Impact of model resolution on scenario outcomes for electricity sector system expansion

Mallapragada, Dharik S.; Papageorgiou, Dimitri J.; Venkatesh, Aranya; Lara, Cristiana L.; Grossmann, Ignacio E.

doi:10.1016/j.energy.2018.08.015

Cited by 62 publications

(34 citation statements)

References 11 publications

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“…We speculate that this may have to do with the fixed storage duration and the use of average 24-hour diurnal profiles per season that do not capture the full benefit of shifting supply from low to high demand periods. To better characterize the potential role of storage, additional work with higher temporal resolution of supply and demand and different parameterizations of storage technology is required 85 . For example, recent work using a model with hourly resolution indicated cost-effective deployment of lithium-ion battery storage in North Carolina by 2030 86 .…”

Section: Resultsmentioning

confidence: 99%

Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study

Thomas

Queiroz

et al. 2019

Environ. Sci. Technol.

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Rigorous model-based analysis can help inform state-level energy and climate policy. In this study, we utilize an open-source energy system optimization model and publicly available datasets to examine future electricity generation, CO2 emissions, and CO2 abatement costs for the North Carolina electric power sector through 2050. Model scenarios include uncertainty in future fuel prices, a hypothetical CO2 cap, and an extended renewable portfolio standard. Across the modeled scenarios, solar photovoltaics represent the most cost-effective low-carbon technology, while trade-offs among carbon constrained scenarios largely involve natural gas and renewables. We also develop a new method to calculate break-even costs, which indicate the capital costs at which different technologies become cost-effective within the model. Significant variation in break-even costs are observed across different technologies and scenarios. We illustrate how break-even costs can be used to inform the development of an extended renewable portfolio standard in North Carolina. Utilizing the break-even costs to calibrate a tax credit for onshore wind, we find that the resultant wind deployment displaces other renewables, and thus has a negligible effect on CO2 emissions. Such insights can provide crucial guidance to policymakers weighing different policy options. This study provides an analytical © 2019. https://doi.org/10.1021/acs.est.9b04184 2 framework to conduct similar analyses in other states using an open source model and freely available datasets. INTRODUCTION Many US states have proposed plans to address the climate change threat 1 . North Carolina is the ninth most populous state, the fourteenth largest CO2 emitter (2014) 2 in the United States, and the first state in the Southeast to adopt a Renewable Energy and Energy Efficiency Portfolio Standard (REPS) 3 . The NC REPS requires investor-owned utilities to meet at least 12.5% of their electricity demand through renewable energy resources or energy efficiency measures by 2021 3 . Three carve outs, representing minimum shares of specific fuel types, were also defined: 0.2% solar by 2018, 0.2% swine waste by 2020, and 900,000 MWh of poultry waste by 2016 3 . The deployment of solar PV has far exceeded the solar carve out, with 4.3% of North Carolina's total generation supplied by solar 4 .This high level of solar PV deployment is largely due to the rapid decline in investment costs and favorable contract terms for third party, utility-scale solar under the Public Utilities Regulatory Policies Act (PURPA). However, electric power producers in the state have had difficulty meeting the swine and poultry waste targets, and have repeatedly filed joint petitions to the North Carolina Utilities Commission (NCUC) seeking relief and delay 5 .These outcomes illustrate the challenge that state policymakers face in developing policy that balances environmental performance, affordability, and stakeholder interests. Debates over energyrelated policies and incentives within the state persist, often ...

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Section: Resultsmentioning

confidence: 99%

Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study

Thomas

Queiroz

et al. 2019

Environ. Sci. Technol.

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“…Therefore, spatial or technological aggregation approaches are reducing the number of time series, but not the number of time steps. As mentioned above, the temporal sub-dimension in ESMs is crucial for the implementation of storages and the description of system dynamics, which is especially important for ESMs considering a high share of intermittent RES [36][37][38][39][40][41]. This applies for both single-node and multi-node ESMs, and the group of aggregation methods employed to tackle this issue is broad and diverse.…”

Section: Motivation and Scope Of The Reviewmentioning

confidence: 99%

“…Instead, the method is sometimes simply called "representative day" [66,67,[108][109][110][111][112][113], "TD" [54,[114][115][116][117][118][119][120][121], "typical daily profiles" [16,17], "typical segment" [122] "time slot" [52], or "time band" [123]. Accordingly, the term "TS" is used by the majority of authors [36,39,51,[104][105][106][107][124][125][126][127][128]. The most frequent distinction is made between the four seasons [16,17,36,39,[104][105][106][107]115,121,124,[126][127][128] or between summer, winter.…”

Section: Time Slicesmentioning

confidence: 99%

“…Accordingly, the term "TS" is used by the majority of authors [36,39,51,[104][105][106][107][124][125][126][127][128]. The most frequent distinction is made between the four seasons [16,17,36,39,[104][105][106][107]115,121,124,[126][127][128] or between summer, winter. and mid-season [40,51,54,66,67,91,[108][109][110]112,117,118,120,123,129,130], but other distinctions such as monthly, bi-monthly, or bi-weekly among others [40,51,54,111,113,114,…”

Section: Time Slicesmentioning

confidence: 99%

“…This can be done, for instance, by downsampling to 2 h TSs [116,118,122], 4 h TSs [40], or a number of different time step sizes to investigate the downsampling impact [66][67][68]. Moreover, day and night cycles (two diurnal TSs) [36,104,[126][127][128], optionally including the peak hour of the day [36,127,128] or other TSs of irregular length [39,54,106,107,112,123,129], were also used. Mavrotas et al [54] also implemented an algorithm for segmenting the chosen TDs to coarser TSs based on ordering of the gradients between time steps and merging the smallest ones.…”

Section: Time Slices/averaging + Downsampling/segmentationmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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Electric power infrastructure planning under uncertainty: stochastic dual dynamic integer programming (SDDiP) and parallelization scheme

2019

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Impact of model resolution on scenario outcomes for electricity sector system expansion

Cited by 62 publications

References 11 publications

Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study

Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study

A Review on Time Series Aggregation Methods for Energy System Models

Electric power infrastructure planning under uncertainty: stochastic dual dynamic integer programming (SDDiP) and parallelization scheme

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