Search

Distribution Asset Management is an important task performed by utility companies to prolong the lifetime of the critical distribution assets and to accordingly ensure grid reliability by preventing unplanned outages. This study focuses on microgrid applications for distribution asset management as a viable and less expensive alternative to traditional utility practices in this area. A microgrid is as an emerging distribution technology that encompasses a variety of distribution technologies including distributed generation, demand response, and energy storage. Moreover, the substation transformer, as the most critical component in a distribution grid, is selected as the component of the choice for asset management studies. The resulting model is a microgrid-based distribution transformer asset management model in which microgrid exchanged power with the utility grid is reshaped in such a way that the distribution transformer lifetime is maximised. Numerical simulations on a test utility-owned microgrid demonstrate the effectiveness of the proposed model to reshape the loading of the distribution transformer at the point of interconnection in order to increase its lifetime., Comment: This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License

The growing proliferation of microgrids and distributed energy resources in distribution networks has resulted in the development of Distribution Market Operator (DMO). This new entity will facilitate the management of the distributed resources and their interactions with upstream network and the wholesale market. At the same time, DMOs can tap into the flexibility potential of these distributed resources to address many of the challenges that system operators are facing. This paper investigates this opportunity and develops a distribution market scheduling model based on upstream network ramping flexibility requirements. That is, the distribution network will play the role of a flexibility resource in the system, with a relatively large size and potential, to help bulk system operators to address emerging ramping concerns. Numerical simulations demonstrate the effectiveness of the proposed model on when tested on a distribution system with several microgrids., Comment: IEEE PES Transmission and Distribution Conference and Exposition (T&D), Denver, CO, 16-19 Apr. 2018

The growing penetration of renewable generation in distribution networks, primarily deployed by end-use electricity customers, is changing the traditional load profile and inevitably makes supply-load balancing more challenging for grid operators. Leveraging the potential flexibility of existing microgrids, that is to help with supply-load balance locally, is a viable solution to cope with this challenge and mitigate existing net load variability and intermittency in distribution networks. This paper discusses this timely topic and determines the microgrid value of ramping based on its available reserve using a cost-benefit analysis. To this end, a microgrid ramping-oriented optimal scheduling model is developed and tested through numerical simulations to prove the effectiveness and the merits of the proposed approach in microgrid ramping valuation., Comment: 2017 IEEE International Conference on Smart Grid Communications (IEEE SmartGridComm), Dresden, Germany, 23-26 Oct. 2017

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits., Comment: 2017 North American Power Symposium (NAPS), Morgantown, WV, 17-19 Sep. 2017

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem., Comment: IEEE Power and Energy Society General Meeting, 2017

The variable nature of the solar generation and the inherent uncertainty in solar generation forecasts are two challenging issues for utility grids, especially as the distribution grid integrated solar generation proliferates. This paper offers to utilize microgrids as local solutions for mitigating these negative drawbacks and helping the utility grid in hosting a higher penetration of solar generation. A microgrid optimal scheduling model based on robust optimization is developed to capture solar generation variability and uncertainty. Numerical simulations on a test feeder indicate the effectiveness of the proposed model., Comment: IEEE Power and Energy Society General Meeting, 2017

Introduction of market mechanisms in distribution systems is currently subject to extensive studies. One of the challenges facing Distribution Market Operators (DMOs) is to implement a fair and economically efficient pricing mechanism that can incentivize consumers to positively contribute to grid operations and to improve economic performance of the distribution system. This paper studies a penalty-based pricing mechanism in distribution markets and further investigates the interrelationship between the locational marginal prices (LMPs) at transmission and distribution levels. As a result, a closed-form relationship between these LMPs is derived. The possibility of zeroing out the settlement profit is further investigated under the proposed pricing mechanism., Comment: Accepted to the IEEE Power and Energy General Meeting 2017

Distributed renewable energy resources have attracted significant attention in recent years due to the falling cost of the renewable energy technology, extensive federal and state incentives, and the application in improving load-point reliability. This growing proliferation, however, is changing the traditional consumption load curves by adding considerable levels of variability and further challenging the electricity supply-demand balance. In this paper, the application of microgrids in effectively capturing the distribution network net load variability, caused primarily by the prosumers, is investigated. Microgrids provide a viable and localized solution to this challenge while removing the need for costly investments by the electric utility on reinforcing the existing electricity infrastructure. A flexibility-oriented microgrid optimal scheduling model is proposed and developed to coordinate the microgrid net load with the aggregated consumers/prosumers net load in the distribution network with a focus on ramping issues. The proposed coordination is performed to capture both inter-hour and intra-hour net load variabilities. Numerical simulations on a test distribution feeder with one microgrid and several consumers and prosumers exhibit the effectiveness of the proposed model., Comment: IEEE Transactions on Power Systems

The levelized cost of energy (LCOE) presents the energy-normalized cost of a generation asset by considering all associated costs (investment and operation) and total generated energy over its life cycle. As LCOE is a levelized value, it provides a quick and easy measure to compare different energy resource technologies with different characteristics. The LCOE calculation for large-scale power plants and distributed generations (DGs) is extensively studied and can be found in the literature. The discussions on the LCOE calculation for energy storage systems, however, is limited. Although still relatively expensive compared to generation technologies, energy storage is gaining significant attention and has been deployed extensively during the past few years, conceivably due to its many benefits such as load shifting, energy arbitrage, and renewable coordination. Therefore, LCOE calculation of energy storage systems plays an important role in economic evaluation of power systems. This paper proposes a method for calculating the LCOE of energy storage, and further provides the sensitivity analysis with respect to changes in capacity, electricity market prices, and efficiency., Comment: CIGRE Grid of the Future Symposium, Oct.30-Nov.1 2016, Philadelphia, PA

Renewable energy has attracted significant attention over the last decade, conceivably due to its environmental benefits and the recent drops in the development and deployment cost of the technology. The increase in renewable generation, however, has resulted in new challenges in supply-load balancing, owing to its intermittent, non-predictable and volatile generation features. Several methods have been introduced to cope with negative impacts of the renewable generation deployment. In this paper, a novel method, i.e., the application of microgrids in capturing the variabilities of distributed renewable generation in distribution networks is proposed and investigated. Utilizing available flexibility of microgrids represents a local and viable solution which leads to lower investments from electric utilities for increasing their flexibility and providing more reserved power. It is investigated that how the system flexibility requirements can be integrated into the microgrid optimal scheduling model to enable microgrids in supporting the grid operators by offering flexibility services. Using the proposed flexibility constraints, intra-hour and inter-hour variabilities at the distribution feeder will be efficiently captured. Numerical simulations on a test distribution feeder, with one microgrid and several renewable-equipped consumers, show the effectiveness of the proposed model., Comment: CIGRE Grid of the Future Symposium, Oct.30-Nov.1 2016, Philadelphia, PA

In spite of all advantages of solar energy, its deployment will significantly change the typical electric load profile, thus necessitating a change in traditional distribution grid management practices. In particular, the net load ramping, created as a result of simultaneous solar generation drop and load increase at early evening hours, is one of the major operational issues that needs to be carefully addressed. In this paper, microgrids are utilized to offer a viable and localized solution to this challenge while removing the need for costly investments by the electric utility. In this regard, first the microgrid ramping capability is determined via a min-max optimization, and second, the microgrid optimal scheduling model is developed to coordinate the microgrid net load with the distribution grid net load for addressing the ramping issue. Numerical simulations on a test distribution feeder with one microgrid exhibit the effectiveness of the proposed model., Comment: Accepted to 2016 IEEE PES Innovative Smart Grid Technologies (ISGT), 6-9 Sep. 2016

In this paper, a flexibility-oriented microgrid optimal scheduling model is proposed to mitigate distribution network net load variability caused by large penetration distributed solar generation. The distributed solar generation variability, which is caused by increasing adoption of this technology by end-use consumers, is mainly addressed by electric utilities using grid reinforcement. Microgrids, however, provide viable and local solutions to this pressing challenge. The proposed model, which is developed using mixed-integer programming and employs robust optimization, not only can efficiently capture distribution network net load variations, mainly in terms of ramping, but also accounts for possible uncertainties in forecasting. Numerical simulations on a test distribution feeder with one microgrid and several consumers/prosumers indicate the effectiveness of the proposed model., Comment: Accepted to 2016 North American Power Symposium (NAPS), Denver, CO, 18-20 Sep. 2016