Engineering › Electrical and Electronic Engineering

Smart Grid Energy Management

Works Count: 86480

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Description

This cluster of papers focuses on demand response in smart grids, exploring topics such as energy management, electricity markets, real-time pricing, renewable energy integration, and the use of game theory for optimizing load control in smart homes. It also covers the application of demand response to achieve controllability of electric loads and the potential of peer-to-peer energy trading in microgrids.

Keywords

Demand Response; Smart Grid; Energy Management; Electricity Markets; Renewable Energy; Real-Time Pricing; Home Energy; Smart Home; Game Theory; Load Control

Real-Time Price-Based Demand Response Management for Residential Appliances via Stochastic Optimization and Robust Optimization

2012-09-24

Zhi Chen , Lei Wu , Yong Fu

This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic optimization and robust optimization approaches. The proposed real-time price-based DR management application can be imbedded … This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic optimization and robust optimization approaches. The proposed real-time price-based DR management application can be imbedded into smart meters and automatically executed on-line for determining the optimal operation of residential appliances within 5-minute time slots while considering uncertainties in real-time electricity prices. Operation tasks of residential appliances are categorized into deferrable/non-deferrable and interruptible/non-interruptible ones based on appliances' DR preferences as well as their distinct spatial and temporal operation characteristics. The stochastic optimization adopts the scenario-based approach via Monte Carlo (MC) simulation for minimizing the expected electricity payment for the entire day, while controlling the financial risks associated with real-time electricity price uncertainties via the expected downside risks formulation. Price uncertainty intervals are considered in the robust optimization for minimizing the worst-case electricity payment while flexibly adjusting the solution robustness. Both approaches are formulated as mixed-integer linear programming (MILP) problems and solved by state-of-the-art MILP solvers. The numerical results show attributes of the two approaches for solving the real-time optimal DR management problem for residential appliances.

An Evaluation of the HVAC Load Potential for Providing Load Balancing Service

2012-03-13

Ning Lü

This paper investigates the potential of providing intra-hour load balancing services using aggregated heating, ventilating, and air-conditioning (HVAC) loads. A directload control algorithm is presented. A temperature-priority-list method is used … This paper investigates the potential of providing intra-hour load balancing services using aggregated heating, ventilating, and air-conditioning (HVAC) loads. A directload control algorithm is presented. A temperature-priority-list method is used to dispatch the HVAC loads optimally to maintain customer-desired indoor temperatures and load diversity. Realistic intra-hour load balancing signals are used to evaluate the operational characteristics of the HVAC load under different outdoor temperature profiles and different indoor temperature settings. The number of HVAC units needed is also investigated. Modeling results suggest that the number of HVAC units needed to provide a ±1-MW load balancing service 24 hours a day varies significantly with baseline settings, high and low temperature settings, and outdoor temperatures. The results demonstrate that the intra-hour load balancing service provided by HVAC loads meets the performance requirements and can become a major source of revenue for load-serving entities where the two-way communication smart grid infrastructure enables direct load control over the HVAC loads.

The concept of demand-side management for electric utilities

1985-01-01

Clark W. Gellings

Demand-side management (DSM) is the planning and implementation of those electric utility activities designed to influence customer uses of electricity in ways that will produce desired changes in the utility's … Demand-side management (DSM) is the planning and implementation of those electric utility activities designed to influence customer uses of electricity in ways that will produce desired changes in the utility's load shape. While the objective of any DSM activity is to produce a load-shape change, the art of successful implementation and the ultimate success of the program rests within the balancing of utility and customer needs. This paper describes demand-side management for electric utilities and discusses the evolution of this concept for load management, strategic conservation, and marketing.

Demand response and smart grids—A survey

2013-11-14

Pierluigi Siano

Distributed generation: definition, benefits and issues

2003-11-22

Guido Pepermans , Johan Driesen , Dries Haeseldonckx +2 more

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Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

2010-08-10

Amir-Hamed Mohsenian-Rad , Alberto Leon‐Garcia

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to … Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates . Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

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Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

2010-07-29

Michael Angelo A. Pedrasa , Ted Spooner , Iain MacGill

We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling … We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.

Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

2012-12-06

Ahmed Zoha , Alexander Gluhak , Muhammad Ali Imran +1 more

Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal … Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

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A summary of demand response in electricity markets

2008-05-22

Mohammed Albadi , Ehab F. El‐Saadany

Achieving Controllability of Electric Loads

2010-11-30

Duncan S. Callaway , Ian A. Hiskens

This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control … This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control objectives, including economic dispatch, automatic generation control, and spinning reserve. The paper then reviews existing initiatives that seek to develop load control programs for the provision of power system services. We then discuss some of the challenges to achieving a load control scheme that balances device-level objectives with power system-level objectives. One of the central premises of the paper is that, in order to achieve full responsiveness, direct load control (as opposed to price response) is required to enable fast time scale, predictable control opportunities, especially for the provision of ancillary services such as regulation and contingency reserves. Centralized, hierarchical, and distributed control architectures are discussed along with benefits and disadvantages, especially in relation to integration with the legacy power system control architecture. Implications for the supporting communications infrastructure are also considered. Fully responsive load control is illustrated in the context of thermostatically controlled loads and plug-in electric vehicles.

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Photovoltaic self-consumption in buildings: A review

2015-01-12

Rasmus Luthander , Joakim Widén , Daniel Nilsson +1 more

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An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

2013-06-06

Zhuang Zhao , Won Cheol Lee , Yoan Shin +1 more

With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the … With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

Demand Response as a Market Resource Under the Smart Grid Paradigm

2010-04-30

Farrokh Rahimi , Ali Ipakchi

Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively … Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively as distributed energy resources (DER). Although much of the DER emerging under smart grid are targeted at the distribution level, DER, and more specifically DR resources, are considered important elements for reliable and economic operation of the transmission system and the wholesale markets. In fact, viewed from transmission and wholesale operations, sometimes the term ¿virtual power plant¿ is used to refer to these resources. In the context of energy and ancillary service markets facilitated by the independent system operators (ISOs)/regional transmission organizations (RTOs), the market products DER/DR can offer may include energy, ancillary services, and/or capacity, depending on the ISO/RTO market design and applicable operational standards. In this paper we first explore the main industry drivers of smart grid and the different facets of DER under the smart grid paradigm. We then concentrate on DR and summarize the existing and evolving programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under smart grid and market paradigms.

Distributed generation technologies, definitions and benefits

2004-05-08

Walid El‐Khattam , M.M.A. Salama

Review of energy system flexibility measures to enable high levels of variable renewable electricity

2015-02-27

Peter D. Lund , Juuso Lindgren , Jani Mikkola +1 more

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Demand side management: Benefits and challenges

2008-11-10

Goran Štrbac

Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

2012-08-13

Pedram Samadi , Hamed Mohsenian‐Rad , Robert Schober +1 more

In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of … In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.

Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

2010-10-01

Pedram Samadi , Amir-Hamed Mohsenian-Rad , Robert Schober +2 more

In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part … In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.

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Real-Time Demand Response Model

2010-10-27

Antonio J. Conejo , Juan M. Morales , Luis Baringo

This paper describes an optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices. The objective of the model is to maximize … This paper describes an optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices. The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy-consumption level, maximum and minimum hourly load levels, and ramping limits on such load levels. Price uncertainty is modeled through robust optimization techniques. The model materializes into a simple linear programming algorithm that can be easily integrated in the Energy Management System of a household or a small business. A simple bidirectional communication device between the power supplier and the consumer enables the implementation of the proposed model. Numerical simulations illustrating the interest of the proposed model are provided.

Demand Side Management in Smart Grid Using Heuristic Optimization

2012-06-07

T. Logenthiran , Dipti Srinivasan , Tan Zong Shun

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers … Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications

2012-08-23

Walid Saad , Zhu Han , H. Vincent Poor +1 more

The future smart grid is envisioned as a large scale cyberphysical system encompassing advanced power, communications, control, and computing technologies. To accommodate these technologies, it will have to build on … The future smart grid is envisioned as a large scale cyberphysical system encompassing advanced power, communications, control, and computing technologies. To accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient and robust operation of such heterogeneous and large-scale cyberphysical systems. In this context, this article is an overview on the potential of applying game theory for addressing relevant and timely open problems in three emerging areas that pertain to the smart grid: microgrid systems, demand-side management, and communications. In each area, the state-of-the-art contributions are gathered and a systematic treatment, using game theory, of some of the most relevant problems for future power systems is provided. Future opportunities for adopting game-theoretic methodologies in the transition from legacy systems toward smart and intelligent grids are also discussed. In a nutshell, this article provides a comprehensive account of the application of game theory in smart grid systems tailored to the interdisciplinary characteristics of these systems that integrate components from power systems, networking, communications, and control.

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

2010-11-30

Amir-Hamed Mohsenian-Rad , Vincent W. S. Wong , Juri Jatskevich +2 more

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy … Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.

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A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

2015-03-18

Ruilong Deng , Zaiyue Yang , Mo–Yuen Chow +1 more

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption … The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

2014-07-22

John S. Vardakas , Nizar Zorba , Christos Verikoukis

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most … The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

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The path of the smart grid

2010-01-01

Hassan Farhangi

Exciting yet challenging times lie ahead. The electrical power industry is undergoing rapid change. The rising cost of energy, the mass electrification of everyday life, and climate change are the … Exciting yet challenging times lie ahead. The electrical power industry is undergoing rapid change. The rising cost of energy, the mass electrification of everyday life, and climate change are the major drivers that will determine the speed at which such transformations will occur. Regardless of how quickly various utilities embrace smart grid concepts, technologies, and systems, they all agree onthe inevitability of this massive transformation. It is a move that will not only affect their business processes but also their organization and technologies.

Energy Storage and Its Use With Intermittent Renewable Energy

2004-05-25

John P. Barton , David Infield

A simple probabilistic method has been developed to predict the ability of energy storage to increase the penetration of intermittent embedded renewable generation (ERG) on weak electricity grids and to … A simple probabilistic method has been developed to predict the ability of energy storage to increase the penetration of intermittent embedded renewable generation (ERG) on weak electricity grids and to enhance the value of the electricity generated by time-shifting delivery to the network. This paper focuses on the connection of wind generators at locations where the level of ERG would be limited by the voltage rise. Short-term storage, covering less than 1 h, offers only a small increase in the amount of electricity that can be absorbed by the network. Storage over periods of up to one day delivers greater energy benefits, but is significantly more expensive. Different feasible electricity storage technologies are compared for their operational suitability over different time scales. The value of storage in relation to power rating and energy capacity has been investigated so as to facilitate appropriate sizing.

Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

2009-07-08

Habib Allah Aalami , Mohsen Parsa Moghaddam , G. Reza Yousefi

Smart Grid Technologies: Communication Technologies and Standards

2011-09-15

Vehbi Çağrı Güngör , Dilan Sahin , Taşkın Koçak +4 more

For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century … For 100 years, there has been no change in the basic structure of the electrical power grid. Experiences have shown that the hierarchical, centrally controlled grid of the 20th Century is ill-suited to the needs of the 21st Century. To address the challenges of the existing power grid, the new concept of smart grid has emerged. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability, and so on. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

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Nonintrusive appliance load monitoring

1992-01-01

George Hart

A nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage … A nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described. The theory and current practice of nonintrusive appliance load monitoring are discussed, including goals, applications, load models, appliance signatures, algorithms, prototypes field-test results, current research directions, and the advantages and disadvantages of this approach relative to intrusive monitoring.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid

2009-12-21

Kristien Clement-Nyns , Edwin Haesen , Johan Driesen

Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular. The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet … Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular. The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park. These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations. Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay. This uncoordinated power consumption on a local scale can lead to grid problems. Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor. The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses. As the exact forecasting of household loads is not possible, stochastic programming is introduced. Two main techniques are analyzed: quadratic and dynamic programming.

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Distributed generation: a definition

2001-04-01

Thomas Ackermann , Göran Andersson , Lennart Söder

Nonintrusive appliance load monitoring: Review and outlook

2011-02-01

Michael Zeifman , Kurt Roth

Consumer systems for home energy management can provide significant energy saving. Such systems may be based on nonintrusive appliance load monitoring (NIALM), in which individual appliance power consumption information is … Consumer systems for home energy management can provide significant energy saving. Such systems may be based on nonintrusive appliance load monitoring (NIALM), in which individual appliance power consumption information is disaggregated from single-point measurements. The disaggregation methods constitute the most important part of NIALM systems. This paper reviews the methodology of consumer systems for NIALM in residential buildings.

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

2011-06-29

Peter Pálenský , Dietmar Dietrich

Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation … Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.

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Virtual power plant and system integration of distributed energy resources

2007-01-01

Danny Pudjianto , Charlotte Ramsay , Goran Štrbac

A concept is presented along with the overarching structure of the virtual power plant (VPP), the primary vehicle for delivering cost efficient integration of distributed energy resources (DER) into the … A concept is presented along with the overarching structure of the virtual power plant (VPP), the primary vehicle for delivering cost efficient integration of distributed energy resources (DER) into the existing power systems. The growing pressure, primarily driven by environmental concerns, for generating more electricity from renewables and improving energy efficiency have promoted the application of DER into electricity systems. So far, DER have been used to displace energy from conventional generating plants but not to displace their capacity as they are not visible to system operators. If this continues, this will lead to problematic over-capacity issues and under-utilisation of the assets, reduce overall system efficiency and eventually increase the electricity cost that needs to be paid by society. The concept of VPP was developed to enhance the visibility and control of DER to system operators and other market actors by providing an appropriate interface between these system components. The technical and commercial functionality facilitated through the VPP are described and concludes with case studies demonstrating the benefit of aggregation (VPP concept) and the use of the optimal power flow algorithm to characterise VPP.

Demand Response in Electricity Markets: An Overview

2007-06-01

Mohammed Albadi , Ehab F. El‐Saadany

This paper presents an overview of demand response (DR) in electricity market. The definition and a classification of demand response will be presented. Different potential benefits as well as cost … This paper presents an overview of demand response (DR) in electricity market. The definition and a classification of demand response will be presented. Different potential benefits as well as cost components of demand response will be presented. The most common indices used for demand response evaluation are highlighted. Moreover, some utilities experiences with different demand response programs will be presented.

Big data driven smart energy management: From big data to big insights

2015-12-11

Kaile Zhou , Chao Fu , Shanlin Yang

A review of the development of Smart Grid technologies

2016-01-22

Maria Lorena Tuballa , Michael Lochinvar S. Abundo

Smart home energy management systems: Concept, configurations, and scheduling strategies

2016-03-26

Bin Zhou , Wentao Li , Ka Wing Chan +4 more

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Aggregate Flexibility of Thermostatically Controlled Loads

2014-10-06

He Hao , Borhan M. Sanandaji , Kameshwar Poolla +1 more

It is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection … It is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection of TCLs can be succinctly modeled as a stochastic battery with dissipation. We next characterize the power limits and energy capacity of this battery model in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set-points. We then describe a direct load control architecture for regulation service provision. Here, we use a priority-stack-based control framework to select which TCLs to control at any time. The control objective is for the aggregate power deviation from baseline to track an automatic generation control signal supplied by the system operator. Simulation studies suggest the practical promise of our methods.

Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers

2017-01-09

Nian Liu , Xinghuo Yu , Cheng Wang +3 more

According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent … According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users' willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers' costs and improving the sharing of the PV energy.

Designing microgrid energy markets

2017-06-23

Esther Mengelkamp , Johannes Gärttner , Kerstin Rock +3 more

A blockchain-based smart grid: towards sustainable local energy markets

2017-08-22

Esther Mengelkamp , Benedikt Notheisen , Carolin Beer +2 more

Peer-to-Peer energy trading in a Microgrid

2018-03-26

Chenghua Zhang , Jianzhong Wu , Yue Zhou +2 more

Peer-to-Peer (P2P) energy trading represents direct energy trading between peers, where energy from small-scale Distributed Energy Resources (DERs) in dwellings, offices, factories, etc, is traded among local energy prosumers and … Peer-to-Peer (P2P) energy trading represents direct energy trading between peers, where energy from small-scale Distributed Energy Resources (DERs) in dwellings, offices, factories, etc, is traded among local energy prosumers and consumers. A hierarchical system architecture model was proposed to identify and categorize the key elements and technologies involved in P2P energy trading. A P2P energy trading platform was designed and P2P energy trading was simulated using game theory. Test results in a LV grid-connected Microgrid show that P2P energy trading is able to improve the local balance of energy generation and consumption. Moreover, the increased diversity of generation and load profiles of peers is able to further facilitate the balance.

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Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants

2018-01-31

Thomas Morstyn , Niall Farrell , Sarah Darby +1 more

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Peer-to-peer and community-based markets: A comprehensive review

2019-01-27

Tiago Sousa , Tiago Soares , Pierre Pinson +3 more

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Spot Pricing of Electricity

1988-01-01

Fred C. Schweppe , Michael C. Caramanis , Richard D. Tabors +1 more

MicroGrids

2003-06-25

R.H. Lasseter

The MicroGrid concept assumes a cluster of loads and microsources (<100 kW) operating as a single controllable system that provides both power and heat to its local area. This concept … The MicroGrid concept assumes a cluster of loads and microsources (<100 kW) operating as a single controllable system that provides both power and heat to its local area. This concept provides a new paradigm for defining the operation of distributed generation. To the utility the MicroGrid can be thought of as a controlled cell of the power system. For example this cell could be controlled as a single dispatchable load, which can respond in seconds to meet the needs of the transmission system. To the customer the MicroGrid can be designed to meet their special needs; such as, enhance local reliability, reduce feeder losses, support local voltages, provide increased efficiency through use waste heat, voltage sag correction or provide uninterruptible power supply functions. This paper provides an overview of the MicroGrid paradigm. This includes the basic architecture, control and protection and energy management.

Applied Dynamic Programming

1962-12-31

Richard Bellman , Stuart E. Dreyfus

This comprehensive study of dynamic programming applied to numerical solution of optimization problems. It will interest aerodynamic, control, and industrial engineers, numerical analysts, and computer specialists, applied mathematicians, economists, and … This comprehensive study of dynamic programming applied to numerical solution of optimization problems. It will interest aerodynamic, control, and industrial engineers, numerical analysts, and computer specialists, applied mathematicians, economists, and operations and systems analysts. Originally published in 1962. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Optimal Dispatch with Renewable Energy Generators for the Mitigation of Carbon Dioxide Emission

2025-06-25

Rosalie Vertudes , Reuel C. Pallugna , Lory Liza D. Bulay-og +3 more | Mindanao Journal of Science and Technology

The reliance on coal-fired power plants for electricity generation in Mindanao, Philippines, poses significant environmental concerns due to their substantial carbon dioxide emissions. According to the Department of Energy, the … The reliance on coal-fired power plants for electricity generation in Mindanao, Philippines, poses significant environmental concerns due to their substantial carbon dioxide emissions. According to the Department of Energy, the total projected projects for coal-fired plants are 2,538 MW, accounting for approximately 30% of the carbon dioxide emissions. There were previous studies on mitigating the carbon footprint of a power system. However, it requires complex computational solutions and analysis, with the assumed required renewable energy to be implemented. To align with the country's commitment to reduce carbon emissions by 75% by 2040, this study explores the optimization of energy dispatch with the available renewable energy sources in the Mindanao grid. To achieve this, PowerWorld simulation software was utilized to perform the technical and economic analysis of the power system. Five-generation mix scenarios were also created based on sustainable load dispatching in Mindanao by 2030. It involved modeling the power system of various scenarios and conducting economic power flow simulations. The simulation results show that Case 3 (mix of coal, biomass, hydro, and geothermal) has the optimal mix with 20.51% carbon emission reduction and the cheapest marginal cost of 379,814.18 pesos/hr. This study highlights the practical potential of optimizing energy dispatch to transition towards a sustainable and clean energy generation system in Mindanao.

Monthly Electricity Billing Display With SMS Feature

2025-06-25

| IJIREEICE

Disaggregation of Hourly Electricity Consumption into Subconsumptions

2025-06-25

Ahmet Yücekaya , Ayşe Hümeyra Bilge , Ergün Yukseltan +2 more | International Journal of Energy Economics and Policy

Electricity consumption in a country, region, or city comprises demand from residential, industrial, commercial, and other specific consumptions such as street lighting, and agricultural related consumptions, along with losses from … Electricity consumption in a country, region, or city comprises demand from residential, industrial, commercial, and other specific consumptions such as street lighting, and agricultural related consumptions, along with losses from leakage and transmission and unauthorized usage. However, tracking the specific consumption components in real time is challenging, as detailed consumption data for each category is typically determined only after billing periods. Understanding the electricity demand of each subcategory and its proportion within total consumption is crucial for effective system planning and operation. This study develops a framework to disaggregate total electricity consumption into subcategories—residential, industrial/ commercial, irrigation, and lighting—at an hourly resolution. The methodology depends crucially on a special day detection approach that identifies consumption patterns on days at which where electricity usage is at its lowest, without any a priori information of the holidays in each country or a region. Based on this, baseline consumption levels for different periods are determined, indices for subcategories are developed, and time series models are generated to analyze consumption trends. For validation, the methodology is applied to Turkish electricity data, where the structure of consumption is analyzed in relation to holidays and special occasions, accounting for the reduction in demand during these periods. The proportions of residential, industrial/commercial, and lighting consumption are derived from total consumption, and the remaining categories are estimated accordingly. Finally, the accuracy and convergence of the results are evaluated, and the findings are presented with their implications for energy planning and grid management.

The Impact of Smart Metering Mobile Application on Residential Electricity Consumption: Evidence from South Korea

2025-06-25

Namho Kwon , Daegon Cho | International Journal of Energy Economics and Policy

This study examines the relationship between mobile app usage and residential electricity consumption. We focus on how smart meter feedback influences energy-saving behaviors under a progressive tariff system in South … This study examines the relationship between mobile app usage and residential electricity consumption. We focus on how smart meter feedback influences energy-saving behaviors under a progressive tariff system in South Korea. The study uses a combination of three datasets—daily electricity consumption, mobile app access logs, and demographic survey data—gathered from 284 households. A panel vector autoregression (VAR) model and a difference-in-difference (DID) approach are used to analyze the dynamic relationship between app engagement and energy use. The results show that daily app access does not significantly affect electricity consumption, on average. However, under a progressive tariff system, households nearing a tariff stage threshold demonstrate a reduction in electricity use when engaging with the app. This effect is strongest among households with smaller living areas, smaller household size, and no children. This study is among the first to provide empirical evidence on the impact of smart metering mobile apps in a real-world setting. Our findings underscore the importance of tailored feedback strategies to maximize energy efficiency through smart meter technology.

PFD Simulator based Deep Reinforcement Learning for Energy Consumption Minimization of Electric RTO

2025-06-24

J. Mo , Myungho Kim | Journal of KIISE

Energy modeling and Life Cycle Assessment of the Italian electricity grid and energy storage system: comparison of current state and forecast to 2030

2025-06-24

Claudio Zuffi , Federico Rossi , Daniele Fiaschi +1 more | The International Journal of Life Cycle Assessment

Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation

2025-06-24

Xiaolu Ye , Zhanshan Wang , Shuran Wang | Applied Sciences

This paper proposes a distributed data-driven adaptive iterative learning control (DAILC) method to address the challenges of modeling and voltage regulation in isolated AC microgrids subject to disturbances and sensor … This paper proposes a distributed data-driven adaptive iterative learning control (DAILC) method to address the challenges of modeling and voltage regulation in isolated AC microgrids subject to disturbances and sensor saturation. Firstly, by utilizing the input–output data from the microgrid, a time-varying linear data microgrid model is developed for the distributed renewable energy generation unit (DREGU) that is independent of the microgrid’s physical information. Subsequently, the DAILC algorithm is developed from the microgrid data model, which only uses the input data and the corresponding saturated outputs from each DREGU, along with embedded measurement disturbances. Additionally, we verify the convergence of this algorithm, demonstrating that it can ensure the error in voltage restoration converges to a small neighborhood around the origin, even under conditions of sensor saturation and disturbances. Finally, through a simulation involving four DREGU nodes, we confirm the effectiveness of the proposed distributed DAILC method.

Residential solar adoption - main determinants and the future of small-scale photovoltaics

2025-06-24

Severin Reissl , Luis Sarmiento , Johannes Emmerling | Environmental Research Energy

Abstract We present a data-driven microsimulation model analyzing residential photovoltaic (PV) adoption in Germany to address uncertainties in future adoption rates. We develop and calibrate a technology adoption model incorporating … Abstract We present a data-driven microsimulation model analyzing residential photovoltaic (PV) adoption in Germany to address uncertainties in future adoption rates. We develop and calibrate a technology adoption model incorporating economic, financial, and behavioral factors to replicate historical adoption patterns from 2007-2020 and project future scenarios. The model simulates household adoption decisions based on expected profitability, environmental attitudes, financial constraints, and peer effects, while differentiating across income groups and geographical locations. Combining household survey data with installation records, we demonstrate that our model accurately reproduces historical adoption patterns. Our projections follow the sigmoid curve typical of innovation diffusion theory, with peer effects accelerating adoption as ownership increases. The baseline scenario predicts approximately 47\% household saturation by 2050, with maximum adoption growth occurring around 2030, though systemic grid-level constraints may limit penetration well before reaching such levels. Alternative scenarios demonstrate the critical role of technology costs, electricity prices, and feed-in tariffs in driving adoption. We find significant disparities between income groups, with the highest income decile reaching 77\% adoption by 2050 compared to only 6\% for the lowest decile. This disparity highlights the regressive distribution of feed-in tariff benefits, with wealthier households receiving disproportionately larger subsidies, though historical technology diffusion patterns suggest such inequities may diminish over time. Our findings provide valuable insights for policymakers regarding grid planning, subsidy design, and the distributional implications of renewable energy policies.

Capacity credit evaluation of generalized energy storage considering strategic capacity withholding and decision-dependent uncertainty

2025-06-24

N. D. Qi , Pierre Pinson , Mads Almassalkhi +3 more | Applied Energy

IOT Based Controlled Smart Distribution Box

2025-06-24

V. S. Patil | International Journal for Research in Applied Science and Engineering Technology

This project introduces an IoT-controlled smart distribution box designed for enhanced energy management and convenience, boasting versatile features for both online and offline usage. Utilizing a NodeMCU microcontroller unit, the … This project introduces an IoT-controlled smart distribution box designed for enhanced energy management and convenience, boasting versatile features for both online and offline usage. Utilizing a NodeMCU microcontroller unit, the system integrates a 4-channel relay for load management via voice commands (Google Assistant, Amazon Alexa), manual switches, and programmable timers and schedules. An LCD 2004 display provides real-time feedback, while a PZEM004T sensor enables precise energy monitoring. Users can also set load limits, with notifications sent when thresholds are reached, further enhancing efficiency and safety. With these comprehensive capabilities, the system empowers users to optimize energy usage, promote sustainability, and simplify control of electrical appliances in residential and commercial settings.

Advancement of a Transmission System with Novel Methods and Tools

2025-06-24

Abdullah Bin Queyam , M. Premkumar , Ramesh Kumar +1 more | CRC Press eBooks

Control and monitoring of stand-alone hybrid renewable energy systems

2025-06-23

Mubarak A. Alanazi | Polityka Energetyczna – Energy Policy Journal

Data-Driven MPC with Multi-Layer ReLU Networks for HVAC Optimization Under Iraq’s Time-of-Use Electricity Pricing

2025-06-23

Alaa Shakir , Ghamgeen Izat Rashed , Yigang He +1 more | Processes

Enhancing the energy management capabilities of modern smart buildings is essential for energy conservation, which is valuable for modern power networks maintaining a tight power balance under high renewable penetration. … Enhancing the energy management capabilities of modern smart buildings is essential for energy conservation, which is valuable for modern power networks maintaining a tight power balance under high renewable penetration. This study introduces a data-driven control strategy based on the model predictive control (MPC) for HVAC (heating, ventilation, and air conditioning) systems considering the time-of-use (ToU) electricity rates in Iraq. A multi-layer neural network is first constructed using time-delayed embedding for the modeling of building thermal dynamics, where the rectified linear unit (ReLU) is used as the activation function for the hidden layers. Based on such piecewise affine approximation, an optimization model is developed within the receding horizon control framework, which incorporates the data-driven model and is transformed into a mixed-integer linear programming facilitating efficient problem solving. To validate the efficiency of the proposed approach, a simulation model of the building’s thermal network is constructed using Simscape considering several thermal effects among the building components. Simulation results demonstrate that the proposed approach improves the economic performance of the building while maintaining thermal comfort levels within acceptable range.

Conditional GAN-based synthetic financial modeling for street vendors in India’s informal economy

2025-06-23

Subhadip Das , S. Mishra , Zefree Lazarus Mayaluri | Digital Finance

Method for Electrolytic Industrial Load Participation in Grid Demand Response Control Considering Production Process Characteristics and Power Regulation Costs

2025-06-23

Lingfang Li , Yixuan Chen , Haocong Xie +1 more | Journal of Electrical Engineering and Technology

Application of hybrid PSOBOA with various inertia weight control strategies for economic scheduling in a grid-connected microgrid network

2025-06-22

S. Bhattacharjee , Aneesa Farhan M A | Energy Sources Part B Economics Planning and Policy

Considering the Uncertainty of Renewable Energy: Bidding Strategy for Virtual Power Plants in the Monthly Centralized Market

2025-06-22

Chi Zhang , Ming Wang , Fei Yang +4 more | Strategic Planning for Energy and the Environment

The execution of the energy transition and “dual carbon” objectives is progressively enhancing the penetration rate of renewable energy in the new power system. This paper examines the bidding strategy … The execution of the energy transition and “dual carbon” objectives is progressively enhancing the penetration rate of renewable energy in the new power system. This paper examines the bidding strategy for virtual power plants (VPPs) incorporating renewable energy within the rolling trading framework of the mid-to-long-term centralised electricity market, in response to the challenges posed by power generation uncertainty and market-driven consumption due to large-scale renewable energy integration. An outer clearing model is developed to enable VPP participation, with the objective of maximising societal welfare, hence determining transaction volumes and clearing prices. Subsequently, taking into account the predicting inaccuracies of wind and solar energy, an internal optimisation model is formulated with the aim of maximising the income of the Virtual Power Plant (VPP), therefore measuring the incremental revenue. A dual-layer optimisation model appropriate for Virtual Power Plant (VPP) participation is developed and subsequently utilised to analyse the optimised bidding strategies for VPPs in the medium to long-term monthly centralised market.

Optimized Demand Side Management for Refrigeration: Modeling and Case Study Insights from Kenya

2025-06-21

Josephine Nakato Kakande , Godiana Hagile Philipo , Stefan Krauter | Energies

According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of … According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for mechanisms to match demand and supply better and increase power system flexibility has led to enhanced attention on demand-side management (DSM) practices to boost technology, infrastructure, and market efficiencies. Refrigeration requirements will continue to rise with development and climate change. In this work, particle swarm optimization (PSO) is used to evaluate energy saving and load factor improvement possibilities for refrigeration devices at a site in Kenya, using a combination of DSM load shifting and strategic conservation, and based on appliance temperature evolution measurements. Refrigeration energy savings of up to 18% are obtained, and the load factor is reduced. Modeling is done for a hybrid system with grid, solar PV, and battery, showing a marginal increase in solar energy supply to the load relative to the no DSM case, while the grid portion of the load supply reduces by almost 25% for DSM relative to No DSM.

Multi-objective optimal scheduling of distribution networks with load aggregator involvement

2025-06-21

Linjin Zheng , Jianfeng Zheng , Haoyang Tang +4 more | Energy Reports

Optimizing smart grid flexibility with a hybrid MINLP framework for renewable integration in urban energy systems

2025-06-21

Sameer Algburi , Omer Al-Dulaimi , Hassan Falah Fakhruldeen +7 more | Energy Reports

Multi-agent system for optimized energy management in multi-smart buildings via deregulated market system

2025-06-21

Abdul Haseeb Sajid , Muhammad Zubair Iftikhar , Syed Ali Abbas Kazmi +3 more | Energy Reports

A Cost Effective IOT Solutions for Energy Usage Management

2025-06-21

Prof. Prachiti Adghulkar | International Journal for Research in Applied Science and Engineering Technology

Thepaperfocusonthedevelopmentofan‘ACostEffectiveIOTSolutionsforEnergyUsageManagement ’. With the growing demand of electricity its somewhere hard to manage the efficient energy. Traditional Meterswhichisfoundineverywhere nowadays requiremanualreadingandoftenleadtoerrorsanddelayin billing.Asmartenergyaddressedtheseissuesbyenablingrealtimemonitoring and automated-billing. Bythe use of smart energy meter, it … Thepaperfocusonthedevelopmentofan‘ACostEffectiveIOTSolutionsforEnergyUsageManagement ’. With the growing demand of electricity its somewhere hard to manage the efficient energy. Traditional Meterswhichisfoundineverywhere nowadays requiremanualreadingandoftenleadtoerrorsanddelayin billing.Asmartenergyaddressedtheseissuesbyenablingrealtimemonitoring and automated-billing. Bythe use of smart energy meter, it allow users to track their energy and get updated all time through an app or a web dashboard. The smart meter is recharged with the help of Esp module. In smart energy meter, energy utilizationand thecorrespondingamountwillbedisplayedonthe LCDasaremainder. Thefeedbackfromtheuserhelp sinidentifyingtheusagesbetweenauthorizedandunauthorizeduserswhichhelpsincontrollingthe power wastage. Esp module is used for sending messages to the local authorities regarding user power consumptions. Also they canmonitor themeterreadingsregularly withoutmakingeffor tstovisiteachhouse fortakingmanualreadings. Thistechnologynotonlyreducethehuman effortsbutalsopromotesenergyconservationby allowing andencouraginguserstomonitorandoptimizetheir energyconsumption.

Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme

2025-06-20

Mohammed Ali , Ahmad H. Besheer , Hassan M. Emara +1 more | Scientific Reports

Abstract The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. This paper introduces a novel cost–benefit approach for scheduling battery energy storage … Abstract The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. This paper introduces a novel cost–benefit approach for scheduling battery energy storage systems (BESS) within microgrids (MGs) that features smart grid attributes. The proposed comprehensive approach accounts for fluctuations of real-time pricing, demand charge tariffs, and battery degradation cost. Using the dynamic programming technique, a novel high-speed BESS scheduling optimization algorithm that incorporates a LiFePO4 battery degradation cost model is developed, achieving substantial monthly operational cost savings for the MG with a fine-grained sampling interval of nine minutes and execution time under one minute. The algorithm utilizes day-ahead forecasts for MG load profiles and photovoltaic output power, enabling the prediction of BESS’s optimal power profile a day in advance. The algorithm’s rapid execution enables real-time adaptability, allowing BESS scheduling to dynamically respond to grid fluctuations. The proposed approach outperforms existing methods in the literature, delivering MG operational cost savings ranging from 33.6% to 94.8% across various scenarios. Consequently, this approach enhances MG operational efficiency and provides significant cost savings.

Optimizing Solar Microgrid Efficiency via Reinforcement Learning: An Empirical Study Using Real-Time Energy Flow and Weather Forecasts

2025-06-20

Indrajit Das , Muhammad Ahmed , Abhay Shukla | International Journal of Computer Applications

Cost and Reliability Optimization of Smart Grids by Demand Response Analysis amidst Uncertainties

2025-06-20

| International journal of intelligent engineering and systems

Enhancing energy-saving behaviors of domestic electricity consumers in Zambia: A demand side management model

2025-06-20

Marius Stan | World Journal of Advanced Engineering Technology and Sciences

Climate change has affected the availability of water, reducing the levels of water for electricity generation, this has resulted into massive load shedding, considering that the region and Zambia in … Climate change has affected the availability of water, reducing the levels of water for electricity generation, this has resulted into massive load shedding, considering that the region and Zambia in particular, is highly dependent on hydropower. Literature suggests that that energy-serving behaviour has a huge potential to reduce energy demand drastically. Nevertheless, the switch and save campaign by Zesco, has not yielded much success, as load shedding is still being experienced despite the campaign. It is for this reason that this research was conducted to develop a demand side management model that will enhance the energy-saving behaviours of domestic electricity consumers. This model is interactive and operates on the principle of information, encouraging the consumer to reduce the excess loads and instead use alternative sources particularly for heating and lighting. Where there is no compliance, the option by the system is to load-shed that particular consumer not complying with the requirements. It is believed the implementation of this system would persuade the domestic electricity consumers to use electricity prudently and reduce the chances of complete electricity black-out. The discussion has also included global diverse energy forms which exist, and are used at various scales: global, regional, national, community and household level. Among these scales, the household level is considered as the terminal link for energy consumption and sustained environmental protection. It is worth noting that these concerns gave rise to an investigation on mechanisms on how to enhance energy saving behaviours of domestic electricity consumers in Zambia: a demand side management model. Although demand-side management (DSM) needs to be more customer centered, either with or without smart technologies for smart grid, less attention has been paid to the developing world in relation to DSM strategy development. The main reasons have been lacking appropriate technology and capital costs. Importantly, there are alternative DSM strategies that require minimum or no cost to implement and provide immediate results, of which energy-saving behaviour for the occupant at residences is one. The Model for Enhancing Energy Saving Behaviour (MEESB) is therefore a demand response technology and strategy, which applies the Time-based, Incentive-based and energy saving-behaviour programs to achieve energy conservation. The study utilized a mixed research method employing both qualitative and quantitative in the research design. The findings of the study were that electrical practitioners agree with the idea of utilising a suitable demand side model that has features for warning the individual domestic householders of their drawing of electricity current beyond the limit set by the supply authority. They are also in agreement that load shedding should only be implemented to electricity consumers who do not comply with the appeal to “switch and save power”. Finally, the electrical practitioners agree with the idea of a provision within the model for enhancing energy saving behaviours, for resetting back to supply, upon isolating the loads consuming excess power, if the household was load shaded for exceeding the set limit of electricity consumption. The study established that domestic electricity consumers have sufficient knowledge of electricity saving technology and its benefits in matters of steady supply of electricity.

Allocation of Cost of Reliability to Various Customer Sectors in a Standalone Microgrid System

2025-06-20

Sakthivelnathan Nallainathan , Ali Arefi , C.P. Lund +1 more | Energies

Due to the intermittent and uncertain nature of emerging renewable energy sources in the modern power grid, the level of dispatchable power sources has been reduced. The contemporary power system … Due to the intermittent and uncertain nature of emerging renewable energy sources in the modern power grid, the level of dispatchable power sources has been reduced. The contemporary power system is attempting to address this by investing in energy storage within the context of standalone microgrids (SMGs), which can operate in an island mode and off-grid. While renewable-rich SMGs can facilitate a higher level of renewable energy penetration, they also have more reliability issues compared to conventional power systems due to the intermittency of renewables. When an SMG system needs to be upgraded for reliability improvement, the cost of that reliability improvement should be divided among diverse customer sectors. In this research, we present four distinct approaches along with comprehensive simulation outcomes to address the problem of allocating reliability costs. The central issue in this study revolves around determining whether all consumers should bear an equal share of the reliability improvement costs or if these expenses should be distributed among them differently. When an SMG system requires an upgrade to enhance its reliability, it becomes imperative to allocate the associated costs among various customer sectors as equitably as possible. In our investigation, we model an SMG through a simulation experiment, involving nine distinct customer sectors, and utilize their hourly demand profiles for an entire year. We explore how to distribute the total investment cost of reliability improvement to each customer sector using four distinct methods. The first two methods consider the annual and seasonal peak demands in each industry. The third approach involves an analysis of Loss of Load (LOL) events and determining the hourly load requirements for each sector during these events. In the fourth approach, we employ the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. The annual peak demand approach resulted in the educational sector bearing the highest proportion of the reliability improvement cost, accounting for 21.90% of the total burden. Similarly, the seasonal peak demand approach identified the educational sector as the most significant contributor, though with a reduced share of 15.44%. The normalized average demand during Loss of Load (LOL) events also indicated the same sector as the highest contributor, with 12.34% of the total cost. Lastly, the TOPSIS-based approach assigned a 15.24% reliability cost burden to the educational sector. Although all four approaches consistently identify the educational sector as the most critical in terms of its impact on system reliability, they yield different cost allocations due to variations in the methodology and weighting of demand characteristics. The underlying reasons for these differences, along with the practical implications and applicability of each method, are comprehensively discussed in this research paper. Based on our case study findings, we conclude that the education sector, which contributes more to LOL events, should bear the highest amount of the Cost of Reliability Improvement (CRI), while the hotel and catering sector’s share should be the lowest percentage. This highlights the necessity for varying reliability improvement costs for different consumer sectors.

Valuing the electricity produced locally in renewable energy communities through noncooperative resources scheduling games

2025-06-20

Louise Sadoine , Zacharie De Grève , Thomas Brihaye | Applied Energy

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Chance-constrained solar PV hosting capacity assessment for distribution grids using Gaussian Process and Logit learning

2025-06-20

Sel Ly , Anshuman Singh , Petr Vorobev +2 more | International Journal of Electrical Power & Energy Systems

The Impacts of Renewable Energy Sources: A Review on Grid Inertia and Frequency Regulation

2025-06-20

Sospeter Gabriel , Francis Mwasilu , Bakari M. M. Mwinyiwiwa | Tanzania Journal of Engineering and Technology

The power system is gradually transitioning into low inertia due to integrating substantial intermittency quantity of converter-based renewable energy sources, such as wind and photovoltaic power, into the current power … The power system is gradually transitioning into low inertia due to integrating substantial intermittency quantity of converter-based renewable energy sources, such as wind and photovoltaic power, into the current power grid network. This integration presents significant inertia and frequency control challenges to the network as a result of a decrease in the percentage of synchronous generators. Moreover, faster frequency deviations are posed by the mismatch between the supply and demand during contingencies, which creates difficulties in preserving the frequency stability of the power system. This research explores the impacts of renewable energy sources (RESs) on grid inertia and frequency management as key parts of preserving the power system's stability. Furthermore, the research article proposes mitigation ways to optimize both conventional synchronous generators and synthetic inertia for consistent and dependable functioning of the grid network while accommodating a growing share of renewable energy. The mitigation measures examined in this review research paper are synthetic inertia, fast frequency response, and battery storage systems

The Impacts of Renewable Energy Sources: A Review on Grid Inertia and Frequency Regulation

2025-06-20

Sospeter Gabriel , Francis Mwasilu , Bakari M. M. Mwinyiwiwa | Tanzania Journal of Engineering and Technology

A Review of Optimization Strategies for Energy Management in Microgrids

2025-06-20

Astrid Esparza , Maude Josée Blondin , João Pedro F. Trovão | Energies

Rapid industrialization, widespread transportation electrification, and significantly rising household energy consumption are rapidly increasing global electricity demand. Climate change and dependency on fossil fuels to meet this demand underscore the … Rapid industrialization, widespread transportation electrification, and significantly rising household energy consumption are rapidly increasing global electricity demand. Climate change and dependency on fossil fuels to meet this demand underscore the critical need for sustainable energy solutions. Microgrids (MGs) provide practical applications for renewable energy, reducing reliance on fossil fuels and mitigating ecological impacts. However, renewable energy poses reliability challenges due to its intermittency, primarily influenced by weather conditions. Additionally, fluctuations in fuel prices and the management of multiple devices contribute to the increasing complexity of MGs and the necessity to address a range of objectives. These factors make the optimization of Energy Management Strategies (EMSs) essential and necessary. This study contributes to the field by categorizing the main aspects of MGs and optimization EMS, analyzing the impacts of weather on MG performance, and evaluating their effectiveness in handling multi-objective optimization and data considerations. Furthermore, it examines the pros and cons of different methodologies, offering a thorough overview of current trends and recommendations. This study serves as a foundational resource for future research aimed at refining optimization EMS by identifying research gaps, thereby informing researchers, practitioners, and policymakers.

An Intelligent Approach for Efficient Demand Side Management Based on Price in Smart Grids Using Advanced Metering Infrastructure Frameworks for Rural Electrification

2025-06-20

S. Kannaki , S. Jaganathan | Journal of Electrical Engineering and Technology

Aggregator-Based Optimization of Community Solar Energy Trading Under Practical Policy Constraints: A Case Study in Thailand

2025-06-20

Sanvayos Siripoke , Varinvoradee Jaranya , Chalie Charoenlarpnopparut +3 more | Energies

This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition … This paper presents SEAMS (Solar Energy Aggregator Management System), an optimization-based framework for managing solar energy trading in smart communities under Thailand’s regulatory constraints. A major challenge is the prohibition of residential grid feed-in, which limits the use of conventional peer-to-peer energy models. Additionally, fixed pricing is required to ensure simplicity and trust among users. SEAMS coordinates prosumer and consumer households, a shared battery energy storage system (BESS), and a centralized aggregator (AGG) to minimize total electricity costs while maintaining financial neutrality for the aggregator. A mixed-integer linear programming (MILP) model is developed to jointly optimize PV sizing, BESS capacity, and internal buying price, accounting for Time-of-Use (TOU) tariffs and local policy limitations. Simulation results show that a 6 kW PV system and a 70–75 kWh shared BESS offer optimal performance. A 60:40 prosumer-to-consumer ratio yields the lowest total cost, with up to 49 percent savings compared to grid-only systems. SEAMS demonstrates a scalable and policy-aligned approach to support Thailand’s transition toward decentralized solar energy adoption and improved energy affordability.

Solar PV Powered Cooling with Thermal Energy Storage for Rural Healthcare Facilities in Malaysia

2025-06-20

Ahmad Zaidi Abdullah , Hasila Jarimi , Tianhong Zheng +7 more | Journal of Climate Change

This paper presents an analysis of an off-grid solar cooling system designed for rural healthcare facilities in Malaysia, where the electricity infrastructure is often unreliable, affecting the delivery of essential … This paper presents an analysis of an off-grid solar cooling system designed for rural healthcare facilities in Malaysia, where the electricity infrastructure is often unreliable, affecting the delivery of essential healthcare services. Aiming to minimize the reliance on electrical battery storage, the system utilizes a DC-powered vapor compression cooling unit directly driven by photovoltaic (PV) solar panels with energy storage using thermal energy storage. Meanwhile, electrical battery storage was designed sufficiently for auxiliary electrical equipment. In addition, the system was designed to maintain indoor air temperatures within a comfortable range as per MS 1525– 2014 and DOSH 2010 standard. In conducting the feasibility study of the proposed idea, we have designed and installed a 5-kW cooling system at a test room facility in Malaysia. The performance of the system was monitored over various conditions, and the results show that indoor temperatures were kept between 22 °C to 26 °C, even when external temperatures reached 35 °C. Additionally, levelized cost of cooling analysis, and a simple payback period indicates that cost per kWh of cooling is only 0.033 USD and less than 2 years respectively. This feasibility study not only demonstrates the technical viability of solar-powered cooling in rural healthcare settings but also highlights its potential for broader application in similar off-grid regions, contributing to sustainable energy solutions in the healthcare sector.

Strengthening Resilience in Distribution Networks through Microgrid Development and Integration of Distributed Energy Resources Using the Group Instruction Optimization Algorithm

2025-06-19

Britto C. John De , T. D. Suresh , R. Hemalatha +3 more | CRC Press eBooks

Research on the Low-Carbon Economic Operation Optimization of Virtual Power Plant Clusters Considering the Interaction Between Electricity and Carbon

2025-06-19

Ting Pan , Qiao Zhao , Jiangyan Zhao +1 more | Processes

Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for … Under carbon emission constraints, to promote low-carbon transformation and achieve the aim of carbon peaking and carbon neutrality in the energy sector, this paper constructs an operational optimization model for the coordinated operation of a virtual power plant cluster (VPPC). Considering the resource characteristics of different virtual power plants (VPPs) within a cooperative alliance, we propose a multi-VPP interaction and sharing architecture accounting for electricity–carbon interaction. An optimization model for VPPC is developed based on the asymmetric Nash bargaining theory. Finally, the proposed model is solved using an alternating-direction method of multipliers (ADMM) algorithm featuring an improved penalty factor. The research results show that P2P trading within the VPPC achieves resource optimization and allocation at a larger scale. The proposed distributed ADMM solution algorithm requires only the exchange of traded electricity volume and price among VPPs, thus preserving user privacy. Compared with independent operation, the total operation cost of the VPPC is reduced by 20.37%, and the overall proportion of new energy consumption is increased by 16.83%. The operation costs of the three VPPs are reduced by 1.12%, 20.51%, and 6.42%, respectively, while their carbon emissions are decreased by 4.47%, 5.80%, and 5.47%, respectively. In addition, the bargaining index incorporated in the proposed (point-to-point) P2P trading mechanism motivates each VPP to enhance its contribution to the alliance to achieve higher bargaining power, thereby improving the resource allocation efficiency of the entire alliance. The ADMM algorithm based on the improved penalty factor demonstrates good computational performance and achieves a solution speed increase of 15.8% compared to the unimproved version.

Quantification of implicit price flexibility of household customers’ load demand with machine learning and Shapley analysis

2025-06-19

Jouni Haapaniemi , Juha Haakana , Otto Räisänen +3 more | Energy

Optimal Placement of Renewable Based DG and DSTATCOM in Distribution Systems to Alleviate the Effects of EVCS

2025-06-19

T. D. Suresh , M. Thirumalai , Ulagammai Meyyappan +4 more | CRC Press eBooks

Loss Reduction and Bus Voltage Improvement in Distribution Network under Unbalanced Conditions using Cat Swarm Optimization Algorithm

2025-06-19

Chava Hari Babu , Hariharan Rajalakshmi Mohanraj | CRC Press eBooks

Design and Implementation of an IoT-Enabled Smart Energy Meter Using Arduino

2025-06-19

Y. Sukhi , P. Kavitha , Sharo E. J. Adlin +6 more | CRC Press eBooks

Intelligent Residential Energy Control with Demand Response Incorporating Renewable Power Sources and Electric Vehicle Integration

2025-06-19

Ulagammai Meyyappan , R. Hemalatha , Britto C. John De +3 more | CRC Press eBooks

Multi-Energy Optimal Dispatching of Port Microgrids Taking into Account the Uncertainty of Photovoltaic Power

2025-06-19

Xiaoyong Wang , Wei Xing , Hanqing Zhang +2 more | Energies

To tackle the problems of high scheduling costs and low photovoltaic (PV) accommodation rates in port microgrids, which are caused by the coupling of uncertainties in new energy output and … To tackle the problems of high scheduling costs and low photovoltaic (PV) accommodation rates in port microgrids, which are caused by the coupling of uncertainties in new energy output and load randomness, this paper proposes an optimized scheduling method that integrates scenario analysis with multi-energy complementarity. Firstly, based on the improved Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) clustering algorithm and backward reduction method, a set of typical scenarios that represent the uncertainties of PV and load is generated. Secondly, a multi-energy complementary system model is constructed, which includes thermal power, PV, energy storage, electric vehicle (EV) clusters, and demand response. Then, a planning model centered on economy is established. Through multi-energy coordinated optimization, supply–demand balance and cost control are achieved. The simulation results based on the port microgrid of the LEKKI Port in Nigeria show that the proposed method can significantly reduce system operating costs by 18% and improve the PV accommodation rate through energy storage time-shifting, flexible EV scheduling, and demand response incentives. The research findings provide theoretical and technical support for the low-carbon transformation of energy systems in high-volatility load scenarios, such as ports.

Reinforcement Learning-assisted Voltage Stability Analysis of the Nigerian Power Grid Using Dynamic Voltage Restorer (DVR) and Battery Energy Storage System (BESS)

2025-06-19

Ndifreke U. Ekanem , Mfonobong A. Umoren , Kufre M. Udofia | Journal of Engineering Research and Reports

This study proposed and validated a reinforcement learning (RL)-assisted framework to enhance voltage stability in Nigeria’s power grid through dynamic coordination of a Dynamic Voltage Restorer (DVR) and Battery Energy … This study proposed and validated a reinforcement learning (RL)-assisted framework to enhance voltage stability in Nigeria’s power grid through dynamic coordination of a Dynamic Voltage Restorer (DVR) and Battery Energy Storage System (BESS). Addressing the Nigerian grid’s vulnerabilities—including ageing infrastructure, frequent voltage sags/swells, and inadequate reactive power support—the framework integrated a Deep Deterministic Policy Gradient (DDPG)-based RL controller with classical voltage stability methodologies rooted in Newton-Raphson power flow, Jacobian matrix eigenvalue analysis, and voltage stability indices (L-Index, VCPI). A realistic 3-bus MATLAB/Simulink model of the Alaoji-Onitsha 330kV transmission corridor was developed, simulating fault-induced instability scenarios with a 500 MVA generator, 800 MW + 300 MVAR industrial load, and transmission line parameters reflective of Nigeria’s grid. The RL agent was trained to minimise voltage deviations and harmonic distortion, and dynamically optimised DVR voltage injection and BESS charge/discharge cycles achieved a 3.25 ms response time of 0.92 p.u. voltage compensation, and a 96% reduction in total harmonic distortion (THD from 9.06% to 0.36%). Comparative analyses demonstrated the RL controller’s superiority over conventional PI, ANN, and PSO methods, with 75% faster transient recovery and 71% lower THD. Empirical validation under IEC 61000-4-30 and IEEE 519-2022 standards confirmed stable voltage regulation within ±0.9% of nominal during asymmetrical faults, while Jacobian eigenvalue analysis revealed a 40% improvement in stability margins (smallest singular value, σ_min, increased by a factor of 2.8). Through the combination of model-free RL adaptability with physics-based grid modelling, the study provided an adaptable solution for weak grids, reducing dependency on pre-trained datasets and offering a cost-effective strategy for mitigating voltage instability in Nigeria’s power system amid growing renewable integration and load volatility.

Decarbonizing the Industry Sector: Current Status and Future Opportunities of Energy-Aware Production Scheduling

2025-06-19

Georgios P. Georgiadis , Christos N. Dimitriadis , Michael C. Georgiadis | Processes

As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy … As industries come under growing pressure to minimize carbon emissions without compromising the efficiency of operations, the integration of energy-aware production scheduling with emerging energy markets, renewable energy, and policy mechanisms is critical. This paper identifies critical shortcomings in current academic and industrial approaches—namely, an excessive reliance on deterministic assumptions, a limited focus on dynamic operational realities, and the underutilization of regulatory mechanisms such as carbon trading. We advocate for a paradigm shift to more robust, adaptable, and policy-compliant scheduling systems that provide space for on-site renewable generation, battery energy storage systems (BESSs), demand-response measures, and real-time electricity pricing schemes like time-of-use (TOU) and real-time pricing (RTP). By integrating recent advances and their critical analysis of limitations, we map out a future research agenda for the integration of uncertainty modeling, machine learning, and multi-level optimization with policy compliance. In this paper, we propose the need for joint efforts from researchers, industries, and policymakers to collectively develop industrial scheduling systems that are both technically efficient and adherent to sustainability and regulatory requirements.

Supervisory control and data acquisition for localized grids in extended reality

2025-06-19

Joseph M. Dowell , Anthony Hill Penrose Cofie , John Fuller +2 more | World Journal of Advanced Engineering Technology and Sciences

Supervisory Control and Data Acquisition (SCADA) systems are essential to the operation of intelligent off-grid power systems. In this study, extended reality (XR) technology is used, to perform control room … Supervisory Control and Data Acquisition (SCADA) systems are essential to the operation of intelligent off-grid power systems. In this study, extended reality (XR) technology is used, to perform control room duties remotely, in a combination of real and digital environments, thus eliminating multiple monitor setups, reducing peripheral devices, and enhancing mobility and comfort such as for operators in control room environments. The XR system enhances communications with built-in video, voice, and text chats, as well as screen casting capabilities. The Apple Vision Pro is utilized for the XR system. To execute the XR system, the off-grid power generation system incorporates the supporting intelligent infrastructure; this includes devices such as sensors, smart meters, inverters, and battery storage systems that connect and transmit data via the Internet. The headset of Apple Vision Pro can access the servers of IoT devices through direct applications or by web browser. Once opened, the data windows on server are configured as desired. This data is then analyzed and exported to create real-time grid alerts that produce notifications directly into the operator’s vision., The XR system allows for quicker response times and the overall protection of the off-grid power generation, can be easily setup and utilized.

ADN source-load-storage cooperative two-layer optimal allocation based on ICSQPSO algorithm

2025-06-19

X.R. Li , Hong Zhang , Liang Zhao +2 more | Research Square (Research Square)

<title>Abstract</title> With the continuous advancement of smart grid technology and the extensive application of renewable energy, the traditional one-way passive distribution network is gradually transforming into a two-way interactive, multi-dimensional, … <title>Abstract</title> With the continuous advancement of smart grid technology and the extensive application of renewable energy, the traditional one-way passive distribution network is gradually transforming into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). However, the stochastic and temporal nature of distributed generation (DG) output, coupled with the volatility of loads, poses significant challenges to the resource allocation and operation regulation of ADNs. To address these challenges, this paper proposes a two-layer optimization method for ADN source-load-storage coordination, taking into account demand response. The upper layer, referred to as the planning layer, aims to determine the optimal siting and capacity setting scheme for each device within the active distribution network. Conversely, the lower layer, known as the operation layer, focuses on deriving the optimal scheduling scheme for each flexible resource, including the demand response load. To manage the complexity inherent in the two-layer planning problem, an improved hybrid cuckoo search-based quantum-behaved particle swarm optimization (ICSQPSO) algorithm is introduced. This algorithm enhances computational efficiency and mitigates the risk of falling into local optima. The effectiveness of the proposed model and algorithm is subsequently verified through simulation using the IEEE33 algorithm.

Reliability Analysis of Distribution Systems with Solar-Based DG and Capacitor Integration Using the Water Cycle Optimization Algorithm

2025-06-19

R. Hemalatha , Britto C. John De , T. Yuvaraj +3 more | CRC Press eBooks