For nearly 20 years, the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges that they were originally designed to examine. Additionally, this paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work.

We develop hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed electric vehicles (EVs) incorporating distribution grid level constraints. The frameworks consist of two detailed mathematical models and coordination algorithms, which can benefit the operation of both entities involved, i.e., the grid operations and EV charging. The first model comprises of a comprehensive optimal power flow model at the distribution grid level, while the second model represents detailed optimal EV charging with reactive power support to the grid. We demonstrate benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that in constrained distribution grids, coordinated charging significantly reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to unity power factor.

This paper presents two probabilistic approaches based on bootstrap method and quantile regression (QR) method to estimate the uncertainty associated with solar photovoltaic (PV) power point forecasts. Solar PV output power forecasts are obtained using a hybrid intelligent model, which is composed of a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on radial basis function neural network (RBFNN) that is optimized by particle swarm optimization (PSO) algorithm. The point forecast capability of the proposed hybrid WT+RBFNN+PSO intelligent model is examined and compared with other hybrid models as well as individual SCM. The performance of the proposed bootstrap method in the form of probabilistic forecasts is compared with the QR method by generating different prediction intervals (PIs). Numerical tests using real data demonstrate that the point forecasts obtained from the proposed hybrid intelligent model can be effectively used to quantify PV power uncertainty. The performance of these two uncertainty quantification methods is assessed through reliability.

Load tap changers (LTCs) are commonly used for voltage control in distribution systems. The mechanical relays on LTCs are controlled in discrete steps; hence, distribution optimal power flow (DOPF) formulations require LTCs to be modeled with integer variables. Integer variables are generally ignored or relaxed and rounded off to reduce the computational burden in DOPF models. However, in this work, we highlight and overcome complex infeasibility issues caused by rounding methods. Moreover, recent advances in convex optimization have improved the computational tractability of the DOPF problem. In this work, we develop and analyze numerical efficiency of different, but exact formulations for incorporating LTCs as integer control assets in a relaxed second-order cone program (SOCP) version of DOPF model. The resulting formulation becomes a mixed-integer SOCP (MISOCP), which is computationally tractable compared to mixed-integer non-linear program (MINLP). To recover the exact optimal solution in the proposed MISOCP-DOPF model, a McCormick relaxation is employed within a sequential bound-tightening algorithm. We show that solutions obtained from MISOCP-DOPF are always AC feasible. Thus, the MISOCP-DOPF yields optimal and realistic AC solutions, and is validated in large distribution feeders and is compared to MINLP counterpart.

This article develops a two-stage volt-VAr control strategy coordinating the discrete controls of legacy grid devices and the operation of smart inverters (SIs) in power distribution grids with high penetration of photovoltaic generation. The first stage dispatch problem optimally coordinates the tap settings of on-load tap changers, on / off status of shunt capacitor banks, and SIs’ active power output. The second stage incorporates the SI volt-VAr mode, i.e., $\mathbf{Q(V)}$ as per IEEE-1547, and an adaptive volt-VAr droop function, $\mathbf{Q(\Delta V)}$ , to dispatch the SI reactive power generation. Both dispatch problems are formulated as mixed-integer linear programming problems to maintain tractable formulations and reduce the complexity and computational burden that usually arise when solving large-scale optimal power flow problems. The proposed two-stage strategy is tested on a modified IEEE 123-bus system considering different droop settings. The dispatch strategy augmented with $\mathbf{Q(\Delta V)}$ droop outperforms the model with only $\mathbf{Q(V)}$ droop in stabilizing the PCC voltage while maintaining less active power curtailment.

High penetration of photovoltaic (PV) generators in active distribution networks (ADNs) cause overvoltage due to reverse power flow. Smart inverter (SI) functionalities can be configured to provide efficient voltage regulation and reactive power support in ADNs. Moreover, the mismatch between peak PV generation and peak demand make battery energy storage systems (BESSs) increasingly important assets in power grids, capable of enabling higher penetration of renewable energy resources by mitigating the system-wide effects of PV intermittency. In this work, we propose the inclusion of local piecewise Volt-VAr and Volt-Watt droop functions of PV (as per the IEEE-1547) as constraints in a multiperiod distribution grid optimal power flow (DOPF) formulation. This approach is an efficient voltage control method for unbalanced multiphase networks, making the inverter dispatch solutions abide by the IEEE-1547 standard and therefore suitable at the local inverter controller level. We adopt a well-known LinDist3Flow version of DOPF, and the resulting multiperiod DOPF becomes a mixed-integer linear programming problem. Simulations on the IEEE 123-node feeder with 15 utility-scale PV and BESS inverters validate the efficacy of the proposed formulation in performing effective voltage regulation and control that abide by the IEEE-1547 standard. The case studies show that SI droop-integrated DOPF can ensure reduced variation in bus voltages, reducing the risk of voltage violations, and net load smoothing can be achieved with BESS, reducing the disparity between inexpensive midday and expensive evening energy price.