Customers' load profiles are critical resources to support data analytics applications in modern power systems. However, there are usually insufficient historical load profiles for data analysis, due to the collec...
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Customers' load profiles are critical resources to support data analytics applications in modern power systems. However, there are usually insufficient historical load profiles for data analysis, due to the collection cost and data privacy issues. To address such data shortage problems, load profiles synthesis is an effective technique that provides synthetic training data for customers to build high-performance data-driven models. Nonetheless, it is still challenging to synthesize high-quality load profiles for each customer using generation models trained by the respective customer's data owing to the high heterogeneity of customer load. In this paper, we propose a novel customized load profiles synthesis method based on conditional diffusion models for heterogeneous customers. Specifically, we first convert the customized synthesis into a conditional data generation issue. We then extend traditional diffusion models to conditional diffusion models to realize conditional data generation, which can synthesize exclusive load profiles for each customer according to the customer's load characteristics and application demands. In addition, to implement conditional diffusion models, we design a noise estimation model with stacked residual layers, which improves the generation performance by using skip connections. The attention mechanism is also utilized to better extract the complex temporal dependency of load profiles. Finally, numerical case studies based on a public dataset are conducted to validate the effectiveness and superiority of the proposed method.
Monitoring a large and geographically dispersed solar generation fleet can be a challenging undertaking. At Dominion Energy, there is currently over 1.7 GW of transmission and distribution-connected solar generation. ...
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Monitoring a large and geographically dispersed solar generation fleet can be a challenging undertaking. At Dominion Energy, there is currently over 1.7 GW of transmission and distribution-connected solar generation. Performing the post-mortem analysis and ensuring the safe and reliable operation on more than 100 solar facilities can take substantial engineering efforts. In this study, a renewable generation data collection platform is presented to address the increasing need for solar generation monitoring. The data platform consists of data collection, event storage, real-time alerts, and data analytics applications. The data analysis system can support various technical aspects in solar integration, including grounding monitoring, inverter fault response and model validation, power quality monitoring, and equipment overvoltage protection. The proposed data platform greatly improves the visibility of solar generation and ensures its successful integration.
Edge computing systems enable data analytics applications at one-hop wireless distances from mobile devices. Enabling and using proximal mobile edge servers opportunistically for data analytics applications can minimi...
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Edge computing systems enable data analytics applications at one-hop wireless distances from mobile devices. Enabling and using proximal mobile edge servers opportunistically for data analytics applications can minimize network latency, lower dependencies on Internet connectivity, and reduce the cost of using cloud services. This paper presents different experimental evaluations on the feasibility of opportunistic usage of mobile edge servers. The paper also highlights key challenges and future research directions.
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