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Optimal operations of energy storage systems in multi‐application scenarios of grid ancillary services based on electricity price forecasting
Author(s) -
Han Xiaojuan,
Hong Zhenpeng,
Su Yu,
Wang Zuran
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6300
Subject(s) - computer science , grid , particle swarm optimization , electricity , electricity market , revenue , operations research , simulation , engineering , economics , geometry , mathematics , electrical engineering , accounting , machine learning
Summary Since the economy of the energy storage system (ESS) participating in power grid ancillary services is greatly affected by electricity price factors, a flexible control method of the ESS participating in grid ancillary services based on electricity price forecasting is proposed in this paper, and the economic evaluation of the ESS participating in ancillary services is realized by the net present value (NPV) method. To improve the accuracy, the Markov chain is adopted to correct the predicted electricity price value obtained by the combination of the particle swarm optimization (PSO) and back propagation (BP) neural network according to the systematic errors in the modelling process. Based on the compatibility of each ancillary service, taking the maximum revenue of the system as the objective function, a flexible control model of the ESS participating in ancillary services based on electricity price forecasting is established, and the YALIMP+CPLEX software toolbox in MATLAB is adopted to solve the problem of electricity price forecasting, and the capacity division of the ESS under the ancillary service application scenarios is realized. The NPV method is used to evaluate the economics of the ESS participating in ancillary services under this strategy. The simulation analysis of the actual operation data from a power grid in China validates the effectiveness of the proposed method. The simulation results show the proposed method can provide theoretical support for the ESS participating in power grid ancillary service markets.