Abstract
In this study, a type-2 fuzzy chance-constrained fractional integrated programming (T2FCFP) approach is developed for the planning of sustainable management in an electric power system (EPS) under complex uncertainties. Through simultaneously coupling mixed-integer linear programming (MILP), chance-constrained stochastic programming (CCSP), and type-2 fuzzy mathematical programming (T2FMP) techniques into a fractional programming (FP) framework, T2FCFP can tackle dual objective problems of uncertain parameters with both type-2 fuzzy characteristics and stochastic effectively and enhance the robustness of the obtained decisions. T2FCFP has been applied to a case study of a typical electric power system planning to demonstrate these advantages, where issues of clean energy utilization, air-pollutant emissions mitigation, mix ratio of renewable energy power generation in the entire energy supply, and the displacement efficiency of electricity generation technologies by renewable energy are incorporated within the modeling formulation. The suggested optimal alternative that can produce the desirable sustainable schemes with a maximized share of clean energy power generation has been generated. The results obtained can be used to conduct desired energy/electricity allocation and help decision-makers make suitable decisions under different input scenarios.
Highlights
Effective planning of electric power systems (EPS) is of great significance for environmental protection and economic development
This study aims to develop an integrated modeling method for EPS management of uncertainties and risks based on type-2 fuzzy programming
The problem is to determine the least cost plan for allocation of electricity supplies and expansion choices under environmental protection and growing power requirement limitations over the 15-year planning horizon; by involving the type-2 fuzzy chance-constrained fractional programming (T2FCFP) method the generation expansion planning (GEP) problem can achieve the aim of maximization renewable energy generation
Summary
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China Institute for Energy, Environment and Sustainability Research, UR-NCEPU, North China Electric Power Institute for Energy, Environment and Sustainable Communities, UR-BNU, 3737 Wascana Parkway, Regina, SK S4S 0A2, Canada
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