Distributed real-time demand response for energy management scheduling in smart grid

Hu, Mian; Xiao, Jiang‐Wen; Cui, Shichang; Wang, Yan‐Wu

doi:10.1016/j.ijepes.2018.01.016

Cited by 68 publications

(30 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [32] and [33], authors performed energy consumption scheduling using DR program to match the ever increasing demand with available power supply. The objectives are to maximize social welfare and reduce energy bills by effectively managing the demand with power supply.…”

Section: Related Workmentioning

confidence: 99%

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

et al. 2020

View full text Add to dashboard Cite

In this study, a novel framework is proposed for efficient energy management of residential buildings to reduce the electricity bill, alleviate peak-to-average ratio (PAR), and acquire the desired trade-off between the electricity bill and user-discomfort in the smart grid. The proposed framework is an integrated framework of artificial neural network (ANN) based forecast engine and our proposed day-ahead grey wolf modified enhanced differential evolution algorithm (DA-GmEDE) based home energy management controller (HEMC). The forecast engine forecasts price-based demand response (DR) signal and energy consumption patterns and HEMC schedules smart home appliances under the forecasted pricing signal and energy consumption pattern for efficient energy management. The proposed DA-GmEDE based strategy is compared with two benchmark strategies: day-ahead genetic algorithm (DA-GA) based strategy, and dayahead game-theory (DA-game-theoretic) based strategy for performance validation. Moreover, extensive simulations are conducted to test the effectiveness and productiveness of the proposed DA-GmEDE based strategy for efficient energy management. The results and discussion illustrate that the proposed DA-GmEDE strategy outperforms the benchmark strategies by 33.3% in terms of efficient energy management. INDEX TERMS Advanced metering infrastructure, artificial neural networks, demand response, energy management, grey wolf modified enhanced differential evolution algorithm, smart grid. NOMENCLATURE SG Smart grid ANN Artificial neural network HEMS Home energy management system MILP Mixed integer linear programming BBSA Binary backtracking search algorithm The associate editor coordinating the review of this manuscript and approving it for publication was Behnam Mohammadi-Ivatloo .

show abstract

Section: Related Workmentioning

confidence: 99%

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Research on the energy management of residential micro-grids mainly focused on the topologies, objectives, and methods. The majority of works are related to the demand-side management [7], [8], off-line optimization methods [9], [10] and online ruled-base power flow control techniques [11], [12]. Other methods using fuzzy controllers and neural networks also have been reported [13], [14].…”

Section: Introductionmentioning

confidence: 99%

A magnetically coupled multi-port, multi-operation-mode micro-grid with a predictive dynamic programming-based energy management for residential applications

Jafari

Malekjamshidi

Zhu

2019

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

This paper presents the development of a residential micro-grid topology based on a combination of common magnetic and electrical buses. The magnetic bus interfaces two low voltage dc buses linking a PV and a fuel cell to a high voltage dc bus connected to a grid-tied single-phase bidirectional inverter. A battery is used to store the surplus energy of the system and stabilise the dc voltage of the fuel cell bus. A synchronised bus voltage balance (SBVB) technique is used to reduce the conduction losses and increase the soft switching operation range of the converters. To improve the maximum power point tracking (MPPT) performance and system efficiency, appropriate control techniques and compensation blocks are designed. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes according to a predictive 2D dynamic programming-based energy management. A mode selection and transition strategy is developed to select the appropriate operation mode and smooth the mode transition. A detailed study of the micro-grid including steady-state operation, small signal modelling, controller design, and energy management is presented. A prototype of the system is developed, and experimental tests are conducted for an energy management scenario.

show abstract

“…A binary integer linear programming (BILP) optimization method is presented for load scheduling. Seeking an efficient energy management scheduling for household appliances, a distributed real-time demand response is suggested in Reference [24]. The energy supply, energy demand and battery energy constraints are considered to form a temporally-spatially coupled optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods

et al. 2019

View full text Add to dashboard Cite

From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions’ set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method.

show abstract

Distributed real-time demand response for energy management scheduling in smart grid

Cited by 68 publications

References 36 publications

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

A magnetically coupled multi-port, multi-operation-mode micro-grid with a predictive dynamic programming-based energy management for residential applications

Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods

Contact Info

Product

Resources

About