The impact of short-term variability and uncertainty on long-term power planning

Bylling, Henrik C.; Pineda, Salvador; Boomsma, Trine Krogh

doi:10.1007/s10479-018-3097-3

Cited by 25 publications

(19 citation statements)

References 36 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: 94%

“…Since, Q sum or Q maxmin must be solved in each iteration, we suggest to apply a problem reduction in order to reduce time usage. 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).…”

Section: Time Aggregation Frameworkmentioning

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 2 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

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: 94%

Section: Time Aggregation Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Buchholz

Gamst

Pisinger

2020

SN Oper. Res. Forum

View full text Add to dashboard Cite

show abstract

“…The RE resources create for the power systems' operation some operational challenges for GEP due to the following feature of their stochastic nature [23][24][25][26]:…”

Section: Generation Expansion Planning With High Share Of Renewable Ementioning

confidence: 99%

Modelling energy systems of Vietnam with integration of renewable power sources

Edelev¹,

Karamov²,

Sidorov³

et al. 2019

The International Workshop on Information, Computation, and Control Systems for Distributed Environments 2019

View full text Add to dashboard Cite

The paper addresses the research of the large-scale penetration of renewable energy into the power system of Vietnam. The proposed approach presents the optimization of operational decisions in different power generation technologies as a Markov decision process. It uses a stochastic base model that optimizes a deterministic lookahead model. The first model applies the stochastic search to optimize the operation of power sources. The second model captures hourly variations of renewable energy over a year. The approach helps to find the optimal generation configuration under different market conditions.

show abstract

“…This raises the question of how the system should evolve with respect to the context, that is, what investments to realize now and in the coming years. Finally, the objective of such studies can be 2‐fold: coming up with theoretically performant solutions for a given environment (eg, Reference 9), or learn on innovative energy system behaviours to provide useful insights (see References 10,11 or Reference 12 for instance).…”

Section: Introductionmentioning

confidence: 99%

Techno‐economic planning of local energy systems through optimization models: a survey of current methods

Cuisinier

Bourasseau

Ruby

et al. 2020

Intl J of Energy Research

View full text Add to dashboard Cite

Summary Energy system planning is a difficult task, since it involves long‐term decision‐makings with multiple dimensions: technical, economic, social, ecological, and political. The rise of distributed and multi‐energy systems including new technologies has further increased this complexity. Techno‐economic studies based on optimization models have recently received much attention in the literature. They are essential to grasp energy systems complexity and provide decision support. This work targets methodological issues related to local systems while comparing them with methods used at larger scales. First, a new framework providing a comprehensive vision of added values and limits of optimization models is presented. Then, the main methodological trends as well as several undertaken research paths are identified based on the analysis of more than 60 research papers. The review results are summarized in a complete and concise table. Finally, future research topics are discussed including the operational facets of investment optimization models in case of high intermittent energy shares, flexibility issues and long‐term operational decisions. The results provide useful information to modellers or researchers that look for appropriate and state‐of‐the‐art optimization methods, or aim to deepen current research paths. Hence, they will facilitate the planning and development of energy systems for the future.

show abstract

The impact of short-term variability and uncertainty on long-term power planning

Cited by 25 publications

References 36 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

Modelling energy systems of Vietnam with integration of renewable power sources

Techno‐economic planning of local energy systems through optimization models: a survey of current methods

Contact Info

Product

Resources

About