考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度

Multi-Microgrid Cooperative Game Optimization Scheduling Considering Multiple Uncertainties and Coupled Electricity-Carbon Transactions

双碳背景下,构建低碳运行的能源系统是实现“双碳”目标的重要方向与实施路径。为了促进多微网系统内部能源的本地消纳以及低碳经济运行,该文对不确定性环境下多微网系统的合作运行及电碳耦合交易展开研究。首先,对于每个微网,构建了电转气碳捕集系统相耦合的热电联产运行模式,基于地方碳交易市场和阶梯碳交易机制,提出了多能源微网的低碳运行模型;其次,考虑到新能源发电和电力市场电价都存在不确定性的实际情况,采用机会约束和鲁棒优化的方法,以降低不确定性影响;再次,基于纳什谈判理论,建立了多个微网电碳耦合的合作博弈模型,各微网主体可同时参与到上级能源市场和地方能源市场中进行电能和碳排放配额的交易;最后,将非凸的合作博弈问题分解为两个线性可求解的子问题,进一步采用交替方向乘子法对问题进行求解。通过算例验证,该文所提方法可以有效提升各微网经济效益并减少碳排放。

In the context of achieving dual carbon goals,establishing a multi-microgrid system with low-carbon operations is crucial for attaining energy conservation and emission reduction goals.In recent years,as the energy market evolves,microgrids have the capability to trade both carbon quotas and electric energy concurrently,which has greatly promoted the local consumption of internal resources in microgrids.However,for multi-microgrid systems,there are challenges arising from differing time scales and topological structures during the trading of electric energy and carbon quotas;at the same time,they will also encounter the influence of various uncertain factors,including the integration of new energy sources and fluctuations in electricity market prices.In order to solve the above problems,this paper constructs a multi-microgrid electricity-carbon coupling trading model that considers multiple uncertainties to proficiently oversee multi-microgrid systems engaged in electricity-carbon coupling trading First,this paper builds a microgrid low-carbon operation model including power-to-gas(P2G)and carbon capture system(CCS)for a single microgrid.In each microgrid,in order to limit the carbon emissions of combined heat and power(CHP)units,a CHP unit operation mode coupling P2G and CCS is proposed.CCS captures CO2 generated by CHP units,and P2G equipment uses CO_(2)to generate CH_(4),which reduces carbon emissions and realizes energy recycling.On this basis,building upon the tiered carbon trading mechanism,we constructed a carbon emission cost model for a single microgrid that incorporates inter-microgrid carbon quota trading.Furthermore,accounting for the variability in new energy output and the uncertainty of electricity prices in the power market during practical operations,opportunity constraints and robust optimization methods are used to reduce the impact of uncertainty.Then,for multi-microgrid systems,utilizing Nash bargaining theory,we established a cooperative game model for the electricity-carbon coupling of multiple microgrids.Each microgrid entity can simultaneously engage in transactions within the central energy market and local energy markets,conducting both electricity and carbon emission quota transactions.In the final step,we decompose the non-convex cooperative game problem into two linearly solvable sub-problems,employing the alternating direction method of multipliers(ADMM)algorithm for iterative problem resolution.In the case analysis,simulation analysis was conducted on three microgrids integrating multiple energy sources,including electricity,gas,and heat,to validate the effectiveness of the proposed method.At the same time,establishing a local energy trading market holds positive significance for enhancing the operation of the system's low-carbon economy.The case study yields the following conclusions:(1)The CHP unit operation mode coupled with P2G and CCS proposed in this article can successfully reduce carbon emissions within the integrated energy system.(2)Contrasted with the independent operation of each microgrid,cooperative operation through electricity-carbon coupling enhances the overall system benefits.Simultaneously,by engaging in carbon quota trading between microgrids,the multi-microgrid system can decrease the quantity of carbon quotas procured from external markets,thereby effectively lowering the system's carbon trading costs.(3)Taking into account the uncertainty of new energy output and electricity market prices enhances the resilience of multi-microgrid systems to operational risks.