The widespread deployment of autonomous inverter-based solutions for mitigating voltage and frequency excursions caused by high-penetration photovoltaic (PV) systems has drawn increased attention due to their potential impact on PV production. It is now important to quantify the amount of solar energy curtailed as a result of the activation of inverter-based grid support functions (GSFs). This study proposes a methodology for estimating the impact of volt-watt on customer PV energy curtailment using smart meter voltage data. This method estimates maximum possible curtailment for a given volt-watt curve based on the customer smart meter voltage during the time period of interest. This study compares the proposed methodology with field measurements using irradiance and customer inverter data from Hawaii as well as with results from a previous simulation-driven study on the impact of advanced inverter GSF activation on PV energy curtailment. Results show that the proposed method for estimating lost PV production caused by volt-watt control aligns reasonably well with field measurements and computer simulations for hundreds of customers. The proposed method could be used to estimate customer energy curtailment, which could inform future compensation mechanisms for utilities leveraging customer-sited resources to mitigate high voltage and defer infrastructure upgrades.
This paper addresses a major utility and regulator concern of characterizing customer net electricity consumption profiles to realize integrated distribution system planning. This is pivotal in assessing the capability of the power system to accommodate net load variability and its impacts on the grid such as voltage rise, narrowing peak demand duration, and reducing the cost of energy storage. Although the extant literature has focused on load clustering, this paper uses a symbolic aggregate approximation-based (SAXbased) dimensionality-reduction and k-means techniques to cluster net consumption of smart meter data for more than 3500 residential customers in a month at different temporal resolutions. This study proposes the use of cumulative explained variance in the principal component analysis to determine the optimal number of segments and dimensionality of the transformed space during discretization while retaining the data integrity instead of using intuition, as proposed by the extant literature. Also, this paper describes a screening methodology to determine the distribution of high-voltage customers among the resulting clusters of customers with and without on-site solar photovoltaic generation at different time resolutions.INDEX TERMS Symbolic aggregate approximation, clustering, net electricity consumption, principal component analysis.
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