Long-term energy planning models used to analyze the integration of variable renewable energy (VRE) often incorporate limited temporal and spatial resolution because of low availability of high-quality data. It is therefore crucial to develop a method for indirectly representing the costs and benefits of the various options to maintain a reliable grid while integrating VRE. Little research has been conducted on how transmission can enable better matching of VRE generation with electricity demand on a spatial basis. Hence, the copper-plate assumption—a transmission system where power can flow unconstrained from any generation site to any demand site—found in most energy modeling tools. Using China’s power sector as a case study, we create an optimization tool to explore the impact of transmission system expansion on electricity production costs and wind generation siting.
We develop a high-resolution linear programming capacity expansion model using hourly load and wind capacity factors data at the province level. Demand comes from Guangdong, while the other 31 provinces can only produce electricity from wind. Generation and transmission capacity expansion is optimized through a cost minimization approach for nine scenarios constrained by a renewable portfolio standard (RPS) – 10, 30, or 50% – and by a restriction on transmission line siting – No transmission (production within Guangdong only), Adjacent transmission (production in any of Guangdong’s adjacent provinces), or Full transmission (production in any provinces).
In all scenarios, the cost-minimized mix locates wind turbines in one province only. Wind and transmission costs can be reduced by up to 38% and 68%, if building transmission lines is allowed from adjacent provinces and all provinces, respectively. The resulting reduction in installed wind capacity ranges from 51% to 73%. Electricity overproduction can be up to 45 times lower when moving from No to Full transmission configurations. Lowest residual peak load occurs in Adjacent configurations. We calculate that the cost error in copper-plate assumption models – corresponding to transmission costs – is at least 17% of the total cost in our Full transmission scenario.
While transmission expansion significantly decreases costs and overproduction, a cost minimizing approach does not systematically favor configurations with highest wind capacity values, as the capacity value is derived from the highest residual peak load hour while costs are minimized over the year. Next steps will consist in applying the same method to other consuming provinces to derive an average error in cost calculation between high- and low-resolution models.
Nils Johnson, Energy Program, IIASA
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Last edited: 03 February 2016
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