Jinxi Yang

An Agent Based Approach to Model Investments in the Power Sector



Country of origin: China
University: Chalmers University of Technology
Supervisor: Kristian Lindgren, Daniel Johansson
Industry partners: The Scottish Government


The objective of this research is to develop a bottom-up model to simulate how power companies make investments decisions on capacity expansions.

The model will represent the power system in the North Sea Region with a necessarily fine time. The decisions criteria are based on the expectations of current and future energy demand, energy supply characteristics, and policies.

The focus of this effort will be on the power production and the challenges faced when increasing shares of renewable supply need to meet a varying demand.

Project objective: expected results and contribution to society

There are 4 main expected results.

(1) An agent-based model (ABM) will be developed capturing key features that characterise which investments are made in the power sector. (This necessarily includes a modelling approach with a time resolution that captures the variability problems of supply and demand.)

(2) Such an ABM provides a possibility for a sensitivity analysis in the broader context of different modelling approaches for the energy systems investigation; this complementary approach allows us to go beyond a traditional parameter sensitivity analysis. 

(3) The model is expected to identify critical issues when the power production system becomes dependent on a larger share of variable renewable supply. (This includes electricity price variations and volatility, as well as the risk for exploitation of market power when only a small number of power companies dominate in a region.)

(4) This research is also expected to give us additional insights of possible effects of different policies by using the ABM in combination with an optimisation modelling.

This research will contribute to better understanding of benefits and limitations of different model approaches, which can support real world planning and decision making.