The increasing penetration of distributed generators (DGs) exacerbates the risk of voltage violations in active distribution networks (ADNs). The conventional voltage regulation devices limited by the physical constraints are difficult to meet the requirement of real-time voltage and VAR control (VVC) with high precision when DGs fluctuate frequently. However, soft open point (SOP), a flexible power electronic device, can be used as the continuous reactive power source to realize the fast voltage regulation. Considering the cooperation of SOP and multiple regulation devices, this paper proposes a coordinated VVC method based on SOP for ADNs. First, a time-series model of coordinated VVC is developed to minimize operation costs and eliminate voltage violations of ADNs. Then, by applying the linearization and conic relaxation, the original nonconvex mixed-integer nonlinear optimization model is converted into a mixed-integer second-order cone programming model which can be efficiently solved to meet the requirement of voltage regulation rapidity. Case studies are carried out on the IEEE 33-node system and IEEE 123-node system to illustrate the effectiveness of the proposed method.

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Coordinated Control Method of Voltage and Reactive Power for Active Distribution Networks Based on Soft Open Point

A fuzzy synthetic evaluation approach for risk assessment: a case of Singapore's green projects

Under the global trend of renewable energy development, various advanced techniques such as forecasting algorithm, intelligent computation, and optimal control are expected to make the complex and uncertain renewable energy system stable and profitable in the near future. This paper presents a new control strategy for large-scale wind energy conversion systems to achieve a balance between power output maximization and operating cost minimization. First, an intelligent maximum power point tracking (IMPPT) algorithm is proposed such that short-term wind speed prediction, wind turbine dynamics, and MPPT are collectively considered to improve system efficiency. Second, in view of a spatial and temporal distribution of wind speed disturbances, a box uncertainty set is embedded in the forecast wind speed, which is likely more realistic for practicing engineers. Then, the IMPPT and box uncertainties are applied to the wind energy conversion system (WECS) control strategy, which is formulated as a min-max optimization problem and efficiently solved with semi-definite programming (SDP). Finally, a comparison with the conventional MPPT control method demonstrates that the proposed approach can obtain higher efficiency, which validates this paper.

Maximum Power Point Tracking Strategy for Large-Scale Wind Generation Systems Considering Wind Turbine Dynamics

A direct-drive permanent magnet synchronous generator (PMSG) with a three-level neutral-point-clamped back-to-back power converter is an attractive configuration for high-power wind energy conversion systems. For such a topology, finite-control-set model-predictive control (FCS-MPC) has emerged as a promising alternative. However, due to its fully model-based concept, variation of system parameters (in particular, the stator and grid filter inductance and rotor permanent-flux linkage) will (seriously) affect the system control performances when using the classical FCS-MPC. In this work, a robust FCS-MPC method with revised predictions is proposed and validated for such a system. With the proposed solution, not only the system robustness against parameter variations is improved, but also the control variable ripples are evidently reduced. The proposed method has been implemented with a fully field-programmable-gate-array-based real-time hardware. Its performance improvements in comparison with the conventional solutions are validated with experimental data.

Robust Predictive Control of Three-Level NPC Back-to-Back Power Converter PMSG Wind Turbine Systems With Revised Predictions

Optimal siting and sizing of soft open points in active electrical distribution networks

An efficient control strategy of a wind energy conversion system (WECS) plays a crucial role in wind power utilization. In this paper, a novel multivariable control strategy for a variable-speed variable-pitch WECS is proposed. It is designed for the complete operating regions of a wind turbine, including both of the partial load region and the full load region, with the objectives of maximizing energy capture, smoothing power output, alleviating drive train transient loads, and reducing pitch actuator activities. In particular, this paper considers the wind speed disturbance as norm-bounded, instead of deterministic or chance constraints in the widely used quadratic control methods such as linear quadratic Gaussian control and model predictive control. Moreover, the WECS control with the norm-bounded disturbance is formulated as a second-order cone programming (SOCP) problem that has not been previously employed for wind power control. Finally, simulation results are provided to demonstrate the validity of the proposed method.

Second-Order Cone Programming-Based Optimal Control Strategy for Wind Energy Conversion Systems Over Complete Operating Regions

Variable wind power output introduces new challenges to power system frequency regulation and control, such as automatic generation control (AGC) which is very important to regulate power system frequency. The existing methods to design control gains for proportional-integral (PI) controllers in AGC are either time consuming or easily affected by the designer's experience. Further, the control gains are usually designed with fixed values for specific scenarios in the studied power system. The desired response may not be achieved when variable wind power is integrated into power systems. To address these challenges, a dynamic gain tuning control (DGTC) method for AGC is proposed in this paper. By the proposed control method, PI control gains can be dynamically adjusted to reach the desired performance. The proposed method is tested in a modified IEEE 39 bus system with actual wind data, and compared with conventional control approach with well-tuned fixed gains in the simulation. The simulation results show that the proposed control method provides better AGC response with less deviation of system frequency and tie-line flow under variable wind power.

Dynamic Gain-Tuning Control (DGTC) Approach for AGC With Effects of Wind Power

Under the global trend of renewable energy development, various advanced techniques such as forecasting algorithm, intelligent computation, and optimal control are expected to make the complex and uncertain renewable energy system stable and profitable in the near future. This paper presents a new control strategy for large-scale wind energy conversion systems (WECSs) to achieve a balance between power output maximization and operating cost minimization. First, an Intelligent Maximum Power Point Tracking (IMPPT) algorithm is proposed such that short-term wind speed prediction, wind turbine dynamics, and MPPT are collectively considered to improve system efficiency. Then, in view of a spatial and temporal distribution of wind speed disturbances, a box uncertain set is embedded in the forecasted wind speed, which is likely more realistic for practicing engineers. Next, IMPPT and box uncertainties are applied to the WECS control strategy, which is formulated as a min-max optimization problem and efficiently solved with semi-definite programming (SDP). Finally, a comparison with the conventional MPPT control method demonstrates that the proposed approach can obtain a higher efficiency, which validates this research work.

Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics

One of the key issues in wind energy is the control design of the wind energy conversion system to achieve an expected performance under both the power system and mechanical constraints which are subject to stochastic wind speeds. Quadratic control methods are widely used in the literature for such purposes, e.g., linear quadratic Gaussian and model predictive control. In this paper, the chance constraints are considered to address the stochastic behavior of the wind speed fluctuation on control inputs and system outputs instead of deterministic constraints in the literature. Also considered are the two different models: the first one assumes that the wind speed measurement error is Gaussian, where the chance constraints can be reduced to the deterministic constraints with Gaussian statistics; whereas the second one assumes that the error is norm bounded, which is likely more realistic to the practicing engineers, and the problem is formulated as a min-max optimization problem which has not been considered in the literature. Then, both of the models are formulated as semi-definite programming optimization problems that can be solved efficiently with existing software tools. Finally, the simulation results are provided to demonstrate the validity of the proposed method.

Zhiqiang Jin

Papers

Maximum Power Point Tracking Strategy for Large-Scale Wind Generation Systems Considering Wind Turbine Dynamics

Second-Order Cone Programming-Based Optimal Control Strategy for Wind Energy Conversion Systems Over Complete Operating Regions

Dynamic Gain-Tuning Control (DGTC) Approach for AGC With Effects of Wind Power

Maximum power point tracking strategy for large-scale wind generation systems considering wind turbine dynamics

Semi-Definite Programming for Power Output Control in a Wind Energy Conversion System