Kenneth Van den Bergh

Optimization and Allocation of Spinning Reserves in a Low-Carbon Framework

Low-carbon electric power systems are often characterized by high shares of renewables, such as wind power. The variable nature and limited predictability of some renewables will require novel system operation methods to properly size and cost-efficiently allocate the required reserves. The current state-of-the-art stochastic unit commitment models internalize this sizing and allocation process by considering a set of scenarios representing the stochastic input during the unit commitment optimization. This results in a cost-efficient scheduling of reserves, while maintaining the reliability of the system. However, calculation times are typically high. Therefore, in this paper, we merge a state-of-the-art probabilistic reserve sizing technique and stochastic unit commitment model with a limited number of scenarios in order to reduce the computational cost. Results obtained for a power system with a 30% wind energy penetration show that this hybrid approach allows to approximate the expected operational costs and reliability of the resulting unit commitment of the stochastic model at roughly one thirtieth of the computational cost. The presented hybrid unit commitment model can be used by researchers to assess the impact of uncertainty on power systems or by independent system operators to optimize their unit commitment decisions taking into account the uncertainty in their system.

The impact of renewable injections on cycling of conventional power plants

Renewable injections from wind and solar affect the variability in residual electricity demand (demand minus renewable injections) to be covered by the conventional electricity generation system. In order to meet the variable residual demand, conventional power plants are required to cycle, meaning that they have to change their generation output by ramping or switching on/off. As cycling of conventional power plants entails technical and economical issues, it is imperative to know the effect of increasing renewable injections on cycling. This paper quantifies conventional power plant cycling as function of the amount of renewable injections. The results follow from a unit commitment model of a realistic electricity system. The study shows that the change in variability in residual demand due to renewable injections is rather limited. The way conventional power plants meet this variable demand, however, changes considerably as renewable injections force more base load plants to cycle. The take-away of this paper is that required flexibility from the conventional power plant portfolio is not the issue; the way this flexibility is delivered is the issue when it comes to conventional power plant cycling.

Long-term cycling costs in short-term unit commitment models

Cycling of conventional power plants is becoming increasingly important in an electricity system with a large penetration of intermittent renewables. Power plant cycling entails short-term costs, e.g., additional fuel costs during start-up, and long-term costs, e.g., additional maintenance costs. Power plant operators should take long-term cycling costs into account when making short-term scheduling decisions, in order to reduce total generation costs. This paper presents a new approach to consider long-term start-up costs in a short-term unit commitment model. The approach is based on an iterative procedure, in which consecutively a unit commitment model is solved and the correct total start-up cost is recalculated. This new approach, referred to as the Cost Redistribution Unit Commitment (CRUC), is applied to a real-life case study based on the 2014 German electricity system. The performance of the CRUC model, in terms of generation costs and computational tractability, is compared with existing start-up cost formulations in the literature. The simulation results show that, for the considered case study, the CRUC model outperforms the existing formulations in terms of generation costs, but requires a longer run time.