Combined cooling, heating, and power system optimal pricing for electricity and natural gas using particle swarm optimization based on bi-level programming approach: Case study of Canadian energy sector

Combined cooling, heating, and power (CCHP) systems are considered energy efficient and they can be economically rewarding only when the inter-dependencies of energy prices for electricity and natural gas (NG) are properly accounted for. The goal of this study is to determine the optimal hourly energy prices for electricity and NG for a non-autonomous CCHP system using particle swarm optimization algorithm based on bi-level programming approach. Based on optimal sizing and configuration of the CCHP system, the rewards for both the distribution utility and the industrial energy consumer are analyzed. For simulation purposes in this study, a CCHP system with known loads operated in Ontario, Canada, based on actual energy prices is examined. For a CCHP system with gas turbine (GT) as prime mover, the optimal energy prices for on-peak and off-peak periods for spring, summer, fall, and winter for electricity and NG are determined. It is found that for the on-peak period, if the electricity prices in winter are 1.16, 1.56, and 1.75 times greater than those in summer, fall, and spring, respectively, and if the NG prices in winter are 1.19, 1.64, and 1.86 times greater than those in summer, fall, and spring, respectively, the rewards for distribution utility and industrial energy consumer are guaranteed. Based on the simulation results, it is determined that, under the optimal hourly energy prices for electricity and NG, the industrial energy consumer meets in excess of 92% of its needed electricity by the CCHP system that is sized and configured optimally with capital recovery of 2-3 years.