Solution Of Reactive Power Dispatch Research Articles

Optimal Solution of reactive power dispatch under normal and contingency condition using Rao algorithms

Rao suggested three straight forward, metaphor-free optimization strategies to address the challenging optimization issue. These algorithms are approaches for optimization without parameters. As a result, no parameter modification unique to an algorithm is needed. This feature makes these algorithms simple to use and effective at resolving a wide range of difficult engineering and scientific challenges. This paper’s main objective is to use the Rao method to resolve the reactive power dispatch problem under both regular and emergency circumstances. The suggested method’s reliability was tested using these techniques in standard IEEE 30 bus test systems. Overall reactive power dispatch (RPD) results of Rao algorithms were matched with those of recently published well-known methods. Rao algorithms produce accurate and competitively priced numerical results.

Optimal Solution of Reactive Power Dispatch in Transmission System to Minimize Power Losses Using Sine-Cosine Algorithm

Optimal reactive power dispatch (ORPD) has a crucial impact to enhance safety, reliability, and economical operation of the electric power system. ORPD is a non-linear, non-convex and mixed variable problem, which has been solved by many researchers via different meta-heuristic algorithms during the last decade. In this work, a novel algorithm named sine-cosine algorithm (SCA) is utilized to solve ORPD problem by considering both dependent and independent control variable constraints. SCA has been tested and validated on standard 14, 30 and 57-bus power systems. To validate the superiority of proposed algorithm, the outcomes obtained through SCA are compared with recent published results attained through particle swarm optimization (PSO), modified Gaussian barebones teaching–learning based optimization (BBTLBO), ant bee colony optimization (ABCO), whale optimization algorithm (WOA) and backtracking search algorithms (BSA). The results attained using SCA show the improvement in the power losses minimization. Thus, with standard 14-bus system, the power losses are minimized from 0.04% to 4.78%. While, using standard 30-bus, the power losses are minimized from 0.4% to 3.4% and with standard 57-bus, power losses are reduced from 0.9% to 1.99%. Furthermore, a comparative analysis with 30 independent runs on the above-mentioned bus systems is performed to examine the functioning of the proposed method in terms of probability density function (PDF) and cumulative density function (CDF). For such analysis, well-known meta-heuristic algorithms such as PSO, WOA, differential evolution (DE) are compared with proposed SCA in solving the ORPD problem. The results of this analysis clearly show that proposed algorithm is robust, effective, and computationally easy in solving the ORPD problem compared to the existing meta-heuristic algorithms.

Optimal Reactive Power Dispatch Solution Using Enhanced Sine Cosine Optimization Algorithm with Selection Operator

In this paper, the sine and cosine algorithm is enhanced (ESCA) by selecting the best elements of the group in the next iterations to reduce active power loss. The sine and cosine algorithm (SCA) converges to an optimal solution through the sinus and cosine-based mathematical relations and the destination or the best global element in each iteration. Also, it uses the current elements for generating new elements without keeping the elements that have a better solution in the next generations. In ESCA this problem is avoided by applying the selection operator for keeping the best elements to the next generation. This method improves the optimal solution and is obtained in most implementations of the algorithm, which reduces the disturbances in obtaining the optimal solution. Also, one of the parameters of the standard method is fixed to the normal one value for the performance improvement of the algorithm. This method has been applied to a typical power network such as IEEE 30-bus and IEEE 118-bus in order to reduce the active power loss, reduce the power loss with the voltage deviation and with the stability of the voltage. Finally, this method proved effective than the standard method and with the previously applied metaheuristic methods.

Sensitive reactive power dispatch solution accomplished with renewable energy allocation using an enhanced coyote optimization algorithm

This study is concerned with solving the optimal reactive power dispatch (ORPD) problem considering the existence of renewable energy resources (RERs). The sensitivity analysis aims at studying the impact of incorporating reactive power resources and wind units for solving the ORPD problem. An enhanced coyote optimization algorithm (ECOA) is developed for solving ORPD problem. The ECOA combines the merits of fuzzy logic principles and the conventional COA with the aim of obtaining the best performance of ORPD solution. The capability of ECOA are examined on IEEE 30-bus, 118-bus test systems while its scalability is tested for standard IEEE 300-bus test system. The impact of different penetration levels of wind energies on achieving the ORPD is assessed. Statistical analyses are carried out to prove the robustness of the proposed ECOA. Therefore, the ECOA leads to more competitive solutions for single and multi-objective cases compared with the reported methods in the literature.

New strategy based modified Salp swarm algorithm for optimal reactive power planning: a case study of the Algerian electrical system (114 bus)

Optimal reactive power planning is an important task for experts and industrials to ensure the reliability of modern power systems. Actually, the structure of practical power systems becomes dynamic and characterised by uncertainty in load and non-linear characteristic of various elements of power systems such as constraints associated to thermal units, constraints associated with FACTS devices and renewable sources. This study introduces an intelligent strategy based new metaheuristic named Salp swarm algorithm (SSA) to improve the solution of reactive power dispatch by optimising the total power loss, the total voltage deviation individually and simultaneously considering static VAR compensators (SVCs). To improve the efficiency of the original algorithm in solving large test systems, a sub SSA is formed to optimise the various objective functions based on a grouped control variable. In this study, four grouped swarms named SSA_PG for active power, SSA_VG for voltages, SSA_Ti for Tap transformers, and SSA_SVC for SVCs are formed to operate in a flexible structure to minimise a specified objective function. The proposed intelligent planning strategy validated on the IEEE 30 bus and to the large electrical test system 114 Bus of the Algerian network at normal condition and considering critical situations such as margin loading stability and contingency. Results found using the proposed strategy compared to those cited recently in the literature proves its particularity in terms of solution quality and convergence characteristics.

Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch

Reactive Power Dispatch (RPD) is a complex, non-continuous and it is famous and essential problem in the power system. The calculation of this problem is really part of optimal load flow calculations. In this study, two types of Particle Swarm Optimization (PSO) algorithm are utilize as an optimization tools to solve RPD problem in order to minimize real Power Loss (PLoss) in the power system and keep voltage at all buses within acceptable limit. First type of PSO algorithm is Conventional PSO and the second type is utilize to improve the searching quality, also to decrease the time calculation and to enhance the convergence characteristic in the first type, it is called Modified PSO (MPSO). These types of PSO algorithm are tested on IEEE Node- 14, Node-30, Node-57 and Node-118 power systems to test their efficiency and ability in solving RPD problem in small and large power systems. The simulation results in four power systems show that the MPSO algorithm has a better performance in decreasing losses, decreasing time calculation and enhancement of voltage profile when compared to the Conventional PSO and other algorithms that reported in the literature.

Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature.

Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

Gravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57- and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems.

Sparse Reactive Power Scheduling by a Penalty Function - Linear Programming Technique

This work presents a linear programming based algorithm to solve reactive power dispatch problems. A mixed set of variables (generated voltages and reactive power injections) and the reactive power model of the fast decoupled load flow algorithm are used to derive linear sensitivities. A suitable criterion is suggested to form a sparse reactive power sensitivity matrix. The sparse sensitivity matrix is in turn modeled as a bipartite graph which is used to define an efficient constraint relaxation strategy to solve linearized reactive power dispatch problems. The penalty function - linear programming technique method is used in a complete reactive power dispatch solution algorithm, the performance of which is discussed by solving a 256-node, 58 control-variable test system.