Meng Yue

Capacity specification for hybrid energy storage system to accommodate fast PV fluctuations

To accommodate the fast power fluctuations associated with PV generation, a hybrid energy storage system (HESS) consisting of a battery energy storage system (BESS) and super-capacitor is evaluated in this paper. A probabilistic approach for determining the power capacity specification for the HESS is proposed. This method would allow the capacities of the BESS and super-capacitor to be properly designed to optimize the characteristics of high energy density of the BESS and high power density of the super-capacitor. Results show that the super-capacitor within the HESS helps to mitigate the high frequency fluctuations, which contributes to the extension of BESS lifetime. In addition, the super-capacitor helps to address the peaks in solar power fluctuations without the severe penalty of round trip losses associated with a BESS. The proposed approach has been simulated using real PV generation data from an existing PV power plant in Long Island, New York.

A Bayesian Approach Based Outage Prediction in Electric Utility Systems Using Radar Measurement Data

Severe weather events such as strong thunderstorms are some of the most significant and frequent threats to the electrical grid infrastructure. Outages resulting from storms can be very costly. While some tools are available to utilities to predict storm occurrences and damage, they are typically very crude and provide little means of facilitating restoration efforts. This paper developed a methodology to use historical high-resolution (both temporal and spatial) radar observations of storm characteristics and outage information to develop weather condition dependent failure rate models for different grid components. Such models can provide an estimation or prediction of the outage numbers in small areas of a utility’s service territory once the real-time measurement or forecasted data of weather conditions become available as the input to the models. Considering the potential value provided by real-time outages reported, a Bayesian outage prediction algorithm is proposed to account for both strength and uncertainties of the reported outages and failure rate models. The potential benefit of this outage prediction scheme is illustrated in this paper.

Design of energy storage system to improve inertial response for large scale PV generation

With high-penetration levels of renewable generating sources being integrated into the existing electric power grid, conventional generators are being replaced and grid inertial response is deteriorating. This technical challenge is more severe with photovoltaic (PV) generation than with wind generation because PV generation systems cannot provide inertial response unless special countermeasures are adopted. To enhance the inertial response, this paper proposes to use battery energy storage systems (BESS) as the remediation approach to accommodate the degrading inertial response when high penetrations of PV generation are integrated into the existing power grid. A sample power system was adopted and simulated using PSS/E software. Impacts of different penetration levels of PV generation on the system inertial response were investigated and then BESS was incorporated to improve the frequency dynamics.

An enhanced probabilistic contingency analysis for development of risk-informed transmission planning

The electric power industry heavily relies on software tools for planning and operation. Some of these tools are widely adopted by utilities. It is foreseen that the industry will continue to use these tools until the next generation of grid analytical tools becomes available and mature. These tools, however, were usually designed to perform deterministic studies or limited probabilistic analyses. Regulators and other industry stakeholders recognize the need of having tools to better account for the increasing uncertainties and provide probabilistic risk assessment (PRA) based decision-making procedures. To address these issues, this study offers a short-term solution by enhancing the PRA capabilities of these popular tools by developing interfaces for the existing tools. These interfaces will handle the statistical distributions of the different input parameters and extract the risk measures and metrics for decision-makers. A widely used transmission planning tool, PSS/E, is selected for demonstration of this solution.

Outage data collection and parameter estimation for an improved probabilistic contigency analysis

Probabilistic risk assessment (PRA) techniques are increasingly being used in electric power industry applications for better coping with uncertainties over deterministic approaches. One application where PRA techniques can add value is data analysis for parameters such as outage frequency. Focusing on a probabilistic contingency analysis (PCA), this study examines the issue of obtaining a point estimate of outage statistics by lumping or pooling outage data records together from different sources. A Pearson Chi-square test is adopted to determine the poolability of data, and a lognormal distribution is used to model the data source variability and capture variations of operation and maintenance practices among different utilities. The distribution parameters representing outage frequencies and durations are calculated from the raw outage data. An improved PCA scheme based on the outcomes of this study is proposed and being implemented.