Multi‐objective expansion planning of park‐level integrated energy system considering the volatility trend of CETP

Abstract

With the rapid development of carbon trading market, the volatility trend of carbon emission trading price (CETP) becomes one of the factors that cannot be ignored in energy system planning. Based on this, this paper proposes a multi-objective expansion planning model for park-level integrated energy system (PIES) that takes into account the volatility trend of CETP. First, the influencing factors of CETP prediction are filtered and downscaled, and a kernel extreme learning machine (KELM) model based on the improved multi-objective grey wolf algorithm optimizer (IMOGWO) is used for probabilistic interval prediction of CETP. Next, the operational characteristics of each carbon emission device are analysed and a model for calculating the cost of carbon trading is proposed. Then, a two-layer PIES planning model is developed with the objective of minimizing the annualized system cost and carbon emissions during the expansion planning cycle, the upper-layer model is a planning model for solving equipment expansion scenarios, and the lower-layer model is an operation model for calculating typical operation schemes. Finally, the simulation effect of the prediction model is verified by the European carbon trading data, and the planning schemes are compared and analysed to prove the effectiveness of the proposed method.