Smarter Subsidies: New Framework Targets Power Grid Losses Where Money Works Hardest

Every year, a startling share of the electricity flowing through distribution grids simply vanishes. It leaks away as heat in conductors and transformers, disappears through faulty meters and unmetered connections, and is siphoned off by unauthorized consumption. In Iran, where distribution losses have hovered between roughly 12 and 15 percent of delivered energy in recent years, this invisible drain represents thousands of gigawatt-hours of energy that was generated, transmitted, and then never billed. Now, a team of researchers has built a decision-support framework that promises to change how governments spend the money they pour into fighting these losses, and the results suggest that a simple reallocation of existing budgets could more than double the national reduction achieved.

The study, published in the journal Results in Engineering by Aidin Shaghaghi, Vahid Rezaei, Rahim Zahedi, and Reza Dashti, tackles a governance problem that has long been obscured by technical complexity. In Iran, responsibility for loss reduction is split between a national tier—the Ministry of Energy, TAVANIR, and the Iran Grid Management Company, which measure system-wide losses and allocate budgets—and a management tier of 39 regional distribution companies that carry out the actual work. The authors found that the way money flows between these tiers bears little relationship to where it can do the most good. In 2022, nearly a third of the national loss-reduction budget went to companies whose losses actually increased, while nine companies that received no funding at all managed to cut their losses by a combined 366.3 gigawatt-hours.

At the heart of the framework is a deceptively simple question: how much of a utility’s measured loss is actually reducible? The answer begins with the concept of intrinsic loss, a technical floor determined by conductor resistance, transformer characteristics, load factor, and network geometry. Using a conventional loss-factor approach, the researchers calculated this floor for each of the 39 companies. The results were striking. Intrinsic losses varied only narrowly, from 2.09 to 5.74 percent of delivered energy, while measured losses ranged from 6.39 to 23.66 percent. In other words, the enormous differences between utilities stem almost entirely from the reducible component—losses caused by poor maintenance, inadequate metering, and unauthorized consumption—rather than from the physics of their networks. Nationally, about 80 percent of measured loss lies above the intrinsic floor and is therefore, in principle, recoverable.

This intrinsic-loss floor serves as an eligibility gate in the framework, and it is one of the study’s original contributions. A company whose measured loss does not exceed its own technical baseline has no reducible loss at its current configuration, and no amount of operational subsidy can buy a reduction the network does not admit. Such companies are excluded from the incentive envelope and referred instead to long-term capital modernization programs. The researchers validated this floor with robustness checks: even if their intrinsic-loss estimates were doubled, only one company would become ineligible, and the top performance tiers would survive estimates inflated by a factor of 2.5.

The second pillar of the framework addresses a phenomenon well known to grid operators but rarely formalized in regulatory design: diminishing returns. The cost of removing an additional kilowatt-hour of loss rises monotonically as losses fall, following an S-shaped curve with distinct regimes. In the descending region, substantial reductions come cheap; in the saturation region, incremental capital yields almost nothing. To detect which regime a utility occupies, the team developed a multi-year saturation index that compares overlapping three-year windows of loss data, smoothing out the measurement noise that plagues single-year comparisons. With a dead band of 0.01 to absorb metering error, the test placed 15 Iranian companies in the descending region, 4 in the saturation band, and 20 in the ascending region—meaning their losses were actually rising. For saturated companies, further subsidy is deadweight; recovery requires structural innovation rather than more of the same spending.

A third original element, the loss reversibility index, captures the fragility of past gains. Capital investment in new conductors, insulated lines, and metering infrastructure eliminates loss only if it is sustained by operational spending—inspection, meter verification, connection tightening, and maintenance. Withdraw the operational expenditure, and losses rebound as assets decay. The index quantifies the loss at risk of returning if that sustaining expenditure is not maintained, linking the durability of reductions to a budget category that regulators often treat as dispensable.

With these diagnostics in place, the framework sorts utilities into four ordered tiers using a deliberately non-compensatory rule. Companies with a descending loss trend and a competitive unit cost enter Tier A or B, where budgets are allocated in proportion to their reducible potential divided by their realized cost of reduction, with Tier A companies earning a bonus if they reduce more loss than their scale predicts. Companies that fail the trend test but carry a large share of national loss fall into Tier C, where they receive restricted funding under containment targets—because letting a systemically heavy utility’s losses escalate would offset gains elsewhere. Smaller failing companies land in Tier D with a minimum envelope and a mandatory audit. Crucially, the thresholds are structural rather than regulator-chosen: zero for eligibility, unity for comparative performance, and one-thirty-ninth for systemic weight, making the allocation reproducible by any regulator without subjective weighting.

The contrast with conventional multi-criteria methods is illuminating. When the researchers solved the same allocation problem using TOPSIS and VIKOR—two standard compensatory techniques that aggregate criteria into a single score—the entropy-derived weights gave the multi-year trend a weight of just 0.043, effectively ignoring it. As a result, five companies with rising or saturated losses ranked in the upper third of the compensatory orderings, their low single-year unit costs compensating for trends showing no lasting improvement. The Spearman correlation between the compensatory rankings and the tier-based ordering was only 0.52 to 0.60, demonstrating that the choice of decision rule materially changes who gets funded.

The practical payoff is quantified in a comparison with the actual 2022 allocation. Iran spent 71.23 million US dollars on loss reduction that year and achieved a net national reduction of 180.7 gigawatt-hours, at an effective cost of 394.2 dollars per megawatt-hour. Simply redistributing the same money among eligible companies according to the framework’s weights—shifting funds away from one utility operating at 371.92 dollars per megawatt-hour toward others at 38 to 68 dollars—would raise the expected reduction to 391.1 gigawatt-hours, cutting the unit cost nearly in half. Going further and transferring the 8.32 million dollars given to restricted-tier companies into the incentive envelope lifts the expected reduction to 511.0 gigawatt-hours at 139.4 dollars per megawatt-hour, a nearly threefold improvement in cost-effectiveness from the same total budget.

The study’s implications reach well beyond Iran. Loss levels vary widely across countries and are governed by economic, geographic, and institutional conditions as well as technical ones, but the logic of the framework—separate the irreducible from the reducible, detect saturation before it wastes money, reward durability, and protect systemically important laggards—applies wherever public funds flow to state-owned or regulated distribution utilities. The authors caution that their analysis is descriptive rather than causal, that the four-year window is short, and that single-year unit costs can be unstable, as illustrated by one company whose tiny reduction implied an implausible 5,785 dollars per megawatt-hour. Future work should extend the panel, estimate intrinsic losses at feeder level, and evaluate the allocation out of sample. But the core message is already actionable: for most utilities, the dominant loss component is not technical, so the highest-return investments are in metering, inspection, and billing—unglamorous tools that, properly funded and properly targeted, could turn one of the power sector’s most stubborn inefficiencies into one of its most solvable problems.

Subject of Research: A decision-support framework for incentive-based regulation and subsidy allocation to reduce power distribution losses under diminishing returns

Article Title: A decision-support framework for incentive-based regulation of power distribution losses with optimizing subsidy allocation under diminishing returns

Article References: Shaghaghi, A., Rezaei, V., Zahedi, R., & Dashti, R. (2026). A decision-support framework for incentive-based regulation of power distribution losses with optimizing subsidy allocation under diminishing returns. Results in Engineering, 32, Article 113365. https://doi.org/10.1016/j.rineng.2026.113365

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113365

Keywords: power distribution, electricity losses, incentive regulation, subsidy allocation, diminishing returns, grid efficiency, non-technical losses, Iran, decision-support framework, energy policy, utility benchmarking, regulation