Systematic Approach to Better Understanding Integration Costs

Stark, Gregory B.¹ 

[1] National Renewable Energy Lab. (NREL), Golden, CO (United States)

关键词: integration costs;    operating costs;    variable generation;    conventional generation;    National Renewable Energy Laboratory;    NREL;   

DOI  :  10.2172/1225498
RP-ID  :  NREL/TP--5D00-64502
PID  :  OSTI ID: 1225498

美国|英语

来源: SciTech Connect

【 摘 要 】

This research presents a systematic approach to evaluating the costs of integrating new generation and operational procedures into an existing power system, and the methodology is independent of the type of change or nature of the generation. The work was commissioned by the U.S. Department of Energy and performed by the National Renewable Energy Laboratory to investigate three integration cost-related questions: (1) How does the addition of new generation affect a system's operational costs, (2) How do generation mix and operating parameters and procedures affect costs, and (3) How does the amount of variable generation (non-dispatchable wind and solar) impact the accuracy of natural gas orders? A detailed operational analysis was performed for seven sets of experiments: variable generation, large conventional generation, generation mix, gas prices, fast-start generation, self-scheduling, and gas supply constraints. For each experiment, four components of integration costs were examined: cycling costs, non-cycling VO&M costs, fuel costs, and reserves provisioning costs. The investigation was conducted with PLEXOS production cost modeling software utilizing an updated version of the Institute of Electrical and Electronics Engineers 118-bus test system overlaid with projected operating loads from the Western Electricity Coordinating Council for the Sacramento Municipal Utility District, Puget Sound Energy, and Public Service Colorado in the year 2020. The test system was selected in consultation with an industry-based technical review committee to be a reasonable approximation of an interconnection yet small enough to allow the research team to investigate a large number of scenarios and sensitivity combinations. The research should prove useful to market designers, regulators, utilities, and others who want to better understand how system changes can affect production costs.