Novel power distribution system reserve capacity optimal configuration method and device

Abstract

The invention discloses a novel power distribution system reserve capacity optimal configuration method and device, and the method comprises the steps: constructing a reserve capacity model of a power distribution system, and calculating the availability of the reserve capacity in time and the accessibility of the reserve capacity in capacity; using Markov chain-Monte Carlo to generate an operation scene based on wind and light historical output information, and then using a K-Means algorithm to delete the operation scene to generate a wind and light output typical scene; and constructing a standby capacity double-layer optimization configuration model and constraint conditions in which the power distribution system participates, solving the standby capacity double-layer optimization configuration model and the constraint conditions based on a KKT condition and a commercial solver, outputting a solving result, and applying the solving result to standby capacity optimization configuration of the power distribution system. The device comprises a processor and a memory. According to the invention, the operation safety of the power system is improved, and the intelligent fusion operation of the system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized operation of power distribution systems, and in particular to a novel method and device for optimizing the configuration of spare capacity of power distribution systems. Background Art

[0002] In the power system, renewable energy generation has strong volatility, indirectness and uncertainty, and the prediction accuracy is difficult to be effectively improved in a short period of time, which will bring short-term high-power disturbances and seriously threaten the safe operation of the power grid. Traditional coal-fired power generation equipment faces the challenges of long startup time and slow adjustment speed in providing backup power. Hydropower has good adjustment ability, but its power generation capacity is limited by seasonal changes and geographical location, and its application scenarios are limited. After the large-scale introduction of new energy, relying on coal and hydropower as backup power sources cannot meet the demand for backup capacity adjustment of the new power system. At the same time, relying heavily on coal-fired power generation will also reduce the economic benefits of the system and contradict the goal of promoting the use of renewable energy. With the continuous development of advanced measurement equipment and management and control methods, user-side resources such as distributed power sources, energy storage systems, electric vehicles and temperature control loads have become high-quality and flexible backup resources. Therefore, the distribution system can participate in the backup auxiliary service market by effectively integrating local demand and flexible resources, making reasonable dispatch, and providing backup support for the upper power grid.

[0003] However, with the large-scale access of new energy to the grid, a large number of distributed resources are also connected to the distribution network, and the coupling between the transmission and distribution networks is becoming increasingly close. The control methods used after the distribution network is connected to large-scale wind power have brought boundary imbalance problems to the transmission and distribution network. In the context of the diversification of system operation scenarios caused by the access of new energy, how to jointly dispatch and allocate the power generation resources of the transmission and distribution network to ensure the safe, efficient, clean, low-carbon, flexible, and intelligent integrated operation of the new power system is a difficult point in research.

[0004] There are still certain difficulties in solving the above problems: the means of quantifying the volatility and uncertainty of new energy still need to be improved, and the rapid response capability of backup capacity is imperfect when sudden power changes occur; it is difficult to obtain data such as the backup needs of users in the system and the safety standards of lines; there are many factors that affect the backup allocation of distribution networks, and the calculation scale is huge; the operating scenarios of the system after the connection of new energy are complex and changeable, especially in extreme weather or emergencies, it is difficult to ensure the safe and stable operation of the entire system. Summary of the invention

[0005] The present invention provides a novel method and device for optimizing the configuration of spare capacity of a power distribution system. The present invention improves the safety of power system operation and realizes the intelligent integration operation of the system. The details are described below:

[0006] In a first aspect, a novel method for optimizing the configuration of spare capacity of a power distribution system is provided, the method comprising:

[0007] Construct a reserve capacity model for the distribution system and calculate the availability of reserve capacity in time and accessibility in capacity;

[0008] Use Markov Chain-Monte Carlo to generate operating scenarios based on historical wind and solar power output information, and then use the K-Means algorithm to delete the operating scenarios to generate typical wind and solar power output scenarios;

[0009] Construct a two-layer optimization configuration model and constraint conditions for the reserve capacity involving the distribution system, solve the two-layer optimization configuration model and constraint conditions for the reserve capacity based on KKT conditions and commercial solvers, output the solution results, and apply the solution results to the optimal configuration of the reserve capacity of the distribution system.

[0010] The availability of the backup capacity in time is: the delivery capability of the backup capacity under the limit of 4 consecutive hours. The accessibility of the backup capacity in capacity is: the backup capacity reaching the coupling node between the power distribution system and the upper-level power grid.

[0011] Among them, the double-layer optimization configuration model of the reserve capacity involving the distribution system is: the upper layer constructs the distribution system operation decision model, and the lower layer constructs the transmission system reserve decision model.

[0012] Furthermore, the objective function of the power distribution system operation decision model is specifically:

[0013]

[0014] Where: f ul is the upper layer objective function; π e,t is the price of electricity in the electricity market; P in,t and P out,t are the electricity purchased and sold by the distribution system in time period t; ru,t and π rd,t To increase the reserve price and decrease the reserve price; π inc,t Provide a backup reward price for system success; π pen,t The penalty price for the system failing to provide backup; κ ru,t and κ rd,t are the percentages of possibility of increasing and decreasing the system reserve, respectively; is the probability that the promised reserve of gas units and energy storage cannot be used, R ua,t and R da,t They are respectively the upward and downward reserve capacity that the distribution system can deliver to the upper-level power grid.

[0015] Furthermore, the transmission system backup decision model is specifically:

[0016]

[0017] Where: f ll is the lower layer objective function; π gen,i,t Provide electricity sales quotation for traditional power generation companies; gen,i,t is the power of traditional power generation companies; π ret,i,t is the power price offered by the load aggregator; P ret,i,t Power purchased for load aggregators; P in,t and P out,t are the electricity bought and sold by the distribution system in time period t respectively; and are the quotations for electricity sales and purchases of the distribution system; π rug,i,t and R u,gen,i,t Increase the quotation and declared capacity of reserve for traditional power generation companies; rdg,i,t and R d,gen,i,t Lower the quotation and declared capacity of reserve for traditional power generation companies; ret,i,t and R u,ret,i,t They are the quoted price and declared capacity of the load aggregator for raising reserve; and R ua,t They are the quoted and declared capacity for the distribution system to increase reserve; and R da,t They are the quoted and declared capacity of the distribution system's reserve reduction; and are the probabilities that the reserves promised by traditional power generation companies, load aggregators and distribution systems cannot be called; κ ru,t and κ rd,t are the probabilities of calling up reserve and down reserve respectively; π inc,t and π pen,t is the incentive and punishment after the backup is successfully called; N k and N r They are respectively a collection of traditional power generation companies and load aggregators.

[0018] The constraints include: standby demand constraints:

[0019]

[0020] In the formula, R u,sys,t and R d,sys,t are the upward and downward reserve capacities required by system users; R u,gen,i,t and R d,gen,i,t They are the upward and downward reserves provided by traditional power plants; R u,ret,i,t It is the upward reserve provided by the load aggregator; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system;ru,t and π rd,t is the dual variable.

[0021] The constraints also include: a distribution system bidding constraint, where the bid reserve capacity of the distribution system meets availability and accessibility requirements:

[0022]

[0023] In the formula, Indicates the upper limit of active power transmitted by the line; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; P in,t and P out,t is the electricity bought and sold by the distribution system in time period t; are the dual variables corresponding to each constraint.

[0024] In a second aspect, a novel device for optimizing the configuration of spare capacity in a power distribution system comprises: a processor and a memory, wherein the memory stores program instructions, and the processor calls the program instructions stored in the memory to enable the device to execute any one of the methods described in the first aspect.

[0025] In a third aspect, a computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes any one of the methods described in the first aspect.

[0026] The beneficial effects of the technical solution provided by the present invention are:

[0027] (1) Compared with the prior art which does not fully consider the continuity of the backup capacity provided by the distribution system, the present invention accurately calculates the availability and accessibility of the backup capacity to ensure that the gas units and energy storage system in the distribution system can respond quickly when needed under different operating conditions, provide stable backup capacity, ensure the safe operation of the system, and improve the safety and stability of the system;

[0028] (2) The present invention uses the MCMC method and the K-Means algorithm to accurately simulate and reduce the uncertainty scenarios of wind and solar power generation, generate representative operation scenarios, and effectively deal with the operation risks caused by fluctuations in new energy sources such as wind power and photovoltaic power in the distribution system, ensuring that the system can still operate safely and reliably under diverse or even extreme conditions, and enhancing the adaptability to the uncertainty of new energy;

[0029] (3) The present invention fully considers the distribution system's ability to flexibly participate in the energy market and the backup market, combines economy, safety and reliability, and optimizes the scheduling of various types of backup resources to ensure the safety and stability of the power grid to the greatest extent possible, thereby improving the system's safe operation capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flowchart of a novel method for optimizing the allocation of reserve capacity in a power distribution system;

[0031] Figure 2 Collect pictures for scenery contribution scenes;

[0032] Figure 3 Adjust the backup demand map up / down for the system;

[0033] Figure 4 It is the timing parameter diagram related to the backup market;

[0034] Figure 5 It is the optimal decision diagram for the distribution system backup;

[0035] Figure 6 A diagram of the backup supply status of each market player;

[0036] Figure 7 Optimized clearing price graph for the power and reserve markets. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0038] The embodiment of the present invention takes into account the impact of the uncertainty of new energy on the backup capacity configuration of the new power system, and constructs a safe and reliable new power system backup capacity optimization configuration method through the active participation of the distribution system in providing backup capacity, so that it can combine the real-time situation of the distribution system to economically and flexibly realize the optimal configuration of the power grid backup capacity, support the reliable, safe and stable operation of the power grid, so as to improve the adaptability of the power system to high-proportion access of new energy and future scenarios such as new market trading models.

[0039] The novel method for optimizing the configuration of the spare capacity of the power distribution system proposed in the embodiment of the present invention has the following specific process: Figure 1 The method includes: distribution system backup capacity modeling and typical scenario generation, a two-layer optimization configuration model of backup capacity involving the distribution system, and two-layer problem solving, as described below:

[0040] Example 1

[0041] 101: Construct a reserve capacity model for the distribution system and calculate the availability of reserve capacity in time and accessibility in capacity;

[0042] The embodiment of the present invention aims to support the distribution system to provide backup capacity to the upper power grid. When a sudden power change occurs in the distribution system, in order to achieve rapid response, the backup capacity supported by the distribution system is provided by the gas units and energy storage within its area. Further, in step 101, the backup capacity provided by the distribution system is provided by the gas units and energy storage within its area. The basic steps are:

[0043] 1011: Construct an energy storage backup model in the power distribution system, which comprehensively considers the charging and discharging state and real-time capacity of the energy storage;

[0044] 1012: Construct a gas generator set standby model in the power distribution system, which takes into account the upper and lower limits of the output and the ramp speed of the gas generator set;

[0045] 1013: Calculate the availability of spare capacity in time, which is expressed as the delivery capability of spare capacity under the limit of continuous 4 hours;

[0046] 1014: Calculate the capacity accessibility of the reserve capacity, which is represented by the reserve capacity that can reach the coupling node between the distribution system and the upper power grid.

[0047] 102: Use Markov Chain-Monte Carlo to generate operating scenarios based on historical wind and solar power output information, and then use the K-Means algorithm to reduce the scenarios to generate typical wind and solar power output scenarios;

[0048] Among them, the Markov chain Monte Carlo (MCMC) method dynamically simulates a Markov chain, whose sampling distribution changes as the simulation progresses, and its goal is to obtain a specific stationary distribution. The MCMC method constructs a Markov chain of wind and solar time series based on historical wind and solar data. As the Markov chain is extended, the resulting simulation sequence can be regarded as an independent sample from the target distribution. When the Markov chain is infinitely long, that is, the number of samplings is close to infinity, the required data distribution is obtained.

[0049] The step 102 is specifically as follows:

[0050] 1021: Construct the Markov chain state transfer equation to represent the random process of wind and solar power historical output;

[0051] 1022: Generate the status of wind and solar power output at each moment based on the Monte Carlo method;

[0052] 1023: Use the K-Means algorithm to reduce the scene, and use the time series of wind and solar power output after reduction as the typical scene.

[0053] Among them, firstly, k initial cluster centers are randomly selected, and each data object is classified into the nearest center according to the distance between the original data point and these centers, so as to form k clusters. Then, a new cluster center is formed in the next iteration, and the original k clusters are used as new data points for further classification, and the iteration is repeated until the cluster center no longer changes. Finally, representative typical scenario time series data is obtained as the input of the double-layer optimization configuration model of the reserve capacity in which the distribution system participates as recorded in the following step 103.

[0054] 103: Construct a two-layer optimization configuration model for reserve capacity involving the distribution system, specifically, the upper layer constructs the distribution system operation decision model, and the lower layer constructs the transmission system reserve decision model, and its objective function is to minimize the distribution system operation cost and the overall operation mode cost of the entire system;

[0055] 104: Construct the constraints of the two-layer optimization configuration model of reserve capacity involving the distribution system, including the constraints of the upper distribution system operation decision model and the lower transmission system reserve decision model, specifically:

[0056] 1041: The constraints of the upper-level distribution system operation decision model include (1) the power flow constraints of the distribution system under normal operation, ensuring that the power flow does not exceed the limit when the distribution system is operating normally; (2) the power flow constraints of the distribution system when calling the reserve, ensuring that the power flow does not exceed the limit when the distribution system calls the reserve; (3) voltage constraints, complying with the voltage drop of the distribution system branch and the node voltage limit;

[0057] 1042: The constraints of the lower-level transmission system reserve decision model include (4) power balance constraints, which ensure the power balance of the entire distribution system; (5) reserve demand constraints, which meet the reserve demand of the upper-level power grid; (6) distribution system bidding constraints, which require the reserve capacity bid by the distribution system to meet availability and accessibility; (7) traditional power generation company bidding constraints, which require their bids to be limited to the installed capacity of the unit; and (8) load aggregator constraints, which require the power supplied to the power grid and its reserve capacity to be within a reasonable range.

[0058] 105: Applying Karush-Kuhn-Tucker (KKT) conditions, the objective function and constraint conditions of the lower-layer transmission system reserve decision model are converted into KKT conditional expressions, and the KKT conditional expressions are added as constraint conditions to the upper-layer distribution system operation decision model, and the two-layer model is converted into a single-layer optimization model with balance constraints; the distribution system power flow constraints are linearized by the second-order cone relaxation method, and the reserve capacity two-layer optimization configuration model with the participation of the distribution system constructed in step 104 is converted into a single-layer mixed integer second-order cone programming model that can be solved by the commercial solver GUROBI;

[0059] 106. Based on the conversion process, the commercial solver GUROBI is used to solve the single-layer mixed integer second-order cone programming model, and the solution is applied to capacity configuration.

[0060] The embodiments of the present invention achieve safe and stable scheduling of backup resources while meeting the backup capacity requirements of the power grid, ensure that the backup resources are economically and reliably collected by the power grid and participate in the power grid backup service, improve the utilization efficiency of the power grid for backup resources, ensure the reliability and rapid response capability of backup resources in the scheduling process, help to enhance the security of the power grid in various operating scenarios, ensure that the distribution system can operate stably and safely in the face of new energy fluctuations or emergencies, and reliably respond to the backup needs of the power grid.

[0061] Example 2

[0062] The solution in Example 1 is further introduced below in conjunction with a specific calculation formula, as described below for details:

[0063] 201: Construct a reserve capacity model for the distribution system and calculate the availability of reserve capacity in time and accessibility in capacity;

[0064] Further, in step 201, the backup of the power distribution system is provided by the gas units and energy storage within the power distribution system area. The basic steps are:

[0065] 2011: Constructing a storage reserve model in the distribution system, which comprehensively considers the charging and discharging status and real-time capacity of the energy storage;

[0066] Among them, when the energy storage is in charging mode, it can provide upward reserve in two ways: one is to respond to the immediate needs of the grid by reducing its charging power; the other is to quickly release the stored energy by switching directly from the charging state to the discharging state. Conversely, to provide downward reserve, the energy storage can do so by increasing its charging power, thereby reducing the overall energy input to the grid.

[0067] In discharge mode, the operation of energy storage is different. To provide upward reserve, energy storage can deliver more energy to the grid by increasing its discharge power. To provide downward reserve service, energy storage can reduce energy output by reducing its discharge power, or by changing its working state, switching from discharge mode to charging mode, thereby reducing energy supply to the grid.

[0068] 0≤P ESS,c,j,t ≤u c,j,t P ESS,j (1)

[0069] 0≤P ESS,d,j,t ≤u d,j,t P ESS,j(2)

[0070] u c,j,t +u d,j,t ≤1 (3)

[0071]

[0072]

[0073] R ESS,j,t,up ≤max{(1-u d,j,t )(P ESS,j +P ESS,c,j,t ),(1-u c,j,t )(P ESS,j -P ESS,d,j,t )} (6)

[0074] R ESS,j,t,down ≤max{(1-u d,j,t )(P ESS,j -P ESS,c,j,t ),(1-u c,j,t )(P ESS,j +P ESS,d,j,t )} (7)

[0075] Where: P ESS,c,j,t and P ESS,d,j,t are the charging and discharging power of energy storage at node j in time period t; u c,j,t and u d,j,t They are binary variables representing the energy storage system in the charging and discharging state. When the value is 1, it means that the energy storage system is in the charging (discharging) state; P ESS,j Indicates the rated charging and discharging power of energy storage; E j,t E is the energy stored by node j in time period t; j,min and E j,max They represent the lower and upper limits of its storage energy respectively; △t is the unit time length of time period t; η c and η d Respectively represent the charging and discharging efficiency of the energy storage at node j; N ESS represents the node set of energy storage; T is the scheduling period; R ESS,j,t,up and R ESS,j,t,down They are the upward and downward reserve capacities provided by the energy storage at node j in time period t, respectively.

[0076] 2012: Construct a gas generator set standby model in the power distribution system, which takes into account the upper and lower limits of the output and the ramp speed of the gas generator set;

[0077] P GT,j,t +R GT,j,t,up ≤P GT,j,max (8)

[0078] P GT,j,t -R GT,j,t,down ≥0 (9)

[0079] P GT,j,t +R GT,j,t,up -P GT,j,t-1 -R GT,j,t-1,down ≤RDN GT,j (10)

[0080] Where: P GT,j,t is the active power output of the gas unit at node j in time period t; R GT,j,t,up and R GT,j,t,down is the upward reserve and downward reserve capacity provided by the gas unit at node j in time period t; P GT,j,max is the upper limit of the active power output of the gas unit at node j; RDN GT,j It is the climbing speed of the gas unit.

[0081] 2013: Calculate the temporal availability of reserve capacity, which is expressed as the ability to deliver reserve capacity under the limit of 4 hours of continuous operation.

[0082] Among them, the backup of the distribution system is provided by its internal energy storage and gas units. In view of the limited storage capacity of energy storage, when providing backup services, the energy storage must take into account its own state of charge (SOC) to ensure that it can continuously output the promised backup capacity without exceeding the upper limit of its energy storage capacity and not falling below its lower limit. In the existing electricity market, there are usually specific regulations on the operating time of energy storage. For example, many markets stipulate that energy storage must have at least 4 hours of continuous operation capacity to meet the market's operating requirements and ensure the stability of power supply. Therefore, the energy storage backup used in the embodiment of the present invention is assumed to have a continuous 4-hour limit delivery capacity.

[0083]

[0084] Where: t' is the continuous four hours from 0h in the t period, ensuring the backup availability within 4 hours; r ESS,j,t+t',up and r ESS,j,t+t',down are the upward and downward reserve capacities of energy storage j in the distribution system during time period t; R ESS,j,t,up and R ESS,j,t,down They are the upward and downward reserve capacities provided by the energy storage at node j in time period t, respectively.

[0085] 2014: Calculate the accessibility of reserve capacity in terms of capacity, which is expressed as the reserve capacity that can reach the coupling node between the distribution system and the upper grid;

[0086] In the operation of the power market, the distribution system not only participates in the bidding process for power, but is also responsible for providing the required reserve capacity to the higher-level power system. In order to effectively perform this responsibility, it is necessary to accurately calculate and determine the reserve capacity that can actually be transmitted to the upper-level power system.

[0087] 0≤R ua,t ≤R t,up (13)

[0088] 0≤R da,t ≤R t,down (14)

[0089] In the formula, R ua,t and R da,t They are the upward and downward reserve capacities that the distribution system can deliver to the upper power grid; R t,up and R t,down They are the sum of the upward and downward reserve capacities provided by the gas-fired units and energy storage in the distribution system.

[0090] 202: Use the Markov chain-Monte Carlo method to generate operating scenarios based on historical wind and solar power output information, and then use the K-Means algorithm to reduce the scenarios to generate typical wind and solar power output scenarios;

[0091] Among them, the Markov chain Monte Carlo (MCMC) method dynamically simulates a Markov chain, whose sampling distribution changes as the simulation progresses, and its goal is to obtain a specific stationary distribution. As the Markov chain is extended, the resulting simulation sequence can be regarded as an independent sampling from the target distribution. The MCMC method constructs a Markov chain of the wind and solar time series based on historical wind and solar data. As the Markov chain is extended, the resulting simulation sequence can be regarded as an independent sampling from the target distribution. When the Markov chain is infinitely long, that is, the number of samplings is close to infinity, the required data distribution is obtained.

[0092] 2021: Construct a Markov chain state transition equation to represent the random process of wind and solar power historical output;

[0093]

[0094] In the formula, z t is the state of wind and solar power output at time t; p ij is the probability of state i at time t transferring to state j at time t+1; i, j are the specific states; n ik is the cumulative number of transitions from state i to state j at adjacent moments. The transition probabilities between all states constitute the state transition matrix P.

[0095]

[0096] The sum of each row in the matrix P is 1, and the cumulative state transfer matrix Q is:

[0097]

[0098] In the formula, q ij The value of

[0099] 2022: Generate the status of wind and solar power output at each moment based on the Monte Carlo method;

[0100] P m =P j,min +u(P j,max -P j,min ) (18)

[0101] Where: P m is the simulated power; P j,max and P j,min are the upper and lower limits of the power interval of state j; u is the cumulative transition probability at the next moment, and its value is uniformly distributed in the interval [0,1]. The next moment state j is extracted from the current state i, and u is compared with the i-th row of the matrix Q. If q i(j-1) <u<q ij , select j as the state at the next moment, and convert state j into the corresponding power according to formula (18).

[0102] 2023: Use the K-Means algorithm to reduce the scenario, and the time series of wind and solar power output after reduction is used as a typical scenario;

[0103] First, k initial cluster centers are randomly selected, which are the representative time series data of wind power, photovoltaic or load on a certain day. According to the distance between the original data point and these centers, each data object is classified into the nearest center to form k clusters. Then, new cluster centers are formed in the next iteration, and the original k clusters are used as new data points for classification. This iteration is repeated until the cluster center no longer changes. Finally, representative typical scenario time series data is obtained as the input of the double-layer optimization configuration model of spare capacity involving the distribution system.

[0104] The traditional K-Means algorithm cannot give the optimal number of clusters. The embodiment of the present invention uses the K-Means algorithm by selecting an appropriate clustering index and analyzing the clustering results within the range of cluster numbers to determine the optimal number of clusters. The range of cluster numbers is usually N is an integer between , and N is the total number of samples in the dataset.

[0105] The Calinski-Harabasz (CH) index is selected as the index for determining the best clustering.

[0106]

[0107] Where: T k and P k They represent the sum of squares of the inter-class and intra-class deviations of the number of classifications k. As the value of k continues to grow, P k The indicator shows a downward trend. At the same time, T k The index shows an upward trend. At a certain k value, the CH index may reach its maximum value, which is usually considered to be a reference for identifying the optimal number of clusters.

[0108] 203: Construct a two-layer optimization configuration model for reserve capacity involving the distribution system, specifically, the upper layer constructs the distribution system operation decision model, and the lower layer constructs the transmission system reserve decision model, and its objective function is to minimize the distribution system operation cost and the overall operation mode cost of the entire system;

[0109] Among them, the double-layer optimization configuration model of reserve capacity involving the distribution system is a double-layer stochastic optimization model based on the distribution system reserve capacity model and the typical operation scenario. The objective function of the upper problem aims to minimize the operating cost of the distribution system.

[0110] The objective function of the upper model is specifically:

[0111]

[0112] Where: f ul is the upper layer objective function; π e,t is the price of electricity in the electricity market; P in,t and P out,t are the electricity purchased and sold by the distribution system in time period t; ru,t and π rd,t To increase the reserve price and decrease the reserve price; π inc,t Provide a backup reward price for system success; π pen,t The penalty price for the system failing to provide backup; κ ru,t and κ rd,t are the percentages of possibility of increasing and decreasing the system reserve, respectively; is the probability that the promised reserve of gas units and energy storage cannot be used, R ua,t and R da,t They are respectively the upward and downward reserve capacity that the distribution system can deliver to the upper-level power grid.

[0113] The lower level problem mainly solves the problem of minimizing the overall operating model cost of the entire system. The objective function is:

[0114]

[0115] Where: f ll is the lower layer objective function; π gen,i,t Provide electricity sales quotation for traditional power generation companies; gen,i,t is the power of traditional power generation companies; π ret,i,t is the power price offered by the load aggregator; P ret,i,t Power purchased for load aggregators; P in,t and P out,t are the electricity bought and sold by the distribution system in time period t respectively; and are the quotations for electricity sales and purchases of the distribution system; π rug,i,t and R u,gen,i,t Increase the quotation and declared capacity of reserve for traditional power generation companies; rdg,i,t and R d,gen,i,t Lower the quotation and declared capacity of reserve for traditional power generation companies; ret,i,t and R u,ret,i,t They are the quoted price and declared capacity of the load aggregator for raising reserve; and R ua,t They are the quoted and declared capacity for the distribution system to increase reserve; and R da,t They are the quoted and declared capacity of the distribution system's reserve reduction; and are the probabilities that the reserves promised by traditional power generation companies, load aggregators and distribution systems cannot be called; κ ru,t and κ rd,t are the probabilities of calling up reserve and down reserve respectively; π inc,t and π pen,t is the incentive and punishment after the backup is successfully called; N k and N r They are respectively a collection of traditional power generation companies and load aggregators.

[0116] 204: Constructing the constraints of the two-level optimization configuration model of reserve capacity involving the distribution system;

[0117] 2041: Constraints of the upper-level distribution system operation decision model:

[0118] (1) Power flow constraints of the distribution system under normal operating conditions to ensure that the power flow does not exceed the limit when the distribution system is operating normally;

[0119]

[0120] Where: P ij,t and Q ij,t is the active and reactive power flowing through branch ij; I ij,t is the current of ij; r ij and xij are the resistance and reactance of ij respectively; p j,t and q j,t is the active and reactive power injected into node j; P in,t and P out,t is the electricity bought and sold by the distribution system during time period t; Q in,t and Q out,t is the reactive power flowing into and out of the distribution system; P GT,j,t is the power of gas unit j; P WP,j,t and P PV,j,t is the power of wind power and photovoltaic power; P ESS,c,j,t and P ESS,d,j,t is the charging and discharging power of energy storage j; U i,t is the voltage of node i; δ(j) represents the set of nodes connected to j

[0121] (2) The power flow constraints of the distribution system when calling the reserve are ensured to ensure that the power flow does not exceed the limit when the distribution system calls the reserve;

[0122]

[0123] In the formula, the mark "*" indicates the standby called state; the parameter meaning is the same as the symbols shown in formula (22).

[0124] (3) Voltage constraints, which comply with the voltage drop and node voltage limits of the distribution system branches;

[0125] U i,min ≤U s,i,t ≤U i,max (28)

[0126] Where U i,max and U i,max are the upper and lower limits of the voltage at node i respectively.

[0127] 2042: Constraints of the lower-level transmission system reserve decision model:

[0128] (4) Power balance constraint to ensure the power balance of the entire system;

[0129]

[0130] Where P gen,i,t is the power of traditional power generation companies; P ret,i,t Power purchased for load aggregators; P in,t and P out,t are the electricity purchased and sold by the distribution system in time period t; e,t is the dual variable.

[0131] (5) Reserve demand constraints to meet the reserve demand of the upper power grid;

[0132]

[0133] In the formula, R u,sys,t and R d,sys,t are the upward and downward reserve capacities required by system users; R u,gen,i,t and R d,gen,i,t They are the upward and downward reserves provided by traditional power plants; R u,ret,i,t It is the upward reserve provided by the load aggregator; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; ru,t and π rd,t is the dual variable.

[0134] (6) Distribution system bidding constraints: the distribution system's bid reserve capacity must meet availability and accessibility requirements;

[0135]

[0136] 0≤R ua,t : l dso,u,t (34)

[0137]

[0138] In the formula, Indicates the upper limit of active power transmitted by the line; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; P in,t and P out,t is the electricity bought and sold by the distribution system in time period t; l dso,t , l dso,u,t , l dso,d,t , l out,t , l in,t , are the dual variables corresponding to each constraint.

[0139] (7) Traditional power generation companies’ bidding constraints, where their bids are limited to the installed capacity of the units;

[0140]

[0141] P gen,i,t -R d,gen,i,t ≥0: l gen,i,t(39)

[0142]

[0143] In the formula, is the installed capacity of the generator; P gen,i,t For traditional power generation companies is the upper limit of the spare capacity that the generator set can provide; l gen,i,t , l u,gen,i,t , l d,gen,i,t , The corresponding dual variable.

[0144] (8) Load aggregator constraints: the power supplied to the grid and its reserve capacity are within a reasonable range.

[0145]

[0146] In the formula, The upper limit of load demand borne by load aggregators; The upper limit of the reserve capacity that the load aggregator can provide; P ret,i,t Power purchased for load aggregators; l ret,i,t , l u,ret,i,t , are the dual variables corresponding to each constraint.

[0147] 205: The objective function and constraint conditions of the lower-level transmission system backup decision model are converted into KKT conditional expressions by using the Karush-Kuhn-Tucker (KKT) condition, and added as constraint conditions to the upper-level distribution system operation decision model. The two-level model is converted into a single-level optimization model with balance constraints. The power flow constraints of the distribution system are linearized by the second-order cone relaxation method. Thus, the entire model is converted into a single-level mixed integer second-order cone programming model that can be solved by the commercial solver GUROBI.

[0148] Use KKT conditions to output the Lagrangian function L of the underlying problem:

[0149]

[0150] In the formula, l dso,t , l dso,u,t , l dso,d,t , l out,t , l in,t , l gen,i,t , l u,gen,i,t , l d,gen,i,t , l ret,i,t , l u,ret,i,t , is the dual variable corresponding to constraints (29)-(44); Indicates the upper limit of active power transmitted by the line; is the installed capacity of the generator; is the upper limit of the spare capacity that the generator set can provide; Provides a power cap on load demand for load aggregators; The upper limit of the spare capacity that a load aggregator can provide.

[0151] The corresponding constraints are:

[0152] g(x П )λ=0 (46)

[0153] 0≤g(x Ш )⊥λ≥0 (47)

[0154] In the formula, x П With x Ш is the decision variable of the proposed two-level optimal configuration model of reserve capacity involving the distribution system; x П is the variable in the equation; x Ш is the inequality variable; λ is the set of dual variables of constraints (29)-(44).

[0155] Here, the linearized power flow constraint is relaxed using a second-order cone:

[0156] ||2P ij,t 2Q ij,t l ij,t -v i,t ||2≤l ij,t +v i,t (48)

[0157] Where P ij,t and Q ij,t are the active and reactive powers flowing through line ij respectively; l ij,t is the square value of the line current; v i,tis the square of the node voltage.

[0158] 206: After calculating the optimal configuration of the distribution system's backup resources, sufficient flexible backup resource support can be provided for the interactive response between the power grid's source, network, load, and storage. This configuration scheme ensures that backup resources can respond to demand quickly and accurately under various operating conditions, thereby enhancing the safety and stability of power grid dispatch, especially in the case of randomness and volatility caused by a high proportion of new energy access, effectively avoiding safety hazards caused by large-scale power disturbances.

[0159] To sum up, based on the optimization configuration results, the economical, safe and stable dispatch of backup resources can be achieved, ensuring that the backup resources are reliably collected by the power grid and participate in the power grid backup service, improving the utilization efficiency of the power grid for backup resources, ensuring the reliability and rapid response capability of backup resources in the dispatching process, which will help enhance the security of the power grid in various operating scenarios, ensure that the distribution system can operate stably and safely in the face of new energy fluctuations or emergencies, and reliably respond to the power grid backup needs.

[0160] Example 3

[0161] The embodiment of the present invention mainly conducts case analysis on the actual situation of large-scale access of wind power and photovoltaic power to the power grid, and the participation of energy storage and gas units in the distribution system in providing backup, and uses the IEEE 33-node system to verify the effectiveness of the operation strategy of the double-layer random scheduling model constructed by the embodiment of the present invention.

[0162] According to the wind and solar output scenario prediction method proposed in the embodiment of the present invention, the MCMC method is first used to simulate the original scenarios of wind and solar power generation output, and then the wind and solar output scenarios are reduced by the K-Means algorithm to generate typical scenarios of wind and solar power output after large-scale grid access. The predicted values ​​of photovoltaic and wind power output under three typical scenarios after clustering are as follows: Figure 2 As shown. According to the two-layer stochastic optimization model, on the basis of the wind and solar power output forecast value, the distribution system purchases / sells electricity from the upper power grid or energy storage according to the backup demand, thereby obtaining the optimal configuration plan for the backup capacity. Assume that the unit cost of purchasing electricity from the upper power grid is 0.5$ / kWh. The unit cost of energy storage charging / discharging is 0.1$ / kWh. The unit risk cost is 2$ / kWh. Assume that the system backup demand is as follows Figure 3 As shown, the increase / decrease of the reserve probability and the corresponding reward and penalty prices are as follows Figure 4As shown. In addition to the Distribution System Operator (DSO), there are other key participants active in the market, including power producers and load aggregators. These participants together constitute the multiple entities of the market and jointly promote the development of the electricity market. In the example of the embodiment of the present invention, it is assumed that there are 10 traditional power generation companies (Generation Company, GC) and 10 load aggregators outside the distribution system participating in market transactions. In this example, it is assumed that the upper limit of the reserve capacity that traditional power generation companies can provide is about 10% of their power generation capacity, and the upper limit of the reserve capacity that load aggregators can provide is about 10% of their load level.

[0163] According to the existing reserve demand and the corresponding parameters of traditional power generation companies and load aggregators, a two-level stochastic optimization scheduling model is constructed with the goal of minimizing the operating cost of the distribution system and maximizing the total social profit. The two-level objective function is transformed into a mixed integer second-order cone programming problem. The YALMIP and GUROBI software in MATLB are used to directly solve the problem to obtain the optimal scheduling strategy for the reserve capacity of the distribution system, such as Figure 5 shown.

[0164] like Figure 5 The optimal decision of the distribution system to increase / decrease the reserve capacity is shown. It can be seen that the distribution system needs to adjust the output of each unit according to the change of reserve demand. In this study, the process of market transaction is designed to bid first and then clear the price. Therefore, the bidding results of the distribution system for increasing / decreasing the reserve capacity will directly affect the clearing price of increasing / decreasing the reserve capacity. The bidding strategy and results of the distribution system become the key factors for adjusting the market price and ensuring the stability of power supply. The distribution system meets part of the reserve demand by purchasing electricity from the ESS. The total reserve supply of the system is as follows: Figure 6 shown.

[0165] Depend on Figure 6 It can be seen that the upward / downward reserve demand is mainly provided by the distribution system, GC7, GC9 and GC10 to meet the overall reserve demand. In this case, taking the upward reserve as an example, the price trough of the upward reserve will appear in time periods 3-8, and the reserve clearing price in this period will be determined by the higher marginal price of the traditional power generation companies participating in the reserve supply.

[0166] And because the state of charge (SOC) level of ESS in the period 3-8 is relatively low, the distribution system in this period provides a lower level of upward reserve and a higher level of downward reserve. In the period 12-24, the SOC level of energy storage is significantly improved, so the distribution system provides a higher level of upward reserve and a lower level of downward reserve. The bidding results of the distribution system for the upward / downward reserve capacity are solved by the bilateral stochastic optimization scheduling model to obtain the clearing price and corresponding electricity price of the upward / downward reserve. Figure 7 shown.

[0167] Example 4

[0168] A new type of distribution system spare capacity optimization configuration device, see Figure 6 The device includes: a processor and a memory, wherein program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute the following method steps in Example 1:

[0169] Construct a reserve capacity model for the distribution system and calculate the availability of reserve capacity in time and accessibility in capacity;

[0170] Use Markov Chain-Monte Carlo to generate operating scenarios based on historical wind and solar power output information, and then use the K-Means algorithm to delete the operating scenarios to generate typical wind and solar power output scenarios;

[0171] Construct a two-layer optimization configuration model and constraint conditions for the reserve capacity involving the distribution system, solve the two-layer optimization configuration model and constraint conditions for the reserve capacity based on KKT conditions and commercial solvers, output the solution results, and apply the solution results to the optimal configuration of the reserve capacity of the distribution system.

[0172] The availability of the backup capacity in time is: the delivery capability of the backup capacity under the limit of continuous 4 hours. The accessibility of the backup capacity in capacity is: the backup capacity reaching the coupling node between the distribution system and the upper-level power grid.

[0173] Among them, the two-layer optimization configuration model of reserve capacity involving the distribution system is as follows: the upper layer constructs the distribution system operation decision model, and the lower layer constructs the transmission system reserve decision model.

[0174] Furthermore, the objective function of the distribution system operation decision model is specifically:

[0175]

[0176] Where: f ul is the upper layer objective function; π e,t is the price of electricity in the electricity market; P in,t and P out,tare the electricity purchased and sold by the distribution system in time period t; ru,t and π rd,t To increase the reserve price and decrease the reserve price; π inc,t Provide a backup reward price for system success; π pen,t The penalty price for the system failing to provide backup; κ ru,t and κ rd,t are the percentages of possibility of increasing and decreasing the system reserve, respectively; is the probability that the promised reserve of gas units and energy storage cannot be used, R ua,t and R da,t They are respectively the upward and downward reserve capacity that the distribution system can deliver to the upper-level power grid.

[0177] Furthermore, the transmission system backup decision model is specifically as follows:

[0178]

[0179] Where: f ll is the lower layer objective function; π gen,i,t Provide electricity sales quotation for traditional power generation companies; gen,i,t is the power of traditional power generation companies; π ret,i,t is the power price offered by the load aggregator; P ret,i,t Power purchased for load aggregators; P in,t and P out,t are the electricity bought and sold by the distribution system in time period t respectively; and are the quotations for electricity sales and purchases of the distribution system; π rug,i,t and R u,gen,i,t Increase the quotation and declared capacity of reserve for traditional power generation companies; rdg,i,t and R d,gen,i,t Lower the quotation and declared capacity of reserve for traditional power generation companies; ret,i,t and R u,ret,i,t They are the quoted price and declared capacity of the load aggregator for raising reserve; and R ua,t They are the quoted and declared capacity for the distribution system to increase reserve; and R da,t They are the quoted and declared capacity of the distribution system's reserve reduction; and are the probabilities that the reserves promised by traditional power generation companies, load aggregators and distribution systems cannot be called; κ ru,t and κ rd,t are the probabilities of calling up reserve and down reserve respectively; π inc,t and π pen,t is the incentive and punishment after the backup is successfully called; Nk and N r They are respectively a collection of traditional power generation companies and load aggregators.

[0180] Among them, the constraints include: Backup demand constraints:

[0181]

[0182] In the formula, R u,sys,t and R d,sys,t are the upward and downward reserve capacities required by system users; R u,gen,i,t and R d,gen,i,t They are the upward and downward reserves provided by traditional power plants; R u,ret,i,t It is the upward reserve provided by the load aggregator; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; ru,t and π rd,t is the dual variable.

[0183] Among them, the constraints also include: distribution system bidding constraints, the distribution system's bid reserve capacity meets availability and accessibility:

[0184]

[0185] 0≤R ua,t : l dso,u,t

[0186] 0≤R da,t : l dso,d,t

[0187]

[0188] In the formula, Indicates the upper limit of active power transmitted by the line; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; P in,t and P out,t is the electricity bought and sold by the distribution system in time period t; l dso,t , l dso,u,t , l dso,d,t , l out,t , l in,t , are the dual variables corresponding to each constraint.

[0189] It should be pointed out here that the device description in the above embodiment corresponds to the method description in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0190] The execution subjects of the above-mentioned processor and memory can be devices with computing functions such as computers, single-chip microcomputers, and microcontrollers. In specific implementation, the embodiments of the present invention do not limit the execution subjects and are selected according to the needs of actual applications.

[0191] The data signal is transmitted between the memory and the processor via a bus, which is not described in detail in the embodiment of the present invention.

[0192] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, the storage medium includes a stored program, and when the program is running, the device where the storage medium is located is controlled to execute the method steps in the above embodiment.

[0193] The computer-readable storage medium includes but is not limited to a flash memory, a hard disk, a solid-state drive, and the like.

[0194] It should be pointed out here that the description of the readable storage medium in the above embodiment corresponds to the description of the method in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0195] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present invention are generated.

[0196] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions may be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer-readable storage medium may be any available medium that can be accessed by the computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium or a semiconductor medium, etc.

[0197] Unless otherwise specified, the models of the components in the embodiments of the present invention are not limited, and any device that can perform the above functions may be used.

[0198] Those skilled in the art will appreciate that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0199] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A novel method for optimizing the configuration of spare capacity of a power distribution system, characterized in that: The method comprises: Construct a reserve capacity model for the distribution system and calculate the availability of reserve capacity in time and accessibility in capacity; Use Markov Chain-Monte Carlo to generate operating scenarios based on historical wind and solar power output information, and then use the K-Means algorithm to delete the operating scenarios to generate typical wind and solar power output scenarios; Construct a two-layer optimization configuration model and constraint conditions for the reserve capacity involving the distribution system, solve the two-layer optimization configuration model and constraint conditions for the reserve capacity based on KKT conditions and commercial solvers, output the solution results, and apply the solution results to the optimal configuration of the reserve capacity of the distribution system.

2. A novel method for optimizing the configuration of spare capacity of a power distribution system according to claim 1, characterized in that: The temporal availability of the backup capacity is: the ability to deliver the backup capacity under the extreme condition of continuous 4 hours. The accessibility of the reserve capacity in terms of capacity is: the reserve capacity reaching the coupling node between the power distribution system and the upper-level power grid.

3. A novel method for optimizing the configuration of spare capacity of a power distribution system according to claim 1, characterized in that: The double-layer optimization configuration model of reserve capacity in which the distribution system participates is as follows: the upper layer constructs a distribution system operation decision model, and the lower layer constructs a transmission system reserve decision model.

4. A novel method for optimizing the configuration of spare capacity of a power distribution system according to claim 3, characterized in that: The objective function of the distribution system operation decision model is specifically: Where: f ul is the upper layer objective function; π e,t is the price of electricity in the electricity market; P in,t and P out,t are the electricity purchased and sold by the distribution system in time period t; ru,t and π rd,t To increase the reserve price and decrease the reserve price; π inc,t Provide a backup reward price for system success; π pen,t The penalty price for the system failing to provide backup; κ ru,t and κ rd,t are the percentages of possibility of increasing and decreasing the system reserve, respectively; is the probability that the promised reserve of gas units and energy storage cannot be used, R ua,t and R da,t They are respectively the upward and downward reserve capacity that the distribution system can deliver to the upper-level power grid.

5. A novel method for optimizing the configuration of spare capacity of a power distribution system according to claim 3, characterized in that: The transmission system backup decision model is specifically: Where: f ll is the lower layer objective function; π gen,i,t Provide electricity sales quotation for traditional power generation companies; gen,i,t is the power of traditional power generation companies; π ret,i,t is the power price offered by the load aggregator; P ret,i,t Power purchased for load aggregators; P in,t and P out,t are the electricity bought and sold by the distribution system in time period t respectively; and are the quotations for electricity sales and purchases of the distribution system; π rug,i,t and R u,gen,i,t Increase the quotation and declared capacity of reserve for traditional power generation companies; rdg,i,t and R d,gen,i,t Lower the quotation and declared capacity of reserve for traditional power generation companies; ret,i,t and R u,ret,i,t They are the quoted price and declared capacity of the load aggregator for raising reserve; and R ua,t They are the quoted and declared capacity for the distribution system to increase reserve; and R da,t They are the quoted and declared capacity of the distribution system's reserve reduction; and are the probabilities that the reserves promised by traditional power generation companies, load aggregators and distribution systems cannot be called; κ ru,t and κ rd,t are the probabilities of calling up reserve and down reserve respectively; π inc,t and π pen,t is the incentive and punishment after the backup is successfully called; N k and N r They are respectively a collection of traditional power generation companies and load aggregators.

6. A novel method for optimizing the configuration of spare capacity of a power distribution system according to claim 1, characterized in that: The constraints include: Standby demand constraints: In the formula, R u,sys,t and R d,sys,t are the upward and downward reserve capacities required by system users; R u,gen,i,t and R d,gen,i,t They are the upward and downward reserves provided by traditional power plants; R u,ret,i,t It is the upward reserve provided by the load aggregator; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; ru,t and π rd,t is the dual variable.

7. A novel method for optimizing the configuration of spare capacity of a power distribution system according to claim 1, characterized in that: Said The constraints also include: distribution system bidding constraints, the distribution system's bid reserve capacity meets availability and accessibility: 0≤R ua,t : λ dso,u,t 0≤R da,t : λ dso,d,t In the formula, Indicates the upper limit of active power transmitted by the line; R ua,t and R da,t It is the upward and downward reserve provided by the distribution system; P in,t and P out,t is the electricity bought and sold by the distribution system in time period t; λ dso,t , λ dso,u,t , λ dso,d,t , λ out,t , λ in,t , are the dual variables corresponding to each constraint.

8. A novel device for optimizing the configuration of spare capacity of a power distribution system, characterized in that: The device comprises: a processor and a memory, wherein program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to execute any one of the methods of claims 1-7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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