Research on Optimization Methods for Management of Electric Thermal Gas Multi Energy Systems

The object of this study is the process of providing informational support to stakeholders of the life cycle of energy to ensure strategic management of energy systems with renewable energy sources. The task to improve and develop information technologies applied to the management of complex energy systems and the issue of their socialization and perception by people were tackled. Considerable attention was paid to the task of data visualization in order to improve the perception of information by users. An important aspect of the interaction of energy life cycle stakeholders during the management of energy systems was explored and the need to ensure their involvement and trust in information technology was emphasized. The research considers the development and implementation of interfaces for a decision support system in the management of energy microgrids using renewable energy sources such as solar panels and wind generators. The results include web application architecture design, user role assignment, user action modeling, and data visualization. Approaches to the distribution of stakeholder authority, database access, interface design and data visualization for full client support were proposed. It is noted that the proposed interface solutions are designed for a decision support system in the management of microgrids with different types of renewable energy sources, such as solar panels and wind generators. Summarizing the results, the relevance of the task to devise methods of information support and interfaces for strategic management of energy systems has been emphasized. The conclusions imply the effectiveness of the proposed solutions for the strategic management of microgrids

A step to clean energy - Sustainability in energy system management in an emerging economy context

Fractional-order calculus (FOC) has gained significant attention in electric vehicle (EV) energy storage and management systems, as it provides enhanced modeling and analysis capabilities compared to traditional integer-order approaches. This review presents a comprehensive survey of recent advancements in the application of FOC to EV energy storage systems, including lithium-ion batteries (LIBs), supercapacitors (SCs), and fuel cells (FCs), as well as their integration within energy management systems (EMS). The review focuses on developments in electrochemical, equivalent circuit, and data-driven models formulated in the fractional-order domain, which improve the representation of nonlinear, memory-dependent, and multi-scale dynamics of energy storage devices. It also discusses the benefits and limitations of current FOC-based models, identifies open challenges such as computational feasibility and parameter identification, and outlines future research directions. Overall, the findings indicate that FOC offers a robust framework with significant potential to advance next-generation EV energy storage and management systems.

In this report, the third in a series, we provide an evaluation of several products that exemplify the current state of practice of Energy Management, Control, and Information Systems (EMCIS). The available features for these products are summarized and analyzed with regard to emerging trends in EMCIS and potential benefits to the federal sector. The first report [1] covered enabling technologies for emerging energy management systems. The second report [2] serves as a basic reference for building control system (BCS) networking fundamentals and includes an assessment of current approaches to open communications. Part 4 of this series will discuss applications software from a user's perspective. It is important for energy managers in the Federal sector to have a high level of knowledge and understanding of these complex energy management systems. This series of reports provides energy practitioners with some basic informational and educational tools to help make decisions relative to energy management systems design, specification, procurement, and energy savings potential.

With the new challenges brought by the high penetration of Renewable Energy Resources (RESs) into the modern grid, developing new solutions and concepts are necessary. Microgrid (MG) is one of the new concepts introduced to overcome upcoming issues in the modern electricity grids. MGs and Multi-Microgrids (MMGs) are defined as the building blocks of smart grids. MGs are the small units, where power generation and consumption happen at the same location and MG makes the decisions by itself. MGs can operate grid-connected or island mode depending on the functionality of the MG. Energy Management System (EMS) is the decision making centre of the MG. The data from the devices is received by the EMS and after processing, the commands are sent to the controllable components. Management of voltage, active and reactive power, neutral current, unit commitment and economic dispatch are of the tasks of EMS. In this PhD thesis, an optimal EMS for MGs and MMGs is developed. The main objective of this project by developing the EMS is to optimize the energy flow in the MGs and MMGs to obtain peak load shaving in a cost beneficial system. In order to achieve an efficient EMS, communication system, forecasting system, scheduling system, and optimization system are modelled and developed. Different types of EMS operation, centralized, decentralized and distributed, are investigated in this work to achieve the best combination for MMG EMS operation. The communication system is mainly utilizing Modbus TCP/IP protocol for data transmission at local level and Internet of Things (IoT) protocols (MQTT) for the global communication level. A communication operation algorithm is proposed to manage the MMG EMS under different communication operation modes and communication failure conditions. Furthermore, a monitoring system is developed to collect the data from different devices in the MG. The data is processed in the MG EMS and the commands are sent to components through the communication infrastructure. The link between MGs and MMGs is through the proposed two-level communication system, where the expansion of MGs to a MMG is investigated. In the MMG, MGs are functioning as a unit while having different priorities and operating under different policies. Each MG has its own MG EMS and the EMSs transfer information through the communication system between each other in either centralized, decentralized, distributed, or no communication modes under the MMG EMS. The forecasting system is required in the EMS to predict the future MG characteristics such as power generation and consumption. The forecasted data is the input to the optimization and scheduling system of EMS. Employing the forecasting system in the EMS would increase the accuracy of the optimization and scheduling systems. In this thesis, the timeseries-based forecasting algorithms are employed to predict next day’s active power using the load data, generation data, weather data and temperature data as the inputs. The heart of EMS is the scheduling and optimization system. The purpose of the scheduling system is to define the amount and the time of energy flow in the MG for different generation sources and consumption loads. Furthermore, scheduling system is responsible for peak load shaving and valley filling. On the other hand, the optimization system has the task of minimizing the operation costs of the MGs. The role of market in the scheduling and optimization is important. Time of Use (ToU) tariff is the pricing system, which determines the peak and off peak hours for energy usage pricing. In order to apply the optimization system, a model of the system, an objective function and systems constraints are defined, where aging of battery energy storage system (BESS), operational cost of components and MG cost benefits are considered. To operate the EMS scheduling and optimization system, IBM CPLEX Optimization Studio solver conducts the optimization while for the scheduling system, objective function and constraints are defined in MATLAB. In this thesis, a rule-based, MILP and MIQP optimization system for commercial MGs including electric vehicles (EVs) are proposed to investigate performance of MG EMS for different case studies. In this thesis, the literature for different scheduling and forecasting systems is investigated and different optimization algorithms are analysed. The communication protocols utilized in this research are described and compared to other protocols in the literature. In different chapters of this thesis, the modelling of MGs and MMG EMS, different modules of EMS, forecasting, optimization, scheduling and communication systems are described and analysed. A novel communication system for MMG EMS operation is proposed for commercial buildings. The performance of MG EMS and MMG EMS is examined for power and neutral current sharing, operation cost optimization, and demand peak shaving applications and results are compared to investigate the performance of proposed algorithms.

Requirement of energy for human sustenance and growth has increased exponentially during the last century. The rate of rise in demand for energy has reached unprecedented levels leading to widening of gap between demand and supply of electric energy due to the scarcity of resources. The harmful effects of excessive usage of energy on the environment pose a great danger to the sustainability of our ecosystem. In this scenario, it becomes pertinent to design a strategy for increased efficiency of electricity utilization with an aim to minimize air pollution and carbon footprint. Hence, energy management systems are the need of the hour to identify the potential for improvements in energy efficiency. However, the implementation of Energy Information and Management System (EIMS) in academic institutes is extremely limited due to lack of awareness and relevant green policies. The current work presents a blueprint of Energy Information and Management System for an academic institute leading to multi-measure energy efficiency through multiple strategies including equipment operational improvements and upgrades, and occupant behavioural changes. The design of Intelligent EIMS enables energy savings relative to a baseline model, which predicts energy consumption from key parameters such as occupancy levels mapped with the timetable and operational schedule. The need for policies to be adopted by educational institutes for optimum utilization of electrical energy has been discussed and presented in the paper. In the present work, the different sub-domains/facilities of the college were primarily divided into three categories, namely facilities mapped with college timetable (like classrooms, laboratories, etc.), facilities mapped with fixed or regular schedule (like hostel mess, corridors, etc.) and facilities independent of college timetable or fixed schedule (like canteen, staffroom, common room, etc.). The two basic categories were further subdivided on the basis of scheduled usage and ad hoc usage of these facilities. Based on these categorizations, policies for energy usage were framed for these facilities, and prototype EIMS was designed and implemented at Maharaja Agrasen College, University of Delhi. 4.8% saving in the power consumption was observed post-EIMS implementation.

Community-scale renewable energy systems planning under uncertainty—An interval chance-constrained programming approach

Buildings can be made more user-friendly and secure by putting “smart” design strategies and technology processes in place. Such strategies and processes increase energy efficiency, make it possible to use resources rationally, and lower maintenance and construction costs. In addition to using wireless technologies and sensors to improve thermal, visual, and acoustic comfort, “smart” buildings are known for their energy, materials, water, and land management systems. Smart buildings use wireless technologies and sensors to improve thermal, visual, and acoustic comfort. These systems are known for managing energy, materials, water, and land. The task of the study is to consider the indicators that form the basis of the framework for evaluating intelligent buildings. The indicators for the development of “smart” buildings are classified into six categories in this paper: green building construction, energy management systems, safety and security management systems, occupant comfort and health, building automation and control management systems, and communication and data sharing. The paper aims to develop a scoring model for the smartness of public buildings. In developing the scoring system, the decision-making process requires an appropriate selection of the optimal solution. The contents of the research are the methods known as the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP), Interval Valued Pythagorean Fuzzy AHP with differences (IVPF-AHP d), and the proposed method Interval Valued Pythagorean Fuzzy AHP (IVPF-AHP p). The research focuses on the IVPF-AHP as one of the methods of Multi-Criteria Decision-Making (MCDM) and its implementation. The comparative analysis of the three presented methods indicates a significant degree of similarity in the ranking, which confirms the ranking similarity. The results highlight the importance of bioclimatic design, smart metering, ecological materials, and renewable energy systems.

Energy management and monitoring systems should take initiatives to accelerate the application of measurement and verification (M&V) and be a focus area of various energy management systems. An M&V plan provides the methodology that will give overall energy savings resulting from specific energy efficiency and conservation projects. The energy management plan which mainly focus on artificial intelligence (AI) is increasingly becoming the predominant area of a new emerging technological area and industrial utilities for energy studies, drawing the increasing attention of scholars, technocrats and experts in recent years. Studies presented in this chapter highlight the role of AI on the environmental factors with respect to carbon footprints, which has been decreasing during the 12th Five-Year Plan as compared to the 11th Five Year Plan in both ferrous and non-ferrous industries globally. AI has a minimum impact on carbon intensity in the worker-intensive and tech-intensive industries. Therefore, the government policy should accelerate the implementation of AI, in line with the metal industries. Deployment of energy management information systems, data analytics and soft computing has the potential to transform the productivity, process control and energy efficiency proficiency in metal industries of recent modern times.

It is Important for energy managers to have a high level of knowledge and understanding of complex energy management systems. This chapter is intended to help energy practitioners with some basic informational and educational tools make decisions relative to energy management systems design, specification, procurement, and energy savings potential. This chapter provides an evaluation of several products that exemplify the current state of practice of Energy Management, Control, and Information Systems (EMCIS). The available features for these products are summarized and analyzed with regard to emerging trends in EMCIS and potential benefits.

The electric grid is radically evolving and transforming into the smart grid, which is characterized by improved energy efficiency and manageability of available resources. Energy management (EM) systems, often integrated with home automation systems, play an important role in the control of home energy consumption and enable increased consumer participation. These systems provide consumers with information about their energy consumption patterns and help them adopt energy-efficient behavior. The new generation EM systems leverage advanced analytics and communication technologies to offer consumers actionable information and control features, while ensuring ease of use, availability, security, and privacy. In this article, we present a survey of the state of the art in EM systems, applications, and frameworks. We define a set of requirements for EM systems and evaluate several EM systems in this context. We also discuss emerging trends in this area.

The implementation of monitoring tools and energy management systems (EnMSs) supports companies in their long-term energy efficiency strategies, and they are essential to analyse the effectiveness of energy performance improvement actions (EPIAs). The first fundamental step towards increasing energy efficiency is the development of energy audits (EAs). EAs provide comprehensive information about the energy usage in a specific facility, identifying and quantifying cost-effective EPIAs. The crucial role of these tools in clean energy transition is remarked by the European Energy Efficiency Directive (EED), which promotes the implementation of EAs and EnMS programmes. The purpose of this work is to better understand the link between EnMSs (specifically ISO 50001) and EAs in the EED Article 8 implementation in two industrial and two tertiary sectors in Italy. Moreover, the impact of company size, energy monitoring systems, and EnMSs on planned and/or implemented EPIAs is analysed. Our findings show that, albeit the complexity of the variables involved in energy efficiency gap, the “energy savings/company” and “EPIA/site” ratios are higher in enterprises with an EnMS and monitoring system. Thus, a correct energy audit must always be accompanied by a specific monitoring plan if it is to be effective and useful to the company decision maker.

This is the third in a series of three articles on enterprise energy management (EEM) systems featured in Strategic Planning for Energy and the Environment. The first article described the current state-of-the-art in EEM systems and their associated benefits in controlling energy cost, quality, and reliability (see Vol. 22, #4). The second piece considered energy in terms of managing the associated cost and reliability risks to businesses (Vol. XX, #X). As the field of energy management matures, so do the tools and best practices available to ensure that the energy required by an organization is used in the most efficient way possible. In the past, energy management practices consisted primarily of replacing inefficient equipment and then using any number of methods to estimate the savings gained. Studies performed by the Department of Energy (DOE) and the Texas State Energy Conservation Office (SECO) have shown, however, that energy savings can be dramatically increased and maintained over time by adopting and implementing consistent energy management practices and recognized measurement and verification procedures. As energy management standards and best practices begin to see widespread adoption, the information systems required to support them will play a crucial role in their implementation and success. The enterprise energy management systems described in the previous articles will not only address shorter term cost, quality, and reliability concerns, but can also provide the detailed data and analysis capabilities required to ensure that energy management strategies and conservation measures are on track throughout an organization. Organizations can apply EEM systems to gain a comprehensive understanding of current energy performance, plan and select cost-effective energy conservation measures, track performance of measures that have been implemented, and verify the savings realized. Over the last several decades, there has been increasing interest and activity in the field of energy management. A Lawrence Berkeley National Labs (LBNL) study of energy efficiency projects completed by US energy service companies over a ten-year period shows that total project spending has increased from roughly $500 million in 1990 to more than $2 billion in 2000 [1]. Energy management practice has traditionally focused exclusively on technologies that increase the energy efficiency of key energy-consuming processes and equipment. Rebuild America, a US Department of Energy (DOE) energy efficiency program, lists lighting and HVAC equipment upgrades among the most commonly implemented energy efficiency measures [2]. The US DOE Energy Information Administration (EIA) lists a variety of energy management activities for several industrial sectors, including waste-heat recovery and deployment of variable-speed drives [3]. Although there is little doubt that upgrading equipment and processes is a key ingredient to increased energy efficiency, there have always been concerns that traditional deployment practices have not resulted in consistent (and long-term) energy savings. While the LBNL study mentioned above notes a steady increase in energy efficiency project spending over time, it also acknowledges that there is a wide variation in typical energy savings [1]. There has been considerable effort over the last several years to define standards and best practices that increase the performance of energy efficiency projects and make the savings realized more predictable and repeatable. The International Performance Measurement and Verification Protocol (IPMVP), for example, provides best-practice methods for measuring and verifying the results of energy efficiency projects in commercial and industrial facilities [4]. MSE 2000, an energy management standard developed by the Georgia Institute of Technology and accredited by ANSI, specifies a management infrastructure for increasing energy efficiency and reducing costs [5]. Both of these standards move beyond traditional energy efficiency practices and into the realm of more comprehensive strategic energy management practices that resemble the structure and discipline found in best-practice management systems like ISO 9000 and 14000.

In the past few years, the application and research community has expressed a lot of interest in managing energy and power while using distributed generation systems. Electricity generation and its usage coordination are vital aspects of energy efficiency that can help in saving energy, decreasing energy costs, and fulfilling global emission objectives. Owing to the relevance of the topic, here, the researchers have presented a comparative and critical review of recent developments in the fields of energy management systems (EMSs) and power management systems (PMSs). Furthermore, the researchers also reviewed the various EMS and PMS methods that could be used for reviewing microgrid (MG) and nanogrid (NG) systems. The EMS for MG and NG systems helps in addressing important economic objectives like minimisation of operational costs after optimising the fuel costs, emission costs, and battery degradation costs, while also improving the life of the MG devices. Alternatively, the PMS helps in addressing technical objectives like improving the stability, flexibility, reliability, and quality of MG and NG systems. The researchers have also discussed the drawbacks and challenges affecting the widespread application of EMSs and PMSs.

Review of electric vehicle energy storage and management system: Standards, issues, and challenges