Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Zhang, Kun; Prakash, Anand Krishnan; Paul, Lazlo; Blum, D.; Alstone, Peter; Zoellick, James; Brown, Richard E.; Pritoni, Marco

doi:10.1016/j.adapen.2022.100099

Cited by 52 publications

(14 citation statements)

References 73 publications

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“…This calculation has a significant impact on the investment and selection of equipment for the system [10,11], ultimately affecting its operational effectiveness [12]. The study of heat and humidity load calculation for underground buildings [13,14] is of practical importance as it allows for adjustments in HVAC design to accommodate the unique characteristics of these buildings and meet their specific thermal comfort or technological requirements [15,16].…”

Section: Nomenclaturementioning

confidence: 99%

Heat and Humidity Transport Analysis Inside a Special Underground Building

Ai,

Xue,

et al. 2023

FHMT

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The calculation of heat and humidity load serves as the cornerstone of Heating, Ventilation, and Air Conditioning (HVAC) design. Nevertheless, as the heat and humidity load characteristics of underground structures differ substantially from those of above-ground structures, it is a challenge to derive their accurate calculation procedure through engineering experience. Therefore, it is particularly important to carry out quantitative research on heat and humidity load. This study used Design Builder software to study the influence of the design state point of air conditioning in underground buildings on energy consumption. The study showed that compared with the single design temperature of 18°C, setting the temperature of 16°C in winter and 22°C in summer could reduce energy consumption by about 59%. And the hourly heat load, cooling load and humidity load in one year are simulated and calculated so as to quantitatively analyze the characteristics of the load. This provides a database for selecting suitable HVAC equipment. It is further emphasized that dehumidification is the key to HVAC design of underground structures, which provides a reference for similar engineering designs.

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Section: Nomenclaturementioning

confidence: 99%

Heat and Humidity Transport Analysis Inside a Special Underground Building

Ai,

Xue,

et al. 2023

FHMT

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“…The energy systems achieved self-sufficiency at 79% and 80%, respectively [22,23]. Moreover, a long-term study conducted by Kun Zhang et al demonstrated that effectively controlling the building energy system could lead to annual electricity cost savings of 12% and a 34% reduction in peak load demand while maintaining indoor thermal comfort [12].…”

Section: Building Energy System Managementmentioning

confidence: 99%

Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control

et al. 2023

Energies

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The development of distributed renewable energy resources and smart energy management are efficient approaches to decarbonizing building energy systems. Reinforcement learning (RL) is a data-driven control algorithm that trains a large amount of data to learn control policy. However, this learning process generally presents low learning efficiency using real-world stochastic data. To address this challenge, this study proposes a model-based RL approach to optimize the operation of existing zero-energy houses considering PV generation consumption and energy costs. The model-based approach takes advantage of the inner understanding of the system dynamics; this knowledge improves the learning efficiency. A reward function is designed considering the physical constraints of battery storage, photovoltaic (PV) production feed-in profit, and energy cost. Measured data of a zero-energy house are used to train and test the proposed RL agent control, including Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) agents. The results show that the proposed RL agents can achieve fast convergence during the training process. In comparison with the rule-based strategy, test cases verify the cost-effectiveness performances of proposed RL approaches in scheduling operations of the hybrid energy system under different scenarios. The comparative analysis of test periods shows that the DQN agent presents better energy cost-saving performances than Q-learning while the Q-learning agent presents more flexible action control of the battery with the fluctuation of real-time electricity prices. The DDPG algorithm can achieve the highest PV self-consumption ratio, 49.4%, and the self-sufficiency ratio reaches 36.7%. The DDPG algorithm outperforms rule-based operation by 7.2% for energy cost during test periods.

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“…A predictive control strategy is one of the key approaches in activating energy flexibility in buildings to meet smart grid needs. Model predictive control (MPC) is considered a suitable technique for performing DR in a building [10,11] and activating building energy flexibility [12,13]. Model Predictive Control (MPC) can enable programming the building operation based on future weather and occupant behaviour.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control for demand response in all-electric school buildings

Morovat,

Athienitis,

Candanedo

2023

J. Phys.: Conf. Ser.

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This paper presents predictive control strategies for all-electric school buildings in cold regions to activate energy flexibility based on changes in electricity prices. A fully electric school building near Montreal, Canada, is used as a case study. This study investigates three scenarios: 1) Reference case with a proportional–integral controller and flat rate electricity price, 2) Model predictive control with flat rate electricity price, and 3) Model predictive control with dynamic electricity price. These scenarios are modelled using the resistance-capacitance thermal networks model, and energy performance is determined and compared over a typical heating season. The proposed approach takes into account the physical parameters of the building, weather predictions, and thermal comfort constraints to maintain optimal energy consumption. A building energy flexibility index is used to quantify the building energy flexibility with a focus on peak demand reduction when the electricity prices are higher than usual. The results show that the MPC strategy can reduce peak power demand by up to 100% and minimize the cost of electricity during demand response events while maintaining acceptable comfort conditions.

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Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Cited by 52 publications

References 73 publications

Heat and Humidity Transport Analysis Inside a Special Underground Building

Heat and Humidity Transport Analysis Inside a Special Underground Building

Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control

Model predictive control for demand response in all-electric school buildings

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