Tomonori Sadamoto

Hierarchical distributed control for networked linear systems

In this paper, we propose a design method of hierarchical distributed controllers for networked linear systems. The hierarchical distributed controller has an advantage that an L2-performance of the closed-loop system improves as improving a performance of local controllers that stabilize disjoint subsystems individually. Towards systematic design, we utilize state-space expansion that enables us to deal with the state variables associated with disjoint subsystems and those associated with interference among hierarchically clustered subsystems in a tractable manner. Moreover, by the integration of a hierarchical distributed observer having good compatibility with the structured controller, we build a framework to implement an observer-based hierarchical distributed control. The efficiency of the proposed control system is shown through an example of power networks.

Glocal (global/local) control synthesis for hierarchical networked systems

This paper is concerned with how to develop a new system control theory for providing systematic design methods of hierarchical networked systems composed of various kinds of subsystems from the glocal (global/local) control view point. Through examinations of energy network control we first explain the idea and concept of glocal control and propose a structure of glocal control system consisting multi-resolved sensor, glocal predictor, and glocal controller. We then provide methods of designing glocal predictor and glocal controller which fit the proposed structure with numerical examples.

Spatiotemporally Multiresolutional Optimization Toward Supply-Demand-Storage Balancing Under PV Prediction Uncertainty

Large-scale penetration of photovoltaic (PV) power generators and storage batteries is expected in recently constructed power systems. For the realization of smart energy management, we need to make an appropriate day-ahead schedule of power generation and battery charge cycles based on the prediction of demand and PV power generation, which inevitably involves nontrivial prediction errors. With this background, a novel framework is proposed to maintain the balance among the total amounts of power generation, demand, and battery charging power with explicit consideration of the prediction uncertainty, assuming that consumer storage batteries are not directly controllable by a supplier. The proposed framework consists of the following three steps: 1) the day-ahead scheduling of the total amount of generation power and battery charging power; 2) the day-ahead scheduling of utility energy consumption requests to individual consumers, which aim to regulate battery charging cycles on the consumer side; and 3) the incentive-based management of the entire power system on the day of interest. In this paper, we especially focus on the day-ahead scheduling problems in steps 1) and 2), and show that they can be analyzed in a manner originating from spatiotemporal aggregation. Finally, we demonstrate the validity of the proposed framework through numerical verification of the power system management.

Planning of Optimal Daily Power Generation Tolerating Prediction Uncertainty of Demand and Photovoltaics.

Abstract The concern with renewable energy has been growing. Large-scale installation of photovoltaic (PV) generation and electricity storage is expected to be installed into the power system in Japan. In this situation, we need to keep supply-demand balance by systematically using not only traditional power generation systems but also the PV generation and storage equipment. Towards this balancing, a number of prediction methods for PV generation and demand have been developed in literature. However, prediction-based balancing is not necessarily easy. This is because the prediction of PV generation and the demand forecasting inevitably includes some uncertainty. Against this background, we formulate a problem to plan battery charge pattern while minimizing the fuel cost of generators with explicit consideration of prediction uncertainty. In this problem, given as interval quadratic programming, the prediction uncertainty is described as a parameter in constraint condition. Furthermore, we propose a method to find a solution to this problem from the viewpoint of monotonicity analysis. Finally, by numerical analysis based on this problem and its solution method, we discuss the relation between the minimal regulating capacity and the required battery charge/discharge pattern to tolerate a given amount of prediction uncertainty.

Low-dimensional functional observer design for linear systems via observer reduction approach

This paper proposes a design method for low-dimensional linear functional observers based on a model reduction approach. In contrast to existing methods for designing approximate observers, this method can guarantee not only stability preservation but also an a priori L2-error bound for the observer approximation. Moreover, owing to the fact that this method can be applied to any Luenberger-type functional observers, the method is compatible with the standard feedback gain determination methods, such as pole placement techniques. The efficiency of the proposed method is shown through a numerical example for electric power network systems.

Retrofit Control of Wind-Integrated Power Systems

In this paper, we address several growing concerns of wind power integration from the perspective of power system dynamics and stability. We propose a new control design technique called retrofit control, by which one can control the rotor voltages of doubly fed induction generators to suppress the oscillations in the tie-line power flows caused by a disturbance inside the wind farm. The controller can be designed in a modular way, and also implemented in a completely decentralized fashion using only local feedback from the wind generator states and the voltage at the point of common coupling without depending on the states of any of the synchronous machines in the rest of the system. We show the effectiveness of the design using simulations of the IEEE 68- bus, 16-machine power system model with two wind farms.

Hierarchical distributed design of stabilizing controllers for an evolving network system

In this paper, we propose a hierarchical distributed design method of stabilizing controllers for an evolving network system where a part of the network system changes as installing a new dynamical system, which is called an evolving component. First, supposing that the dimension of the evolving network system is low enough to make the existing controller design methods applicable, we propose a design method of a stabilizing controller such that the whole evolving network system is kept to be stable as long as the evolving component does not spoil the local stability of the part of the evolving network system. Next, on the basis of this controller design, we propose a hierarchical distributed design method of stabilizing controllers for a large-scale evolving network system. The main idea of the hierarchical distributed design is to apply the proposed distributed design to evolving components that are hierarchically decomposed, thereby realizing a scalable handling of large-scale evolving network systems. Finally, we demonstrate the proposed method through an example of evolving power systems.

Projective state observers for large-scale linear systems

In this paper, towards efficient state estimation for large-scale linear systems, we propose a novel framework of low-dimensional functional state observers, which we call a projective state observer, with the provision of a systematic design procedure. The projective state observer can be regarded as a generalization of functional state observers that is defined by means of the orthogonal projection taking into account the system controllability/observability in a quantitative manner. The efficiency of the proposed observer is shown through a numerical example for a reaction-diffusion system evolving over a directed complex network. Moreover, through this observer design, we discuss a trade-off relation between low-dimensionality and an observation error.

Power Supply Scheduling Optimization from a Viewpoint of Spatio-Temporal Aggregation

Abstract With increased attention of renewable energy, a large number of photovoltaic (PV) power generators are expected to be installed into power systems in Japan. In this situation, we consider a problem to make an appropriate schedule on the following day based on the prediction of demand and PV power generation. Since the PV/demand prediction includes non-negligible uncertainty, we need to devise a method for the power supply scheduling explicitly taking into account the prediction uncertainty. Towards a robust power supply scheduling tolerating the prediction uncertainty, first, we introduce spatio-temporal aggregation and provide a fundamental fact on it. Based on this, we show that the scheduling problem can be divided into two subproblems that involve spatially and temporally aggregated variables, respectively. Then, investigating that spatio-temporal aggregation has potential to reduce the influence of the prediction uncertainty, we show that the feasibility of the scheduling problem is improved by the spatio-temporal aggregation. Finally, we show the efficiency of the power supply scheduling based on the spatio-temporal aggregation through a numerical simulation.

Retrofitting state feedback control of networked nonlinear systems based on hierarchical expansion

In this paper, we propose a systematic method to design retrofit controllers for networked nonlinear systems. The retrofit controller, which consists of a linear state feedback controller and a dynamical compensator, can improve the control performance for a subsystem of interest, while guaranteeing the stability of the whole closed-loop system. Towards the retrofit controller design, we introduce a type of state-space expansion, called hierarchical expansion. The cascade structure of the hierarchical expansion realization enables the systematic design of a stabilizing controller for a low-dimensional linear model extracted from the subsystem of interest. As a result, we can design a retrofit controller without explicit consideration of the dynamics of subsystems other than the subsystem of interest. The effectiveness of the proposed method is demonstrated through a power network example.