The energy flow optimization between new energy power systems is a unit combination optimization problem between new energy sources, traditional sources, and battery energy storage systems, taking into account steady-state constraints and transient stability constraints, while maximizing energy interaction within or between the new energy grid or the transmitting and receiving end grids. The optimization of unit combination in new energy power systems while ensuring a certain level of energy interaction is not only a guarantee for increasing the proportion of new energy consumption, but also a key factor in maximizing the economic, safety, stability, and carbon reduction capabilities of power grid operation. Considering that the future development trend of the power system is gradually moving towards a clean and low-carbon power system with new energy as the main body and multiple energy sources integrated with each other, the high proportion of fluctuations in new energy and the large-scale integration of multiple types of loads into the transmission end system will cause certain interference effects on the steady-state and transient operation status of the transmission end system, as well as within the system, posing great challenges to improving the energy transmission capacity of the transmission end system.
The transmission limit of the sending end system is defined as the maximum value of energy or power that can be borne by the sending end system on the AC/DC transmission channel between the sending end grid and the receiving end grid at a certain time scale, that is, the maximum power that can be transmitted to the receiving end system through the transmission channel during a certain period of time, under the premise of meeting the given constraints of the sending end system and the transmission channel. Among them, the constraint conditions mainly consider the following two points: first, the power supply system must ensure the safety of the system flow and ensure that the power generation equipment in the system operates within a safe range during normal operation; The second is that when there is an operational failure in the sending end system, the sending end system can maintain stable operation, including steady-state stability and transient stability. At the same time, all operating equipment in the system can operate safely before and after the recovery of the operational failure. So, the energy output limit of the sending end system is mainly measured by parameter indicators such as time interval, maximum power supply installed capacity of the sending end system, maximum load of the sending end power grid itself, and maximum reserve capacity demand of the sending receiving end interconnection system. The numerical values of these indicator parameters are influenced by various deterministic and uncertain factors, with the most significant uncertainty factors including fluctuations in electricity load within the transmission system and fluctuations in new energy output.
The optimization algorithm model for the energy output limit of the sending end system is generally based on the known topology, network parameters, equipment parameters, and system operation mode of the sending end system. Then, the optimal power flow method is used to calculate the transmission power limit of the sending end system under various steady-state and transient stability constraints at different time cross-sections. The power limit values at different time cross-sections are integrated within a certain time scale, The energy output limit at this time scale can be obtained.
Using the method of continuous power flow calculation in the power system, starting from the input basic operating parameter data of the transmission end system, the system flow situation during the operation of the transmission end system is calculated. By increasing the output power or load power at certain nodes, multiple cyclic iterations are carried out. When a node in the system experiences power exceeding the limit or constraint range, the iterative solution ends, Using the current calculation results as the evaluation criteria for calculating the output power of the power system at the transmitting end, determine the maximum output power limit of the system. Further quantifying the uncertainty factors affecting cross regional power transmission in the power system, and using Monte Carlo sampling algorithm to analyze the uncertainty factors, a power system maximum transmission capacity calculation and evaluation model based on power flow model was established, effectively reducing the difficulty of calculating the actual transmission capacity of complex power systems for cross regional power transmission.
The use of battery energy storage systems to reduce the energy interaction limit between new energy interconnection systems is limited by uncertain conditions such as system operating state uncertainty and temporary stable backup constraints, which has good application prospects. On the one hand, the battery energy storage system is connected to the AC power grid through a power electronic converter (also known as an energy conversion system: PCS). Its fast control characteristics enable the battery energy storage device to provide sufficient and fast energy support response capability for the sending end system, effectively reducing or even resisting the impact of transient faults in the new energy interconnection system on the safety and stability of the sending end system. On the other hand, the energy spatiotemporal migration characteristics of the energy storage device in the sending end system can provide a certain energy partition coordination ability for the sending end system, which can not only effectively improve the level of new energy consumption, but also play a role in optimizing peak valley differences and increasing the limit of energy delivery. References [69-71] analyze the relationship between energy allocation capacity and the improvement of power grid external transmission capacity, and propose a method for a distributed energy storage system to support and improve power grid external transmission capacity in the event of operational faults in the power grid. The feasibility and practicality of the proposed method are verified by building a simulation model.
By analyzing the power fluctuations and flow changes during the process of microgrid integration, an optimization model for the power transmission capacity of microgrid integration with energy storage devices was established, ensuring the stable operation of the power grid. Based on this, conducting research on using energy storage devices to improve and optimize the output power limit of the sending end system has a very broad application prospect.