A comparative study of time aggregation techniques in relation to power capacity expansion modeling

Buchholz, Stefanie; Gamst, Mette; Pisinger, David

doi:10.1007/s11750-019-00519-z

Cited by 14 publications

(57 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As already mentioned, using a time domain reduction approach has the advantage of regaining tractability in the problem. From the literature [3,4], we see that the solutions found with a time domain reducing approach still provide high-quality solutions. We hereby find a portfolio of good quality solutions in a time comparable to the time of solving the non-aggregated problem.…”

Section: Time Aggregation Frameworkmentioning

confidence: 93%

“…From the research area of capacity expansion energy system models, time-aggregating simplification methods show strong time reductions without compromising the solution quality significantly [3,4]. Therefore, we suggest to aggregate the problem (P) in each iteration k. For this purpose, any aggregation technique ATQ can be used (see [3] for an overview and comparison of different techniques). The basic idea of time aggregation is to smartly reduce the time domain and then solve the problem on the aggregated time domain only.…”

Section: Time Aggregation Frameworkmentioning

confidence: 99%

“…The following case study is used to illustrate the suggested approaches. We reuse the sandbox model from [3] which is a single-region capacity expansion model featuring unit commitment. Both investment capacities and dispatch decisions are optimized such that total system costs are minimized.…”

Section: Case Studymentioning

confidence: 99%

“…The mathematical formulation is described in Appendix A along with the technological input parameters assigned for this case study ( Table 5). We refer to [3] for further details. Technologies available for investments are wind and PV representing variable renewable energy (VRE) capacities, nuclear and coal representing base-load capacities and open and combined cycle gas turbines (OCGT and CCGT) representing flexible capacities.…”

Section: Case Studymentioning

confidence: 99%

“…For capacity expansion models, this is typically seen as regions or supplying units being aggregated into one, or as a disregard of time slices not being part of an intelligent selection. Especially time aggregation has achieved a lot of recent attention with various studies comparing different techniques and experimentally quantifying the gains and errors [3,4,25,32]. The common conclusion is a tremendous speedup at the cost of a slightly compromised solution quality.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Finding a Portfolio of Near-Optimal Aggregated Solutions to Capacity Expansion Energy System Models

Buchholz

Gamst

Pisinger

2020

SN Oper. Res. Forum

Self Cite

View full text Add to dashboard Cite

Energy system models are frequently being influenced by simplifications, assumption errors, uncertainties, incompleteness, and soft constraints which are challenging to model in a good way. In capacity expansion modeling, also the long time horizon and the high shares of renewable energies feed into the uncertainties. Consequently, a single optimal solution might not provide enough information to stand alone. Contrarily, a portfolio of different solutions, all being within an acceptance span of the system costs, would create more valuable decision support tool. This idea is known from the literature where a near-optimal solution space typically is explored by introducing integer cuts that iteratively cut off solutions as they are found. Generalizing this idea, we suggest an approach that explores the near-optimal solution space by iteratively finding new solutions which are as different as possible from earlier solutions with respect to investment decisions. Our method deviates from the literature since it maximizes the difference of the found solutions rather than finding k similar solutions. An advantage of this approach is that the resulting portfolio holds high diversity which creates a better basis for good decision-making. Moreover, it overcomes a potential struggle of getting symmetric solutions and it strengthens the robustness arguments of the different investment decisions. Furthermore, we suggest to search for alternative solutions in an aggregated solution space whereas the original solution space typically has been used for the search in previous work. We hereby exploit the speedup achieved through aggregation to find more solutions, and we observe that these solutions might indicate must have investments of the non-aggregated problem. The suggested approach is tested on a case study for three different limitations on the system costs. Results show that our approach by far outperforms the approach known from the literature when the neighborhood size exceeds 0.7%. Furthermore, using our approach, a portfolio of eight solutions with

show abstract

Section: Time Aggregation Frameworkmentioning

confidence: 93%

Section: Time Aggregation Frameworkmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Finding a Portfolio of Near-Optimal Aggregated Solutions to Capacity Expansion Energy System Models

Buchholz

Gamst

Pisinger

2020

SN Oper. Res. Forum

Self Cite

View full text Add to dashboard Cite

show abstract

Bridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning

Cao

Haas

Sperber

et al. 2021

Energy Science & Engineering

View full text Add to dashboard Cite

The comprehensive evaluation of strategies for decarbonizing large‐scale energy systems requires insights from many different perspectives. In energy systems analysis, optimization models are widely used for this purpose. However, they are limited in incorporating all crucial aspects of such a complex system to be sustainably transformed. Hence, they differ in terms of their spatial, temporal, technological, and economic perspective and either have a narrow focus with high resolution or a broad scope with little detail. Against this background, we introduce the so‐called granularity gaps and discuss two possibilities to address them: increasing the resolutions of the established optimization models, and the different kinds of model coupling. After laying out open challenges, we propose a novel framework to design power systems in particular. Our exemplary concept exploits the capabilities of power system optimization, transmission network simulation, distribution grid planning, and agent‐based simulation. This integrated framework can serve to study the energy transition with greater comprehensibility and may be a blueprint for similar multimodel analyses.

show abstract

Decision support for the technician routing and scheduling problem

Gamst,

Pisinger

2023

Networks

Self Cite

View full text Add to dashboard Cite

The technician routing and scheduling problem (TRSP) optimizes routes for technicians serving tasks subject to qualifications, time constraints, and routing costs. In the literature, the TRSP is solved either to provide actual technician work schedules or to perform what‐if analyses on different TRSP scenarios. A TRSP scenario consists of a given number of tasks, technicians, skills, working hours and so forth. We present a method which builds optimal TRSP scenarios with respect to technician fleet, their skills, their working hours and digitization of task equipment. The scenarios are built such that the combined TRSP costs (OPEX) and investment costs (CAPEX) are minimized. By using a holistic approach we can generate scenarios that would not have been found by studying the investments individually. The proposed method consists of a matheuristic based on column generation. To reduce computational time, the routing costs of a technician are estimated instead of solved to optimality. The proposed method is evaluated on data from the literature and on real‐life data from a telecommunication company. The evaluation shows that the proposed method successfully suggests attractive scenarios. The method especially excels in ensuring that more tasks are serviced, but also in reducing travel time with around 16% in the real‐life instance. We believe that the proposed method could constitute an important strategic tool for routing companies. In the conclusion, we propose future research directions to extend the applicability.

show abstract

A comparative study of time aggregation techniques in relation to power capacity expansion modeling

Cited by 14 publications

References 23 publications

Finding a Portfolio of Near-Optimal Aggregated Solutions to Capacity Expansion Energy System Models

Finding a Portfolio of Near-Optimal Aggregated Solutions to Capacity Expansion Energy System Models

Bridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning

Decision support for the technician routing and scheduling problem

Contact Info

Product

Resources

About