Traditional Energy Management System Research Articles

The Design and Implementation of an Intelligent Carbon Data Management Platform for Digital Twin Industrial Parks

In the face of increasing environmental challenges, carbon emissions from industrial parks have become a global focal point, particularly as electricity consumption serves as a major source of carbon emissions that requires effective management. Despite proactive efforts by governments and industry stakeholders to transition industrial parks toward cleaner production methods, traditional energy management systems exhibit significant limitations in data collection, real-time monitoring, and intelligent analysis, making it difficult to meet the urgent demands for carbon reduction. To address these challenges, this study proposes a carbon data management approach for industrial parks based on digital twin technology and develops an intelligent system that integrates monitoring, environmental surveillance, energy management, and carbon emission monitoring. The system supports efficient energy-saving and carbon-reducing decision making by real-time collection of energy consumption data. By incorporating Building Information Modeling (BIM) and Internet of Things (IoT) technologies, the system facilitates the integration and visualization of multi-source data, significantly enhancing the transparency of carbon data. The results of the carbon reduction validation system demonstrate that the application of this platform and its associated facilities can significantly reduce carbon emissions in the park, providing robust support for the transition of industrial parks toward low-carbon and sustainable development.

Energy scheduling strategy for energy hubs using reinforcement learning approach

The rapid integration of Renewable Energy Sources (RES) into energy systems is critical for achieving a fossil fuel-free future. However, it also introduces significant challenges, such as increased system complexity and uncertainties in energy output. To overcome these challenges, this paper presents a model-free Deep Reinforcement Learning (DRL) method to energy hub scheduling using state-of-the-art algorithms, specifically Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO). Breaking away from traditional energy management systems that typically use discrete action spaces, this approach employs a multi-dimensional, continuous action space, providing a more accurate framework for decision-making. Furthermore, the current study adopts a comprehensive perspective by considering not only energy supply and operational costs, but also the often-overlooked impact of emitted emissions in the objective function. The results yield a Pareto space encompassing various criteria, offering a diverse scheduling framework that balances economic feasibility with environmental sustainability. By presenting a range of optimal solutions, this study contributes to the development of more resilient and sustainable energy systems.

Enhancing Photovoltaic Grid Integration through Generative Adversarial Network-Enhanced Robust Optimization

This paper presents a novel two-stage optimization framework enhanced by deep learning-based robust optimization (GAN-RO) aimed at advancing the integration of photovoltaic (PV) systems into the power grid. Facing the challenge of inherent variability and unpredictability of renewable energy sources, such as solar and wind, traditional energy management systems often struggle with efficiency and grid stability. This research addresses these challenges by implementing a Generative Adversarial Network (GAN) to generate realistic and diverse scenarios of solar energy availability and demand patterns, which are integrated into a robust optimization model to dynamically adjust operational strategies. The proposed GAN-RO framework is demonstrated to significantly enhance grid management by improving several key performance metrics: reducing average energy costs by 20%, lowering carbon emissions by 30%, and increasing system efficiency by 8.5%. Additionally, it has effectively halved the operational downtime from 120 to 60 h annually. The scenario-based analysis further illustrates the framework’s capacity to adapt and optimize under varying conditions, achieving up to 96% system efficiency and demonstrating substantial reductions in energy costs across different scenarios. This study not only underscores the technical advancements in managing renewable energy integration, but also highlights the economic and environmental benefits of utilizing AI-driven optimization techniques. The integration of GAN-generated scenarios with robust optimization represents a significant stride towards developing resilient, efficient, and sustainable energy management systems for the future.

Machine Learning-Driven Energy Harvesting and Storage System Design for IoT Applications in Smart Buildings.

The advent of Internet of Things (IoT) technologies has revolutionized the concept of smart buildings, integrating diverse sensors and devices for enhanced automation and efficiency. Avinash B. Raut, Energy harvesting and storage technologies have emerged as promising solutions to address the power requirements of IoT devices in smart buildings. This abstract provides a comprehensive overview of machine learning-driven energy harvesting and storage system design tailored specifically for IoT applications in smart buildings. Traditional energy management systems often face challenges such as suboptimal energy utilization, limited scalability, and lack of adaptability to dynamic environmental conditions. In contrast, machine learning techniques offer adaptive, data-driven solutions to optimize energy harvesting, storage, and distribution in smart buildings. [1] This paper explores various machine learning algorithms, including supervised learning, reinforcement learning, and deep learning, and their application in optimizing energy harvesting from ambient sources such as solar, kinetic, and thermal energy. Moreover, machine learning enables predictive energy demand modeling by analyzing historical data and environmental factors, thus enhancing the efficiency of energy storage and distribution systems. Real-world case studies and experimental results are presented to demonstrate the effectiveness and potential of machine learning-driven energy management systems in improving energy efficiency, reliability, and autonomy in IoT-enabled smart buildings DOI: https://doi.org/10.52783/tjjpt.v45.i02.6318

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

Considering Saudi Arabia's strategy entails economic diversification and a reduction in dependency on oil, a change towards sustainable and efficient energy management practices is required. Effective energy management is critical for optimising energy consumption, lowering costs, and reducing the environmental footprint. Traditional energy management systems are frequently built on rule-based or deterministic techniques that fail to address the complexity and uncertainty inherent in home energy usage patterns and user behaviours. Fuzzy logic-based solutions have emerged in recent years, holding enormous promise for domestic energy management. Fuzzy logic excels at dealing with imprecise and uncertain input, making it ideal for modelling human-centric systems such as domestic energy management. This study presents a cutting-edge fuzzy-based smart energy management system adapted to Saudi residential buildings. Using fuzzy logic approaches, the system intends to optimize energy usage, reduce peak demand, and improve energy efficiency. It automatically adjusts energy-consuming devices such as air conditioning, lights, and appliances depending on user preferences, occupancy patterns, as well as real-time energy prices using fuzzy decision-making algorithms. The research not only substantiates the success of this strategy through a comparative study, but it also highlights its prospective benefits for Saudi Arabian residential constructions. It also covers the obstacles and potential research possibilities for adopting fuzzy-based smart energy management systems, exposing a promising road towards a more sustainable and safe energy future. The study's findings underscore the superiority of the comparative study alternative, revealing it as the most effective means to evaluate the fuzzy-based system's performance and unique advantages in relation to other established energy management approaches.

Model predictive control of a microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV systems

This paper presents a new energy management system (EMS) based on model predictive control (MPC) for a microgrid with solar photovoltaic (PV) power plants and a quasi-Z-source cascaded H-bridge multilevel inverter that integrates an energy storage system (ES-qZS-CHBMLI). The system comprises three modules, each with a PV power plant, quasi-impedance network, battery energy storage system (BESS), and voltage source inverter (VSI). Traditional EMS methods focus on distributing the power among the BESSs to balance their state of charge (SOC), operating in charging or discharging mode. The proposed MPC-EMS carries out a multi-objective control for an ES-qZS-CHBMLI topology, which allows an optimized BESS power distribution while meeting the system operator requirements. It prioritizes the charge of the BESS with the lowest SOC and the discharge of the BESS with the highest SOC. Thus, both modes can coexist simultaneously, while ensuring decoupled power control. The MPC-EMS proposed herein is compared with a proportional sharing algorithm based on SOC (SOC-EMS) that pursues the same objectives. The simulation results show an improvement in the control of the power delivered to the grid. The Integral Time Absolute Error, ITAE, achieved with the MPC-EMS for the active and reactive power is 20 % and 4 %, respectively, lower than that obtained with the SOC-EMS. A 1,3 % higher charge for the BESS with the lowest SOC is also registered. Furthermore, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is implemented to validate the simulation results.

Decentralized home energy management system to reduce system peak and uncertainty

The increasing penetration of EVs is providing new opportunities for various Energy Management Systems (EMS) due to their flexibilities and energy storage capabilities. However, traditional EMS based on centralized control strategies requires huge investment in Information & Communications Technologies (ICTs) to coordinate the households demands and achieve grid level goals. Alternatively, decentralized EMS require less computation burden and communications but are mostly focused on customer energy cost saving and comfort level, ignoring the grid goals. This paper develops a variancebased optimization algorithm for a decentralized Home Energy Management Systems (HEMS) to achieve centralized performance without excessive computational burdens and coordination communications and to achieve energy bills saving for households at the same time. The performances of the proposed HEMS are assessed against different households at different aggregated grid levels. Results show that the proposed system can reduce system reinforcement investment for network operators by reducing 30% system peak demand and more than 50% demand uncertainty compared to traditional HEMS. Consumers can achieve nearly 12% energy bill saving by only accessing energy market. Further cost savings can be expected, given access to other markets.

A Practical Research Methodology for Implementation of the Ems in Iraqi Enterprises

In practice, energy management encompasses several activities that give objective data on the principal users of fuel and energy, the energy efficiency of various developments and certain types of products, and energy conservation reserves. This research investigates the benefits and stages of EMS that must be adopted in Iraq. The work considers the significance of using EMS technology at Baghdad International Airport. Airport infrastructure necessitates much energy due to its vast size and particular design. Annually, the airport consumes nearly 9.2M KWh, costing 1.2 billion Iraqi Dinars/KWh. As Iraq's current energy crisis is massive, the energy audit will show an accurate picture of the energy consumption. EMS aims to increase the organisation's stability (including financial) and improve its competitive position by reducing costs and increasing management efficiency. The work has accomplished that Iraqi companies need a modern management strategy-energy consumption management. Traditional heads of departments must be updated to be active in fixing the organisation's energy issues. The EMS technology can identify conflicts and re-document structures. The technique also enables addressing any safety issues in the design phase. Therefore, this paper contributes to enhancing and developing the traditional Energy Management System in Iraq.

A Microgrid Energy Management System Based on Non-Intrusive Load Monitoring via Multitask Learning

Non-intrusive load monitoring (NILM) enables to understand the appliance-level behavior of the consumers by using only smart meter data, and it mitigates the requirements such as high-cost sensors, maintenance/update and provides a cost-effective solution. This article presents an efficient NILM-based energy management system (EMS) for residential microgrids. Firstly, smart meter data are analyzed with a multi-task deep neural network-based approach and the appliance-level information of the consumers is extracted. Both consumption and operating status of the appliances are obtained. Afterward, the energy consumption behaviors of the end-users are analyzed using these data. Accordingly, average power consumption, operation cycles, preferred usage periods, and daily usage frequency of the appliances were obtained with an average accuracy of more than 90%. The obtained results were integrated into an EMS to create an efficient and user-centered microgrid operation. The developed model not only provided the optimum dispatch of distributed generation plants in the microgrid but also scheduled the controllable loads taking into account customers' satisfaction. It was demonstrated with the help of simulation that the proposed NILM-based EMS model improves the operation cost/customer satisfaction ratio between 45% and 65% compared to a traditional EMS.

Optimal energy management of microgrid based on multi-parameter dynamic programming

With the wide application of microgrid system, fluctuation and randomness are the characteristics of distributed generation output. The traditional energy management system can’t meet the requirements to ensure the security and stability of the grid. The microgrid energy management is of great significance to the stable operation of power grid. In order to obtain higher economic benefits and pay less environmental costs, reasonable scheduling of various distributed power sources is able to achieve this goal. In this article, microgrid energy management including distributed generation is studied. The objective function includes the economic objective and the environmental objective. The model of energy management is considered as a multi-objectives and multi-parametric optimization problem. The multi-parameter dynamic programming is used to optimize the energy management of microgrid. Finally, the efficiency of the proposed method is examined by the simulation studies.

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis.

In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.

A Multiagent-Based Hierarchical Energy Management Strategy for Maximization of Renewable Energy Consumption in Interconnected Multi-Microgrids

A new energy management framework for multi-microgrid (MMG) systems composed of high renewable energy sources (RES) is proposed in this paper. In traditional energy management system (EMS), battery energy storage system (BESS) is only considered in a single microgrid (MG) optimization model, which leads to underutilization of storage devices and waste of resources. Considering the advantages of local consumption of RES, this paper develops a hierarchical multi-agent energy management strategy. A two-step model aiming at maximizing the usage of RES is established to help determine the strategy. In the first step, the strategy is developed to maximize the local consumption of RES based on the demand response programs (DRP) and BESS within the independent MG. In the second step, the community BESS and BESSs are used to share the RES of multiple MGs for increasing the utilization ratio of RES. Simultaneously, the output of each controllable distributed generator (CDG) and the transaction with power grid are determined through a distributed adjustable power generation plan query library (DGPS). Moreover, the simulation with a three-MG system proves that the strategy can effectively increase the utilization ratio of RES and reduce the transaction with power grid compared to the traditional strategy.

A Novel Stochastic-Programming-Based Energy Management System to Promote Self-Consumption in Industrial Processes

The introduction of non-conventional energy sources (NCES) to industrial processes is a viable alternative to reducing the energy consumed from the grid. However, a robust coordination of the local energy resources with the power imported from the distribution grid is still an open issue, especially in countries that do not allow selling energy surpluses to the main grid. In this paper, we propose a stochastic-programming-based energy management system (EMS) focused on self-consumption that provides robustness to both sudden NCES or load variations, while preventing power injection to the main grid. The approach is based on a finite number of scenarios that combines a deterministic structure based on spectral analysis and a stochastic model that represents variability. The parameters to generate these scenarios are updated when new information arrives. We tested the proposed approach with data from a copper extraction mining process. It was compared to a traditional EMS with perfect prediction, i.e., a best case scenario. Test results show that the proposed EMS is comparable to the EMS with perfect prediction in terms of energy imported from the grid (slightly higher), but with less power changes in the distribution side and enhanced dynamic response to transients of wind power and load. This improvement is achieved with a non-significant computational time overload.

Guest Editorial Distributed Energy Management Systems

Energy management system (EMS) is well known as the brain of power grid operation, for real-time monitoring, security assessment, dispatch schedule, and coordinated control of power grids. With the development of smart grids, a pressing need of transition from a traditional centralized EMS architecture to a distributed one is arising. This special section addresses the challenges and opportunities for the development of distributed EMS for smart grids.

Demand Side Management in Smart Grid Using Heuristic Optimization

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Modern power systems control centers: from EMS to AEMS

Traditional energy management system (EMS) plays an indispensable role in control centers of electric power systems. However, it also has several shortcomings, including lack of real-time closed-loop control, unreliable functional modules, and difficult to exchange data. The electric hybrid control theory (EHCT) has been proposed as an innovative and effective means to overcome these shortcomings. This paper argues that the EHCT provides a theoretical foundation for the advanced energy management system (AEMS) that can achieve multi-objective, near-optimal, and closed-loop control of electric power grids. This paper also discusses the significance for control centers to evolve from the traditional EMS to the AEMS. Furthermore, this paper points out that the traditional EMS can be considered as an integral part of the AEMS and that the AEMS can be built upon the traditional EMS. Thus, the resources that are currently available can be fully utilized to achieve near-optimal and closed-loop control of power system operations.