Intelligent Energy Management Research Articles

Deep reinforcement learning for intelligent energy management systems of hybrid-electric powertrains: Recent advances, open issues, and prospects

Deep reinforcement learning for intelligent energy management systems of hybrid-electric powertrains: Recent advances, open issues, and prospects

Linear Parameter-Varying Model Predictive Control for Intelligent Energy Management in Battery/Supercapacitor Electric Vehicles

Linear Parameter-Varying Model Predictive Control for Intelligent Energy Management in Battery/Supercapacitor Electric Vehicles

Energy Management System Technology in Smart Grid

The smart grid, envisioned as the future of the power industry, aims to revolutionize energy supply with efficiency and sustainability. This paper extensively examines the critical role of Energy Management System (EMS) technology within the smart grid framework. By harnessing advanced technologies like distributed energy storage, intelligent energy management, electric vehicle charging, intelligent energy dispatching, and fault perception diagnosis, the smart grid promises heightened power system efficiency, reliability, and environmental consciousness. In today's dynamic energy landscape, the integration of renewable sources introduces a unique challenge due to their inherent volatility. EMS emerges as the linchpin, offering real-time monitoring and data-driven optimization to ensure grid stability amid fluctuating supply. The smart grid, with advanced information technology and intelligent control strategies, leans heavily on EMS for seamless energy production, transmission, and consumption. However, challenges persist, encompassing stable energy supply, security, infrastructure upgrades, and cross-border standardization. Yet, with sustained technological innovation and collaborative efforts, a future of intelligent, sustainable, and reliable power systems is attainable. In conclusion, EMS integration into the smart grid not only signifies technological progress but also heralds a monumental shift towards a greener, more sustainable energy landscape. This paper provides a comprehensive exploration of how EMS, as the backbone of the smart grid, empowers us to navigate the complexities of modern energy demands, paving the way for a brighter, more efficient future.

Intelligent energy management for an on-grid hydrogen refueling station based on dueling double deep Q network algorithm with NoisyNet

The development of hydrogen-based vehicles (HVs) can help achieve a zero-carbon future; however, the availability of hydrogen refueling stations (HRSs) prevents their widespread adoption as personal vehicles. Existing studies have investigated the energy management of HRSs using various methods. However, there have been no reports of implementing a deep reinforcement learning (DRL) approach to address these uncertainties and achieve real-time decision making. This study proposes an energy management optimization model of an on-grid HRS based on the improved dueling double deep Q network(D3QN) algorithm with NoisyNet. The primary goal is to reduce the cost of operating an HRS and improve voltage stability while satisfying the hydrogen demand of HVs. Notably, this study adopts an improved version of the double deep Q network (DDQN), that is, the NoisyNet-D3QN (NN-D3QN) approach, because NoisyNet can aid efficient exploration and the dueling network can generalize learning across actions. The adopted NN-D3QN algorithm has better performance than other basic algorithms. Compared with the NN-DDQN, D3QN, and DDQN approaches, the reward of the proposed method increases by 19.08 %, 31.66 %, and 39.26 %, respectively.

A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe.

Research on Optimal Dispatching of Renewable Energy and Traditional Power System

This research seeks to determine the most efficient method of incorporating renewable energy into the traditional power system. By analyzing the challenges and obstacles faced in merging renewable energy with the traditional power system, this study outlines the major variations in energy sources, the volatility of renewable energy, the uncertainty caused by climate change and weather patterns, the impact of renewable energy on grid stability. The power system's capacity and dependability were also taken into account. This study presents recommended scheduling suggestions to tackle these issues, encompassing intelligent energy prediction and management, incorporation of different energy storage technologies, enhancements to the distributed energy system, and utilization of smart grid technology.

Control and management of microgrid clusters: development and future research

The paper explores the potential of microgrid clustering technology and its impact on addressing energy and environmental challenges. Over the past decades, there has been a significant interest in using microgrids to ensure stability and reliability in power systems, especially considering the increasing utilization of renewable energy sources. Microgrid clusters coordinate power distribution between microgrids and the main grid, effectively addressing issues such as voltage rise, harmonics, low power factor, power backflows, and deficiencies in protection schemes. However, before implementing microgrid clustering, certain challenges need to be overcome, particularly in terms of design considerations. The paper critically reviews the design challenges of microgrid clustering, highlighting the advantages and challenges that arise during the development of microgrids, especially the problem of inadequate adaptation to large-scale renewable energy sources. The research analyzes various architectures of microgrid clusters and methods of their interaction. The classification of microgrid clustering is based on different architectures according to connection locations. Additionally, a comparison of different power transmission technologies within microgrid clusters, such as alternating current and direct current systems, is conducted with an assessment of the advantages and disadvantages of each technology. Considerable attention is given to the control and management of microgrid clusters, elucidating the possibilities of hierarchical and distributed management structures to ensure optimal power distribution and voltage and frequency regulation. In conclusion, the paper examines promising directions for further research aimed at enhancing the integration of large-scale renewable energy sources, developing intelligent management and energy management systems, as well as establishing reliable control and management systems for microgrid clusters.

How occupant positioning systems can be applied to help historic residences manage energy consumption: A case study in China

Historic residences often suffer from low energy efficiency due to inadequate management and obsolete infrastructure. To ensure these buildings operate in an environmentally friendly manner, intelligent energy control and management are necessary. Accurate indoor user positioning is fundamental to realizing occupant-centric controls. However, historic dwellings, with architectural heritage preservation requirements and diverse operational modes, present multiple challenges in terms of occupant monitoring and energy management. This research proposes a BLE positioning system based on the SAE-CNN algorithm to realize the analysis and prediction of occupant locations in a cost-effective and minimally-intrusive manner. The process begins by dividing spaces based on the mapped BIM model and cluster analysis. The RSSI maps of wireless signals collected from the Bluetooth beacons in each space are then preprocessed by Gaussian filter and sliding window techniques. Lastly, a machine learning algorithm refines the prediction model, which is integrated into a digital twin platform designed for energy consumption management. The system was implemented in a traditional Chinese dwelling in Wufu town, Fujian Province. The results are promising, showing that the positioning accuracy reached 1.5–2 m (95 % confidence interval), while the platform effectively presented the real-time positioning results as well as realized occupant-centric energy management. Moreover, the collected location information shed light on occupants' spatial usage habits, offering valuable insights for devising decarbonization retrofit strategies.

TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities

Quality lifestyle leads to increasing trends in smart cities by offering modern communication and information technologies. Smart cities offer multiple applications with smart management of resources such as smart agriculture, Intelligent transportation systems, waste management and energy management. These applications are based on IoTs that are composed of sensor networks with limited processing and computing capabilities and are connected with different types of networks. Due to limited computational capability, IoT sensor nodes require more time to compute different tasks and are required to offload some tasks to remotely placed cloud servers for task execution. Fog nodes are preferred over the cloud as they are placed in close access to IoT nodes distributed in different networks. Different types of networks make it more vulnerable to malicious attacks. Malicious nodes offload complex and high computing tasks to fog nodes to compromise their performance and create delays in the computing tasks of legitimate nodes. In addition, fog nodes even after removing the malicious nodes are unable to process all the legitimate tasks within a specific time frame. In this work, a Trust-based Efficient Task Execution Scheme (TETES) is proposed for fog node that scrutinizes the offloaded tasks sent by the malicious nodes and efficiently execute most of the trusted tasks within a stipulated time cycle. The simulated results show that TETES execute more offloaded tasks as compared to well-known First Come First Serve (FCFS), Longest Task First (LTF), and Shortest Task First (STF) algorithms.

A case study of a neighborhood in Douala examining the technical feasibility of a hybrid renewable energy system connected to the grid for energy and oxygen production in the fight against respiratory diseases

The world is increasingly focusing its attention on the rapid growth in electricity consumption, a concern shared by both industrialized and emerging nations. Meeting this escalating demand has become crucial, necessitating a shift towards sustainable energy use. Renewable energy sources have emerged as a popular choice for both off-grid and grid-connected installations as a means to bridge the efficiency gap. In this context, the present paper explores the potential of supplying electricity to a neighborhood in Cameroon comprising 100 homes through the integration of solar photovoltaic cells and electrolyzers. It is worth noting that Cameroon as a whole continues to grapple with severe social, economic, and physical challenges. Some regions within the country face significant obstacles in accessing quality healthcare, particularly for conditions like respiratory illnesses. This study employs an intelligent energy management method to design an optimal power flow control system, aimed at enhancing system stability during power outages and load fluctuations by effectively utilizing available storage capacity. The analysis was conducted using the Hybrid Optimization Model for Electric Renewable (HOMER) program. Our calculations reveal that a daily output of 0.123 kg mass of oxygen is produced, with renewable factor (RF) values indicating an 85.1 % integration of renewable sources, and the crucial parameter of Loss of Power Supply Probability (LPSP) reaching 0 %. These findings demonstrate that it is feasible for 100 homes to reliably generate and connect to the grid for electricity. Moreover, this research can serve as a solid foundation for the development of hybrid renewable energy systems to address the challenge of treating respiratory disorders such as COVID-19. These results underscore the significance of this study for various stakeholders, including decision-makers, policymakers, and investors in Cameroon and beyond. The ultimate goal is to promote national growth by improving socio-medical conditions.

Two-Stage experimental intelligent dynamic energy management of microgrid in smart cities based on demand response programs and energy storage system participation

Power variations due to uncertainties create fluctuations in voltage/frequency (V/F). Most critical microgrid’s (MG) challenge in smart cities is V/F stability considering uncertainties in different operating conditions. This study proposes an energy management platform based on an intelligent probabilistic wavelet petri neuro-fuzzy inference algorithm (IPWPNFIA) to control the V/F index in the presence of renewable energy sources (RESs) and battery energy storage system (BESS) facing with various uncertainties. The suggested approach is programmed at two central and local controller stages based on the communication system and time-of-use demand response programs execution. The suggested approach is modeled by considering asymmetric membership functions based on the BESS optimal participation to control uncertainties caused by RESs, plug-and-play operations, and load fluctuations. The proposed platform's performance is verified and compared in different scenarios with different methods. The experimental setup and results are based on the rapid control prototyping of the micro-grid platform, MATLAB/Simulink and RT-LAB software, and hardware infrastructure such as the OPAL-RT (OP5600/OP8660) System. The most important highlights of this research are: fast dynamic response, real-time control based on real data, reducing the calculation time and burden based on learning algorithms, and the suitable coordination to adjust the protection equipment pick-up time.

Futuristic Energy Management Solution: Fuzzy logic controller-Enhanced Hybrid Storage for Electric Vehicles with Batteries and Super Capacitors

The core focus of this study was directed towards devising an energy management strategy tailored for hybrid storage systems (HSS) within electric vehicles, with the prime objective of enhancing the longevity of the battery cycle. The batteries employed in electric vehicles (EVs) are prone to expedited deterioration resulting from harsh charging/discharging cycles and the substantial power surges experienced during acceleration and deceleration phases. While an excessively large energy storage system (ESS) could cater to the elevated power requisites, it inevitably grapples with augmented dimensions, bulk, and cost implications. In a bid to surmount these challenges, an innovative approach has been explored: integrating battery and supercapacitor (SC) elements within the HSS. This synergy aims not only to curtail the overall ESS footprint but also to elongate the operational lifespan of the battery. The pivotal concern revolves around constructing an adept energy management scheme that orchestrates the interplay between the primary energy storage component and the SC, a resource in demand by EVs. Central to this study is the proposition of an intelligent energy management strategy, grounded in fuzzy logic controller (FLC), seamlessly embedded within the within the HSS of the EV. To translate these concepts into tangible outcomes, a comprehensive assessment was conducted. By leveraging the capabilities of MATLAB/Simulink software, the state of charge for the super capacitor and the power dynamics of the battery were scrutinized across diverse driving scenarios over distinct time intervals. The standout feature of this investigation lies in the employment of an ingenious FLC strategy, meticulously regulating the energy and flow of power and energy between the battery and super capacitor elements within the HSS of the EV. In a comparative analysis against conventional control methodologies, this approach shines vividly, yielding superior outcomes and cementing its efficacy.

Power Management Optimization of HES Connected to the Grid-Based IEMS

This paper presents a study of a grid-connected hybrid energy system (HES) consisting of photovoltaic/wind/fuel cell/battery/supercapacitor (SC). The purpose of using SC with the battery is to create a hybrid energy storage system (HESS) with high energy density. To prevent overcharging of the latter, a discharge load was employed (in our case, a resistance). The objectives of this research are to operate the photovoltaic generator and the wind power generator at their maximum power points by applying the perturbation and observation (P&O) technique and the tip speed ratio (TSR) technique, respectively. On the other hand, the energy management of the proposed system aims to achieve an energy balance between the grid and the HES despite climate changes and changes in energy demand to which the system is exposed, and to lengthen the life cycle of the HESS by keeping the state of charge (SOC) within a limited range. This management is accomplished by using a classical energy management strategy. To achieve optimal power management of the proposed system, a new intelligent energy management strategy (IEMS) based on the FLC-FOPI is proposed. A comparative analysis was also conducted between the classical strategy and the proposed strategy. As for the grid-side converter, direct power control based on SOSMC technique (DPC-SOSMC) has been proposed to regulate the flow of active and reactive power according to grid demand based on HRES. To demonstrate the effectiveness of the proposed controllers, simulation results evaluating the proposed system under variable conditions are presented using the Matlab-Simulink platform.

Enhancing energy efficiency with smart grid technology: a fusion of TCN, BiGRU, and attention mechanism

Introduction: Smart Grid (SG) as an intelligent system has become a key element in the efficient operation of the electrical grid. With the continuous increase in global energy demand and escalating environmental concerns, the importance of energy conservation and sustainable energy sources has become increasingly prominent. Especially in energy-intensive sectors such as large-scale buildings, energy supply and management face challenges. These structures require a significant amount of energy supply at specific times, but may encounter energy wastage issues at other times.Method: Smart Grid technology establishes a network that can transmit both electricity and data. By making full use of this data, intelligent decision-making is achieved, optimizing grid operations. Therefore, the application of Smart Grid technology to energy conservation has attracted attention and become a research focus. This study utilizes the TCN-BiGRU model, leveraging spatiotemporal sequence data and incorporating an attention mechanism to predict future energy consumption.Results: The research results indicate that the integration of Smart Grid technology, TCN, BiGRU, and the attention mechanism contributes to accurately and stably predicting energy consumption demands. This approach helps optimize energy scheduling, enhance energy utilization efficiency, and realize more intelligent, efficient, and sustainable energy management and utilization strategies.Discussion: This study provides an innovative solution for applying Smart Grid technology to energy conservation in large-scale buildings. This approach holds the potential to improve the efficiency of energy supply and management, promote sustainable energy utilization, and address the growing global energy demand and environmental issues.

Intelligent Learning Algorithm and Intelligent Transportation-Based Energy Management Strategies for Hybrid Electric Vehicles: A Review

As one of the alternatives to conventional fuel vehicles, hybrid electric vehicles (HEV) offer lower fuel consumption and fewer exhaust emissions. To improve the performance of the HEV, the energy management strategy (EMS) is one of the most critical technologies. Classic EMS can be broadly classified into rule-based and optimization-based. With the development of machine learning technology, the deep reinforcement learning (DRL) algorithm of intelligent learning algorithms has been applied to the EMS. This paper mainly reviews the research progress of the EMS based on DRL from two aspects of the algorithm and training environment, and the EMS research involving combining the intelligent transportation system (ITS) is reviewed. In addition, the experimental test progress situations of DRL-based EMS research are discussed. Finally, the challenge of DRL-based EMSs is analyzed and some solutions are provided. In particular, it also involves some discussion about automotive cyber security in the intelligent transportation environment.

Demand response-based cost mitigation strategy in renewable energy connected microgrid using intelligent energy management system

Demand response-based cost mitigation strategy in renewable energy connected microgrid using intelligent energy management system

An intelligent sleep-awake energy management system for wireless sensor network

An intelligent sleep-awake energy management system for wireless sensor network

Optimizing electric vehicle charging schedules and energy management in smart grids using an integrated GA-GRU-RL approach

Introduction: Smart grid technology is a crucial direction for the future development of power systems, with electric vehicles, especially new energy vehicles, serving as important carriers for smart grids. However, the main challenge faced by smart grids is the efficient scheduling of electric vehicle charging and effective energy management within the grid.Methods: To address this issue, we propose a novel approach for intelligent grid electric vehicle charging scheduling and energy management, integrating three powerful technologies: Genetic Algorithm (GA), Gated Recurrent Unit (GRU) neural network, and Reinforcement Learning (RL) algorithm. This integrated approach enables global search, sequence prediction, and intelligent decision-making to optimize electric vehicle charging scheduling and energy management. Firstly, the Genetic Algorithm optimizes electric vehicle charging demands while minimizing peak grid loads. Secondly, the GRU model accurately predicts electric vehicle charging demands and grid load conditions, facilitating the optimization of electric vehicle charging schedules. Lastly, the Reinforcement Learning algorithm focuses on energy management, aiming to minimize grid energy costs while meeting electric vehicle charging demands.Results and discussion: Experimental results demonstrate that the method achieves prediction accuracy and recall rates of 97.56% and 95.17%, respectively, with parameters (M) and triggers (G) at 210.04 M and 115.65G, significantly outperforming traditional models. The approach significantly reduces peak grid loads and energy costs while ensuring the fulfilment of electric vehicle charging demands and promoting the adoption of green energy in smart city environments.

Preface

The 2023 2nd International Conference on Smart Energy and Energy Internet of Things (SEEIoT 2023), held from June 16th to 18th, 2023 in Guilin, China (virtual event), was a premier academic conference on smart energy and energy Internet of Things for research groups working on related fields. It allowed researchers to share their experiences with other groups from all over the world by presenting scientific work and potential innovations in this research area and in the social events promoting informal get togethers.The works presented in this paper volume cover a broad range of topics including Actuators and Sensors for Smart Grids, Power Grid Dispatching and Automation Control, Ubiquitous Electric Internet of Things, Intelligent Energy Management, etc.The Conference was intended to give students and research groups an opportunity to learn more about smart energy and energy Internet of Things as an important tool in their applications and development. It represented a unique opportunity to meet colleagues and friends, exchanging ideas, and learning about new developments and best practice, while working to advance the understanding of the knowledge base that we will collectively draw upon in the years ahead to meet future challenges. Participants attended the academic presentations by scholars representing both institutes and academia.The Proceedings of SEEIoT 2023 included some excellent papers selected via a peer review process. The Conference Program featured keynote speeches by three world-leading scholars and many sessions for regular paper oral and poster presentations, attracting over 60 participants. Professor Zhenbin Zhang from Shandong University, China gave a speech on Bias-Free Fast Start-Up Strategy and High-Performance Predictive Control Method for DC Energy Routers. Energy routers based on Dual Active Bridge (DAB) converters are key components of hybrid micro-energy systems. They facilitate efficient bidirectional energy flow, and flexible integration of renewable energy sources. Three technical shortcomings are associated with conventional control methods for the DAB converter, viz., poor startup performance, slow dynamic response, and poor parameter robustness. His research in deed proposed effective solutions to these challenges.In making this Conference possible, we want to acknowledge our deep appreciation to all our conference authors for their excellent contributions and reviewers for their hard work on reviewing all the submissions; to all the Committee members for their effort and enthusiasm. Particular thanks go to the staff of Journal of Physics: Conference Series for their time and outstanding publishing work.The Committee of SEEIoT 2023List of Committee Member are available in this pdf.

Multiobjective Intelligent Energy Management for Hybrid Electric Vehicles Based on Multiagent Reinforcement Learning

This paper proposes a multi-objective energy management strategy (EMS) based on multi-agent reinforcement learning (MARL) for a hybrid electric vehicle equipped with an engine-generator set (EGS) and a hybrid energy storage system (HESS, consisting of a battery and ultracapacitor). First, besides improving fuel economy, maintaining battery state of charge (SOC), reducing battery degradation and constraint on ultracapacitor SOC are also taken into consideration, formulating multi-objective energy management. Then, the problem is solved using MARL which combines game theory and reinforcement learning. In this framework, EGS and HESS are viewed as two intelligent agents respectively, and their interactions are described as a general-sum stochastic game. Following the principle of MARL, the two agents can learn the optimal control policy which guarantees the Nash equilibrium of multiple objectives, thus achieving a satisfactory balance among them. In the simulation, the MARL-based EMS is compared with single-agent reinforcement learning (SARL) which ignores the relations of different agents, and dynamic programming (DP) which integrates various targets into a single cost function with weight coefficients. The simulation results verify the superiority of the proposed EMS in optimizing multiple objectives.