Energy Management System Research Articles

Load management design and techno-economic analysis for an islanded hybrid Pv-Teg microgrid

Integration of various renewable energy sources (RES) is one of the critical factors behind microgrid (MG) capacity development and MG expansion. Distributed energy resources with and without RES, electrical loads, controllers, and storage units constitute the essential components of MGs. On the other hand, with the addition of different power generation sources, load prediction on the user side of the MG becomes an important issue. Demand response solutions and changing end-user behavior are the other sources of uncertainty in the performance of MGs. Therefore, matching supply and demand is a very important issue that must be done immediately at every moment of the operation. A load management system (LMS) in a community MG is essential to ensure the system’s adaptability. Fuzzy logic (FL) controllers and energy management systems will ensure optimum use of energy resources and efficient operation of MG and reduce energy waste. In this study, an 85 kW photovoltaic (PV) and thermoelectric generator (TEG) hybrid MG system (PVTEG-MG), which can operate as an island grid in all regions of the World with hot water resources, has been designed for the first time and its techno-economic analysis has been made. The state of charge (SOC) for the proposed PVTEG-MG remained stable at 40% charge state by adding charge management. The average value of the Levelized Cost of Energy (LCOE) is $0.456/kWh for PV, $0.456/kWh for TEG, and 0.399/kWh over the total energy produced, 219,000 kWh for the PV and 493,650 kWh for the TEG. Overall, this PVTEG-MG system is a promising solution for hot water regions that can be used as an island grid to reduce electricity costs, increase efficiency and reliability, and provide a more sustainable energy source.

Optimizing renewable energy systems through artificial intelligence: Review and future prospects

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

As a case study on sustainable energy use in educational institutions, this study examines the design and integration of a solar–hydrogen storage system within the energy management framework of Kangwon National University’s Samcheok Campus. This paper provides an extensive analysis of the architecture and integrated design of such a system, which is necessary given the increasing focus on renewable energy sources and the requirement for effective energy management. This study starts with a survey of the literature on hydrogen storage techniques, solar energy storage technologies, and current university energy management systems. In order to pinpoint areas in need of improvement and chances for progress, it also looks at earlier research on solar–hydrogen storage systems. This study’s methodology describes the system architecture, which includes fuel cell integration, electrolysis for hydrogen production, solar energy harvesting, hydrogen storage, and an energy management system customized for the needs of the university. This research explores the energy consumption characteristics of the Samcheok Campus of Kangwon National University and provides recommendations for the scalability and scale of the suggested system by designing three architecture systems of microgrids with EMS Optimization for solar–hydrogen, hybrid solar–hydrogen, and energy storage. To guarantee effective and safe functioning, control strategies and safety considerations are also covered. Prototype creation, testing, and validation are all part of the implementation process, which ends with a thorough case study of the solar–hydrogen storage system’s integration into the university’s energy grid. The effectiveness of the system, its effect on campus energy consumption patterns, its financial sustainability, and comparisons with conventional energy management systems are all assessed in the findings and discussion section. Problems that arise during implementation are addressed along with suggested fixes, and directions for further research—such as scalability issues and technology developments—are indicated. This study sheds important light on the viability and efficiency of solar–hydrogen storage systems in academic environments, particularly with regard to accomplishing sustainable energy objectives.

A centralized EMPC scheme for PV-powered alkaline electrolyzer

Photovoltaic (PV)-powered alkaline electrolyzer system (PVPAES) is an advanced technique to convert the off-grid and intermittent PV-based solar energy into storable and transportable electrolyzer-based hydrogen energy with zero carbon emissions. However, it is difficult to realize the coordinated control of the off-grid PV module and the alkaline electrolyzer, due to the multiple timescale dynamics. To address this challenge, the PVPAES is decomposed into the slow part and fast part based on the dynamic time scale. Exploiting the decomposed subsystems, the slow one is assumed to be managed well by the auxiliary controller. For the fast one, a centralized economic model predictive control (CEMPC) scheme is constituted. This CEMPC integrates the energy management system and local feedback control into a single optimal control framework. A mathematical model of the PVPAES is established, on the basis of which the CEMPC directly adopts the economic indices as the cost function to realize the flexible power point tracking, power supply-demand balance, and dynamic economic optimization. Moreover, the inherent strong nonlinearity of PVPAES results in the nonconvex mixed-integer nonlinear programming optimization problem in the CEMPC. The exhaustive search algorithm utilizing finite converter switching states is adopted to achieve the global economic optimum. The effectiveness of the proposed CEMPC controller is illustrated through simulations under varying irradiance conditions.

Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision

Active building energy management can facilitate the development of low-carbon buildings and support flexible operations of future smart cities, thanks to advancements in digitalization To fully leverage these benefits, it is essential to integrate diverse objectives and engage multiple stakeholders. However, a gap remains in comprehensive field insights into emission reduction, flexibility provision, and user impacts. This study examined how a real occupied building, with all its energy assets, could function as an emission-aware prosumer with flexible energy consumption. An existing building energy management system was enhanced by integrating a model predictive control strategy. The setup reduced equivalent carbon emissions from electricity imports and provided flexibility to the energy system. The experimental results indicate an emission reduction of 12.5% compared to a rule-based controller that maximized PV self-consumption. In addition, a minimal flexibility provision experiment was demonstrated with a locally emulated distribution system operator. The results suggest that flexibility was provided without the risk of rebound effects, as flexibility was quantified and communicated to the system operator in advance. This study demonstrates the feasibility of low-carbon buildings and their support for flexible energy systems, while also identifying and discussing practical scalability challenges.

Quantum support vector machine for forecasting house energy consumption: a comparative study with deep learning models

The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model’s exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms.

Improving wind power forecast accuracy for optimal hybrid system energy management

Abstract Due to its renewable and sustainable features, the wind energy is growing up around the word. However, the wind speed fluctuation induces the intermittent character of the generated wind power. Thus, wind power estimation, through wind speed forecasting, is very inherent to ensure an effective power scheduling. Four wind speed predictors based on deep learning networks and optimization algorithms, were developed. The designed topologies are the multilayer perceptron neural network, the long short-term memory network, the convolutional short-term memory network and the bidirectional short-term neural network coupled with the Bayesian optimization. The models performance was evaluated through evaluation indicators mainly, the root mean squared error, the mean absolute error and the mean absolute percentage. Based on the simulation results, all of them show a considerable prediction results. Moreover, the combination of the the long short-term memory network and the optimization algorithm is more robust on wind speed forecasting with a mean absolute error equal to 0.23 m/s. The estimated wind power was investigated for an optimal Wind/PV/Battery/Diesel energy management. The handling approach lies on the continuity of the load supply through the renewable sources as priority, the batteries on second order and finally the diesels. The proposed management strategy respects the designed criteria with satisfactory contribution percentage of the renewable sources equal to 71%.

Impact of Engine Inertia on P2 Mild HEV Fuel Consumption

The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively.

Analysis of partial load loss of the PCS and internal storage battery loss in residential PV power generation and storage battery systems

AbstractThis study aims to quantify the amount of loss due to partial load of power conditioning system (PCS) and internal loss of storage battery in residential photovoltaic (PV) power generation and storage battery system by using home energy management system (HEMS) data. After identifying the configuration of the PV power generation and storage battery system, HEMS data on PV power generation, household demand, storage battery charging, storage battery discharging, purchased power and sold power were extracted and analyzed according to the research purpose. As a result, the loss due to partial load of the PCS and the average internal loss of the storage battery were quantified. In addition, it was confirmed that a more realistic system simulation could be performed by considering the impact of the partial load of the PCS and the average internal loss of the storage battery.

A privacy-preserving distributed energy management framework based on vertical federated learning-based smart data cleaning for smart home electricity data

Nonintrusive load monitoring (NILM) is a part of home energy management systems ((H)EMSs), which can advance the systems to leverage and use gathered electricity data (load data) to achieve cost-effective load monitoring for efficient (residential) demand-side management (DSM). Load data monitored by existing (H)EMSs, whose load monitoring is based on intrusive load monitoring, can be used as a preliminary stage for NILM to be accelerated in energy management for the improvement of its practicality. However, they can be polluted. For example, power-consumption data gathered by appliance-level smart plugs may have an incorrect appliance label (a mislabeled appliance ID). Polluted data must be preprocessed through a data cleaning process before they are leveraged and used. Data-cleaning approaches that improve data quality should be developed considering a decentralized paradigm, because a centralized data-cleaning paradigm cannot be applied to future edge-based IoT applications. Additionally, preventing data leakage is paramount in data management for numerous field applications. This study develops a privacy-preserving distributed energy management framework based on vertical federated learning for smart data cleaning as a demonstrative application of smart home electricity data toward distributed NILM. The framework developed in this study with the advent of AI methodology can achieve smart data cleaning for further distributed NILM to be accelerated for its practical applications; its feasibility and effectiveness have been verified experimentally.

Advances in emerging digital technologies for energy efficiency and energy integration in smart cities

Advances and fast development in emerging digital technologies trigger the next generation revolution in energy areas and smart cities, while roles and mechanisms of digital technologies for smart and sustainable transition is unclear. Furthermore, energy flexibility enhancement in intermittent renewable-stochastic demand power management and survival capability in minimizing frequency of power outrage are still not clear, when suffering from climate change and extreme events with emerging digital technologies. In this study, advances in emerging digital technologies have been systematically and comprehensively reviewed, in terms of current development status and mechanisms for energy efficiency and energy integration in energy-efficient systems. Afterwards, roles of energy digitalization technologies are provided for high-efficiency, low-carbon and intelligent building energy systems, including artificial intelligence for dynamic performance predictions, advanced model predictive controls and optimisations of nonlinear systems (e.g., PVs, heat pumps, heat recovery systems and multi-energy storages). Furthermore, digital twin-based building energy digitalization technologies are applied for 3D modeling, monitoring, real-time visualization and virtual reality interaction. Frontier multi-agent based distributed energy systems are comprehensively proposed with multi-agent energy management, including RE-battery-building-EV, RE-building-hydrogen vehicles, and both centralised and distributed energy management systems. Considering the multi-energy system capability for power management (intermittent renewable energy and energy demands) and survival capability when suffering from high-impact and low-probability events, both energy flexibility and energy resilience with energy digitalization technologies are interconnected, for climate change adaption and internet of energy. This study provides a systematic and comprehensive review on emerging digital technologies for energy efficiency and energy integration in smart cities, providing guidelines on sustainable and smart transitions with multi-agent based distributed energy management and energy digitalization technologies.

Application of Smart Energy Management Systems in Green Buildings

Application of Smart Energy Management Systems in Green Buildings

IoT-integrated smart energy management system with enhanced ANN controller for small-scale microgrid

IoT-integrated smart energy management system with enhanced ANN controller for small-scale microgrid

A distributed algorithm with network‐independent step‐size and event‐triggered mechanism for economic dispatch problem

AbstractThe economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network‐independent step sizes and an event‐triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network‐independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency.

District Energy Management Systems: Key Data Points for System Integration and Related Challenges: Lessons Learned from Experts in Germany

District energy management systems (DEMS) offer the capability to harness the energy potential of districts. Currently, there is a lack of research describing the necessary underlying information systems. An overview of lessons learned is presented from a series of workshops with experts from industry, research, and further roles, predominately from Germany. Seven workshops discussing challenges within digitalization, data requirements, district energy system planning, common elements in district energy systems are conducted, ensuring long‐term operation and transferability, and validation of prior findings. Based on these discussions, insights into topics such as the key data and its requirements, typical distinctions in DEMS, their infrastructure, considerations in the information modeling, as well as challenges in data availability, are offered. These findings are mapped to a data value pipeline, illustrating key resources and tools needed to plan and operate DEMS. As demonstrated in this article, the requirements for DEMS highlight their complexity. Distinct categorizations, such as the ownership or heterogeneity in building and technologies, form the landscape of DEMS. The need for standardized, interoperable systems is often a contrast to the uniqueness of individual systems and their needs. Further adoption of the described concepts necessitates sustainable business models.

Standby Power Reduction of Home Appliance by the i-HEMS System Using Supervised Learning Techniques

Electricity consumption in homes is on the rise due to the increasing prevalence of home appliances and longer hours spent indoors. Home energy management systems (HEMSs) are emerging as a solution to reduce electricity consumption and efficiently manage power usage at home. In the past, numerous studies have been conducted on the management of electricity production and consumption through solar power. However, there are limited human-centered studies focusing on the user’s lifestyle. In this study, we propose an Intelligent Home Energy Management System (i-HEMS) and evaluate its energy-saving effectiveness through a demonstration in a standard house in Korea. The system utilizes an IoT environment, PID sensing, and behavioral pattern algorithms. We developed algorithms based on power usage monitoring data of home appliances and human body detection. These algorithms are used as the primary scheduling algorithm and a secondary algorithm for backup purposes. We explored the deep connection between power usage, environmental sensor data, and input schedule data based on Long Short-Term Memory network (LSTM) and developed an occupancy prediction algorithm. We analyzed the use of common home appliances (TV, computer, water purifier, microwave, washing machine, etc.) in a standard house and the power consumption reduction by the i-HEMS system. Through a total of six days of empirical experiments, before implementing i-HEMS, home appliances consumed 13,062 Wh. With i-HEMS, the total consumption was reduced to 10,434 Wh (a 20% reduction), with 9060 Wh attributed to home appliances and 1374 Wh to i-HEMS operation.

Machine learning based on reinforcement learning for smart grids: Predictive analytics in renewable energy management

This paper introduces a groundbreaking approach to energy management in smart cities, leveraging deep reinforcement learning (DRL) and secured blockchain technology. The escalating demand for energy in urban areas necessitates intelligent solutions that optimize energy usage while ensuring data security and integrity. Our proposed technique integrates DRL algorithms with blockchain technology to achieve optimal energy management while addressing security concerns. The core innovation lies in the development of a DRL framework that dynamically adapts to changing energy demands and environmental factors. By continuously learning and optimizing energy allocation strategies, our approach enhances resource utilization efficiency and reduces wastage, contributing to sustainable smart city initiatives. Additionally, the incorporation of blockchain technology ensures data immutability, transparency, and secure transactions within the energy management system. We validate the effectiveness of our method through extensive simulations and real-world experiments, demonstrating significant improvements in energy efficiency, cost savings, and security compared to traditional approaches. Our research paves the way for future advancements in smart city infrastructure, offering a scalable and robust solution for optimal energy management with heightened security measures.

INTEGRATED PIEZOELECTRIC ENERGY HARVESTING: AUTOMATING TOLL COLLECTION AND HIGHWAY STREET LIGHTS

This research paper presents a comprehensive study on the integration of piezoelectric energy harvesting technology for the dual purpose of automatic toll collection and highway street lighting. The project proposes a novel system architecture that leverages the piezoelectric effect to convert mechanical stress from passing vehicles into electrical energy, thereby powering both toll collection mechanisms and street lights. The system incorporates RFID technology for seamless vehicle identification and toll deduction, along with an intelligent control system for efficient energy management. The paper discusses the design, implementation, advantages, challenges, and considerations of the proposed system, highlighting its potential for sustainable infrastructure development and energy efficiency.

METHODS AND TOOLS FOR IN-DEPTH ANALYSIS OF ENERGY CHARACTERISTICS AND MANAGEMENT OF ENERGY CONSUMPTION IN EDUCATIONAL INSTITUTIONS

A factor of the state's national security is reducing energy dependence and taking measures to increase energy efficiency, especially in the budgetary sphere, because about 90% of such buildings do not meet modern energy efficiency requirements. Creating a reliable energy consumption management system, choosing measures to reduce consumption and diversifying energy sources is a difficult task that requires consideration of building energy consumption processes as a complex problem with the use of modern tools of the energy management system, modeling, and the use of modern approaches to automation and measurements. The object of research is energy consumption processes in educational institutions. The subject of the research is energy characteristics and methods of energy consumption management in educational institutions. Research methods. analytical methods, system analysis, synthesis, modeling, systematization, method of comparative and structural analysis. One of the tasks of this study is to review the capabilities of software products that can be used by energy managers of educational institutions to monitor, analyze and forecast energy consumption, as well as to create reports. Also in the work, an analysis of the state of affairs regarding energy consumption management was carried out on the example of KPI named after Igor Sikorskyi; proposals for the development of the automated energy monitoring system were provided, in particular, the architecture of the web application was developed taking into account the support of existing databases to improve the energy monitoring process.

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.