Co-scheduling of HVAC control, EV charging and battery usage for building energy efficiency

Wei, Tianshu; Zhu, Qi; Maasoumy, Mehdi

doi:10.1109/iccad.2014.7001351

Cited by 32 publications

(14 citation statements)

References 13 publications

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“…This configuration is the less frequent for building and its HVAC system control purposes. It is mainly used when the control goals focus on the peak values [108,[139][140][141][142][143][144][145], such as reducing or shifting the power peak load or minimizing the maximum daily PPD value [75].…”

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confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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confidence: 99%

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

et al. 2018

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“…At individual building customer level, it has been shown in our previous work that appropriately managing flexible energy loads such as HVAC systems and battery storage can effectively reduce both peak power demand and total energy cost of buildings [39], [40]. Furthermore, it is essential to control the HVAC energy consumption (by turning on/off air conditioning and changing air flow volume) and the usage of battery storage (by charging/discharging storage battery) collaboratively in a holistic formation to maximize building energy efficiency, as the two aspects have significant impact on each other [39].…”

Section: Intelligent Building Energy Scheduling and Demand Bid Curve mentioning

confidence: 99%

Proactive Demand Participation of Smart Buildings in Smart Grid

Wei

Zhu

2016

IEEE Trans. Comput.

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Abstract-Buildings account for nearly 40 percent of the total energy consumption in the United States. As a critical step toward smart cities, it is essential to intelligently manage and coordinate the building operations to improve the efficiency and reliability of overall energy system. With the advent of smart meters and two-way communication systems, various energy consumptions from smart buildings can now be coordinated across the smart grid together with other energy loads and power plants. In this paper, we propose a comprehensive framework to integrate the operations of smart buildings into the energy scheduling of bulk power system through proactive building demand participation. This new scheme enables buildings to proactively express and communicate their energy consumption preferences to smart grid operators rather than passively receive and react to market signals and instructions such as time varying electricity prices. The proposed scheme is implemented in a simulation environment. The experiment results show that the proactive demand response scheme can achieve up to 10 percent system generation cost reduction and 20 percent building operation cost reduction compared with passive demand response scheme. The results also demonstrate that the system cost savings increase significantly with more flexible load installed and higher percentage of proactive customers participation level in the power network.

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“…Other work proposes to jointly leverage a data center and Plug-in Hybrid EVs in regulation market to maximize the profit [5]. Some approaches co-schedule heating, ventilation, air conditioning (HVAC) and EVs for reducing the energy consumption and the peak energy demand [6].…”

Section: Related Workmentioning

confidence: 99%

Optimizing energy storage participation in emerging power markets

Chen

Liu

Coskun

et al. 2015

2015 Sixth International Green and Sustainable Computing Conference (IGSC)

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Abstract-The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving.

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Co-scheduling of HVAC control, EV charging and battery usage for building energy efficiency

Cited by 32 publications

References 13 publications

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Proactive Demand Participation of Smart Buildings in Smart Grid

Optimizing energy storage participation in emerging power markets

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