Pitfalls of Power Systems Modelling Metrics

Abstract

In power system modelling the unit commitment problem is used to simulate the wholesale electricity market. A solution to the unit commitment problem is a least-cost schedule that contains information regarding the capacity factors of each generator, the total CO2 emissions, and unserved energy per hour. However, since there might be a large variety of (sub)-optimal solutions, these characteristics might be arbitrary and conclusions about them may be presumptuous.In this article, we illustrate this by running multiple experiments on a future European power system. Each scenario was run multiple times by adding additional terms to the objective function such as the minimization and maximization of generator capacity factors, carbon emissions, and loss of load hours. The results showed that schedules can be equivalent in terms of cost, but that relative capacity factors, emissions, and loss of load hours could differ by large factors.

Linking Unserved Energy to Weather Regimes

“…However, similar values of ENS do not imply that they occur at the same bidding zone or time (R. H. Wuijts et al, 2022). Therefore, to make sure our results are robust under different model decisions, a more detailed model that includes generation cost (the Cost Model in Appendix E) was simulated for analysed period of 1982-2010 for the Distributed Energy +10% demand scenario.…”

“…The other key reliability indicator is loss of load expectation (LOLE), i.e., the average number of hours ENS that is expected to occur per year based on simulations. However, in power system models, LOLE can take on arbitrary values making it a less robust indicator than EENS (R. H. Wuijts et al, 2022).…”

The climatological renewable energy deviation index (credi)

“…Energy system models are vital to capture the impact of this variability [4]. However, their complexity results in high computational burdens that grows exponentially with the simulation period [1,[5][6][7][8]. Incorporating large climate datasets that capture energy-meteorological variability in operational hourly energy system models is thus, as of yet, unfeasible [1,7,9].…”

“…However, their complexity results in high computational burdens that grows exponentially with the simulation period [1,[5][6][7][8]. Incorporating large climate datasets that capture energy-meteorological variability in operational hourly energy system models is thus, as of yet, unfeasible [1,7,9]. Even so, understanding the scale of this variability, can aid system operators in their task to ensure both short-and long-term energy security [1,7,10].…”

“…Incorporating large climate datasets that capture energy-meteorological variability in operational hourly energy system models is thus, as of yet, unfeasible [1,7,9]. Even so, understanding the scale of this variability, can aid system operators in their task to ensure both short-and long-term energy security [1,7,10]. Therefore, alternative approaches are needed to assess energy-meteorological variability [1,11].…”