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AI Net Metering Optimization

Machine learning for maximizing value under net metering tariffs

The Problem

“Optimize distributed energy value under net metering tariffs with AI-driven forecasting and dispatch”

Organizations face these key challenges:

1

Net metering tariffs are complex and frequently updated

2

Solar generation and site demand are highly variable

3

Battery dispatch decisions must balance savings, degradation, and resilience

4

Manual planning cannot evaluate enough scenarios fast enough

5

Data is fragmented across meters, SCADA, EMS, weather, and billing systems

6

Operators lack confidence in black-box optimization outputs

7

Emergency scenarios in nuclear and critical energy operations are too rare and complex for manual preparation

8

Regulatory, safety, and audit requirements demand explainable recommendations

Impact When Solved

Increase net metering revenue through better export timingReduce electricity import costs with predictive battery dispatchLower demand charges by forecasting and shaving peaksImprove ROI of solar and storage assetsSupport tariff-aware scheduling across multi-site portfoliosEnable simulation-driven emergency decision support for complex energy operationsReduce operator workload with automated recommendationsImprove resilience planning for outage and contingency scenarios

The Shift

Before AI~85% Manual

Human Does

  • •Review current tariffs, net metering rules, and export compensation changes
  • •Estimate customer load, PV production, and bill impacts using spreadsheets and standard profiles
  • •Run manual scenario comparisons for rate plans, TOU periods, and storage sizing
  • •Investigate billing disputes and annual true-up variances from interval and billing records

Automation

  • •No AI-driven forecasting or optimization is used
  • •Static formulas calculate estimated imports, exports, and bill credits
  • •Basic reports summarize monthly usage, production, and true-up outcomes
With AI~75% Automated

Human Does

  • •Approve tariff assumptions, program rules, and optimization objectives
  • •Decide customer guidance, rate-plan recommendations, and DER operating policies
  • •Review flagged anomalies, billing exceptions, and cases with feeder or interconnection constraints

AI Handles

  • •Forecast interval load, PV generation, imports, exports, and bill impacts by customer and location
  • •Optimize battery dispatch, EV charging alignment, and rate-plan selection under tariff and grid constraints
  • •Simulate tariff, export compensation, and customer behavior scenarios across the portfolio
  • •Monitor interval data for billing anomalies, forecast drift, and unexpected peak or export patterns

Operating Intelligence

How AI Net Metering Optimization runs once it is live

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence82%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

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.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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