Over time, distribution systems have begun to include increased distributed energy resources (DERs) due to the advancement of auxiliary power electronics, information and communication technologies (ICT), and cost reductions. Electric vehicles (EVs) will undoubtedly join the energy community alongside DERs, and energy transfers from vehicles to grids and vice versa will become more extensive in the future. Virtual power plants (VPPs) will also play a key role in integrating these systems and participating in wholesale markets. Energy trading on a peer-to-peer (P2P) basis is a promising business model for transactive energy that aids in balancing local supply and demand. Moreover, a market scheme between VPPs can help DER owners make more profit while reducing renewable energy waste. For this purpose, an inter-VPP P2P trading scheme is proposed. The scheme utilizes cutting-edge technologies of the Avalanche blockchain platform, developed from scratch with decentralized finance (DeFi), decentralized applications (DApps), and Web3 workflows in mind. Avalanche is more scalable and has faster transaction finality than its layer-1 predecessors. It provides interoperability abilities among other common blockchain networks, facilitating inter-VPP P2P trading between different blockchain-based VPPs. The merits of DeFi contribute significantly to the workflow in this type of energy trading scenario, as the price mechanism can be determined using open market-like instruments. A detailed case study was used to examine the effectiveness of the proposed scheme and flow, and important conclusions were drawn.

This paper reviews the use of game theory tools to study the operation and design of modern power grids. The contribution of this work is to summarize the literature to highlight the versatile solution capability of game theory by focusing on the interconnected objectives of energy trading and energy management. This review was conducted with a focus on various applications in energy systems, including general energy markets, micro grids (MGs), virtual power plants (VPP), electric vehicles (EVs), and smart homes, and explores how game theory can summarize the solutions for pricing, bidding, demand side management, and resource optimization. A key finding is the suitability of game theory for modeling decentralized energy systems where strategic incentives can lead to outcomes that benefit both individuals and society. It also discusses the limitations, challenges, and potential benefits of game theory in complex power systems. This study provides researchers and policy makers with a comprehensive overview of current research and insights into the potential of game theory to shape the future of energy systems.

The effective operation of a microgrid (MG) depends on seamless coordination among agents in energy management systems. A pivotal element in this coordination is the multi-agent coordinator, strategically positioned as an intermediate controller between the primary and tertiary control levels. Its role is to derive optimal setpoints from data obtained at the tertiary control level and convey them to the relevant agents at the primary control level. This study presents a novel optimization model for the multi-agent energy management system, aimed at enhancing real-time economic dispatch in the secondary control by managing active and reactive power. Instead of relying on a conventional centralized MG controller, the proposed framework employs multi-agent coordination to improve the reliability, stability, and economic performance of MG operations.

The expansion of peer-to-peer (P2P) energy trading induces more complicated operations not only in physical terms as in network quality but also in economic terms as in benefit distribution. Therefore, this study proposes a framework that considers the network quality problem in the market operation and maintains the optimal benefit distribution for the expanding P2P system. For this, the network quality is measured by maintaining the voltage level of each corresponding node within the voltage limits. To integrate two objectives, a cluster-based bilevel P2P energy trading is incorporated with distributed optimization. Therefore, the alternating direction method of the multipliers is applied to integrate the objectives and provide communication between the two levels of P2P with limited information exchanges. The results demonstrate that the proposed method can improve the voltage level as a representation of network quality and attract many participants as an impact of proper benefit distribution even in the expanding P2P system.