Pith. sign in

REVIEW 5 major objections 4 minor 64 references

A Bottom-Up Approach to Optimizing the Solar Organic Rankine Cycle for Transactive Energy Trading

T0 review · 5 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A solar-ORC microgrid with battery storage can cut operational costs by an average of 16 percent under peer-to-peer energy trading, according to the paper's two-stage optimization models.

desk verdict The 16% cost saving is an artifact of free grid imports; the model has real potential but needs correct cost accounting and a defined baseline. read the letter →

arxiv 2412.01359 v2 pith:XYJIJVF6 submitted 2024-12-02 math.OC

Silvia Anna Cordieri , Chiara Bordin , Sambeet Mishra This is my paper · ORCID
classification math.OC MSC 90C1190C90
keywords SolarorganicRankinecyclepeer-to-peerenergytradingtransactivemixed-integerlinearprogrammingmicrogridbatterystorageoperationsresearchsystemsmodeling
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

This paper tries to show that a solar organic Rankine cycle plant paired with battery storage is economically viable in community microgrids where prosumers trade energy directly. It builds two mixed-integer linear programs: one schedules the solar-ORC and battery to meet a prosumer's demand, and the second optimizes peer-to-peer trades among microgrids using each plant's residual surplus and deficit. The key result is an average 16 percent reduction in operational costs compared with using the grid alone, with larger gains in sunnier locations and seasons. A reader should care because this is a concrete, computable pathway for making distributed solar heat-to-power technology competitive and for embedding it in larger energy-system models.

What carries the argument

The carrying object is the sequential two-model MILP architecture. The S-ORC model minimizes production and battery cycling costs subject to energy balances; it computes each prosumer's hourly surplus or deficit ($e^t_{in}$, $e^t_{out}$). The TET model then minimizes transmission costs $\sum c^t_{T,ij}|f^t_{ij}|$ over energy flows between participants, using those imbalances as parameters. Thermodynamic detail enters through the mass-flow identity $m^t_{ORC} = \rho A^t v$ linking pipe section, density, and velocity, with turbine and pump power from enthalpy differences, and battery aging enters through the lifetime-throughput constraint $B_{fade}/B_{throughput}|b^t-b^{t-1}| \le d^t$.

What would settle it

Price grid imports and exports at the same per-kWh rate used in the grid-only baseline, re-solve the S-ORC and TET models, and compare the cost gaps; if the 16 percent shrinks to near zero, the central cost-saving claim is an artifact of free grid exchange.

Watch

Extended reading notes

Core claim

The central claim is that a Solar-ORC, modeled with thermodynamic detail (working fluid density, mass flow rate, turbine and pump enthalpy differences, collector efficiency) and coupled to a degrading battery, can be scheduled by a MILP and then used in a peer-to-peer trading market to lower total operational costs. Across instances representing different communities, locations (Bologna and Tromsø), and seasons, the two-stage procedure reports an average 16% cost reduction over a grid-only baseline, 19% for Bologna and 14.7% for Tromsø. The paper also finds that choice of working fluid and collector technology mainly affects plant sizing rather than operational decisions, and that the models solve weekly instances in seconds.

Load-bearing premise

The reported savings assume that electricity imported from and exported to the grid is free in the optimized system, so the 16 percent gap may just reflect free grid energy rather than the solar-ORC's value.

Editorial extensions

If this is right

  • If the claim holds, community microgrids can schedule a solar-ORC plus battery and trade residual energy without a central coordinator, cutting operating costs by about 16 percent on average.
  • The same S-ORC model can be dropped into larger energy-system models as a technology-detailed module, because it is a standalone MILP with short solve times.
  • Working-fluid choice (ethanol, methanol, cyclohexane, R134a) and collector efficiency change the required plant size, so design-stage decisions can be separated from operational scheduling.
  • Because battery degradation is penalized, the optimizer will avoid deep cycling, extending battery lifetime and making reported savings more realistic.

Reading between the lines

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

  • Editorial inference: if grid import and export prices were added to the S-ORC objective, the 16 percent gap would likely shrink, because the current formulation treats grid exchange as free while the grid-only baseline presumably pays for it.
  • Editorial inference: the same two-stage structure could test seasonal storage by adding a long-horizon battery or hydrogen state, which the paper lists as future work; Arctic sites like Tromsø would be the natural test case.
  • Editorial inference: linking the operational model to a design-optimization step (also flagged by the authors as future work) would convert the operational-cost percentage into a full net-present-value comparison.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 4 minor

Summary. The paper proposes two mixed-integer linear programs: an S-ORC model for the weekly operational scheduling of a solar organic Rankine cycle coupled with a battery, and a TET model for peer-to-peer transactive energy trading among prosumers in a microgrid community. The S-ORC model includes thermodynamic relationships for the working fluid, solar collector efficiency, and a battery degradation mechanism; the TET model routes residual imbalances between prosumers and the grid. The authors report a sensitivity analysis across working fluids, plant sizes, solar collector technologies, seasons, and two locations (Bologna and Tromsø), and they claim an average 16% reduction in operational costs when the Solar-ORC is used in P2P trading compared with a grid-only baseline. The central economic claim is not supported by the model as written because grid imports and exports are unpriced in the objectives, and the grid-only baseline is never formally defined.

Significance. If the economic claim were properly supported, the paper would make a useful contribution: it demonstrates how a technology-detailed ORC model can be embedded in a tractable MILP scheduling and trading framework, and it provides a broad sensitivity exploration (nine working fluids, five collector types, multiple seasons and locations). The computational tractability (instances solved in seconds) is a genuine strength for potential integration into larger energy system models. However, the load-bearing 16% cost-saving result depends on an incomplete cost accounting in which grid electricity is free in the proposed model and the comparison baseline is not specified. The model also contains unresolved storage and degradation formulation issues. These problems are fixable, but the quantitative results in the current manuscript cannot be relied upon.

major comments (5)
  1. [Section 5.2, Eq. (1) and (4)] The S-ORC objective minimizes only cp*x_t + cb*(b_t_in + b_t_out); grid withdrawals e_t_in and injections e_t_out appear only in inequality (4), with no price term and no upper bound. The optimizer can therefore satisfy demand with arbitrary grid imports at zero cost, and the model is underdetermined because any e_t_in/e_t_out pair satisfying (4) leaves the objective unchanged. The 16% reduction reported in Section 6.3 is computed against 'using the grid as the only source', but that baseline objective is never written down and no grid tariff is specified anywhere in the paper. If the baseline pays for grid imports while the proposed model imports them free, the savings are an accounting artifact rather than a property of the Solar-ORC plus P2P framework.
  2. [Section 5.3, Eq. (26)-(29)] The TET objective prices only transmission costs c^t_T |f^t_ij|, while the system-level grid balances h_t_in and h_t_out in Eq. (29) are free and unbounded. The trading layer can therefore absorb arbitrary mismatches with the grid at zero cost, which again means that the comparison between TET and a grid-only baseline depends on an implicit grid price that is never introduced. In addition, Eq. (27) uses j both as the summation index and on the right-hand side; the right-hand side should presumably be e_t_i,out, or the sums need relabeling.
  3. [Section 5.2, Eq. (13)-(22)] The battery state b_t is never bounded by b_max or by the degradation-limited capacity b_t_max. Constraints (21)-(22) only bound the charging and discharging flows b_t_in and b_t_out, not the accumulated battery level b_t. With b^0 = 0 and no upper bound on b_t in Eq. (13), the storage can accumulate arbitrarily large energy, so the battery capacity and its degradation limit are not actually enforced. A constraint of the form b_t <= b_t_max (and b_t <= b_max) is missing.
  4. [Section 5.2, Eq. (19)] The degradation constraint applies B_fade/B_throughput * |b_t - b_{t-1}| <= d_t as a per-time-step condition and then uses d_t in Eq. (20) as a capacity multiplier. The lifetime-throughput degradation model cited from ref. [23] is cumulative: capacity fade depends on the total energy discharged over the battery's lifetime, not on each individual hour's throughput. As written, d_t can be chosen independently in every period, and there is no accumulation of throughput over the horizon, so the model does not represent progressive aging or remaining lifetime. The degradation state should be accumulated across time steps before limiting capacity.
  5. [Section 6] The numerical values of cp, cb, the grid electricity price, and the demand profiles used for the reported 12% and 16% cost comparisons are not reported. Because the objective contains only cp and cb, and because the grid tariff is decisive for the baseline comparison, the central quantitative claims cannot be checked or reproduced from the paper. The authors should provide a complete data table (or a public repository) with all cost parameters, tariffs, and demand series.
minor comments (4)
  1. [Table 4] The column labeled 'Density [kg/m^3]' contains values that are orders of magnitude too small for the listed fluids (for example, ethanol is around 789 kg/m^3, not 0.253 kg/m^3). Either the column is mislabeled (perhaps it is specific volume) or the density data are incorrect, and this feeds the mass-flow-rate comparison in Figure 5.
  2. [Throughout] There are several typographical and formatting issues: 'Sytem' in the Figure 12 caption, 'Artic' in Section 7, 'T able' in the table captions, and 'Section 5.3 we presents an optimization model' in the methodology text. These should be corrected in a revision.
  3. [Section 5.2, Eq. (19)] Equation (19) contains an absolute value in a MILP; the paper should state how the absolute value is linearized and clarify whether both charge and discharge throughput contribute to degradation or only discharge, given that the lifetime throughput is defined as total dischargeable energy.
  4. [Section 5.1 and Section 8] The conclusions state that the model 'will be made available in the GitHub platform under the name OPTI-ORC', but no repository link or code appendix is provided in the manuscript. For reproducibility, the code and data should be available at the time of review.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: models reuse the authors' earlier constraints but no prediction is fitted to a target; the 16% saving is an accounting gap, not a circular one.

full rationale

The paper's central quantitative claim (16% operational-cost reduction) is not obtained by fitting a parameter to that target, nor by importing a uniqueness theorem, nor by defining the output into the input. The S-ORC model (Eq. 1) minimizes cp*x_t + cb*(b_in+b_out), and e_in/e_out enter only inequality (4), so grid withdrawals are unpriced; the TET model (Eq. 26) prices only transmission fluxes while h_in/h_out are free in Eq. (29). The "grid as the only source" baseline is asserted in Sections 6.1 and 6.3 but never written as an objective, so the cost saving cannot be verified. This is an incomplete-comparison/correctness limitation, not a circular step: no equation in the paper reduces a claimed prediction to its own inputs, and the reused constraints from the authors' prior works [22,23,41] are standard energy-balance and battery-degradation constraints that do not by themselves force the 16% figure. Per the hard rules, circularity is not claimed without an exhibited equation-level reduction. Score 2 reflects the presence of several self-citations that are contextual but not load-bearing for the paper's main conclusion.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim depends on several unstated numeric inputs (costs, enthalpy differences, battery parameters, electricity price) and on modeling simplifications about collector efficiency, battery fade, turbine regulation, market behavior, and the two-stage solution method. None of these are validated against measured data, and several are not even quantified in the text.

free parameters (4)
  • Solar-ORC production cost cp and battery cycling cost cb = not specified in paper
    The objective (1) is a sum of cp times xt and cb times (b_in plus b_out), and all cost results scale with these values, but the numerical values are not reported.
  • Enthalpy differences Delta_h_T and Delta_h_P = not specified in paper
    Equations (6) and (7) convert mass flow to turbine output and pump consumption; without their values the model cannot be parameterized.
  • Battery degradation parameters B_fade and B_throughput = not specified in paper
    Constraints (19) and (20) control capacity fade; the values are said to follow reference [23] but are not stated.
  • Grid electricity price used for both locations = Italian market price, value not stated
    Sections 6.1 and 7.2 use the same electricity cost for Bologna and Tromsø to isolate weather effects; the actual price is not given, and the baseline cost comparison depends on it.
assumptions (5)
  • domain assumption Solar collector efficiency can be modeled as a constant parameter per technology
    Equation (12) uses fixed eta_solar values from Kalogirou [7], ignoring dependence on operating temperature and irradiance; this affects all sizing and cost results.
  • domain assumption Battery degradation is represented by linear capacity fade with per-step cycling
    Constraint (19) applies Bfade over Bthroughput to each hourly change |b_t minus b_{t-1}| without accumulating over time, so capacity can appear to recover; this is an unvalidated simplification.
  • domain assumption Lamination at constant enthalpy is the regulation policy for the ORC turbine
    Section 3.1 states this policy and constraint (10) uses a variable section area A_t; no validation is given that this matches actual plant operation.
  • domain assumption P2P trading can be represented by minimizing transmission costs on residual imbalances after self-consumption
    The TET objective (26) and constraints (27)-(29) ignore energy prices and assume the market follows this cost-minimizing allocation.
  • domain assumption Sequential solution of S-ORC before TET is a valid way to optimize the community
    Algorithm 1 decomposes the problem into independent prosumer schedules followed by a trading layer; no bound or proof is given that this matches the joint optimum.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Bottom-Up Approach to Optimizing the Solar Organic Rankine Cycle for Transactive Energy Trading." pith.science (2026). https://pith.science/paper/XYJIJVF6

@misc{pith2026241201359,
  author       = {Pith},
  title        = {Pith review of: A Bottom-Up Approach to Optimizing the Solar Organic Rankine Cycle for Transactive Energy Trading},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XYJIJVF6}},
  note         = {Machine review of arXiv:2412.01359}
}
read the original abstract

Solar Organic Rankine Cycle (ORC)-based power generation plants leverage solar irradiation to produce thermal energy, offering a highly compatible renewable technology due to the alignment between solar irradiation temperatures and ORC operating requirements. Their superior performance compared to steam Rankine cycles in small-scale applications makes them particularly relevant within the smart grid and microgrid contexts. This study explores the role of ORC in peer-to-peer (P2P) energy trading within renewable-based community microgrids, where consumers become prosumers, simultaneously producing and consuming energy while engaging in virtual trading at the distribution system level. Focusing on a microgrid integrating solar ORC with a storage system to meet consumer demand, the paper highlights the importance of combining these technologies with storage to enhance predictability and competitiveness with conventional energy plants, despite management challenges. A methodology based on operations research techniques is developed to optimize system performance. Furthermore, the impact of various technological parameters of the solar ORC on the system's performance is examined. The study concludes by assessing the value of solar ORC within the transactive energy trading framework across different configurations and scenarios. Results demonstrate an average 16\% reduction in operational costs, showcasing the benefits of implementing a predictable and manageable system in P2P transactive energy trading.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages

  1. [23]

    Smacs model, a stochastic multihorizon approach for charging sites management, operations, design, and expansion under limited capacity conditions,

    C. Bordin and A. Tomasgard, “Smacs model, a stochastic multihorizon approach for charging sites management, operations, design, and expansion under limited capacity conditions,” Journal of Energy Storage , vol. 26, p. 100824,

    work page

  2. [1]

    Distributed multi-generation: A comprehensive view,

    G. Chicco and P. Mancarella, “Distributed multi-generation: A comprehensive view,” Renewable and Sustainable Energy Reviews , vol. 13, no. 3, pp. 535– 551, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S1364032107001578

    work page 2009

  3. [2]

    Clean energy for all europeans pack- age

    European commission. Clean energy for all europeans pack- age. [Online]. Available: https://energy.ec.europa.eu/topics/energy-strategy/ clean-energy-all-europeans-package en

    work page

  4. [3]

    United Nations. Cop23. [Online]. Available: https://unfccc.int/event/cop-23

    work page

  5. [4]

    The 17 goals

    ——. The 17 goals. [Online]. Available: https://sdgs.un.org/goals

    work page

  6. [5]

    Optimal design and operation of an organic rankine cycle (orc) system driven by solar energy with sensible thermal energy storage,

    H. Yu, H. Helland, X. Yu, T. Gundersen, and G. Sin, “Optimal design and operation of an organic rankine cycle (orc) system driven by solar energy with sensible thermal energy storage,” Energy Conversion and Management , vol. 244, p. 114494, 2021. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S0196890421006701

    work page 2021

  7. [6]

    A world overview of the organic rankine cycle market,

    T. Tarti` ere and M. Astolfi, “A world overview of the organic rankine cycle market,” Energy Procedia , vol. 129, pp. 2–9, 2017, 4th International Seminar on ORC Power SystemsSeptember 13-15th 2017 POLITECNICO DI MILANO BOVISA CAMPUS MILANO, ITALY. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1876610217340286

    work page 2017

  8. [7]

    Solar thermal collectors and applications,

    S. A. Kalogirou, “Solar thermal collectors and applications,” Progress in Energy and Combustion Science , vol. 30, no. 3, pp. 231–295, 2004. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0360128504000103

    work page 2004

Show all 64 references
  1. [8]

    Educating the energy infor- matics specialist: opportunities and challenges in light of research and industrial trends,

    C. Bordin, S. Mishra, A. Safari, and F. Eliassen, “Educating the energy infor- matics specialist: opportunities and challenges in light of research and industrial trends,” SN Applied Sciences , vol. 3, pp. 1–17, 2021

    2021

  2. [9]

    Smart energy and power systems modelling: an iot and cyber-physical systems perspective, in the context of energy informatics,

    C. Bordin, A. H ˚ akansson, and S. Mishra, “Smart energy and power systems modelling: an iot and cyber-physical systems perspective, in the context of energy informatics,” Procedia Computer Science, vol. 176, pp. 2254–2263, 2020. [Online]. Available: https://doi.org/10.1016/j....

    2020 doi

  3. [10]

    A society of devices: Integrating intelligent distributed resources with transactive energy,

    K. Kok and S. Widergren, “A society of devices: Integrating intelligent distributed resources with transactive energy,” IEEE Power and Energy Magazine, vol. 14, no. 3, pp. 34–45, 2016. [Online]. Available: https: //ieeexplore.ieee.org/document/7452756

    2016

  4. [11]

    A bidding system for peer-to-peer energy trading in a grid-connected microgrid,

    C. Zhang, J. Wu, M. Cheng, Y. Zhou, and C. Long, “A bidding system for peer-to-peer energy trading in a grid-connected microgrid,” Energy Procedia, vol. 103, pp. 147–152, 2016, renewable Energy Integration with Mini/Microgrid – Proceedings of REM2016. [Online]. Available: http...

    2016

  5. [12]

    Comparative review and discussion on p2p electricity trading,

    C. Park and T. Yong, “Comparative review and discussion on p2p electricity trading,” Energy Procedia, vol. 128, pp. 3–9, 2017

    2017

  6. [13]

    Technology development of challenges of peer to peer energy,

    C. Park, “Technology development of challenges of peer to peer energy,” Seongnam: KETI, 2009

    2009

  7. [14]

    Fluid selection for a low-temperature solar organic rankine cycle,

    B. F. Tchanche, G. Papadakis, G. Lambrinos, and A. Frangoudakis, “Fluid selection for a low-temperature solar organic rankine cycle,” Applied Thermal Engineering, vol. 29, no. 11, pp. 2468–2476, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1359...

    2009

  8. [15]

    Solar energy powered rankine cycle using supercritical co2,

    H. Yamaguchi, X. Zhang, K. Fujima, M. Enomoto, and N. Sawada, “Solar energy powered rankine cycle using supercritical co2,” Applied Thermal Engineering, vol. 26, no. 17, pp. 2345–2354, 2006. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1359431106000780

    2006

  9. [16]

    Microgrid transactive energy: Review, architectures, distributed ledger technologies, and market analysis,

    M. F. Zia, M. Benbouzid, E. Elbouchikhi, S. M. Muyeen, K. Techato, and J. M. Guerrero, “Microgrid transactive energy: Review, architectures, distributed ledger technologies, and market analysis,” IEEE Access, vol. 8, pp. 19 410–19 432,

  10. [17]

    Review of existing peer-to-peer energy trading projects,

    C. Zhang, J. Wu, C. Long, and M. Cheng, “Review of existing peer-to-peer energy trading projects,” Energy Procedia, vol. 105, pp. 2563–2568, 2017, 8th International Conference on Applied Energy, ICAE2016, 8-11 October 2016, Beijing, China. [Online]. Available: https://www.scie...

    2017

  11. [18]

    Overview on artificial intelligence in design of organic rankine cycle,

    D. Zhao, S. Deng, L. Zhao, W. Xu, W. Wang, X. Nie, and M. Chen, “Overview on artificial intelligence in design of organic rankine cycle,” Energy and AI , vol. 1, p. 100011, 2020. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S2666546820300112

    2020

  12. [19]

    Multi-objective optimization of organic rankine cycles for waste heat recovery: Application in an offshore platform,

    L. Pierobon, T.-V. Nguyen, U. Larsen, F. Haglind, and B. Elmegaard, “Multi-objective optimization of organic rankine cycles for waste heat recovery: Application in an offshore platform,” Energy, vol. 58, pp. 538– 549, 2013. [Online]. Available: https://www.sciencedirect.com/sc...

    2013

  13. [20]

    Energy storage technologies and real life applications – a state of the art review,

    M. Aneke and M. Wang, “Energy storage technologies and real life applications – a state of the art review,” Applied Energy , vol. 179, pp. 350–377,

  14. [21]

    Thermal energy storage for solar- powered organic rankine cycle engines,

    E. Casati, A. Galli, and P. Colonna, “Thermal energy storage for solar- powered organic rankine cycle engines,” Solar Energy , vol. 96, pp. 205–219, 32

  15. [22]

    Including power management strategies and load profiles in the mathematical optimization of energy storage sizing for fuel consumption reduction in maritime vessels,

    C. Bordin and O. Mo, “Including power management strategies and load profiles in the mathematical optimization of energy storage sizing for fuel consumption reduction in maritime vessels,” Journal of Energy Storage , vol. 23, pp. 425– 441, 2019. [Online]. Available: https://ww...

    2019

  16. [24]

    A multi-agent system approach for optimal microgrid expansion planning under uncertainty,

    S. Mishra, C. Bordin, A. Tomasgard, and I. Palu, “A multi-agent system approach for optimal microgrid expansion planning under uncertainty,” International Journal of Electrical Power and Energy Systems , vol. 109, pp. 696– 709, 2019. [Online]. Available: https://www.sciencedir...

    2019

  17. [25]

    Thermoeconomic analysis and optimization of a regenerative two-stage organic rankine cycle coupled with liquefied natural gas and solar energy,

    M. Mehrpooya, M. Ashouri, and A. Mohammadi, “Thermoeconomic analysis and optimization of a regenerative two-stage organic rankine cycle coupled with liquefied natural gas and solar energy,” Energy, vol. 126, pp. 899– 914, 2017. [Online]. Available: https://www.sciencedirect.co...

    2017

  18. [26]

    A new coupled energy system consisting of fuel cell, solar thermal collector, and organic rankine cycle; generation and storing of electrical energy,

    Y. Chen, L. Feng, I. B. Mansir, and M. Taghavi, “A new coupled energy system consisting of fuel cell, solar thermal collector, and organic rankine cycle; generation and storing of electrical energy,” Sustainable Cities and Society , vol. 81, p. 103824, 2022. [Online]. Availabl...

    2022

  19. [27]

    A distributed peer-to-peer energy transaction method for diversified prosumers in urban community microgrid system,

    Z. Wang, X. Yu, Y. Mu, and H. Jia, “A distributed peer-to-peer energy transaction method for diversified prosumers in urban community microgrid system,” Applied Energy , vol. 260, p. 114327, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0306261919320148

    2020

  20. [28]

    A novel decentralized platform for peer-to-peer energy trading market with blockchain technology,

    A. Esmat, M. de Vos, Y. Ghiassi-Farrokhfal, P. Palensky, and D. Epema, “A novel decentralized platform for peer-to-peer energy trading market with blockchain technology,” Applied Energy , vol. 282, p. 116123, 2021. [Online]. Available: https://www.sciencedirect.com/science/art...

    2021

  21. [29]

    Auction based energy trading in transactive energy market with active participation of prosumers and consumers,

    M. Khorasany, Y. Mishra, and G. Ledwich, “Auction based energy trading in transactive energy market with active participation of prosumers and consumers,” in 2017 Australasian Universities Power Engineering Conference (AUPEC) , 2017, pp. 1–6. [Online]. Available: https://ieeex...

    2017

  22. [30]

    Performance analysis of an integrated pumped-hydro and compressed-air energy storage system and solar organic rankine cycle,

    M. Marefati, M. Mehrpooya, and F. Pourfayaz, “Performance analysis of an integrated pumped-hydro and compressed-air energy storage system and solar organic rankine cycle,” Journal of Energy Storage , vol. 44, p. 103488,

  23. [31]

    Modelling 33 and optimization of chp based district heating system with renewable energy production and energy storage,

    H. Wang, W. Yin, E. Abdollahi, R. Lahdelma, and W. Jiao, “Modelling 33 and optimization of chp based district heating system with renewable energy production and energy storage,” Applied Energy , vol. 159, pp. 401–421,

  24. [32]

    Modelling and simulation of phase change material latent heat storages applied to a solar-powered organic rankine cycle,

    G. Manfrida, R. Secchi, and K. Sta´ nczyk, “Modelling and simulation of phase change material latent heat storages applied to a solar-powered organic rankine cycle,” Applied Energy, vol. 179, pp. 378–388, 2016. [Online]. Available: https://www.sciencedirect.com/science/article...

    2016

  25. [33]

    A bi-level optimization-based community energy management system for optimal energy sharing and trading among peers,

    E. Fernandez, M. Hossain, K. Mahmud, M. S. H. Nizami, and M. Kashif, “A bi-level optimization-based community energy management system for optimal energy sharing and trading among peers,” Journal of Cleaner Production , vol. 279, p. 123254, 2021. [Online]. Available: https://w...

    2021

  26. [34]

    Peer-to-peer energy trading in a microgrid,

    C. Zhang, J. Wu, Y. Zhou, M. Cheng, and C. Long, “Peer-to-peer energy trading in a microgrid,” Applied Energy, vol. 220, pp. 1–12, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0306261918303398

    2018

  27. [35]

    A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration,

    C. Bordin, H. O. Anuta, A. Crossland, I. L. Gutierrez, C. J. Dent, and D. Vigo, “A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration,” Renewable Energy, vol. 101, pp. 417–430, 2017. [Online]. Av...

    2017 doi

  28. [36]

    Mathematical optimization applied to thermal and electrical energy systems,

    C. Bordin, “Mathematical optimization applied to thermal and electrical energy systems,” 2015. [Online]. Available: https://amsdottorato.unibo.it/6915/ 1/Bordin Chiara tesi.pdf

    2015

  29. [37]

    A review of solar-driven organic rankine cycles: Recent challenges and future outlook,

    R. Loni, O. Mahian, C. N. Markides, E. Bellos, W. G. le Roux, A. Kasaeian, G. Najafi, and F. Rajaee, “A review of solar-driven organic rankine cycles: Recent challenges and future outlook,” Renewable and Sustainable Energy Reviews , vol. 150, p. 111410, 2021. [Online]. Availab...

    2021

  30. [38]

    Battery energy storage technology for power systems—an overview,

    K. Divya and J. Østergaard, “Battery energy storage technology for power systems—an overview,” Electric Power Systems Research, vol. 79, no. 4, pp. 511– 520, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S0378779608002642

    2009

  31. [39]

    Method of testing to determine the thermal performance of solar collectors. (ashrae standard),

    “Method of testing to determine the thermal performance of solar collectors. (ashrae standard),” 11 1985. [Online]. Available: https://www.osti.gov/biblio/ 575023

    1985

  32. [40]

    Available: https://jump.dev/JuMP.jl/stable/

    [Online]. Available: https://jump.dev/JuMP.jl/stable/

  33. [41]

    An lp model for the central energy system,

    S. A. Cordieri, M. Monaci, P. Paronuzzi, and H. Schwaeppe, “An lp model for the central energy system,” in Operations Research and Data Science in Public Services. Cham: Springer International Publishing, 2023, pp. 59–71. [Online]. Available: https://link.springer.com/chapter/...

    2023 doi

  34. [42]

    Lozza, Turbine a Gas E Cicli Combinati

    G. Lozza, Turbine a Gas E Cicli Combinati . Via U.Terracini 30, Bologna: Societ` a Editrice Esculapio, 2016. [Online]. Available: https://bookshop. editrice-esculapio.com/products/lozza-turbine-a-gas-e-cicli-combinati?srsltid= AfmBOopNZtNzCSjwebdp1uSg-EcEc8DQ8GyN3Liw6XicKk5Y0J...

    2016

  35. [43]

    Available: https://support.gurobi.com/hc/en-us/articles/ 360060235871-Gurobi-9-1-2-released

    [Online]. Available: https://support.gurobi.com/hc/en-us/articles/ 360060235871-Gurobi-9-1-2-released

  36. [44]

    Multiobjective optimization of a hybrid wind/solar battery energy system in the arctic,

    M. Huang, T.-T. Nguyen, and T. Bostr¨ om, “Multiobjective optimization of a hybrid wind/solar battery energy system in the arctic,” Journal of Renewable Energy, vol. 2021, 2021. [Online]. Available: https://doi.org/10.1155/2021/882956

    2021 doi

  37. [45]

    Investigation of energy yield on tromsøya by mapping solar potential in arcgis,

    O. F. Eikeland, T. Bostr¨ om, and M. Chiesa, “Investigation of energy yield on tromsøya by mapping solar potential in arcgis,” in 2019 Nordic Workshop on Power and Industrial Electronics (NORPIE) , 2019, pp. 1–8. [Online]. Available: https://ieeexplore.ieee.org/document/9967829

    2019

  38. [46]

    Available: https://pypsa.org/

    [Online]. Available: https://pypsa.org/

  39. [47]

    Available: https://www.ucl.ac.uk/energy-models/models/highres

    [Online]. Available: https://www.ucl.ac.uk/energy-models/models/highres

  40. [48]

    Available: https://energy.mit.edu/genx/

    [Online]. Available: https://energy.mit.edu/genx/

  41. [49]

    Available: https://www.nrel.gov/analysis/sienna.html

    [Online]. Available: https://www.nrel.gov/analysis/sienna.html

  42. [50]

    Assessment of the off-design performance of a solar thermally-integrated pumped-thermal energy storage,

    G. F. Frate, A. Baccioli, L. Bernardini, and L. Ferrari, “Assessment of the off-design performance of a solar thermally-integrated pumped-thermal energy storage,” Renewable Energy , vol. 201, pp. 636–650, 2022. [Online]. Available: https://doi.org/10.1016/j.renene.2022.10.097

    2022 doi

  43. [51]

    Pumped thermal electricity storage for active distribution network applications,

    A. A. Ibrahim, B. Kazemtabrizi, C. Bordin, C. J. Dent, J. D. McTigue, and A. J. White, “Pumped thermal electricity storage for active distribution network applications,” in 2017 IEEE Manchester PowerTech , 2017, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/documen...

    2017

  44. [52]

    Rnr: reliability oriented network restructuring,

    S. Mishra, C. Bordin, and I. Palu, “Rnr: reliability oriented network restructuring,” in 2018 IEEE 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University-RTUCON. IEEE, 2019. [Online]. Available: https://ieeexplore.ieee.org/doc...

    2018

  45. [53]

    A multihorizon approach for the reliability oriented network restructuring problem, considering learning effects, construction time, and cables maintenance costs,

    C. Bordin, S. Mishra, and I. Palu, “A multihorizon approach for the reliability oriented network restructuring problem, considering learning effects, construction time, and cables maintenance costs,” Renewable Energy, vol. 168, pp. 878–895,

  46. [54]

    Behavioural change in green transportation: Micro-economics perspectives and optimization strategies,

    C. Bordin and A. Tomasgard, “Behavioural change in green transportation: Micro-economics perspectives and optimization strategies,” Energies, vol. 14, no. 13, p. 3728, 2021

    2021

  47. [55]

    Optimization of organic rank- ine cycle power systems for waste heat recovery on heavy-duty vehicles considering the performance, cost, mass and volume of the system,

    M. Imran, F. Haglind, V. Lemort, and A. Meroni, “Optimization of organic rank- ine cycle power systems for waste heat recovery on heavy-duty vehicles considering the performance, cost, mass and volume of the system,” Energy, vol. 180, pp. 229–241, 2019

    2019

  48. [56]

    An evaluation of wind turbine waste heat recovery using organic rankine cycle,

    O. Nematollahi, Z. Hajabdollahi, H. Hoghooghi, and K. C. Kim, “An evaluation of wind turbine waste heat recovery using organic rankine cycle,” Journal of Cleaner Production, vol. 214, pp. 705–716, 2019

    2019

  49. [57]

    Features extraction of wind ramp events from a virtual wind park,

    S. Mishra, E. ¨Oren, C. Bordin, F. Wen, and I. Palu, “Features extraction of wind ramp events from a virtual wind park,” Energy reports, vol. 6, pp. 237–249, 2020. 35

    2020

  50. [60]

    Available: https://doi.org/10.1016/j.renene.2020.12.105

    [Online]. Available: https://doi.org/10.1016/j.renene.2020.12.105

    2020 doi

  51. [2013]

    Available: https://www.sciencedirect.com/science/article/pii/ S0038092X13002788

    [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0038092X13002788

  52. [2015]

    Available: https://www.sciencedirect.com/science/article/pii/ S0306261915010909

    [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0306261915010909

  53. [2016]

    Available: https://www.sciencedirect.com/science/article/pii/ S0306261916308728

    [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0306261916308728

  54. [2019]

    Available: https://www.sciencedirect.com/science/article/pii/ S2352152X19303044

    [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S2352152X19303044

  55. [2020]

    Available: https://ieeexplore.ieee.org/document/8966274

    [Online]. Available: https://ieeexplore.ieee.org/document/8966274

  56. [2021]

    Available: https://www.sciencedirect.com/science/article/pii/ S2352152X21011713

    [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S2352152X21011713

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.