Energy Management System Control Research Articles

Research on the Design of Integrated Energy Management and Optimization Control Systems for Novel Power Systems

This research paper is determining the impact of integrated energy management and optimization control for Novel power systems. Research objectives have been made based on integrating power system for reducing power consumption based on optimization parameters. Optimal control is mainly dealing with control law by finding out a given system and delivering certain criteria for achieving goals. There are mainly three parts for problem optimization purposes those are included decision, constraints and objective functions. Four different objectives has been made those are mainly discussed about the importance of the power systems regarding integrated management system. There are various factors that are affecting optimizations including the role of the multigrain recursion, global convergence, properties of the optimization model and local convergence. Along with this, the optimization control system has been played a crucial role for the power systems development purposes. Research has been based on the effective design of energy management for power systems. The primary application for the optimization techniques is based on the storage system, electromagnetic-based design and mapping design for the microwave structure. The rate of unsustainable energy management is increasing and sustainable energy management system is decreasing. The conclusion has been based on the significance of the optimization control for the power systems.

An Optimal Approach to Energy Management Control of a Fuel-Cell Vehicle

This paper presents the design of an energy management control system to improve powertrain efficiency and optimize the amount of fuel used by a hybrid fuel cell vehicle in a route-based scenario. To reach this goal, a complete tank-to-wheel model is developed under the assumption of a known scenario, the speed profile that best minimizes the energy required to complete the test is computed, and a controller able to handle the power request is designed. In particular, a Model Predictive Control architecture is used to split the power request between the primary and the secondary power source (fuel cell and supercapacitors). The effectiveness of the proposed approach is assessed through extensive simulation tests using a realistic model.

Sustainable energy efficiency in aluminium parts industries utilizing waste heat and equivalent volume with energy management control system

Sustainable energy efficiency in aluminium parts industries utilizing waste heat and equivalent volume with energy management control system

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

Optimal Energy Management System Control of Permanent Magnet Direct Drive Linear Generator for Grid-Connected FC-Battery-Wave Energy Conversion

The Wave Energy Conversion System (WECS) control strategy is presented in this study to make sure the system operates at its best under fluctuating wave resource situations. The suggested system consists of a MOPSO based MPC approach, a point absorber WEC oscillating in heave, back-to-back power converter for grid connections, and a linear permanent magnet generator. Despite the benefits of model predictive control, problems including switching frequency variations, steady-state errors, high processing costs, and constrained prediction horizons continue to exist. The article presents a method that incorporates the switching control action into the cost function while maintaining the finite nature of a model predictive control to handle the switching frequency issue. In order to minimise switching frequency variations while also addressing other control goals, such as regulating the direct current linked voltage and controlling the flow of active and reactive power, the switching control weight factors are optimised. In order to increase power quality, a fuel cell-based short-term energy storage system is also included to direct current link between the back-to-back converters.

Investigation of Cooling Systems Faults, Control and Management Models

With the rapid increase in population in the world, people's demands for chilled and frozen foods are also increasing. The healthy and energy efficient cold storage of foods has become an important issue. In this context, studies on the efficiency and performance of energy management models including automatic control techniques on cooling systems have been investigated in recent years. The purpose of this study is to give information about faults, traceable energy management models and control systems in the perspective of cooling systems.
 Maintenance of cooling systems, malfunctions and their late detection cause time and cost problems. The decrease in the performance of the cooling system or the occurrence of malfunctions cause economic costs as well as energy consumption. Controlling the changes in pressure, temperature and electricity consumption values ​​in cooling systems and comparing them with standard operating conditions is one of the methods used for fault detection. Faults that may occur in cooling systems; compressor, condenser, evaporator, expansion element and fan failures, thermal failures, phase protection relay failures, under/over refrigerant charge, probe failures, night curtain electric motor failure, condenser-evaporator surface pollution. Errors and malfunctions occurring in the cycle cause the parameters under normal operating conditions and accordingly the cooling performance coefficient to change. Traceable energy management models are needed to detect faults and prevent them in a short time. With the design and implementation of these models, long-term damage to the system is prevented by reducing energy, maintenance and repair costs.
 In the design of traceable energy management models and control systems for cooling systems; set point and management, alarm notification and management, designing algorithms with precise temperature control will provide energy efficiency.

ANN Optimized Hybrid Energy Management Control System for Electric Vehicles

ANN Optimized Hybrid Energy Management Control System for Electric Vehicles

Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids.

The study of the radiating module of the energy management system of buildings

Purpose. Improving the toolkit of interaction between sensors and the building control system using a triangular microstrip antenna. Methodology. Mathematical modeling by the method of finite differences in the time domain. Findings. The proposed technical solution consists in choosing the optimal design of the radiating module in the form of a triangular microstrip antenna, by means of mathematical modeling, the values of the geometric dimensions of the triangle, the thickness of the dielectric layer and the value of its dielectric constant, the overall dimensions of the radiating module, which ensure optimal coordination of the antenna with the power feeder in the form of a microstrip line. The developed computer model of the triangular microstrip antenna using the finite-difference method in the time domain allows conducting research on the parameters of this antenna that provide stable wireless communication between the system of sensors and the home automation control unit. The condition for increasing the degree of matching the impedance of the antenna and the supply line is the choice of the method of powering the antenna. It was established that the most optimal way of feeding a triangular antenna is the method of connecting the microstrip line to the middle of the side of the triangle, compared to the way of connecting the power line to the top of the triangle. The width of the microstrip power line was optimized according to the criterion of minimizing the module of the reflection coefficient at the antenna input. A study of the degree of matching the impedance of the antenna at operating frequencies of 2,4 and 7 GHz was carried out. At a frequency of 7 GHz, the magnitude of the reflection coefficient module is -23,6776 dB. The three-dimensional and two-dimensional radiation patterns of this antenna are calculated. The radiation pattern has an almost spherical shape, that is, it allows placing the radiating module in any orientation relative to the earth's surface. This allows the triangular microstrip antenna to be used as a radiating module of the building energy management system and the smart home system. Originality. The study of the ways of feeding the triangular microstrip antenna allows to find the optimal coupling of the antenna with the power line, which ensures the formation of radiation characteristics that ensure effective interaction between the sensors and the building's energy management control system. Practical value. The radiation characteristics of a triangular microstrip antenna obtained as a result of the study make it possible to use it to organize a wireless communication channel in a 4% operating frequency band.

Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid

In recent years, the power system has been evolved into micro grids, which are little pockets of self-contained entities. Different distributed, interconnected generation units, loads, and energy storage units make up a typical microgrid system. The increased energy efficiency of these units on micro grids is gaining popularity Day-by-Day. Because of their stochastic behavior, renewable generation causes an imbalance in the power system, which needs microgrid energy management. An efficient energy management system for a small-scale Hybrid Wind-Solar- Battery based microgrid is proposed in this paper. The wind and solar energy conversion systems and battery storage system have been developed along with power electronic converters, control algorithms and controllers to test the operation of hybrid microgrid. The power balance is maintained by an energy management system for the variations of renewable energy power generation and also for the load demand variations. In this system, for reliable and stable operation of a microgrid with multiple DGs, coordinated control of energy management system is proposed. The control algorithms of microgrid system are verified by Matlab Simulation.

Decentralized Energy Management and Voltage Regulation in Islanded DC Microgrids

Battery energy storage systems (BESSs) are essential units in islanded dc microgrids (MGs) with tangible penetration of distributed renewable energy sources. These units play a crucial role in energy management and preferably should be controlled in a decentralized manner. In this regard, the conventional droop control is a well-known technique to implement a decentralized control for load sharing in the presence of various-sized distributed generator (DG) units and variable state-of-charge (SoC) level of BESS units. This method provides plug-and-play (PnP) feature for DG units; however, load sharing accuracy is poor, and voltage is unregulated. This article proposes a novel control system for energy management in islanded dc MGs while providing regulated voltage and highly accurate load sharing simultaneously in a decentralized way. The method uses the SoC level of BESS units in a modified droop control technique. A superimposed ac voltage on the nominal dc voltage of the network is used by the control system, bearing a frequency representing the network's load state. This frequency defines the operation mode of BESS units. Conducting simulation studies using piecewise linear electrical circuit simulation (PLECS) validates the effectiveness of the proposed control system. The experimental results confirm the viability and feasibility of the proposed control system.

Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles

Nowadays, the automotive market has showed great interest in the diffusion of Hybrid Electric Vehicles (HEVs). Despite their low emissions and energy consumptions, if compared with traditional fossil fuel vehicles, their architecture is much more complex and presents critical issues in relation to the combined use of the internal combustion engine (ICE), the electric machine and the battery pack. The aim of this paper is to investigate lithium-ion battery usage when coupled with an optimization-based strategy in terms of the overall energy management for a specific hybrid vehicle. A mathematical model for the power train of a Peugeot 508 RXH was implemented. A rule-based energy management system (EMS) was developed and optimized using real data from the driving cycles of two different paths located in Messina. A mathematical model of the battery was implemented to evaluate the variation of its voltage and state of charge (SOC) during the execution of driving cycles. Similarly, a mathematical model was implemented to analyze the state of health (SOH) of the battery after the application of electrical loads. It was thus possible to consider the impact of the energy management system not only on fuel consumption but also on the battery pack aging. Three different scenarios, in terms of battery usage at the starting SOC values (low, medium, and maximum level) were simulated. The results of these simulations highlight the degradation and aging of the studied battery in terms of the chosen parameters of the rule-based optimized EMS.

Implementasi Sistem Kontrol Manajemen Energi Hibrid Berbasis Mikrokontroller

Currently, world researchers are competing to find the most effective and efficient way to use renewable energy sources, namely energy from sunlight and solar and wind heat or it can be called hybrid energy. The use of this hybrid energy technology can certainly save conventional fuel and reduce the risk of ecosystem damage caused. This study aims to see the energy value resulting from the implementation of a hybrid energy management control system that has been created based on a microcontroller. With the experimental method, the results were obtained that the solar cell voltage value was highest at 11 pm with 28.87 Vdc, then for the voltage of the wind energy was the highest at 27.85 at the 3rd minute at 4 pm. For energy testing of batteries, the results were obtained that if there is only one energy source to charge the battery, it takes 9 hours while if it is a hybrid energy source, it only takes 2.5 hours. This proves that hybrid energy is very effective rather than relying solely on one of the energy sources.

Validation of a lithium-ion commercial battery pack model using experimental data for stationary energy management application.

Background:A cost-effective solution for the design of distributed energy storage systems implies the development of battery performance models yielding a suitable representation of its dynamic behaviour under realistic operation conditions. Methods:In this work, a lithium-ion battery (LIB) is tested to be further modelled and integrated into an existing energy management control system. This specific LIB (5.0 kW /9.8 kWh) is integrated with a commercial inverter and solar photovoltaic (PV) system (3.3 kWp) as part of a microgrid that is also encompassing other energy storage technologies at the University of Évora, Pole of INIESC - National Research Infrastructure for Solar Energy Concentration. A testing protocol fully characterizes the battery and the inverter efficiency to describe their performance better. Then, a battery model is built upon both the existent LIB description and experimental fitting regression. The model allows obtaining the voltage curve, the internal resistance (i.e., to describe instantaneous voltage drop/rise and transients), and the state of charge (SOC) and/or energy capacity based on the current input. The developed model is validated through the comparison with the experimental results. Results:The model approach presented a higher voltage RMSE (root mean square error) of 5.51 V and an MRE (maximum relative error) of 5.68 % in the discharge state. Regarding SOC, the MRE obtained was approximately 6.82 %. In the charge state, the highest RMSE voltage was 5.27 V, with an MRE of 6.74 %. Concerning SOC, the MRE obtained was approximately 6.53 %. Conclusions:The developed model is validated through the comparison with experimental results. Based on computational effort, simplicity of use and the associated model error, the approach is validated to the regular conditions of the commercial battery pack to be incorporated in the next research step, following a bottom-up modelling approach for an increasingly more complex smart grid.

Monitoring and Control System for Energy Management Using Internet of Things

Abstract: The use of technology has become an essential part of improving lifestyle, work efficiency, and a catalyst for economic growth. The benefit of the Internet of Things (IoT) and connected nodes has been on a steep incline in recent years. This paper aims to research, build, test and implement a low-cost energy monitoring and control system using IoT devices. Electrical appliances (e.g., air conditioning units and overhead lighting) can be controlled and monitored using IoT technology from any place in the world. In order to accomplish this goal, a complete front-end to back-end system that includes a smart device application (iOS platform), a cloud-based database, an Application Programming Interface (API), and a hardware development is proposed. A small programmable specialized computing device (e.g., Raspberry Pi) for preliminary testing. This smart node was chosen due to familiarity, and its capabilities, such as general purpose pins and built-in Wi-Fi chip. The end goal is to observe energy efficiency by monitoring and controlling air conditioning appliances and standard overhead lighting units. These smart IoT devices allow for the usage energy data from each unit to be collected and stored in a Cloud-based database that can be analysed and reported for energy conservation and analysis. Keywords: Internet of Things (IoT), Energy Monitoring, Control System, Security.

CntrlDA: A building energy management control system with real-time adjustments. Application to indoor temperature

Rule-Based Control (RBC) and Model Predictive Control (MPC) have been traditionally used to control building heating, ventilation and air conditioning (HVAC) systems. They, however, present shortcomings when faced with efficiently controlling these systems at a larger level. Reinforcement Learning (RL) has recently emerged as a viable alternative, showing promising results compared to previous methods, but still having some difficulties with untrained situations or sudden changes. CntrlDA is our proposal on improving the RL formulation by coupling it with data assimilation (DA), a technique commonly used in numerical weather prediction. Our battery of experiments, in a building simulation environment, shows that training a RL control agent with DA and external data, leads to better performance than training the agent using only the simulation data. The RL control agent with DA maintains the temperature range 15.6% more often than the RL control agent without DA. It is also shown that by including a DA stage in the control process, the agent better deals with unexpected events (which are common in real-life systems and particularly in building energy control scenarios). We show that it maintains the range 15.4% more often than the system without DA with no significant added cost of resources.

Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information

The combination of electrification and advanced dedicated hybrid engines (DHEs) has been attached with promising prospects for the abatement of fuel consumption and emissions. To explore the potential of this solution, experimental data are used from a high-efficiency DHE operated under dual-mode combustion: the lean-burn spark-induced compression ignition (SICI) and the stoichiometric spark ignition (SI). This work endeavors to develop the integrated energy management and engine control system for such DHE-based plug-in hybrid electric vehicles (PHEVs), to this end, the synergy of artificial intelligent control and trip preview can be exploited to boost the control performance, with the engine combustion process being optimally controlled in transient. This paper presents a multistate deep reinforcement learning (M−DRL)-based energy management strategy (EMS) with a discrete–continuous hybrid action space to select optimal combustion mode while output continuous engine power demand, and its state space is expanded to incorporate the multivariate traffic and terrain information processed by a spatio-temporal data processing (STDP) framework in real time. Comprehensive experiments are carried out to validate the capability of the proposed method, based on a standard driving cycle and a real-world driving cycle in China with real-time traffic data recorded. The results indicate that the proposed M−DRL strategy avoids frequent switchover of engine operation modes as well as achieves more than 93% of the dynamic programming’s performance in terms of the fuel consumption and NOx emissions abatement.

Validation of a lithium-ion commercial battery pack model using experimental data for stationary energy management application

Background:A cost-effective solution for the design of distributed energy storage systems implies the development of battery performance models yielding a suitable representation of its dynamic behaviour under realistic operation conditions.Methods:In this work, a lithium-ion battery (LIB) is tested to be further modelled and integrated into an existing energy management control system. This specific LIB (5.0 kW /9.8 kWh) is integrated with a commercial inverter and solar photovoltaic (PV) system (3.3 kWp) as part of a microgrid that is also encompassing other storage technologies at the University of Évora, Pole of INIESC – National Research Infrastructure for Solar Energy Concentration. The battery and the inverter are fully characterized through the implementation of a testing protocol aiming at better describing the battery performance. Then, a battery model is built upon both the existent LIB description and experimental fitting regression, for real-time predictive optimization control development. Considering the pre-determined efficiency of the inverter, the model allows to obtain the voltage curve, the series resistance (i.e., to describe instantaneous voltage drop/rise and transients), and the state of charge (SOC) and/or energy capacity, based on the current input. The developed model is validated through the comparison with the experimental results.Results:In discharge state, the model approach presented a higher voltage RMSE (root mean square error) of 5.51 V and an MRE (maximum relative error) of 5.68 %. Regarding SOC the MRE obtained was approximately 6.82 %. In charge state, the highest RMSE voltage was 5.27 V, with an MRE of 6.74 %. Concerning SOC, the MRE obtained was approximately 6.53 %. Conclusions:The developed setup allowed us to perform the necessary characterization tests under real operating conditions. Based on computational effort, simplicity of use, and the associated model error compared with the experimental data, generally, the model describes the battery behaviour.

Optimal Design for Super Capacitor/Battery Power Management Applied in Electric Vehicle Applications: A Hybrid Methodology

This manuscript presents an optimal control system for energy management of the hybrid energy storage system (HESS) as battery and super capacitor (SC) on electric vehicles (EVs). The proposed system is parallel execution of levy flight with Tunicate swarm optimization. The proposed method is improved by levy flight distribution; hence, it is named Improved Tunicate swarm optimization (ITSA). Here, the HESS method calculates the super capacitor reference voltage in terms of load dynamics and improves the power. At first, compute the super capacitor reference voltage assuming real-time load dynamics. Furthermore, diminish the range of battery power magnitude and power loss concurrently. In the proposed system, the Improved Tunicate swarm optimization is merged for generating the probable HESS control signal data set. Furthermore, the proposed method gains the super capacitor voltage, amount of battery current, variation of battery current. Through the proposed method, the hybrid energy storage system parameters are enhanced, and the proposed system gives a reliable solution. The proposed method is performed on the MATLAB/Simulink work platform. The quality of a hybrid energy storage system is compared with that of other methods.

Techno economic analysis of microgrid with an efficient energy management system and inverter control strategies

A microgrid comprises of distributed energy resources with the capability of operating independently as an islanded mode or in a grid connected mode. The efficacy of a microgrid is based on the performance of the control strategy and the energy management strategy. Therefore, in this paper the feasibility of an efficient inverter control strategy and energy management strategy for microgrid are studied. The proposed microgridis implemented with master-slave energy management control and battery management system for effective power flow control in an islanded and grid connected mode. A three-layer hierarchical energy management strategy comprising of master-slave system to provide continuous supply at all conditions and effective switchover operations between grid connected and islanded modes of operation is proposed. The voltage-frequency control under standalone mode of operation and P-Q control using hysteresis current control under grid-connected mode of operation are developed and the system parameters like real power, reactive power and voltage at the PCC are analyzed after the implementation of proposed controllers under islanded and grid connected modes of operation. The proposed model achieves voltage and frequency regulation under varying system operating conditions. Also, a techno-economic analysis is performed in HOMER software where the cost of energy and return on investment are studied for the proposed microgrid system by which levelized cost and payback period is reduced. The proposed control algorithms are implemented through MATLAB simulation and tested in a real-time for 1 kW grid-connected solar PV system.