Increasing Future Grid Stability Through Linear Optimization of System Capacity Factor – Washington State

• In Event: Planning for Climate and Economic Resilience

Abstract

Energy production functions as an on-demand delivery supply chain, where semi-centralized generation centers service diverse consumer demands. Utility-scale electricity generation is measured in megawatt hours (MWh), which is the electricity needed to supply 1 million watts of consumer demand for one hour. Each electricity generation facility (EGF) has a maximum hourly output capacity in megawatts (MW), which can be compared with its actual production over a period of time to identify its capacity factor.

This unit-less ratio indicates how much productivity a facility or group of facilities lost to downtime, maintenance, or sub-optimal generation over a given period. In 2023, Washington’s EGF's collectively produced electricity at an observed capacity factor of 37.64 percent, meaning 62.36 percent of their production capability was unused. Unused capacity carries expected costs, such as repairs, operator salaries, and in some cases fuel storage.

Maximizing the system’s capacity factor is a direct approach to increasing the efficiency of generating electricity on a per-MWh level, reducing the cost of electricity for consumers, and reducing gaseous. These outcomes, as well as statewide energy independence, can be a direct result of informed public policy. This abstract’s accompanying report provides a vision for a future where by the year 2050, electricity in Washington: i) is generated within a supply chain that improves upon or maintains its centralization; ii) meets consumer demands to an extreme statistical certainty; iii) increases statewide energy independence; iv) lowers the cost of electricity on a per-MWh basis through the elimination of fuel procurement, storage, and facility idling costs; and v) is generated in sustainable manner.

Policy can be informed using a linear optimization problem (LOP). LOPs are an iterative method of minimizing or maximizing one dependent variable by manipulating the independent variables that influence it. In this case, the dependent variable to be minimized is total system capacity, and the independent variables to be changed are the capacities attributed to each generation method.

By replacing existing non-renewable fueled electricity generation facilities, considering geographical, environmental, and seasonal limitations, and accounting for increasing total consumers and their respective demand with hydroelectric nuclear, geothermal, and wind generated electricity, the system can increase it's capacity factor to at most 46.41 percent by 2050. This approach could reduce the state’s 2050 capacity by 9,222 MW (29.3 percent of 2023’s capacity) when compared with generation added at the current capacity factor.

This is just one solution to omptimizing Washington's existing electricity generation network with considerations for the future, but demonstrates how far from an optimized solution the current infrastructure is. Prices for end-consumers, emissions, and system unreliability are all expected costs incurred by consumers, who are effectively a captive market. Improvements must come on the largest-scales, as consumer-level improvements such as rooftop solar panels are only marginal and infeasible on a state-wide socioeconomic scale. This presents an opportunity for mathematically justified long-term public policy, founded on a strong foundation of research with a definitive end goal.

Author

• Alexander Elchev, Library of Congress
  Presenting Author