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

• Published in: Energy conversion and management Vol. 137; pp. 97 - 112
• Main Authors: Yu, L., Li, Y.P., Huang, G.H., An, C.J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2017; Elsevier Science Ltd
• Subjects: Air pollution; Carbon dioxide; Carbon dioxide emissions; Computer simulation; Electric power; Electric power generation; Electric power systems; Electric vehicles; Electrical loads; Electricity; Electricity consumption; Electricity distribution; Electricity pricing; Emissions; Energy costs; Energy demand; Flexible probabilistic programming; Intervals; Mitigation; Monte Carlo simulation; Multiple uncertainties; Municipal energy system; Municipalities; Outsourcing; Peak electricity price; Peak load pricing; Pollutants; Pollution abatement; Pollution control; Pollution load; Probabilistic methods; Statistical analysis; System effectiveness; Transportation; Qingdao China; China; Peak electricity price; Electric vehicles; Multiple uncertainties; Municipal energy system; Flexible probabilistic programming
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2017.01.028

[Display omitted] •A robust flexible probabilistic programming method is developed for planning MES.•Multiple uncertainties with various violations and satisfaction levels are examined.•Solutions of considering peak electricity prices and electric vehicles are analyzed.•RFPP-MES can better improve energy system reliability and abate pollutant emission. 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.