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This paper proposes a comprehensive framework to detect non-technical losses (NTLs) and recover electrical energy (lost by abnormalities or fraud) by means of a data mining analysis, in the Spanish Power Electric Industry. It is divided into four section: data selection, data preprocessing, descriptive, and predictive data mining. The authors insist on the importance of the knowledge of the particular characteristics of the Power Company customer: the main features available in databases are described. The paper presents two innovative statistical estimators to attach importance to variability and trend analysis of electric consumption and offers a predictive model, based on the Generalized Rule Induction (GRI) model. This predictive analysis discovers association rules in the data and it is supplemented by a binary Quest tree classification method. The quality of this framework is illustrated by a case study considering a real database, supplied by Endesa Company. [ABSTRACT FROM PUBLISHER]

In a competitive electricity market, consumers seek strategies to procure their energy needs from different resources (pool, bilateral contracts, and their own generation facilities) at minimum cost while controlling the risk. In this paper, a novel method is proposed to address the electricity procurement problem of large consumers using the concept of information gap decision theory. The method can be used as a tool for assessing the risk levels, considering whether a large consumer is risk-taking or risk-averse regarding its midterm procurement strategies. In the proposed method, procurement decisions are evaluated by means of two criteria. The first criterion is the robustness of the decision against experiencing high procurement costs, and the second one is the opportunity of taking advantage of low procurement costs. The pool price is considered an uncertain variable, and it is assumed that the large consumer has an estimate of the prices. In this study, two models of uncertainty are addressed for the pool price based on the concept of weighted mean squared error using a variance-covariance matrix, and the expected procurement cost is modeled using a joint normal probability distribution function. A case study illustrates the working of the proposed technique. [ABSTRACT FROM PUBLISHER]

This paper proposes a decision-making framework, based on stochastic programming, for a retailer: 1) to determine the sale price of electricity to the customers based on time-of-use (TOU) rates, and 2) to manage a portfolio of different contracts in order to procure its demand and to hedge against risks, within a medium-term period. Supply sources include the pool, self-production facilities and several instruments such as forward contracts, call options, and interruptible contracts. The objective is to maximize the profit and simultaneously to minimize the risks in terms of a multi-period risk measure. Moreover, the risks are measured using conditional value at risk (CVaR) methodology. The reaction of the customers to the retailers' selling prices as well as the competition between the retailers is modeled through a market share function. The problem is formulated as a mixed-integer stochastic programming. It is solved by a decomposition technique, and the decomposed parts are solved by a branch-and-bound algorithm. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel schematic approach for coordinating the selection of distributed generation unit investment proposals submitted by multiple, competing, private investors to achieve maximum investor participation while complying with the technical operational limits of the local distribution company. The concept of domains and commons has been used to identify the largest transgressors to system function, and forms the basis for revisions to the investor proposals. [ABSTRACT FROM AUTHOR]

The rise in temperatures induced by climate change may have important implications for Thailand's electricity demand. This paper investigates how changing climate will affect Thailand's daily, seasonal, and long-term electricity demand. Regression models are applied to capture daily load patterns across each month in the year. Temperature projections from the UK Hadley Centre climate model are then used to assess hourly sensitivity to changes in mean temperatures and diurnal temperature range. These are combined with four representative socioeconomic scenarios from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios to project absolute changes in Thailand's electricity demand. The specific climate and socioeconomic scenarios considered here indicate that mean annual temperatures in Thailand will rise by 1.74 to 3.43° C by 2080, implying increases in Thai peak electricity demand of 1.5%-3.1% in the 2020s, 3.7%-8.3% in the 2050s, and 6.6%-15.3% in the 2080s. [ABSTRACT FROM AUTHOR]

Problems such as price volatility have been observed in electric power markets. Demand-side participation is frequently offered as a potential solution by promising to increase market efficiency when hockey-stick-type offer curves are present. However, the individual end-consumer will surely value electricity differently, which makes demand-side participation difficult as a group and at a bus. In this paper demand is categorized into two groups: one that highly values reliability and one that does not. The two types are modeled separately and a new optimal bidding function is developed and tested based on this model. [ABSTRACT FROM AUTHOR]

The residual demand derivative plays a central role in constructing the best response to competitors' strategies in widely used strategic models such as the Cournot model and the supply function model. In the absence of transportation or transmission constraints, the residual demand derivative is obtained straightforwardly by taking the derivative of the residual demand function with respect to price. However, in an electricity market, the market is embedded in a transmission network. When there is no transmission congestion, the residual demand derivative can be calculated as usual, but when there is transmission congestion, the residual demand derivative is more difficult to calculate. In this paper, we characterize the transmission-constrained residual demand derivative. We use the dc power flow model and characterize the residual demand derivative analytically. The residual demand derivative could also be obtained from the solution of a specific weighted least squares problem. Several properties of the residual demand derivative are implications of the weighted least squares theory. We also characterize the condition under which the residual demand derivative will be bounded or unbounded when there are perfectly elastic supplies/demands at some buses in the system. We verified our results in three examples: a two-bus system, a four-bus two-loop system, and a three-bus one-loop system with one perfectly elastic supply. The residual demand derivative characterization can be used to analyze the strategic behavior in both the Cournot model and the supply function model with transmission constraints, and it can be easily incorporated into sophisticated optimal strategy algorithms. [ABSTRACT FROM AUTHOR]

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns. [ABSTRACT FROM AUTHOR]

In a competitive electricity market, a generation company (Genco) can manage its trading risk through trading electricity among multiple markets such as spot markets and contract markets. The question is how to decide the trading proportion of each market in order to maximize the Genco's profit and minimize the associated risk. Based on the mean-variance portfolio theory, this paper proposes a sequential optimization approach to electric energy allocation between spot and contract markets, taking into consideration the risks of electricity price, congestion charge, and fuel price. Especially, the impact of the fuel market on electric energy allocation is analyzed and simulated with historical data in respect of the electricity market and other fuel markets in the U.S. Simulation results confirm that the proposed analytic approach is consistent with intuition and therefore reasonable and feasible for a Genco to make a trading plan involving risks in an electricity market. [ABSTRACT FROM AUTHOR]

The recent evolution of the electricity business regulation has given new possibilities to the electricity providers for formulating dedicated tariff offers. A key aspect for building specific tariff structures is the identification of the consumption patterns of the customers, in order to form specific customer classes containing customers exhibiting similar patterns. This paper illustrates and compares the results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, hierarchical clustering, K-means, fuzzy K-means) and the self-organizing maps to group together customers with similar electrical behavior. Furthermore, this paper discusses and compares various techniques—Sammon map, principal component analysis (PCA), and curvilinear component analysis (CCA)—able to reduce the size of the clustering input data set, in order to allow for storing a relatively small amount of data in the database of the distribution service provider for customer classification purposes. The effectiveness of the classifications obtained with the algorithms tested is compared in terms of a set of clustering validity indicators. Results obtained on a set of nonresidential customers are presented. [ABSTRACT FROM AUTHOR]

In this paper, an effective ac corrective/preventive contingency dispatch over a 24-h period is proposed based on security-constrained unit commitment (SCUC) model. The SCUC model includes unit commitment, ac security-constrained optimal power flow (SCOPF), and load shedding (LS) for steady state and contingencies. The objective of this SCUC model is to obtain the minimum bid-based system operating cost while maintaining the system security. The prevailing generation constraints, such as hourly power demand, system reserves, fuel and emission limits, ramp up/down limits, and minimum up/down time limits, are included in this model. In addition, system constraints such as time-limited emergency controls for a given contingency and ac network security limits are taken into account. The proposed ac solution for the hourly scheduling of generating units is based on Benders decomposition. Case studies with the six-bus system, the IEEE 118-bus test system, and 1168-bus system are presented in detail in this paper. [ABSTRACT FROM AUTHOR]

This paper develops a state-queuing model to analyze the price response of aggregated loads consisting of thermostatically controlled appliances (TCAs). Assuming a perfectly diversified load before the price response, we show that TCA setpoint changes in response to the market price Will result in a redistribution of TCAs in on/off states and therefore change the probabilities for a unit to reside in each state. A randomly distributed load can be partially synchronized and the aggregated diversity lost. The loss of the load diversity can then create unexpected dynamics in the aggregated load profile. Raising issues such as restoring load diversity and damping the peak loads are also addressed in this paper. [ABSTRACT FROM AUTHOR]

Many problems have appeared with the practical implementation of restructured electrical business in the U.S. and European Union such as lack of generation, network constraints, etc. A good example of these problems is the scarce participation of the demand in the electricity markets-energy, reserve, and other ancillary services-problems that could be solved through new demand responsive programs, aimed to replace the traditional demand side management programs into voluntary demand participation programs. A methodology for the generation of demand side bids and offers in large customer facilities and a real application to a university customer is presented in this paper. The methodology is based on the knowledge of the physical processes involved in the electricity consumption and on the flexibility of the required supply. The result of the methodology proposed is a set of demand packages that can be used to participate in different electricity markets, whose possibilities-in the market arena-will be explored in a consequent paper. [ABSTRACT FROM AUTHOR]