For years, the developers of concentrating solar power (CSP) systems have highlighted the numerous benefits of CSP plants that are integrated with thermal energy storage (TES). However, utilities, grid operators, and state regulators need credible, quantifiable analysis to verify that this technology offers significant value as a dispatchable source of energy. Before these important stakeholders can fully support CSP with TES, the benefits have to be analyzed in an acceptable framework, preferably via simulations created by recognized, commercially available software. A new report by the National Renewable Energy Laboratory (NREL) describes just such an analysis, and quantifies the incremental operational value of CSP with TES in multiple scenarios.
Historically, the industry has used a traditional utility planning tool, the production cost model, to simulate the operation of the grid and to estimate the operational value of different generation mixes. These cost models, however, have not been used very often or very thoroughly to analyze simulations of commercial CSP with TES.
The new report describes the implementation of CSP with TES in a commercial production cost model, and presents results for simulations of grid operations with CSP in a test system, consisting of two balancing areas located primarily in Colorado. This test system, while geographically limited, allowed NREL to better isolate the relative value of TES under various scenarios. The methodology developed under these scenarios is currently being used to investigate the more complex California system controlled by the California Independent System Operator.
NREL implemented CSP with and without storage in the PLEXOS simulation model, and compared it to PV and a baseload generator. The CSP plant with storage was modeled as a trough-type plant with 6 hours of storage. The test system consisted of two balancing areas located in Colorado and Wyoming, using publicly available data to represent the generator mix and system operator within this area. The generation characteristics and fuel prices were based on a 2020 scenario and included two renewable energy (RE) cases—a low RE scenario in which wind and solar provide 13% of the annual generation and a high RE scenario where wind and solar provide 34% of generation, including 8% from photovoltaics (PV).
NREL found that the simulated CSP plants could be dispatched to avoid the highest-cost generation, generally shifting energy production to the morning and evening in non-summer months and shifting energy towards the end of the day in summer months. This minimized the overall system production cost by reducing use of the least-efficient gas generators or preferentially displacing combined cycle generation over coal generation. The simulation also indicated that the system could dispatch CSP during the periods of highest net load, resulting in a very high capacity value.
The difference in value between plants with and without storage, the analysis showed, is highly dependent on both the cost of natural gas and the penetration of other renewable sources, such as PV. With a low penetration of renewables, the inherent coincidence of solar and price patterns illustrated that CSP without storage (and PV) have relatively high value. Combined with a relatively low gas price, of $4.1 per million British thermal units, the analysis yielded an incremental operational value of TES of about $6.6 per megawatt-hour (MWh) over and above that of a plant without TES (at low RE penetration). At higher RE penetration, the difference increases as the value of mid-day generation is reduced. In fact, in the high-RE test system, this difference in operational value grew to $16.7/MWh. Moreover, the capacity value of CSP systems with TES remains high, further increasing the difference in value associated with TES. Under this scenario the total value, including operational and capacity, can be as high as $35.8/MWh over PV systems.
To learn more, read the report here:
Simulating the Value of Concentrating Solar Power with Thermal Energy Storage in a Production Cost Model.