In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

In this paper, a modified version of Imperialist Competitive Algorithm (ICA) is proposed for the optimal energy management of a Microgrid (MG) with Parking Lots (PL) and Distributed Generation (DG) units. A 24-h scheduling for participation in DG units and electric vehicles PLs in two scenarios is done. The PLs are divided into seven group that each group has different trip behavior. Therefore, energy management should be done in such a way as to minimize operating costs according to the charging status of electric vehicles as well as the production capacity of distributed generation sources. Finally, the results of the two scenarios are reviewed separately and compared. The simulation results proved the effectiveness of the proposed method. The MG operation cost is decreased about 63%. Also, the optimization results. The optimization results by the proposed ICA algorithm are compared with the results of genetic algorithm (GA) and particle swarming optimization (PSO) algorithms. The optimization results confirm better performance of the proposed algorithm compared to GA and PSO algorithms.

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

Ensuring the efficiency and robustness of a power system stabiliser is indebted to provide a comprehensive scheme that considers significant uncertainties in variables and parameters of the model. This study proposes a robust type‐II fuzzy logic‐based power system stabiliser scheme that can handle the uncertainties in both loading condition and line parameters. Unlike loading condition, there are many lines in a typical power system and it is necessary to introduce an indicator that can mark lines with significant uncertainty in the parameters. Hence, a criterion based on Oscillatory Stability Index is presented in the study. On the other hand, random samples from line parameters and loading conditions are generated by the roulette‐wheel using probability density functions (PDF) to form the elements of the weighting matrix. Two objective functions including weighted eigenvalue deviations and damping ratios are defined and then optimised to find the parameters of the proposed stabiliser in the uncertain environment. Simulations are carried out in different scenarios and in terms of both load condition and line parameters changes. Finally, the performance of the proposed stabiliser is assessed through comparisons with an optimised lead–lag power system stabiliser and a robust lead–lag power system stabiliser.

In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.