An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG
Xiaohui Yang,
Xiaopeng Wang,
Zhengyang Leng,
Yeheng Deng,
Fuwei Deng,
Zhonglian Zhang,
Li Yang and
Xiaoping Liu
Renewable Energy, 2023, vol. 211, issue C, 307-325
Abstract:
The popularization of combined cooling, heating and power microgrid containing renewable energy sources (RES-CCHP MG) can effectively alleviate the energy crisis and reduce the emission of air pollutants. However, uncertainties in renewable energy generation and load negatively affect the operation of microgrid. To solve this problem, a multi-time scale optimal dispatch strategy combining robust optimization and rolling optimization is proposed. First, the price-based demand response is introduced to establish the RES-CCHP MG model that is more in line with practical application requirements. In the day-ahead dispatching stage, robust adjustment coefficients are introduced to improve the defects of traditional robust optimization which is conservative, and a two-layer robust optimization model is established to improve the ability to resist risks. In the intra-day dispatching stage, in order to reduce the impact of day-ahead prediction error and real-time power fluctuation, the hierarchical rolling optimization model based on model prediction control is established to face the situation that different loads have different response speeds. The simulation results show that compared with the traditional optimal dispatching strategy, the operating cost and the peak-to-valley difference of electric load are reduced by 5.4% and 14.7%, respectively, and the utilization of renewable energy is increased by 4.8%.
Keywords: Renewable energy source; Combined cooling heating and power; Uncertainty; Two-layer robust optimization; Model predicative control (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:211:y:2023:i:c:p:307-325
DOI: 10.1016/j.renene.2023.04.103
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