Demand side management—A simulation of household behavior under variable prices

Gottwalt, Sebastian; Ketter, Wolfgang; Block, Carsten; Collins, John; Weinhardt, Christof

doi:10.1016/j.enpol.2011.10.016

Cited by 348 publications

(183 citation statements)

References 12 publications

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“…The more the load is exchanged between time slots, the darker is the corresponding rectangle. Similar to the findings by Gottwalt et al (2011), a large proportion of the savings arise at night time. In fact, most load is shifted to the time interval from 1 a. m. to 6 a. m..…”

supporting

confidence: 83%

“…More precisely, Gottwalt et al (2011) propose an optimization procedure for load shifting based on real-time pricing. They also analyze the effect at household level, but neglect the financial benefits.…”

Section: Household Levelmentioning

confidence: 99%

“…• Although the electricity retailer gains an immense financial advantage by implementing Demand Response, the average savings per person are relatively small (e. g. Gottwalt et al, 2011;Ossowski, 2012, 2013) and account for only € 11.6 per annum. However, as electricity prices are likely to rise in the future, revenues due to Demand Response 24 will also increase.…”

mentioning

confidence: 99%

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Measuring the financial impact of demand response for electricity retailers

Feuerriegel

Neumann

2014

Energy Policy

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Due to the integration of intermittent resources of power generation such as wind and solar, the amount of supplied electricity will exhibit unprecedented fluctuations. Electricity retailers can partially meet the challenge of matching demand and volatile supply by shifting power demand according to the fluctuating supply side. The necessary technology infrastructure such as Advanced Metering Infrastructures for this so-called Demand Response (DR) has advanced. However, little is known about the economic dimension and further effort is strongly needed to realistically quantify the financial impact. To succeed in this goal, we derive an optimization problem that minimizes procurement costs of an electricity retailer in order to control Demand Response usage. The evaluation with historic data shows that cost volatility can be reduced by 7.74 %; peak costs drop by 14.35 %; and expenditures of retailers can be significantly decreased by 3.52 %.

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supporting

confidence: 83%

Section: Household Levelmentioning

confidence: 99%

mentioning

confidence: 99%

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Measuring the financial impact of demand response for electricity retailers

Feuerriegel

Neumann

2014

Energy Policy

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“…The appointed DR benefit for the transmission and distribution operators of deferring investment in network reinforcement or increased long-term network reliability stated by Siano (2014) is worth investigating. The need for this study is strengthened by the existence of studies defending that DR can cause new demand peaks for electric utilities to deal with when day-ahead hourly prices are applied (Gottwalt, Ketter, Block, Collins, & Weinhardt, 2011).…”

Section: Theory Of Load Managementmentioning

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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“…The components and communication networks of the end user, 13 namely, the sensitized objects and home area networks (HAN), are not integrated. Other studies concentrate on this aspect of smart grid infrastructure (Gottwalt et al, 2011). We presume the availability of remotely controllable devices on the premises of all end users, as this is a necessary condition for the efficient use of DR programs in our setup.…”

Section: Model Assumptionsmentioning

confidence: 99%

Value and granularity of ICT and smart meter data in demand response systems

2016

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The large-scale integration of intermittent resources of power generation leads to unprecedented fluctuations on the supply side. An electricity retailer can tackle these challenges by pursuing strategies of flexible load shifting -socalled demand response mechanisms. This work addresses the associated tradeoff between ICT deployment and economic benefits. The ICT design of a demand response system serves as the basis of a cost-value model, which incorporates all relevant cost components and compares them to the expected savings of an electricity retailer. Our analysis is based on a typical German electricity retailer to determine the optimal read-out frequency of smart meters. For our set of parameters, a positive information value of smart meter read-outs is achieved within an interval of 21 to 57 minutes regarding variable costs.Electricity retailers can achieve a profitable setting by restricting smart meter roll-out to large customers.

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Demand side management—A simulation of household behavior under variable prices

Cited by 348 publications

References 12 publications

Measuring the financial impact of demand response for electricity retailers

Measuring the financial impact of demand response for electricity retailers

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

Value and granularity of ICT and smart meter data in demand response systems

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