A robust flexible-probabilistic programming method for planning municipal energy system with considering peak-electricity price and electric vehicle

Effective electric power systems (EPS) planning with considering electricity price of 24-h time is indispensable in terms of load shifting, pollutant mitigation and energy demand-supply reliability as well as reducing electricity expense of end-users. In this study, a robust flexible probabilistic programming (RFPP) method is developed for planning municipal energy system (MES) with considering peak electricity prices (PEPs) and electric vehicles (EVs), where multiple uncertainties regarded as intervals, probability distributions and flexibilities as well as their combinations can be effectively reflected. The RFPP-MES model is then applied to planning Qingdao’s MES, where electrical load of 24-h time is simulated based on Monte Carlo. Results reveal that: (a) different time intervals lead to changes of energy supply patterns, the energy supply patterns would tend to the transition from self-supporting dominated (i.e. in valley hours) to outsourcing-dominated (i.e. in peak hours); (b) 15.9% of total imported electricity expense would be reduced compared to that without considering PEPs; (c) with considering EVs, the CO2 emissions of Qingdao’s transportation could be reduced directly and the reduction rate would be 2.5%. Results can help decision makers improve energy supply patterns, reduce energy system costs and abate pollutant emissions as well as adjust end-users’ consumptions.