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

Feuerriegel, Stefan; Bodenbenner, Philipp; Neumann, Dirk

doi:10.1016/j.eneco.2015.11.016

Cited by 47 publications

(18 citation statements)

References 23 publications

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“…Then the potential savings implied by the DR system can hardly cover the overall costs (cp. results in Feuerriegel et al, 2013Feuerriegel et al, , 2016. However, even if the DR system is not profitable today, the assumed cost increases for control reserves and balancing energy can potentially lead to a positive financial case in the medium term.…”

Section: Policy Implicationsmentioning

confidence: 99%

Integration Scenarios of Demand Response into Electricity Markets: Load Shifting, Financial Savings and Policy Implications

Feuerriegel

Neumann

2016

SSRN Journal

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Demand Response allows for the management of demand side resources in real-time; i. e. shifting electricity demand according to fluctuating supply.When integrated into electricity markets, Demand Response can be used for load shifting and as a replacement for both control reserve and balancing energy. These three usage scenarios are compared based on historic German data from 2011 to determine that load shifting provides the highest benefit: its annual financial savings accumulate to e3.110 M for both households and the service sector. This equals to relative savings of 2.83 % compared to a scenario without load shifting. To improve Demand Response integration, the proposed model suggests policy implications: reducing bid sizes, delivery periods and the time-lag between market transactions and delivery dates in electricity markets.

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Section: Policy Implicationsmentioning

confidence: 99%

Integration Scenarios of Demand Response into Electricity Markets: Load Shifting, Financial Savings and Policy Implications

Feuerriegel

Neumann

2016

SSRN Journal

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“…In this regard, smart meters could be considered necessary, but not sufficient enabling devices for achieving significant peak load reductions. On one hand, residential consumers may require additional information systems and technologies, such as inhome, always-on displays [45], home area networks [47] or mobile applications that provide detailed consumption feedback allowing them to more easily control and shift their consumption loads. Just as for energy efficiency programs, the degree of technology adoption within a home can influence the households' response to DSM initiatives [32], but this is more pronounced for load management programs because it requires that consumers make repeated decisions about whether to accept the utilities' request for a change in consumption [15].…”

Section: Load Management Effectsmentioning

confidence: 99%

“…In such an arrangement, referred to as direct-load control, the meter is effectively by-passed because the communications occurs directly between the utility and the appliance. Thus, the metering device is less relevant as a factor of success, particularly if the direct load program is managed by a third party intermediary that combine and manage the load of multiple consumers [47].…”

Section: Load Management Effectsmentioning

confidence: 99%

Toward a sustainable modern electricity grid: The effects of smart metering and program investments on demand-side management performance in the US electricity sector 2009-2012

Corbett

Wardle²,

Chen

2018

IEEE Trans. Eng. Manage.

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Building a sustainable modern electricity sector is a key part of the United Nations' 2030 Sustainable Development Goals. Smart meters are expected to contribute to the achievement of this goal by providing new demand-side management opportunities for utilities. Attempting to address existing gaps in the extant literature, this paper examines the effects of metering technologies and demand-side management program investments using panel data from 87 American electricity utilities for the period 2009-2012. Our model provides strong explanatory power with respect to energy efficiency, and shows that automated meter reading (AMR) devices, advanced metering infrastructure (AMI) devices, direct program costs and incentive costs all have a positive influence on energy efficiency effects. In contrast, the results for load management are largely insignificant, with the exception of incentive costs which has a small, but significant, negative impact on peak load reductions. We discuss the implications and potential research directions arising from these findings, in particular the need to consider interactions between different demand-side programs and the influence of different types of information technologies that are emerging as part of the smart grid. Managerial Relevance Statement Panel data analysis is performed to investigate the impacts of smart metering and program financial investments on energy demand management by the US electricity sector. Unlike most existing studies on smart grid implementation which utilize cross-sectional data, the use of panel data in this research allow for the identification of impacts of the smart grid technology by observing the performances of the same utilities over time. Decision makers can then input their specific characteristics, such as numbers of different meters types and their level of investment, to understand and determine what benefits could be expected given a certain level of technology implementation or financial investment. Our research also leads to a number of analytical results which are different from those in the existing literature. In particular, we show that automated meter reading devices, advanced metering infrastructure devices, direct program costs and incentive costs all have a positive influence on energy efficiency effects, but not on load reductions. We also discuss the importance for decision makers to consider the interactions between different types of demand-side initiatives in conjunction with transformations occurring on the supply-side as well as the continuing innovation in smart grid technologies in use by intermediary organizations and consumers.

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“…(1) Smart meter use rate (C6): Smart meters can record electricity consumption in intervals of an hour or less and provide energy use information for monitoring centers, which can help residents and management departments to implement DR plans [61,63]. (2) Smart home application degree (C7): Smart homes include the control and automation of heating, lighting, air conditioning, ventilation and security.…”

Section: Technology Level (Z3)mentioning

confidence: 99%

Performance Evaluation of Residential Demand Response Based on a Modified Fuzzy VIKOR and Scalable Computing Method

Dong

Huang

2018

Energies

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For better utilizing renewable energy resources and improving the sustainability of power systems, demand response is widely applied in China, especially in recent decades. Considering the massive potential flexible resources in the residential sector, demand response programs are able to achieve significant benefits. This paper proposes an effective performance evaluation framework for such programs aimed at residential customers. In general, the evaluation process will face multiple criteria and some uncertain factors. Therefore, we combine the multi-criteria decision making concept and fuzzy set theory to accomplish the model establishment. By introducing trapezoidal fuzzy numbers into the Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR) method, the evaluation model can effectively deal with the subjection and fuzziness of experts' opinions. Furthermore, we ameliorate the criteria weight determination procedure of traditional models via combining the fuzzy Analytic Hierarchy Process and Shannon entropy method, which can incorporate objective information and subjective judgments. Finally, the proposed evaluation framework is verified by the empirical analysis of five demand response projects in Chinese residential areas. The results give a valid performance ranking of the five alternatives and indicate that more attention should be paid to the criteria affiliated with technology level and economy benefits. In addition, a series of sensitivity analyses are conducted to examine the validity and effectiveness of the established evaluation framework and results. The study improves traditional multi-criteria decision making method VIKOR by introducing trapezoidal fuzzy numbers and combination weighing technique, which can provide an effective mean for performance evaluation of residential demand response programs in a fuzzy environment.

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

Cited by 47 publications

References 23 publications

Integration Scenarios of Demand Response into Electricity Markets: Load Shifting, Financial Savings and Policy Implications

Integration Scenarios of Demand Response into Electricity Markets: Load Shifting, Financial Savings and Policy Implications

Toward a sustainable modern electricity grid: The effects of smart metering and program investments on demand-side management performance in the US electricity sector 2009-2012

Performance Evaluation of Residential Demand Response Based on a Modified Fuzzy VIKOR and Scalable Computing Method

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