Self-scheduling model for home energy management systems considering the end-users discomfort index within price-based demand response programs

Javadi, Mohammad Sadegh; Nezhad, Ali Esmaeel; Nardelli, Pedro H. J.; Gough, Matthew; Lotfi, Mohamed; Santos, Sérgio F.; Catalão, João P.S.

doi:10.1016/j.scs.2021.102792

Cited by 112 publications

(29 citation statements)

References 57 publications

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“…The authors of [100] offer a new method for reducing customer discontent, system capacity, and demand rebound by constructing consumer convenience and demand rebound indices and creating objective functions based on these indices. The authors of [101] concentrated on a self-scheduling model for home energy management systems (HEMS), proposing a novel formulation of a linear discomfort index (DI) that incorporates end-user preferences into the everyday operation of home appliances.…”

Section: Blockchain-supported Demand Response In Smart Gridsmentioning

confidence: 99%

Overview of Demand-Response Services: A Review

2022

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It is essential for the electricity sector to analyze and determine the distribution capacity throughput and apply new methods aimed at increasing the capacity of the transmission system. Consequently, the transition to modern electricity networks is two-sided, i.e., involving technological and social modifications. The demand response (DR) redistributes consumption away from peak times when grid load and costs are the highest. It incentivizes customers to use electricity when supply is high and inexpensive due to various market mechanisms. The present DR policy proposals stress the importance of fostering behavioral change through competitive pricing and customer participation in reducing carbon emissions and implementing smart energy solutions (including monitoring tools, such as smart meters and applications). The internet of things (IoT) has been applied to ensure adaptive monitoring of energy consumption and cost-effective and adequate demand-side management (DSM). The article is based on the research of the most recent sources of DR implementation methods applied at the power distribution level. It explains the main concepts, classifications, and entities implementing DSM programs, and suggests new visions and prospects for DSM and DR. Moreover, it discusses the application of blockchain technology potential for the internet of energy.

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Section: Blockchain-supported Demand Response In Smart Gridsmentioning

confidence: 99%

Overview of Demand-Response Services: A Review

2022

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“…A MILP framework was developed in [15] to investigate the end-user comfortoriented HEMS scheduling problem with different demand response programs. Moreover, the self-scheduling problem of HEMS taking into consideration the end-user comfort and operational preferences while addressing price-based demand response programs has been studied in [16]. A controller has been designed in [17] for the HEMS to minimize the amount of the daily electricity bill of a residential end-user with various load types by using dynamic price signals.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

et al. 2022

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“…The above state of the art summarizes various mathematical, 22 heuristic, 19 multi-agent-based, 14,15 and reinforcement learning-based 16 EMS strategies with a wide range of objectives. Furthermore, the quantum computational-based algorithms [28][29][30] are also adopted to resolve the problems of optimal scheduling in MG.…”

Section: State Of the Artmentioning

confidence: 99%

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

Singh

Ding

Raju

et al. 2021

Intl J of Energy Research

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Ever since its inception, the concept and application of demand-side response have continued to evolve and take a new shape in microgrid energy management. The application of demand response programs in the microgrid literature lacks the consideration of flexible price elasticity of different load categories. The realistic characterization of load-responsive models with a combination of both linear and nonlinear models is necessary to study the effect of demand response programs. To cover this research gap, the impact of price-based demand response programs on the optimal scheduling of microgrids is investigated in the presence of linear and nonlinear load models. The flexible elasticity model is adopted to characterize the actual behavior of customer responsiveness towards changes in electricity price. Five load models, namely linear, logarithmic, exponential, power, and hyperbolic, were derived for each price-based demand response program. Furthermore, the stochastic-based scenario modeling is considered to cope with the volatile renewable generation in the microgrid network. The recently reported swarm intelligence-based algorithm called the sparrow search method is intended to solve the proposed microgrid energy management issue for the first time in the literature. Fifteen case studies on the basis of distinct linear and nonlinear load scenarios have been carried out to assess the effectiveness of the methodology proposed. Finally, various techno-economic performance indices were evaluated for all case studies, and a priority-wise ranking is assigned based on the multi-criteria assessment technique.

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Self-scheduling model for home energy management systems considering the end-users discomfort index within price-based demand response programs

Cited by 112 publications

References 57 publications

Overview of Demand-Response Services: A Review

Overview of Demand-Response Services: A Review

A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

A swarm intelligence approach for energy management of grid‐connected microgrids with flexible load demand response

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