In this study, the authors represent a modelling to analyse and simulate renewable power generation for two area power systems in the presence of high penetrated wind farm. The performance of assumed power systems may hazard without appropriate frequency amelioration methodologies. To complete the LFC model for two area power systems, the combination of automatic generation control and automatic voltage regulation of thermal units is considered. Due to the decline in the total inertia of power system associated with wind farm contribution, the self-tuning and adaptive fuzzy-based PID droop can be proposed in the structure of wind turbines instead of the fixed/traditional PID droop in de-loaded area to ameliorate the frequency excursions. Besides, the artificial bee colony algorithm can tune the parameters of membership functions for input and output signals based on a multi-objective function (MOF). The proposed strategy control is proved to be accurately stable under various load changes and yields more satisfactory performance in comparison to the conventional PID droop. This research generally includes wind farm collaboration in the frequency control by inertia, primary and secondary frequency control.

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In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.

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