Optimization of Dispersed Energy Supply —Stochastic Programming with Recombining Scenario Trees

Epe, Alexa; Küchler, Christian; Römisch, Werner; Vigerske, Stefan; Wagner, Hermann‐Josef; Woll, Oliver

doi:10.1007/978-3-540-88965-6_15

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…One problem commonly associated with the use of scenarios in stochastic optimization is the "curse of dimensionality". When considering a number of uncertain parameters with independent scenario probabilities, the number of joint realization scenarios increases exponentially with the number of considered uncertainties [8]. The same issue applies when considering a single uncertain parameter, in our case demand, and a number of time periods.…”

Section: Stochastic Demand Treementioning

confidence: 92%

“…We initially considered the use of recombining trees, which can be used to limit the increase in scenarios from one time period to the next [7]. A recombining tree's defining characteristic is that the order of previous transitions does not matter to a finally reached node, as the result is identical for each variation [8]. See the recombining stochastic tree as outlined on the left-hand side of Fig.…”

Section: Stochastic Demand Treementioning

confidence: 99%

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Strategic generation investment using a stochastic rolling-horizon MPEC approach

Kallabis

Gabriel

2020

Energy Syst

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Investments in power generation assets are multi-year projects with high costs and multi-decade lifetimes. Since market circumstances can significantly change over time, investments into such assets are risky and require structured decision-support systems. Investment decisions and dispatch in electricity spot markets are connected, thus requiring anticipation of expected market outcomes. This strategic situation can be described as a bilevel optimization problem. At the upper level, an investor decides on investments while anticipating the market results. At the lower level, a market operator maximizes welfare given consumer demand and installed generation assets as well as producer price bids. In this paper, we formulate this problem as a mathematical program with equilibrium constraints (MPEC). We consider this model to include a dynamic, rolling-horizon optimization. This structure splits the investment process into multiple stages, allowing the modification of wait-and-see decisions. This is a realistic representation of actors making their decision under imperfect information and has the advantage of allowing the players to adjust their data in between rolls. This more closely models real-world decision-making and allows for learning and other feedback in between rolls. The rolling-horizon formulation also has the beneficial byproduct of computational advantage over a fixed-horizon stochastic optimization formulation since smaller problems are solved and we provide supporting numerical results to this point.

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Section: Stochastic Demand Treementioning

confidence: 92%

Section: Stochastic Demand Treementioning

confidence: 99%

Strategic generation investment using a stochastic rolling-horizon MPEC approach

Kallabis

Gabriel

2020

Energy Syst

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Section: Scenario Selectionmentioning

confidence: 99%

“…In the next section we discuss the involvement of stakeholders in the scenario selection process to maintain relevance and objectivity. An alternative approach is to probabilistically weight scenarios and/or to create scenario trees reflecting future evolutionary paths of the system under study (Epe et al, 2009). Within the consideration of the "timeliness" and relevance of a modelling effort, there is also a tendency for scenario choice to reflect contemporary debates (Trutnevyte, McDowall, Tomei, & Keppo, 2016), meaning that model results may have a "use-by" date beyond which the real-world systems have diverged significantly from the scenario assumptions made.…”

Section: Scenario Selectionmentioning

confidence: 99%

Making energy system models useful: Good practice in the modelling of multiple vectors

Hawker

Bell

2019

WIREs Energy & Environment

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Energy system models which cover multiple vectors have become increasingly used to provide an evidence base for policy and commercial decisions in real-world energy systems undergoing change. In particular, such models are often used to derive 'optimal' pathways to decarbonisation considering the planning or operation of systems with multiple technology options. This paper explores how the concept of 'usefulness'the applicability and relevance of modelling outcomesmay be used to establish criteria for modelling design and practice at the outset, and looks at the difficulties that may be faced in achieving this. The application should inform the choice of modelling framework and the manner in which tractability should be addressed and results meaningfully presented. A process of continuous engagement is proposed which guides modelling work towards 'useful' outcomes, as well as mitigating the danger of results being more reflective of design choices than the properties of the real-world systems being modelled. Because of the difficulties in maintaining and auditing complex datasets spanning expertise from multiple sectors, there is a clear role for independent data curators to facilitate rigour in model parameterisation and to allow consistency between modelling efforts. Specialists from the different disciplines represented should be engaged to ensure that data have been interpreted and applied correctly. All modelling choices should be clearly documented along with advice on their possible implications in respect of use of the results.

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“…The resulting large scale mixed integer linear programming problem is solved using a nested column decomposition algorithm. In [Epe et al, 2009] the authors do address the problem of dealing with the intermittency of wind. Again a multistage stochastic programming approach is taken and the resulting large scale optimization problem is solved using a recombining tree methodology.…”

Section: Related Literature and Contributionsmentioning

confidence: 99%

A stochastic multiscale model for electricity generation capacity expansion

Parpas

Webster

2014

European Journal of Operational Research

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Long-term planning for electric power systems, or capacity expansion, has traditionally been modeled using simplified models or heuristics to approximate the short-term dynamics. However, current trends such as increasing penetration of intermittent renewable generation and increased demand response requires a coupling of both the long and short term dynamics. We present an efficient method for coupling multiple temporal scales using the framework of singular perturbation theory for the control of Markov processes in continuous time. We show that the uncertainties that exist in many energy planning problems, in particular load demand uncertainty and uncertainties in generation availability, can be captured with a multiscale model. We then use a dimensionality reduction technique, which is valid if the scale separation present in the model is large enough, to derive a computationally tractable model. We show that both wind data and electricity demand data do exhibit sufficient scale separation. A numerical example using real data and a finite difference approximation of the Hamilton-Jacobi-Bellman equation is used to illustrate the proposed method. We compare the results of our approximate model with those of the exact model. We also show that the proposed approximation outperforms a commonly used heuristic used in capacity expansion models.

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Optimization of Dispersed Energy Supply —Stochastic Programming with Recombining Scenario Trees

Cited by 6 publications

References 14 publications

Strategic generation investment using a stochastic rolling-horizon MPEC approach

Strategic generation investment using a stochastic rolling-horizon MPEC approach

Making energy system models useful: Good practice in the modelling of multiple vectors

A stochastic multiscale model for electricity generation capacity expansion

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