Development Of Energy Management System Research Articles

Evaluation of the Energy Management System in Water and Wastewater Utilities in the Context of Sustainable Development—A Case Study

Energy management in enterprises is an important issue in the context of improving energy efficiency, energy use, and energy consumption. This is consistent with the Sustainable Development Goals. The purpose of this study was to evaluate the energy management system of water and wastewater utility in the context of sustainable development based on the opinions of managers and employees. The results indicate the involvement of the surveyed enterprise in energy management system development activity. This demonstrates the orientation of the surveyed enterprise to support activities to improve energy performance in line with the implementation of sustainable development. The added value is that the developed research tool can be used in studies of other enterprises to assess the level of energy management.

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.

Design and Control of Split-Pi DC-DC Converter for Vehicle to Grid and Grid to Vehicle Applications with Development of Energy Management System in MATLAB/Simulink

Design and Control of Split-Pi DC-DC Converter for Vehicle to Grid and Grid to Vehicle Applications with Development of Energy Management System in MATLAB/Simulink

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization

A Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors.

IoT-based intelligent energy management system for optimal planning of HVAC devices in net-zero emissions PV-battery building considering demand compliance

To achieve the goal of carbon neutrality, renewable energy resources are now being widely deployed in the building sector, forming the hybrid energy system. For such hybrid energy system, the development of energy management system (EMS) is of great significance for their maximum utilization. In this study, an Internet of things-based (IoT-based) intelligent EMS is developed for a real net-zero emissions photovoltaic-battery (PV-battery) building, where its main purpose is to optimally schedule the heating, ventilation, and air conditioning (HVAC) devices in order to tradeoff between user comfort level and battery remaining energy. The developed EMS mainly consists of two modules, including data-driven forecasting module and mixed-integer programming module. The data-driven forecasting module is firstly developed to forecast future 24-h PV generation, baseload demands, and room thermal dynamic using several data-driven methods. Then, based on the forecast results, the optimal planning problem of HVAC devices is formulated as a multi-objective optimization problem, which is solved by the mixed-integer programming module. Moreover, a novel concept named as demand compliance is proposed, where the user-defined demand important coefficient is considered in the optimization problem. Comprehensive case studies are conducted to compare the developed EMS and existing rule-based control method. The experimental results demonstrated that the developed EMS could achieve similar user comfort level comparing with rule-based method, while its battery remaining energy was significantly higher. This study might provide a new perspective for the demand-side management program of net-zero emissions building.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries, malls, shopping complexes, and domestic applications has significantly increased energy consumption. The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings. The proposed micro-grid model includes four power generators: solar power, wind power, Electricity Board (EB) source, and a Diesel Generator (DG) set, with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources. The core issue in direct current to alternate current conversion is harmonics distortion, a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality. The monthly demand for residential buildings is 13–15 Megawatts. So, almost 433 Kilo-Watts (KW) of electricity is required every day, and if it is used for 8 h per day, 50–60 KW of electricity is needed per hour. The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software, and simulation results are provided. The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.

Development of energy management system using neural network for power electric system

Development of energy management system using neural network for power electric system

Simulation-Based Optimization of Residential Energy Flows Using White Box Modeling by Genetic Programming

The development of energy management systems that optimize the electrical energy flows of residential buildings has become important nowadays. The optimization is formulated as a symbolic regression problem that is solved by genetic programming, which provides near optimal results while being highly performant during application. Additionally, the so-trained energy flow controllers are explainable and therefore address three of the current major disadvantages of most existing solutions. 260 controllers are trained to calculate the optimal gridfeed-in value for an inverter and are evaluated for their ability to minimize the energy costs and to support grid stability and battery lifetime. Additionally, they are compared to two existing energy management systems, a rule-based self consumption optimization and a linear model predictive controller. It is shown that this energy management system can significantly minimize energy costs compared to both reference systems by up to 58.25%, support grid stability and prolong battery lifetime by up to 76.48%.

Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques

In order to develop energy management systems for hybrid ship propulsion plants that are truly optimal and robust, it is important that the test conditions in experimental facilities are as close as possible to real world applications. In this context, a framework for the design and experimental evaluation of power-split control systems for ship propulsion is proposed. Using machine learning, data from ship operation are processed and 20 loading patterns are recognized; representative templates are extracted to be used as marine loading cycles in the energy management system development and testing. A ship propulsion model with wave disturbance is utilized to simulate realistic loading scenarios on the experimental facility. A predictive energy management system is presented, that controls the diesel engine and the electric motor/generator based on a strategy that defines the trade-off between fuel consumption and NOx emissions minimization. In addition the propeller load characteristics that are estimated and a speed predictor are utilized to aid the optimization within the 10 s prediction time window. A parametric simulation study is performed for the trade-off evaluation between fuel consumption and NOx emissions reduction potential of the control scheme. Finally, utilizing an extracted loading cycle, the energy management system is experimentally implemented and tested in real-time operation, where it has to cope with environmental disturbance rejection and follow the desired speed profile while performing the power-split control in respect to the fuel to NOx weighting strategy. Based on the experimental results in a hybrid diesel–electric marine powertrain with a 260 kW diesel engine and a 90 kW electric machine, fuel consumption and NOx emissions reduction by 6% and 8.5% respectively, were achieved over the tested profile. In this framework, the capabilities of the energy management system in realistic operation conditions can be exploited and evaluated.

Online and hard constrained adaptive dynamic programming algorithm for energy storage control in smart buildings

AbstractThe high computational complexity caused by static optimization is the key factor to hinder the development of energy management systems. Adaptive dynamic programming (ADP) is an effective dynamic optimization method to break through the bottleneck. However, the hard constraints of energy system have not been fully considered due to the non‐convexity and nonlinearity of the value function, which makes the theoretical analysis complicated and brings safe security problems in battery systems. In this article, a systematic online ADP control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. The second‐order local expansion at the current state is used to replace the nonlinear value function to simplify the theoretical analysis with the error of the reminder term. Based on the local property of value function, a method for the determination of adaptive parameters is first designed. It is proven that the solution of adaptive parameters not only prevents over‐charged and over‐discharged of the battery but also limits the charging and discharging power of the battery to be less than the rated power. In addition, long‐short term memory (LSTM) neural networks, a type dynamic network with memory characteristics, are used for the implementation of the present algorithm instead of the static networks to help realize the algorithm online. Due to the hidden state of LSTM, the performance of the online algorithm is improved after running the energy system. Numerical results verify the effectiveness of the proposed online control algorithm.

The Impact of Smart Meter Installation on Attitude Change Towards Energy Consumption Behavior Among Northern Ireland Households

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.

A New Modulation–Demodulation Approach to DC Power-line Data Transmission for SRG-Integrated Microgrid

Switched reluctance generators (SRGs) possess a range of advantages, such as simple structure, good heat dissipation, high fault tolerance, which have, thus, been applied to wind power generation systems and other distributed generators. In order to maximize SRG performances, stable and reliable communication is essential to facilitate system monitoring, fault detection, and energy management system development. In this article, we propose a new modulation–demodulation strategy to realize effective power-line communication (PLC) between SRG and other converters in an inverter-interfaced distributed power grid. The newly designed modulation method utilizes and controls the turn- on/off angle (TOA) of an SRG, so as to modulate the voltage ripples to convey information to the receiving end. An innovated demodulation method is also devised in this article, using maximum entropy spectrum estimation (MESE) method, for the purpose of acquiring useful signals from the sending end. Vigorous experimentation is conducted, which verify the functionality of the proposed TOA-MESE modulation–demodulation method for data transmission between SRG and other converter-based installations.

8-1-1 ＣＧＳ，熱源機及び蓄熱槽の最適制御システムの開発

8-1-1 ＣＧＳ，熱源機及び蓄熱槽の最適制御システムの開発

Development of energy management system based on a rule-based power distribution strategy for hybrid power sources

The proton exchange membrane fuel cell has become a good candidate for the power source of future green transportations due to its excellent performance of high efficiency and cleanness. To overcome the slow dynamic characteristic, the fuel cells are always grouped with other energy storage devices such as lithium-ion batteries and supercapacitors. The development of energy management system and design of the power distribution strategy are critical issues for the hybrid power source system. In previous work, few studies have comprehensively considered the criteria of power capabilities of the batteries and supercapacitors which are intimately correlated with the power requirements of accelerating, gradient climbing and regenerative braking. The development of power distribution strategy considering the constraints of the power capability is urgent and critical to the safety and longevity of the hybrid energy storage system. In this paper, a distributed energy management system is developed for the hybrid power source system based on a rule-based power distribution strategy. The presented power distribution strategy has comprehensively considered the criterias of the demand power, the remaining capacity and power capability of the hybrid power source system. Moreover, the Bayes Monte Carlo approach is employed for co-estimation of the remaining capacity and power capability of the batteries and supercapacitors. Compared with the existing rule-based strategy without considering the restrictions of the power capability, the presented strategy has better rationality in terms of fuel economy and dynamic property. The presented energy management strategy can extend the lifespan and improve the economy of the hybrid energy storage system by employing the charge and discharge limits of power capability and residual capacity.

An Optimal Energy Management Technique Using the $\epsilon$ -Constraint Method for Grid-Tied and Stand-Alone Battery-Based Microgrids

The intermittent characteristics of microgrids (MGs) have motivated the development of energy management systems (EMSs) in order to optimize the use of distributed energy resources. In current studies, the implementation of an EMS followed by experimental-based analyses for both grid-tied and stand-alone MG operation modes is often neglected. Additionally, the design of a management strategy that is capable of preserving the storage device lifetime in battery-based MGs using a power gradient approach is hardly seen in the literature. In this context, this work presents the application of an EMS for battery-based MGs which is suitable for both grid-tied and stand-alone operation modes. The proposed EMS is formulated as an optimal power flow (OPF) problem using the $\boldsymbol {\epsilon }$ -constraint method which is responsible for computing the current references used by the EMS to control the MG sources. In the optimization problem, the total generation cost is minimized such that the active power losses are kept within pre-established boundaries, and a battery management strategy based on power gradient limitation is included. Finally, the effectiveness of the proposed EMS is evaluated by two scenarios which enable detailed analyses and validation. The first considers a dispatchable and a non-dispatchable source, whereas the second a dispatchable source and a storage device. The experimental results showed that the proposed EMS is efficient in both operation modes and is also capable of smoothing the state of charge ( SoC ) behavior of the storage device.

Development of Energy Management System for Residential Load in India

Development of Energy Management System for Residential Load in India

A Development of Energy Management System with Semi-Transparent Solar Roof and Off-Cycle Credit Test Methodology for Solar Power Assisted Automobile.

A Development of Energy Management System with Semi-Transparent Solar Roof and Off-Cycle Credit Test Methodology for Solar Power Assisted Automobile.

Development of Energy Management Systems of Russian Companies in the Context of World Tendencies of Improving Energy Efficiency

1. Introduction 1.1 Introduction to the Problem Acknowledged by the world communion concept of sustainable development as a priority area for the next ten years, proved its relevance on the World Summit Rio+20. On that summit meeting for the first time the problems of sustainable energy were widely discussed. The problems of increasing energy efficiency in different economic sectors are becoming more acute for many countries, and the United Nations declared 2014-2024 years as the decade of sustainable economy for everyone. Current energy challenges are international and they require concentrated efforts of all countries. Development of energy management systems meeting the requirements of international standards enables the companies to adopt and implement the principles of sustainable energy. There are many obstacles in this area which restrict and decrease activity of introduction of these systems in Russian enterprises. This requires special research identifying the ways of overcoming the obstacles during development of energy management systems and their further sustainable development. 1.2 Importance of the Problem Studying current world tendencies in improving energy efficiency allows identify prior areas of development of the Russian economy in this sphere. Expansion of energy management systems in Russian companies is possible only on the basis of strong evidence of their feasibility and functioning. This requires comparative analysis of certified status of the company and energy efficiency using the example of the leading world countries. A separate study of the reasons of decreasing energy efficiency in the Russian economy is also needed as well as identification of Russian company's capacities to meet the requirements of international standards and ability to achieve effect from introducing energy management systems. 1.3 Relevant Scholarship Research of international approaches to energy management, development of international and national standardization and increase of economic energy efficiency has a wide scientific reach. Researches in this area were conducted by Bashmakovi (2012), Buchin (2011), Dolmatov (2014), Zverev (2011), Lokteva (2010), Golovanova (2010), Ratner (2012), Dira (2012), and Sedash (2013). Russian companies today face many organizational and technological problems when making a decision about introducing energy management systems. Available scientific publications presenting practical steps and accumulated experience in developing and implementing these systems in certain enterprises provide a solution to these problems. Among the authors publishing their works in this area are Aleksandrova (2014), Kondrasheva (2014), Stepochkin (2014), Gorokhova (2014), Alutin (2011), Karavaikov (2011), Lakhov (2014), Lemesheva (2011), and Reishakhrit (2011). The works of the authors Bobylev (2010), Averchenkov (2010), Solovieva (2010), Kiijushin (2010), Bragina (2011), Makhova (2011), Korobova (2011), Omeltchenko (2012), Savin (2014), Zanosienko (2014), and Tufetulov (2011) are devoted to the problem of development of specific instruments of implementation the concept of energy management on organizational and regional levels. However, scientists have been emphasizing, primarily, experience of certain countries in increasing energy efficiency and systematizing obtained results. The issues of introducing energy management systems in particular Russian companies regarding their industry specifics are studied very well. With this being said, such important aspects as interdependence of energy management systems certification level and growth of economic energy efficiency of the country regarding its gross domestic product are not studied well enough. The same situation is with compliance of traditional systems of energy management systems in Russian companies to international demands. These aspects require a thorough research and further development in fundamental and applied researches. …

Hardware-in-Loop Simulation for the Energy Management System Development of a Plug-in Hybrid Electric Bus

In this paper, a hardware-in-loop experiment that integrated controller area network (CAN) monitor and evaluation function for control system is the research content, and a rule-based energy management system of plug-in hybrid electric bus (PHEB) is developed. The real-time kernel of PHEB model was downloaded into VTSystem platform for the real-time simulation system development. An driver and energy management system-in-loop experiment was carried out to verify the energy management strategy under the China Transit Bus Driving Cycle (CTBDC), and the CAN bus performance features were evaluated by CANoe software. The energy consumption per 100km includes 14.1L diesel and 11.9 kW·h electricity with an initial SoC of 85%.