Multi-Objective Optimal Scheduling for Multi-Renewable Energy Power System Considering Flexibility Constraints

As renewable energy penetration increases, the lack of flexibility in a multi-renewable power system can seriously affect its own economics and reliability. To address this issue, three objectives are considered in this study: power fluctuations on tie-line, operating cost, and curtailment rate of r...

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Bibliographic Details
Published in:Processes 2022-07, Vol.10 (7), p.1401
Main Authors: Yang, Lei, Huang, Wei, Guo, Cheng, Zhang, Dan, Xiang, Chuan, Yang, Longjie, Wang, Qianggang
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Distributed generation
Economic forecasting
Electric rates
Flexibility
Multiple objective analysis
Operating costs
Optimization
Particle swarm optimization
Pollutants
Renewable energy
Renewable resources
Scheduling
Self sufficiency
Standard deviation
Systems stability
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Summary:As renewable energy penetration increases, the lack of flexibility in a multi-renewable power system can seriously affect its own economics and reliability. To address this issue, three objectives are considered in this study: power fluctuations on tie-line, operating cost, and curtailment rate of renewable energy. Presented also is an optimal day-ahead scheduling model based on the MREPS for distributed generations with flexibility constraints. The multi-objective particle swarm optimization (MOPSO) algorithm can be applied to obtain a set of Pareto non-dominated solutions for the day-ahead scheduling strategy with the proposed model. By using fuzzy comprehensive evaluation, the optimal compromise solution is determined in the set. The presented method sacrifices a small amount of economy and power fluctuation, but it can reduce the deviation between forecast and realized power fluctuations on the tie-line, while improving the utilization of renewable energy.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr10071401