District Heating Luleå

Heat Generation Mix, Industrial Contracts & Transition Projections

Overview

Luleå’s district heating system is one of Sweden’s most distinctive — and most exposed. For over 50 years, roughly 90% of the city’s heat has come from a single industrial partner: SSAB’s steel plant on the waterfront. That partnership is now entering its most consequential transition since it began.

Note

2025 heat generation mix — Luleå Energi (official)
86% Recycled · 9% Renewable · 5% Fossil
Source: Luleå Energi environmental declaration 2025

This report visualizes the historical heat generation data, the industrial contract landscape, and scenario projections for the period leading to and following SSAB’s blast furnace shutdown (~2030).

---

Heat generation mix

Historical mix 2019–2025

heat_mix <- data.frame(
  year = c(2019, 2020, 2021, 2022, 2023, 2024, 2025),
  ssab_gas_lulekraft = c(87, 86, 85, 84, 82, 80, 78),
  biofuel_pellets    = c(8,  9,  9,  9,  10, 10, 9),
  electric_boilers   = c(3,  3,  4,  4,  5,  6,  5),
  other              = c(2,  2,  2,  3,  3,  4,  4)
)

heat_long <- heat_mix |>
  pivot_longer(-year, names_to = "source", values_to = "pct") |>
  mutate(source = factor(source,
    levels = c("ssab_gas_lulekraft","biofuel_pellets","electric_boilers","other"),
    labels = c("SSAB gas (Lulekraft)","Biofuel / pellets","Electric boilers","Other")
  ))

p_mix <- ggplot(heat_long, aes(x = factor(year), y = pct, fill = source,
                               text = paste0(source, ": ", pct, "%"))) +
  geom_bar(stat = "identity", width = 0.7) +
  scale_fill_manual(values = c(
    "SSAB gas (Lulekraft)" = "#A32D2D",
    "Biofuel / pellets"    = "#1D9E75",
    "Electric boilers"     = "#185FA5",
    "Other"                = "#BA7517"
  )) +
  labs(x = "Year", y = "Share (%)", fill = NULL) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom",
        panel.grid.major.x = element_blank())

ggplotly(p_mix, tooltip = "text") |>
  layout(legend = list(orientation = "h", y = -0.15))

2025 breakdown — official declaration

The graphic below reflects Luleå Energi’s published 2025 environmental declaration:

mix_2025 <- data.frame(
  category = c("Recycled", "Renewable", "Fossil"),
  pct      = c(86, 9, 5),
  color    = c("#1D9E75", "#BA7517", "#5F5E5A")
)

p_2025 <- plot_ly(mix_2025,
  labels = ~category, values = ~pct,
  type = "pie", hole = 0.55,
  marker = list(colors = mix_2025$color,
                line = list(color = "#FFFFFF", width = 2)),
  textinfo = "label+percent",
  textfont = list(size = 14)
) |>
  layout(
    title = list(text = "Luleå DH energy mix 2025", font = list(size = 16)),
    showlegend = TRUE,
    legend = list(orientation = "h")
  )
p_2025

Emission factors (2025, Luleå Energi):

Factor	Value
Combustion emissions	36.91 g CO₂eq/kWh
Transport emissions	3.60 g CO₂eq/kWh
Bio-oil origin	Hungary, Spain, France, Portugal
Pellet raw material	Sweden

---

Residential prices

prices <- data.frame(
  year            = c(2019,  2020,  2021,  2022,  2023,  2024),
  lulea           = c(9245,  9506,  9754,  9451, 10457, 10919),
  north_avg       = c(12884, 13201, 13550, 13890, 14820, 15671),
  south_avg       = c(15087, 15420, 15890, 16540, 18120, 19654),
  national_median = c(14200, 14600, 15100, 15800, 17900, 19466)
)

price_long <- prices |>
  pivot_longer(-year, names_to = "series", values_to = "sek") |>
  mutate(series = recode(series,
    "lulea"           = "Luleå",
    "north_avg"       = "North Sweden avg",
    "south_avg"       = "South Sweden avg",
    "national_median" = "National median"
  ))

p_price <- ggplot(price_long,
    aes(x = year, y = sek, color = series, linetype = series,
        text = paste0(series, " (", year, "): ", format(sek, big.mark=","), " SEK"))) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3) +
  scale_color_manual(values = c(
    "Luleå"            = "#A32D2D",
    "North Sweden avg" = "#185FA5",
    "South Sweden avg" = "#BA7517",
    "National median"  = "#888780"
  )) +
  scale_linetype_manual(values = c(
    "Luleå"            = "solid",
    "North Sweden avg" = "dashed",
    "South Sweden avg" = "dashed",
    "National median"  = "dotted"
  )) +
  scale_y_continuous(labels = label_comma()) +
  labs(x = "Year", y = "SEK per year (15 000 kWh)", color = NULL, linetype = NULL) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom")

ggplotly(p_price, tooltip = "text") |>
  layout(legend = list(orientation = "h", y = -0.15))

Tip

Luleå has Sweden’s lowest district heating prices, driven by cheap industrial waste gas from SSAB. In 2024 Luleå residents paid 10,919 SEK/year vs a national median of 19,466 SEK — a 44% discount. This advantage is directly tied to the SSAB contract and is at risk as the transition unfolds.

---

Industrial contracts

contracts <- data.frame(
  Partner         = c("SSAB / LuleKraft (BF gas)",
                      "SSAB EAF + biochar (MOU)",
                      "Uniper (hydrogen waste heat)",
                      "LKAB Svartön (sulfuric acid)",
                      "Own reserve boilers",
                      "Thermal storage (Aronstorp)"),
  Status          = c("In transition","In transition","Planned",
                      "Planned","Confirmed","Confirmed"),
  Heat_source     = c("Blast furnace process gas",
                      "EAF waste heat + biochar gas",
                      "Electrolysis waste heat",
                      "Sulfuric acid process heat",
                      "Pellets / bio-oil / electricity",
                      "Buffer / flexibility asset"),
  Contract_start  = c(1970, 2030, 2024, 2026, 1980, 2022),
  Contract_end    = c(2030, 2040, 2035, 2036, 2040, 2042),
  Years_remaining = c(5, 15, 10, 11, 15, 17),
  Confirmed       = c("Yes","MOU only","Yes","Under assessment","Yes","Yes")
)

contracts |>
  kable(col.names = c("Partner","Status","Heat source","Start","End",
                      "Years left","Confirmed"),
        align = c("l","l","l","c","c","c","c")) |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = TRUE, font_size = 13) |>
  row_spec(which(contracts$Status == "In transition"),
           background = "#FFF3CD") |>
  row_spec(which(contracts$Status == "Planned"),
           background = "#D1ECF1") |>
  row_spec(which(contracts$Status == "Confirmed"),
           background = "#D4EDDA")

Partner	Status	Heat source	Start	End	Years left	Confirmed
SSAB / LuleKraft (BF gas)	In transition	Blast furnace process gas	1970	2030	5	Yes
SSAB EAF + biochar (MOU)	In transition	EAF waste heat + biochar gas	2030	2040	15	MOU only
Uniper (hydrogen waste heat)	Planned	Electrolysis waste heat	2024	2035	10	Yes
LKAB Svartön (sulfuric acid)	Planned	Sulfuric acid process heat	2026	2036	11	Under assessment
Own reserve boilers	Confirmed	Pellets / bio-oil / electricity	1980	2040	15	Yes
Thermal storage (Aronstorp)	Confirmed	Buffer / flexibility asset	2022	2042	17	Yes

Contract timeline

contracts_plot <- contracts |>
  mutate(Partner = factor(Partner, levels = rev(Partner)),
         color = case_when(
           Status == "Confirmed"      ~ "#1D9E75",
           Status == "In transition"  ~ "#BA7517",
           Status == "Planned"        ~ "#185FA5"
         ))

p_timeline <- ggplot(contracts_plot,
    aes(y = Partner, xmin = Contract_start, xmax = Contract_end,
        color = Status,
        text = paste0(Partner, "\n", Contract_start, "–", Contract_end,
                      "\nStatus: ", Status))) +
  geom_linerange(linewidth = 6, alpha = 0.85) +
  geom_vline(xintercept = 2025, linetype = "dashed",
             color = "#444441", linewidth = 0.8) +
  annotate("text", x = 2025.5, y = 0.4, label = "Now",
           hjust = 0, size = 3.5, color = "#444441") +
  scale_color_manual(values = c(
    "In transition" = "#BA7517",
    "Planned"       = "#185FA5",
    "Confirmed"     = "#1D9E75"
  )) +
  scale_x_continuous(breaks = seq(1970, 2045, 5)) +
  labs(x = "Year", y = NULL, color = "Status") +
  theme_minimal(base_size = 12) +
  theme(legend.position = "bottom",
        panel.grid.major.y = element_blank(),
        axis.text.x = element_text(angle = 30, hjust = 1))

ggplotly(p_timeline, tooltip = "text") |>
  layout(legend = list(orientation = "h", y = -0.2))

---

Transition gap projection

Warning

These are illustrative estimates, not official projections. They are based on Luleå Energi public statements, the SSAB/Luleå Energi 2023 MOU, and NSD investigative reporting (September 2024). SSAB has not confirmed exact EAF waste heat volumes.

gap <- data.frame(
  year               = 2024:2035,
  ssab_bf_gas        = c(80,78,75,70,60,40, 0, 0, 0, 0, 0, 0),
  ssab_eaf_waste     = c( 0, 0, 0, 0, 0, 0,20,25,30,30,30,30),
  new_industrial     = c( 5, 6,10,12,15,18,22,28,32,35,38,40),
  own_production     = c(10,10,10,10,10,12,15,18,18,18,18,18),
  gap_unconfirmed    = c( 0, 0, 0, 3,10,25,38,24,15,12, 9, 7)
)

gap_long <- gap |>
  pivot_longer(-year, names_to = "source", values_to = "pct") |>
  mutate(source = factor(source,
    levels = c("ssab_bf_gas","ssab_eaf_waste","new_industrial",
               "own_production","gap_unconfirmed"),
    labels = c("SSAB BF gas (phasing out)",
               "SSAB EAF waste heat (est.)",
               "New industrial sources",
               "Own production (backup)",
               "Gap — unconfirmed supply")
  ))

p_gap <- ggplot(gap_long,
    aes(x = factor(year), y = pct, fill = source,
        text = paste0(source, " (", year, "): ", pct, "%"))) +
  geom_bar(stat = "identity", width = 0.75) +
  geom_vline(xintercept = 6.5, linetype = "dashed",
             color = "#A32D2D", linewidth = 0.9) +
  annotate("text", x = 6.6, y = 95,
           label = "SSAB EAF\nonline ~2030",
           hjust = 0, size = 3, color = "#A32D2D") +
  scale_fill_manual(values = c(
    "SSAB BF gas (phasing out)"   = "#A32D2D",
    "SSAB EAF waste heat (est.)"  = "#BA7517",
    "New industrial sources"      = "#1D9E75",
    "Own production (backup)"     = "#185FA5",
    "Gap — unconfirmed supply"    = "#B4B2A9"
  )) +
  labs(x = "Year", y = "Share of total heat demand (%)", fill = NULL) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "bottom",
        panel.grid.major.x = element_blank(),
        axis.text.x = element_text(angle = 30, hjust = 1))

ggplotly(p_gap, tooltip = "text") |>
  layout(legend = list(orientation = "h", y = -0.25))

The grey bars represent the unconfirmed supply gap — the period where current confirmed contracts do not cover projected heat demand. This gap is largest around 2029–2031, the critical window between SSAB’s blast furnace shutdown and the ramp-up of EAF and new industrial partnerships.

---

North vs South comparison

Heat generation mix by region for each year 2019–2024. Shares are calculated from the Energimarknadsinspektionen production registry, aggregating all systems classified as northern or southern by city name.

2019

2020

2021

2022

2023

2024

Note

Reading the tabs: Industrial excess heat is structurally higher in northern systems throughout all years, while waste incineration dominates in southern systems. Northern systems have seen a modest but consistent increase in biofuel share since 2019.

---

Key events timeline

events <- data.frame(
  year  = c(1970, 2019, 2022, 2023, 2024, 2025, 2026, 2030, 2030),
  label = c(
    "SSAB/Luleå Energi collaboration begins",
    "Contract extended to 2030",
    "Thermal storage commissioned (Aronstorp)",
    "SSAB EAF waste heat MOU signed",
    "Uniper hydrogen heat agreement",
    "SSAB EAF groundbreaking (Sep 2025)",
    "LKAB Svartön industrial park (est.)",
    "SSAB blast furnace shutdown (target)",
    "SSAB EAF operations begin (target)"
  ),
  type  = c("Contract","Contract","Infrastructure","MOU",
            "Contract","Milestone","Planned","Critical","Transition"),
  y_pos = c(1,2,1,2,1,2,1,1,2)
)

color_map <- c(
  "Contract"       = "#185FA5",
  "Infrastructure" = "#1D9E75",
  "MOU"            = "#BA7517",
  "Milestone"      = "#534AB7",
  "Planned"        = "#888780",
  "Critical"       = "#A32D2D",
  "Transition"     = "#BA7517"
)

p_tl <- ggplot(events,
    aes(x = year, y = y_pos, color = type,
        text = paste0(year, ": ", label))) +
  geom_point(size = 5) +
  geom_segment(aes(xend = year, yend = 0), linewidth = 0.5, alpha = 0.4) +
  geom_hline(yintercept = 0, linewidth = 0.5, color = "#B4B2A9") +
  scale_color_manual(values = color_map) +
  scale_x_continuous(breaks = seq(1970, 2035, 5)) +
  scale_y_continuous(limits = c(-0.5, 3)) +
  labs(x = "Year", y = NULL, color = "Event type") +
  theme_minimal(base_size = 12) +
  theme(axis.text.y = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        legend.position = "bottom",
        axis.text.x = element_text(angle = 30, hjust = 1))

ggplotly(p_tl, tooltip = "text") |>
  layout(legend = list(orientation = "h", y = -0.25))

---

Data sources

Dataset	Source	URL
Production per price area (2019–2024)	Energimarknadsinspektionen	ei.se technical data
Price per price area (2019–2024)	Energimarknadsinspektionen	ei.se economic data
2025 energy mix declaration	Luleå Energi	luleaenergi.se
SSAB/Luleå Energi 2023 MOU	SSAB press release	ssab.com
Contract extension 2019	ENERGInyheter	energinyheter.se
Heat gap investigation	NSD (Sep 2024)	nsd.se

---

Report prepared as part of research on district heating governance and SSAB’s industrial transition in Luleå, Sweden. George Washington University / Luleå University of Technology, 2025.