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The paper presents a methodology for hot spinning reserve allocation (HSRA) following expected disconnection of big generators during peak demand. The proposed method includes generation rescheduling, optimal reactive power allocation, and load curtailment to prevent system cascaded outages. Hybrid grey wolf optimization (HGWO) and Marine Predators Algorithm (MPA) are implemented to solve the economic HSRA problem with total costs minimization. The conversion process of HGWO is enhanced utilizing two internal loops based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The effectiveness of the optimization algorithms is conducted on a 66-bus three-area test system for cost minimization following the outage of the biggest generator in each area. The optimal solution maximizes the social benefits of participating consumers, keeping other consumers' reliability at minimum total cost. The percentages of load curtailment in the three cases are 6.9%, 8% and 6.7% of system load, respectively, where there was no solution for the optimal power flow without shedding some loads to satisfy the network constraints. HGWO and MPA are used to determine the optimal solution. The optimal solution depends on the energy price of load curtailment, system configuration as well as the cost coefficients of generators. [ABSTRACT FROM AUTHOR]

With the increasing development of EVs, the energy demand from the conventional utility grid increases in proportion. On the other hand, photovoltaic (PV) energy sources can overcome several problems when charging EVs from the utility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PVarray, a lithium-ion battery representing the EV battery, and a lead-acid battery representing the energy storage system (ESS). A bidirectional DC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed controllers achieve maximum power extraction from the PVand regulate the DC-link voltage. It also controls the voltage and current levels of both the ESS and the EV during the charging/discharging process. The study has been applied to two cases with different power levels. Analysis, simulation, and implementation of the proposed system are presented. A 120 W laboratory prototype is carried out to verify the system performance, experimentally. Design guides for higher power levels are proposed to help in choosing the proper parameters of the converters. Both the simulation and experimental results are matched and verify the high performance of the proposed system. [ABSTRACT FROM AUTHOR]

Future smart grids will require an observable, controllable and flexible network architecture for reliable and efficient energy delivery. The use of artificial intelligence and advanced communication technologies is essential in building a fully automated system. This paper introduces a new technique for online optimal operation of distributed generation (DG) resources, i.e. a hybrid fuel cell (FC) and photovoltaic (PV) system for residential applications. The proposed technique aims to minimize the total daily operating cost of a group of residential homes by managing the operation of embedded DG units remotely from a control centre. The target is formed as an objective function that is solved using genetic algorithm (GA) optimization technique. The optimal settings of the DG units obtained from the optimization process are sent to each DG unit through a fully automated system. The results show that the proposed technique succeeded in defining the optimal operating points of the DGs that affect directly the total operating cost of the entire system. [ABSTRACT FROM AUTHOR]