Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages

In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimi...

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Bibliographic Details
Published in:Renewable energy Vol. 184; pp. 776 - 790
Main Authors: He, Yi, Guo, Su, Zhou, Jianxu, Ye, Jilei, Huang, Jing, Zheng, Kun, Du, Xinru
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2022
Subjects:
algorithms
batteries
case studies
cost effectiveness
Data-driven model
decision making
evolution
Hybrid energy storage
Multi-objective evolutionary algorithm
Operation threshold optimization
Planning-operation co-optimization
Renewable energy
thermal energy
wind power
Hybrid energy storage
Data-driven model
Renewable energy
Planning-operation co-optimization
Operation threshold optimization
Multi-objective evolutionary algorithm
ISSN:0960-1481, 1879-0682
Online Access:Get full text
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Summary:In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models; (2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ); (3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions.
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ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2021.11.116