Offshore wind power has seen significant growth in the past decade. The total capacity across the world is expected to grow even up to 1,000GW by 2050. Although most of the existing offshore wind turbines are built on fixed foundations, they are currently restricted to waters less than 60 metres deep. In contrast, floating foundations can be deployed for deeper waters even above 100 metre, which particularly appeals to some of the largest potential markets for offshore wind, such as Japan and the US, where there are few shallow water sites. Unlike conventional static power cable, dynamic power cable used for floating offshore wind is exposed to dynamic motion due to marine conditions, such as wave and tidal current. Therefore, it requires specific mechanical characteristics, such as fatigue resistance, torque balance and bending stiffness. Dynamic power cable system, including the subsea cable geometry, will be designed to achieve these characteristics and further be validated by performing static, dynamic and fatigue analyses.

Off-grid is the power system that is completely separated from the commercial grid, and the system is introduced in remote areas such as islands. Currently, power is mainly supplied from diesel generators, but there are some problems that operation and management costs such as fuel and transportation costs are enormous, and that CO₂ emissions are very large. Therefore, off-grid systems that have installed renewable energy are attracting attention. In this paper, the power system composition of off-grid mainly based on renewable energy, operation methods that enable efficient power supply, the generator capacity determination method that enables stable power supply are proposed and economical and environmental performances, generator capacities and operational parameters are evaluated. In addition, this study uses the data acquired on the actual island and is carried out by simulation.

The enhancement of power system resilience has been recognized as important after large-scale power outages following disasters, and there has been discussion on ensuring frequency stability for loss of large-scale generation. Also, it is expected that renewable energy sources (RES) will be main power sources in the future under the increasing social demand for decarbonization. In order to realize both the enhancement of power system resilience and the large-scale integration of RES in the future, the authors consider that it will be important to establish the emergency frequency control technologies to cope with the large-scale integration of RES, mainly photovoltaics.

This paper discusses the issues of emergency frequency control and the technologies to be developed in the future and provides a method to enhance the function of load-side frequency relay considering the self-disconnection characteristics of RES as one of the mid- to long-term measures. The proposed method has been verified by simulating a loss of generation in 60 Hz interconnected power system using the IEEJ WEST 30-machine system model, and the proposed method has been clarified to contribute to the enhancement of power system resilience. The proposed method would be feasible in the case communication control technology is used appropriately.

This paper proposes distribution state estimation (DSE) using multiple stages considering asynchronous measurement data by dependable parallel multi-population global-best brain storm optimization with differential evolution strategies. In actual distribution systems, measurement data are obtained asynchronously by polling in distribution automation systems. However, conventional DSE methods have not considered the asynchronous measurement data. Therefore, new DSE formulation using multiple stages is proposed in order to consider the asynchronous measurement data. Since actual distribution system equipment causes a nonlinear characteristic of an objective function, evolutionary computation techniques have been applied to DSE problems. Moreover, parallel and distributed processing should be applied to the DSE problems considering penetration of renewable energies. In such a case, appropriate estimation results should be obtained even if various faults of distributed processes occur. Namely, fast and dependable computation is necessary for the DSE problems. The proposed method is verified to be more effective than conventional DSE using a single stage without considering the asynchronous measurement data.

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.

In this paper, we propose a multi-agent based restoration system for islanding distribution system. In the case of restoring an isolated power distribution system, since a power failure is restored using distributed power supplies, the supply capacity is generally insufficient for the demand. For this reason, it is necessary to restore from an important load in consideration of the restoration priority. In this paper, we propose a recovery method considering load priority.

The proposed multi-agent system consists of four types of agent: single facilitator agent (FAAG), several feeder agents (FAGs), several Load agents (LAGs), and several generator agents (GAGs). In the proposed system, FAAG and FAGs play as an important role, which facilitate the power interchange between GAGs and LAGs. From the simulation results, it can be seen the proposed multi-agent system could find suboptimal load distribution considering load priority.

The large-scale integration of wind power generation may deteriorate the power system stability. For example, transient stability can be adversely affected by unscheduled power flow conditions caused by wind power generation. In order to address this problem, it will be a promising way to use an event-based wide-area control system (WACS) with advanced information and communication technology. In the present paper, we develop a novel method that controls active and reactive power of variable-speed wind power generation by WACS for transient stability improvement. Numerical simulation results show that the proposed active power control method improves the transient stability. In addition, the results indicate that coordinated control of the active and reactive power by the proposed method has synergistic effects for transient stability improvement.

Recently, faults of photovoltaic power plants are becoming a serious issue because it may prolong the payout time for power plant installation. Under this situation, not only detection of the photovoltaic module itself but also classification of the kind of faults has been attracting much attention. This study applies supervised the machine learning algorithm using neural networks to fault classification, which is able to lower maintenance costs. However, a large amount of input and output data are required to obtain enough estimation accuracy for machine learning models. In fact, in a single photovoltaic power plant, anomalies are not frequent and it is difficult to collect data to withstand practical use.

In this study, a numerical simulation to generate a large amount of voltage data when the photovoltaic power plant has fault has been developed and the voltage data was generated to train machine learning models. The generated voltage data reproduce measured data from an actual photovoltaic power plant. For faults such as cell fault and shadow, the proposed method functions properly and classified the anomaly at high accuracies.

This paper proposes an electricity market simulator based on single-price auctions. Three kinds of mathematical decision-making models are constituted as economic agents. One is a marginal cost model, and the others are more sophisticated models taking into account market models and load prediction models. The validity of the approach is demonstrated by using a simplified two-bus test system model with multiple power producers. The simulation has shown that electricity prices depend on the behavior of economic agents in a competitive environment. The developed simulator has potential to overcome the limitation of mathematical model-based approaches. The proposed approach can predict possible hidden behaviors of selfish producers that may deteriorate the economic rationality.

Nowadays, the introduction of renewable energy (RE) has been progressed from the perspective of considering environmental issues, there is concern that unstable output will increase. So system frequency control by existing generators becomes more important than ever. Even in system frequency control, Load Frequency Control (LFC) that support unpredictable short-period components of load fluctuations is expected to improve performance. Since Power-Frequency Constant of Area Requirement calculation of LFC changes from moment to moment, it is necessary to correctly estimate and update it to the best value in order to improve LFC performance.

In this study, we propose a new LFC method that has a function of estimating Power-Frequency Constant for the purpose of improving the performance of LFC even in situations when the introduction of RE has expanded. We use reinforcement learning, which is a machine learning method that does not use training data, to estimate Power-Frequency Constant. For the verification, we use a simulated power system consisting of experimental devices such as simulated generators and loads.

Metal strands of composite fiber-optic ground wire (OPGW) are sometimes melted and broken by high-energy lightning strikes. Direct current (DC) arc tests simulating the lightning strikes have thus been performed to obtain the melting and breaking characteristics of OPGW strands. J-Power Transmission Network Co., Ltd. has developed high-strength and high-corrosion-resistant OPGW for long-span 500kV overhead transmission lines crossing straits. This paper describes the melting and breaking characteristics of the OPGW strands due to DC arc discharge simulating high-energy lightning strike. In this research, the tests were performed using two kinds of arc, one was the arc supposing summer lightning whose polarity was negative with a relatively short duration, and the other was the arc supposing winter lightning whose polarity was positive with a relatively long duration. Furthermore, the calculations regarding the melting and breaking of OPGW strands were carried out, and the calculation results showed a tendency similar to the test results.

The isolated power system (off-grid) using only renewable energy resources (PCS supplies) is studied. On the other hand, the off-grid of the Japanese general transmission and distribution company using only PCS supplies has not been realized. To realize and clarify the safety operation of off-grid, in this paper, we simulated the fault aspect using the simple simulation.