Techno-economic analysis and optimal control of battery storage for frequency control services, applied to the German market

Abstract

Optimal investment in battery energy storage systems, taking into account degradation, sizing and control, is crucial for the deployment of battery storage, of which providing frequency control is one of the major applications. In this paper, we present a holistic, data-driven framework to determine the optimal investment, size and controller of a battery storage system providing frequency control. We optimised the controller towards minimum degradation and electricity costs over its lifetime, while ensuring the delivery of frequency control services compliant with regulatory requirements. We adopted a detailed battery model, considering the dynamics and degradation when exposed to actual frequency data. Further, we used a stochastic optimisation objective while constraining the probability on unavailability to deliver the frequency control service. Through a thorough analysis, we were able to decrease the amount of data needed and thereby decrease the execution time while keeping the approximation error within limits. Using the proposed framework, we performed a techno-economic analysis of a battery providing 1 MW capacity in the German primary frequency control market. Results showed that a battery rated at 1.6 MW, 1.6 MWh has the highest net present value, yet this configuration is only profitable if costs are low enough or in case future frequency control prices do not decline too much. It transpires that calendar ageing drives battery degradation, whereas cycle ageing has less impact.

Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

“…Both the revenue and degradation cost are then multiplied by the number of cells. This assumes a high-quality cell balancing and battery management system in which all cells behave equally such that each cell is controlled individually as in [20,52,53]. The purpose of the battery model is to update the capacity of the battery continuously after the cycling to get accurate SoC value to estimate the CM penalties (p).…”

“…Then, the first term in (19) captures SEI layer growth with time, the second term captures the battery loss with mild cycles and the third term captures the beginning of capacity loss in the first cycle/day. With more expansion and contraction of electrode materials during charging/discharging, the negative electrode particles face increased stress that lead to mechanical fracture which results in active material loss (Q AM ) in (20). Equation (20) assumes that there is a negative electrode active material loss with every cycle (N c ).…”

“…With more expansion and contraction of electrode materials during charging/discharging, the negative electrode particles face increased stress that lead to mechanical fracture which results in active material loss (Q AM ) in (20). Equation (20) assumes that there is a negative electrode active material loss with every cycle (N c ). Third, at the cell's beginning of life, the positive electrode storing capability (Q pos ) will slightly increase because it is found that temperature is the main controlling factor in the first few cycles leading to an increased Ah throughput in (21).…”

“…Lithium ion batteries (LIBs) have higher power density, energy density, and cycle life compared to other battery types [16]. Therefore, they provide several grid storage services such as energy arbitrage [17], voltage support [18], peak shaving [19], frequency control [20], and energy reserve [21] which could play a vital role in supporting the CM and energy security [22]. It is found that batteries cannot only enhance its business case by participating in the CM but also reducing the cost of electricity to customers by reducing the shortage hours [23].…”

Research on Energy Storage Optimization for Large-Scale PV Power Stations under Given Long-Distance Delivery Mode

“…Varshosaz et al [42] adopted the saturation control theory to calculate the minimum energy storage capacity to keep the system stable. The authors of [43,44] used the discrete Fourier transform for analyzing the spectrum of renewable energy to determine the compensation range of energy storage, and then proposed a capacity determination method that satisfied the requirements. (3) Establishing objective functions with the objective of minimizing the cost.…”