A resource configuration method and device for a power distribution network

Abstract

The application provides a resource configuration method of a power distribution network. In the execution of the method, resource parameters in the power distribution network are acquired; the resource parameters are input into a pre-established resource configuration model of the power distribution network, including a configuration cost minimization model, an energy interaction minimization model and a power balance determination constraint model; an optimal solution of the resource configuration model of the power distribution network is acquired according to an Epsilon constraint method to obtain Pareto frontier data; and the resource configuration parameters of the power distribution network are adjusted based on the Pareto frontier data. Thus, the resource configuration model of the power distribution network is solved according to the Epsilon constraint method, and each point on the Pareto frontier considers the optimal solution of the energy storage system and demand response planning that optimizes the configuration cost and energy interaction, so that the power distribution network operator can configure various resources in the power distribution network based on the obtained optimal solution, and the target of minimizing the energy interaction cost and configuration budget cost with the upper-level power grid is achieved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network optimization, and more particularly to a method and apparatus for resource allocation in power distribution networks. Background Technology

[0002] In recent years, distributed power generation based on renewable energy has developed rapidly and become an important part of the distribution network. In order to minimize disruption to the safe operation of the distribution network, energy storage systems and demand response technologies have been developed rapidly.

[0003] As the goal of "carbon neutrality" is gradually achieved, distribution network operators tend to build distribution networks with lower energy interaction costs, ensuring that all energy consumed in these networks comes from distributed generation sources and generates no carbon dioxide emissions. However, dispatching demand response resources and allocating energy storage configuration resources typically requires additional budgets from distribution network operators. How to coordinate and configure various resources within the distribution network to minimize energy interaction costs with the upper-level grid and configuration budget costs has become a pressing issue. Summary of the Invention

[0004] In view of this, the present invention provides a resource allocation method and apparatus for a distribution network to obtain the optimal allocation solution for various resources in the distribution network, so as to facilitate the distribution network operator to allocate various resources in the distribution network based on the obtained optimal solution, and achieve the goal of minimizing the energy cost and configuration budget cost of interaction with the upper-level power grid.

[0005] The technical solution is as follows:

[0006] In a first aspect, embodiments of this application provide a resource allocation method for a power distribution network, including:

[0007] Obtain resource configuration parameters in the power distribution network;

[0008] The resource configuration parameters are input into a pre-established distribution network resource configuration model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model.

[0009] The optimal solution of the power distribution network resource allocation model is obtained using the Epsilon constraint method to obtain Pareto front data;

[0010] The resource allocation parameters in the distribution network are adjusted based on the Pareto frontier data.

[0011] Preferably, the configuration cost minimization model is as follows:

[0012] min[C ESS +C DR +C WT +CPV ],

[0013] Among them, C ESS C represents the energy storage cost of the energy storage system in the aforementioned distribution network. DR For load demand response costs, C WT To reduce costs for wind power generation, C PV To reduce the cost of photovoltaic power generation.

[0014] Preferably, the energy interaction minimization model is:

[0015]

[0016] Where T represents the time interval during which energy interaction occurs. It is the power exchanged between the distribution network and the upstream power grid during time period t.

[0017] Preferably, the power balance determination constraint model is obtained by transforming the power balance chance constraint model using the quantiles of the probability distribution;

[0018] The power balance opportunity constraint model is as follows:

[0019]

[0020] Where Pr represents the inequality The probability of it being true. It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. The net load within the power balance area of ​​the distribution network is α, where α is the preset information level.

[0021] The power balance determination constraint model is as follows:

[0022]

[0023] in, It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. Let Φ be the load reduction amount caused by photovoltaic power generation in the distribution network, Φ be the standard normal distribution function, and α be the preset confidence level. For the The corresponding standard deviation For the The corresponding mean.

[0024] Secondly, embodiments of this application provide a resource allocation device for a power distribution network, comprising:

[0025] The parameter acquisition module is used to acquire resource configuration parameters in the power distribution network;

[0026] The model input module is used to input the resource parameters into a pre-established distribution network resource configuration model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model.

[0027] The optimal value solution module is used to obtain the optimal solution of the distribution network resource allocation model according to the Epsilon constraint method, so as to obtain Pareto front data;

[0028] The resource configuration module is used to adjust the resource configuration parameters of the distribution network based on the Pareto frontier data.

[0029] Preferably, the configuration cost minimization model is as follows:

[0030] min[C ESS +C DR +C WT +C PV ],

[0031] Among them, C ESS C represents the energy storage cost of the energy storage system in the aforementioned distribution network. DR For load demand response costs, C WT To reduce costs for wind power generation, C PV To reduce the cost of photovoltaic power generation.

[0032] Preferably, the energy interaction minimization model is:

[0033]

[0034] Where T represents the time interval during which energy interaction occurs. It is the power exchanged between the distribution network and the upstream power grid during time period t.

[0035] Preferably, the power balance determination constraint model is obtained by transforming the power balance chance constraint model using the quantiles of the probability distribution;

[0036] The power balance opportunity constraint model is as follows:

[0037]

[0038] Where Pr represents the inequality The probability of it being true. It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. The net load within the power balance area of ​​the distribution network is α, where α is the preset information level.

[0039] The power balance determination constraint model is as follows:

[0040]

[0041] in, It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. Let Φ be the load reduction amount caused by photovoltaic power generation in the distribution network, Φ be the standard normal distribution function, and α be the preset confidence level. For the The corresponding standard deviation For the The corresponding mean.

[0042] Thirdly, embodiments of this application provide an electronic device, the electronic device comprising:

[0043] Memory, used to store one or more programs;

[0044] A processor; when the one or more programs are executed by the processor, to implement the method described in any of the first aspects above.

[0045] Fourthly, embodiments of this application provide a computer storage medium storing a program that, when executed by a processor, implements the method described in any of the first aspects above.

[0046] The above technical solution has the following beneficial effects:

[0047] This application provides a method and apparatus for resource allocation in a distribution network. When executing the method, resource parameters in the distribution network are acquired; these parameters are input into a pre-established distribution network resource allocation model, which includes a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model; the optimal solution of the distribution network resource allocation model is obtained using the Epsilon constraint method to obtain Pareto front data; and the resource allocation parameters of the distribution network are adjusted based on the Pareto front data. Therefore, in this application embodiment, by solving the configuration cost minimization model, energy interaction minimization model, and power balance deterministic constraint model using the Epsilon constraint method, each point on the obtained Pareto front considers the optimal solution for the energy storage system and demand response planning with optimal configuration cost and energy interaction. This facilitates distribution network operators in configuring various resources in the distribution network based on the obtained optimal solution, achieving the goal of minimizing energy interaction costs and configuration budget costs with the upstream power grid. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0049] Figure 1 A flowchart illustrating a resource allocation method for a power distribution network provided in an embodiment of the present invention;

[0050] Figures 2 to 10 The following diagram illustrates a specific application scenario of a resource allocation method for a power distribution network according to an embodiment of the present invention.

[0051] Figure 11 This is a schematic diagram of the structure of a resource allocation device for a power distribution network provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] To minimize the energy cost and configuration budget cost of interaction with the upper-level power grid, this application provides a resource allocation method for a distribution network. Please refer to [link to relevant documentation]. Figure 1 The method may include:

[0054] Step S101: Obtain resource configuration parameters in the distribution network.

[0055] Specifically, the resources in the distribution network include at least load points, energy storage units, wind power generation, and photovoltaic power generation. The resource configuration parameters are the relevant configuration parameters of the load points, wind power generation, and photovoltaic power generation in the distributed distribution network, such as: the power of the load points, the power of the wind power generation, the power of the photovoltaic power generation, the charging and discharging parameters of the energy storage units, and maintenance costs, etc.

[0056] Step S102: Input the resource configuration parameters into the pre-established distribution network resource configuration model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model.

[0057] Specifically, the resource configuration parameters are input into the pre-established distribution network resource configuration model. The resource configuration model includes a configuration minimization model, an energy interaction minimization model, and a power balance deterministic constraint model. It should be noted that the model part will be explained later.

[0058] Step S103: Obtain the optimal solution of the distribution network resource allocation model according to the Epsilon constraint method to obtain Pareto frontier data.

[0059] Specifically, the Epsilon constraint method is used to transform each model of the power distribution network resource allocation model into a single objective optimization problem, that is, to transform a multi-objective optimization problem into a single-objective optimization problem.

[0060] Alternatively, one objective (energy interaction minimization model) can be used as the objective function, and the other objective (configured as the minimization model) can be transformed into a constraint using the Epsilon constraint method, thereby transforming the multi-objective optimization problem into a single-objective optimization problem.

[0061] The following explains how to solve a multi-objective optimization model using the Epsilon constraint method. Assume the multi-objective optimization includes objective functions F1 and F2. The specific steps are as follows:

[0062] Step S1031: Perform individual optimization for each objective function. At this point, the other objective functions are in an unconstrained state, yielding F1'. * F2' (The optimal value F1' obtained by taking F1 as the optimization objective) * And the values ​​of the other objective function at this time, F2' and F1'. (The optimal value obtained by taking F2 as the optimization objective) And the value of the other objective function at this time (F1');

[0063] Step S1032: The range of values ​​for the non-dominated solution F2 is as follows: Based on the desired number of non-dominated solutions n, select e from the intervals with equal spacing. k (k = 1, ..., n), where the constant value is e k It is obtained at equal intervals from the range of values ​​of F2.

[0064] Step S1033: Set F2 = e k As a condition, it is included in the optimization model with F1 as the optimization objective, and the optimization result is F. 1,k Thus, the kth non-dominated solution (F1 = F) is obtained. 1,k F2=e k ).

[0065] Step S1034: After solving all n non-dominated solutions, the Pareto front data for the multi-objective optimization problem can be constructed.

[0066] Based on the above, the optimal solution of the distribution network resource allocation model is obtained, and the Pareto front data is obtained.

[0067] Step S104: Adjust the resource allocation parameters in the distribution network based on the Pareto frontier data.

[0068] Specifically, each non-dominated solution on the Pareto front is a feasible optimal solution, and the corresponding resource allocation scheme is an optimized scheme. However, the combination of objective function values ​​is different and can be selected according to actual needs (such as a preference for lower configuration budget costs). All of them can achieve the goal of minimizing the energy cost and configuration budget cost of interaction with the upper-level power grid.

[0069] As can be seen from the above technical solution, the embodiments of this application provide a resource allocation method for a distribution network. When executing the method, resource parameters in the distribution network are obtained; the resource parameters are input into a pre-established distribution network resource allocation model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model; the optimal solution of the distribution network resource allocation model is obtained according to the Epsilon constraint method to obtain Pareto front data; and the resource allocation parameters of the distribution network are adjusted based on the Pareto front data. Therefore, in the embodiments of this application, the configuration cost minimization model, energy interaction minimization model, and power balance deterministic constraint model are solved using the Epsilon constraint method. Each point on the obtained Pareto front considers the optimal solution for the energy storage system and demand response planning with optimal configuration cost and energy interaction, facilitating distribution network operators to allocate various resources in the distribution network based on the obtained optimal solution, thereby achieving the goal of minimizing energy interaction costs and configuration budget costs with the upper-level grid.

[0070] As a preferred embodiment, the configuration cost minimization model is as follows:

[0071] min[C ESS +C DR +C WT +C PV ],

[0072] Among them, C ESS C represents the energy storage cost of the energy storage system in the aforementioned distribution network. DR For load demand response costs, C WT To reduce costs for wind power generation, C PV To reduce the cost of photovoltaic power generation.

[0073] It is understandable that the configuration cost minimization model is a solution model that minimizes the energy storage cost, load demand response cost, wind power reduction cost, and photovoltaic power reduction cost of the energy storage system.

[0074] I. Energy storage cost of energy storage systems (C) ESS It includes investment costs and operation and maintenance costs, and its formula can be expressed as:

[0075]

[0076] Where, α ESS Let P be the capital recovery factor for the energy storage equipment installed in the energy storage system, r be the discount rate, n be the operating years of the energy storage equipment in the energy storage system, c2 be the unit power investment cost of the energy storage equipment in the energy storage system, and P be the cost per unit power of the energy storage equipment. ESS.maxC3 represents the maximum power that the energy storage device in the energy storage system can achieve during charging and discharging, and E represents the unit capacity investment cost of the energy storage device in the energy storage system. r M represents the rated energy storage capacity of the energy storage devices in the energy storage system. ESS β represents the one-time investment cost of the energy storage equipment installed in the energy storage system. ESS This is the annual operation and maintenance cost coefficient for energy storage equipment in the energy storage system.

[0077] In practical engineering, the energy storage cost C of an energy storage system ESS The following constraints also need to be considered:

[0078] When an energy storage device is operating normally, the battery's state of charge (SOC) is a crucial parameter reflecting its remaining capacity. The SOC of a battery refers to the ratio of its remaining capacity to its rated capacity after charging and discharging at a given discharge rate. Its expression is as follows:

[0079]

[0080] Wherein, the value of SOC ranges from [0, 1], E(t) is the remaining energy stored in the battery at time t, and Er represents the rated capacity of the battery energy storage.

[0081] In practical engineering, overcharging and over-discharging can reduce the lifespan of energy storage devices. Therefore, it is necessary to constrain the state of charge of energy storage devices, the expression of which is shown below:

[0082] SOC min ≤SOC(t)≤SOC max ;

[0083] Among them, SOC min and SOC max These represent the upper and lower limits of the state of charge of the energy storage device, respectively.

[0084] Energy storage devices may be in a charging or discharging state during operation, and the amount of energy stored in the battery changes constantly depending on the different operating states, as expressed below:

[0085]

[0086]

[0087] Among them, P ch (t), P dis (t) represents the power of the energy storage device charging and discharging the distribution network at time t, and η is the charging and discharging efficiency. This represents the power change of the energy storage device at time t after accounting for charging and discharging losses.

[0088] To ensure that the energy storage device maintains a consistent state at the beginning and end of each scheduling cycle, and that the energy change of the energy storage device is zero within a set time period, the following constraints can be added:

[0089] That is, the energy change value of the energy storage device is 0 within the time period T.

[0090] In practical engineering, the actual charging and discharging power of energy storage devices is constrained by the upper limit of the battery's charging and discharging power, as shown in the following expression:

[0091] 0≤P ch (t)≤P ESS,max ;

[0092] 0≤P dis (t)≤P ESS,max ;

[0093] Among them, P ESS,max This refers to the upper limit of the charging and discharging power of energy storage devices.

[0094] The above explains the energy storage cost and related constraints of the energy storage system in the configuration cost minimization model of this application.

[0095] II. Regarding load demand response cost C DR It includes the total scheduling incentive cost for transferable loads and the total scheduling incentive cost for loads that can be reduced, and its expression can be stated as:

[0096] C DR =C trans +C re ;

[0097] Among them, C trans C represents the total incentive cost for dispatching transferable loads. re The total incentive cost for load scheduling can be reduced.

[0098] Transferable loads are loads that correspond to a flexible demand response mechanism. Because they are not subject to continuity and timing restrictions, they can be flexibly allocated within an acceptable transfer time range. Common loads include ice storage air conditioning, electric vehicle battery swapping stations, and some industrial and commercial loads.

[0099] Transferable loads must meet constraints such as power constraints, dispatch time constraints, and total electricity consumption constraints. The calculation and constraints for transferable load demand response costs are as follows:

[0100]

[0101] Among them, C trans The total incentive cost for dispatching transferable loads, The total amount of load available for transfer. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. and These represent the upper and lower bounds of the transferable load transferable time interval, T. trans For the set of transferable load time periods, y represents the maximum transferable duration of the transferable load. t The time when transferable loads are allowed to be transferred. The incentive price is the transferable load, and Δt is the scheduling time interval.

[0102] Reduceable loads are loads that can be reduced or interrupted by analyzing user comfort and willingness to respond. Common loads include a large number of temperature control loads and building lighting loads.

[0103] Reduceable loads need to meet constraints such as the duration of load reduction. The demand response cost and constraints for reduceable loads are as follows:

[0104]

[0105] Among them, C re To reduce the total incentive cost of load dispatching, The total load that can be reduced; z represents the load reduction amount over a certain period of time. t The time when load reduction is permitted. and These represent the upper and lower bounds of the load reduction and the time interval for load reduction, respectively. re For the set of time periods where load can be reduced, The maximum duration for which the load can be reduced is [specified]. The incentive price is for reducing the load.

[0106] The above explains the load demand response cost and related constraints in the configuration cost minimization model of this application.

[0107] III. Regarding cost reduction in wind power generation C WT And the cost reduction of photovoltaic power generation C PV The expression can be written as:

[0108]

[0109] And the following constraints should be met:

[0110]

[0111] Among them, c wt To reduce the penalty factor for wind power generation, c pvTo reduce the penalty coefficient for photovoltaic power generation, This represents a reduction in wind power generation. This represents the reduction in photovoltaic power generation. This represents the maximum reduction in wind power generation. This represents the maximum reduction in photovoltaic power generation. This is a predicted value for wind power generation. This represents the predicted value for photovoltaic power generation.

[0112] The above explains the cost reduction of wind power generation, cost reduction of photovoltaic power generation, and related constraints in the configuration cost minimization model of this application.

[0113] As a preferred embodiment, the energy interaction minimization model is:

[0114]

[0115] Where T represents the time interval during which energy interaction occurs. It is the power exchanged between the distribution network and the upstream power grid during time period t.

[0116] Understandably, the energy interaction minimization model aims to minimize the energy interaction with the upstream power grid (substation).

[0117] In a preferred embodiment, the power balance deterministic constraint model is obtained by transforming the power balance chance constraint model using the quantiles of the probability distribution;

[0118] The power balance opportunity constraint model is as follows:

[0119]

[0120] Where Pr represents the inequality The probability of it being true. It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. The net load within the power balance area of ​​the distribution network is α, which is a preset information level (generally taken as 5%).

[0121] The net load within the power balance zone of a distribution network can be calculated using the following formula:

[0122]

[0123] in, This is the actual value of the power at the load point. This represents the actual value of wind power generation. This represents the actual value of photovoltaic power generation.

[0124] The actual values ​​of wind power generation and photovoltaic power generation can be expressed by the following formula:

[0125]

[0126] in, This represents the actual value of wind power generation. This represents the actual value of photovoltaic power generation. This is a predicted value for wind power generation. This represents the predicted value of photovoltaic power generation. This represents the prediction error for wind power generation. This represents the prediction error for photovoltaic power generation.

[0127] For the actual power value at the load point, we can assume that the power value at the load point at a certain moment follows a normal distribution, and its probability density function is as follows:

[0128] Right now:

[0129]

[0130] in, This represents the average real-time output power of the load. σ is the standard deviation. L is the coefficient of standard deviation.

[0131] The power balance chance constraint model is transformed into a power balance deterministic constraint model using the quantiles of the probability distribution, including:

[0132] If the transmission power in interval t is negative, it indicates that the power is transmitted from the distribution network to the main grid. In this case, the quantile of the net load at the 5% confidence level is used to represent that the power generation is greater than the average value, which represents the worst operating condition of the distribution network. If the transmission power in interval t is positive, the quantile of the net load at the 95% confidence level is used to represent that the load consumption is greater than the average value, which represents the worst operating condition of the distribution network.

[0133] To reformulate the chance constraint as a linear constraint, we need to find the quantile of the random variable (net load) corresponding to the confidence level and use this quantile to transform it into a linear constraint. The net load follows a normal distribution:

[0134]

[0135] The mean and variance of the net load are as follows:

[0136]

[0137]

[0138] in, This represents the average net load. This represents the variance of the net load; other parameters are described above.

[0139] Based on the above, and according to the transformation properties between the normal distribution and the standard normal distribution, the restated constraint conditions, i.e., the power balance determination constraint model, are as follows:

[0140]

[0141] in, It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. Let Φ be the load reduction amount caused by photovoltaic power generation in the aforementioned distribution network, Φ be the standard normal distribution function, and α be a preset confidence level (generally taken as 5%). For the The corresponding standard deviation For the The corresponding mean.

[0142] The above explains the power balance determination constraint model in the configuration cost minimization model of this application. The power balance determination constraint model enables the established distribution network resource allocation model to fully consider the power fluctuations of power sources and loads, thereby improving the effectiveness and scientific nature of the model.

[0143] The following is a specific application scenario example, combined with the appendix. Figures 2 to 10 To explain, among other things, Figures 2 to 7 The horizontal axis represents time (in hours). Figures 8 to 10 The horizontal axis represents cost (in yuan):

[0144] Taking a distributed power distribution network in eastern China as an example, a certain substation has 10 load points within its power supply range, with a total load of 3900kW, a residential load of 1200kW, a commercial load of 700kW, an administrative load of 1000kW, and an industrial load of 1000kW. The combined wind power and photovoltaic power is 5500kW, and the equivalent penetration rate (peak penetration rate, the total peak power of distributed power sources / peak load) is 170%. Figure 2 The curves showing the changes in load, wind power, and photovoltaic resources in this distribution network over a day are displayed.

[0145] Net load curve as follows Figure 3 As shown, in order to convert the chance constraint into a linear constraint, it is necessary to calculate the quantiles. Therefore, the 5% and 95% quantile curves were plotted. Considering the minimum energy exchange with the upper-level grid in the objective function, the 5% quantile curve was selected in the time intervals of 7:00 to 10:00 and 11:40 to 16:00, and the 95% quantile curve was used at other times.

[0146] The power line capacity is 7800kW, with temperature control load and building lighting load considered as reducible loads. Statistics show that reducible loads account for approximately 29.5% of the total load, while electric vehicles and distributed energy stations are considered transferable loads. Transferable loads account for approximately 15% of the total load, and the incentive cost for both reducible and transferable loads is 0.2 yuan / kW. The planned energy storage system has a unit power cost of 800,000 yuan / MW, a unit capacity cost of 2.1 million yuan / (MW·h), and an operation and maintenance cost of 64 yuan / (kW·a). The energy storage system has an upper limit of 0.9% and a lower limit of 0.1% state of charge, a service life of 10 years, and a discount rate of 9%. The maximum reduction for wind power is 20% of wind power output, with a wind power penalty cost of 0.05 yuan / kW. The maximum reduction for photovoltaic power is 20% of photovoltaic power output, with a photovoltaic penalty cost of 0.1 yuan / kW.

[0147] To solve the model in the basic scenario, we need to calculate two extreme points using the Epsilon constraint method:

[0148] Extreme point 1: When the cost is 0, the calculated minimum transmission capacity is 34MW. This result does not take into account any management factors, and the actual load is the same as the original net load curve.

[0149] Extreme point 2: When the transmitted energy is 0, the minimum cost is 2.09 million yuan. The demand response scheduling and energy storage allocation scheme includes the transferable load, the load that can be reduced, the photovoltaic reduction amount, and the wind power reduction amount per day, as shown below. Figures 4 to 7 As shown.

[0150] Based on the Epsilon constraint method, find the value at ε i For each point, then plot the Pareto front, such as... Figure 8As shown in the figure, each point represents an optimal demand response schedule, which is a trade-off between minimizing energy interaction with the upper-level grid and minimizing configuration budget. This provides distribution network operators with a range of optimal strategies to choose from based on actual needs.

[0151] Figure 9 This paper illustrates the relationship between cost and exchange energy under different discount rates. Energy storage was deployed when the price decreased to 52.9%. Total cost remained constant at this price. As the price of energy storage continued to decrease, management costs also gradually decreased. A comparison of different energy storage prices is shown in the table below:

[0152] Discount factor Cost (million yuan) Energy storage capacity (kWh) Energy storage capacity (kW) 50％ 2.09 0.4398 0.1583 30％ 2.04 758.5579 283.0594 20％ 1.99 1849.8 805.1654

[0153] Figure 10 The effects of different distributed generation penetration rates (at 170%, 140%, and 110%) on the Pareto front are shown. As distributed generation penetration decreases, the cost of regulation measures required to achieve the desired transmission power increases.

[0154] As can be seen from the above technical solution, the embodiments of this application provide a resource allocation method for a distribution network. When executing the method, resource parameters in the distribution network are obtained; the resource parameters are input into a pre-established distribution network resource allocation model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model; the optimal solution of the distribution network resource allocation model is obtained according to the Epsilon constraint method to obtain Pareto front data; and the resource allocation parameters of the distribution network are adjusted based on the Pareto front data. Therefore, in the embodiments of this application, the configuration cost minimization model, energy interaction minimization model, and power balance deterministic constraint model are solved using the Epsilon constraint method. Each point on the obtained Pareto front considers the optimal solution for the energy storage system and demand response planning with optimal configuration cost and energy interaction, facilitating distribution network operators to allocate various resources in the distribution network based on the obtained optimal solution, thereby achieving the goal of minimizing energy interaction costs and configuration budget costs with the upper-level grid.

[0155] The above are some specific implementations of a resource allocation method for a power distribution network provided in the embodiments of this application. Based on this, this application also provides a corresponding device. The device provided in the embodiments of this application will be described below from the perspective of functional modularization.

[0156] See Figure 11 The diagram shows a structural schematic of a resource allocation device for a power distribution network. This device may include:

[0157] The parameter acquisition module 100 is used to acquire resource configuration parameters in the power distribution network.

[0158] The model input module 200 is used to input the resource parameters into a pre-established distribution network resource configuration model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model.

[0159] The optimal value solving module 300 is used to obtain the optimal solution of the distribution network resource allocation model according to the Epsilon constraint method, so as to obtain Pareto frontier data;

[0160] Resource configuration module 400 is used to adjust the resource configuration parameters of the distribution network based on the Pareto frontier data.

[0161] Optionally, the configuration cost minimization model is:

[0162] min[C ESS +C DR +C WT +C PV ],

[0163] Among them, C ESS C represents the energy storage cost of the energy storage system in the aforementioned distribution network. DR For load demand response costs, C WT To reduce costs for wind power generation, C PV To reduce the cost of photovoltaic power generation.

[0164] Optionally, the energy interaction minimization model is:

[0165]

[0166] Where T represents the time interval during which energy interaction occurs. It is the power exchanged between the distribution network and the upstream power grid during time period t.

[0167] Optionally, the power balance deterministic constraint model is obtained by transforming the power balance chance constraint model using the quantiles of the probability distribution;

[0168] The power balance opportunity constraint model is as follows:

[0169]

[0170] Where Pr represents the inequality The probability of it being true. It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. The net load within the power balance area of ​​the distribution network is α, where α is the preset information level.

[0171] The power balance determination constraint model is as follows:

[0172]

[0173] in, It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. Let Φ be the load reduction amount caused by photovoltaic power generation in the distribution network, Φ be the standard normal distribution function, and α be the preset confidence level. For the The corresponding standard deviation For the The corresponding mean.

[0174] It should be noted that the steps and related technical features executed by each module in the resource allocation device for a power distribution network provided in this application correspond to the method provided in the application embodiment. The description of the device part can be found in the embodiments of the aforementioned method part, and will not be repeated here.

[0175] As can be seen from the above technical solutions, the embodiments of this application provide a resource allocation device for a distribution network. The device includes: a parameter acquisition module for acquiring resource allocation parameters in the distribution network; a model input module for inputting the resource parameters into a pre-established distribution network resource allocation model, the distribution network resource allocation model including: a configuration cost minimization model, an energy interaction minimization model, and a power balance deterministic constraint model; an optimal value solution module for obtaining the optimal solution of the distribution network resource allocation model according to the Epsilon constraint method to obtain Pareto front data; and a resource allocation module for adjusting the resource allocation parameters of the distribution network based on the Pareto front data. Therefore, in the embodiments of this application, the configuration cost minimization model, energy interaction minimization model, and power balance deterministic constraint model are solved using the Epsilon constraint method. Each point on the obtained Pareto front considers the optimal solution for the energy storage system and demand response planning with optimal configuration cost and energy interaction, facilitating distribution network operators to allocate various resources in the distribution network based on the obtained optimal solution, thereby achieving the goal of minimizing energy costs and configuration budget costs in interaction with the upper-level grid.

[0176] This application also provides an electronic device, including: a memory for storing one or more programs;

[0177] A processor; when the one or more programs are executed by the processor, the methods described in the above embodiments are implemented.

[0178] This application provides a computer storage medium storing a program that, when executed by a processor, implements the method described in the above embodiments.

[0179] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0180] Those skilled in the art will understand that the flowchart shown is merely an example in which the embodiments of this application can be implemented, and the scope of application of the embodiments of this application is not limited by any aspect of the flowchart.

[0181] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and devices can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0182] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0183] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0184] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A resource allocation method for a power distribution network, characterized in that, include: Obtain resource configuration parameters in the power distribution network; The resource allocation parameters are input into a pre-established distribution network resource allocation model, which includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance determination constraint model; wherein, the configuration cost minimization model includes: a solution model for minimizing the investment cost and operation and maintenance cost of the energy storage system, the total dispatch incentive cost of the transferable load, the total dispatch incentive cost of the load that can be reduced, the wind power reduction cost, and the photovoltaic power reduction cost. The optimal solution of the power distribution network resource allocation model is obtained using the Epsilon constraint method to obtain Pareto front data; Adjust resource allocation parameters in the distribution network based on the Pareto front data; The power balance deterministic constraint model is obtained by transforming the power balance opportunity constraint model using the quantiles of the probability distribution. The transformation from the power balance opportunity constraint model to the power balance deterministic constraint model using the quantiles of the probability distribution includes: if the transmission power in interval t is negative, then the quantile of the net load at the 5% confidence level is used to represent that the power generation is greater than the average value; if the transmission power in interval t is positive, then the quantile of the net load at the 95% confidence level is used to represent that the load consumption is greater than the average value.

2. The method according to claim 1, characterized in that, The configuration cost minimization model is as follows: ， in, The energy storage cost of the energy storage system in the aforementioned distribution network. For load demand response costs, To reduce costs for wind power generation, To reduce the cost of photovoltaic power generation.

3. The method according to claim 1, characterized in that, The energy interaction minimization model is as follows: ， Where T represents the time interval during which energy interaction occurs. It is the power exchanged between the distribution network and the upstream power grid during time period t.

4. The method according to claim 1, characterized in that, The power balance opportunity constraint model is as follows: ， Where Pr represents the inequality The probability of it being true. It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. The net load within the power balance area of ​​the aforementioned distribution network. To preset the credit level; The power balance determination constraint model is as follows: ， in, It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. It is the standard normal distribution function. To preset the credit level, For the The corresponding standard deviation For the The corresponding mean.

5. A resource allocation device for a power distribution network, characterized in that, include: The parameter acquisition module is used to acquire resource configuration parameters in the power distribution network; The model input module is used to input the resource configuration parameters into a pre-established distribution network resource configuration model. The distribution network resource configuration model includes: a configuration cost minimization model, an energy interaction minimization model, and a power balance determination constraint model. The configuration cost minimization model includes: a solution model for minimizing the investment cost and operation and maintenance cost of the energy storage system, the total dispatch incentive cost of transferable loads, the total dispatch incentive cost of loads that can be reduced, the wind power reduction cost, and the photovoltaic power reduction cost. The optimal value solution module is used to obtain the optimal solution of the distribution network resource allocation model according to the Epsilon constraint method, so as to obtain Pareto front data; The resource configuration module is used to adjust the resource configuration parameters of the distribution network based on the Pareto frontier data. The power balance deterministic constraint model is obtained by transforming the power balance opportunity constraint model using the quantiles of the probability distribution. The transformation from the power balance opportunity constraint model to the power balance deterministic constraint model using the quantiles of the probability distribution includes: if the transmission power in interval t is negative, then the quantile of the net load at the 5% confidence level is used to represent that the power generation is greater than the average value; if the transmission power in interval t is positive, then the quantile of the net load at the 95% confidence level is used to represent that the load consumption is greater than the average value.

6. The apparatus according to claim 5, characterized in that, The configuration cost minimization model is as follows: ， in, The energy storage cost of the energy storage system in the aforementioned distribution network. For load demand response costs, To reduce costs for wind power generation, To reduce the cost of photovoltaic power generation.

7. The apparatus according to claim 5, characterized in that, The energy interaction minimization model is as follows: ， Where T represents the time interval during which energy interaction occurs. It is the power exchanged between the distribution network and the upstream power grid during time period t.

8. The apparatus according to claim 5, characterized in that, The power balance opportunity constraint model is as follows: ， Where Pr represents the inequality The probability of it being true. It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. The net load within the power balance area of ​​the aforementioned distribution network. To preset the credit level; The power balance determination constraint model is as follows: ， in, It is the power exchanged between the distribution network and the upstream power grid during time period t. Let t be the energy conversion power of the energy storage system during time period t. Let t be the load output of the distribution network during time period t. Let t be the load input to the distribution network during time period t. Let t represent the load reduction in the distribution network during time period t. The amount of load reduction caused by wind power generation in the distribution network during time period t. To reduce the load on photovoltaic power generation in the aforementioned distribution network. It is the standard normal distribution function. To preset the credit level, For the The corresponding standard deviation For the The corresponding mean.

9. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the one or more programs are executed by the processor, the method as described in any one of claims 1 to 4 is implemented.

10. A storage medium, characterized in that, The storage medium stores a program that, when executed by a processor, implements the method of any one of claims 1 to 4.

Images