C.S. Ozveren

Electric power distribution network restoration: a survey of papers and a review of the restoration problem

This survey addresses the problem of supply restoration following an outage in an electric distribution system. Operator decision support for the restoration of supply after an outage is an active research area. This is significant for enhancing supply continuity and achieving better efficiency. It is important for operators to reach a feasible and speedily implementable restoration plan to restore the electricity service beyond the faulted zone. The restoration plan should satisfy objectives such as minimum number of customers without supply, minimum number of switching operations, and no overloaded components. Service restoration is therefore a multiple-objective problem with some objectives contradictory to one another. This paper provides a comprehensive survey of the conceptual aspects as well as recent algorithmic developments for distribution system restoration. Several fundamentally different approaches are discussed in the paper together with the factors affecting the assumptions of the underlying concepts and the various criteria used in the different approaches are reviewed. The paper considers all areas of the restoration problem including the input data requirements, focusing on the methodological features of the systems reported in the literature. The survey starts with a discussion on the rationale of distribution restoration. It then introduces the postulates upon which the conventional restoration approaches are based. Finally, the different approaches are compared and related by discussing the strengths and weaknesses of restoration schemes as currently practised and recently reported. In conclusion, the review also points out the needs and directions for future research. Critical discussions of individual contributions to the development of the subject are presented only in as much as they treat specific matters of concept, principle or applicability. The question of interfacing to the SCADA system or control centre environment is also briefly discussed.

Short term load forecasting using Multiple Linear Regression

this paper we present an investigation for the short term (up 24 hours) load forecasting of the demand for the South Sulewesis (Sulewesi Island - Indonesia) Power System, using a multiple linear regression (MLR) method. After a brief analytical discussion of the technique, the usage of polynomial terms and the steps to compose the MLR model will be explained. Report on implementation of MLR algorithm using commercially available tool such as Microsoft EXCELTM will also be discussed. As a case study, historical data consisting of hourly load demand and temperatures of South Sulawesi electrical system will be used, to forecast the short term load. The results will be presented and analysed potential for improvement using alternative methods is also discussed.

Electricity load profile classification using Fuzzy C-Means method

This paper presents the Fuzzy C-Means (FCM) clustering method. The FCM technique assigns a degree of membership for each data set to several clusters, thus offering the opportunity to deal with load profiles that could belong to more than one group at the same time. The FCM algorithm is based on minimising a c-means objective function to determine an optimal classification. The simulation of FCM was carried out using actual sample data from Indonesia and the results are presented. Some validity index measurements was carried out to estimate the compactness of the resulting clusters or to find the optimal number of clusters for a data set.

Finite element aided design optimisation of a shaded-pole induction motor for maximum starting torque

A method of optimizing the design of a shaded-pole induction motor for maximum starting torque with the aid of finite element modeling and a modified hybrid global-local search method combining the niching genetic algorithm with a direct search method is presented. By invoking the genetic algorithm and the deterministic method in turn, the solution with the global minimum is secured while simultaneously improving the convergence speed. The performance of the hybrid search method is demonstrated with an ideal mathematical problem first, before applying it to the shaded-pole motor design.

A review of the use of genetic algorithms in economic load dispatch

This paper presents a review of many previous papers on the use of genetic algorithms (GA) for solving the problem of economic load dispatch (ELD) for power systems. The paper will cover several topics, i.e. a brief description of the GA method, the various power system models and topologies solved by the GA method, various GA techniques used for ELD, and hybrids of GA with other techniques for solving ELD.

Application of artificial neural network for short term load forecasting

As accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional loads forecasting methods have been developed. In this paper we present the development of short term load forecaster using artificial neural network (ANN) models. Three approaches have been undertaken to forecast the load demand up to 24 hours ahead. The first model is a model that has 24 output nodes to forecast a sequence of 24 hourly loads at a time. The second ANN model forecasts the peak and valley load and the result is used to forecast the load profile, and finally a system with 24 separate ANNs in parallel, one for each hour of the days is used to forecast the load demand. These models are applied to the South Sulawesi Electricity System and the comparative summary of their performances are evaluated through simulation.

Allocation of transmission losses using three different proportional sharing methods

Recently, allocation transmission losses have become a major issue in both regulated and deregulated system. Two main reasons for this are performance and costs issues. Consequently, there have been a large number of investigations undertaken in the area of transmission loss allocation. These methods encompass a wide variety of techniques ranging from proportional sharing, pro-rata to incremental methods (marginal allocation, and unsubsidised marginal allocation). The issue of loss allocation is going to be at forefront of the discussions due to recent deregulation and privatisation attempts in Indonesia. In this paper after a brief statement of loss allocation problem we will provide a detailed comparison of three alternative algorithms from the proportional sharing (PS) approaches: Power Flow Tracing, Proportional Sharing Allocation, and Tracing Active - Reactive Power Flow method. A 4-bus simple meshed network will be used to test these methods. The results of the calculations will be compared with each other and then conclusions about the applicability of PS approach in Indonesia will be stated.

Renewable energy market potential in U.K.

Energy is crucial to our daily lives and to the needs of business and industries. Affordable, sustainable and reliable energy supplies are key objectives of the governments energy policy. But now a days Green electricity (GE) is a generic term for electricity generated from clean, environmentally preferable energy source. Over the last twenty years following privatisation the current energy policy frame work combining competitive energy markets and effective, independent regulation has been serendipitously successful in meeting the governments goals in terms of the environmental policy as carbon and other emissions were reduced as cleaner, more efficient gas-fired generation replaced coal generation and investment were made to reduce emissions from remaining coal- fired plant. But recent experience has led to questions as to whether we can continue to rely on a policy based on energy markets. Because, wholesale and retail energy prices have risen significantly and remain high and volatile, impacting on industrial competitiveness and fuel poverty. Carbon emission has started to rise as generators increase output from coal fired station in response to rising gas prices [1]. Since 1999 GE has been available to all customers in the UK. The market has had positive beginnings with almost all electricity suppliers offering a green electricity product. Marketing has been launched and consumers are beginning to make the switch to green electricity despite the premium charged. An accreditation scheme guarantees that the green purchases match power entering the grid. For supply side, the government also introduced the Renewable Obligation in 2002 as part of its strategy to cut carbon dioxide emissions. This places an obligation on energy suppliers to source an increasing proportion of their consumer demand from renewable energy however the demand side of the sector also has a significant role to play in reducing emission. For business customers, the Climate Change Levy on gas and electricity bills in 2001 has been introduced and companies were allowed an 80% discount on the levy if they signed Climate Change Agreements. The government estimates that these two programmes working together will save 6.2 MtC per year in 2010. For domestic customers, the government also introduced the Energy Efficiency Commitment (EEC), an obligation on gas and electricity suppliers to increase the efficiency of the energy use of their domestic customers [2]. The UK government has committed to a number of international and domestic targets and goals to reduce emissions of greenhouse gases which contribute to a climate change. In international agreements, the UK has made two ambitious commitments, which are not legally binding: To reduce emissions of carbon dioxide to 20% below 1990 levels by 2010 and to put the UK on a path to reduce carbon dioxide emissions by 60 % compared to 2000 levels, by 2050 with real progress by 2020. With the present energy markets, Utilities Bill and Climate Change Levy, despite aiming to support renewable energy are introducing a number of uncertainties to the market. In this paper we will try and discuss as to how barriers to this potential markets renewable energy can be overcome through government policies.

The Use of Fuzzy Cognitive Agents to Simulate Trading Patterns within the Liberalised UK Electricity Market

The world is becoming increasingly competitive by the action of liberalised national, regional, or global markets. Electrical power systems are not immune to such change, and many national power systems have are now subject to the influence of overtly neo-liberal market models. The resulting electrical markets are subject to increasingly complex rule bases put in place to ensure that individual consumer and national imperatives are met. Further, these markets are also increasingly moving towards real time trading, reducing the time available for critical decisions to be made. In an effort to develop a deeper understanding of these evolving markets and to create effective system support tools for market participants, many simulation techniques such as neural networks and expert systems have been applied. In this paper we report our efforts to develop an effective simulation platform using fuzzy cognitive agents. Our approach is based upon the encapsulation of fuzzy cognitive maps (FCM) generated on the Matlab Simulink platform within commercially available Intelligent Agent software. The approach permits the retention of the visually simple but rich relational complexity of the Matlab Simulink based FCM, whilst enhancing their domain applicability by integrating it with the relational and structural flexibility of the Intelligent Agents. We will describe a potential architecture for the fuzzy cognitive agent and report on our first attempts to integrate the Matlab Simulink based FCM with the jack intelligent agent toolkit will be presented

Short term electric load forecast using artificial neural networks

The application of neural networks (NN) in the area of power system engineering is increasing rapidly. Several researchers have suggested that short-term load forecasting (STLF) is a suitable area for the implementation of the NN approach. This paper presents work to further support two previous reports in which the authors described a method developed using NN and proposed a novel approach in applying NN to the problem of STLF. The proposed approaches to STLF have been developed for the PC environment as the primary target. With the increase in power and performance of networked PCs, such a platform is capable of supporting the large scale databases, high speed communications and processing power, required for the STFL process in the electricity supply industry (ESI). This improved method uses a NN architecture which consists of an ensemble of hidden layers, connected separately to a common input and output layer. The previously reported approaches are improved upon and enhanced to provide 3 hour ahead half hourly forecast together with a sliding window of validated system demand data applied directly to the input layer of the NN. The network includes a data filter that has been developed to remove the generalised noise from the input data set, which is used as an additional input for the NN. Results from annual demand curves with the corresponding forecasts for comparative purposes, which show that the NN approach achieves a high degree of accuracy, comparable with values reported in the literature for STLF, are presented.<<ETX>>