Residential Demand Response Based on Weighted Load Shifting and Reduction Target

Ferraz, Bibiana Petry; Pereira, L.F.A.; Lemos, Fernando de Aguiar; Haffner, Sérgio

doi:10.1007/s40313-019-00517-3

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…One load-management strategy in DSM is load-shifting (Figure 1), a technique to shift peakhour demand to off-peak hours by reallocating the load demand [6] without changing the total energy consumption [7]. This can also reduce the cost [1,6,[8][9][10][11][12][13][14][15] as well as the capacity of the electricity grid. In practice, Yilmaz et al (2019), quoted in [16], defined load-shifting as a "process where consumers time-shift demand, either through behaviour change or automation, in response to particular conditions within the electricity system, and [it] is therefore a potential solution to equilibrate the network".…”

Section: Introductionmentioning

confidence: 99%

“…In practice, Yilmaz et al (2019), quoted in [16], defined load-shifting as a "process where consumers time-shift demand, either through behaviour change or automation, in response to particular conditions within the electricity system, and [it] is therefore a potential solution to equilibrate the network". The fundamental step in achieving load-shifting is to involve and motivate consumers [1,10], underlining the importance of focusing on the residential sector. In load-shifting at the appliance level, some studies have grouped appliances based on their operating times into controllable and uncontrollable load appliances [1,17,18].…”

Section: Introductionmentioning

confidence: 99%

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A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Manembu,

Kewo,

Bramstoft

et al. 2023

Energies

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Load-shifting is a demand-side management (DSM) strategy to support the efficiency of the electricity grid during hours of peak demand. Load-shifting at the appliance level is an interesting topic to review, since appliance usage is one of the main inputs of the load-profile analysis. More literature reviews on load-shifting at the appliance level are required, as this is a specific issue in the body of literature on load-profile research, though only a limited number of studies are available at this time. It is also essential to focus on appliance usage patterns to improve our understanding of the impacts and characteristics of different appliances. Existing studies on load-shifting have used commonly structured literature reviews; our work addresses the transparency of each stage and substage in the selection of the final list of studies. The findings show that efficiency has been achieved in installed-capacity reductions; costs, including those of emission reductions; and peak consumption reductions. The most frequently used method in load-shifting at the appliance level is to develop load-shifting optimization algorithms. This work contributes by providing a transparent process of drawing up a systematicity literature review as a source of knowledge and grounded theory. It also contributes to specific research on load-shifting at the appliance level by highlighting and discussing the key findings for the reader. In particular, it contributes to improving energy efficiency by describing load-shifting methods at the appliance level and identifying both controllable and uncontrollable appliances. This detailed literature review at the appliance level can make valuable contributions in support of decision- and policymaking by illuminating new dynamic systems specifically in load-shifting and in demand-side management in general for energy efficiency purposes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Manembu,

Kewo,

Bramstoft

et al. 2023

Energies

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“…A Smart residential community DR scheduling model presented in [10] efficiently schedules electricity load demanded by the user at different price-based DR plans through interruptible load programs. A residential DR model using Demand Price Elasticity Matrix (DPEM) mathematical model is proposed to determine the crosselasticities based on weighted load shifting and reduction target on real world data collected from south brazil electrical distribution utility [11]. A DR model for renewable energy source based residential smart grid demand response system introduced, in which real-time electricity pricing prediction performed by reformulating non-convex to convex problem to assure optimal solution [12].…”

Section: Introductionmentioning

confidence: 99%

Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach

et al. 2021

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Demand response modelling have paved an important role in smart grid at a greater perspective. DR analysis exhibits the analysis of scheduling of appliances for an optimal strategy at the user's side with an effective pricing scheme. In this proposed work, the entire model is done in three different steps. The first step develops strategy patterns for the users considering integration of renewable energy and effective demand response analysis is done. The second step in the process exhibits the learning process of the consumers using Robust Adversarial Reinforcement Learning for privacy process among the users. The third step develops optimal strategy plan for the users for maintaining privacy among the users. Considering the uncertainties of the user's behavioral patterns, typical pricing schemes are involved with integration of renewable energy at the user' side so that an optimal strategy is obtained. The optimal strategy for scheduling the appliances solving privacy issues and considering renewable energy at user' side is done using Robust Adversarial Reinforcement learning and Gradient Based Nikaido-Isoda Function which gives an optimal accuracy. The results of the proposed work exhibit optimal strategy plan for the users developing proper learning paradigm. The effectiveness of the proposed work with mathematical modelling are validated using real time data and shows the demand response strategy plan with proper learning access model. The results obtained among the set of strategy develops 80 % of the patterns created with the learning paradigm moves with optimal DR scheduling patterns. This work embarks the best learning DR pattern created for the future set of consumers following the strategy so privacy among the users can be maintained effectively.INDEX TERMS Demand response, best strategy, robust adversarial reinforcement learning, renewable energy. NOMENCLATURE n i S -Strategy for set of users In -Incentives announced () n  -cost function for the model and   -Best policy parameters for learning , tt hl -reward and incentive function in learning i   -Policy strategy ( , ) n v  -Optimization function for pricing and cost analysis RL-Reinforcement Learning ADP -Approximate Dynamic Programming RARL -Robust Adversarial Reinforcement learning GNI -Gradient Based Nikaido -Isoda Function

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FAlloc: A Fair Power Limit Allocation-Based Approach to Implement Brownout

Agarwal

2024

J Control Autom Electr Syst

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Residential Demand Response Based on Weighted Load Shifting and Reduction Target

Cited by 6 publications

References 22 publications

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

A Systematicity Review on Residential Electricity Load-Shifting at the Appliance Level

Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach

FAlloc: A Fair Power Limit Allocation-Based Approach to Implement Brownout

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