Flexible Power Grids with Large Scale PV Integration: Control Strategies and Dispatching Capability of Hybrid PV Power Plant

Abstract

Global efforts to decarbonize the electrical grid have led to the widespread adoption of renewable energy sources, including solar photovoltaic (PV) power plants. However, cloud movements over these PV power plants cause the generated power to become stochastic, intermittent, and variable, leading to fluctuations that affect voltage and frequency stability. To manage this variability, many countries have updated grid codes to enforce ramp rate thresholds, with penalties for noncompliance. Concurrently with these regulations, considerable ongoing research focuses on implementing techniques to mitigate cloud-induced power variability, such as output modulation without energy storage, which can lead to energy losses.

The use of battery energy storage systems (BESS), on the other hand, offers a more f lexible solution. Smoothing controllers are employed to regulate BESS during operation, ensuring that the combined output of the BESS and the PV power plant is smoother and less variable than the original PV power profile. However, arbitrary settings can result in poorperformanceoncloudydaysorunnecessarybatteryusageoncleardays,accelerating battery degradation. This research introduces a novel predictive and adaptive smoothingcontroller. It predicts next-day cloud cover and adjusts smoothing parameters in real-time based on power variability, significantly lowering ramp rates and reducing battery degradation.

Conventional charging and discharging schemes cause repetitive State of Charge (SOC) changes, degrading BESS performance. This issue is more pronounced in largescale BESS with parallel units, where the reference power is often shared among units basedontheirSOC.Thiscausestheunitstochargeanddischargesimultaneously, following the same cycling pattern, which contributes to each battery’s degradation and premature aging. To address this, we propose the Reliant Monotonic Charging/Discharging Controller (RMCC), where battery units are divided into two groups that operate concurrently—one charging and the other discharging—to help reduce battery cycles and extend BESS lifetime. This approach prolongs battery life and enhances efficiency, reducing degradation rates significantly compared to traditional methods.

For larger BESS with more than two units, the Smart RMCC(S-RMCC)controller is proposedtomanagemultiplebatteryunitsbydeterminingtheoptimalnumbertoactivate based on forecasted energy needs and the remaining energy of battery units, deciding whether to engage the entire BESS system or a subset of units. It employs two power allocation strategies: conditional unit selection for inter-group support during fluctuations and dynamic unit selection for adjusting the number of active batteries based on the energy needs dictated by ramp events. These strategies enhance battery utilization and prevent scenarios where one group has idle units while the other lacks sufficient energy to counteract the forecasted ramp event. Additionally, this research emphasizes real-time battery aging data for informed decision-making, focusing on degradation balancing. Uneven operational patterns in connected battery units lead to irregular degradation and performance loss. The enhanced S-RMCC controller monitors aging indices and selects units with similar capacities to compensate for power fluctuations.

Collectively, the proposed controllers and algorithms offer promising solutions to the challenges of PV power variability and BESS performance, contributing significantly to the flexible and seamless integration of variable PV power plants into the electrical power grid.

Date of Award	20 Jul 2024
Original language	American English
Supervisor	Mohamed El Moursi (Supervisor)