(418a) An Optimization-Based Decision Framework for Integrating Energy Storage with Fossil Power Plants

Rapidly declining costs of clean renewable energy and increased policy efforts have driven the integration of renewables in electricity grids. However, the intermittency in availability of renewable energy requires electricity grids to be more flexible, resilient and reliable. To incorporate flexibility in electricity grids, the traditionally considered techniques are the increased cycling operation of conventional power plants and the integration of grid-level energy storage. However, the conventional plants are typically designed to operate at base-load and witness increased failure rates when forced to rapidly cycle their output, or while undergoing startup and shutdown operations. Such cycling operation also reduces the power generation efficiency of the plant [1,2]. On the other hand, to effectively manage energy supply-demand fluctuations, grid-scale energy storage integration requires large capital investment with only a limited number of candidate storage technologies [3-5]. To address these power generation and storage challenges, we propose the integration of localized energy storage with individual fossil power plants. Such an energy storage scheme requires less capital investment, reduces the cycling associated with fossil-fueled power plants to improve grid flexibility, and increases the number of candidate storage technologies. Moreover, the operational synergies that exist between the power plant and the localized storage systems provide additional routes for cost savings and system flexibility.

There exists a variety of candidate energy storage technologies which include electrochemical, thermal and/or mechanical storage. However, these technologies have several associated trade-offs in terms of efficiency, lifespan, availability, cost, environmental footprint and safety. To handle these trade-offs and the complex dynamics between energy generation, storage and dispatch systems, we develop an optimization-based framework for the downselection of viable storage alternatives. The overall problem is formulated as a mixed-integer nonlinear programming (MINLP) model considering two objectives: (i) maximizing the total profit obtained from an integrated power generation and storage system, and (ii) minimizing the power plant cycling over a finite time-horizon of operation. To extensively compute integration profitability, the problem considers spot market revenues, power plant operational costs, energy storage investment and operating costs, and power plant cycling operation costs. The overall problem is especially challenging due to the presence of numerous decision variables, dynamic operations of the power plant and storage, and the complex interactions between the various systems components. To address this, we employ surrogate models for both power plant and storage, which are developed based on high-fidelity storage models. We demonstrate the utility of the framework using a case study on storage technology selection for natural gas combined cycle (NGCC) power plants. To ensure that the system meets a time-variant grid demand, detailed dynamic models of the NGCC plant and sodium sulphur (NaS) battery-based electrochemical storage are used where this candidate technology is synergistically integrated with the NGCC plant [6,7]. A sensitivity analysis is performed to determine the combination of cost parameters which facilitate the storage selection. The results indicate that for the objective of profit maximization, the selection of energy storage is economically viable for low storage investment cost as well as for high NGCC variable operating costs, which includes the fuel costs and CO₂ emission cost. In addition, the integration of energy storage of capacity 23% of the power plant nominal output is optimal to minimize power plant cycling while meeting grid demand.

References:

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