DSO Flexibility Need Calculation Methods

The practical methods Swedish DSOs use to quantify their flexibility needs — the calculation step that feeds both the Flexibility Need Assessment (FNA) and the DNDP. For the regulatory framework, FNA scope, deadlines, and reporting structure, see Flexibility Need Assessment; for the per-company DNDP submissions and empirical need figures, see Swedish DNDP First Round — DSO Profiles (2025-2034).

How DSOs calculate flexibility needs in practice

FNA Webinar 7 included concrete worked examples from E.ON Energidistribution and Ellevio — the most granular picture of what “flexibility need” means operationally:

Two binding constraints per substation/connection point:

1. N-1 technical capacity — max load while remaining N-1-safe (lines, transformers, cables)
2. Subscription limit (abonnemangsgräns) to the overlying network — the allocated capacity from regionnät or Svk

E.ON’s method: historical load data (4 years) → load forecast at target year → compare simulated load against the binding constraint → flexibility need = the excess that doesn’t fit. Low-voltage (0.4 kV) excluded; bedömning done per station.

Ellevio’s method: same inputs, explicitly overlaying capacity at connection point to overlying network (2030 and 2035 vintages separately). Flexibility need is the gap between load forecast and the binding constraint line in the time series.

Both methods confirm that the subscription capacity indication from the overlying network (delivered 2025-12-01 from regionnät to lokalnät; 2026-01-20 from Svk to regionnät) is the critical external input — without knowing the overlying subscription limit at 2030/2035, lokalnät cannot assess their constraint-based flex need. (Source - FNA Webinar 7 (2026-03-16))

Practical tools for flexibility need quantification

Energiforsk’s methodology study (2025:1088) describes two specific tools for the need analysis step that go beyond the basic load-vs-limit approach used by major DSOs, and two further Energiforsk reports add complementary methods:

Endre Technologies probabilistic method: Rather than comparing a single forecast against the constraint limit (deterministic approach), Endre’s tool disaggregates load profiles by customer type (household, commercial, industrial) and builds probability distributions for overload scenarios. The result is 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 a binary “congested / not congested” classification. The approach mirrors the N-1 probabilistic dimensioning used at transmission level.

RISE AMI classification tool: Identifies each customer’s heating type (direct electric, heat pump, district heating), EV charging presence, solar panel installation, and price-responsive behaviour patterns — directly from smart meter hourly data, without requiring manual surveys or customer self-reporting. Once customers are classified by resource type, DSOs can estimate aggregated flexibility potential behind any feeder: e.g., “this 100-customer feeder has 23 heat pump customers totalling ~200 kW of thermostat-controllable load.”

Together these tools address the two core data gaps in DSO flexibility need analysis: how severe is the constraint probabilistically (Endre), and what resource potential exists to address it (RISE). Both are being piloted in Sweden; neither is yet standardized or mandated.

(Source - Energiforsk 2025-1088 Metodik Flexibilitet Elnät (2025))

What-if behavioral scenario method (Energiforsk 2024:1043): Rather than producing a single point estimate of the flexibility need, the report proposes publishing explicitly named EV charging behavior scenarios (direct charging / price-optimized / grid-friendly), each producing a different MW × hours/year output. Key insight: the behavioral assumption is the dominant uncertainty — across the report’s two case studies (a Stockholm-local load profile and a generic SE3 profile), the range spans roughly 20–450 kW and 6–427 hours/year, though the two profiles’ figures should not be blended into a single substation-level range (see the source page for the full breakdown). Publishing scenarios enables stakeholders — municipalities, aggregators, customers — to form their own views on which is most plausible. (Source - Energiforsk 2024-1043 DNDP Analys och Flexibilitet (2024))

National top-down baseline method (Energiforsk 2026:1157): For FNA 2028 and beyond, a standardized national top-down method based on official data sources (Energimyndigheten, SCB, Trafikverket) would provide all DSOs with a common baseline scenario that can be broken down to county/municipality level. This would address the comparability problem that currently makes DNDP aggregation unreliable. Phase 1 covers rooftop solar and home EV charging; Phase 2 expands to additional load categories. The method is intended to complement (not replace) local bottom-up analysis of point loads and connection queues. (Source - Energiforsk 2026-1157 Nationell Metod Effekt och Kapacitetsprognoser (2026))

Skånes Effektkommission four-step method

A working group of Skånes Effektkommission (E.ON Energidistribution, Kraftringen Nät, Öresundskraft Elnät) published a practical four-step guide for DSO flexibility need calculation in 2026, calibrated to minimum FNA/NUP reporting requirements for radial grids. (Source - Skånes Effektkommission Flexibilitetsbehov Metod (2026))

The guide complements the methods described by E.ON and Ellevio at Webinar 7: the same two-constraint logic (N-1 technical capacity and subscription limit to overlying grid) is used, but with explicit guidance on standard power templates, worked examples, and double-counting rules.

Step 1 — Define baseline: usually 3–5 years of historical hourly load data at all selected network points. Baseline is set either as the dimensioning peak-load and low-load hour (minimum reporting) or as a full one-year time series with typical years (e.g. warm/medium/cold). Account for network restructuring and operational changes.

Step 2 — Define forecast: identify additional installations per category for 2030 and 2035 (ongoing/planned connections, societal growth, trends in small-scale solar and vehicle charging); estimate their power using aggregated templates from the Energiforsk lathund or typical profiles; add to the baseline.

Aggregated power templates (Energiforsk lathund, peak load):

Non-residential templates are per m² (see the source page).

Worked EV example (1,000 connection points): chargeable cars 15% (2025) → 40% (2030) = +250 vehicles; 0.2 kW/vehicle by day and 1 kW at night gives +50 kW by day and +250 kW at night.

Worked solar example (1,000 connection points): PV share 13% (2025) → 25% (2030) = +120 installations, average 18 kW, aggregated about 16 kW on a summer low-load day and 0 kW on a winter peak day, giving about 1,900 kW of injection on a summer day.

Step 3 — Define capacity constraints: define a limit for every point — technical transfer limits set by the grid owner (e.g. N-1 or 100% of transformer rating) and subscription limits towards the overlying grid for the target years, obtained from the overlying grid owner. Include planned reinvestments/reinforcements for the target years. Voltage constraints, physical-space constraints and the low-voltage network may be excluded.

Step 4 — Identify flexibility need: compare forecast load with the capacity limits and quantify the exceedance, either event by event (overload in MW, period, direction, reason) or over the whole time series (maximum exceedance in MW and accumulated MWh). Report by season, direction (up-regulation / down-regulation) and reason, and locate the need in the grid.

Double-counting rule: lokalnät must separately report flexibility need arising from constraints in their own network versus constraints in the overlying network, to avoid double reporting.

Energiforsk lathund as industry standard — Skåne DSO evidence

The Skåne NUP synthesis provides the first cross-DSO evidence of methodological practice at regional scale: at least 8 of 20 Skåne DSOs explicitly reference the Energiforsk lathund (Effektprognos — en lathund för lokalbolag, 2024:1006) as their primary forecast tool. These include Bjäre Kraft Elnät, Bromölla Energi, Höganäs Elnät, Kraftringen Nät, Olofströms Kraft, Olseröd, Skånska Energi Nät, and Öresundskraft. The Effektkommission’s four-step method guide and Region Skåne’s municipal forecasts also use the same tool.

This makes the Energiforsk lathund the de facto regional standard for DSO flexibility need forecasting in Skåne — and likely beyond, given its development with major national DSOs. The power templates documented in the Skånes Effektkommission four-step method section above (small house 1.6–2.8 kW/dwelling; apartment 0.3–0.5 kW/dwelling) are drawn from this lathund.

Regulatory dependency — SENAB finding: Skånska Energi Nät (SENAB) is documented in the NUP synthesis as explicitly conditioning its flex market participation on “regulatory development and revenue cap incentives.” This is the first public statement from a Swedish DSO directly tying its flex market participation decision to the TOTEX/lösningsneutralitet reform trajectory at Ei. The CAPEX bias is not merely a theoretical problem — it is actively cited by DSOs as a reason not to run markets yet. The FNA 2026 Bilaga V survey (question: “are there sufficient incentives for network companies to consider flexibility solutions?”) will capture this systematically.

Related pages

• Flexibility Need Assessment — the regulatory framework, FNA scope, deadlines, and reporting structure these methods feed into
• Swedish DNDP First Round — DSO Profiles (2025-2034) — the per-company DNDP submissions and empirical need figures these methods produced
• Distribution Network Development Plan — DNDP framework, legal basis, and aggregate Ei PM2025:03 findings
• DSO Flexibility Valuation — Methods and Swedish Evidence — valuing flexibility against grid reinforcement
• Skånes Effektkommission · E.ON Energidistribution · Ellevio — actors developing and applying these methods