CN-121984072-A - Collaborative optimization method and system for energy storage regulation and control based on source-load fluctuation quantification

CN121984072ACN 121984072 ACN121984072 ACN 121984072ACN-121984072-A

Abstract

The application provides a collaborative optimization method and a collaborative optimization system for energy storage regulation and control based on source-load fluctuation quantification, and relates to the technical field of electric power. The method comprises the steps of performing discretization division on a total load fluctuation interval and a total output fluctuation interval of an electric power system, constructing a source-load fluctuation matching matrix, establishing a mapping relation from a source-load fluctuation state to a system regulation and control requirement based on the source-load fluctuation matching matrix, generating a regulation and control requirement signal interval, constructing an energy storage scheduling model based on dynamic programming, taking the regulation and control requirement signal interval as an excitation signal, maximizing the sum of relative achievement rate of the normalized system source-load matching degree improvement quantity and an energy storage scheduling comprehensive benefit index as an optimization target, performing collaborative optimization solution, and outputting an optimal energy storage scheduling strategy corresponding to the optimization target. The application solves the problems of low energy storage participation and limited system regulation capability caused by the fact that the prior art cannot consider energy storage economic benefits and power grid dispatching requirements.

Inventors

ZHAO YUTING
Cheng Dingran
ZHU HAN
CUI YANG
WANG SHUO

Assignees

东北电力大学

Dates

Publication Date: 20260505
Application Date: 20260205

Claims (10)

1. The collaborative optimization method for energy storage regulation and control based on source-load fluctuation quantification is characterized by comprising the following steps: Performing discretization division on a total load fluctuation interval and a total output fluctuation interval of the power system, and constructing a source-load fluctuation matching matrix to quantify source-load fluctuation differences of each period; Based on the source-load fluctuation matching matrix, establishing a mapping relation from a source-load fluctuation state to a system regulation and control requirement, and generating a regulation and control requirement signal interval; And constructing an energy storage scheduling model based on dynamic programming, taking the regulation and control demand signal interval as an excitation signal, maximizing the sum of the relative achievement rate of the normalized system source-load matching degree improvement quantity and the energy storage scheduling comprehensive benefit index as an optimization target, carrying out collaborative optimization solution, and outputting an optimal energy storage scheduling strategy corresponding to the optimization target.
2. The collaborative optimization method for energy storage regulation and control based on source-load fluctuation quantification according to claim 1, wherein the discretizing division is performed on a total load fluctuation interval and a total output fluctuation interval of a power system, and a source-load fluctuation matching matrix is constructed, comprising: Based on the in-group moment of inertia IW under different clustering numbers k, an IW curve is established, inflection points of the IW curve are analyzed, and the optimal clustering number is determined; Calculating the power variation amplitude and the minimum energy block of a single fluctuation subinterval based on the optimal clustering quantity; calculating a load state based on the power variation amplitude of the single fluctuation subinterval; based on the power variation amplitude and the load state of the single fluctuation subinterval, the total output fluctuation interval is expanded and divided into an output state interval sequence; combining the ith load state and the jth output state interval to construct a discrete source-charge full-wave dynamic state combination S ij ; Discrete level index based on the i-th load state L i and the j-th output state interval combination Q j And Calculating the source-load matching degree; and establishing a source-charge fluctuation matching matrix M based on the source-charge matching degree.
3. The collaborative optimization method for energy storage control based on source-load fluctuation quantization according to claim/2, wherein the calculation formula of the intra-group moment of inertia IW is: (1) Where k is the number of clusters, i is the cluster index, Is the sample set of the i-th cluster, For any sample within a cluster, Is a cluster center, and is provided with a plurality of clusters, Representing sample points To its cluster center Square of euclidean distance; Based on the optimal clustering quantity, calculating the power variation amplitude and the minimum energy block of a single fluctuation subinterval through the following formula: (2) wherein: for the power variation amplitude of a single fluctuation subinterval, And Respectively a minimum value and a maximum value of TLFR, A standard duration for each time step; is the minimum energy block, is used for representing the minimum energy required by conversion between quantization states, and k The optimal clustering quantity; based on the power variation amplitude of the single fluctuation subinterval, calculating a load state by the following formula: (3) wherein L i is the ith load state, Is an arbitrary value of the load fluctuation interval at the time t, And The minimum value and the maximum value of the load fluctuation interval are set; Based on the power variation amplitude and the load state of the single fluctuation subinterval, dividing the total output fluctuation interval into an output state interval sequence by the following formula: (4) Wherein Q j is the j-th output state interval; Is the total output at the time t, And Respectively, TOFR, n represents the number of subintervals extending between TOFR minimum and TLFR minimum, m represents TOFR total subintervals increasing compared with TLFR, Q 1 and Q n represent 1 st and nth output state intervals extending to minimum side of total output fluctuation interval, Q n+1 , And 1 st and k th Output state intervals matched with the load state intervals; And Respectively represent the 1 st and the kth of TOFR th expansion to the maximum side Individual output state intervals, L 1 and Respectively represent the 1 st and the k th A plurality of load status intervals; The discrete source-charge full-wave dynamic state combination S ij is represented as: (5) Wherein L is a set of load state intervals, Q is a set of output state intervals; Discrete level index based on the i-th load state L i and the j-th output state interval combination Q j And The source-to-charge matching degree is calculated by the following formula: (6) Wherein m i,j is the source-charge matching degree, the magnitude of the value quantifies the unmatched degree of the source-charge fluctuation state, when m i,j is not equal to 0, the system is in an unbalanced state, and the smaller the value is, the larger the deviation amplitude between the source and the charge layer is represented; the source-charge fluctuation matching matrix M is expressed as: (7) in [ the above ] Represents a dimension k formed by m i,j as an element Line m+k A matrix of columns.
4. The collaborative optimization method for energy storage regulation based on source-charge fluctuation quantification according to claim 3, wherein establishing a mapping relationship from a source-charge fluctuation state to a system regulation demand based on the source-charge fluctuation matching matrix, generating a regulation demand signal interval, comprises: determining a regulation and control demand quantization value based on the source-load fluctuation matching matrix, and constructing a regulation and control demand quantization matrix; Extracting all the quantitative values of the regulation and control demands in the system, and constructing a system regulation and control demand matching degree set; Constructing a regulation and control demand signal interval based on the sequencing positions of the regulation and control demand quantized values in the system regulation and control demand matching degree set; and forming a regulation and control demand signal matrix based on the regulation and control demand signal interval.
5. The collaborative optimization method for energy storage regulation based on source-load fluctuation quantization according to claim 4, wherein the regulation demand quantization value is determined by the following formula: (8) wherein: the quantitative value is used for regulating and controlling the demand; The corresponding relation between the numerical characteristics and the supply and demand states is that when Indicating that supply is greater than demand Indicating supply and supply shortage Representing the system supply and demand balance; the regulation and control demand quantization matrix is expressed as: (9) In the formula, In order to regulate the demand quantization matrix, Expressed in terms of The dimension formed for the elements is k Line m+k A matrix of columns; The system regulation and control demand matching degree set is expressed as: (10) wherein, the XI is a system regulation and control requirement matching degree set; is a set obtained by carrying out weight removal, descending order and arrangement on all matching degrees in the XI, wherein s.t. is a constraint condition; 、 And Is the unique matching degree value of the ascending arrangement after the weight of the Xis removed; The regulation and control demand signal interval is expressed as: (11) wherein: In order to regulate the demand signal interval, And Respectively the quantitative values of the regulation and control demands The upper limit and the lower limit of the corresponding regulation and control demand signal subinterval; the total fluctuation range of the demand signal is regulated and controlled to be a preset constant; step length of signal gear; representing a quantitative value of a regulatory requirement At the collection Ordering of (a) the number of times; Is the total number of unique match values in the system.
6. The collaborative optimization method for energy storage regulation based on source-load fluctuation quantization according to claim 4, wherein the regulation demand signal matrix is expressed as: (12) wherein: the regulation and control demand signal interval corresponding to the state combination S ij at the time t is represented, The demand signal matrix is regulated and controlled; to be expressed by The dimension formed for the elements is k Line m+k A matrix of columns.
7. The collaborative optimization method for energy storage regulation and control based on source-load fluctuation quantification according to claim 1, wherein the energy storage scheduling model based on dynamic programming is constructed by the following modes: establishing a mapping relation between energy storage capacity parameters and a fluctuation interval, wherein the mapping relation is expressed as follows: (13) Wherein L x is the maximum charge and discharge limit configuration of the energy storage, P max is the maximum charge and discharge power of the energy storage, and x is the power variation amplitude of a single fluctuation subinterval C y is the energy storage capacity configuration, SOC max is the rated capacity of the energy storage system, y is the minimum energy block Is a multiple of (2); Based on the mapping relation between the energy storage capacity parameter and the fluctuation interval, an adjustable state set Θ of the energy storage system is established, and the adjustable state set Θ is expressed as: (14) S ij is a source-charge full-wave dynamic state combination, and K L (t) and K Q (t) are respectively a t moment load and an output state value; Determining a state space and decision variables of an energy storage scheduling model, wherein the state space is expressed as S (t, SOC (t)), and the SOC (t) is the time t with the minimum energy block The decision variable is expressed as D (T) = (a (T), b (T), u (T)), a (T) represents the operation type of energy storage, a (T) e { Charge, DISCHARGE, none }, charge, disCharge and None respectively represent charging, discharging and inactivity, b (T) represents the regulation target of the energy storage, b (T) e { Output, load }, output represents the energy storage regulation acting on the source side state, load represents the energy storage regulation acting on the Charge side state, u (T) is the power regulation level, u (T) e {1,2, x }; Based on the decision variable, determining the actual charge and discharge power of the stored energy at the time t, which is expressed as: (17) wherein P (t) is the actual charge and discharge power of energy storage at the moment t; determining a state transition equation and constraint conditions of the energy storage scheduling model, wherein the state transition equation and constraint conditions are respectively expressed as follows: (18) (19) Wherein SOC (t+1) is the next time state calculated according to the state transition equation.
8. The collaborative optimization method for energy storage regulation and control based on source-load fluctuation quantification according to claim 7, wherein the regulation and control demand signal interval is used as an excitation signal, the sum of the normalized system source-load matching degree improvement quantity and the relative achievement rate of the energy storage scheduling comprehensive benefit index is maximized as an optimization target, collaborative optimization solution is performed, and an optimal energy storage scheduling strategy corresponding to the optimization target is output, and the collaborative optimization method comprises the following steps: After energy storage is introduced, an expanded source-charge-storage fluctuation matrix M E and a source-charge-storage regulation and control demand signal matrix are constructed E Expressed as: (15) (16) wherein: Representing the number of adjustable intervals increased on both sides of a source-charge after energy storage participation, and the dimension of the expanded matrix is along with An increase; Is a section adjustment coefficient, P max is the upper limit of the single charge and discharge power of the stored energy, and b max is the maximum adjustment energy block number of the stored energy; Expressed in terms of The dimension formed by the elements is Row of lines A matrix of columns; Expressed in terms of The dimension formed by the elements is Row of lines A matrix of columns; calculating the total quantity of source-load matching degree improvement and the maximum value of energy storage dispatching comprehensive benefit indexes, wherein the calculation formula of the total quantity of source-load matching degree improvement is as follows: (20) wherein: improving the total amount for the source-to-charge matching degree; m (t) is a matching degree improvement value at time t, and M i,j (t) and The matching degree before and after the energy storage participation at the moment t; The calculation formula of the maximum value of the energy storage dispatching comprehensive benefit index is as follows: (21) wherein: for the maximum total gain of energy storage during the phase time, E (t) is the instant benefit (element) at time t, Regulating and controlling the median value of the demand quantization interval for the time t; Normalizing by taking the total quantity of the source-load matching degree improvement and the maximum value of the energy storage scheduling comprehensive benefit index as reference values, and calculating the sum of relative achievement rates; the calculation formula of the sum of the relative achievement rates is as follows: (22) wherein R m and The relative achievement rates of the endogenous-charge matching degree and the benefit of the accumulation period after the participation of energy storage are respectively, Representing the sum of the relative achievement rates; And solving by adopting a dynamic programming method by taking the sum of the relative achievement rates as an optimization target, and obtaining an optimal decision sequence { D (0), D (1) & D (T-1) } which maximizes the sum of the relative achievement rates through reverse recurrence and forward tracking, thereby obtaining the optimal energy storage scheduling strategy.
9. The collaborative optimization method for energy storage regulation and control based on source-load fluctuation quantification according to claim 8, wherein when a dynamic programming method is adopted for solving, inverse recursive solving is carried out through a Bellman equation, and a value function V (t, SOC) is constructed to represent the optimal cumulative gain obtained from the state SOC (t) at the moment t to a programming end point: (23) wherein: The instant profit function is the instant profit function at the time t; when the source-to-charge matching degree is targeted, When the energy storage income is taken as a target: In collaborative optimization, the direct goal of optimization is to maximize the sum of the relative achievement rates of source-charge matching and energy storage returns: (24) The boundary condition is set as the state value at the end of the planning period is reset to zero; And reversely recursing from t=T-1 to t=0, calculating an optimal value function of all states in all time steps, and finding an optimal decision sequence starting from the initial state SOC (0) through forward tracking.
10. A co-optimizing system for energy storage regulation based on source-load fluctuation quantification, for implementing the method according to any one of claims 1 to 9, characterized in that the system comprises: The source-load fluctuation quantification module is configured to discretize and divide a total load fluctuation interval and a total output fluctuation interval of the power system, and construct a source-load fluctuation matching matrix so as to quantify the source-load fluctuation difference of each period; The regulation and control demand determining module is configured to establish a mapping relation from a source-charge fluctuation state to system regulation and control demands based on the source-charge fluctuation matching matrix, and generate a regulation and control demand signal interval; And the dynamic programming module is configured to construct an energy storage scheduling model based on dynamic programming, take the regulation and control demand signal interval as an excitation signal, maximize the sum of the normalized system source-load matching degree improvement quantity and the relative achievement rate of the energy storage scheduling comprehensive benefit index as an optimization target, perform collaborative optimization solution and output an optimal energy storage scheduling strategy corresponding to the optimization target.

Description

Collaborative optimization method and system for energy storage regulation and control based on source-load fluctuation quantification Technical Field The application relates to the technical field of electric power, in particular to a collaborative optimization method and a collaborative optimization system for energy storage regulation and control based on source-load fluctuation quantification. Background As the installed ratio of new energy increases gradually, the fluctuation difference of source-load expands continuously, and the system faces the challenges of peak power supply and low-valley new energy consumption, for example, at the running level of the power system, the seasonal fluctuation characteristic of high-proportion new energy causes the power system to face the double challenges of contradiction between supply and demand balance and consumption. Taking a provincial power grid as an example, when the generating capacity of the new energy reaches 30%, the electricity limit rate of the new energy is also up to 23%, and the defect of the system regulation capability is highlighted. At present, a plurality of researches on independent energy storage participation scheduling are carried out in the prior art, but the energy storage participation power market also has multiple problems that firstly, the guidance of a marketized electricity price mechanism is lacked, the profit space is smaller, secondly, the energy storage participation power is taken as a power generation enterprise to participate in transactions during discharging, the benefits of the traditional power generation main body can be possibly reduced, thirdly, the energy storage is possibly selected to be charged only in a valley electricity period when the energy storage is considered to participate in transactions with a power user during charging, and the flexibility of the energy storage is limited. The method also provides reference for the optimal configuration and market participation of flexible resources such as energy storage and the like. How to guide the energy storage to participate in the power market to solve the three problems, the key aspects are to firstly construct a quantitative source-load fluctuation difference method, secondly construct a regulation signal adapting to the energy storage by utilizing the source-load supply and demand difference, and thirdly construct a cooperative optimization algorithm with the benefit matched with the source load and the weight, so as to ensure the benefit of the energy storage and continuously participate in the scheduling operation of the system, play the role of ballast of medium-long term transaction and reduce the structural price difference between the medium-long term market and the spot market. Aiming at the problem of source-load fluctuation quantification, documents Wu Wenchuan, xu Shuwei, yang Yue, and the like, namely, high-proportion new energy power system probability scheduling [ J ] of risk quantification, power system automation, 2023,47 (15): 3-11 ] firstly provides definition and framework of risk quantification probability scheduling, and provides systematic solutions for promoting safe operation of novel power systems. Literature "Ye J, Xie L, Ma L, et al. Low-carbon optimal scheduling for multi-source power systems based on source-load matching under active demand response[J]. Solar Energy, 2024, 267: 112241" measures the source-load matching degree by introducing a source-load difference index, balances the economic cost and the safety performance of system scheduling, but the paper relies on the traditional peak-valley electricity price and has a slight deficiency in adapting to the fluctuation characteristic of new energy. Document "Li J, Zhao J, Chen Y, et al. Optimal sizing for a wind-photovoltaic-hydrogen hybrid system considering levelized cost of storage and source-load interaction[J]. International Journal of Hydrogen Energy, 2023, 48(11): 4129-4142" proposes two matching metrics to mobilize the aggressiveness of source-load interactions, but the solution objective of the proposed model is oversubscribed by economy while ignoring the impact of source-load fluctuations on system operation. The literature Gu Guangrong, yang Peng, shang Bo, and the like, a method for improving the balance capacity of a power distribution network through cooperative optimization of source-load-storage [ J ]. Chinese motor engineering report 2024,44 (13): 5097-5109 ] provides a quantitative evaluation method for the time sequence balance degree of source-load power based on an information entropy theory, and the method can promote new energy consumption and improve the balance capacity of the power distribution network to the greatest extent. For the problem of constructing a marketized electricity price mechanism for guiding energy storage participation, literature "Zhao D, Jafari M, Botterud A, et al. Strategic energy storage investments: A case study of the CAISO