Ifiok Anthony Umoren, Syeda Sanober Ali Jaffary, Muhammad Zeeshan Shakir, Konstantinos Katzis · 5 authors
This article presents a blockchain-based scheme for energy trading between electric vehicles (prosumers) and critical load (consumer) in a logical network. Unlike traditional wholesale energy markets where retailers sell energy to consumers, our proposed model directly connects prosumers with consumers to meet temporary energy demands. We exploit blockchain technology to establish a trusted energy trading ecosystem and develop an application to remotely monitor energy trading activities between trading entities. Experimental results illustrate that the energy trading system is effective in finding, associating, and routing prosumers to consumers, while protecting privacy of entities. Numerical results show a favorable performance of our optimization model in comparison to traditional frameworks.
With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs.
With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.
Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Thang X. Vu · 6 authors
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively.
Zahra Foroozandeh, Sérgio Ramos, João Soares, Fernando Lezama · 7 authors
Efficient alternatives in energy production and consumption are constantly being investigated and conducted by increasingly strict policies. Buildings have a significant influence on electricity consumption, and their management may contribute to the sustainability of the electricity sector. Additionally, with growing incentives in the distributed generation (DG) and electric vehicle (EV) industries, it is believed that smart buildings (SBs) can play a key role in sustainability goals. In this work, an energy management system is developed to reduce the power demands of a residential building, considering the flexibility of the contracted power of each apartment. In order to balance the demand and supply, the electrical power provided by the external grid is supplemented by microgrids such as battery energy storage systems (BESS), EVs, and photovoltaic (PV) generation panels. Here, a mixed binary linear programming formulation (MBLP) is proposed to optimize the scheduling of the EVs charge and discharge processes and also those of BESS, in which the binary decision variables represent the charging and discharging of EVs/BESS in each period. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis are considered. The results point to a 65% reduction in peak load consumption supplied by an external power grid and a 28.4% reduction in electricity consumption costs.
The combined cooling, heating and power (CCHP) system is a typical distributed, electricity-gas integrated energy scheme in a community. First, it generates electricity by use of gas, and then exploits the waste heat to supply community with heat and cooling. In this paper, we consider a smart city consisting of a number of communities (CCHPs) and an agent of power grid (APG), where CCHPs can sell energy to the APG according to its bid. To study all utilities of entities in such a city from energy trading, a noncooperative Stackelberg game between APG and CCHPs is formulated. Here, the APG gives a bid for buying the energy from CCHPs, then CCHPs respond to the APG with their optimal energy supply that maximizing their utilities according to this bid. We show that the maximum profit to the APG and utilities to the CCHPs can be obtained at the Stackelberg equilibrium, which is guaranteed to exist and unique. Because the complete information about energy supply of each CCHP is unknown to the APG in advance, we propose a distributed algorithm that is able to find the point of equilibrium through a limited number of iterations. Taking privacy protection and transaction security into consideration, we design a blockchain-enabled energy management system. This system is composed of Internet of Energy (IoE) sub-system and blockchain sub-system, where the information interactions as well as energy transactions between APG and CCHPs can be carried out effectively and safely. Finally, security analysis and numerical simulations show the effectiveness and accuracy of our proposed mechanism.
Gijs van Leeuwen, Tarek AlSkaif, Madeleine Gibescu, Wilfried van Sark
In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator. This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to evaluate the impact of combining physical constraints and trading on social welfare of the community and scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented, grid-only where only OPF optimization is implemented and a combined scenario where both are implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are reduced by over 50% in scenarios including grid constraints.
The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d'Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.
To promote coordinated development of electric vehicles (EVs) and power grid under open power selling, a bidding mechanism using blockchain smart contract technology was proposed. By demand respone management (DRM) on and off the blockchain, based on different driving characteristics of EV subgroups, various charging–discharging demands and constraints were fully considered between EV user subgroups and agent. Purchase–sale transaction relationship and unit commitment plan were fully considered between the EV agent and power dispatching center under economic dispatching. Aiming at the lowest power purchase cost of EV users, the highest profit of EV agent and the lowest cost of power economic dispatching, smart contract models with optimal benefits were established among the three. The smart contract models were solved by combining the internal and external optimization relationship of particle swarm and genetic algorithms. The charging–discharging price was optimized by DRM to realize the reasonable allocation of charging–discharging resources of EVs. An example analysis shows that this bidding mechanism can achieve peak–cutting and valley–filling for power load. At the same time, it can effectively protect the benefits of EV users, agent, and power dispatching center. This result can provide a reference for the application of smart contract in bidding of EVs to the power grid.
Mark Stefan, Paul Zehetbauer, Stephan Cejka, Franz Zeilinger · 5 authors
Energy communities will be an essential element of the future energy system. Especially renewable energy communities are gaining high attention in many European countries and their implementation, characteristics and use cases are elaborated in many research and development activities all around the world. Within the Austrian research project Blockchain Grid, a blockchain-based renewable energy community is implemented and field-tested in Heimschuh, Styria. It supports different technical applications like self-consumption optimisation and peer-to-peer energy trading for customers and a novel approach for grid capacity management supporting distribution system operators. These use cases have been implemented and validated in simulative studies showing promising potential for total energy costs for energy community members.
With the gradually opening of energy markets and popularization of Electric Vehicles (EVs), EVs can transmit, dispatch and recharge energy in different markets and domains dynamically. However, in Vehicular Energy Network, EVs may randomly enter and leave a market, it imposes a difficult problem in that how to schedule and distribute energy effectively. Additionally, the location of EV owners usually includes sensitive information such as home addresses, company names, hospital traces, and so on, which may be collected by attackers and may result in the privacy leakage about EV owners. In this article, we propose a decentralized blockchain-enabled energy trading scheme that can trade cross over various domains efficiently, which enables reliable transactions between EVs and energy nodes within short processing delay. It can also preserve the privacy of EV owners, by adopting the k-anonymity method in constructing a united request to hide the location information and creating a clocking area based on undirected graphs. Even though the server is maliciously attacked, the attacker cannot distinguish among EV owners, which breaks the linkage between real locations and identities to preserve EV owners' privacy. Finally, we conduct a comprehensive experimental evaluation to evaluate the trading performance and location privacy protection performance. The simulation results show that our proposed architecture outperforms over most state-of-the-art schemes in terms of processing delay and location privacy awareness.
The adoption of blockchain in the public electrical vehicle charging market has yet to be realized to its full potential, despite existing proof-of-concept. A positive outlook for blockchain is suggested by contemporary research, as well as the need for empirical studies to fully identify blockchain’s barriers to adoption. Through qualitative methods, a focused literature review, and in-depth interviews with subject matter experts, this thesis investigated which barriers to blockchain adoption exist in the public electrical vehicle charging market. The results indicated that the main barrier to blockchain adoption was the structure of the public electrical vehicle charging market itself, since it is an immature market experiencing constant change. This was followed by: coordination, norms and cultures, business process, incumbent technological solutions, regulations and legislations, shared infrastructure, and distributed ledger technology. These barriers were not shown individually, but were affected by each other. It is concluded that blockchain technology is not needed by the market in its current state, as its key defining attributes of privacy, security and decentralization are not deemed worth the cost of its own implementation. This research contributes to, and updates, the knowledge of blockchain utilization for the public electrical vehicle charging market. The results can be used by private and public organizations and scholars as reference material with regards to blockchain adoption for public electrical vehicle charging.
Godwin C. Okwuibe, Zeguang Li, Thomas Brenner, Ole Langniß
The increase in development of electric vehicle(EV) will have a strong impact on the power distribution grid if adequate care is not taken on the high power demand required for charging EV. Consequently, there is need to create a platform to enable charging point operators to effectively manage the EV user’s charging requests and ensure that there charging needs are satisfied while not exceeding the distribution grid capacity. If this is not done, there is no doubt that in few a years, EV owners will be faced with the problems of unavailability of charging stations and congestion in grid sequel to simultaneous charging of many EV’s. This work proposes a smart EV charging infrastructure based on a blockchain platform. With the charging demand (kWh) and maximum duration of the charging event provided by the EV user, the EV load flexibility is determined and utilized through smart charging to achieve a stable grid. EV owners and charging stations are linked through the platform thereby reducing the actors in EV charging ecosystem from six to four. Flexibility (power and time) in charging of EV is traded within the blockchain platform. By this, additional investors will be attracted into the business of EV charging station and through flexible offers, EV loads are shifted from the peak load hours. Consequently, the simulations shows that the acceptance rate of EV users increased by more than 50% when our smart charging system was adopted compared to the normal charging scenario.
With the development and expansion of smart grid systems, vehicle-to-grid (V2G) has become a new type of energy interaction based on Internet of Electric Vehicles (IoEVs). By leveraging the charging/discharging capabilities of EVs, V2G can be implemented in smart grid to enable intelligent energy transactions and reduce the unbalance of supply and demand. However, the implementation of interaction between the existing V2G technology and IoEVs faces the problems of high-complexity energy transaction management, insufficient computing capability, poor scalability, and lack of incentive mechanisms. The three-tier bi-directional energy transaction management strategies based on game and contract theory have been proposed. Firstly, the optimal pricing and EV discharging strategy is obtained based on the non-cooperative Stackelberg game and the energy-price equilibrium. Secondly, in order to optimize the utility of EAG, the information asymmetry incentive mechanism based on contract theory is proposed. This mechanism can effectively stimulate EVs to contribute to V2G energy transaction and further improve social benefits considering the energy transmission loss and battery life cycle degradation. To reduce the communication as well as processing latency and improve the efficiency of energy transaction management, edge computing has been incorporated. Simulation results show that the performance of the proposed scheme significantly outperforms other existing schemes under various scenarios.
Eduardo Francisco, Luís Tiago Ferreira, Carlos Silva, Joaquim Braga
The electric vehicle (EV) market is evolving fast with an expected high penetration of EV in the coming years. These EVs are dependent on charging infrastructure and since most charging will happen at home this will bring challenges to the low-voltage distribution network. The main challenge addressed in this study is the available grid capacity and what could be done to prevent the massive request of residential buildings grid connection reinforcements, which represents significant costs to consumers, large waiting times, which are not compatible with the rising necessities for charging EV and a general oversizing of the distribution network, which will drive the already low utilisation factors even lower. The presented solution for this challenge is to introduce a flexible power grid connection, which takes advantage of smart charging technology and the applications of flexible power contracts to allow the charging of 5–7 times more EV in the common garages of residential buildings without any building grid connection reinforcement and for a fraction of the cost. The distribution system operator (DSO) will represent a key role in the implementation of this solution, not only regarding the technical aspects but also regarding the onboarding of the consumers.
A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with R2score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.
The increasing electric vehicle (EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm based on Lagrangian relaxation. A barrier to the implementation of the coordination algorithm is that there usually does not exist a reliable coordinator of charging stations. This paper shows that an unreliable coordinator may collude with some charging stations and behave dishonestly by disobeying the coordination algorithm. Thus, the collusion coalition can gain more profits while lowering the profits of others and the total social welfare. To provide reliable coordination of charging stations, a novel blockchain-based coordination platform via Ethereum is established, including a coordination structure and a smart contract. A mathematical analysis is given to show that the proposed platform can mitigate the collusion behaviors in the coordination. Simulation results show the consequence of collusion and how blockchain can prevent the collusion.
Yuta Susowake, Hasan Masrur, Tetsuya Yabiku, Tomonobu Senjyu · 7 authors
In Japan, residents of apartments are generally contracted to receive low voltage electricity from electric utilities. In recent years, there has been an increasing number of high voltage batch power receiving contracts for condominiums. In this research, a high voltage batch receiving contractor introduces a demand–response in a low voltage power receiving contract, which maximizes the profit of a high voltage batch receiving contractor and minimizes the electricity charge of residents by utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimization algorithm calculates a Pareto solution for the relationship between two objective trade-offs in the MATLAB ® environment.
The stability and economy of the electronic vehicle distribution network system is increasingly important as the number of electric vehicles in use continues to rise. An electric vehicle (EV) and Internet of things (IoT) charge scheduling method is proposed in this paper which uses smart contract in the distribution network (DN) with uncertain renewable energy output. Based on user charging demand and power grid load level, this paper explores peak load shifting, guiding EV charging options by electricity price to change the demand response of each node, thereby regulating the DN power quality. A smart contract is created between the user and the charging station to realize the electricity price renewal in the power flow calculation cycle. This enhances the rationality of electricity price formulation and reduces deviation between the forecast load and the actual load, ensuring the validity of the method to a certain extent. According to the achievement of the smart contracts signed with the charging station, users are given rewards or fines, which reduces the default rate of the user. This decentralized transaction process improves the security and completeness of the transaction. The feasibility of utilizing this method for the distributed power grid is verified through simulation on a 34-node test system.
Felipe Condon, Mohamed A. Ahmed, José Manuel Martínez, Young-Chon Kim
This paper proposes a blockchain-based energy trading platform for electric vehicles in smart campus parking lots. Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles. The electric vehicle owner may want to charge energy at a low price and sell it during peak hours at a higher price. The proposed system architecture consists of two layers: the physical infrastructure layer and the cyber infrastructure layer. The physical infrastructure layer represents all of the physical components located in the campus distribution power system, such as electric vehicles charging stations, transformers, and electric feeders, while the cyber infrastructure layer supports the operation of the physical infrastructure layer and enables selling/buying energy among participants. Blockchain technology is a promising candidate to facilitate auditability and traceability of energy transactions among participants. A real case of a parking lot with a realistic parking pattern in a university campus is considered. The system consists of a university control center and various parking lot local controllers (PLLCs). The PLLC broadcasts the electricity demand and the grid price, and each electric vehicle owner decides whether to charge/discharge based on their benefits. The proposed system is implemented on Hyperledger Fabric. Participants, assets, transactions, and smart contracts are defined and discussed. Two scenarios are considered. The first scenario represents energy trading between electric vehicles as sellers and the PLLC as a buyer, while the second scenario involves energy trading between electric vehicles as buyers and the PLLC as a seller. The proposed platform provides profits for participants, as well as enables balancing for the university load demand locally.
Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.
Michela Moschella, Pietro Ferraro, Emanuele Crisostomi, Robert Shorten
In this article, we propose a stochastic decentralized algorithm to recommend the most convenient charging station (CS) to plug-in electric vehicles (PEVs) that need charging. In particular, we use different cost functions to describe the possibly different priorities of PEV drivers, such as the preference to minimize charging costs, charging times, or the distance between them and the CS. For this purpose, we leverage on an Internet of Things architecture based on a permissioned distributed ledger technology (DLT) to enforce compliance of drivers and reduces the occurrence of detrimental misbehaviors of drivers. Extensive simulations performed with the mobility simulator SUMO in realistic city-wide networks have been provided to illustrate how the proposed PEV assignment procedure works in practice, and to validate its performance.
Michela Moschella, Pietro Ferraro, Emanuele Crisostomi, Robert Shorten
In this paper we propose a stochastic decentralized algorithm to recommend\nthe most convenient Charging Station (CS) to Plug-in Electric Vehicles (PEVs)\nthat need charging. In particular, we use different cost functions to describe\nthe possibly different priorities of PEV drivers, such as the preference to\nminimize charging costs, charging times, or the distance between them and the\nCS. For this purpose, we leverage on an IoT architecture based on a\npermissioned Distributed Ledger Technology (DLT) to enforce compliance of\ndrivers and reduces the occurrence of detrimental misbehaviours of drivers.\nExtensive simulations performed with the mobility simulator SUMO in realistic\ncity-wide networks have been provided to illustrate how the proposed PEV\nassignment procedure works in practice, and to validate its performance.\n
Anselma Wörner, Arne Meeuw, Liliane Ableitner, Felix Wortmann · 6 authors
Abstract Due to environmental and resiliency benefits, distributed energy resources (DER) are a potential solution for meeting future electricity demand, but their integration into centralized power markets on the large scale is challenging. Many practitioners argue that blockchain technology can create new market structures for DER like local peer-to-peer energy markets which foster renewable generation. To get an understanding of the status quo of the research on blockchain-based energy exchange, we conducted a systematic literature review on the existing academic articles and industry projects. This article describes the design and technical specifications of the first real blockchain-based electricity market in Switzerland derived from this literature review and outlines the implementation of this market in the real world. The findings provide valuable guidelines for the integration of DER into future sustainable energy markets.