This article integrates three previously separate bodies of literature into a single framework that prices construction-phase flexibility as a portfolio of real options, pairing elicitation-derived (FAHP) weights with simulation-derived (Sobol) variance indices, and formalizes the decision as stochastic optimal control under irreversibility.
Abstract
AI-driven growth is pushing data-center electricity demand from 415 TWh (2024) toward 945 TWh by 2030. Demand-response research has matured on operational levers but treats the physical envelope as largely exogenous, leaving construction-phase decisions that bound achievable flexibility unmodeled. This article integrates three previously separate bodies of literature (data-center demand response, grid-interactive efficient buildings, and stochastic optimal control under irreversibility) into a single framework that prices construction-phase flexibility as a portfolio of real options, pairing elicitation-derived (FAHP) weights with simulation-derived (Sobol) variance indices. None of the individual techniques is new; the contribution is their synthesis and the finding that architectural and site decisions carry the dominant financial leverage in the model, whereas a literature-grounded synthetic-persona prior (twenty-five LLM-simulated personas) prioritizes mechanical-electrical systems; a divergence we frame as a screening diagnostic between an LLM prior and the model. The Flex-by-Design Readiness Index (FDRI) is a nineteen-dimension taxonomy across architectural, MEP, and site-urban layers, weighted by Fuzzy AHP. The FDRI–ROV model formalizes the decision as stochastic optimal control under irreversibility. Calibrated to PJM, ERCOT, and CAISO (2024–2026) for a 100 MW plant at N = 10,000 paths over thirty years, it yields +$79 M net option value at Full FDRI for PJM (additive upper bound; substitution-corrected ≈ +$41 M, 1.7× CapEx; 2.4× CapEx PJM, 2.1× ERCOT, 2.8× CAISO); on both the additive (2.4–2.8×) and corrected (1.7×) bases the pre-registered H2 threshold of Vtotal/Ctotal ≥ 3× is not met. Sobol decomposition places architectural and site layers at ST ≈ 0.56 each versus MEP at 0.15, exposing waste-heat-export and regulatory-avoided-cost dimensions as under-recognized leverage. Out-of-sample validation against four hyperscale projects yields 11% MAPE, reported as an n = 4, single-period proof of concept.A pro-rata extrapolation across all ~43 GW of incremental U.S. capacity gives a nominal ~$34 billion through 2035, but this applies the single most optimistic scenario uniformly; applying the substitution-corrected per-plant value with competition, policy, and adoption decay multipliers, the defensible 2035 opportunity is ≈$3–$18 billion (central ≈$7 billion), with $34 billion retained only as an undecayed ceiling.
Solar microgrids represent a technically viable and increasingly cost-competitive pathway to
electricity access in underserved regions of sub-Saharan Africa, South Asia, and the Pacific, yet a
disproportionate number of deployed systems experience abandonment or financial collapse
within five years of commissioning. This paper argues that such failures reflect not primarily
technical inadequacy or affordability barriers in isolation, but a co-optimization failure arising
from frameworks that treat these two dimensions as sequential rather than jointly determined
design problems. A systematic conceptual synthesis was conducted across 168 peer-reviewed
studies, technical reports, and institutional assessments published between 2010 and 2020,
following a structured PRISMA-adapted protocol and drawing on multi-criteria optimization
theory, energy poverty literature, distributed generation engineering, smart-grid control
architectures, and community-level financial modeling. Four primary failure modes emerge from
sequential optimization: the tariff-reliability paradox, stranded capacity, technology-community
mismatch, and governance vacuum. To address these, the paper develops and theoretically
validates a co-optimization framework comprising four integrated modules: a Smart Performance
Core, a Community Affordability Engine, a Bidirectional Feedback Mechanism, and an Adaptive
Governance Layer. Validation against 23 empirical case studies across 14 countries demonstrates
convergent construct validity and internal consistency, with co-optimized systems achieving
average operational lifespans 3.2 years longer and revenue collection rates 28 percentage points
higher than sequentially optimized counterparts. The framework provides operationalizable
principles for system designers, energy planners, and development finance institutions seeking to
reduce system abandonment and accelerate equitable energy access in underserved regions.
Marvin Komi Nenubari, Olaoluwa Adeniji Ibukun· International Journal of Eng...· 0 citations
The vast number and wide geographical distribution of controllable resources on the demand side make their direct participation in demand response (DR) programs challenging. To improve the coordination of demand-side resources in power systems, this paper proposes a dynamic aggregation method for heterogeneous controllable resources based on load baselines. The proposed framework includes quantitative evaluation of resource response potential, generation of target load baselines, and dynamic aggregation guided by indicator weighting. Response capability indicators are used as clustering features, and the load baseline is segmented into ramping, peak, and valley periods through edge-point detection. To accommodate different regulation requirements, a combined Analytic Hierarchy Process and Anti-entropy Weight Method (AHP-AWM) is employed to determine dynamic indicator weights. An improved K-means algorithm incorporating these weights is used to perform adaptive aggregation of controllable resources under multiple regulation scenarios. Numerical simulations demonstrate the applicability of the proposed framework and show that the resulting clusters exhibit distinct response characteristics under different operating conditions.
Yiwei Xiao, Jingjie Huang, Xiaoran Dai et al.· Scientific Reports· 0 citations
Centralized hot water systems in university dormitories can provide significant energy flexibility through demand response (DR). Thus far, however, existing studies have mainly focused on system-level optimization and have failed to provide a quantitative framework that accounts for individual users’ DR participation behavior and the associated uncertainty. In this paper, we develop a data-driven approach to evaluate demand-side flexibility and quantify the uncertainty that arises as a result of user participation. A clustering-based stochastic load prediction model is proposed and validated using real operational data, serving as the baseline for DR load shifting. Four DR strategies for students are designed based on time-of-use pricing and/or academic credit incentives. A survey of nearly 1000 students is used to calibrate participation probabilities, while a binomial distribution model characterizes the uncertainty of user participation, allowing us to derive the probability distribution and expected value of the system’s flexibility potential. Compared with the no-DR baseline, the combined price–credit incentive yields the highest flexibility, achieving a peak-shaving rate of 62.66% and thus significantly outperforming the price-only strategy. Notably, the academic credit incentive alone increases students’ willingness to participate more effectively than price signals. Furthermore, when the number of participating users exceeds 648, the fluctuation range of the estimated flexibility potential falls below 10.5%, enabling stable flexibility evaluation with a moderately large user sample.
Zeju Li, Yanzhe Dou, Qiangang Li et al.· Energies· 0 citations
The
efficiency evaluation focusing on network systems in data envelopment analysis (DEA), utilizing models such as the SBM‐NDEA (network slacks‐based measure model), frequently encounters challenges in defining stage efficiencies, frontier projection, determination of slacks and targets, and the treatment of potential conflicts between stages. In order to overcome the limitations of traditional additive and multiplicative decomposition approaches, this paper proposes the UNSBM (unique stage efficiencies network SBM model). The UNSBM adopts the composition paradigm and a bi‐objective approach based on the SBM‐NDEA multiplier. The main contribution of the model lies in its ability to provide unique and neutral stage efficiency scores, concurrently managing potential conflicts. Furthermore, the study presents a target‐setting model designed to guide inefficient units toward the efficiency frontier. When applied to a real dataset of Taiwanese non‐life insurance companies, the UNSBM demonstrated advantages in the accurate identification of inefficiency sources and the definition of improvement plans, thereby validating its effectiveness, compared to existing methods.
Camila Guimarães Monteiro de Freitas Alves, Lídia Ângulo Meza· International Transactions i...· 0 citations
This paper evaluates the feasibility of introducing a -hour working concept in select bank branches to address growing after-hours customer demand and persistent needs for in-person verification, advisory, and dispute resolution. A quantitative framework is developed by integrating (i) time-band demand decomposition across a 24-hour cycle, (ii) Erlang-C (M/M/c) queueing analysis for staffing and servicelevel assurance under time-varying arrivals, and (iii) investment appraisal using incremental costrevenue modeling, payback, ROI, and NPV. A 25-branch, metro-focused pilot dataset is used to demonstrate end-to-end calculations and decision metrics. Results indicate a mean after-hours share of 55.54% (range 51.6%-58.7%), supporting the case for extended access in urban markets. Queueingbased staffing designed to cap utilization maintains stable performance across time bands, while the financial module shows that 24/7 branches achieve positive incremental monthly profit and 20/25 achieve positive 24-month NPV under the stated assumptions. The findings support a selective scale-up strategy in high-demand corridors, with managerial emphasis on shift design, safety and incentives, service bundling by time band, and measured awareness-building to maximize adoption and value creation.
S. Ramkumar, A.Hussain Syed Ibrahim· International journal of com...· 0 citations
This study investigates strategic capacity planning in an electronics manufacturing firm constrained by capital limitations and compressed timelines. It develops an integrated decision framework to evaluate the trade-offs between internal capacity expansion and subcontracting under demand volatility. A mixed-method approach combining SWOT analysis, 4M-based Root Cause Analysis, and stakeholder-validated Test of Theory assessment was employed to diagnose structural barriers to capacity optimization. Findings identify capital intensity, subcontractor technological immaturity, and the absence of a formalized dual-sourcing architecture as principal constraints. A linear programming model was formulated to minimize total cost subject to capacity and demand constraints, and SAS JMP desirability analysis was applied to rank subcontracting alternatives based on cost competitiveness, technological readiness, and projected savings. Results demonstrate that subcontracting approximately 20 million units per quarter yields the most robust balance between economic efficiency, operational flexibility, and strategic control. The study proposes phased capital deployment and structured dual-sourcing governance as mechanisms for resilient and financially sustainable capacity management.
Erwin A. Sangalang, J. German· 2026 6th International Confe...· 0 citations