Energiforsk 2025-1088 Metodik Flexibilitet Elnät (2025)

Energiforsk Report 2025:1088 — Metodik för att bearbeta flexibilitet i elnäten (Methodology for developing flexibility in electricity grids). Authors: David Olsson (Glava Energy Center) and Lars Olsson (SeniorIT). January 2025. A practical methodology guide in Swedish aimed at helping DSOs develop and deploy flexibility services in their grids — the report states that no established methods for investigating grid flexibility existed at the time, implying a gap-filling role rather than explicitly claiming to be the first such guide.

Commissioned by Energiforsk; a pilot study on Värmland’s grid, in Swedish.

Summary

The report presents a four-step methodology for DSOs wishing to work systematically with flexibility in their grids. It is grounded in a pilot application in Värmland (regional DSO Ellevio + local DSOs) and engages with the tools emerging for flexibility need assessment and resource identification. The core argument is that technology is not the barrier — the real barrier is that incentives for using flexibility (rather than grid investment) are currently unclear, and the report calls for incentive models to be examined in future work.

The four-step methodology

Step 1: Need analysis (behovsanalys)

Identify grid segments with capacity constraints now or in the forecast period. This requires:

• Analysis of hourly load data (ideally AMI data) for each grid segment
• Identification of overload events (frequency, magnitude, duration)
• Forecasting how load growth (EVs, heat pumps, solar) will develop constraints
• Probabilistic quantification: what is the expected number of overload hours per year, and at what severity?

Endre Technologies tool: disaggregates load profiles by customer type (household, commercial, industrial) and builds probability distributions for overload scenarios, expressed as Txx-year return periods (the report’s worked examples use 2-year and 5-year return periods). This allows DSOs to say “there is a 1-in-N-year probability of exceeding X MW on this line” rather than just “the N-1 limit is X MW.”

RISE AMI classification tool: identifies heating type (direct electric, heat pump, district heating), EV charging, solar panels, and price-responsive loads directly from smart meter hourly data — without requiring manual surveys or customer self-reporting. This enables DSO actors to identify the composition of the flexibility potential behind any feeder.

Step 2: Actor mapping (aktörskartläggning)

Identify who has flexibility potential in the relevant grid area. This is not just a database lookup — it requires active dialogue:

• Contact prosumers, aggregators, industrial loads, storage operators
• Identify technical capability (can they respond?), willingness (will they?), and at what price
• Map the gap between technical potential and mobilisable potential (willingness + price sensitivity)

Step 3: Matching need with potential (matchning)

Compare the need identified in Step 1 with the potential mapped in Step 2:

• Is there sufficient volume in the right locations?
• What products (energy, capacity, availability) match the need structure?
• What time windows are relevant (peak load, summer overinjection)?
• Are there multiple services from the same resources (value stacking)?

Step 4: Realisation (realisering)

Choose the instrument:

• Market-based: procure through a flexibility market or bilateral contract
• Non-market: use conditional connection agreements (villkorade avtal), demand tariffs, or other mechanisms
• Combination: LT capacity reservation + ST energy activation

The methodology explicitly notes that villkorade avtal and market mechanisms are complementary, not competing — VA establishes the long-term capacity reservation; the market procures the actual activation signal.

Värmland pilot — key findings

The methodology was applied in Värmland with Ellevio (regional DSO) and several local DSOs. Key empirical results:

Total flexibility potential

Borgvik bottleneck (overinjection focus)

The Borgvik area in Värmland is a case study in asymmetric constraint structure:

• Overinjection (too much generation → reverse power flow → overload): occurs ~83 hours per year
• Overwithdrawal (too much consumption → underdelivery): occurs ~8 hours per year

This 10:1 ratio means the dominant flexibility need in this area is curtailment of wind generation, not load reduction. The flexibility design must therefore be asymmetric: the market/mechanism should primarily procure downward flexibility from wind (or storage charging), not upward flexibility from load.

This is a concrete example of why a generic “demand response” product design may not match local constraint geometry.

Key actors by potential

• Wind farms: largest single category; conditional connection agreements are discussed as a generic/hypothetical example of how flexibility for wind could be structured, not stated as something the interviewed Värmland wind actors already have in place
• Households: large aggregate potential (heat pumps, EVs); difficult to mobilise individually without aggregator
• Industry: moderate potential; higher willingness than households but fewer actors
• Storage: modest absolute volume but high flexibility quality (fast, dispatchable)

The real barrier: incentive misalignment

The report’s most important finding is structural: the technology to identify flexibility needs, map potential, and activate resources already exists — but the report states plainly that the incentives for using it are currently unclear (“Tekniken finns där men incitamenten är i nuläget oklart”), and that incentive models are something that should be examined in future work. The report itself does not frame this in CAPEX/OPEX/TOTEX regulatory-accounting terms or conclude that the methodology will stay unused until TOTEX reform — that causal framing is this wiki’s own cross-reference, not the report’s stated conclusion.

Elsewhere in the wiki, this general incentive-misalignment finding is read alongside Ei R2024:14’s finding that TOTEX reform (SOU 2023:64) is the structural prerequisite for efficient flexibility use, and the European Commission’s LFM study (VITO 2025), which identifies CAPEX-biased regulation as a dominant structural barrier across EU member states — but that CAPEX/OPEX/TOTEX connection is the wiki’s synthesis across sources, not a claim made by this report.

Relevance to wiki topics