Scenario Based Cost Optimization of Water Distribution Networks Powered by Grid-Connected Photovoltaic Systems

Authors

Mirhan Ürkmez, Carsten Skovmose Kallesøe, Jan Dimon Bendtsen, John Leth

Type: Preprint

Publication Date: 2023-01-01

Citations: 0

DOI: https://doi.org/10.48550/arxiv.2307.00845

View Ask PDF

Abstract

The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous study and is designed to reduce the economic costs associated with operating the WDN. To account for the uncertainty of the system, the problem is solved in a scenario optimization framework, where multiple scenarios are sampled from the uncertain variables related to PV power production. To accomplish this, a day-ahead PV power prediction method with a stochastic model is employed. The method is tested on a high-fidelity model of a WDN of a Danish town and the results demonstrate a substantial reduction in electrical costs through the integration of PVs, with PVs supplying $66.95\%$ of the required energy. The study also compares the effectiveness of the stochastic optimization method with a deterministic optimization approach.

Locations

arXiv (Cornell University)
View
DataCite API
View

Ask a Question About This Paper

Summary

Scenario Based Cost Optimization of Water Distribution Networks Powered by Grid-Connected Photovoltaic Systems

2023-08-16

Mirhan Ürkmez , Carsten Skovmose Kallesøe , Jan Dimon Bendtsen +1 more

The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous … The paper presents a predictive control method for the water distribution networks (WDNs) powered by photovoltaics (PVs) and the electrical grid. This builds on the controller introduced in a previous study and is designed to reduce the economic costs associated with operating the WDN. To account for the uncertainty of the system, the problem is solved in a scenario optimization framework, where multiple scenarios are sampled from the uncertain variables related to PV power production. To accomplish this, a day-ahead PV power prediction method with a stochastic model is employed. The method is tested on a high-fidelity model of a WDN of a Danish town and the results demonstrate a substantial reduction in electrical costs through the integration of PVs, with PVs supplying 66.95% of the required energy. The study also compares the effectiveness of the stochastic optimization method with a deterministic optimization approach.

Chat View PDF

Optimizing Photovoltaic Panel Quantity for Water Distribution Networks

2024-12-19

Mirhan Ürkmez , Carsten Skovmose Kallesøe , Jan Dimon Bendtsen +1 more

The paper introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find … The paper introduces a procedure for determining an approximation of the optimal amount of photovoltaics (PVs) for powering water distribution networks (WDNs) through grid-connected PVs. The procedure aims to find the PV amount minimizing the total expected cost of the WDN over the lifespan of the PVs. The approach follows an iterative process, starting with an initial estimate of the PV quantity, and then calculating the total cost of WDN operation. To calculate the total cost of the WDN, we sample PV power profiles that represent the future production based on a probabilistic PV production model. Simulations are conducted assuming these sampled PV profiles power the WDN, and pump flow rates are determined using a control method designed for PV-powered WDNs. Following the simulations, the overall WDN cost is calculated. Since we lack access to derivative information, we employ the derivative-free Nelder-Mead method for iteratively adjusting the PV quantity to find an approximation of the optimal value. The procedure is applied for the WDN of Randers, a Danish town. By determining an approximation of the optimal quantity of PVs, we observe a 14.5\% decrease in WDN costs compared to the scenario without PV installations, assuming a 25 year lifespan for the PV panels.

Chat View PDF

Multi-objective planning of community energy storage systems under uncertainty

2024-03-01

K.B.J. Anuradha , José Iria , Chathurika P. Mediwaththe

This paper evaluates how the planning of a community energy storage system (CESS) under different energy pricing schemes (EPSs) can benefit low-voltage (LV) prosumers and the CESS provider equitably. To … This paper evaluates how the planning of a community energy storage system (CESS) under different energy pricing schemes (EPSs) can benefit low-voltage (LV) prosumers and the CESS provider equitably. To this end, we present a multi-objective stochastic optimization framework to minimize the investment and operating cost of the CESS provider and operating costs of prosumers, taking into account the uncertainties of real and reactive energy consumption and photovoltaic (PV) generation of prosumers. We exploit the hierarchical (ϵ- constraint) method to rank the objectives of the CESS provider and prosumers based on their importance for decision-makers under three EPSs. Furthermore, the uncertainties are modeled using the normal probability distribution. Then, the roulette wheel mechanism (RWM) is exploited to formulate a scenario-based stochastic program. The initial scenarios obtained from the RWM, are then reduced using the K-Means clustering algorithm, to make the problem tractable. Our experiments show that under the EPS where the CESS provider trades energy with prosumers at the average grid energy price, and the objective of the CESS provider is traded-off moderately to improve the objective of prosumers, spreads the benefits for both beneficiaries most equitably.

Chat View PDF

Statistical machine learning–based stochastic planning for photovoltaics

2025-01-01

Xueqian Fu , Chen Zhang , Xianping Wu +1 more

Data-driven Optimization Approaches to the Power System Planning under Uncertainty

2022-01-01

Shuhan Lyu

In order to protect the environment and address fossil fuel scarcity, renewable energy is increasingly used for power generation. However, due to the uncertainties it brings to electricity production, deterministic … In order to protect the environment and address fossil fuel scarcity, renewable energy is increasingly used for power generation. However, due to the uncertainties it brings to electricity production, deterministic optimization is no longer sufficient for operational needs. Therefore, a large number of optimization techniques under uncertainty have been proposed, which provide good ways to address uncertainties. This paper selects three of the more important optimization techniques under uncertainty to introduce: stochastic programming (SP), robust optimization (RO), and a novel approach named distributionally robust optimization (DRO) based on the first two. We explain the basic framework and general process of each approach using specific examples. The focus is on how each method addresses the uncertainties. In addition, we also compare their strengths and weaknesses and discuss future research directions.

Chat View PDF

An Efficient Scenario-based Stochastic Energy Management of Distribution Networks with Distributed Generation, PV Module, and Energy Storage

2019-01-01

Mohammad Rasoul Narimani , Ali Azizivahed , Ehsan Naderi

Incorporating Renewable Energy Sources (RES) incurs a high level of uncertainties to electric power systems. This level of uncertainties makes the conventional energy management methods inefficient and jeopardizes the security … Incorporating Renewable Energy Sources (RES) incurs a high level of uncertainties to electric power systems. This level of uncertainties makes the conventional energy management methods inefficient and jeopardizes the security of distribution systems. In this connection, a scenario-based stochastic programming is introduced to harness uncertainties in the load, electricity price, and photovoltaic generation. Further, a hybrid evolutionary algorithm based on Grey Wolf Optimizer and Particle Swarm Optimisation algorithm is proposed to find the best operation cost, and Energy Not Supplied (ENS) as two important objective functions, which almost always are in stark contrast with each other. The proposed algorithm is applied to the modified IEEE 69-bus test system and the results are validated in terms of efficiency, which indicates a cogent trade-off between the fitness functions addressed above.

Chat View PDF

Energy Scheduling of VPPs with Renewable Generation: A Hybrid Data-Driven Convex Hull Approach

2024-11-29

Zipeng Liang , Safwat Khair Rayeem , C.Y. Chung +3 more

A Stochastic Incentive-based Demand Response Program for Virtual Power Plant with Solar, Battery, Electric Vehicles, and Controllable Loads

2024-05-31

Pratik Harsh , Hongjian Sun , Debapriya Das +2 more

The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power … The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power plant (VPP) facilitates this coordination, thereby amplifying their impact on the transmission level of the power grid. Further, a demand response program enhances the scheduling approach by managing the energy demands in parallel with the uncertain energy outputs of the DERs. This work presents a stochastic incentive-based demand response model for the scheduling operation of VPP comprising solar-powered generating stations, battery swapping stations, electric vehicle charging stations, and consumers with controllable loads. The work also proposes a priority mechanism to consider the individual preferences of electric vehicle users and consumers with controllable loads. The scheduling approach for the VPP is framed as a multi-objective optimization problem, normalized using the utopia-tracking method. Subsequently, the normalized optimization problem is transformed into a stochastic formulation to address uncertainties in energy demand from charging stations and controllable loads. The proposed VPP scheduling approach is addressed on a 33-node distribution system simulated using MATLAB software, which is further validated using a real-time digital simulator.

Chat View PDF

Methodology for distribution network planning considering uncertainty in wind and solar power output

2025-05-16

Hongtao Li , Ergang Zhao , Bo Wang +3 more

The increasing penetration of new energy sources in power systems has significantly heightened uncertainty factors within distribution networks, thereby imposing elevated demands on their planning, operation, and control. This paper … The increasing penetration of new energy sources in power systems has significantly heightened uncertainty factors within distribution networks, thereby imposing elevated demands on their planning, operation, and control. This paper presents a comprehensive methodology to address these challenges. Initially, quantitative modeling of uncertainty factors within distribution networks is conducted, establishing a source-load output model. Subsequently, the correlation between photovoltaic generation and electrical loads is investigated, leading to the development of a probabilistic power flow calculation method that accounts for this correlation. Finally, an optimization framework is constructed with the objective function of minimizing planning costs. The results validate the effectiveness of the proposed methodology in reducing network losses and minimizing network planning expenses. This research contributes to enhancing the reliability and cost-effectiveness of distribution network operations in the face of increased renewable energy penetration and uncertainty.

Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric

2024-12-01

Hamed Haggi , James M. Fenton

The decreasing costs of photovoltaic (PV) systems and battery storage, alongside the rapid rise of electric vehicles (EVs), present a unique opportunity to revolutionize energy use in apartment complexes. Generating … The decreasing costs of photovoltaic (PV) systems and battery storage, alongside the rapid rise of electric vehicles (EVs), present a unique opportunity to revolutionize energy use in apartment complexes. Generating electricity via PV and batteries is currently cheaper and greener than relying on grid power, which is often expensive. Yet, residents in multi-building apartment complexes typically lack access to fast EV charging infrastructure. To this end, this paper investigates the feasibility and energy management of deploying commercial PV-powered battery storage and EV fast chargers within apartment complexes in Orlando, Florida, operated by complex owners. By modeling the complex as a grid-connected microgrid, it aims to meet residents' energy needs, provide backup power during emergencies, and introduce a profitable business model for property owners. To address PV power generation uncertainty, a distributionally robust chance-constrained optimization method using the Wasserstein metric is employed, ensuring robust and reliable operation. The techno-economic analysis reveals that EVs powered by PV and batteries are more cost-effective and environmentally friendly than gasoline vehicles that EV owners can save up to 100 dollars per month by saving on fuel costs. The results also show that integrating PV and battery systems reduces operational costs, lowers emissions, increases resilience, and supports EV adoption while offering a profitable business model for property owners. These findings highlight a practical and sustainable framework for advancing clean energy use in residential complexes.

Chat View PDF

An Efficient Scenario-Based Stochastic Model for Dynamic Operational Scheduling of Community Microgrids with High Penetration Renewables

2017-01-01

Farhad Samadi Gazijahani , Javad Salehi

The supply of electrical energy is being increasingly sourced from renewable generation resources. The variability and uncertainty of renewable generation, compared to a dispatch-able plant, is a significant dissimilarity of … The supply of electrical energy is being increasingly sourced from renewable generation resources. The variability and uncertainty of renewable generation, compared to a dispatch-able plant, is a significant dissimilarity of concern to the traditionally reliable and robust distribution systems. In order to reach the optimal operation of community Micro-grids including various Distributed Energy Resource, the stochastic nature of renewable generation should be considered in the decision-making process. To this end, this paper proposes a stochastic scenario based model for optimal dynamic energy management of Micro-grids with the goal of cost and emission minimization as well as reliability maximization. In the proposed model, the uncertainties of load consumption and also, the available output power of wind and photo-voltaic units are modeled by a scenario-based stochastic programming. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. Finally, an improved meta-heuristic algorithm based on Cuckoo Optimization Algorithm (COA) is implemented to yield the best global optimal solution. The proposed framework is applied in the typical grid-connected Micro-grids in order to verify its efficiency and feasibility.

Chat View PDF

GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks

2017-03-22

Ajay Kumar Sampathirao , Pantelis Sopasakis , Alberto Bemporad +1 more

Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper, we … Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper, we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona.

Chat View PDF

GPU-accelerated stochastic predictive control of drinking water networks

2016-01-01

Ajay Kumar Sampathirao , Pantelis Sopasakis , Alberto Bemporad +1 more

Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper we … Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona.

Chat View PDF

Economic Model Predictive Control of Water Distribution Systems with Accelerated Optimization Algorithm

2024-06-10

Saskia A. Putri , Faegheh Moazeni , Javad Khazaei

Model predictive control (MPC) has emerged as an effective strategy for water distribution systems (WDSs) management. However, it is hampered by the computational burden for large-scale WDSs due to the … Model predictive control (MPC) has emerged as an effective strategy for water distribution systems (WDSs) management. However, it is hampered by the computational burden for large-scale WDSs due to the combinatorial growth of possible control actions that must be evaluated at each time step. Therefore, a fast computation algorithm to implement MPC in WDSs can be obtained using a move-blocking approach that simplifies control decisions while ensuring solution feasibility. This paper introduces a least-restrictive move-blocking that interpolates the blocked control rate of change, aiming at balancing computational efficiency with operational effectiveness. The proposed control strategy is demonstrated on aggregated WDSs, encompassing multiple hydraulic elements. This implementation is incorporated into a multi-objective optimization framework that concurrently optimizes water level security of the storage tanks, smoothness of the control actions, and cost-effective objectives. A fair comparison between the proposed approach with the non-blocking Economic MPC is provided.

Chat View PDF

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

2024-04-10

Vincent Henkel , Lukas Peter Wagner , Felix Gehlhoff +1 more

The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen … The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen production via electrolysis offers a viable solution for converting and storing this volatile renewable energy. However, the inherent fluctuations of renewable energy sources present challenges for consistent utilization and integration of green hydrogen. This work proposes a two-stage optimization approach, combining site-wide optimization and real-time optimization for managing systems of electrolyzers. By adapting an existing static optimization model, dual use is achieved in both site-wide optimization and real-time optimization. The hierarchical optimization structure, characterized by distinct temporal resolutions, enables effective responses to both dynamic changes and long-term trends. The side-wide optimization layer generates long-term plans based on forecast data, while the real-time optimization layer refines these plans in real-time, accommodating immediate fluctuations and ensuring efficient operation. The results from the case study on a system of electrolyzers demonstrate the method's effectiveness in aligning electrolyzer operation with actual availability of renewable energy. This approach offers a robust framework for optimizing the operation of electrolyzers but also other types of flexible energy resources, contributing to sustainable and economically viable energy management.

Chat View PDF

A Control-Inspired Approach for Energy Transition Planning Under Uncertainty

2025-05-01

Molly A. McDonald , Eric D. Larson , Chris Greig +1 more

Multi-Objective Planning of Community Energy Storage Systems Under Uncertainty

2023-01-01

K.B.J. Anuradha , Chathurika P. Mediwaththe

This paper evaluates how the planning of a community energy storage (CES) system under different energy trading schemes (ETSs) can benefit low voltage (LV) prosumers and the CES provider equitably. … This paper evaluates how the planning of a community energy storage (CES) system under different energy trading schemes (ETSs) can benefit low voltage (LV) prosumers and the CES provider equitably. First, we consider an ETS where the CES provider trades energy with prosumers at the average grid energy trading price, second, an ETS where the CES provider trades energy at a higher price than the grid energy trading price, and third, an ETS where the CES provider trades energy at a lower price than the grid energy trading price. To this end, we present a multi-objective stochastic optimization framework to minimize the investment and annual operating costs of the CES provider and annual operating costs of prosumers, taking into account the uncertainties of real and reactive energy consumption and photovoltaic (PV) generation of prosumers. The uncertainties are modeled using the normal probability density function. Then, the roulette wheel mechanism (RWM) is exploited to formulate a scenario-based stochastic program. The initial scenarios obtained from the RWM, are then reduced using the K-Means clustering algorithm, to make the problem tractable. Our experiments show that the ETS where the CES provider trades energy at the average grid energy trading price benefits prosumers and the CES provider more equitably than the other two ETSs.

Chat View PDF

Wasserstein Distance-Based Renewable Scenario Generation Method

2024-11-29

Zhi Cai , Shengnan Xu , Linghao Li +1 more

Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints

2022-05-04

Dafeng Zhu , Bo Yang , Chengbin Ma +4 more

Chat View PDF

Stochastic MPC for energy hubs using data driven demand forecasting

2023-01-01

Francesco Micheli , Varsha Behrunani , Jonas Mehr +2 more

Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase … Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.

Chat View PDF