An interactive approach to demand side management based on utility functions

Matsumoto, Keinosuke; Takamuki, Y.; Mori, Naoki; Kitayama, Masashi

doi:10.1109/drpt.2000.855654

Cited by 11 publications

(16 citation statements)

References 5 publications

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“…As a result, Independent System Operators (ISOs) and Reliability Transmission Organizations (RTOs) have been implementing DSM for its potential to lower market prices, reduce price volatility, improve customer options, and increase the elasticity from wholesale to retail market [32]. Researches on DSM have addressed the minimization of energy consumption, maximization of customer utility, the minimization of customer discomfort, the stabilization of electricity prices, and multi-objective optimizations from the customer side [33][34][35][36][37][38][39][40]. In addition, there have also been studies on the integration of DSM and renewable uncertainty [41], centralized or distributed demand control algorithms [14,30,[42][43][44][45][46], demand-side storage [47,48], models of customer behavior [49], and prediction of DSM participation potential [50][51][52].…”

Section: Motivationmentioning

confidence: 99%

Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

Jiang

Farid

2015

Applied Energy

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Section: Motivationmentioning

confidence: 99%

Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

Jiang

Farid

2015

Applied Energy

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“…Much research work has been undertaken concerning datamining techniques, for example as reported in [14][15][16][17][18]. In the present study, the intention is to make a comparative analysis of the results presented in such studies.…”

Section: B Proposed Frameworkmentioning

confidence: 99%

“…Yang [14] applied patternbased clustering methods and mixture models to establish customer segmentation. In his work, Matsumoto [18] used online estimation of the functions in his modelling of a demandside management system as a means of solving resource allocation, scheduling, and optimization problems. Finally, an outlier analysis reported in [19] can be performed in order to discover irregularities in consumption by detecting abnormalities in payment patterns or irregularities in load profiling.…”

Section: B Proposed Frameworkmentioning

confidence: 99%

Customer information systems for deregulated ASEAN countries

Nizar¹,

Zhang²,

Liebman³

2005

2005 International Power Engineering Conference

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-In similar fashion to western countries, ASEAN countries are also gearing up towards deregulation. Despite potentially different motivating drivers, the ultimate objectives are free market competition leading to efficient pricing signals as well as providing customers with the freedom to choose their electricity provider and benefit from competitive prices. This paper provides an ASEAN electricity market analysis and describes the development of electricity deregulation in ASEAN countries. By way of background it also highlights the objectives of deregulation, the potential challenges and also the impact areas focusing on existing Customer Information Systems (CIS) that have been developed by other utilities. In addition, this paper proposes a new framework for improving CIS for ASEAN utilities facing deregulation. The framework outlines a CIS, which has intelligent features enabling the utility to estimate and predict customer behaviour with respect to consumption patterns. It describes how these features can assist the utility companies to retain their existing customers as well as attract more customers.

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“…The demand side management dispatch schedule is jointly determined by suppliers and customers [25]. The simplest form of social welfare maximization is often mentioned in power systems textbooks [46] and commonly used in academic research.…”

Section: Academic Literaturementioning

confidence: 99%

“…The industrial and academic literature propose different methods and goals for DSM implementations. The most adopted method among academic researches is to maximize the net benefit from electricity consumption and generation [25,[27][28][29][30]. On the other hand, industrial practice in the US electricity market uses historical data to predict a baseline of the electricity consumption that would have occurred without DSM [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Industrial Baseline Errors on Costs and Social Welfare in the Demand Side Management of Day-Ahead Wholesale Markets

Jiang

Farid

2015

Volume 2: Photovoltaics; Renewable-Non-Renewable Hybrid Power System; Smart Grid, Micro-Grid Concepts; Energy Storage; Solar Ch

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Demand Side Management (DSM) has been recognized for its potential to counteract the intermittent nature of renewable energy, increase system efficiency, and reduce system costs. While the popular approach among academia adopts a social welfare maximization formulation, the industrial practice in the United States electricity market compensates customers according to their load reduction from a predefined electricity consumption baseline that would have occurred without DSM. This paper is an extension of a previous paper studying the differences between the industrial & academic approach to dispatching demands. In the previous paper, the comparison of the two models showed that while the social welfare model uses a stochastic net load composed of two terms, the industrial DSM model uses a stochastic net load composed of three terms including the additional baseline term. That work showed that the academic and industrial optimization method have the same dispatch result in the absence of baseline errors given the proper reconciliation of their respective cost functions. DSM participants, however, and very much unfortunately, are likely to manipulate the baseline in order to receive greater financial compensation. This paper now seeks to study the impacts of erroneous industrial baselines in a day-ahead wholesale market context. Using the same system configuration and mathematical formalism, the industrial model is compared to the social welfare model. The erroneous baseline is shown to result in a different and more importantly costlier dispatch. It is also likely to require more control activity in subsequent layers of enterprise control. Thus an erroneous baseline is likely to increase system costs and overestimate the potential for social welfare improvements. NOMENCLATURE

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An interactive approach to demand side management based on utility functions

Cited by 11 publications

References 5 publications

Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

Demand side management in a day-ahead wholesale market: A comparison of industrial & social welfare approaches

Customer information systems for deregulated ASEAN countries

Impacts of Industrial Baseline Errors on Costs and Social Welfare in the Demand Side Management of Day-Ahead Wholesale Markets

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