Data-Driven Generation of Mixed-Integer Linear Programming Formulations for Model Predictive Control of Hybrid Energy Storage Systems using detailed nonlinear Simulation Models

Holtwerth, Alexander; Xhonneux, André; Müller, Dirk

doi:10.1109/osmses54027.2022.9769104

2022 Open Source Modelling and Simulation of Energy Systems (OSMSES) 2022

DOI: 10.1109/osmses54027.2022.9769104

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Data-Driven Generation of Mixed-Integer Linear Programming Formulations for Model Predictive Control of Hybrid Energy Storage Systems using detailed nonlinear Simulation Models

Alexander Holtwerth¹,

André Xhonneux²,

Dirk Müller

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Cited by 3 publications

References 17 publications

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Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

Holtwerth,

Xhonneux,

Müller

2024

Energy Conversion and Management: X

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Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

Holtwerth,

Xhonneux,

Müller

2024

Energy Conversion and Management: X

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Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System

Holtwerth,

Xhonneux,

Müller

2024

Energies

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Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell.

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Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities

Rathod

Subramanian

2022

Sustainability

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To fulfill fast-growing energy needs, all energy sources should be utilized. Renewable energy is infinite and clean. However, its main disadvantage is that renewable energy sources are intermittent. A Hybrid Renewable Energy System (HRES) is built by integrating several distinct energy sources to deal with this problem. In regards to energy economy, economics, dependability, and flexibility, these hybrid systems can surpass the limits of individual energy producing technologies. The power capacity of HRESs increased from 700 GW to 3100 GW globally over the period 2000–2021. This study aimed to offer and analyze a comprehensive literature review of recently published works by several researchers in the area of HRESs. The HRES contains different Hybrid Energy Systems (HESs), which are categorized into three parts, namely, PV_Other, Wind_Other and PV_Wind_Other. These systems, based on different optimization techniques/software with techno-economic objective functions and constraints, are reviewed in this paper. The optimal sizing, control, and power management strategies of the HRES are elaborately discussed to harness its potential. It has been determined that Metaheuristic (MH) methods and HOMER software are mostly employed in the fields of HRES sizing, control, power management, and optimization. The review provides a critical analysis of the shortcomings of the existing HRES systems, while choosing optimization parameters, and control and power management schemes. Moreover, the study encapsulates the various challenges/barriers in adopting HRESs. Finally, this review highlights possible future opportunities for PV, Wind, and other HESs in the area of control, power management, optimization, and optimal sizing.

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Data-Driven Generation of Mixed-Integer Linear Programming Formulations for Model Predictive Control of Hybrid Energy Storage Systems using detailed nonlinear Simulation Models

Cited by 3 publications

References 17 publications

Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System

Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities

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