With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations.

The smart control strategy in the operation of renewable energy station is of great significance to alleviating the shortage of fossil energy, improving the efficiency of renewable energy utilization and the safe operation of power system. Firstly, this paper took the renewable energy stations represented by wind and solar energy as the research object, summarized the smart control strategy of single unit power generation, and elaborate introduced the mechanical structure control, maximum power point tracking control and virtual synchronous machine control in electrical converter level. Secondly, the self-protection control of renewable energy power generation station in the case of power system faults were analysed. The key issues involved in the low voltage ride-through, high voltage ride-through, protection circuit of rotor side, stator side and converters were summarized. Thus, a brief overview of smart control of renewable energy station operation is presented in this paper, hoping to provide technical guide for prosperous development of renewable energy in the future.

Featured Application: This paper is a policy article that is used to alleviate the problem of power deviation caused by the volatility and uncertainty of wind power. It is also suitable for promoting "source-network coordination" between the power system and the wind farm operator.Abstract: Fluctuation and prediction errors of wind power would cause a large amount of automatic generation control (AGC) adjustment costs, which lead to the problem of power curtailment. A reasonable mechanism of grid-connection electricity price may encourage wind farms to take measures to reduce the deviation between output power and schedule power, which is helpful for source-network coordination and reducing wind power curtailment. An alterable electricity pricing mechanism considering wind power deviation rate is proposed. In each schedule cycle, electricity price is adjusted according to the deviation rate and its historical change trend. In this way, wind farms will be encouraged to configure energy storage to promote the accordance of wind output power with schedule power to the greatest extent. Given the statistical characteristic of prediction errors of wind power, this paper proposes a schedule power model, taking least squares of output power deviation as objective function, and then puts forward an engineering application method for determining schedule power. This paper analyzes the overall cost and revenue of a wind farm to configure energy storage and determine the optimal energy storage capacity with the goal of maximizing the profit of the wind farm. In the case analysis, the effect of the deviation rate and its historical change trend, the deviation rate tolerance coefficient on electricity price is analyzed. The case analysis demonstrates the effectiveness of the proposed alterable electricity pricing mechanism and shows that the mechanism is helpful at reducing wind power output deviation and wind curtailment.