Smart Households’ Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs

Wang, Fei; Xiang, Biao; Li, Kangping; Ge, Xinxin; Lu, Hai; Lai, Jingang; Dehghanian, Payman

doi:10.1109/tia.2020.2966426

Cited by 167 publications

(36 citation statements)

References 30 publications

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“…4) Energy forecasting subjects: A diversification of energy forecasting subjects was predicted five years ago. Since then, we have seen forecasting studies for wave energy forecasting [102], reactive power [23], demand response capacity forecasting [103], which were rarely studied before.…”

Section: A a Historical Forecastmentioning

confidence: 99%

Energy Forecasting: A Review and Outlook

Hong

Pinson

Wang

et al. 2020

IEEE Open J. Power Energy

362

124

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Forecasting has been an essential part of the power and energy industry. Researchers and practitioners have contributed thousands of papers on forecasting electricity demand and prices, and renewable generation (e.g., wind and solar power). This paper offers a brief review of influential energy forecasting papers; summarizes research trends; discusses importance of reproducible research and points out six valuable open data sources; makes recommendations about publishing highquality research papers; and offers an outlook into the future of energy forecasting.

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Section: A a Historical Forecastmentioning

confidence: 99%

Energy Forecasting: A Review and Outlook

Hong

Pinson

Wang

et al. 2020

IEEE Open J. Power Energy

362

124

View full text Add to dashboard Cite

show abstract

“…The adoption of very-short-term predictions may minimize and mitigate the influences on the optimal results. Further information on e forecasting accuracy of the PV power output considering different time scales can be found in (Li et al, 2019;Wang et al, 2020a;Wang et al, 2020b). A day-ahead PV power prediction framework, combining a deep learning approach and time correlation principles, is described in (Wang et al, 2020a).…”

Section: Control Algorithmmentioning

confidence: 99%

“…Further information on e forecasting accuracy of the PV power output considering different time scales can be found in (Li et al, 2019;Wang et al, 2020a;Wang et al, 2020b). A day-ahead PV power prediction framework, combining a deep learning approach and time correlation principles, is described in (Wang et al, 2020a). Authors in (Wang et al, 2020a) presented a minute solar irradiance prediction model considering the real-time surface irradiance mapping method to attain higher prediction accuracy.…”

Section: Control Algorithmmentioning

confidence: 99%

“…A day-ahead PV power prediction framework, combining a deep learning approach and time correlation principles, is described in (Wang et al, 2020a). Authors in (Wang et al, 2020a) presented a minute solar irradiance prediction model considering the real-time surface irradiance mapping method to attain higher prediction accuracy. A PV-load decoupling model is investigated in (Li et al, 2019) to increase the performance of customer baseline load estimation considering distributed photovoltaic systems.…”

Section: Control Algorithmmentioning

confidence: 99%

“…Authors presented a comprehensive overview of the current state of the art mechanisms and an analysis of the business strategies for resource aggregators. A forecasting framework with the objective to support load aggregators to predict the existing aggregated smart households' demand response capacity is reported in (Wang et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High Level Controller-Based Energy Management for a Smart Building Integrated Microgrid With Electric Vehicle

2020

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This paper presents an effective approach for the modeling and optimization of a smart building integrated microgrid (BIM). The main objective is the development of a smart building energy management system (BEMS) which is in charge of optimally controlling the operation of a building-integrated-microgrid. More specifically, we consider a BIM consisting of a roof-top solar photovoltaic, wind turbine, energy storage unit, electric vehicle, micro-CHP, metering infrastructure, and loads. The BIM loads are used to satisfy the electric needs of occupants, while operating flexibility of thermal loads, which include building heating and hot water demand, are also considered. Emphasis is given to the optimal performance of the BIM, where the objective is to ensure the power balance between production and loads in the microgrid (electric and thermal). A centralized algorithm is implemented for the power management of all components as well as power exchanges with the electrical grid. The developed algorithm is tested through a case study, where the effects of both loads and renewable resources' variabilities on the operation of the BIM are analyzed via numerical simulations.

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Efficient hybrid pricing for optimal DSM of home energy management system utilizing load precedence

Swain

Lakhara

Khetan

et al. 2021

Int Trans Electr Energ Syst

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Summary This article proposes a home energy management system (HEMS) to minimize the energy consumption for residential loads by demand side management (DSM) based on pricing signals. With introduction of DSM, the electricity utility offers several pricing schemes to influence the consumers to participate actively in DSM by means of flexible load shifting. This article proposes a new, efficient, and hybrid pricing scheme and compares its effect on minimizing energy cost with existing pricing methods. The article has two main contributions, the first one being the introduction of the hybrid pricing strategy, which performs better than the existing pricing schemes with reduced complexity in terms of implementation. Secondly, it also studies the effect of load precedence in case of flexible load shifting, that is, when the consumers decide to shift their peak time load to a off‐peak slot, the order at which individual loads are shifted affects the performance of the HEMS. The article also draws a comparison of the pricing schemes for a HEMS and proves the superiority of the proposed hybrid pricing method.

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Smart Households’ Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs

Cited by 167 publications

References 30 publications

Energy Forecasting: A Review and Outlook

Energy Forecasting: A Review and Outlook

High Level Controller-Based Energy Management for a Smart Building Integrated Microgrid With Electric Vehicle

Efficient hybrid pricing for optimal DSM of home energy management system utilizing load precedence

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