Climate2Energy Framework

• Climate2Energy is a framework that converts climate simulation outputs into energy system inputs using bias correction and technology-specific conversions.
• It integrates renewable generation and weather-sensitive demand analysis to optimize energy planning with stochastic modeling and hydropower assessments.
• The approach supports multi-scale and industrial applications, aligning climate neutrality objectives with actionable decarbonization strategies.

Climate2Energy (C2E) denotes a class of frameworks that connect climate information, climate targets, or carbon constraints to energy-system modeling and decision-making. In its most specific formulation, C2E is a framework that “consistently convert[s] climate model outputs into energy system model inputs,” covering renewable generation and heating and cooling demand, applying bias correction, using established open-source tools where possible, and introducing a hydropower model based on river discharge (Wohland et al., 13 Aug 2025). In related literature, the same label is also used more broadly for workflows that translate climate neutrality objectives, carbon budgets, or carbon circulation into energy planning, operation, procurement, and industrial decarbonization decisions (Gong et al., 2023, Jayan et al., 2022, Shi et al., 2022).

1. Definition and scope

The 2025 Climate2Energy formulation is explicitly climate-to-model: it begins with climate simulations, performs bias correction, converts climate variables into energy-relevant supply and demand series, and supplies those data to energy system optimization models (Wohland et al., 13 Aug 2025). Its immediate motivation is that future energy systems depend on weather through wind, solar, hydropower, and electrified heating and cooling, so climate change and variability must be represented consistently rather than through historical weather alone.

A broader C2E usage appears across adjacent work. In urban energy planning, FOCUS is described as well suited to act as the optimization core of a C2E workflow that links climate goals such as Fit for 55 and net-zero cities with concrete multi-scale energy-system designs and operations (Gong et al., 2023). At facility scale, SuRE couples occupancy-driven demand forecasting, renewable electricity procurement, and carbon offset planning to support Net-Zero and RE100 pathways (Jayan et al., 2022). In carbon-oriented multi-energy systems, C2E-like reasoning places carbon allowances, exchange, and circulation at the center of system planning rather than treating emissions as a secondary constraint (Shi et al., 2022). This suggests that C2E is best understood as both a specific climate-data pipeline and a wider modeling paradigm for embedding climate constraints directly into energy decisions.

2. Climate-to-energy workflow

The core C2E workflow begins with dedicated CESM2.1.2 climate simulations at hourly resolution, configured to provide variables needed by energy models rather than generic climate diagnostics (Wohland et al., 13 Aug 2025). The simulations cover Europe, use the SSP3-7.0 scenario for 2080–2100, and include multiple realizations to represent internal variability and different NAO phases. Historical and future realizations are combined through bias correction, yielding nine future realizations for downstream energy analysis.

Bias correction is performed with delta quantile mapping, univariately and per grid cell, relative to ERA5 over 1995–2015 (Wohland et al., 13 Aug 2025). The framework applies this to primary variables such as surface solar radiation, temperature, 100 m wind speed, and river discharge, while using specific humidity, 10 m wind, and air density without bias correction. The rationale is methodological rather than cosmetic: the climate-to-energy mapping is nonlinear, so inconsistent treatment of variables can distort relative technology performance and system-wide trade-offs.

After bias correction, C2E converts gridded climate fields into technology-specific capacity factors and weather-dependent demand series. These are then aggregated into country-level, population-weighted time series and exported as hourly CSV inputs tailored to energy system models such as AnyMOD (Wohland et al., 13 Aug 2025). The framework also filters wind and solar sites to retain locations better than the median resource, aligning climate inputs with plausible siting assumptions in energy planning models.

3. Representation of renewable supply and weather-sensitive demand

C2E covers solar PV, onshore wind, offshore wind, hydropower, and electricity demand for heating and cooling in a unified pipeline (Wohland et al., 13 Aug 2025). For wind, 3D wind fields are interpolated to 100 m, bias-corrected there, and then translated to turbine hub height with a dynamic power law,

$$s(z,\vec{x},t) = s(100\ \mathrm{m},\vec{x},t)\left(\frac{z}{100\ \mathrm{m}}\right)^{\alpha(\vec{x},t)} .$$

The resulting winds are passed through turbine-specific power curves using windpowerlib. Air density effects are incorporated through pseudo wind speed, which matters because wind power depends strongly on both wind speed and density.

For solar PV, C2E uses the Global Solar Energy Estimator with bias-corrected surface solar radiation and temperature, together with 10 m wind, under explicit panel assumptions: silicon PV, south-facing orientation, and a tilt of $35^\circ$ (Wohland et al., 13 Aug 2025). The output is a grid-cell-level PV capacity factor series that can be spatially aggregated without mixing inconsistent weather datasets or incompatible conversion methods.

Hydropower is treated differently. C2E introduces a new hydropower model based on routed river discharge rather than simpler runoff proxies (Wohland et al., 13 Aug 2025). Monthly river discharge $Q$ is downscaled to daily resolution using daily runoff $R$,

$$Q(\vec{x},\text{month},\text{day}) = Q'(\vec{x},\text{month}) \cdot \frac{R(\text{month},\text{day})}{R_{\text{mean}(\text{month})}} .$$

Country-level hydropower generation and reservoir inflows are then calibrated from capacity-weighted discharge using piecewise linear regressions against ENTSO-E data. This is one of the framework’s distinctive contributions because routed discharge is directly relevant to hydropower operations and is rarely available in standard climate-to-energy workflows.

Heating and cooling demand are converted with the same climate basis rather than appended from external scenarios (Wohland et al., 13 Aug 2025). This is important because the demand side proves especially climate-sensitive in system optimization. In the European SSP3-7.0 application, heating demand declines by $-10\%$ to $-50\%$, Southern European hydropower potentials decline by $-10\%$ to $-40\%$, and cooling demand increases by more than $100\%$ (Wohland et al., 13 Aug 2025).

4. Optimization and decision-support ecosystem

C2E does not end with data conversion; its significance lies in how climate-conditioned inputs reshape optimal system design and operation. In the 2025 formulation, stochastic optimization with AnyMOD confirms that energy systems are highly sensitive to climate conditions, particularly on the demand side (Wohland et al., 13 Aug 2025). That result places demand modeling on equal footing with renewable supply modeling, rather than treating heating and cooling changes as secondary corrections.

Related frameworks show how C2E-style inputs can be operationalized across scales.

Framework	Role in broader C2E practice	Source
FOCUS	Multi-scale MILP planning for prosumer, district, and city systems	(Gong et al., 2023)
SuRE	Facility-scale forecasting, renewable procurement, and offset planning	(Jayan et al., 2022)
EnCortex	Extensible decision framework for dispatch, bidding, and storage control	(Roy et al., 10 Mar 2025)

FOCUS is a three-level, sector-coupled MILP framework spanning prosumer, district, and city levels, with multi-objective support for annuity, operating costs, CO$35^\circ$0 emissions, and self-consumption, returning Pareto-optimal fronts for trade-off analysis (Gong et al., 2023). SuRE provides short- and medium-term demand forecasts, renewable electricity procurement planning, and optimization-based carbon offset recommendations, and it has been used in production for four facilities with an increase in average green power utilization from 80.91% to 87.74%, with a full potential of 96.13% (Jayan et al., 2022). EnCortex provides abstraction, environment, and optimizer layers for weather-dependent energy entities, supporting MILP, MPC, simulated annealing, and deep reinforcement learning, and reports significant cost and carbon footprint savings across battery arbitrage, microgrid, and multi-market bidding scenarios (Roy et al., 10 Mar 2025).

Taken together, these systems illustrate a broader C2E stack: climate-conditioned inputs inform forecasting and optimization, while optimization translates them into capacities, schedules, procurement decisions, and carbon outcomes.

5. Sector coupling, carbon circulation, and industrial applications

A second major branch of C2E literature treats climate constraints as carbon constraints embedded directly in multi-energy systems. The carbon-oriented framework for deep decarbonization divides carbon flows into allowance initialization and allocation, exchange and pricing, and circulation, and combines optimization, game theory, and machine learning to model electricity, natural gas, hydrogen, heat, and their associated emission intensities (Shi et al., 2022). In that formulation, climate targets become carbon caps and allowance structures, which then propagate into energy dispatch and investment.

This logic appears in electricity-gas coupled systems with CCUS and power-to-gas. A two-stage planning model for an electricity-gas coupled integrated energy system retrofits thermal plants into carbon capture power plants, adds PtG, and evaluates deterministic, stochastic, and robust planning under carbon tax and carbon price uncertainty (Xuan et al., 2021). It reports that under a deterministic case with carbon tax 50 \$35^\circ$1/ton, total cost falls from 5952.10 M\$35^\circ$2 and emissions fall from 19.36 Mt to 2.21 Mt (Xuan et al., 2021). This is a direct example of climate-policy variables being translated into energy-system topology and operation.

Industrial applications extend the same pattern. In national sector-coupled optimization, electrified chemical production is modeled as part of the pathway to German net-zero, with electricity, low-temperature heat, and mobility transitioning before the chemical sector because heat pumps and battery electric vehicles yield higher emissions abatement per unit of electricity (Mayer et al., 3 Mar 2025). The fully electrified 2045 case relies on clean energy imports to cover 41% of electricity needs, while a partially electrified, diversified chemical industry provides flexibility by enabling electrified production when renewable electricity is available (Mayer et al., 3 Mar 2025). Other studies use C2E reasoning for embedded CO$35^\circ$3 electroreduction in ethylene oxide manufacturing, where renewable electricity drives on-site CO$35^\circ$4 recycling and can reduce plant emissions by about 80% (Barecka et al., 2020), and for remote renewable energy hubs producing synthetic methane from Morocco for delivery to Belgium, where carbon sourcing configuration shifts delivered e-methane cost from 136 €/MWh to 158 €/MWh (Fonder et al., 2023).

6. Findings, limitations, and research directions

The clearest empirical finding associated with the formal Climate2Energy framework is that climate change alters both supply and demand in ways large enough to change optimized energy systems materially (Wohland et al., 13 Aug 2025). The strongest reported sensitivities are on the demand side, with large reductions in heating demand and large increases in cooling demand, while Southern European hydropower potential declines substantially. This undermines the common practice of combining future renewable supply estimates with static or historically derived demand patterns.

A recurring limitation across the wider C2E literature is that consistency is difficult to maintain when moving from climate data to multi-sector decision models. FOCUS notes ongoing work on temporal aggregation, calibration of district and city layers, and more use cases for validation (Gong et al., 2023). SuRE reports negative adjusted $35^\circ$5 values in some forecasting tasks, reflecting the difficulty of demand prediction under limited features and abrupt regime shifts such as the pandemic (Jayan et al., 2022). EnCortex emphasizes forecast uncertainty and the need for scalable, production-grade decision frameworks under variable renewable output and prices (Roy et al., 10 Mar 2025). Carbon-market and credit architectures add further requirements for measurement, verification, and interoperability (Saraji et al., 2021).

The broader research direction is therefore not only to make climate-aware energy models more detailed, but to make them coherent across data processing, physics-based conversion, optimization, carbon accounting, and implementation. In that sense, Climate2Energy is less a single model than a modeling discipline: climate information must be transformed into energy-system inputs in a way that preserves temporal structure, spatial heterogeneity, cross-sector coupling, and policy relevance (Wohland et al., 13 Aug 2025).