CN-121984075-A - Energy storage configuration method for improving new energy bearing capacity based on collaborative optimization

CN121984075ACN 121984075 ACN121984075 ACN 121984075ACN-121984075-A

Abstract

The invention discloses an energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization, which relates to the technical field of new energy power systems, and is characterized in that an upper constant volume model is constructed, a load curve is optimized based on a Logistic function, and initial energy storage configuration is solved; the method comprises the steps of constructing a middle-layer simulation operation model, calculating the upper limit of the bearing capacity by using a double target with minimum system operation cost and maximum new energy bearing capacity, constructing a lower-layer optimization model, optimizing configuration of a grid-structured power supply and a camera, completing safety constraint verification, adopting a generalized Benders decomposition algorithm to iteratively solve the three-layer model, and outputting a global optimal energy storage configuration and new energy bearing capacity lifting result. The method and the system have the advantages that strategies of a load side, a power side and a system side are optimized in a layered and collaborative mode, economy, reliability and safety are considered, and new energy bearing and absorbing capacity of the power grid is effectively improved.

Inventors

HAN ZIFEN
LIU SHENGHONG
WANG ZHENDAN
LIU ZONGYANG
WU GUODONG
MA YANHONG
WANG WEIWEI
Ou sheng
DONG HAIYING

Assignees

国网甘肃省电力公司
国网甘肃省电力公司平凉供电公司

Dates

Publication Date: 20260505
Application Date: 20260401

Claims (6)

1. The energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization is characterized by comprising the following steps of: S1, constructing an upper constant volume model, constructing a load demand response model based on a Logistic function, calculating a load transfer rate through a time-of-use electricity price, optimizing a load curve, comprehensively considering new energy output constraint, hydropower operation constraint and energy storage system operation constraint, and solving to obtain an initial energy storage configuration scheme and a smoothed net load curve by taking the minimum investment cost and operation and maintenance cost of energy storage capacity as targets; s2, constructing a middle-layer simulation operation model, receiving an initial energy storage configuration scheme and a net load curve which are output by an upper-layer constant volume model, taking the minimum total operation cost of the system and the maximum new energy carrying capacity as double targets, obtaining a comprehensive objective function through normalization weighting, completing the simulation operation of the system by combining power balance constraint and abandoned wind and abandoned light rate constraint, calculating the upper limit of new energy carrying capacity of the power grid meeting the reliability standard of the lost load probability, and outputting the operation state result of the system; S3, constructing a lower-layer optimization model, optimizing configuration capacity of a grid-structured power supply and a camera based on a system operation state result of the middle-layer simulation operation model, taking minimum investment and operation total cost of the grid-structured power supply and the camera as targets, combining short-circuit ratio constraint, node voltage constraint, line power flow constraint and branch power constraint, completing system safety constraint verification, and feeding back a safety verification result to the middle-layer simulation operation model in a linear constraint mode; And S4, carrying out iterative solution on the three-layer collaborative optimization model by adopting a generalized Benders decomposition algorithm until the safe and stable convergence condition is met, and outputting a globally optimal energy storage configuration result and a new energy bearing capacity lifting result.
2. The energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization according to claim 1, wherein the objective function F of the upper constant volume model is: Where Δc ESS,p represents the stored-energy power configuration cost, Δc ESS represents the stored-energy capacity configuration cost, and Δc ESS,w represents the stored-energy maintenance cost.
3. The energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization according to claim 1, wherein the objective function of the middle layer simulation operation model is: With the aim of minimizing the total running cost of the system: Wherein f 1 is the total operation cost of the system, C coal is the operation cost of the thermal power unit, C carbon is the carbon emission cost, C h is the operation cost of the hydroelectric power unit, C WT is the wind power operation cost, C PV is the photovoltaic operation cost, and C ESS is the energy storage operation cost; the maximum expression of the new energy bearing capacity is as follows: Wherein f 2 is the maximum bearing capacity of new energy, P load,max is the maximum load of the system, P line is the maximum exchange power with the external network, P T,min is the minimum technical output of a conventional unit in the system, k represents the new energy electricity rejection rate of the system operation, and the total installation of the Sigma C ess energy storage unit; the two objective functions of the total running cost and the new energy bearing capacity of the system are normalized and weighted to be combined into a comprehensive objective function F: In the formula, The weight of the objective function f 1 is (0, 1), f 1,max ,f 1,min is the maximum value and the minimum value of the objective function f 1 , and f 2,max ,f 2,min is the maximum value and the minimum value of the objective function f 2 .
4. The energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization according to claim 1, wherein the lower optimization model constructs an objective function with the minimum sum of investment and operation cost of a network-structured new energy power supply and a camera: Wherein, C is the total configuration cost, C n,in 、C n,m is the network construction type power supply investment cost and the operation maintenance cost respectively, C syn,in 、C syn,m is the network deployment type power supply investment cost and the operation maintenance cost respectively, C n,in is the network construction type power supply investment cost coefficient of unit capacity, C n,m is the network construction type power supply operation maintenance cost coefficient of unit capacity, C syn1,in 、c syn2,in is the distributed and centralized network deployment type power supply investment cost coefficient of unit capacity respectively, C syn1,m 、c syn2,m is the distributed and centralized network deployment type power supply operation maintenance cost coefficient of unit capacity respectively, S n is the single network construction type power supply grid-connected capacity, Q syn1 is the single distributed network deployment type power supply grid-connected capacity, and Q syn2,i is the i node configuration centralized network deployment type power supply capacity; 、 the network power supply and the distributed camera are respectively configured for the node i.
5. The energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization according to claim 1, wherein the specific flow of iteratively solving the three-layer collaborative optimization model by adopting a generalized beacons decomposition algorithm is as follows: The method comprises the steps of setting a middle-layer simulation running model as a main problem to be responsible for processing discrete decision variables and taking the minimum total running cost of a system as a target, taking an upper-layer constant volume model and a lower-layer optimization model as sub-problems to respectively process continuous variables, taking the middle-layer model as the main problem, transmitting a solution containing the discrete decision variables to the upper-layer model sub-problem, generating a feasible cut after the upper-layer model is solved based on the solution and feeding back the feasible cut to the middle-layer model main problem to guide the main problem to adjust the solution in the next iteration, generating the feasible cut aiming at safety constraint after the lower-layer model is solved, transmitting the feasible cut to the middle-layer model in a linear constraint mode, adjusting the running scheme by the middle-layer model according to the linear constraint mode to ensure the safety and economical synergy, gradually tightening the upper and lower bounds by iteration solution until the safety and stability conditions are met, and finally outputting a globally optimal energy storage configuration result and a new energy bearing capacity lifting result.
6. The energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization according to claim 1, wherein the system safety constraint verification adopts node voltage out-of-limit probability as a quantization index: Wherein Y i (u) is the threshold value of the node voltage at the node, G i (u) is the threshold value of the node voltage at the node, and S ev (w i ) is the severity of the threshold value of the node voltage at the node; Wherein V i is the per unit value of the node voltage of the node, w i is the limit value of the node voltage, and V max and V min are the maximum value and the minimum value of the node voltage.

Description

Energy storage configuration method for improving new energy bearing capacity based on collaborative optimization Technical Field The invention relates to the technical field of new energy power systems, in particular to an energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization. Background At present, aiming at the problem of the new energy carrying capacity of the power grid meeting the constraints of safety, stability and power supply protection, it is critical to research the problem of the upper limit of the new energy carrying capacity of the power grid meeting the constraints of safety, stability and power supply protection, and meanwhile, the problems of increased uncertainty, more complex multi-factor coupling and the like faced by 'adequacy' and 'safety' are urgently needed to be solved. The reasonable starting scale and comprehensive utilization rate of the new energy of the power grid are defined, the new energy development suggestion of the power grid under the conditions of supply protection, consumption protection and security is provided, and guidance is provided for the safe and stable operation and planning of the power grid in the period of new energy conversion promotion. The sustainable development of the novel power system is supported, and the foundation for safe and reliable supply of electric power and efficient consumption of new energy sources is firmed. Along with the large-scale development of new energy power generation and the year-by-year construction of direct current transmission engineering, especially the actual development speed of the current new energy greatly exceeds the expected speed, great challenges are brought to the planning design and operation control of the power system. On one hand, the new energy power generation capacity has randomness and fluctuation, the duck-shaped characteristic of the net load curve of the power grid is more and more outstanding, the situations of peak regulation, power abandonment and late peak power shortage of the new energy in the noon coexist for a long time, the power and electricity space-time distribution is extremely unbalanced, rich and short interweaving, and the method brings abundant challenges. In addition, the novel energy power generation equipment has low disturbance resistance and weak support, the novel energy power generation equipment replaces a conventional unit on a large scale, the effective power supply capacity is not obviously increased along with the installation scale of the novel energy, and the reliable power supply still needs the adjustable power sources such as a coal motor unit, a hydroelectric unit and the like to play a bottom protection role, thereby bringing security challenges. The novel electric power system material and the technical foundation of which the new energy duty ratio is gradually improved continuously change, the supporting capability of the new energy to the load peak power balance and the safe operation of the system is limited, and the problems of insufficient power supply, new energy consumption and safe and stable are frequently occurred, so that the novel electric power system material and the technical foundation are the problems to be solved in the development of the novel electric power system. Therefore, a layered collaborative multi-objective optimized energy storage configuration method is needed, equipment regulation and configuration strategies at a load side, a power side and a system side are comprehensively coordinated, economical efficiency, reliability and safety of a power grid are considered, and effective improvement of new energy bearing capacity is realized. Disclosure of Invention In view of the above, the invention provides an energy storage configuration method and system for improving the bearing capacity of new energy based on collaborative optimization, which considers the load demand response under the time-of-use electricity price so as to achieve the effect of smoothing load. In addition, an energy storage capacity configuration optimization model for improving the bearing capacity of the new energy is built, the maximum bearing capacity of the new energy is calculated, meanwhile, the new energy consumption of the system is improved through cooperative optimization of a grid-built power supply and a camera, and the effect of improving the bearing capacity of the new energy is achieved through configuration of the energy storage capacity. In order to achieve the above purpose, the present invention adopts the following technical scheme: the energy storage configuration method for improving the bearing capacity of new energy based on collaborative optimization comprises the following steps: S1, constructing an upper constant volume model, constructing a load demand response model based on a Logistic function, calculating a load transfer rate through a time-of-use electricity price, optim