Presentations

This paper deals with a model of a real-time electricity market for a smart grid, and discusses how to decide wholesale and retail prices of electricity among market participants: power consumers, power suppliers and an independent system operator. In this paper, we consider a linearized AC model of a power grid with a direct current process and show that the wholesale price for suppliers is determined as the optimal Lagrange multiplier corresponding to the nodal power balance constraints. In addition, we propose a decision procedure for the retail pricing of electricity for consumers. In order to improve the convergence speed of the algorithm, we apply the iterative gradient method with line search to a retail pricing decision procedure, in which the optimal step size is determined in each iteration step. Simulation results, finally, show the effectiveness of the proposed retail pricing decision procedure.

In this paper, we study cooperative control problems with a multi-UAV system. Specifically, we propose a c consensus-based cooperative formation control strategy with collision-avoidance capability for a group of multiple unmanned aerial vehicles (UAVs). To cooperatively fly in formation, a consensus-based algorithm and a leader-follower structure are applied to the UAVs. Collisions among the UAVs can occur while they are flying with the cooperative control algorithm. Therefore, we describe how the cooperatively controlled UAVs avoid collisions while they are flying to desired position for formation. The collision-avoidance strategy is based on an artificial potential approach. The convergence is guaranteed even when the cooperative formation control algorithm and the collision-avoidance control algorithm are simultaneously applied to the quadrotors. Finally, the proposed approach is validated by some simulations.

The purpose of this research is to detect a person with a sensor network that uses CO2 sensors and to guide a rescue robot to the position at which the person is located. Whena person is in a position where sensors are arranged, that person can be detected, but when a person is in a position where sensors are not arranged, detection cannot be performed. Therefore,in this paper, we propose interpolating CO2 concentrations between sensors so that detection of a person can be performed in regions where sensors are not arranged. First, the wind speed required for gas concentration interpolation is estimated from the observed sensor values. Then, the algorithm for interpolating the CO2 concentration between sensors using the estimated wind speed is shown. Finally, the validity of the proposed method is verified by simulation.

In this paper, we study cooperative control problems with a multi-UAV system expressed as a fourth-order system using a consensus algorithm. We first show that a linearized model of quadrotors is expressed as a fourth-order system, and then propose a formation control algorithm for the fourth-order system after formulating a problem. The proposed control law is based on a consensus algorithm and a leader-follower structure is also applied to the control law so that a leader can individually provide each of the quadrotors with information of their desired states. And then, we show that the proposed control algorithm can guarantee accurate formation keeping when fundamental assumptions about the network composed of the multiple UAVs are satisfied. Finally, the proposed approach is validated by some simulations.

Dynamic pricing (a.k.a. real-time pricing) is a method of invoking a response in demand pricing electricity at hourly (or more often) intervals. Several studies have proposed dynamic pricing models that maximize the sum of the welfares of consumers and suppliers under the condition that the supply and demand are equal. They assume that the cost functions of suppliers are convex. In practice, however, they are not convex because of the startup costs of generators. On the other hand,many studies have taken startup costs into consideration for unit commitment problems (UCPs) with a fixed demand. The Lagrange multiplier of the UCP, called convex hull pricing (CHP), minimizes the uplift payment that is disadvantageous to suppliers.However, CHP has not been used in the context of demand response. This paper presents a new dynamic pricing model based on CHP. We apply CHP approach invented for the UCP to a demand response market model, and theoretically show that the CHP is given by the Lagrange multiplier of a social welfare maximization problem whose objective function is represented as the sum of the customer’s utility and supplier’s profit. In addition, we solve the dual problem by using an iterative algorithm based on the subgradient method. Numerical simulations show that the prices determined by our algorithm give sufficiently small uplift payments in a realistic number of iterations.

In this paper, the problem of determining the size of battery storage used in grid-connected photovoltaic(PV) systems is discussed. In this problem, our goal is to find optimal capacity and charge/discharge planning for Li-ion batterys to minimize the power purchase cost. We propose a novel charge/discharge planning method based on optimization technique considering Li-ion battery characteristics to expand battery life. Finally, the effectiveness of the proposed method is shown via simulations.

This paper proposes a cooperative control of multi-agent system to capture target object. When the agents capture the target object, they may be subjected to reaction force from it. Therefore, we have to consider the physical contact between the agent and target object. First, we introduce the second order model to consider reaction force from target object. The network topology among agents is time-varying and connected, and information of target object is allowed to be available to at least one agent. Second, we show the proposed control law guarantees the agents capture the target object. The control law consists of graph theory and consensus algorithm, and we analyze the convergence property. Finally, simulation results show the effectiveness of the proposed control law.

This paper deals with Multi-Robot SLAM for large scale map building. Specifically, each robot estimates a local map using EKF, and we merge these local maps into a global map. In this paper, we provide a new RLS based algorithm for map merging. First, we transform local maps into relative information which is considered as measurements for the global map. Then, we update the state estimate by RLS considering the weighting of measurements, which is determined by error propagation from the EKF SLAM. We prove the convergence of the error covariance matrix in this algorithm. In experimental results, we confirm the validity of the proposed algorithm and correctness of derived theorems for the convergence.

This paper deals with control of smart grid by using distributed Model Predictive Control to control the load frequency of the power network. The control objective is to minimize the cost function while still keeping the frequency deviation constrained to a safe level. Some different control structures are proposed and control law and constraints are derived. Lastly we show the effectiveness of the different controllers by using a large scale power network simulation.

In this paper, we study cooperative control problems with a multi-UAV system expressed as a first-order system. Specifically, we describe a problem that cooperatively controlled UAVs change geometric configuration between multiple UAVs while the UAVs are flying in formation. In other words, each of the UAVs must intensionally change relative position between the UAV and the leader while the UAVs are cooperatively controlled. We propose a control algorithm to change geometric configuration among the UAVs arbitrarily while the cooperatively controlled UAVs are flying in formation. The control algorithm is based on a consensus algorithm. A leader-follower structure is also applied to provide the UAVs with commands from a leader. The control algorithm has an advantage in finding a controller gain to stabilize the multi-UAV system. In addition, the convergence speed does not depend on the network structure of the system. Finally, the proposed approach is validated by some simulations.

This paper deals with the simultaneous localization and mapping (SLAM) problem via the H∞ filter with a known landmark. By adding the observation of a known landmark to those of unknown landmarks, the linearized SLAM model satisfies its observability, and its estimation accuracy is improved. To prove the improvement theoretically, this paper shows that the determinant of the estimated error covariance matrix with the observation of a known landmark becomes small compared with that of the conventional H∞ filter. The convergence of the error covariance matrix is also proven in this paper. With simulations and experimental results, we confirm that the derived theorems for the convergence are correct and that we can accurately estimate the state of the robot and the environment.

This paper addresses wind power prediction which is known to be a key technology in EMS(Energy Management Systems). In recent years, an introductory expansion of renewable energy is expected and the prediction of wind power generation is needed for taking in wind power generation. The goal of this work is to predict the amount of generation in the next day from the past actual data and the weather forecast data of wind. In this paper, 24 hours ahead power prediction method using a filtering theory is proposed for wind power generation. The prediction method is a simple algorithm, the procedure of prediction consists of two steps, the data processing and the calculation of predicted values. In the data processing, in order to get the correlative data from the database, we employ JIT(Just-In-Time) Modeling. In the calculation of predicted value, we provide the regression model for wind speed and wind power, and the unknown parameters are estimated via constrained kalman filter. Finally, the advantages of the proposed method over the conventional method are shown through simulations.

This paper considers a day-ahead market with batteries and accumulators to level power generation. First, we model a consumer with battery, a generator with battery, and an accumulator that each act to maximize their own profit. Then, we not only propose the optimal pricing algorithm based on dual decomposition and the steepest descent method but also prove the stability of this algorithm. Finally, we demonstrate the effectiveness of the algorithm through some numerical simulation.

This paper proposes a game theoretic real-time pricing method based on dual decomposition and its application to the load frequency control of power networks. The proposed pricing method aims to solve the constrained optimization problem,which consists of each player’s utility, and social welfare under selfish players. A selfish player’s decision is a Nash equilibrium solution that considers his/her own cost function. We show that this method can influence a selfish player’s decision toward social welfare maximization. Finally, we show that the proposed method is effective for load frequency control on the basis of simulation results.

In this paper, a method for passivity- and robust integral of the sign of the error (RISE)-based control of a nonlinear teleoperation system with communication delays is proposed. This control strategy can accurately achieve coordinate positioning under conditions with viscous friction and unmodeled effects compensation errors, and its stability condition is independent of robot model parameter uncertainties and time delay. Using passivity-based stability analysis, the stability and tracking performance of this system are demonstrated, and experimental trials are performed to verify its effectiveness.

This paper discusses cyber attacks and faults diagnosis method for networked electrical power systems. In this paper, a power system is divided into some areas, and then, some parameters are estimated by Kalman filter in each area. Then we propose a diagnosis method by using called state and output fault diagnosis matrices which are composed of the estimated values, output matrix and so on. These diagnosis matrices can immediately judge which nodes of which area is attacked. Finally, some simulation results are shown to validate the proposed approach.

エネルギー問題や地球温暖化は世界的な課題であり、省エネルギー、コスト削減の観点から世界中で太陽光発電や風力発電バイオマス発電、コージェネレーションなどの分散型電源が大量に電力系統に連系されようとしています。導入自体は地球環境保護の観点からは好ましいことですが、分散型再生可能エネルギーを本格的に導入するためには、大規模・複雑化していくシステムをどのような形態で接続するべきかという電力ネットワークトポロジーの視点から、電力システムの安定性、最適性、経済性を再考する必要が生じます。 本講習会では、このような分散型電源の大量導入に伴う問題や、それらの管理・最適制御・運用技術とその最先端の研究動向について、分かり易く講義します。

A control problem of power network systems including renewable energy generators is one of the hottest issues in the world. This talk deals with a game theoretic real-time pricing method based on dual decomposition and its application to load frequency control of electrical power networks. The goal of this real-time pricing methodology is to solve the constrained optimization problem consist of each players' utility and social welfare under selfish players. We can show that selfish players‘ decision can be expressed via a kind of a Nash equilibrium solution considering their own cost functions and it can lead selfish players‘ decision to social welfare maximization. Finally the proposed method is applied to a load frequency control problem of power networks and the effectiveness can be shown via some numerical simulation results.

電力需要／発電予測とデマンドレスポンス、大規模システムに対する分散・予測・最適制御、分散型負荷周波数制御を解説。

A control problem of power network systems including renewable energy generators is one of the hottest issues in the world. This talk deals with a game theoretic real-time pricing method based on dual decomposition and its application to load frequency control of electrical power networks. The goal of this real-time pricing methodology is to solve the constrained optimization problem consist of each players' utility and social welfare under selfish players. We can show that selfish players' decision can be expressed via a kind of a Nash equilibrium solution considering their own cost functions and it can lead selfish players' decision to social welfare maximization. Finally the proposed method is applied to a load frequency control problem of power networks and the effectiveness can be shown via some numerical simulation results.