Energy Trading in Smart Grids Using Game Theoretic Approach

An Ethereum-based solution for energy trading in smart grids

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.

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

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.

This paper presents a comprehensive review of advanced control and communication technologies of smart grid (SG). Various func-tions/features of SG include real-time pricing, voltage/VAr control, frequency control, power quality, demand response management, distri-bution system restoration, and reconfiguration, distributed generation dispatch and storage. There is a strong requirement to control these functions, as well as to communicate within themselves. Suppose, if two-way communication is not possible then it cannot be considered as a function of SG. When trying to track or monitor a function, one must be able to control, command, and communicate with it. In order to control observation ability and measure of some type of numerical quantity that is obtained from a real time measuring device being moni-tored is necessary. Some of these measurable quantities are: overvoltage limits, security measures and contingency analysis. This paper addresses various challenges of control and communication techniques of SG.

The global energy landscape has always been dominated by fossil fuels, but with the intensification of climate change and environmental issues, people realize that the traditional energy system must be transformed. In order to integrate renewable energy and improve the sustainability and resilience of the energy system, smart grids have emerged. Smart grid is a modern grid system based on advanced communication, control and information technology, which can integrate renewable energy more effectively. As one of the renewable energy sources, solar energy plays an important role in the smart grid. It has the advantages of wide distribution, environmental protection, and renewable, and with technological progress and cost reduction, solar power generation is widely used in smart grids. Similarly, wind energy, another important renewable energy source, also plays an important role in the smart grid. The continuous development of wind turbine technology has made wind power generation more efficient and reliable, and the integration with smart grid infrastructure has further improved the efficiency of wind energy utilization. In addition, hydrogen energy has great potential as a clean energy carrier, but the storage and distribution of hydrogen energy in smart grid systems still face challenges.

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.

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

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.

Chapter 4 - The role of peer-to-peer energy trading strategy in future modern energy networks

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.

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments.

Smart grids are electric networks that employ advanced monitoring, control, and communication technologies to deliver reliable and secure energy supply, enhance operation efficiency for generators and distributors, and provide flexible choices for prosumers. Smart grids are a combination of complex physical network systems and cyber systems that face many technological challenges. In this paper, we will first present an overview of these challenges in the context of cyber-physical systems. We will then outline potential contributions that cyber-physical systems can make to smart grids, as well as the challenges that smart grids present to cyber-physical systems. Finally, implications of current technological advances to smart grids are outlined.

The smart grid is the solution to the increased grid complexity and energy demand. It is the concept of advanced automatic control, communication technique, and information technology. The nervous system of electric grid is the smart grid, and for consumer, these provide ability to monitor and control energy consumption in real time. This paper provides an overview about what a smart grid is importance of power quality into smart grid, quality evaluation method, expectations of smart grid, and possible solutions.