Abstract-The paper proposes a probabilistic methodology for minimizing wind spillage and maximizing capacity of the deployed wind generation, whilst improving system reliability. Capacities of the connected wind units are initially determined by using a method developed by the industry. A probabilistic approach is applied for the day-ahead planning to find maximum deployable wind sources so that the prescribed wind spillage is not exceeded. This is done using the optimum power flow, where wind spillages are prioritised with the probabilistic 'cost coefficients'. Further improvement of wind energy utilization is achieved by installing FACTS devices and making use of realtime thermal ratings (RTTR). Two ranking lists are developed to prioritize location of SVCs and TCSCs, and they are then combined into a unified method for best FACTS placement. The entire methodology is realized in two sequential Monte Carlo procedures, and the probabilistic results are compared with the state enumeration ones. Results show improved wind utilization, network reliability and economic aspects.
In deregulation, growth in electrical loads necessitates improving power delivery, while nondiscriminatory access to transmission grid is a requirement. Deregulation causes a significant rise in transactions, which requires adequate transfer capability to secure economic transactions. In sustainable power delivery, FACTS devices are deployed to enhance available transfer capability (ATC). However, the high investment cost of FACTS makes the problem formulation a multiobjective optimization: power transfer maximization and minimization of FACTS sizes. Furthermore, due to the complexity in optimizing the control variables of voltage source converter types of FACTS, often the solution results in local optima and high computational time. This paper proposes a hybrid of real power flow performance index sensitivity (∂P I) and particle swarm optimization (PI-PSO) to solve the multiobjective optimization of ATC maximization with minimum FACTS sizes using continuation power flow. ∂P I identifies some high-potential locations with enhanced ATC at minimum FACTS size to constitute the PSO's reduced search space. As ∂P I may exhibit masking effects, iterative nexponent and Newton's divided difference approaches are proposed to reduce masking. The proposed PI-PSO is implemented with a thyristor control series compensator and static synchronous series compensator for both bilateral and multilateral transactions. Results show the effectiveness of the proposed PI-PSO over PSO regarding convergence characteristics, avoidance of local optima, and superior ATC values.
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