Showing papers by "Surya Santoso published in 2022"

Blackstart of Unbalanced Microgrids Using Grid-Forming Inverter with Voltage Balancing Capability

Abstract: This paper analyzes a blackstart operation of an unbalanced microgrid using a grid-forming inverter. The inverter sets the magnitude and frequency by generating its own frequency reference. It controls the positive- and negative-sequence voltages separately to mitigate voltage unbalance at its terminals. The current limiter of the inverter operates in the abc-frame to regulate the unbalanced current effectively. The performance of the grid-forming inverter is evaluated by blackstarting the islanded microgrid with a motor load. The inverter aims to achieve the following objectives during a blackstart process: 1) control the grid voltage at the rated value, 2) limit the inverter current during motor starting, and 3) maintain balanced voltage. The microgrid and grid-forming inverter are modeled and simulated in the PSCAD/EMTDC environment. The simulation results show that the grid-forming inverter is able to blackstart an islanded microgrid and maintain balanced voltage while supplying unbalanced loads. It is also shown that the inverter could start a motor while limiting the associated inrush current.

A Resilience Evaluation Concept for Permanently Islanded Military Nanogrids

Abstract: The ongoing electrification of warfare intensifies the U.S. military's reliance on electrical energy. Military nanogrids are small, permanently islanded, highly mobile, electrical systems, with a generation capacity less than 25 kW. This paper proposes a resilience evaluation concept with metrics for mobile military nanogrids. The proposed metrics evaluate resilience against a disruption in primary generation, loss of a distribution component, loss of fuel supply, and electrical faults. A method for classifying distribution networks into radial, loop, or mesh topologies is also presented. The intent is to quantitatively evaluate a military nanogrid's resilience and drive the design of future systems with enhanced resilience.

Optimal Black-Start Restoration Assisted by Mobile Energy Storage

Abstract: This paper studies a novel mixed-integer linear programming (MILP) formulation on the application of mobile energy storage (MES) to assist with black-start restoration following the full blackout of an electrical network. By synthesizing techniques in the literature to model generator black start and MES activity, the formulation is the first to integrate the two concepts. Furthermore, it recognizes that the manner in which MES facilitates black-start (BS) restoration may differ depending on what component damages occurred during the event that induced the blackout. Within the IEEE 14-Bus System, testing of the formulation has not only confirmed its efficacy but also underscored circumstances where BS restoration could especially benefit from MES intervention in practice. With an MES sized at 2.59% of total MW generation capacity, in certain damage configuration categories the median load energy unserved is reduced by as much as 45.52 MWh (8.26%), and the median final load supplied is raised by as much as 22.98 MW (10.39%).

Modeling and Simulation of Short-Circuit Faults in Inverter-Based Microgrids Using Steady-State Equivalent Circuits

Abstract: This work proposes the modeling and simulation of short-circuit faults in inverter-based microgrids using steady-state equivalent models. A grid-forming inverter is modeled as a voltage source with equivalent positive- and negative-sequence impedances, while a grid-following inverter is represented with a current source injecting constant power. Both equivalent circuit models are developed based on voltage and current control loops and current limiters of detailed models designed for time-domain simulation. The accuracy of the proposed models is validated by comparing short-circuit voltages and currents computed using OpenDSS to those obtained from time-domain simulation using PSCAD/EMTDC. Simulation results showed that the steady-state equivalent circuit models precisely reproduce the response of the detailed models and improve computational efficiency.

Demonstration of Quasi-Static Time-Series Power Flow Studies for Islanded Three-Phase Microgrids

Abstract: The increasing penetration of distributed energy resources (DERs) in distribution systems led to an increase in interest in islanded microgrids. As a consequence, new simulation tools are required by utilities to leverage the benefits of these new system configurations. In this context, this work presents quasi-static time-series (QSTS) power flow studies using a method that utilizes the solution mechanism from OpenDSS. The system modeling and power flow solution are conducted using OpenDSS, simplifying the implementation of islanded microgrid studies. This paper implements and demonstrates the application of our proposed method in OpenDSS. Specifically, voltage regulation and power and energy balance studies are carried out on EPRI ckt 5, which is based on a real-world distribution feeder.

Smart Meter Data Applied to Customer Rephasing for Technical Losses and Voltage Unbalance Mitigation

Abstract: Smart meters have been deployed for replacing electromechanical energy meters around the world. As these new devices have the capability to transmit information, utilities seek for new applications and functionalities to add value for the new meters. In this context, this paper presents a method for customer rephasing using measurements of active and reactive powers acquired from customers' smart meters. The main purposes of such an application are to reduce feeders' technical losses and voltage unbalance. A representative express feeder from a Brazilian utility is utilized as a test system. Results show that the proposed method can reduce 33% of technical losses and up to 80% of the voltage unbalance in the system. Additionally, the probabilistic assessment shows that the method performance is robust to missing data, e.g., its efficiency is not impacted for cases when reactive power measurements are not available.

Optimal Mobile Energy Storage Pre-Placement for Black-Start Restoration

Abstract: This paper studies a novel mixed-integer linear programming (MILP) formulation on the pre-blackout placement of mobile energy storage (MES) for black-start (BS) restoration of a transmission network. The formulation is a stochastic program that, rather than re-energization over a multi-interval horizon, for each scenario considers the final energization states of generators in a steady-state power flow. If a scenario of the event inducing the blackout cuts off an island from the network, the island is re-energized following the blackout only if it contains a generator with either BS capability or a pre-placed MES. Besides the model, this paper also explores the novel analytical concept of a discretized expected value realization.

A QSTS Simulation Tool Using Equivalent Circuit Inverter Models for Permanently Isolated Nanogrids

Abstract: This paper demonstrates a tool for quasi-static time series (QSTS) simulation for permanently isolated military nanogrids using steady-state equivalent circuit inverter models. Grid-forming and grid-following inverters are connected to PV generation and energy storage systems. The tool facilitates planning and operation of such grids by analyzing steady-state operation, fuel consumption, energy endurance, and faults in grid-forming and grid-following inverter modes. The equivalent inverter models are computationally efficient and well suited for rapid studies requiring QSTS with varying irradiance conditions and loads.

Voltage Balancing Capability of Grid-Forming Inverters

Abstract: The objective of this paper is to analyze and identify the range of voltage balancing capability of grid-forming inverters serving three-phase unbalanced loads. These inverters are designed to compensate for voltage imbalance by controlling the negative-sequence components of voltage and current. However, the magnitude of the negative-sequence current an inverter can supply is limited by its relatively low rated current. Moreover, it becomes more challenging to estimate the amount of current needed for an unbalanced load when the inverter is interfaced using a delta-wye grounded interconnection transformer. Therefore, we investigate the range of negative-sequence current the inverter can supply and derive formulas to determine the minimum inverter’s capacity required to compensate for voltage imbalance while supplying unbalanced loads connected through a delta-wye grounded transformer. The proposed formulas can be used to estimate the capacity of an inverter in lieu of detailed analyses and electromagnetic transient simulations. The proposed equation is implemented in small and large scale microgrid systems and validated using a detailed model developed in PSCAD/EMTDC.

Structural Tensor Learning for Event Identification with Limited Labels

Abstract: —The increasing uncertainty of distributed energy resources promotes the risks of transient events for power systems. To capture event dynamics, Phasor Measurement Unit (PMU) data is widely utilized due to its high resolutions. Notably, Machine Learning (ML) methods can process PMU data with feature learning techniques to identify events. However, existing ML-based methods face the following challenges due to salient characteristics from both the measurement and the label sides: ( 1 ) PMU streams have a large size with redundancy and correlations across temporal, spatial, and measurement type dimensions. Nevertheless, existing work cannot effectively uncover the structural correlations to remove redundancy and learn useful features. ( 2 ) The number of event labels is limited, but most models focus on learning with labeled data, suffering risks of non-robustness to different system conditions. To overcome the above issues, we propose an approach called Kernelized Tensor Decomposition and Classification with Semi-supervision (KTDC-Se). Firstly, we show that the key is to tensorize data storage, information filtering via decomposition, and discriminative feature learning via classification. This leads to an efficient exploration of structural correlations via high-dimensional tensors. Secondly, the proposed KTDC-Se can incorporate rich unlabeled data to seek decomposed tensors invariant to varying operational conditions. Thirdly, we make KTDC-Se a joint model of decomposition and classification so that there are no biased selections of the two steps. Finally, to boost the model accuracy, we add kernels for non-linear feature learning. We demonstrate the KTDC-Se superiority over the state-of-the-art methods for event identification using PMU data.