Tulipa Energy Model Skill

Tulipa Energy Model

Julia optimization model for energy system investment and operation decisions.

Why This Matters

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:

GeoToken (g): geothermal electricity generation, dispatch optimization
ThermalToken (d): direct-use heating, district heating network
ComputeToken (c): compute powered by geothermal, load scheduling
LumenToken (b): fiber optic capacity allocation
BrineToken (n): mineral extraction from geothermal brine

The model determines optimal investment timing (when to drill, when to expand) and operation (how to dispatch across revenue streams each hour).

Installation

using Pkg
Pkg.add("TulipaEnergyModel")

Core Concepts

Assets

Producer: geothermal plant, solar array
Consumer: compute load, district heating demand
Conversion: heat → electricity (ORC), electricity → compute
Storage: thermal storage, battery
Transport: transmission lines, heat pipes, fiber

Optimization

Mixed-integer linear programming (MILP)
Multi-period (hourly, daily, seasonal)
Investment + operation co-optimization
Uses HiGHS solver (open source)

Usage for Glass-Line Modeling

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)

Integration with Allocator

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.

Key Files

| 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 |

References

Repo: https://github.com/TulipaEnergy/TulipaEnergyModel.jl
Docs: https://TulipaEnergy.github.io/TulipaEnergyModel.jl/stable/
DOI: CITATION.cff in repo
bmorphism starred this — connection to glass-line RWA thesis
Related: worlds/g/rwa_manifest_destiny.md, gayfnox-allocator/

Agent Skills: Tulipa Energy Model

Install this agent skill to your local

Skill Files