Secure Energy Research Articles

The Effect of Green Intellectual Capital on Green Competitive Advantage in Sustainable City Management

Resource-based view (RBV) is a valuable theoretical foundation in analyzing organizational strategic resources to achieve Green Competitive Advantage in green city management. The purpose of this study is to analyze the strategic resources of the Semarang municipal government and this public sector capability in achieving a green competitive advantage in the management of green city. The application of the green city concept aims to create a more sustainable and environmentally friendly environment, by optimizing the use of renewable energy and reducing emissions. The use of this renewable energy can also provide a more stable and secure energy source because it does not depend on the irregular supply of fossil fuels and is vulnerable to price fluctuations. The method used is by quantitative techniques through Structural Equation Modeling (SEM) with a number of 116 respondents in Semarang City municipal government was taken as the sample through purposive sampling technique. The findings through quantitative analysis shows that green intellectual capital, eco-innovation and transformational leadership factors have a significant effect on Green Competitive Advantage in the context of green city management. As a suggestion, it is advisable to enhance the capacity for sharing a common vision, facilitating communication among parties involved, implementing sustainable management practices, nurturing human resources, and employing a collaborative governance framework for the purpose of building a green city in Semarang, Indonesia.

Deep-Reinforcement-Learning-Based IRS for Cooperative Jamming Networks Under Edge Computing

Effective energy design and secure communications are critical in the Internet of Things (IoT). A cooperative jamming (CJ) scheme based on deep reinforcement learning (DRL) is proposed for intelligent reflecting surface (IRS) aided secure cooperative network with eavesdroppers. Our goal is to maximize the average secrecy rate, or energy efficiency with secrecy constraints by jointly optimising the transmit, jamming beamforming matrices and IRS phase-shift matrix. In order to figure out the challenging non-convex optimization problem, the deep deterministic policy gradient (DDPG) optimization algorithm is proposed to solve the energy efficiency maximization problem. In order to speed up task processing and reduce service delay, the edge computing server is employed for DRL training to improve computing speed and promote secure communication efficiency. By taking advantages of the features of proximity to end devices and computing resources, the edge devices provide a low latency efficient training support to the legal users and real-time control to the IRS units. By this way, a high security and low energy consumption IoT system is built. Numerical results show that the novel scheme improves the secrecy rate and energy efficiency compared with the benchmark scheme.

The overarching role of electric vehicles, power‑to‑hydrogen, and pumped hydro storage technologies in maximizing renewable energy integration and power generation in Sub-Saharan Africa

Globally, efforts are underway to keep global warming well below 1.5–2 °C as outlined in the Paris Agreement. Ghana plans to achieve 10 % renewable energy (RE) participation in the national energy mix by 2030. In the Renewable Energy Masterplan 2030, the target is a total installed RE utility scale of 1094.63 MW. In the current work, we propose a better pathway to gradual decarbonization of the Ghanaian energy sector (power, transport, industry, residential, and others) at higher technical levels of variable RE (VRE) penetration, 0 critical excess electricity production (CEEP), and lower cost than the 2030 Business-as-Usual (BAU) scenario. In our proposed scenario, High Renewable Energy Penetration (HREP) 2030, we assess the overarching role of electric vehicle integration, power-to-gas (hydrogen), and pumped hydro storage technologies in maximizing VRE penetration in Ghana. The results from the current study show that it is technically feasible to include solar and wind power penetrations of 32.66 % and 38.11 % in the 2030 HREP scenario compared to the 2.64 % and 1.99 % of the 2030 BAU case, respectively. The HREP scenario will ultimately lead to 26.45, 30.86, and 23.34 % contribution of solar, wind, and thermal in the total electricity production in 2030 compared to the 2.36, 1.78, and 74 % of the BAU scenario, respectively. The CO2 emissions and its cost in the HREP case are 22.5 and 22.81 % lower than in the BAU case, and the total annual cost of the former is 12 % lower than the latter. The total electricity production from wind and solar energy alone in the 2030 HREP will create an additional 723 jobs and 11.96 Mt avoided CO2 emissions. The proposal made in the current study could usher in a long-term transition pathway that leads from the current fossil-based system to an affordable, efficient, sustainable, and secure energy future for Ghana. The findings presented in the current study should be of significance to decision-makers in formulating and adopting zero and low-carbon strategies for long-term decarbonization targets and planning in Ghana beyond 2030.

An approach for sizing a PV–battery–electrolyzer–fuel cell energy system: A case study at a field lab

Hydrogen is becoming increasingly popular as a clean, secure, and affordable energy source for the future. This study develops an approach for designing a PV–battery–electrolyzer–fuel cell energy system that utilizes hydrogen as a long-term storage medium and battery as a short-term storage medium. The system is designed to supply load demand primarily through direct electricity generation in the summer, and indirect electricity generation through hydrogen in the winter. The sizing of system components is based on the direct electricity and indirect hydrogen demand, with a key input parameter being the load sizing factor, which determines the extent to which hydrogen is used to meet seasonal imbalance. Technical and financial indicators are used to assess the performance of the designed system. Simulation results indicate that the energy system can effectively balance the seasonal variation of renewable generation and load demand with the use of hydrogen. Additionally, guidelines for achieving self-sufficiency and system sustainability for providing enough power in the following years are provided to determine the appropriate component size. The sensitivity analysis indicates that the energy system can achieve self-sufficiency and system sustainability with a proper load sizing factor from a technical perspective. From an economic perspective, the levelized cost of energy is relatively high because of the high costs of hydrogen-related components at this moment. However, it has great economic potential for future self-sufficient energy systems with the maturity of hydrogen technologies.

Analyzing the impact of demand response and reserves in islands energy planning

Small Islands usually rely on fossil fuels for their energy supply and face common challenges such as high energy costs and carbon dioxide emissions. For these reasons they represent interesting cases for analysing the transition towards a clean and secure energy system. Nevertheless, integrating non-dispatchable Renewable Energy Sources in the power grid causes stability issues and this is particularly true for island grids. Such issue is not fully considered in long-term energy planning; indeed, an important factor that should be considered in order to ensure the reliability of the grid are Reserves. There are different types of Reserves depending on the reactiveness/response time and the duration of the service. In this paper, primary and secondary reserves have been analysed in order to plan the long-term energy transition of the small island of Favignana, Italy by means of the new version of H2RES, a Linear Programming single-objective optimisation model able to provide a long-term capacity investment and dispatching optimisation. It has been found that biomass generators are favoured to both photovoltaic and wind turbines for their ability to provide reserves and also decrease the unpredictability of the supply. Batteries and Electrolysers are also used mostly for reserve provision.

Secure Computation Offloading for Marine IoT: An Energy-Efficient Design via Cooperative Jamming

In smart ocean, the Marine Internet of Things (M-IoT) is ubiquitously used for data acquisition, communication connections, task computation, and so on. However, high-efficient energy consumption and secure yet low-latency transmission are subject to IoT devices' computation capacity and transmit power limitation, as well as wireless communication's broadcast characteristics. In this paper, we investigate the secure computation offloading in M-IoT with the assistance of unmanned surface vehicles (USVs) subject to the eavesdropping attack. In particular, we consider a scenario in which USVs are firstly scheduled to set up a high-quality communication link to the high altitude platform (HAP) and then exploited to provide cooperative jamming for the communication security at the physical layer when the HAP is performing computation offloading. We jointly optimize the USVs' positions, the duration of data uploading, the workload offloaded by HAP, the HAP's transmission power, and each USV's jamming signal power to minimize the system-wise energy consumption for completing the total workloads under the latency constraint. Despite the non-convexity, we decompose the problem vertically into a top problem that optimizes the USVs' positions and a bottom problem that optimizes the other variables. The Polyblock outer Approximation and bisection Search based algorithm (PAS-Algorithm) is proposed to alternatively optimize the variables in the bottom problem. And the Code bAsed croSs Entropy algorithm (CASE-Algorithm) is proposed to obtain the suboptimal solution to the formulated joint optimization problem. Numerical results validate the accuracy and effectiveness of our algorithms as well as the performance of optimizing the total energy consumption compared with the benchmark algorithms

Preface

China is currently the world’s largest producer of wind and solar energy. China has made great efforts in promoting decarbonization in energy and reducing greenhouse gas emissions. In the future, China will accelerate the development of low-carbon and zero-carbon technologies to help promote green development. China has a clear pathway to build a more sustainable, secure and inclusive energy future.Following this concept, the 2022 International Conference on Renewable Energy and Electrical Technology (ICREET 2022) took place held during December 16th-18th, 2022 in Xi’an, China (virtual event). The conference provided a platform of international standards where all the participants discussed and shared persuasive key advances in renewable energy and electrical technology. It also offered a unique venue for renewing professional relationships and for remaining up-to-date variations in the challenging and expanding fields.This volume contains the papers presented at the 2022 International Conference on Renewable Energy and Electrical Technology. The papers covering various topics including Renewable Energy Power Generation, Natural Gas Hydrate Development Technology, Energy Internet Technology, Electric Motors and Drives, Power System Analysis, etc. have been through rigorously reviewed by experts and meet the requirements of publication.The online conference attracted about 60 individuals, and three sophisticated professors performed their keynote speeches during the conference. Among them, Prof. Pierluigi Siano from University of Salerno, Italy, one of our keynote speakers, shared with us his research on Energy Communities in Smart Grids. A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) was proposed in this study. To consider both reserve capacity and reserve energy, day-ahead and real-time stages in the ASM were considered. A method, based on hybrid Variable Neighborhood Search (VNS) and distributed parallel optimization was designed for the day ahead and real-time optimization. All the keynote speakers made brilliant speeches combing their own research domains and experiences, and shared unique insights, creating a great platform for academic communication.On behalf of the Conference Organizing Committee, we would like to extend our sincere acknowledgement to all the keynote speakers, peer reviewers, and all the participants. Particularly, our heartfelt thanks and appreciation go to the Editors and Managers, Journal of Physics: Conference Series for their help and kind cooperation during the preparation of the proceedings. Hopefully, all participants and other interested readers can benefit significantly from the proceedings and find it rewarding.The Committee of ICREET 2022List of Committee member is available in this Pdf.

Navigating the Energy Trilemma: Oil and Gas Industry Making Strides

_ Energy is vital to human flourishing. Since the dawn of time, humans have sought to find more efficient ways to power and propel their lives forward. With each generation, we’ve made progress in unlocking energy density—moving from burning wood for warmth inside caves thousands of years ago through our current age, where a revolution in modern energy is transpiring. However, recent global events have certainly tested the upstream oil and gas sector’s “can-do” attitude and spirit of innovation. In the 3 years since West Texas Intermediate Crude (WTI) futures dipped below zero—dropping more than 300%—to settle at negative $37.63/bbl, there has been a global COVID-19 pandemic and recovery, supply chain and logistical challenges, and a war. The oil and gas industry is more adept at riding the market volatility wave than in the past. WTI futures climbed out of the negative, reaching as high as $123.70/bbl on 8 March 2022, as the pandemic began to ease, and the lockdown ended in many regions worldwide. Geopolitical tension with Russia and its full-scale invasion of Ukraine in the first half of 2022 contributed to the crude oil price increases. Also contributing to higher pricing in the first half were concerns over supply levels. The Russian invasion of Ukraine came during eight consecutive quarters of crude oil level decreases, according to the US Energy Information Administration (EIA). In the year since, the price has dropped but not nearly as profoundly, with recent prices ping-ponging in the mid-$60 to mid-$80/bbl range. The EIA cited as reasons for the lower price concerns over a possible economic recession and China’s severe COVID-19 containment measures as keeping global demand lower. The Russian invasion of Ukraine turned global markets upside down. The International Energy Agency (IEA) cited it as the trigger of a worldwide energy crisis that “has the potential to hasten the transition to a more sustainable and secure energy system,” in its World Energy Outlook 2022. “Energy markets and policies have changed as a result of Russia’s invasion of Ukraine, not just for the time being, but for decades to come,” said IEA Executive Director Fatih Birol at the time of the outlook’s release. “Even with today’s policy settings, the energy world is shifting dramatically before our eyes.” Transition and Trilemma The energy transition changes how the world views and uses oil and gas. While it is not the first energy transition the world has faced, it is proving to be the most significant and complicated. “It is needed if we are to prevent the 1.5°C increase in temperature,” Kjetil Hove, the executive vice president of development and production in Norway for Equinor, said during his keynote address at the SPE/IADC International Drilling Conference and Exhibition held in Stavanger on 7 March. “The future carbon intensity of the energy mix needs to be reduced significantly. Last year’s energy crisis and the war in the middle of Europe showed that the energy transition needs to be done wisely and that it addresses the energy trilemma,” he said.

System integration analysis of a zeolite 13x thermal energy storage

Nowadays a major energy transition aimed at cutting CO2 emissions has begun, with the ambitious goal of drastically reducing the dependence on fossil fuels and promoting the use of renewable energy sources. In this context, energy storage systems play a key role in ensuring a stable, secure and time-aligned energy availability. Open sorption thermal energy storages (TESs) can contribute to such transition, since they are simple and cheap devices able to provide a hot airflow with a high energy density and to store energy for long time. In this work, open sorption TES systems operated with Zeolite13X are investigated with a focus on the integration in practical applications. First, the experimental analysis and modelling of the sole TES system is described. Then, the integration of the TES at system level is carried out. The analysis evidences that a significant reduction of storage capacity occurs if air humidification is necessary before the adsorption process or afterwards to keep adequate indoor comfort conditions for the occupants. For the exemplary case study considered, an energy density of 150 kWhm−3 can drop to 40 kWhm−3 in case of complete compensation of the adsorbed water through a humidifier. This paper provides useful guidelines for evaluating the performance of a TES integrated system in terms of specific energy and efficiency. It also identifies applications involving high latent loads (e.g., swimming pools, locker rooms) or for which low relative air humidity is required (e.g. interior of vehicles to prevent condensation on windows) as particularly beneficial for using a TES system.

Energy Efficient and Secure Neural Network–based Disease Detection Framework for Mobile Healthcare Network

Adopting mobile healthcare network (MHN) services such as disease detection is fraught with concerns about the security and privacy of the entities involved and the resource restrictions at the Internet of Things (IoT) nodes. Hence, the essential requirements for disease detection services are to (i) produce accurate and fast disease detection without jeopardizing the privacy of health clouds and medical users and (ii) reduce the computational and transmission overhead (energy consumption) of the IoT devices while maintaining the privacy. For privacy preservation of widely used neural network– (NN) based disease detection, existing literature suggests either computationally heavy public key fully homomorphic encryption (FHE), or secure multiparty computation, with a large number of interactions. Hence, the existing privacy-preserving NN schemes are energy consuming and not suitable for resource-constrained IoT nodes in MHN. This work proposes a lightweight, fully homomorphic, symmetric key FHE scheme (SkFhe) to address the issues involved in implementing privacy-preserving NN. Based on SkFhe, widely used non-linear activation functions ReLU and Leaky ReLU are implemented over the encrypted domain. Furthermore, based on the proposed privacy-preserving linear transformation and non-linear activation functions, an energy-efficient, accurate, and privacy-preserving NN is proposed. The proposed scheme guarantees privacy preservation of the health cloud’s NN model and medical user’s data. The experimental analysis demonstrates that the proposed solution dramatically reduces the overhead in communication and computation at the user side compared to the existing schemes. Moreover, the improved energy efficiency at the user is accomplished with reduced diagnosis time without sacrificing classification accuracy.

Deep Reinforcement Learning Based UAV for Securing mmWave Communications

This paper focuses on the unmanned aerial vehicle (UAV) enabled millimeter (mmWave) communications from physical layer security perspective. A UAV is arranged as an aerial base station to provide ubiquitous connectivity for terrestrial users in the presence of multiple eavesdroppers. With statistical channel state information (CSI) of eavesdroppers, the beamforming vector and trajectory of UAV as well as user scheduling are jointly optimized to minimize the weighted sum of UAV flight period and secrecy outage duration. The considered problem is a combinatorial optimization problem with complicated objective function, and thus difficult to be solved by convex optimization-based methods. To this end, we formulate this problem as a Markov decision process (MDP) and develop a deep reinforcement learning (DRL) based method to optimize all variables simultaneously. Simulation results validate superiority of the proposed method over benchmarks and demonstrate its ability to obtain a compromise between secure transmission and energy efficiency.

A framework for exploring futures of complex urban energy systems

In order to address the climate crisis and provide citizens with clean, secure and affordable energy, urban energy systems need to transition. This is significant as urban energy systems are increasingly seen as complex systems for their close interactions with local urban society, while being interdependent with higher levels of governance. Decisions taken today will continue to influence the inhabitants of our cities for well over 50 years, locking in energy consumption patterns of the future. How, then, do we make decisions on the interventions needed to bring about a desirable future, and prepare for the probable and possible futures? In this paper, we consider the key characteristics of urban energy systems from a complexity science perspective in order to explore what methodologies in futures and foresight scholarship could be beneficial in supporting urban energy decision-making. To do this we have undertaken an integrative review—a method that allows review, synthesis, critique, and analysis of new and emerging topics across multiple disciplines and multiple literature types—and consider the findings in light of their usefulness in understanding complex systems, which are inherently uncertain. We consider how futures and foresight theories and methods can be applied in urban and energy studies, highlighting examples of where around the world these have been applied by organizations seeking to shape transitions. The many methods and approaches that exist under the futures' umbrella have not been applied to anywhere near their full potential in urban energy studies, despite the limitations of many of the planning and modeling exercises currently used. We use key learnings from existing futures and foresight scholarship, along with our understanding of urban energy systems as complex adaptive systems, to propose a theoretical and practical framework for exploring their futures. The framework encompasses concepts of futures, contextualization, mapping uncertainty, participatory processes, and futures governance. Although there is much further research work needed to test and operationalize this framework in an applied way with city stakeholders, we hope this charts a way forward in addressing the critical challenges faced by urban energy planners and their partners.

Advances in Solar-Derived Chemical Fuel Systems

Fuel cells are essential components of a large portfolio for developing a competitive, secure, and sustainable clean energy economy as they possess the ability to efficiently convert a variety of fuels into electricity. They convert chemical energy from fuels into electricity through chemical reactions with an oxidizing agent. Fuel cells are highly efficient and can produce electricity with very little pollution. They are used in a variety of applications, including powering buildings and vehicles, and as a backup power source. However, the infrastructure for fuel cells is still not fully developed and the cost of fuel cells is currently high, hindering their widespread adoption. This article discusses various advanced fuel cell types with descriptions of their working principles and applications. It provides some insights on the requirements of solar-derived chemical fuel cells as well as some novel materials for the fabrication of solar-derived chemical fuel cells. Discussions on the limitations of solar-derived fuel cells were provided in relation to environmental hazards involved in the use of these cells.

Legal and Policy Issues While Evaluating the Sustainability of a Floating Storage Regasification Unit: The Case of Alexandroupoli Greece

Floating Liquid Natural Gas (FLNG) facilities are increasingly being used in developing countries since floating regasification and storage units (FSRU) are proven to be more cost-effective per thermal unit than traditional land-based facilities. The purpose of this study is to assess the main issues and the sustainability of an FSRU project, namely the regional and international energy policies and the need to develop a novel regulatory framework, considering all relevant international policies and legislation. Therefore, the Alexandroupoli FSRU was elected because it has several advantages for Greece, the Balkans and the European Union since it supports the basis for a competitive, secure and time-consuming energy market. In addition, the project helps the E.U. to achieve its energy goals and climate objectives in line with the Paris Agreement and provide affordable, safe and sustainable energy to all citizens. Most importantly, the project was elected to demonstrate the volatility of this specific market in light of the Russo–Ukrainian conflict.

Experimental validation of an AI-embedded FPGA-based Real-Time smart energy management system using Multi-Objective Reptile search algorithm and gorilla troops optimizer

This paper proposes an AI-embedded FPGA-based Smart Energy Management System (SEMS) that ensures intelligent, secure, consistent, and synchronous energy management in an isolated microgrid. The proposed techno-economic SEMS comprises two levels of control to achieve optimal management and operation for an isolated microgrid. The first level adopts the use of the FPGA as a central controller, which is characterized by its high processing speed and small settling time. The second level aims to develop a coordinated operation strategy based on the optimal operation and management of an isolated microgrid in order to optimize the coordinated use of backup sources. An efficient multi-objective optimization problem for optimal operation and management of the microgrid is formulated. Two multi-objective optimization algorithms namely, Gorilla Troops Optimizer (GTO) and Reptile Search Algorithm (RSA) are applied to solve the optimization problem. The three main objectives considered in this study are to minimize the operating costs, the loss of power supply probability (LPSP), and the surplus power consumed by the dummy load. The results prove the superiority of the RSA algorithm in achieving the goals of the objective functions. Within 100 min of the experimental testing, it achieves the lowest operating cost 166.2423 $. The cost savings reach about 6.467 % when using the RSA, while it is 6.0363 % when using the GTO. The developed SEMS reduces the wasted power in the dummy load. In addition, it achieves the lowest value of LPSP about zero, which is considered the best value as it ensures continuous supply.

Research on Foran Energy Transformation Strategy

Hydrogen energy is a critical component of a sustainable and secure energy future, as well as an important development direction of the global energy technology revolution. Accelerating the development of the hydrogen energy industry can help not only to deal with the global environmental crisis, but also to ensure energy supply and achieve sustainable national energy development. By the end of 2021, there had been more than 10 provincial and municipal hydrogen energy industry plans, as well as more than 30 regional and county hydrogen energy special plans, and the central and local governments at all levels issued policies to promote the increase of hydrogen density, as well as further increased subsidies. Foran Energy is one of many companies making the switch to hydrogen. This article will be based on Foran Energy's data for the first half of 2022, combining the surrounding environmental situation to solve the phenomenon of low profit margins following Foran Energy's transformation. The study found that the company can improve its profit margin by adapting to the world situation and adjusting its development strategy.

Petroleum++: Envisioning the Future of SPE

The main values of SPE are providing our members with the latest advancements in all petroleum engineering technical disciplines and enabling professional development throughout their careers from university until retirement. This is achieved through the programs and services SPE offers to us, members, and evolves with the industry and technology. That necessitates that the SPE Board of Directors update the Society’s Strategic Plan as needed. Recently, updating the Strategic Plan has been done about every 5 years. The last exercise to that effect started in June 2022, and the final version was approved during the most recent Board meeting in January 2023. We engaged a consultant to help guide us through the process, and reached out to members in many ways to gather their feedback on challenges and future directions: - One-on-one virtual interviews with members of the SPE Foundation Board of Trustees, members, and chairs of SPE committees, industry members/key sponsors, academia, and SPE section leaders - Focus groups with members including past presidents, advisory councils, young professionals, and academicians - SPE Connect General Discussion community where questions were posted for member feedback - Online survey with mostly open-ended questions that was advertised to all members through JPT, monthly and publication newsletters, and on SPE.org - Session at ATCE where attendees could ask questions and make comments Here are the main results of this work. The entire Strategic Plan can be found here. The core purpose of SPE is to provide shared expertise, resources, and lifelong learning opportunities to fuel the success of our members and the future of the industry. Our mission is to connect a global community of engineers, scientists, and related energy professionals to exchange knowledge, innovate, and advance their technical and professional competence regarding the exploration, development, and production of oil and gas and related energy resources to achieve a safe, secure, and sustainable energy future. SPE is committed to these core values: - Technical Excellence and Professionalism—committed to innovation, lifelong learning, and the highest standards of professional conduct and expertise - Member Focus—consistently provide accessible, relevant resources to help members thrive in the ever-evolving energy industry - Global Scale and Local Relevance—leverage insights and resources to better serve members’ needs both globally and locally - Collaboration—dedicated to knowledge dissemination and exchange to solve complex problems through internal and external networking and volunteer opportunities - Diversity and Inclusion—a community that embraces diversity and inclusion as essential to the success of our industry and society - Environmental and Social Stewardship—leadership to achieve a safe, secure, and sustainable energy future SPE’s vision is to advance the oil- and gas-producing and related energy communities’ ability to meet the world’s energy needs in a safe, secure, and sustainable manner.

A Novel Reconfigurable Coordinated Control for Autonomous Low Voltage DC Microgrid

DC microgrids with a small electrical network of locally available energy resources and distributed loads are becoming popular in real-life applications, especially in remote areas and in some specific urban applications. This, in turn demands a serious insight into the control and reliability enhancement of such systems so that the availability of secure and safe energy can be assured to the users. But the integration of intermittent renewable energy resources in dc microgrids lead to a serious concern regarding the stability of the microgrid bus voltage under continuous change in available source power and the loading conditions. While the microgrid management is still a challenge under steady state operating conditions, it becomes much complex under some emergency events like faults, failure of controllers/sources and overloads etc. This paper proposes a novel reconfigurable coordinated control architecture for a Low Voltage DC (LVDC) microgrid comprises of solar photovoltaic, fuel cell based renewable energy sources supported Energy Storage System (ESS) formed with battery and ultra-capacitor banks. The reliability of the system is ensured in the proposed reconfigurable coordinated control with three layered hierarchy viz. Emergency control layer sandwiched between the supervisory and local control layers. The proposed control strategy is implemented through a new Microgrid Bus Controller (MGBC) strengthened by an “emergency” control layer which handles the responsibility in emergency conditions to coordinate the energy sources, loads and controllers. The MGBC facilitates the fault identification and isolation to ensure the reliable operation of the healthy microgrid sections and speedy fault recovery. The MGBC is dedicated for observing the bus parameters and identify the fault location by monitoring the bus branching currents. Some of the key hardware and simulations results validating the proposed architecture along with the details of the control implementation are presented in this paper.

A Federated Learning and Blockchain-Enabled Sustainable Energy Trade at the Edge: A Framework for Industry 4.0

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy-trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and artificial intelligence, enables smart and secure microgrid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and federated learning-enabled solution provide secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.

Blockchain-Driven Real-Time Incentive Approach for Energy Management System

In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost.