含可逆固体氧化物燃料电池的虚拟电厂随机鲁棒优化OA

Under the background of"dual carbon",virtual power plant(VPP)has attracted widespread attention for their ability to integrate renewable energy,improve energy efficiency and reduce carbon emissions.In order to adapt to the cleanliness trend of low carbon,a VPP architecture containing reversible solid oxide fuel cell(RSOC)and hydrogen storage is proposed,namely RSOC-VPP.In allusion to the significant volatility and corresponding cost changes caused by frequent charging and discharging during RSOC operation,an RSOC-VPP structure is established and a mathematical model is constructed with full consideration of its volatility cost.Taking the maximum daily revenue of a VPP as the optimization objective,conditional value at risk is used to measure the operational risk of VPP.Based on the stochastic robust optimization theory,the uncertainties of electricity market prices,photovoltaic output and flexible loads are characterized by means of scenario generation and confidence intervals,and an optimal scheduling model of RSOC-VPP is established.The example analysis is conducted,and the results show that VPP can make full use of photovoltaic power generation and meet the charging and energy supply demands of electricity and hydrogen loads by power-hydrogen coupling.In comparison with the stochastic optimization,the stochastic robust optimization can show higher stability and reliability when dealing with uncertainty.In comparison with robust optimization,the stochastic robust optimization can achieve higher economic benefits while maintaining system robustness.