Optimal Coordinated Dispatching Strategy of Multi-Sources Power System with Wind, Hydro and Thermal Power Based on CVaR in Typhoon Environment

Abstract

Typhoons and other natural disasters affect the normal operation of power systems thus it is an important goal for strong and intelligent power grid construction to improve the ability of power systems to resist typhoons and other natural disasters. Especially, an effective coordinated and optimized dispatching strategy for a multi-source power system is greatly helpful to cope with the impact of typhoons and other natural disasters on power system operation. Given this background, a typhoon wind circle model considering the temporal and spatial distribution of typhoons is established to obtain the input wind speed of the wind farm at first. Second, based on the initial input wind speed of wind farms, a typical scenario set of wind power output is constructed to reflect its fluctuation and uncertainty. Next, an optimal coordinated dispatching model of a multi-source power system with wind, hydro and thermal power based on the conditional value at risk (CVaR) is established with the target of minimizing the total cost of system dispatching, in which a 72 h pre-dispatching mode is studied to optimize the system operation for 72 h on the day before, on and after the typhoon. Finally, a revised 24-node transmission network system in a coastal area with typhoon is served as a case for demonstrating the effectiveness of the proposed model, and the simulation result shows that the proposed model could take the advantages of the coordination and complementarity of multi-sources power system and decrease the total cost of system dispatching and improve the renewable energy consumption level.

Research on optimal scheduling of thermal power system in thermal power plant based on deep reinforcement learning

“…In formula (5), 𝑌 represents the steam flow rate after depressurization, 𝛽 represents the share of unevaporated water in the total eruption amount, and 𝐻 represents the specific enthalpy of desuperheating water. 𝐻 represents the specific enthalpy of saturated water flowing out of the desuperheater and pressure reducer, 𝐻 represents the specific enthalpy of the inlet steam, and 𝐻 represents the specific enthalpy of the steam after depressurization.…”

“…The parameters for setting the SAC algorithm are shown in Table 1. The reference [5] method and reference [6] method are selected as the comparison methods of the proposed method, and the three methods are used in the experiments. The performance of the proposed method is tested by comparing the optimal scheduling results of the three methods.…”

“…Thermal power system type of thermal power plant The proposed method Reference [5] method Reference [6] method Figure 1. Comparison of total fuel consumption of the three methods According to Figure 1, it can be seen that for boilers, steam turbines, and auxiliary thermal systems, the total fuel consumption of the proposed method after optimal scheduling is smaller than that of the method of reference [5] method and reference [6] method. It can be seen that the total fuel consumption of the proposed method is small and the scheduling is optimal.…”

“…At this stage, the research on the optimal scheduling of thermal power system in thermal power plants has gone deep enough, and more excellent research results have been achieved. Qian et al [5] proposed an optimal coordination scheduling method for wind, water and thermal power multi-power systems based on CVaR in a typhoon environment. Firstly, the typhoon related information of the meteorological observatory is collected, and the time-space distribution characteristics of typhoon are analyzed in depth to calculate the wind speed of the wind farm location.…”

An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm

“…An optimal dispatch model for the multi-source system containing wind, thermal and hydro storage in typhoon environment based on risk analysis with CVaR considering wind speed first and then identification of output scenario is presented in ref. [17]. CVaR is considered risk exposure due to the uncertainties present in the stochastic model for energy and reserve scheduling of renewable-based microgrid under an energy market environment, which is further solved by using multi-objective mixed-integer linear programming [18].…”