Chance-Constrained Optimal Configuration of BESS Considering Uncertain Power Fluctuation and Frequency Deviation Under Contingency

Abstract

With the accelerating integration of variable renewable energies (VREs), power systems become more vulnerable to active power disturbances, and more drastic frequency dynamics emerge. The battery energy storage system (BESS) is able to handle the uncertainties of VREs, and the decreasing system inertia and frequency regulation capability. This paper proposes a chance-constrained optimal configuration scheme for the BESS to maintain both the uncertain power fluctuations and frequency deviation within predefined limits. First, the required frequency regulation capability of the BESS constrained by the maximum transient frequency deviation (MTFD) and quasi-steady-state frequency deviation (QSSFD) is estimated. Then, the kernel density estimation method is utilized to model the net power fluctuations of VREs and load. A multiobjective chance-constrained programming model accounting for the life cycle cost, energy arbitrage, uncertain power fluctuation, MTFD, and QSSFD is established to optimize the capacity of the BESS. Furthermore, the Bernstein approximation is utilized to process the chance constraint, and transform the optimization model into a deterministic form. Based on the linear weighted method and Benders decomposition, the optimization model is solved through alternating iteration. Case studies were conducted to validate the proposed scheme, showing superior performance in smoothing uncertain power fluctuations, and reducing frequency deviation under contingencies.

A hierarchical multi‐area capacity planning model considering configuration ratios of renewable energy and energy storage systems with multi‐area coordination

“…• Thermal Power Plants Constraints: To reduce the computational burden, a cluster-based unit commitment model is adopted here [43]. Constraint (38) reflects the logical relationship between the online, start-up, and shut-down variables, and constraint (39) indicates that these variables cannot exceed the installed number of the thermal power plant. The generation range of each thermal power plant is restricted by its minimum and maximum online capacity, as shown in (40).…”

“…the sum of renewable power output and discharging/charging power of ESS units) and the corresponding typical generation curve $$P_{t,\omega }^{{\mathrm{WPS}},{\mathrm{Ref}}}$$ for RESs. It is noteworthy that the LA planning problem is formulated as a multi‐objective optimization model, which can be transformed into a single‐objective form by the linear weighted method [39]. Thus, a penalty cost function is used instead of directly using power output deviations, as shown in (10).…”

A Unified Control Platform and Architecture for the Integration of Wind-Hydrogen Systems Into the Grid

“…In [6], a cooperative control solves the problem of power fluctuations by using a hybrid energy storage system based on predictive and adaptive smoothing mechanisms. A multi-objective chanceconstrained optimal configuration scheme for battery energy storage systems (BESSs) has been presented in [7] to maintain both the uncertain power fluctuations and frequency deviation within predefined limits. In [8], a BESS has been controlled to reduce the cycles of battery charge/discharge, and then increase its lifetime.…”

A Coordinated Control Strategy of Multi-Type Flexible Resources and Under-Frequency Load Shedding for Active Power Balance

“…Based on this, the study investigates a hierarchical coordinated control approach for controllable resources in the context of active distribution networks [18,19]. It fully utilizes controllable resources to enhance the flexibility and response speed of the power system [20,21].…”