Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon Emissions

TL;DR

This paper presents a stochastic multi-objective optimization method for microgrid sizing that balances cost and carbon emissions, incorporating battery degradation and using an advanced genetic algorithm for improved solutions.

Contribution

It introduces a novel SMOSO model considering battery degradation and carbon emissions, solved by an enhanced self-adaptive genetic algorithm, advancing microgrid planning techniques.

Findings

The proposed method outperforms other algorithms in solution quality and diversity.

Case studies demonstrate effective microgrid size determination balancing cost and emissions.

The model accurately captures battery degradation effects in optimization.

Abstract

Microgrid serves as a promising solution to integrate and manage distributed renewable energy resources. In this paper, we establish a stochastic multi-objective sizing optimization (SMOSO) model for microgrid planning, which fully captures the battery degradation characteristics and the total carbon emissions. The microgrid operator aims to simultaneously maximize the economic benefits and minimize carbon emissions, and the degradation of the battery energy storage system (BESS) is modeled as a nonlinear function of power throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is proposed to solve the SMOSO model, and this algorithm is enhanced by pre-grouped hierarchical selection and self-adaptive probabilities of crossover and mutation. Several case studies are conducted to determine the microgrid size by analyzing Pareto frontiers, and the simulation results validate that the proposed method has superior performance over other algorithms on the solution quality of optimum and diversity.