In this paper, a novel risk-based, two-objective (technical and economical) optimal reactive power DISPATCH method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

The main objective of this paper is to develop a distributed model using grid environment through which the economic load DISPATCH solutions of multi-area power systems can be obtained continuously. Grid computing is a viable solution in order to exploit the enormous amount of computing power available across Internet to solve large interconnected power system problems. A grid service model is proposed for economic load DISPATCH, which provides the solutions at specific intervals of time. The proposed model is designed in such a way that any node in the grid can provide the economic load DISPATCH (ELD) solution. The node which serves as a server node at a specific instance in the grid can obtain the power system data from other client grid nodes and responds with economic load DISPATCH solutions. The proposed model is highly distributed and has inherent features of scalability, reliability and also uses all available computing power, hence economic feasibility of analyzing power system problems was taken care implicitly.

Reactive power DISPATCH plays a key role in secure and economic operation of power systems. Optimal reactive power DISPATCH (ORPD) is a non-linear optimization problem which includes both continues and discrete variables. Due to complex characteristics, heuristic and evolutionary based optimization approaches have become effective tools to solve the ORPD problem. In this paper, a new optimization approach based on improved differential evolution (IDE) has been proposed to solve the ORPD problem. IDE is an improved version of differential evolution optimization algorithm in which new solutions are produced in respect to global best solution. In the proposed approach, IDE determines the optimal combination of control variables including generator voltages, transformer taps and setting of VAR compensation devices to obtain minimum real power losses. In order to demonstrate the applicability and efficiency of the proposed IDE based approach, it has been tested on the IEEE 14 and 57-bus test systems and obtained results are compared with those obtained using other existing methods. Simulation results show that the proposed approach is superior to the other existing methods.

In this paper, Brent method is proposed to solve dynamic economic DISPATCH (DED) problem with transmission losses. The proposed algorithm involves the selection of the values of incremental fuel costs (lambda) and then the evaluation of optimal lambda is done by Brent method. The constraint of ramp rate limits distinguishes the DED problem from traditional static economic DISPATCH (ED) problem. The DED problem divides the entire DISPATCH period into a number of small time intervals and then static economic DISPATCH problem is solved in each interval by incorporating the ramp rate limits. The proposed method has been tested on 6- and 15- units. The simulation results of the proposed method are compared with conventional lambda iterative method. The simulation results show that the proposed method achieves qualitative solution with less computational time than the conventional lambda iterative method.

This paper presents a Teaching-Learning-Based Algorithm (TLBO) to solve Economic Load DISPATCH (ELD) problems involving dierent linear and non-linear constraints. The problem formulation also considers non-convex objective functions including the eect of valve-point loading and the multi-fuel option of large-scale thermal plants. Many diculties, such as multimodality, dimensionality and dierentiability, are associated with the optimization of large scale non-linear constraint based non-convex economic load DISPATCH problems. TLBO is a population-based technique which implements a group of solutions to proceed to the optimum solution. TLBO uses two dierent phases, ' Teacher Phase' and ' Learner Phase', and uses the mean value of the population to update the solution. Unlike other optimization techniques, TLBO does not require any parameter to be tuned, thus, making its implementation simpler. TLBO uses the best solution of the iteration to change the existing solution in the population, thereby increasing the convergence rate. In the present paper, Teaching-Learning-Based Optimization (TLBO) is applied to solve such types of complicated problems eciently and eectively, in order to achieve a superior quality solution in a computationally ecient way. Simulation results show that the proposed approach outperforms several existing optimization techniques. Results also proved the robustness of the proposed methodology.