Implications of Model Structure and Detail for Utility Planning: Scenario Case Studies Using the Resource Planning Model

Mai, Trieu; Barrows, Clayton; Lopez, Anthony; Hale, Elaine; Dyson, Mark; Newman, Alexandra M.

doi:10.2172/1334388

Cited by 10 publications

(17 citation statements)

References 14 publications

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“…As seen in the fourth column of Table 2, the portion of renewable energy sources from this basic + CO 2 + RPS model is increasing from 2.1%, initially, to 7.6% in 2020, to 13.0% in 2030, with respect to the generation amount because of the RPS constraint. Table 6 shows a more detailed result about the generation amount of each energy source and total renewable percentage of each year, which satisfies the RPS constraint in Equation (9). Table 1 shows similar trend of renewable energy portion with respect to generation capacity (4%, initially, to 15.8% in 2020, to 27.3% in 2030).…”

Section: Optimization Resultsmentioning

confidence: 81%

“…Thus, in order to more actively consider carbon emission problems, the RPS constraint in Equation (9) and PV RPS constraint in Equation (10) were added to the above basic + CO 2 model. As seen in the fourth column of Table 2, the portion of renewable energy sources from this basic + CO 2 + RPS model is increasing from 2.1%, initially, to 7.6% in 2020, to 13.0% in 2030, with respect to the generation amount because of the RPS constraint.…”

Section: Optimization Resultsmentioning

confidence: 99%

“…So far, various energy mix or expansion optimization models have been developed [6][7][8][9][10] since a simple linear programming model was proposed [11]. Certain models considered the gap between long term investment and short-term operation [12], thermal operation in generation expansion planning [13], unit commitment constraint [14][15][16], and other issues, such as economics, finance, regulation, and uncertainty [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Energy Mix with Renewable Portfolio Standards in Korea

Geem

Kim

2016

Sustainability

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Korea is a heavily energy-dependent country whose primary energy consumption ranks ninth in the world. However, at the same time, it promised to reduce carbon emission and planned to use more renewable energy. Thus, the objective of this study is to propose an optimal energy mix planning model in electricity generation from various energy sources, such as gas, coal, nuclear, hydro, wind, photovoltaic, and biomass, which considers more renewable and sustainable portions by imposing governmental regulation named renewable portfolio standard (RPS). This optimization model minimizes various costs such as construction cost, operation and management cost, fuel cost, and carbon emission cost while satisfying minimal demand requirement, maximal annual installation potential, and renewable portfolio standard constraints. Results showed that this optimization model could successfully generate energy mix plan from 2012 to 2030 while minimizing the objective costs and satisfying all the constraints. Therefore, this optimization model contributes more efficient and objective method to the complex decision-making process with a sustainability option. This proposed energy mix model is expected to be applied not only to Korea, but also to many other countries in the future for more economical planning of their electricity generation while affecting climate change less.

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Section: Optimization Resultsmentioning

confidence: 81%

Section: Optimization Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Energy Mix with Renewable Portfolio Standards in Korea

Geem

Kim

2016

Sustainability

View full text Add to dashboard Cite

show abstract

“…Indeed, it has been argued that, having more operating hours per year in a transmission model is more important than representing Kirchhoff's voltage law [28]. However, others who have studied the impact of more temporal granularity on generation expansion [29] have concluded that adding dispatch periods slows down computations while having a little apparent effect on generation expansion decisions. (iii) Long-term temporal granularity: Although many TEP models are based on a single investment decision stage ('one-shot' or 'static' planning) [30], dynamic TEP models [4] have become increasingly popular because of improved computational abilities and the need for plans to include timing of investments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(vi) Network representation: The 'pipes-and-bubbles' (P&B) (transshipment) networks used in many planning models have been proposed to be replaced by more realistic but practical networks to solve approximations of power flow, such as the DC optimal power flow (OPF) [35]. However, as Mai et al [29] shows, in a largescale system, DC OPF modelling can dramatically slow solution times and may have little impact on investment recommendations, compared with transshipment networks that lack Kirchhoff's voltage law. An intermediate level of complexity is the hybrid power flow [36].…”

Section: Literature Reviewmentioning

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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A systematic evaluation of wind's capacity credit in the Western United States

et al. 2021

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The degree to which wind energy can contribute to the capacity needed to meet resource adequacy requirements, also known as capacity credit (CC), can be impacted by many factors. The CC varies regionally with wind resource and correlation to net load and may also vary with wind technology (land‐based versus offshore) and turbine specifications. We use a probabilistic resource adequacy tool to systematically assess the CC of multiple technologies for near‐term wind deployment across the Western US power system to help inform planning decisions. We find that wind CC varies by weather year and location but averages 16% for land‐based turbines and 41% for offshore turbines, with large regional variations. The average wind CC increases to 20% for land‐based and 53% for offshore when considering only the sites in the top 25th percentile of capacity factor. The CC of land‐based wind is generally lower than its capacity factor, which indicates a low correlation of generation to system needs. However, offshore wind shows a substantially higher CC, which is driven by not only a higher capacity factor but also a greater correlation with system needs.

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Implications of Model Structure and Detail for Utility Planning: Scenario Case Studies Using the Resource Planning Model

Cited by 10 publications

References 14 publications

Optimal Energy Mix with Renewable Portfolio Standards in Korea

Optimal Energy Mix with Renewable Portfolio Standards in Korea

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

A systematic evaluation of wind's capacity credit in the Western United States

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