The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R2 scores of 0.874 and 0.9375.
Abstract A global push to achieve universal electricity access, paired with drastic reductions in the cost of decentralized electricity technologies, has led to significant research on how best to roll out access to rural communities in sub-Saharan Africa. Various geospatial electrification models have been developed to aid the decision-making process considering decentralized grid alternatives such as mini-grids and solar home systems. Despite these tools suggesting that in many cases, decentralized systems are a more cost-effective electricity access pathway, grid extension still predominates in practice. This is due, at least in part, to institutional structures in most countries that provide significant direct and indirect subsidies to grid extension projects, commonly through publicly-owned utilities. These sources of finance are generally not available to primarily privately operated off-grid energy service providers. However, the subsidy provided for grid extension projects is not well understood. In this paper, we employ utility grid extension costs and revenue data, and geospatial grid infrastructure data to estimate the size and distribution of subsidy implicitly provided to rural grid extension projects for 129 communities in Mombasa County, Kenya. We also estimate subsidies for hypothetical off-grid electricity systems in the same communities that would deliver equivalent services to the grid. We allocate the cost of shared medium voltage (MV) distribution infrastructure using a marginal and an average cost method for grid extension and compare these with subsidies for off-grid systems. We find that the average of average subsidy per customer across communities for grid extension is US$5,118 and US$5,330 for the two MV cost allocation methods respectively, while for the off-grid systems the corresponding average of average subsidies are US$3,380, using a real discount rate of 1.3% evaluated from a nominal discount rate of 8% and inflation rate of 6.7%. Our results show that in the communities in our case study, 40% and 37% of the communities would command less subsidy while served by minigrids over the grid, and the switch would save 50% and 54% of the total cost for average and marginal cost allocation methods respectively. We also show that by using a multi-model approach to electrification and by reallocation of implicit subsidies that have been exclusive to grid extension to other technology options utilities can cast the net wider, without an increase in budgets.
Tehreem Ashfaq, Muhammad Irfan Khalid, Gauhar Ali, Mohammad El Affendi · 10 authors
In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.
Francesco Lo Franco, Vincenzo Cirimele, Mattia Ricco, Vítor Monteiro · 6 authors
Electric car-sharing (ECS) is an increasingly popular service in many European cities. The management of an ECS fleet is more complex than its thermal engine counterpart due to the longer ”refueling“ time and the limited autonomy of the vehicles. To ensure adequate autonomy, the ECS provider needs high-capacity charging hubs located in urban areas where available peak power is often limited by the system power rating. Lastly, electric vehicle (EV) charging is typically entrusted to operators who retrieve discharged EVs in the city and connect them to the charging hub. The timing of the whole charging process may strongly differ among the vehicles due to their different states of charge on arrival at the hub. This makes it difficult to plan the charging events and leads to non-optimal exploitation of charging points. This paper provides a smart charging (SC) method that aims to support the ECS operators’ activity by optimizing the charging points’ utilization. The proposed SC promotes charging duration management by differently allocating powers among vehicles as a function of their state of charge and the desired end-of-charge time. The proposed method has been evaluated by considering a real case study. The results showed the ability to decrease charging points downtime by 71.5% on average with better exploitation of the available contracted power and an increase of 18.8% in the average number of EVs processed per day.
With the increasing popularity of new energy vehicles (NEVs), a large number of automotive batteries are intensively reaching their end-of-life, which brings enormous challenges to environmental protection and sustainable development. This paper establishes a closed-loop supply chain (CLSC) model composed of a power battery manufacturer and a NEV retailer. The benchmark scenario of CLSC members without blockchain technology is analyzed, and the optimal recycling strategy of the manufacturer and retailer using traceability information based on blockchain technology is discussed. We generate our findings from three aspects, as follows: the manufacturer or retailer is more willing to take the responsibility of recycling when the proportion of retired batteries that can be used in echelons is relatively high. Meanwhile, when the recycling cost is large, both manufacturers and retailers should adopt blockchain technology to obtain greater profits. Furthermore, with the improvement of traceability information transmission efficiency, a higher profit growth rate will be achieved.
Feng Xue, Kang Chang, Wei Li, Qin Wang · 8 authors
In this paper, a blockchain-enabled energy trading method is proposed to deal with the inefficiency and security issues in energy trading for electric vehicles in smart grids. It includes the design of a smart contract and the excitation mechanism of energy sharing. The credit points of each vehicle as a node are considered in the design of the smart contract, which is used to supervise the process of energy trading. A strategy to estimate the credit points of each node and describe the excitation mechanism is illustrated. The connection between the credit points and the probability that a node would be accepted for energy trading has been established. To control the energy trading access, a double auction method is used to choose the part of the nodes participating in energy trading. Only selected nodes with winning bids can supply or request energy from the blockchain-based platform. Then it reaches the conclusion that the higher the credit points they have, the more trading initiative they would have during the energy transaction and transmission. The smart contract design and the excitation mechanism proposed in this paper would reward the vehicles that perform well and punish the beguiling vehicles for regulating the trading process.
Syed Muhammad Ahsan, Hassan Abbas Khan, and Naveed-ul-Hassan
Smart buildings are being built as a synergetic deployment of electric vehicles (EVs) and renewable energy sources. Smart charging of EVs and vehicle-to-everything (V2X) technologies are seen as way forward in this context in terms of achieving economic, technological, and environmental advantages. This paper proposes a framework for multi-objective techno-economic optimization for profit maximization of multiple inter-connected buildings (with bilateral contracts) and scheduling of EVs. The optimization problem is modeled as mixed integer linear programming problem, which is solved using CPLEX solver in ILOG optimization studio. The primary building owns the photovoltaic system coupled with storage and charging infrastructure for the fleet of EVs. The optimized charging of EVs at affordable rates using local resources at primary building assists the grid in managing the EVs’ load during peak hours. Results indicate that the primary building gains up to 62% daily profit after factoring in solar, storage, and charging station deployment costs. Additionally, secondary buildings (without solar, storage and charging facilities) earn up to 20% cost savings depending upon the nature of bilateral contracts with primary building. The results further suggest that fleet of EVs gains 35%–65% savings in charging cost based on lower charging rates and V2X operations with primary and secondary buildings.
Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical study problem. The problem is further intensified due to the scarcity of charging stations (CSs) in near EV proximity. Moreover, as energy transactions occur over open channels, it presents critical security, privacy, and trust issues among decentralized channels. To address the open limitations of trusted energy management and optimize the pricing control among EV entities (i.e., prosumers and consumers), the paper proposes a scheme that integrates blockchain and a truthful double auction strategy for trustful EV trading. To address the transaction scalability, we integrate an Interplanetary File System (IPFS) with a double auction mechanism handled through the Remix Smart Contract environment. The double auction leverages an optimal payoff condition between peer EVs. To address the communication latency, we present the scheme at the backdrop of Fifth Generation (5G) networks that minimizes the optimal payoff response time. The scheme is simulated against parameters such as convergence, profit for consumers, computation time, and blockchain analysis regarding node commit latency, collusion attacks, and EV energy consumption. The results indicate the scheme’s viability against traditional (non-blockchain) approaches with high reliability, scalability, and improved cost-efficiency.
The upcoming development in vehicle to grid network (V2G) allows for the flow of energy from battery powered Electric Vehicle (EV) to grid as well as the exchange of information between them. However, during the information exchange, the EV’s confidential information should be transferred from one charging station to another in a secure manner. Furthermore, the anonymity of the EV and charging station should be preserved. Despite the fact that many works on anonymous authentication and privacy preservation exist, there is an increase in computational cost in existing surveys. In this work, the new charging station authenticates the EV using blockchain technology without the involvement of a trusted entity, resulting in a reduction in computational time. Moreover, an efficient revoking mechanism is suggested to block the misbehaving charging station from the V2G network. In addition, security analysis section proves the resistant of our work against several possible well known attacks. Finally, to evaluate the performance of the work, the simulation is performed using CYGWIN platform and the results are proved to be noteworthy.
Electric Vehicles (EVs), as well as the number of applications for their management, are rapidly increasing due to the fact that Internet of Vehicles (IoV) becomes more informative and industrialized. According to the literature, IoV requires decentralization towards the energy Demand Response (DR) management, with secure energy trading, efficient charging scheduling, and incentives for making the drivers to participate. Blockchain and Distributed Ledger Technologies (DLTs) can be used to enable a foundational environment to support DR management. However, and to the best of our knowledge, none of the existing research works discussed in the literature adopts a holistic approach to address them. As such there is a demand for exploring a unified blockchain-based framework for distributed DR management. Therefore, the aim of this work is focused on the blockchain adoption in IoV-assisted smart cities. More specifically, we propose a blockchain based approach, to be used for building a secure and user-centric DR management framework. Our proposition aims to address the DRP in IoV through charging scheduling based on the generation of EV driving profiles, and optimal Vehicle-to-Vehicle/Grid (V2V | V2G) energy trading.
As a representative of clean energy, photovoltaic is expected to become a major supplier of electricity in the future. The combination of electric vehicle (EV) battery and charging station provides a feasible way to promote the effective consumption of photovoltaic. However, the efficiency of mobile power supply is limited by information asymmetry and security problems, and it is urgent to optimize the distribution process. Firstly, the article introduces the energy blockchain to improve the security level of electricity transaction, and designs the photovoltaic-energy storage-charging supply chain. Secondly, based on the selected road network and the actual situation of EV mobile power emergency distribution, the distribution logistics network with 50 distribution points is built. Thirdly, taking the delivery time and comprehensive cost as objective functions, the mathematical model of emergency distribution route optimization for EV mobile power supply is established, and the adaptive NSGA-II algorithm is adopted for example analysis. Finally, the parameter variation of NSGA-II and comparison with two algorithms of GA and MOPSO are carried out to validate the feasibility and applicability of proposed method. The purpose of the research is to quickly and effectively select the optimal distribution route of mobile power supply from many roads by maximizing customer demands and reducing costs, so as to promote the photovoltaic consumption.
In recent years, advancements in electric vehicle (EV) technology and rising petrol prices have increased the demand for EVs and also made them important for the Smart Grid (SG) economy. During the high energy demand, Vehicle to Grid (V2G) comprises a notable feature that returns the stored energy back to the grid. However, due to dynamic nature of energy prices and EVs availability, determining the best charging and discharging strategy is quite difficult. The existing approaches need a model to predict the uncertainty and optimize the scheduling problem. Further, other issues like security, scalability, and real-time data accessibility of EVs energy trading (ET) data at low cost also exist. Though many solutions exist, they are not adequate to handle the aforementioned issues. This paper proposes a Secure V2G-Energy Trading (SV2G-ET) scheme using deep Reinforcement Learning (RL) and Ethereum Blockchain Technology (EBT). The proposed SV2G-ET scheme employs a deep Q-network for EVs scheduling for charging/discharging. SV2G-ET scheme uses InterPlanetary File System (IPFS) and smart contract (SC) for secure access of EV’s ET data in real time. The experimental results prove the efficacy of the proposed SV2G-ET scheme that leads to improved scalability, saving the EVs charging cost, low ET data storage cost, and increased EV owner’s profit.
Energy management in the Internet of Vehicles (IoV) is becoming more prevalent as the usage of distributed Electric Vehicles (EV) grows. As a result, Demand Response (DR) management has been introduced to achieve efficient energy management in IoV. Through DR management, EV drivers are allowed to adjust their energy consumption and generation based on a variety of parameters, such as cost, driving patterns and driving routes. Nonetheless, research in IoV DR management is still in its early stages, and the implementation of DR schemes faces a number of significant hurdles. Blockchain is used to solve some of them (e.g., incentivization, privacy and security issues, lack of interoperability and high mobility). For instance, blockchain enables the introduction of safe, reliable and decentralized Peer-to-Peer (P2P) energy trading. The combination of blockchain and IoV is a new promising approach to further improve/overcome the aforementioned limitations. However, there is limited literature in Demand Response Management (DRM) schemes designed for IoV. Therefore, there is a need for a systematic literature review (SLR) to collect and critically analyze the existing relevant literature, in an attempt to highlight open issues. Thus, in this article, we conduct a SLR, investigating how blockchain technology assists the area of DRM in IoV. We contribute to the body of knowledge by offering a set of observations and research challenges on blockchain-based DRM in IoV. In doing so, we allow other researchers to focus their work on them, and further contribute to this area.
The main roles of an advanced Battery Management System (BMS) are to dynamically monitor the battery packs and ensure the efficiency and reliability of the Battery Energy Storage System (BESS). Estimating the State of Charge (SoC), State of Health (SoH), State of Power (SoP), State of Energy (SoE), State of Temperature (SoT), and State of Safety (SoS) depends on collecting, aggregating, and analyzing real-time data of the BESS. Based on the applications of BESSs and their sizes, there are several restrictions in accessing and sharing data between battery manufacturers, power grid operators, and electricity consumers, while building necessary communication infrastructure is required. To resolve such issues, a conceptual and technological Blockchain-based system is developed in this paper to securely share the real-time data collected from BESSs for monitoring and control purposes, i.e., state estimation. The proposed system benefits from integrating the Internet of Things (IoT) devices in a decentralized structure and connectivity of such IoT nodes, data privacy, and transparency and auditability. The proposed Blockchain-based system is capable of accurately estimating SoC, SoH, SoP, SoE, SoT, and SoS of BESSs in small-and grid-scales.
Decarbonisation of energy sector is crucial to deliver the future net zero energy system with promoting and facilitating the large-scale electrification of end-user sectors. It is necessary to provide sustainable, cost-effective, resilient and scalable energy solutions to exploit the power of citizens to contribute to the clean energy transition, increasing the flexibility of the overall energy system. Energy community, as the new actor, create an integrated pan energy market by bringing together the local consumers and energy market players. However, diversity of energy community brings huge challenges in integration of decentralized renewables with regulated framework, interaction of decentralized marketplaces, as well as interoperability of the cross-border energy sectors with privacy, security and incentives. This paper intends to provide an in-depth investigation on the role of microgrid and blockchain, alone and together, in facilitating the energy community as the “enabling framework” to boost the potential solutions of electrification in the transportation, building, and industrial sectors, as well as rural/remote areas and islands towards a networking green ecosystem. This paper serves as a comprehensive reference to understand the modern microgrid on its control and communication technology with integration of blockchain services in promoting the techno-socio-economic innovations for the restructuring of the sustainable energy supply chain.
Haseeb Javed, Muhammad Irfan, Moazzam Shehzad, Hafiz Abdul Muqeet · 7 authors
Peer-to-peer (P2P) energy trading platform is an upcoming energy generation and effective energy managing strategy that rewards proactive customers (acting as prosumers) in which individuals trade energy for products and services. On the other hand, P2P trading is expected to give multiple benefits to the grid in minimizing the peak load demand, energy consumption costs, and eliminating network losses. However, installing P2P energy trading on a broader level in electrical-based networks presents a number of modeling problems in physical and virtual network layers. As a result, this article presents a thorough examination of P2P studies of energy trade literature. An overview is given with the essential characteristics of P2P energy trading and comparatively analyzed with multiple advantages for the utility grid and individual prosumers. The study then addresses the physical and virtual levels that systematically categorize the available research. Furthermore, the technological techniques have been gone through multiple problems that need to overcome for P2P energy trading in electrical networks. Finally, the article concludes with suggestions for further research.
The synergy of clean energy and electric vehicles (EVs) is highly relevant in achieving low-carbon development. To promote the coordinated development of EVs and photovoltaics (PV) under the background of open power selling, a PV–EV bidding model that considers carbon emissions based on the distributed storage and smart contract technology of a blockchain is proposed in this study. This model aims to provide economic benefits to market players, ensure the safety and environmental protection of power grid operations, and integrate the ladder-type carbon emission trading mechanism. Then, it divides the electricity market into the electricity sales layer, i.e., active distribution networks (ADNs) and PV systems, and the electricity purchase layer, i.e., EVs. Smart contracts are signed with the agent layer, i.e., charging stations. The improved particle swarm optimization algorithm is used to solve the price and power of PV systems and EVs in the optimal contract, and the revenue of PV systems and EVs can be increased by over 35%. Moreover, the orderly charging and discharging of EVs is coordinated with the characteristics of the daily power generation and evening load peaks of PV systems. The proposed model can effectively provide an optimal scheduling scheme for peak load shifting and valley filling while reducing the carbon emissions of ADNs. It also promotes the local consumption of PV.
The Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks.
Due to its highly energy-intensive process, Bitcoin has attracted the global attention of climate research and media. At the time of this submission, behind-the-meter Bitcoin mining has gained significant traction; however, not a single environmental impact assessment has been conducted on this type of operation. This study seeks to fill the gap, applying the established Life Cycle Assessment methodology to estimate the environmental footprint of a single case study. A life cycle assessment methodology of a natural gas power plant mining Bitcoin behind-the-meter in the state of New York following the ISO 14040 guidelines was applied. The functional unit (FU) is defined as the attributed generation capacity of 14 MW over the course of a regular full-calendar year in the attributional model. The FU is scaled to 22 MW and 104 MW in the predictive models to represent planned expansion. The TRACI 2.1 method was applied to characterize the environmental impact. The environmental impact categories considered in this study included global warming, acidification, smog formation, and particulate emissions. Located in New York State, Greenidge LLC, a natural gas power plant produces an estimated 88,440 metric tons of CO 2 -eq per year to mine Bitcoin behind-the-meter. Annual emissions would total 656,983 metric tons of CO 2 -eq if the plant devotes 100% of its generation to Bitcoin mining. The primary driver of greenhouse gas emissions is the generation of electricity itself, accounting for ~ 79% of the total emissions. At full capacity, annual emissions are comparable to the annual emissions of 140,000 passenger vehicles or the emissions resulting from the burning of 600 million lb of coal. Further, additional planned cases could produce an estimated 1.9 million tons tCO 2 -eq per annum. Behind-the-meter Bitcoin mining makes the power plant a significant contributor to global warming at a time when New York State is attempting to radically reduce its greenhouse gas emissions by 85% by 2050 and to have 100% carbon-free electricity by 2040. The environmental impact of this business model is not limited to individual sites but is spread out over upstream impacts as well. In combination, we see that behind-the-meter Bitcoin mining not only goes against local climate initiatives but also poses a significant danger to national initiatives due to feasible scalability, caused by an availability of existing infrastructure and favorable financials.
Governments’ net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced.
In recent years, in order to reduce the consumption of petroleum energy and environmental pollution, China has actively promoted the development of new energy vehicles and achieved good results. Car ownership has surged year after year. However, the development of supporting infrastructure (charging pile) for electric vehicles is seriously backward, and the layout of electric vehicles and charging piles is extremely uneven. The difficulty of charging has gradually become the main factor restricting the development of electric vehicles. Block-chain technology has the technical advantages of decentralization, traceability and non tampering, and is widely used. The proposed blockchain-based charging piles maintenance system is able to provide an end-to-end transparent and high reliability management mode for multi-agents including the charging pile, power distribution station, maintenance, calibration and supervision institutions. In order to propose this method, we first design realization of electric vehicle charging pile sharing system and decentralized scheduling model. Then we design functions of the blockchain based charging pile maintenance system. At last, we provide simulation results and analysis to verify the efficiency of our proposed method.
Marthe Fogstad Dynge, Ugur Halden, Gro Klæboe, Ümit Cali
Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging concepts like local energy markets. The FiT approach has often been assumed as the lower economic bound for a prosumer's willingness to participate in such markets but is now being phased out in several countries. In this paper, a new pricing mechanism based on the Levelized Cost of Electricity is proposed, with the intention of securing profitability for the prosumers, as well as creating a transparent and fair price for all market participants. The mechanism is designed to function on a Distributed Ledger Technology-based platform and is further set up from a holistic perspective, defining the market framework as interactions in a Cyber-Physical-Social-System. Schemes based on both fixed and variable contracts with the wholesale supplier are analyzed and compared with both the conventional FiT and to its proposed replacement options. The results show a cost reduction for the consumers and a slight loss in revenue for the prosumers compared to the FiT scheme. Comparing it to the actual suggested replacements to the FiT, however, it is clear that the pricing mechanism proposed in this study provides a substantial increase of benefits for both prosumers and consumers.
The energy transition to renewable energy in a democratic way is directly connected to the development of energy communities and community microgrids. Los Molinos del Rio Aguas (LMRA), an ecological community in the south of Spain, offers a promising case study for an off-grid community-owned microgrid. In this paper, the interconnection of autonomous solar home systems is proposed with the addition of community assets in order to create an off-grid community microgrid that is financially beneficial for the community. Based on this scenario, a Local Energy Market (LEM) based on Distributed Ledger (DL) technologies is implemented in order to foster the energy exchange and contribute to the social welfare of the community. The results provide a win-win scenario for the community and provides an example of an off-grid community microgrid in combination with a LEM that takes into consideration the social aspect of the community.