Load management in a residential energy hub with renewable distributed energy resources

Rastegar, Mohammad; Fotuhi‐Firuzabad, Mahmud

doi:10.1016/j.enbuild.2015.07.028

Cited by 125 publications

(38 citation statements)

References 21 publications

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“…The maximum and minimum power constraints of every generation unit at every hour of the operation period is presented in (10). In other words, the generation unit cannot generate power beyond the upper and lower limits.…”

Section: Units' Power Constraintmentioning

confidence: 99%

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Nonlinear demand response programs for residential customers with nonlinear behavioral models

Rahmani-Andebili

2016

Energy and Buildings

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Section: Units' Power Constraintmentioning

confidence: 99%

“…The studies presented in [9][10][11][12][13], have investigated the effects of DR programs on the residential customers demand.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear demand response programs for residential customers with nonlinear behavioral models

Rahmani-Andebili

2016

Energy and Buildings

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“…A decentralized model predictive control strategy was proposed in [8] to improve the operation of the coupled electricity and gas network system by considering predicted behavior and operational constraints. A residential energy hub is designed to coordinate solar energy with load demand and determine the scheduling of electric vehicles [9]. Particularly, there are multiple participants inside an EI, how to coordinate and optimize the interests of each participant is addressed in [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Until recently, the research of the EI has drawn wide attention, and its topics may include architecture system [5][6][7], coordination control [8,9] and energy management [10][11][12]. A future electric power distribution system was proposed in [5] to suit for the plug-and-play of DERs and distributed storage devices.…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

et al. 2018

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Community Energy Internet (CEI) integrates electric network and thermal network based on combined heat and power (CHP) to improve the economy of energy system in Smart Community. In the CEI, an energy sharing framework for prosumers equipped with photovoltaic-thermal (PVT) system and heat pump (HP) is introduced. Supporting by the PVT and HP, the prosumer has four role attributes with either heat or electricity producer/consumer. A social welfare maximization model is built for the CEI, including PVT-HP prosumers, CHP system, and utility grid. Considering there are multiply participants in the local market of CEI, the social welfare maximization problem is decoupled by using Lagrange multiplier method. Moreover, a consensus-based fully distributed algorithm is designed to solve the problem. Finally, six residential buildings are selected as the case study to validate the effectiveness of the proposed method.

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“…Therefore, there is need to decrease the electricity market price and the electricity bill price and to improve the economy [12][13][14]. A smart house can regulate the power demand by being associated with a smart grid [15][16][17][18]. When this operation method is performed, controllable loads, like a fixed battery and Heat Pump (HP), are used, so it is possible to adjust.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

et al. 2016

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Abstract:A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

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Load management in a residential energy hub with renewable distributed energy resources

Cited by 125 publications

References 21 publications

Nonlinear demand response programs for residential customers with nonlinear behavioral models

Nonlinear demand response programs for residential customers with nonlinear behavioral models

Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

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