Source - 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. The first practical methodology guide in Swedish specifically aimed at helping DSOs develop and deploy flexibility services in their grids.

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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), draws on international examples (GOPACS, Piclo, etc.), 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 barriers are incentive misalignment (DSOs are not rewarded for avoiding grid investments through flexibility procurement) and the absence of a systematic process for DSOs to identify where and how to act.

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. This allows DSOs to say “there is a 1-in-20-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; many already have bilateral agreements or grid connection conditions
• 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)

International examples cited

GOPACS (Netherlands)

Described as a leading example of coordinated TSO-DSO flexibility procurement. Key features highlighted:

• Shared order book: Dutch DSOs + TenneT in the same market session
• Overbooking to 150%: procure 150% of estimated flexibility need to ensure sufficient volume
• Counterbid matching: FSPs bid price; SOs post counter-bids; matched by clearing algorithm
• Connected to EPEX SPOT and ETPA for liquidity
• Minimum bid: 100 kW, 15-minute resolution
• Pay-as-bid pricing
• Open-source platform (Linux Foundation)

Piclo Flex (UK)

Platform model: DSO posts flexibility need; FSPs submit bids; DSO clears manually or semi-automatically. Noted for its transparency (public posting of needs and results) and as an example of DSO-led market operation without a platform neutrality requirement.

The real barrier: incentive misalignment

The report’s most important finding is structural:

“The technology to identify flexibility needs, map potential, and activate resources exists. The barrier is that DSOs currently have no financial incentive to use it. Under CAPEX-biased regulation, avoiding a grid investment by procuring flexibility does not generate revenue — it generates cost (OPEX). Until TOTEX reform changes this accounting, the methodology will remain unused even where it would be economically optimal.”

This aligns with Ei R2024:14’s finding that TOTEX reform (SOU 2023:64) is the structural prerequisite for efficient flexibility use — and with the European Commission’s LFM study (VITO 2025), which identifies CAPEX-biased regulation as the dominant structural barrier across EU member states.

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