Relaxation of quantitative energy objectives on generation expansion planning: A computational and policy study

Abstract

Summary In the present research, a decision support tool is presented for the optimization of generation expansion planning (GEP) of semiliberalized electricity markets under specified quantitative energy objectives. The evolution of the anticipated System Marginal Price is estimated overtime, in conjunction with power sector's structure. A hybrid solver has been used for that task. It comprises of the Improved Stochastic Ranking Evolution Strategy (ISRES) and the Interior Point Algorithm (IPA), in an attempt to exploit their distinct characteristics. The quantitative energy objectives are represented as constraints. A relaxation factor is applied on the aforementioned constraints and its effect on the power sector's formation and the optimization procedure is demonstrated through a series of representative computational experiments. The results derived from using various relaxation levels are compared with a base case where the energy constraints are not embedded. Capacity orders are extracted denoting the optimal steps towards accomplishing the energy objectives. The evolution of some core indicators of the energy system is compared amongst the investigated cases. Relaxation facilitated the optimization procedure without intervening in GEP scheduling by much. Moreover, ISRES‐IPA is benchmarked against commonly used solvers, demonstrating improved performance on the GEP model's optimization. The decision support tool presented and the conclusions extracted from this study may be of interest to energy policy makers, planners, and investors.