Energy Internet comprehensive service station resource optimization configuration method and system

Abstract

The present invention proposes a resource optimization configuration method and system for an energy internet integrated service station, the method comprising: constructing a flexible resource universal power generation model for an integrated service station; the flexible resource universal power generation model is used to describe the power generation characteristics of different types of flexible resources connected to an integrated service station; the flexible resource universal power generation model is a resource for serving and dynamically balancing the supply and demand of an energy consumption system; summing the power generation parameters of the flexible resources in the universal power generation model to obtain the aggregate power generation characteristics of the integrated service station; constructing a resource optimization configuration model for the integrated service station according to the aggregate power generation characteristics and the cost set by the integrated service station; solving the resource optimization configuration model for the integrated service station. Based on this method, a resource optimization configuration system for an energy internet integrated service station is also proposed. The present invention realizes the optimal configuration of the resources of the integrated service station and maximizes the interests of investors by constructing a resource optimization configuration model and solving the model to obtain the optimal resource combination method.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and in particular relates to a method and system for optimizing resource configuration of an energy internet integrated service station. Background Art

[0002] The business of integrated energy services has broad prospects and huge space. We must demand efficiency from management and tap the potential of existing stocks. Through integrated energy services, we can reduce social energy costs and increase corporate profit margins to achieve a win-win situation. We must strengthen research on systems and mechanisms, focus on key areas, use new smart cities and county-level integrated energy as carriers, carry out in-depth resource management, accelerate the construction of local collaborative work mechanisms and capital ties, and accelerate the construction of a development support system that taps the value of tangible and intangible assets, optimizes system and mechanism management and operations, and guarantees mutual protection of hardware and software. Integrated energy services and multi-station integration have become the main business directions. Among them, the integration of three stations is to transform the original substation into a substation, a charging and swapping station (energy storage station), and a data center station.

[0003] In the integrated service station environment, the problem of optimal allocation of multiple types of distributed resources is one of the key issues affecting the sustainable profitability of the project. Reasonable allocation of the capacity of wind power, photovoltaics, energy storage and conventional units in the integrated service station can reduce system costs. The diversity and dispersion of interactive resources in the integrated service station can provide flexible adjustment capabilities for the integrated service station aggregate. However, the uncertainty and intermittency of distributed resource output and the uncertainty of electricity prices in the power market also bring great risks to the operation and benefits of the integrated service station. The heterogeneity of resources increases the difficulty of coordinated operation of the integrated service station. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an energy Internet comprehensive service station resource optimization configuration method and system, which realizes the optimal configuration of comprehensive service station resources, reduces system costs, and maximizes the interests of investors.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The method for optimizing resource configuration of energy internet comprehensive service station includes the following steps:.

[0007] Constructing a general flexible resource power generation model for an integrated service station; the general flexible resource power generation model is used to describe the power generation characteristics of different types of flexible resources connected to the integrated service station; the flexible resources are resources that dynamically balance the supply and demand of the service and energy consumption system;

[0008] According to the general power generation model, the power generation parameters of the flexible resources in the general power generation model are summed to obtain the aggregate power generation characteristics of the integrated service station;

[0009] According to the aggregated power generation characteristics and the setting cost of the integrated service station, a resource optimization configuration model of the integrated service station is constructed;

[0010] The resource optimization configuration model of the comprehensive service station is solved to obtain the optimal resource combination mode.

[0011] Furthermore, the process of constructing the flexible resource universal power generation model of the integrated service station is as follows:

[0012] Define the flexible resource related parameters of the integrated service station; the parameters include wind power resource type W, photovoltaic power resource type S, energy storage power resource type E, time period T divided into unit time, time period number t, integrated service station node number i, integrated service station node i connected to the j-th type of flexible power generation resource, integrated service station node i connected to the j-th type of flexible power generation resource number k;

[0013] Determine the power generation characteristics of the flexible resource in a certain period of time according to the relevant parameters; use a six-tuple to describe the power generation characteristics of the flexible resource in a certain period of time; the six-tuple is:

[0014] in is the minimum power generation value of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period; is the maximum power generation of the j-th type and k-numbered flexible resource connected to the integrated service station node i in the t-th time period; is the minimum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the maximum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the minimum climbing rate of the flexible resource of the jth type and numbered k connected to the integrated service station node i in the tth time period; is the maximum value of the climbing rate of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period;

[0015] The general power generation model is constructed according to the power generation characteristics; the general power generation model is:

[0016]

[0017] Where δ is the length of time period T.

[0018] Furthermore, the lumped power generation characteristics of the integrated service station are obtained by summing the power generation parameters of the flexible resources in the general power generation model according to the general power generation model, including:

[0019] Obtain the overall steady-state aggregation characteristics of the integrated service station, wherein the overall steady-state aggregation characteristics of the integrated service station include the overall power P of the integrated service station t , raise the boundary P t,ub , lower the boundary P t,lb , Maximum upward climbing rate R t,ub , Maximum downward climbing rate R t,lb , upward regulation can last for T t,ub and downward regulation can last for T t,lb ;

[0020] The process of solving the lumped power generation characteristics includes:

[0021] Calculate the overall power of the integrated service station:

[0022]

[0023] Calculate the upward and downward adjustment boundaries of the integrated service station as:

[0024]

[0025] Calculate the duration of the upward / downward regulation of the integrated service station

[0026]

[0027] Calculate the maximum up / down ramp rate of the integrated service station as:

[0028]

[0029] Furthermore, the method for determining the setting cost of the integrated service station is: defining the access cost of each resource as λ i,j,k And define the unit incentive price of each resource as

[0030] Furthermore, the resource optimization configuration model for building the comprehensive service station is:

[0031]

[0032] Among them, V i,j,k is the decision variable, V i,j,k is an integer variable of 0 or 1; i,j,k is the access cost of resources; V i,j,k λ i,j,k The access cost; is the unit incentive price of the resource; is the installed capacity of the flexible resource of type j and numbered k under the integrated service station node i; is the call cost;

[0033] Calling resource capacity for integrated service stations; Comprehensive service station climbing rate;

[0034] Aggregate the relationship between characteristics and resource characteristics for integrated service stations;

[0035] is the power flow constraint of the distribution network, namely, the voltage constraint and the power flow reverse constraint; the node voltage constraint satisfies U min ≤U i ≤U max ; Power flow backflow constraint satisfaction in, is the net load of node i after aggregation by the integrated service station; P i Output power for interactive resources of integrated service station, Represents the net load of node i.

[0036] Furthermore, the process of solving the resource optimization configuration model of the integrated service station includes:

[0037] Sampling the resource optimization configuration model to generate several scenario sets;

[0038] Solve resource combination methods in different scenarios and obtain several resource combination methods;

[0039] Count the number of occurrences of each resource combination and determine the resource combination with the highest number of occurrences.

[0040] Furthermore, the process of sampling the resource optimization configuration model to generate several scenario sets is as follows:

[0041] The nonlinear voltage constraint is converted into the net load of each node. The linear constraints adjusted within the interval are in the optimized form:

[0042] Monte Carlo simulation is used to predict the output power of each resource unit Generate a set of scenarios.

[0043] The present invention also proposes a resource optimization configuration system for an energy internet integrated service station, comprising a first construction module, a summing module, a second construction module and a solving module;

[0044] The first construction module is used to construct a flexible resource universal power generation model of the integrated service station; the flexible resource universal power generation model is used to describe the power generation characteristics of different types of flexible resources connected to the integrated service station; the flexible resources are resources that dynamically balance the supply and demand of the service and energy consumption system;

[0045] The summing module is used to sum the power generation parameters of the flexible resources in the general power generation model according to the general power generation model to obtain the aggregate power generation characteristics of the integrated service station;

[0046] The second construction module is used to construct a resource optimization configuration model of the integrated service station according to the aggregated power generation characteristics and the integrated service station setting cost;

[0047] The solution module is used to solve the resource optimization configuration model of the comprehensive service station to obtain the optimal resource combination method.

[0048] Furthermore, the first construction module includes a parameter definition submodule, a power generation characteristic determination submodule, and a power generation model construction submodule;

[0049] The parameter definition submodule is used to define the flexible resource related parameters of the integrated service station; the parameters include wind power generation resource type W, photovoltaic power generation resource type S, energy storage power generation resource type E, time period T divided into unit time, time period number t, integrated service station node number i, integrated service station node i connected to the j-th type of flexible power generation resource, integrated service station node i connected to the j-th type of flexible power generation resource number k;

[0050] The power generation characteristic determination submodule is used to determine the power generation characteristics of the flexible resource in a certain period of time according to the relevant parameters; the power generation characteristics of the flexible resource in a certain period of time are described by using a six-tuple; the six-tuple is:

[0051] in is the minimum power generation value of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period; is the maximum power generation of the j-th type and k-numbered flexible resource connected to the integrated service station node i in the t-th time period; is the minimum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the maximum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the minimum climbing rate of the flexible resource of the jth type and numbered k connected to the integrated service station node i in the tth time period; is the maximum value of the climbing rate of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period;

[0052] The power generation model construction submodule is used to construct the general power generation model according to the power generation characteristics; the general power generation model is:

[0053]

[0054] Where δ is the length of time period T.

[0055] Further, the solution module includes a sampling submodule, a solution submodule and a determination submodule;

[0056] The sampling submodule is used to sample the resource optimization configuration model to generate several scenario sets;

[0057] The solving submodule is used to solve resource combination methods in different scenarios and obtain several resource combination methods;

[0058] The determination submodule is used to count the number of occurrences of each resource combination mode and determine the resource combination mode with the largest number of occurrences.

[0059] The effects provided in the content of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0060] The present invention proposes a method and system for optimizing the configuration of resources of an energy internet integrated service station. The method includes the following steps: constructing a universal power generation model of flexible resources of the integrated service station; the universal power generation model of flexible resources is used to describe the power generation characteristics of different types of flexible resources connected to the integrated service station; the flexible resources are resources that serve the dynamic supply and demand balance of the energy consumption system; the universal power generation model sums the power generation parameters of the flexible resources in the universal power generation model to obtain the aggregate power generation characteristics of the integrated service station; constructing a resource optimization configuration model of the integrated service station according to the aggregate power generation characteristics and the cost set by the integrated service station; solving the resource optimization configuration model of the integrated service station to obtain the optimal resource combination method. Based on the resource optimization configuration method of the energy internet integrated service station, a resource optimization configuration system for the energy internet integrated service station is also proposed. The present invention realizes the optimal configuration of the resources of the integrated service station by constructing a resource optimization configuration model and solving the model to obtain the optimal resource combination method, thereby reducing the system cost and maximizing the interests of investors. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] like Figure 1 This is a flow chart of a method for optimizing resource configuration of an energy internet integrated service station according to Embodiment 1 of the present invention;

[0062] like Figure 2 This is a schematic diagram of a general model of a wind turbine generator system according to Embodiment 1 of the present invention;

[0063] like Figure 3 This is a schematic diagram of a general model of a photovoltaic generator according to Embodiment 1 of the present invention;

[0064] like Figure 4A general model of electric vehicles and energy storage resources in Example 1 of the present invention is provided;

[0065] like Figure 5 It is a schematic diagram of the aggregated power generation characteristic curve of the collective service station;

[0066] like Figure 6 The figure shows the node data of the IEEE-33 node system proposed in Embodiment 1 of the present invention;

[0067] like Figure 7 A flowchart of the algorithm solution of Embodiment 1 of the present invention is given;

[0068] like Figure 8 This is a schematic diagram of the resource optimization configuration system of the energy internet comprehensive service station according to Example 2 of the present invention. DETAILED DESCRIPTION

[0069] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits the description of known components and processing techniques and processes to avoid unnecessary limitations on the present invention.

[0070] Example 1

[0071] Embodiment 1 of the present invention proposes a method for optimizing the configuration of resources of an energy internet integrated service station, in order to optimize the configuration of integrated service station resources, reduce system costs, and maximize the interests of investors. Figure 1 This is a flow chart of the method for optimizing resource configuration of an energy internet integrated service station according to Example 1 of the present invention.

[0072] In step S100, a flexible resource universal power generation model of the integrated service station is constructed; flexible resources refer to resources that can increase the flexibility, elasticity and flexibility of the energy supply and demand system and serve the dynamic supply and demand balance of the energy consumption system. Due to the diversity, dispersion and heterogeneity of distributed power sources, a universal flexible resource universal power generation model is constructed, that is, different flexible resources are first uniformly modeled, and then models of specific types of distributed power sources such as wind power, photovoltaics, and energy storage are constructed. The flexible resource universal power generation model is used to describe the power generation characteristics of various types of flexible resources connected to the integrated service station.

[0073] The method for constructing a flexible resource universal power generation model of an integrated service station includes: defining flexible resource related parameters of the integrated service station, wherein the parameters include: wind power generation resource type W, photovoltaic power generation resource type S, energy storage power generation resource type E, time period T divided into unit time, time period number t, number i of the integrated service station node, j-th type of flexible power generation resource connected to the integrated service station node i, and number k of the j-th type of flexible power generation resource connected to the integrated service station node i;

[0074] Determine the power generation characteristics of the flexible resource in a certain period of time according to relevant parameters; use a six-tuple to describe the power generation characteristics of the flexible resource in a certain period of time; the six-tuple is:

[0075] in is the minimum power generation value of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period; is the maximum power generation of the j-th type and k-numbered flexible resource connected to the integrated service station node i in the t-th time period; is the minimum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the maximum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the minimum climbing rate of the flexible resource of the jth type and numbered k connected to the integrated service station node i in the tth time period; is the maximum value of the climbing rate of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period;

[0076] The general power generation model is constructed according to the power generation characteristics; the general power generation model is:

[0077]

[0078] Where δ is the length of time period T.

[0079] like Figure 2 Schematic diagram of a general model of a wind turbine generator system according to Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of a general model of a photovoltaic generator according to Embodiment 1 of the present invention.

[0080] For the wind power and photovoltaic power generation units connected to the integrated service station, it is assumed that the maximum output forecast for each period of the next day is or And assuming that it can be from 0 to Or from 0 to If the unit is continuously adjusted, the upper and lower limits of its power and cumulative power generation are:

[0081]

[0082] exist Figure 2 middle, and are the upper bounds of the cumulative power generation of wind turbine k at two different times. Assuming that wind turbine k starts generating power at time 0, when it generates power at the maximum predicted power in each time period When generating electricity, the upper limit of its cumulative power generation will correspond to its fastest power generation trajectory (AC fold line); when the power generation of wind turbine k is always 0, the power generation trajectory (AB straight line) formed is the lower limit of its cumulative power generation. Figure 3 middle, and are the upper bounds of the cumulative power generation of photovoltaic generator set k at sunrise and sunset respectively. When generating electricity, the upper limit of its cumulative power generation will correspond to its fastest power generation trajectory (AC fold line); when the power generation of photovoltaic generator set k is always 0, the power generation trajectory (AB straight line) formed is the lower limit of its cumulative power generation. In this way, the upper and lower limits of the instantaneous power generation and cumulative power generation of wind power and photovoltaic generator sets describe the feasible range of their power generation over time. In other words, any power generation trajectory that meets these two sets of constraints is feasible for the generator set.

[0083] like Figure 4 A general model of electric vehicles and energy storage resources is given in Example 1 of the present invention; assuming that electric vehicle k is at t a Arrive at the charging station at t b When leaving at any time, the upper and lower bounds of its power generation over time can be defined as follows:

[0084]

[0085] Where: and are the rated discharge power and rated charging power of the electric vehicle, respectively. (Assume that the owner of the electric vehicle allows its power battery to feed power to the grid.) When the owner does not allow the battery of his electric vehicle to discharge, he only needs to set its rated discharge power to 0.

[0086] The upper and lower bounds of the cumulative power generation of electric vehicles over time are as follows: Figure 4 As shown in the figure, They are the upper and lower bounds of the state of charge (SOC) of the electric vehicle battery; B is the initial charge state of the electric vehicle when it arrives at the charging station; i,V,k is the battery capacity of the electric vehicle; is the minimum state of charge required for an electric vehicle to leave. The lower bound of the accumulated power generation of an electric vehicle This corresponds to the fastest charging trajectory (broken line ABC). On this trajectory, the electric vehicle Once connected to the grid, the rated charging power Charge until its state of charge reaches the maximum allowable state of charge In another extreme case (i.e. the upper limit of the cumulative power generation of electric vehicles ), the cumulative power generation of electric vehicles follows the trajectory (broken line ADFG). On this trajectory, electric vehicles Once connected to the grid, the rated discharge power Discharge until its state of charge reaches the minimum allowable state of charge s i,V,k When the time approaches the departure time of the electric vehicle, the discharge stops. When the electric vehicle is charged at rated power Charge the battery to the minimum state of charge required by the user For electric vehicles that are unwilling to participate in discharge, the upper bound of their battery cumulative power generation will not include the discharge process when it first arrives, and the corresponding upper bound trajectory of cumulative power generation is AIG. In this way, the upper and lower bounds of the instantaneous power generation of electric vehicles and the cumulative power generation describe the feasible range of the charging and discharging power of electric vehicles over time. In other words, any trajectory of power generation that satisfies these two sets of constraints is feasible for the electric vehicle. It should be noted here that the charging and discharging rate of electric vehicles is determined by the characteristics of the battery and the requirements of the power grid.

[0087] Similarly, the above model can be used to describe the power generation characteristics of energy storage resources (ESS, Energy Storage System), and the upper and lower bounds of the instantaneous power generation of energy storage are its rated discharge power and negative rated charging power. Since the energy storage resource does not have a minimum requirement to leave the state of charge in the last period, its state of charge can be anywhere between the minimum and maximum allowable state of charge. Therefore, the upper and lower bounds of its cumulative power generation correspond to Figure 4 ADFH and ABC on.

[0088] In step S200, the power generation parameters of the flexible resources are summed according to the general power generation model of the flexible resources to obtain the aggregate power generation characteristics of the integrated service station.

[0089] like Figure 5 The overall steady-state aggregation characteristics of the integrated service station are shown in the power generation baseline P base , raise the boundary Pt,ub , lower the boundary P t,lb , Maximum upward climbing rate R t,ub , Maximum downward climbing rate R t,lb , upward regulation can last for T t,ub and downward regulation can last for T t,lb Description. The power generation baseline of the integrated service station refers to the power generation of the system when the integrated service station does not regulate the integrated resources. The upward / downward regulation can last for T t,ub 、T t,lb It refers to the maximum continuous operation time of the integrated service station according to the power increase / decrease boundary. According to the general flexible resource power generation model established in step S101, the corresponding parameters of each flexible resource are summed up, which can concisely describe the aggregate characteristics of multiple flexible resources.

[0090] Obtain the overall steady-state aggregation characteristics of the integrated service station, wherein the overall steady-state aggregation characteristics of the integrated service station include the overall power P of the integrated service station t , raise the boundary P t,ub , lower the boundary P t,lb , Maximum upward climbing rate R t,ub , Maximum downward climbing rate R t,lb , upward regulation can last for T t,ub And downward regulation can last for T t,lb ;

[0091] The process of solving the lumped power generation characteristics includes:

[0092] Calculate the overall power of the integrated service station:

[0093]

[0094] Calculate the upward and downward adjustment boundaries of the integrated service station as:

[0095]

[0096] Calculate the duration of the upward / downward regulation of the integrated service station

[0097]

[0098] Calculate the maximum up / down ramp rate of the integrated service station as:

[0099]

[0100] In step S300, a resource optimization configuration model of the integrated service station is constructed according to the aggregated power generation characteristics and the setting cost of the integrated service station;

[0101] Define cost information for integrated service stations.

[0102] Specifically, the goal of resource portfolio optimization of integrated service stations is to make their aggregation characteristics reach the expected level under the constraints of distribution network at the lowest cost.

[0103] When the integrated service station integrates various internal control resources, it needs to invest a certain cost, including communication, control terminals, computing resources, etc. Suppose the access cost of each resource is λ i,j,k At the same time, in order to compensate for the losses of each resource when responding to regulation, it is necessary to implement certain incentives for each resource. The comprehensive service station needs to formulate incentive prices based on the advantages and disadvantages of resource characteristics and contributions to the comprehensive service station. Here, it is assumed that the incentive is proportional to the installed capacity of the resource, and the unit incentive price of each resource is defined as (Yuan / kW).

[0104] The resource optimization configuration model for building a comprehensive service station is:

[0105]

[0106] Among them, V i,j,k is the decision variable, V i,j,k is an integer variable of 0 or 1; i,j,k is the access cost of resources; V i,j,k λ i,j,k The access cost; is the unit incentive price of the resource; is the installed capacity of the flexible resource of type j and numbered k under the integrated service station node i; is the call cost;

[0107] Calling resource capacity for integrated service stations; Comprehensive service station climbing rate;

[0108] Aggregate the relationship between characteristics and resource characteristics for integrated service stations;

[0109] is the power flow constraint of the distribution network, namely, the voltage constraint and the power flow reverse constraint; the node voltage constraint satisfies U min ≤U i ≤U max ; Power flow backflow constraint satisfaction

[0110] It should be noted that, when the integrated service station is involved, the distribution network is still the receiving power grid and does not transmit power to the upper network. is the net load of node i after aggregation by the integrated service station; P i Output power for interactive resources of integrated service stations, Represents the net load of node i.

[0111] In step S400, the resource optimization configuration model of the integrated service station is solved to obtain the optimal resource combination mode.

[0112] like Figure 6 The node data of the IEEE-33 node system proposed in Example 1 of the present invention is shown. Before solving the resource optimization configuration model of the integrated service station, Figure 6 To verify the effectiveness of this method. The system reference voltage is 12.66kV, the node voltage allowable range is 0.93~1.07pu, the power reference value is 10MVA, and the total network load is 37.15MW+j23Mvar. The node data of the IEEE-33 node system is as follows Figure 6 As shown in the figure, since this model does not involve the power flow calculation part, the branch data is not listed here. The data in the table are all converted to per unit value. The node parameter diagram of the IEEE 33-bus system is given in Table 1 below.

[0113]

[0114]

[0115] The available access resource information is shown in Table 2 below, where a total of 24 nodes access the flexible interactive resources. The output power of the interactive resources of each node can be controlled to achieve the effect of changing the net load of each node. Table 2 is a diagram of the access interactive resources of the IEEE 33-node system (pu).

[0116]

[0117]

[0118] This application considers two access scenarios of interactive resources. In the first scenario, the distribution network does not constrain the power of node 1; in scenario 2, the distribution network implements congestion management, and the power of node 1 is reduced to 60% of the original load. The access costs of wind power and photovoltaic power refer to the grid-connected electricity prices issued by the National Development and Reform Commission. The guidance prices are 0.34 yuan / kWh and 0.45 yuan / kWh respectively. Considering the service life and the average annual power generation of wind power and photovoltaic power, the relevant information of the access costs of various resources after conversion is shown in Table 3 below; electric vehicles refer to the unified clearing price of 30 yuan / kW adopted by Shandong Energy Shandong in the winter demand response in 2018. Table 3 shows the cost of each resource.

[0119]

[0120] First, the resource optimization configuration model is sampled to generate several scenario sets.

[0121] Specifically, since the voltage constraint in the distribution network is a nonlinear constraint, in order to improve the calculation speed, the nonlinear voltage constraint is first converted into the net load of each node. The linear constraints adjusted within the interval, the optimization problem is improved to the following form:

[0122]

[0123] The output uncertainty of internal resources, wind power, photovoltaic power and electric vehicles in the integrated service station comes from the prediction error of resource output characteristics. Monte Carlo simulation is used to predict the output power of each resource unit. Generate a set of scenarios.

[0124] Wind power and photovoltaic output power prediction, taking the historical wind and light data of a certain place throughout the year as samples, extracting a certain time point t as a scene, and reading the wind power and photovoltaic output power corresponding to the time node t (power t ∈[0,1]); the access resource capacity is When the integrated service station does not regulate the integrated resources, the output power of wind power and photovoltaic power in this scenario The maximum output forecast for this period is:

[0125]

[0126] EV output power forecast, capacity of electric vehicles installed at each node The node can be connected to a maximum of k electric vehicles, and all electric vehicles participating in the integrated service station interaction use AC charging piles. The capacity of each electric vehicle is C = 50kwh, with a rated power P N =7kw for charging and discharging, assuming that the state of charge of the electric vehicle follows a uniform distribution, and using random numbers to generate the electric vehicle SOC. If SOC>0.7, the electric vehicle is considered to be in a discharging state, otherwise it is in a charging state; the power capacity of each node electric vehicle that can be used for regulation in this scenario is:

[0127]

[0128] According to the aggregated amount of the integrated service station, the resource combination optimization model of the integrated service station is established to solve the mixed integer nonlinear programming problem, and the decision variable is the integer variable V i,j,k , you can call CPLEX 12.6 solver to solve. Figure 7 A flowchart of the algorithm solution of embodiment 1 of the present invention is given.

[0129] Solve resource combination methods in different scenarios and obtain several resource combination methods.

[0130] Count the number of occurrences of each resource combination and determine the resource combination with the highest number of occurrences. Output of virtual resources It is a deterministic variable that satisfies the constraints of the general flexible resource model. In each scenario solution, the number of Monte Carlo simulations is 1000, and the resource combinations of the integrated service stations obtained in each scenario are counted. The more times the same combination appears, the more scenarios the combination can cope with, the greater the probability of meeting various adjustment requirements, and the stronger the robustness. Based on this, the set of solutions with the highest probability of occurrence is taken as the final optimal combination.

[0131] Example 2

[0132] Based on the method for optimizing resource configuration of an energy internet integrated service station proposed in Example 1 of the present invention, Example 2 of the present invention further proposes a system for optimizing resource configuration of an energy internet integrated service station, such as Figure 8 A schematic diagram of a resource optimization configuration system for an energy internet integrated service station according to Embodiment 2 of the present invention is given.

[0133] The system includes a first building module, a summing module, a second building module, and a solving module;

[0134] The first building module is used to build a flexible resource universal power generation model of the integrated service station; the flexible resource universal power generation model is used to describe the power generation characteristics of different types of flexible resources connected to the integrated service station; the flexible resources are resources that dynamically balance the supply and demand of the service and energy consumption system;

[0135] The summing module is used to sum the power generation parameters of the flexible resources in the general power generation model according to the general power generation model to obtain the aggregate power generation characteristics of the integrated service station;

[0136] The second building module is used to build a resource optimization configuration model of the integrated service station according to the aggregated power generation characteristics and the setting cost of the integrated service station;

[0137] The solution module is used to solve the resource optimization configuration model of the comprehensive service station to obtain the optimal resource combination mode.

[0138] The first construction module includes a parameter definition submodule, a power generation characteristic determination submodule, and a power generation model construction submodule;

[0139] The parameter definition submodule is used to define the flexible resource related parameters of the integrated service station; the parameters include wind power generation resource type W, photovoltaic power generation resource type S, energy storage power generation resource type E, time period T divided into unit time, time period number t, integrated service station node number i, integrated service station node i connected to the j-th type of flexible power generation resource, integrated service station node i connected to the j-th type of flexible power generation resource number k;

[0140] The power generation characteristic determination submodule is used to determine the power generation characteristics of the flexible resource in a certain period of time according to the relevant parameters; the power generation characteristics of the flexible resource in a certain period of time are described by using a six-tuple; the six-tuple is:

[0141] in is the minimum power generation value of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period; is the maximum power generation of the j-th type and k-numbered flexible resource connected to the integrated service station node i in the t-th time period; is the minimum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the maximum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the minimum climbing rate of the flexible resource of the jth type and numbered k connected to the integrated service station node i in the tth time period; is the maximum value of the climbing rate of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period;

[0142] The power generation model construction submodule is used to construct the general power generation model according to the power generation characteristics; the general power generation model is:

[0143]

[0144] Where δ is the length of time period T.

[0145] The solution module includes a sampling submodule, a solution submodule and a determination submodule;

[0146] The sampling submodule is used to sample the resource optimization configuration model and generate several scenario sets;

[0147] The solution submodule is used to solve resource combination methods in different scenarios and obtain several resource combination methods;

[0148] The determination submodule is used to count the number of occurrences of each resource combination and determine the resource combination with the largest number of occurrences.

[0149] The present invention realizes the optimal allocation of comprehensive service station resources, reduces system costs and maximizes the interests of investors by constructing a resource optimization allocation model and solving the model to obtain the optimal resource combination mode.

[0150] The division of modules and units in the present invention is only a division of logical functions. In actual implementation, there may be other division methods, for example, multiple modules and / or units may be combined or integrated into another system. The modules and units described as separate components may be physically separated or inseparable. The components displayed as units may be physical units or may not be physical units, that is, they may be located in a specific place or distributed in grid units. Therefore, some or all of the units may be selected according to actual needs to implement the scheme of the embodiment.

[0151] Although the above describes the specific implementation of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. For those skilled in the art, other different forms of modifications or deformations can be made on the basis of the above description. It is not necessary and impossible to list all the implementation methods here. On the basis of the technical solution of the present invention, various modifications or deformations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.

Claims

1. A method for optimizing resource configuration of an energy internet integrated service station, characterized in that: The following steps are involved: A general flexible resource power generation model for an integrated service station is constructed; the general flexible resource power generation model is used to describe the power generation characteristics of different types of flexible resources connected to the integrated service station; the flexible resources are resources that dynamically balance the supply and demand of the service and energy consumption system; the process of constructing the general flexible resource power generation model for an integrated service station is: Define the flexible resource related parameters of the integrated service station; the parameters include wind power resource type W, photovoltaic power resource type S, energy storage power resource type E, time period T divided into unit time, time period number t, integrated service station node number i, integrated service station node i connected to the j-th type of flexible power generation resource, integrated service station node i connected to the j-th type of flexible power generation resource number k; Determine the power generation characteristics of the flexible resource in a certain period of time according to the relevant parameters; use a six-tuple to describe the power generation characteristics of the flexible resource in a certain period of time; the six-tuple is: in is the minimum power generation value of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period; is the maximum power generation of the j-th type and k-numbered flexible resource connected to the integrated service station node i in the t-th time period; is the minimum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the maximum value of the cumulative power generation of the j-th type and numbered k-th flexible resource connected to the integrated service station node i in the t-th time period; is the minimum climbing rate of the flexible resource of the jth type and numbered k connected to the integrated service station node i in the tth time period; is the maximum value of the climbing rate of the flexible resource of the jth type and numbered k connected to the node i of the integrated service station in the tth time period; The general power generation model is constructed according to the power generation characteristics; the general power generation model is: Where δ is the length of time period T; According to the general power generation model, the power generation parameters of the flexible resources in the general power generation model are summed to obtain the aggregate power generation characteristics of the integrated service station; According to the aggregated power generation characteristics and the setting cost of the integrated service station, a resource optimization configuration model of the integrated service station is constructed; Solving the resource optimization configuration model of the comprehensive service station to obtain the optimal resource combination mode; The process of solving the resource optimization configuration model of the comprehensive service station includes: Sampling the resource optimization configuration model to generate several scenario sets; Solve resource combination methods in different scenarios and obtain several resource combination methods; Count the number of occurrences of each resource combination and determine the resource combination with the highest number of occurrences.

2. The method for optimizing resource configuration of an energy internet integrated service station according to claim 1, characterized in that: The step of summing the power generation parameters of the flexible resources in the general power generation model to obtain the aggregate power generation characteristics of the integrated service station includes: Obtain the overall steady-state aggregation characteristics of the integrated service station, wherein the overall steady-state aggregation characteristics of the integrated service station include the overall power P of the integrated service station t , raise the boundary P t,ub , lower the boundary P t,lb , Maximum upward climbing rate R t,ub , Maximum downward climbing rate R t,lb , upward regulation can last for T t,ub and downward regulation can last for T t,lb ; The process of solving the lumped power generation characteristics includes: Calculate the overall power of the integrated service station: Calculate the upward and downward adjustment boundaries of the integrated service station as: Calculate the duration of the upward / downward regulation of the integrated service station Calculate the maximum up / down ramp rate of the integrated service station as:

3. The method for optimizing resource configuration of an energy internet integrated service station according to claim 2, characterized in that: The method for determining the setting cost of the integrated service station is: define the access cost of each resource as λ i,j,k And define the unit incentive price of each resource as The resource optimization configuration model for building a comprehensive service station is: Among them, V i,j,k is the decision variable, V i,j,k is an integer variable of 0 or 1; i,j,k is the access cost of resources; V i,j,k λ i,j,k The access cost; is the unit incentive price of the resource; is the installed capacity of the flexible resource of type j and numbered k under the integrated service station node i; is the call cost; Calling resource capacity for integrated service stations; Comprehensive service station climbing rate; Aggregate the relationship between characteristics and resource characteristics for integrated service stations; is the power flow constraint of the distribution network, namely, the voltage constraint and the power flow reverse constraint; the node voltage constraint satisfies U min ≤U i ≤U max ; Power flow backflow constraint satisfaction in, is the net load of node i after aggregation by the integrated service station; P i Output power for interactive resources of integrated service stations, Represents the net load of node i.

4. The method for optimizing resource configuration of an energy internet integrated service station according to claim 3, characterized in that: The process of sampling the resource optimization configuration model to generate several scene sets is as follows: The nonlinear voltage constraint is converted into the net load of each node. The linear constraints adjusted within the interval are as follows, and the optimized form is: Monte Carlo simulation is used to predict the output power of each resource unit Generate a set of scenarios.

5. An energy internet integrated service station resource optimization configuration system, used to execute the energy internet integrated service station resource optimization configuration method according to any one of claims 1 to 4, characterized in that: It includes a first building module, a summing module, a second building module and a solving module; The first construction module is used to construct a flexible resource universal power generation model of the integrated service station; the flexible resource universal power generation model is used to describe the power generation characteristics of different types of flexible resources connected to the integrated service station; the flexible resources are resources that dynamically balance the supply and demand of the service and energy consumption system; The summing module is used to sum the power generation parameters of the flexible resources in the general power generation model according to the general power generation model to obtain the aggregate power generation characteristics of the integrated service station; The second construction module is used to construct a resource optimization configuration model of the integrated service station according to the aggregated power generation characteristics and the integrated service station setting cost; The solution module is used to solve the resource optimization configuration model of the comprehensive service station to obtain the optimal resource combination method.

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