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Incorporating demand response of electric vehicles in scheduling of isolated microgrids with renewables using a bi-level programming approach

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper claims that a two-level scheduling model with real-time pricing can use electric-vehicle demand response to shave peaks in isolated microgrids while cutting costs for both the microgrid operator and EV users.

desk verdict The missing availability constraints in the lower-level EV model make the peak-shaving result an artifact, so the paper's central claim is not established. read the letter →

arxiv 1908.11197 v1 pith:OOT6ZIPI submitted 2019-08-18 eess.SP cs.SYeess.SYmath.OC

Yang Li , Kang Li This is my paper · ORCID
classification eess.SPcs.SYeess.SYmath.OC
keywords microgridschedulingdemandresponseelectricvehiclesrenewablegenerationsbi-levelprogrammingreal-timepricingJAYAalgorithmsequenceoperationtheory
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to show that demand response of electric vehicles, driven by real-time prices, can be incorporated into the scheduling of an isolated microgrid with wind, solar, and storage, and that doing so benefits both sides. It formulates the problem as a bi-level program: the upper level minimizes the microgrid's net operating cost, the lower level minimizes EV users' charging cost, and a real-time price signal links the two levels. A hybrid solution algorithm combining the JAYA heuristic with an interior-point method is proposed. On a test microgrid, the joint strategy reduces the peak-to-valley load difference and improves both the MG net operating cost and the EV charging cost compared with strategies that optimize only one side. The result matters because isolated microgrids need economical ways to absorb renewable variability and EV load without access to a main grid.

What carries the argument

The load-bearing structure is the bi-level program itself, coupled by a real-time price rule: after the lower level returns an EV charging plan, the upper level sets the price proportional to the ratio of the combined EV and original load to a reference load, and the plan is re-optimized until a joint criterion stops improving. Supporting machinery includes sequence operation theory, which turns probabilistic renewable outputs into discrete sequences and converts the chance constraint on spinning reserve into a deterministic 0–1 constraint, and the JAYA-interior-point hybrid algorithm, where JAYA searches the upper level and the interior-point method solves the lower level.

What would settle it

Run the same case study with an added constraint that EV i can only charge between its sampled arrival and departure times shown in the paper's Fig. 4, with each EV's total energy requirement fixed, and compare the load curves and costs; if the peak-to-valley improvement and the joint cost reductions shrink or vanish, the central result depends on the missing time-window constraint.

Watch

Extended reading notes

Core claim

The central claim is that a bi-level scheduling model with real-time pricing coordinates EV charging with microgrid operation so that EV users, pursuing only their own charging-cost minimization, end up shifting load away from peak periods and lowering the microgrid's net operating cost as well. In the reported test system, the joint optimization strategy outperforms the strategy that ignores EV costs on EV charging cost, and outperforms the strategy that ignores MG revenue on MG net operating cost; the load curves show reduced EV charging in peak-load periods and increased charging in off-peak periods. The authors frame this as a coordination of renewable generation uncertainty and EV demand response in a single scheduling model.

Load-bearing premise

The load-bearing premise is that EV charging power can be scheduled in any hour: the lower-level constraints never limit each EV to its sampled arrival-to-departure window, so the flexibility used to create the peak-shaving result is not tied to the actual times EVs are present.

Editorial extensions

If this is right

  • In the reported test case, joint optimization lower the MG net operating cost compared with the strategy that ignores MG revenue, and lowers EV charging cost compared with the strategy that ignores EV costs.
  • The real-time price mechanism shifts EV charging out of peak-load periods, reducing the peak-to-valley load difference in the microgrid.
  • The chance-constraint transformation handles renewable uncertainty without Monte Carlo simulation, keeping the bi-level solution process tractable.
  • The proposed framework yields a single schedule that balances both parties' interests, making it usable as a negotiation tool between a microgrid operator and an EV charging-station operator.

Reading between the lines

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

  • An extension the paper does not pursue: the real-time price rule is a purely load-proportional signal, so comparing it with a price that also reflects renewable surplus or storage state would reveal whether the peak-shaving result is driven by the price shape or by the model's assumed EV flexibility.
  • The reported results likely depend on the lower-level model allowing EVs to charge in any hour; constraining each EV to its arrival–departure window would give a stiffer test of the peak-shaving claim.
  • The same bi-level template could be applied to other price-responsive resources, such as water heaters or home batteries, where the lower level minimizes a user's energy bill and the upper level manages a distribution feeder.
  • The joint objective treats MG and EV interests symmetrically; a different weighting would shift the compromise, so the 'win–win' conclusion is tied to that particular squared-deviation choice.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript proposes a bi-level programming model for scheduling an isolated microgrid with renewable generation and electric-vehicle charging under real-time pricing. The upper level minimizes microgrid net operating cost subject to probabilistic spinning-reserve constraints; the lower level minimizes EV charging cost. A real-time price is computed by Eq. (24) as a linear function of total load plus EV load, and the bi-level problem is solved by an iterative JAYA-interior-point method. A 24-hour case study with 20 EVs compares three strategies and two demand-response cases, reporting that the joint strategy reduces both microgrid operating cost and EV charging cost and achieves peak shaving.

Significance. If the results were valid, the paper would offer a practical framework for coordinating EV demand response with renewable uncertainty in isolated microgrids, with a reusable heuristic solver. The paper deserves credit for modeling the probabilistic reserve constraint via sequence operation theory and for presenting a complete case study with multiple strategies and sensitivity cases. However, the central qualitative claim—that EV demand response achieves peak shaving and a win-win outcome—is not supported by the model as formulated, because the lower-level EV model lacks temporal availability constraints and the pricing rule cannot represent supply-demand dynamics. The reported results are therefore likely an artifact of the modeling assumptions, which substantially reduces the significance of the contribution.

major comments (3)
  1. [Section III-B, Eqs. (20)-(23)] The lower-level EV charging model omits the arrival and departure time windows that are generated in Section II-B and displayed in Fig. 4. Eq. (22) imposes only per-period min/max charging power limits, and Eq. (23) only bounds cumulative energy over the scheduling horizon; nothing prevents P_i,t^EV from being positive at hours when EV i is not connected to the charging station. Because the real-time price in Eq. (24) is monotonically tied to P_t^EV + P_t^L, the lower-level cost minimization will artificially shift all EV charging to the global off-peak hours. The peak-shaving result in Fig. 10 and the 'win-win' cost comparison in Fig. 5 therefore follow from unrealistic flexibility rather than from the proposed demand-response mechanism. This is a load-bearing modeling omission, not a minor detail: without availability constraints, the model permits charging an EV at 03:00 when it arrives at 18:00, so the central claim of the paper is not established for realistic EV behavior.
  2. [Section III-C, Eq. (24)] The real-time price formula depends only on the sum of EV charging power and original load, normalized by a reference power, with no term representing renewable generation, system marginal cost, or the operating state of the microgrid. The paper states in Section III-C that the price reflects the dynamic relationship between supply and demand, but the supply side (PV, WT, MT, ESS) does not enter Eq. (24) at all. As a result, the proposed 'demand response' is essentially an incentive to shift load toward periods of low total load, and the claimed coordination between EV demand response and renewable generation uncertainty is not embodied in the pricing mechanism. The case-study conclusion that demand response helps consume renewable generation is therefore not supported by the model.
  3. [Section IV, Steps 4-15, and Section V-A] The JAYA-IPM solution method is not validated. The termination criterion in Step 12 is only whether the current iteration exceeds a pre-defined maximum number of iterations, and the parameter settings are ambiguous: Section V-A sets N_maxiter to 20 while also stating a JAYA maximum of 1500 iterations, without clarifying which limit applies to the bi-level loop. The paper does not compare JAYA-IPM with an exact or benchmark solver, does not report optimality gaps, and does not test convergence on the bi-level model. Since the quantitative comparisons among Strategies 1-3 in Fig. 5 and the demand-response cases in Figs. 8-10 depend on the quality of the optimized solutions, the reader cannot assess whether the reported costs and schedules are meaningful.
minor comments (5)
  1. [Table 3] The off-peak interval is listed as '00:60-07:00', which is not a valid time interval; it should presumably be '00:00-07:00'.
  2. [Section II-B and Fig. 4] The paper states that EVs leave the charging station immediately once charged to expected capacity, and Fig. 4 plots EV arrival and departure times; however, these departure times are never used in the lower-level optimization, which is confusing and should be clarified.
  3. [Eq. (6) versus Eqs. (20)-(23)] Eq. (6) defines the charging time T_i,CH in terms of rated power and charging efficiency, but the lower-level model uses only an aggregate P_t^EV; the relationship between per-EV charging power P_i,t^EV and the aggregate variable is not stated, and Eq. (23) does not connect the cumulative energy bound to T_i,CH.
  4. [Throughout] The manuscript contains numerous typographical and grammatical errors (for example, 'is it has become', 'considerate both'), which should be corrected in any revision.
  5. [Eq. (11)] In Eq. (11), SOC_t is treated as stored energy with units of kWh, while SOC elsewhere denotes a dimensionless state of charge; the notation should be clarified to avoid unit inconsistency.

Circularity Check

2 steps flagged · score 6.0 of 10

Peak-shaving result is contained in the real-time price rule (Eq. 24) and the lower-level objective (Eq. 20); EV arrival/departure windows are generated but never enforced.

  1. self definitional [Section III-B, Eq. (20); Section III-C, Eq. (24)]
    "The EV charging cost is calculated by the following formula: min F2 = W^c/m + 365 Σ_t ω_rt,t P_t^EV (20) ... the real-time electricity price is calculated according to the following formula in the upper level: ω_rt,t = ω_REF (Σ_i P_i,t^EV + P_L,t)/P_REF^L (24)."

    Substituting (24) into (20) makes the lower-level objective, up to constants, 365·(ω_REF/P_REF^L)·Σ_t [(P_t^EV)^2 + P_L,t·P_t^EV]. With a fixed total EV energy demand and no temporal availability constraints, this convex objective is minimized by placing EV power in hours where the original load P_L,t is small. The paper's reported finding that 'EVs users decrease the charging powers in the peak-load periods, while they increase the charging powers in off-peak periods' is therefore an algebraic consequence of the chosen price rule, not an emergent property discovered by the scheduling model. The peak-shaving claim is constructed into the objective.

  2. other [Section II-B, Section V-B, and Section III-B Eqs. (21)-(23)]
    "It is assumed that ... the default EV charging mode is that an EV leaves the charging station immediately once it is charged to its expected capacity. ... EV arrival and departure times at the EV charging station are illustrated in Fig. 4."

    The arrival and departure times generated as basic data never appear in the lower-level constraints. Eq. (21) only caps aggregate EV charging by available MG power, Eq. (22) imposes per-period min/max limits, and Eq. (23) bounds cumulative energy; no constraint forces P_i,t^EV to be zero outside an EV's arrival-to-departure window. The optimizer can charge an EV at 03:00 even if it arrives at 18:00. This fictitious temporal flexibility is the enabling condition that lets the model move EV load into the off-peak valley in Fig. 10, so the peak-shaving conclusion is an artifact of the omitted availability constraint rather than a robust demand-response result. The paper's own limitations list omits this issue.

full rationale

The central qualitative claim—that price-based EV demand response achieves peak shaving—is not derived from independent data or external benchmarks; it follows from the model's own construction. Eq. (24) defines the real-time price as proportional to the sum of EV load and original load, so minimizing EV charging cost in Eq. (20) is algebraically equivalent to minimizing EV charging during high original-load hours. Since Eqs. (21)-(23) do not enforce the arrival/departure windows that were explicitly generated in Section II-B and shown in Fig. 4, the lower-level solver has unrestricted freedom to concentrate EV charging in the global off-peak hours, producing the Fig. 10 shape by construction. This is a genuine reduction of the headline result to the input assumptions. The self-citation to [19] for the pricing mechanism adds no independent validation of the load-shaping effect, because the same formula is simply adopted as an ansatz. However, the paper also contains independent computational content: the JAYA-IPM hybrid solution procedure, the chance-constraint transformation via sequence operation theory, and the bi-level cost trade-off in Fig. 5 are not simply restatements of the peak-shaving conclusion. Weighing these, the peak-shaving prediction is partly circular (score 6) rather than fully circular: the cost-balance and algorithmic claims retain separate content, but the flagship result is substantially an artifact of Eq. (24) and the missing availability constraints.

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

The model relies on standard probability models for renewables and EV behavior from cited surveys, but the key assumptions that enable the peak-shaving conclusion are ad hoc: the real-time price formula (Eq. 24) makes price proportional to load, and the lower-level charging problem omits arrival-departure time windows. These assumptions are not validated against data or a market model.

free parameters (4)
  • alpha (regulatory factor) = 0.4
    Introduced in Eq. (21) to cap EV charging power as a fraction of available non-renewable generation; chosen by hand, directly constrains the EV charging feasibility and thus the achievable load shift.
  • omega_REF (reference electricity price) = 0.6 $/kWh
    Used in Eq. (24) to set the real-time price level; taken from [19] and not justified by the MG cost structure; the price signal determines the strength of demand response.
  • P_REF^L (reference load power) = 80 kW
    Normalization constant in the real-time price formula (Eq. 24); set to 80 kW based on [19]; it affects the price level and therefore the cost-minimization behavior of EVs.
  • confidence level gamma = 95%
    Chance-constraint parameter in Eq. (19) that sets the required spinning reserve; the paper varies it (85%, 90%, 95%) but does not tie it to actual reliability targets; affects reserve costs and schedules.
assumptions (6)
  • domain assumption PV output follows a Beta distribution, WT output follows a Weibull distribution, and original load follows a normal distribution.
    Section II.A cites [2,19] for these probability models; the paper does not estimate distribution parameters from data for the test system, and the specific parameters are not given.
  • domain assumption EV arrival time and daily mileage follow normal/lognormal distributions based on the 2009 US National Household Travel Survey.
    Section II.B uses these distributions to generate the EV sample; no verification is provided that the generated sample matches the distribution.
  • standard math Sequence operation theory (SOT) and addition-type-convolution can transform the chance constraint into a deterministic equivalent.
    Eqs. (25)-(29) rely on SOT from [34] to discretize and convolve probability distributions; this is a known technique.
  • ad hoc to paper The real-time price in Eq. (24) is a valid representation of supply-demand dynamics.
    The price is defined proportional to (EV load + original load), ignoring renewable generation and MG marginal costs; it is imposed for this paper and drives the DR result.
  • ad hoc to paper EV users minimize charging cost with no temporal availability constraints in the lower-level model.
    The lower-level problem (20)-(23) omits arrival/departure windows computed in Section II.B; this assumption creates the flexibility that produces peak shaving.
  • ad hoc to paper The bi-level iterative JAYA-IPM procedure converges to an optimal or balanced solution.
    Section IV.F terminates after a fixed number of iterations; no convergence proof or optimality gap is provided.

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Pith. "Pith review of Incorporating demand response of electric vehicles in scheduling of isolated microgrids with renewables using a bi-level programming approach." pith.science (2026). https://pith.science/paper/OOT6ZIPI

@misc{pith2026190811197,
  author       = {Pith},
  title        = {Pith review of: Incorporating demand response of electric vehicles in scheduling of isolated microgrids with renewables using a bi-level programming approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OOT6ZIPI}},
  note         = {Machine review of arXiv:1908.11197}
}
read the original abstract

In this work, a novel optimal scheduling approach is proposed for isolated microgrids (MGs) with renewable generations by incorporating demand response of electric vehicles (EVs). First, a bi-level programming-based MG scheduling model is proposed under real-time pricing environments, where the upper- and lower- levels seek to minimize the MG net operating cost and the EV charging cost. Second, a hybrid solution algorithm called JAYA-interior point method is put forward to solve the model. And finally, the simulation results demonstrate that incorporating demand response of electric vehicles is able to guide EV users to actively participate in MG scheduling and achieve the peak load shaving, which offers a fundamental way to balance the interests between MG and EV users.

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