From asi
Optimizes energy system investments and operations across electricity, hydrogen, heat, gas using Julia MILP model TulipaEnergyModel.jl, including geothermal bore modeling for glass-line RWA.
npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
Julia optimization model for energy system investment and operation decisions.
Models glass-line bores for Plurigrid ASI infrastructure using Julia schemas, integrating fiber optic sensing, geothermal energy, communication, and materials extraction.
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Provides expertise in electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management for C&I facilities.
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Julia optimization model for energy system investment and operation decisions.
TulipaEnergyModel.jl optimizes across multiple energy sectors — electricity, hydrogen, heat, gas. The glass-line RWA (geothermal bore + fiber + compute at Plurigrid Portal, Portland) produces exactly these revenue streams. Tulipa can model:
The model determines optimal investment timing (when to drill, when to expand) and operation (how to dispatch across revenue streams each hour).
using Pkg
Pkg.add("TulipaEnergyModel")
using TulipaEnergyModel
# Define the geothermal bore as a Producer asset
# Portland Cascadia volcanic arc: 40-60°C/km gradient
# 2km bore → 80-120°C fluid temperature
# Binary ORC cycle → ~10% thermal-to-electric efficiency
# Model inputs:
# - Drilling cost: $5M-10M per bore
# - Thermal output: ~5 MWth continuous
# - Electric output: ~500 kWe (binary cycle)
# - Revenue: electricity + heat + compute + fiber + brine
# - DOE EGS grant: up to $25M match
# The optimization tells you:
# 1. When to invest (NPV maximizing drill date)
# 2. How to dispatch (hourly revenue allocation)
# 3. Break-even timeline (years to payback)
# 4. Optimal capacity sizing (bore depth, ORC rating)
The Move allocator's glass-line strategy (TRIT_MINUS, conservative/physical) reads its observed_apy_bps from a Tulipa model run:
Tulipa model run (offline, Julia)
→ annual revenue estimate ($/yr)
→ convert to APT at current price
→ set as observed_apy_bps in Move strategy
→ allocator rebalance() fold includes physical yield
This bridges continuous (DeFi) and discontinuous (physical) yield.
| File | Purpose |
|---|---|
src/TulipaEnergyModel.jl | Main module |
src/model.jl | Optimization model formulation |
src/io.jl | Data input/output |
docs/ | Full documentation |
benchmark/ | Performance benchmarks |