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Scenarios generation is a critical part in planning and operation in high renewable energy penetratied power systems. However, the statistical assumptions of traditional parametric methods may not hold for all types of wind farms. In this paper, a data-driven artificial intelligence approach is presented to generate wind power output scenarios based on generative adversarial networks (GANs). Unlike traditional probabilistic model-based techniques which are typically difficult to scale or sample, the proposed method is data-driven and captures patterns of wind power generation. First, the GAN network structure is constructed, and the Wasserstein distance is employed as the discriminator’s loss function. The GAN training then enables the generator to learn random noise and actual history data. Finally, the scenario generation approach based on Monte Carlo simulation and GANs are compared. It shows that the scenarios generated by proposed method can accurately describe the uncertainty of wind power output.

Delaying responsibility allocation is one of the important contents in the construction management of power grid infrastructure projects. Due to the large investment in power grid infrastructure projects, long construction period and complicated operation procedures, many delays occurred during the construction process. Based on this, combined with the theory of time difference theory, this paper proposes a delay analysis method that considers the logical relationship between engineering processes. Secondly, the route time difference theorem is proposed, and the calculation steps and rules of the delay analysis method considering the logical relationship between engineering processes are given. Finally, the substation main structure construction project is selected as an example analysis, and compared with other methods, which effectively proved that the validity and feasibility of the proposed delay analysis method. The method of delay responsibility allocation proposed in this paper also provides ideas and directions for the subsequent research on delay assignment of similar construction projects.

With the increasing proportion of distributed power supplies connected to the power grid, the application of a battery energy storage system (BESS) to a power system leads to new ideas of effectively solving the problem of distributed power grid connections. There is obvious uncertainty involved in distributed power output, and these uncertainties must be considered when optimizing the scheduling of virtual power plants. In this context, scene simulation technology was used to manage the uncertainty of wind power and photovoltaic output, forming a classic scenario. In this study, to reduce the influence of the uncertainty of wind and photovoltaic power output on the stable operation of the system, the time-of-use (TOU) prices and BESS were incorporated into the optimal scheduling problem that is inherent in wind and photovoltaic power. First, this study used the golden section method to simulate the wind and photovoltaic power output, second, the day-ahead wind and photovoltaic power output were used as the random variables, third, a wind and photovoltaic power BESS robust scheduling model that considers the TOU price was constructed. Finally, this paper presents the Institute of Electrical and Electronics Engineers (IEEE) 30 bus system in an example simulation, where the solution set is based on the Pareto principle, and the global optimal solution can be obtained by the robust optimization model. The results show that the cooperation between the TOU price and BESS can counteract wind and photovoltaic power uncertainties, improve system efficiency, and reduce the coal consumption of the system. The example analysis proves that the proposed model is practical and effective. By accounting for the influence of uncertainty of the optimal scheduling model, the actual operating cost can be reduced, and the robustness of the optimization strategy can be improved.

The energy consumed by buildings makes up a significant part of total social energy consumption. The energy use rate of the traditional cooling and heating unit is low. A distributed cooling, heating, and power (CHP) system can achieve cascade use of energy and reduce the long-distance transportation of energy. Along with the wide use of ground-source heat pumps and energy storage technology, the combined cooling, heating, and power (CCHP) system coupled with a ground-source heat pump and energy storage technology is increasingly being used. Firstly, we proposed the construction of a CCHP system driven by distributed energy resources (DERs) including three subsystems of an electricity subsystem, a CCHP subsystem and an auxiliary heating subsystem as the object of study in this paper. Besides, with the goals of reducing carbon emissions, increasing energy efficiency, and minimizing system cost, a constraint mechanism based on the DOM-PSO (dynamic object method/particle swarm optimization) algorithm was applied. Finally, taking Tianjin Eco-City as an example, we used the PSO algorithm to analyze the operating characteristics of the cold and power cogeneration system under the uncertainty of the wind power output. The simulation results show that the joint optimization mode operation strategy can balance the results of different optimization modes by increasing the robust coefficient of wind power. Of all scenarios examined, the CCHP system coupled with the ground-source heat pump and energy storage technology performed best.