Evaluation of performance improvement by model predictive control in a renewable energy system with hydrogen storage

Morin, David; Stevenin, Yoann; Grolleau, Cédric; Brault, Pascal

doi:10.1016/j.ijhydene.2018.09.118

Cited by 25 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model predictive control (MPC) is very suitable to use for temperature control applications in PEFC systems because of its stronger robustness and faster response compared with that of conventional controllers. 21 The main advantage of the MPC is that it allows the current timeslot to be optimized while keeping future timeslots in account, which features its ability to anticipate future events and take control accordingly. More importantly, it has the feature of solving systems with constraints.…”

Section: Discussionmentioning

confidence: 99%

“…A PEFC system is a complicated nonlinear and strongly coupled system, with many constraints that need to be considered to ensure the coordination and balance between every subsystem, which makes the controller design even more complex. Model predictive control (MPC) is very suitable to use for temperature control applications in PEFC systems because of its stronger robustness and faster response compared with that of conventional controllers . The main advantage of the MPC is that it allows the current timeslot to be optimized while keeping future timeslots in account, which features its ability to anticipate future events and take control accordingly.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temperature control strategy for polymer electrolyte fuel cells

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

Summary A polymer electrolyte fuel cell (PEFC) is an electrochemical device that converts chemical energy directly to electrical energy, and its performance greatly depends on its operating temperature. Therefore, in this paper, a novel thermodynamic PEFC model with the airflow cooling method is firstly developed for the PEFC system. Then, a novel model predictive control (MPC) controller is designed to control the stack temperature at an optimal value by adjusting the air flow rate on the basis of the developed thermodynamic PEFC model. The thermodynamic PEFC model and the designed controlling strategies are simulated and analysed in Matlab/Simulink. Three tests are conducted to estimate the reliability of the developed controllers concerning different operating conditions: (a) typical perturbation in the current load, (b) any perturbation in the current load, and (c) variation of the ambient temperature. The simulation results demonstrate that the MPC controller can effectively control the stack temperature at the desired value. Moreover, the MPC controller shows much superior effects compared with the conventional proportional integral derivative (PID) controller. In addition, the developed coolant circuit model can be easily applied to various PEFC systems. The MPC controller shows potential also for other controlling issues of PEFC systems due to its strong robustness and fast response.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temperature control strategy for polymer electrolyte fuel cells

et al. 2020

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…Some papers (e.g., [6,22]) neglect the term (((NOCT − 20)/800)G) to give a simpler equation. Other simplified expressions for the output can be found in [6,7,32]. A detailed expression is presented in [14].…”

Section: Pv-based Systemsmentioning

confidence: 99%

“…e system is connected by a central DC bus to which the AC components are connected via AC/DC inverters. Several papers in the selection adapted this type of configuration, e.g., [2,6,17,22,24,27]. ey justify their choice by stating that the DC configuration has more efficiency, less cost, less occupied space, lower lifetime cost, and high reliability [34].…”

Section: Feed-in-tariff (Fit)mentioning

confidence: 99%

See 1 more Smart Citation

Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Essayeh

Fenni

Dahmouni

et al. 2021

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Having neither precise definition nor a commonly accepted scope, the term “MicroGrid” tends to be used differently across researchers and practitioners alike. The management of energy usage within a microgrid is one of the topics that was handled from numerous perspectives. This study presents systematic literature review (SLR) of research on architectures and energy management techniques for microgrids, providing an aggregated up-to-date catalogue of solutions suggested by the scientific community. The SLR incorporated 45 papers selected according to inclusion/exclusion criteria and defined a priori. The selection process was based on an automated search and covered three known digital libraries. The extraction process covers three main questions. (i) The architectures of microgrids including their components, their bus configuration, and the adopted utility grid policy. (ii) The employed methods to ensure an optimal usage of energy under uncertainty. (iii) The confronted challenges and constraints of the suggested strategies. The findings of this SLR indicate a great diversity of methods and a rich background. Finally, the SLR suggests that future research should take into account the uncertainty aspect relating to energy management rather than the direct use of historical data as it is commonly done in most research papers. A sensitivity analysis should be provided in the latter case.

show abstract

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

Gassi

Baysal

2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end‐energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources‐based microgrid and develops its centralized energy management model for both off‐grid and on‐grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector‐valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi‐parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self‐consumption, energy efficiency, and myopic decision‐making model using the energy level of storage devices obtained from the full look‐ahead optimization model. Highlights The optimization problem combining electricity, heating, and cooling is formulated The electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model. Accurate energy storage level information into the myopic energy management model improves the model decisions. The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding.

show abstract

Evaluation of performance improvement by model predictive control in a renewable energy system with hydrogen storage

Cited by 25 publications

References 27 publications

Temperature control strategy for polymer electrolyte fuel cells

Temperature control strategy for polymer electrolyte fuel cells

Energy Management Strategies for Smart Green MicroGrid Systems: A Systematic Literature Review

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

Contact Info

Product

Resources

About