As more variable energy resources (VER) are added to electric company power generation portfolios, traditional baseload power plants have been adopting more flexible operations to balance out the variable generation of renewables resources. Power generation resources previously operated in a baseload mode now often have to adjust their power output on an hourly or more granular basis to make up for generation shortfalls (as renewables go offline) and surpluses (when renewables generate more power than needed). The power plants that need to operate more flexibly than they originally were designed to do incur a real—yet difficult to quantify—cost of cycling, including efficiency losses, increased component damage, and reduced component life. These factors are important for operations and maintenance (O&M) planners tasked with developing efficient and reasonable future budget estimates and planning for long-term asset maintenance.
This report describes a parametric study of the relative damage incurred as a result of cycling a representative power plant, derived using a proprietary modeling tool and informed by review and meta-analysis of long-term economic forecasts and public power plant operations data to determine inputs. Using this model, we developed an analytic framework designed to place boundaries around the extent of those factors and how they might change in the coming years. We developed a series of potential scenarios for an illustrative, representative power plant and performed an analysis to estimate component damage for the power generation unit. These results show how scenarios lead to different expected levels of cycling damage and how this damage impacts the variable O&M costs associated with increasing power plant operating flexibility. This information can be used to understand the potential physical and economic impacts associated with increased cycling of existing power plants and can help electric companies, state regulators, and others to develop long-term plans to maintain these facilities and improve future estimates of the financial costs of O&M.