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

Yang Zhang,Zheng Li,Qing Hu,Zhenghui Fu,Huaicheng Guo,Yulei Xie

doi:10.3390/su12104272

Abstract

In this study, a comprehensive research framework coupled with electric power demand forecasting, a regional electric system planning model, and post-optimization analysis is proposed for electric power system management. For dealing with multiple forms of uncertainties and dynamics concerning energy utilization, capacity expansions, and environmental protection, the inexact two-stage stochastic robust programming optimization model was developed. The novel programming method, which integrates interval parameter programming (IPP), stochastic robust optimization (SRO), and two-stage stochastic programming (TSP), was applied to electric power system planning and management in Harbin, China. Furthermore, the Gray-Markov approach was employed for effective electricity consumption prediction, and the forecasted results can be described as interval values with corresponding occurrence probability, aiming to produce viable input parameters of the optimization model. Ten scenarios were analyzed with different emissions reduction levels and electricity power structure adjustment modes, and the technique for order of preference by similarity to ideal solution (TOPSIS) was selected to identify the most influential factors of planning decisions by selecting the optimal scheme. The results indicate that a diversified power structure that dominates by thermal power and is mainly supplemented by biomass power should be formed to ensure regional sustainable development and electricity power supply security in Harbin. In addition, power structure adjustment is more effective than the pollutants emission control for electricity power system management. The results are insightful for supporting supply-side energy reform, generating an electricity generation scheme, adjusting energy structures, and formulating energy consumption of local policies.

Highlights

Top speed economic development, sustained industrial expansion, and soaring energy demands all place a higher pressure on decision-makers to establish energy-related plans that address, for example, energy utilization, structure adjustment, and pollutant emission reduction, especially for China
In the process of establishing a regional electric power management scheme, decision-makers are confronted with multiple forms of uncertain information relating to generation and consumption parameters, which are introduced from the features and purposes of the energy system itself, as well as to social–economic and technology factors
An electricity consumption system is a gray system that exhibits the features of uncertainty and dynamic; the Gray-Markov forecasting approach is an effective method for electricity consumption prediction of the regional electric power system, which is based on the Gray forecasting model (GM) and the Markov chain forecasting model

Summary

Introduction

Top speed economic development, sustained industrial expansion, and soaring energy demands all place a higher pressure on decision-makers to establish energy-related plans that address, for example, energy utilization, structure adjustment, and pollutant emission reduction, especially for China. In the process of establishing a regional electric power management scheme, decision-makers are confronted with multiple forms of uncertain information relating to generation and consumption parameters, which are introduced from the features and purposes of the energy system itself, as well as to social–economic and technology factors. Such uncertainties directly weaken the viability of generating expected electric power system management strategies [3,4,5]. Wu et al proposed a general optimization framework by integrating the inexact fuzzy programming method and the inexact stochastic programming method to the heat supply management of an actual wind power heating system, where uncertainties were presented in multiple forms [13]

Objectives

Methods

Results

Conclusion