Generation expansion planning under uncertainty: An application of stochastic methods to the German electricity system

@article{Kendziorski2017GenerationEP,
  title={Generation expansion planning under uncertainty: An application of stochastic methods to the German electricity system},
  author={Mario Kendziorski and Mona Setje-Eilers and Friedrich Kunz},
  journal={2017 14th International Conference on the European Energy Market (EEM)},
  year={2017},
  pages={1-7}
}
Renewable energies are expected to be the main electricity generation source. However, the variability of renewable energy supply poses challenges to the generation expansion modelling as uncertainty of hourly generation need to be adequately taken into account. This paper analyzes the implications of different approaches to optimization under uncertainty, ranging from stochastic to robust optimization. We apply these specific approaches to the German electricity system in 2035 and compare them… Expand
6 Citations

Figures and Tables from this paper

Generation Expansion Planning Considering High Share Renewable Energies Uncertainty
TLDR
Mixed integer linear programming is carried out using MATLAB software to solve the proposed GEP model for a 20 years planning period and proposes an optimal planning model for long-term GEP with high sharing of RES considering the uncertainty. Expand
Generation expansion planning under correlated uncertainty of mass penetration renewable energy sources
This study presents a methodology for generation expansion planning (GEP) under the presence of uncertainty of multiple renewable energy sources (RES). Both long- and short-term uncertainties areExpand
Two-stage robust generation expansion planning considering long- and short-term uncertainties of high share wind energy
Abstract This paper introduces a two-stage robust generation expansion planning (GEP) methodology under the presence of wind energy uncertainty. Both long- and short-term uncertainties areExpand
Open Power System Data - Frictionless data for electricity system modelling
TLDR
It is argued that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. Expand

References

SHOWING 1-10 OF 19 REFERENCES
The Costs of Electricity Systems with a High Share of Fluctuating Renewables: A Stochastic Investment and Dispatch Optimization Model for Europe
Renewable energies are meant to produce a large share of the future electricity demand. However, the availability of wind and solar power depends on local weather conditions and therefore weatherExpand
Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios
TLDR
In this research, a preliminary GEP model is proposed with available input data from various sources and two related optimization models are presented and solved to find the optimal results under uncertainty. Expand
Robust Optimization for Transmission Expansion Planning: Minimax Cost vs. Minimax Regret
Due to the long planning horizon, transmission expansion planning is typically subjected to a lot of uncertainties including load growth, renewable energy penetration, policy changes, etc. InExpand
A survey of stochastic modelling approaches for liberalised electricity markets
TLDR
An overview and classification of stochastic models dealing with price risks in electricity markets is given and shortcomings of existing approaches and open issues that should be addressed by operation research are discussed. Expand
Electric Utility Capacity Expansion Planning with Uncertain Load Forecasts
Abstract This paper is concerned with the role and impact of uncertainty in the forecast of electricity demand. In particular, the emphasis is upon how uncertainty about future demand affects currentExpand
Two-Stage Robust Generation Expansion Planning: A Mixed Integer Linear Programming Model
This paper presents a new uncertainty handling framework for optimal generation expansion planning (GEP) amalgamating the notions of single-stage and two-stage robust optimization (RO). The proposedExpand
Electricity planning under uncertainty Risks, margins and the uncertain planner
Abstract The uncertainty of plant performance and unpredictable demand for electricity has always been a problem for power system planners. This consideration has come to dominate all others and thisExpand
Robust Optimization for Power Systems Capacity Expansion under Uncertainty
We develop a robust optimization model for planning power system capacity expansion in the face of uncertain power demand. The model generates capacity expansion plans that are both solution andExpand
A Greenfield Model to Evaluate Long-Run Power Storage Requirements for High Shares of Renewables
We develop a dispatch and investment model to study the role of power storage and other flexibility options in a greenfield setting with high shares of renewables. The model captures multiple systemExpand
Stochastic Programming Models in Energy
We give the reader a tour of good energy optimization models that explicitly deal with uncertainty. The uncertainty usually stems from unpredictability of demand and/or prices of energy, or fromExpand
...
1
2
...