(181c) A Decision Framework for Integrating Energy Storage with Power Plants: Technology Selection, Design and Optimization

The power grids experience significant daily and seasonal variabilities due to fluctuating electricity demands and prices, integration of intermittent renewables, and changing fuel supply and costs. The conventional fossil power plants frequently vary their operations to meet the highly fluctuating demands while compensating for the variabilities. Such cycling operation leads to significant damage of the critical components and reduces the efficiency of a power plant [1,2]. Energy storage systems can not only help to reduce these adverse impacts of load-following, but can greatly increase the grid flexibility in case of large and rapid discrepancy between the supply and demand. However, most of the previous works have considered large-scale storage at the grid level, resulting in conservative estimates of storage capacities and costs [3-5]. For a seamless integration of renewable energy to the grid, challenges arising from intermittency and spatio-temporal variability need to be addressed at the power plant level. Moreover, the grid-scale integration greatly limits the choice of potential storage technologies. To counter the aforementioned challenges, this work considers a decentralized scheme for energy storage that can be integrated with individual power plants. Decentralized storage requires less capital investment and can reduce the ramp rates in fossil-fueled power plants. Moreover, the operational synergies that exist between the power generation and storage systems provide additional flexibility and cost savings.

A variety of electrochemical, thermal and/or mechanical technologies can be considered for energy storage. These technologies, however, exhibit trade-offs among several characteristics such as efficiency, useful lifespan, availability, cost, environmental footprint and safety. We present an optimization-based framework that accounts for these trade-offs and the dynamic interactions between the different energy generation, storage and distribution components for the downselection of viable storage alternatives. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model with the objective of maximizing the total profit of an integrated power generation and storage system over a time-horizon of operation. The profit includes revenues earned by the system in the spot market, operational costs of power plant, investment and operating costs of energy storage, and costs from cycling operation. The model involves discrete decisions to select from a variety of storage options such as thermal, mechanical, chemical and electrochemical storage. The presence of many discrete and continuous decisions, the dynamic operations of the power plant and storage, and the interactions between the various systems components result in a highly complex and large-scale model. To solve this discrete and dynamic optimization problem, we employ surrogate models for both power plant and storage, which are developed based on high-fidelity models. The framework will be illustrated using a case study on storage technology selection for natural gas combined cycle (NGCC) power plants. To ensure that the system meets a time-varying grid demand, detailed dynamic models of the NGCC plant , sodium sulfur (NaS) battery-based electrochemical storage, molten salt and/or phase change material-based thermal energy storage, and compressed air energy storage (CAES) are used where these candidate technologies are synergistically integrated with the NGCC plant [6,7]. Initial results suggest that the optimal storage technologies and their design capacity strongly depend on the candidate technologies considered, their operating constraints, and cost. It was also observed that an optimal synergistic integration can improve the power plant efficiency during load-following while reducing the ramp rate in the power plant considerably.

References:

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[2] Mechleri E, Fennell PS, Mac Dowell N. Optimisation and evaluation of flexible operation strategies for coal-and gas-CCS power stations with a multi-period design approach. Int J Greenh Gas Control. 2017;59:24-39.

[3] Denholm P, Ela E, Kirby B, Milligan M. Role of Energy Storage with Renewable Electricity Generation.; 2010.

[4] Trifkovic M, Marvin WA, Daoutidis P, Sheikhzadeh M. Dynamic real-time optimization and control of a hybrid energy system. AIChE J. 2014;60(7):2546-2556.

[5] Heuberger CF, Staffell I, Shah N, Mac Dowell N. A systems approach to quantifying the value of power generation and energy storage technologies in future electricity networks. Comput Chem Eng. 2017;107:247-256.

[6] Wang Y, Bhattacharyya D, Turton R. Dynamic Modeling And Control Of A Natural Gas Combined Cycle Power Plant For Load-Following Operation. In: Computer Aided Chemical Engineering. Vol 47. Elsevier; 2019:101-106.

[7] Vudata SP, Bhattacharyya D, Turton R. Optimal Thermal Management of a High-Temperature Sodium Sulphur Battery. In: 2018 AIChE Annual Meeting. ; 2018.