Pricing-Based Demand Response for a Smart Home With Various Types of Household Appliances Considering Customer Satisfaction

Liu, Yi; Xiao, Li-Ye; Yao, Guo‐Dong; Bu, Siqi

doi:10.1109/access.2019.2924110

Cited by 87 publications

(50 citation statements)

References 20 publications

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“…Regarding controllable appliances (loads) of households, they can be divided into three types: (1) interruptible appliances such as pool pump and EVs, (2) non-interruptible appliances like washing machines and dishwashers, and (3) thermostatically controlled appliances such as air conditioners and water heaters [80]. The user could define the preferred interval for operating particular appliances.…”

Section: Flexible Resources At Customer Levelmentioning

confidence: 99%

“…Following the user's preference, the HEMS determines the optimal operation time taking into account the type of appliances. For example, interruptible appliances are allowed to operate at any time during the user's predefined interval whereas the operation of non-interruptible appliances cannot be interrupted while they are working [80].…”

Section: Flexible Resources At Customer Levelmentioning

confidence: 99%

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Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

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Section: Flexible Resources At Customer Levelmentioning

confidence: 99%

Section: Flexible Resources At Customer Levelmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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“…Recently, numerous studies have been published on the development of HEMS optimization algorithms [2][3][4][5][6][7][8][9][10][11][12]. These studies address the scheduling of the energy consumption for home appliances and DERs, while maintaining the consumer's comfort level using mixed-integer nonlinear programming (MINLP) [2], the load scheduling using mixed-integer linear programming (MILP) for single and multiple households [3,4], robust optimization for scheduling of home appliances to resolve the uncertainty of consumer behavior [5], and distributed HEMS architectures consisting of local and global HEMSs [6]. A quickly distributed HEMS algorithm was developed for a large number of households using the MINLP approach with a nonconvex relaxation [7].…”

Section: Introductionmentioning

confidence: 99%

Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

Lee

Choi

2020

Sensors

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This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor–critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference.

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“…In most of the existing studies for the economic power sharing in the MG [4][13] [14], loads are proportionally supplied by DERs according to their capacity ratings in a prespecified manner. In fact, frequency regulation by adjusting power provision is one of ancillary services which can be carried out with the consideration of appropriate incentives [15]. An economic power sharing should incorporate the individual participation willingness of each DER for the regulation ancillary service.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Economic Frequency Regulation for Partitioned Microgrids Based on Cost-Driven Droop Function

Hu¹,

Bu²

2020

Preprint

Self Cite

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With the increase of the number and geographic dispersion of distributed energy resources (DERs), the microgrid (MG) has become scattered along with the higher complexity of control and communication. This paper proposes a two-level regulation strategy to achieve the local objective of frequency regulation and power sharing as well as the global objective of economic operation in the real time for the scattered MG. Firstly, the MG integrated with multiple DERs is partitioned into several<i> </i>nanogrids (NGs) such that DERs connecting on the same feeder are grouped in the same NG for the efficient and cost-effective communication and control. Then<i> </i>in the NG level, the total power mismatch of the entire MG can be learned and an optimal incremental cost can be agreed by each NG through the fastest distributed linear averaging (FDLA) and discrete-consensus algorithm, respectively. In the DER level, a cost-driven droop gain is developed to indicate the willingness of each DER in the NG to participate into the frequency regulation service. The pinning-based protocol is formulated to regulate the frequency and meanwhile enable the proportional power sharing among DERs based on the economic droop function. Case studies satisfactorily demonstrate the effectiveness of the proposed regulation strategy for the economic frequency regulation in the tested MG.

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Pricing-Based Demand Response for a Smart Home With Various Types of Household Appliances Considering Customer Satisfaction

Cited by 87 publications

References 20 publications

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach

Real-Time Economic Frequency Regulation for Partitioned Microgrids Based on Cost-Driven Droop Function

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