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A two-stage optimal coordinated scheduling strategy for micro energy grid integrating intermittent renewable energy sources considering multi-energy flexible conversion

Ju, Liwei, Tan, Qinliang, Lin, Hongyu, Mei, Shufang, Li, Nan, Lu, Yan, Wang, Yao
Energy 2020 v.196 pp. 117078
algorithms, carbon, case studies, cell membranes, cooling, electricity, energy, energy costs, financial economics, heat, income, models, natural gas, solar energy, system optimization, uncertainty, wind power, China
In this paper, a two-stage coordinated scheduling model for dealing with the optimization problem of multiple-energy (power, heating, and cooling) synergistic supply in the micro energy grid (MEG) is proposed. To overcome the uncertainty impact of wind power and photovoltaic power, the scheduling cycle was divided into a day-ahead phase and a real-time phase. Then, the day-ahead forecasting results were taken as random variables for the upper-layer model. The actual value was taken as the realization of random variables for the lower-layer model, including the energy storage revised model and demand response (DR) scheduling model. The cell membrane and chaotic search algorithms were employed to improve the particle swarm algorithm. The “international low carbon park” in China was selected for a case study. The case study showed the following results: (1)The two-stage optimization model and solution algorithm achieved the optimal synergistic supply of multiple energy forms. (2) Power-to-gas could convert the surplus electricity into natural gas, realizing multi-directional energy cascade conversion. (3)MEG could flexibly interact with the upper-grade energy network for obtaining more economic benefits. (4)The price-based DR could smooth the energy load curve and maximize the operation revenue of the MEG by utilizing complementary characteristics of energy prices.