Flexibility of Commercial Building HVAC Fan as Ancillary Service for Smart Grid Mehdi Maasoumy † , Jorge Ortiz ∗ , David Culler ∗ and Alberto Sangiovanni-Vincentelli ∗ Abstract—In this paper, we model energy use in commercial buildings using empirical data captured through sMAP, a cam- pus building data portal at UC Berkeley. We conduct at-scale experiments in a newly constructed building on campus. By modulating the supply duct static pressure (SDSP) for the main supply air duct, we induce a response on the main supply fan and determine how much ancillary power flexibility can be provided by a typical commercial building. We show that the consequent intermittent fluctuations in the air mass flow into the building does not influence the building climate in a human-noticeable way. We estimate that at least 4 GW of regulation reserve is readily available only through commercial buildings in the US. Based on predictions this value will reach to 5.6 GW in 2035. We also show how thermal slack can be leveraged to provide an ancillary service to deal with transient frequency fluctuations in the grid. We consider a simplified model of the grid power system with time varying demand and generation and present a simple control scheme to direct the ancillary service power flow from buildings to improve on the classical automatic generation control (AGC)-based approach. Simulation results are provided to show the effectiveness of the proposed methodology for enhancing grid frequency regulation. I. I NTRODUCTION Total primary energy consumption in the world increased more than 27% over the last decade; from 400 Quadrillion Btu in 2000 to 510 Quadrillion Btu in 2010 [1]. A sustainable energy future requires significant and widespread penetra- tion of renewable energy sources (RES) than the current level. However, the volatility, uncertainty, and intermittency of renewable energy sources present a daunting challenge to integrate them into the power grid at large scale. Balancing generation and load instantaneously and continu- ously, given the randomness in the dynamics of generation and demand, is difficult. Minute-to-minute load variability results from random de/activation of millions of individual loads. Long-term variability results from predictable factors such as the daily and seasonal load patterns as well as more random events like shifting weather patterns. Generators also introduce unexpected fluctuations because they do not follow their generation schedules exactly and they trip unexpectedly due to a range of equipment failures [2]. Significant deviation in supply-demand balance can lead to large frequency deviation, which in turn jeopardizes the stability of the grid. To avoid this catastrophic event, several so called “ancillary services” such as regulation and load following, have been formalized to better manage supply- demand balance at all time. The Federal Energy Regulatory † Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA. Corresponding author. Email: mehdi@me.berkeley.edu ∗ Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1740, USA. Commission (FERC) has defined such services as those “nec- essary to support the transmission of electric power from seller to purchaser given the obligations of control areas and transmitting utilities within those control areas to maintain reliable operation of the interconnected transmission system.” This quote highlights the importance of ancillary services for both bulk-power reliability and support of commercial transactions [2]. Buildings consume about 75% of US electricity, with roughly equal shares for residential and commercial build- ings [3]. Commercial buildings are suitable for providing ancillary services due to the following reasons: 1) More than 30% of commercial buildings have adopted Building Energy Management System (BEMS) technology [4] which facilitates the communication with the grid system operators for providing real-time ancillary services. The majority of these buildings are also equipped with variable frequency drives, which in coordination with BEMS, can manipulate the heating, ventilation and air conditioning (HVAC) system power consumption very frequently (in the order of several seconds). 2) Compared to typical residential buildings, commercial buildings have larger HVAC systems and therefore consume more electricity and present an opportunity for manipulating and controlling the buildings power draw. HVAC system fans account for about 15% of electricity consumed in commercial buildings. Since we can directly control their power-draw rate, upward or downward fans are an ideal candidate for ancillary service. A. Related Works Model-based optimal control strategies such as Model Pre- dictive Control (MPC) are promising for energy efficiency in buildings and for integrating time-of-use rates for shifting loads [5]–[10]. In a more recent paper [11] simulation data show that commercial buildings can provide significant ancil- lary service for more robust operation of the grid. The same paper postulates 6.6 GW of regulation capacity from about 5 million commercial buildings in the US. In this paper, we attempt to verify this claim via experiments on a real building. Regulation is a zero-energy service, making it an ideal candidate for supply by storage. In [2], storage technologies are acknowledged to be ideal suppliers of several ancillary services, including regulation. Storage using chemical batteries however, has two important drawbacks: 1) It is expensive and 2) it is not environmentally-friendly. There is an emerging consensus that flexible loads with thermal storage capabilities, also known as Thermostatically Controllable Loads (TCL) will play an important role in regulating grid frequency and in effect, enable deep penetration of RES.
[1]
Prahlad Patel,et al.
Modeling and optimal control algorithm design for HVAC systems in energy efficient buildings
,
2013
.
[2]
Mehdi Maasoumy.
Modeling and Optimal Control Algorithm Design for HVAC Systems in Energy Efficient Buildings
,
2014
.
[3]
Hadi Saadat,et al.
Power Systems Analysis
,
2002
.
[4]
Alberto Sangiovanni-Vincentelli,et al.
Optimal Control of Building HVAC Systems in the Presence of Imperfect Predictions - eScholarship
,
2014
.
[5]
Biao Huang,et al.
System Identification
,
2000,
Control Theory for Physicists.
[6]
Arthur R. Bergen,et al.
Power Systems Analysis
,
1986
.
[7]
B. J. Kirby,et al.
Spinning Reserve From Responsive Loads
,
2003
.
[8]
Alberto L. Sangiovanni-Vincentelli,et al.
Development of Building Automation and Control Systems
,
2012,
IEEE Design & Test of Computers.
[9]
Jerry C. Whitaker.
AC Power Systems Handbook
,
1991
.
[10]
Alberto Sangiovanni-Vincentelli,et al.
Model-Based Hierarchical Optimal Control Design for HVAC Systems
,
2014
.
[11]
B. Kirby,et al.
Frequency Regulation Basics and Trends
,
2005
.
[12]
Ian A. Hiskens,et al.
Achieving Controllability of Electric Loads
,
2011,
Proceedings of the IEEE.
[13]
Alberto L. Sangiovanni-Vincentelli,et al.
Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control
,
2012,
IEEE Design & Test of Computers.
[14]
Sean P. Meyn,et al.
Ancillary service for the grid via control of commercial building HVAC systems
,
2013,
2013 American Control Conference.
[15]
Atif S. Debs,et al.
Modern power systems control and operation
,
1988
.
[16]
Manfred Morari,et al.
Reducing peak electricity demand in building climate control using real-time pricing and model predictive control
,
2010,
49th IEEE Conference on Decision and Control (CDC).
[17]
David E. Culler,et al.
sMAP: a simple measurement and actuation profile for physical information
,
2010,
SenSys '10.
[18]
Pooya Soltantabar.
Annual Energy Outlook
,
2015
.
[19]
Babu Narayanan,et al.
POWER SYSTEM STABILITY AND CONTROL
,
2015
.
[20]
Johanna L. Mathieu,et al.
Modeling and Control of Aggregated Heterogeneous Thermostatically Controlled Loads for Ancillary Services
,
2011
.
[21]
Goran Strbac,et al.
Demand side management: Benefits and challenges ☆
,
2008
.
[22]
Johanna L. Mathieu,et al.
State Estimation and Control of Heterogeneous Thermostatically Controlled Loads for Load Following
,
2012,
2012 45th Hawaii International Conference on System Sciences.
[23]
S. M. Shahidehpour,et al.
Effects of ramp-rate limits on unit commitment and economic dispatch
,
1993
.