Model Predictive Control of Building On/Off HVAC Systems to Compensate Fluctuations in Solar Power Generation

Dong, Jin; Olama, Mohammed M.; Kuruganti, Teja; Nutaro, James; Winstead, Chris; Xue, Yaosuo; Melin, Alexander M.

doi:10.1109/pedg.2018.8447840

Cited by 21 publications

(23 citation statements)

References 14 publications

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“…To get an effective and high-accuracy estimation model, the Elman NN offline training based on a set of sample data is essential and needs to be done in advance. Thus, the training problems of the Elman NN can be denoted as follows: Given a set of sample dataset as TrainingSet(T amb (in), T amb (out)) (17) By selected training algorithms, the Elman NN model parameters such as W h , W t,i (i = 1, . .…”

Section: Outdoor Temperature Predictionmentioning

confidence: 99%

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An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

et al. 2020

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Being reliant on Air Conditioning (AC) throughout the majority of the year, desert countries with extremely hot weather conditions such as Qatar are facing challenges in lowering weariness cost due to AC On-Off switching while maintaining an adequate level of comfort under a wide-range of ambient temperature variations. To address these challenges, this paper investigates an optimal On-Off control strategy to improve the AC utilization process. To overcome complexities of online optimization, a Elman Neural Networks (NN)-based estimator is proposed to estimate real values of the outdoor temperature, and make off-line optimization available. By looking up the optimum values solved from an off-line optimization scheme, the proposed control solutions can adaptively regulate the indoor temperature regardless of outdoor temperature variations. In addition, a cost function of multiple objectives, which consider both Coefficient of Performance (COP), and AC compressor weariness due to On-Off switching, is designed for the optimization target of minimum cost. Unlike conventional On-Off control methodologies, the proposed On-Off control technique can respond adaptively to match large-range (up to 20 ∘ C) ambient temperature variations while overcoming the drawbacks of long-time online optimization due to heavy computational load. Finally, the Elman NN based outdoor temperature estimator is validated with an acceptable accuracy and various validations for AC control optimization under Qatar’s real outdoor temperature conditions, which include three hot seasons, are conducted and analyzed. The results demonstrate the effectiveness and robustness of the proposed optimal On-Off control solution.

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Section: Outdoor Temperature Predictionmentioning

confidence: 99%

“…Due to its simplicity, On-Off control is widely applied for temperature regulation application scenarios. However, On-Off control also have eminent drawbacks, which include temperature oscillation and non-optimal operation which negatively impacts the AC moving parts and its energy consumption [13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

et al. 2020

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“…The case study also shows that MFC is a very computationally efficient algorithm and can be deployed for very small-time scales. Specifically, each iteration (time step) run for MFC requires 3 milliseconds to make control decisions using Matlab on a standard four-core personal computer, while it requires 600 milliseconds for the traditional modelpredictive control (MPC) [38]. Thus, MFC is 200 times faster.…”

Section: Case Studymentioning

confidence: 99%

Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

et al. 2020

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A transition from generation on demand to consumption on demand is one of the solutions to overcome the many limitations associated with the higher penetration of renewable energy sources. Such a transition, however, requires a considerable amount of load flexibility in the demand side. Demand response (DR) programs can reveal and utilize this demand flexibility by enabling the participation of a large number of grid-interactive efficient buildings (GEB). Existing approaches on DR require significant modelling or training efforts, are computationally expensive, and do not guarantee the satisfaction of end users. To address these limitations, this paper proposes a scalable hierarchical model-free transactional control approach that incorporates elements of virtual battery, game theory, and model-free control (MFC) mechanisms. The proposed approach separates the control mechanism into upper and lower levels. The MFC modulates the flexible GEB in the lower level with guaranteed thermal comfort of end users, in response to the optimal pricing and power signals determined in the upper level using a Stackelberg game integrated with aggregate virtual battery constraints. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable even on small embedded devices. The effectiveness of this approach is demonstrated through a large-scale case study with 10,000 heterogenous GEB. The results show that the proposed approach can achieve peak load reduction and profit maximization for the distribution system operator, as well as cost reduction for end users while maintaining their comfort. INDEX TERMS Demand response, model-free control, Stackelberg game, thermostatically controlled loads, transactive control, virtual battery K. Amasyali et al.: Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

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“…Furthermore, several control strategies for buildings' TCLs have been developed in the literature to support grid-interactive efficient buildings [10][11][12][13][14][15][16][17]. They can be categorized into model-based and data-driven control strategies.…”

Section: Introductionmentioning

confidence: 99%

“…In [13], a comparison study between dynamic programming and a genetic algorithm is performed to implement control of HVAC for demand response (DR). In [14,15], an MPC strategy is presented to optimally dispatch a group of on/off and variable-air-volume HVAC systems, respectively, to compensate fluctuations in solar power generation. In [16,17], a model-free control (MFC) strategy and a signal temporal logic control (STL) strategy are presented, respectively, to also compensate fluctuations in solar power generation.…”

Section: Introductionmentioning

confidence: 99%

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

et al. 2020

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In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

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Model Predictive Control of Building On/Off HVAC Systems to Compensate Fluctuations in Solar Power Generation

Cited by 21 publications

References 14 publications

An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

An Optimal Air-Conditioner On-Off Control Scheme under Extremely Hot Weather Conditions

Hierarchical Model-Free Transactional Control of Building Loads to Support Grid Services

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

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