An Ethereum-based Energy Trading Protocol (EETP)

Smart grid is considered as next generation power system which performs automated control and monitoring operations to make it self-resilience. Advanced Metering Infrastructure is an essential part of smart grid which enables two-way communication thereby providing real-time communication link between consumer and grid operators. Emergence of Distributed Energy Resources (DERs) at end user make AMI's operation more challenging. Smart Meters (SMs) are key components in AMI that sends Power Consumption Data (PCD) of home appliances to Distributed System Operator (DSO). PCD received at DSO can be used for real time pricing applications, real time energy consumption, automatic diagnostic, daily energy metering and tasks such as billing, monitoring, planning and predicting of energy usage. In the context of selling of excess energy generated at DER's to the grid, SMs participates in Demand Response (DR) applications to perform energy trading. Revealing PCD of a home leads to privacy breach, hence it is necessary to achieve secure communication between SM and DSO. At the same time, it is essential to make energy trading transparent. To address this issue, this paper proposes a cloud based private blockchain framework to accomplish secure communication between SM and DSO without compromising privacy. Furthermore, trust between SM and DSO is achieved through smart contracts. Performance evaluation of the proposed blockchain shows its applicability to the said environment.

The smart grid is widely considered as an efficient and intelligent power system. With the aid of communication technologies, the smart grid can enhance the efficiency and reliability of the grid system through intelligent energy management. However, with the development of new energy sources, storage and transmission technologies together with the heterogeneous architecture of the grid network, several new features have been incorporated into the smart grid. These features make the energy trading more complex and pose a significant challenge on designing efficient trading schemes. Based on this motivation, in this paper, we present a comprehensive review of several typical economic incentive approaches adopted in the energy-trading control mechanisms. We focus on the technologies that address the challenges specific to the new features of the smart grid. Furthermore, we investigate the energy trading in a new cloud-based vehicle-to-vehicle energy exchange scenario. We propose an optimal contract-based electricity trading scheme, which efficiently increases the generated profit.

Energy demand is increasing rapidly due to rapid growth and industrialization. It is becoming more and more complex to manage generation and distribution due to the diversification of energy sources to minimize carbon emissions. Smart grids manage reliable power generation and distribution efficiently and cater to a large geographical area and population, but their centralized structure makes them vulnerable. Cybersecurity threats have become a significant concern with these systems’ increasing complexity and connectivity. Further transmission losses and its vulnerability to the single point of failure (SPOF) are also major concerns. Microgrids are becoming an alternative to large, centralized smart grids that can be managed locally with fewer user bases and are safe from SPOF. Microgrids cater to small geographical areas and populations that can be easily managed at the local level and utilized for different sources of energy, like renewable energy. A small group of consumers and producers are involved, but microgrids can also be connected with smart grids if required to exchange the excess energy. Still, these are also vulnerable to cybersecurity threats, as in the case of smart grids, and lack trust due to their decentralized nature without any trusted third party. Blockchain (BC) technology can address the trust and cybersecurity challenges in the energy sector. This article proposes a framework for implementing a BC-based microgrid system for managing all the aspects of a microgrid system, including peer-to-peer (P2P) energy trading, Renewable Energy Certificate (REC), and decentralized energy trading, that can be utilized in the case of Saudi Arabia. It can integrate cybersecurity standards and protocols, as well as the utilization of smart contracts, for more secure and reliable energy generation and distribution with transparency.

Given the ongoing transition towards a more decentralised and adaptive energy system, the potential of blockchain-enabled smart contracts for the energy sector is being increasingly recognised. Due to their self-executing, customisable and tamper-proof nature, they are seen as a key technology for enabling the transition to a more efficient, transparent and transactive energy market. The applications of smart contracts include coordination of smart electric vehicle charging, automated demand-side response, peer-to-peer energy trading and allocation of the control duties amongst the network operators. Nevertheless, their use in the energy sector is still in its early stages as there are many open challenges related to security, privacy, scalability and billing. In this paper, we systematically review 178 peer-reviewed publications and 13 innovation projects, providing a thorough analysis of the strengths and weaknesses of smart contracts used in the energy sector. This work offers a broad perspective on the opportunities and challenges that stakeholders using this technology face, in both current and emergent markets, such as peer-to-peer energy trading platforms. To provide a roadmap for researchers and practitioners interested in the technology, we propose a systematic model of the smart contracting process, by developing a novel 6-layer architecture, as well as presenting a sample energy contract in pseudocode form and as open-source code. Our analysis focuses on the two mainstream application areas we identify for smart contract use in this area: energy and flexibility trading, and distributed control. The paper concludes with a comprehensive, critical discussion of the advantages and challenges that must be addressed in the area of smart contracts and blockchains in energy, and a set of recommendations that researchers and developers should consider when applying smart contracts to energy system settings.

Renewable energy resources, notably wind power, are expected to provide considerable portion of the world energy requirements in the near future. Many system operators around the world are challenged by the problems associated with integrating these intermittent resources into the grid. As one of the potential solutions, demand response (DR) is expected to play a major role for mitigating integration issues of intermittent renewable energy resources. In this context, this paper proposes a DR program which helps to integrate wind power by reshaping the load of the system. The DR program provides a framework to procure load reduction from DR resources in the wholesale energy market. The participants in the program submit their offer packages to provide load reduction in the day-ahead energy market. A day-ahead network-constrained market clearing formulation is also proposed which considers the load reduction provided by the DR program participants as an energy market commodity. The proposed method, which is in the mixed-integer linear programming format, determines commitment state of generating units, schedules the energy and spinning reserve provided by generating units, and schedules the load reduction provided by the DR program participants. To reveal the features of the proposed method, several numerical studies are conducted on the IEEE-RTS. The results presented indicate that integrating load reduction in the energy market provides a powerful tool to selectively modify the system load to support wind power integration, while making significant economic and technical benefits for the system.

When blockchain meets smart grids: A comprehensive survey

Competition in smart distribution grids

The smart grid is the emerging power grid that uses the advanced communication, control, and automation technologies to generate, transmit, distribute, and trade energy. The flow of energy in the smart grid is bidirectional and the consumers are evolved into active participants in energy trading. The energy when produced in surplus is either sold back to the grid (main energy supplier) or is stored in energy storage systems and traded within the grid with other users using peer-to-peer model. In this paper, an approach is developed that integrates the direct trading mechanism between the users and utility, as well as the peer-to-peer trading model in a smart grid. The advantage of trading with other prosumers (ones that consume and produce energy) in the peer-to-peer (P2P) model in comparison to the direct trading with the utility is studied. The game theory auction-based approach is introduced in the P2P energy trading model. It was concluded that the integration of forecasting models with the auction models increased the overall cost savings of prosumers in the P2P energy trading model.

With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.

Powering mobile edge computing (MEC) with a hybrid supply of smart grid (SG) and renewable energy source (RES) offers an opportunity to utilize clean energy and cut down energy expenses under two-way energy transactions. We propose two-timescale online resource allocation and energy management (TSRE) for MEC with a hybrid power supply, adapting to dynamic task and RES arrivals, wireless channels, and energy prices. The TSRE minimizes the time-averaged cost of predictive energy planning and real-time energy trading of base stations (BSs), and the energy usage of mobile users. By generalizing the Lyapunov optimization and stochastic subgradient method, the energy planning (sub)problem is solved upon RES arrivals using only historical data. The real-time offloading schedules, energy trading decisions and CPU configurations are decoupled over time and (asymptotically) optimally made in a distributed manner. The feasibility and the asymptotic optimality of the TSRE are proved. Numerical results demonstrate that the TSRE saves system cost significantly by 33.7%, compared to its baselines.

The smart grid idea was implemented as a modern interpretation of the traditional power grid to find out the most efficient way to combine renewable energy and storage technologies. Throughout this way, big data and the Internet always provide a revolutionary solution for ensuring that electrical energy linked intelligent grid, also known as the energy Internet. The blockchain has some significant features, making it an applicable technology for smart grid standards to solve the security issues and trust challenges. This study will present a rigorous review of blockchain implementations with the cyber security perception and energy data protections in smart grids. As a result, we describe the major security issues of smart grid scenarios that big data and blockchain can solve. Then, we identify a variety of recent blockchain‐based research works published in various literature and discuss security concerns on smart grid systems. We also discuss numerous similar practical designs, experiments, and items that have recently been developed. Finally, we go through some of the most important research problems and possible directions for using blockchain to address smart grid security concerns.

Autonomous Integration of Distributed Energy Sources and Home Appliances Coordination Scheme in Future Smart Grid Networks

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.

As the energy internet develops, it will become possible to carry out peer-to-peer energy trading among prosumers. Different from traditional commodity trading, the process of P2P electricity trading will be affected by physical constraints such as network congestion. To avoid transaction failure, a superconducting energy storage unit is introduced. First, we established the overall structure of the electric vehicle P2P energy trading, and designed the blockchain network and smart contracts; then analyze the benefits of each user entity in the system, and optimize the user matching in the transaction process; After that, we used the co-operative game algorithm to realize P2P electricity transaction pricing model. Finally, the simulation and analysis results show that the use of superconducting energy storage has effectively improved the success rate and demand consumption rate of electric vehicle P2P energy trading.

In this paper, we have proposed a two-level, dual-stage demand side management technique to tackle the energy procurement tariff and power dispatch constraints confronted by a retailer who acts as an aggregator agent in smart grid to eliminate the communication gap between consumers and wholesale energy market. In primal stage the consumers’ demand response has been characterized by Stackelberg game in accordance with the retail tariff; therefore, the primal stage has two additional stages. A risk-aversive dispatched power secretarial to anticipate the market tariff uncertainties has been modeled by using the linear robust optimization and contains objective uncertainties in the secondary stage. The proposed algorithm is converted into a joint integral linear program by conjointly employing the duality approach. In addition, we have designed a heuristic technique to choose the parameters within the disjunctive problems depending on the elucidation of Lagrange multiplier. Moreover we have also proposed a solution for additional linear programming model to attain a probable enhanced bidding technique which assures the Pareto efficiency in the retailer’s profits over the entire uncertain period. The intense performance evaluation by different case studies shows the effectiveness of the proposed mathematical model.