A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning

Keerthisinghe, Chanaka; Verbič, Gregor; Chapman, Archie C.

doi:10.1109/tsg.2016.2629470

Cited by 110 publications

(75 citation statements)

References 27 publications

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“…Similarly, the quantity of electrical energy sold in a slot t is given by x − t , and its price is denoted by s − t . While the energy surplus of Type 1 prosumers in P comes entirely from the PV system, each prosumer Type 2 in P uses its HEMS to optimize its self-consumption, considering their demand and energy surplus by solving the following mixed-integer linear programming (MILP) problem [9]:…”

Section: B Household Agent Modelmentioning

confidence: 99%

Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network

Guerrero

Chapman

Verbič

2019

IEEE Trans. Smart Grid

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485

282

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The increasing uptake of distributed energy resources (DERs) in distribution systems and the rapid advance of technology have established new scenarios in the operation of lowvoltage networks. In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions. Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario. A major challenge to implementing P2P energy trading is that of ensuring that network constraints are not violated during the energy exchange. This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints. The proposed method is tested on a typical UK low-voltage network. The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture.

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Section: B Household Agent Modelmentioning

confidence: 99%

Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network

Guerrero

Chapman

Verbič

2019

IEEE Trans. Smart Grid

Self Cite

485

282

View full text Add to dashboard Cite

show abstract

“…In [12], Huang et al formulated a chance-constrained programming optimization problem to minimize energy cost of appliances considering uncertainties in a smart home. In [13], Keerthisinghe et al proposed a scheme to schedule distributed energy resources in a smart home using an approximate dynamic programming with temporal difference learning. In [14], Zhang et al developed a learning-based demand response strategy for an HVAC system in a smart home to minimize energy cost without affecting customer's comfort of living.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Jiang

Zou

2019

IEEE Trans. Smart Grid

115

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In this paper, we investigate the problem of minimizing the sum of energy cost and thermal discomfort cost in a long-term time horizon for a sustainable smart home with a Heating, Ventilation, and Air Conditioning (HVAC) load. Specifically, we first formulate a stochastic program to minimize the time average expected total cost with the consideration of uncertainties in electricity price, outdoor temperature, renewable generation output, electrical demand, the most comfortable temperature level, and home occupancy state. Then, we propose an online energy management algorithm based on the framework of Lyapunov optimization techniques without the need to predict any system parameters. The key idea of the proposed algorithm is to construct and stabilize four queues associated with indoor temperature, electric vehicle charging, and energy storage. Moreover, we theoretically analyze the feasibility and performance guarantee of the proposed algorithm. Extensive simulations based on real-world traces show the effectiveness of the proposed algorithm.

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“…The HEMS monitors and controls DER, and facilitates customer participation in demand response (DR) programs. Smart home energy management systems have been used in the recent literature to demonstrate how residential customers can effectively maximise their benefits for participating in DR schemes [1,2,[7][8][9]. T tou Time-of-use energy charge…”

Section: Background and Motivationmentioning

confidence: 99%

“…On the contrary, authors in [8,126,127] used nonlinear efficiency curves both for the battery and the inverter, since, in reality, efficiencies are not constant. For batteries, they depend on many factors like SOC, temperature, charge and discharge rates, etc.…”

Section: Battery Operating Modelmentioning

confidence: 99%

“…By contrast, the authors in [128] use an explicit battery model based on a linear dependence of the battery voltage on the SOC, and a nonlinear inverter efficiency based on a quadratic interpolation of an experimental curve and a quadratic function representing the losses. The inverter and battery efficiency curves used in [8], are shown in Figure 4 for illustration. The nonlinear battery efficiency is generic and will vary depending on the chemistry.…”

Section: Battery Operating Modelmentioning

confidence: 99%

See 1 more Smart Citation

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

Azuatalam

Paridari

et al. 2019

Renewable and Sustainable Energy Reviews

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The home energy management problem has many different facets, including economic viability, data uncertainty and quality of strategy employed. The existing literature in this area focuses on individual aspects of this problem without a detailed, holistic analysis of the results with regards to practicality in implementation. In this paper, we fill this gap by performing a comprehensive comparison of seven different energy management strategies, each with different levels of practicality, sophistication and computational requirements. We analyse the results in the context of these three characteristics, and also critique the modelling assumptions made by each strategy. Our analysis finds that using a more sophisticated energy management strategy may not necessarily improve the performance and economic viability of the PV-battery system due to the effects of modeling assumptions, such as the treatment of uncertainties in the input data and battery degradation effects.

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A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning

Cited by 110 publications

References 27 publications

Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network

Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network

Online Energy Management for a Sustainable Smart Home With an HVAC Load and Random Occupancy

Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation

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