Solar photovoltaic (PV) is the most promising renewable energy source available on Earth. Three topologies based on a switched-inductor capacitor and non-isolated high-step-up Cuk converter have been proposed for solar PV. These topologies of the Cuk converter have higher boosting ability than conventional Cuk and boost converters and can reduce the voltage stress of the main switch. A small voltage rating and on-state resistance can give higher efficiency of the converter. The voltage boosting ability of all three topologies was compared to each other and with a conventional Cuk converter. The boosting capability of the third converter was 11 times at 0.75 duty cycle with a solar PV source. These converters do not use a coupled inductor and transformer, which leads to less volume, reducing coupling/core saturation loss, and thus the cost of the converter. A solar PV system of 12 volts was used for boosting with these converters for analysis of the feasibility of use with renewables. The three topologies of the switched-inductor and switched-capacitor (SLSC) Cuk converter were designed and simulated in MATLAB/Simulink to evaluate their effectiveness.

No abstract

No abstract

No abstract

No abstract

A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are

No abstract

No abstract