--In China, as in other parts of the world, many of the best resources for wind generation are located far away from load centers. Large generating facilities connected to distant load centers by long ac transmission lines face numerous technical challenges, regardless of the type of generating facility. This paper addresses some of the most significant challenges for wind generation facilities, including voltage control, reactive power management, dynamic power-swing stability, and behavior following disturbances in the power grid.Wind generation technology has evolved significantly over the past several years, and proven solutions to these technical challenges now exist. Controls integrated into the power electronics and mechanical controls of individual wind-turbinegenerators, combined with integrated wind-farm control systems, have the capability of controlling numerous wind turbines so that they act as one unified generating plant at the point of interconnection with the power grid. This advanced hierarchical control of both real and reactive power output can provide dynamic performance that is, in many cases, superior to that achievable with modern conventional synchronous generation. This paper describes: a. Wind farm control functions, including performance for controlling grid voltage in quasi-steady-state and dynamic conditions. b. Low-voltage ride-through characteristics, including performance following severe system disturbances c. Dynamic power control functions within wind turbinegenerators, including transient and dynamic performance for power swings.

Accurate retrieval of the power equipment information plays an important role in guiding the full-lifecycle management of power system assets. Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process. Knowledge graph has been widely developed in large part owing to its schema-less nature. It enables the knowledge graph to grow seamlessly and allows new relations addition and entities insertion when needed. This study proposes an approach for constructing power equipment knowledge graph by merging existing multisource heterogeneous power equipment related data. A graphsearch method to illustrate exhaustive results to the desired information based on the constructed knowledge graph is proposed. A case of a 500 kV station example is then demonstrated to show relevant search results and to explain that the knowledge graph can improve the efficiency of power equipment management.

A study on power market price forecasting by deep learning is presented. As one of the most successful deep learning frameworks, the LSTM (Long short-term memory) neural network is utilized. The hourly prices data from the New England and PJM day-ahead markets are used in this study. First, a LSTM network is formulated and trained. Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes. Its performance is also compared with other existing methods. The forecasted results demonstrate that, the LSTM deep neural network can outperform the others under different application settings in this problem.

In China, as in other parts of the world, many of the best resources for wind generation are located far away from load centers. Large generating facilities connected to distant load centers by long ac transmission lines face numerous technical challenges, regardless of the type of generating facility. This paper addresses some of the most significant challenges for wind generation facilities, including voltage control, reactive power management, dynamic power-swing stability, and behavior following disturbances in the power grid. Wind generation technology has evolved significantly over the past several years, and proven solutions to these technical challenges now exist. Controls integrated into the power electronics and mechanical controls of individual wind-turbine-generators, combined with integrated wind-farm control systems, have the capability of controlling numerous wind turbines so that they act as one unified generating plant at the point of interconnection with the power grid. This advanced hierarchical control of both real and reactive power output can provide dynamic performance that is, in many cases, superior to that achievable with modern conventional synchronous generation. This paper describes: a. Wind farm control functions, including performance for controlling grid voltage in quasi-steady-state and dynamic conditions. b. Low-voltage ride-through characteristics, including performance following severe system disturbances c. Dynamic power control functions within wind turbine-generators, including transient and dynamic performance for power swings.

CIM/E is an easy and efficient electric power model exchange standard between different Energy Management System vendors. With the rapid growth of data size and system complexity, the traditional relational database is not the best option to store and process the data. In contrast, the graph database and graph computation show their potential advantages to handle the power system data and perform real-time data analytics and computation. The graph concept fits power grid data naturally because of the fundamental structure similarity. Vertex and edge in the graph database can act as both a parallel storage unit and a computation unit. In this paper, the CIM/E data is modeled into the graph database. Based on this model, the parallel network topology processing algorithm is established and conducted by applying graph computation. The modeling and parallel network topology processing have been demonstrated in the modified IEEE test cases and practical Sichuan power network. The processing efficiency is greatly improved using the proposed method.