Meta-heuristics Applied to Power Systems

Several heuristic tools have evolved in the last decade that facilitate solving optimization problems that were previously difficult or impossible to solve. These tools include evolutionary computation, simulated annealing, tabu search, particle swarm, etc. Reports of applications of each of these tools have been widely published. Recently, these new heuristic tools have been combined among themselves and with knowledge elements, as well as with more traditional approaches such as statistical analysis, to solve extremely challenging problems. Developing solutions with these tools offers two major advantages: 1) development time is much shorter than when using more traditional approaches, and, 2) the systems are very robust, being relatively insensitive to noisy and/or missing data.

Annals of Operations Research, 2003

Due to a variety of political, economic, and technological factors, many national electricity industries around the globe are transforming from non-competitive monopolies with centralized systems to decentralized operations with competitive business units. A key challenge faced by energy restructuring specialists at the World Bank is trying to simultaneously optimize the various criteria one can use to judge the fairness and commercial viability of a particular power districting plan. This research introduces and tests a new algorithm for solving the electrical power districting problem in the context of the Republic of Ghana and using a random test problem generator. We show that our mimetic algorithm, the Simulated Annealing Genetic Algorithm, outperforms a well-known Parallel Simulated Annealing heuristic on this new and interesting problem manifested by the deregulation of electricity markets.

A specialized genetic algorithm applied to the solution of the electric grid interdiction problem is presented in this paper. This problem consists in the interaction of a disruptive agent who aims at maximizing damage of the power system (measured as load shed), and the system operator, who implements corrective actions to minimize system load shed. This problem, also known as “the terrorist threat problem”, is formulated in a bi-level programming structure and solved by means of a genetic algorithm. The solution identifies the most vulnerable links of the network in terms of a terrorist attack, providing signals for future reinforcement of the network or more strict surveillance of critical elements. The proposed approach has been tested on three case studies: a didactic five-bus power system, a prototype of the Colombian power system and the IEEE Reliability Test System. Results show the robustness and applicability of the proposed approach.

International Journal of Logistics Economics and Globalisation, 2010

Well established conventional algorithms are available for solving the optimum power flow (OPF) problem. But the recent trend is to use the tools such as genetic algorithms (GAs) evolutionary programming technique, etc., because of some of their superior qualities. Simulated annealing (SA) is one such tool which can be used for solving optimisation problems. In this paper, SA technique has been applied for solving OPF problem which is simultaneously composed by economic dispatch (ED) and load flow problems (LFP). This technique is compared with GA, which represents a class of general purpose stochastic search techniques which simulate natural inheritance by genetics, illustrated by considering a three bus system.

JETIR, 2018

Unit commitment (UC) is considered one of the essential activities in power system planning and operation. The complexity and non-linearity nature of the UC problem make metaheuristic optimizations techniques more relevant for their solutions. Apart from minimizing the operational cost, the increased public awareness regarding the harmful effects of atmospheric pollutants on the environment and other environmental regulations have led researchers to focus on environmental effect as another unit commitment objective function. This paper reviews some published research papers based on metaheuristic techniques considering their single and multi-objective functions. However, the practical, technical and economic importance of the unit commitment problem is proven by the enormous amount of the available literature for attaining their solutions.

2009

Each interconnected electric system has to define its operating mode and how expansion and operation planning are made. This paper presents the first phase of the work of implementing a tool with application to Colombian power system operative planning. The tool uses genetic algorithms to optimize the cost functions that arise in which the elements of the system are the variables that can be operated to reduce losses and fulfill the operation restrictions. Tests results with IEEE systems and two fitness functions proposed by the authors are presented in this work; from those results is estimated the computing time needed to evaluate the Colombian system.

In this paper, a preventive control action that involves both generation rescheduling and load curtailment is proposed for enhancing the dynamic security of large interconnected power systems. The control action is formulated as a security-constrained optimization problem that is solved by mean-variance mapping optimization (MVMO) integrated with a self-adaptive penalization technique and artificial neural networks to develop a fast and effective methodology. The proposed methodology is applied to a 16-generator 68-bus test system to solve the security-constrained optimization problem with both continuous and discrete decision variables. To find a proper and cost-effective solution for the control actions within an acceptable time, dynamic security assessment methodology based on artificial neural networks is integrated into the optimization process for predicting the violations of security constraints brought about by the candidate solutions. The proposed method effectively integrates a variety of popular heuristic optimization algorithms, including MVMO, differential evolution, particle swarm optimization, genetic algorithms, big bang-big crunch, and artificial bee colony. MVMO outperforms all the others in various aspects such as reliability and robustness.

Ain Shams Engineering Journal, 2015

Reactive power planning (RPP) is generally defined as an optimal allocation of additional reactive power sources that should be installed in the network for a predefined horizon of planning at minimum cost while satisfying equality and inequality constraints. The optimal placements of new VAR sources can be selected according to certain indices related to the objectives to be studied. In this paper, various solution methods for solving the RPP problem are extensively reviewed which are generally categorized into analytical approaches, arithmetic programming approaches, and meta-heuristic optimization techniques. The research focuses on the disparate applications of meta-heuristic algorithms for solving the RPP problem. They are subcategorized into evolution based, and swarm intelligence. Also, a study is performed via the multi-objective formulations of reactive power planning and operations to clarify their merits and demerits.

2008

The key goal of electric power distribution companies is to provide a high quality of service with a low cost of operation. The growing customer needs requires a re-distribution of the Power over various nodes of the Distributed Generation (DG) facilitates. The re-distribution might cause over load on various parts of the networks which if not correctly optimized might increase the cost of maintenance and affect the overall network reliability. This is why it is urgently requited to find a methodology that can effectively provide a schema for re-distribution of the power and achieve both customers and power companies contracting objectives. In this paper, we explore our new proposed idea of using a simulated annealing based local search technique to provide an efficient power load distribution for distributed generation network. On doing this, we will apply our approach on the famous IEEE14 and IEEE30 power systems as two test cases. The developed results show the significant of the proposed approach.

2008

This work reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. Results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus and a large realistic system.