Evaluation of residential customer elasticity for incentive based demand response programs

Asadinejad, Ailin; Rahimpour, Alireza; Tomsovic, Kevin; Qi, Hairong; Chen, Chien

doi:10.1016/j.epsr.2017.12.017

Cited by 99 publications

(44 citation statements)

References 23 publications

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“…During the past few years, DSR has been identified as a promising measure for, among others, providing system flexibility to reduce electricity demand, shifting peak demand, providing reserve capacity for unforeseen events, balancing electricity supply and demand and reducing congestion of the network and thus avoiding additional investments in new plants and network reenforcements (Bradley et al 2013;Strbac 2008). As a result, there are numerous studies focusing on consumers' reactions to pricing rates of various DSR schemes, such as static TOU, dynamic TOU and DLC (Asadinejad et al 2018;Bradley et al 2016;D'hulst et al 2015;Faruqui and George 2005;Roldán Fernández et al 2017;Thorsnes et al 2012). For instance, BC Hydro's residential TOU/CPP pilot study in British Columbia found that higher price ratio between peak and off-peak rates increased customers' reduction of energy use (Woo et al 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…There have been numerous studies aiming to explore consumers' engagement in DSR programmes. Most of them focused on consumers' aggregated reactions to pricing signals, such as static TOU tariff or dynamic pricing, but did not consider the possible influences of numerous socio-demographic factors (Asadinejad et al 2018;Roldán Fernández et al 2017). It has, however, been suggested that, in addition to financial incentives, socio-demographic backgrounds might also play a crucial role in consumers' decisions to participate in DSR (He et al 2012;Horne and Kennedy 2017;Xu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Investigating UK consumers’ heterogeneous engagement in demand-side response

Keppo

Xenitidou

et al. 2020

Energy Efficiency

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Demand-side response (DSR), the incentivised time-shifting of energy use by consumers away from peak times, is regarded as a potentially effective measure to balance electricity supply and demand. This will be even more important in the low-carbon energy system of the future, with a high share of nondispatchable power, such as variable renewable energy and nuclear power. Most DSR programmes require consumers' active engagement in shifting end-use activities. Previous studies have, however, rarely revealed sociodemographic factors influential for consumers' willingness-to-shift specific end-use activities. This study thus aims to fill this research gap and, using a multinomial logistic model to analyse a nationwide survey, identify factors influential for DSR-related decisions. The nationwide survey for 1004 respondents was carried out to collect data about consumers' willingness-to-shift their daily activities. We focused on the activities that constitute the major part of domestic energy consumption, i.e. cooking, dish-washing, entertainment, heating, laundry and showering. According to the results, consumers' original timing of the end-use activities, sociodemographic factors, ownership of specific appliances and level of concern for energy-saving are influential for their willingness-to-shift activities. These findings can not only help policymakers make more targeted DSR promotion plans but also help to improve broader modelling tools to better consider consumers' willingness-to-shift their demand.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating UK consumers’ heterogeneous engagement in demand-side response

Keppo

Xenitidou

et al. 2020

Energy Efficiency

View full text Add to dashboard Cite

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“…Many models integrate survey data with a detailed residential load model to identify customer elasticity for incentives. Asadinejad et al [13] identified incentive-based elasticity at the individual appliance level (by load disaggregation) and showed that lighting had the highest elasticity, but HVAC has the highest share in the aggregated load, resulting in more effective load shifting. Klaassen et al [14] analyzed household flexibility based on the load shifting of smart appliances and the overall peak load towards dynamic tariffs.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs

2019

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Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized.

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“…Therefore, compared with punishment, residential users prefer incentive mechanism [27]. From this perspective, many experts and scholars believe that incentive-based DR is more suitable for residential users [28,29]. In order to encourage residential users to participate in DR and energy saving plans, Gonzalez Cabrera and Gutierrez-Alcaraz [30] proposed an incentive-based solution which can effectively assist national grid system operators to achieve load reduction and load transfer during peak hours, and can help residents to save power costs.…”

Section: Dr Researchmentioning

confidence: 99%

A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization

Liu

Lin

et al. 2018

Energies

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The optimal operation of microgrids is a comprehensive and complex energy utilization and management problem. In order to guarantee the efficient and economic operation of microgrids, a three-layer multi-agent system including distributed management system agent, microgrid central control agent and microgrid control element agent is proposed considering energy storage units and demand response. Then, based on this multi-agent system and with the objective of cost minimization, an operation optimization model for microgrids is constructed from three aspects: operation cost, environmental impact and security. To solve this model, dynamic guiding chaotic search particle swarm optimization is adopted and three scenarios including basic scenario, energy storage participation and demand response participation are simulated and analyzed. The results show that both energy storage unit and demand response can effectively reduce the cost of microgrid, improve the operation and management level and ensure the safety and stability of power supply and utilization.

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Evaluation of residential customer elasticity for incentive based demand response programs

Cited by 99 publications

References 23 publications

Investigating UK consumers’ heterogeneous engagement in demand-side response

Investigating UK consumers’ heterogeneous engagement in demand-side response

On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs

A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization

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