A probabilistic multi-objective daily Volt/Var control at distribution networks including renewable energy sources

At the moment, due to technology improvements and governmental incentives for the use of green energies, Renewable Energy Sources (RESs) appears to be a promising approach for electricity generation. This motivates the implementation of Wind Farms (WFs) and Fuel Cell Power Plants (FCPPs) over a mass scale by Distribution Companies (DisCos). As RESs become a larger and larger portion of the generation mix, many aspects of the distribution systems operation and planning has changed. In the context of Volt/Var control problem, proliferation of RESs becomes a challenging issue for DisCos. Since wind power acts as a variable energy source, probabilistic load flow techniques are going to be necessary to analyze the utility system. This paper presents a multi-objective probabilistic method to solve the Volt/Var control problem in distribution system with high wind power penetration. A probabilistic load flow approach using Point Estimate Method (PEM) is employed to model the uncertainty in load demands and electrical power generation of WFs. To regard the operational and economic assessment of system containing WFs and FCPPs, different objective functions have been taken into account. Cost of electrical power generated by WFs, FCPPs, and DisCos, electrical energy losses, emissions produced by WFs, FCPPs and DisCos for the next day are selected as objective functions. A new powerful optimization technique based on a Modified shuffled Frog Leaping Algorithm (MSFLA) is proposed to achieve the optimal values for active and reactive power of WFs and FCPPs, reactive power of capacitors and transformers tap positions for the next day ahead. In order to tackle the optimization problem with non-commensurable objectives, the objectives are fuzzified and max-min operator is employed. The results are compared with other evolutionary methods on a 69-bus distribution feeder in terms of efficiency and accuracy. (C) 2011 Elsevier Ltd. All rights reserved.