Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach

Ampimah, Benjamin Chris; Sun, Mei; Han, Dun; Wang, Xueyin

doi:10.1016/j.apenergy.2017.07.097

Cited by 45 publications

(16 citation statements)

References 24 publications

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“…Ampimah et al [87] proposed a constrained non linear programming (CNLP)-based scheme for optimization of residential consumption of electricity. The objective of the proposed scheme is to reduce EC and PAR.…”

Section: Nlp and Minlp Techniquesmentioning

confidence: 99%

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

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Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

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Section: Nlp and Minlp Techniquesmentioning

confidence: 99%

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

et al. 2020

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Section: Peak Shaving and Load Sheddingmentioning

confidence: 99%

“…Peak shaving as a tool for DSM was applied in [19,20], while load shifting and shedding research was noticed in [21][22][23]. For example, both load shifting and shedding in a nonlinear programming problem were achieved in [21] toward maximizing consumers' satisfaction in an incentive based demand response approach. Moreover, the incentive concept was shown in [22] as a part of the definition of demand flexibility where the problem was formulated as a linear problem with the objective to reduce the aggregator 2 cost for electricity demand bid in a wholesale market.…”

Section: Peak Shaving and Load Sheddingmentioning

confidence: 99%

See 1 more Smart Citation

Enabling electricity access: revisiting load models for AC‐grid operation ‐ Part I

Al-Sumaiti

Salama

Moursi

et al. 2019

IET Generation, Transmission & Distribution

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Meeting electricity demand in remote communities and non-electrified regions in the poor developing world is a challenge. Power generation is in shortage compared to electricity demand. Electric utilities either would enforce grid's zonal load curtailment or not electrify regions. Controlling electricity demand can play a vital role in enabling electricity access; however, weather uncertainty drives electricity demand variability. This paper provides an overview of current demand side management research, identify research gaps and propose a more promising approach to enable electricity access. Also, it proposes manipulating appliances models to fit their operation in applications where power supply shortage is an issue such. The proposed work considers the effect of the probabilistic nature of weather and meeting AC grid codes of operation.

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“…The DRXO clears the DR bidding and aims at minimizing the DR transaction cost. The importance of aggregator and market modelling involving multiple aggregators as an independent mediator between the users and EMO is a challenging task as recognized by researchers in [33][34][35]. Automated DR for an industry in a smart grid paradigm is an exciting research area.…”

Section: Introductionmentioning

confidence: 99%

The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

Mohammad

Mishra

2018

Energies

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This paper presents an interactive trading decision between an electricity market operator, generation companies (GenCos), and the aggregators having demand response (DR) capable loads. Decisions are made hierarchically. At the upper-level, an electricity market operator (EMO) aims to minimise generation supply cost considering a DR transaction cost, which is essentially the cost of load curtailment. A DR exchange operator aims to minimise this transaction cost upon receiving the DR offer from the multiple aggregators at the lower level. The solution at this level determines the optimal DR amount and the load curtailment price. The DR considers the end-user’s willingness to reduce demand. Lagrangian duality theory is used to solve the bi-level optimisation. The usefulness of the proposed market model is demonstrated on interconnection of the Pennsylvania-New Jersey-Maryland (PJM) 5-Bus benchmark power system model under several plausible cases. It is found that the peak electricity price and grid-wise operation expenses under this DR trading scheme are reduced.

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Optimizing sheddable and shiftable residential electricity consumption by incentivized peak and off-peak credit function approach

Cited by 45 publications

References 24 publications

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

Enabling electricity access: revisiting load models for AC‐grid operation ‐ Part I

The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

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