Distributionally Fair Peer-to-Peer Electricity Trading

Estibalitz Ruiz Irusta; Juan M. Morales

REVIEW 4 major objections 5 minor 1 cited by

Distributionally Fair Peer-to-Peer Electricity Trading

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper proposes an optimization mechanism that redistributes peer-to-peer electricity trades to minimize the largest Wasserstein distance between groups defined by energy poverty, cutting unfairness by up to 70.1% at a chosen…

desk verdict A useful group-level fairness mechanism for P2P electricity markets, but the headline reductions are computed from an upper bound, not the true Wasserstein distance of the final solution. read the letter →

arxiv 2501.09713 v1 pith:CEGFCTNG submitted 2025-01-16 math.OC

Estibalitz Ruiz Irusta , Juan M. Morales This is my paper · ORCID

classification math.OC MSC 90C2690C90

keywords peer-to-peerelectricitytradingdistributionalfairnessWassersteindistanceenergypovertyalternatingalgorithmbilinearoptimizationIEEE33-buscommunityPVplant

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a peer-to-peer electricity market can trade fairly across groups with different energy poverty levels, not just efficiently. It defines unfairness as the maximum Wasserstein distance between the distributions of traded electricity across groups, then proposes an optimization model that reshuffles trades to shrink that distance while limiting how much profit the community gives up. On a simulated 1600-household community on the IEEE 33-bus grid, the model cuts unfairness by up to 70.1% at one hour and by 23% on average, with a user-chosen sacrifice parameter. The paper also shows that a small non-profit community photovoltaic plant can push unfairness close to zero. If correct, the work offers a practical lever for community energy managers who want equity without fully abandoning market outcomes.

What carries the argument

The central object is the transaction matrix $X$, where $X_{ij}$ is the electricity sold by peer $i$ to peer $j$, from which each group $g$'s transaction distribution $T^g_i = \sum_{j\in\mathcal{P}}(X_{ij}+X_{ji})$ is built by summing traded electricity per peer. Unfairness is measured as $D_{\max} = \max\{W(g,g')\}$, the largest Wasserstein distance between any two group distributions, where each $W(g,g')$ is the minimum transportation cost between the two distributions. The fairness model minimizes $D_{\max}$ subject to energy-balance, bid-matching, and LinDistFlow grid constraints, plus a group-level profit floor $(1-\epsilon)|\gamma^*_g|$, a utility-trade cap, and a curtailment cap. The Wasserstein distance introduces a bilinear term $\pi^{g,g'}_{ij} d^{g,g'}_{ij}$ because both the transportation plan and the transaction distances are variables, so the paper solves the nonconvex problem with an alternating algorithm that fixes one variable set, solves the resulting LP, then swaps and iterates until the two $D_{\max}$ values agree within tolerance.

What would settle it

Solve the same hourly instances (08/07/2024, each epsilon value) with an exact mixed-integer linear reformulation of the bilinear Wasserstein constraint and compare the optimal $D_{\max}$ with the alternating algorithm's values; if the exact optima are materially lower, the reported reductions come from the heuristic, not from the model.

Watch

Extended reading notes

Core claim

Under selfish peer-to-peer trading, electricity trades concentrate among energy-rich households, and the group that is discriminated against changes with tariff conditions. The paper's central claim is that a distributionally fair trading model can substantially reduce this group-level inequality: it minimizes the maximum Wasserstein distance among the transaction distributions of groups defined by energy poverty, subject to a user-selected profit-sacrifice level epsilon and constraints that prevent fairness from being achieved by shifting trades to the utility or by extra curtailment. Solving this bilinear problem with an alternating algorithm on the IEEE 33-bus test system with 1600 peers, the model reduces the unfairness level by up to 70.1% at 18:00 and by 23% on average, and adding a 20 kW non-profit community PV plant lowers unfairness by 51.95% on average at epsilon = 1%, with near-complete elimination at some hours. The model also identifies which group is currently discriminated against, and this target group changes over time depending on surplus and tariffs.

Load-bearing premise

The load-bearing premise is that the alternating algorithm's output is close enough to the true optimum that the reported unfairness levels and plateaus reflect the model's real capability; the paper states in the Conclusions that the algorithm can stop at local minima and that global optimality is not guaranteed.

Editorial extensions

If this is right

Community managers can reduce group-level trading inequality by a specified amount while capping profit loss, since epsilon is a user-selected parameter.
The model identifies the discriminated group in each hour, and that target group changes with market conditions, so fairness actions can be adaptive rather than fixed.
Unfairness decreases as the allowed sacrifice grows, but only up to a per-hour fairness limit; beyond that limit, additional sacrifice yields no further improvement.
Adding a non-profit community PV plant reduces unfairness further, with a 20 kW plant cutting average unfairness by 51.95% relative to the reference at epsilon = 1%.
The same mechanism transfers to other group definitions, such as tariff type, PV ownership, or consumption patterns, because the group partition is an input to the model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: Because profits are constrained only at the group level, two households within the same group could still end up with very different trade volumes; a within-group dispersion constraint would be a natural check that this model does not provide.
Editorial inference: The plateau behavior suggests that when total surplus is structurally smaller than total deficit, reallocating trades alone cannot eliminate inequality; changing the resource endowment, as the community PV plant does, is the lever that pushes unfairness toward zero.
Editorial inference: The same Wasserstein-minimax objective could apply to other community resource-allocation settings, such as water or data-bandwidth sharing, wherever per-agent allocations can be grouped and feasibility constraints are linear.
Editorial inference: A practical deployment could track $D_{\max}$ in real time from the transaction matrix alone, without solving the fair model, and trigger re-optimization only when the fairness metric rises above a threshold.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Thanks for the report. I read it against the paper, and the stress-test concern holds. Algorithm 1 never evaluates the actual unfairness of the transaction matrix it outputs. In each loop, D1max is the true distance of the current X, but then line 11 fixes π and solves (9a)-(9k) to get a new X and D2max. That fixed π is feasible for any X because its marginals depend only on group sizes, so D2max is an upper bound on the true distance of the new X, not the true value. Line 14 averages the two, and the final X is never checked. Tables 2-3 and the 70.1% and 23% claims are therefore built on a quantity that can be materially larger than the actual unfairness. This is independent of the local-minima issue: even exact subproblem solves leave the bug. The fix is straightforward—evaluate the true max Wasserstein distance of the final X and report that—but without it the main quantitative results are not trustworthy.

What the paper does well: it brings Ye et al.'s distributionally fair max-Wasserstein objective into a P2P clearing model with energy-poverty groups and a user-set profit sacrifice. The LP formulation is mostly clear, the constraints on group profit, curtailment, and utility trades are sensible, and the IEEE 33-bus case with 1600 peers is a real stress test. The authors also honestly flag the local-minima limitation in the conclusions. That is a legitimate new application, not a new theory.

The buyer-side profit term in Eq. (4) and (9c) uses λus_j where it should use λus_i for the importing peer, and the bid indices in (9c) look swapped too. This mis-specifies group profits, and since the sacrifice level ε is defined relative to those profits, the fairness/efficiency tradeoff could shift. Minor but should be fixed. The abstract's 'full elimination' also overstates the 0.01 kWh floor in Table 3; the body correctly says 'nearly complete.' No code or data are provided, so I could not check the numerics independently.

Overall: the central idea is coherent and the flaws are addressable. As-is, I would not rely on the headline numbers, but the paper deserves referee time and a request for corrected tables and, ideally, code. Send it to review rather than desk reject.

Referee Report

4 major / 5 minor

Summary. The paper proposes an optimization-based mechanism for distributionally fair peer-to-peer electricity trading. Peers are partitioned into groups by energy poverty level, and the mechanism minimizes the maximum Wasserstein distance between the distributions of traded electricity across groups, subject to grid constraints, balance constraints, and a user-specified profit sacrifice level. The resulting bilinear problem is solved with an alternating algorithm. A case study on an IEEE 33-bus system with 1600 peers reports reductions in the unfairness metric of up to 70.1% at certain hours and near-elimination when a non-profit community PV plant is added. The paper also analyzes how the unfairness level varies across the day and with tariff structures.

Significance. The core idea of defining group-level fairness through the Wasserstein distance between transaction distributions is a useful and original contribution to P2P market design. The paper is careful to include grid constraints through the LinDistFlow approximation, and the case study is large and realistic, which strengthens the practical relevance. The authors explicitly acknowledge the NP-hard nature of their model and the local-minimum limitation of their alternating algorithm. If the technical issues below are corrected, the mechanism could be a valuable tool for community managers who want to trade off collective fairness against profits. However, the reported quantitative claims currently rest on a mis-specified profit term and on an algorithm whose output is not the true unfairness of the final trade matrix.

major comments (4)

[Eq. (4) and Eq. (9c)] The buyer-side profit term is mis-specified. In Eq. (4), the profit of peer i from buying X_ji should include the utility price for that buyer, λ^us_i, not the seller's utility price λ^us_j. As written, the term X_ji(λ^us_j - (λ^s_j + λ^b_i)/2) values the purchase using the wrong utility price. In Eq. (9c), the analogous term is X_ji(λ^us_j - (λ^s_i + λ^b_j)/2), which also uses the wrong bid/ask average; the settled price for a transaction where j sells to i should be (λ^s_j + λ^b_i)/2. Because Eq. (9c) defines the per-group profit sacrifice constraint, all results that depend on ε (Tables 2-4, the 70.1% claim, and the 'fairness limit' plateaus) are not guaranteed to correspond to the stated sacrifice levels. The indices must be corrected and the case study re-run.
[Algorithm 1, lines 7-14] The reported D_max is not the true maximum Wasserstein distance of the output trade matrix. At each iteration, D1_max is the true distance of the current X (computed via T, d, and the optimal π), but then line 11 solves (9a)-(9k) with π fixed, producing a new X and D2_max. For that new X, the fixed π is only a feasible transport plan, so D2_max is an upper bound on the true distance, not its value. The loop terminates when |D1_max - D2_max| ≤ tol, and line 14 reports the average of the two. The final X from the last line-11 solve is never re-evaluated for its true unfairness. Consequently, the entries in Tables 2 and 3, and the derived reduction percentages, are computed from a quantity that can differ from the true max Wasserstein distance of the reported outcome. The termination criterion also compares a true distance from one iterate with an upper bound from the next, so it does not certify stabilization of the true distances. The algorithm should be modified to compute the true D_max of the final X (by solving (7a)-(7c) for that X) and to base termination on true consecutive values.
[Eq. (9g)] The definition of s^{g,g'}_{ij} is inconsistent with the index sets. The equation states s^{g,g'}_{ij} = T^{g'}_i - T^g_j for i∈g and j∈g', but T^{g'}_i is not defined because i does not belong to g'. The intended absolute-value representation should be s^{g,g'}_{ij} = T^{g'}_j - T^g_i (or equivalently the negative of t). As printed, the constraints (9h) do not enforce d^{g,g'}_{ij} = |T^g_i - T^{g'}_j|, and the model would not compute the correct Wasserstein distance. This needs to be corrected and the implementation checked against the corrected equation.
[Abstract and Table 3] The abstract claims 'even achieving a full elimination when including a non-profit community photovoltaic plant', but Table 3 shows strictly positive unfairness values in all hours, with the 20 kW case yielding 0.01 kWh at 09:00 and 0.75 kWh at 18:00. The main text (Section 5.3) correctly says 'nearly complete elimination achieved at certain hours'. The abstract and the Introduction overstate the result; they should be aligned with the actual table values.

minor comments (5)

[Section 5.2] The claim that the fair model reduces unfairness 'by 23% on average' is not clearly defined. It does not match the reduction implied by the total row in Table 2 (from 1,117.22 to 907.16, about 18.8%), and it is ambiguous whether the average is over hours, over dates, or computed differently. The authors should specify the calculation.
[Throughout] The paper uses both 'sell to the highest bidder' and a reference model that maximizes sellers' revenue, but the objective in (1a) also includes the utility buyback price λ^ub_i for each seller. The exact interpretation of the reference profit should be clarified, and the derivation of the reference model should be expanded.
[Section 4.1 and Fig. 3] The tariff data for the two dates are described, but the dates are not clearly connected to the rest of the case study. The reader has to infer that 15/10/2022 and 08/07/2024 are the simulation days. A short statement in Section 4.1 would improve readability.
[Section 6] The conclusion acknowledges local minima, but it does not discuss the possibility of multiple local optima or the sensitivity of the results to the initialization procedure described in Section 5.2. Since the paper already mentions warm-starting from adjacent ε, a brief sensitivity discussion would help the reader trust the reported plateaus.
[Table 4] The computational times vary widely (e.g., 1492 s at 12:00 for ε=2% vs. 96 s at 09:00 for ε=100%), but no solver version, hardware, or MIP gap (if any) is provided. Adding these details would make the performance claims reproducible.

Circularity Check

0 steps flagged · score 1.0 of 10

No load-bearing circularity; the headline reduction is the optimized Wasserstein objective itself, with the main caveats being algorithmic (local minima and Dmax reporting), not definitional circularity.

full rationale

The central quantitative claim—that the Distributionally Fair P2P model lowers unfairness by up to 70.1%—is measured with the same quantity that the model minimizes: Dmax, the maximum Wasserstein distance between group trade distributions (Eq. (8) vs. objective (9a)). This is self-referential in the sense that the reported improvement is the optimizer's own objective improvement rather than an independent, externally anchored validation. It is not, however, a circular derivation of the kind the rubric targets: nothing is fitted from the reported outcome to predict that same outcome; the sacrifice level ε is a user input, not a fitted parameter; no uniqueness theorem or ansatz is imported from the authors' prior work; and no known result is merely renamed. The feasibility tradeoff is nontrivial because the objective reduction is obtained under the profit-sacrifice constraints (9c), the no-extra-utility/curtailment bounds (9d)-(9e), and the LinDistFlow grid constraints (1e)-(1f). The paper itself flags the alternating algorithm's susceptibility to local minima in the Conclusions. An additional, unstated algorithmic caveat is that Algorithm 1 reports Dmax = (D1max + D2max)/2 after stopping, where D1max is the true max distance of the previous iterate and D2max is an upper bound from the fixed-π solve (line 11); the returned X is never re-evaluated for its true Dmax, so Tables 2-3 carry a computational-accuracy risk. These are correctness concerns that lower confidence in the headline numbers, but they are not definitional circularity and do not justify a high circularity score.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The model's quantitative findings depend on a small set of hand-picked simulation parameters (power peaks, PV ownership, tariff mixes, noise) and on the user-defined sacrifice level. The fairness metric is assumed to be a valid measure of equity, and the alternating solver is heuristic. No new physical entities are introduced; the non-profit community PV plant is a modeled real-world actor with zero selling price.

free parameters (6)

sacrifice level epsilon = swept over 1% to 100%
User-defined bound on how much each group's profit may fall relative to the reference solution; Table 2 is parameterized by epsilon.
group power peaks (R/M/P) = 5.1 / 3.9 / 2.1 kW
Hand-chosen from the literature on household power peaks; they determine consumption and production profiles and thus the feasible trades and the measured unfairness.
PV ownership shares (R/M/P) = 80% / 20% / 0%
Assumed shares based on investment-cost reasoning; they define which groups can sell surplus and strongly affect group-level trade distributions.
tariff plan mix per group = R 80/10/10, M 50/10/40, P 20/10/70 (dynamic/double/flat)
Assumed tariff assignment; combined with EPEX prices on the two selected days, it drives bid prices and trade matching.
profile noise level = 10% normal noise
Added to IEA profiles to make households unique; no seed is reported, so replication is impossible.
algorithm tolerance and max iterations = tol = 0.01, iter = 15
Stopping rules for the alternating heuristic; they define how close the reported Dmax values are to a fixed point.

assumptions (4)

domain assumption Wasserstein distance between per-peer trade distributions is an adequate operationalization of unfairness
Defined in Section 2.3 as the max pairwise distance; no external validation or comparison with other fairness metrics.
domain assumption Peers' bids coincide with their utility tariff prices, with no strategic behavior
Section 2 sets lambda_s_i and lambda_b_i from utility tariffs and excludes bidding games, which simplifies the market-clearing model.
standard math LinDistFlow approximation accurately captures grid voltage limits in the case study
Used in constraints (1e)-(1f); no error analysis relative to exact AC power flow is reported.
ad hoc to paper The alternating algorithm's fixed point approximates the global optimum of the bilinear fair model
Algorithm 1 is heuristic; Section 6 states global optimality cannot be guaranteed, so reported Dmax values depend on this assumption.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Distributionally Fair Peer-to-Peer Electricity Trading." pith.science (2026). https://pith.science/paper/CEGFCTNG

@misc{pith2026250109713,
  author       = {Pith},
  title        = {Pith review of: Distributionally Fair Peer-to-Peer Electricity Trading},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CEGFCTNG}},
  note         = {Machine review of arXiv:2501.09713}
}

read the original abstract

Peer-to-peer energy trading platforms enable direct electricity exchanges between peers who belong to the same energy community. In a semi-decentralized system, a community manager adheres to grid restrictions while optimizing social welfare. However, with no further supervision, some peers can be discriminated against from participating in the electricity trades. To solve this issue, this paper proposes an optimization-based mechanism to enable distributionally fair peer-to-peer electricity trading. For the implementation of our mechanism, peers are grouped by energy poverty level. The proposed model aims to redistribute the electricity trades to minimize the maximum Wasserstein distance among the transaction distributions linked to the groups while limiting the sacrifice level with a predefined parameter. We demonstrate the effectiveness of our proposal using the IEEE 33-bus distribution grid, simulating an energy community with 1600 peers. Results indicate that up to 70.1% of unfairness can be eliminated by using our proposed model, even achieving a full elimination when including a non-profit community photovoltaic plant.

Figures

Figures reproduced from arXiv: 2501.09713 by the authors.

**Figure 1.** Community’s distribution system. Energy consumption and production profiles are obtained from (IEA, 2019). These profiles have been first normalized to obtain typical consumption and production patterns. Additionally, 10% of normal noise was added to randomly alter the profiles and make them unique. Then, each profile was scaled by the power peak chosen for each energy group category. Energy-rich households usuall… view at source ↗

**Figure 2.** Households’ production and consumption profiles. [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗

**Figure 3.** Electricity tariffs on 15/10/2022 (left) and 08/07/2024 (right). [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Cummulative PV production, consumption, and surplus. [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]

**Figure 5.** Figure 5: Trade distributions in reference and fair ( [PITH_FULL_IMAGE:figures/full_fig_p020_5.png]

**Figure 6.** Figure 6: Trade distributions in reference and fair ( [PITH_FULL_IMAGE:figures/full_fig_p021_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Scalable Fairness Shaping with LLM-Guided Multi-Agent Reinforcement Learning for Peer-to-Peer Electricity Markets
eess.SY 2025-08 reject novelty 4.0 of 10

FairMarket-RL injects LLM-computed fairness scores into PPO rewards and reports 30-90 day P2P market simulations with roughly 54% local trade share, 27-30% consumer bill savings, and a profitable grid.

Reference graph

Works this paper leans on

24 extracted references · 15 canonical work pages · cited by 1 Pith paper

[9]

Australian Journal of Agricultural and Resource Eco- nomics doi:10.1111/1467-8489.12487

Solar PV and energy poverty in australia’s 27 residential sector. Australian Journal of Agricultural and Resource Eco- nomics doi:10.1111/1467-8489.12487. IEA,

work page arXiv
[11]

Energy Research & Social Science doi: 10.1016/j

Revisiting energy poverty measurement for the european union. Energy Research & Social Science doi: 10.1016/j. erss.2024.103420. Lei, J., Gao, S., Wei, X., Shi, J., Huang, T., Hatziargyriou, N.D., Chung, C.Y.,

arXiv 2024
[12]

IEEE Transactions on Power Systems doi:10.1109/TPWRS.2022.3225656

A shareholding-based resource sharing mechanism for promoting energy equity in peer-to-peer energy trading. IEEE Transactions on Power Systems doi:10.1109/TPWRS.2022.3225656. Moret, F., Pinson, P.,

arXiv 2022
[13]

IEEE Transactions on Power Systems doi:10.1109/TPWRS.2018.2808961

Energy collectives: A community and fairness based approach to future electricity markets. IEEE Transactions on Power Systems doi:10.1109/TPWRS.2018.2808961. Nussbaumer, P., Bazilian, M., Modi, V.,

work page arXiv 2018
[15]

IEEE Access doi: 10.1109/ ACCESS.2020.3041838

Peer-to-peer energy transaction mechanisms con- sidering fairness in smart energy communities. IEEE Access doi: 10.1109/ ACCESS.2020.3041838. Sadeghi, R., Sadeghi, S., Memari, A., Rezaeinejad, S., Hajian, A.,

arXiv 2020
[16]

Journal of Cleaner Production doi: 10.1016/j.jclepro.2024.141880

A peer-to-peer trading model to enhance resilience: A blockchain-based smart 28 grids with machine learning analysis towards sustainable development goals. Journal of Cleaner Production doi: 10.1016/j.jclepro.2024.141880. Sahih, A.Z., Abbasi, A., Ghasri, M.,

work page arXiv 2024
[17]

Journal of Cleaner Production doi: 10.1016/j.jclepro.2024

Blockchain-enabled solutions for fair and efficient peer-to-peer renewable energy trading: An experimental comparison. Journal of Cleaner Production doi: 10.1016/j.jclepro.2024. 142301. Schwidtal, J., Piccini, P., Troncia, M., Chitchyan, R., Montakhabi, M., Fran- cis, C., Gorbatcheva, A., Capper, T., Mustafa, M., Andoni, M., Robu, V., Bahloul, M., Scott, ...

work page doi:10.1016/j.jclepro.2024 2024
[18]

Renew- able and Sustainable Energy Reviews doi: 10.1016/j.rser.2023.113273

Emerg- ing business models in local energy markets: A systematic review of peer-to- peer, community self-consumption, and transactive energy models. Renew- able and Sustainable Energy Reviews doi: 10.1016/j.rser.2023.113273. Siksnelyte-Butkiene, I., Streimikiene, D., Lekavicius, V., Balezentis, T.,

arXiv 2023

Show all 24 references

[20]

Applied Energy doi: 10.1016/j.apenergy.2024.123933

Review on fairness in local energy systems. Applied Energy doi: 10.1016/j.apenergy.2024.123933. Taheri, B., Gupta, R.K., Molzahn, D.K.,

2024
[21]

Vieira, G., Zhang, J.,

Optimizing parameters of the LinDistFlow power flow approximation for distribution systems URL: https://doi.org/10.48550/arXiv.2404.05125. Vieira, G., Zhang, J.,

doi
[22]

Renewable and Sustainable Energy Reviews doi:10.1016/j.rser.2021.110900

Peer-to-peer energy trading in a microgrid leveraged by smart contracts. Renewable and Sustainable Energy Reviews doi:10.1016/j.rser.2021.110900. 29 Ye, Q., Hanasusanto, G.A., Xie, W.,

2021
[23]

Zhou, Y., Lund, P.D.,

Distributionally fair stochastic op- timization using wasserstein distance URL: https://doi.org/10.48550/ arXiv.2402.01872. Zhou, Y., Lund, P.D.,

doi arXiv
[24]

Renewable Energy doi: 10.1016/j.renene

Peer-to-peer energy sharing and trading of renew- able energy in smart communities - Trading pricing models, decision-making and agent-based collaboration. Renewable Energy doi: 10.1016/j.renene. 2023.02.125. 30

2023 doi
[2012]

Renewable and Sustainable Energy Reviews doi:10.1016/j.rser.2011.07.150

Measuring energy poverty: Focusing on what matters. Renewable and Sustainable Energy Reviews doi:10.1016/j.rser.2011.07.150. Oh, E., Son, S.Y.,

2011 doi
[2013]

Power Systems, IEEE Transactions on doi:10.1109/TPWRS

Branch Flow Model: Relaxations and Convexifi- cation—Part I. Power Systems, IEEE Transactions on doi:10.1109/TPWRS. 2013.2255317. Ghorbani-Renani, N., Preziuso, D., Odonkor, P.,

2013
[2015]

26 Besagni, G., Premoli Vil` a, L., Borgarello, M.,

Energy Policy doi: 10.1016/j.enpol.2017.11.027. 26 Besagni, G., Premoli Vil` a, L., Borgarello, M.,

2017 doi
[2018]

Counting energy poverty in spain between 2004 and

2004
[2019]

Energy and Buildings doi: 10.1016/j.enbuild.2019.03

Towards a multiple-indicator approach to energy poverty in the european union: A review. Energy and Buildings doi: 10.1016/j.enbuild.2019.03

2019 doi
[2020]

Buildings doi: 10.3390/buildings10120217

Italian household load pro- files: A monitoring campaign. Buildings doi: 10.3390/buildings10120217. Casta˜ no-Rosa, R., Sol ´ ıs-Guzm´ an, J., Rubio-Bellido, C., Marrero, M.,

doi
[2021]

Sustainable Cities and Society doi: 10.1016/j

Energy poverty indicators: A systematic literature review and comprehen- sive analysis of integrity. Sustainable Cities and Society doi: 10.1016/j. scs.2021.102756. Soares, J., Lezama, F., Faia, R., Limmer, S., Dietrich, M., Rodemann, T., Ramos, S., Vale, Z.,

2021
[2022]

Addressing social vul- nerability in community microgrids: an equity-centered peer-to-peer elec- tricity trading model, in: Proceedings of the 9th ACM International Confer- ence on Systems for Energy-Efficient Buildings, Cities, and Transportation, ACM. pp. 310–314. doi: 10.11...
[2023]

Energy Reports doi: 10.1016/j.egyr.2023.10.034

A fair and effective approach to managing distributed energy resources through peer-to-peer energy trading with load prioritization among smart homes. Energy Reports doi: 10.1016/j.egyr.2023.10.034. Al Kez, D., Foley, A., Lowans, C., Del Rio, D.F.,

2023 doi
[2024]

Energy Conversion and Management doi:10.1016/j.enconman.2024

Energy poverty assessment: Indicators and implications for developing and developed coun- tries. Energy Conversion and Management doi:10.1016/j.enconman.2024. 118324. Aristondo, O., Onaindia, E.,

2024 doi
[2050]

Accessed September 16,

Retrieved from https://www.iea.org/reports/example-of-daily-load-profile-for-solar-pv- production-relative-to-electricity-demand-in-2050. Accessed September 16,

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [9]

Australian Journal of Agricultural and Resource Eco- nomics doi:10.1111/1467-8489.12487

Solar PV and energy poverty in australia’s 27 residential sector. Australian Journal of Agricultural and Resource Eco- nomics doi:10.1111/1467-8489.12487. IEA,

work page arXiv

[2] [11]

Energy Research & Social Science doi: 10.1016/j

Revisiting energy poverty measurement for the european union. Energy Research & Social Science doi: 10.1016/j. erss.2024.103420. Lei, J., Gao, S., Wei, X., Shi, J., Huang, T., Hatziargyriou, N.D., Chung, C.Y.,

arXiv 2024

[3] [12]

IEEE Transactions on Power Systems doi:10.1109/TPWRS.2022.3225656

A shareholding-based resource sharing mechanism for promoting energy equity in peer-to-peer energy trading. IEEE Transactions on Power Systems doi:10.1109/TPWRS.2022.3225656. Moret, F., Pinson, P.,

arXiv 2022

[4] [13]

IEEE Transactions on Power Systems doi:10.1109/TPWRS.2018.2808961

Energy collectives: A community and fairness based approach to future electricity markets. IEEE Transactions on Power Systems doi:10.1109/TPWRS.2018.2808961. Nussbaumer, P., Bazilian, M., Modi, V.,

work page arXiv 2018

[5] [15]

IEEE Access doi: 10.1109/ ACCESS.2020.3041838

Peer-to-peer energy transaction mechanisms con- sidering fairness in smart energy communities. IEEE Access doi: 10.1109/ ACCESS.2020.3041838. Sadeghi, R., Sadeghi, S., Memari, A., Rezaeinejad, S., Hajian, A.,

arXiv 2020

[6] [16]

Journal of Cleaner Production doi: 10.1016/j.jclepro.2024.141880

A peer-to-peer trading model to enhance resilience: A blockchain-based smart 28 grids with machine learning analysis towards sustainable development goals. Journal of Cleaner Production doi: 10.1016/j.jclepro.2024.141880. Sahih, A.Z., Abbasi, A., Ghasri, M.,

work page arXiv 2024

[7] [17]

Journal of Cleaner Production doi: 10.1016/j.jclepro.2024

Blockchain-enabled solutions for fair and efficient peer-to-peer renewable energy trading: An experimental comparison. Journal of Cleaner Production doi: 10.1016/j.jclepro.2024. 142301. Schwidtal, J., Piccini, P., Troncia, M., Chitchyan, R., Montakhabi, M., Fran- cis, C., Gorbatcheva, A., Capper, T., Mustafa, M., Andoni, M., Robu, V., Bahloul, M., Scott, ...

work page doi:10.1016/j.jclepro.2024 2024

[8] [18]

Renew- able and Sustainable Energy Reviews doi: 10.1016/j.rser.2023.113273

Emerg- ing business models in local energy markets: A systematic review of peer-to- peer, community self-consumption, and transactive energy models. Renew- able and Sustainable Energy Reviews doi: 10.1016/j.rser.2023.113273. Siksnelyte-Butkiene, I., Streimikiene, D., Lekavicius, V., Balezentis, T.,

arXiv 2023