Power distribution network multi-scene intelligent planning method considering alternative energy storage

Abstract

The invention relates to a power distribution network multi-scene intelligent planning method considering alternative energy storage, and the method comprises the following steps: 1, extracting the output characteristics of an alternative energy storage access power distribution network: accurately describing the behaviors of an alternative energy storage system under various working conditions through the analysis of the energy storage capacity, the charge-discharge rate, the efficiency and the life output characteristics; a data support is provided for a subsequent intelligent planning model; step 2, constructing a multi-scene power distribution network intelligent planning model based on alternative energy storage: optimizing configuration and scheduling of an energy storage system; 3, performing instance analysis; the method has the advantages that power characteristics are extracted, behaviors are accurately described, an intelligent planning model is constructed, and coordination and optimization are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution networks, and specifically relates to a multi-scenario intelligent planning method for distribution networks considering alternative energy storage. Background Art

[0002] The distribution network is composed of overhead lines, reactive power compensators and other facilities, which can ensure a stable supply of electricity. At present, due to the complexity and uncertainty of the distribution network, the results of intelligent planning are uncertain, which affects the economic benefits of the distribution network. In order to solve these problems, people have proposed a variety of intelligent planning methods, such as using SOP and photovoltaics to intelligently plan multi-scenario distribution networks, using SOP's power flow control and photovoltaics' clean energy optimization structure to improve the flexibility and reliability of the system. However, this kind of planning requires a large amount of real-time data and historical data, which are often incomplete, affecting the planning effect; another example is the method of realizing multi-scenario distribution network intelligent planning through load phase switching, which can improve the efficiency and reliability of the power grid and reduce losses. However, this method has high requirements for high-precision equipment and intelligent devices. , the technical implementation is difficult; the multi-scenario distribution network intelligent planning method uses community graph theory to divide the power grid into manageable communities. Within each community, depth-first search can optimize the efficiency and reliability of conductor planning; however, this method may fall into local optimality and affect the planning effect; in the two-stage stochastic optimization method of multi-scenario distribution network planning: the first stage creates scenarios to reflect future uncertainties such as load changes and energy output; the second stage establishes a planning model for each scenario to find the optimal solution; however, random algorithms introduce randomness and uncertainty, resulting in deviations in planning results; therefore, it is very necessary to provide a multi-scenario intelligent planning method for distribution networks that considers alternative energy storage, which can extract power characteristics, accurately describe behavior, build intelligent planning models, and achieve coordination and optimization. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a multi-scenario intelligent planning method for distribution networks that takes into account alternative energy storage, which can extract power characteristics, accurately describe behaviors, build intelligent planning models, and achieve coordination and optimization.

[0004] The object of the present invention is achieved by: a multi-scenario intelligent planning method for a distribution network considering alternative energy storage, comprising the following steps:

[0005] Step 1: Extract the output characteristics of alternative energy storage connected to the distribution network: By analyzing the output characteristics of energy storage capacity, charge and discharge rate, efficiency, and life, accurately describe the behavior of alternative energy storage systems under various working conditions, and provide data support for subsequent intelligent planning models;

[0006] Step 2: Build a multi-scenario distribution network intelligent planning model based on alternative energy storage: optimize the configuration and scheduling of energy storage systems;

[0007] Step 3: Example analysis.

[0008] The step 1 of extracting the output characteristics of the alternative energy storage connected to the distribution network is specifically: by analyzing the output characteristics such as energy storage capacity, charge and discharge rate, efficiency, and life, the behavior of the alternative energy storage system under various working conditions is accurately described to obtain the output characteristics of the distribution network. The calculation formula is as follows: t k =T·P i (2), where P i It is the output of the alternative energy storage connected to the distribution network; N k is the energy storage power; N y is the power range; t k is the time period corresponding to the energy storage output interval; T is the time scale of power generation in the distribution network.

[0009] The construction of a multi-scenario distribution network intelligent planning model based on alternative energy storage in step 2 is specifically as follows: the distribution network intelligent planning takes the economic efficiency of distribution network operation as the goal, optimizes the configuration and scheduling of the energy storage system according to the multi-scenario grid demand and energy storage characteristics; considering alternative energy storage, the construction expression of the distribution network intelligent planning model is as follows: minf = f l +f e +f w +f d +f b (3), where minf is the expression of the intelligent planning model of distribution network; f l is the distribution line investment cost target; e is the energy storage system investment cost target; w is the power generation cost target of the generator set; d Cost targets for demand response compensation; b The cost of purchasing electricity off-grid for the transmission grid.

[0010] The distribution line investment cost target f l for: Where i is the discount rate; n is the operating life of the distribution network; c ij is the unit cost of branches i and j; It is the project of branches i and j to be planned in article p.

[0011] The energy storage system investment cost target f e for: In the formula, Ω p is the set of branches to be planned; r p is the alternative energy storage operation coefficient; r mis the alternative energy storage maintenance factor; r s is the alternative energy storage cost conversion factor; r d is the depreciation factor; Ω e is the energy storage configuration node set; c p is the unit power of energy storage; c e is the unit capacity cost of energy storage; is the rated power of alternative energy storage r configured at node k on a typical day s; is the rated capacity of alternative energy storage r configured at node k on a typical day s.

[0012] The target power generation cost of the generator set is f w for: In the formula, Ω w is the node set of the distribution network generators; Γ is the typical day set; T is the distribution network planning period; d s is a typical day planning scenario; c w is the unit cost of curtailed electricity; is the maximum output of the generator set j in period t on a typical day s; p w,t,s,j is the actual output of the generator set at period t on a typical day s.

[0013] The demand response compensation cost target f d for: In the formula, is the compensation price for interruptible load and bonus load in period t; is the set of contract nodes for the two loads; is the planned load of node m during periods s and t; is the interruption load of node n during the period s and t.

[0014] The off-grid electricity purchase cost of the transmission network is f b is: f b =c b ΔP b (8), where ΔP b is the off-grid power consumption; c b is the ΔP purchased from the transmission grid b Unit price.

[0015] Beneficial effects of the present invention: The present invention is a multi-scenario intelligent planning method for distribution networks considering alternative energy storage. In use, traditional distribution network intelligent planning methods focus on optimizing the distribution network structure without considering load demand, which increases the cost of distribution network planning. Therefore, the present invention designs a multi-scenario distribution network intelligent planning method considering alternative energy storage, extracts the power characteristics of alternative energy storage, and accurately describes the behavior of alternative energy storage systems under multi-scenario distribution network operating conditions; constructs a multi-scenario distribution network intelligent planning model based on alternative energy storage, which coordinates and optimizes the charging and discharging power of the energy storage system, the selection of charging and discharging time of the energy storage system, and the distribution of charging and discharging power; simulation experiments verify that the method has better planning performance and can be applied to real life; the present invention has the advantages of extracting power characteristics, accurately describing behavior, constructing intelligent planning models, and achieving coordination and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of daily output and correction of the power distribution network of the present invention.

[0017] Figure 2 This is a schematic diagram of a 10 kV power distribution network of the present invention.

[0018] Figure 3 This is the distribution network load planning result diagram of the present invention. DETAILED DESCRIPTION

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Example 1

[0021] like Figure 1-3 As shown in FIG. 1 , a multi-scenario intelligent planning method for a distribution network considering alternative energy storage includes the following steps:

[0022] Step 1: Extract the output characteristics of alternative energy storage connected to the distribution network: By analyzing the output characteristics of energy storage capacity, charge and discharge rate, efficiency, and life, accurately describe the behavior of alternative energy storage systems under various working conditions, and provide data support for subsequent intelligent planning models;

[0023] In the present invention, the output characteristics of alternative energy storage access to the distribution network are mainly reflected in the regulation ability of the power grid, the improvement of stability, the enhancement of flexibility and the support for renewable energy. The battery energy storage system quickly adjusts the power grid load, and the alternative energy storage is applied to various scenarios such as power peak regulation, black start, and load tracking to meet the different needs of power grid operation. Through the analysis of output characteristics such as energy storage capacity, charge and discharge rate, efficiency, and life, the behavior of the alternative energy storage system under various working conditions is accurately described to provide data support for subsequent intelligent planning models. The daily output power of the distribution network is as follows: Figure 1 shown.

[0024] like Figure 1 As shown, P k is the median value of the power range; t k is the height of the column; P is the constant sequence of the distribution network output; the area of ​​the column is the planned power of the distribution network under the power range; after connecting the alternative energy storage, the backslash column moves to the left to obtain the slash column, which represents the charging regulation of the distribution output; thus, the output characteristics of the distribution network are obtained, and the calculation formula is as follows: t k =T·P i (2), where P i It is the output of the alternative energy storage connected to the distribution network; N k is the energy storage power; N y is the power range; t k is the time period corresponding to the energy storage output interval; T is the time scale of power generation in the distribution network.

[0025] Step 2: Build a multi-scenario distribution network intelligent planning model based on alternative energy storage: optimize the configuration and scheduling of energy storage systems;

[0026] In the present invention, the intelligent planning of the distribution network takes the economic efficiency of the distribution network operation as the goal, optimizes the configuration and scheduling of the energy storage system according to the multi-scenario grid demand and energy storage characteristics; considering the alternative energy storage, the construction expression of the distribution network intelligent planning model is as follows: minf = f l +f e +f w +f d +f b (3), where minf is the expression of the intelligent planning model of distribution network; f l is the distribution line investment cost target; e is the energy storage system investment cost target; w is the power generation cost target of the generator set; d Cost targets for demand response compensation; b The cost of purchasing electricity off-grid for the transmission grid.

[0027] ① Where i is the discount rate; n is the operating life of the distribution network; c ij is the unit cost of branches i and j; It is the project of branches i and j to be planned in Article p;② In the formula, Ω p is the set of branches to be planned; r p is the alternative energy storage operation coefficient; r m is the alternative energy storage maintenance factor; r s is the alternative energy storage cost conversion factor; r d is the depreciation factor; Ω eis the energy storage configuration node set; c p is the unit power of energy storage; c e is the unit capacity cost of energy storage; is the rated power of alternative energy storage r configured at node k on a typical day s; is the rated capacity of alternative energy storage r configured at node k on a typical day s; ③ In the formula, Ω w is the node set of the distribution network generators; Γ is the typical day set; T is the distribution network planning period; d s is a typical day planning scenario; c w is the unit cost of curtailed electricity; is the maximum output of the generator set j in period t on a typical day s; p w,t,s,j is the actual output of the generator set in period t of a typical day s; In the formula, is the compensation price for interruptible load and bonus load in period t; is the set of contract nodes for the two loads; is the planned load of node m during periods s and t; is the interruption load of node n during s and t; ⑤f b =c b ΔP b (8), where ΔP b is the off-grid power consumption; c b is the ΔP purchased from the transmission grid b Unit price;

[0028] Set normal power supply scenarios, such as conventional load demand and renewable energy generation forecasts; emergency power supply scenarios, that is, power supply interruptions due to natural disasters and equipment failures; economic dispatch scenarios; and environmentally friendly scenarios. In each scenario, minf can be minimized to ensure the economy of distribution network planning.

[0029] Step 3: Example analysis.

[0030] In summary, the present invention ensures the long-term sustainable development of the power grid through system access optimization and capacity configuration. The method of the present invention not only improves the ability of participating in the coordinated operation of the power grid by optimizing the configuration of different types of alternative energy storage, but also optimizes the investment benefits and reduces the economic burden that may be caused by power grid upgrades or renovations. By implementing the present invention, power system operators can support and promote the application of flexible resources such as alternative energy storage while ensuring the safety and stability of the power grid, and promote the realization of energy transformation and environmental protection goals.

[0031] The present invention is a multi-scenario intelligent planning method for distribution networks considering alternative energy storage. In use, traditional distribution network intelligent planning methods focus on optimizing the distribution network structure without considering load demand, which increases the cost of distribution network planning. Therefore, the present invention designs a multi-scenario distribution network intelligent planning method considering alternative energy storage, extracts the power characteristics of alternative energy storage, and accurately describes the behavior of alternative energy storage systems under multi-scenario distribution network operating conditions; constructs a multi-scenario distribution network intelligent planning model based on alternative energy storage, which coordinates and optimizes the charging and discharging power of the energy storage system, the selection of the charging and discharging time of the energy storage system, and the distribution of the charging and discharging power; simulation experiments verify that the method has better planning performance and can be applied to real life; the present invention has the advantages of extracting power characteristics, accurately describing behavior, constructing intelligent planning models, and achieving coordination and optimization.

[0032] Example 2

[0033] like Figure 1-3 As shown in FIG. 1 , a multi-scenario intelligent planning method for a distribution network considering alternative energy storage includes the following steps:

[0034] Step 1: Extract the output characteristics of alternative energy storage connected to the distribution network: By analyzing the output characteristics of energy storage capacity, charge and discharge rate, efficiency, and life, accurately describe the behavior of alternative energy storage systems under various working conditions, and provide data support for subsequent intelligent planning models;

[0035] Step 2: Build a multi-scenario distribution network intelligent planning model based on alternative energy storage: optimize the configuration and scheduling of energy storage systems;

[0036] Step 3: Example analysis.

[0037] In the present invention, simulation experiment: In order to verify whether the method of the present invention can meet the requirements of intelligent planning of distribution networks in multiple scenarios, the present invention built a simulation experiment platform and carried out simulation analysis on the above method. The final experimental results are presented in the form of comparison of planning methods based on SOP and photovoltaics, load commutation, and alternative energy storage. The specific experimental preparation process and planning results are shown below.

[0038] ① Experimental preparation: Matlab simulation software is used to perform intelligent planning simulation on the actual 10 kV distribution network. The actual 10 kV distribution network consists of 54 nodes. The present invention simulates 13-54 nodes, simplifies the simulation steps of the distribution network, and installs reactive compensation capacitors at 13, 15, 19, 26, 46, 49 and 54. The upper and lower limits of voltage regulation are 1.05 pu and 1.0 pu, respectively.

[0039] like Figure 2As shown, F is the access point of alternative energy storage 1; Q refers to the access point of the energy storage charging and discharging station; E is the access point of alternative energy storage 2; at nodes 19 and 45, F and E are connected, and the planned line parameters of the distribution network are shown in Table 1 below.

[0040] Table 1 Distribution network planning line parameters

[0041]

[0042] As shown in Table 1, four cases are set according to the distribution network planning: Case 1: Under the condition of unchanged total load, the planning results without considering the access of alternative energy storage and demand-side response, where n19-22 = 0, n19-23 = 1, n19-24 = 0, n19-30 = 0, n28-29 = 1, n28-32 = 0, n51-50 = 0, n51-31 =; Case 2: Under the condition of unchanged total load, only the planning results of alternative energy storage are considered, where n19-22 = 0, n19-23 = 1, n19-24 = 0, n19-30 = 0, n28-29 = 1, n28-32 = 0, n51-50 = 0, n51-31 =; 1-31=1; Case 3: When the total load increases by 20% synchronously, only alternative energy storage needs to be considered to obtain the planning result, among which, n19-22=0, n19-23=0, n19-24=0, n19-30=1, n28-29=1, n28-32=0, n51-50=0, n51-31=0; Case 4: When the total load increases by 20% synchronously, the planning result is obtained by comprehensively considering alternative energy storage and demand-side response, among which, n19-22=1, n19-23=0, n19-24=0, n19-30=0, n28-29=0, n28-32=1, n51-50=1, n51-31=0.

[0043] ② Experimental results: Under the above experimental conditions, the present invention plans the load of the distribution network, and the load planning is as follows Figure 3 shown.

[0044] like Figure 3 As shown, a is the maximum load; b is the load demand; c is the planned load of the present invention; it can be seen from the figure that after the multi-scenario intelligent distribution network planning method designed by the present invention considers alternative energy storage, the load demand is close to the planned load and can meet the load planning requirements; on this basis, the present invention selects four cases to analyze the energy storage configuration power and planning cost, and compares the planning performance based on SOP and photovoltaics, the planning performance based on load commutation, and the planning performance considering alternative energy storage designed in this paper. The experimental results are shown in Table 2 below.

[0045] Table 2 Experimental results

[0046]

[0047] Table 2 shows that in Case 4, a higher minf (>600,000 yuan) can be obtained using planning based on SOP and PV, indicating lower economic benefits; planning based on load switching also has a higher minf (≤600,000 yuan), but other costs are still high. However, the method of the present invention takes alternative energy storage into consideration and achieves the lowest minf in Case 2, with better storage effect and higher planning benefits, which is consistent with the target.

[0048] To sum up, in recent years, with the large-scale access of distributed energy sources such as wind power and photovoltaics, the complexity and uncertainty of distribution networks have gradually become prominent, and the reliability of power supply of distribution networks cannot be met. Therefore, the present invention designs a multi-scenario distribution network intelligent planning method considering alternative energy storage, which suppresses the fluctuation of renewable energy and improves the regulation capability of the power grid from the aspects of distribution network power, planning model, and charging and discharging power coordination.

[0049] The present invention is a multi-scenario intelligent planning method for distribution networks considering alternative energy storage. In use, traditional distribution network intelligent planning methods focus on optimizing the distribution network structure without considering load demand, which increases the cost of distribution network planning. Therefore, the present invention designs a multi-scenario distribution network intelligent planning method considering alternative energy storage, extracts the power characteristics of alternative energy storage, and accurately describes the behavior of alternative energy storage systems under multi-scenario distribution network operating conditions; constructs a multi-scenario distribution network intelligent planning model based on alternative energy storage, which coordinates and optimizes the charging and discharging power of the energy storage system, the selection of the charging and discharging time of the energy storage system, and the distribution of the charging and discharging power; simulation experiments verify that the method has better planning performance and can be applied to real life; the present invention has the advantages of extracting power characteristics, accurately describing behavior, constructing intelligent planning models, and achieving coordination and optimization.

Claims

1. A multi-scenario intelligent planning method for distribution networks considering alternative energy storage, characterized by: The method comprises the following steps: Step 1: Extract the output characteristics of alternative energy storage connected to the distribution network: By analyzing the output characteristics of energy storage capacity, charge and discharge rate, efficiency, and life, accurately describe the behavior of alternative energy storage systems under various working conditions, and provide data support for subsequent intelligent planning models; Step 2: Build a multi-scenario distribution network intelligent planning model based on alternative energy storage: optimize the configuration and scheduling of energy storage systems; Step 3: Example analysis.

2. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 1, characterized in that: The step 1 of extracting the output characteristics of the alternative energy storage connected to the distribution network is specifically: by analyzing the output characteristics such as energy storage capacity, charge and discharge rate, efficiency, and life, the behavior of the alternative energy storage system under various working conditions is accurately described to obtain the output characteristics of the distribution network. The calculation formula is as follows: t k =T·P i (2), where P i It is the output of the alternative energy storage connected to the distribution network; N k is the energy storage power; N y is the power range; t k is the time period corresponding to the energy storage output interval; T is the time scale of power generation in the distribution network.

3. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 1, characterized in that: The construction of a multi-scenario distribution network intelligent planning model based on alternative energy storage in step 2 is specifically as follows: the distribution network intelligent planning takes the economic efficiency of distribution network operation as the goal, optimizes the configuration and scheduling of the energy storage system according to the multi-scenario grid demand and energy storage characteristics; considering alternative energy storage, the construction expression of the distribution network intelligent planning model is as follows: minf = f l +f e +f w +f d +f b (3), where minf is the expression of the intelligent planning model of distribution network; f l is the distribution line investment cost target; e is the energy storage system investment cost target; w is the power generation cost target of the generator set; d Cost targets for demand response compensation; b The cost of purchasing electricity off-grid for the transmission grid.

4. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 3, characterized in that: The distribution line investment cost target f l for: Where i is the discount rate; n is the operating life of the distribution network; c ij is the unit cost of branches i and j; It is the project of branches i and j to be planned in article p.

5. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 3, characterized in that: The energy storage system investment cost target f e for: In the formula, Ω p is the set of branches to be planned; r p is the alternative energy storage operation coefficient; r m is the alternative energy storage maintenance factor; r s is the alternative energy storage cost conversion factor; r d is the depreciation factor; Ω e is the energy storage configuration node set; c p is the unit power of energy storage; c e is the unit capacity cost of energy storage; is the rated power of alternative energy storage r configured at node k on a typical day s; is the rated capacity of alternative energy storage r configured at node k on a typical day s.

6. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 3, characterized in that: The target power generation cost of the generator set is f w for: In the formula, Ω w is the node set of the generators in the distribution network; Γ is the typical day set; T is the distribution network planning period; d s is a typical day planning scenario; c w is the unit cost of curtailed electricity; is the maximum output of the generator set j in period t on a typical day s; p w,t,s,j is the actual output of the generator set at period t on a typical day s.

7. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 3, characterized in that: The demand response compensation cost target f d for: In the formula, is the compensation price for interruptible load and bonus load in period t; is the set of contract nodes for the two loads; is the planned load of node m during periods s and t; is the interruption load of node n during the period s and t.

8. The multi-scenario intelligent planning method for distribution network considering alternative energy storage as claimed in claim 3, characterized in that: The off-grid electricity purchase cost of the transmission network is f b is: f b =c b ΔP b (8), where ΔP b is the off-grid power consumption; c b is the ΔP purchased from the transmission grid b Unit price.

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