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.

OPTIMAL REACTIVE POWER DISPATCH (ORPD) is a MULTI-variable problem with nonlinear constraints and continuous/discrete decision variables. Due to the stochastic behavior of loads, the ORPD requires a probabilistic mathematical model. In this paper, Monte Carlo Simulation (MCS) is used for modeling of load uncertainties in the ORPD problem. The problem is formulated as a nonlinear constrained MULTI OBJECTIVE (MO) optimization problem considering two OBJECTIVEs, i.e., minimization of active POWER losses and voltage deviations from the corresponding desired values, subject to full AC load flow constraints and operational limits. The control variables utilized in the proposed MO-ORPD problem are generator bus voltages, transformers’ tap ratios and shunt REACTIVE POWER compensation at the weak buses. The proposed probabilistic MO-ORPD problem is implemented on the IEEE 30-bus and IEEE 118-bus tests systems. The obtained numerical results substantiate the effectiveness and applicability of the proposed probabilistic MO-ORPD problem.

The key of REACTIVE POWER Planning (RPP), or Var planning and REACTIVE POWER DISPATCH, are the OPTIMAL allocation of REACTIVE POWER sources considering location and size. Traditionally, the locations for placing new Var sources were either simply estimated or directly assumed. Recent research works have presented some rigorous optimization based methods in RPP. The REACTIVE POWER DISPATCH and planning is a large-scale non-convex, nonlinear programming (NLP) problem with real and discrete variables that presents a high degree of complexity for application in real electric POWER systems (EPS). This paper addresses an improved Honey Bee Mating Optimization (HBMO) with strength Pareto front to find the feasible OPTIMAL solution of the RPP problem with various generator constraints in POWER systems. For practical generator operation, many nonlinear constraints of the generator, such as ramp rate limits, generation limits, transmission line loss and non-smooth cost functions are all considered using the proposed method. Effectiveness of the proposed method is demonstrated for two different systems, including 6 and 54 unit generating in comparison with the performance of the other recently optimization algorithms reported in the literature in terms of the solution quality and computation efficiency. The results analysis confirms that the proposed approach has an excellent capability to determine OPTIMAL solution of the RPP problems over the other existing methods and enhances efficiently the solutions quality of the POWER systems.

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.

The measure of REACTIVE POWER optimization of main POWER network mainly considers on-load tap changer, the OPTIMAL capacity of the capacitor, the voltage of generator under the steady load. In This paper, a MULTI-OBJECTIVE optimization methodology to the OPTIMAL REACTIVE POWER Flow (ORPF) problem is proposed in which the e-constrained approach is implemented for the MULTI-OBJECTIVE Mathematical Programming (MMP) formulation. The OBJECTIVEs functions of the proposed model include optimize the total fuel cost, the active POWER losses, and the system load ability. Since the control variables include discrete variables (var sources and transformer tap ratios), the ORPF is inherently a mixed-integer nonlinear programming (MINLP) problem. The optimum tap settings of transformers are directly determined in terms of the admittance matrix of the network since the admittance matrix is constructed in the optimization framework as additional equality constraints. To show the effectiveness of proposed method the results are compared with single-OBJECTIVE, two-OBJECTIVEs and three-OBJECTIVEs. Finally, the method has been implemented in GAMS and solved using DICOPT solver to obtain OPTIMAL solutions. In this research, for selection of the best compromising answer among the Pareto OPTIMAL solutions has been designed a fuzzy decision-maker tool. The algorithm is tested on standard IEEE 14- bus test system. Simulation results show that the proposed algorithm is able to control REACTIVE POWER flow effectively and optimize the selected OBJECTIVE functions.

The modern POWER system has become very complex in nature with conflicting requirements. Heavy load demands and distributed generation compromise the cost of generation. Also the pollution hazards and inherent losses contribute towards an inefficient system. A scenario of coordination between cost, emission and loss will echo a pareto-OPTIMAL generation. The oft handled iterative approaches and optimization techniques have been replaced by a Dynamic Programming technique involving novel recursive approach for realizing production cost minimization, with an emission constrained and loss reduced condition. A study of test system exhibits the computational efficiency and accuracy of the solution.

The main mission of modern POWER systems is to supply the load in the most economical and reliable methods. One of the most challenging issues in this regard is the OPTIMAL REACTIVE POWER DISPATCH (ORPD), since the crucial focus of planning and operation studies is mainly on only supplying the active POWER. The primary purpose of the ORPD issue, as a complex and nonlinear problem, is to identify the relevant control variables to minimize some OBJECTIVE functions, i. e. active POWER losses considering the system constraints. As the literature review shows, the application of meta-heuristic techniques to find the OPTIMAL solution to the ORPD problem is of great importance in this field. This paper, as a comparative case study, attempts to investigate the capability of some POWERful meta-heuristic optimization algorithms to tackle the ORPD problem. The control variables are the generated POWER by the POWER plants, the voltage magnitude of PV buses, the installed capacity of parallel capacitors, and on-load transformer tap changers. All the simulations were implemented on the two case study systems, including the IEEE 30-, and 57-buses. The applied meta-heuristic algorithms to the problem are Orthogonal Crossover based Differential Evolution (OXDE), Hybrid Grey Wolf Optimization, and Particle Swarm Optimization Algorithm (HGWO-PSO), Sine Cosine Algorithm (SCA), and Hybrid PSO and Genetic Algorithm (HPSO-GA).

In this paper, a novel method for a MULTI-OBJECTIVE and risk-based OPTIMAL REACTIVE POWER DISPATCH is proposed. The method includes two main OBJECTIVE functions: technical and economic. The technical OBJECTIVE involves minimizing the risks of voltage instability, voltage deviation, and flow violation, and the economic OBJECTIVE involves minimizing the costs of REACTIVE POWER generation, active POWER losses, load shedding, and active POWER rescheduling. Using these functions and assigning different weighting factors for each sub-OBJECTIVE, the risk of the events or uncertainties to customers or the grid can be managed. In addition, moment matching is used to discretize and create scenarios from continues probability distribution functions of wind speed and electrical energy uncertainties. As the number of uncertain variables increases, so does the number of scenarios and the simulation time. Therefore, the fast-forward selection algorithm is applied to reduce the number of scenarios. To reduce the computational complexity and the number of topological scenarios, a new contingency filtering method based on high-risky events is proposed. A modified MULTI-OBJECTIVE PSO algorithm based on a hybrid PSO with sine-cosine acceleration coefficients is proposed to find the Pareto front of solutions. The method is implemented on the modified IEEE 30-bus test system. To demonstrate the effectiveness of the proposed method, the results are compared with previously published literature. The results show that risk-based scheduling increases system reliability and cost-effectiveness compared to traditional scheduling.

This paper presents a solution to the OPTIMAL POWER Flow (OPF) problem combined economic DISPATCH with valve-point effect and Emission Index (EI) in electrical POWER networks using the physics-inspired optimization method, which is the Gravitational Search Algorithm (GSA). Our main goal is to minimize the OBJECTIVE function necessary for the best balance between energy production and its consumption which is presented in a nonlinear function, taking into account equality and inequality constraints. The OBJECTIVE is to minimize the total cost of active generations, the active POWER losses, and the emission index. The GSA method has been examined and tested on the standard IEEE 30-bus test system with various OBJECTIVE functions. The simulation results of the used methods have been compared and validated with those reported in the recent literature. The results are promising and show the effectiveness and robustness of the used method. It should be mentioned that from the base case, the cost generation, the active POWER losses, and the emission index are significantly reduced to 823 ($/h), 6.038 (MW), and 0.227 (ton/h), which are considered 5.85%, 61.61%, and 44.63%, respectively.