5,672 research outputs found
Embedded intelligence for electrical network operation and control
Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
Inverse Reinforcement Learning in Swarm Systems
Inverse reinforcement learning (IRL) has become a useful tool for learning
behavioral models from demonstration data. However, IRL remains mostly
unexplored for multi-agent systems. In this paper, we show how the principle of
IRL can be extended to homogeneous large-scale problems, inspired by the
collective swarming behavior of natural systems. In particular, we make the
following contributions to the field: 1) We introduce the swarMDP framework, a
sub-class of decentralized partially observable Markov decision processes
endowed with a swarm characterization. 2) Exploiting the inherent homogeneity
of this framework, we reduce the resulting multi-agent IRL problem to a
single-agent one by proving that the agent-specific value functions in this
model coincide. 3) To solve the corresponding control problem, we propose a
novel heterogeneous learning scheme that is particularly tailored to the swarm
setting. Results on two example systems demonstrate that our framework is able
to produce meaningful local reward models from which we can replicate the
observed global system dynamics.Comment: 9 pages, 8 figures; ### Version 2 ### version accepted at AAMAS 201
Distributed multi-agent algorithm for residential energy management in smart grids
Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power
An Exchange Mechanism to Coordinate Flexibility in Residential Energy Cooperatives
Energy cooperatives (ECs) such as residential and industrial microgrids have
the potential to mitigate increasing fluctuations in renewable electricity
generation, but only if their joint response is coordinated. However, the
coordination and control of independently operated flexible resources (e.g.,
storage, demand response) imposes critical challenges arising from the
heterogeneity of the resources, conflict of interests, and impact on the grid.
Correspondingly, overcoming these challenges with a general and fair yet
efficient exchange mechanism that coordinates these distributed resources will
accommodate renewable fluctuations on a local level, thereby supporting the
energy transition. In this paper, we introduce such an exchange mechanism. It
incorporates a payment structure that encourages prosumers to participate in
the exchange by increasing their utility above baseline alternatives. The
allocation from the proposed mechanism increases the system efficiency
(utilitarian social welfare) and distributes profits more fairly (measured by
Nash social welfare) than individual flexibility activation. A case study
analyzing the mechanism performance and resulting payments in numerical
experiments over real demand and generation profiles of the Pecan Street
dataset elucidates the efficacy to promote cooperation between co-located
flexibilities in residential cooperatives through local exchange.Comment: Accepted in IEEE ICIT 201
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