Power distribution network planning and optical storage locating and sizing method and system based on optimal benefit

Abstract

The invention discloses a power distribution network planning and optical storage locating and sizing method and system based on benefit optimization, and belongs to the technical field of electric power economics, and the method comprises the steps: collecting a grid structure of an existing line, constructing a power distribution network line model, and carrying out the topological description of a power distribution network structure; obtaining the future growth prediction quantity of the existing load nodes of the power distribution network, the newly added load nodes in the future and the existing photovoltaic data; constructing a power distribution network frame planning model, taking power distribution network investment minimization and operation efficiency maximization as an overall planning target, considering power distribution network control constraint conditions, and performing frame expansion planning on the power distribution network line model; and on the basis of the power distribution network line model after expansion planning, according to the network parameters of the expanded power distribution network frame and the data obtained in the above steps, constructing an optical storage locating and sizing model to carry out optimal configuration on optical storage, and by taking the operation efficiency maximization of the optical storage as the target and considering the constraint condition of optical storage locating and sizing, carrying out optimal configuration on the optical storage. And optimizing the photovoltaic light abandoning amount and the voltage stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power economy, and in particular relates to a method and system for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency. Background Art

[0002] With the increasing attention paid to environmental issues and the development of new energy generation technologies, more and more distributed power sources are connected to the distribution network, and the traditional single-power radial power distribution network has gradually evolved into a complex distribution network. The development of intelligent distribution network technology is conducive to promoting the development of clean and renewable energy and improving the quality of power supply services. The output and load of distributed power sources have their uncertainty and randomness, which has a great impact on the power supply reliability of the distribution network. Therefore, how to achieve higher distribution network reconstruction economy and reliability while considering these uncertainties has become a problem that needs to be studied. As an important measure for optimizing the operation of the power system, distribution network reconstruction is often used to improve the reliability and economy of the power system.

[0003] The existing photovoltaic storage access method is mainly based on the existing load and grid structure. However, this method has certain limitations in actual application: photovoltaic storage access has a certain time period, and planning based on the existing grid structure is difficult to meet the development of the future distribution network. In order to be able to access photovoltaic storage to the distribution network more efficiently, it is necessary to expand the grid before optimizing the configuration of photovoltaic storage. Summary of the invention

[0004] The purpose of the present invention is to provide a distribution network expansion planning and photovoltaic storage site selection and capacity determination method based on optimal investment benefits, aiming to solve the problems of distribution network load growth and grid optimization after photovoltaic access, as well as the economy and safety of photovoltaic storage access after the expansion of the distribution network.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of the present invention provides a method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency, comprising the following steps:

[0007] Collect the grid structure of existing lines, which includes network topology and line parameters, build a distribution network line model based on the existing parameters, and describe the distribution network structure topologically;

[0008] Obtain the future growth forecast of existing load nodes in the distribution network, future newly added load nodes and existing photovoltaic data;

[0009] Construct a distribution network framework planning model, with the overall planning goals of minimizing distribution network investment and maximizing operation efficiency, taking distribution network control constraints into consideration, and based on the data obtained in the above steps, perform network expansion planning for the distribution network line model;

[0010] Based on the distribution network line model after the expansion plan, according to the network parameters of the expanded distribution network and the data obtained in the above steps, a photovoltaic storage site selection and sizing model is constructed to optimize the configuration of photovoltaic storage. With the goal of maximizing the operating efficiency of photovoltaic storage, the constraints of photovoltaic storage site selection and sizing are considered to optimize the amount of photovoltaic abandoned light and voltage stability.

[0011] Optionally, the overall planning objectives of the distribution network include the network loss cost of the distribution network, the investment cost of new lines and the cost of purchasing electricity from the superior power grid:

[0012] minF1=F loss +F L +F EN

[0013]

[0014] In the formula, F1 is the annual cost of line planning, r is the discount rate, and F loss is the distribution network loss cost, C e is the grid electricity price, τ max is the maximum load loss hours of the power grid, ΔP i is the line power loss under maximum load condition; F L is the investment cost of the new line, m is the planning period of line operation, in years; F EN is the cost of purchasing electricity from the upper grid calculated by the maximum load hour utilization; T max is the maximum load utilization hours of the power grid, P w is the total active load of the distribution network; C li is the construction cost of the i-th line; L i is the total length of the ith line; n i Lines added for planning.

[0015] Optionally, the distribution network control constraints include system operation cost constraints, new line investment cost constraints, power flow balance constraints, branch current constraints, node voltage constraints, node photovoltaic output constraints and network topology constraints.

[0016] Optionally, the system operation cost constraint and the new line investment cost constraint are:

[0017] F loss +F EN ≤F system,total

[0018] F L ≤F L,total

[0019] In the formula, F system,totalis the upper limit of system operation cost, F L,total It is the upper limit of investment cost for new lines.

[0020] Optionally, the power flow balance constraint is:

[0021]

[0022] Where i and j are node numbers; G ij and B ij are the conductance and susceptance of the line between nodes i and j respectively; P i and Q i are the active power and reactive power of the node respectively; P PV,i is the active power of the distributed generation at node i; U i and U j is the voltage at nodes i and j; θ ij is the voltage phase difference between nodes i and j.

[0023] Optionally, the branch current constraint is:

[0024]

[0025] In the formula, I ij is the branch current between nodes i and j; and are the minimum and maximum values ​​of the current allowed to pass through branch ij respectively;

[0026] The node voltage constraint is:

[0027]

[0028] Where U i is the node voltage of node i; and are the minimum and maximum values ​​of the voltage allowed at node i, respectively;

[0029] The photovoltaic output constraint of the node is:

[0030]

[0031] In the formula, and are the minimum and maximum values ​​of the distributed photovoltaic output connected to node i, respectively.

[0032] Optionally, the network topology constraint is:

[0033] g k ∈G

[0034] In the formula, g kis the planned network structure; G is the planning set of feasible radial network structures.

[0035] Optionally, constructing a photovoltaic storage site selection and capacity determination model to optimize the configuration of photovoltaic storage includes:

[0036] In the site selection and capacity determination of photovoltaic storage, the goal is to minimize the amount of photovoltaic abandoned light, and the voltage stability is used to measure the voltage quality, and the site selection and capacity determination of the system are iteratively carried out:

[0037]

[0038] Where, F2 is the amount of photovoltaic abandoned light; is the photovoltaic forecast value during period t, is the actual output power value; F3 is the voltage stability, is the average voltage during the dispatch period, U j,t is the line node voltage.

[0039] Optionally, the constraints for the photovoltaic storage site selection and capacity determination include photovoltaic storage investment and system operation cost constraints, access number restrictions for selected nodes, photovoltaic storage capacity constraints, energy storage power constraints, upper and lower limit constraints on energy storage charge state and energy storage capacity constraints, as well as power flow balance constraints, branch current constraints, node voltage constraints and node photovoltaic output constraints in distribution network control constraints;

[0040] The constraints of PV storage investment and system operation cost are:

[0041] F pv,ess +F om ≤F inv,total

[0042] F buy +F loss ≤F system,total

[0043]

[0044] In the formula, F inv,total The upper limit of total cost of PV storage configuration and operation and maintenance; F system,total is the upper limit of system operation cost; F pv,ess is the PV storage investment cost, c inv,PV and c inv,ESS are the unit capacity investment costs of PV and ESS respectively, i,PV and E i,ESS are the installed capacity of PV and ESS of the i-th node, N PV and N ESS are the number of PV and ESS to be installed, r is the discount rate; F om is the optical storage operation and maintenance cost, c om,PV and c om,ESSare the unit capacity operation and maintenance costs of PV and ESS respectively; F buy is the cost of purchasing electricity from the upper power grid calculated by using the typical day*365 days method, C e is the grid electricity price, P t,buy is the interaction power between the distribution network and the upper power grid during period t; F loss is the distribution network loss cost, is the network loss in period t;

[0045] The number of nodes to be selected is limited to:

[0046] N PV ≤M PV

[0047] N ESS ≤M ESS

[0048] Where M pv is the maximum number of PV nodes connected, M ESS is the maximum number of ESS accesses;

[0049] The optical storage capacity constraint is:

[0050]

[0051]

[0052] In the formula, and are the upper and lower limits of the PV and ESS capacity that the distribution network company can install at node i, respectively. i,PV Install capacity for the PV of the i-th node;

[0053] The energy storage power, upper and lower limits of energy storage charge state, and energy storage capacity constraints are:

[0054] -P ESS ≤P ESS (t)≤P ESS

[0055] SOC min ≤SOC(t)≤SOC max

[0056] E ESS (t) = E ESS (t-1)+P ESS (t)·η C

[0057] E ESS (t) = E ESS (t-1)-P ESS (t) / η Dis

[0058] Where P ESS is the energy storage rated power; SOC min With SOC max are the lower and upper limits of the energy storage SOC, respectively. The SOC value range is set to 0.1-0.9, and the SOC value is 0.5 at the beginning and end of the operation cycle; η C With η Dis are the charging and discharging efficiencies of the i-th energy storage unit respectively.

[0059] The second aspect of the present invention provides a distribution network planning and photovoltaic storage site selection and capacity determination system based on optimal efficiency. Based on the distribution network planning and photovoltaic storage site selection and capacity determination method based on optimal efficiency described in the first aspect of the present invention, the system includes:

[0060] The distribution network line model construction module, the distribution network load and photovoltaic data collection module, the distribution network grid planning module and the photovoltaic storage site selection and sizing optimization module, the distribution network line model construction module is used to construct the distribution network line model according to the grid structure of the existing line; the distribution network load and photovoltaic data collection module is used to obtain the future growth forecast of the existing load nodes in the distribution network, the future newly added load nodes and the existing photovoltaic data; the distribution network grid planning module includes a distribution network grid planning model, which is used to carry out grid expansion planning for the distribution network line model; the photovoltaic storage site selection and sizing optimization module includes a photovoltaic storage site selection and sizing model, which is used to optimize the configuration of photovoltaic storage, with the goal of maximizing the operating efficiency of photovoltaic storage, considering the constraints of photovoltaic storage site selection and sizing, and optimizing the photovoltaic abandoned light amount and voltage stability.

[0061] Compared with the prior art, the beneficial effects of the present invention include at least the following: Based on the background of load growth and access to distributed power sources, the present invention provides a distribution network expansion planning and photovoltaic storage site selection and sizing method based on optimal investment benefits. The grid planning scheme proposed in the first stage of this method can effectively adapt to the high proportion of new load nodes and distributed power sources in the distribution network. With limited investment, it selects a set of lines that minimizes the investment cost and system operating cost to meet the operation of the system, reduce the line loss of the distribution network, and improve the economy and stability of the line operation. The photovoltaic storage site selection and sizing scheme proposed in the second stage can effectively reduce the amount of abandoned light in the system under various investment constraints, and select the optimal access node and configuration capacity in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a two-stage hierarchical planning architecture provided by an embodiment of the present invention;

[0063] Figure 2 It is the initial structure of the grid provided by the embodiment of the present invention;

[0064] Figure 3is a random planning grid planning result provided by an embodiment of the present invention;

[0065] Figure 4 is a grid planning result based on an objective function provided by an embodiment of the present invention;

[0066] Figure 5 This is the voltage distribution before and after the photovoltaic storage is connected under the second solution provided by the embodiment of the present invention;

[0067] Figure 6 It is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0069] In Example 1, the present invention provides a method for planning a distribution network and selecting a location and determining the capacity of a photovoltaic storage system based on optimal benefits. It is worth noting that in this embodiment, a medium-voltage distribution network is used as an implementation object. Figure 6 As shown, the distribution network expansion planning and photovoltaic storage site selection and capacity determination method specifically includes the following steps:

[0070] Step 1: Collect the grid structure of existing lines. The grid structure includes network topology and line parameters, and build a distribution network line model based on the existing parameters.

[0071] In a further preferred but non-limiting embodiment, in the step one, the construction of the distribution network line model also includes a topological description of the complex distribution network structure, retaining key parameters such as the line parameters and node information of the distribution network as the structure to describe the distribution network, thereby simplifying the distribution network model and facilitating the analysis and modeling of the distribution network.

[0072] Step 2: Obtain the future growth forecast of existing load nodes in the distribution network, future newly added load nodes and existing photovoltaic data;

[0073] In a further preferred but non-limiting embodiment, the future growth prediction parameters of the load node include load size and load nature, and the photovoltaic data of the load node include photovoltaic installed capacity and output power.

[0074] Step 3: Construct a distribution network grid planning model, taking line construction cost, network loss cost, and power purchase cost as targets, and considering the operation constraints of the distribution network. Based on the data obtained in step 2, the distribution network line model constructed in step 1 is planned for grid expansion to minimize investment and maximize operation efficiency.

[0075] Specifically, the distribution network expansion planning model also includes:

[0076] On the basis of existing loads and distributed photovoltaics, taking into account future development, integrating future forecast loads and photovoltaic data as well as future new node loads, the dual-power distribution network adopts the rule of "closed-loop planning, open-loop operation", and effectively plans the lines according to the extended planning model that considers various investment costs and system operation costs, so as to achieve safe operation of the lines and maximize the investment returns of the lines under the condition of minimizing investment costs. In addition, when planning the lines, this method uses the method of basic ring vectors in graph theory, which not only maintains the open-loop and radiation state of the distribution network, but also greatly saves search space and time.

[0077] In a further preferred but non-limiting embodiment, in step 3, the overall planning target of the medium voltage distribution network is divided into the distribution network loss cost, the new line investment cost and the cost of purchasing electricity from the superior power grid, that is,

[0078] minF1=F loss +F L +F EN (1)

[0079]

[0080] Where: F1 is the annual cost of line planning, F loss Refers to the distribution network loss cost, C e represents the grid electricity price, τ max Indicates the maximum load loss hours of the power grid, ΔP i Indicates the line power loss when operating at maximum load; F L refers to the investment cost of the new line, m refers to the planning period of line operation, the unit is usually years; F EN is the cost of purchasing electricity from the upper grid, calculated by the maximum load hour utilization method, T max Indicates the maximum load utilization hours of the power grid, P w Represents the total active load of the distribution network; C li refers to the construction cost of the i-th line; L i Refers to the total length of the ith line; n i Refers to the planned additional lines.

[0081] Specifically, the control constraints of the medium-voltage distribution network mainly include system operation cost constraints, new line investment cost constraints, power flow balance constraints, branch current constraints, node voltage constraints, node photovoltaic output constraints and network topology constraints.

[0082] The system operation cost constraints and line construction cost constraints are:

[0083] F loss +F EN ≤F system,total (3)

[0084] F L ≤F L,total (4)

[0085] Where: F system,total is the upper limit of system operation cost; F L,total It is the upper limit of investment cost for new lines.

[0086] The power flow balance constraint is:

[0087] P i +P PV,i =U i ∑ j∈i U j (G ij cosθ ij +B ij sinθ ij ) (5)

[0088] Q i =U i ∑ j∈i U j (G ij sinθ ij +B ij cosθ ij ) (6)

[0089] Where: i and j represent node numbers; G ij and B ij Respectively represent the conductance and susceptance of the line between nodes i and j; P i and Q i Respectively represent the active power and reactive power of the node; P PV,i represents the active power of distributed generation at node i; U i and U j represents the node voltage; θ ij is the voltage phase difference between nodes i and j.

[0090] The branch current constraint is:

[0091]

[0092] Where: I ij is the branch current between nodes i and j; and are the minimum and maximum values ​​of the current allowed to pass through branch ij respectively.

[0093] The node voltage constraints are:

[0094]

[0095] Where: U i is the node voltage of node i; and are the minimum and maximum allowed voltages at node i, respectively.

[0096] The node photovoltaic output constraint is:

[0097]

[0098] Where: and are the minimum and maximum values ​​of the distributed photovoltaic output connected to node i, respectively.

[0099] The topology before and after planning remains radial, and the network topology constraints are:

[0100] g k ∈G(10)

[0101] Where: g k is the planned network structure; G is the planning set of all feasible radial network structures.

[0102] Step 4: Based on the distribution network line model after the expansion plan in step 3, according to the network parameters of the expanded distribution network and the existing load and photovoltaic data, a photovoltaic storage site selection and capacity determination model is constructed to optimize the configuration of photovoltaic storage. Under the constraints of photovoltaic storage configuration, operation and maintenance costs and system operation costs, the amount of photovoltaic abandoned light and voltage stability are optimized to achieve efficient operation of photovoltaic storage.

[0103] Specifically, the solar-storage site selection and capacity determination model also includes:

[0104] A two-layer operation-planning joint optimization model is established for the site selection and capacity determination of photovoltaic storage. The configuration cost of photovoltaic storage is considered as a constraint at the planning layer, and the amount of photovoltaic power abandonment and voltage stability are used as objective functions at the operation layer. The photovoltaic storage operation and maintenance cost and system operation cost are also used as constraints to achieve dual guarantees for the economy and safety of the system by the photovoltaic storage access results.

[0105] Furthermore, when solving the photovoltaic storage site selection and sizing model, this model uses a genetic algorithm to solve it, and the cost function constructed for the model contains costs of different time scales. When solving, the fitness function is divided into two parts: a planning layer composed of annual costs and an operation layer composed of daily costs. In the planning layer, the installation costs of energy storage and photovoltaics are calculated by the equal annual value method; in the operation layer, the photovoltaic storage configuration model constructed by this method is solved by the cplex solver, and the operation is optimized with the minimum amount of abandoned light and voltage stability as the goal, and the optimal amount of abandoned light and voltage quality under this photovoltaic storage configuration are obtained. Through this method, the annual calculation of the photovoltaic storage investment cost and the daily optimization of the distribution network operation are taken into account.

[0106] In a further preferred but non-limiting embodiment, the solar energy storage site selection and capacity determination model in step 4 specifically includes:

[0107] In the site selection and capacity determination of photovoltaic storage, the goal is to minimize the amount of photovoltaic abandoned light, and the voltage stability is used to measure the voltage quality, and the site selection and capacity determination of the system are iteratively carried out:

[0108]

[0109] Where: F2 represents the amount of photovoltaic abandoned light; represents the photovoltaic forecast value during period t, Indicates the actual output power value; F3 indicates the voltage stability. is the average voltage during the dispatch period, U j,t is the line node voltage.

[0110] The constraints for PV-storage site selection and capacity determination include: PV-storage investment and system operation cost constraints, restrictions on the number of nodes to be selected, PV-storage capacity constraints, energy storage power constraints, upper and lower limits of energy storage charge state constraints, and energy storage capacity constraints.

[0111] The constraints of PV storage investment and system operation cost are:

[0112] F pv,ess +F om ≤F inv,total (13)

[0113] F buy +F loss ≤F system,total (14)

[0114]

[0115] Where: F inv,total The upper limit of total cost of PV storage configuration and operation and maintenance; F system,total is the upper limit of system operation cost; F pv,ess represents the PV storage investment cost, cinv,PV and c inv,ESS represents the unit capacity investment cost of PV and ESS, P i,PV and E i,ESS represents the installed capacity of PV and ESS at the i-th node, N PV and N ESS represents the number of PV and ESS to be installed, r represents the discount rate; F om represents the optical storage operation and maintenance cost, c om,PV and c om,ESS represents the unit capacity operation and maintenance cost of PV and ESS; F buy It represents the cost of purchasing electricity from the upper power grid, calculated by the method of typical day*365 days, C e represents the grid electricity price, P t,buy It is represented by the interaction power between the distribution network and the upper power grid during period t; F loss represents the distribution network loss cost, Indicates the network loss in time period t.

[0116] Limitation on the number of nodes to be selected:

[0117] N PV ≤M PV (16)

[0118] N ESS ≤M ESS (17)

[0119] Where: M pv is the maximum number of PV nodes connected, M ESS The maximum number of access points to the ESS.

[0120] The optical storage capacity constraint is:

[0121]

[0122] Where: and are the upper and lower limits of the PV and ESS capacities that the distribution network company can install at node i, respectively.

[0123] The energy storage power, upper and lower limits of energy storage charge state, and energy storage capacity constraints are:

[0124] -P ESS ≤P ESS (t)≤P ESS (20)

[0125] SOC min (t)≤SOC max (twenty one)

[0126] E ESS (t) = E ESS(t-1)+P ESS (t)·η C (twenty two)

[0127] E ESS (t) = E ESS (t-1)-P ESS (t) / η Dis (twenty three)

[0128] Where: P ESS is the energy storage rated power; SOC min With SOC max They are the lower and upper limits of the energy storage SOC, respectively, to prevent overcharge and overdischarge of the energy storage. The SOC value range is set to 0.1-0.9, and to ensure the normal operation of the energy storage, the SOC value is 0.5 at the beginning and end of the operation cycle; η C With η Dis are the charging and discharging efficiencies of the i-th energy storage unit respectively.

[0129] In addition to the constraints added in equations (13)-(23), the PV-storage site selection and capacity determination model also includes distribution network operation constraints in equations (5)-(9).

[0130] In summary, the present invention, based on the comprehensive consideration of existing node loads and photovoltaic distribution, first plans to expand the distribution network so that the distribution network can adapt to the newly added loads and photovoltaic access. On the expanded distribution network grid structure, the access to photovoltaic storage is optimized. The simulation results show that this method can effectively plan the distribution network grid and photovoltaic storage access, comprehensively consider various types of investment benefits and the amount of abandoned light from new energy sources, and plan the distribution network lines and select the location and capacity of photovoltaic storage under limited investment costs and system operating costs, thereby reducing the operating costs of the distribution network system and achieving the maximum benefit of photovoltaic storage access to the distribution network.

[0131] The present invention provides a distribution network planning and photovoltaic storage site selection and capacity determination system based on optimal benefit in Example 2. Based on the distribution network planning and photovoltaic storage site selection and capacity determination method based on optimal benefit described in Example 1 of the present invention, the system includes:

[0132] The distribution network line model construction module, the distribution network load and photovoltaic data collection module, the distribution network grid planning module and the photovoltaic storage site selection and sizing optimization module, the distribution network line model construction module is used to construct the distribution network line model according to the grid structure of the existing line; the distribution network load and photovoltaic data collection module is used to obtain the future growth forecast of the existing load nodes in the distribution network, the future newly added load nodes and the existing photovoltaic data; the distribution network grid planning module includes a distribution network grid planning model, which is used to carry out grid expansion planning for the distribution network line model; the photovoltaic storage site selection and sizing optimization module includes a photovoltaic storage site selection and sizing model, which is used to optimize the configuration of photovoltaic storage, with the goal of maximizing the operating efficiency of photovoltaic storage, considering the constraints of photovoltaic storage site selection and sizing, and optimizing the photovoltaic abandoned light amount and voltage stability.

[0133] In order to more clearly introduce the outstanding essential features of the present invention and the significant progress it brings to the prior art, an application example of implementing the present invention is introduced below.

[0134] The following is a detailed description of an embodiment of the present invention in conjunction with the accompanying drawings, and the application example specifically includes:

[0135] like Figure 1 As shown in the figure, a two-stage hierarchical planning framework is presented, which includes distribution network expansion planning and photovoltaic storage site selection and capacity determination method based on optimal investment benefits.

[0136] The original topology of the distribution network is as follows Figure 2 As shown in the figure, the numbers are load points. The distribution network constructed in this paper has two power supply points, S1 and S2 are the upper grid substations, the solid line represents the original line, and the dotted line represents the possible new line set. The planning parameters are set as follows.

[0137] 1) The power purchase price of the distribution network from the upper power grid is set at 0.4 yuan / kWh, the network loss price is 0.35 yuan / kWh, the planning period of the transmission line is 10 years, the rated voltage is 10kV, and the construction cost of the unit length line is 100,000 yuan. According to relevant information, the maximum load utilization hours of the system are set to 2144h, and the maximum load loss hours of the system are set to 1700h.

[0138] 2) Genetic algorithm parameters: The population size is set to 50, the crossover rate is set to 0.8, and the mutation rate is set to 0.05. Distributed power sources are connected to nodes 3, 10, 17, 25, and 28 of the system.

[0139] Based on the initial grid and the parameters of the area to be planned, the genetic algorithm is used to adjust and optimize the initial distribution network grid structure. The line planning results considering load growth and distributed power access are shown in Table 1. The present invention shows two planning methods. The planning scheme only considers the radial topology constraint and obtains the planning scheme by randomly planning the line, such as Figure 3As shown; Planning Scheme 2 is optimized by building a model in this paper to obtain the planning scheme, as shown in Figure 4 shown.

[0140] Table 1 Distribution network simulation results under different planning schemes

[0141]

[0142] The simulation results show that after line expansion, the newly added nodes of the distribution network can be effectively connected to the distribution network and maintain radial network operation. In addition, after optimization of the planning model based on the cost function, compared with the random planning results, although the annual investment cost of the distribution network line construction has increased, the network loss has dropped significantly, and the annual network loss cost has dropped by 2.534 million yuan, and the cost of purchasing electricity from the distribution network to the superior power grid has dropped significantly.

[0143] The load data of the newly added node includes the node number and its complex power, specifically:

[0144] Node No. 24, (4200+2000j)kVA; Node No. 25, (600+250j)kVA; Node No. 26, (600+250j)kVA; Node No. 27, (600+200j)kVA; Node No. 28, (1200+700j)kVA; Node No. 29, (2000+600j)kVA.

[0145] After the first phase of grid planning is completed, the photovoltaic and energy storage sites are selected and the capacity is determined on the planned grid. The energy storage nodes are 2, 3, 8, 14, 17, 21, and 27, and the photovoltaic nodes are 2, 4, 12, 15, 22, 23, and 25. The unit capacity investment and operation and maintenance costs of energy storage are 1,300 yuan / kW and 0.05 yuan / kW, and the rated capacity of each energy storage group is 10kW (20kWh); the unit capacity investment and operation and maintenance costs of photovoltaics are 4,000 yuan / kW and 0.01 yuan / kW, and the single photovoltaic capacity is 50kW. The economic applicability of photovoltaics is 20 years, and the economic applicability of energy storage is 10 years.

[0146] In the model simulation, two optimal access points for photovoltaic and energy storage are selected for the improved distribution network. The results of the photovoltaic and energy storage optimization configuration of the distribution network system are shown in Table 2.

[0147] Table 2 Results of optimized configuration of photovoltaic and energy storage

[0148]

[0149] In addition, due to the access of photovoltaics and energy storage, the network flow is further optimized, and the network loss is further reduced compared with the grid structure of Planning Scheme 2, reduced to 844,000 yuan. The electricity bill for purchasing electricity from the superior power grid is reduced to 18.789 million yuan with the access of new energy.

[0150] Figure 5 The voltage per unit value of the distribution network system before and after the access of photovoltaic storage after the transformation of the planning scheme 2 is displayed. The simulation results show that after the access of photovoltaic storage, the voltage fluctuation of the distribution network line drops from 19.7 to 19.2, and the node voltage of node 19-node 23 increases, which improves the voltage quality and enhances the operation stability of the distribution network.

[0151] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency, characterized in that: The steps include: Collect the grid structure of existing lines, which includes network topology and line parameters, build a distribution network line model based on the existing parameters, and describe the distribution network structure topologically; Obtain the future growth forecast of existing load nodes in the distribution network, future newly added load nodes and existing photovoltaic data; Construct a distribution network framework planning model, with the overall planning goals of minimizing distribution network investment and maximizing operation efficiency, taking distribution network control constraints into consideration, and based on the data obtained in the above steps, perform network expansion planning for the distribution network line model; Based on the distribution network line model after the expansion plan, according to the network parameters of the expanded distribution network and the data obtained in the above steps, a photovoltaic storage site selection and sizing model is constructed to optimize the configuration of photovoltaic storage. With the goal of maximizing the operating efficiency of photovoltaic storage, the constraints of photovoltaic storage site selection and sizing are considered to optimize the amount of photovoltaic abandoned light and voltage stability.

2. A method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 1, characterized in that: The overall planning objectives of the distribution network include the network loss cost of the distribution network, the investment cost of new lines and the cost of purchasing electricity from the superior power grid: minF1=F loss +F L +F EN In the formula, F1 is the annual cost of line planning, r is the discount rate, and F loss is the distribution network loss cost, C e is the grid electricity price, τ max is the maximum load loss hours of the power grid, ΔP i is the line power loss when operating at maximum load; F L is the investment cost of the new line, m is the planning period of line operation, in years; F EN is the cost of purchasing electricity from the upper grid calculated by the maximum load hour utilization; T max is the maximum load utilization hours of the power grid, P w is the total active load of the distribution network; C li is the construction cost of the ith line; L i is the total length of the ith line; n i Lines added for planning.

3. The method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 1, characterized in that: The distribution network control constraints include system operation cost constraints, new line investment cost constraints, power flow balance constraints, branch current constraints, node voltage constraints, node photovoltaic output constraints and network topology constraints.

4. The method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 3 is characterized in that: The system operation cost constraint and new line investment cost constraint are: F loss +F EN ≤F system,total F L ≤F L,total In the formula, F system,total is the upper limit of system operation cost, F L,total It is the upper limit of investment cost for new lines.

5. The method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 3 is characterized in that: The power flow balance constraint is: Where i and j are node numbers; G ij and B ij are the conductance and susceptance of the line between nodes i and j respectively; P i and Q i are the active power and reactive power of the node respectively; P PV,i is the active power of the distributed generation at node i; U i and U j is the voltage at nodes i and j; θ ij is the voltage phase difference between nodes i and j.

6. A method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 3, characterized in that: The branch current constraint is: In the formula, I ij is the branch current between nodes i and j; and are the minimum and maximum values ​​of the current allowed to pass through branch ij respectively; The node voltage constraint is: Where U i is the node voltage of node i; and are the minimum and maximum values ​​of the voltage allowed at node i, respectively; The photovoltaic output constraint of the node is: In the formula, and are the minimum and maximum values ​​of the distributed photovoltaic output connected to node i, respectively.

7. The method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 3 is characterized by: The network topology constraints are: g k ∈G In the formula, g k is the planned network structure; G is the planning set of feasible radial network structures.

8. The method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 1 is characterized in that: The construction of a photovoltaic storage site selection and capacity determination model to optimize the configuration of photovoltaic storage includes: In the site selection and capacity determination of photovoltaic storage, the goal is to minimize the amount of photovoltaic abandoned light, and the voltage stability is used to measure the voltage quality, and the site selection and capacity determination of the system are iteratively carried out: Where, F2 is the amount of photovoltaic abandoned light; is the photovoltaic forecast value during period t, is the actual output power value; F3 is the voltage stability, is the average voltage during the dispatch period, U j,t is the line node voltage.

9. A method for distribution network planning and photovoltaic storage site selection and capacity determination based on optimal efficiency according to claim 1 or 3, characterized in that: The constraints of the photovoltaic storage site selection and capacity determination include photovoltaic storage investment and system operation cost constraints, access number restrictions for selected nodes, photovoltaic storage capacity constraints, energy storage power constraints, upper and lower limit constraints of energy storage charge state and energy storage capacity constraints, as well as power flow balance constraints, branch current constraints, node voltage constraints and node photovoltaic output constraints in distribution network control constraints; The PV storage investment and system operation cost constraints are: F pv,ess +F om ≤F inv,total F buy +F loss ≤F system,total In the formula, F inv,total The upper limit of total cost of PV storage configuration and operation and maintenance; F system,total The upper limit of system operation cost; F pv,ess is the PV storage investment cost, c inv,PV and c inv,ESS are the unit capacity investment costs of PV and ESS respectively, i,PV and E i,ESS are the installed capacity of PV and ESS of the i-th node, N PV and N ESS are the number of PV and ESS to be installed, r is the discount rate; F om is the optical storage operation and maintenance cost, c om,PV and c om,ESS They are the unit capacity operation and maintenance costs of PV and ESS respectively; F buy is the cost of purchasing electricity from the upper power grid calculated by using the typical day*365 days method, C e is the grid electricity price, P t,buy is the interaction power between the distribution network and the upper power grid during period t; F loss is the distribution network loss cost, is the network loss in period t; The number of nodes to be selected is limited to: N PV ≤M PV N ESS ≤M ESS Where M pv is the maximum number of PV nodes connected, M ESS is the maximum number of ESS accesses; The optical storage capacity constraint is: In the formula, and are the upper and lower limits of the PV and ESS capacity that the distribution network company can install at node i, respectively. i,PV Install capacity for the PV of the i-th node; The energy storage power, upper and lower limits of energy storage charge state, and energy storage capacity constraints are: -P ESS ≤P ESS (t)≤P ESS SOC min ≤SOC(t)≤SOC max AND ESS (t)=E ESS (t-1)+P ESS (t)·η C AND ESS (t)=E ESS (t-1)-P ESS (t) / η Dis Where P ESS is the energy storage rated power; SOC min With SOC max are the lower and upper limits of the energy storage SOC, respectively. The SOC value range is set to 0.1-0.9, and the SOC value is 0.5 at the beginning and end of the operation cycle; η C With η Dis are the charging and discharging efficiencies of the i-th energy storage unit respectively.

10. A distribution network planning and photovoltaic storage site selection and capacity determination system based on optimal efficiency, based on a distribution network planning and photovoltaic storage site selection and capacity determination method based on optimal efficiency as described in any one of claims 1 to 9, characterized in that: The system includes: The distribution network line model construction module, the distribution network load and photovoltaic data collection module, the distribution network grid planning module and the photovoltaic storage site selection and sizing optimization module, the distribution network line model construction module is used to construct the distribution network line model according to the grid structure of the existing line; the distribution network load and photovoltaic data collection module is used to obtain the future growth forecast of the existing load nodes in the distribution network, the future newly added load nodes and the existing photovoltaic data; the distribution network grid planning module includes a distribution network grid planning model, which is used to carry out grid expansion planning for the distribution network line model; the photovoltaic storage site selection and sizing optimization module includes a photovoltaic storage site selection and sizing model, which is used to optimize the configuration of photovoltaic storage, with the goal of maximizing the operating efficiency of photovoltaic storage, considering the constraints of photovoltaic storage site selection and sizing, and optimizing the photovoltaic abandoned light amount and voltage stability.

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