Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm

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

doi:10.1016/j.ejor.2018.05.039

Cited by 106 publications

(36 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One of the most common partitional clustering algorithms used in energy system optimization is the k-means algorithm, which has been used in a variety of studies [14,15,24,37,57,58,63,69,74,78,[83][84][85][86][87]97,[137][138][139]141,142,[145][146][147][148][153][154][155][156][157][158][159][160][161]. The objective of the k-means algorithm is to minimize the sum of the squared distances between all cluster members of all clusters and the corresponding cluster centers, i.e., min…”

Section: Partitional Clusteringmentioning

confidence: 99%

“…Determining a more robust energy system by exposing the once optimized energy systems to different input data scenarios. With respect to iterative approaches, however, it must be called into question as to whether these approaches are more efficient than well-known iterative decomposition approaches such as Benders-or Dantzig-Wolfe decomposition [193] (e.g., as used by Lara et al [24] and Schwele et al [194]).…”

Section: Overview and Trends In The Integration Of Additional Informamentioning

confidence: 99%

“…The spatial sub-dimension is focused on the number of regions and their connections to each other, as energy systems on a national or even larger scale usually face the challenge of taking energy transmission between different regions into account. The temporal sub-dimension is divided into two aspects, namely temporal resolution (TR), often referred to as time steps, and the overall time horizon [9], which also concerns questions of storage modeling [14][15][16][17][18][19][20] as well as the linking of dynamic processes [21][22][23] and investment dynamics [24,25]. In contrast, the techno-economic dimension deals with how the components are represented in the model and whether their design and/or operation are optimized or, if their operational behavior is simply simulated, how they are mathematically represented and if the impact of supply and demand on energy prices is dynamically modeled or not [13].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Partitional Clusteringmentioning

confidence: 99%

Section: Overview and Trends In The Integration Of Additional Informamentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Kotzur et al [385] established three singlenode energy models. Lara et al [386] presented a Multi-node energy model for Texas. Neniškis and Galinis [387] presented an electricity and heat energy model of Lithuania.…”

Section: Rajesh Et Al [349] Established a Multi-stage Mixedmentioning

confidence: 99%

Recent Updates on Energy Planning Models - A Review

Hafeez

Gulshan

Tahira

2020

Journal of Mechanical and Energy Engineering

View full text Add to dashboard Cite

Energy is the most important part for community and financial growth of any country. Energy planning models play a vital role in strategy formulation and power sector progress. In this paper a systematic review and comparison of energy planning models developed and applied from 1977-2019 has been presented. The review indicates that most of the energy planning models has been presented and applied in developed nations. Only few numbers of energy planning models have been presented and applied in under developing countries. The review also shows the comparison of energy planning models applied in developed and under-developed countries. This review article will assistance the energy managers, scholars and strategy makers broadly.

show abstract

“…As variáveis de decisão para ambos os problemas modelo VRS tem como característica um grande número de empates para DMU's eficientes se comparado ao modelo CRS. ConformeAli (1993), no VRS, aquelas DMU's com menor valor de um dos inputs ou o maior valor de um dos outputs são consideradas eficientes, mesmo quando as relações com as demais variáveis não sejam boas. Nesse caso, essas DMU's são denominadas de falsamente eficientes.…”

unclassified

Análise da sustentabilidade de empresas: uma aplicação da análise envoltória de dados

Ferreira

2019

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

View full text Add to dashboard Cite

Essa pesquisa apresenta uma análise da eficiência no tocante à sustentabilidade de oito empresas, denominadas por A, B, C, D, E, F, G e H, de diversos segmentos, por meio da metodologia Análise Envoltória de Dados (DEA) tendo em vista os parâmetros: faturamento, número de funcionários, dimensões – geral, econômica, social e ambiental, elucidados pela Revista Exame – Guia Exame Sustentabilidade para os períodos de 2012 a 2014. A metodologia DEA faz uso de programação matemática e de uma fronteira não-paramétrica de eficiência. O objetivo é verificar se a eficiência das empresas selecionadas se mantém ao longo dos anos 2012, 2013 e 2014, segundo os parâmetros considerados. Para a aplicação da DEA foram definidos o input como número de funcionários e os outputs dimensões geral, econômica, social, ambiental e faturamento. Os resultados mostraram que apenas as empresas C, do ramo de serviços e E, do segmento de material de construção, preservaram as suas eficiências ao longo desses períodos, dentre as empresas avaliadas neste estudo.

show abstract

Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm

Cited by 106 publications

References 35 publications

A Review on Time Series Aggregation Methods for Energy System Models

A Review on Time Series Aggregation Methods for Energy System Models

Recent Updates on Energy Planning Models - A Review

Análise da sustentabilidade de empresas: uma aplicação da análise envoltória de dados

Contact Info

Product

Resources

About