Xendee Corporation

Michael Stadler

Adib Nasle

Scott K Mitchell

Zachary K Pecenak

Patrick James Mathiesen

Kelsey T Fahy

In an embodiment, a method comprises: obtaining input data including a plurality of parameters related to an energy system design; reducing the input data to a subset of the input data; in a first stage: feeding the subset of input data into a reduced version of a model for estimating an energy system configuration; estimating, using the reduced version of the model, a first energy system configuration and a lower bound solution for the first energy system configuration based on the subset of input data; and in a second stage following the first stage: feeding the input data and the lower bound solution into a full version of the model; and estimating, using the full version of the model, a second energy system configuration based on the input data and the lower bound solution.

Robust and fast design of microgrids, der systems, and other energy systems using staged hybrid investment planning

The embodiments disclosed in this document are directed to an AI-enabled microgrid and DER planning platform that uses AI methods and takes into account cost calculations, emission calculations, technology investments and operation. In an embodiment, the computing platform is deployed on a network (cloud computing platform) that can be accessed by a variety of stakeholders (e.g., investors, technology vendors, energy providers, regulatory authorities). In an embodiment, the planning platform implements machine learning (e.g., neural networks) to estimate various planning parameters, where the neural networks are trained on observed data from real-world microgrid/minigrid and DER projects.

Artificial intelligence microgrid and distributed energy resources planning platform

Systems, methods and non-transitory computer-readable storage mediums are disclosed for cloud computing engineering, solar PV (SPV) or solar PV with storage (SPV/S) system configuration, pricing, quoting, advertising messaging, sales lead generation, and content marketing.

Cloud computing smart solar configurator

The disclosed embodiments combine an energy balance (e.g., averaged energy profiles) with original time series data having smaller time steps to establish an energy balance (e.g., averaged profiles) and a power balance (e.g., smaller time step with original time series data) for a Distributed Energy Resources (DER), microgrid, or other energy system. In an embodiment, a method comprises: solving, with at least one processor, a first optimization problem on time series data related to energy system planning, the first optimization including applying a power balancing framework to the time series data that captures intra-hour variability; and selecting, with the at least one processor, technology assets and sizing for the energy system based on an average hourly and sub-hourly datasets.

Growing community of inventors

Encinitas, CA, United States of America

Michael Stadler