arXiv subjects

Malhar Padhee

Publications and source records attributed to Malhar Padhee.

6 recordsLinked to original sources

arXiv2021

Optimal BESS Allocation in Large Transmission Networks Using Linearized BESS Models

The most commonly used model for battery energy storage systems (BESSs) in optimal BESS allocation problems is a constant-efficiency model. However, the charging and discharging efficiencies of BESSs vary non-linearly as functions of their state-of-charge, temperature, charging/discharging powers, as well as the BESS technology being considered. Therefore, constant-efficiency models may inaccurately represent the non-linear operating characteristics of the BESS. In this paper, we first create technology-specific linearized BESS models derived from the actual non-linear BESS models. We then incorporate the linearized BESS models into a mixed-integer linear programming framework for optimal multi-technology BESS allocation. Studies carried out on a 2,604-bus U.S. transmission network demonstrate the benefits of utilizing the linearized BESS models from the model accuracy, convexity, and computational performance viewpoints.

math.OC↗

arXiv2021

Fast DTW and Fuzzy Clustering for Scenario Generation in Power System Planning Problems

Power system planning problems become computationally intractable if one accounts for all uncertain operating scenarios. Consequently, one selects a subset of scenarios that are representative of likely/extreme operating conditions, e.g. heavy summer, heavy winter, light summer, and so on. However, such an approach may not be able to accurately capture the dependencies that exist between renewable generation (RG) and system load in RG-rich power systems. This paper proposes the use of fast dynamic time warping (FDTW) and fuzzy c-means++ (FCM++) clustering to account for key statistical properties of load and RG for scenario generation for power system planning problems. Case studies using a U.S. power network, and comparison with existing scenario generation techniques demonstrate the benefits of the proposed approach.

eess.SP↗

arXiv2019

A Fixed-Flexible BESS Allocation Scheme for Transmission Networks Considering Uncertainties

Battery energy storage systems (BESSs) can play a key role in mitigating the intermittency and uncertainty associated with adding large amounts of renewable energy to the bulk power system (BPS). Two BESS technologies that have gained prominence in this regard are Lithium-ion (LI) BESS and Vanadium redox flow (VRF) BESS. This paper proposes a fixed-flexible BESS allocation scheme that exploits the complementary characteristics of LI and VRF BESSs to attain optimal techno-economic benefits in a wind-integrated BPS. Studies carried out on relatively large transmission networks demonstrate that benefits such as reduction in system operation cost, wind spillage, voltage fluctuations, and discounted payback period, can be realized by using the proposed scheme.

eess.SP↗

arXiv2018

Health Monitoring of Critical Power System Equipments using Identifying Codes

High voltage power transformers are one of the most critical equipments in the electric power grid. A sudden failure of a power transformer can significantly disrupt bulk power delivery. Before a transformer reaches its critical failure state, there are indicators which, if monitored periodically, can alert an operator that the transformer is heading towards a failure. One of the indicators is the signal to noise ratio (SNR) of the voltage and current signals in substations located in the vicinity of the transformer. During normal operations, the width of the SNR band is small. However, when the transformer heads towards a failure, the widths of the bands increase, reaching their maximum just before the failure actually occurs. This change in width of the SNR can be observed by sensors, such as phasor measurement units (PMUs) located nearby. Identifying Code is a mathematical tool that enables one to uniquely identify one or more {\em objects of interest}, by generating a unique signature corresponding to those objects, which can then be detected by a sensor. In this paper, we first describe how Identifying Code can be utilized for detecting failure of power transformers. Then, we apply this technique to determine the fewest number of sensors needed to uniquely identify failing transformers in different test systems.

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arXiv2017

Analyzing Effects of Seasonal Variations in Wind Generation and Load on Voltage Profiles

This paper presents a methodology for building daily profiles of wind generation and load for different seasons to assess their impacts on voltage violations. The measurement-based wind models showed very high accuracy when validated against several years of actual wind power data. System load modeling was carried out by analyzing the seasonal trends that occur in residential, commercial, and industrial loads. When the proposed approach was implemented on the IEEE 118-bus system, it could identify violations in bus voltage profiles that the season-independent model could not capture. The results of the proposed approach are expected to provide better visualization of the problems that seasonal variations in wind power and load might cause to the electric power grid.

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arXiv2017

Finding $K$ Contingency List in Power Networks using a New Model of Dependency

Smart grid systems are composed of power and communication network components. The components in either network exhibit complex dependencies on components in its own as well as the other network to drive their functionality. Existing, models fail to capture these complex dependencies. In this paper, we restrict to the dependencies in the power network and propose the Multi-scale Implicative Interdependency Relation (MIIR) model that address the existing limitations. A formal description of the model along with its working dynamics and a brief validation with respect to the 2011 Southwest blackout are provided. Utilizing the MIIR model, the $K$ Contingency List problem is proposed. For a given time instant, the problem solves for a set of $K$ entities in a power network which when failed at that time instant would cause the maximum number of entities to fail eventually. Owing to the problem being NP-complete we devised a Mixed Integer Program (MIP) to obtain the optimal solution and a polynomial time sub-optimal heuristic. The efficacy of the heuristic with respect to the MIP is compared by using different bus system data. In general, the heuristic is shown to provide near optimal solution at a much faster time than the MIP.

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