Masakazu Koike

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.

Mass density sensor for liquids using ZnO-film/Al-foil Lamb wave device

An application of A/sub 0/ mode Lamb wave devices employing ZnO-film/Al-foil composite structures to mass density sensors for liquids is discussed. By using the variational technique, the effect of liquid-loading upon A/sub 0/ mode Lamb wave propagation in the device is analyzed, and a simple relation between the A/sub 0/ mode Lamb wave velocity and physical conditions is derived. The results suggest that the mass density sensor could be realized by measuring the fractional change in A/sub 0/ mode Lamb wave velocity. It is experimentally shown that the center frequency of the fabricated A/sub 0/ mode Lamb wave device distinctly shifts due to various liquid-loading. The fractional change in the velocity is almost proportional to the mass density of liquids. The error of the measured mass density for seven different liquids is within about 5%.<<ETX>>

Lamb Wave Devices Employing ZnO-Film/Al-Foil Composite Structure

The paper proposes Lamb wave devices employing ZnO-film/Al-foil composite structure for the application to liquid and gas sensors. Theoretical analysis suggested that in particular, the velocity of the lowest-order antisymmetrical Ao mode is significantly reduced by loading liquids, which is mainly due to the mass-loading effect. This was experimentally confirmed from the centre-frequency shift in water-loaded devices. It is concluded that devices based on Ao mode possess the possibility of developing a mass-density sensor in liquids.

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.

Multiresolved control of discrete-time linear systems based on redundant realization via Wedderburn rank reduction

In this paper, we propose a design method of multiresolved control for discrete-time linear systems. In the proposed control system, we implement a transitory compensator specialized in controlling a short-term system behavior into a standard controller that is designed for controlling a long-term behavior. To establish such control architecture, we construct a low-rank model having the same reachable and observable subspaces as those of the original system in the range of its rank. Then, we derive a redundant state-space realization associated with the low-rank model. A cascaded structure of the redundant realization enables to systematically design a transitory compensator that stabilizes the short-term system behavior while cooperating with a standard controller. The efficiency of the multiresolved control is shown through an illustrative example of frequency control in power networks.

Multivariate Control Design Considering Quantization Error and Input Time-Delay for Pneumatic Isolation Table

An active control in a pneumatic isolation table which is driven by on-off air valves is considered in the present paper. A control method for the table with quantized input action caused by the on-off valves has been proposed for rapid vibration suppression. The proposed method determines the control input by using the Lyapunov function for avoiding instability against the quantization of the control input. The method exhibits vibration attenuation performances that are superior to the conventional methods in some experiments. However, this method suppresses vibrations in only the vertical direction. Therefore, improvement of the vibration suppression performance is required in not only the vertical direction but also the roll and pitch directions. In the present paper, a conventional method is extended to a multivariate control system. The effects of the extended method are verified by numerical simulations and experiments.Copyright

Power system operation with battery charge/discharge scheduling based on interval analysis

AbstractThe use of photovoltaic (PV) generation forecasts in economic load dispatching control, which includes the unit commitment of conventional power plants, is essential to ensure the economic performance and the reliability of power systems. In the previous study, we developed a day-ahead charge and discharge scheduling method of battery energy storage systems based on interval analysis using prediction intervals of a PV generation forecast; this interval forecast considers forecast errors and gives not only the forecasted output but also the possible range of the actual output with a certain confidence. In this study, we evaluate the proposed scheduling method by numerical simulations in terms of the power system supply and demand operation.

Hierarchical decentralized observers for networked linear systems

In this paper, we propose a design method of hierarchical decentralized observers for networked linear systems. In this method, based on suitable state-space expansion of the network systems, we, first, find a high-dimensional dynamical compensator that can achieve ideal performance for decentralized state estimation. Next, fully utilizing model reduction techniques, we extract a subspace that is essentially relevant to the decentralized state estimation, from the high-dimensional state-space of the dynamical compensator. This procedure successfully produces a lower-dimensional compensator that guarantees not only the stability of the estimation error but also desirable estimation performance with respect to tracking the system behavior for external input signals. Finally, the efficiency of the proposed method is shown through an illustrative example of power networks.

Day-ahead scheduling for supply-demand-storage balancing - model predictive generation with interval prediction of photovoltaics

Large-scale penetration of photovoltaic (PV) power generators and storage batteries is expected into the power system in Japan. To maintain the supply-demand balance with energy storage, the optimal power generation and the charge/discharge power of storage batteries can be determined in a manner of the model predictive control of generators. In view of this, this paper addresses a problem of the day-ahead scheduling for the supply-demand-storage balance with explicit consideration of the model predictive power generation. This scheduling is performed by using demand prediction, whose uncertainty is expressed in terms of interval prediction. Formulating the day-ahead scheduling problem as an interval-valued allocation problem, we give a solution to it by taking an approach based on the monotonicity analysis with respect to the optimal solution. Finally, the efficiency of the proposed method is verified through a numerical simulation, where we use an interval prediction of PV power generation constructed by experimental data.