The Brookhaven Energy System Optimization Model: Its Variants and Uses

Kydes, Andy S.

doi:10.1007/978-94-009-8751-7_7

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…As highlighted above, this review focuses on the TSA methods in bottom-up energy system optimization models that include generation expansion planning (GEP), as well as unit commitment (UC) and have constantly emerged and evolved since the late 1970s and 1980s [9,11]. Among the early model frameworks, one group focuses on long-term system planning and has usually only one time step per year such as LEAP [43], EFOM [44], and BESOM [45], which are not subject to aggregation techniques and thus neglected in the following. The temporal dimension of the other major group of early bottom-up ESMs such as TIMES [46][47][48][49] and its predecessors MARKAL [50], MESSAGE [12,51], IKARUS [9], and PERSEUS [52] are based on time slice formulations (in the case of PERSEUS called "time slots"), which are explained in more detail in Section 3.2.1.…”

Section: Methodology and Structure Of The Reviewmentioning

confidence: 99%

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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Section: Methodology and Structure Of The Reviewmentioning

confidence: 99%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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“…The input information required for both models falls into seven. general categories: (1) supply and demand efficiencies; (2) costs; (3) environmental impact factors; (4) resource availabilities; (5) basic energy demands; (6) permitted tnarke~ penetration; and (7) demand load characteristics of all electric service demands by season and time of day.…”

Section: Appendix Amentioning

confidence: 99%

Comparative review of the Time-stepped Energy System Optimization Model (TESOM) and the IEA Market Allocation Model (MARKAL)

Kydes¹

1980

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“…The complexity of these kinds of models is high if spatial, temporal, and/or technological dimension is high [1,2]. Many references in scientific literature deal with capacity planning of energy systems of countries [3,4] or even networks of more than one country [5,6]. Because electricity is very hard to store and has to be produced at the same time it is consumed, most of these models focus very much on electricity supply.…”

Section: Introduction To Solving Time-coupled Energy System Optimizationmentioning

confidence: 99%

Efficient Solving of Time-Coupled Energy System MILP Models Using a Problem Specific LP Relaxation

Kirschbaum,

Powilleit,

Schotte

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Energy system optimization models (ESOMs) often contain time coupling constraints, some of which couple short time frames as of daily storages or load changes of components, while other constraints couple longer periods like seasonal storages, peak load prices, or upper bounds to overall yearly CO2 consumption. Those ESOMs have binary constraints for minimal loads, efficiency curves, or discontinuous energy prices that are relevant for the short-term operation of the equipment. Calculation times for solving a whole year or longer as a coupled MILP problem are in many cases too high for practical applications that normally should not exceed one hour. Typical decomposition strategies to reduce calculation times are often designed for subclasses of energy system models and are not generally applicable. In order to have a generalized approach to solve these models efficiently, we investigate strategies that are based on a problem specific relaxation of integer constraints and downsampling of the input time series of the models. A rolling horizon strategy is proposed that relaxes and downsamples the time steps from the end of the rolling horizon to the end of the year to consider the operation during the rest of the year. In order to reduce the error of the relaxation, binary constraints are reformulated to get the best LP approximation of the original MILP model. Using this rolling horizon strategy, models that are almost unsolvable as coupled MILP can be solved efficiently and very robustly and deliver a result that is feasible for the original problem and very close to the optimum of the original problem.

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The Brookhaven Energy System Optimization Model: Its Variants and Uses

Cited by 12 publications

References 4 publications

A Review on Time Series Aggregation Methods for Energy System Models

A Review on Time Series Aggregation Methods for Energy System Models

Comparative review of the Time-stepped Energy System Optimization Model (TESOM) and the IEA Market Allocation Model (MARKAL)

Efficient Solving of Time-Coupled Energy System MILP Models Using a Problem Specific LP Relaxation

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