Decentralized Coordination of a Building Manager and an Electric Vehicle Aggregator

Contreras-Ocaña, Jesus E.; Sarker, Mushfiqur R.; Ortega‐Vazquez, Miguel A.

doi:10.1109/tsg.2016.2614768

Cited by 48 publications

(33 citation statements)

References 38 publications

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“…Then, we limit an upper estimate of the indoor temperature to be under the maximum temperature limit and a lower estimate to be over the minimum limit. This stands in contrast to the typical RC circuit model which produces a central estimate of indoor temperature [3], [17]- [19], [26]. Thus, when the temperature is underestimated in the RC circuit model, the maximum temperature limit could be violated.…”

Section: A Tractable and Robust Approximation Of The Feasible Rementioning

confidence: 92%

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Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Contreras-Ocaña

Ortega‐Vazquez

Kirschen

et al. 2018

IEEE Trans. Power Syst.

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Controllable building loads have the potential to increase the flexibility of power systems. A key step in developing effective and attainable load control policies is modeling the set of feasible building load profiles. In this paper, we consider buildings whose source of flexibility is their HVAC loads. We propose a data-driven method to empirically estimate a robust feasible region of the load using coarse data, that is, using only total building load and average indoor temperatures. The proposed method uses easy-to-gather coarse data and can be adapted to buildings of any type. The resulting feasible region model is robust to temperature prediction errors and is described by linear constraints. The mathematical simplicity of these constraints makes the proposed model adaptable to many power system applications, for example, economic dispatch, and optimal power flow. We validate our model using data from EnergyPlus and demonstrate its usefulness through a case study in which flexible building loads are used to balance errors of wind power forecasts.

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Section: A Tractable and Robust Approximation Of The Feasible Rementioning

confidence: 92%

“…The first is that our use of clusters to model thermal dynamics with several linear functions instead of a single one. The second is that the traditional RC circuit model fails to use p hvac t to predict indoor temperature during time t [3], [17], [26].…”

Section: Comparison Against the Rc Circuit Modelmentioning

confidence: 99%

Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Contreras-Ocaña

Ortega‐Vazquez

Kirschen

et al. 2018

IEEE Trans. Power Syst.

Self Cite

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“…Reference [33] claims 24% cost savings based on simulating one sunny day in the summer in Chicago, but battery degradation costs are not considered and uncertainties in building energy consumption and EV behavior are left for further work. Reference [34] focusses on households and small buildings and does not provide comparable savings data; uncertainty in EV behavior is also neglected. Reference [35] uses V2B for a purpose different from ours: to minimize the penalty that a customer has to pay for power consumption that exceeds the subscribed power according to a pricing scheme used by a utility in Quebec, Canada.…”

Section: Related Research In Electric Vehicle Battery Exploitationmentioning

confidence: 99%

Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings

2018

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Vehicle-to-building (V2B) technology permits bypassing the power grid in order to supply power to a building from electric vehicle (EV) batteries in the parking lot. This paper investigates the hypothesis stating that the increasing number of EVs on our roads can be also beneficial for making buildings sustainably greener on account of using V2B technology in conjunction with local photovoltaic (PV) generation. It is assumed that there is no local battery storage other than EVs and that the EV batteries are fully available for driving, so that the EVs batteries must be at the intended state of charge at the departure time announced by the EV driver. Our goal is to exploit the potential of the EV batteries capacity as much as possible in order to permit a large area of solar panels, so that even on sunny days all PV power can be used to supply the building needs or the EV charging at the parking lot. A system architecture and collaboration protocols that account for uncertainties in EV behaviour are proposed. The proposed approach is proven in simulation covering one year period for three locations in different climatic regions of the US, resulting in the electricity bill reductions of 15.8%, 9.1% and 4.9% for California, New Jersey and Alaska, respectively. These results are compared to state-of-the-art research in combining V2B with PV or wind power generation. It is concluded that the achieved electricity bill reductions are superior to the state-of-the-art, because previous work is based on problem formulations that exploit only a part of the potential EV battery capacity.

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“…Zhao and Zhang [12] developed a framework to aggregate price-responsive loads represented by a second-order ETP model. However, little attention has been paid to an integrated formulation of the smart building with smart appliances [13]- [15], which is crucial to accurately capture the potential flexibility of their buildings.…”

Section: Introductionmentioning

confidence: 99%

“…Although, they model one of each load type that a building may have, the building heating dynamics were disregarded, thus ignoring the effect of the comfort constraints. Contreras-Ocaña et al [13] presented one of the most completed building models and analyzed possible interactions between commercial buildings and an aggregator of electric vehicles. Gonzalez et al [15] presented a residential load simulator with appliances.…”

Section: Introductionmentioning

confidence: 99%

How Can Smart Buildings Be Price-Responsive?

Fernández-Blanco

Moralesu

Pineda

2019

2019 IEEE Milan PowerTech

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The prospective participation of smart buildings in the electricity system is strongly related to the increasing active role of demand-side resources in the electrical grid. In addition, the growing penetration of smart meters and recent advances on home automation technologies will spur the development of new mathematical tools to help optimize the local resources of these buildings. Within this context, this paper first provides a comprehensive model to determine the electrical consumption of a single-zone household based on economic model predictive control. The goal of this problem is to minimize the electricity consumption cost while accounting for the heating dynamics of the building, smart home appliances, and comfort constraints. This paper then identifies and analyzes the key parameters responsible for the price-responsive behaviour of smart households.Index Terms-Model predictive control, price-responsive loads, smart appliances, smart buildings.

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Decentralized Coordination of a Building Manager and an Electric Vehicle Aggregator

Cited by 48 publications

References 38 publications

Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Tractable and Robust Modeling of Building Flexibility Using Coarse Data

Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings

How Can Smart Buildings Be Price-Responsive?

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