Microgrids are a group of interconnected loads, distributed energy resources (including conventional energy sources and renewables) and energy storage systems at a distribution level with distinct electrical boundaries. A microgrid has black start capability and can operate either in isolated or non-isolated mode in connection with other microgrids or main electricity grid.
This energy exchange strategy development motivates microgrid operators to adapt their energy trading actions with the main grid and/or other microgrids according to the current electricity price and trading conditions in order to minimize energy production running cost (fuel cost), ensure maximum utilization of renewables, maximize economic benefits of the energy storage systems. To achieve this, specific energy management system should have to be put in place [1,2,3,4].
Reference [12] presents a genetic algorithm (GA) for optimal unit sizing of an isolated microgrid considering multiple objectives including life-cycle cost minimization, renewable energy penetration maximization, and emission reduction. In [13, 14], particle swarm optimization (PSO) has been applied for real-time energy management of stand-alone microgrids.
In most of the literatures reported above, regarding energy management strategies in microgrids, a single energy storage unit is considered. The integration and combined optimal storage management of microgrids containing two or more energy storage units (ESUs) have not been considered so far. Moreover, the PSO is seen to suffer from stagnation once particles have prematurely converged to any particular region of the search space in the energy management strategies that have applied the standard version of PSO for solving the energy management optimization problem [15].
In this paper, we propose a RegPSO approach to optimally solve the EMS optimization model. To evaluate and compare the performances of this approach, another modern optimization method, genetic algorithm (GA) was also implemented.
The rest of the paper is organized as follows. Section II discusses the formulations of the objective and constraint functions. In Section III, the proposed method of optimal energy management strategy and the RegPSO algorithm are presented. The case study simulation results are discussed and performance comparisons are provided in Section IV, and finally the paper is concluded in Section V.
This objective function is subjected to six decision variables: the charging/discharging power of the VRB, state of charge (SOC) of the VRB, charging/discharging power of the Li-Ion battery, SOC of the Li-Ion battery, the diesel generator power output, and the quantity of power exchange with the main grid.
The objective functions are formulated independently by considering three operational cases based on the microgrid operating mode and the power flow directions between the microgrid and the main grid. In case I, the objective function for the isolated mode of operation is considered. In case II, the microgrid is in grid-connected mode and it receives (buys) power from the main grid. While in case III, the microgrid is also in grid-connected mode but it sends (sells) power to the main grid.
Where, n is the number of time steps for a scheduling day; m indicates the number of all types of dispatchable DGs; q is the number of all types of energy storage units within the microgrid; P i (t) is the power output of the ith dispatchable DG at time t and F i (P i (t)) is the corresponding fuel cost function, and for a diesel generator it is defined as:
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