A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid

Year : 2025 | Volume : 12 | Issue : 03 | Page : 29 38
    By

    Mohd Mussa,

  • Parwinder Singh,

  • Kamaljeet Singh,

  1. Post Graduate Student, Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, India
  2. Assistant Professor, Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, India
  3. Assistant Professor, Department of Electrical Engineering, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, India

Abstract

Due to advancement in power electronics field, it is becoming more feasible to integrate renewable energy into power grid. Renewable energy sources are prompting more and more small investors to invest in generation and distribution of renewable energy at microgrid level. The increased competition requires energy producers to offer energy at minimum possible cost to gain the confidence of consumers, which needs efficient methods to schedule energy generation among the available renewable energy sources. To reduce the cost of power generation, the Energy Management System (EMS) uses optimal hourly scheduling of power generation. In a power grid system consisting of renewable energy source, we use energy management system which should gather all the needed information, solve an optimization problem, and communicate the correct allocation of energy back to each distributed energy resource (DER). The main objective of this project is to find optimal power scheduling at each hour with minimum cost among a number of DERs by using the memory-based genetic algorithm. It shares optimally the power generation in a microgrid. Generally, a microgrid consist of wind plants, photovoltaic plants, and a combined heat and power system. Let us consider two cases for optimization of power generation. In the first case we take five renewable generators and one conventional generator, and renewable generators are considered dispatchable. In the second case, two renewable and four conventional generators are considered, and renewable generators are considered non-dispatchable. Finally, to evaluate performance of our designed grid system we will compare both the case with existing literature methods.

Keywords: Microgrid, renewable energy sources, distributed energy resources, economic dispatch, energy management system, optimization, genetic algorithm, memory-based genetic algorithm, power scheduling, cost minimization, dispatchable generators, non-dispatchable generators

[This article belongs to Journal of Semiconductor Devices and Circuits ]

How to cite this article:
Mohd Mussa, Parwinder Singh, Kamaljeet Singh. A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid. Journal of Semiconductor Devices and Circuits. 2025; 12(03):29-38.
How to cite this URL:
Mohd Mussa, Parwinder Singh, Kamaljeet Singh. A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid. Journal of Semiconductor Devices and Circuits. 2025; 12(03):29-38. Available from: https://journals.stmjournals.com/josdc/article=2025/view=227927


References

  1. Guan X, Xu Z, Jia QS. Energy efficient buildings facilitated by microgrid. IEEE Trans Smart Grid. 2010; 1(3): 243–52.
  2. Cristoni E, Raugi M, Shorten R. Plug and play distributed algorithm for optimized power generation in a microgrid. IEEE Trans Smart Grid. 2014; 5(4): 2145–54.
  3. Kim YS, Eom YK, Lee KY, Park JH. Economic load dispatch for piecewise quadratic function using Hopfield neural network. IEEE Trans Power Syst. 1993; 8(3): 1030–8.
  4.  Najibi F, Niknam T. Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties. Energy Convers Manag. 2015; 98: 484–99.
  5. Bahmani-Firouzi B, Azizipanah-Abarghooee R. Optimal sizing of battery storage for microgrid operation management using a new improved bat algorithm. Int J Electr Power Energy Syst. 2014; 56: 42–54.
  6. Kumar KP, Saaravanan B, Swarup KS. Optimization of renewable energy sources in a microgrid using artificial fish swarm algorithm. Energy Procedia. 2016; 90: 107–13.
  7. Fadaee M, Radzi MAM. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: a review. Renew Sustain Energy Rev. 2012; 16(5): 3364–9.
  8. Maulik A, Das D. Optimal operation of microgrid using four different optimization techniques. Sustain Energy Technol Assess. 2017; 21: 100–20.
  9. Amrollahi MH, Bathaee SMT. Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response. Appl Energy. 2017; 202: 66–77.
  10. Azaza M, Wallin F. Multi objective particle swarm optimization of hybrid micro-grid system: a case study in Sweden. Energy. 2017; 123: 108–18.
  11. Li B, Roche R, Miraoui A. Microgrid sizing with combined evolutionary algorithm and MILP unit commitment. Appl Energy. 2017; 188: 547–62.
  12. Zhang J, Wu Y, Guo Y, Wang B, Wang H, Liu H. A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl Energy. 2016; 183: 791–804.
  13. Deihimi A, Zahed BK, Iravani R. An interactive operation management of a micro-grid with multiple distributed generations using multiobjective uniform water cycle algorithm. Energy. 2016; 106: 482–509.
  14. Sedighizadeh M, Esmaili M, Eisapour-Moarref A. Voltage and frequency regulation in autonomous microgrids using hybrid big bang-big crunch algorithm. Appl Soft Comput. 2017; 52: 176–89.
  15. Sahin C, Shahidehpour M, Erkmen I. Allocation of hourly reserve versus demand response for security-constrained scheduling of stochastic wind energy. IEEE Trans Sustain Energy. 2013; 4(1): 219–28.
Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 31/07/2025
Accepted 05/08/2025
Published 27/08/2025
Publication Time 27 Days
33


My IP

PlumX Metrics