Hiroyuki Kita |
Faculty of Information Science and Technology Systems Science and Informatics System Synthesis |
Professor |
This paper proposes the model reduction method for smart inverters having Frequency-Watt control. The proposed reduction method consists of the following three steps: (1) derive the mathematical model of the smart inverter connected to low-voltage feeder, (2) combine the smart inverter mathematical model developed in (1) with the mathematical models for upstream electric circuits consisting of series impedances, line impedances, and transformers, and (3) aggregate the combined models found in (2) using the Padé approximation method or the Routh approximation, which can derive the aggregate transfer functions for the system with different poles. The proposed reduction method is also designed with the current limit of smart inverters. The proposed model reduction method can generate an equivalent aggregated dynamic model for smart inverters, seen from the distribution substation. The validity of the proposed reduction method is ascertained through computational simulation on the Matlab/Simulink environment.
The authors focus on a microgrid (MG) having hydrogen production processes as a part of demand. The studied MG consists of a photovoltaic (PV), a battery storage system, two different type electrolyzers, and electric/fuel steam boilers and supplies for the electric, thermal and hydrogen demands. This paper proposes detailed operation scheme which consists of two stages, namely, the day ahead scheduling and the real-time (RT) control. The day ahead scheduling optimizes the operations of system components in the next day based on the forecasts for PV output and demands. The RT control provides the actual control command to individual system component based on the actual PV output and demands monitored online. The effectiveness of the proposed scheme is ascertained through some computational case studies. Performance of the proposed scheme is evaluated in terms of the total energy cost, the curtailed PV output and the hydrogen production volume.
Off-grid distribution system would be a promising option for a marginally viable community. However, technical challenge regarding photovoltaic generation employment in an off-grid distribution system is its uncertain and fluctuating generation output, which would require larger energy buffer and regulation capability. In this paper, it is proposed a probabilistic operation scheduling method for an off-grid considering the uncertainties in photovoltaic generation output and electricity demand. The proposed scheduling method uses both the probabilistic power flow analysis and the optimal power flow technique. Effectiveness of the proposed scheduling method is validated through numerical case studies for the assumed test off-grid model.
In distribution systems, a large amount of photovoltaic systems (PVs) is being introduced, which may make the voltage profile more complicated. In order to accommodate PVs as much as possible, more advanced voltage management scheme should be developed. For such purpose, online voltage profile monitoring would be helpful. however, it needs installation of many sensors into the distribution system. On the other hand, smart meters and IT switches are being installed in Japan. That is, the distribution systems are being rich-information circumstances. Based on the above recent trend, this paper proposes voltage profile estimation methods based on the state estimation technique. The numerical case studies for the distribution network model with 2,160 consumers were carried out to validate the estimation performance.
1.はじめに
長谷川淳氏は,電力系統工学が独立した技術分野として歩み始めた昭和40年代初頭より,電力系統の解析・計画・運用・制御に関する研究を推進され,40年以上の長きにわたり,電力および電力系統技術の発展に貢献された。特に,次世代の新しい電力システムとして,「高柔
Recent growth of renewable energy (RE) generations with natural variability, would make the demand and supply regulation in a whole power system more difficult, and therefore, alternatives for demand and supply regulation resources would be required. The authors focus on co-generation system (CGS) owned by the consumers as one of regulation resources and have proposed a novel optimal operation strategy of CGSs to provide the demand and supply regulation. This paper discusses the optimal installation design including two configurations of CGS based on the economic viewpoints considering the energy supply cost and the financial incentive associated with the contribution for demand and supply regulation. The discussions are based on numerical case studies with the actual electrical and thermal demand profiles and equipment cost of CGS.
Recent growth of renewable energy (RE) generations with natural variability, such as photovoltaic generation and wind turbine generation, would make the demand and supply control in a whole power system more difficult, and therefore, alternatives for demand and supply regulation resources would be required. The authors focus on co-generation system (CGS) as one of regulation resources. In order to procure adequate volume of regulation capability, an aggregator coordinates a number of CGSs efficiently and flexibly considering the wide variety of electricity/thermal demands of CGS owners. This paper proposes a novel optimal operation strategy of CGS coordinated by the aggregator considering the energy balance and operation cost of individual CGS owner. This paper also demonstrates the availability of CGSs for regulation capability by numerical case studies in which the actual consumption profile is employed.
From growing interests in the environment issues, promotion of photovoltaic power generation (PV) is accelerated in the world. Meanwhile, rapid chargers (RCs) for popularized electric vehicles are being installed in urban areas. These two trends in distribution system might cause severer voltage fluctuation problems. On the other hand, a RC can provide the reactive power support, which is capable of voltage regulation. Based on this viewpoint, this paper proposes a new framework of voltage regulation, in which the reactive power compensation by RCs is actively utilized. The proposed voltage regulation method combines two different control functions with consideration for over-compensation avoidance. This paper ascertains the validity of proposed voltage regulation method through numerical simulations.
Recently, a large number of renewable energy (RE) sources such as wind farms (WFs), photovoltaic generations (PVs) have been introduced to power systems as a solution for the worldwide environmental issue. On the other hand, with the deregulation of the electric power industry such as the full liberalization of retail sector, power producer and suppliers who own RE sources have traded the generated electricity in the electric market. However, power outputs of RE sources fluctuate every moment and it is impossible to predict the power output perfectly. Thus, to operate RE sources according to the generation schedule notified in the market while suppressing their power output fluctuation, cooperation of energy storage systems with RE sources are required. This paper proposes a method for determining the scheduled generation of WFs by cooperating the predicted WF power output and heat pumps (HP)/biogas engine generator (BG) heat supply system (HP/BG heat supply system) which is a new energy storage technology developed by the authors. The proposed methods consist of two steps. In the first step, optimal operations of HP and BG are determined by utilizing the predicted WF generation so that the flexibility of HP/BG system can be ensured. In the second step, the operations of HP and BG are modified based on actual WF outputs including predicted errors. The validity and effectiveness of the proposed method are investigated through some computational simulations using MATLAB.
This paper focuses on the minimization of energy costs for a factory with a non-utility generation facility, where exact load following is difficult. The factory purchases any shortfall in energy from utilities under a demand contract. In the demand contract, the amount of energy that can be purchased in a 30 minute period is limited. To reduce energy costs, we propose load follow-up PI control with bias and fuzzy PI control based on the Takagi-Sugeno fuzzy model. The fuzzy PI control method considers the elapsed time and purchasable energy during each 30 minute period. We also optimize the control parameters and the purchasable energy in the demand contract by Particle Swarm Optimization (PSO). The evaluation function in PSO is the increase in monthly costs considering the energy cost per unit for the generation facility, and the purchasing price and basic contract rate for the utilities. The function is estimated by simulation using the available data for the factory. The optimization of the function is carried out using actual load data. The validity of the proposed methods is evaluated by comparing the approximate cost increase with that under fixed energy output from the generation facility.
Computational simulation is essential to evaluate the impact wind turbine penetration on the demand and supply power control. The observed time-series of wind turbine output in entire power system is required as input data for computational simulation, but it is difficult to obtain. In this study, we propose the method to generate simulated time-series of wind turbine output with conversion of wind speed by location and height based on Weibull distribution. In particular, wind speed data published by Japan Meteorological Agency and Weibull parameter by NEDO are utilized.
The output fluctuation of renewable energy generation may disturb the stable power system operation. Against this issue, the authors are investigating the feasibility of Power to Heat (P2H) technology, in which co-generation systems and other heat-supply devices are utilized cooperatively to provide adjustable electricity generation/consumption. This paper introduces the heat pump (HP) and the biogas engine generator (BG) system as one of the P2H technology, and proposes the detailed control strategies. The proposed control strategies are composed of "day-ahead scheduling" and "real-time operation". Day-ahead scheduling decides operation schedules for HP and BG which minimize total output fluctuation using forecasted output of wind farm. Real-time operation revises the result of day-ahead scheduling to satisfy the control target using actual output of wind farm. Effectiveness of those proposed control strategies are evaluated computational simulations.