Forecasting flexibility in electricity demand with price/consumption volume signals

Gorria, Carlos; Jimeno, Joseba; Laresgoiti, I.; Lezaun, M.; Ruiz, Nerea

doi:10.1016/j.epsr.2012.09.011

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…Recent years, with the consideration of the tight relation of demand, price and the resulting profits in the electricity market, demand uncertainty has been widely used, and the relevant literature contains models for predicting loads, which aim at providing generation companies with an estimation of future loads and demand dynamics [1][2][3][4][5]. Paper [1] built a mathematical model to predict the aggregated demand of domestic consumers signed up to financial incentives of demand management program.…”

Section: Introductionmentioning

confidence: 99%

“…Paper [1] built a mathematical model to predict the aggregated demand of domestic consumers signed up to financial incentives of demand management program. Paper [2] invented a hybrid forecast model by Wavelet-ARIMA, where demand series is decomposed into a set of constitutive series to build different pertinent models with better regression effects.…”

Section: Introductionmentioning

confidence: 99%

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Demand elasticity analysis by least squares support vector machine

Xie

Zheng

2013

2013 6th International Congress on Image and Signal Processing (CISP)

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Demand-side management (DSM) has been introduced and paid much effort in the electricity industry for such a long time, and become more and more focused in the most of the electricity markets recent years, because of its effect on the modification of consumer demand for energy through various methods especially financial incentives. But due to the complexity of the influence factors, the demand elasticity analysis on electric load is along one of focused and unsolved problems in the researches of electricity market. This paper presents a mathematical tool based on optimization models that is intended to be used to evaluate the load elasticity. Here one effective way of tackling the elasticity computation problem is proposed to simulate the accurate demand response for its influence factors, where least square support vector machine (LS-SVM) is generalized to build the simulation model. First to overcome the limitations of traditional parametric regression methods based on least square rule, load mapping model are accomplished by LS-SVM that is a strong nonlinear regression tool and outperforms standard support vector machine in the regression accuracy and modeling velocity by obtaining the solutions of a set of linear equations instead of solving a quadratic programming problem. Based on that model, numerical simulation is performed to simulate the changes in load and its influence factor in consistency with the latent mapping relationship. Then demand elasticity models are deduced, whose elasticity coefficients are computed by the generated samples. The method embodies clear concepts, so it can be easily understood theoretical and computationally realized. Case studies are used to test the proposed model. In this scenario, the goal is to show the response of demands to different influence signals. Alternative analyses based on assessing the elasticity of power to other input parameters could be also carried out.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Demand elasticity analysis by least squares support vector machine

Xie

Zheng

2013

2013 6th International Congress on Image and Signal Processing (CISP)

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“…Thus, in daily energy demand peaks, energy can be supplied into the electric grid, saving the owner's money. Many papers have concentrated on the proper setups for energy storage in order to react to the varying prices of electricity throughout the day and sell the excess energy or buy some if needed [1]. The absence of crude oil is expected in the following decades due to an alarming rate of the diminishment of fossil-fuel reserves in the world [2].…”

Section: Introductionmentioning

confidence: 99%

Small Cogeneration Unit with Heat and Electricity Storage

2021

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A micro cogeneration unit based on a three-cylinder internal combustion engine, Skoda MPI 1.0 L compressed natural gas (CNG), with an output of 25 kW at 3000 RPM is proposed in this paper. It is a relatively simple engine, which is already adopted by the manufacturer to operate on CNG. The engine life and design correspond to the original purpose of use in the vehicle. A detailed dynamic model was created in the GT-SUITE environment and implemented into an energy balance model that includes its internal combustion engine, heat exchangers, generator, battery storage, and water storage tank. The 1D internal combustion engine model provides us with information on engine start-up time, actual effective power, friction power, and the amount of heat going to the cooling system and exhaust pipe. The catalytic converter was removed from the exhaust pipe, and the engine was always operating at full load; thus, engine power control is not considered. An energy storage system for an island operation of the entire power unit for a large, detached house was designed to withstand accumulated energy for a few days in the case of a breakout. To reach a low initial system cost, the possible implementation of worn-out battery packs toward emission reduction in terms of the second life of the battery is proposed. The energy and emission balance are carried out, and the service life of the engine is also discussed.

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“…Another reason for the study of new tools to balance supply and demand is that transmission networks in Europe, due to new market mechanisms, are becoming a platform to increasing energy flows (UCTE, 2007) (Zeilinger, 2011) (Gyamfi & Krumdieck, 2012) (Gorria, Jimeno, Laresgoiti, Lezaun, & Ruiz, 2013).…”

Section: Load Management and Real-time Pricingmentioning

confidence: 99%

Methodology to simulate the impact of a large deployment of a residential energy management system in the electricity grid

Miguel

Neves

Martins

2014

Electric Power Systems Research

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This work proposes a methodology to evaluate the energy and power resources that can be made available through the deployment and use of a residential energy management system, as the Energy Box (EB) proposed by Livengood & Larson (2009). The impact on the electricity distribution system of such technology will result of changes in the electricity usage by end-use customers in response to stimuli like real-time changes in the electricity price, corresponding to the widely known demand response (DR). However, due to the dispersed and uncontrolled nature of the management of end-use appliances, estimating the energy and power output of the aggregation of a high number of EB's requires a specific approach which can take into account the random nature of load response.The approach that was followed simulated a certain universe of consumers from one particular city, using information regarding total electricity consumption as well as the segregated consumption per main appliance. The proposed methodology makes use of queried data regarding the willingness of consumers to postpone the start of appliances, as well as prototypes of hourly price diagrams. These prototypes were obtained through a data clustering exercise that was also proposed as part of the methodology. The possible environmental impact of the aggregated use of EB's was also estimated.The output of the methodology includes the range of released network capacity as well as load rebound, both caused by the aggregated EB response. In particular, load rebound is a relevant phenomenon that presents new challenges to the management of the grid, and for which some preventive measures are suggested.The analysis of the environmental significance of the aggregated EB resource shows that DR is not per se a tool to reduce CO 2 emissions, being strongly dependent of the generation technologies that are used to compensate demand variations. ResumoEste trabalho propõe uma metodologia para avaliar os recursos de energia e de potência que podem ser disponibilizados através da disseminação e utilização de um sistema de gestão de energia no sector residencial, como a Energy Box (EB) proposta por Livengood & Larson (2009). O impacto de tal tecnologia sobre o sistema de distribuição de energia elétrica irá resultar de mudanças no uso da eletricidade por utilização final de clientes em resposta a estímulos como mudanças em tempo real no preço da electricidade, correspondendo ao amplamente conhecido conceito de gestão da procura (DR). No entanto, a natureza dispersa e não controlada da gestão destas cargas exige uma abordagem que tenha em conta o caráter aleatório do comportamento desta procura para efetuar uma estimativa agregada do consumo de energia e da potência causados por um elevado número de dispositivos EB.A abordagem que foi seguida exigiu a simulação de um determinado universo de consumidores de uma determinada cidade, utilizando informações sobre o consumo total de energia eléctrica, bem como o consumo por eletrodoméstico. A metodologia proposta faz uso de...

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Forecasting flexibility in electricity demand with price/consumption volume signals

Cited by 14 publications

References 10 publications

Demand elasticity analysis by least squares support vector machine

Demand elasticity analysis by least squares support vector machine

Small Cogeneration Unit with Heat and Electricity Storage

Methodology to simulate the impact of a large deployment of a residential energy management system in the electricity grid

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