Christian Hinrichs

Distributed hybrid constraint handling in large scale virtual power plants

In many virtual power plant (VPP) scenarios, numerous individually configured units within a VPP have to be scheduled regarding both global constraints (i.e. external market demands) and local constraints (i.e. technical, economical or ecological aspects for each unit). Approaches for global and local constraint handling have been discussed in the relevant literature independently. A hybrid approach is proposed that combines a decentralized combinatorial optimization heuristic with the encoding of individually constrained search spaces into unconstrained representations by means of support vector data description. The approach is applied to simulated VPP.

A Decentralized Heuristic for Multiple-Choice Combinatorial Optimization Problems

We present a decentralized heuristic applicable to multi-agent systems (MAS), which is able to solve multiple-choice combinatorial optimization problems (MC-COP). First, the MC-COP problem class is introduced and subsequently a mapping to MAS is shown, in which each class of elements in MC-COP corresponds to a single agent in MAS. The proposed heuristic “COHDA” is described in detail, including evaluation results from the domain of decentralized energy management systems.

On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty

Multi-agent systems often consist of heterogeneous agents with different capabilities and objectives. While some agents might try to maximize their systems utility, others might be self-interested and thus only act for their own good. However, because of their limited capabilities and resources, it is often necessary that agents cooperate to be able to satisfy given tasks. To work together on such a task, the agents have to solve a task allocation problem, e.g., by teaming up in groups like coalitions or distributing the task among themselves on electronic markets. In this paper, we introduce two algorithms that allow agents to cooperatively solve a dynamic task allocation problem in uncertain environments. Based on these algorithms, we investigate the influence of inter-agent variation on the systems behavior. One of these algorithms explicitly exploits inter-agent variation to solve the task without communication between the agents, while the other builds upon a fixed overlay network in which agents exchange information. Throughout the paper, the frequency stabilization problem from the domain of decentralized power management serves as a running example to illustrate our algorithms and results.

Conjoint dynamic aggregation and scheduling methods for dynamic virtual power plants

The increasing pervasion of information and communication technology (ICT) in energy systems allows for the development of new control concepts on all voltage levels. In the distribution grid, this development is accompanied by a still increasing penetration with distributed energy resources like photovoltaic (PV) plants, wind turbines or small scale combined heat and power (CHP) plants. Combined with shiftable loads and electrical storage, these energy units set up a new flexibility potential in the distribution grid that can be tapped with ICT-based control following the long-term goal of substituting conventional power generation. In this contribution, we propose an architectural model and algorithms for the self-organization of these distributed energy units within dynamic virtual power plants (DVPP) along with first results from a feasibility study of the integrated process chain from market-driven DVPP formation to product delivery.

Supporting Renewable Power Supply Through Distributed Coordination of Energy Resources

Renewable Energy Sources (RES) are considered a solution for a sustainable power supply. But integrating these decentralized power sources into the current power grid designed for a centralized power supply is a challenging task. We suggest distributed, agent-based and self-organized control algorithms for distributed units in a “Smart Grid” as a promising but challenging solution. Dynamical Virtual Power Plants (DVPP) are introduced as a first prototype of distributed controlled components of a Smart Grid. Tools and methods for a comprehensive evaluation of such new Smart Grid control methods in terms of technological indicators as well as sustainability indicators will be the next challenge in research and development for computer scientists in this domain.

COHDA: A Combinatorial Optimization Heuristic for Distributed Agents

Solving Distributed Constraint Optimization Problems has a large significance in today’s interconnected world. Complete as well as approximate algorithms have been discussed in the relevant literature. However, these are unfeasible if high-arity constraints are present (i.e., a fully connected constraint graph). This is the case in distributed combinatorial problems, for example in the provisioning of active power in the domain of electrical energy generation. The aim of this paper is to give a detailed formalization and evaluation of the COHDA heuristic for solving these types of problems. The heuristic uses self-organizing mechanisms to optimize a common global objective in a fully decentralized manner. We show that COHDA is a very efficient decentralized heuristic that is able to tackle a distributed combinatorial problem, without being dependent on centrally gathered knowledge.

A distributed combinatorial optimisation heuristic for the scheduling of energy resources represented by self-interested agents

The aggregation of controllable distributed energy resources (DER) to virtual power plants (VPPs) forms a possible integration path for DER in future energy systems. The authors present a fully distributed scheduling heuristic for VPPs. The approach is realised by representing each participant of a VPP by a self-interested agent. Both the global, operator-driven scheduling objective of a VPP as well as arbitrary individual local objectives of the agents are integrated efficiently in a fully distributed coordination paradigm. Convergence and termination of the heuristic are proven in the presence of unreliable environments, e.g., with communication delays.

The Effects of Variation on Solving a Combinatorial Optimization Problem in Collaborative Multi-Agent Systems

In collaborative multi-agent systems, the participating agents have to join forces in order to solve a common goal. The necessary coordination is often realized by message exchange. While this might work perfectly in simulated environments, the implementation of such systems in a field application usually reveals some challenging properties: arbitrary communication networks, message delays due to specific communication technologies, or differing processing speeds of the agents. In this contribution we interpret these properties as sources of variation, and analyze four different multi-agent heuristics with respect to these aspects. In this regard, we distinguish synchronous from asynchronous approaches, and draw conclusions for either type. Our work is motivated by the use case of scheduling distributed energy resources within self-organized virtual power plants.

Generalized cascade classification model with customized transformation based ensembles

Classification of high-dimensional data with imbalanced classes poses problems. Especially such time series classification tasks are problematic, because the ordering of each time step (feature) is important and therefore dimensionality reduction and feature selection cannot be applied. The cascade classification model was developed for such time series classification tasks. The cascade classifier splits high-dimensional classification tasks into a cascade of low-dimensional tasks. But the cascade classification model can only handle data sets with a data structure that can be easily learned in low-dimensional space. In this paper, we propose a generalized version of the cascade classification model that can also deal with data sets with more complex data structures. Generalization is achieved with time series transformations and an ensemble of classifiers based on the time series classifier: transformation based ensembles. For this purpose the cascade classifier is integrated into transformation based ensembles with some adjustments. In a simulation study we apply the generalized cascade classification model to predict the realizability (feasibility) of power production time series for pools of different numbers of micro combined heat and power plants. We show that the choice of the aggregation scheme for the ensemble members in the generalized cascade classification model has a strong impact on the overall classification results. But the choice of a weighting scheme showed hardly any influences on the classification result. Furthermore, data sets of different complexity (different structures in data space) yielded very similar classification results.

Local soft constraints in distributed energy scheduling

In this contribution we present an approach on how to include local soft constraints in the fully distributed algorithm COHDA for the task of energy units scheduling in virtual power plants (VPP). We show how a flexibility representation based on surrogate models is extended and trained using soft constraints like avoiding frequent cold starts of combined heat and power plants. During the task of energy scheduling, the agents representing these machines include indicators in their choice for a new operation schedule. Using an example VPP we show that our approach enables the agents to reflect local soft constraints without sacrificing the global result quality.