Zahra Soltani

The burgeoning penetration of distributed energy resources (DERs) can pose challenges to the secure operation of transmission and Distribution Systems (DSs). In this paper, using statistical information obtained from different DS conditions and data-mining algorithms, an Independent System Operator-DS Operator-DER Aggregator (ISO-DSO-DERA) coordination framework is proposed, which allows DER aggregators to participate in the wholesale electric market considering DS limits. The performance of this framework is compared with the case where the ISO has no visibility over the DS limits while making decisions on the aggregator’s energy and ancillary service awards. A detailed unbalanced AC optimal power flow based on the current and voltage (IVACOPF) model is utilized for emulating DSO-DERAs coordinated operations to manage DS limits while considering DERAs promised services to ISO. The effect of VAr support capability of roof-top PV unit smart inverters (SIs) is evaluated in increasing the DS flexibility to improve the deployability of the aggregators promised awards. The VAr capability of PV SIs is based on the IEEE 1547-2018 standard, formulated by mixed-integer linear constraints. An IEEE 118-bus system and unbalanced 240-bus distribution test system are used to compare performance of the different ISO-DSO-DERA coordination architectures and, transmission and distribution management during uncertain events.

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.

The high R/X ratio of typical distribution systems makes the system voltage vulnerable to the active power injection from distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to the voltage control. This article proposes a two-stage stochastic optimization strategy to optimally place the photovoltaic (PV) smart inverters with Volt-VAr capability for distribution systems with high PV penetration to mitigate voltage violation issues. The proposed optimization strategy enables a planning-stage guide for upgrading the existing PV inverters while considering the operation-stage characteristics of the Volt-VAr control. One advantage of this planning strategy is that it utilizes the local control capability of the smart inverter that requires no communication, thus avoiding issues related to communication delays and failures. Another advantage is that the Volt-VAr control characteristic is internally integrated into the optimization model as a set of constraints, making placement decisions more accurate. The objective of the optimization is to minimize the upgrading cost and the number of the smart inverters required while maintaining the voltage profile within the acceptable range. Case studies on an actual 12.47 kV, 9-km-long Arizona utility feeder have been conducted using OpenDSS to validate the effectiveness of the proposed placement strategy in both static and dynamic simulations.

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.

Recently there has been a considerable increase in the penetration level of distributed energy resources (DERs) due to various factors, such as the increasing affordability of these resources, the global movement towards sustainable energy, and the energy democracy movement. However, the uncertainty and variability of DERs introduce new challenges for power system operations. Advanced techniques that account for the characteristics of DERs, i.e., their intermittency and human-in-the-loop factors, are essential to improving distribution system operations. This paper proposes a behavior-aware approach to analyze and aggregate prosumers’ participation in demand response (DR) programs. A convexified AC optimal power flow (ACOPF) via a second-order cone programming (SOCP) technique is used for system scheduling with DERs. A chance-constrained framework for the system operation is constructed as an iterative two-stage algorithm that can integrate loads, DERs’ uncertainty, and SOCP-based ACOPF into one framework to manage the violation probability of the distribution system’s security limits. The benefits of the analyzed prosumers’ behaviors are shown in this paper by comparing the optimal system scheduling with socially aware and non-socially aware approaches. The case study illustrates that the socially aware approach within the chance-constrained framework can utilize up to 43% more PV generation and improve the reliability and operation of distribution systems.

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).

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.

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The ACOPF uses the measured voltage magnitudes, derived active power measurements, and the feeder head measurements to obtain a complete active power and reactive power capture of the feeder loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed is verified in two stages: by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.