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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.

Renewable Energy Production Simulation Platform (REPS) is developed by China Electric Power Research Institute (CEPRI) to simulate the operation of renewable energy in the power system. REPS takes into account the characteristics of China’s electric power system, it can assess the accommodation of renewable energy power and simulate the impact of different renewable energy capacity on the operation of power system. Assessment model and calculation process of REPS V1.3 is introduced in this article, and annual consumptive capacity in one provincial power grid of China is evaluated with the platform. REPS is of great guiding significance to electrical source planning. With the sustained and rapid growth of renewable energy in China, power system will be more and more dependent on REPS.

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.

With the increasing of PV power integration, the uncertainty of PV power affects the security of the grid seriously. It is necessary to master random characteristics of Photovoltaic(PV) power for power system planning and operation. An analysis method of random characteristics of PV power considering weather type is proposed. The clear sky model of PV is built and the actual PV power output can be divided into the product of the clear sky output and relative output, which is decomposed into the benchmark output and fluctuation output caused by different clouds and weather conditions, a complete PV output model description is formed. Then, the curves are divided into four categories based on the weather fluctuation characteristics, corresponding to the sunny, cloudy, overcast and variation weather. Furthermore, the distribution characteristics of benchmark output and fluctuation output are also analyzed in this paper.