Configuration Planning of Expressway Self-Consistent Energy System Based on Multi-Objective Chance-Constrained Programming

Abstract

Regarding the problem of the optimal configuration of self-consistent energy systems based on a 100% renewable energy supply for expressway electricity demand in no-grid areas, this paper proposes a multi-objective planning model based on chance-constrained programming (CCP) to achieve the optimization objectives of low cost and high reliability. Firstly, the number of units of different types of wind turbines (WT), the capacity of photovoltaic (PV) cells, and the number of sets of energy storage systems (ESS) are selected for the design variables in our configuration plan. After defining the load grading shedding and ESS scheduling strategy, the Monte Carlo Simulation (MCS) method and the backward reduction method are applied to model the uncertainties of electric load and renewable energy sources. Finally, the set of Pareto solutions are optimized by the non-dominated sorted genetic algorithm-II (NSGA-II) and its unique best solution is determined by the Criteria Importance Though Intercriteria Correlation (CRITIC) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. Making use of the wind speed and solar radiation intensity historical data of an area in northwest China in the last five years, eight case studies of two typical scenarios are designed and carried out to explore in-depth the impact of different confidence levels and load fluctuation ranges on the planning results. The results verify that the proposed method can effectively improve the robustness of the system and satisfy the power demand in confidence scenarios.