Energy-Independent Selection Criteria

• Energy-independent selection criteria are frameworks that decouple decision metrics from immediate energy output by emphasizing reliability, quality, and uncertainty.
• They are applied across disciplines such as thermodynamic MCDM, evolutionary dynamics, and energy market algorithms to provide unbiased, robust rankings.
• These criteria support resilient design and automated optimization in systems facing uncertain energy prices and fluctuating operational conditions.

Energy-independent selection criteria encompass frameworks and mathematical formulations used for decision making, optimization, and evolutionary modeling in contexts where selection or ranking of alternatives is intentionally decoupled from direct energy output or immediate energetic advantage. This concept spans thermodynamic approaches in multi-criteria decision making (MCDM), cooperative strategy evolution, automated energy plan switching, robust energy system design, and prebiotic chemical network persistence. These criteria rely on theoretical, statistical, or procedural mechanisms that prioritize attributes such as reliability, quality, uncertainty, robustness, and non-energetic constraints, enabling more nuanced, generalizable, or resilient choices across scientific and engineering domains.

1. Thermodynamic Indicators in Multi-Criteria Decision Making

A key instantiation of energy-independent selection criteria arises in the thermodynamic approach to MCDM, in which analogies between physical concepts—energy, exergy, and entropy—inform the selection and ranking of decision alternatives (Verma et al., 2015).

• Energy (U): Represents the aggregate magnitude of expert ratings weighted by criterion importance ($U = w \cdot r$); this indicator operates similarly to traditional TOPSIS, capturing quantitative ratings without adjusting for reliability.
• Exergy (X): Defined as the “useful” portion of energy, incorporating a quality factor $q$ based on rating consistency ($X = q \cdot U$; $q = 1 - (|r_i - \bar{r}|/\bar{r})$ for crisp criteria), reflecting dispersion among experts. Exergy enables quality-adjusted ranking and penalizes alternatives with high rating variability even if their mean score is high.
• Entropy (S): Quantifies the unevenness among ratings ($S = U - X$); higher entropy corresponds to less reliable aggregate judgments.

The comprehensive workflow includes assembling the decision matrix, normalization, weight assignment, energy/exergy/entropy calculation (including fuzzy environments using triangular fuzzy numbers), and final ranking based on exergy. Case studies demonstrate exergy-based ranking distinguishes alternatives with similar energy scores but differing rating reliability, asserting the superiority of exergy over energy for robust selection (Verma et al., 2015).

2. Evolutionary Dynamics and Multi-Level Selection

Energy-independent selection criteria are fundamental in evolutionary models that decouple fitness or success from direct energy-yield maximization (Amado et al., 2016). In models comparing metabolic strategies:

• Efficient strain (C): High-yield, low-rate resource conversion.
• Inefficient strain (D): High-rate, low-yield resource conversion.

Mathematical formulations capture the resource uptake and ATP conversion (e.g., Eqns. 1–3), with selection for optimized energy usage measured by mutant invasion and fixation probability. The crucial insight is that:

• In well-mixed populations, the efficient strain's advantage is tightly constrained by immediate energy payoff, limiting evolutionary stability to non-dilemma domains.
• Structured populations (small groups, kin selection, group splitting) expand the parameter space where the efficient strain thrives, driven by group benefits and relatedness rather than direct energy advantage.

Thus, population structure and group dynamics act as energy-independent selection criteria—success is determined by social context, not energy extraction alone. These models elucidate evolutionary transitions (e.g., to multicellularity), showing that cooperative behaviors may arise via higher-level mechanisms decoupled from individual energetic benefit (Amado et al., 2016).

3. Competitive Algorithms in Energy Plan Selection

In retail energy markets with choice, the challenge of optimal energy plan selection can be framed as a metrical task system (MTS) problem, with performance guarantees that are robust to future energy price or demand uncertainty (Zhai et al., 2019).

• Selection algorithms: Both deterministic (gCHASEₛ, 3-competitive) and randomized (gCHASEₛʳ, 2-competitive) algorithms use cumulative cost differences ($\Delta(t)$, $\hat{\Delta}(t)$) and switching cost dynamics to determine when to “stay or switch” plans.
• Energy independence: Algorithms require only past and current data, do not rely on predictive energy metrics, and maintain optimality regardless of fluctuating rates, usage, or cancellation fee structures.
• Empirical results: Deterministic online algorithms achieve near-optimal cost savings (14.6–16.2%), validating the robustness of selection criteria that do not depend on precise energy forecasts.

The approach enables automated, transparent, and energy-independent plan recommendation in complex retail environments, supported by worst-case optimality proofs (Zhai et al., 2019).

4. Flexible Design Selection in Energy Systems

In multi-objective energy system design, energy-independent selection criteria are established via the flexible here-and-now decision (“flex-hand”) methodology (Hollermann et al., 2019).

• Optimization strategy: A fixed design is selected to minimize the $$-indicator distance between its Pareto front (across all operational choices) and the ideal Pareto front (achieved via design-specific optimization per trade-off point):$$ I(\mathcal{P}(x^{\mathrm{f}}), \mathcal{P}^*) = \min \left{\epsilon : \forall q \in \mathcal{P}^*, \exists p \in \mathcal{P}(x^{\mathrm{f}}) \text{ such that } p_i - q_i \leq \epsilon \;\forall i \right}

$$</p> <ul> <li><strong>Criteria considered:</strong> Economic (annualized costs) and environmental (global warming impact), often with conflicting priorities.</li> <li><strong>Energy independence:</strong> The methodology avoids objective weighting and pre-specification, instead selecting a design that delivers maximum operational adaptability for arbitrary future priorities or scenarios.</li> <li><strong>Robustness to uncertainty:</strong> Extensions handle parameter uncertainty by minimizing the worst-case indicator value across multiple discrete scenarios.</li> <li><strong>Case paper:</strong> Achieved at most a 3.6</ul> <p>This procedure supports system design choices unaffected by single-energy objectives, accommodating future shifts in policy or operational focus (<a href="/papers/1906.08621" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Hollermann et al., 2019</a>).</p> <h2 class='paper-heading' id='energy-independent-multi-criteria-evaluation-in-renewable-energy'>5. Energy-Independent Multi-Criteria Evaluation in Renewable Energy</h2> <p>Energy-independent selection criteria also underpin evaluation frameworks for renewable energy technologies via MCDM methods (<a href="/papers/2303.17520" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Bhatia et al., 2023</a>).</p> <ul> <li><strong>MCDM techniques:</strong> TOPSIS and MOORA rank alternatives using a multi-objective formulation ($$F(x) = (f_1(x), ..., f_k(x))^\top$) subject to technical, economic, environmental, and social criteria.

Objective weighting: Standard deviation and entropy methods quantify criteria dispersion and uncertainty, removing subjective bias.

Case application: Alternative PV panels (A1–A30) are ranked strictly via performance parameters (efficiency, cost, lifetime, emissions), not direct energy output alone.

Significance: The criteria remain stable irrespective of final energy yield, supporting systematic, unbiased evaluation for sustainable technology selection (Bhatia et al., 2023).

6. Site Selection Methodologies Under Non-Energetic Constraints

Methodologies for renewable energy site selection further demonstrate the application of energy-independent criteria via mathematical programming and benefit metrics (Sen et al., 2023).

• Two-phase selection: GIS+AHP for initial filtering, followed by coarse- and fine-grained optimization.
• Coarse-grained metrics: Interval Utility (IU), Cumulative Sub-Interval Utility (CSIU; both submodular with approximation algorithms), and Minimum Sub-Interval Utility (MSIU).
• Fine-grained optimization: Integer Linear Programming (ILP) models with network flow constraints, maximizing demand coverage rather than location-specific energy output.
• Experimental validation: Synthetic datasets verify that robust site selection and demand coverage are achievable with constraints on budget, supply, and transmission, illustrating resilience under a wide range of operational conditions (Sen et al., 2023).

7. Abiogenesis and Thermodynamic Selection

In prebiotic chemistry and the theory of abiogenesis, energy-independent selection criteria are formalized as an energy-budget-based viability inequality (Prosser, 24 Apr 2025):

• Viability inequality: A reaction network persists only if aggregated environmental energy input $z(t)$ plus cumulative reaction release $\sum r_i$ minus expenditure $\sum x_i$ is non-negative ($y(t) = z(t) + \sum r_i - \sum x_i \geq 0$).
• Selection filter: This condition acts as a sieve, retaining only "energetically compatible" networks, irrespective of heredity or genetic encoding.
• Extensions: Models include stored energy, entropic dynamics, and spatial constraints to account for realistic chemical environments.
• Generalization: TALM (Thermodynamic Abiogenesis Likelihood Model) estimates persistence likelihood under fluctuating energy regimes and diverse planetary scenarios.
• Empirical prospects: Laboratory simulations of fluctuating environments and computational studies provide avenues for testing the energy-independent selection concept (Prosser, 24 Apr 2025).

Summary Table: Domains and Applications of Energy-Independent Selection Criteria

Domain	Methodology	Criteria/Metric
MCDM/Thermodynamics	Energy/Exergy/Entropy indicators	Reliability-adjusted rating quality
Evolutionary Biology	Multilevel selection models	Group structure, kin selection
Energy Markets	Competitive online algorithms	Cost-difference thresholds, switching
Energy Systems Design	Flex-hand Pareto indicator	Distance to ideal multidimensional trade-offs
Renewable Tech MCDM	TOPSIS/MOORA, SD/entropy weights	Intrinsic parameters, not direct output
Site Selection	ILP, coarsed/fine-grained metrics	Demand coverage maximization
Abiogenesis	Energy-budget viability inequality	Reaction persistence under energy flux

Concluding Perspective

Energy-independent selection criteria systematically extend selection, optimization, and decision frameworks beyond the simplistic maximization of instantaneous energetic output. By integrating thermodynamic measures of reliability, multi-level and kin-based interactions, robust operational adaptability, and principled mathematical programming, these criteria enable resilient, unbiased, and context-aware choice mechanisms in engineering, evolutionary, and physical systems. Their rigorous theoretical structures and empirical grounding across diverse scientific domains underscore their centrality to modern decision-making and modeling methodologies.