Environmental and Economic Optimization Model for Electric System Planning in Ningxia, China: Inexact Stochastic Risk-Aversion Programming Approach

Abstract

The main goal of this paper is to provide a novel risk aversion model for long-term electric power system planning from the manager’s perspective with the consideration of various uncertainties. In the proposed method, interval parameter programming and two-stage stochastic programming are integrated to deal with the technical, economics, and policy uncertainties. Moreover, downside risk theory is introduced to balance the trade-off between the profit and risk according to the decision-maker’s risk aversion attitude. To verify the effectiveness and practical application of this approach, an inexact stochastic risk aversion model is developed for regional electric system planning and management in Ningxia Hui Autonomous Region, China. The series of solutions provide the decision-maker with the optimal investment strategy and operation management under different future emission reduction scenarios and risk-aversion levels. The results indicated that pollution control devices are still the main measures to achieve the current mitigation goal and the adjustment of generation structure would play an important role in the future cleaner electricity system with the stricter environmental policy. In addition, the model can be used for generating decision alternatives and helping decision-makers identify desired energy structure adjustment and pollutants/carbon mitigation abatement policies under various economic and system-reliability constraints.

A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China

“…Ahn and Han proposed a two-stage stochastic model to form a mixed network, and optimized public utility supplement and carbon dioxide emission reduction strategies [15]. Ji et al proposed a method that combined two-stage stochastic programming and interval parameter programming to reflect the uncertainties of management, technology, economy, and policy in the power system of Ningxia Hui Autonomous Region, China [16]. Zhang et al proposed an interval two-stage stochastic programming model for grasping the direction of regional energy structure adjustment, controlling resources allocation patterns, and formulating local energy consumption policies in Heilongjiang Province, China [17].…”

A Birandom Chance-Constrained Linear Programming Model for CCHP System Operation Management: A Case Study of Hotel in Shanghai, China

“…It is thus desired that effective uncertain optimization models be advanced. Currently, a number of inexact optimization methods have been widely applied in the energy system [21][22][23][24][25][26][27][28][29][30][31]. For instance, Ersoz and Colak developed four stochastic simulation methods for evaluating the investment feasibility of the CCHP system, including the parametric method, the Monte-Carlo method, the historical trend method, and the scenario-based method [22].…”

Dynamic planning method of economic operation optimization model

“…How to maximize the use of limited resources is an important issue facing modern society 2,3 . With the development of economic research and the exploration of operational research, the dynamic programming method was proposed by American Mathematician Bellman 4‐7 . It is a very effective method to solve the optimization of multi‐level decision‐making process with the principle of optima which has been widely used in many fields.…”

“…2,3 With the development of economic research and the exploration of operational research, the dynamic programming method was proposed by American Mathematician Bellman. [4][5][6][7] It is a very effective method to solve the optimization of multi-level decision-making process with the principle of optima which has been widely used in many fields. Its advantage lies in that it can transform an n-stage decision-making problem into n-stage problems, and solve them one by one, which cannot be done by the classical extreme method.…”