• Energy Research

This paper proposes a multi-data driven hybrid learning method for weekly photovoltaic (PV) power scenario forecast that is coordinately driven by weather forecasts and historical PV power output data. Patterns of historical data and weather forecast information are simultaneously captured to ensure the quality of the generated scenarios. By combining bicubic interpolation and bidirectional long-short term memory (BiLSTM), a super resolution algorithm is first presented to enhance the time resolution of weather forecast data from three hours to one hour and increase the precision of weather forecasting. A weather process-based weekly PV power classification strategy is proposed to capture the coupling relationships between meteorological elements, continuous weather changes and weekly PV power. A gated recurrent unit (GRU)-convolutional neural network (CNN)-based scenario forecast method is developed to generate weekly PV power scenarios. Evaluation indices are presented to comprehensively assess the quality of the generated weekly scenarios of PV power. Finally, the PV power, weather observation and weather forecast data collected from five PV plants located in Northeast Asia are used to verify the effectiveness and correctness of the proposed method.

Determining an economical and reliable spare transformer strategy is an essential and challenging task for power utility companies. This study proposes a probabilistic cost‐benefit analysis‐based spare transformer strategy incorporating condition monitoring information. Not only the actual wearing condition of each transformer, but also the impacts of a transformer ageing process on spare transformer strategy are considered by the proposed method. First, the actual wearing condition and functional age of each transformer are estimated based on condition monitoring information. Secondly, the ageing process and ageing failure rate of each transformer are modelled by combining a Weibull distribution and its functional age. Thirdly, based on probabilistic cost‐benefit analysis, the outage costs, as well as economic benefits under different inventory sizes, are estimated. The transformer's ageing process is modelled and considered in the determination of the optimal number and the acquisition timing of spare transformers. Finally, a transformer substation group and its condition monitoring information, and the real spare transformer strategy collected from a regional power grid in China were used to demonstrate an application of the proposed method. The results indicate that the spare transformer strategy determined by the proposed method not only enables the power utilities to avoid economic losses but also guarantees a high cost‐benefit ratio of spare transformer strategy.

This paper proposes a bilevel programming model for the planning of tidal current farms (TCFs). The micrositing strategy of tidal current turbines (TCTs) and the collector system planning scheme are coordinated to achieve a better balance of energy yields, TCF capital investment and power systems economic operation using the proposed method. The power output of the TCF is modeled considering the characteristics of tidal current velocity and wake effects. A coordinated planning model consisting of one upper level model and two lower level models is developed to maximize comprehensive profit. Not only the investment and maintenance costs of TCTs and submarine cables, but also the operation cost of the collector system and the impact of the TCF integration on the operation of power systems are all taken into account to ensure the long-term benefits of both TCF owners and power systems. An efficient solution for the proposed planning model is developed by combining a genetic algorithm with a mixed integer programming. The effectiveness and adaptability of the proposed method are demonstrated using the measured data of tidal current velocity with distinct characteristics and the IEEE 30- and 118-bus test systems.