AI Urban Energy Planning
Intelligent city-scale energy planning and infrastructure optimization
The Problem
“City-scale EV charging and battery planning is too static for modern urban energy systems”
Organizations face these key challenges:
EV charging demand is highly variable across time, sites, and user behavior
Battery sizing and dispatch decisions are often disconnected from charging operations
Grid capacity limits and tariff structures create complex trade-offs
Planners lack a unified model for autonomy, cost, resilience, and emissions
Manual scenario analysis does not scale across districts or city portfolios
Existing tools are difficult for non-technical planners to operationalize
Impact When Solved
The Shift
Human Does
- •Collect and reconcile load, asset, building, mobility, and policy inputs from utility, city, and third-party sources
- •Set planning assumptions for demand growth, electrification, DER adoption, reliability targets, and emissions goals
- •Run periodic feeder, capacity, and scenario reviews and compare upgrade, DER, and non-wires options
- •Review study results with stakeholders and approve capital plans, interconnection priorities, and program actions
Automation
- •Limited spreadsheet calculations for load growth and peak demand projections
- •Static map and report generation for service areas, constraints, and planned projects
- •Basic rule-based aggregation of historical usage, weather, and asset data
- •Manual scenario summaries with little continuous updating between planning cycles
Human Does
- •Set planning objectives, policy constraints, reliability thresholds, and acceptable cost-emissions tradeoffs
- •Review AI-generated scenarios and approve grid upgrades, non-wires alternatives, DER programs, and district strategies
- •Handle exceptions where forecasts conflict with local knowledge, regulatory requirements, or stakeholder commitments
AI Handles
- •Continuously ingest and reconcile utility, city, weather, building, EV, and DER data into current planning views
- •Generate granular demand, electrification, and DER adoption forecasts with probabilistic feeder and district scenarios
- •Identify constraint risks, hosting capacity opportunities, and least-cost portfolios across upgrades, storage, demand response, and local generation
- •Monitor new data for anomalies and planning changes, then refresh scenarios and prioritize areas needing human review
Operating Intelligence
How AI Urban Energy Planning runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve grid upgrades, non-wires alternatives, distributed energy resource programs, or district strategies without planner sign-off [S2][S5].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Urban Energy Planning implementations:
Key Players
Companies actively working on AI Urban Energy Planning solutions: