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

TL;DR

This work tackles microgrid sizing under uncertainty by formulating a scenario-based stochastic multi-objective optimization that minimizes economic cost $f^E$ and pollutant emissions $f^P$, while capturing nonlinear battery degradation effects. A novel self-adaptive genetic algorithm, SAMOGA, with pre-grouped hierarchical selection and convergence-aware crossover/mutation, solves the resulting MINLP efficiently. The model integrates WT, PV, BESS, and DG with a nonlinear degradation mechanism that updates time-varying BESS capacity and degradation costs, while employing a comprehensive set of operational and balance constraints. Case studies show that the proposed approach outperforms benchmark GAs in both Pareto-optimal quality and diversity, achieving substantial reductions in CO2 emissions as renewable penetration increases, and highlighting the importance of accounting for degradation in life-cycle sizing.

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.

Figures (12)

• Figure 1: System framework of a typical microgrid.
• Figure 2: Illustration of the crossover and mutation operation.
• Figure 3: Each scenario contains generations of WT and PV as well as the load demand and the total scenario number is 125 ($5^{3}$): (a) 5 typical scenarios of WT's output power (kW); (b) 5 typical scenarios of PV's output power (kW); (c) 5 typical scenarios of load requirement (kW).
• Figure 4: The histogram describes the proportion of renewable energy generation (%) in blue with the left axis and PEC ($kg$) in red with the right axis of different solutions in the Pareto optimal frontier (S1 is the abbreviation of solution 1).
• Figure 5: The operations of DERs in one day including generation of WT, PV and DG as well as BESS behavior (charging if positive and discharging if negative).