Skip to content

Resource Planner

Renewable energy procurement analysis

Enhances✓ Available Now

What You Do Today

Evaluate renewable energy procurement options — utility-scale solar, wind, storage PPAs, build-own-transfer agreements. Compare levelized costs, integration costs, capacity value, and contract terms.

AI That Applies

AI benchmarks PPA pricing against market trends, models integration costs including firming and shaping, and evaluates contract risk factors across proposals.

Technologies

How It Works

The system aggregates vendor performance data — pricing, delivery, quality metrics, and contract compliance. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

PPA evaluation becomes more rigorous with AI-powered benchmarking against a broader market dataset.

What Stays

Negotiating contract terms, evaluating counterparty credit risk, and making strategic decisions about technology and timing that shape the utility's portfolio for decades.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for renewable energy procurement analysis, understand your current state.

Map your current process: Document how renewable energy procurement analysis works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Negotiating contract terms, evaluating counterparty credit risk, and making strategic decisions about technology and timing that shape the utility's portfolio for decades. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support LevelTen tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long renewable energy procurement analysis takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

Which vendor evaluation criteria could be scored automatically from data we already collect?

They're prioritizing which operational processes to automate

your process improvement or lean lead

What's our current contract renewal process, and where do we miss optimization opportunities?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.