Bi-Level Emission Reduction Model of the Hybrid Power Market Based on Carbon Emission Flow Theory and Source–Load Coordination

Abstract

Limited by the influence of network topology and other factors, the theory of carbon emission flow is unreasonable in the allocation of carbon responsibility on the user side, which leads to the low enthusiasm of users to respond to emission mitigation. The emergence of bilateral transactions provides users with the freedom to choose the type of power supply which is of great significance to exploring the potential of users to reduce emissions and promote the consumption of new energy. For this reason, this paper proposes a bi-level emission reduction model of the hybrid electricity market considering carbon emission flow and source–load coordination. The upper level aims to maximize the revenue of wind, photovoltaic, and thermal power generators and establishes a market-clearing model based on the trading rules of the hybrid electricity market to obtain the bid-winning power of each generator and the hybrid market electricity price. After the market is cleared, the carbon emission liability of the user side is calculated by using the carbon emission flow theory. The lower level takes the minimum cost of electricity consumption as the target and uses electricity price and carbon responsibility as incentives to establish a decision-making model for users to purchase electricity and guide users to actively choose green energy for consumption. The results of the example show that compared with the single pool market trading model the carbon emissions of the system are reduced by 11.9% while the income of the new energy power generations is increased by 9.84% and the electricity cost of the user is reduced by 21.2%, which underlines a mutually beneficial outcome for all stakeholders in the market.

Multi-Objective Optimal Power Flow Calculation Considering Carbon Emission Intensity

“…Additionally, in light of carbon capture systems and the theory of carbon emission flow, a low-carbon optimal learning and dispatching method for the power system is introduced. In Zhong et al [18], a bi-level optimization model is proposed to guide users to actively choose green energy consumption. The higher level seeks to maximize the benefits of wind power, photovoltaic, and coal-fired power plants, and the lower level aims at minimizing the cost of electricity consumption, utilizing electricity prices and carbon responsibility as motivating factors.…”