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This paper presents an approach to transform the active distribution network with distributed energy resources into multiple autonomous microgrids. The distribution network consists of several generation resources and demand entities, that are clustered into autonomous microgrids. The proposed problem is formulated as a bi-level optimization problem that leverages the Eigen decomposition in the graph spectra of the distribution network to determine the boundaries for microgrids and a mixed-integer programming problem that minimizes the expansion cost within microgrids. The presented approach is evaluated in a case study for a distribution network considering the imposed reliability constraints. The outcomes indicate the effectiveness of the proposed algorithm to determine the expansion strategies to form autonomous microgrids in active distribution networks. [ABSTRACT FROM PUBLISHER]

This paper develops a day-ahead price-based scheduling strategy for the coordination of wind and storage units in a generating company (GENCO). The proposed strategy is based on the stochastic price-based unit commitment (PBUC) for the scheduling of wind energy and storage units, which considers volatilities in day-ahead intrahour market prices and wind power generation. The proposed approach firms up the hourly sum of wind and storage unit generation, and mitigates potential wind energy imbalance charges for participating GENCOs in electricity markets. Although the proposed approach would apply to any kind of storage, we consider the pumped-storage (PS) hydro unit as an example in this study. Numerical examples illustrate a GENCO's day-ahead coordinated scheduling results for wind and PS units. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]