Version 1
: Received: 29 July 2018 / Approved: 2 August 2018 / Online: 2 August 2018 (07:45:36 CEST)
Version 2
: Received: 19 December 2018 / Approved: 20 December 2018 / Online: 20 December 2018 (08:51:38 CET)
Version 3
: Received: 12 January 2019 / Approved: 14 January 2019 / Online: 14 January 2019 (10:15:30 CET)
s: Hossain MA, Pota HR, Squartini S, Abdou AF, Modified PSO algorithm forreal-time energy management in grid-connected microgrids, Renewable Energy (2019), doi: https://doi.org/10.1016/j.renene.2019.01.005.
s: Hossain MA, Pota HR, Squartini S, Abdou AF, Modified PSO algorithm forreal-time energy management in grid-connected microgrids, Renewable Energy (2019), doi: https://doi.org/10.1016/j.renene.2019.01.005.
s: Hossain MA, Pota HR, Squartini S, Abdou AF, Modified PSO algorithm forreal-time energy management in grid-connected microgrids, Renewable Energy (2019), doi: https://doi.org/10.1016/j.renene.2019.01.005.
s: Hossain MA, Pota HR, Squartini S, Abdou AF, Modified PSO algorithm forreal-time energy management in grid-connected microgrids, Renewable Energy (2019), doi: https://doi.org/10.1016/j.renene.2019.01.005.
Abstract
Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating an cost function, it is suitably analysed and then a dynamic penalty function to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for the real-time energy management.
Keywords
converter-based microgrids; renewable energy sources; optimum battery control; real-time energy management; particle swarm optimisation
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
