On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

Velarde, Pablo; Valverde, Luís; Maestre, José María Maestre; Ocampo‐Martínez, Carlos; Bordons, Carlos

doi:10.1016/j.jpowsour.2017.01.015

Cited by 84 publications

(39 citation statements)

References 41 publications

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“…To quantify the effects of maintenance on the degradation over time, predictions over the complete maintenance horizon should be conducted [29] for the three degradation scenarios, namely,…”

Section: E Degradation Modelmentioning

confidence: 99%

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

Núñez

Jamshidi

Wang

2019

IEEE Trans. Ind. Inf.

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Abstract1 -This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box acceleration measurements. The assessment of the welds is stochastic in nature and variant over time, so a set of robust and predictive key performance indicators is defined to capture the weld degradation dynamics during a given maintenance period. Using a scenario-based approach, two objective functions are defined, performance and the number of weld replacements. Evolutionary multi-objective optimization is employed to optimize the objective functions so that the trade-offs between performance and cost support decision-making for railway network maintenance. The results of the proposed methodology show that the infrastructure manager can localize field inspections and maintenance efforts on the area with the most critical welds. To showcase the capability of the proposed methodology, measurements from a regional railway network in Transylvania, Romania are employed.

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Section: E Degradation Modelmentioning

confidence: 99%

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

Núñez

Jamshidi

Wang

2019

IEEE Trans. Ind. Inf.

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“…The development of predictive strategies to control hybrid energy systems was addressed in several studies . In Reference , the authors implemented an optimal control technique for an islanded microgrid based on non‐linear MPC.…”

Section: Introductionmentioning

confidence: 99%

“…This contrasts with the approach presented herein, where the SOC is actively regulated to a reference value by a smart EMS in a HESS integrated by two different storage technologies. In Reference , the authors compared three different types of stochastic MPC controllers to satisfy the power demanded by local consumers in a microgrid. The microgrid under study was a lab‐scale system consisting of an emulated solar field, a battery bank, a hydrogen system, and emulated local consumers.…”

Section: Introductionmentioning

confidence: 99%

“…The development of predictive strategies to control hybrid energy systems was addressed in several studies. [24][25][26][27][28][29][30][31][32][33] In Reference 24, the authors implemented an optimal control technique for an islanded microgrid based on non-linear MPC. The microgrid consisted of different renewable sources, battery energy storage, as well as residential and industrial loads.…”

Section: Introductionmentioning

confidence: 99%

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Predictive energy management for a wind turbine with hybrid energy storage system

et al. 2019

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Summary Hybrid energy storage systems (HESSs) help mitigating the fluctuations and variable availability of certain renewable sources, such as wind power, as they can provide support in different time scales. Therefore, regulating their state‐of‐charge (SOC) becomes crucial to ensure that the hybrid system complies with generation commitments agreed in time‐ahead markets despite subsequent unexpected wind speed variations. So far, research has been mainly targeted at avoiding extreme SOC situations in the storage devices, whereas the regulation of this parameter to specific values has often been disregarded. A novel approach is proposed in this work, where model predictive control (MPC) is used to regulate the SOC of a HESS under variable wind and grid demand scenarios. The MPC‐based supervisory controller developed for the hybrid system has been implemented and simulated under different situations. This controller monitors the future variation of the SOC with the aim of having the HESS available to develop its assigned functions successfully. The results show that a proper regulation of the SOC in the HESS increases the capacity to manage the active power supplied to the grid by the hybrid system based on wind power, as well as the level of compliance with generation commitments established time ahead.

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“…The advantage of Scenario-based SMPC is that it can consider the full probability information of the disturbance for prediction. The SMPC approach has been applied in constrained network control systems [4], energy management [5], and stock option market studies [6][7][8][9]. In [10], Bemporad proposed the Discrete Hybrid Stochastic Automata (DHSA) model and gave an SMPC optimization algorithm of this model.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

et al. 2018

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Abstract:Wind energy has been drawing considerable attention in recent years. However, due to the random nature of wind and high failure rate of wind energy conversion systems (WECSs), how to implement fault-tolerant WECS control is becoming a significant issue. This paper addresses the fault-tolerant control problem of a WECS with a probable actuator fault. A new stochastic model predictive control (SMPC) fault-tolerant controller with the Conditional Value at Risk (CVaR) objective function is proposed in this paper. First, the Markov jump linear model is used to describe the WECS dynamics, which are affected by many stochastic factors, like the wind. The Markov jump linear model can precisely model the random WECS properties. Second, the scenario-based SMPC is used as the controller to address the control problem of the WECS. With this controller, all the possible realizations of the disturbance in prediction horizon are enumerated by scenario trees so that an uncertain SMPC problem can be transformed into a deterministic model predictive control (MPC) problem. Finally, the CVaR object function is adopted to improve the fault-tolerant control performance of the SMPC controller. CVaR can provide a balance between the performance and random failure risks of the system. The Min-Max performance index is introduced to compare the fault-tolerant control performance with the proposed controller. The comparison results show that the proposed method has better fault-tolerant control performance.

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On the comparison of stochastic model predictive control strategies applied to a hydrogen-based microgrid

Cited by 84 publications

References 41 publications

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

Pareto-Based Maintenance Decisions for Regional Railways With Uncertain Weld Conditions Using the Hilbert Spectrum of Axle Box Acceleration

Predictive energy management for a wind turbine with hybrid energy storage system

Stochastic Model Predictive Fault Tolerant Control Based on Conditional Value at Risk for Wind Energy Conversion System

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