Scenario reduction, network aggregation, and DC linearisation: which simplifications matter most in operations and planning optimisation?

Shayesteh, Ebrahim; Hobbs, Benjamin F.; Amelin, Mikael

doi:10.1049/iet-gtd.2015.1404

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…Other potential enhancements might result in significant improvements of plans; examples include improved network reductions or explicit N − 1 (or N-K) contingency constraints [63]. Although some papers have shown how such network model enhancements can change operations or investment plans as well as cost estimates, and compared those changes to other model enhancements [64], their VOME has not been calculated or compared with that of other enhancements. Finally, VOME can provide useful insights not only for users of transmission planning models but also for other types of planning optimisation problems in power and other infrastructure systems.…”

Section: Resultsmentioning

confidence: 99%

Value of model enhancements: quantifying the benefit of improved transmission planning models

Hobbs

2019

IET gener. transm. distrib.

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A framework to quantify the value of model enhancements (VOME) in transmission planning models is proposed and applied to a case study of the large-scale, long-term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability-weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long-run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. But less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the value of model enhancements in any planning context, such as integrated resource planning. Nomenclature () Expected present worth of system cost of making decision , based on the model with all enhancements () Binary parameter: if () = 1, then enhancement i is included in the model with setting ; if zero, then the enhancement is excluded. For instance, if there are three candidate enhancements, then 1 (*) = 1, 2 (*) = 0, 3 (*) = 1 indicates a model with only Enhancements 1 and 3 implemented. Set of enhancements, indexed by i, j = 1…n Optimal first stage transmission investments ("decision") from a model with enhancements setting specified by. (E.g., ω * , where 1 (*) = 1, 2 (*) = 0, 3 (*) = 1 , indicates investments from a model with only Enhancements 1 and 3 implemented.) Decision of no transmission investments in stage 1 Optimal decision from the model with all enhancements (i.e., () = 1 for all i). Model enhancement setting, describing what enhancements are included in the model formulation. Ω The set of all possible permutations of enhancements other than i

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Section: Resultsmentioning

confidence: 99%

Value of model enhancements: quantifying the benefit of improved transmission planning models

Hobbs

2019

IET gener. transm. distrib.

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“…As we mention at the beginning of this section, this work focuses on the reduction of temporal information. However, there are other types of reduction techniques to deal with the computational burden in long-term planning models, such as transmission network aggregation [18], [19]; exogenous estimation of curtailment reduction, curtailment itself, and capacity value [9], [20]. These methods are compatible with the models proposed in this paper and could be combined to further improve the reduction of the computational burden.…”

Section: B Literature Reviewmentioning

confidence: 86%

Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis

Tejada-Arango

Domeshek

Wogrin

et al. 2018

IEEE Trans. Power Syst.

109

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This paper analyzes different models for evaluating investments in energy storage systems (ESS) in power systems with high penetration of renewable energy sources. First of all, two methodologies proposed in the literature are extended to consider ESS investment: a unit commitment model that uses the "system states" (SS) method of representing time; and another one that uses a "representative periods" (RP) method. Besides, this paper proposes two new models that improve the previous ones without a significant increase of computation time. The enhanced models are the "system states reduced frequency matrix" model which addresses short-term energy storage more approximately than the SS method to reduce the number of constraints in the problem, and the "representative periods with transition matrix and cluster indices" (RP-TM&CI) model which guarantees some continuity between representative periods, e.g., days, and introduces long-term storage into a model originally designed only for the short term. All these models are compared using an hourly unit commitment model as benchmark. While both system state models provide an excellent representation of long-term storage, their representation of short-term storage is frequently unrealistic. The RP-TM&CI model, on the other hand, succeeds in approximating both shortand long-term storage, which leads to almost 10 times lower error in storage investment results in comparison to the other models analyzed.Index Terms-Energy storage systems, power system planning, power system modeling, system states, representative days.

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“…For the impacts on electric power supply, we assume the U.S. federal standard temperature threshold of 32 °C, 2,8,9 above which generators requiring water for cooling at those locations are not available on that day. We then solve for the hourly dispatch of generators to meet demand in the Power System Model (PSM), a model of the WECC system that represents the high-voltage transmission network (consisting of 312 locations and 654 transmission lines) developed by Hobbs and colleagues at Johns Hopkins University 14,15,16,17 and intertemporal operational constraints of generators, as well as a given scenario of hourly availability of all generators over the year based on the WBM input.…”

Section: Methodsmentioning

confidence: 99%

Thresholds, Timing, and Teleconnections: A coupled water-power-economic analysis

Fisher‐Vanden

Webster

Lammers

et al. 2021

Preprint

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Previous studies have failed to capture critical dynamics stemming from overlapping hydrological, power, and economic networks when assessing the economic impacts of future water stress on the power system. In this paper, we employ a coupled water-power-economy model to capture these important interactions. We identified three channels that are key to assessing economic impacts – thresholds, teleconnections, and timing. We find that not all reductions in reserve electricity generation capacity result in impacts (thresholds), and that when they occur, intermittent interruptions in electricity supply occurring at critical times of the day, week, and year account for most of the economic impacts (timing). Lastly, we find that impacts may be in different locations from the original water stress (teleconnections). This work underscores the importance of a coupled modeling approach that captures interactions across systems while retaining spatial, temporal, and sectoral detail.

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Scenario reduction, network aggregation, and DC linearisation: which simplifications matter most in operations and planning optimisation?

Cited by 14 publications

References 19 publications

Value of model enhancements: quantifying the benefit of improved transmission planning models

Value of model enhancements: quantifying the benefit of improved transmission planning models

Enhanced Representative Days and System States Modeling for Energy Storage Investment Analysis

Thresholds, Timing, and Teleconnections: A coupled water-power-economic analysis

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