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.

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.

The growing reliance of integrated power and gas systems (IPGSs) on information and communication technology has left them prone to cyber-attacks. To protect IPGSs against such attacks, having comprehensive knowledge about possible attack strategies and their impacts is essential. On this basis, this paper (i) presents a new family of coordinated stealthy false data injection attacks (FDIAs) whose aims are to disconnect gas-fired power plants (GFPPs) by targeting IPGS measurements, (ii) develops a preventive defense strategy against the introduced family of attacks by improving the security of high-risk measurements, and (iii) proposes an online learning-based detection scheme using secured measurements. Initially, a two-stage attack model is developed to construct the FDIA vector. In the first stage, power system measurements are manipulated to maximize the grid reliance on the targeted GFPPs, while bypassing bad data detection schemes. In the second stage, the measurements of gas networks (GNs) are falsified to portray a fake leakage, so misleading the operator to disconnect under-attack pipelines and interrupting the supply of fuel to targeted GFPPs. Each stage is formulated as an attacker-operator bi-level optimization problem, which is solved as mixed-integer linear programming (MILP) by converting the operator level to its corresponding Karush-Kuhn-Tucker (KKT) conditions. Afterwards, based on possible attack scenarios, high-risk measurements are identified to be secured as a preventive defense strategy. Finally, a multilayer perceptron (MLP) model is trained to detect cyber-attacks against IPGS. The performance of the proposed method is corroborated through simulations on IEEE 24-Bus 20-Node IPGS and IEEE 118-bus 135-node IPGS.

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.