Enhancing Dual-Mode Power-Split HEV Performance: An Intelligent Energy Management Considering Powertrain Dynamic

As the world energy crisis continues to burden our natural resources, it is imperative that current technologies be employed to make energy usage more efficient. In this paper, an intelligent residential home energy management system is discussed. A Master Energy Controller was developed to forecast future power demand based on user's life style and environmental/social factors affecting power consumption; this controller uses a branch and bound scheduling algorithm for managing appliance runtime schedules. Further, time-of-use pricing is employed to close the loop between the residential customer and the utility company. The focus of this paper is on how the appliance network architecture and master controller can link to the outside world through the internet. A gateway architecture is used for this interface and details of the communication process and message configurations are presented. This architecture provides a simple, yet powerful approach in linking each intelligent residential energy management system to an energy grid with the goal of improving energy usage.

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

Buildings consume a large portion of the world’s energy and are a source of greenhouse gas emissions. The nature of buildings is changing from static and passive structures to dynamic and active environments for working and living. The concept of sustainable and intelligent buildings is emerging as an important area for the smart grid initiative. In addition, effective home energy management is becoming more feasible using the innovative smart grid technologies. This special section is focused on intelligent buildings and home energy management in a smart grid environment. After careful peer reviews, 17 original research and practical contributions from both academia and industry have been chosen and included in this special section.

The search for sustainable energy solutions has become urgent against the current energy crisis and increasing environmental pollution. Biomass energy conversion and utilization technology, a clean and renewable energy source, constantly evolves, particularly in biomass power generation technology. This technology can effectively reduce greenhouse gas emissions and dependence on fossil fuels. Biomass conversion and utilization techniques include combustion, gasification, and biochemical methods that generate different energy forms, including heat, electricity, and fuel. Developing these technologies provides strong technical support for optimizing energy structures and protecting the environment. The article explores the application of biopower technology in intelligent energy management of electricity and improves its efficiency. The efficiency of biopower technology and its influencing factors were investigated using the Data Envelopment Analysis (DEA) model and Tobit regression analysis. A study was conducted on ten biopower energy companies. The energy management efficiency score of 1 was present in six of these companies, suggesting a relatively high operational efficiency level. Company C has a super-efficiency value of 2.654, indicating its outstanding energy management efficiency. Factors such as human resource input, total investment, and annual production costs significantly affect energy management efficiency. By optimizing resource allocation and improving management efficiency, biopower technology can be applied more effectively to intelligent energy management, thus promoting sustainable development.

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Smart cities are systematically promoting the transition to sustainable and effective energy systems by promoting policies for energy efficiency, regionalized/distributed renewable energy generation, and intelligent energy management. In particular, this transition toward a more integrated and intelligent energy supply has created a plethora of energy meta-information made available through the IoT smart grid, thereby allowing big data analytical services to forecast energy consumption and to manage usage patterns. In this article, we propose a context-aware framework for intelligent power equipment management. Our contribution is to present a design of the proposed framework based on context awareness, the definition of a context ontology for power equipment management, a specification of the inference rules for the context ontology, and a context-aware inference service for power equipment management. The proposed system has broad applications to handle system monitoring and express system controls, so as to be easily and effectively applied to various application domains.

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modeling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent energy management scenario.

Single contract power optimization: A novel business model for smart buildings using intelligent energy management

In grid-tied photovoltaic (PV) generation systems, intelligent energy management is required to maximize its performance. In this article, a novel three-port energy router with optimized control is proposed for this application. The proposed converter can interface among three ports (PV source, battery, and dc-link) with high integration. The PV panel is connected to the battery through an interleaved boost structure on the primary side. The primary-side PV-battery system and secondary-side dc-link are connected through a dual-active-bridge (DAB) converter. The battery operates as an energy buffer to compensate for the power mismatch due to the intermittent nature of the PV source. According to the daily power profile and intelligent power management between the grid and battery, six operating modes are possible. Optimized control strategies are customized in different operating modes. The maximum-power-point-tracking (MPPT) of the PV panel can be always realized. The zero-voltage-switching (ZVS) condition for all <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s, reducing circulating current over a wide range, and flexible energy flow among three ports are all taken into considerations in the design. To verify the proposed concept, a 500 W rated prototype is designed. The designed prototype exhibits high efficiency in various operating modes. The experimental results agree well with the theoretical analysis.

In our current situation, intelligent energy management is essential, it is necessary not only change the way how it is delivered, but also how users use it. Small changes in our life daily, such as turning off lights or air conditioning when there are not people who use it, can result in substantial energy savings. In this document, we provide information about of a support system for energy conservation using sensors, actuators and agents. The wireless network system is an embedded interconnected. Agents, sensors for monitoring a room and actuators for gradually manipulate elements like windows, blinds, led lamps and air conditioning, besides the application of optimization techniques and fuzzy logic. The energy required to give comfort, comes from two main sources possible, artificial (by the company supply electricity) and natural (natural air flows, solar lighting, etc.). Our main goal is through bio-inspired optimization algorithms and fuzzy logic control provides an adequate lighting and temperature settings according to an activity that takes place in the room.

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

This paper presents a management strategy focused on the intelligent use of information based on the implementation of proactive methods and practices using data acquisition, supervision and control aimed at reducing electricity expenditure in public institutions. The article proposes the implementation of an energy management system emphasizing the opportunity of (i) control of energy expenditure, (ii) awareness of the agents involved in the rational use of energy and (iii) reduction in costs associated with management by the demand side. The main steps necessary for the implementation of a system based on NBR ISO 50.001 / 2018 (1) will be described, focusing on the characteristics of a public institution of higher education. The article also presents a case study of electrical energy contracting for a public company, the Brazilian Air Force Unit in Santa Maria, where new optimum demand values were established for peak and off-peak periods from the consumption profile and the contracted energy and demand tariff values. The result was positive with a reduction in cost compared to the initial scenario, with the possibility of increasing this result through the implementation of an energy management system, demonstrating that investment in management based on information and methods for intelligent decision making is the way to the rational use of energy with cost reduction in public buildings.

This study proposes an integrated design approach for a multifunctional UAV using composite materials, combining vacuum infusion, CFD-based aerodynamic analysis, and an STM32-based energy management system. CFD results showed a lift coefficient C L = 0.812, drag coefficient C D = 0.055, and L / D = 14.7, representing a 28 % improvement over aluminum structures. FEM analysis indicated a maximum stress of 312.4 MPa with a safety factor of 1.12, while vacuum infusion achieved 98.7 % resin impregnation, enhancing stiffness by 28 % and reducing weight by 25 %. The automated energy management system increased energy efficiency by 16.3 %, extending flight duration and improving operational stability.

: Intelligent energy management is getting increasingly important for the operation of industrial processes in a globalized world and this trend is expected to continue in the future. This article shows that energy management systems will also be increasingly important for sustainable investment planning such as building or extending a site or plant. Typical investment support methods range from simple spreadsheet-based tools to accurate but resource-intensive simulation studies. However, there is often a need to compare many alternative scenarios quickly to narrow the search space early. Hence, the expectation is not to achieve the level of accuracy of comprehensive studies. Instead, the task is to be as accurate as possible using the limited data available during preliminary frontend engineering and design (preFEED). To address this challenge, a framework was developed that provides more accurate investment decisions by integrating operational aspects in the planning phase. By this, a significant increase in accuracy can be achieved, while obtaining tangible recommendations within acceptabe time. We outline the principal functionality of this framework and share our insights by means of case studies in three different industrial application domains. Finally, we describe the faced challenges and share some lessons learned.

Intelligent energy management in off-grid smart buildings with energy interaction