A two-level control strategy with fuzzy logic for large-scale photovoltaic farms to support grid frequency regulation

Abstract

Physical value of active power, in Watts Value expressed in per-unit system of Ratio value expressed in percent Index of local agent in a PV farm Present step of sampling time in control Superscripts * Reference value Meas Measured actual value Pre Predicted value Min Minimum value Max Maximum value TrS Value computed in the transient state StS Value computed at the steady state Diff Difference value This study proposes a two-level coordinated control strategy with fuzzy logic for appropriately adjusting the total active power supplied to a grid by large-scale photovoltaic (PV) farms in order to regulate grid frequency. For a solar farm, the strategy includes a central coordinating controller and many local controllers at PV power assemblies, treated as agents. In detail, the central controller uses a frequency regulation module based on a new automatic-tuning fuzzy-logic controller scheme to compute the appropriate reference values according to the total power needed. Then, the individual reference value for each local controller is determined. Each local controller governs all power-electronic converters installed at the PV agent to inject power into the grid according to the individual reference value received. Additionally, each local controller uses an algorithm to manage the state-of-charge of the battery bank installed at the agent so that it remains in the safe range of 20-80% while operating and close to the desired idle value of 50% at the steady state. Besides, a special control mode is developed and integrated into the overall strategy to aid rapid recovery of the grid frequency under emergency conditions. Numerical simulations demonstrate that the suggested strategy has the good response in terms of injecting an appropriate amount of power into the grid to regulate the frequency deviation into acceptable ranges of ±0.2 (Hz) in the transient state and ±0.05 (Hz) at the steady state, even when the weather conditions (solar radiation, air temperature), AC system load, and important control parameters of the grid suddenly change. Furthermore, the effectiveness in improving the grid-frequency stabilization by using the proposed strategy is validated within a four-area power system, where four PV farms are connected and the operating parameters of the grids at the areas are fairly different.

A Comprehensive Review on Grid Connected Photovoltaic Inverters, Their Modulation Techniques, and Control Strategies

“…The authors in [190] used FLC along with the back-stepping approach to maintain a DC-link voltage and deliver the power to the grid with a unity power factor. Moreover, the authors in [191] [146]; (c) predictive controllers (PC) [147]; (d) deadbeat controllers [22]; (e) MPC [149]; (f) robust controllers [153]; (g) hysteresis controllers HC [173], (h) adaptive controllers [175], (i) repetitive controllers (RC) [184]; and (j) fuzzy logic controllers (FLC) [186].…”

“…The authors in [190] used FLC along with the back-stepping approach to maintain a DC-link voltage and deliver the power to the grid with a unity power factor. Moreover, the authors in [191] proposed an FLC to regulate the grid frequency in case of large PV integration.…”

An Enhanced Two-Stage Grid-Connected Linear Parameter Varying Photovoltaic System Model for Frequency Support Strategy Evaluation

“…Taking this into account, the LPV model is extended to include the offsets in the state, input and output (ẋ, x, u and y) variables. Using the equilibrium offsets, the LPV model in (10) and (11) is represented by (15) and (16), which is an affine form of the LPV system:…”

“…Frequency stability with declining system inertia can be maintained with the use of fast frequency support from renewable energy sources to slow the rate of change of frequency (RoCoF) and limit the frequency zenith/nadir during frequency transients [8]. In such scenarios with high penetration levels of renewable energy, various frequency support techniques can be implemented from wind turbines, energy storage systems (ESS) and PVs [7,[9][10][11][12][13]. A typical PV system has no rotational inertia and minimal energy storage.…”

Tensor Product‐Based Model Transformation and Optimal Controller Design for High Order Nonlinear Singularly Perturbed Systems

“…The optimal control design of nonlinear systems is one of the most challenging and difficult subjects in control theory. When considering complex large-scale systems, optimal control problems have attracted intene attention due to their wide variety of applications [1][2][3][4][5][6][7][8][9].…”