Conjoint Dynamic Aggregation and Scheduling Methods for Dynamic Virtual Power Plants

Nieße, Astrid; Beer, Sebastian; Bremer, Jörg; Hinrichs, Christian S.; Lünsdorf, Ontje; Sonnenschein, Michael

doi:10.15439/2014f76

Cited by 34 publications

(30 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The desired target schedule is given by ζ. F (Ui) denotes the individual set of feasible schedules that are operable for unit U i without violating any (technical) constraint. Solving this problem without unit independent constraint handling leads to specific implementations that are not suitable for handling changes in VPP composition or unit setup without having changes in the implementation of the scheduling algorithm [8].…”

Section: Methodical Backgroundmentioning

confidence: 99%

Partitioning the Data Domain of Combinatorial Problems for Sequential Optimization

Hinrichs

Bremer

Martens

et al. 2016

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

Abstract-Following the long-term goal of substituting conventional power generation with cleaner energy will lead to an integration of a large share of small energy generation units imposing large problem sizes for coordination. The expected huge number of entities leads to a need for new techniques reducing the computational effort for coordination. Predictive scheduling is a frequent task in energy grid control. For a number of energy resources, schedules have to be found that fulfill several objectives at the same time. Considering day-ahead scenarios with 96-dimensional schedules imposes additional challenges to this already hard combinatorial problem. We explore the effects of reducing complexity by partitioning the data domain of the optimization problem for a sequential approach that integrates energy models for constraint handling directly into the optimization process. We explore the effects of different partitioning schemes and evaluate the trade-off between accuracy and effort with several simulation studies.

show abstract

Section: Methodical Backgroundmentioning

confidence: 99%

Partitioning the Data Domain of Combinatorial Problems for Sequential Optimization

Hinrichs

Bremer

Martens

et al. 2016

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although parts of the planned work regarding local minima convergence reduction is specific for COHDA (the algorithmic parts), the general problem analysis is valid for all heuristic distributed algorithms using a continuous flexibility model. In the following, COHDA is described in a concise fashion, based on the description in [26].…”

Section: Distributed Energy Schedulingmentioning

confidence: 99%

“…[26]) as visualized in Fig. 3: 1) perceive: When an agent a i receives a message κ p from one of its neighbors (say, a p ), it imports the contents of this message into its own memory.…”

Section: A Introducing Cohdamentioning

confidence: 99%

On local minima in distributed energy scheduling

Nieße

Bremer

Lehnhoff

2017

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

Abstract-Distributed energy scheduling constitutes a tough task for optimization algorithms, as the underlying problem structure is highdimensional, multimodal and non-linear. For this reason, metaheuristics and especially distributed algorithms have been in the focus of research for several years with promising results. The modeling of the distributed energy units' flexibility is a specific research task, with different concepts like comfort-level based approaches, enumeration of possible schedules, and continuous schedule representation using machine learning and decoder techniques. Although a continuous representation of flexibility has shown better results regarding the global optimization goal, there have been hints that the susceptibility to local minima traps enlarges compared to the enumeration of distinct schedules. In this contribution, we present an exemplary system for predictive scheduling of distributed energy units consisting of a continuous flexibility modelling approach and a fully distributed planning heuristic. A prestudy is presented, where we analyze the problem structure regarding local minima and describe planned work to reduce the heuristic's susceptibility to be kept in these.

show abstract

“…Virtual power plants are a well-known means for aggregating and controlling DER (Awerbuch and Preston, 1997) and their requisite integration into current and future market structures recently led to VPP systems that frequently re-configure themselves for a market and product-led alignment (Nieße et al, 2014). Based on the size ADCAIJ: Advances in Distributed Computing and Articial Intelligence Journal.…”

Section: Introductionmentioning

confidence: 99%

“…6 N. 3 (2017), 29-44 2. Related Work Market integration of virtual power plants sometimes demands for dynamic aggregation when it comes to energy product centric clustering of units for better trading positions (Nieße et al, 2014).…”

Section: Introductionmentioning

confidence: 99%