From home energy management system local flexibility to low-voltage predictive grid management

Lopes, Diogo; Simões, Micael; Sampaio, Gil; Rua, David; Machado, Paulo Fernando; Bessa, Ricardo; André, Ricardo; Moreira, João Manoel Losada; Abreu, Cláudia; Madureira, André

doi:10.1049/oap-cired.2021.0031

Cited by 1 publication

(2 citation statements)

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“…and H d are respectively, the gas-consuming appliances' heat generation and total household heat demand. Equation (10) determines the gas-consuming appliances' output heat which is calculated by the received natural gas amount at each hour of a day. In (11) and ( 12), E CHP (t) and H CHP (t) are calculated by ν(t), η gas−heat , and η gas−elec.…”

Section: Bi-level Model Formulationmentioning

confidence: 99%

“…These limitations have a significant impact on the result of the implementation of DR applications, that is, energy-saving and reduction, which significantly affect the system's flexibility level. It is shown that using an HEMS leads to the well-organized contribution of residential prosumers in DR applications to provide flexibility [9,10]. The HEMSs calculate and send prosumers' load profile signals to an LDC.…”

Section: Introductionmentioning

confidence: 99%

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Flexibility provision of residential energy hubs with demand response applications

Ostovar

Moeini‐Aghtaie

Hadi

2022

IET Generation Trans & Dist

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Smart grids help local distribution companies (LDCs) facilitate the communication between grid operators and residential prosumers. While the prosumers are striving to optimize their daily load profile by home energy management systems (HEMSs) deployment, the LDCs attempt to enhance the operation of the grid in an efficient way and decrease network losses. This paper attempts to develop a new bi-level probabilistic optimization framework wherein an HEMS optimizes its respective daily load profile, and determines its flexibility provision, which is communicated to an LDC. In the proposed framework, a decentralized approach is used to achieve the flexibility product, which is the modulation of energy, through the incentive-based demand response (DR) programs. Finally, the LDC applies the prosumer's flexibility' offers to optimize its operational performance. The twopoint estimate method (2PEM) is employed to model the uncertainties. The applicability of the framework is demonstrated by applying it to a system with one residential feeder.

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Section: Bi-level Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%