Development of a stochastic simulation–optimization model for planning electric power systems – A case study of Shanghai, China

• Published in: Energy conversion and management Vol. 86; pp. 111 - 124
• Main Authors: Piao, M.J., Li, Y.P., Huang, G.H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2014; Elsevier
• Subjects: Applied sciences; Chance-constrained; Computer simulation; Cost engineering; Electric power systems; Electricity; Electricity consumption; Energy; Exact sciences and technology; Inductively coupled plasma; Interval; Mathematical models; Monte Carlo; Monte Carlo methods; Support-vector-regression; Uncertainty; Asia; China; China, People's Rep., Shanghai; China, People's Rep; Uncertainty; Chance-constrained; Support-vector-regression; Monte Carlo; Electric power systems; Interval; Simulation model; Planning; Optimization
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2014.05.011

•A stochastic simulation–optimization model (SSOM) is developed for planning EPS.•It can reflect risk of violating system constraints under uncertainty.•SSOM can predict electricity demand and optimize energy allocation.•Scenarios associated with SO2-emission mitigation policy are analyzed.•Results create tradeoffs among system cost, energy generation and SO2 mitigation. In this study, a stochastic simulation–optimization model (SSOM) is developed for planning electric power systems (EPS) under uncertainty. SSOM integrates techniques of support-vector-regression (SVR), Monte Carlo simulation, and inexact chance-constrained programming (ICP) into a general framework. SVR coupled Monte Carlo technique is used to predict the electricity consumption amount; ICP is effective for reflecting the reliability of satisfying (or risk of violating) system constraints under uncertainty. The SSOM can not only predict the electricity demand exactly, but also allows uncertainties presented as interval values and probability distributions. The developed SSOM is applied to a real-case study of planning the EPS of Shanghai, with an objective of minimizing system cost and under constraints of resources availability and environmental regulations. Different scenarios associated with SO2-emission policies are analyzed. Results are valuable for (a) facilitating predicting electricity demand, and generating useful solutions including the optimal strategies regarding energy sources allocation, electricity conversion technologies, and capacity expansion schemes, (b) resolving of conflicts and interactions among economic cost, electricity generation pattern, SO2-emission mitigation, and system reliability, and (c) identifying strategies for improving air quality in Shanghai through analyzing the economic and environmental implications associated with SO2-emission reduction policies.