Feasibility Studies

• Customer Project Screening Request Form

• This initial phase helps us quickly determine if a project is a good fit for further detailed analysis.

• Quick Look with REopt & Screening Tools:

  • How we use it: We start with REopt's web tool for a fast, high-level overview of a project's potential. We input basic energy use and utility rate information. We also use quick, free tools like PVWatts for rough solar estimates and the EPA's CHP Screening Tool for initial CHP potential.

  • What it helps us do: This gives us a preliminary idea of potential cost savings, resilience improvements, and environmental benefits. It helps us decide if the project is worth a deeper dive and gives us talking points for early client discussions.

In this phase, we gather more precise information and build detailed models of individual energy systems.

• Gathering Detailed Site & Energy Data:

  • How we use it: We use Autodesk Revit to create or import a 3D model of the building, which helps us understand the space and plan where new equipment might go. We work with the client's MEP engineers to get precise hourly electricity usage data. We also use Google Forms to efficiently collect all necessary project details from the client. Raw models and unstructured data are securely stored in Google Drive, while all structured data is directly ingested into our PostgreSQL database.

  • What it helps us do: We get a clear picture of the building's energy needs and physical layout, organized for detailed analysis and future design.

• Understanding Utility Costs:

  • How we use it: We obtain all the details on the client's current and future electricity rates from the utility company. We organize and analyze this information in PostgreSQL database.

  • What it helps us do: We gain a complete understanding of how the client is currently charged for electricity, which is fundamental to finding savings.

• Designing & Modeling Individual Energy Systems (Solar, Batteries, CHP, Geothermal):

  • How we use it: Helioscope and SolarEdge Designer for precise solar panel designs and energy yield estimates. We then use SAM to model the exact performance and costs of these solar systems, as well as battery storage (BESS), combined heat and power (CHP) units, and geothermal systems. SAM helps us understand how each piece of equipment will perform individually and its exact cost.

  • What it helps us do: We get highly accurate predictions of how much energy each system will produce (or save) and its associated costs, ensuring our designs are realistic and financially sound.

  • Integration: The results from these models are seamlessly integrated into PostgreSQL database for easy access and collaboration.

• Preliminary Environmental & Regulatory Review:

  • How we use it: We conduct an initial assessment of the environmental impact of the chosen technologies and review applicable regulations to identify any early permitting considerations.

  • What it helps us do: We get an early heads-up on potential environmental and regulatory hurdles.

Here, we use the precise data from Phase 1 to find the absolute best combination of energy systems and then analyze every possible way to maximize their value.

• Finding the Best Energy System Mix (REopt - Refined Optimization):

  • How we use it: We load all the precise data we gathered (from PostgreSQL database and SAM outputs) into REopt. REopt then runs a sophisticated optimization to find the perfect combination and size of solar, batteries, and CHP that will give the client the most savings, the best resilience, and the lowest emissions.

  • What it helps us do: We get a highly precise recommendation for the ideal size and type of each energy system, along with a detailed plan for how they should operate together throughout the year.

  • Integration: The results are seamlessly integrated back into PostgreSQL database for team review and further analysis.

• Unlocking All Possible Value (DER-VET - Deep Dive):

  • How we use it: We take the optimized system from REopt (now in our PostgreSQL database) and input it into DER-VET. This tool specializes in finding every single way to make money or save costs from batteries and microgrids. It analyzes benefits like reducing peak demand charges, energy arbitrage (buying cheap, selling expensive), and even participating in energy markets. If the warehouse wants a microgrid, DER-VET also models how all the systems will work together seamlessly, even if the main power grid goes down, ensuring maximum resilience.

  • What it helps us do: We get a detailed breakdown of all potential financial benefits, showing exactly how the systems will operate to generate the most value and enhance power reliability.

  • Integration: The detailed results are seamlessly integrated into the PostgreSQL database for comprehensive review.

We use a complementary set of software for our owner-side optimization and financial analysis. These tools are the foundation for our reports, which maximize value for our clients by identifying the most cost-effective, resilient, and dispatchable DER solutions. They also allow us to understand the potential of individual DERs and DER portfolios for providing stacked services to the grid.

REopt Energy Integration & Optimization - National Renewable Energy Laboratory (NREL)
Also from NREL, ReOpt Lite is a rapid optimization tool with a public API. We use it for initial, high-level feasibility screening and resilience assessments. Its speed allows our engineers to quickly explore thousands of initial scenarios to identify the most promising projects before committing to a more detailed analysis with our other tools.

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• • API Documentation: https://developer.nrel.gov/docs/energy-optimization/reopt/ (For programmatic access via Python)

  • Analysis Scripts (GitHub): https://github.com/NREL/REopt-Analysis-Scripts (Contains example Python scripts and Jupyter Notebooks)

  • User Guides & Tutorials: Available on the REopt website, including video tutorials for the web tool and API.

  • Community Forum: A user forum is available for support. 

System Advisor Model - SAM - National Renewable Energy Laboratory (NREL)
Developed by the National Renewable Energy Laboratory (NREL), SAM is our go-to simulation tool for detailed techno-economic analysis. We use SAM to perform long-term (20-30 year) financial pro forma, validate performance, and model specific component degradation and tax incentive impacts for the optimal solutions identified by our other tools.

https://sam.nrel.gov/

• • Download: Available for Windows, Mac, and Linux directly from the website.

  • Documentation & User Guides: Comprehensive guides and tutorials are available on the SAM website.

  • PySAM (Python SDK): Information and downloads for the Python integration can be found on the SAM website under the "SDK" section.

  • Community Forum: An active user forum is available for questions and support. 

Distributed Energy Resource Value Estimation Tool - DER-VET - Electric Power Research Institute (EPRI)
This is our primary open-source optimization engine for BTM and microgrid design. Developed by the Electric Power Research Institute (EPRI), DER-VET excels at co-optimizing DER sizing and dispatch for a wide range of services, including customer bill savings, resilience, and grid service participation. It is a Python-based tool and is designed to integrate with grid simulation tools like OpenDSS.

https://www.der-vet.com/

• • Software Download: The GUI (Desktop App) can be downloaded from the website.

  • User Guide & Technical Documentation: Available on the DER-VET website and EPRI's StorageWiki.

  • ESIC Working Group/Task Force: Opportunities to engage with the developer community for deeper insights and feedback.

Distributed Energy Resources Customer Adoption Model - DER-CAM - Lawrence Berkeley National Laboratory (LBNL)
Developed by Lawrence Berkeley National Laboratory (LBNL), DER-CAM is a powerful optimization tool used to find optimal DER investments and dispatch strategies, particularly for multi-energy systems that include thermal loads and resources (e.g., Combined Heat and Power). It is a valuable alternative for projects with a strong thermal component.
https://gridintegration.lbl.gov/der-cam 

EnergyPlus - NREL
Whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption—for heating, cooling, ventilation, lighting and plug and process loads—and water use in buildings. Can be used to model thermal energy storage.
https://energyplus.net/ 

OpenDSS (Open Distribution System Simulator)
This is our foundational tool for distribution system analysis (DSA). We use this powerful open-source simulator to perform detailed power flow, hosting capacity, and time-series studies. It allows us to build and model real-world feeder configurations to precisely assess how DERs affect grid voltage, line loading, and protective equipment, identifying potential stress points and opportunities for improvement.
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OpenDSS-G (Graphical User Interface)
This GUI is an invaluable companion to OpenDSS. While our automated workflow drives the core OpenDSS engine via Python, our engineers use the GUI to build and visualize new circuit models, inspect results, and debug our analytical scripts. It helps us ensure the accuracy and integrity of our programmatic analysis.
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NREL SMART-DS (Synthetic Models for Advanced, Realistic Testing)
This synthetic dataset from NREL is essential for developing and training our analytical engine. It provides high-quality, realistic models of large-scale distribution grids in OpenDSS format. We use this data to generate millions of simulation runs, allowing us to build and test our AI models and methodology without requiring real utility data—which can be difficult to obtain—to validate our solutions.
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• Official Python Website: https://www.python.org/downloads/ (for direct Python installers)

• Recommended Distribution: Anaconda or Miniconda (simplifies package and environment management).

  • Download: https://www.anaconda.com/products/distribution or https://docs.conda.io/en/latest/miniconda.html

• Key Concepts: Familiarity with virtual environments (conda env or python -m venv) and package installation (pip install) is crucial for managing project dependencies.

• Relevant Libraries: requests (for APIs), pandas (for data manipulation), matplotlib (for plotting).

Data Exchange between DER Software Packages

• Action: Python is used to extract outputs from one DER software (e.g., SAM's performance data) and format them as inputs for another (e.g., REopt's optimization). This data is now exchanged through the centralized PostgreSQL database, ensuring consistency and a single source of truth for all steps in the analysis pipeline.

• Benefit: Creates a seamless, automated workflow between our specialized tools, ensuring data consistency and maximizing the accuracy and defensibility of our designs. This iterative data flow is essential for optimizing economical, resilient, and sustainable outcomes.

• PySAM (Python SDK): 

  • Information and downloads for the Python integration can be found on the SAM website under the "SDK" section. 

• DER-VET Python Source Code

  • The core analytical engine for programmatic use.

  • GitHub: https://github.com/epri-dev/DER-VET

We will use PostgreSQL as our primary relational database. This open-source solution will be installed on our Azure Virtual Machine and will serve as the single source of truth for all structured project data. It provides the data integrity, query speed, and security necessary for a robust, automated workflow.

• Benefit: This moves us beyond the limitations of Google Sheets, ensuring our data is consistent, easily searchable, and scalable for future growth and potential SaaS conversion.

• Key Python Library: SQLAlchemy is our Python toolkit for interacting with the database.

• • Action: Python scripts utilize Google APIs (e.g., Google Sheets API, Google Docs API, Google Slides API) to automatically pull data from our PostgreSQL database and populate predefined report templates.

  • Benefit: This significantly reduces manual data transfer, minimizes errors, and accelerates the generation of customized feasibility studies and final reports. This automation directly translates to more economical project delivery and allows engineers to focus on analysis rather than data manipulation.

• How it Works: Ollama is a platform that allows us to download and run open-source LLMs (such as Llama 3 or Mistral) directly on our local servers. It provides a simple API that our Python scripts can call, allowing us to send complex prompts and receive structured responses without any data leaving our internal network.

• Role in the Program: The Ollama-powered LLM will serve as the "brain" that translates natural language requests (from a future internal UI or command-line interface) into the precise, multi-step Python commands needed to run analyses across REopt, SAM, DER-VET, and other tools. This allows for more intuitive and powerful automation of our entire feasibility study process.

• Benefits:

  • Data Privacy: All sensitive client and project data remains on our own hardware.

  • Cost Control: No per-token or per-query fees, enabling unlimited internal use and experimentation.

  • Customization: The ability to eventually fine-tune the LLM on our own project data to improve its performance and understanding of DER-specific terminology.

• Getting Started:

  • Download: https://ollama.com/

  • API Documentation: https://github.com/ollama/ollama/blob/main/docs/api.md (For programmatic access via Python and other languages)