Venkataramana Ajjarapu

Ramakrishnan Venkatraman, Siddhartha Kumar Khaitan, Venkataramana Ajjarapu

Combined Transmission and Distribution Systems (CoTDS) simulation for power systems requires development of algorithms and software that are numerically stable and at the same time accurately simulate dynamic events that can occur in practical systems. The dynamic behavior of transmission and distribution systems are vastly different, especially with the increased deployment of distribution generation. The time scales of simulation can be orders of magnitude apart making the combined simulation extremely challenging. This has led to increased research in applying co-simulation techniques for integrated simulation of the two systems. In this paper, a rigorous mathematical analysis on convergence of numerical methods in co-simulation is presented. Two methods for co-simulation of CoTDS are proposed using parallel and series computation of the transmission system and distribution systems. Both these co-simulation methods are validated against total system simulation in a single time-domain simulation environment. The series computation co-simulation method is shown to have better numerical stability at larger integration time steps. The series computation co-simulation method is additionally validated against commercial EMTP software and the results show remarkable correspondence.

Alok Kumar Bharati, Venkataramana Ajjarapu

This paper emphasizes the importance of including the unbalance in the distribution networks for stability studies in power systems. The paper aims to: discuss the various simulation methods for power system analysis; highlight the need for modeling unbalanced distribution system for accurate load margin assessment; demonstrate the influence of net-load unbalance (NLU) on voltage stability margin (VSM). We also share a T&D co-simulation interface with commercial power system solvers. The distribution system is evolving rapidly with high proliferation of distributed energy resources (DERs); these are not guaranteed to proliferate in a balanced manner and uncertainty resulting due to these DERs is well acknowledged. These uncertainties cannot be captured or visualized without representing the distribution system in detail along with the transmission system. We show the impact of proliferation of DERs in various 3-phase proportions on voltage stability margin through T&D co-simulation. We also study the impact of volt/VAR control on voltage stability margin. This analysis is only possible by representing the distribution system in detail through T&D co-simulation. Higher percentage of net-load unbalance (NLU) in distribution system aggravates the stability margin of the distribution system which can further negatively impact the overall stability margin of the system.

Ahmed Alkhonain, Kiran Kumar Challa, Amarsagar Reddy Ramapuram Matavalam et al.

The rapid growth of inverter-based resources (IBRs) and distributed energy resources (DERs) has fundamentally altered the long-term voltage stability characteristics of modern power systems. This article leverages the advantages of machine learning (ML) for the online estimation of long-term voltage stability margin (VSM) and enhancement of VSM through coordinated transmission system operator-distribution system operator (TSO-DSO) optimization. An explicit analytical VSM expression is derived from offline T&D co-simulation data using a physics-informed ML-trained model under probabilistic loading and generation mix scenarios, while accounting for unbalanced distribution modeling. The resulting closed-form VSM representation is linearized and embedded into the TSO optimization problem, enabling real-time enforcement of minimum VSM constraints. We further enhance operational efficiency by incorporating VSM sensitivities into both transmission and distribution optimization, allowing prioritization of the most influential reactive power resources. Simulation studies conducted on the IEEE 30-bus transmission network integrated with multiple IEEE 37-node distribution feeders validate that the proposed framework successfully achieves the desired VSM enhancement while maintaining high estimation accuracy.

Kiran Kumar Challa, Alok Kumar Bharati, Venkataramana Ajjarapu

With a steady increase in the inverter technology integration to the grid, frequency response of the large inter-connection system becomes more unpredictable. This leads to a significant change in the boundaries of the coherent region, which highly depends on the changing disturbance locations and operating conditions. While most of the existing coherency identification is based on a single large generator outage, it is important to identify these boundaries in view of wide range of disturbances. With large amount of inverters in the system, there is increase in the dynamic interactions of the various grid components leading to a need for such boundary identification. This paper presents the multi-view consensus algorithm to identify coherency in the case of variable grid operating conditions and wide range of disturbances. The proposed approach is demonstrated by identifying the coherent regions in the miniWECC 240 bus test system.

Alok Kumar Bharati, Venkataramana Ajjarapu, Zhaoyu Wang

This paper analyses the impact of conservation by voltage reduction (CVR) on voltage stability margin (VSM) considering transmission and distribution (T&D) systems. VSM is determined by P-V curve analysis using PSSE and GridLAB-D solvers to co-simulate the T&D systems under CVR and No CVR conditions. ZIP loads with profile [ZIP] = [0.4 0.3 0.3] are used to model the load. The paper discusses the counterintuitive result: under CVR, the VSM is reduced. Theoretical justification for the reduced VSM under CVR is the increase in the effective impedance between generation and load and this is proved using an extended 2-bus system. The paper shares T&D co-simulation results with IEEE 9-bus transmission system and a larger 123-bus distribution system and with distributed generation (DG) in unity power factor (UPF) and volt-VAR control (VVC) mode.

Mohammad Almomani, Ahmed Alkhonain, Venkataramana Ajjarapu

The increasing integration of Distributed Energy Resources (DERs) in distribution networks presents new challenges for voltage regulation and reactive power support. This paper extends a sensitivity-aware reactive power dispatch algorithm tailored to manage smart inverters operating under different control modes, including PQ, PV, and Volt-Var (VV). The proposed approach dynamically optimizes reactive power dispatch and voltage setpoints, enabling effective coordination among distribution systems as a virtual power plant (VPP) to support the transmission network. The algorithm is applied to the IEEE 13-bus and IEEE-123 bus test systems, and its performance is validated by comparing results with OpenDSS simulations across various operating scenarios. Results show that the maximum error in the voltages is less than 0.015 pu.

Mohammad Almomani, Muhammad Sarwar, Venkataramana Ajjarapu

This paper presents a novel Short-Term Voltage Stability Index (STVSI), which leverages Lyapunov Exponent-based detection to assess and quantify short-term stability triggered by Over Excitation Limiters (OELs) or undamped oscillations in voltage. The proposed method is measurement-based and decomposes the voltage trajectory into two key components using Empirical Mode Decomposition (EMD): a residual part, which indicates delayed voltage recovery, and an oscillatory part, which captures oscillations. The residual component is critical, as it can detect activation of OELs in synchronous generators or Low Voltage Ride-Through (LVRT) relays in inverter-based resources, potentially leading to instability within the quasisteady-state time frame. Meanwhile, the oscillatory component may indicate either a stable or unstable state in the short term. To accurately assess stability, STVSI employs an entropy-based metric to measure the proximity of the system to instability, with specific indices for short-term voltage stability based on oscillations and recovery. Simulations on the Nordic power system demonstrate that STVSI effectively identifies and categorizes voltage stability issues. Moreover, STVSI not only detects voltage stability conditions but also qualitatively assesses the extent of stability, providing a nuanced measure of stability.

Mohammad Almomani, Muhammad Sarwar, Venkataramana Ajjarapu

Ensuring accurate violation detection in power systems is paramount for operational reliability. This paper introduces an enhanced voltage recovery violation index (EVRVI), a comprehensive index designed to quantify fault-induced delayed voltage recovery (FIDVR). EVRVI enhances traditional entropy-based methods by leveraging Empirical Mode Decomposition (EMD) to extract key features from the voltage signal, which are then used to quantify over-voltage (OV) and under-voltage (UV) events. Our simulations on the Nordic system, involving over 245k scenarios, demonstrate EVRVI's superior ability to identify and categorize voltage recovery issues compared to the traditional entropy-based measure. EVRVI not only significantly reduces false negatives in violation detection but also provides a reliable framework for over-voltage detection, making it an invaluable tool for modern power system studies.

Amarsagar Reddy Ramapuram Matavalam, Kishan Prudhvi Guddanti, Yang Weng et al.

This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning. Further, a parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations. Without these modifications to the training procedure, the RL agent failed for most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL learning. The agent was tested by RTE for the 2019 learning to run the power network challenge and was awarded the 2nd place in accuracy and 1st place in speed. The developed code is open-sourced for public use.

Mohammad Almomani, Ahmed Alkhonain, Venkataramana Ajjarapu

This paper presents TSO-DSO coordinated reactive power dispatch, with a focus on real-time implementation. A sensitivity-aware, mixed-integer linear programming (MILP) formulation is developed to model the IEEE 1547-compliant droop-based control modes Volt VAR (VV), Volt Watt (VW), and Watt VAR (WV) of smart inverters. The algorithm employs a hierarchical optimization strategy using Special Ordered Sets (SOS1) to enhance computational efficiency and supports limited measurement scenarios through Recursive Least Squares (RLS) estimation. The proposed method is tested on the IEEE 13-bus and 123-bus distribution networks, which are connected to a 9-bus transmission system. Results demonstrate the feasibility and effectiveness of the real-time dispatch framework in improving voltage regulation and minimizing power curtailment.

Mohammad Almomani, Muhammad Sarwar, Venkataramana Ajjarapu

Existing short term voltage stability (STVS) methods typically address either voltage oscillations or delayed voltage recovery; however, the coexistence of both phenomena has not been adequately covered in the literature. Moreover, existing real-time STVS assessment methods often provide only binary stability classifications. This paper proposes novel indices that enable early detection and quantify the degree of stability. The proposed method decomposes post-fault voltage trajectories using Empirical Mode Decomposition (EMD) into residual and oscillatory components. It then employs Lyapunov Exponents (LEs) to characterize the dynamic behavior of each component and evaluates the stability degree using Kullback Leibler (KL) divergence by comparing the LEs of each component with those of a predefined critical signal. The proposed indices assess oscillatory stability significantly faster than the traditional LE method applied directly to the original signal. Specifically, they detect stability within 0.6 seconds after a fault, compared to approximately 10 seconds for the conventional LE approach. In addition, the delayed-recovery index can identify generator trips caused by over-excitation limits within 3 seconds, well before the actual trip occurs at approximately 20 seconds, thereby providing operators and controllers sufficient time to take preventive actions. Furthermore, thresholds are derived to distinguish between stable and unstable cases, offering a graded measure of the stability margin. Simulation studies on the Nordic test system under varying load conditions demonstrate the effectiveness of the proposed indices.

Subhrajit Sinha, Pranav Sharma, Umesh Vaidya et al.

In this paper, we present a novel approach to identify the generators and states responsible for the small-signal stability of power networks. To this end, the newly developed notion of information transfer between the states of a dynamical system is used. In particular, using the concept of information transfer, which characterizes influence between the various states and a linear combination of states of a dynamical system, we identify the generators and states which are responsible for causing instability of the power network. While characterizing influence from state to state, information transfer can also describe influence from state to modes thereby generalizing the well-known notion of participation factor while at the same time overcoming some of the limitations of the participation factor. The developed framework is applied to study the three bus system identifying various cause of instabilities in the system. The simulation study is extended to IEEE 39 bus system.