A blockchain-based efficient data storage and sharing scheme for renewable energy trading in smart grid

An Ethereum-based solution for energy trading in smart 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.

This paper studies the real-time energy trading problem in a smart community consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. We propose a peer-to-peer (P2P) energy trading system, which integrates energy trading with energy management, enabling each prosumer to jointly manage its energy consumption, storage scheduling and energy trading in a dynamic manner for smart communities consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. The proposed community-based P2P energy trading system combines an online energy control and trading algorithm with a double auction mechanism. The energy control and trading algorithm is designed based on the Lyapunov theory, allowing each prosumer to independently determine its bid in each time slot only based on its current energy supply condition, while the trading price, which is determined via the double auction mechanism, reflects the collective energy supply conditions of all prosumers participating in energy trading. The integration of the Lyapunov-based energy control and trading algorithm and the double auction mechanism yields a dynamic energy trading pricing mechanism that induces the prosumers to participate in energy trading in a coordinated manner by influencing the energy consumption, energy charging/discharging and energy trading decisions of the prosumers. Numerical simulation results demonstrate that energy exchange in the proposed scalable energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while ensuring the fair sharing of the benefits reaped from energy trading among the prosumers.

This work aims to present a global thorough review of peer-to-peer (P2P) energy trading. P2P is an emerging bi-directional bilateral energy-trading model. For a P2P trading a player must find an optimal energy buyer/seller as per available network capacity complying with trading rules. This work identifies these requirements as three pillars of P2P trading: power market economics, network capacity allocation, and trading policy and regulation. Market economics contributes all the way from optimal determination of bidding strategy to market settlement for a player’s maximum payoff. A system operator ensures system stability by optimally allocating the network capacity for a requested P2P transaction in the power system. Whereas the trading policy and regulations aid in dispute minimization by controlling the players’ impromptu and irrational trading behavior. A successful P2P transaction requires a synergy between these three stated pillars, yet no previous works have studied them and their impacts collectively. Among the different reported literature in P2P trading, 60–65% have focused on a P2P business model design using market economics, 35–38% on power system for optimal network scheduling and capacity allocation, and 1–2% of which only have delved on the P2P trading policy and regulations requirements. This work articulates each pillar’s significance and reasoning for their interventions at different timestamps in a P2P transaction. A stepwise prosumer-to-business novel blockchain-led P2P model is designed to showcase the same for a comprehensive understanding. Further, a game theory-based numerical optimization model considering network constraints is demonstrated to clarify the problem formulation approach of P2P trading.

Incentive-compatible and budget balanced AGV mechanism for peer-to-peer energy trading in smart grids

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 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.

When blockchain meets smart grids: A comprehensive survey

Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading

Decision letter for "A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids"

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

In this paper, we investigate the cooperative distributed energy generation and energy trading for future smart grid. In our model, a group of energy users, who are equipped with the capabilities of distributed energy generation, are allowed to trade energy in a cooperative manner with the goal to minimize their total energy-provisioning cost while meeting the local demand of each individual energy user. Moreover, each user also expects to benefit from the cooperative energy generation and trading with the others. Motivated by these objectives, we first jointly determine the optimal energy scheduling decisions for all energy users such that their total energy-provisioning cost can be minimized. Then, based on the optimal energy scheduling decisions, we further determine the optimal transaction costs associated with the users' energy trading to ensure that each of them can positively benefit from the cooperation. Extensive numerical results are provided to show the advantages of the proposed cooperative energy generation and trading model as well as our proposed algorithms to achieve the optimal solutions.

This paper introduces a scalable blockchain-based framework for decentralized energy trading within smart grids, in order to improve energy allocation among participants, which promotes auction-based algorithms. To ensure safe, open, and effective transactions, the system uses a combinatorial auction mechanism that is implemented via smart contracts on a private blockchain. To address scalability concerns, second-layer solutions such as sharding and payment channels are incorporated, enhancing transaction throughput and minimizing latency and costs. A mathematical model is used to improve the utility of participants while complying with the constraints of energy balance, fairness, and grid capacity constraints. The system is implemented using smart contracts on a private blockchain, with second-layer solution such as sharding and payment channels in order to address scalability issues. The test results demonstrate notable improvements in the system’s growth potential, transaction processing speed. The proposed framework uses blockchain and auction-based algorithms to create a decentralized system for energy trading, in which the participants can trade energy directly without relying on centralized entities. The findings of this research suggest that the proposed framework could transform energy trading, encouraging the adoption of sustainable and decentralized energy systems. Future directions include the integration of IoT-enabled predictive analytics and deployment in real-world smart grids.