Advanced control solutions for enhanced resilience of modern power-electronic-interfaced distribution systems

Abstract

Modern power delivery systems are rapidly evolving with high proliferation of power-electronic (PE)interfaced distributed energy resources (DERs). Compared to the conventional sources of generation, the PE-interfaced DERs, e.g., solar and wind resources, are attributed substantially different characteristics such as lower overload capability and limited frequency response patterns. This paper focuses on effective management and control mechanisms for PE-interfaced DERs in power distribution systems with high penetration of renewables, particularly under fault, voltage-sag, load variations, and other prevailing conditions in the grid. Aiming at the solutions to enhance the system performance resilience, we introduce an advanced model predictive control (MPC) based scheme to control the DER units, minimize the impact of transients and disruptions, speed up the response and recovery of particular metrics and parameters, and maintain an acceptable operation condition. The performance of the suggested control scheme is tested on a modified IEEE 34-bus test feeder, where the proposed solution demonstrates its effectiveness to minimize the system transient during faults, with an enhanced grid-edge and system-wide resilience characteristics in voltage profiles.

A review of power system resilience assessment and enhancement approaches by focusing on wind farms and wind turbines

“…In [73], by using genetic algorithm, a method to optimize the maximum output power of WT in the states of low the rated wind speed is proposed which results to enhancement of frequency response. In [74], the proposed optimization method employs the switching method based on space vector modulation where the switching signals are obtained by sampling the voltage, current, active and reactive power of the system. In [35], the authors studied the cyber‐security of variable speed WT.…”

“…• an advanced model predictive control (MPC) to control the distributed energy resources (DER), minimize the impact of transient disruptions and speed up the response, and recovery time of the system [74] • a control between demand and supply by using ramp rate data of wind [48] • coordinated control between DER to manage of the demand and supply sides by the contribution of distribution system operator [82] • an optimizing method to control the islanded MG based on the multi-agent deep reinforcement learning by management of RES and load curtailment [17] • a bi-level method based on contingencies of the active distribution network against the wind storm, 1 st level: minimize the total cost, and 2 nd level: extracts the worst-case realization of the uncertainties to quickly start micro turbine (MT) and ESS after windstorm [13] • a controlling scheme of power flow according to demand and supply management to have a coordinated control in a DC highway [83] • coordinated control of resources and hourly network reconfiguration [81] • post-catastrophe system reconfiguration for distributed system [21] • CVR to control the power demand [24] • using DGs in islanded MG to keep the critical loads alive [84] • Coordination of WT allocation and network reconfiguration improve the performance of WT in islanded mode [80] • prioritizing the recovery of critical loads through the consideration of customer interruption cost [71] Planning Before an event, PA…”

Grid Integration of Solar Photovoltaic System Using Machine Learning-Based Virtual Inertia Synthetization in Synchronverter

“…In general, all AI-based controllers for synchronverter exhibit faster convergence speed compared to other control strategies because of its adaptive J and intelligent control of P. The most notable AI controllers are FL [19], fuzzy secondary controller (FSC) [9], fuzzy adaptive differential evolution (FADE) [20], adaptive neuro-fuzzy inference system (ANFIS) [10], adaptive dynamic programming (ADP) [18] and fuzzy adaptable logic controller based on differential evolution (DE) [20]. Apart from AI, model predictive control (MPC) is also a popular technique to control inverter [11].…”

“…The most notable AI controllers for synchronverter are FL [9] and adaptive neuro-fuzzy inference system (ANFIS) [10]. Apart from AI, model predictive control (MPC) is also a popular technique to control inverter [11].…”

Resiliency Enhancement and Power Quality Optimization of Converter-Based Renewable Energy Microgrids

“…The control method applied to the converter-based renewable micro grids is an opportunity to overcome the integration challenge. It should guarantee the stability, reliability, and resiliency of the micro grids according to advanced grid codes under normal and abnormal conditions [2], [3]. Besides many methods that have been provided for the control of micro grids under normal conditions, various control methods have been proposed to overcome the problems of grid unbalanced conditions for single and three-phase converts [4], [5].…”