Over the past decades, the concept of embedded energy production popularly known as microgrids is getting implemented in a wider range due to numerous benefits which include flexibility, efficiency, improved stability, and cost-effectiveness. Opportunities for microgrids in the electricity market are becoming higher these days due to which consumers are now becoming prosumers. Energy trading in microgrids happens in peer to peer manner where there is a need for third-party involvement. To eliminate this involvement, a technology is needed to make the entire system a decentralized one which is "Blockchain". In this paper, a systematic review is conducted on how blockchain technology can be implemented in the energy sector for mitigating the issues and how peer to peer energy trading happens in the microgrids thereby smart contracts are created for purpose of P2P energy trading using solidity tool.
The popularization and rapid development of distributed energy becomes a trend of the times. Distributed energy prosumers should be able to trade with each other to reduce losses, increase efficiency, flexibility and economy. The traditional centralized power settlement scheme is not suitable for the utilization on the situation of distributed energy transaction settlement. Energy internet as the next generation energy system integrating cuttingedge information technologies with energy system could realize peerto- peer energy services. The distributed interactive concept of the energy trading is highly consistent with the principle of blockchain. In this paper, aiming at the problems of information disunity, trust system difficult to establish, power deviation waste and cost advance caused by power pre-sale, a power transaction asynchronous settlement system for microgrid is proposed based on blockchain technology. The experiment results illustrate that the system obtains promising performance by reasonable set grid structure which could meet the requirements of practical applications.
Jacob Eberhardt, Marco Peise, Dong-Ha Kim, Stefan Tai
The production of renewable energies by individual households typically is small-scale and not profitable without public subsidies, yet a critical factor in preventing further global warming. Unlike market-based peer-to-peer trading solutions, which require households to engage in costly peer-to-peer trading activities, we propose a community-based approach where households in a local distribution grid share the energy they produce in a netting process to maximize internal consumption. The technical instantiation of this idea in real-world energy grids comes with several challenges. Households within a community do not necessarily trust each other or electric utilities. Furthermore, energy consumption data is highly sensitive and must be protected. Further idiosyncrasies of national energy markets, regulatory frameworks, and current grid infrastructure exist. Addressing all these challenges, we propose a blockchain-based system that leverages zero-knowledge off-chain computations to facilitate automated energy sharing within a community in a trustless and privacy-preserving way. We provide a proof- of-concept implementation using the ZoKrates framework for verifiable off-chain computations and the Ethereum Blockchain. To support our claims, we provide evaluation results obtained in the context of a major German national research project on blockchain-based energy networks.
Vanh Khuyen Nguyen, Quan Z. Sheng, Adnan Mahmood, Wei Emma Zhang · 5 authors
The growth in distributed energy resources (DER) has produced positive impacts on energy grid systems. However, there are still significant challenges for deployment of DER systems. In this paper, we bring out the latest advancements in the domains of the internet of things (IoT), artificial intelligence, and distributed ledger technology in tandem to create the next generation of a smart, distributed, and efficacious energy management and trading system in Australia. The system is comprised of cost-effective and easy-to-assimilate IoT devices, e.g., smart sockets and inverters connected to existing devices or renewable energy sources, to formulate a mobile-friendly platform that provides the energy consumers with intuitive analytics, programmable control, and real-time energy monitoring and trading so as to assist them in improving their energy-efficiency.
The electricity industry has always been under scrutiny in order to improve the quality of electricity supply, measurement and billing services to have the at most user transparency, while providing these services with the highest efficiency. Although many solutions have emerged, of which the smart meter was considered a viable option, it was quick to perish under the prodigious complications with the real-life feasibilities. El DApp- An electricity power consumption tracking application solution, harnessing both the IoT and Blockchain utilities to provide a decentralized and secure recording mechanism, that provides an improved architecture to the smart meter is proposed in this article. The El DApp provides a high security and cost efficient decentralized live electricity power consumption recording of the user that is maintained by a Raspberry Pi based Ethereum network.
Shivam Saxena, Hany E. Z. Farag, Hjalmar Turesson, Henry Kim
Transactive energy systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilise distributed generation units to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary grid service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivisation, contract auditability, and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this study proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by (i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, (ii) utilising smart contracts to enforce the validity of each transaction and penalise reputation ratings in case of a mitigation failure, and (iii) automating the negotiation and bidding of agent services by implementing the contract net protocol as a smart contract. Experimental results on both simulated and real‐world ADNs are executed to demonstrate the efficacy of the proposed system.
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.
Varun Deshpande, Laurent George, Hakim Badis, Alemayehu Addisu Desta
In the context of smart grids, Demand Response (DR) is used to manage energy imbalance by smoothing consump¬tion peaks through voluntary rationing of energy by participants. However, it largely remains centralized and opaque with little to no traceability. To resolve this, we propose a blockchain-based framework in which a consortium of DR allotters and certify¬ing authorities maintain the blockchain. This brings in more transparency, traceability, and complete decentralization along with trustlessness, non-repudiation, and immutability. Further, the framework uses distinct components/concepts like Secure Elements, Escrow Accounts, Applied Smart Contracts in unison to fix the impediments of previous blockchain-based propositions. Next, we propose a fair and efficient DR allotment mechanism for a distributed DR marketplace whose execution time is less than 1 minute for more than 20,000 participants. Further, through simulations, we show the impact of different parameters on it and demonstrate its ability to delicately balance various paradigms of DR metrics. Finally, we conclude with our findings on system reliability and its inordinate effects on DR allotment metrics.
European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.
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.
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.
Distributed ledgers are nothing new. They actually have been around for centuries. Blockchain has simply created a framework for having a secured, shared system of distributed ledgers, which is digitized to enable rapid, automated synchronization across the entire distributed system. The core value of a distributed ledger is that it allows for an agreed-upon record of past events as a shared basis for future action. A distributed ledger that does not rely on a centralized authority to stay synchronized in its contents allows for higher trust among the parties using the ledger. The degree of trust will depend on the level of shared governance and immutability of the contents. The popularization of blockchain applications for an electronic cash system and elsewhere in the financial industry has been the foundation for a tremendous amount of research and development on blockchain and other distributed ledger technologies.
Nowadays, unlike depleting fossil fuel resources, the integration of different types of renewable energy, as distributed generation sources, into power systems is accelerated and the technological development in this area is evolving at a frantic pace. Thus, inappropriate use of them will be irrecoverably detrimental. The power industry will reach a turning point in the pervasiveness of these infinite energy sources by three factors. Climate changes due to greenhouse gas accumulation in the atmosphere; increased demand for energy consumption all over the world, especially after the genesis of Bitcoin and base cryptocurrencies; and establishing a comprehensive perspective for the future of renewable energy. The increase in the pervasiveness of renewable energy sources in small-scale brings up new challenges for the power system operators to manage an abundant number of small-scale generation sources, called microsources. The current structure of banking systems is unable to handle such massive and high-frequency transactions. Thus the incorporation of cryptocurrencies is inevitable. In addition, by utilization of IoT-enabled devices, a large body of data will be produced must be securely transferred, stored, processed, and managed in order to boost the observability, controllability, and the level of autonomy of the smart power systems. Then the appropriate controlling measures must be performed through control signals in order to serve the loads in a stable, uninterruptible, reliable, and secure way. The data acquires from IoT devices must be analyzed using artificial intelligence methods such as big data techniques, data mining, machine learning, etc. with a scant delay or almost real-time. These measures are the controversial issues of modern power systems, which are yet a matter of debate. This study delves into the aforementioned challenges and opportunities, and the corresponding solutions for the incorporation of IoT and blockchain in power systems, particularly in the distribution level and residential section, are addressed. In the last section, the role of IoT in smart buildings and smart homes, especially for energy hubs schemes and the management of residential electric vehicle supply equipment is concisely discussed.
The increase of the number of electric vehicles leads to serious valley imbalance in the power grid. In order to achieve peak cutting and valley filling, according to the energy storage characteristics of electric vehicles, this paper proposes an electric vehicle group (EVG) energy trading method based on smart contract and double auction matching mechanism, and constructs the electric energy transaction process of the electric vehicle and electric vehicle in the electric vehicle group under the unbalanced load of the power grid. Through deploying the double auction matching algorithm to the smart contract, the automatic execution of matching transaction and automatic clearing of transaction cost are realized, which saves the economic cost of manual matching mode. In addition, the multi-stage quotation method proposed greatly improves the number of transactions. The simulation experiment based on Monte Carlo simulation shows that the power transaction method can not only effectively alleviate the valley problem of power grid load, achieve the effect of reducing and raising Valley, but also can improve the transaction efficiency.
Gonzalo Munilla Garrido, Daniel Miehle, André Luckow, Florian Matthes
The increase of renewable energy generated in certain countries has outpaced the expansion of their power grid, causing grid congestion. Currently, grid operators use flexibility measures to counter this challenge. However, these measures struggle to cope with the growth in renewables. There are numerous proposals to improve flexibility measures using distributed energy resources such as electric vehicles (EVs). However, there is a need for a platform whereby EVs can be leveraged directly by grid operators. In answer to the decentralized quality of EVs and the requirements defined by our automotive industry partner, we propose a platform based on a distributed ledger technology (DLT). To achieve this goal, we first designed a concept for a decentralized flexibility market for the stakeholders of the ecosystem. The concept serves as the blueprint for the implementation of the platform. With the design and its implementation and simulation, we validated the use case and technical feasibility of the chosen DLT. We conclude that our prototype has the potential to allow grid operators to leverage idle EVs in aggregation to mitigate congestion.
The future of renewable energy transportation and distribution is dynamic and complex, with distributed renewable resources in required distributed control. It is suggested that Distributed Ledger Technology (DLT) is a timely innovation with the potential to facilitate this future. The transition to full renewable energy requires an infrastructure capable of handling intermittent production that has a low marginal cost. This requires a distributed control logic where devices with embedded intelligence coordinate local production, a decentralized energy market where prices are not primarily based on production, and an underlying digital infrastructure to enable both. Simulations and experiments have demonstrated great potential in such a digital infrastructure, but real-life tests have identified scalability as a remaining challenge. In this paper, we propose a DLT-based architecture for the energy grid with the development of existing solution concepts by implementing scalability solutions. To this end, we derive energy market components as a framework for building efficient microgrid. Then, we discuss the microgrid as a case study of such a market according to the required components within energy production, transmission, and distribution; distributed ledger platform operations, IoT device manufacturing,; software development; and research in IoT, edge and cloud computing, and energy systems.
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.
The optimal deployment of heterogeneous energy storage (HES), mainly consisting of electrical and thermal energy storage, is essential for increasing the holistic energy utilization efficiency of multienergy systems. Consequently, this article proposes a risk-averse method for HES deployment in a residential multienergy microgrid (RMEMG), considering the diverse uncertainties and multienergy demand-side management (DSM). Apart from the HES size and location planning, its optimal investment phase is also determined by maximizing the system equivalent daily profit (EDP) and minimizing the risk. To handle the system uncertainties from renewable energy sources, power demands, outdoor temperature, and residential hot water needs, the multistage adaptive stochastic optimization approach is utilized. Then, through the constraint linearization and stochastic scenario sampling, the original nonlinear deployment model is converted to a mixed-integer linear programming one and tested on an IEEE 33-bus distribution network based RMEMG. The effectiveness of the proposed method is verified by comparing it with the existing practices. The comparison results indicate that the proposed risk-averse deployment method can effectively increase the system EDP and more immune to the uncertainties. Besides, this method can be practically applied for the emerging RMEMGs, such as smart buildings, intelligent homes, etc., which get long-term DSM contracts.