In this paper, the coordinated integration of aggregated plug-in electric vehicle (PEV) fleets and renewable energy sources (wind energy) in power systems is studied by stochastic security-constrained unit commitment (Stochastic SCUC) model, which minimizes the expected grid operation cost while considering the random behavior of the many PEVs. PEVs are mobile and distributed devices with deferrable options for the supply/utilization of energy at various times and locations. The increased utilization of PEVs, which consume electricity rather than fossil fuel for driving, offers unique economic and environmental opportunities, and brings out new challenges to electric power system operation and planning. The storage capability of PEVs could help power systems mitigate the variability of renewable energy sources and reduce grid operation costs. Vehicle-to-grid (V2G) enables PEVs to have bi-directional power flows once they are connected to the grid, i.e., they can either inject power to, and draw power from, the grid which adds further complexity to power system operations. PEVs signify customers' random behavior when considering their driving patterns, locational energy requirements, topological grid interconnections, and other constraints imposed by the consumers. Numerical tests demonstrate the effectiveness of the proposed approach for analyzing the impact of PEVs on the grid operation cost and hourly wind energy dispatch.Index Terms-Load aggregation, plug-in electric vehicles, renewable energy sources, stochastic security-constrained unit commitment, V2G.

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In this paper, the application of high reliability distribution system (HRDS) in the economic operation of a microgrid is studied. HRDS, which offers higher operation reliability and fewer outages in microgrids, is applied to looped networks in distribution systems. The microgrid model in this study is composed of distributed energy resources (DER) including distributed generation (DG), controllable loads, and storage. The microgrid would utilize the local DER as well as the main grid for supplying its hourly load economically which is subject to power quality and reliability requirements. The HRDS implemented at Illinois Institute of Technology (IIT) is used as a case study along with the local DER to increase the load point reliability and decrease the operation cost of the IIT microgrid. The availability of distribution lines, main grid supply, and microgrid generation is considered using the Markov chain Monte Carlo simulation in the microgrid scenarios. The reliability indices based on frequency and duration of outages are measured at the microgrid level and the load point level, and the potential system enhancements are discussed for improving the economic operation of the IIT microgrid.Index Terms-High reliability distribution system, microgrid economics, stochastic security constrained unit commitment, storage.

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This paper presents a stochastic hourly coordination strategy for wind units and cascaded hydro generation as storage to firm up the hourly dispatch in a generating company (GENCO). The proposed strategy is based on the stochastic price-based unit commitment (Stochastic PBUC) formulation which includes wind energy imbalance charges. The forecast errors of electricity market price and wind speed are simulated with the Monte Carlo method via a scenario approach. The risk-aversion constraints are considered for limiting a GENCO's financial risks when considering uncertain wind power generation. The proposed optimization model is solved by mixed-integer linear programming (MIP) and illustrative examples examine the effectiveness of the proposed risk-based coordination model for optimizing a GENCO's payoff.Index Terms-Cascaded hydro and wind coordination, risk-aversion, stochastic price-based unit commitment.