Model predictive control under forecast uncertainty for optimal operation of buildings with integrated solar systems

Liu, Xiaoqi; Paritosh, Parth; Awalgaonkar, Nimish M.; Bilionis, Ilias; Karava, Panagiota

doi:10.1016/j.solener.2018.06.038

Cited by 21 publications

(6 citation statements)

References 55 publications

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“…A transfer function correlates a control system's input and output signal for all possible input values. The second-order transfer functions are commonly used in convention for a dynamic system to exhibit oscillations and are usually represented as Equation 2 (Liu et al, 2018).…”

Section: System Identification Algorithmmentioning

confidence: 99%

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Savarimuthu,

Victor,

Davaraj

et al. 2024

JST

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The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed.

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Section: System Identification Algorithmmentioning

confidence: 99%

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Savarimuthu,

Victor,

Davaraj

et al. 2024

JST

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“…Thermal and electrical energy supplies will need to be smartly handled to satisfy user needs technically and economically. However, the current solar hybrid solution controllers seem not to be capable of ensuring the required performance [43,44].…”

Section: Overall System Controlmentioning

confidence: 99%

A Solar Dually PVT Driven Direct Expansion Heat Pump One-Year Field Operation Results at Continental Climate

Sanz

Martín²,

Pereda

et al. 2022

Energies

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The high energy-consuming building sector needs to meet both electricity and heat demands. In a nearly zero energy building scenario, most of the consumed energy would be generated locally by means of renewable solutions that nowadays seem not to provide an attractive performance or cost-competitiveness. Solar-based technologies tend to be the most promising ones, but for high densely populated areas, the usual photovoltaic or thermal single approaches may not be efficient enough. The current work is focused on the analysis of the dual use of the solar resource by means of hybrid PVT collectors and their smart combination with direct expansion heat pumps through predictive control strategies. To that end, a system was developed, installed in a real-use single-family house at a continental climate for domestic hot water application, operated and monitored for one entire year. The average day indicator results show 83% renewable energy share, 220% self-sufficiency ratio, 41% heat pump self-consumption and 46% of the solar fraction.

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“…However, the model predictive control (MPC) approach has the ability to consider both of them. In order to take these advantages, MPC-based control approaches are presented in [219], [220] for uncertainty mitigation of wind and solar energy systems. A robust nonlinear controller is presented in [221] for grid connected solar PV system.…”

Section: Uncertainty Issuesmentioning

confidence: 99%

High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions

et al. 2020

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The utilization of renewable energy sources (RESs) has become significant throughout the world, especially over the last two decades. Although high-level RESs penetration reduces negative environmental impact compared to conventional fossil fuel-based energy generation, control issues become more complex as the system inertia is significantly decreased due to the absence of conventional synchronous generators. Some other technical issues, high uncertainties, low fault ride through capability, high fault current, low generation reserve, and low power quality, arise due to RESs integration. Renewable energy like solar and wind are highly uncertain due to the intermittent nature of wind and sunlight. Cutting edge technologies including different control strategies, optimization techniques, energy storage devices, and fault current limiters are employed to handle those issues. This paper summarizes several challenges in the integration process of high-level RESs to the existing grid. The respective solutions to each challenge are presented and discussed. A comprehensive list of challenges and solutions, for both wind and solar energy integration cases, are well documented. Finally, the future recommendations are provided to solve the several problems of renewable energy integration which could be key research areas for the industry personnel and researchers.

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Model predictive control under forecast uncertainty for optimal operation of buildings with integrated solar systems

Cited by 21 publications

References 55 publications

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

Solar Energy Prediction Based on Intelligent Predictive Controller Algorithm

A Solar Dually PVT Driven Direct Expansion Heat Pump One-Year Field Operation Results at Continental Climate

High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions

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