Ukraine needs distributed energy resilience, not just green megawatts. ESMP helps investors, foundations, MDBs and municipalities identify where capital can survive missile risk, scale through reconstruction, and matter for energy independence — with deterministic financial models and source-linked data on every number.
| What is this? | A pre-investment screening platform for green-energy projects in Ukraine. Information digital twins of cities + an AI-assisted analyst that drafts investor-grade memos. |
| Who is it for? | Investors, family offices, foundations, MDBs/DFIs, IPPs, and Ukrainian municipalities preparing fundable projects. |
| What problem? | War risk, grid attacks, regulatory chaos and donor overlap make Ukrainian projects hard to screen. Capital sits on the sidelines while distributed resilience is needed now. |
| What can I do now? | Run a playbook above · explore 11 data layers across 4 MVP cities · read the methodology · review the risk framework. |
Russian strikes have repeatedly knocked out >50% of Ukraine's centralised generation. Distributed renewables + storage are the only hedge.
Hospitals, water utilities and shelters need autonomous power. Microgrids are no longer optional.
$486B Ukraine RDNA. Capital is mobilising, but pipeline-quality projects with verified data are scarce.
Every MW of domestic renewables reduces fossil import dependence and frees fiscal space.
Ukraine's 2030 RES targets and EU acquis alignment open ETS, CBAM-safe and Article 6 ITMO revenue.
EBRD, IFC, USAID, KfW, EIB all chasing similar opportunities — ESMP surfaces gaps and overlap.
Protect Ukraine — Protect the Planet
The Ukraine Energy Security Marshall Plan (UESMP) is a strategic initiative to bring investments in distributed clean energy systems to accelerate Ukraine's energy security, weaken Russia's war economy, and advance the global clean energy transition. Authored by Alex Cornell du Houx through Elected Officials to Protect America (EOPA), with the PVBLIC Foundation and aligned partners, it pairs AI-powered municipal digital twins with blended finance so cities can rapidly design, finance, and deploy resilient local energy systems — embedding affordability, resilience, and security into the infrastructure that civilian life depends on.
The Public Leadership Certificate (PLC) and Master's in Public Leadership with the University of San Francisco — energy security, AI-enabled planning, climate finance and governance — teams compete for a Policy Prize that drives real reform.
Deploy AI-enabled digital twins and secure, sovereign data infrastructure across Ukrainian and partner municipalities for energy-security planning, resilient deployment, and reconstruction readiness.
Convene PVBLIC's global network of family offices, philanthropy, multilaterals and private capital — in coordination with the Office of the President of Ukraine — to mobilise blended finance and technology transfer.
Municipal coordination mechanisms, strategic financing structures, and cross-sector partnership frameworks that turn plans into deployment at both national and local levels.
Support Ukrainian delegates at COP, the Ukraine Recovery Conferences, the UN General Assembly, Climate Weeks, and the Yalta European Strategy (YES) Annual Meeting.
Every ESMP memorandum maps shortlisted opportunities to one or more delivery strategies:
Grant-funded installations on critical infrastructure — hospitals, schools, shelters.
US & EU municipalities paired with Ukrainian counterparts for co-investment and knowledge transfer.
Decentralised solar + storage microgrids hardened against grid disruption from missile strikes.
Aligning EBRD, IFC, USAID, KfW and private capital to avoid overlap and maximise impact.
Structuring projects so US-connected entities can claim Inflation Reduction Act clean-energy credits.
Each city twin is a structured JSON document. Every numeric value carries provenance: source URL, retrieval date, and a 0–1 confidence score. See the Sources & Provenance page for the full per-field index.
Indicative figures. Click Sources & Provenance for source URL, retrieval date, confidence and method per field.
ESMP is an AI-assisted screening engine with deterministic financial models and source-linked data. It is not a black-box LLM and not investment advice — it is a research tool that compresses weeks of desk-based screening into minutes, with every number traceable.
A Claude (Anthropic) reasoning model plans the analysis, picks tools, and writes prose — never the numbers.
numpy-financial calculates IRR / NPV / payback. War-risk and corruption scores use fixed formulas.
Every figure carries source URL, retrieval date and confidence. No silent assumptions.
Outputs are screening drafts. A human analyst signs off before any memo leaves ESMP.
You see the 0–1 confidence score on every layer; low-confidence fields are flagged.
A community of 15,204 people, 20 km from Lviv and ~1,000 km from the nearest active frontline. The municipality submitted a verified ESMP Municipality Data Request in May 2026 with a ranked 4-project pipeline, monthly meter readings from its Umuni energy-management platform, and explicit 10% co-financing capacity. Below: headline economics from the April 2026 EOPA Investment Brief.
Off-take basis (deterministic, via finance_calc): utility-scale output is modelled at a
corporate PPA of €95–110/MWh for grid feed-in — not the retail tariff.
Rooftop output is valued behind-the-meter at the avoided retail cost (~€150/MWh self-consumption);
biogas is power-only with district-heat (CHP) revenue as upside. Assumptions: 13% solar capacity factor, 20-year life, 9% discount — see
Methodology. These are screening estimates, not a closing memo.
Regulatory tailwind: Ukraine Law No. 4777-IX
in force since 11 March 2026 (flexible grid connection, FiP replaces CfD, green auctions to 2034).
Municipality contact: Khrystyna Baran, Energy Management focal point —
routed via ESMP (contact@energysecuritymarshallplan.org).
Loading cities...
Select a layer above.
Every numeric output ESMP shows is produced by an explicit, reproducible formula. The LLM picks tools and writes prose. The Python tools below compute the numbers. This page documents what each formula does, what inputs it consumes, and what its limits are.
Computed by finance_calc using the numpy-financial library. The LLM never does arithmetic.
| Field | Unit | Description |
|---|---|---|
| capex_eur | EUR | Total capital cost, year 0 outflow. |
| capacity_mw | MW | Nameplate generation capacity. |
| capacity_factor | 0–1 | Annual energy / (capacity × 8760 h). |
| ppa_eur_per_mwh | EUR/MWh | Constant offtake price (or LCOE proxy). |
| opex_pct_per_year | 0–1 | O&M as fraction of capex per year. |
| life_years | years | Project life for cash-flow horizon. |
| discount_rate | 0–1 | WACC / hurdle rate for NPV. |
numpy_financial.irr(cashflows) — the discount rate that makes NPV = 0. Returns NaN if no real root exists (typical when cumulative revenue never recovers capex).
Discounted payback is exposed as payback_discounted_years when the LLM requests it.
Computed by warrisk_score. A weighted composite of four observable signals. Higher = more dangerous.
| Component | Source | Normalisation |
|---|---|---|
| strike_intensity | ACLED event counts · Ukrainian official strike reports · NASA FIRMS thermal anomalies | strikes per 100k pop / month, log-scaled to 0–10 |
| grid_damage_index | Ukrenergo / DTEK incident reports · satellite night-light delta (NOAA VIIRS) | fraction of substations / generation capacity offline 12-month rolling |
| frontline_proximity | ISW / DeepState frontline geometry | 1 − min(distance_km / 300, 1), so <30 km → ~1 |
| air_defense_uncertainty | Open-source defence reporting; analyst override | 0–1 subjective, default 0.5 with confidence flag |
miga_quote and the SCM block — the score itself stays hazard-only.Pulled from the city energy layer (peak_demand_mw, annual_consumption_gwh). Source: Ukrenergo regional load reports + oblast statistical bulletins. When the LLM needs implied per-capita demand:
Wartime distortions (IDP flux, industrial shutdowns) are reflected as reduced confidence on the field, not silently smoothed.
Source: PVGIS-SARAH3, Global Solar Atlas, NASA POWER — cross-checked, lowest-confidence wins.
Mean wind speed at 100 m AGL from Global Wind Atlas. Capacity factor is looked up from a turbine class curve; not modelled from scratch.
Suitable roof area derived from OpenStreetMap building footprints × oblast suitability factor.
A 0–10 composite from the governance and regulatory layers.
Read directly from the crime layer:
These are not combined into a single number on purpose — investors weight them differently.
Every field carries a confidence in [0,1]:
| Range | Meaning |
|---|---|
| ≥ 0.80 | Official primary source, retrieved within 12 months, no known contradictions. |
| 0.50–0.79 | Secondary source or modelled estimate; some triangulation. |
| < 0.50 | Single weak source, stale, or analyst inference. Do not use without verification. |
Investing in Ukrainian energy assets means pricing risks that do not appear in standard EU/US deal templates. This framework names the twelve risks ESMP tracks, rates how material they are at MVP-city level, and lists the mitigation tools available to investors today.
| Risk profile | Best-fit capital | Example city / use-case |
|---|---|---|
| Very high (frontline) | Grant + concessional only; no commercial debt | Kharkiv hospital microgrid — Direct Aid (EOPA-1) |
| High | Blended: grant first-loss + DFI debt | Kyiv resilience microgrids — EOPA-3 |
| Medium | DFI debt + private equity, MIGA-wrapped | Lviv 10 MW solar+BESS — EOPA-3 / EOPA-4 |
| Low | Commercial PPA, IPP-style structure | Lviv / Pustomyty rooftop and small wind |
Ukraine's State Compensation Mechanism (SCM) has been operational since
1 January 2026, with UAH 1 billion allocated from the 2026 State Budget
and operated by the Export Credit Agency (ECA). It directly de-risks war-exposed energy assets
and reshapes the financing case — surfaced automatically by warrisk_score and miga_quote.
| Component | What it does | Cap / scope |
|---|---|---|
| Component 1 — asset compensation | Partial compensation for war-damaged business assets in the 10 frontline oblasts (Chernihiv, Dnipropetrovsk, Donetsk, Kharkiv, Kherson, Mykolaiv, Odesa, Poltava, Sumy, Zaporizhzhia). | Capped UAH 30M per business. Of the MVP cities, only Kharkiv qualifies. |
| Component 2 — premium subsidy | Brings the cost of war-risk insurance down toward 1% for businesses (nationwide). | Capped UAH 3M per business. |
The ECA is being seeded as a national war-risk reinsurer — endorsed by Ukraine's Insurance Taskforce (~30 participants including WTW, Marsh, Allianz, Aon, the World Bank and FCDO): the local market underwrites and cedes to the ECA, which is backstopped by donors/IFIs for high layers, with private international reinsurance participating where feasible. International comparables: Pool Re (UK), TRIA (US), the Caribbean catastrophe pool (CCRIF) and African Risk Capacity.
SCM is a financing mitigant, not a reduction to the physical hazard score: the war-risk score still reflects strikes, frontline proximity and grid damage, while SCM lowers the net cost of insuring and recovering the asset.
These are indicative pipeline candidates assembled from the MVP city twins, not committed projects. They show what kind of opportunities ESMP surfaces and how risk ↔ capital pairing works in practice. Real pipeline access requires data-room sign-off — request access.
| Project | City | Type (EOPA) | Size | CapEx (EUR) | Status | Risk | Funding fit |
|---|---|---|---|---|---|---|---|
| Hospital solar + storage | Kharkiv | Direct Aid (1) | 500 kW + 1 MWh | ~€1.4M | concept | Very high (8.4) | Grant first-loss |
| School shelter microgrid | Kyiv | Resilience (3) | 250 kW + 0.5 MWh | ~€0.7M | data needed | Medium (4.6) | Blended (donor + DFI) |
| Municipal rooftop solar portfolio | Lviv | IPP / PPA (3,5) | 3 MW (15 sites) | ~€3.0M | pre-feasibility | Low (2.1) | Commercial debt + equity |
| Solar + BESS hybrid | Lviv | Distributed Resilience (3) | 10 MW + 4 MWh | ~€11.9M | feasibility | Low–Med | DFI debt + MIGA wrap |
| Community solar | Pustomyty | Direct Aid + Sister City (1,2) | 5 MW | ~€4.5M | pre-feasibility | Low (1.8) | Sister-city co-invest + grant |
| Onshore wind | Lviv oblast | IPP / PPA (3,4) | 20 MW | ~€26M | concept | Low (2.1) | Commercial + ECA |
| Water utility microgrid | Kharkiv | Resilience (3) | 1.5 MW + 3 MWh | ~€3.8M | data needed | Very high (8.4) | Donor + MIGA |
A scoped universe of clean-energy and energy-security technologies relevant to Ukraine's reconstruction — from commercial workhorses (solar PV, lithium BESS) to frontier plays like Quaise-style millimeter-wave deep geothermal and small modular reactors. Each entry is scored on two axes that matter for a war-exposed grid:
Ranking below is ESMP's composite priority (0.55 × security + 0.45 × cost) for Ukraine 2025–2030. Figures are indicative ranges from public sources (IEA, IRENA, Lazard LCOE+, BloombergNEF, vendor disclosures); validate with the Sources tab before underwriting.
A bankable Ukraine project stack typically layers four capital classes against the technology's TRL and risk profile:
| Layer | Capital type | Suits which tech | Typical % |
|---|---|---|---|
| 1. First-loss | Grants (USAID, EU Modernisation Fund, Innovation Fund) | Frontier tech demonstrators, retrofits, microgrids for critical infra | 10–30% |
| 2. Concessional | DFI debt (EBRD, EIB, IFC, EDC, JBIC) below-market | Commercial renewables in war zone; LDES pilots; SMRs | 40–60% |
| 3. Risk-mitigant | USDFC / MIGA partial-risk & war-risk guarantees | All technologies operating in Ukraine | wrap |
| 4. Commercial | Senior debt, equity (utilities, infra funds, strategics) | Mature tech (PV, wind, BESS) after de-risking layers above | 30–50% |
ESMP's role is to structure the stack, not provide capital — we match technology + city + sponsor + capital classes against deal-specific risk, then surface bankable opportunities to the right financiers.
Bankability depends on stacking multiple revenue lines. Below are the streams ESMP tracks per project:
Every numeric value in an ESMP city twin carries a provenance object:
source_url, retrieved_at (ISO date), confidence (0–1),
method (official / estimated / modeled / inferred), and an optional note.
The Explore Layers tab shows this on every individual field. The catalogue below summarises the
primary sources by data layer.
| Layer | Primary sources | Typical method | Typical confidence |
|---|---|---|---|
| economy | State Statistics Service of Ukraine (Ukrstat); oblast statistical yearbooks; Ministry of Economy bulletins; World Bank Ukraine country data | official + estimated (wartime gaps) | 0.55–0.80 |
| demographics | Ukrstat; UNHCR Ukraine Refugee Situation; IOM DTM (IDPs); Ministry of Social Policy registers | official + modeled (IDP flux) | 0.55–0.85 |
| social | Ministry of Health facilities registry; Ministry of Education school registry; oblast administration sites | official | 0.80–0.95 |
| crime | Transparency International Ukraine CPI; NABU / SAPO case statistics; KIIS & Razumkov trust polls; Prozorro analytics | official + modeled | 0.55–0.80 |
| energy | Ukrenergo regional load reports; NEURC tariff orders; SAEE renewable installations registry; DTEK disclosures | official | 0.75–0.90 |
| investment | UkraineInvest; oblast IPA bulletins; IFC / EBRD project disclosures; FDI Markets | official + secondary | 0.55–0.80 |
| military | ACLED conflict events; ISW / DeepState frontline maps; NASA FIRMS; Ukrainian official strike reports; NOAA VIIRS night-lights | modeled composite | 0.50–0.75 |
| governance | Hromada / city council official sites; Ukrainian Government Portal; mayor / deputy contact pages; Diia.City registry | official | 0.80–0.95 |
| regulatory | NEURC; SAEE; Verkhovna Rada legislation; Article 17-1 wartime regime; Energy Community Secretariat reports | official | 0.80–0.95 |
| infrastructure | Ukravtodor; Ukrzaliznytsia; State Agency for Infrastructure; OpenStreetMap; oblast plans | official + OSM | 0.65–0.85 |
| renewables | PVGIS-SARAH3; Global Solar Atlas; Global Wind Atlas; NASA POWER; SAEE installations registry; OpenStreetMap building footprints | modeled (resource) + official (installed) | 0.75–0.95 |
| emissions | State Environmental Inspectorate; GPC inventories where available; IEA CO2 grid factor for Ukraine; SBTi sectoral pathways | modeled | 0.60–0.80 |
| Component | strike_intensity | ACLED Ukraine dataset; UA MoD daily situation reports | 0.85 |
| Component | grid_damage_index | Ukrenergo + DTEK incident logs; NOAA VIIRS night-light delta | 0.70 |
| Component | frontline_proximity | DeepState / ISW frontline geometry, distance < 30 km | 0.90 |
| Component | air_defense_uncertainty | analyst override, low transparency | 0.40 |
| Composite | 8.4 / 10 | weighted sum, weights {0.35, 0.25, 0.30, 0.10} | 0.72 |
Read this as: high-confidence on strikes and frontline, medium on grid damage, low on air-defense effectiveness — overall confidence 0.72.
When you reproduce ESMP data, please cite as:
Each underlying source retains its own licence; ESMP does not relicense third-party data.