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Register a new userHarmonic Power-Flow Study of Polyphase Grids with Converter-Interfaced Distributed Energy Resources, Part II: Model Library and Validation
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In Part I, a method for the Harmonic Power-Flow (HPF) study of three-phase power grids with Converter-Interfaced Distributed Energy Resources (CIDERs) is proposed. The method is based on generic and modular representations of the grid and the CIDERs, and explicitly accounts for coupling between harmonics. In Part II, the HPF method is validated. First, the applicability of the modeling framework is demonstrated on typical grid-forming and grid-following CIDERs. Then, the HPF method is implemented in Matlab and compared against time-domain simulations with Simulink. The accuracy of the models and the performance of the solution algorithm are assessed for individual resources and a modified version of the CIGRE low-voltage benchmark microgrid (i.e., with additional unbalanced components). The observed maximum errors are 6.3E-5 p.u. w.r.t. voltage magnitude, 1.3E-3 p.u. w.r.t. current magnitude, and 0.9 deg w.r.t. phase. Moreover, the scalability of the method is assessed w.r.t. the number of CIDERs and the maximum harmonic order ($leqslant$25). For the maximum problem size, the execution time of the HPF method is 6.52 sec, which is 5 times faster than the time-domain simulation. The convergence of the method is robust w.r.t. the choice of the initial point, and multiplicity of solutions has not been observed.
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Power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). This complicates the analysis and mitigation of harmonics, whose creation and propagation are facilitated by the interactions of converters and their controllers through the grid. In this paper, a method for the calculation of the so-called Harmonic Power-Flow (HPF) in three-phase grids with CIDERs is proposed. The distinguishing feature of this HPF method is the generic and modular representation of the system components. Notably, as opposed to most of the existing approaches, the coupling between harmonics is explicitly considered. The HPF problem is formulated by combining the hybrid nodal equations of the grid with the closed-loop transfer functions of the CIDERs, and solved using the Newton-Raphson method. The grid components are characterized by compound electrical parameters, which allow to represent both transposed or non-transposed lines. The CIDERs are represented by modular linear time-periodic systems, which allows to treat both grid-forming and grid-following control laws. The methods accuracy and computational efficiency are confirmed via time-domain simulations of the CIGRE low-voltage benchmark microgrid. This paper is divided in two parts, which focus on the development (Part I) and the validation (Part II) of the proposed method.
The present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The CATV sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the state of a distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored buses using random forest. The effectiveness of the proposed approach was demonstrated using a 1572-bus feeder from the SMART-DS data set for two case studies - passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.
In this paper, we propose and experimentally validate a scheduling and control framework for distributed energy resources (DERs) that achieves to track a day-ahead dispatch plan of a distribution network hosting controllable and stochastic heterogeneous resources while respecting the local grid constraints on nodal voltages and lines ampacities. The framework consists of two algorithmic layers. In the first one (day-ahead scheduling), we determine an aggregated dispatch plan. In the second layer (real-time control), a distributed model predictive control (MPC) determines the active and reactive power set-points of the DERs so that their aggregated contribution tracks the dispatch plan while obeying to DERs operational constraints as well as the grids ones. The proposed framework is experimentally validated on a real-scale microgrid that reproduces the network specifications of the CIGRE microgrid benchmark system.
In this paper, we present a data-driven secondary controller for regulating to some desired values several variables of interest in a power system, namely, electrical frequency, voltage magnitudes at critical buses, and active power flows through critical lines. The power generation system is based on distributed energy resources (DERs) interfaced with either grid-forming (GFM) or grid-following (GFL) inverters. The secondary controller is based on online feedback optimization leveraging the learned sensitivities of the changes in the system frequency, voltage magnitudes at critical buses, and active power flows through critical lines to the changes in inverter active and reactive power setpoints. To learn the sensitivities accurately from data, the feedback optimization has a built-in mechanism for keeping the secondary control inputs persistently exciting without degrading its performance. The feedback optimization also utilizes the learned power-voltage characteristics of photovoltaic (PV) arrays to compute DC-link voltage setpoints so as to allow the PV arrays to track the power setpoints. To learn the power-voltage characteristics, we separately execute a data-driven approach that fits a concave polynomial to the collected power-voltage measurements by solving a sum-of-squares (SoS) optimization. We showcase the secondary controller using the modified IEEE-14 bus test system, in which conventional energy sources are replaced with inverter-interfaced DERs.
Quantifying the impact of inverter-based distributed generation (DG) sources on power-flow distribution system cases is arduous. Existing distribution system tools predominately model distributed generation sources as either negative PQ loads or as a PV generator and then employed a PV-PQ switching algorithm to mimic Volt/VAR support. These models neglect the unique characteristics of inverter-based distributed generation sources, have scalability and convergence issues, and are ill-suited for increasing solar penetration scenarios. This work proposes an inverter-based DG model accounting for the inverters topology, sensing position, and control strategies. The model extends recently introduced analytical positive sequence generator models for three-phase studies. The use of circuit-simulation based heuristics help achieve robust convergence. Simulation of the PG&E prototypical feeders using a prototype solver demonstrate the models accuracy and efficacy.
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