Design Of Energy Management System Research Articles

Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.

An SDP relaxation in the complex domain for the efficient coordination of BESS and DGs in single-phase distribution grids while considering reactive power capabilities

This research proposes an efficient energy management system design for medium-voltage distribution networks from a perspective of convex optimization. The exact nonlinear programming problem that represents the operation of distributed energy resources (DERs), such as batteries and photovoltaic sources, is approximated as an equivalent semi-definite programming (SDP) problem. This SDP approximation is achieved in the complex domain using Hermitian matrices, which are the equivalent of positive semi-definite matrices in the real number domain. Two test feeders, one with 27 nodes and the other with 33 nodes, are modified to match the characteristics of Colombian rural and urban networks. The 27-bus grid is adapted to accommodate the average demand and solar generation conditions of the municipality of Capurganá, while those of the city of Medellín are used for the 33-bus grid. One of the main contributions of this research is that it considers a DER operation with variable power factors, which allows for significant improvements compared to the traditional use of a unitary power factor scheme. Numerical comparisons are conducted using metaheuristic optimizers, including parallel versions of the particle swarm optimizer, the vortex search algorithm, and the continuous version of the genetic algorithm. These comparisons demonstrate the effectiveness of the proposed SDP approximation. All simulations are carried out in the MATLAB software (version 2022b), using the disciplined convex optimizer (CVX) and the SDPT3 and SEDUMI solvers.

Design of an Intelligent Energy Management System for Electric Vehicle Charging Station

In today’s world there are more outcomes in environmental change due to the overutilization of petroleum products in this manner prompting a genuine effect on the climate. So there is a need for a substitute answer for lessen the consumption of such non – sustainable assets. One such exertion made in the field of Freeways is the advancement of "Solar Freeways" which can be an elective arrangement. Sun oriented streets consolidate various arrangements in one – it can assist us with improving the creation of power utilizing sun based boards, to give a computerized stage to our future country's ventures like Smart Cities, and to work with the arising electric vehicles that supplant the petroleum driven vehicles and substantially more. Motivated by the fact that there are numerous amount of clean and sustainable energy we receive from roadways, the following study puts forward some of the event and application of an innovative charging method for the renewable energy driven electric cars, buses by using the roadway and also implementation of revolutionary nanotechnology along with the latest best in the house power electronics and power system analysis tools. A small scale prototype model was made by our team to attest the working of smart inductive charging process. Our project team was successful to improve the working of the model by improving the use of the preinstalled solar panels and also implement its use on the very concept we are trying to improve. On the vehicle, there will be the use of coils which are experimentally made for the flow of charges that are needed to provide charge to a moving electric vehicle (EV). The detailed strategy is presented in this report. Key Words: Environmental change, Overutilization of petroleum products, Solar Freeways, Renewable energy, Electric vehicles (EVs), Smart Cities, Nanotechnology

Design and development of energy management system for solar electric vehicle with dynamic power split approach

ABSTRACT The integration of on-roof solar photovoltaic (PV) systems in electric vehicles (EVs) represents a groundbreaking advancement in the evolution of sustainable transportation. The objective of the research is to design and validate an integrated system for on-roof solar PV integration in EVs, optimising energy utilisation and reducing reliance on grid electricity. This paper explores the implementation of an Adaptive Maximum Power Point Tracking (MPPT) system, which maintains the output of the PV system at a constant level, leading to a significant improvement of approximately 10.20% in energy output compared to conventional MPPT systems. Additionally, a dynamic power split approach (DPAS) is employed to intelligently manage the allocation of power between the solar PV system and the vehicle battery, ensuring continuous fulfilment of energy demand while simultaneously recharging the battery. The proposed system reduces 65% of energy demand from grid and hence, reliance on grid. Furthermore, the dynamic power split approach enable substantial reductions in battery capacity requirements, with a potential decrease of 25–35% observed. These advancements not only enhance the performance and efficiency of EVs but also contribute to cost savings and the transition towards a cleaner and more sustainable transportation ecosystem.

Evaluation of IoT-Enabled Energy Management Systems: Uncovering Sustainable Energy Storage Parameters and Their Significance

This research begins an in-depth examination of IoT-enabled energy management systems at a time when IoT (Internet of Things) technology has a profoundly revolutionary impact. Finding the fundamental sustainable energy storage characteristics that support their operational effectiveness and environmental sustainability is its main goal. By taking a comprehensive approach, the paper carefully analyzes the existing IoT-based systems, exploring their complex architectural features, real-world uses, and related difficulties. Parallel to that, a thorough literature study is conducted, highlighting the crucial aspects of sustainable energy storage and their inherent significance in energy management systems. The results of this in-depth analysis unequivocally demonstrate how crucial these factors are for improving the effectiveness, performance, and ecological soundness of energy management systems. While highlighting the current environment and key variables, it is important to recognize the research's constraints, which are confined to an analysis of existing IoT-based energy management systems and a literature study. The design and implementation of IoT-driven energy management systems infused with the indicated sustainable energy storage characteristics are anticipated to be included in further research projects. The investigation of practical application, scalability, and a dedication to keeping up with the rapidly changing technology and practices that characterize this dynamic sector would be some of these objectives.

Design and implementation of smart home energy management system for Indian residential sector

Residential power consumption has surged with the widespread adoption of smart appliances, prompts utilities to implement Demand Response(DR) programs. Home Energy Management Systems (HEMS) plays a vital role in these initiatives within the smart grid infrastructure. The rapid advancement of wireless communication technologies, especially the Internet of Things (IoT), will enable successful architectural designs for HEMS. This study proposes an incentive-based DR implementation using IoT-enabled HEMS for the Indian residential sector that represents 25% of the total power demand. The proposed HEMS automates the management of categorized loads during peak hours through load shifting and scheduling. The proposed algorithm is deployed in real-time residential setup using IoT modules. Emphasizing sustainability and consumer affordability, the work integrates a Photo-Voltaic (PV) based Battery Storage System (BSS) to support critical loads during peak hours. Consumer load preferences obtained through a survey are incorporated into the deployment of the proposed HEMS. Experimental studies demonstrated significant energy savings of 49% with the loads under consideration and substantial cost savings like approximately 49% with Load Management (LM) alone, 53% with the inclusion of Time of Day (ToD) tariff, and an impressive 78% with the proposed incentives for participating in DR. Additionally, this study highlights the synergistic savings realized through the combined implementation of the proposed LM strategy and ToD tariff in India.

Design and modelling of a neural network-based energy management system for solar PV, fuel cell, battery and ultracapacitor-based hybrid electric vehicle

Design and modelling of a neural network-based energy management system for solar PV, fuel cell, battery and ultracapacitor-based hybrid electric vehicle

A Robust Conic Programming Approximation to Design an EMS in Monopolar DC Networks with a High Penetration of PV Plants

This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software.

Methodology for energy management strategies design based on predictive control techniques for smart grids

This article focuses on the development of a general energy management system (EMS) design methodology using on model-based predictive control (MPC) for the control and management of microgrids. Different MPC-based EMS for microgrids have been defined in the literature; however, there is a lack of generality in the proposed that would facilitate adapting to new architectures, energy storage system technology, nature of the bus, application, or purpose. To fill this gap, a novel general formulation that is parameterizable, simple, easily interpretable, and reproducible in different microgrid architectures is presented. This is the result of the development of a novel methodology, which is also presented. It considers the state space formulation of the controller from the initial modelling phase, from the dynamics of the energy storage systems represented by their models to the subsequent definition of the optimisation problem. This is developed through the design of the general cost function and the formulation of constrains, by means of general guidelines and reference values. To evaluate the performance of the developed methodology, simulation tests were carried out for four different microgrid architectures, with different applications and objectives, also considering different generation conditions, demand profiles, and initial conditions. The results showed that, with some simple guidelines and regardless of the case study, the developed MPC controller design methodology can address the technical-economic optimisation problem associated with energy management in microgrids in an easy and intuitive way.

Energy Management System Design for Hybrid and Intelligent Light Train

Energy Management System Design for Hybrid and Intelligent Light Train

Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles

As the process of urbanization continues to accelerate, the demand for sustainable cities has become more critical than ever before. The incorporation of electric vehicles (EVs) is a key component in creating sustainable cities. However, the development of smart cities for EVs entails more than just the installation of charging stations. Software engineering plays a crucial role in realizing smart cities for electric vehicles. This paper examines the role of software engineering in the creation of smart cities for electric vehicles, the techniques utilized in electric vehicle charging infrastructure, the obstacles faced by software engineers, and the future of software engineering in sustainable cities. Specifically, the paper explores the significance of software engineering in integrating EVs into the transportation system, including the design of smart charging and energy management systems, and the establishment of intelligent transportation systems. Additionally, the paper offers case studies to demonstrate successful software engineering implementations for smart cities. Finally, the paper concludes with a discussion of the challenges that software engineers encounter in implementing intelligent transportation systems for EVs and provides future directions for software engineering in sustainable cities.

Development and Application of an Energy Management System for Electric Vehicles Integrated with Multi-input DC-DC Bidirectional Buck-Boost Converter

The rise in environmental pollution, demand for fossil fuels, and higher fuel economy vehicles has raised concerns about the creation of new and efficient transportation vehicles in recent days. These days, most developments in electric vehicles concentrate on making the vehicles more pleasant to ride in. Nonetheless, the emphasis now should be on energy and its most efficient use. To do this, you must give your attention to the origin of the automobile. The answer to this problem may be found in hybrid energy storage systems (HESS). This work is concerned with the design and implementation of an effective energy management system in electric vehicles (EVs) equipped with an active HESS consisting of a battery and a super capacitor via the incorporation of load sharing into this hybridization under a variety of load demand scenarios. To address the demands of high fuel efficiency vehicles, automotive firms are focusing on the development of diesel-engine operated vehicles, electric vehicles, fuel-cell vehicles, plug-in electric vehicles, and hybrid electric vehicles. A Multi-input Bidirectional Buck-Boost (MIB3) DC-DC converter is proposed in this dissertation to provide a greater conversion ratio to the input DC voltage. The multi-input converter recommended has fewer components and a simpler control method, making it more trustworthy and cost-effective. This converter also has bidirectional power flow functionality, making it suitable for charging the battery during regenerative braking in an electric or hybrid vehicle. Three different energy sources are used in the suggested topology: a photovoltaic (PV) panel, a battery, and an ultra-capacitor

Bi-level power management strategies optimization for multi-stack fuel cell system-battery hybrid power systems

A bi-level multi-mode power management strategy (PMS) is proposed in this work to improve the efficiency, remaining useful life (RUL), life-cycle cost (LCC) of multi-stack fuel cell system-battery hybrid power systems (MHPSs). In the first level PMS, the output power allocated between the multi-stack fuel cell system (MFCS) and the battery is determined by smoothing the MHPS power demand. The smoothing effects of different MHPS power demands smoothing algorithms are analyzed. In the second level PMS, the multi-mode MFCS PMS is investigated. The second level PMS is validated through system efficiency and hydrogen consumption simulation in a specific application scenario. As a result, the proposed second level PMS could have lower hydrogen usage costs and higher efficiency than the equal allocation, daisy chain, and adaptive allocation PMSs. The proposed MHPS bi-level PMS can provide recommendations for the future MHPS advanced and efficient real-time energy management system design.

A Systematic Literature Review of the Interplay between Renewable Energy Systems and Occupant Practices

The development of renewable energy systems offers a potential solution to energy consumption in the residential sector. These systems face many barriers and challenges regarding the nature of home energy demand and behaviors of household occupants. These barriers are discussed in innovation theory, which describes how people assess new technologies. A systematic literature review of 123 journals was conducted to explore the interrelationship between energy systems, home energy demand and occupant practices. This identified key gaps in the literature and important takeaways from past research showing the limitations of renewable energy systems in integrating into everyday lives. There are numerous personal and social barriers that inhibit behavior change and limit the penetration of renewable systems. Additionally, the development of social norms and institutional rhythms have resulted in people living in a lock-in lifestyle, with limited flexibility for change. This review discusses the role of technology, consumers and policies, and how they must all interact to create a sustainable and effective energy solution to this climate emergency. The next step is to reevaluate the design of home automation and energy management systems to consider the impacts of different lifestyles and routines.

Design and Key Technology of Energy Management System Based on Wind Storage System

With the goal of “carbon peaking and carbon neutrality” and the proposal of building a new power system, more and more research and applications of wind power supporting energy storage have been conducted in scientific and technological research and engineering construction. The energy management system technology of wind storage system plays a key role in improving system operation efficiency, improving grid security and wind power utilization, and reducing the cost of the whole life cycle. This paper sorts out the development trend of energy management systems, introduces the management objects, system architecture, network architecture, and functional architecture of the wind storage system energy management system, and finally expounds on the key technologies of the wind storage energy management system.

Advanced Energy Management System (A-EMS) Design of a Grid-Integrated Hybrid System

Advanced Energy Management System (A-EMS) Design of a Grid-Integrated Hybrid System

Design and implementation of an intelligent energy management system for smart home utilizing a multi-agent system

Green Hydrogen Microgrid System has been selected as a source of clean and renewable alternative energy because it is undergoing a global revolution and has been identified as a source of clean energy that may aid the country in achieving net-zero emissions in the coming years. The study proposes an innovative Microgrid Renewable hybrid system to achieve these targets. The proposed hybrid renewable energy system combines a photovoltaic generator (PVG), a fuel cell (FC), a supercapacitor (SC) and a home vehicle power supply (V2H) to provide energy for a predefined demand. The proposed architecture is connected to the grid and is highly dependent on solar energy during peak periods. During the night or shading period, it uses FC as a backup power source. The SC assists the FC with high charge power. SC performs this way during load transients or quick load changes. A multi-agent system (MAS) was used to build a real energy management system (RT-HEMS) for intelligent coordination between components (MAS). The scheduling algorithm reduces energy consumption by managing the required automation devices without the need for additional network power. It will meet household energy requirements regardless of weather conditions, including bright, cloudy or rainy conditions. Implementation and discussion of the RT-HEMS ensures that the GHS is functioning properly and that the charge request is satisfied.

Multi-Agent-Based Controller for Microgrids: An Overview and Case Study

A microgrid can be defined as a grid of interconnected distributed energy resources, loads and energy storage systems. In microgrid systems containing renewable energy resources, the coordinated operation of distributed generation units is important to ensure the stability of the microgrid. A microgrid needs a successful control scheme to achieve its design goals. Undesirable situations such as distorted voltage profile and frequency fluctuations are significantly reduced by installing the appropriate hardware such as energy storage systems, and control strategies. The multi-agent system is one of the approaches used to control microgrids. The application of multi-agent systems in electric power systems is becoming popular because of their inherent benefits such as autonomy, responsiveness, and social ability. This study provides an overview of the agent concept and multi-agent systems, as well as reviews of recent research studies on multi-agent systems’ application in microgrid control systems. In addition, a multi-agent-based controller and energy management system design is proposed for the DC microgrid in the study. The designed microgrid is composed of a photovoltaic system consisting of 30 series-connected PV modules, a wind turbine, a synchronous generator, a battery-based energy storage system, critical and non-critical DC loads, the grid and the control system. The microgrid is controlled by the designed multi-agent-based controller. The proposed multi-agent-based controller has a distributed generation agent, battery agent, load agent and grid agent. The roles of each agent and communication among the agents are designed properly and coordinated to achieve control goals, which basically are the DC bus voltage quality and system stability. The designed microgrid and proposed multi-agent-based controller are tested for two different scenarios, and the performance of the controller has been verified with MATLAB/Simulink simulations. The simulation results show that the proposed controller provides constant DC voltage for any operation condition. Additionally, the system stability is ensured with the proposed controller for variable renewable generation and variable load conditions.

Design of an efficient energy management system for renewables based wireless electric vehicle charging station

AbstractThe radical decline in climatic conditions coerces the world to take steps progressive toward implementing green energy technologies in all facets. The electric vehicle is one such huge leap toward providing a pollution‐free environment to the future generation, but the charging techniques in most cases utilize the power that is obtained using thermal power stations, and so on. This concludes it to be not completely green energy, therefore in this paper, considered two far‐reaching sources viz. photovoltaic and wind energy storage systems. The sources are also supported with the presence of an additional storage unit of battery energy storage system (BESS) and the overall system is managed by energy management system (EMS) that is designed using a simple state flow algorithm instead of complex machine learning methods. The electric vehicle (EV) is charged using wireless power transfer technology. The model is built using MATLAB—Simulink software and the simulation results are validated against the physical calculation carried out over the equivalent circuit model reduction method. The values were taken from the TATA Nexon EV for consideration and the output values are 400 V and 15 A to be provided for the charging of the EV at the receiving end of the wireless charging.

Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms

Rural areas have increased directly with the growth of the world population. Although, isolated rural areas do not have the energy infrastructure to supply essential services such as electricity. In Ecuador, as in the rest of the world, these areas of the rural population usually present access problems due to the topography of the place. This makes the construction of power lines to connect them to the Interconnected National Energy System unfeasible. In this regard, isolated microgrids have emerged as a great solution to cover the energy demands in these locations. However, an optimal implementation of isolated microgrids depends on several factors, such as geographical location, weather conditions, sizing, load demand, operating costs, and social impacts. Therefore, this study proposes the design of a new energy management system (EMS) for isolated microgrids comprising a photovoltaic system, diesel generator, and battery energy storage system (ESS). Since fuzzy logic control (FLC) has proven to be a powerful tool for dealing with the nonlinearities of a microgrid and the application of fuzzy-based EMS for isolated microgrids is rarely reported in the literature, this study proposes the application of an FLC for the EMS's design of an isolated microgrid. The proposed fuzzy-based EMS uses generation and demand forecasts, enhances the operating time of diesel generators (DLG), and takes advantage of the available solar resource to supply the energy required by a community while preserving the ESS lifetime. An adjustment of the FLC parameters by Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms is performed to improve the behavior of the proposed EMS. Furthermore, a battery degradation model is applied to estimate the ESS State of Health (SOH). To highlight the advantages of the proposed approach, a case study in a specific community in Ecuador is presented. In this location, the proposed EMS is compared with an EMS without parameter adjustment developed in previous work, demonstrating improved performance in DLG limits, preserving the battery lifespan by controlling the battery SOC limits more efficiently, and minimizing the microgrid operating costs by maximizing the use of photovoltaic power (i.e., reducing the wasted photovoltaic energy). The results provide evidence that the EMS adjusted with the PSO algorithm presents an enhanced behavior than the one adjusted by the CS algorithm. Finally, the fuzzy-EMS is validated using Matlab® and Hardware-in-the-Loop Typhoon HIL-402 device. Results demonstrate that the proposed approach limits DLG usage, make the most of the available solar resources, and extends the battery life by controlling overcharges and deep discharges.