This paper proposes a new computerized educational approach to teach the power electronics laboratory. It describes PSpice implementation of the core power electronic circuits that depend on thyristor circuits to identify behaviors with load variations. It uses the developed simulation models to support and enhance power electronics education at the undergraduate level. These simulations successfully integrate the contents of the power electronic laboratory course. A study of the impact of these simulations on the results of the students showed that it helped them to master the course contents and to gain better grades. ß

SUMMARYThis paper addresses the risk-based self-scheduling problem using a hybrid technique between Lagrangian relaxation (LR) and particle swarm optimizer (PSO). The paper analyses a self-scheduling model that accounts for profit and risk simultaneously. The effect of risk is explicitly modelled in the self-scheduling problem taking into account the variance of the market-clearing prices. The forecasted hourly probabilities that spinning and non-spinning reserves are called and generated are also considered in the formulation to simulate the reserve uncertainty. Artificial neural network (ANN) is applied for forecasting the hourly reserve probability. The proposed approach is applied to a 36 unit test system.

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Problems with voltage and stability have arisen as a result of the dramatic development in renewable energy generating units, notably solar energy systems linked to low and medium voltage networks, and the influence of active loads that vary rapidly from time to time during the day. Because reactive power is directly proportional to voltage, its use to renewable energy producing units can only improve their efficiency. Better performance for these controllers is possible via the usage of several controller types. In this paper, we use a salp swarm optimization algorithm (SSA) to design fractional order proportional-integral (FO-PI) controllers, whose job it is to regulate the active and reactive power of solar inverters by compensating for the overvoltage and undervoltage presented by the inverters' ability to absorb and produce reactive power. After that, we compared the FO-PI controller's results to those of the standard PI controller. Grid-connected photovoltaic (PV) system modeling and simulation were performed using the MATLAB/ Simulink modeling and simulation tools. After that, the full PV system was simulated in the most likely situations across a range of grid and weather conditions. We may infer from the simulation results that this model is credible, reliable, and applicable to the analysis of grid-connected PV systems.

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