Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility
Open accessAbstract
In recent years, electric vehicle (EV) charging stations have witnessed rapid growth. However, effective management of charging stations is challenging due to individual EV owners' privacy concerns, competing interests of different stations, and the coupling distribution network constraints. To cope with this challenge, this paper proposes a two-stage scheme. In the first stage, the aggregate EV power flexibility region is derived by solving an optimization problem. We prove that any trajectory within the obtained region corresponds to at least one feasible EV dispatch strategy. By submitting this flexibility region instead of the detailed EV data to the charging station operator, EV owners' privacy can be preserved and the computational burden can be reduced. In the second stage, a distributed coordination mechanism with a clear physical interpretation is developed considering the AC power flow based network constraints. We prove that the proposed mechanism converges to the centralized optimum. Case studies validate the theoretical results. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed scheme.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes a two-stage scheme for coordinating EV charging stations: first deriving an aggregate EV power flexibility region that preserves EV owner privacy, then using a distributed coordination mechanism with AC power flow constraints. It proves the mechanism converges to the centralized optimum and reports lower costs, reduced power losses, and improved battery storage utilization in case studies.
Key findings
- The aggregate EV power flexibility region is approximated by lower and upper power trajectories, and Proposition 1 proves that any trajectory within the region corresponds to at least one feasible EV dispatch strategy.
- The distributed coordination mechanism is guaranteed to converge to the centralized optimum (Proposition 2), reaching convergence in about 42 iterations in the simulation.
- Compared with the conventional uniform-price baseline, the proposed mechanism reduces charging-station total cost from 293.36 USD to 212.00 USD (27.73%), DSO cost from 547.89 USD to 517.86 USD, and overall system total cost from 841.24 USD to 729.87 USD (13.24%).
- The proposed method reduces power losses, promotes local energy transactions, and greatly improves battery storage utilization; scalability tests show CSO and DSO computational times remain about 7 s and 150 s as the number of charging stations increases from 4 to 20.
Methods
- Two-stage optimization: Stage 1 solves an optimization problem to approximate each charging station’s aggregate EV power flexibility region; Stage 2 uses a distributed iterative coordination mechanism between charging station operators and the DSO, with locational energy trading prices accounting for power losses and AC power flow constraints.
- Simulation setup: modified IEEE 33-bus test system with four charging stations (CS1–CS4) equipped with PV and batteries, a 24-hour horizon with 1-hour slots, and specified EV, battery, charger, and network parameters.
- Analysis: case studies validate Propositions 1 and 2, check second-order conic relaxation exactness, compare costs and power losses against a conventional baseline, examine energy trading prices and battery SOC, and test scalability.
Limitations
- The paper does not include an explicit limitations section.
- Evident from the text: the aggregate EV power flexibility is an approximation (F_i ≈ ˜F_i), and the second-order conic relaxation’s exactness is stated to hold for radial distribution networks when power injections are not too large and bus voltages remain around nominal values.
- Evident from the text: privacy protection is described as preserved “to some extent,” and the numerical validation is limited to a modified IEEE 33-bus system with four charging stations and specific parameter settings.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
3 of 9 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
- Premisecitation§ I. INTRODUCTION · p. 1
The sudden pulse-like and high charging load of charging stations strains the distribution grid because of random and relatively high EV charging power.
“However, due to the random and relatively high EV charging power [2], the sudden pulse-like and high charging load of the charging stations strains the distribution grid [3].”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [2] [2] Z. J. Lee, G. Lee, T. Lee, C. Jin, R. Lee, Z. Low, D. Chang, C. Ortega, and S. H. Low, 'Adaptive charging networks: A framework for smart electric vehicle charging,' IEEE Transactions on Smart Grid , vol. 12, no. 5, pp. 4339-4350, 2021. - source
The Abstract (page unavailable) says limited infrastructure capacity prevents many sites from installing more than a few charging ports, and §C, “Energy Delivery With Constrained Infrastructure” (page unavailable), describes congestion at an oversubscribed transformer. These passages support a concern about charging loads straining local infrastructure, but they do not establish that sudden, pulse-like station loads strain the wider distribution grid. The sentence cites a different source for random and relatively high EV charging power. (LLM, conf=0.83)
Experimental — accuracy not yet measured - cites [3] [3] K. Clement-Nyns, E. Haesen, and J. Driesen, 'The impact of charging plug-in hybrid electric vehicles on a residential distribution grid,' IEEE Trans. Pow. Syst. , vol. 25, no. 1, pp. 371-380, 2010. - source
Section VI, “Impact on the Distribution Grid” (page unavailable), says uncoordinated PHEV charging increases loads and voltage drops and affects grid performance. But “Charging Periods” (page unavailable) focuses on charging at home on residential grids; the copy does not establish a sudden, pulse-like load from charging stations. The separate statement about random and relatively high EV charging power is attached to [2], not this source. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Premisecitation§ 3: repeat · p. 6
The exactness of the second-order conic relaxation has been verified for the radial distribution network when the power injection at each bus is not too large, and the bus voltage maintains around the nominal value.
“The exactness of relaxation has been verified for the radial distribution network [30] when the power injection at each bus is not too large, and the bus voltage maintains around the nominal value.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [30] [30] M. Farivar and S. H. Low, 'Branch flow model: Relaxations and convexification-Part I,' IEEE Trans. Pow. Syst. , vol. 28, no. 3, pp. 25542564, 2013. - source
The Summary states that the conic relaxation is exact for radial networks, but gives a different condition: no upper bounds on real and reactive loads. The cited paper does not establish exactness specifically when each bus’s power injection is not too large and voltage stays near nominal; its Extensions section instead discusses other sufficient conditions involving load and voltage bounds. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Findingnone given§ I. INTRODUCTION · p. 2
Compared with the traditional method, the proposed method greatly improves the utilization of battery storage in charging stations.
“Compared with the traditional method, the proposed method can achieve significant total cost savings, reduce power losses, and greatly improve the utilization of battery storage in charging stations.”
Quote in the paperSubstantial claim - Findingnone given§ I. INTRODUCTION · p. 2
Compared with the traditional method, the proposed method reduces power losses.
“Compared with the traditional method, the proposed method can achieve significant total cost savings, reduce power losses, and greatly improve the utilization of battery storage in charging stations.”
Quote in the paperSubstantial claim - Findingown data§ Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility · p. 1
Case studies validate the theoretical results.
“Case studies validate the theoretical results.”
Quote in the paperNot a substantial claim
- Findingnone given§ I. INTRODUCTION · p. 2
Compared with the traditional method, the proposed method achieves significant total cost savings.
“Compared with the traditional method, the proposed method can achieve significant total cost savings, reduce power losses, and greatly improve the utilization of battery storage in charging stations.”
Quote in the paperNot a substantial claim - Findingown data§ D. Performance Evaluation · p. 8
For the DSO, the proposed mechanism achieves a lower total cost (517.86 USD) than the baseline (547.89 USD).
“For the DSO, the proposed mechanism can also achieve a lower total cost (517.86 USD) than the baseline (547.89 USD).”
Quote in the paperSubstantial claim - Findingown data§ B. Aggregate EV Power Flexibility · p. 7
With the quadratic term (e.g., w = 0.01), the power flexibility region is more evenly distributed.
“On the contrary, with the help of quadratic term (e.g., w = 0 . 01 ), the power flexibility region is more evenly distributed.”
Quote in the paperNot a substantial claim - Findingown data§ B. Aggregate EV Power Flexibility · p. 8
At the equilibrium point, all voltages of buses with charging stations are within the allowable range.
“We can see that all the voltages are within the allowable range.”
Quote in the paperSubstantial claim - Findingown data§ D. Performance Evaluation · p. 9
When the four charging stations are located far from each other (at bus 7, 22, 25, and 33), their trading prices are different and divergent.
“As seen, the trading prices are different and divergent.”
Quote in the paperNot a substantial claim - Findingreasoning§ D. Performance Evaluation · p. 9
When the number of charging stations increases, the total computational time of a CSO and DSO remains about 7s and 150s, respectively.
“when the number of charging stations increases, the total computational time of a CSO and DSO remains about 7s and 150s, respectively.”
Quote in the paperSubstantial claim - Findingown data§ D. Performance Evaluation · p. 8
Compared with the baseline, the proposed mechanism reduces the amount of energy bought from the utility grid.
“compared with the baseline, the proposed mechanism can reduce the amount of energy bought from the utility grid.”
Quote in the paperSubstantial claim - Findingown data§ D. Performance Evaluation · p. 9
Under the baseline, battery storage was not fully utilized, particularly for CS2-CS4.
“according to the SOC level, the battery storage was not fully utilized in the baseline, particularly for CS2-CS4”
Quote in the paperSubstantial claim - Findingreasoning§ D. Performance Evaluation · p. 8
The trading prices of the four charging stations follow the same trend.
“First, the trading prices of the four charging stations follow the same trend.”
Quote in the paperNot a substantial claim - Findingown data§ B. Aggregate EV Power Flexibility · p. 8
The number of iterations to reach convergence for the proposed distributed coordination mechanism is about 42.
“It can be found that the number of iterations to reach convergence is about 42, which indicates that the convergence speed is fast and acceptable.”
Quote in the paperSubstantial claim - Findingown data§ D. Performance Evaluation · p. 8
Under the proposed mechanism, the energy trading costs C_g,i for CS2, CS3, and CS4 are negative.
“under the proposed mechanism, the energy trading cost C g,i for charging stations (CS2, CS3, and CS4) are negative”
Quote in the paperSubstantial claim - Findingown data§ D. Performance Evaluation · p. 8
The power loss cost of the proposed method is lower than that of the baseline.
“In addition, the power loss cost of the proposed method is also lower than that of the baseline.”
Quote in the paperSubstantial claim - Findingown data§ B. Aggregate EV Power Flexibility · p. 7
The recovered dispatch of one EV power trajectory in CS1 satisfies the charging power, charging status, and SOC constraints, validating Proposition 1.
“The charging power, charging status, and SOC all satisfy the constraints, which validates Proposition 1.”
Quote in the paperSubstantial claim - Findingown data§ B. Aggregate EV Power Flexibility · p. 8
All gap values between the left hand side and the right hand side of inequality (24) are less than 10^-6.
“As shown in Fig. 8, all gap values are less than 10 -6 , which is very small.”
Quote in the paperSubstantial claim - Findingown data§ D. Performance Evaluation · p. 8
The proposed mechanism achieves a total overall system cost of 729.87 USD, compared with 841.24 USD for the baseline, a decrease of 13.24%.
“the proposed mechanism (729.87 USD) outperforms the baseline (841.24 USD) in terms of the total cost of the overall system, with a great decrease (13.24%).”
- Also stated in: § VI. CONCLUSION · p. 10 · plain
“Compared with the traditional method using a uniform price, the proposed coordination mechanism can achieve a significant total cost reduction of 13.24%.”
Quote in the paperSubstantial claim - Also stated in: § VI. CONCLUSION · p. 10 · plain
- Findingown data§ B. Aggregate EV Power Flexibility · p. 7
Without the quadratic term (w = 0), the power flexibility region becomes more concentrated in some periods.
“In the case of w = 0 , i.e., without the quadratic term, the power flexibility region becomes more concentrated in some periods.”
Quote in the paperNot a substantial claim - Premisecitation§ I. INTRODUCTION · p. 1
Centralized schemes may jeopardise the privacy of charging stations by gathering their private data to make a central decision.
“The above centralized schemes may jeopardise the privacy of charging stations by gathering their private data to make a central decision [16].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] [16] Y. Liu, Y. Li, H. B. Gooi, Y. Jian, H. Xin, X. Jiang, and J. Pan, 'Distributed robust energy management of a multimicrogrid system in the real-time energy market,' IEEE Trans. Sustain. Energy , vol. 10, no. 1, pp. 396-406, 2017. - source
- Interpretationown data§ Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility · p. 1
Any aggregate power trajectory within the derived aggregate EV power flexibility region corresponds to at least one feasible EV dispatch strategy.
“We prove that any trajectory within the obtained region corresponds to at least one feasible EV dispatch strategy.”
- Also stated in: § I. INTRODUCTION · p. 2 · plain
“We prove that any trajectory within this region corresponds to at least one feasible EV dispatch strategy.”
- Also stated in: § B. Approximation of Aggregate EV Power Flexibility · p. 4 · plain
“Proposition 1: Let { ˆ p ∗ d,i , ˇ p ∗ d,i , ∀ t } be the optimal solution of (1). For any aggregate power trajectory { p d,i ( t ) , ∀ t } that satisfies p d,i ( t ) ∈ [ˇ p ∗ d,i ( t ) , ˆ p ∗ d,i ( t )] for all time slots t ∈ T , there exists a feasible EV dispatch strategy.”
Quote in the paperSubstantial claim - Also stated in: § I. INTRODUCTION · p. 2 · plain
- Interpretationreasoning§ Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility · p. 1
The proposed distributed coordination mechanism is guaranteed to converge to the centralized optimum.
“We prove that the proposed mechanism is guaranteed to converge to the centralized optimum.”
- Also stated in: § I. INTRODUCTION · p. 2 · boosted
“We prove that the proposed mechanism is guaranteed to converge to the centralized optimum.”
- Also stated in: § 3: repeat · p. 6 · boosted
“Proposition 2: The proposed distributed coordination mechanism is guaranteed to converge to the centralized optimum.”
- Also stated in: § VI. CONCLUSION · p. 10 · plain
“We prove that the proposed mechanism can converge to the centralized optimum.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The DSO will help coordinate energy trading by announcing an energy trading price to each charging station that accounts for grid buying/selling prices, distance from the utility grid, and other charging stations.
- Also stated in: § I. INTRODUCTION · p. 2 · boosted
- Interpretationreasoning§ Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility · p. 1
Submitting the aggregate EV power flexibility region instead of detailed EV data to the charging station operator preserves EV owners' privacy.
“By submitting this flexibility region instead of the detailed EV data to the charging station operator, EV owners' privacy can be preserved and the computational burden can be reduced.”
- Also stated in: § I. INTRODUCTION · p. 2 · plain
“This can protect individual EV owner's privacy.”
- Also stated in: § B. Approximation of Aggregate EV Power Flexibility · p. 4 · hedged
“The operator only knows the aggregate flexibility but not the detailed information of each EV, which can protect EV owner's privacy to some extent.”
Quote in the paperSubstantial claim- Rests on an assumption: EV owners are not willing to reveal their private information to the CSO, distribution network constraints are only available to the DSO, and the DSO does not know the batteries and PV generations in the charging stations.
- Also stated in: § I. INTRODUCTION · p. 2 · plain
- Interpretationreasoning§ Distributed Coordination of Charging Stations Considering Aggregate EV Power Flexibility · p. 1
Submitting the aggregate EV power flexibility region instead of detailed EV data reduces computational burden.
“By submitting this flexibility region instead of the detailed EV data to the charging station operator, EV owners' privacy can be preserved and the computational burden can be reduced.”
- Also stated in: § I. INTRODUCTION · p. 2 · plain
“Moreover, the challenging computational complexity of managing a large number of EVs can be resolved.”
Quote in the paperSubstantial claim - Also stated in: § I. INTRODUCTION · p. 2 · plain
- Premisecitation§ I. INTRODUCTION · p. 2
With an increasing number of EVs, solving an optimization problem with detailed EV data and constraints will be time consuming.
“In addition, with an increasing number of EVs, solving an optimization problem with detailed EV data and constraints will be time consuming [26].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] [26] H. Zhang, Z. Hu, Z. Xu, and Y. Song, 'Optimal planning of PEV charging station with single output multiple cables charging spots,' IEEE Trans. Smart Grid , vol. 8, no. 5, pp. 2119-2128, 2017. - source
- Interpretationown data§ D. Performance Evaluation · p. 9
With the proposed coordination mechanism and energy trading price, the utilization of energy storage can be greatly promoted.
“Thus, it can be deduced that with the proposed properly designed coordination mechanism and energy trading price, the utilization of energy storage can be greatly promoted.”
- Also stated in: § VI. CONCLUSION · p. 10 · plain
“Energy trading enhances the utilization rate of battery storage deployed in the charging stations.”
Quote in the paperSubstantial claim - Also stated in: § VI. CONCLUSION · p. 10 · plain
- Interpretationreasoning§ B. Aggregate EV Power Flexibility · p. 7
The quadratic term provides more available dispatchable opportunities for charging station.
“This provides more available dispatchable opportunities for charging station.”
Quote in the paperSubstantial claim - Interpretationnone given§ D. Performance Evaluation · p. 8
Under the proposed mechanism, the energy trading price is determined by the dual variable of (17), which reflects the value of electricity at different buses, accounting for load and generation levels and physical limits of transmission lines.
“under the proposed mechanism, the energy trading price is determined by the dual variable of (17), which well reflects the value of electricity at different buses, accounting for the load and generation levels, and the physical limits of the transmission lines, etc.”
Quote in the paperNot a substantial claim - Interpretationown data§ B. Aggregate EV Power Flexibility · p. 8
The second-order conic relaxation is exact.
“That is, equality almost holds for the inequality constraint (24), so the second-order conic relaxation is exact.”
Quote in the paperSubstantial claim - Interpretationown data§ D. Performance Evaluation · p. 9
The proposed mechanism is not sensitive to the number of charging stations.
“Therefore, the proposed mechanism is not sensitive to the number of charging stations.”
Quote in the paperSubstantial claim - Interpretationreasoning§ B. Proof of Proposition 2 · p. 11
After equivalent transformations, the proposed mechanism turns into the classical ADMM framework.
“After the equivalent transformations, we can find that our proposed mechanism turns into the classical ADMM framework.”
Quote in the paperSubstantial claim - Interpretationnone given§ D. Performance Evaluation · p. 9
The calculations of charging stations can be run in parallel.
“This is because the calculations of charging stations can be run in parallel.”
Quote in the paperSubstantial claim - Interpretationreasoning§ D. Performance Evaluation · p. 8
Negative energy trading costs mean that CS2, CS3, and CS4 can earn revenue from selling energy to other charging stations or the utility grid instead of paying more on buying energy from the utility grid under the baseline.
“which means that they can earn revenue from selling energy to other charging stations or the utility grid instead of paying more on buying energy from the utility grid under the baseline.”
Quote in the paperSubstantial claim
- May combine two claims. 15
- Classified as probably not a substantial claim. 8
May combine two claims.
Findingown data§ B. Aggregate EV Power Flexibility · p. 7Within the aggregate power flexibility area, any power trajectory is dispatchable and can satisfy the EVs' charging requirements.
“Within this area, any power trajectory is dispatchable and can satisfy the EVs' charging requirements.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Findingnone given§ B. Aggregate EV Power Flexibility · p. 7The area between the upper and lower power trajectories is the aggregate power flexibility that is available to dispatch by charging stations in the second stage.
“The area between the upper and lower power trajectories is the aggregate power flexibility that is available to dispatch by charging stations in the second stage.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ D. Performance Evaluation · p. 8The proposed mechanism achieves a total cost of 212 USD for charging stations, compared with 293.36 USD for the baseline, a reduction of 27.73%.
“The proposed mechanism (212 USD) performs better than the baseline (293.36 USD) with a significant total cost reduction (27.73%).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ D. Performance Evaluation · p. 9Under the proposed mechanism, the battery SOCs of the four charging stations vary in the same trend and experience a complete charging and discharging cycle.
“while under the proposed mechanism, the battery SOCs of the four charging stations vary in the same trend and experience a complete charging and discharging cycle.”
Quote in the paperSubstantial claimMay combine two claims.
Findingreasoning§ A. Proof of Proposition 1 · p. 11The constructed EV dispatch strategy is feasible and satisfies the EV charging constraints.
“We prove that it is a feasible EV dispatch strategy as follows.”
- Also stated in: § A. Proof of Proposition 1 · p. 11 · boosted
“Therefore, we have constructed a feasible EV dispatch strategy, which completes the proof.”
Quote in the paperSubstantial claim- Also stated in: § A. Proof of Proposition 1 · p. 11 · boosted
May combine two claims.
Findingown data§ D. Performance Evaluation · p. 8In the circled area covering the charging stations and bus 1, the proposed method generates much thinner lines than the baseline, indicating less power loss in these lines.
“In the circled area covering the charging stations and bus 1, the proposed method (Fig. 10 (a)) generates much thinner lines than the baseline (Fig. 10 (b)). This indicates less power loss in these lines”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S1 (asserted), S2 (checkable), S4 (based).
Premisenone given§ I. INTRODUCTION · p. 2How to characterize the maximal potential power flexibility of EVs in a charging station remains to be investigated.
“Despite the above efforts, how to characterize the maximal potential power flexibility of EVs in a charging station remains to be investigated.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone given§ I. INTRODUCTION · p. 1Existing studies on energy trading mechanisms mainly focus on the design of the mechanism without consideration of distribution network constraints.
“However, the above studies mainly focus on the design of energy trading mechanism without consideration of distribution network constraints.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationreasoning§ D. Distributed Coordination Mechanism · p. 6The private data of individual charging stations and the network parameters owned by the DSO are well protected.
“Therefore, the private data of individual charging stations and the network parameters owned by the DSO are well protected.”
Quote in the paperSubstantial claimMay combine two claims.
Premisereasoning§ B. Energy Trading with Other Charging Stations/Utility Grid · p. 4Power transmissions in the distribution network result in power losses, so giving charging stations the same price is unfair and the DSO may need to pay for the power losses.
“However, since the charging stations are located at different buses and the power transmissions in distribution network result in power losses, giving them the same price is unfair. Also in that case, the DSO may need to pay for the power losses.”
Quote in the paperSubstantial claimMay combine two claims.
Premisecitation§ A. Modelling of Charging Station · p. 4The charging power dispatched by the charging station may be less than the upper boundary, which may lead to dissatisfaction of EVs or decrease their utility.
“The charging power dispatched by the charging station may be less than the upper boundary, which may lead to dissatisfaction of EVs or decrease their utility [9].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9] [9] N. Liu, X. Yu, C. Wang, and J. Wang, 'Energy sharing management for microgrids with PV prosumers: A stackelberg game approach,' IEEE Trans. Ind. Informat. , vol. 13, no. 3, pp. 1088-1098, Jun. 2017. - source
Classified as probably not a substantial claim.
Premisenone given§ I. INTRODUCTION · p. 2EV charging stations have constraints distinct from other resources, such as the limitation due to a finite number of chargers.
“In particular, EV charging stations has some constraints distinct from other resources, such as the limitation due to a finite number of chargers.”
Quote in the paperSubstantial claimMay combine two claims.
Premisecitation§ I. INTRODUCTION · p. 2Centralized methods for EV charging scheduling require the charging station to know the data of all EVs, which may violate the privacy of EV owners.
“However, the above methods require the charging station to know the data of all EVs, which may violate the privacy of EV owners.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [24] [24] Q. Yan, B. Zhang, and M. Kezunovic, 'Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation,' IEEE Trans. Smart Grid , vol. 10, no. 2, pp. 2096-2106, Mar. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [25] [25] J. Qi, C. Lai, B. Xu, Y. Sun, and K.-S. Leung, 'Collaborative energy management optimization toward a green energy local area network,' IEEE Trans. Ind. Informat. , vol. 14, no. 12, pp. 5410-5418, 2018. - source
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone given§ D. Distributed Coordination Mechanism · p. 6The data exchanged between the charging stations and the DSO consists solely of the energy trading schedule/profile and the trading prices.
“The data exchanged between the charging stations and the DSO consists solely of the energy trading schedule/profile and the trading prices.”
Quote in the paperNot a substantial claimMay combine two claims.
Premisecitation§ I. INTRODUCTION · p. 1Various techniques such as intermittent renewable generation and distributed energy storage in the charging station and distribution network further complicate the operation of charging stations and threaten power system reliability.
“Meanwhile, various techniques, such as intermittent renewable generation and distributed energy storage, begin to appear in the charging station and distribution network [4]. This further complicates the operation of charging stations and threatens the power system reliability.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] [4] K. Chaudhari, A. Ukil, K. N. Kumar, U. Manandhar, and S. K. Kollimalla, 'Hybrid optimization for economic deployment of ESS in PV-integrated EV charging stations,' IEEE Trans. Ind. Informat. , vol. 14, no. 1, pp. 106-116, Jan. 2018. - source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationreasoning§ D. Performance Evaluation · p. 8The trading prices of the four charging stations follow the same trend because they are located close to each other.
“This is because they are located close to each other.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown data§ D. Performance Evaluation · p. 8The proposed mechanism benefits both the charging stations and the distribution network by reducing power losses and costs, resulting in a win-win situation.
“In conclusion, the proposed mechanism can not only benefit the charging stations, but also the distribution network by reducing power losses and costs, resulting in a win-win situation.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown data§ B. Aggregate EV Power Flexibility · p. 8The convergence speed of the proposed distributed coordination mechanism is fast and acceptable.
“It can be found that the number of iterations to reach convergence is about 42, which indicates that the convergence speed is fast and acceptable.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone given§ D. Performance Evaluation · p. 8Less power loss in these lines is beneficial to prolong the service life of transmission lines.
“which is beneficial to prolong the service life of transmission lines.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown data§ D. Performance Evaluation · p. 8The proposed coordination mechanism can promote local transactions and reduce the power loss of the distribution network.
“It can be concluded that the proposed coordination mechanism can promote local transactions and reduce the power loss of the distribution network.”
- Also stated in: § VI. CONCLUSION · p. 10 · plain
“The energy sharing of charging stations affects the local power flow of the distribution network and results in a lower power loss.”
Quote in the paperSubstantial claim- Also stated in: § VI. CONCLUSION · p. 10 · plain
Classified as probably not a substantial claim. May combine two claims.
Interpretationnone given§ VI. CONCLUSION · p. 10Using an aggregate EV power flexibility region instead of complete information about EVs can protect EV owners' privacy and be much easier for the charging station to dispatch.
“In contrast to the existing approaches that assume the charging station has complete information about the EVs, we use an aggregate EV power flexibility region instead. This can protect EV owners' privacy and be much easier for the charging station to dispatch.”
Quote in the paperSubstantial claim
The authors state this as an assumption.
The DSO will help coordinate energy trading by announcing an energy trading price to each charging station that accounts for grid buying/selling prices, distance from the utility grid, and other charging stations.
“To facilitate the energy trading, here we assume that the DSO will help to coordinate the trading by announcing an energy trading price λ p,i ( t ) to each charging station that takes into account multiple factors such as the grid buying/selling prices, the distance from the utility grid and other charging stations, etc.”
The authors state this as an assumption.
Each charging station is equipped with renewables such as PV panels.
“In addition, each charging station is assumed to be equipped with renewables such as PV panels for reducing the energy bought from the grid.”
The authors state this as an assumption.
The utility electricity sale price is less than the utility electricity buy price.
“Further, they should meet the requirement λ s g ( t ) < λ b g ( t ) to ensure that the distribution network won't arbitrage by buying from and selling back to the utility at the same time.”
The paper relies on this without support.
The DSO is in charge of distribution network management and will regulate energy transactions among charging stations.
“Generally, the DSO is in charge of the distribution network management and will regulate the energy transactions among charging stations.”
The paper relies on this without support.
EV owners are not willing to reveal their private information to the CSO, distribution network constraints are only available to the DSO, and the DSO does not know the batteries and PV generations in the charging stations.
“The EV owners are not willing to reveal their private information (such as the charging needs) to the CSO; the distribution network constraints is only available to DSO; the DSO does not know the batteries and PV generations in the charging stations.”
The paper relies on this without support.
Both charging and discharging cause battery degradation.
“Both charging and discharging can cause battery degradation and the degradation cost is described by:”
The paper relies on this without support.
The charging station operator has some flexibility in charging EVs instead of a fixed load curve.
“Instead of a fixed load curve, the charging station operator has some flexibility in charging the EVs.”
The paper relies on this without support.
The number of EVs charging at a charging station reflects how busy the station is.
“In general, the number of EVs charging at a charging station reflects how busy the station is.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Most mentioned studies overlook the potential benefits of an integrated DER environment, such as DERMS, where EVs could operate in coordination with other DERs to enhance overall grid stability and flexibility.
“Second, most studies overlook the potential benefits of an integrated DER environment, such as DERMS, where EVs could operate in coordination with other DERs to enhance overall grid stability and flexibility.”
From Improving distribution system flexibility using electric vehicles under the distributed energy resource management system framework (2026) · cites this paper as [10]
The Introduction (p. 1) says some earlier energy-trading studies did not consider distribution-network constraints, and the Overview (p. 2) describes coordinating EVs with PV and battery storage for reliable operation. The Conclusion (p. 10) reports lower power loss from the proposed coordination scheme. But the paper does not establish that most studies overlook integrated DER coordination or discuss DERMS specifically. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Cites
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075 · see citing works
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,863 · see citing works
Network reconfiguration in distribution systems for loss reduction and load balancing
Baran, M. E., & Wu, F. F. (1989) · IEEE Transactions on Power Delivery · doi:10.1109/61.25627 · Cited by 5,111 · cited 1× · see citing works
“In this section, we evaluate the performance of the proposed two-stage scheme, including the aggregate EV power flexibility evaluation and the distributed coordination mechanism using a IEEE 33-bus test system [31] with four charging stations: CS1, CS2, CS3, and CS4, as shown in Fig.”
MethodNetwork Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing
Baran, M. E., & Wu, F. F. (1989) · IEEE Power Engineering Review · doi:10.1109/mper.1989.4310642 · Cited by 3,350 · see citing works
The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid
Clement-Nyns, K., Haesen, E., & Driesen, J. (2010) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2036481 · Cited by 2,847 · cited 1× · see citing works
“However, due to the random and relatively high EV charging power [2], the sudden pulse-like and high charging load of the charging stations strains the distribution grid [3].”
BackgroundBranch Flow Model: Relaxations and Convexification—Part I
Farivar, M., & Low, S. H. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2255317 · Cited by 1,541 · cited 1× · see citing works
“The exactness of relaxation has been verified for the radial distribution network [30] when the power injection at each bus is not too large, and the bus voltage maintains around the nominal value.”
BackgroundPeer-to-Peer Trading in Electricity Networks: An Overview
Tushar, W., Saha, T. K., Yuen, C., Smith, D. B., & Poor, H. V. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2020.2969657 · Cited by 821 · cited 1× · see citing works
“A comprehensive review summarizing various P2P energy trading mechanisms can be found in [15].”
BackgroundIncentivizing Energy Trading for Interconnected Microgrids
Wang, H., & Huang, J. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2614988 · Cited by 520 · see citing works
Yan, Q., Zhang, B., & Kezunović, M. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2788440 · Cited by 451 · cited 1× · see citing works
“[24] solved a centralized optimization problem to determine EV charging schedules and to manage the power balance in the charging station.”
BackgroundEnergy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach
Liu, N., Yu, X., Wang, C., & Wang, J. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2654302 · Cited by 425 · cited 2× · see citing works
“The charging power dispatched by the charging station may be less than the upper boundary, which may lead to dissatisfaction of EVs or decrease their utility [9].”
BackgroundMethodHybrid Optimization for Economic Deployment of ESS in PV-Integrated EV Charging Stations
Chaudhari, K., Ukil, A., Kumar, K. N., Manandhar, U., & Kollimalla, S. K. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2713481 · Cited by 384 · cited 1× · see citing works
“Meanwhile, various techniques, such as intermittent renewable generation and distributed energy storage, begin to appear in the charging station and distribution network [4].”
BackgroundThree-Party Energy Management With Distributed Energy Resources in Smart Grid
Tushar, W., Chai, B., Yuen, C., Smith, D. B., Wood, K. L., Yang, Z., & Poor, H. V. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2341556 · Cited by 379 · cited 1× · see citing works
“To model the interaction between participants, a leader-follower game based peer-to-peer (P2P) scheme was widely adopted for sharing of facilities [8] and among multiple PV prosumers [9].”
MethodA Geometric Approach to Aggregate Flexibility Modeling of Thermostatically Controlled Loads
Zhao, L., Zhang, W., Hao, H., et al. (2017) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2674699 · Cited by 357 · cited 1× · see citing works
“The aggregate flexibility of thermostatically controllable loads was explored in [28] that aims at providing ancillary services to the grid.”
BackgroundGrid Influenced Peer-to-Peer Energy Trading
Tushar, W., Saha, T. K., Yuen, C., et al. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2937981 · Cited by 298 · cited 1× · see citing works
“A cooperative game based scheme was proposed to form prosumer coalitions to meet their demands at the minimum total cost [11].”
BackgroundBranch Flow Model: Relaxations and Convexification—Part II
Farivar, M., & Low, S. H. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2013.2255318 · Cited by 288 · see citing works
Hybrid Energy Sharing for Multiple Microgrids in an Integrated Heat–Electricity Energy System
Liu, N., Wang, J., & Wang, L. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2861986 · Cited by 243 · cited 1× · see citing works
“To protect the privacy of microgrids, a distributed algorithm based on subgradient method was proposed in [17].”
MethodAggregate Power Flexibility in Unbalanced Distribution Systems
Chen, X., Dall’Anese, E., Zhao, C., & Li, N. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2920991 · Cited by 242 · cited 1× · see citing works
“An alternative approach is to derive the aggregate flexibility of the large number of resources first, which can simplify further analysis [27].”
BackgroundPeer-to-Peer Energy Sharing Among Smart Energy Buildings by Distributed Transaction
Cui, S., Wang, Y., & Xiao, J. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2906059 · Cited by 241 · cited 1× · see citing works
“Energy trading optimization schemes designed for interconnected microgrids were proposed in [10], [11].”
BackgroundAggregate Flexibility of Virtual Power Plants With Temporal Coupling Constraints
Wang, S., & Wu, W. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3106646 · Cited by 241 · cited 1× · see citing works
“The flexibility of virtual power plants was represented by a combination of a virtual battery and a virtual generator to make the high-dimensional problem tractable [32].”
MethodDistributed Robust Energy Management of a Multimicrogrid System in the Real-Time Energy Market
Liu, Y., Li, Y., Gooi, H. B., Jian, Y., Xin, H., Jiang, X., & Pan, J. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2017.2779827 · Cited by 238 · cited 1× · see citing works
“The above centralized schemes may jeopardise the privacy of charging stations by gathering their private data to make a central decision [16].”
BackgroundPeer-to-Peer Energy Trading With Sustainable User Participation: A Game Theoretic Approach
Tushar, W., Saha, T. K., Yuen, C., Liddell, P., Bean, R. W., & Poor, H. V. (2018) · IEEE Access · doi:10.1109/access.2018.2875405 · Cited by 233 · see citing works
Jing, R., Xie, M., Wang, F. X., et al. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114551 · Cited by 215 · cited 1× · see citing works
“The energy trading model was further extended to the field of multi-energy systems [12].”
BackgroundDirect Energy Trading of Microgrids in Distribution Energy Market
Kim, H., Lee, J., Bahrami, S., et al. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2926305 · Cited by 213 · cited 1× · see citing works
“A distributed mechanism based on ADMM was proposed to determine the amount of energy traded among networked charging stations [19] and interconnected microgrids [20].”
MethodBargaining-based cooperative energy trading for distribution company and demand response
Fan, S., Ai, Q., & Piao, L. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.095 · Cited by 185 · see citing works
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
Lee, Z. J., Lee, G., Lee, T., Jin, C., Lee, R., Low, Z., Chang, D., Ortega, C., & Low, S. H. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3074437 · Cited by 174 · cited 1× · see citing works
“However, due to the random and relatively high EV charging power [2], the sudden pulse-like and high charging load of the charging stations strains the distribution grid [3].”
BackgroundOptimal Planning of PEV Charging Station With Single Output Multiple Cables Charging Spots
Zhang, H., Hu, Z., Xu, Z., & Song, Y. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2517026 · Cited by 171 · cited 1× · see citing works
“In addition, with an increasing number of EVs, solving an optimization problem with detailed EV data and constraints will be time consuming [26].”
BackgroundYi, Z., Xu, Y., Gu, W., Yang, L., & Sun, H. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3085885 · Cited by 168 · cited 1× · see citing works
“The aggregate flexibility of thermostatically controllable loads (TCLs) [28] and various distributed energy resources [29] has been studied.”
BackgroundElectrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling
Wang, S., Bi, S., Zhang, Y. A., et al. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2810274 · Cited by 162 · cited 1× · see citing works
“THE proliferation of electric vehicles (EVs) has spurred the rapid development of EV charging stations [1].”
BackgroundDistributed Coordination of EV Charging With Renewable Energy in a Microgrid of Buildings
Yang, Y., Jia, Q., Deconinck, G., Guan, X., Qiu, Z., & Hu, Z. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2707103 · Cited by 143 · see citing works
Zhang, J., Che, L., Wan, X., & Shahidehpour, M. (2022) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3123351 · Cited by 123 · cited 1× · see citing works
“A distributed mechanism based on ADMM was proposed to determine the amount of energy traded among networked charging stations [19] and interconnected microgrids [20].”
MethodHierarchical Coordination of Heterogeneous Flexible Loads
Xu, Z., Callaway, D. S., Hu, Z., & Song, Y. (2016) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2016.2516992 · Cited by 121 · see citing works
Energy management of cooperative microgrids with P2P energy sharing in distribution networks
Liu, T., Tan, X., Sun, B., et al. (2015) · IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgridcomm.2015.7436335 · Cited by 115 · see citing works
Affolabi, L., Shahidehpour, M., Gan, W., et al. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3131959 · Cited by 90 · cited 1× · see citing works
“A centralized energy trading framework of EV charging stations was proposed in [14] that integrated a power distribution network.”
BackgroundChen, G., Zhang, H., Hui, H., et al. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2021.3100846 · Cited by 80 · cited 1× · see citing works
“The aggregate flexibility of thermostatically controllable loads (TCLs) [28] and various distributed energy resources [29] has been studied.”
BackgroundArbitrage Strategy of Renewable-Based Microgrids via Peer-to-Peer Energy-Trading
Nezamabadi, H., & Vahidinasab, V. (2020) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2020.3045216 · Cited by 62 · cited 1× · see citing works
“The P2P energy trading problem of microgrids was formulated as a bi-level programming, which was then transformed into a single level optimization that can be solved centrally [15].”
BackgroundAn Asynchronous Online Negotiation Mechanism for Real-Time Peer-to-Peer Electricity Markets
Guo, Z., Pinson, P., Wu, Q., Chen, S., Yang, Q., & Yang, Z. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3111869 · Cited by 50 · cited 1× · see citing works
“Similarly, the energy sharing problem of prosumers in [21] adopted the traditional ADMM method and considered a simplified LinDistFlow model.”
MethodCollaborative Energy Management Optimization Toward a Green Energy Local Area Network
Qi, J., Lai, C., Xu, B., Sun, Y., & Leung, K.-S. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2796021 · Cited by 46 · cited 1× · see citing works
“A centralized scheme based on improved particle swarm optimization was presented in [25] to schedule EVs’ charging for cost reduction.”
MethodAn Energy Sharing Mechanism Considering Network Constraints and Market Power Limitation
Chen, Y., Zhao, C., Low, S. H., et al. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3198721 · Cited by 46 · cited 1× · see citing works
“To better unlock prosumer’s flexibility, generalized Nash game based P2P schemes were developed [10].”
MethodSelf-citationRana, R., & Mishra, S. (2018) · IEEE Systems Journal · doi:10.1109/jsyst.2018.2851618 · Cited by 44 · cited 2× · see citing works
“This kind of web-based reservation system can be found in [34].”
BackgroundA Distributed Online Algorithm for Promoting Energy Sharing Between EV Charging Stations
Yan, D., & Chen, Y. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3203522 · Cited by 44 · cited 1× · see citing works
“Distributed optimization is another way to design P2P energy trading schemes, which has been applied in the field of energy buildings [12], multi-energy systems [13], and charging stations [14].”
MethodSelf-citationA Hierarchical Distributed Energy Management for Multiple PV-Based EV Charging Stations
Zhang, J., Zhang, Y., Li, T., et al. (2018) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2018.8591742 · Cited by 41 · see citing works
Self-citationReview of energy sharing: Business models, mechanisms, and prospects
Chen, Y., & Zhao, C. (2022) · IET Renewable Power Generation · doi:10.1049/rpg2.12438 · Cited by 37 · cited 1× · see citing works
“A new market mechanism with properly designed prices is desired [7].”
BackgroundSelf-citationLearning-Based Predictive Control via Real-Time Aggregate Flexibility
Li, T., Sun, B., Chen, Y., et al. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3094719 · Cited by 31 · cited 1× · see citing works
“A learning-based method was proposed to provide real-time aggregate flexibility feedback of controllable loads [33].”
MethodSelf-citationDistributed Coordination of EV Charging with Renewable Energy in a Microgrid of Buildings
Yang, Y., Jia, Q., Deconinck, G., et al. (2019) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm40551.2019.8973991 · Cited by 15 · see citing works
Direct Energy Trading of Microgrids in Distribution Energy Market
Kim, H., Lee, J., Bahrami, S., et al. (2019) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm.2019.8909772 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Xu, H., Chen, Y., Wang, H., Wang, H., Meng, Y., & Gu, Q. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104345 · Cited by 94 · see citing works
Self-citationA Comprehensive Review of Smart Charging Strategies for Electric Vehicles and Way Forward
Dahiwale, P. V., Rather, Z. H., & Mitra, I. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3365581 · Cited by 89 · see citing works
“Reference [55] presents a privacy-preserving and distributed charging mechanism with conflicting objectives of agents viz., EV user: achieve the charging requirement, charging station operator: minimize the operating cost, DSO: power loss minimization with system security.”
BackgroundZhang, M., Xu, Y., Shi, X., et al. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3274198 · Cited by 79 · see citing works
“References [19] and [20] formulate an optimization problem to maximize the upper and lower bounds difference, also known as box-based method.”
BackgroundMethodMeng, W., Song, D., Huang, L., et al. (2024) · Energy · doi:10.1016/j.energy.2024.130332 · Cited by 75 · see citing works
Wen, Y., Hu, Z., He, J., & Guo, Y. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3340157 · Cited by 66 · see citing works
“Therefore, many researchers are exploring practical and viable methods to address DERs and ADNs at the transmission level [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25].”
BackgroundWang, W., & Wu, L. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3422330 · Cited by 37 · see citing works
“Reference [35] considers uncertain EV arrivals via a real-time adjustment method, yet neglects the EV departure uncertainties that are critical for optimizing the real-time charging schedule to meet EV users’ needs.”
Yang, H., Chen, Q., Tang, K., et al. (2025) · Energy · doi:10.1016/j.energy.2025.137770 · Cited by 37 · see citing works
A novel forecasting approach to schedule aggregated electric vehicle charging
Brinkel, N., Visser, L., van Sark, W. G. J. H. M., & AlSkaif, T. A. (2023) · Energy and AI · doi:10.1016/j.egyai.2023.100297 · Cited by 30 · see citing works
Bahmani, M. H., Shayan, M. E., & Fioriti, D. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.110106 · Cited by 30 · see citing works
Kioumarsi, K., & Bolurian, A. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.114914 · Cited by 27 · see citing works
Day-Ahead Scheduling and Online Dispatch of Energy Hubs: A Flexibility Envelope Approach
Feng, S., Wei, W., & Chen, Y. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3337629 · Cited by 25 · see citing works
“We further extend the flexibility concept in [32], [33].”
BackgroundMethodInfluentialSelf-citationUllah, Z., Yan, L., Rehman, A. U., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3439019 · Cited by 25 · see citing works
“Building sustainable transportation choices and lowering the burden on power lines are two international aims that have given rise to charging stations based on renewable energy resources (RERs) as a particularly efficient solution [6].”
BackgroundDecarbonising the GB Power System via Numerous Electric Vehicle Coordination
Qiu, D., Dong, Z., Wang, Y., et al. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3342168 · Cited by 20 · see citing works
Li, Y., & Li, Z. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3397057 · Cited by 19 · see citing works
“To name a few, the full-day flexibility of EVs is studied in [5], [6], and the real-time flexibility of EVs is investigated in [2], [7].”
BackgroundDynamic scheduling of electricity demand for decentralized EV charging systems
Yadav, K., & Singh, M. K. (2024) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2024.101467 · Cited by 19 · see citing works
Xiao, T., Peng, Y., & Chen, C. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109761 · Cited by 18 · see citing works
Guo, S., Zhou, B., Chung, C. Y., et al. (2025) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2025.3635922 · Cited by 14 · see citing works
Lee, S., & Jung, J. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116292 · Cited by 13 · see citing works
Han, Q., Li, X., & He, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110264 · Cited by 13 · see citing works
Dai, X., Chen, C., Wang, J., et al. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.111106 · Cited by 12 · see citing works
Gupta, S., Yadav, V. K., & Singh, M. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3558313 · Cited by 12 · see citing works
Aggregation Feasible Region of Full Power Spectrum Electric Vehicles Based on Polyhedron Projection
Yuan, L., Yu, Q., Yao, X., et al. (2025) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2025.3544640 · Cited by 11 · see citing works
Li, L., Lin, C., Guo, Y., et al. (2025) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2025.3564652 · Cited by 11 · see citing works
Pourramezani, K., Baghaee, H. R., & Gharehpetian, G. B. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.08.049 · Cited by 12 · see citing works
Sarmokadam, S., & Mathew, R. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.03.032 · Cited by 10 · see citing works
Stochastic modeling for the aggregated flexibility of distributed energy resources
Wen, Y., Guo, Y., Hu, Z., et al. (2024) · Electric Power Systems Research · doi:10.1016/j.epsr.2024.110628 · Cited by 10 · see citing works
Zhang, Y., Guo, Z., Jiao, F., et al. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3579812 · Cited by 10 · see citing works
“Efforts have focused on utilizing the charging flexibility of EVs at charging stations (CSs) to adjust DN load fluctuations [3], reduce the operational costs of CSs [4], [5], [6], and lower charging costs for users [7].”
Carbon-Aware Quantification of Real-Time Aggregate Power Flexibility of Electric Vehicles
Wang, X., & Chen, Y. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3595577 · Cited by 10 · see citing works
“An inner approximation method was proposed to maximize the EV flexibility [9].”
Self-citationHuang, S., Yan, D., & Chen, Y. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.109971 · Cited by 10 · see citing works
“Several distributed algorithms have been developed for energy sharing among DCs [18], charging stations [19], microgrids [20], etc.”
BackgroundSelf-citationSarmokadam, S., Suresh, M., & Mathew, R. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.03.032 · Cited by 10 · see citing works
Berna, C., Álvarez-Piñeiro, L., & Blanco, D. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16060312 · Cited by 9 · see citing works
“Similarly, studies like Yan et al. [33] have investigated distributed coordination mechanisms for multiple charging stations, focusing on characterizing aggregate EV power flexibility and optimizing energy trading.”
Pan, Y., Hu, J., Fang, Y., et al. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3590923 · Cited by 7 · see citing works
“Different from most existing aggregation methods [8], [9], [10], [11], which Authorized licensed use limited to the terms of the applicable license agreement with IEEE.”
Zhang, G., Feng, X., Xie, T., et al. (2025) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2025.104267 · Cited by 7 · see citing works
Wang, S., Du, L., & Chen, X. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3555951 · Cited by 6 · see citing works
“Note that this aggregate flexibility can be integrated with other types of flexibility, such as thermal flexibility in integrated energy systems [4] and electric vehicle charging flexibility [5], [6].”
Peng, Y., Ye, L., Ouyang, W., Xi, Q., Wang, J., & Wang, X. (2025) · IEEE Access · doi:10.1109/access.2025.3582530 · Cited by 6 · see citing works
Su, C., Wang, Y., & Štrbac, G. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115977 · Cited by 6 · see citing works
Online Deadline-Aware EV Charging Considering State-of-Charge-Dependent Peak Charging Rate
Wang, X., Chen, Y., Du, L., et al. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3591027 · Cited by 6 · see citing works
“To solve the offline problem, information on future charging behaviors of EVs is needed [1], [2], but this information is typically difficult to obtain or predict accurately, which calls for online EV charging scheduling methods.”
Self-citationLi, K., Fang, J., Ai, X., et al. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125029 · Cited by 6 · see citing works
Wang, X., Wang, S., Du, L., & Chen, Y. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3593904 · Cited by 6 · see citing works
“In [30], DSO and CSOs interacted to leverage flexibility from EVs to reduce power loss.”
Self-citationA Cooperative Multi-Agent Charging Scheduling by Regrouping EVs With Differentiated Deadlines
Cai, L., Guo, G., & Wu, D. O. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3529088 · Cited by 4 · see citing works
“Additionally, these approaches fail to acknowledge the fact that EV parking duration often exceeds the required charging time, thereby disregarding the consideration of charging flexibility that can support future economic and secure power sys-tem operations [19].”
Wu, P., Liu, D., Zhang, S., et al. (2025) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2025.3581203 · Cited by 4 · see citing works
Distributed control of electric vehicle clusters for user-based power scheduling
Cao, X., Ziras, C., Engelhardt, J., & Marinelli, M. (2023) · IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific) · doi:10.1109/itecasia-pacific59272.2023.10372266 · Cited by 4 · see citing works
Dong, P. X., Sui, K., Liu, M., et al. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.110939 · Cited by 3 · see citing works
Bahmani, M. H., Shayan, M. E., & Lorenzini, G. (2023) · Journal of Engineering Thermophysics · doi:10.1134/s1810232823040094 · Cited by 3 · see citing works
“However, as soon as changing the viewpoint from a single vehicle to the bulk of available vehicles in a region, their aggregated behavior would be much predictable and the results of modeling will be far more reliable [46, 71–73].”
Hierarchical Communication Network for Electric Vehicles Aggregation and Control
Wu, X., Yu, Y., Liu, Y., et al. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3395476 · Cited by 3 · see citing works
Choudhary, D., Mahanty, R. N., & Kumar, N. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3611268 · Cited by 3 · see citing works
“The strategies [12], [13], [14] and [15] consider various EVCS constraints.”
Song, Z., Lv, Z., Jia, B., et al. (2024) · Frontiers in Energy Research · doi:10.3389/fenrg.2024.1420039 · Cited by 2 · see citing works
Goh, H. H., Huang, C., Yew, W. K., Xie, H., Zhang, D., Dai, W., Kurniawan, T. A., Pang, W. L., Vaithilingam, C. A., & Phang, S. K. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.108053 · Cited by 3 · see citing works
Hai, C., Liu, H., Yue, K., et al. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127386 · Cited by 2 · see citing works
Pan, L., Liu, J., Gao, H., et al. (2026) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2026.111641 · Cited by 2 · see citing works
Bo, W., Zhou, P., Zhang, C., et al. (2026) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2026.3679752 · Cited by 2 · see citing works
Rocha, P. E. D., Dias, F. J. M., Escalante, S. L., et al. (2024) · IEEE Latin America Transactions · doi:10.1109/tla.2024.10620391 · Cited by 2 · see citing works
“Also, the use of renewable energy and electric vehicles significantly reduces carbon dioxide CO 2 emissions [12], [13].”
BackgroundWei, C., Chen, Z., Wang, Y., et al. (2024) · Energy · doi:10.1016/j.energy.2024.134170 · Cited by 2 · see citing works
Probabilistic flexibility assessment of shared charging stations for electric vehicles
Nukić, E., Levi, V. A., Ćetenović, D., et al. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.102014 · Cited by 2 · see citing works
Electric vehicle navigation and cluster dispatch for reliable low-carbon traffic–Power systems
Wang, S., Xiang, Y., Chen, L., et al. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1164198 · Cited by 2 · see citing works
Zhang, X., Zhao, Q., Guo, L., & Wang, S. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3594615 · Cited by 1 · see citing works
“Efficient methods have been developed using maximum-volume inner approximations [11], while two-stage frameworks that preserve user privacy have achieved up to 13.24% cost reduction compared with traditional methods [12].”
InfluentialYu, R., Ye, R., Zhang, Q., et al. (2025) · Processes · doi:10.3390/pr13061803 · Cited by 1 · see citing works
Two-Tier Aggregation of Distributed Energy Storage Units Considering Network Access Purchase
Zhang, M., Xu, Y., Yang, L., et al. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3600677 · Cited by 1 · see citing works
“Several effective methods have been proposed to determine the maximum envelope of aggregate power where any trajectory is achievable, such as [14] and [15].”
Gao, T., Wang, X. (., Zhang, J., Zhang, J., & Parisio, A. (2026) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2026.111598 · Cited by 1 · see citing works
Mekapati, S. R., Choudhury, N. B. D., Kasireddy, I., & Biswas, S. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-48891-7_4 · Cited by 1 · see citing works
Low Voltage Station Area Distributed Energy Resources Aggregation Method Based on Virtual Battery
Yu, C., Huang, J., Gao, M., et al. (2024) · Power System and Green Energy Conference (PSGEC) · doi:10.1109/psgec62376.2024.10720966 · Cited by 1 · see citing works
“The current references aggregates resources such as temperature controlled loads [3], electric vehicles [4-5], and distributed generators [6] to participate in grid dispatching or market transactions, but mostly focuses on single resource aggregation, ignoring the differences in different resources.”
BackgroundOptimizing Charge and Discharge Control for Plug-In Electric Vehicles in Power Distribution Systems
Mekapati, S. R., Choudhury, N. B. D., & Kasireddy, I. (2024) · International BIT Conference (BITCON) · doi:10.1109/bitcon63716.2024.10985126 · Cited by 1 · see citing works
“EV charging process via Optimizing Procedures electricity pricing or grid balancing will lead to less overload and an even greater increase in quality [11], [24].”
2023 Index IEEE Transactions on Sustainable Energy Vol. 14
(2023) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2023.3315084
Calculation method of carbon flow distribution in load-intensive regional energy centers
Wang, S., Chen, H., Li, D., et al. (2024) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-247175
“In MEFN, different types of energy are supplied, transmitted and utilized through various energy devices [10].”
Tiwari, K., & Singh, B. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3515940
“For ac-based vehicle charging, a commonly discussed power supply configuration involves an ac bus system [15].”
Numerous Electric Vehicle Charging Aggregation, Bidding and Management
Tan, L., Liu, X., Wang, S., et al. (2024) · Asian Conference on Frontiers of Power and Energy (ACFPE) · doi:10.1109/acfpe63443.2024.10800886
“To obtain high-quality data, EVs with charging times under 1 hour and charging energy below 10% SOC during the parking period will be excluded, as they offer very little flexibility [8].”
Research on the Quantification Method of Electric Vehicle Cluster Flexibility
Wang, Z., Wang, Y., Xie, L., et al. (2024) · doi:10.1109/epee63731.2024.10875468
Abdulnasser, G., Ali, A., Shaaban, M. F., & Mohamed, E. E. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33912-7.00002-5
Sarmokadam, S., & Suresh, M. (2025) · AIP Advances · doi:10.1063/5.0266157
Multi-Load and Electric Vehicle Aggregation Participate in Market Scheduling Research
Ma, Z., Zhang, H., Zhang, X., et al. (2025) · doi:10.1109/nesp65198.2025.11040262
“As in literature [10], it is assumed that electric vehicle owners submit the next day's charging information to the charging station.”
Aggregation of 5G Base Station Backup Batteries for Flexibility Enhancement of Power System
Xiong, K., Liu, X., Zhao, Z., et al. (2025) · IEEE International Conference on Power Science and Technology (ICPST) · doi:10.1109/icpst65050.2025.11089316
“Common approaches include box-based [7-8], ellipsoid-based [9-10], and polytope-based [11-16] MIA methods.”
An Efficient Reduction Method for Available Time Intervals of Large-Scale Demand-Side Resources
Wei, H., Jiang, H., Zhang, J., et al. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3597362
“In contrast, inner-approximation methods usually establish unified representations such as ellipses and polytopes to guarantee dispatch feasibility [13], [14], [15], [16].”
Sun, Y., Liu, X., Li, M., et al. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105355
Ding, J., Lu, S., Huang, X., et al. (2026) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2026.3665226
“Similarly, Chen et al. [17] and Yan et al. [24] also overlook the time-coupling relationship in their proposed algorithm, which utilizes the box-based aggregate model.”
Privacy-Preserving Estimation of Aggregated Flexibility from Local Energy CommunitiesPreprint
Tostado‐Véliz, M., Hasanien, H. M., Siano, P., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6258199
Improving voltage flexibility index in MGs by optimal charging scheduling of PHEVs
Hafezimagham, A., Baghaee, H. R., Gharehpetian, G. B., et al. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109141
Mohanapriya., S., Dharun Karthik., A., Mahathi Krishnaa, A., et al. (2026) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears67481.2026.11416795
Inverse Optimization-based Aggregation Model of Residential Controllable Loads
Jin, X., Qian, T., Hu, Q., et al. (2026) · doi:10.1109/pesim67009.2026.11438610
“Inner approximations, on the other hand, lie strictly within the true feasible region, guaranteeing feasibility but possibly omitting some feasible points [9]–[11].”
Self-citationWang, Z., Yu, X., Li, H., Liu, Y., Zang, T., Ruan, J., & Chen, S. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.148218 · Cited by 1 · see citing works
A Smart Charging Strategy for Electric Vehicles in Constrained Distribution Network
Dahiwale, P. V., & Rather, Z. H. (2026) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2026.3690683
Convex Mean-Field Charging Scheduling of Large-Scale EV Populations
Chen, G., Zhang, J., Su, P., & Deng, H. (2026) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee69242.2026.11524468
“However, realizing this potential requires a control framework capable of coordinating the charging and discharging behaviors of vast populations of EVs [9].”
A hierarchical stochastic coordination strategy for residential electric vehicle charging
(146229), M. Y., Jia, J., Wang, B., et al. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08939-w
“However, existing coordination strategies primarily target public charging stations, employing meth-ods like distributionally robust model predictive control [21] and reinforcement learning [22], or focus on aggregated EVs through decentralized approaches [23, 24].”
Yihu, R., & Lin, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6904227
Preference-aware flexibility envelopes for coordinated operation of energy communities
Tostado‐Véliz, M., Hasanien, H. M., Siano, P., et al. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102493
Two-Stage Game Theory Using CNN-BILSTM for Effective EV Charging Management and Power Allocation
Levingstan, Y., Saji, K. S., Jarin, T., et al. (2026) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan66520.2026.11588500
“Additionally, different EVs may dynamically and randomly arrive at the electrical charging station to charge [8].”
An aggregation method of HVAC Loads Based on an Improved Polyhedral Inner Approximation
Lv, J., Xia, Q., & Zhang, Z. D. (2026) · doi:10.1109/epsic70071.2026.11590838
“Common techniques include box[7]-[8], zonotope[9]-[10], homogeneous polyhedral[11]-[14], and ellipsoidal approximations[15].”
An Electric Vehicle Optimization Scheduling Strategy Based on TSM-NSGA-III Algorithm
Wu, Y., Wang, Z., Du, Y., et al. (2026) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2026.p1209
Hai, C., Haoming, L. I. U., Wang, J., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7202514
Coordinated power regulation of clustered IBRs within grid-connected local energy communities
Alexakis, Z. A., Papageorgiou, P. C., Alexandridis, A. T., et al. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128539
Jin, X., Qian, T., Hu, Q., & Dou, X. (2026) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2026.105224
Charging station planning strategy considering imbalanced distribution of response propensity
Wang, Y., Xie, D., Gu, C., et al. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.114096
Guo, Z., Li, Y., Yang, J., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7395296
Tan, T., Xu, Q., & Yang, Y. (2026) · World Electric Vehicle Journal · doi:10.3390/wevj17090468
Hou, S., Sang, Y., Zhao, X., Yu, Y., & Xue, Q. (2026) · Energies · doi:10.3390/en19184245
Wang, L., Chen, L., Li, J., et al. (2026) · IET Smart Grid · doi:10.1049/stg2.70126
- Authors
- Yan, D., Ma, C., & Chen, Y.
- Affiliations
- Chinese University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2023-01
- Volume
- 14
- Issue
- 1
- Pages
- 356-370
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W4313536130
- Semantic Scholar
- e9e17e053d16c36b9e18ef0d7637b4aeefefcd2e
- Cited by
- 96
- References
- 36
- Citations by year
- 2026: 27 · 2025: 34 · 2024: 25 · 2023: 9 · 2022: 1
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Sustainable Energy (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Chinese University of Hong Kong Direct Grant for Research (4055169)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Keywords
- electric vehicle charging stations, distributed coordination, privacy preservation, AC power flow constraints, distribution network constraints, two-stage optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-19.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-19.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
