Demand reduction in building energy systems based on economic model predictive control

Ma, Jingran; Qin, S. Joe; Salsbury, Timothy I.; Xu, Peng

doi:10.1016/j.ces.2011.07.052

Cited by 327 publications

(176 citation statements)

References 15 publications

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“…This can be performed by choosing n in equ. (11) to be a larger ( > 2) even integer. The infinity norm could be used in the cost function, but this tends to make finding the weights more difficult for standard optimization algorithms.…”

Section: Final Scientific Technical Reportmentioning

confidence: 99%

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Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS

Wenzel¹

2013

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Executive SummaryThis project provides algorithms to perform demand response using the thermal mass of a building. Using the thermal mass of the building is an attractive method for performing demand response because there is no need for capital expenditure. The algorithms rely on the thermal capacitance inherent in the building's construction materials. A near-optimal "day ahead" predictive approach is developed that is meant to keep the building's electrical demand constant during the high cost periods. This type of approach is appropriate for both time-of-use and critical peak pricing utility rate structures. The approach uses the past days data in order to determine the best temperature setpoints for the building during the high price periods on the next day. A second "model predictive approach" (MPC) uses a thermal model of the building to determine the best temperature for the next sample period. The approach uses constant feedback from the building and is capable of appropriately handling real time pricing. Both approaches are capable of using weather forecasts to improve performance.Algorithm performance was tested on a municipal library in Sacramento, CA and a large office building in Milwaukee, WI. In both cases the algorithms were capable of storing thermal energy in the building mass in such a way that the electrical demand was reduced during periods of high cost. Comparison of Actual Performance to Project ObjectivesAs reported through the past two years, fluctuating business decisions regarding the prioritization of this work against other needs has had a continued impact on the actual performance to project objectives, resulting in early termination of work against this grant. All work done under the grant to date is currently planned to be implemented into product in 2014.Project risks previously stated included 1) non-standardized industry utility rate structures and 2) availability of resources due to other committed project needs. Both of these risks also impacted the progress of the project.There are several rate structures used in the industry. This made it necessary to develop two demand response algorithms that focused on the most common rates. A "day ahead" approach was developed suitable for the time-of-use and critical peak pricing rate structures and a "model predictive" approach was developed to additionally be suitable for real-time-pricing profiles.Secondly, development resources to produce the manufacture-ready proof of concept were not available due to previously stated business decisions and the need to focus on other committed projects. Therefore, the project scope was changed to include a prototype to perform field tests of the algorithm. However, this prototype used a personal computer to communicate to the building automation system and was not commercially viable. This prototype was still able to meet the success criteria of demonstrating the capability of shifting electrical demand by controlling rate of heat transfer into and out of building mass and demonstrating an autom...

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Section: Final Scientific Technical Reportmentioning

confidence: 99%

“…It is worth noting that Equation (23) is a linear equation with the first term and has a max function in the second term. Reference [11] illustrates how Equation (23) can be rewritten as a linear equation by utilizing constraints on the optimization problem. The resulting cost function can then be expressed as:…”

Section: Weighting Of Cost Function Termsmentioning

confidence: 99%

Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS

Wenzel¹

2013

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“…In particular, annual direct and scattered radiation values in the file can be imported into the simulation engine for annual daylighting simulation. Historical measurements of ambient temperature, relative humidity, and solar radiation [29] contained in this weather file were used for energy consumption simulation.…”

Section: Parametric Simulation Modelingmentioning

confidence: 99%

Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions

Han

Sun

2017

Sustainability

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In the current context of increasing energy demand, timber-glass buildings will become a necessary trend in sustainable architecture in the future. Especially in severe cold zones of China, energy consumption and the visual comfort of residential buildings have attracted wide attention, and there are always trade-offs between multiple objectives. This paper aims to propose a simulation-based multiobjective optimization method to improve the daylighting, energy efficiency, and economic performance of timber-glass buildings in severe cold regions. Timber-glass building form variables have been selected as the decision variables, including building width, roof height, south and north window-to-wall ratio (WWR), window height, and orientation. A simulation-based multiobjective optimization model has been developed to optimize these performance objectives simultaneously. The results show that Daylighting Autonomy (DA) presents negative correlations with Energy Use Intensity (EUI) and total cost. Additionally, with an increase in DA, Useful Daylighting Illuminance (UDI) demonstrates a tendency of primary increase and then decrease. Using this optimization model, four building performances have been improved from the initial generation to the final generation, which proves that simulation-based multiobjective optimization is a promising approach to improve the daylighting, energy efficiency, and economic performances of timber-glass buildings in severe cold regions.

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“…Typically, an upper layer provides the optimal set-point (from the economic point of view) to the MPC, which is responsible for stabilizing the system and reject any disturbances (from the dynamical point of view). Although recent, it has been applied, for example, to building temperature and smart grid management [13], [14]. To our best knowledge, the EMPC framework has never been applied to autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of economic model predictive control for an autonomous truck

Lima

Trincavelli

Nilsson

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-In this paper, we propose a controller for smooth autonomous path following. The controller is formulated as an economic model predictive controller. The economic cost introduced in the objective function leads to a smooth driving, since we minimize the first and second derivatives of the curvature function (i.e., we encourage linear curvature profiles). Since the curvature in clothoids varies linearly with the path arc-length, we use the smoothness and comfort characteristics of clothoid-driving to obtain a compact and intuitive controller formulation. We enforce convergence of the controller to the reference path with soft constraints that avoid deviations from the reference path. Finally, we present real life experiments where the controller is deployed on a Scania construction truck that show that the proposed controller outperforms a purepursuit controller. Moreover, we detail how the few tuning parameters can affect the obtained solution in practice.

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Demand reduction in building energy systems based on economic model predictive control

Cited by 327 publications

References 15 publications

Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS

Final Scientific Technical Report: INTEGRATED PREDICTIVE DEMAND RESPONSE CONTROLLER FOR COMMERCIAL BUILDINGS

Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions

Experimental evaluation of economic model predictive control for an autonomous truck

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