Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature

Optimalna lokacija i parametri za FACT uređaj za kompenzaciju reaktivne snage koristeći algoritam harmonijskog pretraživanja

Reactive Power Compensation (RPC) is an important issue in the operation and control of power system. In this paper, two FACTS controller like Static Var Compensator (SVC) and Thyristor Controlled Series Capacitor (TCSC) are considered for RPC. RPC is a multi-objective nonlinear optimization problem that minimizes the bus voltage deviation and real power loss. In this work, Harmony Search Algorithm (HSA) is used to determine the optimal location and setting of SVC and TCSC respectively. The efficacy of HSA is demonstrated on modified IEEE 30 bus power system for two operating conditions. A comparison of simulation results reveals the effectiveness of proposed algorithm over other well established population based optimization technique like Simple Genetic Algorithm (SGA),Particle Swarm Optimization (PSO) and Differential Evolution (DE).Kompenzacija reaktivne snage (RPC) važan je zadatak pri radu i upravljanju energetskim sustavima. U ovome radu razmatra se FACT regulator kao što su statički kompenzator (SVC) i tiristorski serijski kondenzator (TCSC). RPC je više kriterijski nelinearni optimizacijski problem gdje se minimizira odstupanje napona sabirnice i gubitci snage. Korišten je HSA algoritam (engl. Harmony Search Algorithm) za određivanje položaja i parametara SVC i TCSC. Efikasnost sustava demonstrirana je na modificiranom energetskom sustavu IEEE 30 za dva različita uvjeta. Usporedbna simulacijskih rezultata prikazuje efikasnost predloženog algoritma u odnosu na ostale metode kao što su genetski algoritmi (SGA), čestična optimizacija roja (PSO) i diferencijalna evolucija (DE)

Assessing Effectiveness of Research for Load Shedding in Power System

The research on loadshedding issues dates back to 1972 and till date many studies were introduced by the research community to address the issues. A closer review of existing techniques shows that still the effectiveness of loadshedding schemes are not yet benchmarked and majority of the existing system just considers the techniques to be quite symptomatic to either frequency or voltage. With an evolution of smart grids, majority of the controlling features of power system and networks are governed by a computational model. However, till date not enough evidences of potential computational model has been seen that claims to have better balance between the load shedding schemes and quality of power system performance. Hence, we review some significant literatures and highlights the research gap with the existing technqiues of load balancing that is meant for assisting the researcher to conclude after the selection process of existing system as a reference for future direction of study

Adopting Scenario-Based approach to solve optimal reactive power Dispatch problem with integration of wind and solar energy using improved Marine predator algorithm

The penetration of renewable energy resources into electric power networks has been increased considerably to reduce the dependence of conventional energy resources, reducing the generation cost and greenhouse emissions. The wind and photovoltaic (PV) based systems are the most applied technologies in electrical systems compared to other technologies of renewable energy resources. However, there are some complications and challenges to incorporating these resources due to their stochastic nature, intermittency, and variability of output powers. Therefore, solving the optimal reactive power dispatch (ORPD) problem with considering the uncertainties of renewable energy resources is a challenging task. Application of the Marine Predators Algorithm (MPA) for solving complex multimodal and non-linear problems such as ORPD under system uncertainties may cause entrapment into local optima and suffer from stagnation. The aim of this paper is to solve the ORPD problem under deterministic and probabilistic states of the system using an improved marine predator algorithm (IMPA). The IMPA is based on enhancing the exploitation phase of the conventional MPA. The proposed enhancement is based on updating the locations of the populations in spiral orientation around the sorted populations in the first iteration process, while in the final stage, the locations of the populations are updated their locations in adaptive steps closed to the best population only. The scenario-based approach is utilized for uncertainties representation where a set of scenarios are generated with the combination of uncertainties the load demands and power of the renewable resources. The proposed algorithm is validated and tested on the IEEE 30-bus system as well as the captured results are compared with those outcomes from the state-of-the-art algorithms. A computational study shows the superiority of the proposed algorithm over the other reported algorithms

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv

Optimal Allocation of DSTATCOM in Distribution Network Using Whale Optimization Algorithm

This paper deals with a new approach implemented to decrease power losses and improve voltage profile in distribution networks using Distribution STATic COMpensator (DSTATCOM). DSTATCOM location can be determined by the voltage stability index (VSI) and sizing can be identified by nature inspired, recently developed whale optimization algorithm (WOA). To check efficacy, the proposed technique is tested on two standard buses: Indian rural electrification 28-bus and IEEE 69-bus distribution systems. Obtained results show that optimal allocation of DSTATCOM effectively reduces power losses and improves voltage profile