In this paper, two new computationally efficient improved stochastic algorithms for solving multi-area DC Optimal Power Flow (DC-OPF) in interconnected power systems have been presented. These algorithms are based on the combined application of Fuzzy Logic strategy incorporated in both Evolutionary Programming (EP) and Tabu Search (TS) algorithms, hence termed as Fuzzy Mutated Evolutionary Programming (FMEP) and Fuzzy Guided Tabu Search (FGTS). Multi-area DC-OPF calculations determine optimum generation schedule, optimal control variables and system quantities of each area with due consideration of generation and transmission system limitations for efficient power system operation. The popularity of EP and TS algorithms are due to their significant property of dealing with the optimization problems without any restrictions on the structure or type of the function to be optimized and due to the ease of computation. The proposed methods are tested on single area, two area and four area IEEE 30-bus interconnected systems. The optimal solutions obtained using SLP (Successive Linear Programming), EP, TS, FMEP and FGTS are compared and analyzed. The analysis reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for real-time applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.

Transmission expansion planning (TEP) is one of the most important parts of expansion planning in power systems. Competition in these systems has resulted in essential changes in TEP models and criteria. While methods introduced so far are essentially based on dc load flow, the proposed method utilizes the ac optimal power flow (OPF) in order to model the real world condition and obtaining optimal plans. Furthermore, in order to model the operation and investment costs, transmission tariffs are used. Investigation on 8-bus system confirms the advantages of the proposed method as compared with previously presented approaches.

In this paper, a combinational optimization algorithm is introduced to obtain the best size and location of Static Compensator (STATCOM) in power systems. Its main contribution is considering contingency analysis where lines outages may lead to infeasible solutions especially at peak loads and it commonly can be vanished by load shedding.The objective of the proposed algorithm is firstly to prevent infeasible power flow solutions without undesired load-shedding, which is critical in contingency analysis; and secondly to mitigate overall power losses and costs. Moreover, active and reactive powers generation costs are considered in the proposed objective function. Since there are various constraints such as lines outages number, cost and their duration that must betaken to account, Bacterial Foraging oriented by Particle Swarm Optimization (BF-PSO) algorithm combined with Optimal Power Flow (OPF) is used to solve and overcome the complexity of this combinational nonlinear problem. In order to validate the accuracy of the proposed method, two test systems, including IEEE 30 bus standard system and Azarbaijan regional power system of Iran, are applied in simulation studies. All obtained optimization results show the effectiveness of the suggested combinational method in loss and cost reduction and preventing load-shedding.

By adding renewable energy sources, such as solar and wind, advanced metering infrastructure, and energy storage systems, the traditional power grid is becoming a smart grid. To prevent the uneconomic operation of a smart grid and increase the penetration of renewable resources, the Demand Response (DR) method is crucial for reducing the peak load and passing critical conditions. In this context, this study presents a multi-objective optimization of the AC optimal power flow (AC-OPF) problem with respect to DR. The novelty of the proposed demand-response-based OPF approach consists of decreasing the system cost through the simultaneous participation of active and reactive power in DR, considering the physical constraints of the AC network and various renewable energy sources in the smart grid, and increasing the calculation accuracy by demand prediction based on previous data using deep learning methods. Finally, using the TOPSIS method, the best DR value was determined according to multi-objective optimization. The effectiveness and resiliency of the proposed method were validated using a modified IEEE 24-bus testing system. The results illustrate that the optimal demand response (20%) achieved not only peak reduction and valley filling in active and reactive power but also minimized the total voltage deviation and system cost.

This paper presents a new AC optimal power flow (AC OPF) model for sub-transmission networks. This model, which consists of sub-transmission and distribution bus-bar switching actions, can avoid undesirable over-current (OC) status and subsequent actions of OC relays. The proposed AC OPF optimizes the bus-bar switching actions along with optimizing sub-transmission control actions. Also, to consider the impact of OC relays’ actions in the proposed AC OPF, the cost of load shedding caused by these relay actions is included in the objective function and is minimized along with the sub-transmission operation cost. The bus-bar switching actions are modeled using binary decision variables. Therefore, the proposed AC OPF model is formulated as a Mixed Integer Non-linear Programming (MINLP) optimization problem. The effectiveness of the proposed model is illustrated on a real-world sub-transmission network of Iran’ s power system.