論文

The frequency synchronisation problem of wide-area control from the higher level of networked power systems is considered. We investigate the asymptotic convergence and robustness of the passivity-based distributed averaging control used in damping the inter-area oscillations of power networks and coping with the time-varying power demand. A strict composite storage function is constructed that allows us to quantify the asymptotic convergence rate of the closed-loop system. Furthermore, the power sharing and (Formula presented.) gain performance are utilized as a tool to quantify the impact of time-varying power demand on control areas. We simulate the controller performance on a four-area network which is equivalent to the IEEE New England 39-Bus system.

The integration of renewable energy resources in modern power systems promotes flexible demand response but poses challenges to power balance and frequency stability due to their intermittent generation. To address this problem, this study deals with the frequency control of power system in the presence of demand responses. The dynamic electricity pricing scheme enabling both demand response participants and suppliers to contribute to the frequency regulation via their own decision-making process is proposed. The main concern in the controller design is the integration of physical and human systems on the same timescale, encompassing controllers based on frequency dynamics and dynamic pricing. Therefore, event-triggered conditions are proposed to decrease the communication frequency while ensuring system stability, leveraging the passivity property of the system. Under the proposed event-triggered conditions, the authors clearly demonstrate the asymptotic stability around the equilibrium point of the entire system. Furthermore, a numerical simulation using a four areas power network system is performed, confirming the effectiveness of the proposed control scheme and the stability of the system.

The problem of decentralized control for overall system stability has received increasing attention due to heterogeneity in power networks. To address this problem, two passivity indices are introduced to characterize the shortage of nonlinear negative imaginary property and (Formula presented.) stability of the coupling network. In a modular decomposition, novel passivity-short-based conditions in a decentralized manner are investigated to ensure asymptotically stability of the overall system and (Formula presented.) stability of the network simultaneously, which is so-called system-wide stability with (Formula presented.) gain. Furthermore, the local subsystems are equipped with a linear matrix inequality-based controller, implementing decentralized stabilization. A numerical simulation with three heterogeneous bus dynamics is illustrated to demonstrate the effectiveness of the proposed controller design considering load change and measurement errors, offering insights into system robustness.

This paper proposes a framework for compensating fluctuations of renewable energy generation by adjusting charging amount of Electric Vehicles (EV) in a microgrid. We have three purposes: matching supply and demand, minimizing the microgrid’s cost, and maintaining the convenience of EV while adjusting charging amount depending on the generation from renewable sources. In order to achieve these purposes, we propose two-stage framework, Stackelberg-game approach, and considering expected utility of EV. As a result, the microgrids could determine their energy management strategy while considering their cost and EV’s convenience, and therefore positive economic effect has been created, matching supply and demand.

This paper proposes a framework for compensating fluctuations of renewable energy generation by adjusting charging amount of Electric Vehicles (EV) in a microgrid. We have three purposes: matching supply and demand, minimizing the microgrid’s cost, and maintaining the convenience of EV while adjusting charging amount depending on the generation from renewable sources. In order to achieve these purposes, we propose two-stage framework, Stackelberg-game approach, and considering expected utility of EV. As a result, the microgrids could determine their energy management strategy while considering their cost and EV’s convenience, and therefore positive economic effect has been created, matching supply and demand.

This paper discusses optimal energy management considering battery characteristics for smart grid and microgrid systems. Energy storage systems are very important for energy peak shifting and peak load cutting in microgrid and smart grid systems, but optimal battery formulation and energy management in the use of expensive energy storage system and methods of taking account of battery characteristics are still in the developmental stage. First, the optimal battery placement for a smart grid system with lithium-ion batteries is considered as a means of lifetime extension. The battery choice index for this optimal battery utilization problem of optimal energy management is discussed. Second, a smart grid system with a distributed battery system for grid frequency control with consideration of distributed control and battery characteristics is discussed. Optimal controllers for distributed battery systems to reduce the battery load and to expend the battery lifetime are designed. Simulations are used to demonstrate the effectiveness of the proposed method in comparison with conventional methods.

© 2018 IEEE. Ahstract-This paper deals with merging control of automated vehicles using distributed model predictive control. We consider a road model which has two main lanes. In the distributed control algorithm, vehicles repeat sharing the planned future trajectory with other vehicles traveling in the vicinity of each vehicle by using communication and replanning its own trajectory considering the future trajectory of other vehicles. Moreover, in order to further improve the stability of model predictive control, terminal constraints are set and feasible conditions for optimization problems under the terminal conditions are derived. At the last part of this paper, we confirm the effectiveness of proposed methods through simulations.

© 2018 The Society of Instrument and Control Enginners - SICE. This paper addresses a merging problem of connected and automated vehicles at single lane with two different freeways roundabout. First, we assume that the merging vehicle already knew the environment of the roadway beforehand. After that, we formulate the optimization problem based on model predictive control framework which includes lane keeping and collision avoidance. Finally, we show in the simulation results that the proposed solution is capable of merging the vehicle at the roundabout effectively.

© 2018 The Society of Instrument and Control Enginners - SICE. In this paper, we deal with the charging scheduling optimization problem of electric vehicle using Stackelberg game. Stackelberg game is one of game theory classified as hierarchical, repeating, and non-cooperative game. The charging station determines the price to maximize its own profit from selling energy and each EV determines the energy demand to maximize the charge benefit by competing with other EVs. At this time, we guarantee that Nash equilibrium exists within the EV group. Finally, using numerical simulation, we show that the game reaches an Stackelberg equilibrium.

© 2018 The Society of Instrument and Control Enginners - SICE. In this paper, load frequency control by optimal control algorithm using quasi-Newton method for microgrid introducing diesel generator and battery is discussed. In the proposed method, we consider microgrid model including diesel generator and battery. Also, saturation is taken into account as a constraint on the input to the model of diesel generator and battery. Then, a cost function considering the storage capacity constraint is formulated. Therefore, we propose an algorithm using the quasi-Newton method to determine inputs that minimize the formalized cost function. In addition to the quasi-Newton method, we propose an algorithm using Backward Sweep to match the terminal condition. Finally, we confirm the effectiveness of the proposed method through numerical simulations.

© 2018 AACC. This paper addresses optimal power demand management in the electricity market. First, we model the behavior of players, consumers, aggregators, and the market. Each consumer entity acts to maximize its own profit. The aggregator decides how much individual consumer power demand should be reduced if total power demand exceeds power generation constraints. We propose a method for an aggregator to make this decision using mechanism design and matching theory. Finally, we show that an algorithm can manage power demand and improve consumers' profits using simulation results.

In this paper, load frequency control problem for power system is discussed. The control is conducted to retain power demand supply balance by controlling output of generators in power network. However, it becomes harder to retain the balance because of large volume injection of renewable energy whose output is uncertain. To resolve the problem, some approaches are considered in this research field. One is to use dispersion type power sources like battery in addition to conventional generators like thermal power generator and hydroelectric generator. Another is to apply new control theories and methods. The other is the load frequency control considering demand response by using a controllable load on the consumer side. Especially among them, in this paper, power system which holds large volume wind turbine generator, turbine generator and battery is focused on and new control method is proposed. Concretely, load frequency control based moving horizon estimation and robust model predictive control is proposed in order to satisfy the constraints of state and control input.

© 2019 IEEE. This paper addresses the optimal control problem (optimal acceleration and deceleration to minimize fuel consumption) for Connected and Automated Vehicles (CAVs) to cross an intersection with state and control constraints. First, we obtain the crossing time for the CAVs to cross the intersection with Mixed Integer Linear Programming (MILP). Then, the crossing time is used as a terminal time to formulate the optimal control problem and we utilized analytical solution (Hamiltonian analysis) to solve the problem. Finally, we simulated the proposed approach and the results show that, even with the frequently changing crossing time, the controller is capable of providing the solution to the optimal control problem.

This paper presents the optimal motion planning problem for connected and automated vehicles (CAVs) to cross a conflict area at an intersection with state and control constraints. First, we formulate the scheduled merging (or crossing) time for all CAVs as a mixed integer linear programming (MILP) problem where the merging time is solved frequently. Second, we formulate the optimal motion planning problem so that the CAVs can achieve their scheduled merging time as well as minimizing the energy consumption. Since we solve the motion planning problem analytically, not all the solutions are feasible to comply with the frequently updated merging time. To solve this problem, we propose a feasibility enforcement period (FEP). Then, we validate the proposed solution through simulation, and the results show that even the merging time is frequently updated, the CAVs can still achieve the merging time with a minimal deviation. Besides, our proposed framework also shows a significant improvement in terms of travel time as compared to the conventional one.