Occupant-oriented economic model predictive control for demand response in buildings

Frahm, Moritz; Zwickel, Philipp; Wachter, Jan; Langner, Felix; Strauch, Pascal; Matthes, Jörg; Hagenmeyer, Veit

doi:10.1145/3538637.3538864

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…The most widely used approach among MPC types is the so-called economic MPC that minimises the power consumption or energy cost consumption [12,13]. Some examples are [14] that manages the cooling demand of a house to reduce the peak periods, and [15] which proposes an economic MPC based on electricity variable prices. Some studies focus on demand response actions instead of a direct reduction on the power, such as [16] in which a hierarchical MPC is used for load shifting in a building, and [17] to perform DR actions at a building taking advantage of installed PV.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control with Self-Learning Capability for Automated Demand Response in Buildings

Urrutia

Pascual²,

Iribarren

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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This paper presents an optimal management strategy, called Building Optimizer, based on a Model Predictive Control (MPC) approach with self-learning capabilities for buildings. This research is framed in the development of an agent-based architecture to provide demand response services in an Energy Community to optimise the management of renewable energy sources and provide grid stability. The proposed MPC is a key enabler of cooperative demand response strategies at community level, ensuring the allocation of an optimal demand profile at each participating member of the community according to an optimal consumption reference defined by a complementary agent at community level. The MPC calculates the optimal setpoints of the HVAC system's terminal units, considering the expected usage of the buildings and the outdoor conditions, and exploiting the building's thermal inertia. The models embedded in the MPC are grey-box models representing a thermal zone of the building. To reduce measurement and model uncertainties, these models incorporate self-learning capabilities implemented as Moving Horizon Estimators that perform a continuous calibration based on real-time operational measurements. This solution allows full automation for model calibration and management of the terminal units. This paper presents a case study in which a baseline MPC with fixed model parameters obtained by an offline calibration is compared to the Building Optimizer with selflearning capabilities. The Building Optimizer is able to track a requested power consumption providing up to 20% of flexibility compared to the reference consumption without demand management and guaranteeing thermal comfort, at least 98% of the time. For this scenario, the Building Optimizer proves more reliable in guaranteeing the thermal comfort and a better match to the requested consumption compared to the baseline MPC. Demand-side management by the MPC can be translated into up to 15% energy shift from peak hours to valley hours.

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Section: Introductionmentioning

confidence: 99%

Model Predictive Control with Self-Learning Capability for Automated Demand Response in Buildings

Urrutia

Pascual²,

Iribarren

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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“…MPC finds the optimal input trajectory for the HVAC system's control outputs over a future time horizon by solving an optimization problem under consideration of future system dynamics, forecasts, and constraints. Therefore, it requires a dynamic thermal building model and forecasts of OB and weather [13]. The development of models and forecasts can make MPC less practicable and more expensive for real-world applications [11].…”

Section: Introductionmentioning

confidence: 99%

Occupant-Oriented Demand Response with Room-Individual Building Control

Frahm¹,

Zwickel²,

Maaß³

et al. 2023

Preprint

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In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants' Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC) and a conventional hysteresis-based two-point control as upper and lower benchmarks, respectively. We evaluate the three controllers in a multi-zone environment for cooling a building in summer and consider two different scenarios that differ in how much the permitted temperatures vary. The results show that PSC strongly outperforms the conventional control approach in both scenarios with regard to the electricity costs and OTS. It leads to 50 % costs reduction and 15 % comfort improvements while the ideal MPC achieves costs reductions of 58 % and comfort improvements of 29 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems.

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Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder

Werling,

Beichter,

Heidrich

et al. 2023

Lecture Notes in Computer Science

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To successfully increase the share of renewable energy sources in the power system and for counteract their fluctuating nature in view of system stability, forecasts are required that suit downstream applications, such as demand side management or management of energy storage systems. However, whilst many forecast models to create these forecasts exist, the selection of the forecast model best suited to the respective downstream application can be challenging. The selection is commonly based on quality measures (such as mean absolute error), but these quality measures do not consider the value of the forecast in the downstream application. Thus, we introduce a meta-learning framework for forecast model selection, which automatically selects the forecast model leading to the forecast with the highest value in the downstream application. More precisely, we use a meta-learning approach that considers the selection task as a classification problem. Furthermore, we empirically evaluate the proposed framework on the downstream application of a smart building’s photovoltaic-battery management problem known as dispatchable feeder on building-level with a data set containing time series from 300 buildings. The results of our evaluation demonstrate that the proposed framework reduces the cost and improves the accuracy compared to existing forecast model selection heuristics. Furthermore, compared to a manual forecast model selection, it requires noticeably less computational effort and leads to comparable results.

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Occupant-oriented economic model predictive control for demand response in buildings

Cited by 10 publications

References 31 publications

Model Predictive Control with Self-Learning Capability for Automated Demand Response in Buildings

Model Predictive Control with Self-Learning Capability for Automated Demand Response in Buildings

Occupant-Oriented Demand Response with Room-Individual Building Control

Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder

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