Pith. sign in

REVIEW 3 major objections 7 minor 112 references

Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization

T0 review · 3 major / 7 minor · reviewed 2026-08-16 · deepseek-v4-flash

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

Pith's one-line read This paper claims that the Power System Transition Planning framework makes multistage stochastic, industry-aligned power system planning tractable on realistic 144-bus systems by combining SDDP decomposition with parallel…

desk verdict A broad, data-rich multistage stochastic planning framework with a real modeling bug in the DTR linearization and no optimality evidence for the headline 144-bus result; worth a serious referee but needs major revision. read the letter →

arxiv 2505.01331 v1 pith:HESXFE6K submitted 2025-05-02 eess.SY cs.SY

Ahmed Al-Shafei , Nima Amjady , Hamidreza Zareipour , Yankai Cao This is my paper · ORCID
classification eess.SYcs.SY
keywords powersystemtransitionplanningmultistagestochasticprogrammingdualdynamichigh-performancecomputingmixed-integerlinearthermalratingparalleldecompositiongridexpansion
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper introduces Power System Transition Planning (PSTP) as a category of long-term optimization that aligns multistage stochastic programming with the scenario-based planning practices of system operators. Its central claim is that the resulting mixed-integer linear program, although intractable monolithically for realistic grids, becomes solvable through Stochastic Dual Dynamic Programming (SDDP) with a Markov-chain scenario representation and parallel high-performance computing. The model co-optimizes a wide palette of planning factors: renewable zones, gas with carbon capture, small modular reactors, hydrogen turbines, battery and pumped-hydro storage, transmission lines, dynamic thermal rating sensors, and modular series compensation devices. The contribution is a blueprint formulation plus a scalability demonstration: a six-bus case solved to the same objective as a monolithic solver, and a 144-bus, 1000-zone, up to 20-stage case solved in about a week on 80 cores. If the framework is right, it gives planners and policymakers a tractable, transparent way to compare adaptive transition pathways toward zero-emission networks.

What carries the argument

The engine of the paper is MC-SDDP, a Markov-chain variant of Stochastic Dual Dynamic Programming: a sampling-based Benders decomposition that builds piecewise-linear approximations of future cost-to-go functions and collapses the scenario tree into a Markov chain with one expected cost-to-go function per stage. This decomposition reduces the monolithic mixed-integer program to a sequence of much smaller stage-wise subproblems that can be solved in parallel on high-performance computing clusters. Around this engine, the paper constructs linearizations for several planning factors: a combined dynamic-thermal-rating and new-line capacity constraint handled through auxiliary binary variables, an SSSC flow-injection model with cut-in conditions handled through big-M binaries, and a state-of-charge-dependent battery degradation model expressed as linear constraints.

What would settle it

A concrete test: compute a valid lower bound for the 20-stage 144-bus problem (for example, solve the extensive-form LP relaxation or build a Lagrangian bound) and compare it with the reported $30.30$ billion best solution; if that gap is large, or if the bound exceeds the reported value, the SDDP policy is not verified. A complementary test would fix the reported first-stage decisions, simulate the policy on held-out weather and demand years, and check that realized operational costs match the claimed optimum.

Watch

Extended reading notes

Core claim

The central claim of the paper is that the PSTP framework, formulated as a multistage stochastic mixed-integer linear program and solved by Markov-chain SDDP with parallel high-performance computing, is tractable for realistic power system transition planning. In the six-bus test case, the SDDP algorithm is reported to converge to the same optimal solution as the monolithic mixed-integer program, $13.56$ billion dollars, while using less wall-clock time; the paper also computes a value of the stochastic solution of $2.17$ billion dollars on that case, showing that deterministic scenario-based planning underperforms adaptive recourse. In the larger 144-bus test case with 1000 candidate renewable zones, the framework solves problems with 2, 5, 10, and 20 transition stages on 80 cores, with the best reported solution improving from $33.89$ to $30.30$ billion dollars as the number of stages increases. The paper presents this as evidence that a broad, industry-aligned planning model need not be reduced to deterministic forecasts or toy networks to remain computable.

Load-bearing premise

The load-bearing premise is that SDDP applied to the mixed-integer PSTP problem, where convexity is lost and optimality is not guaranteed (as the paper itself states), converges to near-optimal solutions on the 144-bus case, even though the only comparison against an exact monolithic solution is the small six-bus case.

Editorial extensions

If this is right

  • Multistage stochastic planning becomes feasible at operator scale: systems with hundreds of buses, thousands of candidate renewable zones, and many decision stages can be solved within planning-cycle runtimes rather than restricted to 6-24 bus academic cases.
  • The model lets a planner compare, under the same uncertainty layers, conventional transmission expansion against modular non-wired alternatives such as dynamic thermal rating sensors and series compensation devices, and against storage and new clean generation.
  • The reported value of the stochastic solution implies that deterministic scenario-based industry tools can understate transition cost by a material margin ($2.17$ billion on the six-bus case), so recourse-based planning may change investment recommendations.
  • For a 20-year horizon, the results suggest diminishing returns beyond about five decision stages when the long-term scenarios evolve gradually, meaning planners may need only five-year decision intervals to capture most of the benefit.

Reading between the lines

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

  • Because SDDP scales with the number of stages and scenarios rather than with subproblem size, the reported week-long runtime on 80 cores would likely grow sharply if the network were enlarged without also decomposing each stage subproblem; the paper leaves such temporal and network decomposition as future work.
  • The zone-resolution experiment suggests an implicit planning guideline: roughly 1000 representative renewable zones capture nearly all of the cost benefit while staying below the regime where solution time becomes polynomial of order greater than two; comparable tuning could be applied in other regions.
  • If this framework is used for regulatory decisions, the missing optimality certificate on the large case would need to be filled by a valid lower bound or confidence interval; the paper does not supply one, so an independent bound would be the next natural check.
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

3 major / 7 minor

Summary. The paper introduces the Power System Transition Planning (PSTP) problem, formulated as a multistage stochastic MILP with a wide range of planning factors (VRES, storage, DTR, SSSC, new transmission, CCS retrofits, etc.), geospatial input processing, and a scenario-generation pipeline based on clustering of weather and load data. The model is first solved monolithically on a 6-bus Alberta-derived test case (AESO-6), where the authors report that the stochastic dual dynamic programming (SDDP) implementation converges to the same optimal value ($13.56b) as the monolithic MILP. The framework is then applied to a 144-bus, 1000-zone test case (AESO-144) with up to 20 stages, reporting a best solution of $30.30b and a wall-clock time of about one week on 80 cores. The central claim is that this combination of model breadth, SDDP decomposition, and HPC parallelism makes industry-scale transition planning tractable.

Significance. If fully supported, the paper would constitute a substantial scalability demonstration for multistage stochastic power-system planning, with a unusually broad set of planning factors and an openly described data pipeline. The AESO-6 SDDP-vs-monolithic match is clean, and the release of test-case data and the detailed geospatial methodology are genuine strengths. However, the current manuscript does not establish the accuracy of the AESO-144 solution: the mixed-integer, non-convex nature of the model means standard SDDP lower-bound arguments do not apply, and no bound, confidence interval, or intermediate benchmark is reported. In addition, the DTR linearization in Eqs. (48)-(53) is algebraically inconsistent with the expression it is claimed to replace. These issues are load-bearing for the paper's main tractability and novelty claims, so the contribution cannot be accepted in its present form.

major comments (3)
  1. [§2.4.13, Eqs. (48)-(53)] The proposed DTR linearization is not equivalent to the original expression. Let A = Σ_{τ≤y} xL_{l,τ,s} and B = Σ_{τ≤y} xD_{l,τ,s}. Expanding (48) gives fl ≤ SST,N A + SST,E + (SDTR,N − SST,N) A B. Substituting V = Σ_{τ≤y} v_{l,τ,s} into (49) (with the per-stage product v_{l,y,s} constrained by (50)-(53)) gives fl ≤ SST,N A − SST,E + (SDTR,N − SST,N) V + (SST,E + SDTR,E) B. The constant term, the coefficient of B, and the replacement of A·B by Σ_{τ≤y}(xL_{τ}·xD_{τ}) are all different from (48); in general A·B ≠ Σ_{τ≤y}(xL_{τ}·xD_{τ}). Thus the DTR constraint actually implemented is not the one derived, and all results involving DTR (cases E and F in Table 5, and the AESO-144 runs) correspond to a model different from the stated formulation. The authors must correct the linearization or explicitly justify an alternative intended form.
  2. [§6.4 (Table 8) and §7.1] No optimality evidence is reported for the AESO-144 case. The only validation is the AESO-6 comparison in Section 6.1, which has 9 long-term scenarios, 7 subproblems, and 7,823 variables per subproblem; AESO-144 has up to 95 subproblems and 5.9×10^7 variables per subproblem. The stopping rule described in Section 6.1 (lower bound stalling after 25 iterations) is not a valid optimality certificate for a mixed-integer, non-convex SDDP, and the text itself concedes in Section 7.1 that optimality is not guaranteed. Table 8 reports only a 'Best Solution' value with no lower bound, upper-bound confidence interval, or number of iterations of the gap trajectory. Consequently, the $30.30b figure is merely the cost of a feasible policy, and the paper's headline scalability claim that the framework 'converges' to a planning solution is unsupported. The authors should report, for each AESO-144 run, the lower-bound trajectory (even if not a rigorous bound), the Monte Carlo upper-bound statistics, the stopping-rule parameters actually used, and ideally a comparison on an intermediate-size case solvable by both SDDP and a monolithic or heuristic benchmark.
  3. [§3 and §7.1] The statement in Section 3 that 'finite convergence is proven [58]' is misleading in context. Reference [58] concerns SDDP convergence for classes of problems with convex value functions and generally continuous recourse; it does not apply to the present model, which contains integer transition variables in every stage and, as the text acknowledges, loses convexity. The single AESO-6 instance cannot establish the reliability of the method for this non-convex problem. The authors should either provide a formal justification (e.g., under which conditions the built cuts retain validity for the mixed-integer model) or, more realistically, soften the claims and provide empirical evidence on several instances with different sizes and integer-variable structures, including a comparison against a strong lower bound (such as a convex relaxation or a Lagrangian bound) for AESO-144.
minor comments (7)
  1. [Eq. (36)] The index in 'V L,min ≤ vL,n,y,o,y,s ≤ V L,max' appears to be a typo; it should probably read vL,n,t,o,y,s.
  2. [Eqs. (3), (11)-(12), (20)] The retrofit output variable is denoted pR in constraints (11)-(12) but pC in the objective (3) and the nodal balance (20); the notation should be unified.
  3. [Eq. (49)] The fourth term is written as Σ_{τ≤y} v_{l,y,s}; the summation index should be τ, i.e., Σ_{τ≤y} v_{l,τ,s}.
  4. [Eqs. (47)-(48)] The sums use τ<y in (47) but τ≤y in (48) and (49); the convention for whether stage-y investments are available in stage-y operations or only from the next stage should be stated clearly and applied consistently.
  5. [Table 7] The 'Scenarios' row compares 9 (monolithic) with 144 (SDDP), but the monolithic model also embeds four short-term conditions per node; the counting of scenarios and sample paths should be explained so the comparison is meaningful.
  6. [References] References [8] and [26] are the same paper, and references [57] and [58] are also the same; duplicate entries should be removed.
  7. [Section 6.2] The sentence 'remains larger than any network in comparable work' is an unsupported superlative and should be qualified with a concrete comparative basis (e.g., number of buses, variables, subproblems, or scenarios in the cited works).

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the derivation chain is self-contained and externally benchmarked.

full rationale

The paper's central chain — model formulation (Sec. 2), MC-SDDP decomposition (Sec. 3), scenario construction (Sec. 4), monolithic benchmark and VoSS (Sec. 5), and SDDP comparison (Sec. 6) — does not reduce to its own inputs. The AESO-6 SDDP result is validated against an independent monolithic Gurobi solve of the same model ('converging to the same optimal solution of $13.56b'), not fitted to it; the stopping rule (lower-bound stall for 25 iterations with 1e-4 tolerance) and upper-bound Monte Carlo check are standard SDDP diagnostics rather than constructed predictions. VoSS is computed by the standard definition EEV - RP with operational costs re-optimized under fixed investment decisions, which is a definitional but not circular comparison. Scenario aggregation borrows from the authors' prior work [76], but that method is independently published and the paper supplies its own out-of-sample mutual-information validation (NMI 0.7520, AMI 0.7497), so the self-citation is not load-bearing. Weather, DTR, load, cost, and technology data come from external sources (CaSPAr, IEEE-738, NREL ATB, AESO). The acknowledged lack of a guaranteed optimality gap for the mixed-integer AESO-144 case (Sec. 7.1: 'optimality is not [guaranteed]') is a correctness or evidence limitation, not circularity: the reported $30.30b best solution is an output of the algorithm, not a fitted input used to produce it. No equation in the paper defines a predicted quantity in terms of the quantity it is said to predict.

Assumptions & free parameters 10 free parameters · 8 assumptions · 0 invented entities

The framework introduces no new physical or mathematical entities; it assembles existing technologies (DTR, SSSC, CCS, storage, VRES) into a multistage stochastic program. The free parameters listed above are data-driven or heuristic choices that shape the numerical results. The axioms are standard approximations in the power systems planning literature, with the SDDP convergence and representative-day axioms being the most paper-specific.

free parameters (10)
  • Number of representative days per node = 4
    Daily profiles of wind, solar, DTR, and load are clustered via multivariate DTW; 4 medoids represent a year. Out-of-sample NMI=0.752 and AMI=0.75 support adequacy, but the count is a modeling choice (Section 4.4.1).
  • Number of VRES candidate zones = 25 (AESO-6), 1000 (AESO-144)
    Chosen from the cost-vs-time trade-off experiment in Section 6.3; 1000 is described as an intermediate choice balancing solution quality and runtime.
  • VRES area reduction factor = 10
    All candidate areas are reduced by a factor of 10 based on Alberta's protected area map, without precise boundaries (Section 4.2).
  • DTR sensor spacing = 3 km
    Assumed spacing for sag monitoring devices; makes DTR capital cost a function of line length (Section 4.5).
  • Value of lost load (VOLL) = $100/MW
    Used for load-shedding penalty, taken from reference [84] (Section 4.5).
  • Curtailment penalty = Slightly lower than lowest fuel cost
    Subjective penalty based on a marginal-value argument [85]; exact value is not reported (Section 4.5).
  • Battery degradation linear fit coefficients = a1=-0.00102, b1=0.00051; a2=-0.000151, b2=0.00015 (per hour)
    Two-line fit to the degradation-vs-SOC curve from [40] with R^2>0.98, used in Eq. (29a)-(30).
  • Long-term scenario probabilities = Equal (1/3 or 1/5 per state)
    Assigned equal probabilities to Baseline/Moderate/Optimistic states to avoid bias in socioeconomic projections (Section 5).
  • Stage length = 5 years (AESO-6); 1/2/4/10 years depending on stage count (AESO-144)
    Defines Y_s in the cost calculation; planning horizon is fixed at 20 years (Sections 4.5, 6.4).
  • Big-M constants for SSSC cut-in = M_f = 2 * max(SDTR, SST)
    Empirical bound for the linearized cut-in constraints (Section 2.4.14, Eq. 60-64).
assumptions (8)
  • domain assumption DC power flow approximation is adequate for transition planning
    Used in nodal balance and power flow constraints (Section 2.4.6, Eq. 20-22); ignores losses, voltage, and VAR limits.
  • domain assumption Short-term uncertainty is stage-wise independent and can be collapsed into a Markov chain with three to five states
    MC-SDDP requires collapsible scenario trees (Section 3, Fig. 3); the paper notes this precludes full-fidelity time-dependent uncertainty.
  • ad hoc to paper A small set of representative days (4) reproduces yearly operational statistics
    Multivariate DTW clustering of 365 days into 4 medoids (Section 4.4.1); out-of-sample NMI=0.75 is good but 4 is a small number.
  • domain assumption Long-term scenarios are equally probable
    Assignment of equal probability to Baseline/Moderate/Optimistic states, justified by avoiding bias in probabilistic projections (Section 5).
  • ad hoc to paper SDDP cut convergence remains valid for the mixed-integer, non-convex problem
    The paper relies on [58] for finite convergence but concedes optimality is not guaranteed (Sections 3 and 7.1); integer variables break convexity.
  • domain assumption CaSPAr HRDPS weather data for 2022-2023 is representative of planning-horizon climate
    Used for wind, solar, and DTR inputs and zone medoids (Sections 4, 4.1, 4.2); future climate is captured only by scaling factors from CMIP6/SSP.
  • domain assumption Solar and wind conversion models with eta=22% and a typical turbine power curve are adequate
    Section 4.2 uses simplified PV and wind models, ignoring temperature effects on panels, to convert weather data to power outputs.
  • ad hoc to paper Battery degradation can be represented by the piecewise-linear fit of Figure 1
    Equations (29a)-(30) use a two-line fit to the degradation-vs-SOC curve from [40]; the constraint sums degradation only over a representative day, which does not obviously enforce the lifetime limit across the full year and stages.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization." pith.science (2026). https://pith.science/paper/HESXFE6K

@misc{pith2026250501331,
  author       = {Pith},
  title        = {Pith review of: Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HESXFE6K}},
  note         = {Machine review of arXiv:2505.01331}
}
read the original abstract

This work introduces the category of Power System Transition Planning optimization problem. It aims to shift power systems to emissions-free networks efficiently. Unlike comparable work, the framework presented here broadly applies to the industry's decision-making process. It defines a field-appropriate functional boundary focused on the economic efficiency of power systems. Namely, while imposing a wide range of planning factors in the decision space, the model maintains the structure and depth of conventional power system planning under uncertainty, which leads to a large-scale multistage stochastic programming formulation that encounters intractability in real-life cases. Thus, the framework simultaneously invokes high-performance computing defaultism. In this comprehensive exposition, we present a guideline model, comparing its scope to existing formulations, supported by a fully detailed example problem, showcasing the analytical value of the solution gained in a small test case. Then, the framework's viability for realistic applications is demonstrated by solving an extensive test case based on a realistic planning construct consistent with Alberta's power system practices for long-term planning studies. The framework resorts to Stochastic Dual Dynamic Programming as a decomposition method to achieve tractability, leveraging High-Performance Computing and parallel computation.

Figures

Figures reproduced from arXiv: 2505.01331 by the authors.

Figure 1
Figure 1. Degradation vs. SOC curve and its linear approxima [PITH_FULL_IMAGE:figures/full_fig_p019_1.png] view at source ↗
Figure 2
Figure 2. The SSSC device characteristics, field-proven by S [PITH_FULL_IMAGE:figures/full_fig_p023_2.png] view at source ↗
Figure 3
Figure 3. (a) Scenario tree representation. (b) Markov chai [PITH_FULL_IMAGE:figures/full_fig_p025_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Heat-map spanning AESO-6 produced from a single pa [PITH_FULL_IMAGE:figures/full_fig_p027_4.png]
Figure 5
Figure 5. Figure 5: Clustered locations with their medioids The vast number of data points heavily increases the computational bur￾den. Thus, the AESO-6 test case uses a reduced set of zones as seen in [PITH_FULL_IMAGE:figures/full_fig_p028_5.png]
Figure 6
Figure 6. Figure 6: AESO-6 system projected on AESO’s planning areas. [PITH_FULL_IMAGE:figures/full_fig_p029_6.png]
Figure 7
Figure 7. Figure 7: An illustration of array preparation for multivar [PITH_FULL_IMAGE:figures/full_fig_p030_7.png]
Figure 8
Figure 8. Figure 8: An illustration of the out-of-sample analysis tes [PITH_FULL_IMAGE:figures/full_fig_p031_8.png]
Figure 9
Figure 9. Figure 9: Nominal capacity of allocated resources in the firs [PITH_FULL_IMAGE:figures/full_fig_p036_9.png]
Figure 10
Figure 10. Figure 10: Nominal capacity of allocated resources in the se [PITH_FULL_IMAGE:figures/full_fig_p036_10.png]
Figure 11
Figure 11. Figure 11: Volume of total curtailment of RE and load for the w [PITH_FULL_IMAGE:figures/full_fig_p037_11.png]
Figure 12
Figure 12. Figure 12: In-sample and out-of-sample scenario-wise ex-p [PITH_FULL_IMAGE:figures/full_fig_p039_12.png]
Figure 13
Figure 13. Figure 13: AESO-144 test case 42 [PITH_FULL_IMAGE:figures/full_fig_p042_13.png]
Figure 14
Figure 14. Figure 14: Solution quality based on number of zones (log sca [PITH_FULL_IMAGE:figures/full_fig_p043_14.png]
Figure 15
Figure 15. Figure 15: The solution time rises considerably after the 1000 [PITH_FULL_IMAGE:figures/full_fig_p044_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

112 extracted references · 54 canonical work pages

  1. [58]

    Löhndorf, D

    N. Löhndorf, D. Wozabal, S. Minner, Optimizing trading decisions for hydro storage systems using approximate dual dynamic progr amming, Oper. Res. 61 (2013) 810–823

    2013

  2. [1]

    rep., IEA, Accessed Oct

    IEA, Net zero by 2050, Tech. rep., IEA, Accessed Oct. 30, 2 024 (2021)

    2021

  3. [2]

    Moreira, D

    A. Moreira, D. Pozo, A. Street, E. Sauma, Reliable Renewa ble Gener- ation and Transmission Expansion Planning: Co-Optimizing System’s Resources for Meeting Renewable Targets, IEEE Trans. Power Syst. 32 (4) (2017) 3246–3257. doi:10.1109/TPWRS.2016.2631450

    arXiv 2017

  4. [3]

    Baringo, A

    L. Baringo, A. Baringo, A Stochastic Adaptive Robust Opt i- mization Approach for the Generation and Transmission Expa n- sion Planning, IEEE Trans. Power Syst. 33 (1) (2018) 792–802 . doi:10.1109/TPWRS.2017.2713486

    work page arXiv 2018

  5. [4]

    S. Yin, J. Wang, Generation and Transmission Expansion P lanning Towards a 100% Renewable Future, IEEE Trans. Power Syst. 37 ( 4) (2022) 3274–3285. doi:10.1109/TPWRS.2020.3033487

    work page arXiv 2022

  6. [5]

    A. J. Conejo, M. Carrión, J. M. Morales, Decision Making U nder Un- certainty in Electricity Markets, Springer, New York, 2010 , chapters 1 and 4 highlight practical utility of stochastic methods in regulatory environments

    2010

  7. [6]

    NREL, Annual technology baseline, https://atb.nrel.gov/, Ac- cessed on Oct 30, 2024 (2023)

    2023

  8. [7]

    doi:10.59327/IPCC/AR6-9789291691647.001

    IPCC, Summary for policymakers, Synthesis report, Inte rgovernmen- tal Panel on Climate Change, Geneva, Switzerland, in Press ( 2023). doi:10.59327/IPCC/AR6-9789291691647.001

    doi:10.59327/ipcc/ar6-9789291691647.001 2023

Show all 112 references
  1. [9]

    Z. Wei, L. Yang, S. Chen, Z. Ma, H. Zang, Y. Fei, A multi- stage planning model for transitioning to low-carbon integ rated elec- tric power and natural gas systems, Energy 254 (2022) 124361 . doi:10.1016/j.energy.2022.124361

    2022

  2. [10]

    C. Li, N. Wang, X. Shen, Y. Zhang, Z. Yang, X. Tong, F. Maré chal, L. Wang, Y. Yang, Energy planning of Beijing towards low-car bon, 49 clean and efficient development in 2035, CSEE J. Power Energy S yst (2022). doi:10.17775/CSEEJPES.2021.03620

    2022

  3. [11]

    N. Zhao, Y. Tao, F. You, Renewable power systems transit ion plan- ning using a bottom-up multi-scale optimization framework , in: Y. Ya- mashita, M. Kano (Eds.), 14th International Symposium on Pr ocess Systems Engineering, Vol. 49 of Computer Aided Chemical Eng ineer- ing, E...

    2022 doi

  4. [12]

    Flores-Quiroz, K

    A. Flores-Quiroz, K. Strunz, A distributed computing f ramework for multi-stage stochastic planning of renewable power system s with en- ergy storage as flexibility option, Appl. Energy 291 (2021) 1 16736. doi:10.1016/j.apenergy.2021.116736

    2021

  5. [14]

    L. C. da Costa, F. S. Thome, J. D. Garcia, M. V. F. Pereira, Reliability- Constrained Power System Expansion Planning: A Stochastic Risk- A verse Optimization Approach, IEEE Trans. Power Syst. 36 (1) (2021) 97–106. doi:10.1109/TPWRS.2020.3007974

    2021

  6. [15]

    W. Shen, J. Qiu, K. Meng, X. Chen, Z. Y. Dong, Low-Carbon Electricity Network Transition Considering Retirement of Aging Coal Generators, IEEE Trans. Power Syst. 35 (6) (2020) 4193–4205 . doi:10.1109/TPWRS.2020.2995753

    2020

  7. [16]

    J. M. Ramirez, A. Hernandez, J. Marmolejo, A robust mult istage ap- proach to solve the generation and transmission expansion p lanning problem embedding renewable sources, Electr. Power Syst. R es. 186 (2020) 106396. doi:10.1016/j.epsr.2020.106396

    2020

  8. [17]

    Asgharian, M

    V. Asgharian, M. Abdelaziz, Voltage Stability Constra ined Low- Carbon Generation & Transmission Expansion Planning, in: E EE Can. 50 Conf. Electr. Comput. Eng., IEEE, London, ON, Canada, 2020, pp. 1–

    2020

  9. [18]

    doi:10.1109/CCECE47787.2020.9255798

    2020

  10. [19]

    S. L. Gbadamosi, N. I. Nwulu, A multi-period composite g eneration and transmission expansion planning model incorporating r enewable energy sources and demand response, Sustainable Energy Tec hnol. As- sess. 39 (2020) 100726. doi:10.1016/j.seta.2020.100726

    2020

  11. [20]

    Alanazi, M

    M. Alanazi, M. Mahoor, A. Khodaei, Co-optimization gen eration and transmission planning for maximizing large-scale sola r PV in- tegration, Int. J. Electr. Power Energy Syst. 118 (2020) 105 723. doi:10.1016/j.ijepes.2019.105723

    2020

  12. [21]

    Z. Zhou, C. He, T. Liu, X. Dong, K. Zhang, D. Dang, B. Chen, Reliability-Constrained AC Power Flow-Based Co- Optimization Planning of Generation and Transmission Sys- tems With Uncertainties, IEEE Access 8 (2020) 194218–19422 7. doi:10.1109/ACCESS.2020.3032560

    2020

  13. [22]

    Asadi Majd, E

    A. Asadi Majd, E. Farjah, M. Rastegar, Composite genera tion and transmission expansion planning toward high renewable ene rgy pene- tration in Iran power grid, IET Renewable Power Gener. 14 (9) (2020) 1520–1528. doi:10.1049/iet-rpg.2019.0673

    2020

  14. [23]

    Zeinaddini-Meymand, M

    M. Zeinaddini-Meymand, M. Rashidinejad, A. Abdollahi , M. Pourakbari-Kasmaei, M. Lehtonen, A Demand-Side Managem ent- Based Model for G&TEP Problem Considering FSC Al- location, IEEE Systems Journal 13 (3) (2019) 3242–3253. doi:10.1109/JSYST.2019.2916166

    2019

  15. [24]

    Zhang, H

    H. Zhang, H. Cheng, L. Liu, S. Zhang, Q. Zhou, L. Jiang, Co ordina- tion of generation, transmission and reactive power source s expansion planning with high penetration of wind power, Int. J. Electr . Power Energy Syst. 108 (2019) 191–203. doi:10.1016/j.ijepes.20 19.01.006

    2019 doi

  16. [25]

    Parzen, H

    M. Parzen, H. Abdel-Khalek, E. Fedotova, M. Mahmood, M. M. Frysz- tacki, J. Hampp, L. Franken, L. Schumm, F. Neumann, D. Poli, A. Kiprakis, D. Fioriti, Pypsa-earth. a new global open ener gy system optimization model demonstrated in africa, Appl. Energy 34 1 (2023) 121096. doi...

    2023 doi

  17. [26]

    C. L. Lara, J. D. Siirola, I. E. Grossmann, Electric powe r infrastructure planning under uncertainty: Stochastic dual dynamic integ er program- ming (SDDiP) and parallelization scheme, Optim. Eng. 21 (4) (2020) 1243–1281. doi:10.1007/s11081-019-09471-0. URL https://doi.org/10...

    2020 doi

  18. [27]

    S. Hou, Y. Fan, B.-W. Yi, Long-term renewable electrici ty planning using a multistage stochastic optimization with nested dec omposition, Comput. Ind. Eng. 161 (2021) 107636. doi:10.1016/j.cie.20 21.107636

    2021 doi

  19. [28]

    M. V. F. Pereira, L. M. V. G. Pinto, Multi-stage stochast ic optimization applied to energy planning, Math. Program. 52 (1-3) (1991) 3 59–375. doi:10.1007/BF01582895

    1991 doi

  20. [29]

    J. Hole, A. Philpott, O. Dowson, Capacity planning of re newable energy systems using stochastic dual dynamic programming, Eu- ropean Journal of Operational Research 322 (2) (2025) 573–5 88. doi:https://doi.org/10.1016/j.ejor.2024.12.031

    2025 doi

  21. [30]

    Denholm, T

    P. Denholm, T. Mai, R. W. Kenyon, B. Kroposki, M. O’Malle y, Inertia and the power grid: A guide without the spin, Tech. Rep. NREL/ TP- 6A20-73856, National Renewable Energy Laboratory (NREL), Golden, CO, USA, Accessed Oct. 30, 2024 (2020). doi:10.2172/165982 0. URL https://ww...

    2020

  22. [31]

    A. M. Bukar, M. Asif, Technology readiness level as- sessment of carbon capture and storage technologies, Re- newable and Sustainable Energy Rev. 200 (2024) 114578. doi:https://doi.org/10.1016/j.rser.2024.114578

    2024

  23. [32]

    30, 2024 (2021)

    Ministry of Energy, Alberta hydrogen roadmap, accesse d Oct. 30, 2024 (2021). URL https://open.alberta.ca/

    2021

  24. [33]

    M. Rawa, Z. M. AlKubaisy, S. Alghamdi, M. M. Refaat, Z. M. Ali, S. H. A. Aleem, A techno-economic planning model for int e- grated generation and transmission expansion in modern pow er sys- tems with renewables and energy storage using hybrid Runge K utta- gradient-based opti...

  25. [34]

    S. M. Jordaan, C. Combs, E. Guenther, Life cycle assess- ment of electricity generation: A systematic review of spa- tiotemporal methods, Adv. Appl. Energy 3 (2021) 100058. doi:https://doi.org/10.1016/j.adapen.2021.100058

    2021

  26. [35]

    P. C. Nikolaos, F. Marios, K. Dimitris, A review of pumpe d hydro storage systems, Energies 16 (11) (2023). doi:10.3390/en1 6114516

    2023 doi

  27. [36]

    National Renewable Energy Laboratory, Ramping up the r amping ca- pability: India’s power system transition, Tech. Rep. NREL /TP-6A20- 77639, National Renewable Energy Laboratory (NREL), Golde n, CO, USA, Accessed Oct. 30, 2024 (2023). URL https://research-hub.nrel.gov/

    2023

  28. [37]

    R. S. Jorge, T. R. Hawkins, E. G. Hertwich, Life cycle ass essment of electricity transmission and distribution—part 1: powe r lines and cables, Int. J. Life Cycle Assess. 17 (2012) 9–15

    2012

  29. [38]

    Stott, J

    B. Stott, J. Jardim, O. Alsac, Dc power flow revis- ited, IEEE Trans. Power Syst. 24 (3) (2009) 1290–1300. doi:10.1109/TPWRS.2009.2021235

    2009

  30. [39]

    Abou-Jaoude, C

    A. Abou-Jaoude, C. S. Lohse, L. M. Larsen, N. Guaita, I. T rivedi, F. C. Joseck, E. Hoffman, N. Stauff, K. Shirvan, A. Stein, Meta-anal ysis of advanced nuclear reactor cost estimations, Tech. rep., U.S . Department of Energy Office of Scientific and Technical Information, Acce sse...

    2024

  31. [40]

    Y. Wang, Z. Zhou, A. Botterud, K. Zhang, Q. Ding, Stochas tic coordi- nated operation of wind and battery energy storage system co nsidering battery degradation, J. Mod. Power Syst. Clean Energy 4 (4) ( 2016) 581–592. doi:10.1007/s40565-016-0238-z

    2016 doi

  32. [41]

    J. D. Glover, T. O. Sarma, M. S. Billinton, Power System A nalysis and Design, 6th Edition, Cengage Learning, 2012

    2012

  33. [43]

    J. L. Higle, S. W. Wallace, Stochastic programming: Opt imization when uncertainty matters, INFORMS Trans. on Educ. 5 (2) (200 5) 9–19. doi:10.1287/educ.1053.0016. 53

  34. [44]

    Vanderbeck, A nested decomposition approach to a thr ee-stage, two- dimensional cutting-stock problem, Manage

    F. Vanderbeck, A nested decomposition approach to a thr ee-stage, two- dimensional cutting-stock problem, Manage. Sci. 47 (6) (20 01) 864–879. doi:10.1287/mnsc.47.6.864.9809

    doi

  35. [45]

    URL https://www.smartwires.com/smartvalve/

    SmartWires, Smartvalve: digital power flow control, Ac cessed Oct 30, 2024 (2023). URL https://www.smartwires.com/smartvalve/

    2023

  36. [46]

    Toledo, E

    F. Toledo, E. Sauma, S. Jerardino, Energy Cost Distorti on Due to Ignoring Natural Gas Network Limitations in the Scheduling of Hy- drothermal Power Systems, IEEE Trans. Power Syst. 31 (5) (20 16) 3785–3793. doi:10.1109/TPWRS.2015.2502184

  37. [47]

    Helseth, M

    A. Helseth, M. Fodstad, B. Mo, Optimal Medium-Term Hy- dropower Scheduling Considering Energy and Reserve Capaci ty Markets, IEEE Trans. Sustainable Energy 7 (3) (2016) 934–94 2. doi:10.1109/TSTE.2015.2509447

    2016

  38. [48]

    R. P. Liu, A. Shapiro, Risk neutral reformulation appro ach to risk averse stochastic programming, Eur. J. Oper. Res. 286 (1) (2 020) 21–

  39. [49]

    Alvarez, S

    M. Alvarez, S. K. Rönnberg, J. Bermúdez, J. Zhong, M. H. J . Bollen, A Generic Storage Model Based on a Future Cost Piecewise-Lin ear Approximation, IEEE Trans. Smart Grid 10 (1) (2019) 878–888 . doi:10.1109/TSG.2017.2754288

    2019

  40. [50]

    J. L. Morillo, L. Zephyr, J. F. Pérez, A. Cadena, C. L. And erson, Distribution-free chance-constrained load balance model for the oper- ation planning of hydrothermal power systems coupled with m ultiple renewable energy sources, Int. J. Electr. Power Energy Syst . 142 (2022)...

    2022

  41. [51]

    doi:10.1016/j.ejor.2020.01.060

    2020 doi

  42. [52]

    A. B. Philpott, V. L. de Matos, L. Kapelevich, Distribut ionally ro- bust sddp, Computational Manage. Sci. 15 (3–4) (2018) 431–4 54. doi:10.1007/s10287-018-0314-0

    2018 doi

  43. [53]

    Guevara, F

    E. Guevara, F. Babonneau, T. H. de Mello, Uncertainty dy nam- ics in energy planning models: An autoregressive and markov chain modeling approach, Comput. Ind. Eng. 191 (2024) 11008 4. doi:https://doi.org/10.1016/j.cie.2024.110084

    2024

  44. [54]

    Löhndorf, A

    N. Löhndorf, A. Shapiro, Modeling time-dependent rand omness in stochastic dual dynamic programming, Eur. J. Oper. Res. 273 (2) (2019) 650–661. doi:https://doi.org/10.1016/j.ejor.20 18.08.001

    2019 doi

  45. [56]

    Infanger, D

    G. Infanger, D. P. Morton, Cut sharing for multistage st ochastic linear programs with interstage dependency, Math. Program. 75 (2) (1996) 241–256. doi:10.1007/BF02592154

    1996 doi

  46. [57]

    I. P. on Climate Change (IPCC), Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Ass essment Report of the Intergovernmental Panel on Climate Change, Ca mbridge University Press, 2023

    2021

  47. [59]

    Füllner, S

    C. Füllner, S. Rebennack, Stochastic dual dynamic prog ramming and its variants – a review, Preprint, Karlsruhe Institute of Te chnology (2023). 55

    2023

  48. [60]

    Papavasiliou, Y

    A. Papavasiliou, Y. Mou, L. Cambier, D. Scieur, Applica tion of stochas- tic dual dynamic programming to the real-time dispatch of st orage under renewable supply uncertainty, IEEE Trans. Sustainab le Energy 9 (2) (2018) 547–558. doi:10.1109/TSTE.2017.2748463

    2018

  49. [61]

    Papavasiliou, Y

    A. Papavasiliou, Y. Mou, L. Cambier, D. Scieur, Applica tion of stochas- tic dual dynamic programming to the real-time dispatch of st orage under renewable supply uncertainty, IEEE Trans. Sustainab le Energy 9 (2) (2018) 547–558

    2018

  50. [62]

    IEEE, Ieee standard for calculating the current-tempe rature relation- ship of bare overhead conductors, IEEE Std 738-2012 (Revisi on of IEEE Std 738-2006 - Incorporates IEEE Std 738-2012 Cor 1-2013) (2 013) 1– 72doi:10.1109/IEEESTD.2013.6692858

    2012

  51. [63]

    J. Mai, K. C. Kornelsen, B. A. Tolson, V. Fortin, N. Gasse t, D. Bouhemhem, D. Schäfer, M. Leahy, F. Anctil, P. Coulibaly, The canadian surface prediction archive (caspar), Bull. Am. Me teorol. Soc. 101 (3) (2020) E341 – E356. doi:10.1175/BAMS-D-19-0143.1

    2020 doi

  52. [64]

    URL https://developers.google.com/maps/documentation

    Google maps platform documentation, Accessed Oct 30, 2 024 (2023). URL https://developers.google.com/maps/documentation

    2023

  53. [65]

    El Hammoumi, S

    A. El Hammoumi, S. Chtita, S. Motahhir, A. El Ghzizal, So lar pv en- ergy: From material to use, and the most commonly used techni ques to maximize the power output of pv systems: A focus on solar tr ack- ers and floating solar panels, Energy Reports 8 (2022) 11992– 12010. doi:...

    2022 doi

  54. [66]

    30, 2024 (2024)

    AESO, Bulk transmission line technical requirements s ection 503.22, https://www.aeso.ca/rules-standards-and-tariff/, Ac- cessed Oct. 30, 2024 (2024)

    2024

  55. [67]

    30, 2024 (2024)

    PV Education, Photovoltaic education network, Access ed Oct. 30, 2024 (2024). URL https://www.pveducation.org/

    2024

  56. [68]

    Burton, N

    T. Burton, N. Jenkins, D. Sharpe, E. Bossanyi, Wind Ener gy Hand- book, 2nd Edition, John Wiley & Sons, Chichester, West Susse x, UK, 2011

    2011

  57. [69]

    S. N. Laboratories, Pv performance modeling collabora tive (pvpmc), Accessed Oct. 30, 2024 (2013). URL https://pvpmc.sandia.gov/

    2013

  58. [70]

    Wind, Wind Turbine Platform 1S MW, Accessed Oct

    G. Wind, Wind Turbine Platform 1S MW, Accessed Oct. 30, 2 024 (2023). URL https://www.goldwindamericas.com/

    2023

  59. [71]

    30, 2024 (2024)

    AESO, Transmission utilization map, https://www.aeso.ca/grid/, Accessed Oct. 30, 2024 (2024)

    2024

  60. [72]

    D. J. Berndt, J. Clifford, Using dynamic time warping to fi nd patterns in time series, in: KDD Workshop, 1994, pp. 359–370. 56

    1994

  61. [73]

    30, 2024 (2 024)

    Canadian Parks and Wilderness Society (CPA WS) Norther n Alberta, Government of alberta’s renewable energy policy fails to pr otect nature, hinders economic diversification, Accessed Oct. 30, 2024 (2 024). URL https://cpawsnab.org/

    2024

  62. [74]

    30, 2024 (2022)

    AESO, Hourly load by area and region, https://www.aeso.ca/ market/, Accessed Oct. 30, 2024 (2022)

    2022

  63. [75]

    30, 2024 (2024)

    Alberta Electric System Operator, Transmission costs , https: //www.aeso.ca/grid/grid-planning/transmission-costs/, Ac- cessed Oct. 30, 2024 (2024)

    2024

  64. [76]

    Al-Shafei, Aeso test cases, https://github.com/SolidAhmad/ AESO-test-cases.git, Accessed Nov

    A. Al-Shafei, Aeso test cases, https://github.com/SolidAhmad/ AESO-test-cases.git, Accessed Nov. 2, 2024 (2024)

    2024

  65. [77]

    Romano, N

    S. Romano, N. X. Vinh, J. Bailey, K. Verspoor, Adjusting for chance clustering comparison measures, J. Mach. Learn. Res. 17 (1) (2016) 4635–4666

    2016

  66. [78]

    Shokoohi-Yekta, J

    M. Shokoohi-Yekta, J. Wang, E. J. Keogh, On the non-triv ial gener- alization of dynamic time warping to the multi-dimensional case, in: SDM, 2015, pp. 289–297

    2015

  67. [79]

    Sarajpoor, L

    N. Sarajpoor, L. Rakai, N. Amjady, H. Zareipour, Genera lizing time ag- gregation to out-of-sample data using minimum bipartite gr aph match- ing for power systems studies, IEEE Trans. Power Syst. 39 (3) (2024) 5352–5365. doi:10.1109/TPWRS.2023.3327969

    2024

  68. [80]

    3 0, 2024 (2024)

    Government of Canada, Cmip6 scenarios, Accessed Oct. 3 0, 2024 (2024). URL https://climate-scenarios.canada.ca/?page= cmip6-scenarios

    2024

  69. [81]

    N. X. Vinh, J. Epps, J. Bailey, Information theoretic me asures for clusterings comparison: is a correction for chance nece ssary?, in: Information theoretic measures for clusterings compar ison: is a correction for chance necessary?, ICML ’09, Association f or Computing Machi...

    2009

  70. [82]

    group/en/unpacking-nuclear/latest-ipcc-climate-repo rt, Ac- cessed Oct

    Orano Group, Latest IPCC climate report, https://www.orano. group/en/unpacking-nuclear/latest-ipcc-climate-repo rt, Ac- cessed Oct. 30, 2024 (2024). 57

    2024

  71. [83]

    Karimi, P

    H. Karimi, P. Musilek, A. Knight, Dynamic thermal ratin g of trans- mission lines: A review, Renewable Sustainable Energy Rev. (2018) 600–612doi:https://doi.org/10.1016/j.rser.2018.04.001

    2018 doi

  72. [84]

    30, 2024 (2023)

    Laki Power, Lkx-multi, Accessed Oct. 30, 2024 (2023). URL https://www.lakipower.com/lkxmulti

    2023

  73. [85]

    X. Rui, M. Sahraei-Ardakani, T. R. Nudell, Linear model ling of series facts devices in power system operation models, IET Gener. T ransm. Distrib. 16 (6) (2022) 1047–1063. doi:10.1049/gtd2.12348

    2022 doi

  74. [86]

    W. E. Hart, J.-P. Watson, D. L. Woodruff, Pyomo: modeling and solving mathematical programs in python, Math. Program. Co mput. 3 (3) (2011) 219–260

    2011

  75. [87]

    pdf, Accessed Oct

    AESO, Summary of pricing and mitigation approaches in other jurisdictions, https://www.aeso.ca/assets/Uploads/ Attachment-1-Pricing-Approaches-in-Other-Jurisdicti ons-FINAL. pdf, Accessed Oct. 30, 2024 (2019)

    2019

  76. [88]

    Klinge Jacobsen, S

    H. Klinge Jacobsen, S. T. Schröder, Curtailment of rene wable generation: Economic optimality and incentives, Energy Po l- icy 49 (2012) 663–675, special Section: Fuel Poverty Comes of Age: Commemorating 21 Years of Research and Policy. doi:https://doi.org/10.1016/j.enpol.2012.07.004

    2012 doi

  77. [89]

    30, 2024 (2023)

    Gurobi Optimization, LLC, Gurobi optimizer reference manual, Ac- cessed Oct. 30, 2024 (2023). URL https://www.gurobi.com

    2023

  78. [90]

    S. M. Millett, Should probabilities be used with scenar ios, J. Future Stud. 13 (2009) 61–68

    2009

  79. [91]

    Meinshausen, Z

    M. Meinshausen, Z. R. J. Nicholls, J. Lewis, M. J. Gidden , E. Vogel, M. B. Freund, U. B. et al., The shared socio-economic pathway (ssp) greenhouse gas concentrations and their extensions to 2500 , Geosci. Model Dev. (2020). 58

    2020

  80. [92]

    Homem-de Mello, V

    T. Homem-de Mello, V. de Matos, E. Finardi, Sampling str ategies and stopping criteria for stochastic dual dynamic programming : A case study in long-term hydrothermal scheduling, Energy Syst. 2 (2011) 1–

    2011

  81. [93]

    Dowson, L

    O. Dowson, L. Kapelevich, SDDP.jl: a Julia package for s tochastic dual dynamic programming, INFORMS J Comput. 33 (2021) 27–33 . doi:https://doi.org/10.1287/ijoc.2020.0987

    2021

  82. [94]

    Shapiro, Analysis of stochastic dual dynamic progra mming method, Tech

    A. Shapiro, Analysis of stochastic dual dynamic progra mming method, Tech. Rep. 2509, Optimization Online (2009). URL https://optimization-online.org/wp-content/uploads/ 2009/12/2509.pdf

    2009

  83. [95]

    com/operations/power/canyon-creek-pumped-storage/, Accessed Oct

    TC Energy, Canyon creek pumped storage, https://www.tcenergy. com/operations/power/canyon-creek-pumped-storage/, Accessed Oct. 30, 2024 (2024)

    2024

  84. [96]

    doi:10.1007/s12667-011-0024-y

    doi

  85. [97]

    23, 2024 (2024)

    Arcus Power, Nrg stream, Accessed: Oct. 23, 2024 (2024) . URL https://www.arcuspower.com/nrg-stream

    2024

  86. [98]

    rep., Alberta Electric System Operator, Accessed, Oct

    AESO, Transmission utilization assessment 2023, Tech . rep., Alberta Electric System Operator, Accessed, Oct. 30, 2024 (2023). URL https://www.aeso.ca/assets/Uploads/ grid/transmission-utilization-map/ Tx-Utilization-Assessment-2023.pdf

    2023

  87. [99]

    GmbH, Digsilent powerfactory, https://www.digsilent.de, ac- cessed: 2025-04-16 (2024)

    D. GmbH, Digsilent powerfactory, https://www.digsilent.de, ac- cessed: 2025-04-16 (2024)

    2024

  88. [100]

    Fragkos, J.-F

    I. Fragkos, J.-F. Cordeau, R. Jans, Decomposition meth ods for large- scale network expansion problems, Transp. Res. Part B Metho dol. 144 (2021) 60–80. doi:10.1016/j.trb.2020.12.002

    2021 doi

  89. [101]

    energyexemplar.com/plexos, accessed: 2025-04-16 (2024)

    Energy Exemplar, Plexos integrated energy model, https://www. energyexemplar.com/plexos, accessed: 2025-04-16 (2024)

    2024

  90. [102]

    Brown, J

    T. Brown, J. Hörsch, F. Neumann, Pypsa - python for power system analysis, https://pypsa.org, open-source tool maintained by KIT. GitHub: https://github.com/PyPSA (2024). 59

    2024

  91. [103]

    ENTSO-E, Ten-year network development plan (tyndp), https:// tyndp.entsoe.eu, used for scenario-based planning by European TSOs (2022)

    2022

  92. [104]

    Corporation, Powerworld simulator, https://www.powerworld

    P. Corporation, Powerworld simulator, https://www.powerworld. com, accessed: 2025-04-16 (2024)

    2024

  93. [105]

    National Renewable Energy Laboratory (NREL), Region al energy de- ployment system (reeds) model, https://www.nrel.gov/analysis/ reeds/, dOE-supported long-term capacity expansion model (2023)

    2023

  94. [106]

    Kammen, S

    D. Kammen, S. D. Team, Switch power system planning mod el, https://switch-model.org, open-source planning tool developed at UC Berkeley (2024)

    2024

  95. [107]

    Department of Energy, Quadrennial technology re view (qtr), https://www.energy.gov/quadrennial-technology-review-0, ac- cessed: 2025-04-09 (2015)

    U.S. Department of Energy, Quadrennial technology re view (qtr), https://www.energy.gov/quadrennial-technology-review-0, ac- cessed: 2025-04-09 (2015)

    2015

  96. [108]

    Vernova, Planos - power system planning tools by ge v ernova, https://www.gevernova.com/digital, accessed: 2025-04-16

    G. Vernova, Planos - power system planning tools by ge v ernova, https://www.gevernova.com/digital, accessed: 2025-04-16. Lim- ited public documentation (2023)

    2023

  97. [109]

    S. N. Laboratories, Quest - quantifying energy storag e tool, https://energy.sandia.gov/energy/ssrei/renewable-energy/ grid-integration/quest/, suite of tools for grid planning and energy storage modeling (2022)

    2022

  98. [110]

    R. D. Zimmerman, C. E. Murillo-Sánchez, R. J. Thomas, M atpower: Steady-state power system analysis toolbox, https://matpower.org, mATLAB-based OPF and power flow tool. GitHub: https://github. com/MATPOWER/matpower (2024)

    2024

  99. [111]

    nationalgrideso.com/future-energy/future-energy-scenarios, accessed: 2025-04-09 (2023)

    National Grid ESO, Future energy scenarios, https://www. nationalgrideso.com/future-energy/future-energy-scenarios, accessed: 2025-04-09 (2023)

    2023

  100. [112]

    ENTSO-E, Ten-year network development plan (tyndp), https:// tyndp.entsoe.eu/, accessed: 2025-04-09 (2022)

    2022

  101. [113]

    Alberta Electric System Operator, Long-term transmi s- sion plan, https://www.aeso.ca/grid/grid-planning/ long-term-transmission-plan/, accessed: 2025-04-09 (2023). 60

    2023

  102. [114]

    Electric Reliability Council of Texas, Long-term sys tem assessment, https://www.ercot.com/gridinfo/planning/ltsa, accessed: 2025- 04-09 (2023)

    2023

  103. [116]

    Appendix A

    International Energy Agency, Developing capacity fo r long-term energy policy planning: A roadmap, https://www.iea.org/reports/ developing-capacity-for-long-term-energy-policy-pla nning-a-roadmap, accessed: 2025-04-09 (2020). Appendix A. Additional Tables Table A.9: Pan-region...

    2020

  104. [6479]

    doi:10.1016/j.egyr.2022.04.066. 52

    2022 doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.