Abstract: 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.

Abstract: A machine learning based multiyear projection planning for energy systems is disclosed. In some embodiments, a method comprises: obtaining input data for an energy system; determining one or more projection factors based on the input data; determining, based on a machine learning model, an operation or investment associated with the energy system to achieve lower cost or improve one or more metrics of the energy system for the multiyear horizon based at least in part on the one or more projection factors and a description of technology or infrastructure of the energy system; generating a recommended operation or investment decision for the energy system based at least in part on output of the machine learning model; and storing the recommended operation or investment decision.

Abstract: 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.

Abstract: 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.

Abstract: 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.