(623c) A Two-Level Optimization Framework with Consideration of Economic Benefits and Long-Term Capacity Fading for Battery Energy Storage Systems

To achieve greenhouse gas emission targets, renewable energy must be developed with unprecedented scale worldwide. The grid is facing the emerging challenge of intermittency and dispatchability caused by variable renewable energy. To tackle these problems, grid-scale energy storage has attracted much attention because it can decouple the electricity generation from usage. With the decreasing cost, high energy density, and low overall energy loss, electrochemical energy storage, especially Li-ion battery technology, provides a promising solution [1]. One drawback of Li-ion batteries is that they face capacity fading caused by two dominant side reactions during charging: the growth of a solid-electrolyte interface (SEI) and lithium plating [2]. Most of the previous battery control algorithms either ignore capacity fading or use an oversimplified fading model [3–7]. In practice, this causes battery energy storage systems (BESS) to perform sub-optimally, due to the model not adequately representing these important phenomena.

To tackle the above challenges, we proposed a two-level optimization framework for a BESS by addressing the economic benefits in the upper-level optimization. This economic optimization is coupled with a strategy to reduce battery capacity fading, which is handled in the lower-level optimization problem. The lower-level problem uses a detailed electrochemical Li-ion battery model, with mathematical representations of both SEI growth and Li plating.

The flow chart of the proposed framework is shown in Figure 1. Given forecasted electricity prices, the current state of charge (SOC), and the limits of the SOC, the upper-level optimization will generate and solve an economic maximization problem. From the solution, the changes in SOC can be obtained. Due to the mechanism of the Li-ion battery, side reactions only happen during battery charging. The lower-level optimization only accounts for optimizing the charging current given the desired SOC changes resulting from the upper-level problem. Due to the complexity of the Li-ion mechanism, the optimization problem is nonlinear and non-convex. This problem is solved using particle swarm optimization. The objective function is described in Eq. 1 and is comprised of two terms. The first term (with the a weighting factor) is utilized to align the upper-level optimal solution with the lower-level problem. The second term (with the b weighting factor) represents an effort to maximize the Faraday efficiency of the battery, which results in minimal capacity degradation. A proper balance of these two terms will achieve ideal economic performance while mitigating the long-term consequences of battery degradation.

To validate the performance of the proposed framework, a case study is carried out with electricity prices obtained from the California Independent System Operator (CAISO). A rule-based charging strategy, constant current constant voltage (CCCV), is used as the benchmark. The results are summarized in Table 1. With the proposed framework, the lifespan of the BESS is extended by about 5.1% and the total revenue generated within the lifespan also increases by about 9.8%. Moreover, thanks to the inclusion of the Faraday efficiency in the evaluation function of the PSO, the proposed framework increases the overall BESS efficiency by about 1.3%.

Reference

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