Distribution network source-storage collaborative planning method, system, equipment and medium considering driving factors

Abstract

The present invention discloses a distribution network source-storage collaborative planning method, system, device and medium taking into account driving factors, and the method includes: S1, establishing a mathematical model of technical driving factors, policy driving factors and market-oriented driving factors; S2, constructing a multi-stage double-layer optimization configuration model of photovoltaic storage based on the mathematical model established in step S1; the multi-stage double-layer optimization configuration model of photovoltaic storage includes an upper planning layer and a lower operating layer; the planning layer takes the lowest total cost within the planning period of the distribution network as the goal, and the decision variables of the planning layer are photovoltaic and energy storage site selection and capacity determination; the operating layer takes the lowest active power loss and node voltage offset of the system within the total planning stage as the goal, and the decision variables of the operating layer are the scheduling strategy of energy storage; S3, using a particle swarm algorithm to solve the multi-stage double-layer optimization configuration model of photovoltaic storage, considering constraints when solving, and obtaining the optimal solution as the optimal distribution network planning scheme. The present invention improves the utilization rate of new energy and the adaptability of planning schemes.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a distribution network source-storage collaborative planning method, system, equipment and medium taking driving factors into account. Background Art

[0002] Source and storage site selection and sizing is the process of optimizing the address and capacity of the power generation source and energy storage system in the power system. Through scientific site selection and precise sizing, the use of renewable energy can be maximized and the dependence on fossil fuels can be reduced. The addition of energy storage systems can help better integrate renewable energy, solve the problem of fluctuations in new energy power generation, and improve the grid's ability to absorb renewable energy. Scientific distribution network planning is an important part of the construction of low-carbon power systems. Driven by smart grid technology, the source-storage characteristics and role characteristics have undergone fundamental changes. At present, the research on the site selection and sizing of distribution network sources and storage has been very in-depth, but most of the existing research focuses on system design and optimization under static conditions, and does not consider the morphological evolution of the distribution network over time and its impact on the site selection and sizing of sources and storage. Therefore, taking into account the impact of morphological evolution in the distribution network planning stage can enhance the adaptability of planning, which is of great significance to the construction of new power systems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a distribution network source-storage collaborative planning method, system, device and medium taking into account driving factors. The impact of technological progress, policy guidance and market orientation on distribution network planning is quantified from an economic perspective, and the impact of the triple driving factors on the distribution network photovoltaic-storage planning configuration is studied.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A distribution network source-storage collaborative planning method taking driving factors into account comprises the following steps:

[0006] S1. Establish mathematical models of technology drivers, policy drivers and market-oriented drivers;

[0007] S2. Constructing a photovoltaic and energy storage multi-stage double-layer optimization configuration model based on the mathematical model established in step S1; the photovoltaic and energy storage multi-stage double-layer optimization configuration model includes an upper planning layer and a lower operation layer; the planning layer aims to minimize the total cost within the planning period of the distribution network, and the decision variables of the planning layer are photovoltaic and energy storage site selection and capacity determination; the operation layer aims to minimize the active power loss and node voltage offset of the system within the total planning stage, and the decision variables of the operation layer are the scheduling strategy of energy storage;

[0008] S3. The particle swarm algorithm is used to solve the multi-stage two-layer optimization configuration model of photovoltaic storage. The constraints are taken into account when solving the model, and the optimal solution is obtained as the optimal distribution network planning scheme.

[0009] To optimize the above technical solutions, the specific measures taken also include:

[0010] Furthermore, S1 is specifically as follows: S1.1. Establishing a mathematical model of technology driving factors; specifically as follows:

[0011] The literature analysis method is used to collect sub-technologies of key technologies of power systems. A key technology includes several sub-technologies. The number of patent applications and scientific papers published over the years for each sub-technology is collected, and the number of patent applications and scientific papers published over the years are fitted with a logarithmic curve to obtain the technology maturity curve, and then the technology maturity of each sub-technology is obtained.

[0012] The sub-technologies are coupled to obtain the technical maturity of key technologies;

[0013] S1.2. Establish a mathematical model of policy drivers; the details are as follows:

[0014] The calculation formula for establishing photovoltaic power generation subsidies is as follows:

[0015]

[0016] In the formula, For photovoltaic in stage T h Unit power generation subsidies; Subsidy for PV unit electricity generation in the initial year; For stage T h The decline coefficient of photovoltaic power generation subsidies; For stage T h +Δt is the decline coefficient of photovoltaic power generation subsidy; Υ is the decline coefficient at different stages; Δt represents the number of planning stages;

[0017] S1.3. Establish a mathematical model of market-oriented driving factors; the details are as follows:

[0018] The cost learning curve is used to predict the future trend of photovoltaic and energy storage investment costs. The power function form of the cost learning curve is shown as follows:

[0019]

[0020] LR=1-2 α

[0021] Where: is the unit investment cost of equipment in year y; is the initial annual unit investment cost of the equipment; W0 is the initial annual installed capacity of the equipment; W y is the cumulative installed capacity of the equipment in year y, α is the learning index obtained by fitting historical data, and LR is the learning rate;

[0022] S1.4. Couple the market-oriented driving factors with the technology-driven factors, introduce the technology maturity correction factor to correct the cost learning curve. The formula of the corrected cost learning curve is as follows:

[0023]

[0024] In the formula, represents the revised unit investment cost of equipment in year y, For stage T h The technology maturity correction factor is based on the integrated technology maturity coefficient. get.

[0025] Furthermore, in S2, the planning layer aims to minimize the total cost within the planning period of the distribution network, and the objective function of the planning layer is as follows:

[0026]

[0027] In the formula, C Total represents the total cost of the distribution network within the planning period, C invest represents the sum of the investment costs of photovoltaic and energy storage; C operation The sum of the operation and maintenance costs of photovoltaics and energy storage; Subsidy for photovoltaic power generation; C buy The cost of purchasing electricity from the upper grid;

[0028]

[0029] In the formula, represents the investment cost of energy storage; represents the investment cost of photovoltaics;

[0030]

[0031] In the formula, is the unit installation cost of photovoltaic power, is the unit installed cost of energy storage capacity, is the unit installed cost of energy storage power, and the three are obtained from the modified cost learning curve;

[0032] For stage T h PV installed capacity of intrinsic node i; and For stage T h The energy storage capacity and power installation capacity of the internal node i; R is the capital recovery coefficient; r is the discount rate; T h represents the planning stage; Ω pv Represents the set of photovoltaic installation points; ΩESS represents the set of installation points of energy storage; represents a collection of planning stages; is a 0-1 decision variable. , which means that at T h Install photovoltaic equipment on node i in phase i. , which means that at T h The photovoltaic equipment is not installed at the i-node in the stage; is a 0-1 decision variable, indicating stage T h Whether the internal node i is equipped with energy storage; Year is the service life of the equipment;

[0033] C operation is the system operation and maintenance cost, expressed as:

[0034]

[0035] In the formula, The operation and maintenance cost of photovoltaic power generation is is the operation and maintenance cost of energy storage. Among them, photovoltaic only considers the operation and maintenance cost, while energy storage considers the battery replacement cost while considering the operation and maintenance cost. The specific formula is as follows:

[0036]

[0037]

[0038] In the formula, For photovoltaic in stage T h Unit capacity maintenance cost; For energy storage in stage T h The unit capacity maintenance cost is: k is the energy storage replacement rate;

[0039]

[0040] In the formula, C t,buy is the electricity price at time t; P t,buy is the power purchased by the main grid at time t; For stage T h Subsidies for photovoltaic power generation in China; For stage T h The photovoltaic power at time t.

[0041] Further, in S2, the operation layer takes the minimum active power loss and node voltage offset of the system in the overall planning stage as the goal, and the overall objective function of the operation layer is composed of a first sub-objective function and a second sub-objective function;

[0042] The expressions of the first sub-objective function and the second sub-objective function are as follows:

[0043]

[0044] Where f1 is the first sub-objective function of the operation layer, f2 is the second sub-objective function of the operation layer, P CL is the active power loss of the system during the overall planning stage; For stage T h Active power at branch ij at time t; For stage T h Reactive power at branch ij at time t; For stage T h The voltage at node i at time t; r ij is the resistance at line ij; U offest is the node voltage offset of the system in the overall planning stage; N is the number of nodes; U k,min is the minimum rated voltage of node k; For the planning stage h The voltage amplitude of node k at time t;

[0045] The overall objective function F of the operation layer is:

[0046]

[0047] Where ω1 and ω2 are the weight coefficients of system active power loss and node voltage offset, ω1+ω2=1; f1 * and is the normalized sub-objective function value.

[0048] Furthermore, in S3, the constraints include: power flow constraints, power balance constraints, node voltage constraints, main grid purchased power constraints, photovoltaic installation capacity constraints, photovoltaic active output constraints, line capacity constraints, multi-stage coordination constraints and energy storage equipment constraints.

[0049] Furthermore, the power flow constraint condition is:

[0050]

[0051] Where: and The planning stage is T h The voltage of nodes i and j at time t; and The planning stage is T h Active and reactive power of internal node i; For stage T h The voltage phase difference between nodes i and j at time t, G ij and B ijare the conductance and susceptance of branch ij respectively; Ω Th,ij For stage T h The collection of system nodes within;

[0052] The power balance constraint condition is:

[0053]

[0054] Where P t,buy is the main grid power purchase at time t, P load,t is the power of the distribution network at time t; For photovoltaic in stage T h Output power at time t; For energy storage in stage T h The charge and discharge power at time t, represents the discharge of energy storage; Represents energy storage charging;

[0055] The node voltage constraint condition is:

[0056]

[0057] Where: U min It is the minimum voltage allowed during system operation; The planning stage is T h The voltage at node i at time t, U max It is the maximum voltage allowed during system operation;

[0058] The main grid power purchase constraint condition is:

[0059] P t,buy ≥0

[0060] The photovoltaic installation capacity constraint condition is:

[0061]

[0062] For stage T h PV installed capacity of intrinsic node i; is the maximum photovoltaic capacity that can be installed at node i;

[0063] The photovoltaic active output constraint condition is:

[0064]

[0065] In the formula, represents the photovoltaic output of node j; For stage T h PV installed capacity of intrinsic node j;

[0066] The line capacity constraint condition is:

[0067]

[0068] S ij,max is the capacity of line ij; For stage T h Active power at branch ij at time t; For stage T h Reactive power at branch ij at time t;

[0069] The multi-stage coordination constraints are:

[0070] When planning photovoltaic and energy storage, the capacity of energy storage and photovoltaic equipment is constrained. The formula is as follows:

[0071]

[0072] For stage T h -1 PV installed capacity of intrinsic node i; For stage T h PV installed capacity of intrinsic node i; For stage T h -1 the energy storage capacity of the internal node i, For stage T h The energy storage capacity of the intrinsic node i;

[0073] The energy storage device constraints are:

[0074] The constraints that need to be met during the operation of energy storage equipment include charging and discharging constraints, remaining capacity constraints, and battery capacity constraints. The formula is as follows:

[0075]

[0076] is the maximum installed capacity of the energy storage device at the i-node, and For stage T h The energy storage charging and discharging state at time t at internal node i is a 0-1 variable. When the energy storage device is in charging state, When , the energy storage device is in a non-charging state; When the energy storage device is in the discharge state, When , the energy storage device is in a non-discharging state; For the energy storage device stage T h Charging power at internal node i at time t; and Stage Th The upper and lower limits of the charging power at the internal node i; For the energy storage device stage T h The discharge power at internal node i at time t; and Stage T h The upper and lower limits of the discharge power at the internal node i; For stage T h The remaining power of the energy storage device at the internal node i at time t; and is the charging and discharging efficiency of the energy storage device at node i; For stage T h The rated capacity of the energy storage device at the internal node i; and are the minimum and maximum proportions of the remaining capacity of the energy storage device at node i to its rated maximum capacity; Indicates stage T h The energy storage operation state constraint at the internal node i at time t, Indicates stage T h The remaining power of the energy storage device at the internal node i at time t; Indicates stage T h The remaining capacity of the energy storage device at internal node i at time t-1, Δt represents the charging and discharging time.

[0077] Furthermore, the specific process of coupling the sub-technologies to obtain the technical maturity of the key technology is as follows:

[0078] The sub-technology maturity matrix T is obtained from the technology maturity of each sub-technology;

[0079]

[0080] Where, T n Indicates the technology maturity level of the nth sub-technology, where n is the number of sub-technology;

[0081] Determine the integration relationship between each sub-technology and obtain the integration maturity matrix I;

[0082]

[0083] In the formula, I km Indicates the integration relationship between the k-th sub-technology and the m-th sub-technology;

[0084] Normalize the integration maturity matrix I and the sub-technology maturity matrix T and then multiply them to get the integration maturity level matrix S;

[0085]

[0086] In the formula, S k represents the integration maturity level of the k-th sub-technology;

[0087] Standardize the integration maturity level matrix S,

[0088]

[0089] In the formula, represents the integration technology maturity coefficient, n k is the number of sub-technologies that have an integration relationship with the kth sub-technology, and n is the number of sub-technologies.

[0090] The present invention also proposes a distribution network source-storage collaborative planning system taking driving factors into account, comprising:

[0091] Driver modeling module, which is used to build mathematical models of technology drivers, policy drivers and market-oriented drivers;

[0092] A photovoltaic and energy storage multi-stage double-layer optimization configuration model establishment module is used to construct a photovoltaic and energy storage multi-stage double-layer optimization configuration model based on a mathematical model of technology-driven factors, policy-driven factors and market-oriented driving factors; the photovoltaic and energy storage multi-stage double-layer optimization configuration model includes an upper planning layer and a lower operation layer; the planning layer aims to minimize the total cost within the planning period of the distribution network, and the decision variables of the planning layer are photovoltaic and energy storage site selection and capacity determination; the operation layer aims to minimize the active power loss and node voltage offset of the system within the total planning stage, and the decision variables of the operation layer are the scheduling strategy of energy storage;

[0093] The solution module is used to solve the multi-stage two-layer optimization configuration model of photovoltaic storage using a particle swarm algorithm, taking constraints into account when solving the problem, and obtaining the optimal solution as the optimal distribution network planning solution.

[0094] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distribution network source-storage collaborative planning method taking driving factors into account as described above is implemented.

[0095] The present invention also proposes a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the distribution network source-storage collaborative planning method taking driving factors into account as described above.

[0096] The beneficial effects of the present invention are:

[0097] The present invention constructs a distribution network source-storage collaborative planning method that takes into account driving factors, and comprehensively considers three driving factors in the evolution of the power system morphology. That is, technological progress, policy guidance, and market orientation. Based on collaborative planning, the present invention establishes a photovoltaic-energy storage collaborative multi-stage two-layer planning model to achieve the overall planning of photovoltaic equipment installed capacity expansion and energy storage equipment investment and construction. In the planning process, the balance and economy between photovoltaics and energy storage are maintained, and the economic indicators and technical indicators during system operation are fully considered. Economic indicators include the investment cost, operating cost, photovoltaic power generation subsidy and the system's cost of purchasing electricity from the superior power grid. The technical indicator is the active power loss of the system. The particle swarm algorithm is used to obtain the optimal solution of the distribution network multi-stage planning model, and the optimal distribution network planning scheme is obtained. The utilization rate of new energy and the adaptability of the planning scheme are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 This is a schematic diagram of the evolution of the distribution network;

[0099] Figure 2 A multi-stage dual-layer optimization configuration model for optical storage. DETAILED DESCRIPTION

[0100] The present invention will now be described in further detail with reference to the accompanying drawings.

[0101] Technological progress, policy guidance, and market orientation have a significant impact on the planning of distribution networks. With the continuous development and updating of technology, the equipment and systems of distribution networks are also constantly updated and upgraded. New technologies also provide more options and possibilities for distribution network planning. Figure 1 As shown. Due to the uncertainty and diversity of technological progress, considering the impact of key technological progress in the power system during the distribution network planning stage has a strong supporting role in building a new power system. At the same time, policy guidance and market orientation also play a positive role in promoting the development of distributed energy and the integration of clean energy.

[0102] In one embodiment, the present invention provides a distribution network source-storage collaborative planning method taking into account driving factors, comprising the following steps:

[0103] S1. Establish mathematical models of technology drivers, policy drivers and market-oriented drivers; specifically:

[0104] S1.1. Establish a mathematical model of technology driving factors; the details are as follows:

[0105] A key technology of the power system usually has a large number of sub-technologies. For example, photovoltaic power generation technology involves sub-technologies such as photovoltaic module technology, photovoltaic array layout technology, photovoltaic inverter technology, photovoltaic power generation system management technology, etc. The sub-technologies of key technologies of the power system are collected by literature analysis. A key technology includes several sub-technologies, which are then evaluated by an expert group, expanded and deleted to obtain the final sub-technologies. The number of patent applications and scientific papers published over the years for each sub-technology are collected, and the number of patent applications and scientific papers published over the years are fitted with a logarithmic curve to obtain a technology maturity curve, and then the technology maturity of each sub-technology is obtained.

[0106] The sub-technologies are coupled to obtain the technical maturity of the key technologies. The specific process is as follows:

[0107] The sub-technology maturity matrix T is obtained from the technology maturity of each sub-technology;

[0108]

[0109] Where, T n Indicates the technology maturity level of the nth sub-technology, where n is the number of sub-technology;

[0110] Determine the integration relationship between the sub-technologies, compare the patent applications of each sub-technique, take 0 if the two sub-technologies do not overlap, take 9 if the two sub-technologies completely overlap, and take the corresponding value according to the degree of overlap to obtain the integration maturity matrix I;

[0111]

[0112] In the formula, I km Indicates the integration relationship between the k-th sub-technology and the m-th sub-technology;

[0113] Normalize the integration maturity matrix I and the sub-technology maturity matrix T and then multiply them to get the integration maturity level matrix S;

[0114]

[0115] In the formula, S k represents the integration maturity level of the k-th sub-technology;

[0116] Standardize the integration maturity level matrix S,

[0117]

[0118] In the formula, represents the integration technology maturity coefficient, n k is the number of sub-technologies that have an integration relationship with the kth sub-technology, n is the number of sub-technologies; S iThe value range is [0,1].

[0119] S1.2. Establish a mathematical model of policy drivers; policy drivers play an important role in promoting and supporting distribution network planning, which is reflected in policy orientations in energy transformation and carbon neutrality. It is also reflected in policy incentives in terms of fiscal subsidies, tax incentives, and service innovation. In this article, photovoltaic and energy storage subsidy coefficients are selected as policy drivers. Excessive subsidies will bring fiscal pressure and overcapacity, while too low subsidies will lead to insufficient momentum for industrial development. Research on reasonable subsidy retreat policies can promote the sustainable development of distribution networks while alleviating fiscal pressure. The details are as follows:

[0120] The calculation formula for establishing photovoltaic power generation subsidies is as follows:

[0121]

[0122]

[0123] In the formula, For photovoltaic in stage T h Unit power generation subsidies; Subsidy for PV unit electricity generation in the initial year; For stage T h The decline coefficient of photovoltaic power generation subsidies; For stage T h +Δt is the decline coefficient of photovoltaic power generation subsidy; Υ is the decline coefficient at different stages; Δt represents the number of planning stages;

[0124] S1.3. Establish a mathematical model of market-oriented driving factors. The typical manifestation of user market driving factors in distribution network planning is to promote the continuous growth of renewable energy equipment and energy storage equipment. With the continuous increase in user-side renewable energy demand, as well as energy system optimization and enterprise transformation and upgrading. The supply side of raw energy can continuously expand production capacity and increase the installed capacity of renewable energy and energy storage. While meeting user needs, low-carbon, efficient and sustainable economic development can be achieved. The details are as follows:

[0125] The cost learning curve is used to predict the future trend of photovoltaic and energy storage investment costs. The learning curve is an effective model for analyzing the economic efficiency of technology. It establishes a mathematical expression based on the empirical relationship between cumulative output and unit cost, and predicts future costs based on this. This model reflects the role of learning, time and experience accumulation in reducing production costs. At present, the learning curve method is widely used internationally to measure technological progress. The power function form of the cost learning curve is shown as follows:

[0126]

[0127] LR=1-2 α

[0128] Where: is the unit investment cost of equipment in year y; is the initial annual unit investment cost of the equipment; W0 is the initial annual installed capacity of the equipment; W y is the cumulative installed capacity of the equipment in the yth year, α is the learning index, which is obtained by fitting historical data and is a constant between (0,1); LR is the learning rate;

[0129] S1.4. Couple the market-oriented driving factors with the technology-driven factors, introduce the technology maturity correction factor to correct the cost learning curve. The formula of the corrected cost learning curve is as follows:

[0130]

[0131] In the formula, represents the revised unit investment cost of equipment in year y, For stage T h The technology maturity correction factor is based on the integrated technology maturity coefficient. The value range is [0,1].

[0132] S2, based on the mathematical model established in step S1, a multi-stage dual-layer optimization configuration model for photovoltaic storage is constructed; the block diagram of the multi-stage dual-layer optimization configuration model for photovoltaic storage is as follows: Figure 2 As shown in the figure, it includes the upper planning layer and the lower operation layer; the planning layer aims to minimize the total cost within the planning period of the distribution network, and the decision variables of the planning layer are the site selection and capacity determination of photovoltaic and energy storage; the operation layer aims to minimize the active power loss and node voltage offset of the system within the total planning stage, and the decision variables of the operation layer are the scheduling strategy of energy storage; S2 is specifically:

[0133] S2.1. Construct a planning layer with the goal of minimizing the total cost within the planning period of the distribution network. The objective function of the planning layer is as follows:

[0134]

[0135] In the formula, C invest represents the sum of the investment costs of photovoltaic and energy storage; C operation The sum of the operation and maintenance costs of photovoltaics and energy storage; Subsidy for photovoltaic power generation; C buy The cost of purchasing electricity from the upper grid;

[0136]

[0137] In the formula, represents the investment cost of energy storage; represents the investment cost of photovoltaics;

[0138]

[0139] In the formula, is the unit installation cost of photovoltaic power, is the unit installed cost of energy storage capacity, is the unit installed cost of energy storage power, and the three are obtained from the modified cost learning curve;

[0140] For stage T h PV installed capacity of intrinsic node i; and For stage T h The energy storage capacity and power installation capacity of the internal node i; R is the capital recovery coefficient; r is the discount rate; T h represents the planning stage; Ω pv Represents the set of photovoltaic installation points; Ω ESS represents the set of installation points of energy storage; represents a collection of planning stages; is a 0-1 decision variable. , which means that at T h Install photovoltaic equipment on node i in phase i. , which means that at T h The photovoltaic equipment is not installed at the i-node in the stage; is a 0-1 decision variable, indicating stage T h Whether the internal node i is equipped with energy storage; Year is the service life of the equipment;

[0141] C operation is the system operation and maintenance cost, expressed as:

[0142]

[0143] In the formula, The operation and maintenance cost of photovoltaic power generation is is the operation and maintenance cost of energy storage. Among them, photovoltaic only considers the operation and maintenance cost, while energy storage considers the battery replacement cost while considering the operation and maintenance cost. The specific formula is as follows:

[0144]

[0145] In the formula, For photovoltaic in stage T h Unit capacity maintenance cost; For energy storage in stage T h The unit capacity maintenance cost is: k is the energy storage replacement rate;

[0146]

[0147] In the formula, C t,buy is the electricity price at time t; P t,buy is the power purchased by the main grid at time t; For stage T h Subsidies for photovoltaic power generation in China; For stage T h PV power at time t;

[0148] S2.2, the operation layer aims to minimize the active power loss and node voltage offset of the system in the overall planning stage. The objective function is as follows:

[0149]

[0150] Where f1 is the first sub-objective function of the operation layer, f2 is the second sub-objective function of the operation layer, P CL is the active power loss of the system during the overall planning stage; For stage T h Active power at branch ij at time t; For stage T h Reactive power at branch ij at time t; For stage T h The voltage at node i at time t; r ij is the resistance at line ij; U offest is the node voltage offset of the system in the overall planning stage; N is the number of nodes; U k,min is the minimum rated voltage of node k; For the planning stage h The voltage amplitude of node k at time t;

[0151] The overall objective function of the operation layer is:

[0152]

[0153] Where ω1 and ω2 are the weight coefficients of system active power loss and node voltage offset, ω1+ω2=1; f1 * and is the normalized sub-objective function value.

[0154] S3. The particle swarm algorithm is used to solve the multi-stage two-layer optimization configuration model of photovoltaic storage. The constraints are taken into account when solving the model, and the optimal solution is obtained as the optimal distribution network planning scheme.

[0155] The constraints include:

[0156] (1) Power flow constraints

[0157]

[0158] Where: and The planning stage is T h The voltage of nodes i and j at time t; and The planning stage is T h Active and reactive power of internal node i; For stage T h The voltage phase angle difference between nodes i and j at time t. ij and B ij are the conductance and susceptance of branch ij respectively; Ω Th,ij For stage T h The collection of system nodes within;

[0159] (2) Power balance constraints

[0160]

[0161] Where P load,t is the power of the distribution network at time t; For photovoltaic in stage T h Output power at time t; For energy storage in stage T h The charge and discharge power at time t, represents the discharge of energy storage; Represents energy storage charging;

[0162] (3) Node voltage constraints

[0163]

[0164] Where: U min It is the minimum voltage allowed during system operation; U max It is the maximum voltage allowed during system operation;

[0165] (4) Main grid power purchase constraints

[0166] P t,buy ≥0

[0167] (5) PV installation capacity constraints

[0168]

[0169] is the maximum photovoltaic capacity that can be installed at node i;

[0170] (6) Photovoltaic active output constraints

[0171]

[0172] In the formula, represents the photovoltaic output of node j;

[0173] (7) Line capacity constraints

[0174]

[0175] S ij,max is the capacity of line ij;

[0176] (8) Multi-stage coordination constraints

[0177] When planning photovoltaic and energy storage, the capacity of energy storage and photovoltaic equipment is constrained to ensure the connection of each stage plan. The formula is as follows:

[0178]

[0179] (9) Energy storage equipment constraints

[0180] The constraints that need to be met during the operation of energy storage equipment include charging and discharging constraints, remaining capacity constraints, and battery capacity constraints. The formula is as follows:

[0181]

[0182] It is the maximum installed capacity of the energy storage device at the i-node. and For stage T h The energy storage charging and discharging state at time t at internal node i is a 0-1 variable. When the energy storage device is in charging state, When , the energy storage device is in a non-charging state; When the energy storage device is in the discharge state, When , the energy storage device is in a non-discharging state; For the energy storage device stage T h Charging power at internal node i at time t; and Stage T h The upper and lower limits of the charging power at the internal node i; For the energy storage device stage T h The discharge power at internal node i at time t; and Stage T h The upper and lower limits of the discharge power at the internal node i; For stage T h The remaining power of the energy storage device at the internal node i at time t; and is the charging and discharging efficiency of the energy storage device at node i; For stage T h The rated capacity of the energy storage device at the internal node i; and are the minimum and maximum proportions of the remaining capacity of the energy storage device at node i to its rated maximum capacity; Indicates stage T h The energy storage operation state constraint at the internal node i at time t, Indicates stage T h The remaining power of the energy storage device at the internal node i at time t; Indicates stage T h The remaining capacity of the energy storage device at internal node i at time t-1, Δt represents the charging and discharging time.

[0183] In another embodiment, the present invention proposes a distribution network source-storage collaborative planning system taking driving factors into account corresponding to the method of embodiment 1, comprising:

[0184] Driver modeling module, which is used to build mathematical models of technology drivers, policy drivers and market-oriented drivers;

[0185] A photovoltaic and energy storage multi-stage double-layer optimization configuration model establishment module is used to construct a photovoltaic and energy storage multi-stage double-layer optimization configuration model based on a mathematical model of technology-driven factors, policy-driven factors and market-oriented driving factors; the photovoltaic and energy storage multi-stage double-layer optimization configuration model includes an upper planning layer and a lower operation layer; the planning layer aims to minimize the total cost within the planning period of the distribution network, and the decision variables of the planning layer are photovoltaic and energy storage site selection and capacity determination; the operation layer aims to minimize the active power loss and node voltage offset of the system within the total planning stage, and the decision variables of the operation layer are the scheduling strategy of energy storage;

[0186] The solution module is used to solve the multi-stage two-layer optimization configuration model of photovoltaic storage using a particle swarm algorithm, taking constraints into account when solving the problem, and obtaining the optimal solution as the optimal distribution network planning solution.

[0187] The implementation methods of each module and the functions of the modules in the system are completely consistent with the steps of the method in Example 1, so they will not be repeated here.

[0188] In another embodiment, the present invention proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distribution network source-storage collaborative planning method taking into account driving factors as described in Embodiment 1 is implemented.

[0189] In another embodiment, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the distribution network source-storage collaborative planning method taking driving factors into account as described in the first embodiment.

[0190] The IEEE33 node system is used to simulate and analyze the method proposed in the present invention. Assume that the capacity of a single photovoltaic grid-connected power generation device is 100KW, and the upper limit of the capacity of a single node is 3MW; the capacity of a single energy storage device is 10kw (40kwh); in order to further study the planning method proposed in this article, it is assumed that the system does not contain photovoltaic and energy storage equipment in the initial stage, and the entire system is powered by the upper power grid. Assume that the number of planning stages is 3, 5 years is one planning stage, the stage growth rate of load demand is 5%, the electricity price of each year is the same, and the initial penetration rate of photovoltaic and energy storage capacity is 0. The specific simulation model parameters are shown in Table 1. The model is essentially a large-scale nonlinear programming model, which is optimized and solved using the Gurobi solver.

[0191] Since photovoltaic output has a strong seasonal characteristic, the planning stage within the planning period is divided into two typical seasons: winter and summer.

[0192] Table 1 Model simulation parameters

[0193]

[0194] The final statistics of the installed capacity, quantity, network loss cost of photovoltaic and energy storage, and the investment and operation and maintenance costs of each equipment in each planning stage are shown in Table 2.

[0195] Table 2 Planning results

[0196]

[0197] As can be seen from Table 2, after considering the influence of evolutionary driving factors, the proposed method of the present invention tends to increase the installed capacity of photovoltaics in the first planning stage, but with the continuous decline of policy driving factors and photovoltaic subsidy mechanisms, the installed capacity of photovoltaics gradually decreases in the second and third stages, but the total photovoltaic penetration rate continues to increase. The installed capacity of energy storage is roughly the same in the three planning stages, but with the continuous development of technology, the cost of energy storage is constantly decreasing.

[0198] Through the above analysis, it can be seen that the present invention takes into account the impact of the driving factors of power system evolution in distribution network planning and realizes the reasonable planning of photovoltaic and energy storage.

[0199] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0200] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0201] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A distribution network source-storage collaborative planning method taking into account driving factors, characterized in that: The following steps are involved: S1. Establish a mathematical model of technology-driven factors, policy-driven factors and market-oriented driving factors; S1 is specifically as follows: S1.

1. Establish a mathematical model of technology-driven factors; the details are as follows: The literature analysis method is used to collect sub-technologies of key technologies of power systems. A key technology includes several sub-technologies. The number of patent applications and scientific papers published over the years for each sub-technology is collected, and the number of patent applications and scientific papers published over the years are fitted with a logarithmic curve to obtain the technology maturity curve, and then the technology maturity of each sub-technology is obtained. The sub-technologies are coupled to obtain the technical maturity of key technologies; S1.

2. Establish a mathematical model of policy drivers; the details are as follows: The calculation formula for establishing photovoltaic power generation subsidies is as follows: In the formula, For photovoltaic in stage T h Unit power generation subsidies; Subsidy for PV unit electricity generation in the initial year; For stage T h The decline coefficient of photovoltaic power generation subsidies; For stage T h +Δt is the decline coefficient of photovoltaic power generation subsidy; Υ is the decline coefficient at different stages; Δt represents the number of planning stages; S1.

3. Establish a mathematical model of market-oriented driving factors; the details are as follows: The cost learning curve is used to predict the future trend of photovoltaic and energy storage investment costs. The power function form of the cost learning curve is shown as follows: LR=1-2 α Where: is the unit investment cost of equipment in year y; is the initial annual unit investment cost of the equipment; W0 is the initial annual installed capacity of the equipment; W y is the cumulative installed capacity of the equipment in year y, α is the learning index obtained by fitting historical data, and LR is the learning rate; S1.

4. Couple the market-oriented driving factors with the technology-driven factors, introduce the technology maturity correction factor to correct the cost learning curve. The formula of the corrected cost learning curve is as follows: In the formula, represents the revised unit investment cost of equipment in year y, For stage T h The technology maturity correction factor is based on the integrated technology maturity coefficient. get; S2. Constructing a photovoltaic and energy storage multi-stage double-layer optimization configuration model based on the mathematical model established in step S1; the photovoltaic and energy storage multi-stage double-layer optimization configuration model includes an upper planning layer and a lower operation layer; the planning layer aims to minimize the total cost within the planning period of the distribution network, and the decision variables of the planning layer are photovoltaic and energy storage site selection and capacity determination; the operation layer aims to minimize the active power loss and node voltage offset of the system within the total planning stage, and the decision variables of the operation layer are the scheduling strategy of energy storage; S3. The particle swarm algorithm is used to solve the multi-stage two-layer optimization configuration model of photovoltaic storage. The constraints are taken into account when solving the model, and the optimal solution is obtained as the optimal distribution network planning scheme.

2. The distribution network source-storage collaborative planning method taking driving factors into account according to claim 1, characterized in that: In S2, the planning layer aims to minimize the total cost within the planning period of the distribution network. The objective function of the planning layer is as follows: In the formula, C Total represents the total cost of the distribution network within the planning period, C invest represents the sum of the investment costs of photovoltaic and energy storage; C operation The sum of the operation and maintenance costs of photovoltaics and energy storage; is the power generation subsidy for photovoltaic power generation; Cbuy is the cost of purchasing electricity from the upper grid; In the formula, represents the investment cost of energy storage; represents the investment cost of photovoltaics; In the formula, is the unit installation cost of photovoltaic power, is the unit installed cost of energy storage capacity, is the unit installed cost of energy storage power, and the three are obtained from the modified cost learning curve; For stage T h PV installed capacity of intrinsic node i; and For stage T h The energy storage capacity and power installation capacity of the internal node i; R is the capital recovery coefficient; r is the discount rate; T h represents the planning stage; Ω pv Represents the set of photovoltaic installation points; Ω ESS represents the set of installation points of energy storage; represents a collection of planning stages; is a 0-1 decision variable. , which means that at T h Install photovoltaic equipment on node i in phase i. , which means that at T h The photovoltaic equipment is not installed at the i-node in the stage; is a 0-1 decision variable, indicating stage T h Whether the internal node i is equipped with energy storage; Year is the service life of the equipment; C operation is the system operation and maintenance cost, expressed as: In the formula, The operation and maintenance cost of photovoltaic power generation is is the operation and maintenance cost of energy storage. Among them, photovoltaic only considers the operation and maintenance cost, while energy storage considers the battery replacement cost while considering the operation and maintenance cost. The specific formula is as follows: In the formula, For photovoltaic in stage T h Maintenance cost per unit capacity; For energy storage in stage T h The unit capacity maintenance cost; k is the energy storage replacement rate; In the formula, C t,buy is the electricity price at time t; P t,buy is the power purchased by the main grid at time t; For stage T h Subsidies for photovoltaic power generation in China; For stage T h The photovoltaic power at time t.

3. The distribution network source-storage collaborative planning method taking driving factors into account according to claim 1, characterized in that: In S2, the operation layer aims to minimize the active power loss and node voltage offset of the system in the overall planning stage, and the overall objective function of the operation layer is composed of a first sub-objective function and a second sub-objective function; The expressions of the first sub-objective function and the second sub-objective function are as follows: Where f1 is the first sub-objective function of the operation layer, f2 is the second sub-objective function of the operation layer, P CL is the active power loss of the system during the overall planning stage; For stage T h Active power at branch ij at time t; For stage T h Reactive power at branch ij at time t; For stage T h The voltage at node i at time t; r ij is the resistance at line ij; U offest is the node voltage offset of the system in the overall planning stage; N is the number of nodes; U k,min is the minimum rated voltage of node k; For the planning stage h The voltage amplitude of node k at time t; The overall objective function F of the operation layer is: Where ω1 and ω2 are the weight coefficients of system active power loss and node voltage offset, ω1+ω2=1; and is the normalized sub-objective function value.

4. The distribution network source-storage collaborative planning method taking driving factors into account according to claim 1, characterized in that: In S3, the constraints include: power flow constraints, power balance constraints, node voltage constraints, main grid purchased power constraints, photovoltaic installation capacity constraints, photovoltaic active output constraints, line capacity constraints, multi-stage coordination constraints and energy storage equipment constraints.

5. The distribution network source-storage collaborative planning method taking driving factors into account according to claim 4, characterized in that: The power flow constraint condition is: Where: and The planning stage is T h The voltage of nodes i and j at time t; and The planning stage is T h Active and reactive power of internal node i; For stage T h The voltage phase difference between nodes i and j at time t, G ij and B ij are the conductance and susceptance of branch ij respectively; Ω Th,ij For stage T h The collection of system nodes within; The power balance constraint condition is: Where Pt,buy is the main grid purchase power at time t, P load,t is the power of the distribution network at time t; For photovoltaic in stage T h Output power at time t; For energy storage in stage T h The charge and discharge power at time t, represents the discharge of energy storage; Represents energy storage charging; The node voltage constraint condition is: Where: U min It is the minimum voltage allowed during system operation; The planning stage is T h The voltage at node i at time t, U max It is the maximum voltage allowed during system operation; The main grid power purchase constraint condition is: Pt,buy≥0 The photovoltaic installation capacity constraint condition is: For stage T h PV installed capacity of intrinsic node i; is the maximum photovoltaic capacity that can be installed at node i; The photovoltaic active output constraint condition is: In the formula, represents the photovoltaic output of node j; For stage T h PV installed capacity of intrinsic node j; The line capacity constraint condition is: Sij,max is the capacity of line ij; For stage T h Active power at branch ij at time t; For stage T h Reactive power at branch ij at time t; The multi-stage coordination constraints are: When planning photovoltaic and energy storage, the capacity of energy storage and photovoltaic equipment is constrained. The formula is as follows: For stage T h -1 PV installed capacity of intrinsic node i; For stage T h PV installed capacity of intrinsic node i; For stage T h -1 the energy storage capacity of the internal node i, For stage T h The energy storage capacity of the intrinsic node i; The energy storage device constraints are: The constraints that need to be met during the operation of energy storage equipment include charging and discharging constraints, remaining capacity constraints, and battery capacity constraints. The formula is as follows: is the maximum installed capacity of the energy storage device at the i-node, and For stage T h The energy storage charging and discharging state at time t at internal node i is a 0-1 variable. When the energy storage device is in charging state, When , the energy storage device is in a non-charging state; When the energy storage device is in the discharge state, When , the energy storage device is in a non-discharging state; For the energy storage device stage T h Charging power at internal node i at time t; and Stage T h The upper and lower limits of the charging power at the internal node i; For the energy storage device stage T h The discharge power at internal node i at time t; and Stage T h The upper and lower limits of the discharge power at the internal node i; For stage T h The remaining power of the energy storage device at the internal node i at time t; and is the charging and discharging efficiency of the energy storage device at node i; For stage T h The rated capacity of the energy storage device at the internal node i; and are the minimum and maximum proportions of the remaining capacity of the energy storage device at node i to its rated maximum capacity; Indicates stage T h The energy storage operation state constraint at the internal node i at time t, Indicates stage T h The remaining power of the energy storage device at the internal node i at time t; Indicates stage T h The remaining capacity of the energy storage device at internal node i at time t-1, Δt represents the charging and discharging time.

6. The distribution network source-storage collaborative planning method taking driving factors into account according to claim 1, characterized in that: The specific process of coupling the sub-technologies to obtain the technical maturity of the key technology is as follows: The sub-technology maturity matrix T is obtained from the technology maturity of each sub-technology; Where, T n Indicates the technology maturity level of the nth sub-technology, where n is the number of sub-technology; Determine the integration relationship between each sub-technology and obtain the integration maturity matrix I; In the formula, I km Indicates the integration relationship between the k-th sub-technology and the m-th sub-technology; Normalize the integration maturity matrix I and the sub-technology maturity matrix T and then multiply them to get the integration maturity level matrix S; In the formula, S k represents the integration maturity level of the k-th sub-technology; Standardize the integration maturity level matrix S, In the formula, S represents the integration technology maturity coefficient, n k is the number of sub-technologies that have an integration relationship with the kth sub-technology, and n is the number of sub-technologies.

7. A distribution network source-storage collaborative planning system taking into account driving factors, characterized in that: include: Driver modeling module, used to build mathematical models of technology drivers, policy drivers and market-oriented drivers; The mathematical model of establishing technology driving factors, policy driving factors and market-oriented driving factors specifically includes: S1.1, establishing a mathematical model of technology driving factors; specifically as follows: The literature analysis method is used to collect sub-technologies of key technologies of power systems. A key technology includes several sub-technologies. The number of patent applications and scientific papers published over the years for each sub-technology is collected, and the number of patent applications and scientific papers published over the years are fitted with a logarithmic curve to obtain the technology maturity curve, and then the technology maturity of each sub-technology is obtained. The sub-technologies are coupled to obtain the technical maturity of key technologies; S1.

2. Establish a mathematical model of policy drivers; the details are as follows: The calculation formula for establishing photovoltaic power generation subsidies is as follows: In the formula, For photovoltaic in stage T h Unit power generation subsidies; Subsidy for PV unit electricity generation in the initial year; For stage T h The decline coefficient of photovoltaic power generation subsidies; For stage T h +Δt is the decline coefficient of photovoltaic power generation subsidy; Υ is the decline coefficient at different stages; Δt represents the number of planning stages; S1.

3. Establish a mathematical model of market-oriented driving factors; the details are as follows: The cost learning curve is used to predict the future trend of photovoltaic and energy storage investment costs. The power function form of the cost learning curve is shown as follows: LR=1-2 α Where: is the unit investment cost of equipment in year y; is the initial annual unit investment cost of the equipment; W0 is the initial annual installed capacity of the equipment; W y is the cumulative installed capacity of the equipment in year y, α is the learning index obtained by fitting historical data, and LR is the learning rate; S1.

4. Couple the market-oriented driving factors with the technology-driven factors, introduce the technology maturity correction factor to correct the cost learning curve. The formula of the corrected cost learning curve is as follows: In the formula, represents the revised unit investment cost of equipment in year y, For stage T h The technical maturity correction factor is obtained based on the integrated technology maturity coefficient S; a photovoltaic storage multi-stage double-layer optimization configuration model establishment module is used to construct a photovoltaic storage multi-stage double-layer optimization configuration model based on a mathematical model of technology driving factors, policy driving factors and market-oriented driving factors; the photovoltaic storage multi-stage double-layer optimization configuration model includes an upper planning layer and a lower operation layer; the planning layer aims to minimize the total cost within the planning period of the distribution network, and the decision variables of the planning layer are photovoltaic and energy storage site selection and capacity determination; the operation layer aims to minimize the active power loss and node voltage offset of the system within the total planning stage, and the decision variables of the operation layer are the scheduling strategy of energy storage; The solution module is used to solve the multi-stage two-layer optimization configuration model of photovoltaic storage using a particle swarm algorithm, taking constraints into account when solving the problem, and obtaining the optimal solution as the optimal distribution network planning solution.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the distribution network source-storage collaborative planning method taking into account driving factors as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program enables the computer to execute the distribution network source-storage collaborative planning method taking into account driving factors as described in any one of claims 1-6.

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