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In general, the load demand of a standard microgrid system, changes on an hourly basis. Keeping in line with the rise and fall of this load demand curve, utilities fix different prices at different hours, which is termed as time of usagebased electricity pricing. Elastic and inelastic are the two types of load that comprise the hourly demand of the microgrid system. Demand-side management (DSM) shifts the elastic loads from peak load hours to those hours when the utility charges less thereby, restructuring the entire demand model-based on demand-price elasticity. Considering that the elastic loads contribute about 5% to 20% of the total load consumed during an hour, this paper implements a novel hybrid CSAJAYA algorithm to minimize the overall cost of a microgrid system considering DSM strategy. The various cost components taken into consideration are fuel cost, penalized emission cost, the cost of operation and maintenance, the cost of depreciation, etc. Numerical results depict that 30% to 40% decrement in overall generation cost was realized when DSM-based energy management microgrid system was performed using the novel hybrid algorithm when compared to those available in the literature. Measures of central tendencies analysis claim the superiority of the proposed optimization algorithm.

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Electric vehicle (EV) penetration in the transport section is increasing and replacing the conventional fossil fuel based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location in the electrical network affects the energy loss and voltage deviation. Therefore, the test system considered here, is a road network of urban area overlapped with a 33-bus radial network. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation have been attempted simultaneously using 2-layer optimization. Differential Evolution and Harris Hawks Optimization are the two tools have been used to solve the problem and the final results have been validated using eight other established optimization techniques. 2m point estimation method has been used to take care of uncertainties related to EV and PV. Monte-carlo simulation is also applied to cross verify the performance. The land cost, customer accessibility to charging station have been taken into account to allocate it at proper places. The whole work has been performed based on the 24-hrs dynamically varying EV flows and PV outputs.