Abstract
In this study, a Copula-based stochastic industry-energy system management (CSIE) model was developed based on Copula-based stochastic programming and interval linear programming. CSIE model can not only deal with extreme random events in industry-energy system (IES) of resource-dependent cities, but also quantify the risks of industrial energy demand-supply. To prove the practicability, a case study of IES planning in Yulin city was represented. Reasonable solutions of energy production and industrial energy consumption strategy were obtained, which can guarantee that pollutant emission meets the environmental requirements, and the system cost gets the lowest during 2021-2035. Furthermore, CSIE model could be spread to IES management in similar resource-dependent cities.
Highlights
Industry-energy system (IES) is filled with extreme random events, especially in resource-dependent cities
In view of the above information, there are many extreme random events that exist in IES of Yulin city, including: (a) the energy structure is extremely unreasonable in Yulin city, because more than 80% of its energy consumption is from burning coal; (b) the industrial structure is extremely irrational, as the GDP of coal related industries occupies more than 50% of the city; (c) due to extremely uneven spatial and temporary distribution of natural conditions, Yulin city shows extremely fragile ecological characteristics
A Copula-based stochastic industry-energy system management (CSIE) model could be formulated based on Copula-based stochastic programming and interval linear programming, aiming at system cost minimization, and considering uncertain factors including energy availability, industrial product demand, Gaussian dispersion of pollutant and environmental requirements
Summary
Industry-energy system (IES) is filled with extreme random events, especially in resource-dependent cities. The extremely unreasonable energy consumption structure, the extremely irrational industrial structure, and the extremely fragile ecological environment [1]. Planning IES by optimization model is one of the most effective ways to cope with energy shortage and atmosphere environment pollution, as well as guarantee sustained industrial production. Extreme random events would interfere inversion analysis of IES through traditional models, reduce the reliability of planning schemes [3]. The objective of this paper is to develop a Copula-based stochastic industryenergy system management (CSIE) model for dealing with extreme random uncertainties and supporting IES management of resource-dependent cities, and taking Yulin city, China as an example to verify the practicability of the model
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