Generation expansion planning within the context of electricity markets

Abstract

Abstract Generation expansion planning is defined as the problem determining the optimal type of energy technologies to be installed, time, and construction location. This problem becomes even more complicated within the context of electricity markets since market conditions have to be taken into consideration, including their volatilities and shocks. This work addresses the problem of optimal generation expansion planning, taking into account the market operation through the introduction of unit commitment constraints. In addition, it considers the penetration of distributed energy resources into the system, including energy storage systems and demand response programs. The overall methodological framework is based on mixed-integer programming techniques and has been tested in an illustrative power system under various assumptions regarding CO2 emissions limits as well as CO2 and natural gas fuel prices. The results highlight that renewable power units are installed in all cases, while non-renewable power units are installed only if they are low-carbon (biomass and nuclear) or they are equipped with a CCS technology (hard coal and natural gas). In addition, there is a positive correlation with the installation of energy storage systems; namely, the more the capacity of the installed RES units, the more the capacity of the installed energy storage systems. The increase in the price of natural gas fuel is able to lead to power mixes with higher CO2 intensity, if it is combined with a low CO2 emissions price. Energy storage systems, accompanied by demand response programs to some extent, play a decisive role in both energy and reserves balance in the zero-emissions power mix. System operators, regulatory authorities, and potential investors can utilize the developed optimization framework to quantify the roadmap and the long-term dynamics of the studied power system to optimize the investment strategy of their resources and portfolios.