Incentive-Driven Energy Trading in the Smart Grid

When blockchain meets smart grids: A comprehensive survey

The popularity of renewable energy is increasing due to its cost effectiveness. However, not everyone can generate and fulfill their energy demand, so energy trading is necessary. Current solutions are centralised and charged at a high fee for energy trading as they have a monopoly in the market. Energy trading requires the storage, verification, and sharing of data related to the trade while keeping records tamper-proof. Traditional database solutions are centralised and susceptible to data tempering. In our proposed scheme, we aim to solve those problems with the help of blockchain technology by storing data on blockchain and verifying transactions with the PoA consensus algorithm for faster processing. We tested our scheme against the Ethereum network and found that our scheme has a significant improvement in cost and processing. In the future, with the help of machine learning, pricing for each transaction can be optimised.

The evolution of conventional power systems into smart grids demands intelligent and efficient energy management solutions. Accurate load forecasting is essential for optimal generation scheduling, demand-side management, and system reliability. However, traditional methods often fail to capture the nonlinear and time-varying nature of electrical loads. This work proposes a machine learning–based approach for local load forecasting and grid stability enhancement. Techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are utilized to analyze historical load and weather data for improved prediction accuracy. In addition, a predictive control strategy is implemented to maintain system stability by mitigating voltage fluctuations, frequency deviations, and load imbalances. The proposed model dynamically adjusts control actions such as demand response and load shedding. Simulation results demonstrate reduced forecasting errors (MAE, RMSE) and improved voltage and frequency regulation, ensuring enhanced reliability, efficiency, and stable operation of modern smart grid systems. Keywords: Smart Grid; Machine Learning; Load Forecasting; Grid Stability; Predictive Control.

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.

Nowadays, the proportion of the global supply of green energy sources (e.g., solar and wind) is increasing dramatically. Integrating these sources into the current smart grid is very challenging, given that the smart grid has centralized topology which is costly and inefficient. In particular, the current procedures used in the wholesale energy market are slow, time-consuming and thus expensive for small-scale energy sources (e.g., small-scale solar energy farms). Indeed, these sources are excluded in practice from the energy market. Thus, to integrate the increasing number of small-scale energy sources, there is a need to transform the smart grid into a decentralized system. A promising technology to realize this decentralization is Blockchain. With its characteristics like immutability, security, and decentralization, we believe that Blockchain will be the core technology for the future decentralized smart grid. In this paper, we propose a peer-to-peer (P2P) energy trading system based on Ethereum. The objective is to allow any participant (e.g., big/small producers and consumers) to securely trade energy with minimum cost. The proposed approach moves most of the processing and storage off-chain, with the objective to minimize the cost of on-chain (i.e., smart contract) processing and storage while guaranteeing the security of the trading process. Stable coins are used to pay for the trade so as to overcome the problem of the instability regarding cryptocurrency exchange rates. Our proposal has been implemented, tested and deployed on the Ethereum official test network Ropsten. The results show that the proposed approach implements the P2P trading process with very small cost, making it an affordable solution for all participants, including small-scale sources.

Buildings consume a large portion of the world’s energy and are a source of greenhouse gas emissions. The nature of buildings is changing from static and passive structures to dynamic and active environments for working and living. The concept of sustainable and intelligent buildings is emerging as an important area for the smart grid initiative. In addition, effective home energy management is becoming more feasible using the innovative smart grid technologies. This special section is focused on intelligent buildings and home energy management in a smart grid environment. After careful peer reviews, 17 original research and practical contributions from both academia and industry have been chosen and included in this special section.

There is a growing interest in 'smart grid' technologies in both industry and academic circles. Few attempts have been made to develop a written specification consummated with standards agreed upon by members of both coteries, due to lack of government support. Utilities in the state of California are obligated, by state legislature, to create a more: efficient, reliable, and intelligent electric power system. This initiative along with Florida Power & Lighting's 'Smart Grid' pilot program has created a sense of exigency within the industry regarding smart grid technologies and standardizations. Their accomplishments are beginning to shape the policies and standards with marginal input from academic societies, ushering in a very lopsided, and business acclimatized set of standards. We will present and analyze, a SCE 'Smart Grid' use case, in which the utilities back office applications interact with the customer's meter, and provide technical recommendations for system security improvements.

Peer-to-peer trading in energy networks is expected to be exclusively conducted by the prosumers of the network with negligible influence from the grid. This raises the critical question: how can enough prosumers be encouraged to participate in peer-to-peer trading so as to make its operation sustainable and beneficial to the overall electricity network? To this end, this paper proposes how a motivational psychology framework can be used effectively to design peer-to-peer energy trading to increase user participation. To do so, first, the state-of-the-art of peer-to-peer energy trading literature is discussed by following a systematic classification, and gaps in existing studies are identified. Second, a motivation psychology framework is introduced, which consists of a number of motivational models that a prosumer needs to satisfy before being convinced to participate in energy trading. Third, a game-theoretic peer-to-peer energy trading scheme is developed, its relevant properties are studied, and it is shown that the coalition among different prosumers is a stable coalition. Fourth, through numerical case studies, it is shown that the proposed model can reduce carbon emissions by 18.38% and 9.82% in a single day in Summer and Winter respectively compared to a feed-in-tariff scheme. The proposed scheme is also shown to reduce the cost of energy up to 118 ¢ and 87 ¢ per day in Summer and Winter respectively. Finally, how the outcomes of the scheme satisfy all the motivational psychology models is discussed, which subsequently shows its potential to attract users to participate in energy trading.

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

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.

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modeling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent energy management scenario.

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency.

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.

Selection of the most appropriate communications technology for a smart grid (SG) application is far from trivial. We propose such a feasibility assessment starting from identification of key performance indicators (KPIs) required for peer-to-peer (P2P) energy trading and grid control operations from a communications perspective. A set of cross-disciplinary KPIs, both quantitative and qualitative, are considered from communications, power, business, actor involvement, financial, and demand side management categories. They serve as a general baseline for use cases, as there have been few previous works attempting to capture the essential features of P2P SG operations. The KPIs are briefly identified along with their relations to P2P energy trading and grid control. A straightforward comparison of the quantitative and qualitative KPIs' impact on technology selection is not feasible. This paper addresses the comparison with: 1) a prioritization of the KPIs using the analytic hierarchy process; 2) a comparison of technology solutions evaluated in our previous works against the KPIs' requirements; and 3) a total feasibility evaluation of the solutions against selected KPIs. The prioritization shows latency, reliability, security, scalability, robustness, costs of information and communication technologies (ICT) devices, and costs of ICT deployment are the most important KPIs in enabling P2P energy trading and grid control. Further, the technology feasibility assessment enables identification of the most suitable candidates for an SG application.

In this article, we consider ultra-reliable low-latency communication (URLLC) for efficient energy trading over a smart grid (SG) network using home-based smart meters (SM). We develop a cost-friendly privacy preservation framework based on existing demand-side energy management by employing random bidirectional energy trading among customers. Customers in our design can be either producers or consumers and mostly both (‘ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prosumers</i> ’). Our aim is to develop a decentralized optimization framework that not only reduces energy costs, but also improves privacy preservation and energy trading ability directly from the customer’s end. One of the vital costs for energy consumers is the supply charge. Our method can minimize it by orchestrating energy trading among customers in a decentralized adaptive fashion. To predict the energy demand by optimizing between privacy and cost, we employ an extension of the follow the regularized leader (FTRL) algorithm. We perform a theoretical analysis to demonstrate the convergence of the FTRL, the benefits of URLLC for the SG network, and the cost-effective privacy preservation ability of the proposed model. In addition to enabling energy trading efficiently, our extensive simulation results demonstrate that our proposed framework outperforms the state-of-the-art methods in terms of the cost-friendly privacy of SMs.