Constraint-Handling with Support Vector Decoders

Bremer, Jörg; Sonnenschein, Michael

doi:10.1007/978-3-662-44440-5_14

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…4, that can be calculated for all kinds of devices and, therefore, understood as a unified representation of their flexibility (Chicco et al 2020). Bremer and Sonnenschein (2014) introduce an approach to model and map the flexibility of power devices in the context of distributed optimization for operational planning in an abstracted form. Feasible operation plans for different types of power devices are generated based on their device specific constraints by sampling the search space of the devices (Bremer and Sonnenschein 2013).…”

Section: Multienergy Nodementioning

confidence: 99%

“…Feasible operation plans for different types of power devices are generated based on their device specific constraints by sampling the search space of the devices (Bremer and Sonnenschein 2013). In the mentioned approach by Bremer and Sonnenschein (2014), this feasible region forms a multidimensional and nonlinear search space for each device. The resulting solution spaces can be understood as a unified flexibility representation across all types of power devices.…”

Section: Multienergy Nodementioning

confidence: 99%

“…This happens by using a Gaussian kernel to geometrically compare the analyzed operation plan with the support vectors of the spherical solution space. Additionally, Bremer and Sonnenschein (2014) present a decoder that makes it possible to move infeasible operation plans into the feasibility region and thus ensures the feasibility of stepwise optimized operation plans.…”

Section: Support Vector Data Descriptionmentioning

confidence: 99%

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Choosing the right model for unified flexibility modeling

et al. 2022

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Using aggregated flexibility from distributed small-scale power devices is an extensively discussed approach to meet the challenges in modern and increasingly stochastic energy systems. It is crucial to be able to model and map the flexibility of the respective power devices in a unified form to increase the value of the cumulative flexibility from different small-scale power devices by aggregation. In order to identify the most suitable approach for unified flexibility modeling we present a framework to evaluate and compare the advantages and disadvantages of already existing modeling approaches in different levels of detail. As an introduction to flexibility modeling and as a basis for the evaluation process we initially provide a comprehensive overview of the broad range of flexibility models described in scientific literature. Subsequently, five selected modeling approaches allowing the generation of a unified flexibility representation for different power devices are presented in detail. By using an evaluation metric we assess the suitability of the selected approaches for unified flexibility modeling and their applicability. To allow a more detailed performance analysis, the best evaluated models are implemented and simulations with different small-scale devices are performed. The results shown in this paper highlight the heterogeneity of modeling concepts deriving from the various interpretations of flexibility in scientific literature. Due to the varying complexity of the modeling approaches, different flexibility potentials are identified, necessitating a combination of approaches to capture the entire spectrum of the flexibility of different small-scale power devices. Furthermore, it is demonstrated that a complex model does not necessarily lead to the discovery of higher flexibility potentials, and recommendations are given on how to choose an appropriate model.

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Section: Multienergy Nodementioning

confidence: 99%

Section: Multienergy Nodementioning

confidence: 99%

Section: Support Vector Data Descriptionmentioning

confidence: 99%

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Choosing the right model for unified flexibility modeling

et al. 2022

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“…More detailed information regarding this methodology can be consulted in [38] and [34]. The information in equation ( 23) can be integrated in any meta-heuristic optimization framework (like EPSO) to optimize the availability flexibility according to the end-user goals [52].…”

Section: Surrogate Model For the Hems Flexibilitymentioning

confidence: 99%

Multi-period flexibility forecast for low voltage prosumers

Pinto

Bessa

Matos

2017

Energy

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Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low voltage residential clients, will play a crucial role on the flexibility provision to both system operators and market players like aggregators. Modeling and forecasting multi-period flexibility from residential prosumers, such as battery storage and electric water heater, while complying with internal constraints (comfort levels, data privacy) and uncertainty is a complex task. This papers describes a computational method that is capable of efficiently learn and define the feasibility flexibility space from controllable resources connected to a HEMS. An Evolutionary Particle Swarm Optimization (EPSO) algorithm is adopted and reshaped to derive a set of feasible temporal trajectories for the residential net-load, considering storage, flexible appliances, and predefined costumer preferences, as well as load and photovoltaic (PV) forecast uncertainty. A support vector data description (SVDD) algorithm is used to build models capable of classifying feasible and non-feasible HEMS operating trajectories upon request from an optimization/control algorithm operated by a DSO or market player.

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“…It includes classical optimization strategies and problems such as the application of evolutionary algorithms or constraint handling (e.g. [3] or [9]). The generation of such schedules is of great interest for aggregators, DER system providers and system operators as it determines financial compensations, grid congestion questions and other aspects.…”

Section: Optimization and Scheduling Of Energy Resourcesmentioning

confidence: 99%

Dynamic Aggregation of Virtual Power Plants with a Registry System for Distributed Energy Resources

Dethlefs

Preisler

Renz

2017

IFIP Advances in Information and Communication Technology

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A growing number of Distributed Energy Resources will be connected to the grid. These distributed energy resources have different technical capabilities, operational constraints and are distributed throughout the electricity network which results in an increasing operational complexity. Aggregators controlling these resources in a virtual power plant might need to reconfigure it in case of failing distributed energy resources (e.g. when the weather changes) or due to changing grid conditions on a system operators request. This means that both, aggregator and system operator, must maintain operational reserves such as frequency control which means also rising costs for the actors. Different approaches such as dynamic virtual power plants have been introduced to address these issues. Extending these approaches, the increasing automation and information as well as communication capabilities will allow the actors to exchange and share real-time data about these resource capabilities on demand, allowing the aggregator to dynamically reconfigure the virtual power plant with external capabilities of distributed energy resources prior operation and during runtime, allowing the virtual power plant to be adaptive towards changing situations. For this purpose a common, active registry system for energy resource capabilities is necessary. In this paper, we will describe a process to dynamically aggregate distributed energy resource capabilities a priori as well as during runtime allowing the adaptive reconfiguration of virtual power plants.

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Constraint-Handling with Support Vector Decoders

Cited by 13 publications

References 16 publications

Choosing the right model for unified flexibility modeling

Choosing the right model for unified flexibility modeling

Multi-period flexibility forecast for low voltage prosumers

Dynamic Aggregation of Virtual Power Plants with a Registry System for Distributed Energy Resources

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