Method for evaluating on-site consumption capability of new energy by considering distributed shared energy storage

Abstract

The invention relates to a method for evaluating the on-site consumption capability of new energy by considering distributed shared energy storage. Firstly, based on a regional power system flexibility supply and demand balance mechanism, a regional power system space-time flexibility quantitative analysis method considering adjustment capability of multi-type flexibility resources and flexibility-related attribute characteristics is proposed; secondly, considering the flexible adjustment capability of a distributed power supply, a demand response load and a distributed energy storage device, and by taking the minimum wind curtailment and light curtailment of the regional power distribution network as a target, constructing a regional power distribution network new energy on-site consumption evaluation model containing distributed shared energy storage; and finally, based on the evaluation model, solving to obtain the maximum local absorption potential of the new energy of the regional power distribution network by taking the maximum new energy capacity which can be accepted by the regional power distribution network as a target.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to an evaluation method for local consumption capacity of new energy taking into account distributed shared energy storage. Background Art

[0002] With the large-scale grid connection of new energy represented by wind and solar, the volatility and intermittent characteristics will have a great impact on power system safety issues such as voltage stability and power balance when they are connected to the power system. New energy has the characteristics of poor flexibility, strong volatility and high intermittency, which limits the power system's ability to absorb new energy. Fully tapping the flexibility resources on the source-load-storage side can reduce the problem of wind and solar abandonment caused by the lack of grid flexibility, which is of great significance to promoting carbon emission reduction and low-carbon operation of the grid. Energy storage itself has the characteristics of power time and space transfer and is a high-quality flexibility resource. Therefore, how to accurately evaluate the local absorption capacity of new energy considering distributed shared energy storage has become a technical problem that needs to be solved urgently.

[0003] However, in actual use, there is a problem: the existing technology cannot effectively and accurately evaluate the local consumption capacity of new energy in the distribution network, resulting in a reduced utilization rate of new energy. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide an evaluation method for the local consumption capacity of new energy taking into account distributed shared energy storage.

[0005] The technical solution adopted by the present invention is: a method for evaluating the local consumption capacity of new energy considering distributed shared energy storage, comprising the following steps:

[0006] S100: Based on the flexibility supply and demand balance mechanism of regional power system, a quantitative analysis method of spatiotemporal flexibility of regional power system is proposed, which takes into account the regulation capacity and flexibility-related attribute characteristics of multiple types of flexibility resources;

[0007] S200: Considering the flexible adjustment capabilities of distributed power sources, demand response loads and distributed energy storage devices, and taking the goal of minimizing wind and solar power abandonment in the regional distribution network, a new energy on-site consumption evaluation model for the regional distribution network with distributed shared energy storage is constructed;

[0008] S300: Based on the above evaluation model, with the goal of maximizing the renewable energy capacity that the regional distribution network can accommodate, the maximum on-site consumption potential of renewable energy in the regional distribution network is solved.

[0009] Specifically, in step S100:

[0010]

[0011] In the formula, ft N is the grid flexibility demand at time t; β l,t is the power grid load power at time t; β NE,t is the power generation of new energy at time t; β n,t is the net load power of the power grid at time t;

[0012] The time domain fluctuation of net load reflects the direction of its flexibility demand. If the net load increases at time t+1, the power grid needs to have upward flexibility, denoted as F t SD,up Otherwise, downward flexibility is required, denoted as F t SD,dn .

[0013] The upward flexibility provides F t SD,up and downward flexibility supply F t SD,dn The expressions are:

[0014]

[0015] In the formula, They are the upward flexibility supply of distributed generation, energy storage, and demand response load at time t; are the downward flexibility supply of distributed generation, energy storage, and demand response load at time t; β DG,t , β DG,max , β DG,min are the output of distributed generation and its upper and lower limits at time t; R up , R down are the up and down climbing rates respectively; β is the energy storage output at time t, which can be positive or negative; C,max , β S,max are the maximum charging and discharging powers of energy storage at time t, both are positive numbers; E ES,t 、E ES,max 、E ES,min are the energy storage capacity and its upper and lower limits at time t; η C , η S are the charge and discharge efficiency respectively; n D is the demand response coefficient; β DR,max , β DR,min Load capping and lowering for participation in demand response; They are the load that can be reduced, the load that can be transferred, and the load that can be translated.

[0016] The expression of the flexibility quantitative index is:

[0017]

[0018] In the formula, for the upward flexibility difference; is the downward flexibility difference. Define the flexibility difference F di , when F di >0 indicates that the grid flexibility resources are insufficient and there is a gap in flexibility; when F di <0 indicates that the grid flexibility resources are sufficient and there is a margin of flexibility. t SR This indicator provides a quantitative assessment of flexibility. t SR When the value is positive, it means that the flexibility in the corresponding direction is sufficient, otherwise it is insufficient;

[0019]

[0020] In step S200, the flexible adjustment capability of distributed power sources, demand response loads and distributed energy storage devices is considered, and the local consumption evaluation model of new energy in the regional distribution network containing distributed shared energy storage is constructed with the goal of minimizing the abandonment of wind and solar power in the regional distribution network:

[0021] (1) Objective function

[0022]

[0023] In the formula, is the photovoltaic output power of node i at time t; is the wind power output of node i at time t; is the photovoltaic output power consumed by node i at time t; is the wind power output consumed by node i at time t;

[0024] (2) Constraints

[0025] The active-reactive power flow constraint of the distribution network optimal dispatching model is:

[0026]

[0027] Where: P i t , Q i t is the active load and reactive load at node i at time t; P ij t , Q ij t is the active power and reactive power on line (i, j) at time t; i ij t is the current amplitude on line (i, j) at time t; r kit 、x ki t is the resistance and inductance value of line (i, j) at time t; v i t is the voltage amplitude at node i at time t;

[0028] If distributed new energy, power storage equipment, flexible loads, etc. are installed at node i, the net injected active power and reactive power can be expressed as:

[0029]

[0030] Where: is the energy storage charging power and discharging power at node i at time t; is the active power and reactive power of the distributed renewable energy at node i at time t; is the demand response load adjustment amount connected to node i at time t; is the distributed generation power at node i at time t;

[0031] The reserve constraint of the distribution network optimal dispatching model is:

[0032]

[0033] Where: ε% is the system reserve rate;

[0034] The node voltage and branch current safety constraints of the distribution network optimization dispatching model are:

[0035]

[0036] Where: v i,max 、v i,min is the maximum and minimum value of the voltage amplitude at node i; i ij,max is the maximum value of the current amplitude of branch ij;

[0037] The safe operation constraints of the energy storage device in the distribution network optimization dispatching model are:

[0038]

[0039] Where: is the power of the energy storage system connected to the i-th node at time t; is the charging power and discharging power of the energy storage system connected to the i-th node at time t; η ch , η dh is the charging and discharging efficiency of the energy storage system; Δt is the scheduling time interval; is the upper limit of the charging and discharging power of the energy storage system; E i,ESS,min 、E i,ESS,maxThe upper and lower limits of the energy storage system. Considering that the energy storage system cannot be charged and discharged at the same time at any time, a 0-1 variable is introduced. and And introduce Constraint means that at any time the energy storage system can be in one of the three states of charging, discharging, and neither charging nor discharging, and there is no physically impossible phenomenon of both charging and discharging;

[0040] The safe operation constraints of distributed renewable energy in the distribution network optimization dispatching model are:

[0041]

[0042] Where: is the maximum output of DG at time t, is the power factor angle;

[0043] The demand response constraint of the distribution network optimal dispatch model is:

[0044]

[0045] Where: μ is the demand response coefficient, which is used to characterize the relationship between power reduction and power increase for different loads; The load power increase at time t is: is the load reduction power at time t;

[0046] The distributed generation operation constraints of the distribution network optimization dispatching model are:

[0047]

[0048] Where: are the output and upper and lower limits of distributed generation respectively; They are the upper and lower limits of the ramp rate of distributed power sources respectively; is the power factor of the distributed power source;

[0049] The tie line transmission power constraint of the distribution network optimization dispatching model is:

[0050]

[0051] Where: represents the interaction power between the distribution network and the upper power grid at time t; Respectively represent the maximum and minimum values ​​of the interaction power;

[0052] The SVC operation constraints of the distribution network optimization dispatch model are:

[0053]

[0054] Where: represents the reactive compensation power of SVC at node i at time t; They represent the maximum and minimum values ​​of the reactive compensation power of the SVC at node n respectively.

[0055] In step S300, the objective function of the model for evaluating the maximum local consumption potential of new energy in the regional distribution network is:

[0056]

[0057] Where: P i PV,AC is the photovoltaic access capacity of node i; P i WT,AC is the wind power absorptive capacity of node i.

[0058] Beneficial effects of the invention: The invention is based on the evaluation of the local absorption capacity of new energy in the regional distribution network, with the minimum wind and solar power abandonment and the maximum potential for local absorption of new energy as the objective functions, considering various types of flexibility resources and devices as well as network operation constraints, to establish and solve a local absorption capacity evaluation model for new energy in the regional distribution network considering distributed shared energy storage, and according to the solution results of the scheduling model, the local absorption capacity evaluation results of new energy before and after considering distributed shared energy storage are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of a method for evaluating the local consumption capacity of new energy considering distributed shared energy storage provided by an exemplary embodiment of the present invention;

[0060] Figure 2 is a topological diagram of a power distribution system provided by an exemplary embodiment of the present invention;

[0061] Figure 3 is a base load curve of Lankao County provided by an exemplary embodiment of the present invention;

[0062] Figure 4 It is a typical daily curve of photovoltaic four seasons provided by an exemplary embodiment of the present invention;

[0063] Figure 5 This is a typical daily curve of wind power in four seasons provided by an exemplary embodiment of the present invention;

[0064] Figure 6 is a new energy consumption curve for scenario 1 provided by an exemplary embodiment of the present invention;

[0065] Figure 7 is a new energy consumption curve for scenario 2 provided by an exemplary embodiment of the present invention;

[0066] Figure 8 It is a scene 2 energy storage charging and discharging and SOC curve provided by an exemplary embodiment of the present invention;

[0067] Fig. 9 is a demand response load regulation curve for scenario 2 provided by an exemplary embodiment of the present invention;

[0068] Fig.10 is a distributed power output curve of scenario 2 provided by an exemplary embodiment of the present invention;

[0069] Fig.11 is an output curve of a reactive power compensation device in scenario 2 provided by an exemplary embodiment of the present invention;

[0070] Fig.12 is a flexibility margin rate curve before and after connecting to energy storage provided by an exemplary embodiment of the present invention;

[0071] Fig.13 It is a comparison of the new energy consumption capacity before and after connecting to energy storage provided by an exemplary embodiment of the present invention;

[0072] Fig.14 It is a scene 4 energy storage charging and discharging and SOC curve provided by an exemplary embodiment of the present invention;

[0073] Fig.15 is an output curve of a reactive power compensation device in scenario 4 provided by an exemplary embodiment of the present invention;

[0074] Fig.16 It is a load demand response curve of scenario 4 provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention, and are specifically described in combination with the embodiments below.

[0076] like Figure 1 As shown, the evaluation method 100 for the local consumption capacity of new energy considering distributed shared energy storage includes the following steps:

[0077] Step 101, based on the flexibility supply and demand balance mechanism of the regional power system, a quantitative analysis method of the spatiotemporal flexibility of the regional power system is proposed considering the regulation capabilities and flexibility-related attribute characteristics of multiple types of flexibility resources;

[0078] Step 102, considering the flexible adjustment capabilities of distributed power sources, demand response loads and distributed energy storage devices, with the goal of minimizing wind and solar power abandonment in the regional distribution network, constructing a new energy on-site consumption evaluation model for the regional distribution network including distributed shared energy storage;

[0079] Step 103, based on the above evaluation model, with the maximum capacity of new energy that the regional distribution network can accommodate as the goal, solve and obtain the maximum local consumption potential of new energy in the regional distribution network.

[0080] Specifically, the present invention provides an evaluation method for the local absorption capacity of new energy considering distributed shared energy storage. First, based on the flexible supply and demand balance mechanism of the regional power system, a quantitative analysis method for the spatiotemporal flexibility of the regional power system considering the adjustment capacity and flexibility-related attribute characteristics of multiple types of flexibility resources is proposed; then, an evaluation model for the local absorption capacity of new energy in the regional distribution network with distributed shared energy storage is established, and the optimal dispatching result of the distribution network with the participation of the distributed energy storage resources is solved; finally, based on the optimal dispatching result of the distribution network containing distributed shared energy storage, the absorption capacity and maximum absorption potential of new energy before and after considering distributed energy storage are determined.

[0081] Step S100: Based on the flexibility supply and demand balance mechanism of the regional power system, a quantitative analysis method of the spatiotemporal flexibility of the regional power system is proposed, which takes into account the regulation capabilities and flexibility-related attribute characteristics of multiple types of flexibility resources;

[0082] Step S200: Considering the flexible adjustment capabilities of distributed power sources, demand response loads and distributed energy storage devices, and taking the goal of minimizing wind and solar power abandonment in the regional distribution network, a new energy on-site consumption evaluation model for the regional distribution network including distributed shared energy storage is constructed;

[0083] Step S300, based on the above evaluation model, with the goal of maximizing the capacity of new energy that the regional distribution network can accommodate, the maximum local consumption potential of new energy in the regional distribution network is solved.

[0084] In a specific embodiment, the spatiotemporal flexibility of the regional distribution network of distributed shared energy storage is first characterized to obtain a quantitative index reflecting the flexibility of the regional distribution network.

[0085] Furthermore, step S100 includes: system flexibility demand analysis, system flexibility supply analysis, and system flexibility quantitative indicators.

[0086] Specifically, net load fluctuations are used to characterize the flexibility demand of the power grid, and flexibility supply resources include distributed power sources, energy storage, and interruptible loads. Based on the relationship between flexibility supply and demand, a flexibility gap can be defined to represent the difference between flexibility demand and supply.

[0087] Furthermore, step S100 also includes:

[0088] Step S110: Grid flexibility demand analysis;

[0089] Step S120: Grid flexibility supply analysis;

[0090] Step S130: quantifying grid flexibility indicators.

[0091] Net load fluctuation can characterize the grid flexibility demand, so the grid flexibility demand f t N The grid load power β l,t and renewable energy power generation β NE,t Joint decision:

[0092] Formula 1:

[0093] The time domain fluctuation of net load reflects the direction of its flexibility demand. If the net load increases at time t+1, the power grid needs to have upward flexibility, denoted as F t SD,up Otherwise, downward flexibility is required, denoted as F t SD,dn .

[0094] Step S120: Grid flexibility supply analysis;

[0095] System upward flexibility supply F t SD,up The expression formula is as follows:

[0096] Formula 2:

[0097] System downward flexibility supply F t SD,dn The expression formula is as follows:

[0098] Formula 3:

[0099] Where: They are the upward flexibility supply of distributed generation, energy storage, and demand response load at time t; are the downward flexibility supply of distributed generation, energy storage, and demand response load at time t; β DG,t , β DG,max , β DG,min are the output of distributed generation and its upper and lower limits at time t; R up , R down are the up and down climbing rates respectively; β is the energy storage output at time t, which can be positive or negative; C,max , β S,max are the maximum charging and discharging powers of energy storage at time t, both are positive numbers; E ES,t 、E ES,max 、E ES,min are the energy storage capacity and its upper and lower limits at time t; η C , η S are the charge and discharge efficiency respectively; nD is the demand response coefficient; β DR,max , β DR,min Load capping and lowering for participation in demand response; They are the load that can be reduced, the load that can be transferred, and the load that can be translated.

[0100] Step S130: quantifying grid flexibility indicators.

[0101] According to the flexibility supply and demand relationship, the flexibility gap F can be defined di , which represents the difference between flexibility demand and supply, and also has upward / downward flexibility differentials Two types.

[0102] Formula 4:

[0103] When F di >0 indicates that the grid flexibility resources are insufficient and there is a gap in flexibility; when F di <0 indicates that the grid flexibility resources are sufficient and there is a margin of flexibility. t SR This indicator provides a quantitative assessment of flexibility. t SR When the value is positive, it means that the flexibility in the corresponding direction is sufficient, otherwise it is insufficient.

[0104] Formula 5:

[0105] Furthermore, S200 includes: considering the flexible adjustment capabilities of distributed power sources, demand response loads and distributed energy storage devices, with the goal of minimizing wind and solar power abandonment in the regional distribution network, and constructing an evaluation model for the local consumption of new energy in the regional distribution network containing distributed shared energy storage;

[0106] (1) Objective function

[0107] In order to accurately and effectively optimize and evaluate the local consumption capacity of regional distribution networks for renewable energy and make full use of the flexibility resources in the distribution network, the objective function is to minimize the abandonment of wind and solar power in the regional distribution network within a complete dispatch cycle (taken as 24 hours):

[0108] Formula 6:

[0109] Where: is the photovoltaic output power of node i at time t; is the wind power output of node i at time t; is the photovoltaic output power consumed by node i at time t; is the wind power output consumed by node i at time t.

[0110] (2) Constraints

[0111] In order to ensure the safety of system operation and the feasibility of the optimization plan, the constraints that need to be considered include power flow constraints, transmission power constraints, distribution network and flexibility resource operation constraints, and backup constraints.

[0112] 1) Active-reactive power flow equation constraints of distribution network:

[0113] The Distflow flow form of the radial distribution network is as follows:

[0114] Formula 8:

[0115] Where: P i t , Q i t is the active load and reactive load at node i at time t; P ij t , Q ij t is the active power and reactive power on line (i, j) at time t; i ij t is the current amplitude on line (i, j) at time t; r ki t 、x ki t is the resistance and inductance value of line (i, j) at time t; v i t is the voltage amplitude at node i at time t.

[0116] If distributed new energy, power storage equipment, flexible loads, etc. are installed at node i, the net injected active power and reactive power can be expressed as:

[0117] Formula 9:

[0118] Where: is the energy storage charging power and discharging power at node i at time t; is the active power and reactive power of the distributed renewable energy at node i at time t; is the demand response load adjustment amount connected to node i at time t; is the distributed generation power at node i at time t.

[0119] 2) Alternative constraints:

[0120] Taking into account the randomness and volatility of distributed renewable energy output and load demand, the power system needs to have a certain amount of backup capacity to ensure safe and reliable power supply to meet users' electricity needs.

[0121] Formula 10:

[0122] In the formula: ε% is the system reserve rate.

[0123] 3) Constraints on safe operation of distribution network:

[0124] The safe operation constraints of the distribution network mainly consider node voltage and branch current.

[0125] Formula 11:

[0126] Where: v i,max 、v i,min is the maximum and minimum value of the voltage amplitude at node i; i ij,max is the maximum value of the current amplitude in branch ij.

[0127] 4) Constraints on safe operation of energy storage devices:

[0128] The safe operation constraints of power storage equipment mainly include charge and discharge state constraints, charge and discharge power constraints and charge state constraints, specifically:

[0129] Formula 12:

[0130] Where: is the power of the energy storage system connected to the i-th node at time t; is the charging power and discharging power of the energy storage system connected to the i-th node at time t; η ch , η dh is the charging and discharging efficiency of the energy storage system; Δt is the scheduling time interval; is the upper limit of the charging and discharging power of the energy storage system; E i,ESS,min 、E i,ESS,max The upper and lower limits of the energy storage system. Considering that the energy storage system cannot be charged and discharged at the same time at any time, a 0-1 variable is introduced. and And introduce Constraint means that at any time the energy storage system can be in one of the three states: charging, discharging, and neither charging nor discharging, and there is no physically impossible phenomenon of both charging and discharging.

[0131] 5) Safe operation constraints of distributed new energy:

[0132] Formula 13:

[0133] Where: is the maximum output of DG at time t, is the power factor angle.

[0134] 6) Demand response constraints:

[0135] Formula 14:

[0136] Where: μ is the demand response coefficient, which is used to characterize the relationship between power reduction and power increase for different loads; The load power increase at time t is: is the load reduction power at time t.

[0137] 7) Distributed power operation constraints:

[0138] Formula 15:

[0139] Where: are the output and upper and lower limits of distributed generation respectively; They are the upper and lower limits of the ramp rate of distributed power sources respectively; is the power factor of the distributed power supply.

[0140] 8) Tie line transmission power constraints:

[0141] Formula 16:

[0142] Where: represents the interaction power between the distribution network and the upper power grid at time t; Represent the maximum and minimum values ​​of interaction power respectively.

[0143] 9) SVC operation constraints:

[0144] Formula 17:

[0145] Where: represents the reactive compensation power of SVC at node i at time t; They represent the maximum and minimum values ​​of the reactive compensation power of the SVC at node n respectively.

[0146] Further, step S300 includes: based on the above evaluation model, with the goal of maximizing the capacity of new energy that the regional distribution network can accommodate, solving for the maximum local consumption potential of new energy in the regional distribution network.

[0147] The objective function of the model for evaluating the maximum local consumption potential of new energy in regional distribution networks is:

[0148] Formula 7:

[0149] Where: P i PV,AC is the photovoltaic access capacity of node i; P i WT,ACis the wind power absorptive capacity of node i.

[0150] At this point, the evaluation model of the local consumption capacity of new energy in the regional distribution network considering distributed shared energy storage has been established and can be applied to actual examples. In order to verify the effectiveness of the proposed method, the example is set as follows:

[0151] Example: The above-mentioned evaluation method is verified based on the new energy and load data of Lankao County, Henan Province in 2022.

[0152] The topology of the power distribution system is as follows: Figure 2 As shown in Figure 1, photovoltaic and wind power are connected at different nodes, with a maximum capacity of 1200kW, and flexible resources such as energy storage system (ESS), distributed generation (DG), reactive power compensation device (SVC), and demand response load (IL) are connected to the distribution system to verify the effectiveness of the proposed evaluation method. The basic load curve and wind and solar output curve of Lankao County are shown in Figure 1. Figure 3 , 4 , 5. Table 1 shows the setting of flexibility resource parameters.

[0153]

[0154] Table 1 System flexibility resource parameters

[0155] First, we set up the following four typical scenarios based on typical days in spring and autumn for comparative analysis. Scenarios 1 and 2 are analyses of the existing renewable energy absorption capacity of the regional distribution network before and after the connection to distributed shared energy storage. Scenarios 3 and 4 are analyses of the maximum renewable energy absorption potential of the regional distribution network before and after the connection to distributed shared energy storage.

[0156] Scenario 1: Assessment of local consumption capacity of new energy including DG, IL and SVC.

[0157] Scenario 2: Assessment of local absorption capacity of new energy including DG, IL, SVC and distributed shared energy storage.

[0158] Scenario 3: Assessment of the maximum local consumption potential of new energy including DG, IL and SVC.

[0159] Scenario 4: Assessment of the maximum on-site consumption potential of new energy including DG, IL, SVC and distributed shared energy storage.

[0160] Scenario 1, that is, the new energy consumption curve before configuring distributed shared energy storage is as follows: Figure 6 As shown. It can be seen that in scenario 1, new energy cannot be fully absorbed during the noon period. By comparing with the load demand curve and the wind and solar output curve, it can be seen that there are abundant wind and solar resources at noon, so the photovoltaic output and wind power output cannot be fully absorbed during the noon period, which is a period with a low new energy absorption rate.

[0161] Scenario 2, that is, the new energy consumption curve after configuring distributed shared energy storage is as follows: Figure 7 As shown. It can be seen that after the configuration of distributed shared energy storage, only a very small part of the wind power output at points 1-2 cannot be fully absorbed, while the photovoltaic output is fully absorbed. As can be seen from Table 2, after the configuration of distributed shared energy storage, the system's local absorption capacity of new energy has been greatly improved. However, since new energy has not been fully absorbed, it is necessary to study the maximum local absorption capacity of new energy as a basis, and further study the reasonable configuration of distributed shared energy storage to maximize the utilization of flexible resources.

[0162]

[0163] Table 2 Comparison of new energy consumption rates in different scenarios

[0164] The charging and discharging and SOC curves of distributed shared energy storage at different times are as follows: Figure 8 As shown. It can be seen that the charging and discharging of distributed shared energy storage shows certain similarities and continuity. During the evening peak, the load level is high, and photovoltaic power generation is basically zero, so energy storage needs to be continuously discharged to meet the load demand. From morning to noon, photovoltaic power generation is large and the load level is low, so energy storage is continuously charged. The correlation between the charging and discharging curve of distributed shared energy storage and the output curve and load curve of new energy shows the role of distributed shared energy storage in the consumption of new energy.

[0165] The output of other flexible resources in scenario 2 is as follows: Figure 9-11 As shown in the figure, the load demand response is adjusted upward at noon to improve the system's ability to absorb new energy during the noon period. Distributed power sources have a larger output in the evening and at night to make up for the insufficient output of new energy during this period and the load gap during the evening peak.

[0166] Tables 3 and 4 are the upward and downward supply diagrams of flexibility resources for scenarios 1 and 2, respectively. From morning to noon, due to the low load level and abundant wind and solar power, the downward supply of flexibility is relatively scarce. From dusk to night, due to the almost no output of photovoltaic power and the peak load, the upward supply of flexibility is relatively scarce. At the same time, after adding distributed energy storage, distributed shared energy storage can provide the system with stable and abundant flexibility supply at most times.

[0167]

[0168] Table 3 Upward supply of flexible resources

[0169]

[0170] Table 4 Flexible resources supply downward

[0171] The flexibility resource abundance ratio before and after energy storage is connected is shown in Table 5 and Fig.12 As shown. After configuring distributed energy storage, the system's flexibility margin level has been significantly improved. Energy storage itself has the characteristics of power time and space transfer and is a high-quality flexibility resource. At the same time, the SOC of energy storage is rarely at the upper limit, so it can provide more flexibility to the system than other flexibility resources.

[0172]

[0173] Table 5 Flexibility Adequacy Data

[0174] Scenarios 3 and 4 study the maximum local consumption potential of new energy sources including DG, IL, SVC, and distributed shared energy storage, with the goal of maximizing the wind and solar capacity that can be consumed locally under the condition of existing flexible resources.

[0175] The optimization calculation results are shown in Table 6. Before configuring distributed shared energy storage, when the wind power capacity does not exceed 0.26MW, the photovoltaic capacity does not exceed 1.46MW, and the total capacity of distributed new energy is 1.72MW, the distribution network can fully absorb the new energy locally. After configuring distributed shared energy storage, when the wind power capacity does not exceed 1.99MW and the photovoltaic capacity does not exceed 1.49MW, the distribution network can fully absorb the new energy locally. It can be seen that with the access of distributed shared energy storage, the flexibility of the system is improved, and more potential for local absorption of new energy in the distribution network is tapped. At this time, the optimization results of each flexibility resource are as follows: Figure 13-16 shown.

[0176]

[0177] Table 6 Comparison of local consumption capacity of new energy

[0178] In addition to typical days in spring, the new energy consumption capacity of the distribution network based on distributed shared energy storage and flexibility resources was evaluated on typical days in summer, autumn and winter. The results show that except for spring, all new energy sources connected in other seasons can be consumed on site. It is not difficult to find from the load and new energy output curves in the four seasons that the output of new energy in spring is large and the load output is small, which brings greater consumption pressure to the distribution network. In the other three seasons, the capacity of distributed shared energy storage is relatively sufficient, and its application in other aspects can be further explored in subsequent research to maximize the utilization of distributed shared energy storage functions.

[0179] Therefore, based on the evaluation of the local absorption capacity of renewable energy in the regional distribution network, the present invention takes the minimum wind and solar power abandonment and the maximum potential of local absorption of renewable energy as the objective functions, considers various types of flexibility resources and devices as well as network operation constraints, establishes and solves the local absorption capacity evaluation model of the regional distribution network considering distributed shared energy storage, and obtains the evaluation results of the local absorption capacity of renewable energy before and after considering distributed shared energy storage according to the solution results of the scheduling model.

[0180] In one example of the present invention, the flexible adjustment capabilities of distributed power sources, demand response loads and distributed energy storage devices are taken into consideration, and a new energy on-site consumption evaluation model for regional distribution networks based on distributed shared energy storage is constructed in combination with flexibility indicators to obtain an evaluation result of the new energy on-site consumption capacity before and after considering distributed shared energy storage.

[0181] After adopting the technical solution of the present invention, the following technical effects can be achieved:

[0182] (1) Through the study of the flexibility supply and demand mechanism, a flexibility adequacy index is proposed based on the analysis of the upward / downward flexibility supply capacity, which can effectively quantify the spatiotemporal flexibility of the power system;

[0183] (2) The constructed evaluation model for local consumption of new energy in regional distribution networks based on distributed shared energy storage can coordinately optimize various flexibility resources, quantitatively evaluate the flexibility supply of different flexibility resources, and quickly and accurately evaluate the local consumption capacity of existing new energy in regional distribution networks. At the same time, it can be further applied to the evaluation of the maximum consumption potential of new energy in regional distribution networks.

[0184] (3) Through simulation research, it is found that when the system photovoltaic penetration rate is high, the trough of new energy consumption often occurs at noon. At this time, the unplanned access of distributed shared energy storage may still lead to the problem of wind and solar power abandonment, so it is necessary to further optimize the configuration of distributed shared energy storage. At the same time, since the access of distributed shared energy storage needs to consider the absorption capacity of one year, and the output of new energy and load in each season is significantly different, except for the season of large new energy generation, when all distributed shared energy storage is used for absorption, distributed shared energy storage has the potential to participate in other auxiliary services in other seasons.

Claims

1. A method for evaluating the local consumption capacity of new energy sources considering distributed shared energy storage, characterized in that: The steps include: S100: Based on the flexibility supply and demand balance mechanism of regional power system, a quantitative analysis method of spatiotemporal flexibility of regional power system is proposed, which takes into account the regulation capacity and flexibility-related attribute characteristics of multiple types of flexibility resources; S200: Considering the flexible adjustment capabilities of distributed power sources, demand response loads and distributed energy storage devices, and taking the goal of minimizing wind and solar power abandonment in the regional distribution network, a new energy on-site consumption evaluation model for the regional distribution network with distributed shared energy storage is constructed; S300: Based on the above evaluation model, with the goal of maximizing the renewable energy capacity that the regional distribution network can accommodate, the maximum on-site consumption potential of renewable energy in the regional distribution network is solved.

2. The method for evaluating the local consumption capacity of new energy sources considering distributed shared energy storage according to claim 1, characterized in that: In the step S100: In the formula, f t N is the grid flexibility demand at time t; β l,t is the power grid load power at time t; β NE,t is the power generation of new energy at time t; β n,t is the net load power of the power grid at time t; The time domain fluctuation of net load reflects the direction of its flexibility demand. If the net load increases at time t+1, the power grid needs to have upward flexibility, denoted as F t SD,up Otherwise, downward flexibility is required, denoted as F t SD,dn .

3. The method for evaluating the local consumption capacity of new energy sources considering distributed shared energy storage according to claim 2 is characterized in that: The upward flexibility provides F t SD,up and downward flexibility supply F t SD,dn The expressions are: In the formula, They are the upward flexibility supply of distributed generation, energy storage, and demand response load at time t; are the downward flexibility supply of distributed generation, energy storage, and demand response load at time t; β DG,t , β DG,max , β DG,min are the output of distributed generation and its upper and lower limits at time t; R up , R down are the up and down climbing rates respectively; β is the energy storage output at time t, which can be positive or negative; C,max , β S,max are the maximum charging and discharging powers of energy storage at time t, both are positive numbers; E ES,t 、E ES,max 、E ES,min are the energy storage capacity and its upper and lower limits at time t respectively; η C , η S are the charge and discharge efficiency respectively; n D is the demand response coefficient; β DR,max , β DR,min Load capping and lowering for participation in demand response; They are the load that can be reduced, the load that can be transferred, and the load that can be translated.

4. The method for evaluating the local consumption capacity of new energy sources considering distributed shared energy storage according to claim 3 is characterized in that: The expression of the flexibility quantitative index is: In the formula, for the upward flexibility difference; is the downward flexibility difference. Define the flexibility difference F di , when F di >0 indicates that the grid flexibility resources are insufficient and there is a gap in flexibility; when F di <0 indicates that the grid flexibility resources are sufficient and there is a margin of flexibility. t SR This indicator provides a quantitative assessment of flexibility. t SR When the value is positive, it means that the flexibility in the corresponding direction is sufficient, otherwise it is insufficient; 5. The method for evaluating the local consumption capacity of new energy considering distributed shared energy storage according to claim 1 is characterized in that: In step S200, the flexible adjustment capability of distributed power sources, demand response loads and distributed energy storage devices is considered, and the local consumption evaluation model of new energy in the regional distribution network containing distributed shared energy storage is constructed with the goal of minimizing the abandonment of wind and solar power in the regional distribution network: Objective Function In the formula, is the photovoltaic output power of node i at time t; is the wind power output of node i at time t; is the photovoltaic output power consumed by node i at time t; is the wind power output consumed by node i at time t.

6. The method for evaluating the local consumption capacity of new energy considering distributed shared energy storage according to claim 1 is characterized in that: In step S300, the objective function of the model for evaluating the maximum local consumption potential of new energy in the regional distribution network is: Where: P i PV,AC is the photovoltaic access capacity of node i; P i WT,AC is the wind power absorptive capacity of node i.

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