Multiobjective Scheduling of Hybrid Renewable Energy System Using Equilibrium Optimization

Salil Madhav Dubey,Hari Mohan Dubey,Surender Reddy Salkuti,Manjaree Pandit

doi:10.3390/en14196376

Abstract

Due to increasing concern over global warming, the penetration of renewable energy in power systems is increasing day by day. Gencos that traditionally focused only on maximizing their profit in the competitive market are now also focusing on operation with the minimum pollution level. The paper proposes a multiobjective model capable of finding a set of trade-off solutions for the joint optimization problem, considering the cost of reserve and curtailment of power from renewable sources. Managing a hybrid power system is a challenging task due to its stochastic nature mixed with the objective function and complex practical constraints associated with it. A novel metaheuristic Equilibrium Optimizer (EO) algorithm incepted in the year 2020 utilizes the concept of control volume and mass balance for finding equilibrium state is proposed here for computing the optimal schedule and impact of renewable energy integration on profit and emission for different optimization objectives. In this paper, EO has shown dominant performance over well-established metaheuristic algorithms such as particle swarm optimizer (PSO) and artificial bee colony (ABC). In addition, EO produces well-distributed Pareto-optimal solutions and the fuzzy min-ranking is used as a decision maker to acquire the best compromise solution.

Highlights

The electricity demand is increasing day by day due to the growth and evolution of industrial establishments and changing lifestyles
A substantial part of the demand is still being met by thermal power generation, which depends on fossil fuels such as coal, natural gas and petroleum, which are considered the main sources of harmful emissions and air pollution
The performance of the Equilibrium Optimizer (EO) algorithm is tested on standard test cases under dynamic constraints [38,39,40]

Summary

Introduction

The electricity demand is increasing day by day due to the growth and evolution of industrial establishments and changing lifestyles. The power generation sector contributes more than 30% of carbon dioxide emissions to the atmosphere [1]. These pollutant gases affect humans but are responsible for the destruction of other lifeforms. Due to growing concern over environmental considerations, there is a demand for sufficient and secured electricity at the lowest price along with a minimum level of pollution to stabilize the environment. It is possible by multi-objective optimization that considers power generation cost and emission both for minimization. Metaheuristic and hybrid approach includes evolutionary algorithm (EA) [2], genetic algorithm (GA) [4], non-dominating sorting genetic algorithm (NSGA) [5,6,7], particle swarm optimization (PSO) [8,9,10], harmony search (HS) [11,12], differential evolution (DE) [13], hybrid bat algorithm (HBA) [14], kernel search optimization (KSO) [15], time-varying acceleration coefficient particle swarm optimization (TVAC-PSO)

Results

Discussion

Conclusion