1.School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;2.State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830000, China;3.Altay Power Supply Company of State Grid Xinjiang Electric Power Co., Ltd., Altay 836300, China;4.China Electric Power Research Institute, Beijing 100192, China
There are many types of sources and loads in a regional integrated energy system, and there are correlation characteristics between different energy sources, between loads, and between energy sources and loads. Therefore, a planning method for regional integrated energy systems considering the correlation of sources and loads is proposed. Firstly, Nataf transform and Cholesky decomposition are used to improve Latin hypercube sampling, and sample matrices are obtained considering the correlation of wind power, photovoltaic and power, electricity, gas, cooling and heat loads. Then, the typical source and load scenarios of the regional integrated energy system are obtained by improving cluster centers. Secondly, a planning model of regional integrated energy systems considering the correlation of multiple heterogeneous sources and loads is established. The objective function of the model is to minimize the sum of investment, operation, external energy transaction cost and curtailment cost of wind power and photovoltaic power. Finally, a test example based on the combination of the IEEE 39-bus power grid system, Belgium 20-bus gas grid system, and 14-bus cooling/heat grid system is simulated to verify the necessity of considering the correlation of multiple heterogeneous sources and loads in the planning of the regional integrated energy system and the feasibility of the proposed method.
This work is supported by National Key R&D Program of China (No. 2018YFE0208400) and State Grid Corporation of China.
[1] | YANG Ruopu, LIU Jia, ZENG Pingliang, et al. Planning of Regional Integrated Energy System Considering Correlation of Multiple Heterogeneous Sources and Loads[J]. Automation of Electric Power Systems,2022,46(16):31-39. DOI:10.7500/AEPS20220321022 |