Industrial Energy Management Systems Research Articles

Regional deep energy Q-Net-based energy scheduling for industrial energy management system

Regional deep energy Q-Net-based energy scheduling for industrial energy management system

A non-functional requirements-based ontology for supporting the development of industrial energy management systems

Non-functional requirements (NFRs) are essential in the development of industrial energy management systems (IEMS). This paper presents a comprehensive ontology model for NFRs in IEMS, derived from an extensive survey of NFRs in both the software industry and the energy domain. The proposed ontology encompasses four critical factors influencing the quality attributes of IEMS: technologies, stakeholders, markets, and regulations. Implemented using the OWL 2 DL standard, the ontology model aims to provide a clear and consistent understanding of NFRs and their relationship to the energy domain, as well as identify which factors have a significant impact on the environmental performance of IEMS. The ontology is evaluated through various methods, such as technical validation, user evaluations, its applications in RDF data management, and application-based evaluation, including software architecture, knowledge base data model design, and regulatory framework design. By understanding the link between software quality requirements and the characterizing factors of the energy domain, as provided by our ontology model, this information can be used to inform life cycle assessments and quantify potential reductions in energy consumption, emissions, waste generation, and other environmental impacts associated with implementing cleaner and more sustainable solutions. The results demonstrate that the proposed ontology effectively supports IEMS development and serves as a foundation for developing reusable and adaptable software systems across different industrial domains.

A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

A Dynamic Optimal Scheduling Strategy for Multi-Charging Scenarios of Plug-in-Electric Vehicles Over a Smart Grid

Green transportation has become our top priority due to the depletion of the earth’s natural resources and rising pollutant emission levels. Plug-in electric vehicles (PEVs) are seen as a solution to the problem because they are more cost and environment friendly. Due to rapid industrialization and government incentives for zero-emission transportation, a significant challenge is also constituted in power grids by the self-interested nature of PEVs, with the asymmetry of information between the charging power demand and supply sides. In this paper, we propose an optimal strategy in industrial energy management system, based on evolutionary computing, to characterize different charging situations. The proposed approach considers stochastic, off-peak, peak, and electric power research institute charging scenarios for attaining the vehicle-to-grid capacity in terms of optimal cost and demand. An extensive scheduling of charging cases is studied in order to avoid power outages or scenarios in which there is a significant supply-demand mismatch. Furthermore, the proposed scheme model also reduces the greenhouse gases emission from generation side to build a sustainable generation infrastructure, which maximizes the utility of fuel-based energy production in the presence of certain nonlinear constraints. The simulation analysis demonstrates that PEVs can be charged and discharged in a systematic manner. The participation of transferable load through the proposed methodology can significantly reduce the economic costs, pollutant impacts, efficiency, and security of power grid operation.

IoT-based industrial energy management system

The ESCOM (Electricity Supply Corporation of Malawi) currently faces difficulties in monitoring and controlling critical parameters, such as electricity generation, transmission, and distribution in real-time. Inefficient energy management, lack of fault detection mechanisms, manual interventions, and limited data-driven decision-making capabilities are hindering ESCOM's ability to deliver reliable and efficient electricity services to the people of Malawi. The main objective of this work is to address the pressing challenges faced by ESCOM (Electricity Supply Corporation of Malawi) in effectively managing and optimizing electricity distribution within their infrastructure. This paper introduces an IoT-based industrial management system designed to tackle these challenges and enhance ESCOM's operational efficiency, energy management, and overall performance.

Anomalous energy consumption detection using a Naïve Bayes approach

Background: Industrial energy management has emerged as an important component in monitoring energy consumption particularly with the recent trend of migrating towards IR 4.0. The capability to detect anomalies is essential as it serves as a precautionary step for real-time response to mitigate the maximum demand penalty. The purpose of this research was to develop a high accuracy anomalies detection algorithm to identify anomalies in the energy consumption data recorded by a smart meter. Methods: The proposed algorithm utilized supervised and unsupervised machine learning techniques, namely Isolation Forest and Gaussian Naïve Bayes. The data were first labeled by using Isolation Forest to categorize them into normal and abnormal groups. This was followed by Gaussian Naïve Bayes to classify and predict the anomalies of the smart meter reading. Results: These machine learning techniques showed significant accuracy in predicting the anomalies in smart meter readings. The data used were simulated data collected in less than a month with 30-minute reading intervals. The data were divided into testing and validation sets according to a ratio of 7:3. The balanced accuracy score in predicting anomalies for each different smart meter was above 89%. The average precision, average recall and average F1 score for the normal data were 98%, 99% and 98%, respectively. Whereas the corresponding scores for the abnormal data set were 95%, 90% and 92%. Conclusions: The proposed algorithm is a hybrid approach based on Isolation Forest and Gaussian Naïve Bayes and it provided satisfactory accuracy in anomaly electricity consumption detection based on smart meter readings. The study presents a quick and simple method for categorizing energy consumption data as normal or abnormal, which assists in automatically labelling vast datasets of energy consumption readings. The proposed approach establishes a fundamental framework for predicting the occurrence of anomalies in the industrial energy management system.

Intrusion Detection and Prevention against Cyber Attacks for an Energy Management System

Industrial Microgrids (IμG) are the large-scale buildings fortified with onsite Distributed Generations (DGs), energy storage, and demand response strategies. For optimal handling of these energy resources, storages, and loads to better match the power demand with the generation, a management system is developed termed as Industrial Energy Management System (IEMS). The optimal operation of IEMS depends on the accuracy of information flowing through the communication links between user and IEMS as well as the IEMS and utility grid. Communication links have some associated cybersecurity issues such as unauthorized access, data modification, and disrupt disclosure to change the operating conditions in today’s era of computation. The main goal of this paper is to devise a technique to efficiently detect and prevent internal as well as external apparition attempts. For this purpose, an Intrusion Detection and Prevention System (ID/PS) against cyberattacks is developed using a Linux operating system based on Smooth-sec software. The proposed ID/PS would continuously monitor and record the traffic within the local IμG network to distinguish a legal command from a real attack by decoding the attack packets to create its templates with defined ID/PS rules to take prevention measures and triggered an alert, alarm or Splunk for the security administrator. The devised novel approach significantly contributes to intrusion attention to vulnerabilities and security analysis of cyber secured equipped IEMS for an IμG.

Industrial Energy Management System: Design of a Conceptual Framework Using IoT and Big Data

Industrial activities consume a large portion of the total energy demand worldwide and thus significantly contribute to greenhouse gas emissions. One of the most effective ways to reduce energy consumption in the industrial sector is to implement an energy management system. Current research into Industrial Energy Management System (IEnMS) remains insufficient, and to the best of our knowledge, a holistic framework for an IEnMS using the Internet of Things (IoT) and big data does not exist. This paper provides a comprehensive systematic literature review of the existing academic publications on IEnMS from where the main requirements and components of an IEnMS are identified. We further verify this study by conducting a detailed survey with specialized employees of ten (10) large companies to acquire expert opinion about using the modern technologies like IoT, big data, and data analytics in IEnMS. We have then proposed a theoretical framework for the IEnMS using IoT, big data and data analytics to construct an effective cyber-physical system architecture including steps from data acquisition to the end-user decision-making process. These findings demonstrate how the suggested framework provides an objective methodology for selecting the most appropriate IEnMS for various businesses based on their specific needs.

Industrial Energy Management Systems [President's Message

Presents the President's message for this issue of the publication. The data from the U.S. Energy Information Administration, the average monthly electricity bill of industry in the United States is more than US$5.69 billion. Manufacturers are looking for the most cost-effective way to run their plants and produce products. Industrial energy management systems (IEMSs) play a very important role in fulfilling this mission. The main function of an IEMS is to schedule and control the operation of equipment and monitor the machines' operating conditions and energy consumption. Though this is adequate for a traditional, vertically integrated utility system, electrical power infrastructures are dramatically changing around the globe. Since 1990, many countries have introduced deregulated electricity markets. In the meantime, environmental concerns and smart grid development make the electrical system more dynamic and enable customer participation.

Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries

Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.

Method for transforming energy and production systems into mobile demonstrators

Practical experiences and simple demonstrations within learning factories improve the learning process of trainees; however, being bound to a specific location is a disadvantage of learning factories that limits the target audience. Mobile demonstrators address this barrier to the dissemination of knowledge because they can be displayed at trade shows or transported to companies for on-site workshops. To fulfill the mobility requirement, demonstrators must be compact and easily transportable, therefore, abstractions of complex production-related processes are necessary while educational objectives must be maintained. This article presents a method for systematically deriving mobile demonstrators from complex production systems. The development of a demonstrator for industrial energy management systems serves as an example for the application of the developed method. In the demonstrator, a common and relatable daily process, namely brewing coffee, represents the industrial production process and a simplified energy management system is developed to visualize energy flows.

Industrial Energy Management Systems and Energy-Related Decision-Making

Transitions is today’s debate in almost all topics both in academia and practice. Energy transitions among others, have received relatively more attention, due to the global demand for increasing energy efficiency and lowering environmental impacts. In recent decades, energy management systems, through implementing energy management programs and related practices within industrial companies, have played a vital role in enhancing industrial energy efficiency performance levels. However, still there are problems at very first step of energy management program installation, which is decision-making. Despite market and non-market failures, lack of information, inadequate knowledge, the consequent increase in the perception of risk and uncertainty can be addressed as potential reasons for mentioned problems. Another essential reason can be explained through how an energy program is characterized by people who are attending at an energy-related decision desk. Keeping in mind that allocation of the budget for any investment should not only have financial conformation, but also a strategic value for the company, this paper aims to discuss the impacting parameters on industrial energy-related decision-making and behavior patterns with respect to the critical role of industrial energy management systems.

Gap analysis of industrial energy management systems in Slovenia

Industrial energy management systems, which comprise software solutions, upfront services, and ongoing monitoring and management, enable industrial companies to actively manage their energy consumption and energy procurement activities. Energy management systems are usually tailored to the specific industrial needs but may offer limited functionalities, mostly as a result of different identified gaps (process simplifications, improper measurement points, a lack of motivation, etc.). A survey was conducted in order to analyse the gaps and use of energy management systems in Slovenian industry. The results of the survey presented in this paper demonstrate that the use of energy management systems in industry is recognised as a potential competitive advantage by most of the addressed companies. Furthermore, motivation was highlighted as an important prerequisite for process and structural improvements and reported to be thus far insufficiently addressed. Furthermore, the importance of strong cooperation with actors at different levels of industry, namely the executive and shop floor levels, is addressed. In the conclusion, possibilities for new opportunities in the exploitation of energy efficiency through the use of industrial energy management systems are discussed.

An IoT-based energy-management platform for industrial facilities

Interconnectivity and interoperability are very important features in the development of integrated energy management systems for industrial facilities. A simple and common strategy for exchanging energy-related information among the entities in a facility is currently lacking. To this end, the purpose of this study is to present an IoT-based communication framework with a common information model to facilitate the development of a demand response (DR) energy management system for industrial customers. Additionally, we developed and implemented an IoT-based energy-management platform based on a common information model and open communication protocols, which takes advantage of integrated energy supply networks to deploy DR energy management in an industrial facility. The experimental results of this study demonstrate that the proposed platform can not only improve the interconnectivity of the entities in industrial energy management systems but also reduce the energy costs of industrial facilities.

Industrial Energy Management Systems in Italy: State of the Art and Perspective

Despite the economic crisis, the impact of industry sector share on the total primary energy demand in Italy is still significant. The certification of companies according to the standard ISO 50001:2011 (“Energy management systems - Requirements and guidelines for use”), can represent a key element in the achievement of objectives set in the 20-20-20 Climate-Energy Package.This paper illustrates the state of implementation of ISO 50001 certifications in Italy, reporting on the results of a questionnaire carried out as a part of a master's thesis project at Sapienza, University of Rome in collaboration with FIRE (Italian Federation for the Rational Use of Energy) that included the major certification bodies, certified companies and consultants. The purpose is to outline the current situation, identify the perspectives and highlight the pros and cons related to the implementation of an Energy Management System (EnMS).The big picture shows that Italy, one of the leading countries in energy efficiency policies, suffers from a significant delay in the implementation of the EnMS in industry with respect to Germany.The results of the survey also show that the definition of energy performance indicators, as well as the individuations of an energy baseline and a monitoring plan constitute the requirements most critical to comply with for companies than for consultants. It also appears that more than 35% of companies already ISO 50001 certified have received benefits in terms of cumulative energy saving above 5%, and that the main reason why they have implemented an EnMS is related to the potential impact on increasing the competitiveness of the core business.