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The high proportional integration of variable renewable energy sources (RESs) has greatly challenged traditional approaches to the safe and stable operation of power systems. Considering the complementary characteristics of various RESs, an optimization model is proposed in this study for cascade hydropower stations coupled with renewable-energy-based power generation methods such as wind and photovoltaics (PV). Building on the autoregressive moving average (ARMA) model and improved vine-copula theory, a joint distribution model for wind and PV power is built with measured data to capture the spatial and temporal correlations between wind and solar plants, and sufficiently representative scenarios for renewable energy generation are explored. Due to nonlinearities in the forebay elevation vs. reservoir volume, tailwater elevation vs. water flow, and head-sensitive power generation in hydropower stations, several linearization approaches are used to reformulate the optimization model into a more tractable form. The hydraulic relationship and time delay of water flow between cascade reservoirs are established to make full use of the controllable complementarity of hydropower generation. Optimized coefficients of coordinated operations in different seasons are obtained by a heuristic algorithm for cascade reservoirs. A detailed case study is undertaken in a basin with wind farms and solar arrays in Southwest China, and the simulation results demonstrate the potential of a large-scale hydro–wind–solar hybrid system to meet export power transmission demands. Finally, the optimal capacity of wind and PV plants is determined to inform the future planning and construction of actual systems.

Due to the clear differences in the characteristics of different power sources, it is necessary to take both the economic and security requirements of the power grid into account simultaneously to achieve an optimal operation strategy for a multienergy power system. This paper focuses on the optimal day-ahead dispatching of a hybrid power system that includes wind power, solar photovoltaic power, cascade hydropower, thermal power, and pumped-storage power. Considering the complementary characteristics of these different sources, a day-ahead joint optimal dispatching model of the multi-energy power system is established, with objectives of maximizing the security level, economic benefits and renewable energy consumption. A three-stage method based on Benders decomposition is proposed to solve this complex multiobjective problem. By decoupling the security and economic problems of the hybrid power system, an efficient solution for the scheduling strategy is achieved. The influences of factors such as the diversity of natural scenarios and the security requirements of system operation on the scheduling results are fully discussed in the case study section. The computational efficiencies of the proposed three-stage method and of the direct method are also comparatively analyzed. The simulation results verify the superiority of the proposed scheduling strategy and algorithm.

Due to the advantages of simple, fast and applicability, the intelligent algorithm can be used to simulate unknown massive scenes in the future. This paper mainly introduces the medium and long-term simulation method used to evaluate the source-network collaborative development planning program. Based on the actual situation of the large-scale new energy access power system, a time series Monte Carlo simulation method is used to establish the mid-to-long term operation evaluation model. According to the known power supply planning, load conditions, and grid layout planning, input large amounts of data to simulate the operating status of the power system, and calculate the technical and economic indicators of the power supply and grid.

In order to assess the risk of grid frequency deviation caused by power fluctuations in a wind farm more precisely, an annual second-level wind power fluctuation model should be established. Firstly, the annual second-level wind speed considering the spatial correlation in a wind farm is constructed. The annual average wind speed with auto-correlation and satisfying the Weibull distribution is simulated, with which the second-level wind speed incorporating the spatial correlation of wind farm is calculated based on the Kaimal power spectrum in frequency domain. Finally, the wind power fluctuation is determined using the wind speed obtained previously and is utilized to estimate the subsequent frequency deviation. The effectiveness of the generated annual second-level wind power fluctuations is validated via simulations performed in MATLAB/Simulink.

With the gradual development of social informatization construction, the construction planning of Data Center is larger and larger; operation and maintenance personnel on the data center room operation and maintenance requirements are also increasing. Traditional machine room monitoring and maintenance based on 2D graphics can not meet the three-dimensional operation needs. We need through three-dimensional way to make up for two-dimensional computer room operating data problem which data display is not rich. At the same time, 3D computer room monitoring is facing complex and changeable work, such as old equipments scrapped and new equipment came on-line, etc. In this case, a 3D scene editor will be needed to distribute informatization three-dimensional room in the form of dynamic scenes, which can provide maintainability, scalability of system. In this paper, the advantages and disadvantages of the 2D scene and 3D scene are studied and compared, then a 3D scene editor is designed and implemented to solve the problem of changing position of physical environments in the computer room, finally we validate it through experiments.

To discover knowledge from simulation results of power systems, effective data mining techniques are important for both academic and industry. In this paper, a method to visualize transient dynamics of power systems based on manifold learning is proposed, which is aiming at clustering stable and unstable simulation cases concisely. Regarding as a collection of time series data, results of one simulation case are approximately presented as a state point in a visualization space. Different dimension reduction methods in the manifold learning family are applied to enable such visualizations. Then, stable and unstable simulation cases are clustered according to their Euclid distances in the visualization space by the Fuzzy C-means method. Using the IEEE 39 bus system, the proposed method is tested and validated. Comparisons with other dimension reduction methods are presented. It can be seen that the Isomap manifold learning method helps to reveal stable and unstable case clusters effectively and accurately.