Energy Management System Research Articles

Droop-based Control Strategy to operate a DC Nanogrid under the Net Zero Energy Concept

This work presents a droop-based control strategy for a dc nanogrid designed to operate under the Net Zero Energy (NZE) concept, enabling seamless transitions between grid-connected and islanded modes. The nanogrid integrates photovoltaic (PV) generation, a fast electric vehicle charging station (EVSE), and a battery energy storage system (BESS) into a $700~$V dc bus interfacing with the ac grid via a bidirectional three-phase AC-DC converter. An isolated DC-DC converter establishes a secondary $48~$V dc bus for powering dc loads. An energy management system (EMS) defines an optimal day-ahead power dispatch for the BESS to meet NZE objectives. At the primary control level, a modified power-to-voltage droop strategy ensures accurate power tracking and parallel operation with the AC-DC converter. This approach enables continuous voltage regulation of the main bus under instantaneous power imbalances caused by schedule deviations or operational mode transitions. Additionally, the proposed strategy eliminates the need for secondary control or high-bandwidth communication. The system is validated through hardware-in-the-loop (HIL) simulations using the Typhoon HIL 604 platform, with the control strategy implemented on a DSP. Real-time simulation results confirm stable performance under various operating conditions, including transitions between modes.

Cognitive fuzzy logic-integrated energy management for self-sustaining hybrid renewable microgrids

The Sustainable Energy Resource integrated with Energy Storage System is deployed inside a microgrid, using a power management method to effectively regulate energy consumption during peak demand. Demand-based energy management measures, such as distributing load and stalling appliance usage amid peak hours are executed. An Integrated Energy Management System (EMS) was proposed employing fuzzy logic as a solution to manage the energy needs of loads in this work. The system effectively used a combination of the hybrid utility grid, photovoltaic (PV), wind, and battery to optimise the utilisation of renewable energy resources for load supply. The implementation of an effective management system is intended to control the energy production of a novel hybrid electrical power system by changes in load. The EMS concomitantly considers power demand, renewable power, and State of Charge (SoC) through the incorporation of fuzzy logic control. Cost analysis employing three optimisation techniques like Firefly, PSO and Genetic Algorithm for the equivalent load profile and sources also conducted. Fuzzy EMS enhances energy management by 41.40% LCOE in comparison to the Firefly Algorithm. It decreases expenses by 24.09% more effectively than the PSO Algorithm. In comparison to the Genetic Algorithm, the Fuzzy EMS demonstrates a 45.02% reduction in LCOE, hence establishing its capacity to provide a more economical energy solution. This technique considers security constraints and makes an intelligent choice of energy sources based on grid electricity costs. Maximising system resilience and making the most effective feasible utilisation of green energy sources are the primary goals.

Deep learning approaches for robust prediction of large-scale renewable energy generation: A comprehensive comparative study from a national context

Precise forecasting of renewable energy generation is crucial for ensuring grid stability and enhancing the efficiency of energy management systems. This research develops and rigorously evaluates a range of deep learning models—such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidirectional LSTM (BiLSTM) architectures—for predicting solar, wind, and total renewable energy production at a national scale. These models are systematically benchmarked against traditional machine learning approaches and gradient boosting methods to determine their predictive capabilities. The findings demonstrate that deep learning models incorporating memory mechanisms consistently surpass conventional methods, with BiLSTM standing out as the most precise and dependable model. Furthermore, the study investigates fully connected artificial neural networks (ANNs) and ConvLSTM2D models, reinforcing the advantages of memory-based architectures in modeling temporal relationships. By introducing a robust deep learning framework for large-scale renewable energy forecasting, this research represents a considerable leap forward compared to traditional machine learning techniques. The results highlight the transformative potential of deep learning in improving forecasting accuracy, thereby facilitating more effective energy planning and the smooth integration of renewable energy into national power grids.

Smart Energy Efficiency: Energy Management System in Smart Buildings based on Voting Ensemble and battery bank

Energy management in smart buildings requires strategies to improve power consumption, ensuring reliability and cost reduction. This thesis addresses the solution of the problem by implementing a predictive model based on an artificial intelligence technique working in conjunction with a proposed battery bank in order to better store energy and mitigate the high peaks of electricity demand in different periods as well as in atypical scenarios. The research addresses two aspects: accuracy in consumption prediction, and energy management, the latter including energy storage efficiency and mitigating high peak power consumption. The proposal aims for the expected results to reduce electricity consumption by 3% to 7%, validating the viability of this scalable and sustainable solution to improve energy efficiency in smart infrastructures.

Empirical study of decentralized group-based battery energy management (DBEM) for PV-battery nanogrid systems in islanded mode

Abstract Conventional battery management systems struggle to optimize the control of battery fleets with varying capacities and states of charge, resulting in reduced lifespan and inefficient renewable energy utilization, especially under dynamic loading and generation conditions in nanogrids. This paper proposes a Decentralized Group-Based Battery Energy Management (DBEM) system to address these challenges. The DBEM system was empirically validated using a prototype islanded nanogrid at Kasetsart University Sriracha Campus, Thailand, featuring three systems, each equipped with a 3.25 kWp photovoltaic array and 1200 Ah, 48 V battery groups comprising lead-acid and lithium-ion types. DBEM organizes batteries with similar characteristics into groups managed by a centralized controller, which optimizes charging and discharging processes in real-time. Empirical results demonstrate that DBEM significantly improves energy efficiency and prolongs battery lifespan, highlighting its potential to improve the performance of islanded nanogrid PV-battery systems.

Development of an IoT-Enabled Smart Electricity Meter for Real-Time Energy Monitoring and Efficiency

Smart meters play an important role in energy management systems as they provide essential parameters for real-time monitoring, protection, and control that enable informed decisions for the end-users and the utility grid. However, available systems are high-cost solutions with different hardware and software, which provide limited measuring parameters with certain accuracy. This work aims to develop and implement an innovative smart electricity meter (SEM) system that surpasses conventional designs by incorporating advanced features like noninvasive sensors, manual signal calibration, and flexible communication modes. The developed SEM supports real-time data transmission via IoT and provides superior accuracy in measuring harmonics and frequency, addressing key challenges in energy monitoring. This work contributes to real-time energy monitoring and energy management systems in residential and industrial applications.

GAP-ANALYSIS OF ENERGY MONITORING PROCESSES STRATEGIC PLANNING AT THE MUNICIPAL FACILITIES

The reliable energy management in energy management systems should be based on accurate, complete, authentic and actual data on energy consumption and greenhouse gas emissions provided by energy monitoring systems. Errors during the implementation and use of energy monitoring systems will lead to ineffective energy management and will not allow achieving the stated goals. The article presents the results of a gap-analysis of regulatory support that affects the effectiveness of the implementation and long-term application of energy monitoring systems in the municipalities and proposes measures to bridge the gaps. The gap-analysis results illustrate the key gaps between current regulatory standards and the goals of implementing energy monitoring systems. The article contains recommendations for improving the regulatory framework to support energy efficiency and sustainable development of communities. Thanks to its systemic approach, the article can be useful for scientists and professionals interested in the development of sustainable energy practices at the local level, and serves as a basis for further research in this area.

EXPERIENCE IN CREATING AND OPERATING ENERGY MONITORING SYSTEMS AT MUNICIPAL FACILITIES

The urgency of creating a national energy monitoring system in Ukraine is driven by the need for rational use of fuel and energy resources against the backdrop of current challenges, including the energy crisis caused by the military aggression of the Russian Federation. The introduction of such a system will ensure continuous monitoring and analysis of energy consumption at all levels: from government agencies to enterprises, institutions and organisations. The use of modern digital technologies to monitor the use of fuel and energy resources will help to promptly identify inefficient use of resources, reduce costs and improve environmental performance. In the context of Ukraine's integration into the European Union and achievement of sustainable development goals, a national energy monitoring system can become an important tool for fulfilling international commitments in the field of energy efficiency and sustainable development. Therefore, the creation of such a system is critical for ensuring the country's energy independence and sustainability. Object of research: energy management and energy monitoring systems, organisational and technical elements of the systems. Subject of the study: processes and mechanisms of the energy monitoring system, its impact on energy efficiency, energy security and the possibility of forming a national energy monitoring system. Research methods: analytical methods, questionnaire method. The practical significance of the results lies in the development of a national system for monitoring the efficiency of fuel and energy resources, the implementation of which will provide institutions and local governments with the necessary information to analyse resource use and further optimise energy consumption.

Characterization and thermophysical property enhancement of paraffin wax PCM using single and hybrid nanoparticles

Organic phase change materials (PCMs) are extensively used across various applications but are limited by their low thermal conductivity. Enhancing PCMs with thermally conductive nanoparticles presents a promising approach to improve their thermal properties. This study explores the thermophysical characteristics of innovative nanocomposite PCMs incorporating boron nitride (BN) and zinc oxide (ZnO) nanoparticles, the composites are synthesized with a constant nanoparticle concentration of 1.0 wt%. Detailed analyses reveal that BN and ZnO nanoparticles are uniformly dispersed without altering paraffin’s chemical structure. The hybrid nanocomposite HnCPCM 3, containing 0.5% ZnO and 0.5% BN, achieves an optimal latent heat of fusion of 204.24 kJ/kg and the highest thermal conductivity of 0.437 W/m K compared to pure paraffin. Additionally, the HnCPCMs show no phase separation, and improved thermal stability. These findings highlight the significant potential of BN and ZnO hybrid nanocomposites for advancing thermal energy storage and management systems.

Implementation of real-time optimal load scheduling for IoT-based intelligent smart energy management system using new decisive algorithm

This paper presents the implementation of a real-time optimal load scheduling system for an IoT-based intelligent smart energy management system (SEMS) using a novel decisive algorithm. The increasing use of electrical equipment by consumers often leads to a mismatch between demand and supply, posing significant challenges to the energy sector. The proposed system addresses these challenges by optimizing load distribution and enhancing energy efficiency through advanced demand-side management techniques. By leveraging real-time data from IoT sensors and incorporating user preferences, the new algorithm dynamically adjusts power consumption to avoid peak-hour overloads, thus preventing widespread power outages. Experimental results demonstrate that the system effectively reduces overall energy consumption while maintaining user comfort and optimizing costs. The innovative approach of controlled partial load shedding based on consumer priorities ensures a balanced and resilient energy supply. This study highlights the potential of IoT and advanced algorithms in transforming energy management practices and providing sustainable solutions for the future.

Development of an Energy-Efficient Electrical Load Model for Optimizing Electricity Consumption in the Headquarters Building of PT Semen Padang Using the PDCA Approach

The increasing electricity consumption in the public building sector has a significant impact on greenhouse gas (CO2) emissions, which is proportional to the volume of energy used. This study aims to develop an energy-efficient electrical load model to optimize electricity consumption in the headquarters building of PT Semen Padang using the Plan-Do-Check-Action (PDCA) approach. In the Plan stage, problem identification was conducted through a Pareto diagram and root cause analysis using a fishbone diagram. The dominant issue identified was energy waste in the lighting system. The solution implemented included the installation of motion sensor-based switch control devices to regulate lighting use automatically. In the Do phase, the devices were installed and tested, while in the Check phase, the evaluation showed a reduction in electricity consumption from over 85,000 kWh per month to below 80,000 kWh per month, resulting in an electricity cost savings of IDR 31 million per month. The Action phase established standards for inputs, processes, and outputs to ensure the sustainability of the improvements made. This study not only provides financial benefits but also supports the company's Energy Management System policy. With the PDCA approach, improvement processes can be carried out systematically and continuously, making a tangible contribution to energy efficiency in the public building sector.

Deep learning for sensible cooling and heating loads associated with solar panels of residential buildings in arid climate

PurposeThis study aims to develop accurate prediction models for heating and cooling demands in buildings equipped with solar panels. By integrating renewable energy technologies, the goal is to design nearly energy-neutral buildings that significantly reduce energy consumption and enhance overall efficiency.Design/methodology/approachThe research utilizes deep learning models to address variables in building design, an area that previous studies have not fully explored. A dataset from arid climate regions was used to train and test two deep learning models to predict building energy consumption and solar energy output. The evaluation focused on how well the models predicted heating and cooling needs, as well as the amount of energy solar panels would need to generate in order to meet these demands. This approach represents an advancement over previous methodologies by integrating deep learning techniques with energy prediction in the context of arid climates, where energy efficiency is a critical concern.FindingsThe deep learning models developed in this study were highly accurate in predicting both heating and cooling requirements and the energy output from solar panels. This suggests that these models can effectively support the design of energy-efficient buildings, ensuring that the solar panels provide enough energy to cover the building’s needs.Originality/valueThis study introduces a novel method for predicting solar panel performance by integrating building characteristics with energy consumption, moving beyond traditional reliance on environmental factors. It optimizes energy management systems for arid climates, enhancing accuracy and applicability. The use of deep learning models with optimization techniques ensures precise and flexible predictions, providing a holistic solution for energy-efficient building design. The findings provide useful insights for architects and builders looking to create nearly zero-energy buildings, advancing the field of green building technologies.

Terminal group engineering of Ti3C2Tx MXene on thermal emitter performance

Ti3C2Tx MXene has emerged as a promising material for diverse nanophotonics applications. In this study, we investigate how Ti3C2Tx MXene terminal groups (–F, –O–, –OH) influence the performance of a planar thermal emitter with a VO2/SiO2/Ti3C2Tx MXene structure. By examining four variants of Ti3C2Tx MXene across the 2–20 µm spectral range, we demonstrate that the hysteresis loop threshold temperature remains constant for all MXene types due to the VO2 phase change material. The average differential emissivity (Δε) between the semiconductor and metallic states of VO2 varies significantly with terminal group composition. The VO2/SiO2/Ti3C2F2 structure exhibits the highest differential emissivity of Δε = 0.42, while VO2/SiO2/Ti3C2(OH)2 shows the lowest of Δε = 0.33. The remaining structures; VO2/SiO2/Ti3C2 and VO2/SiO2/Ti3C2O2, demonstrate intermediate differential emissivity values of Δε = 0.41 and 0.38, respectively. These findings establish a foundation for controlling emissivity in applications such as energy harvesting, thermophotovoltaics, and radiative cooling systems. The ability to tailor thermal emission through MXene terminal group engineering presents opportunities for designing tunable photonic devices with precise thermal control capabilities for the next-generation of energy management systems.

Explaining the role of reducing energy consumption in strengthening healthy cities in smart cities

Different dimensions and interventions can lead to the outcome of being a healthy, smart city. One of them is the smart management of energy, via a smart infrastructure—the system component that certainly should be considered in a smart city. Improving the quality of life is one of the big aims of smart cities. For this reason, the management of energy is one of the factors that directly and indirectly affect it and public health. The present study is a descriptive-analytical type, and through a meta-synthesis methodology, the analysis explores past studies on decreasing energy consumption and moving toward healthy and smart city initiatives. The conclusion provides actionable recommendations for realizing this vision. Studies indicate that achieving smart, healthier cities requires the integration of smart grids and renewable energy, the implementation of data-driven energy management systems, and the promotion of citizen engagement and behavioral change.

Economically Effective and Environment Friendly Smart Energy Management System for Textile Industries

Economically Effective and Environment Friendly Smart Energy Management System for Textile Industries

Optimal energy management for multi-energy microgrids using hybrid solutions to address renewable energy source uncertainty

Research in industrial grid energy management is essential due to increasing energy demands, rising costs, and the integration of renewable sources. Efficient energy management can reduce operational costs, enhance grid stability, and optimize resource allocation. Addressing these challenges requires advanced techniques to balance supply, demand, and storage in dynamic industrial settings. In this study, a new hybrid algorithm is used for system modelling and low-cost, optimal management of Micro Grid (MG) networked systems. Optimizing micro-sources to reduce electricity production costs through hourly, day-ahead, and real-time scheduling was the process’ primary goal.This research proposes a Quadratic Interpolation and New Local Search for Greylag Goose Optimisation (QI-NLS-G2O) and Gaussian Radius Zone Perceptron Net (GRZPNet) technique based energy management scheme for Multi-Energy Microgrids (MEMG) to help the Energy Management System (EMS) formulate optimal dispatching strategies under Renewable Energy Source (RES) uncertainty. To precisely represent the MEMG’s operational state, the scheduling process incorporates an off-design performance model for energy conversion devices. Utilising MG inputs such as Wind Turbines (WT), Photovoltaic arrays (PV), and battery storage with associated cost functions, the GRZPNet learning phase based on QI-NLS-G2O is utilised to forecast load demand. The QI-NLS-G2O optimises the MG configuration according to the load demand. The MATLAB/Simulink working platform is used to implement the suggested hybrid technique, which is then contrasted with alternative approaches to solving problems.The proposed model significantly improves dispatching accuracy, reducing RES uncertainty impacts by approximately 15% and enhancing MEMG performance efficiency by up to 20% in simulations.

Steady-state data-driven dynamic stability assessment in the Korean power system

The extensive research on dynamic security assessment stability prediction has focused on data preprocessing techniques to improve accuracy because it was assumed that high-resolution postfault data exist. For practical users, the acquisition and application of high-resolution measurement data present significant challenges. Installing phasor measurement units on all power system nodes is deemed impractical due to high costs. In this work, we aimed to develop a rotor angle stability prediction model using steady-state data that can be easily generated from the current energy management system. Note that the steady-state measurement data refer to a pre-contingency operation condition characterized by real and reactive loads, generation levels, flows, as well as voltages and angles. The proposed framework comprises three stages: it finds physical meaning from the extended equal-area criterion to move away from the black-box approach, proposes a feature data extraction strategy to reduce the dimensionality of the input space in the support vector machine, and partition time-series power flow data by month to consider system topology changes. By utilizing 5-min-interval power flow data, unstable cases are determined, and two main feature data are extracted to train the support vector machine. The obtained results showed the effectiveness of the proposed framework in responding to a critical line fault event in real time.

A Comparative Study of Energy Management Strategies for Battery-Ultracapacitor Electric Vehicles Based on Different Deep Reinforcement Learning Methods

An efficient energy management strategy (EMS) is crucial for the energy-saving and emission-reduction effects of electric vehicles. Research on deep reinforcement learning (DRL)-driven energy management systems (EMSs) has made significant strides in the global automotive industry. However, most scholars study only the impact of a single DRL algorithm on EMS performance, ignoring the potential improvement in optimization objectives that different DRL algorithms can offer under the same benchmark. This paper focuses on the control strategy of hybrid energy storage systems (HESSs) comprising lithium-ion batteries and ultracapacitors. Firstly, an equivalent model of the HESS is established based on dynamic experiments. Secondly, a regulated decision-making framework is constructed by uniformly setting the action space, state space, reward function, and hyperparameters of the agent for different DRL algorithms. To compare the control performances of the HESS under various EMSs, the regulation properties are analyzed with the standard driving cycle condition. Finally, the simulation results indicate that the EMS powered by a deep Q network (DQN) markedly diminishes the detrimental impact of peak current on the battery. Furthermore, the EMS based on a deep deterministic policy gradient (DDPG) reduces energy loss by 28.3%, and the economic efficiency of the EMS based on dynamic programming (DP) is improved to 0.7%.

Comprehensive Review on Challenges of Integration of Renewable Energy Systems into Microgrid

The integration of renewable energy systems (RES) into microgrids faces challenges from technical, economic, and socio-environmental perspectives. Despite their potential to address energy access and climate change challenges, RES-based microgrids face significant barriers, including technical complexities, economic constraints, socio-cultural resistance, regulatory inadequacies, and environmental concerns. Some of the technical issues, like energy intermittency and lack of compatibility with other energy sources, are managed by the energy management systems (EMS) and the integrated battery systems. These economic barriers include high capital investment and unpredictable revenue sources, which are addressable through chosen microgrid architecture, flexible payment structures, and tariffs. Community opposition and lack of local knowledge are overcome by employing socio-cultural mitigation measures that pertain to partaking in planning processes and developing training programs. These gaps are addressed by the use of standardized regulatory and policy structures, as well as streamlined permitting procedures, while environmental issues are managed by the application of life cycle assessment (LCA)-based solutions and environmentally sustainable materials. Furthermore, the paper addresses more recent developments, including energy management by artificial intelligence (AI), peer-to-peer (P2P) energy trading, and microgrids with an emphasis on improvement and prospects. Finally, the policy implications are presented, stressing the need for systemic solutions to address the observed tendencies. This paper systematically reviews the multifaceted challenges of integrating RES into microgrids. It presents innovative solutions, including AI-driven energy management, peer-to-peer energy trading, modular microgrid designs, and policy frameworks that enhance efficiency, reliability, and sustainability for a scalable energy transition. This review provides a diverse view to enhance the future growth of microgrids and provides several insights for the stakeholders related to the future development of microgrid technology for making energy transition scalable and sustainable.

Forecasting Residential Energy Consumption with the Use of Long Short-Term Memory Recurrent Neural Networks

In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10−6 for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model’s accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices.