Research on Double-Layer Optimized Configuration of Multi-Energy Storage in Regional Integrated Energy System with Connected Distributed Wind Power

Zhang, Quanming; Ren, Zhichao; Ma, Ruiguang; Tang, Ming; He, Zhaoshui

doi:10.3390/en12203964

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…In this way, the agent can learn how to obtain the highest reward value according to different States in the environment to obtain the optimal control strategy. MDP (Markov Decision Process) is usually used to model the reinforcement learning problem [8].…”

Section: Loss Optimization Of the Distribution Network Based On A Sin...mentioning

confidence: 99%

“…The electromagnetic transient model of DG is established in PSCAD/EMTDC, and the equivalent dynamic phasor model is established in MATLAB/Simulink. Reference [6] proposes a decision-making method for DG location and capacity of the distribution network considering voltage stability assessment and uses the multi-objective decision-making method to decide the location and capacity of DG access to the distribution network.…”

Section: Introductionmentioning

confidence: 99%

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Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity

Shi¹,

Huang²

2022

J. Phys.: Conf. Ser.

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With the wide application of distributed generation technology, in order to maintain the stable and safe operation of the distribution network, this paper considers the uncertainty of power generation and a load of distributed energy storage. It takes the distribution network with distributed energy storage as the research object, models and analyzes the optimization problem, and studies the problem of DG configuration by using the reinforcement learning method. In the case of distributed grid connection, the voltage distribution change of the distribution network and the impact of network loss are analyzed. The line power flow of the distribution network is calculated by using the MATLAB platform as a platform, and the power flow calculation of the distribution network is simulated and analyzed by the MATLAB platform. The system loss is significantly reduced. The experimental results show that the system loss variance is 29.19. After adding energy storage, the loss variance is 14.77, which is reduced by 49.41%. According to the scheme, the voltage stability and operating loss of the system can be effectively improved.

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Section: Loss Optimization Of the Distribution Network Based On A Sin...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity

Shi¹,

Huang²

2022

J. Phys.: Conf. Ser.

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“…The operation cost of the battery banks is similar as the cost of life of the renewable energy generation equipment (C bl ) [8,15,[35][36][37][38][39][40]. This is presented in Equation (18), where L loss is the battery life loss and C init-bat is the initial investment cost [8].…”

Section: Operation Cost Of the Battery Banks (F 3 (X))mentioning

confidence: 99%

Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling

2020

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Planning the operation scheduling with optimization heuristic algorithms allows microgrids to have a convenient tool. The developments done in this study attain this scheduling taking into account the impact of energy storage useful life in the microgrid operation. The scheduling solutions, proposed for the answer of an optimization problem, are obtained by using a metaheuristic algorithm called Differential Evolutionary Particle Swarm Optimization (DEEPSO). Thanks to the optimization that is conducted in this study, it is possible to formulate dispatches of the existent microgrid (MG) by always looking for the ideal dispatch that implies a lower cost and provides a greater viability to any project related to renewable energy, electric vehicles and energy storage. These advances oblige the battery manufacturers to start looking for more powerful batteries, with lower costs and longer useful life. In this way, this paper proposes a scheduling tool considering the energy storage useful life.

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“…In the optimal configuration of energy storage, double-layer optimization is a common method. For instance, the upper layer of the literature optimizes the configuration of operating targets, and the lower layer optimizes the economic objective [8][9] .…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-objective Optimal Configuration of Distributed Energy Storage System

Cao

Zhang

2023

J. Phys.: Conf. Ser.

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The purpose is to improve the absorption capacity of new energy generation added to the power system, the distributed energy storage system (ESS) is introduced. Considering the new energy generation as the main distributed power supply is united to the power grid, the mathematical model of wind-photoelectric-energy storage system is constructed. Taking active power loss, voltage offset and comprehensive cost as objectives, a multi-objective internal and external layer optimization energy storage device configuration model is established. The outer layer uses branch and bound method to locate the location of energy storage. In the inner layer, NSGA-II algorithm is used to optimize the capacity and the situation of storing or releasing power. Eventually, the IEEE 30 node test system with distributed power supply is used for simulation, and the optimal location, capacity and charging/discharging strategy of the ESS in the grid topology are obtained. The results verify the rationality of the configured energy storage, and show that system can absorb distributed energy to a certain extent.

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Research on Double-Layer Optimized Configuration of Multi-Energy Storage in Regional Integrated Energy System with Connected Distributed Wind Power

Cited by 13 publications

References 21 publications

Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity

Distribution Network Distributed Energy Storage Configuration Optimization Method Considering Variance of Network Loss Sensitivity

Impact of Energy Storage Useful Life on Intelligent Microgrid Scheduling

Research on Multi-objective Optimal Configuration of Distributed Energy Storage System

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