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This paper describes a model for power distribution grid simulation with distributed generation, designed to be a simulation environment of two proposed elements: (1) an algorithm to reconfigure the circuit topology to enable a microgrid during an outage of power supply, and (2) a specific limiter to allow safe operation of distributed generation in the network. Moreover, an interface of the simulation model with a SCADA was designed to suggest possible operational screens for both: the generator and the utility. The simulation model was designed with a synchronous generator with all cogeneration typical controls: speed, active power, voltage, reactive power and power factor. Thus, it is possible to simulate critical situations with all control modes available in a typical cogeneration plant in addition to the proposed limiter. Due to the immense number of possible combinations of circuit breakers in the grid, the proposed algorithm is based on genetic algorithms to enable a fast enough autonomous reconfiguration to avoid the outage perception by the customer. [ABSTRACT FROM AUTHOR]

This paper presents a particle swarm optimization (PSO) as a tool for loss reduction study. This issue can be formulated as a nonlinear optimization problem. The proposed application consists of using a developed optimal power flow based on loss minimization function by expanding the original PSO. The study is carried out in two steps. First, by using the tangent vector technique, the critical area of the power system is identified under the point of view of voltage instability. Second, once this area is identified, the PSO technique calculates the amount of shunt reactive power compensation that takes place in each bus. The proposed approach has been examined and tested with promising numerical results using the IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for a high-resolution urban spatial load demand forecasting. This methodology is meant to improve the visualization, analysis and inference of load density information in the electric distribution systems in the near future. The proposed methodology converts input data into grid maps and then divides the grid map into larger regions, which will have their expected growth according to convolution matrices and weighting factors that search for characteristics in the history of this region. The definition of the characteristics of the region's growth is obtained by processing the imperialist competitive algorithm that searches the best array of convolution, which will set the expected growth of the region. Thus, it is possible to obtain a spatial growth forecast of high resolution and with great precision, which are important factors for smart-grid planning. [ABSTRACT FROM AUTHOR]

Summary: In a demand‐side management (DSM) program, a set of activities are designed to influence electricity consumption in a way that may produce desired changes in the utility's load shape. Detailed analysis when planning DSM programs is crucial because the utility intends to achieve technical and economic benefits but does not wish to lose revenue unnecessarily. To assist utilities in planning for price‐based demand response (DR) programs (an approach to DSM), this paper proposes an optimization system focused on the targeting of residential customers to enroll in a new time‐of‐use tariff. The developed system uses optimal power flow to estimate the reduction needed to achieve the proposed objectives of the DR program in each bus, followed by the selection of the customers that will provide this reduction via binary particle swarm optimization. The optimization system has been tested using real data acquired from a Brazilian utility including customers' load curves, radial distribution feeder data, a time‐of‐use tariff, and estimated elasticity matrices from the literature. The main results show that the system can be of great value supporting power utilities' implementation of price‐based DR programs. [ABSTRACT FROM AUTHOR]