Strength Pareto Evolutionary Algorithm for the Dynamic Economic Emission Dispatch Problem incorporating Wind Farms and Energy Storage Systems

K Alqunun

doi:10.48084/etasr.3508

Abstract

Intermittent and stochastic characteristics of wind energy sources cause many challenges for the existing power networks. One of these challenges is the violation of the energy balance constraint due to the high penetration of wind power. The use of Energy Storage Systems (ESS) can facilitate the high penetration of wind power and mitigate the effect of its intermittency. Within this context, ESS incorporate the Dynamic Economic Emission Dispatch (DEED) problem. The problem is formulated as a multi-objective problem and the Strength Pareto Evolutionary Algorithm (SPEA) is used for its resolution. Simulations were carried out on a well-known ten-unit system and the results show the importance of using ESS in reducing the total production cost of electricity and total emissions.

Highlights

Due to the energy shortage and the increasing pressure to protect the environment, wind energy is gaining attention
It is required to handle these random characteristics of energy sources
A method based on the Strength Pareto Evolutionary Algorithm (SPEA) technique is proposed for solving the Dynamic Economic Emission Dispatch (DEED) problem incorporating wind power

Summary

Introduction

Due to the energy shortage and the increasing pressure to protect the environment, wind energy is gaining attention. In wind-thermal systems, it is important to allocate perfectly the generation of all units including wind power in order to alleviate wind power curtailment This problem is referred to as the power dispatch problem. Several research works have handled the optimal dispatch for the windthermal systems The resolutions of such problems have been based on quadratic programming, Genetic Algorithm (GA) [3], Particle Swarm Optimization (PSO) [4], simulated annealing [5], harmony search [6], firefly algorithm [7], chemical reaction optimization [8], etc. The uncertainty of the wind power output is handled with different manners, such as scenario method [9], forecast error method [10], stochastic programming [11], probability theory-based model [12], fuzzy logic [13], and chance constraint model [14]. In [16], the intermittent nature of the wind power was described by evaluating its underestimation and overestimation costs and was incorporated in the wind-based economic emission dispatch problem

Methods

Results

Conclusion