This paper considers a hierarchical decentralized management of virtual power plants. Virtual power plants serve as aggregator of energy resources, including generators, storage and controllable loads, to support their balancing market integration. A virtual power plant becomes a largescale distributed system having a large number of power equipment, and a centralized management methodology may not be suitable for its operation. We investigate the real-time pricing strategy and individual optimization by each participants. The proposed hierarchical decentralized management methodology can mitigate the effects of uncertainties in the power generation and load profiles and allow plug-and-play type operation of power equipment. The effectiveness of the proposed hierarchical decentralized management methodology is evaluated through numerical experiments of the virtual power plant consisting 58 power equipment.
This paper constructs a control system in which one highly mobile agent navigates autonomous multiple agents escaping from the agent according to nonlinear interaction. This study is motivated by the sheepdog system: a flock of thousands of sheep are controlled by only a few sheepdogs. Inspired by the sheepdog system, we have proposed a control law of a sheepdog to solve the problem of one sheep converging on a circular trajectory around a goal by one sheepdog, which is called the mobile control. In this paper, we extend this controller to applicable to a flock of sheep by treating multiple sheep as “a disc with a certain flock radius”. This paper deals with the problem of navigating a flock of sheep to converge their centers of gravity on a circular trajectory around a goal position. For this problem, we propose a flock model of sheep and an extended mobile control designed by considering the suitable distance between a sheepdog and a center of the flock. Finally, we verified the validity of the proposed method through numerical simulation and robot demonstrations.
The projection type iterative learning identification method has several advantages such as: (i) no time-derivatives of input/output signals are required and (ii) it gives unbiased estimations. However, this identification method requires a parameterized model obtained by projecting a tracking error signal onto a finite-dimensional subspace, and the parameterized model must be estimated in advance. The model is called a parameter space representation in this paper. This paper presents an approach for estimating the parameter space representation required for the projection type iterative learning identification method. The proposed method is based on the projection of the estimated error signal onto the finite-dimensional signal subspace whose basis is determined by the closed-loop system with the estimated model. The benefits of the proposed method in comparison with existing method are illustrated with simulation studies.