This paper proposes an efficient algorithmic approach that overcomes the critical challenges in real-time unbalanced distribution system state estimation, topology error processing, and outage identification simultaneously. These challenges include (1) limited locations of measurement devices and unsynchronized measurement data as well as missing and bad data, (2) complicated mixed-phase switch actions and mutual impedances and shunt admittances, and (3) the nonlinear nature of unbalanced distribution system power flow with distributed energy resources (DERs). A single snap-shot mixed-integer quadratic programming (MIQP) optimization framework is proposed to cope with these challenges, which simultaneously identifies real-time network topology, estimates system state, and detects the outages via analytical constraints. An AC optimal power flow (ACOPF) approach is proposed to accurately model unbalanced distribution systems. In the proposed MIQP formulation, nonlinearities due to the complicated mixed-phase switch operations and the ACOPF approach are linearized. The effectiveness of the proposed approach is verified on an actual distribution feeder in Arizona. The results illustrate that the proposed model is robust, accurate, and computationally efficient for implementation in a distribution management system (DMS).

New resource mix, e.g., renewable resources, are imposing operational complexities to modern power systems by intensifying uncertainty and variability in the system net load. This issue has motivated independent system operators (ISOs), e.g., California ISO (CAISO), to add flexible ramping products (FRPs) to their day-ahead (DA) market models. Such structural changes in the DA market formulation require further analyses and detailed design to ensure adequate operational flexibility, market efficiency, and reliability. This paper conducts a comprehensive study to: (a) augment existing DA market models with enhanced FRP design to schedule ramp capabilities that are more adaptive with respect to the real-time (RT) 15-min net load variability and uncertainty, and (b) design corresponding market payment policies that accurately reflect the value of the added flexibility through enhanced FRP design. The proposed FRP design can be implemented in present-day system operations with minimal disruption to existing DA market models. Performance of the proposed DA market model, which includes the enhanced FRP design, is compared against the DA market model with existing FRP design through a validation methodology based on RT unit commitment model. This validation methodology mimics fifteen-minute markets of CAISO. The proposed method is tested on an IEEE 118-bus test system.

Topology detection and state estimation in real-time are critical for modern distribution systems management and control. However, the number of sensors in distribution networks is limited, and communication links between switch devices and distribution management system are not well-established. In this regard, this article proposes mixed-integer quadratic programming formulations to determine the topology of distribution network and estimate distribution system states simultaneously using micro-phasor measurement units and smart meter data. Two approaches based on ac optimal power flow are proposed and analyzed: polar power-voltage (PPV) formulation and rectangular current-voltage (RIV) formulation. The proposed models can identify multiple simultaneous switching actions and different topology configurations including radial and meshed networks. Only measurement data at each time interval are needed to identify the topology and states of the system correctly. The proposed models are tested on a modified IEEE-33 bus system with realistic load data from the Pecan Street Inc. database and a real distribution feeder of an electric utility. The results confirm that both models can identify system topology and states with remarkable accuracy in real-time, whereas the RIV model outperforms the PPV model. Moreover, the impact of missing data on the performance of the model is evaluated.

System operators are required to serve the load in the most cost-effective way while maintaining the integrity of the system and heeding reliability requirements. In the day-ahead market, operators acquire reserves in an attempt to guarantee N-1 reliability; yet, reserve deliverability is not guaranteed. Prior research has shown that the use of transmission switching, or topology control, may help improve reserve deliverability. In this paper, transmission switching is used as a corrective mechanism to help the system achieve N-1-1 reliability, where not only has the system lost a single element, but also it experiences the loss of a second major element after an adjustment period. In an attempt to preserve N-1-1 reliability, for this paper, a day-ahead unit commitment model that acquires supplementary reserves is solved. The day-ahead market solution is then tested for N-1-1 reliability using contingency analysis models with and without transmission switching. The methodology can be employed at the day-ahead time stage to ensure the system has acquired sufficient supplemental reserves. The results demonstrate that not only can corrective transmission switching be beneficial post-contingency without inhibiting the ability to return to N-1 reliability, but it can also help obtain an N-1-1 reliable solution.

The proliferation of distributed energy resources (DERs) imposes new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution networks, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. Moreover, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effectiveness of the proposed ACOPF for unbalanced systems in providing an optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simulations show that the optimal settings cause no stability issue for the distribution system.

Modern distribution systems are changing from passive load serving networks to active load and generation inclusive networks. Accurate distribution systems modeling is critical for effective distribution system control, operation, and planning. This paper proposes novel nonlinear and convex AC optimal power flow (ACOPF) models based on current-voltage (IVACOPF) formulation for an unbalanced distribution system with distributed energy resources (DERs). In the proposed formulation, untransposed distribution lines, shunt elements of distribution lines, distribution transformers, and DERs are modeled. The proposed nonlinear IVACOPF model is linearized and convexified using the Taylor series. The accuracy of the proposed nonlinear and convex IVACOPF approaches for modeling unbalanced distribution systems is compared with OpenDSS and the widely used LinDistFlow method. The proposed accurate convex IVACOPF model has multiple applications for distribution systems management, planning, and operation. Applications of the proposed model on two key parts of advanced distribution management systems (ADMS), (i) DERs scheduling and (ii) state estimation, are presented in this paper. The proposed models are tested on distribution feeder of an electric utility in Arizona with 2100 primary nodes and a large number of rooftop photovoltaic units. The results confirm the accuracy and effectiveness of the proposed IVACOPF for both examined ADMS applications.