Bridging the AI–Energy Paradox: A Compute-Additionality Covenant for System Adequacy in Energy Transition

Abstract

As grids decarbonize and end-use sectors electrify, the rapid penetration of artificial intelligence (AI) and hyperscale data centers reshapes the electrical load profile and power quality requirements. This leads not only to higher consumption but also coincident demand in constrained urban nodes, steeper ramps and tighter power quality constraints. The article investigates to what extent a compute-additionality covenant can reduce resource inadequacy (LOLE) at an acceptable $/kW-yr under realistic grid constraints, tying interconnection/capacity releases to auditable contributions (ELCC-accredited firm-clean MW in-zone or verified PCC-level services such as FFR/VAR/black-start). Using two worked cases (mature market and EMDE context) the way in which tranche-gated interconnection, ELCC accreditation and PCC-level services can hold LOLE at the planning target while delivering auditable FFR/VAR/ride-through performance at acceptable normalized costs is illustrated. Enforcement relies on standards-based telemetry and cybersecurity (IEC 61850/62351/62443) and PCC compliance (e.g., IEEE/IEC). Supply and network-side options are screened with stage-gates and indicative ELCC/PCC contributions. In a representative mature case, adequacy at 0.1 day·yr⁻¹ is maintained at ≈$200 per compute-kW-yr. A covenant term sheet (tranche sizing, benefit–risk sharing, compliance workflow) is developed along an integration roadmap. Taken together, this perspective outlines a governance mechanism that aligns rapid compute growth with system adequacy and decarbonization.

1. Introduction

The global energy system is being reshaped by the imperative to decarbonize, by rapid electrification and deep decarbonization and by the emergence of AI-enabled technologies [1]. IEA projects that electricity consumption by data centers could approach ~945 TWh by 2030, with AI as a major driver, representing just under 3% of global electricity in the base case and growing at ~15% per year from 2024 to 2030 [2]. Consistent with this uncertainty, demand is treated parametrically rather than deterministically, using PUE bands [1.10–1.30] combined with low/central/high compute-growth scenarios to bound outcomes, anchored in the empirical spread between best-in-class hyperscale operations (fleet PUE ~1.09) and the sectoral average (~1.58) [2,3]. Figure 1 summarizes the parametric AI/data-center demand envelope to 2030 and 2035, combining low/central/high compute growth with the PUE bands [1.10–1.30] defined above, and anchoring the central 2030 point to the IEA’s ~945 TWh estimate.

In parallel, decarbonization strategies are favoring electrification as the primary route to mitigate GHG emissions. This includes road transport shifts from internal combustion to electromobility, buildings’ transition towards electric heating and cooling, and the electrification of industrial processes that were once reliant on direct fossil combustion [4]. The consequence is an increase not only in aggregate consumption but in the coincidence of load growth with urban nodes and industrial clusters, where hosting capacity, fault-level margins and power-quality tolerances are already binding. In this paper, a “symbiosis/paradox” lens is adopted. AI is simultaneously: (i) an increasingly dominant, locationally concentrated consumer of electricity and (ii) a force-multiplier capable of accelerating discovery, engineering and deployment of clean, reliable power. In this manner a lagged but governable feedback loop between demand and supply is created. Operationally, this implies that voltage-regulation bands, harmonic-distortion limits and ride-through behavior at the point of common coupling (PCC) must be treated as first-order constraints (IEEE 519-2022 [5]; EN 50160:2022 [6]; IEC 61000-4-34 + A1:2009 + A2:2025 [7]).

AI and robotics are expected to play a significant part in the growing demand for electricity. The growth of data centers driven by cloud services, machine-learning workflows and pervasive IoT connectivity is reshaping the demand profile, since high-performance computing and advanced analytics are typically operated on a round-the-clock basis, with tight requirements for voltage regulation, harmonics control and ride-through capability. According to recent analyses, the compound annual growth rate of data-center energy consumption may approach 15% over the next decade in the IEA base case, with AI as the dominant driver [8,9]. Such growth reflects not only electricity used behind the meter but also induced needs for grid stabilization, reactive-power support and backup capacity, in order to satisfy reliability criteria. In jurisdictions with explicit adequacy targets (e.g., LOLE ≤ 0.1 day·yr⁻¹), accredited capacity contributions are assessed via ELCC to ensure that incremental compute does not degrade reliability [10]. As a result, demand forecasting has to be coupled with explicit modeling of the power-electronics interface, which includes UPS behavior, inverter set-points, fault-ride-through and fast-frequency response, since these elements condition the net load seen by the system operator and determine the extent to which flexibility can be sourced locally without degrading power quality. Accordingly, procurement and design for large data centers should be framed in terms of services (e.g., FFR, primary/secondary VAR, black-start contribution where applicable) as well as energy, with compliance demonstrated against PCC-level power-quality standards.

At the same time, while developed economies benefit from advanced grid infrastructure and integrated portfolios of renewables and flexibility assets, emerging markets and developing economies (EMDEs) face a dual challenge. Rapid urbanization and industrialization in parts of sub-Saharan Africa and Asia are expected to accelerate demand growth, yet access to affordable capital and specialized technical expertise remains constrained. Countries in these regions must scale both capacity and resilience under tight fiscal conditions and with limited institutional capabilities [11]. Consequently, investments in modern grid architectures, which include transmission backbones, primary and secondary substations and digital protection schemes, together with energy storage and renewable generation, become critical policy imperatives. As is clear, sequencing is important, with renewable deployments located near load, reinforcement of high-factor transmission corridors and targeted storage for peak shaving and contingency cover so that delivery risk is controlled. The scale of the challenge is underscored by current access metrics (~600 million Africans without electricity access) and the investment gap identified by IEA/ESMAP [12,13]. A complementary priority for EMDEs is to develop local manufacturing and maintenance capability for priority technologies so that dependence on imported equipment does not become a binding risk during scale-up.

Even as wind and solar expand rapidly, their intermittency necessitates additional flexibility at multiple timescales. Investments in storage in the form of lithium-ion batteries for fast response, longer-duration pumped hydro storage and hydrogen-based options are increasing [14]. Yet, several solutions remain at relatively low technology readiness levels when benchmarked against widely deployed solutions, which sustains uncertainty around meeting peak demand without recourse to fossil-fuel-based backup. It is therefore the case that demand-side measures, which included load shifting, response aggregation and controlled curtailment of non-critical processes, are required as complements, and that interconnection, dynamic line rating and advanced voltage-VAr control are deployed to raise effective hosting capacity in the near term while maintaining system security. In parallel, AI methods, which include surrogate modeling, active learning and closed-loop lab automation, can compress design–build–test–learn cycles for selected options with system value (e.g., EGS/CPG near load centers, HTS urban backbones, CSP + TES for evening ramps and perovskite–Si tandems), provided that governance links incremental compute to open telemetry, testbeds and milestone-based funding.

Furthermore, governments are enacting measures intended to accelerate the energy transition. These measures include capital subsidies for renewable installations, carbon-pricing mechanisms and regulatory reforms that facilitate grid modernization. Integrated policy frameworks underscore that robust transitions depend on aligning economic incentives with technological progress while preserving reliability and affordability [15]. In practice, this alignment is operationalized through transparent evaluation, measurement and verification (M&V) of delivered system services (e.g., IPMVP Core Concepts 2022 (EVO 10000-1:2022)-consistent baselining [16]) and through procurement that explicitly values flexibility. Where interconnection backlogs are material, process reforms (e.g., cluster-study approaches and readiness screens) are critical complements to zone-level resource adequacy [17,18]. To make the symbiosis operational, two coupling mechanisms are emphasized: (i) conditional interconnection/permitting for large compute loads tied to verifiable additionality of firm-clean capacity or certified grid-service portfolios in the same zone, and (ii) open-data requirements (with privacy safeguards) for data centers’ level telemetry, to improve grid-operations models and evaluate learned controllers.

For operators, the near-term part of the paradox is tangible. Dense compute clusters are required to be supported by continuous thermal management and high-density power distribution, while the UPS/inverter subsystem is set to control regimes whose set-points, harmonic performance, short-circuit behavior and ride-through characteristics condition the available hosting capacity. At the same time, these parameters ought to be specified in order to ensure compliance with voltage regulation, flicker limits, EMC and fault-ride-through thresholds under a steady state and contingency operation [19,20,21,22].

What is not yet known in definite terms is the expansion in the use of robots. Recent industry commentaries have suggested that autonomous machines may diffuse across manufacturing, logistics and selected services at scale, thereby creating a distributed yet synchronous demand class whose coincidence factors and duty cycles are still uncertain. In this paper, robotics demand is treated as an upper-bound stress class, separate from core data-center loads, and is developed with bounded S-curve adoption, duty-cycle assumptions and power-draw distributions in Section 3.

At the same time, it is acknowledged that AI and robotics do not solely act as additional loads. Significant opportunities are provided to reduce consumption in conventional sectors when data and control are embedded within advanced energy management systems. In industrial settings, AI-driven predictive maintenance has been shown to optimize plant operation, reduce unplanned outages and lower energy wastage by anticipating degradation and scheduling interventions at non-disruptive windows [23]. In the buildings sector, smart control systems that learn occupancy and thermal inertia adjust HVAC operation dynamically and deliver measurable savings relative to static set-point control with field and meta-analytic evidence spanning ~5–20% HVAC/whole-building savings [24,25,26,27]. Realized system benefits, however, depend on baselines being established credibly, on rebound effects being constrained and on the separation between energy-aware inference at the edge and energy-intensive training in the cloud being made explicit, so that the net effect on the grid is understood. In practice, this requires measurement and verification of delivered services, disclosure of duty cycles for AI workloads and co-siting or contractual coupling with a clean and flexible power supply, so that local hosting capacity is respected. These requirements double as the “governance layer” that can channel AI’s acceleration capabilities toward technologies with near-term system value.

Finally, the implications for grid operators and policymakers are high, since the dual nature of AI, acting both as an efficiency enabler and as an increasing consumer, must be reflected in planning standards, interconnection procedures and procurement signals. Grid modernization programs are therefore required to account for the additional load imposed by data-center clusters and robotic operations, while regulatory frameworks need to be structured in a way to incentivize energy-efficient AI architectures and to promote timely investments in renewable generation and flexibility, so that the carbon footprint of digital demand is contained. It is well established that the integration of distributed energy resources with intelligent load-management systems provides a pathway by which supply and demand can be balanced in real time with reduced curtailment, improved hosting capacity and enhanced resilience [28]. It becomes an imperative to bring into the mainstream interoperability standards, transparent telemetry, contracts that value fast-frequency response, voltage support and ride-through capability, as well as cybersecurity requirements, so that digital loads participate in system services rather than free-ride on them. In conditional interconnection tied to additionality, service-based procurement, and open testbeds constitute the practical mechanisms that bind incremental compute to measurable system value.

To summarize, as AI-enabled computation and robotics expand, new electricity requirements are introduced at precisely the nodes where power-quality tolerances are tightest, yet complementary reductions are unlocked when predictive maintenance and smart control are deployed within credible baselines. Since the demand shock and the acceleration benefits will potentially unfold in different time scales, ~1–5 years for grid adequacy and power-electronics operability versus ~5–15 years for AI-accelerated hardware and materials, the central policy task has to be the governance of this lagged feedback so that near-term stress finances and, at the same time, de-risks medium-term solutions. Whether the balance resolves to an AI net burden or a net benefit will depend on disciplined interface design, on data-center and robotics operations being aligned with a clean and flexible supply, and on policy frameworks that embed intelligent demand as an active participant in secure, low-carbon system operation. Accordingly, we pose the following research questions to guide the analysis: Under a planning target of LOLE = 0.1 day·yr⁻¹, to what extent can tranche-gated interconnection of compute load, conditioned on PCC compliance and provision of FFR/VAR, maintain system adequacy (ΔELCC, ΔLOLE) and at what normalized cost (USD per compute-kW-yr) across mature and EMDE contexts? (SQ1) What accredited firm-clean capacity (ELCC) and PCC-level services (FFR/VAR/ride-through) per MW of compute are required to hold a zonal LOLE constant? (SQ2) What telemetry resolution and cybersecurity architecture (IEC 61850 [29]/IEC 62443 [30]) are necessary for auditable enforcement at an acceptable compliance cost? Which tranche-release and cost-sharing structures minimize interconnection delays without degrading power quality? The remainder of the paper therefore proceeds in two parts: AI as burden and AI as accelerator, joined by a governance layer that makes the “symbiosis/paradox” visible, measurable and financeable.

To reconcile rapid growth in compute load with net-zero and system-adequacy constraints, a compute-additionality covenant is proposed to be established between large compute providers and system operators (see Section 4.3). Interconnection capacity for hyperscale clusters (~50–100+ MW) is released in tranches, conditional on: (i) provision of verifiable, PCC-level grid services—fast frequency response/inertial contribution, dynamic VAR support and voltage regulation, fault-ride-through—demonstrated within IEEE 519-2022, EN 50160:2022 and IEC 61000-4-34:2005 + A1:2009 + A2:2025 limits; and/or (ii) underwriting effective load-carrying capability (ELCC)-accredited firm, clean capacity within the same capacity zone, so that loss of load expectation is not degraded relative to the counterfactual. Compliance relies on open, timestamped IEC 61850 (series), telemetry with IEC 62443 (series), cyber-hardening and settlement-grade M&V, so that performance and payment are aligned and auditable. At the same time, the covenant internalizes hosting-capacity and protection-upgrade costs through a transparent tariff, giving regulators a tractable mechanism to align compute expansion with reliability and decarbonization objectives. In effect, generic “green PPAs” are upgraded to system-adequacy commitments. To clarify how the proposed covenant relates to familiar instruments,

Table 1. Covenant vs. green PPAs vs. carbon pricing.

Dimension	Green PPA	Carbon Pricing (Tax/ETS)	Compute-Additionality Covenant
Contract object	Energy (MWh) and REC attributes from a project	Emissions externality priced per tCO2e	Interconnection/capacity access conditioned on auditable grid services
Primary measurable	Metered MWh and certificates	Verified emissions	ELCC-accredited firm-clean MW and/or verified PCC services (FFR/VAR/black-start)
Spatial/temporal granularity	Often zonal; hourly/15 min energy	Economy-wide; coarse temporal	Same-zone accreditation; sub-second-to-1 s telemetry for services; tranche-based timing
Link to system adequacy	Indirect; depends on grid mix and deliverability	Indirect; changes dispatch/investment over time	Direct; credits tied to accredited capacity and PCC compliance that shift LOLE/ELCC
Additionality mechanism	Financial offtake may be additional, not guaranteed	Price signal; depends on policy stringency	Access conditional on net new accredited capacity/services; tranche gates enforce additionality
Enforcement and audit	Contractual energy delivery and REC audits	Regulator-run MRV	Standards-based telemetry (IEC 61850/62351/62443), test protocols, sampling/audit cadence
Risk allocation	Market/shape risk borne via contract terms	Policy risk borne by market	Benefit–risk sharing defined in term sheet (penalties, credits, downtime), tied to service performance
Effect on interconnection	None directly	None directly	Prioritized or staged capacity releases conditional on verified delivery
Fit for EMDEs	Constrained by creditworthiness and grid bottlenecks	Requires robust institutions	Configurable to weak grids via tranche sizing, local PCC tests, community benefit provisions

compares it with green PPAs and carbon pricing, along enforceability and adequacy linkages.

The key contributions of the paper are as follows: (i) in terms of sustainability, the tranche-gated additionality with community-benefit earmarks, in order to protect adequacy and equity, (ii) in terms of computing the schedulability together with standards-based telemetry/cyber (IEC 61850/62351/62443) for auditable enforcement, and (iii) in terms of renewables, the ELCC-accredited firm-clean build and PCC services tied to capacity accreditation together with LOLE targets. The rest of the paper is developed as follows: Section 2 sets out the modeling stack and the methods flowchart, Section 3 develops the scenario inputs and deterministic reports with the Monte Carlo results, Section 4 formalizes the covenant’s PCC/ELCC tests and the governance workflow, Section 5 summarizes technology screening and cost–risk bands and Section 6 concludes with policy design, together with implementation guidance. Supplementary Notes S1–S4 provide equations, parameter tables and reproducible excel workbooks, in order to ensure transparency and repeatability.

2. Methods

This section sets out the modeling framework, which underpins both the probabilistic demand envelope and the evaluation of the compute-additionality covenant. Benchmarked base demand, compute-growth trajectories, PUE distributions, the training–inference mix, robotics adoption, coincidence factors and regional modifiers are integrated into a correlated Monte Carlo engine in order to generate hourly time-series realizations of load and technology performance. Afterwards, these traces are subjected to two screening layers, namely: (i) a system-adequacy screen which estimates marginal ELCC and the induced shift in LOLE/LOLP; and (ii) a PCC-compliance screen which tests harmonics (THD), voltage/flicker and ride-through behavior, fast-frequency-response sizing, and interconnection constraints under standards-based telemetry and cybersecurity. Costs and risks are then normalized to common units ($/kW-yr, $/MWh-yr), with explicit accounting for audit/telemetry burden, downtime penalties and benefit–risk sharing, yielding decision metrics—ΔELCC, ΔLOLE, PCC violations avoided, interconnection delays mitigated and cost per avoided LOLE hour—with uncertainty bands. Implementation details, equations and reproducible workbooks are provided in Supplementary Note S4. Figure 2 presents the transition framework, linking current constraints to covenant-based pathways and the target state aligned with adequacy and PCC obligations, while Figure 3 summarizes the pipeline from scenario inputs through Monte Carlo synthesis, adequacy/PCC screens and cost normalization for decision metrics.

2.1. Demand Model: Compute vs. Robotics, Training vs. Inference, and PUE

2.1.1. Structure and State Variables

The total digital electricity is decomposed into (i) data-center compute, which is split into training and inference, parameterized by a time-varying mix ϕ_t ϵ [0, 1] (share of training in total compute energy at year t) and (ii) robotics (industrial arms—I, AMR/AGV—M, humanoids—H). The model enforces a common 2024 baseline, back-solved from a 2030 anchor (IEA total) [8], ensuring visual and analytic consistency across deterministic and probabilistic views.

Computation of baseline and trajectories

E_anchor,2030 denotes the IEA 2030 total. The 2024 baseline is as follows:

$$E_{base,2024} = {\displaystyle \frac{E_{anchor,2030}}{{(1+g_{central})}^{2030-2024}}}$$

Deterministic low/central/high paths apply constant CAGRs g_low, g_central, g_high:

$$E_{compute}^x(t) ={ E}_{base,2024} · {(1+g_x)}^{t-2024}, x\in \{low,central,high\}.$$

Training vs. inference split

The annual compute energy is split by ϕ_t

$$E_{train}(t)={\phi }_t{ E}_{compute}(t), E_{infer}(t)=(1-{\phi }_t) E_{compute}(t).$$

Hourly load shapes $s_{t,h}^{train}$ and $s_{t,h}^{infer}$ map energy to power, allowing distinct coincidence factors (e.g., training more schedulable, inference more demand-coupled). These profiles feed adequacy and PCC analyses.

Power Usage Effectiveness (PUE)

Site energy is adjusted using a PUE random variable centered on best-practice operations:

$$E_{site}^{(i)}(t) =E_{IT}^{(i)}(t) · {\displaystyle \frac{{PUE}^i}{{pue}_{mode}}}$$

with PUEⁱ calculated using a triangular distribution over [pue_min, pue_max] with mode pue_mode.

2.1.2. Robotics Energy

Installed units for each class k ϵ {I,M,H} follow a logistic function:

$$N_k(t) = {\displaystyle \frac{K_k}{1 + exp\{-r_k (t-t_{0,k})\}}}$$

and annual energy (TWh/year) is as follows:

$$E_k(t)={\displaystyle \frac{N_k(t)\overline{P_k}{h}_k·365}{{10}^9}}$$

$$E_{robotics}\left(t\right)=\sum _{kϵ\left\{I,M,H\right\}}{E}_k(t)$$

Central (deterministic) $\overline{P_k}{, h}_k$ pair with uncertainty ranges in Section 2.4. The adoption baselines are calibrated to public industrial/AMR statistics and laboratory-reported power envelopes for humanoids. See Supplementary Note S2 for more details. Unless stated otherwise, all electricity quantities are reported as AC site energy at the facility boundary. Data-center totals, therefore, include PUE (cooling/auxiliaries), while robotics totals refer to AC energy at the charger and include conversion/charging overheads (device-side DC loads are not reported).

2.2. Resource-Adequacy Pathway: ELCC/LOLE

2.2.1. Reliability Target and Metrics

A planning reliability target of LOLE = 0.1 day/year (“1 in 10” criterion) is adopted [31].

2.2.2. ELCC Calculation

The effective load-carrying capability (ELCC) of a portfolio, R, is the horizontal shift in demand at the reliability target after adding R:

$${\Delta }_{ELCC}(R) = max\{\Delta L:LOLE(L+\Delta L | R) = LOLE(L | \varnothing )\}$$

Incremental ELCC is calculated for (i) covenant-backed firm-clean capacity (e.g., CSP + TES, storage, geothermal) and (ii) accredited demand-side flexibility (FFR, ride-through curtailment windows), bundled with compute interconnections. ELCC is estimated using a probabilistic stack (sequential Monte Carlo for weather/outages) [32].

2.3. PCC Services and Compliance

2.3.1. Targeted Services and Minimum Performance

Obligated services at the point of common coupling (PCC) are specified, against standards and grid-code products:

• Fast frequency response (FFR): sub-second to 10 s response envelopes. These are mapped to existing procurement products (e.g., dynamic containment/regulation/moderation) to define set-points, deadbands and durations for data-center-sited inverters/BESS or controllable UPS [33].
• Voltage/VAR support and harmonics: steady-state voltage quality per EN 50160. Harmonic current/voltage limits at the PCC per IEEE 519-2022. Reactive capability must be demonstrated across operating ranges.
• Ride-through and immunity to dips: compliance demonstrated by IEC 61000-4-34 class tests (≥16 A per phase equipment), including 2025 Amendment 2 updates.

2.3.2. Telemetry, Cyber, and Data Formats

• Telemetry and semantics: IEC 61850 logical nodes and MMS/GOOSE/sampled values for event-speed signaling. Sub-second data are retained for FFR verification, with 1 s to 10 s aggregation for settlement [34].
• Cybersecurity: defense-in-depth per ISA/IEC 62443 across zones/conduits. Protocol-level security aligned with IEC 62351 [35] (esp. parts 5–6 for legacy telecontrol and 61850 security).
• Measurement class: power-quality measurements consistent with IEC 61000-4-30 Class A [36] and IEEE 1159-2019 [37] for monitoring practice and PQDIF exchange, where applicable.

2.3.3. Verification and M&V

Performance claims (e.g., delivered FFR MW, kvar at power-factor set-points, harmonic compliance) are audited via:

• Sampling plans and event tests (scheduled dispatch and disturbance-triggered).
• Measurement chain documented with Class-A requirements.
• Settlement baselines consistent with IPMVP Core Concepts/FEMP M&V guidance (uncertainty and regression-based adjustments for conditions).

Detailed test procedures, planning/compatibility thresholds, and IEC 61850/IEEE C37.118 [38] telemetry mappings for all PCC criteria are consolidated in Appendix A.

2.4. Uncertainty Approach

2.4.1. Parameter Priors and Draws

Uncertain inputs are represented with transparent priors:

• Compute: Triangular distribution is utilized.

$g~Tri(g_{min},g_{mode},g_{max})$ and $PUE~Tri({pue}_{min},{pue}_{mode},{pue}_{max})$

• Training share: φ_t evolves via a bounded random walk towards φ_∞, with annual step $\Delta {\phi }_t~N(o,{\sigma }_{\phi }^2)$ in boundaries [0, 1].
• Robotics: for each k ϵ {I,M,H}, fleet scale s_k, active power $\overline{P_k}$, active hours h_k are based on a triangular distribution. Adoptions N_k(t) follow the logistic central path with optional stochastic K_k, r_k, t_0.k sensitivities.
• Adequacy: weather-year resampling, unit FORs, and correlated renewable availability; storage round-trip efficiency and duration as scenario parameters.

Triangular inverse transform was used in MS Excel for a draw U~U(0,1) with (a, b, c) = (min, mode, max):

$$X=\left\{\begin{array}{c}a+\sqrt{U\left(b-a\right)\left(c-a\right),} U\le {\displaystyle \frac{c-a}{b-a}}\\ b-\sqrt{\left(1-U\right)\left(b-a\right)\left(b-c\right)}, otherwise\end{array}\right.$$

2.4.2. Correlations and Propagation

Rank–correlation coupling is allowed between: (i) compute growth g and PUE improvement (negative correlation), (ii) training share ϕ_t and FFR headroom (positive) and (iii) humanoid fleet size and hours h_H (positive). Each Monte Carlo trial combines demand, robotics, and adequacy states to form an hourly net load and resource availabilities. Risk metrics are computed per trial.

2.4.3. Reporting and Visualization

For each year, t, the empirical uncertainty band is calculated:

$$\left[E_{10}\left(t\right),E_{90}(t)\right]=\left[{Quantile}_{0.10}\left\{E_{total}^{\left(i\right)}(t)\right\}, {Quantile}_{90}\left\{E_{total}^{\left(i\right)}(t)\right\}\right]$$

Check Supplementary Note S3 for more information.

2.5. Value-of-Information (VoI) for Telemetry Resolution

Telemetry resolution is evaluated as a design variable, using a value-of-information (VoI) lens. Let Δt denote the sampling granularity (e.g., sub-second vs. 1 s). For a given covenant tranche, the decision loss L(Δt) aggregates (i) enforcement misclassification costs, i.e., undetected non-compliance or false fails on PCC/FFR tests that distort ELCC crediting and curtailment decisions and (ii) system-model error propagated into adequacy metrics (ΔLOLE, ΔELCC). The incremental cost of tighter telemetry C(Δt) includes metering/communications CAPEX, OPEX for storage and audit, together with cybersecurity/privacy overhead.

The expected value of moving from Δt₁ to Δt₂ is defined as the following:

$$VoI\left(\Delta t_1\to \Delta t_2\right)\mathbb{E}\left[L\left(\Delta t_1\right)\right]-\mathbb{E}\left[L\left(\Delta t_2\right)\right]-(C(\Delta t_2-C\left(\Delta t_1\right))$$

A positive VoI justifies higher-resolution telemetry. Otherwise, coarser sampling is preferred. Practically, Δt is mapped to detection performance (ROC curves) for events of interest (e.g., fast-frequency-response windows, voltage excursions, harmonics estimation), and the avoided penalties, avoided wrongful credits/curtailments, together with reductions in interconnection delays that are attributable to faster, lower-error compliance testing, are monetized. Finally, this creates a stage-gate for telemetry design: the covenant specifies a baseline Δt and permits an upgrade whenever VoI > 0, under local cost and risk parameters, in order to ensure transparent, auditable decisions. This approach treatment follows decision analytic VoI [39] and adapts sensor-selection results in graphical models (Krause and Guestrin) [40] to grid telemetry, consistent with information-theoretic approaches to PMU design in power systems [41].

3. Scenarios and a Probabilistic Electricity-Demand Envelope for AI Compute and Robotics

The concept behind this section is to formalize the envelope of plausible electricity demand that is attributable to AI-intensive digital infrastructure, together with emerging robotic systems over 2024–2035. Section 2 details the model construction and the outcomes are synthesized here, in order to delineate credible lower-to-upper bounds (P10–P90) that are decision-useful for resource adequacy, capacity expansion planning and power-system risk management. The envelope is built by coupling (i) a compute-centric demand model anchored to an externally benchmarked 2030 waypoint and parameterized by compounded growth and data-center efficiency (PUE), with (ii) a robotics load model spanning industrial arms, autonomous mobile robots (AMRs) and early humanoids, via adoption and duty-cycle dynamics. Together, these components define an integrated AI–automation demand surface.

Two principles govern this interpretation. First is power versus energy. Annual electricity is reported (TWh yr⁻¹), while intra-annual load shapes (peaks, ramps) that further condition system impacts are treated implicitly through PUE and duty-cycle assumptions. Second, irreducible growth is addressed. Even under conservative parameter draws for growth and utilization, the lower bound rises materially above today’s aggregate, implying that the net effect of AI deployment is to increase electricity consumption under any credible scenario. Efficiency gains shift trajectories but do not reverse the sign of change.

The compute module compounds a baseline demand with a triangular growth draw, together with a PUE draw (triangular 1.1–1.3, mode 1.2), anchored in 2030 to a sectoral electricity waypoint used in the Methods Section to ensure external consistency (anchor: 945 TWh for data-center electricity in 2030), while preserving uncertainty around the pathway to and beyond that point. Robotics loads are computed bottom-up from fleet-size trajectories (logistic adoption for industrial arms and humanoids; empirically grounded growth for AMRs) and duty-cycle × power draws, with Monte Carlo variation in scale, active power, hours and charging/overheads. Parameter sets for adoption plateaus and slopes, as well as duty cycles by class, are provided in Section 2 and in Supplementary Notes S2 and S3.

The results indicate that robotics adds a persistent and growing “edge-AI” load, which is non-trivial in absolute terms, and strategically important due to its spatial distribution (factories, logistics hubs and, later, commercial/residential environments). In the central path, aggregate robotics electricity rises from ≈19.3 TWh in 2024 to ≈33.0 TWh in 2030 and ≈43.5 TWh in 2035. The envelope broadens over time: P10–P90 spans ≈28.7–54.4 TWh in 2030 and ≈38.2–71.6 TWh in 2035. The composition is dominated by industrial arms; AMRs contribute a rising share, whereas humanoids remain de minimis through 2030 under conservative utilization assumptions. These values reflect realistic duty cycles, together with charging losses. As a result, they remain robust to plausible efficiency improvements in actuators, power electronics and path-planning, as seen in Figure 4. Projections beyond 2035 are characterized by high speculation. As such, and in order not to influence the 2024–2035 envelope, Appendix B “Robotics Beyond 2035” documents a non-normative boundary case, compiling public projections, together with order-of-magnitude energy accounting, to provide a more holistic view of how things could evolve in the future.

Robotics are integrated with compute, the total AI-intensive digital electricity demand expands across all percentiles of the distribution:

• 2024 baseline (central): ≈428 TWh (≈409 TWh compute + ≈19 TWh robotics).
• 2030: central ≈978 TWh; P10–P90 ≈873–1327 TWh—i.e., ~2.0× (P10) to ~3.1× (P90) the
• 2024 baseline, with the anchor respected.
• 2035: central ≈1944 TWh; P10–P90 ≈1569–3407 TWh—i.e., ~3.7× to ~8.0× 2024.

Two features are noticeable. First, the lower bound is monotone increasing. Even the P10 draw approximately doubles the 2024 demand by 2030, and nearly quadruples it by 2035, given conservative utilization and efficiency assumptions. Second, the distribution is right-skewed. The tail risk reflects correlated high adoption with slower-than-assumed PUE improvements (or rising auxiliary loads such as cooling for high rack-power densities), thereby raising the upper-percentile outcomes, as presented in Figure 5.

The conclusion that AI increases electricity demand does not rely on a single assumption, but it follows from the joint structure of the system:

• Scale effects dominate efficiency effects. Even with optimistic PUE improvements, service demand (compute tokens, model training runs, inference call-volumes and embodied AI in robots) scales faster than unit-energy intensity declines, and a canonical rebound/Jevons dynamic in digital services is reinforced by rapid model-capability gains.
• Robotics and “AI at the edge” externalize energy use from data centers to millions of devices with non-coincident duty cycles and heterogeneous charging/overheads, creating an additive load floor that is largely orthogonal to hyperscale efficiency measures.
• The 2030 compute anchor ensures that the central trajectory is calibrated to sectoral benchmarks. Monte Carlo draws perturb the pathway and not the direction of travel. As a result, every credible percentile rises, relative to 2024.

Finally, from a power-system perspective, the envelope implies the following:

• Capacity expansion: accelerated clean generation with firming resources, to meet a materially higher electricity budget by the early-to-mid 2030s;
• Grid-aware siting: co-location of compute with low-marginal-cost, low-carbon supply, grid-constrained nodes and thermal-host opportunities to valorize waste heat;
• Flexibility and demand response: exploiting schedulable AI workloads (training, batch inference) and robotic duty-cycle buffers to provide contingent curtailment and ancillary services;
• Standards and disclosure: mandatory reporting of PUE-like metrics and robotics energy intensity per task-hour, in order to reduce informational asymmetry and to enable procurement, which prices externalities.

4. The AI-Driven Breakthrough Paradigm

While artificial intelligence is altering current operational practices in the power sector, at the same time, it is impacting the research and commercialization pathways of new energy technologies. These two interrelating dimensions can be the foundation of a real disruption in the evolution of the power sector. In this paper, “breakthrough paradigm” is used to denote the coupling of (i) AI-enabled closed-loop discovery (self-driving labs, active learning, Bayesian optimization) with (ii) bankable diffusion into grids via interface standards and service accreditation. The path towards artificial general intelligence (AGI) and the prospect of artificial superintelligence (ASI) are considered as catalysts for research and development in low-TRL domains, where experimental iteration, material exploration and control-system tuning have historically been time-consuming. It has been argued that recent progress in large language models (LLMs) and multi-agent architecture signal systems has allowed them to be capable of a broad range of cognitive tasks at near-human level, with non-trivial reasoning and pattern recognition already demonstrated [42]. The current paradigm follows distinct steps. Initially, new technologies (e.g., computation, materials, control, etc.) are conceived in laboratories, and as these technologies mature through successive iterations, they climb the TRL scale before real-world pilots are deployed, and eventually, these technologies are commercialized. This paper’s contribution focuses on making those steps auditable by specifying guardrails, telemetry and procurement triggers that link accelerated R&D to grid-operational value.

4.1. Approaching AGI and the Prospect of ASI

Considering AGI as an accelerator, it is recognized that the convergence of compute, data availability and refined algorithms has enabled automated generation of hypotheses, high-throughput simulation and optimization across large design spaces. The consequence is that candidate materials, converter topologies and plant configurations can be screened in orders of magnitude faster than traditional workflows, and that digital-twin environments can be kept in line with experimental campaigns, so that uncertainty is reduced in each cycle [42]. In low-TRL areas, iteration has been constrained by the time constants of fabrication, instrumentation and diagnostic feedback. Under an AGI-assisted reality, multi-physics surrogate models and inverse-design kernels coupled with lab automation (“self-driving labs”) tune process parameters continuously, with objective functions that embed manufacturability, reliability and interface compatibility, rather than single-metric efficiency [43,44,45]. It has further been suggested that, as AGI systems mature, recursive self-improvement could be induced, thereby opening a path toward ASI capacities that would compress development timelines considerably. Such a trajectory would mobilize discovery at rates that are incompatible with conventional standards of verification [46].

Moving beyond AGI to ASI, the implications need to be treated cautiously, since acceleration without appropriate governance would externalize the risk to critical infrastructure. It becomes a necessity to ensure that risk management and assurance (safety envelopes, data provenance, model-risk controls, auditability) are built into research platforms from inception (NIST AI RMF 1.0 (2023) [43], EU AI Act). Furthermore, measurement and verification are specified for learned controllers before field exposure, and cyber-physical interfaces are hardened so that failure modes induced by distribution shifts or adversarial inputs are contained (e.g., IEC 62443-2-4:2023 for IACS security; NERC guidelines for inverter-based resource performance [10]) [42,46]. In practice, this means that learned policies, e.g., grid control, electrolyzer dispatch or inverter droop, are coupled with supervisory logic, rollback mechanisms are provided during commissioning and telemetry with sufficient temporal granularity is streamed for post-event analysis. When controllers are destined for grid-connected assets, early prototypes should target grid-code-adjacent performance envelopes aligned with IEEE 1547-2018 (DER interconnection) [47], IEEE 2800-2022 (transmission-level IBR interconnection) [48] and ENTSO-E grid-forming guidance. The need for proper regulation of ASI is self-evident. Regulatory guardrails that can be utilized include disclosure of training boundaries, constraints on autonomous experimentation with hazardous materials, and explicit coupling of research on compute with a clean and flexible supply, so that the energy cost of discovery does not erode decarbonization goals.

4.2. Innovation Spillovers: From Lab to Grid

The historical record of innovation spillovers provides the pathway by which laboratory breakthroughs become bankable infrastructure [49]. It has been observed that materials that were first developed for aerospace and defense, such as carbon-fiber composites, advanced alloys and ceramics, were subsequently redeployed to lower mass and raise durability in wind and solar hardware, thereby extending lifetimes and trimming balance-of-system costs. Control and data architectures that optimized robotic manufacturing lines were adapted to grid operations. The same families of algorithms that aligned throughput and quality on factory floors were repurposed to balance variable renewable generation, detect anomalies on feeders and dispatch distributed energy resources so that net demand could be shaped inside network constraints [28]. In parallel, an AI-enabled experimental design, which includes high-throughput screening, active learning and Bayesian optimization, has migrated from pharma to energy materials (catalysts, solid-state electrolytes, photo-absorbers), shrinking cycle time from candidate identification to prototype validation [44,45,46].

In the past, knowledge transfer has depended on structured collaboration between research centers, equipment manufacturers, system operators and regulators. Funding instruments that require co-development across disciplines, open data repositories that lower replication barriers and pre-competitive consortia that settle on interoperable interfaces have been used to move inventions across the “valley of death” at speed [49]. Under an AGI-assisted paradigm, these mechanisms are extended. Lab protocols are formalized so that results are reproducible across facilities, metadata standards are enforced for simulation and experiment linkage and interface specifications for power electronics (e.g., grid-forming controls, fault-ride-through behavior, harmonic emission limits) are embedded in early prototypes so that later compliance with grid codes is not treated as an afterthought. It follows that demonstration plants are instrumented to expose service delivery (i.e., fast frequency response, voltage support, inertia-like behavior), so that value can be procured transparently and so that operating envelopes are known to planners. Where telemetry crosses organizational boundaries, the communications stack should align with IEC 61850 (series) (utility automation) and be protected per IEC 62443-2-4:2023 (IACS cybersecurity) [50].

Finally, the operability conditions for absorbing AGI-enabled breakthroughs into the grid are crucial. Siting, interconnection and market participation must be aligned so that technologies emerging from accelerated pipelines do not stall at the point of connection. Programs that integrate DERs with intelligent load management and the procureability of fast services (FFR/VAR/fault ride-through) provide the bridge from prototype to revenue. Policy is then required to value flexibility explicitly, to license data flows necessary for verification while protecting security and commercial sensitivities and to sequence backbone transmission, storage and secure low-carbon additions, so that the arrival of new options is matched by system-level absorptive capacity. It is therefore the case that roadmaps incorporate AGI-accelerated R&D as a contingent supply, with procurement triggers tied to demonstrated performance, rather than forward claims, and that the energy budget of discovery itself is accounted for when planning research compute.

To summarize, the breakthrough paradigm that can be brought forward by AI presents a dual narrative. On the one hand, the approach to AGI and the possibility of a step toward ASI offers to compress development cycles for low-TRL energy technologies through automated hypothesis generation, rapid simulation and closed-loop experimentation. On the other hand, the well established logic of innovation spillovers shows how laboratory advances become grid assets once transfer mechanisms, interface standards and economic signals are aligned. Realization requires verifiable guardrails (risk management, M&V, cybersecurity), early embedding of grid-code-consistent behavior in prototypes and service-based procurement that rewards measurable contributions to stability and adequacy. If these elements are advanced in a harmonized manner, the combined effect of the accelerated discovery and robust diffusion can be mobilized to deliver clean, reliable power at the scales demanded by a digital and electrified economy.

4.3. Coupling Mechanisms and Guardrails

To convert acceleration into system value, coupling mechanisms are required at the point where AI demand meets infrastructure. Conditional interconnection and permitting for large compute clusters should be tied to the verifiable additionality of firm-clean capacity or certified grid-service portfolios (fast-frequency response, reactive support, black-start) in the same interconnection zone. For controllers and plant-level policies, conformance should be demonstrated against the relevant interconnection standard (IEEE 1547-2018 or IEEE 2800-2022) and the evolving ENTSO-E grid-forming specifications, with staged exposure and rollback during commissioning. Open, privacy-preserving telemetry from campuses enables surrogate models and learned controllers to be evaluated against real disturbances before full exposure, ensuring that acceleration does not erode operability.

Governance must embed model-risk management and auditability for learned policies used in grid control, electrolyzer dispatch or inverter droop. Commissioning should include rollback mechanisms and post-event analysis under standardized M&V. Cyber-physical security and data governance should align with IEC 62443-2-4:2023 (IACS) and with the NIST AI RMF for model lifecycle risk controls. Within the EU, the AI Act establishes obligations for high-risk systems that will apply to grid-facing AI controls. In practice, these guardrails convert AGI-assisted discovery into bankable assets by aligning accelerated prototypes with grid-code compliance and procurement pathways from the outset.

5. Technology Portfolio for Scaling Clean, Reliable Power: Status, Integration and System Value

The transition from paradigm to praxis is investigated by examining specific technology families through a common evaluative lens that prioritizes the system value over headline efficiency. In detail, the aspects considered are as follows:

• Grid services and temporal value. These include dispatchability, ramping capability and grid-forming behavior under contingencies.
• Scalability and manufacturability. This includes supply chain depth, siting constraints and deployable modularity at standard voltages and footprints.
• Technology readiness and credible cost trajectories. This aspect is anchored in demonstrated milestones, learning rates and bankable delivery risk, rather than speculative performance.
• Integration complexity at the power-electronics and controls interface. This includes protection coordination, harmonic emissions, fault-ride-through settings and interoperability, with state estimation and market dispatch.
• Governance, measurement and verification, and risk.

The technologies investigated in the next sections are organized into three major categories:

• Generation and storage.
• Direct conversion.
• Transmission and system infrastructure.

The concept behind this section is to move from paradigm to praxis by stress-testing technology families against a system-value rubric which prioritizes accredited adequacy (ELCC), deliverable PCC-level services and integration risk over headline efficiency. Within generation and storage, CSP + TES and EGS/CPG equipped with supercritical CO₂ cycles are evaluated for multi-hour dispatchability, fast ramping capability and bankable learning trajectories. At the same time, high-yield yet variable options (perovskite–Si tandems, high-altitude wind) are assessed for energy contribution and grid-forming behavior, noting that ELCC is a function of storage, hybridization and siting. In direct conversion, photoelectrochemical pathways are treated as solar-to-molecule routes, which can decouple electricity and fuel balancing while introducing distinct balance-of-plant and M&V considerations. Afterwards, within transmission and system infrastructure, HTS corridors are examined for urban hosting-capacity relief and power-quality gains, alongside cryogenic availability and protection-coordination challenges. Finally, frontier options (e.g., fusion, space-based PV) are tracked as a long-dated option value rather than near-term adequacy. In order to ensure transparency, detailed appraisals covering indicative ELCC/FFR/VAR attributes, manufacturability and cost trajectories, PCC integration complexity (harmonics, ride-through, interoperability) and governance/M&V risks, are presented in Appendix C.

6. Policy, Economic and Grid Implications

Innovative technologies, such as high-temperature superconducting (HTS) transmission systems, introduce a new class of options for energy supply and delivery, promising to relax structural constraints that have traditionally governed generation, transport and end-use. At the same time, their effective deployment has to rely on a policy and regulatory framework that recognizes the specific risks of low technology readiness level (TRL) solutions and, accordingly, provides the required economic signals and institutional support for early uptake. Concepts at very low TRL are scoped, as the research tracks with clear decision gates. For options already demonstrated at distribution or transmission voltages (e.g., 10 kV/40 MVA HTS with series SFCL in Essen [51]), certification pathways, service accreditation and market access are specified.

Figure 6 presents the integration logic. An AI-driven demand shock is translated, via a compute-additionality covenant, into interconnection and capacity-tranche levers, technology gates and PCC-level services and auditable outcomes aligned with ELCC and IEC/IEEE standards. The concept behind the covenant is to map a set of inputs into a set of mechanisms. The inputs are compute capacity (MW), PUE/duty-cycle assumptions, the zone’s ELCC quota (α) and PCC obligations (FFR coefficient, minimum power factor) and the telemetry/audit cadence. These inputs are mapped, namely, to tranche-gated interconnection, together with cost-/benefit sharing, to the delivery of PCC services (fast-frequency response, dynamic VAR/voltage support, ride-through) through technology gates, and to verification/governance aligned with IEEE 519, EN 50160, IEC 61000-4-34, IEC 61850 and IEC 62443 with IPMVP-consistent M&V. Finally, the mapping yields auditable outputs in the form of ΔELCC and LOLE status, relative to the 0.1 day·yr⁻¹ planning criterion, PCC-compliance rates, normalized costs ($/ELCC-kW-yr; $/compute-kW-yr) and tranche releases. Finally, Figure 7 presents a visual map summarizing inputs (compute MW, α, PCC thresholds, telemetry cadence), mechanisms (tranche-gated interconnection; technology gates; governance via IEEE/IEC/ISA with IPMVP M&V), and outputs (ΔELCC/LOLE vs. 0.1 day·yr⁻¹; PCC compliance; normalized cost per ELCC-kW and compute-kW; tranche releases).

6.1. Investment and Regulatory Frameworks

The introduction of low-TRL energy systems necessitates substantial, multi-year investments that bridge laboratory proof-of-concept to commercial demonstration, since component reliability, manufacturability and field performance are still evolving; therefore, public authorities and private actors ought to co-design instruments that de-risk capital allocation and, in parallel, accelerate learning. Public–private partnerships (PPPs) have repeatedly proven to be effective vehicles for this purpose, as they apportion risk across entities with complementary mandates and the cost of capital structures, while keeping an innovation pipeline aligned with explicit performance targets. Afterwards, and in order to transform pilot outcomes into bankable assets, contingent support—through milestone-based grants, innovation tax credits and government-backed concessional loans—has to be combined with access to standardized power purchase agreements and long-tenor offtake contracts that stabilize revenue streams during the fragile early years of deployment. Where assets deliver services (FFR, VAR, black-start) in addition to energy, contracts should pay for verified service delivery under recognized M&V frameworks (e.g., IPMVP Core Concepts 2022 (EVO 10000-1:2022)) and publish service-quality telemetry to reduce verification costs over time.

Regulation, which was predominantly optimized around conventional assets, needs to evolve to reflect the operational particularities of novel options, such as HTS systems that operate under cryogenic regimes with distinct safety and maintenance requirements [52]. For HTS corridors, certification should explicitly cover cryostat integrity, quench detection/limiting logic, protection selectivity with superconducting fault-current limiters (SFCLs), electromagnetic compatibility at the point of common coupling, and cyber-secure controls (e.g., IEC 61850 (series), ISA/IEC 62443 (series)). It is therefore essential to update siting, interconnection and grid-code provisions so that technology-specific criteria are explicitly recognized. Queue-management reforms (e.g., such as the cluster studies and readiness screens under FERC Order No. 2023) should be mirrored in TSO/DSO practice to bring accelerated pilots to connection without displacing mature projects [17,18]. Clear certification pathways which cover materials characterization, cryostat integrity, quench protection, electromagnetic compatibility and cyber-secure control interfaces, will improve considerably investor and insurer confidence and, at the same time, reduce transaction costs for project developers. Finally, regulatory sandboxes can be used as controlled environments where performance, reliability and market interactions are observed under real operating conditions before full codification, while tariff structures are adjusted to reflect the system-level value (loss reductions, stability support, resilience) of these disruptive assets (e.g., Ofgem’s Energy Regulation Sandbox) [53].

6.2. Integrating Disruptive Innovations into Existing Energy Systems

When inserting breakthrough technologies into meshed grids that were engineered for conventional thermal generation and resistive lines, system operators have to prioritize resilience, cybersecurity and operational adaptability. HTS cables, by virtue of near-lossless transmission and high current densities, can relieve corridor congestion in dense urban settings. However, cryogenic balance-of-plant transients, quench behavior and protection coordination with SFCLs must be integrated into protection schemes and operational planning so that dynamic load sharing is maintained over a broad envelope of contingencies [52]. A disciplined KPI set for pilots should include mean time between cryo maintenance, quench detection-to-isolation time, planned/unplanned downtime, and corridor ampacity vs. XLPE for a fixed right-of-way. At the same time, supervisory control and data acquisition (SCADA) layers and energy management systems (EMS) should incorporate state estimators and model-based diagnostics that account for cryogenic transients, ensuring that preventative maintenance can be scheduled without jeopardizing network security. Where telemetry spans asset owners and system operators, the communications stack should align with IEC 61850 (series) for utility automation, and be protected as per the IEC 62443-2-4:2023 for IACS cybersecurity.

For space-based photovoltaics (SBPVs), recent in-orbit demonstrations (Caltech SSPD-1, 2023–2024) have validated key subsystems, including wireless power transfer in space and a detectable beam to Earth, placing SBPVs on a transparent research track with measurable milestones, rather than in speculative territory [54]. If matured, SBPVs would present non-synchronous injections with distinctive siting, spectrum allocation and safety considerations. The space elevator concept, if paired with orbital solar arrays and high-capacity power transfer mechanisms, introduces intermittent but potentially very large injections at designated nodes. Grid architectures will thus have to accommodate variable, non-synchronous inflows, whose temporal profiles differ from terrestrial wind and solar. Early grid-integration studies should therefore focus on interconnection standards for non-traditional sources, beam availability statistics, and interaction with demand-side electro-intensive assets (e.g., electrolyzers) to absorb surplus. Demand-side management (DSM) schemes, spanning flexible industrial loads, electro-thermal storage and hydrogen production have to be co-optimized with these supply side innovations, so that spillage is minimized and ancillary services are procured in a cost-effective manner.

From an economic perspective, the appraisal cannot be restricted to initial capital outlays; rather, it has to internalize long-run benefits arising from reduced electrical losses, an improved security of supply, deferred network reinforcements and lower greenhouse gas emissions. Cost–benefit analysis (CBA) should explicitly value stacked revenues from ancillary services, incorporate reliability metrics (ELCC for capacity accreditation) and run sensitivity bands for manufacturing yield, O&M, and interconnection lead-times documented in current queue analyses. Cost–benefit analysis therefore must include externalities such as environmental impact, air-quality co-benefits and distributional effects on vulnerable consumers, while sensitivity testing captures the uncertainty envelopes that are inherent in low-TRL trajectories. Finally, policy decisions should be informed by multi-criteria assessments that weigh technical perquisites, financial risk, institutional readiness and societal acceptance, since only coherent integration with existing infrastructures will deliver a resilient, sustainable energy future.

6.3. Coupling Compute Growth to System Value

A compute-additionality covenant has to be adopted, whereby interconnection capacity for data centers is released in tranches at the zone level (T–D interface/grid-supply point) and phased with either of the following:

• Verified delivery of local services (FFR, VAR, black-start) meeting PCC-level power-quality standards (IEEE 519-2022; EN 50160:2022; IEC 61000-4-34:2005+ A1:2009+ A2:2025);
• Contracted firm-clean MW entering service in the same zone, with accredited ELCC.

Procurement ought to pay for delivered services such as FFR, VAR and black-start from data centers and proximate pilots, not energy alone, so incentives match operator needs and learning accelerates where the system is most stressed.

Interconnection process reforms (cluster studies, readiness screens, alternative transmission evaluations) should be leveraged to couple data centers’ build-out to grid-enhancing investments (advanced conductors, DLR, topology optimization). M&V should follow IPMVP-consistent methods, with open, privacy-preserving telemetry (IEC 61850 (series) models), cyber-secured per IEC 62443 (series) and standardized reporting to enable controller validation and post-event diagnostics. Regulatory sandboxes at candidate nodes should license data flows for verification, codify telemetry formats and standardize M&V for accelerated pilots (EGS, HTS, CSP + TES, perovskites), keeping acceleration, interconnection and market participation on a single auditable track and turning the paradox into an investable program (with queue data transparency and performance-based triggers for tranche releases).

Operationalizing the compute-additionality covenant in sub-Saharan Africa (SSA) has to pair interconnection tranche releases with a financing stack that lowers the cost of capital and ring-fences currency risk. At the same time, sequencing ought to align disbursements with verifiable service delivery so that near-term stress finances durable assets. Recent analysis indicates that, by 2030, Africa’s energy investment must double to >USD 200 billion per year, with ~USD 25 billion per year for access alone [55]. Practical instruments to close bankability gaps at the grid supply (GS) node include IDA/IFC standardized documentation and partial-risk guarantees proven under scaling solar [56], MIGA political risk and currency inconvertibility cover [57], and local currency hedging, which includes long tenor, inflation-linked swaps, through TCX [58]. For early-stage de-risking and preparation, AfDB’s SEFA, the Alliance for Green Infrastructure in Africa (AGIA) and Mission 300 by World Bank and AfDB can supply concessional/blended capital and wraps to move clean-firm and flexibility assets (e.g., EGS; CSP + TES) to bankability [59,60]. Under the covenant, tranche-release conditions should bind data center clusters to (i) verified PCC-level service delivery (e.g., FFR/VAR/black-start via standardized M&V) and (ii) ELCC-accredited clean-firm MW in the same zone; a defined share of proceeds should be earmarked for distribution upgrades and last-mile connections in surrounding communities to advance SDG7.

To ensure durability and local value capture, localization must be sequenced toward capabilities with high employment elasticity and low cost-uplift risk while prioritizing O&M training, testing/commissioning and spares/assembly before heavy-manufacturing mandates. Peer-reviewed evidence from South Africa’s REIPPPP and cross-country comparisons shows that rigid local content rules can raise project costs without securing durable supply chains [61,62]. Accordingly, the covenant should condition localization on credible vendor plans and accredited workforce programs and quality systems, and measure outcomes in uptime/service quality, rather than nominal spend. A two-node pilot template can de-risk replication:

• Designate one urban GS point and one coastal node as regulatory sandboxes;
• Finance interconnection-tied clean-firm additions with concessional tranches and MIGA/IDA wraps, overlay TCX hedges for local currency revenues and procure campus-plus-pilot services under standardized M&V;
• Publish 24 month telemetry and settlement records to establish lender-grade performance baselines;
• Reserve a defined percentage of each tranche for feeder reinforcement and mini-grid interties in adjacent underserved communities, consistent with the Mini-Grids for Half a Billion People playbook [63].

6.4. Covenant Term Sheet

In order to operationalize the “compute-additionality covenant” as a contractible, auditable mechanism, the roles and responsibilities, trance mechanics, benefit–risk sharing and a standards aligned compliance workflow are required. They are presented in the form of a term sheet.

• A. Roles and responsibilities
• A1. Compute provider (service delivery and ELCC underwriting)

• Obligation portfolio: deliver either (i) PCC-level services meeting accredited set-points (FFR, dynamic VAR/voltage regulation, harmonic limits, ride-through) or (ii) ELCC-accredited firm-clean capacity inside the same capacity/BA zone, or a hybrid that meets the zone’s reliability target.
• ELCC underwriting: commit to an $ELCC quota\ge \alpha ·{PeakMW}_{compute}$ (typical α = 0.6–1.0, depending on baseline LOLE and coincidence factors). Demonstrate deliverability and accreditation method (ELCC or equivalent capacity credit) recognized by the ISO/RTO/DSO.
• Telemetry and M&V: provide IEC 61850-structured telemetry at specified resolutions. Maintain settlement-grade records. Enable audit sampling and event replays.
• Curtailment and remediation: Accept automated curtailment rights (see also D3) if out of compliance. fund remediation per the clause below.

• A2. ISO/TSO/DSO (accreditation, tranche governance)

• Accreditation: define resource accreditation tests (ELCC/FFR/VAR/ride-through) and approve covenant-eligible resources; publish methods and seasonal updates.
• Tranche governance: administer tranche release (capacity blocks) upon verified compliance, manage queue priority and readiness screens and execute claw-backs for non-performance.
• System data: provide locational hosting capacity, short-circuit levels and PQ envelopes to parameterize obligations.

• A3. Regulator (tariff, sandbox oversight)

• Tariffing: approve cost-reflective tariffs for protection upgrades/hosting capacity enhancements and a covenant surcharge/credit reflecting delivered services and ELCC.
• Sandbox: authorize regulatory sandboxes (time-bounded, with exit criteria) to pilot telemetry and accreditation innovations. Require periodic public reporting.

• A4. Community (planning participation, impact benefits)

• Participation: formal role in siting consultations. Right to review non-sensitive performance summaries and local upgrade plans.
• Benefit pathways: eligibility for last-mile connections, distribution upgrades, workforce programs and DER enablement, funded via the benefit-sharing clause.

• B. Tranche mechanics
• B1. Tranche size and staging

• Block size: e.g., 25–50 MW per tranche (HV/MV dependent). Initial tranche is limited (e.g., 25 MW) in weak grids.
• Staging: T0_00 “provisional energization” ≤25 MW for on-site commissioning. Subsequent tranches are contingent on verified performance.

• B2. Release triggers (any one or hybrid)

• PCC-services path: demonstrate, over a rolling 90 day window, ≥X MW FFR within Y s, dynamic VAR capability [−Q, +Q] across load range, THD within IEEE 519-2022 limits and ride-through per IEC 61000-4-34 A2. Pass n disturbance/event tests.
• ELCC path: Procure/underwrite Z MW ELCC-accredited firm-clean capacity in-zone sufficient to hold LOLE constant. Submit the ISO’s ELCC letter.
• Hybrid: Weighted combination achieving the same LOLE target.

• B3. Audit cadence

• Settlement: monthly
• Conformance audit: quarterly
• Annual re-accreditation: full test suite.
• Data retention: raw sub-second buffers ≥30 days; 1 s aggregates ≥24 months (see C2).

• B4. Remediation and claw-back

• Cure period: 30–60 days after first material breach. During cure, tranche cap reduced to last verified level.
• Financials: performance bond or LC sized to 90 day replacement cost of obligations. Forfeiture funds immediate substitute resources.
• Claw-back: sustained non-performance (>2 consecutive audits) triggers tranche revocation and queue reversion.

• C. Benefit–risk sharing clause
• C1. Earmarked upgrades

• Allocation: 10–20% of the covenant-related interconnection proceeds (or an equivalent recurring contribution) earmarked for distribution upgrades and last-mile electrification within the host municipality/feeder group.
• Prioritization: projects that increase hosting capacity (e.g., advanced VVC, DLR sensors, protection upgrades) and connect unserved/underserved loads.

• C2. Transparency for bankability

• Public telemetry summaries: publish 24 month rolling settlement-grade time series at aggregated granularity (e.g., 1 min/5 min), including delivered FFR MW, kvar range, PQ compliance rates, and availability factors.
• Lender packages: provide secure data rooms with hashed IDs, audit trails, and attestations from the ISO/DSO and an independent verifier, to enable non-recourse financing of obligation portfolios.

• C3. Community impact

• Set-asides: define MW or € set-asides for community DER pilots (e.g., feederside BESS co-optimized for PQ/FFR), with M&V plans and a reporting template aligned to IPMVP/FEMP concepts.

• D. Compliance workflow (testing → approval)
• D1. Pre-connection conformance

• Design dossier: single-line diagrams, inverter/UPS settings, IEC 61850 data models (logical nodes, reports/GOOSE/SV), cybersecurity zones/conduits per ISA/IEC 62443.
• Bench tests and factory acceptance: PQ immunity (IEC 61000-4-34), harmonic filters, FFR latency; certificate pack.
• Site acceptance tests (SAT): staged energization, disturbance injections, ride-through, harmonic scan; Class-A measurement chain (IEC 61000-4-30) documented.

• D2. Provisional connection (T₀ tranche)

• Provisional window: 30–90 days to accumulate evidence runs; telemetry streamed to ISO/DSO historian in IEC 61850 (MMS reports for 1 s aggregates; GOOSE/SV for sub-second events).
• Pass criteria: meet PCC specs over ≥95% of intervals; complete n commanded FFR set-point tests; no PQ violation exceeding EN 50160/IEEE 519 limits.

• D3. Full approval and ongoing monitoring

• Connection approval: release next tranche(s) per Section B.
• Sampling and audits: quarterly ISO/DSO audits; blind event replays; spot harmonic campaigns.
• Automated controls: if a material deviation is detected, ISO/DSO may curtail up to the non-compliant tranche share until a successful re-test.

• D4. Privacy-preserving telemetry

• Minimization: publish only aggregated performance metrics. Hash device IDs.
• Access control: role-based access. Cryptographic signing/time-stamping for audit logs; IEC 62351/IEC 62443 controls for transport and device security.
• Data sharing: research access via differentially private aggregates or k-anonymized datasets, governed by ethics and data-sharing agreements.

• E. Parameterization for mature vs. emerging grids

• Tranche size: mature grids 50 MW. Emerging grids 10–25 MW initial, with ramp-up contingent on PQ outcomes.
• ELCC ratio α: mature 0.6–0.8. Emerging 0.8–1.0 (until LOLE stabilizes).
• Audit cadence: mature quarterly. Emerging bi-monthly in first year.
• Benefit earmark: mature 10–15%. Emerging 15–20% with explicit feeder-level upgrades and connections.

Furthermore, covenant schedules (term, tranche calendar, cure periods), definitions (e.g., how FFR MW are measured) and appendices (telemetry schema, test scripts) should be annexed to the PPA/interconnection agreement to ensure contract enforceability and consistent accreditation by the system operator.

Finally, in order to parameterize tranche pricing and the covenant’s risk screen, within-technology cost–risk bands for four enabling options—CSP + TES (Gen3), EGS/CPG with scCO₂ cycles, HTS urban backbones (cables + SFCL), and perovskite–Si tandem PV (2T/4T)—are compiled based on recent public benchmarks (See Appendix D, Figure A1, Figure A2, Figure A3 and Figure A4). These bands serve as priors for sensitivity tests and stage-gate thresholds and are not intended for cross-technology magnitude comparisons.

6.5. Case Studies

6.5.1. Purpose and Scope

The concept behind this section is to operationalize the proposed compute-additionality covenant through two illustrative, non-site-specific examples: namely, a mature market and an emerging sub-Saharan Africa (SSA) context. In both cases, the same screening logic is applied, i.e., PCC-level service obligations together with an ELCC-anchored adequacy quota, under transparent parameters drawn from the literature. Full equations, parameterization and reproducible spreadsheets are provided in Supplementary Note S4 and in the accompanying workbook. The aim is not to optimize a particular site, but to demonstrate how a covenant can be parameterized, audited and stress-tested for adequacy and power-quality outcomes within realistic policy bands.

6.5.2. Mature Market Case Study

A 200 MW compute campus is examined, connected to a mature grid operating with a PF floor of 0.98, an FFR obligation equal to 0.15 MW per MW of compute and an adequacy quota of α = 0.7 (policy band 0.6–0.8). The design couples a 230 MW/4 h BESS with 35 MW geothermal; standard CRFs and FOM are used, while an ancillary adder reflects PCC-level FFR/VAR enablement. Results indicate that adequacy passes at the central α and that service obligations are fulfilled with linear, auditable sizing, referenced to compute demand. Derived costs are reported per compute kW-yr and per ELCC-kW-yr. The inputs are presented in

Table 2. Inputs.

Category	Symbol	Unit	Value
Compute load	Pcomp	MW	200
PCC: FFR coefficient	fFFR	MW per MW	0.15
PCC: power-factor minimum	PFmin	—	0.98
Adequacy quota (central; band)	α	—	0.70 (0.60–0.80)
BESS power	PBESS	MW	230
BESS duration	H	h	4
BESS ELCC credit	κΒΕΣΣ	—	0.65
BESS CAPEX		$/kWh	250
BESS lifetime		years	12
BESS WACC		%	8
BESS FOM		$/kW-yr	8
Geothermal power		MW	35
Geothermal ELCC credit	κGEO	—	0.9
Geothermal CAPEX		$/kW	4000
Geothermal lifetime		Years	25
Geothermal WACC		%	8
Geothermal FOM		$/kW-yr	110
DR power	PDR	MW	0
DR ELCC credit	κDR	—	0.5
DR program cost		$/kW-yr	40
Ancillary adder (compute-side)		$/kW-yr	25

and the results in

Table 3. Results for a mature economy.

Metric	Value
FFR required	30.00 MW
FFR delivered	30.00 MW
Reactive headroom (PF = 0.98)	40.612 MVAr
BESS energy	920 MWh
ELCC total (BESS + DR)	181.0 MW
ELCC target @ α = 0.9	140.0 MW
ELCC delivered	140.0 MW
ΔELCC (delivered − target)	0 MW
ELCC from BESS	108.5 MW
ELCC from geothermal	31.5 MW
Adequacy (central α)	PASS
LOLE relative to 0.1 day·yr−1	Held at target (by construction under α)
Annualized cost	54.325 M$/yr
Cost per compute	271.62 $/kW-yr
Cost per ELCC	300.14 $/ELCC-kW-yr

.

See Supplementary Note S4 (Table S3) for a consolidated summary of FFR/VAR delivery, ELCC targets/actuals, ΔELCC, normalized costs, and LOLE adequacy for both scenarios.

6.5.3. Emerging Markets and Developing Economies (EMDEs) Case Study

A 25 MW compute campus is considered in EMDE context under the same PCC obligations and with α = 0.9 (policy band 0.8–1.0). The configuration employs a 30 MW/4 h BESS, together with 6 MW of demand response (DR); BESS financing reflects blended concessional terms (WACC = 6%). At the central α, the design passes adequacy with lower normalized costs than the mature case, while the PCC obligations remain transparent and enforceable. Sensitivity to higher α is examined via firm-clean backfill in the supplementary workbook. The inputs are presented in

Table 4. Inputs for SSA country.

Category	Symbol	Unit	Value
Compute load	Pcomp	MW	25
PCC: FFR coefficient	fFFR	MW per MW	0.15
PCC: power-factor minimum	PFmin	—	0.98
Adequacy quota (central; band)	α	—	0.90 (0.80–1.00)
BESS power	PBESS	MW	30
BESS duration	H	h	4
BESS ELCC credit	κΒΕΣΣ	—	0.65
BESS CAPEX		$/kWh	250
BESS lifetime		years	12
BESS WACC		%	6
BESS FOM		$/kW-yr	8
Geothermal power		MW	0
Geothermal ELCC credit	κGEO	—	0.9
Geothermal CAPEX		$/kW	4000
Geothermal lifetime		Years	25
Geothermal WACC		%	8
Geothermal FOM		$/kW-yr	110
DR power	PDR	MW	6
DR ELCC credit	κDR	—	0.5
DR program cost		$/kW-yr	40
Ancillary adder (compute-side)		$/kW-yr	25

and the results in

Table 5. Results for developing country.

Metric	Value
FFR required	3.75 MW
FFR delivered	3.75 MW
Reactive headroom (PF = 0.98)	5.076 MVAr
BESS energy	120 MWh
ELCC total (BESS + DR)	22.5 MW
ELCC target @ α = 0.9	22.5 MW
ELCC delivered	22.5 MW
ΔELCC (delivered − target)	0 MW
ELCC from BESS	19.5 MW
ELCC from DR	3 MW
Adequacy (central α)	PASS
LOLE relative to 0.1 day·yr−1	Held at target (by construction under α)
Annualized cost	4.683 M$/yr
Cost per compute	187.33 $/kW-yr
Cost per ELCC	208.15 $/ELCC-kW-yr

.

See Supplementary Note S4 (Table S3) for a consolidated summary of FFR/VAR delivery, ELCC targets/actuals, ΔELCC, normalized costs, and LOLE adequacy for both scenarios.

6.5.4. Discussion of Case Study Results

The core mechanics of the covenant are applicable across contexts. In both cases, the PCC obligations scale linearly with compute (FFR and dynamic VAR headroom), thereby offering clear measurement points for compliance. Adequacy is governed by an ELCC quota. Under the central α, the mature system carries headroom (181 MW ELCC versus a 140 MW target), whereas the SSA baseline exactly meets its quota (22.5 MW vs. 22.5 MW). Normalized costs are lower in the SSA case (187 $/kW-yr; 208 $/ELCC-kW-yr), owing to smaller scale, the DR contribution and concessional financing. Raising the quota to α = 1.0 in SSA triggers firm-clean backfill (≈2.78 MW at κ = 0.9), increasing the compute-normalized cost to ~241 $/kW-yr while preserving adequacy by construction. Finally, the ancillary adder is modest relative to total costs, but material for PCC readiness underlines the value of auditable grid services alongside capacity accreditation. A consolidated outcomes summary is provided in Supplementary Note S4 (Table S3).

7. A Roadmap for Future Research and Commercialization

The transition from exploratory research to market-ready systems is inherently interdisciplinary and requires an explicit roadmap that aligns scientific agendas, funding instruments and staged demonstrations; at the same time, the management of uncertainty—technical, economic and regulatory—has to be embedded from the outset so that learning is codified and investment signals are preserved. Accordingly, the pathway is organized into transparent stage-gates that couple technology readiness levels (TRLs) to manufacturing readiness levels (MRLs) and to integration readiness at the grid edge (IRL): laboratory proof (TRL 3–4) → prototype in a relevant environment (TRL 5–6) with formal MRL assessments → pre-certified field pilots (TRL 7–8) with IRL metrics explicitly tied to interconnection and market participation. Funding and scale-up have to be synchronized with these gates through de-risking instruments (e.g., EU Innovation Fund, U.S. DOE Title 17/LPO and ARPA-E SCALEUP), so that tranche releases are indexed to verified performance and safety milestones, rather than forward claims, which would help towards disciplined learning and bankability. Anchoring the TRL–MRL–IRL stage-gates introduced above,

Table 6. What solves what, by when?

Technology	TRL/MRL/IRL—Today	2025–2030 Gate (Bankability)	2030–2035 Gate (Scale/Outcomes)	Grid Services (Operator-Relevant)	ELCC Range (Qualitative)	Pilot KPI Set (Auditable)	Dominant Risks
CSP + TES (Gen3 pathway)	Commercial CSP + molten-salt: TRL~9; Gen3 particle subsystems: TRL~6–7; MRL: mid; IRL: mid.	≥50 MW_net plant with ≥6–10 h TES; ≥95% availability over ≥2 summers; verified derates; TES round-trip efficiency; independent ELCC dossier; particle-receiver pilot at ≥700 °C.	First deployments of Gen3 receivers/power blocks at commercial scale with standardized accreditation; demonstrated curtailment reduction at PV hubs and evening-ramp coverage; maturing O&M cost evidence.	Dispatchable evening-ramp coverage; voltage/VAR and frequency support via synchronous machine or grid-forming controls at power block.	High with multi-hour TES; accredit site-specifically (portfolio-dependent).	Availability (%), TES RTE, receiver flux/temperature stability, field derate factors, ELCC study sign-off, summer-season M&V.	Receiver durability; TES life; water management; salt/particle handling O&M.
EGS/CPG with scCO2 cycles	Prototype in relevant environment: TRL~5–6 (FORGE-class connectivity shown); MRL: low-mid; IRL: low-mid.	Season-scale injector–producer connectivity; induced-seismicity thresholds per injected MWh under traffic-light protocols; validated reservoir ↔ surface co-models; initial O&M evidence; independent ELCC based on co-simulation.	Multi-year circulation with acceptable thermal drawdown; scCO2 Brayton reliability across transients; bankable ELCC accreditation and deliverability; progression to programmatic replication.	Firm, dispatchable capacity; frequency/VAR via converter or synchronous coupling; nodal adequacy contribution.	High (firm), site-specific; accredit via coupled reservoir-plant models.	Connectivity retention (flow/pressure); temperature-decline slope; seismic events per injected MWh; plant availability; ELCC study sign-off; O&M metrics.	Induced seismicity; flow sustainability; geochemical interactions; scCO2 materials/sealing; execution risk.
HTS urban backbones (cables + SFCL)	Field-demonstrated at distribution voltages (e.g., 10 kV/40 MVA AmpaCity); corridor pilots pre-commercial: TRL~7–8; MRL: mid; IRL: low-mid (protection/cyber integration).	Replicated urban pilots under regulated programs with transparent M&V; certification of cryostat integrity, quench detection/limiting logic, SFCL coordination, EMC at PCC, IEC 61850 (series)/62443 (series)—aligned controls.	Standardized multi-km corridors and inter-substation backbones; service-based procurement (loss reduction, fault-level management, hosting-capacity unlock); EMS/SCADA models incorporating cryo transients.	Loss reduction; fault-level management (via embedded SFCL); thermal headroom/hosting-capacity increase; congestion relief.	N/A as a transmission asset (indirectly raises portfolio ELCC via curtailment/constraint relief).	MTBM (cryo); quench detection-to-isolation time; planned/unplanned downtime; corridor ampacity gain vs. XLPE per right-of-way; SFCL coordination success.	Cryogenic reliability; protection coordination; lifecycle/repair logistics and total cost of ownership.
Perovskite–Si tandem PV (2T/4T)	Pilot-scale modules (mid-20%); TRL~6–7; MRL: low-mid (factory yield bottleneck); IRL: mid-high (PV interconnection standard), product-specific bankability pending.	Full-BOM IEC pass (85/85, TC, HF, UV, PID); ≥2 summers with ≤0.5–0.6%/year post-stabilization slope; lead-containment; independent yield assessment; bankable 25 year warranty; second-source for key layers.	Multi-GW manufacturing with stable yield; LCOE advantage vs. best-in-class Si at equal BOS realized in BOS-constrained sites; field reliability generalized across climates.	Non-dispatchable energy; VAR and fast-frequency support feasible via advanced inverters where specified; curtailment–BOS relief in space-limited sites.	Low → moderate, penetration-dependent; accredit per operator studies.	IEC pass/fail across BOM; annualized field-slope (%/year); yield (factory) and repair/replace stats; warranty terms; hazardous-material containment verification.	Ion migration and phase segregation; interface recombination/sputter damage; moisture/oxygen and UV ingress; lead leakage; factory-yield stability.

translates the portfolio into auditable checkpoints, mapping each technology’s present maturity to 2025–2030 bankability gates and 2030–2035 scaling outcomes, while specifying operator-facing services, indicative ELCC contributions, pilot KPIs, and the dominant risks that shape deployment priority.

7.1. Coordinated Research Agendas and Funding Priorities

A comprehensive research agenda must span materials science, structural engineering, cryogenics, power systems and information technologies, because binding constraints in disruptive technologies often reside at the interfaces between subsystems, rather than within any single domain. Coordinated programs, structured around shared testbeds and open data policies, enable faster replication of results and reduce the probability of design dead-ends. This coordination is reinforced when funding calls specify performance metrics and field-validation milestones that are relevant to commercial use-cases. In practice, multi-institution testbeds (e.g., NREL’s ARIES) should host hardware-in-the-loop campaigns that exercise converter controls, communications stacks and cyber-physical responses under realistic disturbances before site deployment [64]. Key priorities therefore include the following:

• EGS/closed-loop geothermal: fracture-network creation, circulation stability and induced-seismicity monitoring following recent field tests;
• PV tandems: perovskite–Si durability pathways (thermal/humidity/UV) with field-relevant degradation models and bankability data;
• HTS cable systems: improved cryostat reliability, quench detection/mitigation logic, device terminations and SFCL coordination;
• EMS/DSSE: architectures that integrate AI-assisted forecasting, fault localization and secure actuation with standardized substation/data models (IEC 61850 (series));
• In general, program calls should require publishable telemetry schemas and reference controller implementations and minimum viable certification test plans to reduce downstream transaction costs.

In parallel, research on grid modernization has to advance smart-grid functionalities including phasor measurement, adaptive protection, wide-area control, and standardized cybersecurity frameworks that are capable of resisting coordinated attacks without degrading system observability. Establishing collaborative research centers and innovation hubs concentrates specialized equipment and talent (e.g., metrology labs, cryogenic facilities, high-voltage halls) and, at the same time, provides neutral fora where utilities, manufacturers and regulators converge to define test procedures and pre-certification steps. Afterwards, codified knowledge should be transferred into modular design tool chains that are accessible to planners and project developers, so that feasibility screening, siting and interconnection studies can be executed consistently across jurisdictions. Ground cyber-physical safety in EN IEC 62443 (series) [65] (with cross-references to NIST SP 800-82 Rev.3 (2023) [66]) apply EN ISO/IEC 27019:2024 [65] and EN/IEC 62351 (series) for power-system controls, and align interface profiles to IEC 61850 (series) logical nodes to ensure interoperable, auditable event records, consistent with NIS2 and the EU Network Code on Cybersecurity.

7.2. Transition Pathways and Pilot Demonstrations

Because low-TRL technologies carry legitimate doubts about scalability and field robustness, transition pathways must be organized around pilot plants and demonstrations that expose systems to real-world stresses and operational complexity. For HTS corridors, replicated urban pilots (building on Essen’s 10 kV/40 MVA AmpaCity link with integrated SFCL) should report a minimal KPI set (e.g., quench detection-to-isolation time, cryo O&M hours per km-year, failure rates of rotating equipment, and corridor ampacity vs. XLPE for a fixed right-of-way), with third-party verification. For geothermal reservoirs, staged EGS programs should progress from short duration to extended circulation with quantified injectivity/thermal drawdown. Where wireless power or space-based solar concepts are pursued, they should remain on research tracks with subsystem milestones and safety cases before any grid interconnection studies are initiated.

These pilots perform a second function: they build stakeholder confidence by converting abstract performance claims into measured outcomes that lenders, insurers and regulators can interrogate. Public–private consortia are therefore essential, since they pool resources, share risks and align the demonstration environment with realistic operating conditions; procurement should reward learning: e.g., by indexing tranche releases to verified performance metrics, so that feedback loops remain explicit and the pace of iteration is preserved. Finance should be laddered:

• Grant-funded for TRL 5–6 prototyping;
• Grant or concessional/first-of-a-kind debt via EU Innovation Fund or Title 17/LPO at TRL 7–8;
• Commercial debt/equity post-certification.

Each step has to be contingent on KPI attainment and safety sign-off. Finally, lessons from other sectors (wind, solar, geothermal) confirm that iterative testing, transparent data and early engagement with affected communities accelerate acceptance and shorten the path to bankability.

Techno-economic evaluation must accompany each stage, using deterministic and probabilistic (Monte Carlo) analyses to quantify sensitivity to capital costs, failure rates, load growth and policy parameters; environmental impact assessments should be run in parallel so that design choices internalize lifecycle burdens and do not externalize risks to ecosystems or public health. Recommendations include the following:

• ISO 14040:2006 + AMD 1:2020/ISO 14044:2006+ AMD 1:2017 + AMD 2:2020-conformant LCA for pilots and product lines [67];
• Resource-adequacy accreditation using reliability metrics (LOLE/ELCC) to score a firm contribution of flexible assets;
• Uncertainty bands reflecting the manufacturing yield, interconnection lead-times and O&M learning.

At the same time, standardization bodies should be engaged early to codify test methods and safety margins, thereby reducing future re-design cycles and avoiding stranded R&D. For grid-facing equipment, pre-normative work should be coordinated through IEC/CEN-CENELEC/IEEE/CIGRE working groups, so that emerging practices (e.g., HTS cable and SFCL testing, IEC 61850 (series) profiles) are captured in technical brochures and standards updates before mass procurement.

To summarize, an actionable roadmap to commercialization integrates coordinated research agendas with targeted funding, focused pilot demonstrations and progressive regulatory codification. If these elements are aligned—through clear metrics, risk-sharing instruments and open, verifiable learning—then disruptive energy technologies can transition from laboratories to operational grids in a manner that preserves reliability and affordability, while contributing materially to decarbonization and system resilience. Finally, by embedding uncertainty management and stakeholder engagement throughout, the pathway remains adaptable to evidence, which is the necessary condition for durable, large-scale adoption. Stage-gate rigor, secure/open telemetry, and accreditation-based procurement can turn today’s prototypes into tomorrow’s bankable, grid-integrated assets.

8. Conclusions and Future Outlook

The convergence of artificial intelligence with low technology readiness level (TRL) innovations delineates a conditionally credible pathway to satisfy the escalating energy demands of the coming decades; at the same time, it reframes system design and operation from component-by-component optimization to integrated, evidence-driven orchestration. The present paper has traversed a wide spectrum of emergent options—advanced concentrated solar power coupled with next-generation thermal storage, enhanced/closed-loop geothermal, perovskite–Si tandems, high-temperature superconducting (HTS) urban backbones, artificial photosynthesis and space-based solar power—whose joint consideration suggests that the global energy infrastructure can be reconfigured towards architectures that are sustainable, resilient and equitable, provided that deployment is guided by explicit performance metrics and staged validation with verifiable system services and capacity accreditation.

Artificial intelligence has already altered the cadence of research and the practice of operation and maintenance, since data-centric methods shorten iteration cycles, improve fault prognosis and allow real-time optimization at multiple temporal and spatial scales. When this capability is applied to low-TRL technologies—many of which were, until recently, confined to controlled laboratory settings—lead times from invention to fielded systems can be reduced, because design hypotheses are confronted early, with operational data and models updated in situ. Afterwards, as electrification and digitalization increase aggregate demand and load variability, AI-enabled forecasting, dispatch and predictive maintenance can be embedded within energy management systems (EMS) to preserve reliability while absorbing novel supply and conversion pathways, so that the combined portfolio can meet and, in specific contexts, exceed present energy service requirements with lower environmental burdens.

This transformation, however, cannot be expected to unfold in isolation from policy and finance. It has to be anchored by proactive, coordinated instruments that de-risk early deployment and translate experimental success into bankable assets. Targeted programs—such as ARPA-E in the United States and Horizon 2020 in Europe—have already demonstrated that mission-oriented calls, milestone-based grants and public–private partnerships effectively compress time-to-demonstration and crowd-in private capital by sharing technical and market risks. It follows that regulatory frameworks should codify clear certification pathways, standard interconnection prerequisites and transparent tariff rules for emergent assets, so that lenders and insurers can price risk and off-takers can commit to long-tenor contracts; such measures are expected to support the continuity of learning-by-performing across successive project cohorts.

Integrating disruptive options into existing grids remains a primary engineering challenge. High-temperature superconducting (HTS) transmission promises near-lossless transport and high current densities in constrained corridors, but it imposes cryogenic balance-of-plant, quench protection and distinctive protection coordination that system operators have to internalize (as shown in the Essen 10 kV/40 MVA HTS link with a series SFCL and associated protection studies). For space-based photovoltaics (SBPVs), recent in-orbit demonstrations (Caltech SSPD-1) validated key subsystems (wireless power transfer in space; detectable beam to Earth). SBPVs should therefore remain on a transparent research track with subsystem milestones and safety cases before any grid interconnection is contemplated. At the same time, grid architectures will have to evolve towards smart, cyber-secure platforms with wide area measurement, adaptive protection and model-based control, where AI tools support anomaly detection and corrective action under fast ramps and contingencies. Demand-side management, electro-thermal storage and hydrogen production can then be co-optimized with these supply-side innovations to minimize spillage and enhance resilience. Capacity accreditation for flexible assets should be computed via reliability metrics (ELCC/LOLE) to make contributions bankable in resource-adequacy planning and markets.

The role of interdisciplinary research is, under these conditions, decisive. A coordinated roadmap, which aligns research agendas, shared testbeds, pilot demonstrations and commercialization strategies, has to be established so that technical, regulatory and economic uncertainties are exposed early and addressed iteratively. Collaborative research centers and innovation hubs concentrate on specialized infrastructure and foster repeatable, open-data experiments; industry consortia translate findings into standards, test methods and pre-certification protocols; regulators operate sandboxes in which operational behavior is observed before full codification. Such alignment ensures that the trajectory of technology maturation is matched by an agile institutional environment, and that distributional outcomes are considered explicitly, so benefits are equitably shared across regions and consumer classes.

Looking forward, the opportunity set is large, but so are the requirements for disciplined execution. Next-generation photovoltaic architectures, enhanced geothermal systems and artificial photosynthesis can be combined with AI-assisted decision-making to yield a low-carbon, fault-tolerant energy system that meets future demand growth without compromising quality of service. Uncertainty will persist, e.g., in costs, in performance learning rates and in public acceptance, yet this uncertainty can be managed through staged pilots, transparent data and probabilistic appraisal, so that scale-up proceeds as evidence accumulates. Finally, the coupling of planning models with operational data will allow planners to update siting, sizing and dispatch rules in near-real time, thereby closing the loop between strategy and field performance.

The concept behind this article is to advance a contracting and accreditation mechanism, rather than to perform a full resource-adequacy or production-cost study. Therefore, the results are based on stylized scenarios and screening-level equations instead of nodal simulations. Consequently, network congestion, commitment/dispatch effects, and deliverability under transmission constraints are not estimated and could shift realized adequacy and costs. The probabilistic demand envelope (compute + robotics) reflects parameter and structural uncertainty (e.g., PUE distributions, training/inference splits, duty cycles), whereas regional transferability is bounded by heterogeneity in market design, institutional capacity, interconnection backlogs and tariff structures. Quantitative outputs are contingent on portfolio composition, weather-year selection and outage correlations, siting/deliverability and the capacity-accreditation method adopted by the system operator. These factors limit external validity beyond the specific case constructions presented here. Covenant performance is conditioned on telemetry access, cybersecurity and privacy compliance and auditability at the PCC, including Class-A power-quality metrology (IEC 61000-4-30) and secure telemetry per IEC 61850/IEC 62443. Deviations from these assumptions can bias PCC-compliance inference and settlement. Claims regarding interconnection-delay mitigation are procedural (tranche-gating plus readiness screens) and depend on institutional adoption. No queue-time model is estimated. At the same time, the referenced technology pathways (e.g., Gen-3 CSP with TES, EGS/CPG, HTS) carry TRL/MRL/IRL risks, cost-dispersion and supply chain constraints, which could shift ELCC and cost outcomes. Learning rates and supply chain shocks could materially re-rank options. In this paper, α has been used as a screening proxy. In nodal resource adequacy studies, deliverability, accreditation and ELCC are resolved under network constraints, and LOLE is computed with nodal contingencies. Finally, AI “acceleration” effects, together with potential rebound, are only approximated here. Net system benefits ought to be verified through pilots with measured LOLE/ELCC deltas and documented compliance costs. To summarize, these limitations frame the covenant as a decision framework to be validated, and not as a guarantee of adequacy or cost recovery.

To summarize, while the magnitude of future energy needs is daunting, the joint application of AI and select low-TRL technologies provides a practical route to deliver sustainable and equitable outcomes. A compute-additionality covenant, service-based procurement, interconnection reform and secure/open telemetry are necessary conditions for progress; robust standards, cybersecurity and grid modernization are the operational means by which integration will be secured. If these elements are aligned, through clear metrics, accountable governance and open, verifiable learning, then the energy system that emerges will not only respond to today’s constraints but will also empower future generations to thrive in a cleaner and more resilient world. The policy task is thus to ensure that near-term stress from digital demand finances and de-risks the medium-term solutions that AI helps accelerate, making the symbiosis visible, measurable and financeable.