CN-115481781-B - Combined planning method for power system and readable storage medium

Abstract

The application provides a power system joint planning method, which comprises the steps of establishing a shared energy storage joint planning model according to the topological relation of a power system, taking the comprehensive economical efficiency of the power system as an upper optimization target, taking the operation effect of an energy storage system in the power system in a typical scene as a lower optimization target, and solving the shared energy storage joint planning model by adopting a double-layer optimization method to obtain the operation plan of the energy storage system. The shared energy storage joint planning model uses a power grid information flow and energy flow to connect a source end model for describing the output of the wind turbine generator set based on a fuzzy opportunity constraint planning method, a load end model for describing price excitation to influence the user demand load response and a storage end model considering the service life of an energy storage power station. The application considers the randomness and uncertainty of the source end and the load end of the power system, thereby realizing the dispatching balance of the power system, improving the energy utilization efficiency and reducing the comprehensive cost investment of the system.

Inventors

• BAO WEIZHONG
• ZHANG GEJUN
• Rong Shuquan
• GUO JIJUN
• WU CUICUI

Assignees

• 江苏龙源风力发电有限公司

Dates

Publication Date

20260505

Application Date

20220818

Claims (2)

1. 1. The joint planning method for the electric power system is characterized by comprising the following steps of: S100, establishing a shared energy storage joint planning model according to the topological relation of the power system; the step 100 of establishing a shared energy storage joint planning model according to the topological relation of the power system comprises the following steps: s110, establishing a source end model, indirectly describing uncertainty of the output of the wind turbine by the output prediction error of the wind turbine, wherein the method comprises the following steps: S111, modeling the output prediction error epsilon wind by adopting a fuzzy theory: , The prediction error has two expression forms, namely positive error if the actual output is higher than the predicted output, and negative error if the actual output is lower than the predicted output; S113, calculating the membership grade mu wind of the prediction error by using the Cauchy distribution: , wherein E + wind 、E - wind represents the statistical average value of the positive error and the negative error respectively; s114, establishing a source end model of wind turbine generator output represented by a credibility measure C r (ξ≤ε wind ) of a prediction error xi): ; the step of establishing the shared energy storage joint planning model according to the topological relation of the power system by the S100 comprises the following steps: s120, establishing a load end model, wherein the steps comprise: S121, a demand response model based on the incentive price is established, if the upper bound of a demand response coefficient of the load participation demand side response is ρ up , and the lower bound is ρ down , the upper bound and the lower bound are linear relations with the incentive price x, and the expression is as follows: , ; the demand response p 0 ∈[ρ down ,ρ up at price x is stimulated, S122, calculating the total response load S of participating in demand response at the incentive price: , Wherein, the user participation demand response is recorded as an event j, the number of the participating users is N j , the incentive price is x j , The response coefficient for users to participate in the demand response has the value range of S j is the total amount of demand load of a certain user; s123, calculating the total incentive cost expenditure C s at the incentive price x: ; s124, establishing an influence model of the excitation price on the load participation demand response, wherein the form of the load participation response is divided into three types of transferring, reducing and interrupting, namely a transferable load model, a reducible load model and an interruptible load model, and dividing a day into N regulation and control load periods T: the load model can be cut down: , Where ρ LAr,t is a coefficient by which the load can be reduced at time T, S t is the user load capacity at time T, P LAr,t is the power of the load reduction at time T, and W LAr,T is the amount by which the load can be reduced during time T; And (3) with The start and stop times of load shedding, respectively; And (3) with A lower limit and an upper limit of the capacity which can be cut down for the load of the T period respectively; transferable load model: , Wherein ρ LAs,t is the transferable coefficient of the load at time t, and P LAs,t is the transferable power of the load at time t; And (3) with W LAs,T is the transferable amount of the load in the period T as the starting and ending time of the load transfer; And (3) with The upper and lower limits of the load transferable capacity in the T period are respectively set; interruptible load model: , P LAt,t is the interruptible power of the load at the moment t, and ρ LAt,t is the interruptible coefficient of the load at the moment t; And (3) with W LAt,T is the interruptible amount of the load in the period of T; And (3) with The upper limit and the lower limit of the interruption capacity of the interruptible load are respectively; The step of establishing the shared energy storage joint planning model according to the topological relation of the power system by the S100 comprises the following steps: S130, establishing a storage end model to describe the influence of the charge and discharge process on the service life of the battery, wherein after n times of discharge behaviors are experienced, the actual electric quantity expression of the battery is as follows: , wherein i is the count of the number of discharges, Is a dimensionless coefficient, The rated life of the energy storage system, Loss amount for the ith discharge process: , wherein D i represents the depth of discharge of some energy storage operation in the non-rated state; p R represents the power of the energy storage battery in the rated state, and a, b and c are all influence coefficients; S200, taking the comprehensive economical efficiency optimization of the power system as an upper-layer optimization target, taking the operation effect of an energy storage system in the power system under a typical scene as a lower-layer optimization target, and solving the shared energy storage joint planning model by adopting a double-layer optimization method to obtain an operation plan of the energy storage system; the shared energy storage joint planning model uses a power grid information flow and an energy flow to connect the following sub-models: describing a source end model of the output of the wind turbine generator based on a fuzzy opportunity constraint planning method; describing a load end model of price excitation influencing user demand load response; Considering a storage end model of the service life of the energy storage power station; The step 200 of obtaining a daily operation plan of the energy storage system by solving the shared energy storage joint planning model by a double-layer optimization method by taking the comprehensive economy optimization of the power system as an upper optimization target and the operation effect of the energy storage system in the power system as a lower optimization target in a typical scene comprises the following steps: S210, determining an objective function of an upper layer optimization objective by using comprehensive economy optimization, wherein the objective function is as follows: , Wherein F upper is an objective function of an upper layer decision model, C d inv、 、C d FOM is daily average investment cost and fixed maintenance cost of the energy storage system respectively, and epsilon sc is occurrence probability of a scene sc; variable maintenance costs for the energy storage system under a typical operating scenario sc; the operation cost of the power grid under a typical operation scene sc is; cs is the demand response cost; s220, determining an objective function of the lower-layer objective optimization, which comprises the following steps: the optimization target 1, the net load variance of the power grid is minimum; the optimization target 2 is that the net load variation square of the power grid is minimum; An optimization target 3, wherein the energy storage system can provide the maximum power support level when the power grid fails; the step 200 of obtaining a daily operation plan of the energy storage system by solving the shared energy storage joint planning model by a double-layer optimization method by taking the comprehensive economy optimization of the power system as an upper optimization target and the operation effect of the energy storage system in the power system as a lower optimization target in a typical scene comprises the following steps: s230, solving an upper layer decision model by taking a plurality of group layering chaotic differential particle groups as a basic framework, and solving the optimal comprehensive operation cost; and S240, optimizing the lower three targets by using a fuzzy satisfaction degree maximum method, solving the multi-target linear membership degree, respectively solving the three target function linear membership degrees of the lower decision model by adopting a halfpace membership degree decreasing function curve so as to evaluate the satisfaction degree of the corresponding target function, calculating to obtain the maximum fuzzy satisfaction degree, taking the maximum fuzzy satisfaction degree as the target function of the lower decision model, and solving the optimal operation plan of the energy storage system under different typical operation scenes.
2. 2. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the power system joint planning method of claim 1.

Description

Combined planning method for power system and readable storage medium Technical Field The invention relates to the technical field of power planning of power systems, in particular to a power system joint planning method and a readable storage medium. Background With the gradual transition of energy systems in China to clean and renewable energy, the duty ratio of clean electric energy in social energy is continuously improved, and a novel electric power system mainly generating clean energy such as wind power, photovoltaic and the like is gradually used for replacing a high-carbon emission electric power system mainly generating traditional fossil energy. The new energy output has the characteristics of randomness, volatility and the like, and has great influence on the safe and stable operation of the power grid, and mainly comprises the problems of wind and light abandoning, peak shaving, frequency modulation, stability and the like. The traditional power grid and energy storage configuration operation mode does not consider the flexibility of the response of a user side, so that the energy storage side and the user side are insufficient in resource utilization, wind power cannot be effectively consumed, the phenomenon of 'wind abandoning and light abandoning' occurs sometimes, and the lack of coordination among power grid modules of 'source-network-load-storage' causes resource waste and equipment investment is increased. Disclosure of Invention In order to solve the problem that the existing power system lacks response to user side change and lacks coordination among all modules of the power system, the invention provides a power system joint planning method and a readable storage medium. The invention adopts the following power system joint planning method, which comprises the following steps: S100, establishing a shared energy storage joint planning model according to the topological relation of the power system; S200, taking the comprehensive economical efficiency optimization of the power system as an upper-layer optimization target, taking the operation effect of an energy storage system in the power system under a typical scene as a lower-layer optimization target, and solving the shared energy storage joint planning model by adopting a double-layer optimization method to obtain an operation plan of the energy storage system; the shared energy storage joint planning model uses a power grid information flow and an energy flow to connect the following sub-models: ① Describing a source end model of the output of the wind turbine generator based on a fuzzy opportunity constraint planning method; ② Describing a load end model of price excitation influencing user demand load response; ③ Consider the storage end model of the service life of the energy storage power station. Specifically, the step of establishing the shared energy storage joint planning model according to the topological relation of the power system in S100 includes: s110, establishing a source end model, indirectly describing uncertainty of the output of the wind turbine by the output prediction error of the wind turbine, wherein the method comprises the following steps: S111, modeling the output prediction error epsilon wind by adopting a fuzzy theory: The predictive error has two expression forms, namely positive error if the actual output is higher than the predicted output, and negative error if the actual output is lower than the predicted output; S113, calculating the membership grade mu wind of the prediction error by using the Cauchy distribution: Wherein E +wind、E-wind represents the statistical average value of the positive error and the negative error respectively; S115, establishing a source end model of wind turbine generator output represented by a credibility measure C r(ξ≤εwind) of a prediction error xi): Specifically, the step of establishing the shared energy storage joint planning model according to the topological relation of the power system in S100 includes: s120, establishing a load end model, wherein the method comprises the following steps of S121, a demand response model based on the incentive price is established, if the upper bound of a demand response coefficient of the load participation demand side response is ρ up, and the lower bound is ρ down, the upper bound and the lower bound are linear relations with the incentive price x, and the expression is as follows: the demand response p 0∈[ρdown,ρup at price x is stimulated, S122, calculating the total response load S of participating in demand response at the incentive price: The user participation demand response is recorded as an event j, the number of the participating users is N j, the incentive price is x j,ρ(xj), the response coefficient of the user participation demand response is the response coefficient of the user participation demand response, and the value range is ρ (x j)∈[ρdown,ρup],Sj is the total demand load of a certain user; s123, calculat