Optimal coordination of electric vehicle charging and photovoltaic power curtailment in unbalanced low voltage networks: An experimental case
Open accessAbstract
This study introduces a quadratic programming‐based optimisation method to coordinate electric vehicle (EV) charging and photovoltaic (PV) curtailment in unbalanced low voltage (LV) networks. The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non‐linear models and surpassing the limitations of local solutions derived from meta‐heuristics algorithms reported in the literature. The coordination is carried out through a centralised controller installed at the header of the LV feeder. The objective of the proposed strategy is to minimise the power curtailment of all PV systems and maximise the power delivered to all EVs by optimising at every time step a suitable setpoint for the PV units and the charging rate of each EV connected without surpassing network constraints. A new energy‐boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival‐and‐desired energy states of the vehicle to compute its charging trajectory optimally. The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware‐in‐the‐Loop setup.
AI snapshot
Written from the paper’s full text.
TL;DR
This paper proposes a convex quadratic programming (QP) coordination strategy for EV charging and PV curtailment in unbalanced low-voltage networks, including a new energy-boundary model for EV charging trajectories. It validates the strategy experimentally in a Power Hardware-in-the-Loop laboratory setup with two commercial EVs and a PV inverter.
Key findings
- The QP model minimises PV power curtailment and maximises EV charging while respecting network constraints, with convexity guaranteeing the global optimum and avoiding local solutions from metaheuristics.
- In the first scenario, the Nissan Leaf started charging 1 min after connection at ΔP = 1.38 kW (6 A), increased to 3.7 kW, and reached nominal battery capacity at interval 730 (12:10 h), leaving 40 min of availability.
- In the second scenario, the BMW i3-based emulated EVs followed the optimal setpoint until the internal CCCV algorithm reduced charging; in the long-distance trip case, a peak of 2.3 kW appeared in phase a at interval 772 (12:52 h).
- An intentional charging-station disconnection caused measured energy to deviate from the computed trajectory, producing higher ΔE at the end, especially for phases b and c.
Methods
- A centralised controller at the LV feeder head solves a QP optimisation at each time step, using linearised network constraints based on voltage- and loading-sensitivity matrices computed with a Perturb-and-Observe method in PowerFactory and Python.
- Experimental tests were performed at the Smart Grid Technology Lab, TU Dortmund University, using a Power Hardware-in-the-Loop setup with an RWE eSTATION charging station, Phoenix Contact charging controller, KoCoS EPPE CX power quality analyser, 60 kVA SMA inverter, 200 kVA EGSTON power amplifiers, and an RT-Lab PV model.
- Two commercial EVs were tested: a Nissan Leaf 2012 (24.1 kWh, single-phase, up to 3.7 kW) and a BMW i3 2016 (33.2 kWh, three-phase, up to 11 kW); scenarios used 10 households, 1-min resolution, and assumed all vehicles arrived at 11:00 h.
Limitations
- The paper states that a unique set of sensitivity matrices is used for the whole optimisation period, only the main service cable loading is evaluated, and network reconfiguration effects are not considered.
- The paper states that the convexity assumption requires a convex formulation and linearised constraints, and that the lack of feedback signals prevents corrective action under perturbations; above almost 95% battery energy, the EV’s internal CCCV algorithm dictates charging power.
- Evident from the text: only one charging outlet could be measured, requiring emulation and a scale factor of 10 for the PV inverter, and SOC was not directly accessible, so arrival energy was estimated from test drives.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 23 cited sources checked · 0 supported · 0 flagged
- Findingown data
In the long-distance trip case, the measured charging power in the emulated EVs closely follows the optimal charging setpoint before interval 763 (12:43 h), when the CCCV algorithm in the BMW i3 automatically starts to decrease the charging power in all three phases.
“In Figure 11, the measured charging power in the emulated EVs closely follows the optimal charging setpoint before the interval 763 (12:43 h), when the CCCV algorithm in the BMW i3 automatically starts to decrease the value of the charging power in all three vehicles, that is, in the three phases.”
Quote in the paperNot a substantial claim - Findingown data
In the long-distance trip case, the measured energy is kept constant for about 3 min from interval 739 (12:19 h) because the charging station was intentionally disconnected to test the recovery of the charging process in all three phases.
“However, the measured energy is kept constant for about 3 min from interval 739 (12:19 h) because the charging station was intentionally disconnected (Figure 11) in order to test the recovery of the charging process in all three phases.”
Quote in the paperSubstantial claim - Findingreasoning
In the long-distance trip case, the measured energy path of all phases deviates from the computed trajectory, deriving in a higher ΔE at the end of the charging process, especially for phases b and c.
“By doing so, the measured energy path of all phases deviates from the computed trajectory, deriving in a higher Δ E at the end of the charging process, especially for phases b and c.”
Quote in the paperSubstantial claim - Findingown data
The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment using two commercial EVs and a PV inverter in a Power Hardware-in-the-Loop setup.
“The effectiveness of the proposed coordination strategy was successfully proven through three scenarios in a laboratory environment, making use of two commercial EVs and a PV inverter in a Power Hardware-in-the-Loop setup.”
Quote in the paperSubstantial claim - Findingown data
In the first scenario, the Nissan Leaf charging power continues increasing up to the maximum charging power of 3.7 kW.
“Thereafter, the charging power continues increasing up to the maximum charging power, that is, 3.7 kW.”
Quote in the paperNot a substantial claim
- Findingown data
In the long-distance trip case, the optimal and measured charging energy paths in all three EVs follow a similar trajectory within their energy boundaries at the beginning of the charging process.
“In terms of energy, Figure 12 shows that the optimal and measured charging energy paths in all three EVs follow a similar trajectory within their energy boundaries at the beginning of the charging process.”
Quote in the paperNot a substantial claim - Findingown data
In the first scenario, the Nissan Leaf reached the nominal battery capacity value at interval 730 (12:10 h).
“In Figure 6 it is observed that the Nissan Leaf reached the nominal battery capacity value at the interval 730 (12:10 h)”
Quote in the paperNot a substantial claim - Findingcitation
In the second scenario, the measured power tries to follow the optimal set point but keeps below the reference for EVs in households 12 and 21.
“In this case, the measured power tries to follow the optimal set point, but it keeps below the reference for EVs in households 12 and 21.”
Quote in the paperNot a substantial claim - Findingown data
When the battery energy level is over almost 95%, about 10.5 kWh per emulated vehicle, the internal CCCV algorithm dictates the charging power regardless of the control signal sent to the external charging controller.
“When the battery energy level is over almost 95% (i.e., ≈10.5 kWh per emulated vehicle), no matter the control signal sent to the external charging controller, the internal CCCV algorithm dictates the charging power, as shown in both scenarios.”
Quote in the paperSubstantial claim - Findingown data
In the first scenario, the measured power follows the optimal charging set point with small deviations associated with the instant where the measurement was taken.
“It can also be noticed that the measured power follows the optimal charging set point with small deviations associated with the instant where the measure was taken (a zoom view in Figure 6).”
Quote in the paperNot a substantial claim - Findingown data
Due to the difference between the optimal charging power and the measured power, an energy variation ΔE exists at the end of the charging period.
“Due to the difference between the optimal charging power and the measured power, an energy variation (Δ E ) exists at the end of the charging period, as shown in Figure 9.”
Quote in the paperNot a substantial claim - Interpretationown data
The output power of the real PV inverter presents fast variations due to its maximum power point tracker algorithm, particularly for low power values.
“However, the output power of the real inverter presents fast variations due to its maximum power point tracker algorithm, particularly for low power values.”
Quote in the paperNot a substantial claim - Interpretationreasoning
PV units can provide local voltage support through active power curtailment to maintain the voltage profile below a normative upper limit.
“Considering that all households with a PV unit can export the surplus energy to the grid, these units can also provide local voltage support through the active power curtailment set up for maintaining the voltage profile below a normative upper limit.”
Quote in the paperSubstantial claim- Rests on an assumption: All households with a PV unit can export surplus energy to the grid.
- Interpretationreasoning
The proposed model surpasses the limitations of local solutions derived from meta-heuristics algorithms reported in the literature.
“The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature.”
Quote in the paperSubstantial claim - Premisecitation
The voltage sensitivities in refs [9,10] were obtained assuming three-phase balanced LV networks.
“However, the voltage sensitivities were obtained assuming three-phase balanced LV networks.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The nominal energy value of all three vehicles is reached before their maximum disconnection time because the charging process followed the upper energy boundary.
“It is also noted that the nominal energy value of all three vehicles is reached before their maximum disconnection time because the charging process followed the upper energy boundary.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The proposed model guarantees the optimal global solution of the problem.
“The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature.”
Quote in the paperSubstantial claim - Interpretationreasoning
The convexity assumption leads to simplifying models by not including certain constraints or not fully assuring control requirements.
“This leads to simplifying models by not including certain constraints or do not fully assure the control requirements.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The optimisation problem is convex or a convex formulation is used.
- Interpretationown data
Experimental studies proved the concept of the coordination schema by comparing the charging process of two commercial EVs and the power curtailment of a real PV unit.
“Experimental studies proved the concept of the coordination schema in Figure 3 by comparing the charging process of two commercial EVs and the power curtailment of a real PV unit.”
Quote in the paperSubstantial claim - Interpretationreasoning
The new energy-boundary model computes the EV charging trajectory optimally.
“A new energy-boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival-and-desired energy states of the vehicle to compute its charging trajectory optimally.”
Quote in the paperSubstantial claim - Interpretationnone given
A limitation of the proposed approach is that only the loading level of the main service cable of the feeder is evaluated.
“and that only the loading level of the main service cable of the feeder is evaluated.”
Quote in the paperNot a substantial claim - Interpretationnone given
The proposed quadratic programming-based optimisation model is convex.
“The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature.”
Quote in the paperNot a substantial claim - Interpretationreasoning
A limitation of the proposed approach is the utilisation of a unique set of sensitivity matrices for the whole optimisation period.
“Some limitations of the proposed approach are related to the utilisation of a unique set of sensitivity matrices for the whole optimisation period”
Quote in the paperNot a substantial claim - Interpretationnone given
A new energy-boundary model is proposed to meet the energy requirements of all EVs.
“A new energy-boundary model is also proposed to meet the energy requirements of all EVs, which is based on a recurrent function that depends on the arrival-and-desired energy states of the vehicle to compute its charging trajectory optimally.”
Quote in the paperSubstantial claim - Interpretationreasoning
The proposed model avoids the complexity of non-linear models.
“The proposed model is defined as a convex model that guarantees the optimal global solution of the problem avoiding the complexity of non-linear models and surpassing the limitations of local solutions derived from meta-heuristics algorithms reported in the literature.”
Quote in the paperSubstantial claim - Interpretationreasoning
Using a unique set of sensitivity matrices means the sensitivity values cannot be expected to exactly match those computed for the load and generation levels at time t+1 on the feeder.
“For the former, it means that the sensitivity values cannot be expected to exactly match those that could be computed for the load and generation levels at time t + 1 on the feeder”
Quote in the paperNot a substantial claim
- May combine two claims. 20
- Classified as probably not a substantial claim. 12
May combine two claims.
Findingown dataAlthough the output power of the PV inverter varied significantly due to its size, the power curtailment factor was successfully applied based on the optimisation results.
“Although the output power of the PV inverter varied significantly due to its size, the power curtailment factor was successfully applied based on the optimisation results.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe energy necessary to reach the rated battery capacity of the Nissan Leaf was 3.8 kWh and of the BMW i3 was 2.9 kWh.
“During this test, it was found that the energy necessary to reach the rated battery capacity of the Nissan Leaf and the BMW i3 was 3.8 and”
- Also stated in: § -237 · plain
“2.9 kWh respectively.”
Quote in the paperSubstantial claim- Also stated in: § -237 · plain
May combine two claims.
FindingreasoningIn the long-distance trip case, the BMW i3 arrival energy level was approximately 13.2 kWh, or 4.38 kWh per emulated vehicle.
“At the arrival, the energy level of the vehicle was approximately equal to 13.2 kWh (4.38 kWh per emulated vehicle), which was estimated through e arr j ¼ e Bmax j -ECR j ⋅ d j , knowing the travelled distance d, the battery capacity and an ECR = 0.153 kWh/km from the interface of the vehicle.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenIn the second scenario, the charging process for the three vehicles is delayed 2 min after being connected.
“For the three vehicles, the charging process is delayed 2 min after being connected.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the first scenario, the Nissan Leaf charging process followed the upper energy boundary, which left an availability interval of 40 min.
“due to the charging process followed the upper energy boundary, which left an availability interval of 40 min (blue line in Figure 5).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenIn the second scenario, the initial charging power equals 1.38 kW and continues to increase up to 3.7 kW.
“Like the above scenario, the initial charging power equals 1.38 kW and continues to increase up to 3.7 kW.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationIn the long-distance trip case, when the charging process is almost finished, a peak power value of 2.3 kW appears in phase a at interval 772 (12:52 h), which slowly decreases for approximately 8 min up to the disconnection time.
“However, when the charging process is almost finished, a peak power value of 2.3 kW appears in phase a at interval 772 (12:52 h), which slowly decreases for approximately 8 min up to the -241 disconnection time.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the first scenario, the Nissan Leaf starts charging 1 min after being connected with a ΔP = 1.38 kW, that is, 6 A, which is the minimum current to activate the charging controller.
“It can be seen in both figures that the EV starts charging 1 min after being connected with a Δ P = 1.38 kW, that is, 6 A, which is the minimum current to activate the charging controller.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe optimality of meta-heuristic problems highly depends on the parametrisation of the algorithm and the complexity of non-linear terms, so Pareto front solutions may not be the optimal global value.
“However, similar to ref. [5], the optimality of the problem highly depends on the parametrisation of the meta-heuristic algorithm and the complexity of the non-linear terms of the model, which implies that each solution, in this case, in the Pareto front may not be the optimal global value of the problem.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
InterpretationreasoningConvexity allows for theoretical guarantees that the proposed quadratic optimisation model converges to the optimal solution.
“Convexity allows for theoretical guarantees that the proposed quadratic optimisation model converges to the optimal solution.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The optimisation problem is convex or a convex formulation is used.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningChanges in feeder admittance due to network re-configuration will modify the sensitivity values of the grid.
“Another consideration for the sensitivity coefficients is the effect of network re-configuration, as the changes in feeder admittance will modify the sensitivity values of the grid.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe proposed weighting formulation evenly distributes the power curtailment among all PV units in the network.
“A weighting formulation is proposed to evenly distribute the power curtailment among all PV units in the network.”
Quote in the paperNot a substantial claim- Rests on an assumption: All PV units are likely to contribute to the aggregated reverse power flow.
May combine two claims.
Interpretationown dataFor the EVs, slight variations in power with respect to the optimal power value were found for the first vehicle, whereas for the second vehicle these variations were more significant due to the internal charging control algorithm of the EV.
“For the EVs, slight variations in power with respect to the optimal power value were found for the first vehicle, whereas for the second vehicle, these variations were more significant due to the internal charging control algorithm of the EV.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisereasoningThe algorithms in [11-15] were tested through simulations, disregarding real-world conditions such as non-idealistic charging characteristics, so their performance is only approximate and may differ in real applications.
“In addition, the algorithms were tested through simulations, disregarding relevant real-world conditions, such as non-idealistic charging characteristics, so their performance is only approximate and may differ in real applications.”
Quote in the paperSubstantial claim0 of 5 sources checked- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenAll five studies [11-15] except [12] use additional energy storage to buffer energy between EVs and PV units.
“All five studies, except [12], use additional energy storage to buffer the energy between the EVs and the PV units.”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe energy values needed to reach rated battery capacity can vary for the same route depending on driving mode, tyre state, average velocity, and other external factors that modify the vehicle energy consumption rate.
“Note that these values can vary for the same route because they highly depend on the driving mode, state of the tyres, average velocity, and other external factors that can modify the energy consumption rate (kWh/km) of the vehicles.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationUsing meta-heuristic approaches increases the complexity of the problem and highly leads to getting stuck in a local solution.
“Nonetheless, using the meta-heuristic approach increases the complexity of the problem as it is treated like a non-linear nonconvex optimisation problem that highly leads to getting stuck in a local solution.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationown dataA new coordination strategy to maximise EV charging and minimise PV curtailment in real LV networks was presented and tested in a laboratory environment to demonstrate its effectiveness in maintaining or improving the energy service provision by the DSO.
“A new coordination strategy to maximise the EV charging and minimise the PV curtailment in real LV networks was presented and tested in a laboratory environment to demonstrate its effectiveness in maintaining or improving the energy service provision by the DSO.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe adaptive charging algorithm in refs [16-18] seeks to maximise the charging current within the capacity limits of the network and reduce charging times.
“The algorithm is sought to maximise the charging current within the capacity limits of the network and reduce charging times.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA time resolution of 1 min was considered accurate for this study.
“A time resolution of 1 min was considered to be accurate for this study [27].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningThe proposed approach requires a convex formulation of the optimisation problem because, without convexity, the model is not guaranteed to converge.
“On the other hand, the convexity assumption causes some limitations in terms of applicability, that is, the proposed approach requires a convex formulation of the optimisation problem because, without convexity, this model is not guaranteed to converge.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The optimisation problem is convex or a convex formulation is used.
May combine two claims.
InterpretationreasoningEvaluating only the loading level of the main service cable means that an overload condition in any of the branches cannot be evaluated, although these can be included in the construction of the sensitivity matrix.
“and for the latter means that if there is an overload condition in any of the branches, it cannot be evaluated; although these can be included in the construction of the sensitivity matrix.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe longer the available range of EV connection, the narrower its energy boundaries, and therefore the maximum time of delaying to start charging.
“Besides, by comparing Figures 9 and 12, it can be observed that the longer the available range of EV connection, the narrower its energy boundaries, and therefore, the maximum time of delaying to start charging.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningOnly one set of sensitivities is calculated along the test period, reducing the computational burden and execution time of the algorithm.
“Only one set of sensitivities is calculated along the test period, as this approach reduces the computational burden and execution time of the algorithm.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe BMW i3 on-board charger injects unbalanced charging currents, with current through phase a greater than in phase b, and current in phase b higher than in phase c.
“According to the charging tests carried out in the same laboratory by Caro et al. [28], this happens because the on-board charger injects unbalanced charging currents, that is, the current through phase a is greater than in phase b, and the current in phase b is higher than in phase c.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Caro et al. [28]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningThe deviation in measured energy is due to the lack of feedback signals in the proposed coordination strategy, so no correction action can be executed under these perturbations in the optimisation.
“This effect is due to the lack of feedback signals in the proposed coordination strategy, that is, it is not possible to execute a correction action under these perturbations in the optimisation.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe adaptive charging algorithm in refs [16-18] is based on actual charging current measurements instead of predefined load curves, so no modelling or data about the EVs is necessary.
“The algorithm is based on the actual charging current measurements instead of predefined load curves, which means that no modelling or data about the EVs is necessary.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S4 (based).
InterpretationreasoningTo achieve the above response by the PV inverter, it must be capable of limiting the active power through external signals.
“To achieve the above response by the PV inverter, it must be capable of limiting the active power through external signals.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe energy level that can be guaranteed is that in which the external control signal dictates the charging power; above that, the internal CCCV algorithm is a limitation that cannot be controlled as it depends on the manufacturer.
“Therefore, this is a limitation that cannot be controlled as it depends on the manufacturer, and therefore, the energy level that can be guaranteed is that in which the external control signal dictates the charging power.”
Quote in the paperSubstantial claim
The paper relies on this without support.
When the number of PVs and EVs increases in the LV network without control action, the voltage and loading levels of the grid can be negatively affected.
“When the number of PVs and EVs increases in the LV network without any control action, the voltage and loading levels of the grid can be negatively affected.”
The authors state this as an assumption.
All households have installed smart metres with load and generation control capability.
“It is assumed that all households have installed smart metres with load and generation control capability.”
The paper relies on this without support.
No paper considering a combined testbed for smart EV charging and PV curtailment is found in the literature.
“However, to the knowledge of the authors, no paper considering a combined testbed for smart EV charging and PV curtailment is not found in the literature.”
The paper relies on this without support.
All PV units are likely to contribute to the aggregated reverse power flow.
“As all the PV units are likely to contribute to the aggregated reverse power flow”
The paper relies on this without support.
The energy state previous to the EV connection is unknown.
“As the energy state previous to the EV connection is unknown”
The paper relies on this without support.
All households with a PV unit can export surplus energy to the grid.
“Considering that all households with a PV unit can export the surplus energy to the grid”
The authors state this as an assumption.
All PV units have a rated power of 5 kVA and operate with a unity power factor.
“Besides, it was assumed that all PV units have a rated power of 5 kVA operated with a unity power factor.”
The authors state this as an assumption.
The optimisation problem is convex or a convex formulation is used.
“On the other hand, the convexity assumption causes some limitations in terms of applicability, that is, the proposed approach requires a convex formulation of the optimisation problem because, without convexity, this model is not guaranteed to converge.”
The paper relies on this without support.
At the LV level, the number of possible network re-configurations is almost null, and these operational states do not occur frequently.
“However, at the LV level, the number of possible re-configurations is almost null, and these operational states do not occur frequently.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Cable and transformer loading constraints omit PV curtailment
MajorThe paper says the voltage and loading approximations were modified to include the PV curtailment factor. Unlike the voltage constraint, the stated cable and transformer loading constraints use PV power without that factor, so changing the curtailment decision cannot change their calculated loading.
“In order to evaluate the effect of the voltage and loading level of the feeder in the coordination strategy, the linear approximations for these two parameters in ref. [6] were modified to include the PV power curtailment factor ζ i,t .”
“P Line l;ϕ;t f c þ X h∈H h≠∅ � C h;ϕ;l ⋅ p EV h;t þ D h;ϕ;l ⋅ p PV h;t � ≤ S Line l;ϕ rated”
Real-network citation points to an EV-charging study
MinorThe feeder description cites [26] for the real LV network. Reference 26 is instead a study of temporal resolution in EV charging-load modelling.
“a distribution feeder from one real LV network in the North West of England [26] was modelled in PowerFactory”
“26. Simolin, T., et al.: Assessing the influence of the temporal resolution on the electric vehicle charging load modeling accuracy.”
Time-resolution citation points to a harmonic-distortion study
MinorThe stated choice of a 1-minute time resolution cites [27], but reference 27 concerns the influence of state of charge on charging harmonic distortion.
“A time resolution of 1 min was considered to be accurate for this study [27].”
“27. Caro, L.M., et al.: State of charge influence on the harmonic distortion from electric vehicle charging.”
RT-Lab citation points to an LV-network source
MinorThe experimental setup cites [25] for the RT-Lab PV model, but reference 25 is an Electricity North West entry about low-voltage network solutions.
“a single cell PV model developed in RT-Lab [25].”
“25. West, Electricity North.: Low Voltage Network Solutions (2019). https://www.enwl.co.uk/lvns”
Caro study is cited as [28] but listed as reference 27
MinorThe text attributes the charging-current observation to Caro et al. [28], whereas the provided reference list numbers the Caro et al. paper 27.
“According to the charging tests carried out in the same laboratory by Caro et al. [28], this happens because the on-board charger injects unbalanced charging currents”
“27. Caro, L.M., et al.: State of charge influence on the harmonic distortion from electric vehicle charging.”
Cites
Renewables 2020 - Global status report
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Electric Vehicles: A Comprehensive Review of Technologies, Integration, Adoption, and Optimization
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- Authors
- Cortés Borray, A. F., Rauma, K., & Torres, E.
- Affiliations
- TECNALIA, Basque Research and Technology Alliance (BRTA) Derio Spain
- TU Dortmund University (DE)
- University of the Basque Country (ES)
- Venue
- IET Smart Grid
- Published
- 2022-10-18
- Volume
- 6
- Issue
- 3
- Pages
- 233-245
- ISSN
- 2515-2947 (ISSN-L)
- OpenAlex
- W4306690134
- Cited by
- 7
- References
- 21
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 3
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IET Smart Grid (publishedVersion, cc-by-nc)
- Communities in ADDI (University of the Basque Country) (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- Hispana (submittedVersion, cc-by-nc)
- Funders
- Bundesministerium für Verkehr und Digitale Infrastruktur
- European Commission (957755)
- Eusko Jaurlaritza (IT1191-19)
- Euskal Herriko Unibertsitatea (18/181)
- Ministerio de Ciencia e Innovación
- Technische Universität Dortmund
- 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, quadratic programming, convex optimization, photovoltaic inverter, power hardware-in-the-loop, grid integration of EVs
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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.
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