Leveraging Owners' Flexibility in Smart Charge/Discharge Scheduling of Electric Vehicles to Support Renewable Energy Integration

Amarnath Banerjee; Mohammad Javad Feizollahi; Pouya Sharifi

arxiv: 1907.07722 · v1 · pith:4E44Q63Ynew · submitted 2019-07-17 · 🧮 math.OC · math.DS

Leveraging Owners' Flexibility in Smart Charge/Discharge Scheduling of Electric Vehicles to Support Renewable Energy Integration

Pouya Sharifi , Amarnath Banerjee , Mohammad Javad Feizollahi This is my paper

Pith reviewed 2026-05-24 20:08 UTC · model grok-4.3

classification 🧮 math.OC math.DS

keywords electric vehiclesvehicle-to-gridwind integrationsmart chargingmixed-integer quadratic programmingrolling-horizon optimizationmicro-grid scheduling

0 comments

The pith

Optimal EV charge and discharge scheduling maximizes wind integration and minimizes owner costs via V2G flexibility.

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

The paper establishes that EV owners' flexibility can be harnessed through coordinated scheduling to absorb wind power fluctuations in a vehicle-to-grid setup. It develops a static multi-objective mixed-integer quadratic program that assumes day-ahead knowledge of all parameters and a dynamic rolling-horizon version that incorporates real-time updates on EV availability, wind forecasts, and prices. The dual objectives are higher wind utilization in the micro-grid and lower charging costs for owners, while factoring in battery degradation and owner concerns about unexpected events. Simulations with 100 EVs show marked gains relative to uncoordinated charging.

Core claim

An optimal scheduling algorithm for aggregated EV fleets, formulated first as a static MIQP with multi-objective optimization under day-ahead parameter certainty and then as a rolling-horizon dynamic program with updated forecasts, increases wind generation integration and reduces EV charging costs while addressing battery degradation and participation barriers.

What carries the argument

Mixed-integer quadratic programming (MIQP) multi-objective optimization for the static day-ahead model, extended by rolling-horizon optimization for real-time dynamic scheduling.

If this is right

Wind power absorption rises substantially in the micro-grid relative to uncoordinated EV charging.
EV owners incur lower average charging costs under the optimized schedule.
Battery degradation is explicitly traded off against other objectives in the formulation.
Real-time updates to EV arrivals, wind forecasts, and prices are accommodated without full re-optimization from scratch.
Owner concerns about unexpected events are mitigated by the flexibility built into the V2G contract.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same scheduling logic could be tested on solar or other variable renewables whose forecast uncertainty profiles differ from wind.
Scaling to thousands of vehicles would likely require decomposition or distributed algorithms to keep the MIQP tractable.
Coupling the model with home battery storage or demand-response programs could further enlarge the flexibility pool.
Empirical driver behavior data would be needed to validate whether the modeled participation rates hold outside simulation.

Load-bearing premise

The static model assumes every parameter including wind output and energy prices is known a day ahead of scheduling.

What would settle it

A field trial in which forecast errors cause the coordinated schedule to deliver no measurable gain in wind utilization or cost reduction compared with uncontrolled charging would falsify the practical benefit.

Figures

Figures reproduced from arXiv: 1907.07722 by Amarnath Banerjee, Mohammad Javad Feizollahi, Pouya Sharifi.

**Figure 6.** Figure 6: Comparison of the models with quadratic vs linear degradation function. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗

**Figure 8.** Figure 8: Performance evaluation under different values of [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 11.** Figure 11: Comparison of dynamic model with perfect forecast vs MDP wind forecast. (a) Total grid supply (kWh) (b) Wind utilization (%) (c) Total cost (¢) that aggregators can exploit the presence of advanced communication technology in smart grid, flexibility of EV drivers, and the V2G technology to support high integration of intermittent wind energy into the power system. Unlike some of the previous research, the… view at source ↗

read the original abstract

High integration of intermittent renewable energy sources (RES), in particular wind power, has created complexities in power system operations. On the other hand, large fleets of Electric Vehicles (EVs) are expected to have great impact on electricity consumption, and uncoordinated charging process will add load uncertainty and further complicate the grid scheduling. In this paper, we propose a smart charging approach that uses the flexibility of EV owners to absorb the fluctuations in the output of RES in a vehicle-to-grid (V2G) setup. We propose an optimal scheduling algorithm for charge/discharge of aggregated EV fleets to maximize the integration of wind generation as well as minimize the charging cost for EV owners. Challenges for people participation in V2G, such as battery degradation and feeling insecure for unexpected events, are also addressed. We first formulate a static model using mixed-integer quadratic programming (MIQP) with multi-objective optimization assuming that every parameter of the model is known a day ahead of scheduling. Subsequently, we formulate a dynamic (dis)charging schedule after EVs arrive into the system with updated information about EV availabilities, wind generation forecast, and energy price in real-time, using rolling-horizon optimization. Simulations using a group of 100 EVs in a micro-grid with wind as primary resource demonstrate significant increase in wind utilization and reduction in charging cost compared to uncontrolled charging scenario.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Standard MIQP plus rolling-horizon application to EV V2G scheduling that improves on uncontrolled charging in simulation but skips forecast-error checks in the dynamic case.

read the letter

This paper sets up a multi-objective MIQP to schedule aggregated EV charge and discharge so that wind output is absorbed while owners pay less and batteries degrade less. It then moves to a rolling-horizon version that re-optimizes as new wind forecasts and EV arrival data arrive. The static model assumes perfect day-ahead knowledge of everything; the dynamic one relaxes that by updating in real time. Both are compared to an uncontrolled baseline on a 100-EV micro-grid example, and the numbers show higher wind utilization and lower owner cost. That combination of objectives and the rolling update is the concrete piece that is new relative to earlier V2G papers that usually fix one or two of those elements. The formulation itself is clean and the constraints on owner flexibility and degradation are stated explicitly, which is useful if someone wants a ready template. The main gap is exactly the one the stress-test flagged: the dynamic schedules are built on wind forecasts, yet the paper reports no injected forecast error or sensitivity runs. Wind forecast error is large enough that an optimistic trajectory can produce schedules that look good on paper but fail when the actual wind arrives. Without that check the utilization gains stay plausible but unproven for real operations. The multi-objective weights are also free parameters with no discussion of how they were chosen or whether the results are sensitive to them. This is a solid applied optimization paper for readers who already work on EV-grid coordination and want one more worked example with battery wear included. It is not a methods advance, so it will not change how most people formulate these problems. I would send it to peer review for a power-systems journal; the core model is coherent and the simulation setup is reproducible enough that referees can ask for the missing robustness tests without starting from scratch.

Referee Report

3 major / 1 minor

Summary. The paper proposes a static MIQP multi-objective optimization model assuming day-ahead perfect information and a dynamic rolling-horizon formulation for aggregated EV charge/discharge scheduling in a V2G micro-grid to maximize wind utilization while minimizing owner charging costs and addressing battery degradation. Simulations with 100 EVs report significant gains in wind integration and cost reduction versus an uncontrolled baseline.

Significance. If the simulation results prove robust, the work would provide a practical scheduling framework linking EV flexibility to renewable integration in micro-grids. The dual static/dynamic formulations and explicit handling of owner concerns are constructive contributions, but the absence of forecast-error sensitivity analysis limits the strength of the practical claim.

major comments (3)

[Abstract / dynamic formulation] Abstract and dynamic model paragraph: the rolling-horizon formulation ingests real-time wind forecasts yet contains no sensitivity study or injected forecast-error scenarios; because the headline utilization gains rest on these forecasts, the lack of robustness quantification is load-bearing for the central claim that the method supports renewable integration in practice.
[Abstract / static model] Abstract / static model paragraph: the claim of day-ahead scheduling rests on the assumption that every parameter (including wind output and prices) is known perfectly; no comparison or degradation analysis under imperfect information is supplied, weakening the transferability of the reported gains.
[Simulation results] Simulation results paragraph: the reported improvements for 100 EVs lack error bars, dataset provenance, or verification that the multi-objective weights were not tuned post-hoc; without these, the quantitative claims cannot be assessed for statistical reliability.

minor comments (1)

[Model formulation] Notation for the multi-objective weights and battery degradation cost term should be defined explicitly with units in the model section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestions. We address each major comment below and indicate the revisions we will make to the manuscript.

read point-by-point responses

Referee: [Abstract / dynamic formulation] Abstract and dynamic model paragraph: the rolling-horizon formulation ingests real-time wind forecasts yet contains no sensitivity study or injected forecast-error scenarios; because the headline utilization gains rest on these forecasts, the lack of robustness quantification is load-bearing for the central claim that the method supports renewable integration in practice.

Authors: We acknowledge the importance of assessing robustness to forecast errors. The rolling-horizon formulation is designed to incorporate updated forecasts at each step, which provides some inherent adaptability. However, to directly address this concern, we will add a new subsection in the simulation results that includes sensitivity analysis with perturbed wind forecasts to demonstrate the impact on wind utilization gains. revision: yes
Referee: [Abstract / static model] Abstract / static model paragraph: the claim of day-ahead scheduling rests on the assumption that every parameter (including wind output and prices) is known perfectly; no comparison or degradation analysis under imperfect information is supplied, weakening the transferability of the reported gains.

Authors: The static MIQP model is formulated under the assumption of perfect day-ahead information to provide a benchmark for the best possible performance. The dynamic rolling-horizon model is then introduced to handle real-time updates with forecast information. We will revise the abstract and the model description to better highlight this distinction and add a brief discussion on the potential degradation under imperfect information, noting it as a direction for future research. revision: partial
Referee: [Simulation results] Simulation results paragraph: the reported improvements for 100 EVs lack error bars, dataset provenance, or verification that the multi-objective weights were not tuned post-hoc; without these, the quantitative claims cannot be assessed for statistical reliability.

Authors: The simulation results are based on deterministic optimization using wind generation and electricity price data from a specific micro-grid case study (we will add the exact data sources and references in the revised manuscript). The multi-objective weights were selected through preliminary analysis to achieve a balanced trade-off between the objectives, and we will include a justification for these choices. As the underlying model is deterministic, statistical error bars are not directly applicable; however, we will report results across multiple EV arrival scenarios to provide a sense of variability. revision: yes

Circularity Check

0 steps flagged

No circularity: optimization outputs are produced by standard MIQP/rolling-horizon solvers against external benchmark

full rationale

The paper formulates a static MIQP multi-objective optimization and a subsequent rolling-horizon dynamic scheduler that take day-ahead parameters, real-time forecasts, and EV availability as exogenous inputs. The reported simulation gains (higher wind utilization, lower charging cost) are generated by solving these programs and comparing the resulting schedules to an uncontrolled-charging baseline. No equation reduces a claimed prediction to a fitted parameter inside the model, no self-citation supplies a load-bearing uniqueness result, and no ansatz is smuggled via prior work by the same authors. The derivation chain is therefore self-contained and externally falsifiable via the benchmark comparison.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard mixed-integer programming assumptions plus the domain assumption that day-ahead forecasts are perfect for the static model; no new entities are postulated and the only free parameters appear to be the relative weights in the multi-objective function.

free parameters (1)

multi-objective weights
The model balances wind utilization against charging cost via unspecified weights that must be chosen by the modeler.

axioms (1)

domain assumption All model parameters (wind generation, prices, EV availability) are known perfectly one day ahead for the static formulation.
Explicitly stated in the abstract for the static MIQP model.

pith-pipeline@v0.9.0 · 5784 in / 1350 out tokens · 21439 ms · 2026-05-24T20:08:51.649642+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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Q. Yan, M. Kezunovic, Impact analysis of electric vehicle charging on distribution system, in: 2012 North 16 A PREPRINT - JULY 19, 2019 American Power Symposium (NAPS), IEEE, 2012, pp. 1–6

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Schuller, C

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Gottwalt, A

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Ghofrani, A

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Honarmand, A

M. Honarmand, A. Zakariazadeh, S. Jadid, Optimal scheduling of electric vehicles in an intelligent parking lot considering vehicle-to-grid concept and battery condition, Energy 65 (2014) 572–579

work page 2014

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work page 2018

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work page 2014

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Apostolaki-Iosiﬁdou, P

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work page 2017

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Sortomme, M

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work page 2011

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Golden, CO

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