Model Predictive Control Based Energy Management System Literature Assessment for RES Integration

Tumeran, Nor Liza; Yusoff, Siti Hajar; Gunawan, Teddy Surya; Hanifah, Mohd Shahrin Abu; Zabidi, Suriza Ahmad; Pranggono, Bernardi; Yunus, Muhammad Sharir Fathullah Mohd; Sapihie, Siti Nadiah Mohd; Halbouni, Asmaa Hani

doi:10.3390/en16083362

Cited by 7 publications

(3 citation statements)

References 65 publications

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“…The framework maximizes the use of renewable energy sources, including solar photovoltaic, wind turbines, and energy storage systems (ESS), to enable a more sustainable and efficient energy generation and distribution. In one study, microgrid management operational expenses were decreased and prediction accuracy increased with the use of an MPC-based EMS [31]. Another study gives hope that, when the load needs to exceed the capacity of the battery converter or when the stored battery energy is insufficient to meet demand during peak hours, the MPC strategy can be applied and the grid can be adjusted to act as storage to absorb any extra PV energy [32].…”

Section: Microgrid Ems-mpcmentioning

confidence: 99%

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

Chowdhury,

Yusoff,

Gunawan

et al. 2024

Energies

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A supervisory control system using Model Predictive Control (MPC) has been designed to evaluate the efficiency of wind and solar power and is consistent with the cost function in the supervisory MPC optimization problem. A two-layer Economic Model Predictive Control (EMPC) framework has been developed and has improved results such as cost reductions compared to recent advanced methods. A speed Generalized Predictive Control (GPC) scheme intended for wind energy conversion systems was developed last year, with simulation results indicating superior performance over previous models. A Hierarchical Distributed Model Predictive Control (HDMPC) can work under different weather conditions with improved economic performance and keep a good balance between power delivery and load demand. An energy management system (EMS), built on the basis of MPC, can be quite lucrative for the sphere in the present climate scenario, with the selection and testing of suitable algorithms, controlled processes, cost functions, and a set of constraints as well as with proper optimizations carried out. Previous research indicates that an MPC-based EMS has the potential to be a good solution to manage energy well and also introduced it to the world experimentally. The key intention of this research study is to explore the existing advances that have been introduced and to analyze their performance in terms of cost function, different sets of constraints, variant conversion processes, and scalability to achieve more optimized operation of MPC-based EMS.

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Section: Microgrid Ems-mpcmentioning

confidence: 99%

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

Chowdhury,

Yusoff,

Gunawan

et al. 2024

Energies

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“…The ocean waves and energy consumption data are always stochastic and dynamic by nature due to the daily fluctuation of the weather conditions and power consumption. Deterministic model-based strategies proved to be ineffective for the prediction of such datasets [31,32], since the time-series data to be forecasted are influenced by several factors, with the most significant factor being the forecasting horizon. The forecasting horizon is defined as the length of time during which output data are predicted in the future.…”

Section: Long Short-term Memory Neural Networkmentioning

confidence: 99%

Data-Driven Management Systems for Wave-Powered Renewable Energy Communities

Iqbal,

Mehran

2024

Energies

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This research focus on the essential task of precise prediction for power generation and energy consumption of wave energy converters (WECs) within the framework of contemporary wave-powered renewable energy sources (RESs). Utilizing real-time wave data, we introduce a deep learning methodology featuring a long short-term memory (LSTM) model. Additionally, we propose an online management system for RESs aimed at optimizing interactions among WECs, energy storage systems (ESSs), super capacitor (SC), and load. This approach leads to significant enhancements in mean square error (MSE) for critical variables such as wave height, time period, and direction, improving predictive accuracy by factors of 8.37, 9.30, and 16.14, respectively. Through diverse scenario-based experimental evaluations, our solution exhibits competitive performance when compared to benchmark strategies and ideal solutions. These findings underscore the potential of the LSTM-NN model to advance the efficiency and reliability of wave energy forecasting and management systems. As wave energy technology evolves, this study contributes to ongoing efforts to enhance practical applicability, especially in coastal regions with substantial wave energy potential.

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“…𝑢 𝑚𝑖𝑛 < 𝑢 𝑘 < 𝑢 𝑚𝑎𝑥 𝑥 𝑚𝑖𝑛 < 𝑥 𝑘 < 𝑥 𝑚𝑎𝑥 (9) where 𝑈 is the set of control inputs over the prediction horizon. By employing MPC in an HESS, operators can achieve optimal energy management, maximize energy efficiency, enhance system stability, and meet operational requirements while considering dynamic changes in energy generation, consumption, and grid conditions [60]. MPC offers a flexible and robust control framework for an HESS, enabling effective integration of renewable energy sources, energy storage devices, and grid interactions.…”

mentioning

confidence: 99%

Optimizing Performance of Hybrid Electrochemical Energy Storage Systems through Effective Control: A Comprehensive Review

Clemente,

Arias,

Gevorkov

et al. 2024

Electronics

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The implementation of energy storage system (ESS) technology with an appropriate control system can enhance the resilience and economic performance of power systems. However, none of the storage options available today can perform at their best in every situation. As a matter of fact, an isolated storage solution’s energy and power density, lifespan, cost, and response time are its primary performance constraints. Batteries are the essential energy storage component used in electric mobility, industries, and household applications nowadays. In general, the battery energy storage systems (BESS) currently available on the market are based on a homogeneous type of electrochemical battery. However, a hybrid energy storage system (HESS) based on a mixture of various types of electrochemical batteries can potentially provide a better option for high-performance electric cars, heavy-duty electric vehicles, industries, and residential purposes. A hybrid energy storage system combines two or more electrochemical energy storage systems to provide a more reliable and efficient energy storage solution. At the same time, the integration of multiple energy storage systems in an HESS requires advanced control strategies to ensure optimal performance and longevity of the system. This review paper aims to provide a comprehensive overview of the control systems used in HESSs for a wide range of applications. An overview of the various control strategies used in HESSs is offered, including traditional control methods such as proportional–integral–derivative (PID) control, and advanced control methods such as model predictive control (MPC), droop control (DC), sliding mode control (SMC), rule-based control (RBC), fuzzy logic control (FLC), and artificial neural network (ANN) control are discussed. The paper also highlights the recent developments in HESS control systems, including the use of machine learning techniques such as deep reinforcement learning (DRL) and genetic algorithms (GA). The paper provides not only a description and classification of various control approaches but also a comparison between control strategies from the evaluation of performance point of view. The review concludes by summarizing the key findings and future research directions for HESS control systems, which is directly linked to the research on machine learning and the mix of different control type strategies.

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Model Predictive Control Based Energy Management System Literature Assessment for RES Integration

Cited by 7 publications

References 65 publications

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

Analysis of Model Predictive Control-Based Energy Management System Performance to Enhance Energy Transmission

Data-Driven Management Systems for Wave-Powered Renewable Energy Communities

Optimizing Performance of Hybrid Electrochemical Energy Storage Systems through Effective Control: A Comprehensive Review

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