End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts

Erdinç, Ozan; Taşçıkaraoğlu, Akın; Paterakis, Nikolaos G.; Eren, Yavuz; Catalào, João P. S.

doi:10.1109/pesgm.2017.8274516

Cited by 21 publications

(23 citation statements)

References 38 publications

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“…In the future, we plan to investigate the online HVAC control in a commercial building [34], e.g., how to allocate the air supply rate of every zone or room in realtime while taking thermal discomfort of occupants into consideration. Moreover, we plan to investigate the impact of HVAC load aggregation [35] in a residential building on end-user comfort, e.g., given some households enrolled in a demand response program, how to minimize the average thermal discomfort of these households without violating the total power reduction/increase requirement during a demand response event.…”

Section: Discussionmentioning

confidence: 99%

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

Jiang

Zou

2019

IEEE Trans. Smart Grid

117

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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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Section: Discussionmentioning

confidence: 99%

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

Jiang

Zou

2019

IEEE Trans. Smart Grid

117

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“…Reference [17] applies the Fanger index as a realistic measure for thermal comfort with the ASHRAE 55 standard to evaluate the range for thermal comfort. Reference [18] applies the contracted allowed minimum comfort violation limits of the demand resource providers to limit the comfort level. Moreover, the utility function of demand resource providers is applied to estimate the participation rate of end-use customers.…”

Section: Sumentioning

confidence: 99%

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

Ding

Zhu

Wang

et al. 2019

2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS)

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With the rapidly increased penetration of renewable generations, incentive-based demand side management (DSM) shows great value on alleviating the uncertainty and providing flexibility for microgrid. However, how to price those demand resources becomes one of the most significant challenges for promoting incentive-based DSM under microgrid environments. In this paper, a flexible demand resource pricing scheme is proposed. Instead of using the utility function of end users like most existing literatures, the economic benefit of flexible demand resources is evaluated by the operation performance enhancement of microgrid and correspondingly the resource is priced based on a benefit sharing approach. An iteration-based chance-constrained method is established to calculate the economic benefit and shared compensation for demand resource providers. Meanwhile, the financial risks for the microgrid operator due to uncertain factors are mitigated by the chanceconstrained criterion. The proposed scheme is examined by an experimental microgrid to illustrate its effectiveness.Index Terms-Demand side management, resource pricing, renewable energy integration, microgrid, risk management

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“…In this paper, the occupancy state is detected by the method developed in [34] with information obtained from temperature and CO2 sensors, and it is predicted by a queueing model-based approach developed in [35]. The weather conditions including solar radiation and outdoor temperature and humidity are forecasted by the method presented in [36] with the actual data obtained from the meteorological station [32]. By using these methods with the actual data, the range of the prediction error for the case studies in this paper can be estimated.…”

Section: Remarksmentioning

confidence: 99%

PMV-based event-triggered mechanism for building energy management under uncertainties

Spanos

et al. 2017

Energy and Buildings

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This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic eventtriggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.

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End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts

Abstract: End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts. IEEE Transactions on Smart Grid, 8(1), 362-372. [7457313].

Cited by 21 publications

References 38 publications

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

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

Flexible Demand Resource Pricing Scheme: A Stochastic Benefit-Sharing Approach

PMV-based event-triggered mechanism for building energy management under uncertainties

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