Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested.

Neural networks for short-term load forecasting: a review and evaluation

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.

Evolutionary programming techniques for economic load dispatch

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Over the past half-century, Optimal Power Flow (OPF) has become one of the most important and widely studied nonlinear optimization problems. In general, OPF seeks to optimize the operation of electric power generation, transmission, and distribution networks subject to system constraints and control limits. Within this framework, however, there is an extremely wide variety of OPF formulations and solution methods. Moreover, the nature of OPF continues to evolve due to modern electricity markets and renewable resource integration. In this two-part survey, we survey both the classical and recent OPF literature in order to provide a sound context for the state of the art in OPF formulation and solution methods. The survey contributes a comprehensive discussion of specific optimization techniques that have been applied to OPF, with an emphasis on the advantages, disadvantages, and computational characteristics of each. Part I of the survey (this article) provides an introduction and surveys the deterministic optimization methods that have been applied to OPF. Part II of the survey examines the recent trend towards stochastic, or non-deterministic, search techniques and hybrid methods for OPF.

Optimal power flow: a bibliographic survey I

This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus voltage magnitude limits, and generator reactive capabilities, are included as penalties in the GA fitness function (FF). Advanced and problem-specific operators are introduced in order to enhance the algorithm's efficiency and accuracy. Numerical results on two test systems are presented and compared with results of other approaches.

Optimal power flow by enhanced genetic algorithm

This paper presents the development of an artificial neural network based short-term load forecasting model for an autonomous power system. Problems encountered in data preparation, network structure definition and the model's sensitivity to temperature, together with suggested solutions, are discussed. The proposed model can provide next week's load forecasts. Experiences obtained during the application of the model to predict daily load curves in the autonomous power system of the Greek island of Crete are presented.

Short-term load forecasting in an autonomous power system using artificial neural networks

The difference between the lambda values of the economic dispatch and the unit commitment problems is presented, based on the economic interpretation of the Lagrangian relaxation solution framework. Although both sets of lambdas represent marginal cost of electricity, the underlying assumptions on their computation in the two problems are different, as explained. Results of two case studies that support the above differences are presented.

Lambda of Lagrangian relaxation solution to unit commitment problem

Different optimisation methods developed for the solution of the nonlinear OPF problem with both continuous and discrete variables are compared. Two mathematical programming methods are compared with two metaheuristics, an enhanced genetic algorithm and a particle swarm optimisation implementation. Test results from the application of the methods to several IEEE systems are presented and compared. Useful conclusions are drawn concerning the execution times and the ‘optimum’ costs provided by all four tested methods.

Comparison of two metaheuristics with mathematical programming methods for the solution of OPF

This paper presents the development of an Artificial Neural Network (ANN) based short-term load forecasting model for the Dispatching Center of the Greek Public Power Corporation (PPC) in the island of Crete. The model can forecast daily load profiles with a lead time of one to seven days. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set pre-processing are described in the paper.

C.E. Zoumas

Papers

Optimal power flow by enhanced genetic algorithm

Short-term load forecasting in an autonomous power system using artificial neural networks

Lambda of Lagrangian relaxation solution to unit commitment problem

Comparison of two metaheuristics with mathematical programming methods for the solution of OPF

Data pre-processing for short-term load forecasting in an autonomous power system using artificial neural networks