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arxiv: 2604.25016 · v1 · submitted 2026-04-27 · 📡 eess.SY · cs.SY

Recognition: unknown

A Novel Two-Step Approach for Reactive Power Demand Calculation Using Integrated Voltage Stability Analysis

Hassan Abouelgheit , Hendrik Lens
Authors on Pith no claims yet

Pith reviewed 2026-05-08 01:26 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords reactive power demandvoltage stabilitypower system planningquasi-dynamic simulationQ-V analysisdynamic simulationtime-series analysisvoltage violations
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The pith

A two-step simulation method directly calculates the reactive power a power grid needs by checking voltage stability over a full year.

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

The paper introduces a methodology that calculates reactive power demand by running a sequence of simulations that together assess both long-term and short-term voltage stability. It combines quasi-dynamic simulation, Q-V analysis, and dynamic simulation iteratively across all 8760 hours of a year, judging violations by their number, severity, and duration. A sympathetic reader would care because knowing the true demand lets planners install the right amount of compensation equipment to keep voltages stable without waste or risk. This differs from prior work that usually optimizes equipment placement in a single simulation step rather than first measuring the actual demand through back-to-back checks.

Core claim

The proposed two-step approach directly calculates actual reactive power demand by integrating Quasi-Dynamic Simulation, Q-V analysis, and dynamic simulation sequentially in an iterative framework that performs comprehensive time-series analysis over 8760 hours with multi-criteria assessment of voltage violations. In the case study, the method identified and addressed all buses experiencing voltage issues and produced the total reactive power demand required across the network.

What carries the argument

The two-step back-to-back simulation framework that sequentially applies quasi-dynamic simulation, Q-V analysis, and dynamic simulation with annual time-series and multi-criteria violation scoring.

If this is right

  • All voltage-violating buses in the network can be systematically identified and resolved through the integrated checks.
  • The exact total reactive power demand for the entire network can be quantified for use in planning compensation equipment.
  • Single-simulation optimization approaches are insufficient because they miss the combined long-term and short-term stability picture.
  • Time-series analysis over 8760 hours provides a more complete demand figure than methods using only representative snapshots.

Where Pith is reading between the lines

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

  • The approach could be extended to grids with high shares of renewables to track how reactive needs shift with variable generation patterns.
  • It might support real-time grid operators by updating demand estimates as conditions change throughout the year.
  • Computational cost for very large networks would need testing, since running full dynamic simulations hourly could become expensive.

Load-bearing premise

The sequential combination of these three simulation types over a full year accurately reflects the grid's true reactive power needs without missing interactions or introducing large modeling errors.

What would settle it

Applying the calculated reactive power amounts in a follow-up simulation or real grid test and still observing voltage violations that exceed the multi-criteria thresholds of number, severity, and duration would show the method underestimates demand.

Figures

Figures reproduced from arXiv: 2604.25016 by Hassan Abouelgheit, Hendrik Lens.

Figure 1
Figure 1. Figure 1: Proposed two-step approach for reactive power demand calculation highest-priority bus, to calculate the reactive power demand (∆Qi) required to compensate for the volt￾age violation (∆Vi) at that specific bus. A reactive power compensation equipment is then installed at the identified bus location, followed by another QDS to assess the system’s improved performance and pos￾sible new voltage violations. Thi… view at source ↗
Figure 2
Figure 2. Figure 2: Illustrative graph showing TVI and its boundaries [12] analyze voltage stability [6]. It illustrates the rela￾tionship between the reactive power Q injected or absorbed at a specific bus and the resulting voltage V at that bus. The vertical axis represents the re￾active power, while the horizontal axis represents the bus voltage in per unit (p.u.). Reference [27] provides detailed information on Q-V-curve … view at source ↗
Figure 3
Figure 3. Figure 3: Voltage magnitudes of all buses in the 39- view at source ↗
Figure 4
Figure 4. Figure 4: Voltage magnitudes of the buses that vio view at source ↗
Figure 6
Figure 6. Figure 6: Q-V Curve for Bus 04 in iteration 1 view at source ↗
Figure 9
Figure 9. Figure 9: Voltage magnitudes of all buses in the transmission system in iteration 3 view at source ↗
Figure 8
Figure 8. Figure 8: Analysis of simulation results using generic view at source ↗
Figure 10
Figure 10. Figure 10: TVI analysis and voltage curves of all buses in iteration 1 A contingency list was defined including a short circuit fault on line 09-39 at t = 2.5 s with a fault clearing time of 100 ms combined with a generator event in which G 03 went out of service at t = 2.66 s. These two events have caused a large disturbance in the system with a noticeable voltage drop following the short-circuit recovery, see 10. … view at source ↗
Figure 11
Figure 11. Figure 11: Q-V Analysis for Bus 07 after iteration 1 view at source ↗
Figure 13
Figure 13. Figure 13: TVI analysis and voltage curves of all buses – after compensation in step 2 voltage violations. This could lead to an overestima￾tion of the actual reactive power demand. Moreover, in real networks it is not feasible to install a compen￾sation equipment at each critical bus that experiences voltage deviations due to space limitations and regu￾latory constraints. Looking ahead, future work will focus on ap… view at source ↗
read the original abstract

The assessment of reactive power demand plays an instrumental role in power system planning. This paper presents a methodology for calculating reactive power demand based on a two-step approach. Unlike existing methodologies in the literature that focus primarily on optimization of reactive power compensation equipment placement and sizing through single-simulation approaches, this methodology directly calculates the actual reactive power demand through a comprehensive back-to-back simulation framework. While existing methods address either long-term or short-term voltage stability using either steady-state analysis or individual dynamic simulations, the proposed approach integrates both stability assessments sequentially through iterative Quasi-Dynamic Simulation, Q-V analysis and dynamic simulation. Furthermore, this methodology employs comprehensive time-series analysis over a full annual period (8760 hours) with multi-criteria violation assessment (number, severity and duration of voltage violations). In the final section of this paper, a case study was conducted to demonstrate the application of the proposed methodology. Simulations were performed to validate the effectiveness of the methodology, with the results showing that all buses with voltage issues were successfully addressed and finally the total reactive power demand across the network was calculated.

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.

Referee Report

2 major / 2 minor

Summary. The paper proposes a two-step methodology for calculating reactive power demand in power systems by sequentially integrating Quasi-Dynamic Simulation, Q-V analysis, and dynamic simulation over a full annual period (8760 hours), employing multi-criteria violation assessment based on number, severity, and duration of voltage issues. Unlike single-simulation optimization approaches in the literature, it claims to directly compute the actual demand through this back-to-back framework. A case study is presented in which all buses with voltage issues were successfully addressed and the total reactive power demand across the network was calculated.

Significance. If the mapping from violation metrics to a reproducible demand value can be made explicit, the integrated long-term/short-term stability assessment over comprehensive time-series data could provide a more complete alternative to existing placement/sizing optimization methods. The multi-criteria, full-year evaluation is a methodological strength relative to steady-state-only or single-dynamic-simulation techniques.

major comments (2)
  1. [Abstract and case-study section] Abstract and final (case-study) section: the central claim that the framework 'directly calculates the actual reactive power demand' requires an explicit, reproducible aggregation rule that converts the multi-criteria violation statistics (number/severity/duration over 8760 h) into a single numerical Q value or set of per-bus injections. No such formula, threshold definitions, or iterative feedback procedure between the three simulation layers is supplied; the text only states that violations 'were successfully addressed' and demand 'was calculated.' This mapping is load-bearing for the claimed advantage over optimization-based methods.
  2. [Methodology and case-study sections] Methodology and case-study sections: no quantitative performance metrics, error bars, baseline comparisons against existing Q-compensation sizing methods, or sensitivity analysis on simulation parameters are reported. The case study supplies only the qualitative statement that 'all buses with voltage issues were successfully addressed,' which is insufficient to substantiate the demand-calculation claim.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one key quantitative outcome (e.g., total MVAR demand or number of buses corrected) from the case study.
  2. [Methodology] Notation for the three simulation stages and the violation criteria should be defined consistently when first introduced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough and constructive review. The comments identify key areas where the manuscript can be strengthened to better substantiate the proposed methodology. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and case-study section] Abstract and final (case-study) section: the central claim that the framework 'directly calculates the actual reactive power demand' requires an explicit, reproducible aggregation rule that converts the multi-criteria violation statistics (number/severity/duration over 8760 h) into a single numerical Q value or set of per-bus injections. No such formula, threshold definitions, or iterative feedback procedure between the three simulation layers is supplied; the text only states that violations 'were successfully addressed' and demand 'was calculated.' This mapping is load-bearing for the claimed advantage over optimization-based methods.

    Authors: We acknowledge that the explicit mapping from multi-criteria violation statistics to the computed reactive power demand values was not presented with sufficient detail. In the revised manuscript we will add a new subsection in the Methodology section that defines the violation thresholds (for number, severity, and duration), describes the iterative feedback procedure linking the quasi-dynamic, Q-V, and dynamic simulation layers, and provides the aggregation formula that converts the violation metrics into per-bus reactive power injections and the network-wide total demand. This addition will make the calculation reproducible and directly address the referee's concern. revision: yes

  2. Referee: [Methodology and case-study sections] Methodology and case-study sections: no quantitative performance metrics, error bars, baseline comparisons against existing Q-compensation sizing methods, or sensitivity analysis on simulation parameters are reported. The case study supplies only the qualitative statement that 'all buses with voltage issues were successfully addressed,' which is insufficient to substantiate the demand-calculation claim.

    Authors: We agree that the case-study results are currently limited to qualitative statements. In the revision we will expand the case-study section to include quantitative metrics (e.g., total violation duration reduction, severity index before/after compensation), error bars derived from repeated simulations with varied initial conditions, direct numerical comparisons against two established optimization-based Q-placement methods, and a sensitivity analysis on key parameters such as quasi-dynamic time step and Q-V curve resolution. These additions will provide the necessary quantitative support for the demand-calculation claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; simulation framework is independent of target result

full rationale

The paper describes a two-step methodology that chains quasi-dynamic simulation, Q-V analysis, and dynamic simulation over 8760 hours, using multi-criteria voltage violation detection to arrive at a reactive power demand figure. No equations, parameters, or steps are shown that reduce the final demand value to a fitted input, self-citation, or definitional tautology. The derivation relies on external simulation outputs and explicit (if unstated in the abstract) aggregation rules rather than constructing the result from itself. This is the normal case of a self-contained engineering procedure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are detailed. Standard power-system assumptions about simulation fidelity are implicit but not enumerated.

pith-pipeline@v0.9.0 · 5488 in / 1282 out tokens · 105550 ms · 2026-05-08T01:26:48.620095+00:00 · methodology

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Reference graph

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