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arxiv: 2604.06536 · v1 · submitted 2026-04-08 · 📡 eess.SY · cs.SY

Multi-Region Optimal Energy Storage Arbitrage

Md Umar Hashmi , Harsha Nagarajan , Dirk Van Hertem1 This is my paper

Pith reviewed 2026-05-10 18:37 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords energy storage arbitragemulti-region participationmixed-integer linear programminginterconnector lossesday-ahead marketsbattery cyclingcross-border tradingoptimal scheduling
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The pith

A grid-scale battery can earn over 40 percent more arbitrage revenue by trading across two interconnected day-ahead markets instead of one.

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

The paper develops an optimization model for a battery that can buy low and sell high in two separate electricity markets connected by an interconnector. It converts the problem into a mixed-integer linear program that handles battery limits, losses, and the requirement to act in both markets at once. This matters for storage operators because more regions are linking their power grids, yet existing tools do not capture the added value of cross-border trades. Tests on eight years of Belgian and UK price data show the multi-market approach raises revenue by more than 40 percent compared with local-only trading, though congestion on the link reduces some of the extra profit. The work also adds a way to skip battery cycles that would not pay off, improving long-term use.

Core claim

The authors present an exact mixed-integer linear programming formulation for multi-region energy storage arbitrage. A battery located at one end of an interconnector can participate in two day-ahead markets by charging or discharging simultaneously from both while respecting capacity, ramping, and loss constraints. Disjunctive linearization removes the nonlinearities that arise from the simultaneous market actions. Numerical results from eight years of real price data confirm that this cross-border strategy increases arbitrage revenue by more than 40 percent over single-market operation, with interconnector congestion shown to limit the gains. A pseudo-efficiency term is added to eliminate

What carries the argument

The mixed-integer linear program obtained by disjunctive linearization of the multiregion arbitrage problem, which forces simultaneous battery operation across all markets and incorporates losses and market-specific prices.

Load-bearing premise

The battery must charge or discharge from all participating markets at the exact same time, and all losses plus market prices must be known in advance for the schedule to be optimal.

What would settle it

If a physical battery following the model's schedules on the Belgian-UK interconnector achieves revenue gains noticeably below 40 percent over a multi-year period that includes congestion events, the claimed advantage of multi-region participation would not be supported.

Figures

Figures reproduced from arXiv: 2604.06536 by Dirk Van Hertem1, Harsha Nagarajan, Md Umar Hashmi.

Figure 1
Figure 1. Figure 1: Occurrences of negative prices in the DA market in Belgium and the UK for the last 8 years [39]. The classical formulation of the EA problem relies on the assumption of non-negative electricity prices, which ensures the problem is convex and solvable via methods like LP. This assumption is now frequently violated in markets with high renewable penetration. The presence of negative prices introduces a non-c… view at source ↗
Figure 2
Figure 2. Figure 2: Stylized grids connected via an AC or DC interconnector with an offshore wind injection located in the centre. + = Region A Ci A Ci B Segment 1 Region B xi A xi B xi = [Xmin, Xmax ] Objective function for multi-regional arbitrage Segment 1 Segment 1 Segment 2 Segment 2 Segment 2 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Piecewise objective function for MREA optimization. power (or energy change) is typically represented as xi = x ch i − x dis i (see Sec. II-D2), with a binary variable enforcing that at most one of x ch i and x dis i is nonzero, thereby preventing simultaneous charging and discharging. The disjunctive binary model in (18)–(19) is different. In the proposed MREA formulation, the binary variable enforces a s… view at source ↗
Figure 4
Figure 4. Figure 4: Battery prices (per kWh) based on BloombergNEF’s 2025 survey show average lithium-ion pack costs dropping 8% year-on-year, extending the decline of 93% since 2010 [51]. The case studies are performed for a battery connected at one end of the NEMO link connecting mainland Belgium to the UK grid via a 140 km undersea cable with a 1 GW capacity 3The notation xC-yC expresses how quickly a battery can be charge… view at source ↗
Figure 5
Figure 5. Figure 5: (a) The DA prices for Belgium and the UK for 30th June 2024 and (b) stylized interconnector flow. TABLE II: Comparing arbitrage models for one and multiple regions for 1 day Market Battery Model NEMO Profit Cycles profit/ participation location flow (euro) cycle single BE LP n/a 136.1 2.43 56.0 noDis n/a 134.2 1.96 68.3 DP n/a 136.1 2.43 56.0 MILP n/a 136.1 2.43 56.0 UK LP n/a 236.5 2.77 85.4 noDis n/a 236… view at source ↗
Figure 7
Figure 7. Figure 7: Profit per 100% cycle of battery operation. Note that considering the per kWh cost of 100 euros, the total battery cost of 1 MWh (0.5C-0.5C)is 100,000 euros, which has 178% return for P MR MILP model without reaching the storage cycle life of 7200 cycles. Refer to the battery parameters in Table I. D. Case study 3 This case study aims to improve the utilization of grid-scale batteries. Batteries have a lim… view at source ↗
Figure 8
Figure 8. Figure 8: Limiting cycles of operation. 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 pseudo 1000 2000 3000 4000 5000 6000 7000 Cycles 100 150 200 250 300 Revenue (in thousand euros) Cycles of operation Revenue for 8 years (euros) [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparing cycles of operation with revenue for different ηpseudo. Online repository The MILP-based MREA, P MR MILP, described in this paper, is publicly available at github.com/umar-hashmi/Inter-Refional￾Energy-Arbitrage. V. CONCLUSION AND FUTURE WORK This paper successfully developed and validated a novel Mixed-Integer Linear Programming (MILP) model for Multi￾Region Energy Arbitrage (MREA), enabling a si… view at source ↗
read the original abstract

The increasing interconnection of power systems through AC and DC links enables energy storage units to access multiple electricity markets yet most existing arbitrage models remain limited to singlemarket participation This gap restricts understanding of the economic value and operational constraints associated with crossborder storage operation To address this an optimal multiregion energy storage arbitrage model is developed for a gridscale battery located at one end of an interconnector linking two distinct dayahead markets The formulation incorporates battery capacity and ramping limits converter and interconnector losses and marketspecific buying and selling prices Using disjunctive linearization of nonlinear terms this work exactly reformulates the multiregion energy arbitrage optimization as a mixedinteger linear programming problem The proposed formulation ensures that the battery either charges or discharges from all participating energy markets simultaneously at any given time Case studies using eight years of BelgianUK price data demonstrate that multiregion participation can increase arbitrage revenue by more than 40% compared to local energy arbitrage operation only while also highlighting the negative impact of interconnector congestion on achievable gains The results indicate that crossborder market access substantially enhances storage profitability while considering the cycle of battery and that the proposed formulation provides a computationally efficient framework for evaluating and operating storage assets in interconnected power systems Finally a pseudoefficiency term is introduced to improve battery utilization by discarding less profitable charging and discharging battery cycles

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

0 major / 3 minor

Summary. The paper develops an optimal multi-region energy storage arbitrage model for a grid-scale battery at one end of an interconnector between two day-ahead markets. It incorporates battery limits, ramping, converter and interconnector losses, and market-specific prices, then uses disjunctive linearization to exactly reformulate the problem as a mixed-integer linear program (MILP) that enforces simultaneous same-mode (charge or discharge) participation across markets. Case studies with eight years of Belgian-UK historical prices report that multi-region participation increases arbitrage revenue by more than 40% relative to local-only operation, while interconnector congestion reduces gains; a pseudo-efficiency term is added to discard low-profit cycles and improve utilization.

Significance. If the exact MILP reformulation holds, the work provides a computationally efficient framework for quantifying the value of cross-border storage participation and the sensitivity to interconnector limits. The use of eight years of real price data lends credibility to the >40% revenue-gain claim and the congestion analysis. The explicit physical motivation for the simultaneous-mode constraint and the exact linearization (rather than approximation) are strengths that support practical application to interconnected power systems.

minor comments (3)
  1. [Abstract] Abstract: several compound terms lack spaces (singlemarket, dayahead, multiregion, crossborder); these should be corrected for readability.
  2. [Abstract / formulation] The pseudo-efficiency threshold is listed as a free parameter in the model; its selection rule, sensitivity, and effect on the claimed optimality of the MILP should be stated explicitly (e.g., in the formulation or results section).
  3. [Case studies] Data exclusion rules and any preprocessing of the eight-year Belgian-UK price series are not described; these details are needed to reproduce the reported revenue figures.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our manuscript and the recommendation for minor revision. The provided summary accurately captures the core contributions, including the multi-region arbitrage formulation, the exact MILP reformulation via disjunctive linearization, the incorporation of physical constraints such as losses and ramping, and the empirical findings from eight years of Belgian-UK data showing revenue gains exceeding 40%.

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper constructs an MILP formulation for multi-region arbitrage directly from physical battery limits, ramp rates, converter/interconnector losses, and exogenous day-ahead prices. Disjunctive linearization is applied as a standard exact reformulation technique to handle the nonlinear loss and simultaneous-mode constraints; this step is algebraic and does not depend on the numerical results. Revenues and the >40% gain are obtained by solving the resulting MILP on eight years of independent historical Belgian-UK price data, not by fitting parameters to the same data and re-predicting it. The pseudoefficiency term is an explicit post-processing modeling choice introduced to discard low-profit cycles, not a derived quantity that feeds back into the core optimization. No self-citations are invoked as load-bearing uniqueness theorems, and no equation reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on standard optimization assumptions plus one new term and one domain constraint introduced in the abstract.

free parameters (1)
  • pseudo-efficiency threshold
    A new term introduced to discard less profitable cycles; its specific value or fitting procedure is not stated in the abstract.
axioms (1)
  • domain assumption Battery must charge or discharge simultaneously across all markets at each time step
    Explicitly stated as part of the formulation in the abstract.
invented entities (1)
  • pseudo-efficiency term no independent evidence
    purpose: To improve battery utilization by skipping low-profit charge-discharge cycles
    New concept introduced in the abstract with no independent evidence provided.

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Reference graph

Works this paper leans on

56 extracted references · 56 canonical work pages

  1. [1]

    MacLeod, T

    N. MacLeod, T. An, L. Shi, and X. Li,Plans for HVDC Grids. Cham: Springer Nature Switzerland, 2025, pp. 2327–2350. [Online]. Available: https://doi.org/10.1007/978-3-031-27353-7 54

    work page doi:10.1007/978-3-031-27353-7 2025

  2. [2]

    [Online]

    (2023) Victorian big battery: Australia’s largest lithium-ion battery will boost the reliability of our power supply. [Online]. Available: https://tinyurl.com/3eyjhftp

    work page 2023

  3. [3]

    [Online]

    (2023) Hornsdale wind farm stage 2 fcas trial. [Online]. Available: https://arena.gov.au/projects/hornsdale-wind-farm-stage-2/

    work page 2023

  4. [4]

    Grid-scale bat- tery storage: frequently asked questions,

    T. Bowen, I. Chernyakhovskiy, and P. L. Denholm, “Grid-scale bat- tery storage: frequently asked questions,” National Renewable Energy Lab.(NREL), Golden, CO (United States), Tech. Rep., 2019

    work page 2019

  5. [5]

    Green mountain power (gmp): significant revenues from energy storage,

    S. Schoenung, R. H. Byrne, T. Olinsky-Paul, and D. R. Borneo, “Green mountain power (gmp): significant revenues from energy storage,” Sandia National Laboratories: Albuquerque, NM, USA, 2017

    work page 2017

  6. [6]

    Power curves of megawatt-scale battery storage technologies for frequency regulation and energy trading,

    L. Koltermann, M. C. Cort ´es, J. Figgener, S. Zurm ¨uhlen, and D. U. Sauer, “Power curves of megawatt-scale battery storage technologies for frequency regulation and energy trading,”Applied Energy, vol. 347, p. 121428, 2023

    work page 2023

  7. [7]

    [Online]

    (2024) PSEUDO PARTICIPATING GENERATOR AGREEMENT. [Online]. Available: https://tinyurl.com/2dvykr82

    work page 2024

  8. [8]

    [Online]

    (2024) Registration of Pseudo Generation Settlement Facilities by Aggregating Standalone Intermittent Generation Sources for Market Participants. [Online]. Available: https://tinyurl.com/mr2jrb6s

    work page 2024

  9. [9]

    High V oltage Direct Current Electricity – technical information: Fact- sheet NationalGrid,

    “High V oltage Direct Current Electricity – technical information: Fact- sheet NationalGrid,” http://tinyurl.com/mr35rj26, accessed: 2023-05-25

    work page 2023

  10. [10]

    Energy storage for the grid: Policy options for sustaining innovation,

    D. M. Hart, W. B. Bonvillian, and N. Austin, “Energy storage for the grid: Policy options for sustaining innovation,”MIT Energy Initiat. Work. Pap, vol. 1, p. 33, 2018

    work page 2018

  11. [11]

    Cost projections for utility-scale battery storage: 2021 update,

    W. Cole, A. W. Frazier, and C. Augustine, “Cost projections for utility-scale battery storage: 2021 update,” National Renewable Energy Lab.(NREL), Golden, CO (United States), Tech. Rep., 2021

    work page 2021

  12. [12]

    Co-optimizing the value of storage in energy and regulation service markets,

    K. Anderson and A. El Gamal, “Co-optimizing the value of storage in energy and regulation service markets,”Energy Systems, 2017

    work page 2017

  13. [13]

    Co-optimizing battery storage for the frequency regulation and energy arbitrage using multi-scale dynamic programming,

    B. Cheng and W. B. Powell, “Co-optimizing battery storage for the frequency regulation and energy arbitrage using multi-scale dynamic programming,”IEEE Transactions on Smart Grid, 2016

    work page 2016

  14. [14]

    Energy storage in madeira, portugal: Co-optimizing for arbitrage, self-sufficiency, peak shaving and energy backup,

    M. U. Hashmi, L. Pereira, and A. Bu ˇsi´c, “Energy storage in madeira, portugal: Co-optimizing for arbitrage, self-sufficiency, peak shaving and energy backup,” in2019 IEEE Milan PowerTech. IEEE, 2019, pp. 1–6

    work page 2019

  15. [15]

    Optimal allocation of energy storage in a co- optimized electricity market: Benefits assessment and deriving indicators for economic storage ventures,

    V . Krishnan and T. Das, “Optimal allocation of energy storage in a co- optimized electricity market: Benefits assessment and deriving indicators for economic storage ventures,”Energy, vol. 81, pp. 175–188, 2015. 24th Power Systems Computation Conference PSCC 2026 Limassol, Cyprus — June 8-12, 2026

    work page 2015

  16. [16]

    A closed-loop analysis of grid scale battery systems providing frequency response and reserve services in a variable inertia grid,

    R. Lee, S. Homan, N. Mac Dowell, and S. Brown, “A closed-loop analysis of grid scale battery systems providing frequency response and reserve services in a variable inertia grid,”Applied Energy, vol. 236, pp. 961–972, 2019

    work page 2019

  17. [17]

    Hurta, M

    A. Hurta, M. ˇZilka, and F. Freiberg, “Impact of the splitting of the german–austrian electricity bidding zone on investment in a grid-scale battery energy storage system deployed for price arbitrage with gray and green power in austrian and german day-ahead power markets,”Energy Reports, vol. 8, pp. 12 045–12 062, 2022

    work page 2022

  18. [18]

    Inefficient arbitrage in inter-regional electricity transmission,

    D. Bunn and G. Zachmann, “Inefficient arbitrage in inter-regional electricity transmission,”J. of regulatory economics, vol. 37, pp. 243– 265, 2010

    work page 2010

  19. [19]

    A novel settlement/arbitrage scheme-use of asynchronous inter-regional links in india,

    S. Soonee, P. Agarwal, and R. Porwal, “A novel settlement/arbitrage scheme-use of asynchronous inter-regional links in india,” inInterna- tional Conf. on Power System Operation in Deregulated Regime, ITBHU, 2006

    work page 2006

  20. [20]

    (2021) World’s largest battery facility gets bigger

    Vistra Corporation. (2021) World’s largest battery facility gets bigger. [Accessed 25-02-2026]. [Online]. Available: https://vistracorp.com/ worlds-largest-battery-facility-gets-bigger/

    work page 2021

  21. [21]

    Edwards & Sanborn Solar + Energy Storage — mortenson.com,

    Mortenson Company, “Edwards & Sanborn Solar + Energy Storage — mortenson.com,” https://www.mortenson.com/projects/ edwards-sanborn-solar-plus-storage, 2024, [Accessed 25-02-2026]

    work page 2024

  22. [22]

    Project plan for large-scale bat- tery storage system — EnBW — enbw.com,

    Energie Baden-W ¨urttemberg AG, “Project plan for large-scale bat- tery storage system — EnBW — enbw.com,” https://www.enbw.com/ press/philippsburg-battery-storage-project.html, 2025, [Accessed 25-02- 2026]

    work page 2025

  23. [23]

    Battery energy storage systems in transmission network expansion planning,

    J. Aguado, S. de La Torre, and A. Trivi ˜no, “Battery energy storage systems in transmission network expansion planning,”Electric Power Systems Research, vol. 145, pp. 63–72, 2017

    work page 2017

  24. [24]

    A comprehensive review of the integration of battery energy storage systems into distribution networks,

    M. Stecca, L. R. Elizondo, T. B. Soeiro, P. Bauer, and P. Palensky, “A comprehensive review of the integration of battery energy storage systems into distribution networks,”IEEE Open Journal of the Industrial Electronics Society, vol. 1, pp. 46–65, 2020

    work page 2020

  25. [25]

    Energy arbitrage analysis for market-selection of a battery energy storage system-based venture,

    I. U. Khan and M. Jamil, “Energy arbitrage analysis for market-selection of a battery energy storage system-based venture,”Energies, vol. 18, no. 16, p. 4245, 2025

    work page 2025

  26. [26]

    Battery energy storage systems for energy arbitrage: market selection and price forecasting optimization,

    I. U. Khan, “Battery energy storage systems for energy arbitrage: market selection and price forecasting optimization,” Ph.D. dissertation, Memorial University of Newfoundland, 2026

    work page 2026

  27. [27]

    Optimized operation of standalone battery energy storage systems in the cross-market energy arbitrage business,

    L. van Sandbergen, “Optimized operation of standalone battery energy storage systems in the cross-market energy arbitrage business,”arXiv preprint arXiv:2509.21337, 2025

    work page arXiv 2025

  28. [28]

    Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming,

    K. Tian, Y . Zang, J. Wang, and X. Zhang, “Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming,”International Journal of Electrical Power & Energy Systems, vol. 162, p. 110330, 2024

    work page 2024

  29. [29]

    Belgian Bidding Zone day-ahead reference price,

    E. Group, “Belgian Bidding Zone day-ahead reference price,” https:// www.elia.be/en/grid-data/transmission/day-ahead-reference-price, 2026, [Accessed 01-03-2026]

    work page 2026

  30. [30]

    Energy storage arbitrage under price uncertainty: Market risks and opportunities,

    Y . Wu, B. Xu, and J. Anderson, “Energy storage arbitrage under price uncertainty: Market risks and opportunities,” in2025 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2025, pp. 1–5

    work page 2025

  31. [31]

    The value of arbitrage for energy storage: Evidence from european electricity markets,

    D. Zafirakis, K. J. Chalvatzis, G. Baiocchi, and G. Daskalakis, “The value of arbitrage for energy storage: Evidence from european electricity markets,”Applied energy, vol. 184, pp. 971–986, 2016

    work page 2016

  32. [32]

    HVDC converter,

    “HVDC converter,” https://en.wikipedia.org/wiki/HVDC converter, ac- cessed: 2023-05-25

    work page 2023

  33. [33]

    Optimal control of storage under time varying electricity prices,

    M. U. Hashmi, A. Mukhopadhyay, A. Bu ˇsi´c, and J. Elias, “Optimal control of storage under time varying electricity prices,” in2017 IEEE International Conference on Smart Grid Communications (SmartGrid- Comm). IEEE, 2017, pp. 134–140

    work page 2017

  34. [34]

    Optimal storage arbitrage under net metering using linear program- ming,

    M. U. Hashmi, A. Mukhopadhyay, A. Bu ˇsi´c, J. Elias, and D. Kiedanski, “Optimal storage arbitrage under net metering using linear program- ming,” inSmartGridComm. IEEE, 2019

    work page 2019

  35. [35]

    Optimal bidding strategy of battery storage in power markets considering performance- based regulation and battery cycle life,

    G. He, Q. Chen, C. Kang, P. Pinson, and Q. Xia, “Optimal bidding strategy of battery storage in power markets considering performance- based regulation and battery cycle life,”IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2359–2367, 2015

    work page 2015

  36. [36]

    The value of electricity storage arbitrage on day-ahead markets across europe,

    T. Mercier, M. Olivier, and E. De Jaeger, “The value of electricity storage arbitrage on day-ahead markets across europe,”Energy Economics, vol. 123, p. 106721, 2023

    work page 2023

  37. [37]

    Modeling arbitrage of an energy storage unit without binary variables,

    Z. Shen, W. Wei, D. Wu, T. Ding, and S. Mei, “Modeling arbitrage of an energy storage unit without binary variables,”CSEE Journal of Power and Energy Systems, vol. 7, no. 1, pp. 156–161, 2020

    work page 2020

  38. [38]

    Arbitraging variable efficiency energy storage using analytical stochastic dynamic programming,

    N. Zheng, J. Jaworski, and B. Xu, “Arbitraging variable efficiency energy storage using analytical stochastic dynamic programming,”IEEE Transactions on Power Systems, vol. 37, no. 6, pp. 4785–4795, 2022

    work page 2022

  39. [39]

    Transparency Platform — newtransparency.entsoe.eu,

    ENTSO-e, “Transparency Platform — newtransparency.entsoe.eu,” https://newtransparency.entsoe.eu/, 2025, [Accessed 16-10-2025]

    work page 2025

  40. [40]

    W. B. Powell,Approximate Dynamic Programming: Solving the curses of dimensionality. John Wiley & Sons, 2007, vol. 703

    work page 2007

  41. [41]

    Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints,

    M. U. Hashmi, D. Van Hertem, A. van der Meer, and A. Keane, “Linear energy storage and flexibility model with ramp rate, ramping, deadline and capacity constraints,” in2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE, 2024, pp. 1–6

    work page 2024

  42. [42]

    Electricity price prediction for energy storage system arbitrage: A decision-focused approach,

    L. Sang, Y . Xu, H. Long, Q. Hu, and H. Sun, “Electricity price prediction for energy storage system arbitrage: A decision-focused approach,”IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2822–2832, 2022

    work page 2022

  43. [43]

    Cross-border electricity trading: towards flow-based market coupling,

    K. Leuven, “Cross-border electricity trading: towards flow-based market coupling,”Eifact Sheet, vol. 2, 2015

    work page 2015

  44. [44]

    Computability of global solutions to factorable nonconvex programs: Part i—convex underestimating problems,

    G. P. McCormick, “Computability of global solutions to factorable nonconvex programs: Part i—convex underestimating problems,”Math- ematical programming, vol. 10, no. 1, pp. 147–175, 1976

    work page 1976

  45. [45]

    An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs,

    H. Nagarajan, M. Lu, S. Wang, R. Bent, and K. Sundar, “An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs,”Journal of Global Optimization, vol. 74, pp. 639–675, 2019

    work page 2019

  46. [46]

    Optimal power flow in distribution networks under N–1 disruptions: A multistage stochastic programming approach,

    H. Yang and H. Nagarajan, “Optimal power flow in distribution networks under N–1 disruptions: A multistage stochastic programming approach,” INFORMS Journal on Computing, vol. 34, no. 2, pp. 690–709, 2022

    work page 2022

  47. [47]

    An overview of ancillary services and hvdc systems in european context,

    A. Kaushal and D. Van Hertem, “An overview of ancillary services and hvdc systems in european context,”Energies, vol. 12, no. 18, p. 3481, 2019

    work page 2019

  48. [48]

    Electrical interconnec- tors: market opportunities, regulatory issues, technology considerations and implications for the gb energy sector,

    C. MacIver, K. R. Bell, G. P. Adam, and L. Xu, “Electrical interconnec- tors: market opportunities, regulatory issues, technology considerations and implications for the gb energy sector,”Energy Strategy Reviews, 2021

    work page 2021

  49. [49]

    Flexible operation of low-inertia power systems connected via high voltage direct current interconnec- tors,

    V . Trovato, A. Mazza, and G. Chicco, “Flexible operation of low-inertia power systems connected via high voltage direct current interconnec- tors,”Electric Power Systems Research, vol. 192, p. 106911, 2021

    work page 2021

  50. [50]

    Meeting uk heat demands in zero emission renewable energy systems using storage and interconnectors,

    T. G. Cassarino and M. Barrett, “Meeting uk heat demands in zero emission renewable energy systems using storage and interconnectors,” Applied energy, vol. 306, p. 118051, 2022

    work page 2022

  51. [51]

    Lithium-Ion Battery Pack Prices Fall to $108 Per Kilowatt-Hour, Despite Rising Metal Prices: BloombergNEF — BloombergNEF — about.bnef.com,

    BloombergNEF , “Lithium-Ion Battery Pack Prices Fall to $108 Per Kilowatt-Hour, Despite Rising Metal Prices: BloombergNEF — BloombergNEF — about.bnef.com,” https://tinyurl.com/y436xfye, 2025, [Accessed 01-03-2026]

    work page 2025

  52. [52]

    [Online]

    Nemo link’s loss factor. [Online]. Available: https://tinyurl.com/ 6dp6zv47

    work page

  53. [53]

    Long-term revenue estimation for battery performing arbitrage and ancillary services,

    M. U. Hashmi, W. Labidi, A. Bu ˇsi´c, S.-E. Elayoubi, and T. Chahed, “Long-term revenue estimation for battery performing arbitrage and ancillary services,” inSmartGridComm. IEEE, 2018

    work page 2018

  54. [54]

    [Online]

    intlinprog: Mixed-integer linear programming (milp). [Online]. Available: https://nl.mathworks.com/help/optim/ug/intlinprog.html

    work page

  55. [55]

    HiGHS - High-performance parallel linear optimization software — highs.dev,

    “HiGHS - High-performance parallel linear optimization software — highs.dev,” https://highs.dev/, 2026, [Accessed 21-02-2026]

    work page 2026

  56. [56]

    Limiting energy storage cycles of operation,

    M. U. Hashmi and A. Busic, “Limiting energy storage cycles of operation,” in2018 IEEE Green Technologies Conference (GreenTech). IEEE, 2018. 24th Power Systems Computation Conference PSCC 2026 Limassol, Cyprus — June 8-12, 2026

    work page 2018