Distributed Energy Technologies Planning and Sizing in a Sample Virtual Power Plant Using Speedy Particle Swarm Optimization Algorithm
Subject Areas : International Journal of Smart Electrical Engineering
Mohammad Hosein Salehi
1
,
Mohammadreza Moradian
2
*
,
Majid Moazzami
3
,
Ghazanfar Shahgholian
4
1 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University.
2 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University
3 - Najafabad Branch, Islamic Azad University
4 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran
Keywords:
Abstract :
In modern power networks, once the restructuring of production units is done, traditional power plants will operate as virtual power plants (VPPs), which are actually a collection of distributed generation (DG) units and energy storage systems (ESSs) that form an integrated power plant. Commercial VPPs can replace the current traditional power plants in the near future, because they have many advantages such as organizing distributed energy resources (DER) and hydrogen and electricity storage systems. Considering that energy management and planning of DER resources in VPP have challenging issues, therefore, thoughts such as changes in instantaneous power generation, consumption, energy price and availability of system components should be taken into consideration, so that simulations and future research with problems will not accompanied. Since microgrids have the ability to monitor and control real-time power in power grids, determining the number of DER resources in VPPs is deliberated essential in order to reduce planning costs. For this purpose, in this paper, the optimal sizing of DERs is done using speed particle swarm optimization (SPSO) algorithm. In proposed optimization algorithm, the coefficients c1 and c2 are not constant and is changing according to the number of iterations, which makes the search in the problem solving space more efficient and its convergence is improved by 26% compared to the traditional PSO algorithm. Consequently, the number and sizing of solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), electrolyzer, hydrogen storage and battery resources in a 20-year time horizon will be achieved with the lowest cost.