Abstract
In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
Highlights
In order to make a robust and more reliable power grid, peak demand is taken into account rather than the average demand
V, particles’ pressure, t = 0, peak hours (PHs), off-peak hours (OPHs), H and PB = 0, 1; Generate initial random population; for t = 1 to T do for h = 1 to H do for i=1 to P do Assign random positions and velocities to air particles; Evaluate fitness of each air parcel Equation (3); Identify the best solution among all air parcels; while number of iterations reached to specified limits do if t == PHs swap (OPH, PH); if EnergyConsumption == high
Four hours are taken as PHs such that the PHs vary from season to season [43]
Summary
In order to make a robust and more reliable power grid, peak demand is taken into account rather than the average demand. A DSM program provides support towards power grid functionalities in various areas, such as electricity market control, infrastructure maintenance and management of decentralized energy resources [2]. In electricity markets, it informs the load controller about the latest load schedule and possible load reduction capabilities for each time step of the day. Electricity bills and aggregated power consumption are reduced in [22] by using mixed integer non-linear programming These techniques do not take into account the large number of different household appliances.
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