A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

Nezhad, Ali Esmaeel; Rahimnejad, Abolfazl; Nardelli, Pedro H. J.; Gadsden, S. Andrew; Sahoo, Subham; Ghanavati, Farideh

doi:10.1109/access.2022.3158346

Cited by 21 publications

(3 citation statements)

References 40 publications

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“…In this case study, as expressed by the authors, the RH approach was included both for dealing with uncertainty and also to promptly react to changes in the users' requirements. A similar procedure was presented in [24].…”

Section: Model Predictive Control-based Architectures For Emsmentioning

confidence: 97%

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Caminiti,

Merlo,

Fotouhi Ghazvini

et al. 2024

Energies

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This study aims to develop an adaptable home energy management system capable of integrating the bidirectional smart charging of electric vehicles. The final goal is to achieve a user-defined objectives such as cost minimization or maximizing renewable self-consumption. Industrialwise, the present work yields valuable outcomes in identifying operational frameworks and boundary conditions. Optimal scheduling benefits both users and the electric network, thus enhancing grid utilization and increasing renewable energy integration. By coordinating power interactions with dynamic time-of-use tariffs, the energy management system minimizes user costs and aids the grid by cutting peak hour energy consumption. Charging and discharging operations in electric vehicles comply with energy level constraints outlined by bidirectional charging protocols. The proposed approach ensures the scheduling of cycles that minimize detrimental effects on battery health when evaluating an economically ageing mechanism. Compared to uncontrolled charging, optimal scheduling resulted in a significant reduction in the total operational cost of the dwelling. Trade-off conditions between renewable integration and potential savings are identified and numerically evaluated by means of multiobjective optimization. In contrast to scheduling-based models, the proposed architecture possesses the ability to iteratively adapt decision variables in response to system changes, thus responding effectively to external stochastic uncertainty.

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Section: Model Predictive Control-based Architectures For Emsmentioning

confidence: 97%

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Caminiti,

Merlo,

Fotouhi Ghazvini

et al. 2024

Energies

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show abstract

“…In [4], Shareef et al gave a comprehensive summary of heuristic optimization techniques used for home energy management systems. In [5], Nezhad et al presented a model predictive controller for a home energy management system with loads, photovoltaic (PV) and battery electric storage. They formulated the MPC as a mixed-integer programming problem and evaluated its economic performance under different energy pricing schemes.…”

Section: Optimization-based Energy Management Systemsmentioning

confidence: 99%

“…• Total Consumption constraint in (5) ensures that the scheduled total power consumption until each slot s is not greater than the summation of the stored energy, B, at the beginning of the scheduling window and the total generation until s. This constraint is used as we are considering a completely off-grid system.…”

Section: System Setup and Optimization Problemmentioning

confidence: 99%

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Nakıp

Çopur²,

Biyik

et al. 2023

Applied Energy

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Integrated Supervisory Control and Data Acquisition System for Optimized Energy Management: Leveraging Photovoltaic and Phase Change Material Thermal Storage

Shehram,

Hamidi,

Wahab

et al. 2024

Energy Storage

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Reliable energy sources are crucial for both economic growth and quality of life. In developing countries, where expensive fuels are often the primary energy source, governments are exploring innovative solutions like small‐scale, IoT‐based projects to achieve energy independence in buildings. This research investigates the integration of renewable energy technologies, statistical modeling, cloud computing, and IoT to develop a self‐managing energy system for buildings. The system prioritizes renewable sources, specifically monocrystalline solar cells with 20% efficiency for photovoltaic (PV) energy and flat plate collectors with 90% efficiency and minimal energy loss for thermal energy. Thermal energy is stored in paraffin wax, chosen for its high storage efficiency and thermal properties. The system also utilizes an absorption chiller with a high coefficient of performance (COP) to provide cooling using solar thermal energy. The building's energy loads are categorized as A, B, C, and D, each utilizing both PV and thermal energy. A SCADA system oversees the operation, monitoring the on–off status of these loads. The system is designed for continuous operation, with simulations conducted using Anaconda Jupyter Notebook and Python. This model aims to offer a sustainable and efficient energy solution for buildings, meeting energy demands while optimizing energy use.

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A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 21 publications

References 40 publications

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Integrated Supervisory Control and Data Acquisition System for Optimized Energy Management: Leveraging Photovoltaic and Phase Change Material Thermal Storage

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