(373f) Scheduling of Baseload Power Plants and Batteries with Integration of Renewables

Due to changes in the global climate, societal and cultural pressure, and a rapid decrease in the cost of renewables, an increase in the penetration of intermittent renewable energy sources into the power grid is expected in the near future^[1]. At high penetration rates, integrating intermittent renewables into the grid becomes more challenging due to the limited alignment between wind/solar energy generation and electricity demand. For example, the period when the availability of the renewable energy sources peaks is not the same period when the electricity demand peaks. Also, an increase in cycling of baseload fossil-fueled power plants due to renewables may lead to prohibitive stresses and wear-and-tear on equipment, thus limiting ramp up/down rates to balance the system. Without a sufficiently flexible grid, thermal power plants cannot reduce their power output and wind and solar generation need to be restricted^[2]. For instance, a key limiting factor for deploying photovoltaics (PV) is curtailment, i.e., the PV energy would need to be rejected by system operators to maintain the supply/demand balance of the system^[3]. As curtailment increases, the solar/wind energy offsets less fossil-based generation. Demand response and storage are enabling technologies that can reduce curtailment and facilitate higher penetrations of intermittent renewables into the grid^[2]. Due to the challenges in quantifying the point at which energy storage becomes the least-cost flexibility option, evaluating the role of these interventions in power systems with high penetration of intermittent renewables requires continued analysis, improved data, and implementation of advanced process systems techniques.

In this work, a new optimization strategy employing mixed-integer nonlinear programming is explored to integrate the fossil-fueled power plants, such as supercritical pulverized coal (SCPC) and natural gas combined cycle (NGCC) power plants, with energy storage units. This strategy aims to maximize the use of intermittent renewables during scheduling while minimizing the cost under the constraint of maintaining reliability of the grid. The storage unit for the current study comprises sodium sulfur batteries, which are advanced secondary batteries that can be used for various power system applications. At the grid level, sodium sulfur batteries have high potential for electrical storage due to their high energy density, low cost of the reactants, and high open-circuit voltage^[4]. Also, a creep-fatigue damage model for the most stressed components in the power plant is considered in order to determine the optimal ramping rates and scheduling while maintaining acceptable component life^[5]. Ultimately, the generated ramping rates and schedules of different energy modules can be sent to a lower-level optimizer^[6] and advanced model-based controllers^[7].

References

[1] REN21 Renewable Energy Policy Network for 21^st Century. Available at: http://www.ren21.net/status-of-renewables/global-status-report/. Accessed on April 16, 2018.

[2] GREENING THE GRID. Demand Response and Storage. Available at: http://www.greeningthegrid.org/integration-in-depth/demand-response-and-.... Accessed on April 7, 2019

[3] NATIONAL RENEWABLE ENERGY LABORATORY (NREL) (2016). Energy Storage Requirements for Achieving 50% Solar Photovoltaic Energy Penetration in California. Technical Report NREL/TP-6A20-66595.

[4] VUDATA, S.P., BHATTACHARYYA, D., & TURTON, R. (2017). Development of Dynamic Model and Thermal Management Strategies for High-Temperature Sodium Sulfur Batteries. AIChE Annual Meeting, Minneapolis, MN.

[5] WANG, Y., BHATTACHARYYA, D., & TURTON, R. (2018). Dynamic Modeling and Control of a Natural Gas Combined Cycle (NGCC) Power Plant with a Damage Model. AIChE Annual Meeting, Pittsburgh, PA.

[6] KIM, R., LIMA, F. V. (2019). Multi-objective and Dynamic Real-time Optimization for Postcombustion MEA-based CO₂ Processes under Cycling Conditions. In preparation for publication.

[7] HE, X. & LIMA, F. V. (2019). A Modified SQP-based Model Predictive Control Algorithm: Application to Supercritical Coal-fired Power Plant Cycling. Submitted for publication.