In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively.

In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

Due to the increased deployment of new technologies such as renewable sources and smart meters, complex and unexpected interactions are occurring in power systems. The dynamic instability in power systems are caused by the existing imbalance between mechanical input and output power. For damping these oscillations, power system stabilizer (PSS), and static synchronous compensator (STATCOM) can be used. In this paper, a unique fuzzy controller for dynamic stability of PSS and STATCOM in multi-machine power system is designed. Particle swarm optimization (PSO) combined with genetic algorithm (GA) is used for fuzzy controller optimization. Proposed method is tested on IEEE 39-bus system under disturbance. The simulation results verify the efficiency of the proposed method for dynamic stability of power system.

STATCOMs is used widely in power systems these days. Traditionally, this converter was controlled using a double-loop control or Direct Output Voltage (DOV) controller. But DOV controller do not function properly during a three-phase fault and has a lot of overshoot. Also, the number of PI controllers used in double-loop control is high, which led to complexities when adjusting the coefficients. Therefore, in this paper, an improved DOV method is proposed which, in addition to a reduced number of PI controllers, has a higher speed, lower overshoots and a higher stability in a wider range. By validating the proposed DOV method for controlling the STATCOMs, it has been attempted to improve the dynamical behaviors of induction motor using Matlab/Simulink, and the results indicate a better performance of the proposed method as compared to the other methods.

This paper proposes an attack-resilient distributed secondary controller for microgrids. It is designed to restore frequency even in the presence of compromised distributed generators (DGs). The proposed algorithm is capable of detecting and compensating the effect of various types of attack (e.g., scale, replay, and data integrity attacks) on DGs by utilizing two-hop communication information. The proposed method consists of two key stages: detection and compensation. In the detection stage, each DG’s controller uses its own data, information from neighboring DGs, and three attack detection strategies to identify compromised DGs. Subsequently, in the compensation stage, each DG’s controller determines the effect of attacks on the consensus control law and generates a compensation signal accordingly. In instances where attacks are not compensable, compromised DGs are isolated to ensure a consensus. The effectiveness of the proposed method is demonstrated over a modified IEEE 16-bus test system in MATLAB/Simulink. The simulation results show that the proposed method is resilient to various types of attacks, including scaling attacks, false data injection (FDI) attacks, and replay attacks.

STATCOMs is used widely in power systems these days. Traditionally, this converter was controlled using a double-loop control or Direct Output Voltage (DOV) controller. But DOV controller do not function properly during a three-phase fault and has a lot of overshoot. Also, the number of PI controllers used in double-loop control is high, which led to complexities when adjusting the coefficients. Therefore, in this paper, an improved DOV method is proposed which, in addition to a reduced number of PI controllers, has a higher speed, lower overshoots and a higher stability in a wider range. By validating the proposed DOV method for controlling the STATCOMs, it has been attempted to improve the dynamical behaviors of induction motor using Matlab/Simulink, and the results indicate a better performance of the proposed method as compared to the other methods.