Integrated Regional Renewable and Nonrenewable Energy Policies Identified through Interval Stochastic Semi-Infinite Programming

This paper proposes an interval stochastic semi-infinite programming (ICCSIP) method for identification of regional renewable and nonrenewable energy policies under multiple uncertainties. Relative to conventional methods (e.g., interval linear programming and chance-constrained programming), it has the advantages of addressing the association of system cost with nonrenewable energy price, generating reliable solutions with a low risk of system failure, and providing flexible capital availability that varies with respect to nonrenewable energy price. A case study is used to illustrate the performance of ICCSIP with respect to two scenarios. Scenario 1 neglects control of air pollutant emissions and scenario 2 attempts to address it. Nonrenewable energy sources would be primarily used under scenario 1; in particular, coal would play the most important role with respect to optimum energy supply schemes. Under scenario 2, hydrological and wind energy would be the first and second choices, respectively, in terms of electricity generation. Moreover, air pollutant emissions under scenario 2 would be dramatically less than scenario 1; however, an increased system cost would be required for control of air pollutant emissions.