Search

It is necessary to consider the carbon emission trading between prosumers when designing a distributed transaction mechanism in a distribution system. In this paper, a peer-to-peer (P2P) electricity-carbon transaction mechanism considering the carbon footprint of electricity trading is established. First, the carbon emission characteristic of energy storage system is analyzed, and a carbon emission flow model considering energy storage system is established. Next, a P2P electricity-carbon transaction model based on the carbon emission flow model is established. Based on the improved Benders decomposition method, the original problem is decomposed into the main problem considering network constraints and the subproblem of optimal scheduling for prosumers. Through distributed solutions, the optimal P2P electricity-carbon trading amount of prosumers is solved to protect the privacy of prosumers. Then, a P2P electricity-carbon trading settlement model based on the Nash bargaining model is established, and the cooperation benefit is distributed according to the contribution of prosumers in the electricity-carbon transaction. Finally, the effectiveness of the proposed model is validated by case studies on the improved IEEE 33-bus distribution system. The proposed model can not only ensure the secure operation of the distribution system, but also promote the reduction of demand-side carbon emission and fairly distribute the benefit among cooperative prosumers.

Smart buildings， as prosumers， not only facilitate the local consumption of renewable energy sources （RES） but also coordinate internal flexible loads to reduce electricity consumption costs through peer-to-peer （P2P） electrical energy transactions. Based on this， the paper analyzes wind-photovoltaic （WPV） resources and flexible loads in smart buildings and proposes a P2P optimal scheduling strategy that incorporates joint transactions of electricity and regulation capacity. Initially， flexible load adjustments are used to provide regulation capacity， eliminating the prediction deviations of WPV outputs. Subsequently， the paper introduces a combined transaction mechanism for electricity and regulation capacity， where both electricity and regulation capacity are traded as market commodities in P2P transactions. Finally， case studies are conducted to analyze the proposed method and model. The results demonstrate that the proposed model not only effectively increases the local consumption of WPV but also encourages market participants to actively engage in P2P transactions.

As China progresses with its electricity market reforms in pursuit of "carbon peak and carbon neutrality" objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental principles of electricity financial derivatives, assesses their applicability to the Chinese market through an analysis of international experiences from the United States, Nordic countries, and Australia, and highlights critical issues for the construction of a robust market framework. It offers strategic recommendations regarding the structural and developmental aspects of China's electricity financial market and proposes derivative instruments tailored to China's market to improve liquidity and risk management mechanisms, thereby facilitating the renewable energy transition. The study demonstrates that these derivatives are instrumental in mitigating price volatility, managing transmission congestion, and supporting the shift to renewable energy. This provides a pragmatic approach for the reform and advancement of China's electricity financial market, aligning with global strategies and addressing the unique challenges of China's energy transition. [ABSTRACT FROM AUTHOR]

The global development and enhancement of electricity financial markets aim to mitigate price risk in the electricity spot market. Power producers utilize financial derivatives for both hedging and speculation, necessitating careful selection of portfolio strategies. Current research on investment strategies for power financial derivatives primarily emphasizes risk management, resulting in a lack of a comprehensive investment framework. This study analyzes six short-term electricity futures contracts: base day, base week, base weekend, peak day, peak week, and peak weekend. A multi-agent deep reinforcement learning algorithm, Dual-Q MADDPG, is employed to learn from interactions with both the spot and futures market environments, considering the hedging and speculative behaviors of power producers. Upon completion of model training, the algorithm enables power producers to derive optimal portfolio strategies. Numerical experiments conducted in the Nordic electricity spot and futures markets indicate that the proposed Dual-Q MADDPG algorithm effectively reduces price risk in the spot market while generating substantial speculative returns. This study contributes to lowering barriers for power generators in the power finance market, thereby facilitating the widespread adoption of financial instruments, which enhances market liquidity and stability. [ABSTRACT FROM AUTHOR]