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The integration of renewable energy generation affects the operating characteristics of a power system, such as electric losses, voltage profile, generation cost, system stability, and reliability of the system. The installation of renewable energy generation units in non-optimal locations may increase system losses, costs, voltage fluctuations, etc. The main hurdle in integrating renewable energy generation units with an existing electrical grid is the uncertainty of renewable sources. This paper presents the impact of wind farm integration on the system economy in a wind-integrated deregulated power market. The importance of deregulation in terms of the system generation cost, bus voltage profile, and locational marginal pricing (LMP) are also studied in this work. LMP is the main parameter responsible for handling the system economy (i.e., profit of generating units and profit of customers). Considering the variable nature of wind flow, three different real-time wind speed datasets are used to validate this work. Bus sensitivity factor (BSF) is considered for equating the optimal position of the wind farm in the integrated system. Five different optimization techniques, i.e., sequential quadratic programming (SQP), artificial bee colony (ABC) algorithms, particle swarm optimization (PSO), ant colony optimization (ACO) algorithm, and slime mold algorithm (SMA), are introduced to solve the optimal power flow problem. The SMA and ACO are used for the first time in this type of economic assessment (i.e., impact valuation of LMP) in a deregulated power system, which is the novelty of this work. The entire work is performed in a modified IEEE 30 bus test system.

Impact of Wind Farm Integration on LMP in Deregulated Energy Markets