Optimizing commercial building participation in energy and ancillary service markets

Pavlak, Gregory S.; Henze, Gregor P.; Cushing, Vincent

doi:10.1016/j.enbuild.2014.05.048

Cited by 85 publications

(58 citation statements)

References 19 publications

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“…A number of studies have deployed the purely physical models such as EnergyPlus to achieve reducedorder RC (resistance-capacitance) models for simulating thermal dynamics and DR performance in commercial buildings [17,38,39,40,41,42,43]. Fast power demand response strategies that involve HVAC systems in commercial buildings were evaluated based on the RC model [44,45], which demonstrate that building HVAC systems are quite suitable for demand response.…”

Section: Current Approaches To Quantifying Building Dr Potentialmentioning

confidence: 99%

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

et al. 2016

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This paper presents a novel demand response estimation framework for residential and commercial buildings using a combination of EnergyPlus and two-state models for thermostatically controlled loads. Specifically, EnergyPlus models for commercial and multi-dwelling residential units are applied to construct exhaustive datasets (i.e., with more than 300M data points) that capture the detailed load response and complex thermodynamics of several building types. Subsequently, regression models are fit to each dataset to predict DR potential based on key inputs, including hour of day, set point change and outside air temperature. For single residential units, and residential thermostatically controlled loads (i.e. water heaters and refrigerators) a two-state model from the literature is applied. The proposed framework is then validated for commercial buildings through a comparison with a dataset composed of 11 buildings during 12 demand response events. In addition, the use of the proposed simplified DR estimation framework is presented in terms of two cases (1) peak load shed prediction in an individual building and (2) aggregated DR up/down capacity from a large-scale group of different buildings.

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Section: Current Approaches To Quantifying Building Dr Potentialmentioning

confidence: 99%

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

et al. 2016

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“…This could pose extra demand response incentives on this scale, in order to limit the stress on the distribution grid [23]. In a commercial building context, the magnitude of the electricity demand could be large enough such that direct interaction with the electricity system (on the generation and transmission level) is possible, for example for providing ancillary services [24].…”

Section: What Is the Right Scale In Thinking About A Smart Grid As Itmentioning

confidence: 99%

Ten questions concerning integrating smart buildings into the smart grid

Lawrence

Boudreau

Helsen

et al. 2016

Building and Environment

121

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Recent advances in information and communications technology (ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be 'prosumers' on the grid (both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations. For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems. Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and nontechnical areas.

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“…At instant k, the controller state estimates are available, thus J is a function of z k only. Since the regulation signal represents the change in the power consumed by the HVAC unit, u (6) where u Baseline k+i|k is the optimal value for the manipulated variable at ith prediction horizon step without considering the impact of the regulation signal, i.e. the optimal control signal such that the room air temperature is as close as possible to its set point.…”

Section: Control Design For a Vav Hvac Unitmentioning

confidence: 99%

“…The use of commercial building HVAC systems for providing ancillary services ia examined in [6][7][8][9][10][11][12][13][14][15][16][17][18]. In particular, the works in [6][7][8] address the usage of commercial buildings for demand response programs, which typically involve reduction of peak power in emergency situations.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the works in [6][7][8] address the usage of commercial buildings for demand response programs, which typically involve reduction of peak power in emergency situations. The works in [9][10][11][12] illustrate the potential promise of model predictive control (MPC) for energy efficiency in buildings and for integrating time-of-use rates for shifting loads.…”

Section: Introductionmentioning

confidence: 99%

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Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid

et al. 2018

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Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.

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Optimizing commercial building participation in energy and ancillary service markets

Cited by 85 publications

References 19 publications

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

Quantifying flexibility of commercial and residential loads for demand response using setpoint changes

Ten questions concerning integrating smart buildings into the smart grid

Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid

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