CN118281917B - Distribution network source-storage collaborative planning method, system, equipment and medium considering driving factors - Google Patents
Distribution network source-storage collaborative planning method, system, equipment and medium considering driving factors Download PDFInfo
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Abstract
本发明公开计及驱动因素的配电网源‑储协同规划方法、系统、设备和介质，方法包括：S1、建立技术驱动因素、政策驱动因素和市场导向驱动因素的数学模型；S2、基于步骤S1建立的数学模型构建光储多阶段双层优化配置模型；所述光储多阶段双层优化配置模型包括上层的规划层和下层的运行层；规划层以配电网规划年限内总成本最低为目标，规划层的决策变量为光伏和储能选址定容；运行层以系统在总规划阶段内的有功损耗及节点电压偏移量最小为目标，运行层的决策变量为储能的调度策略；S3、采用粒子群算法求解光储多阶段双层优化配置模型，求解时考虑约束条件，得到最优解作为最优的配电网规划方案。本发明提升了新能源的利用率和规划方案的适应性。
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
The invention relates to the technical field of power distribution network planning, in particular to a power distribution network source-storage collaborative planning method, system, equipment and medium considering driving factors.
Background
The source storage location and volume determination is a process of optimizing and configuring addresses and capacities of a power generation source and an energy storage system in an electric power system, renewable energy sources can be utilized to the maximum extent through scientific location and accurate volume determination, dependence on fossil fuels is reduced, the energy storage system is added, the renewable energy sources can be integrated better, the problem of fluctuation of the power generation amount of new energy sources is solved, and the capacity of the power grid for absorbing the renewable energy sources is improved. Scientific power distribution network planning is an important ring of low-carbon power system construction, and source-storage characteristics and role characteristics are radically changed under the promotion of smart grid technology. At present, the research on the source storage site selection and volume fixation of the power distribution network is very in depth, but most of the existing researches are focused on system design and optimization under static conditions, and the form evolution of the power distribution network along with time and the influence of the form evolution on the source storage site selection and volume fixation are not considered. Therefore, the planning adaptability can be enhanced by considering the influence of morphological evolution in the planning stage of the power distribution network, and the method has great significance for the construction of a novel power system.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a power distribution network source-storage collaborative planning method, a system, equipment and a medium considering driving factors. And the influence of the triple driving factors on the light-storage planning configuration of the power distribution network is researched by quantifying the technical progress, policy guidance and market guiding on the power distribution network planning in the aspect of economy.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a power distribution network source-storage collaborative planning method considering driving factors comprises the following steps:
S1, establishing a mathematical model of a technical driving factor, a policy driving factor and a market-oriented driving factor;
s2, constructing a light storage multi-stage double-layer optimization configuration model based on the mathematical model established in the step S1, wherein the light storage multi-stage double-layer optimization configuration model comprises an upper planning layer and a lower operation layer, the planning layer takes the minimum total cost in the planning period of the power distribution network as a target, and decision variables of the planning layer are photovoltaic and energy storage site selection and volume determination;
and S3, solving the optical storage multi-stage double-layer optimization configuration model by adopting a particle swarm algorithm, and taking constraint conditions into consideration when solving to obtain an optimal solution as an optimal power distribution network planning scheme.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, the S1 is specifically S1.1, a mathematical model of a technical driving factor is established, and the method specifically comprises the following steps:
collecting the number of annual patent applications and the number of scientific papers published by each sub-technology, performing logarithmic curve fitting on the number of the annual patent applications and the number of the scientific papers published by each sub-technology to obtain a technology maturity curve, and further obtaining the technology maturity of each sub-technology;
Coupling the sub-technologies to obtain the technical maturity of the key technology;
S1.2, establishing a mathematical model of a policy driving factor, wherein the mathematical model is specifically as follows:
the calculation formula for building the photovoltaic power generation patch is as follows:
In the formula, The unit power generation patch of the photovoltaic in the stage T h is carried out; the photovoltaic unit power generation patch of the initial year is carried out; Backslide coefficients complementary to the photovoltaic power generation at stage T h; Backslide coefficients for supplementing photovoltaic power generation in a phase T h +delta T, wherein gamma is backslide coefficients in different phases, and delta T represents the number of planning phases;
s1.3, establishing a mathematical model of market-oriented driving factors, wherein the mathematical model is specifically as follows:
and predicting future change trend of photovoltaic and energy storage investment cost by adopting a cost learning curve, wherein the power function form of the cost learning curve is shown as the formula:
LR=1-2α
Wherein: investment cost for the equipment unit of the y-th year; The method comprises the steps of obtaining a unit investment cost of equipment in an initial year, obtaining an initial year installed capacity of the equipment by W 0, obtaining an accumulated installed capacity of the equipment in a y-th year by W y, obtaining a learning index by fitting historical data, and obtaining a learning rate by LR;
S1.4, coupling market-oriented driving factors and technical driving factors, and introducing technical maturity correction factors to correct a cost learning curve, wherein the formula of the corrected cost learning curve is as follows:
In the formula, Indicating the equipment investment cost of the modified y-th year,For the technology maturity correction factor in stage T h, the technology maturity coefficient is integratedObtained.
Further, in S2, the planning layer targets that the total cost of the power distribution network in the planning period is the lowest, and the objective function of the planning layer is as follows:
wherein, C Total represents the total cost in the planning period of the power distribution network, C invest represents the sum of the investment costs of the photovoltaic and the energy storage, and C operation is the sum of the operation and maintenance costs of the photovoltaic and the energy storage; c buy is the electricity purchasing cost of the system to the upper power grid;
In the formula, Representing the investment cost of the stored energy; Representing the investment cost of the photovoltaic;
In the formula, Is the unit installation cost of the photovoltaic,Is the unit installation cost of the energy storage capacity,The unit installation cost of the energy storage power is obtained by the corrected cost learning curve;
Photovoltaic installation capacity for node i within stage T h; And The method comprises the steps of setting energy storage capacity and power installation capacity of a node i in a stage T h, setting R as a fund recovery coefficient, setting R as a cash register, setting T h as a planning stage, setting omega pv as a photovoltaic installation point set, and setting omega ESS as an energy storage installation point set; representing a set of planning phases; Is a 0-1 decision variable when When the photovoltaic device is installed on the i node of the T h stage,When, representing that the photovoltaic equipment is not installed at the i node in the stage T h; a decision variable of 0-1 is used for indicating whether energy storage is installed at a node i in a stage T h;
C operation is the operation and maintenance cost of the system, expressed as:
In the formula, For the operational and maintenance costs of the photovoltaic,The photovoltaic only considers the operation and maintenance cost, and the energy storage also considers the replacement cost of the battery while considering the operation and maintenance cost, and the specific formula is as follows:
In the formula, Maintenance costs per unit capacity for photovoltaic at stage T h; the maintenance cost is the unit capacity of the energy storage in the stage T h;
Wherein, C t,buy is the electricity price at the time t, P t,buy is the main network purchase power at the time t; The photovoltaic power generation patch in the stage T h is carried out; Is the photovoltaic power at time T in phase T h.
Further, in S2, the operation layer targets that the active loss and the node voltage offset of the system in the overall planning stage are minimum, 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:
Wherein f 1 is a first sub-objective function of an operation layer, f 2 is a second sub-objective function of the operation layer, and P CL is the active loss of the system in a total planning stage; Active power at branch ij at time T in phase T h; Reactive power at branch ij at time T in phase T h; The voltage at a node i at T in a stage T h, r ij as a resistor at a line ij, U offest as a node voltage offset of the system in a total planning stage, N as the number of nodes, U k,min as the minimum rated voltage of a node k; the voltage amplitude of the k node at the moment T in the planning stage T h is set;
the overall objective function F of the run layer is:
Wherein ω 1 and ω 2 are the weighting coefficients of the system active loss and the node voltage offset, ω 1+ω2＝1;f1 * and ω Is the sub objective function value after normalization processing.
Further, in S3, the constraint conditions comprise a power flow constraint condition, a power balance constraint condition, a node voltage constraint condition, a main network purchase power constraint condition, a photovoltaic installation capacity constraint condition, a photovoltaic active output constraint condition, a line capacity constraint condition, a multi-stage coordination constraint condition and an energy storage device constraint condition.
Further, the power flow constraint condition is:
Wherein: And The voltages of nodes i and j at the moment T in the planning stage T h are respectively; And Active power and reactive power of a node i in the planning stage T h respectively; For the voltage phase angle difference between nodes i and j at time T in the phase T h, G ij and B ij are respectively the conductance and susceptance of the branch ij, and omega Th,ij is the set of system nodes in the phase T h;
the power balance constraint conditions are as follows:
Wherein P t,buy is the main network purchase power at the time t, and P load,t is the power of the power distribution network at the time t; The output power of the photovoltaic at the moment T in the phase T h is obtained; To store the charge and discharge power at time T in the phase T h, Represents energy storage discharge; Representing energy storage charging;
the node voltage constraint conditions are as follows:
Wherein U min is the minimum voltage allowed during system operation; The voltages of the node i at the moment T in the planning stage T h are respectively, and U max is the maximum voltage allowed during the operation of the system;
the constraint conditions of the main network purchase power are as follows:
Pt,buy≥0
the photovoltaic installation capacity constraint conditions are as follows:
Photovoltaic installation capacity for node i within stage T h; Is the maximum photovoltaic installed quantity installable at the node i;
the photovoltaic active power output constraint conditions are as follows:
In the formula, Representing the photovoltaic output of node j; Photovoltaic installation capacity for node j within stage T h;
The line capacity constraint conditions are:
S ij,max is the capacity of the line ij; Active power at branch ij at time T in phase T h; Reactive power at branch ij at time T in phase T h;
The multi-stage coordination constraint conditions are:
When planning the photovoltaic and energy storage, the energy storage and the photovoltaic equipment capacity are constrained, and the formula is expressed as follows:
photovoltaic installation capacity for node i within stage T h -1; Photovoltaic installation capacity for node i within stage T h; for the energy storage capacity of node i within phase T h -1, The energy storage capacity of the node i in the phase T h;
The constraint conditions of the energy storage equipment are as follows:
Constraint conditions to be met when the energy storage device operates include charge and discharge constraint, residual capacity constraint and storage battery capacity constraint, and the formula is expressed as follows:
For maximum installed capacity of the energy storage device at the inode, AndThe energy storage charging and discharging state at the point T of the node i in the stage T h is 0-1 variable, whenWhen the energy storage device is in a charging state,When the energy storage device is in a non-charging state; when the energy storage device is in a discharge state, When the energy storage device is in a non-discharge state; The charging power at the moment T at the node i in the energy storage equipment stage T h; And The upper limit and the lower limit of the charging power at the node i in the stage T h are respectively; The discharge power at the moment T at the node i in the energy storage equipment stage T h; And The upper limit and the lower limit of the discharge power at the node i in the stage T h are respectively; the residual electric quantity of the energy storage device in the moment T at the node i in the stage T h; And Charging and discharging efficiency of the energy storage device at node i; rated capacity of the energy storage device for node i in stage T h; And The minimum and maximum proportions of the residual capacity of the energy storage equipment at the node i to the rated maximum capacity of the energy storage equipment are respectively; represents the energy storage operating state constraint at time T at node i in phase T h, Representing the residual electric quantity of the energy storage device in the moment T at the node i in the stage T h; the residual electric quantity of the energy storage device in the time T-1 at the node i in the stage T h is represented, and the delta T represents the charge and discharge time.
Further, the specific process of coupling the sub-technologies to obtain the technical maturity of the key technology is as follows:
obtaining a sub-technology maturity matrix T from the technology maturity of each sub-technology;
Wherein T n represents the technical maturity level of the nth sub-technology, and n is the number of sub-technologies;
Judging the integration relation among all the sub-technologies to obtain an integrated maturity matrix I;
wherein, I km represents the integration relationship between the kth sub-technology and the mth sub-technology;
normalizing the integrated maturity matrix I and the sub-technology maturity matrix T, and multiplying to obtain an integrated maturity grade matrix S;
wherein S k represents the integrated maturity level of the kth sub-technology;
the integrated maturity rating matrix S is normalized,
In the formula,And (3) representing the maturity coefficient of the integrated technology, wherein n k is the number of the sub-technologies with the integrated relation with the kth sub-technology, and n is the number of the sub-technologies.
The invention also provides a power distribution network source-storage collaborative planning system considering driving factors, which comprises the following steps:
The driving factor modeling module is used for establishing mathematical models of technical driving factors, policy driving factors and market-oriented driving factors;
The optical storage multi-stage double-layer optimization configuration model building module is used for building an optical storage multi-stage double-layer optimization configuration model based on a mathematical model of a technical driving factor, a policy driving factor and a market guiding driving factor, wherein the optical storage multi-stage double-layer optimization configuration model comprises an upper planning layer and a lower running layer, the planning layer aims at the lowest total cost in the planning period of a power distribution network, decision variables of the planning layer are photovoltaic and energy storage site selection and volume determination, the running layer aims at the minimum active loss and node voltage offset of the system in the total planning period, and decision variables of the running layer are energy storage scheduling strategies;
And the solving module is used for adopting a particle swarm algorithm to solve the optical storage multi-stage double-layer optimization configuration model, and taking constraint conditions into consideration when solving to obtain an optimal solution as an optimal power distribution network planning scheme.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the power distribution network source-storage collaborative planning method considering driving factors when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program for causing a computer to execute the power distribution network source-storage collaborative planning method considering driving factors.
The beneficial effects of the invention are as follows:
The invention constructs a power distribution network source-storage collaborative planning method considering driving factors, and comprehensively considers three driving factors in the form evolution of a power system. I.e. technical progress, policy guidance, market guidance. The invention takes collaborative planning as a starting point, and establishes a photovoltaic-energy storage collaborative multi-stage double-layer planning model so as to realize overall planning of capacity expansion of a photovoltaic equipment installation and energy storage equipment construction. In the planning process, balance and economy between photovoltaic and energy storage are maintained, and economic indexes and technical indexes during system operation are fully considered. The economic indexes comprise investment cost of photovoltaic and energy storage, operation cost, and electricity purchasing cost of the photovoltaic power generation patch and system to the upper power grid. The technical index is the active loss of the system. And obtaining an optimal solution of the multi-stage planning model of the power distribution network by adopting a particle swarm algorithm, and obtaining an optimal planning scheme of the power distribution network. The utilization rate of new energy and the adaptability of a planning scheme are improved.
Drawings
FIG. 1 is a schematic diagram of the morphological evolution of a power distribution network;
FIG. 2 is a multi-stage optical storage dual-layer optimization configuration model.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings.
Technological advances, policy guidance and market targeting have important effects on the planning of power distribution networks, and with the continuous development and updating of technology, equipment and systems of power distribution networks are also being continuously updated and upgraded. New technologies also offer more options and possibilities for power distribution network planning. The form evolution schematic diagram of the power distribution network is shown in fig. 1. Because of uncertainty and diversity of technical progress, the influence brought by the key technical progress of the power system is considered in the planning stage of the power distribution network, and the method has a strong supporting effect on the construction of a novel power system. At the same time, policy guidance and market targeting also play a positive role in facilitating distributed energy development, clean energy integration.
In an embodiment, the invention provides a power distribution network source-storage collaborative planning method considering driving factors, which comprises the following steps:
s1, establishing a mathematical model of a technical driving factor, a policy driving factor and a market guiding driving factor, wherein the mathematical model specifically comprises the following steps:
s1.1, establishing a mathematical model of a technical driving factor, wherein the mathematical model is specifically as follows:
For a key technology of an electric power system, there are a large number of sub-technologies, such as a photovoltaic power generation technology, and the related sub-technologies include a photovoltaic module technology, a photovoltaic array layout technology, a photovoltaic inverter technology, a photovoltaic power generation system management technology and the like. Collecting the number of past patent applications and the number of scientific papers published by each sub-technology, carrying out logarithmic curve fitting on the number of past patent applications and the number of scientific papers published by each sub-technology to obtain a technical maturity curve, and further obtaining the technical maturity of each sub-technology;
the sub-technologies are coupled to obtain the technical maturity of the key technology, and the specific process is as follows:
obtaining a sub-technology maturity matrix T from the technology maturity of each sub-technology;
Wherein T n represents the technical maturity level of the nth sub-technology, and n is the number of sub-technologies;
Judging the integration relation among the sub-technologies, comparing the patent applications of the two-by-two sub-technologies, and if the two sub-technologies do not overlap to obtain 0, acquiring corresponding values according to the overlapping degree if the two sub-technologies overlap to obtain 9, so as to obtain an integrated maturity matrix I;
wherein, I km represents the integration relationship between the kth sub-technology and the mth sub-technology;
normalizing the integrated maturity matrix I and the sub-technology maturity matrix T, and multiplying to obtain an integrated maturity grade matrix S;
wherein S k represents the integrated maturity level of the kth sub-technology;
the integrated maturity rating matrix S is normalized,
In the formula,The maturity coefficient of the integration technology is represented, n k is the number of the sub-technologies with the integration relation with the k sub-technology, n is the number of the sub-technologies, and the value range of S i is [0,1].
S1.2, establishing a mathematical model of a policy driving factor, wherein the policy driving factor plays an important role in promotion and support in power distribution network planning, namely, the policy guiding in the aspects of energy transformation, carbon neutralization and the like is embodied. But also shows policy incentives in aspects of financial subsidies, tax offers, service innovations and the like. In the method, photovoltaic and energy storage subsidy coefficients are selected as policy driving factors, excessive subsidy can bring financial pressure and surplus capacity, insufficient industrial development power can be caused by excessive subsidy, reasonable subsidy and step-out policies are researched, and sustainable development of a power distribution network can be promoted while the financial pressure is relieved. The method comprises the following steps:
the calculation formula for building the photovoltaic power generation patch is as follows:
In the formula, The unit power generation patch of the photovoltaic in the stage T h is carried out; the photovoltaic unit power generation patch of the initial year is carried out; Backslide coefficients complementary to the photovoltaic power generation at stage T h; Backslide coefficients for supplementing photovoltaic power generation in a phase T h +delta T, wherein gamma is backslide coefficients in different phases, and delta T represents the number of planning phases;
s1.3, establishing a mathematical model of market-oriented driving factors, wherein the typical expression form of the user market driving factors in power distribution network planning is to promote the continuous growth of renewable energy equipment and energy storage equipment. With the increasing demand for renewable energy at the user side, energy system optimization and enterprise transformation upgrades. The energy production can be continuously expanded at the supply end of the energy production source, and the installation quantity of the renewable energy source and the energy storage is increased. The low-carbon, high-efficiency and sustainable economic development is realized while the requirements of users are met. The method comprises the following steps:
And predicting future change trend of photovoltaic and energy storage investment cost by adopting a cost learning curve. The learning curve is an effective model for analyzing technical economy, which builds a mathematical expression based on the empirical relationship between accumulated yield and unit cost, and predicts future costs accordingly. The model reflects the reduced effect of learning, time and experience accumulation on production costs. At present, the progress of the technology is measured by a method of learning a curve generally in the world. The power function form of the cost learning curve is shown as follows:
LR=1-2α
Wherein: investment cost for the equipment unit of the y-th year; The method comprises the steps of determining a unit investment cost of equipment in an initial year, determining W 0 as initial year installed capacity of the equipment, determining W y as accumulated installed capacity of the equipment in a y-th year, determining alpha as learning index, fitting historical data to obtain a constant, wherein the constant is between (0 and 1), and determining LR as learning rate;
S1.4, coupling market-oriented driving factors and technical driving factors, and introducing technical maturity correction factors to correct a cost learning curve, wherein the formula of the corrected cost learning curve is as follows:
In the formula, Indicating the equipment investment cost of the modified y-th year,For the technology maturity correction factor in stage T h, the technology maturity coefficient is integratedThe value range is [0,1].
S2, constructing a light storage multi-stage double-layer optimization configuration model based on the mathematical model established in the step S1, wherein a block diagram of the light storage multi-stage double-layer optimization configuration model is shown in FIG. 2, the light storage multi-stage double-layer optimization configuration model comprises an upper planning layer and a lower operation layer, the planning layer aims at the minimum total cost in the planning period of a power distribution network, decision variables of the planning layer are photovoltaic and energy storage site selection and volume determination, the operation layer aims at the minimum active loss and node voltage offset of the system in the total planning stage, and decision variables of the operation layer are energy storage scheduling strategies, and the S2 specifically comprises:
s2.1, constructing a planning layer by taking the lowest total cost in the planning period of the power distribution network as a target, wherein the objective function of the planning layer is as follows:
Wherein, C invest represents the sum of investment costs of the photovoltaic and the energy storage, and C operation represents the sum of operation and maintenance costs of the photovoltaic and the energy storage; c buy is the electricity purchasing cost of the system to the upper power grid;
In the formula, Representing the investment cost of the stored energy; Representing the investment cost of the photovoltaic;
In the formula, Is the unit installation cost of the photovoltaic,Is the unit installation cost of the energy storage capacity,The unit installation cost of the energy storage power is obtained by the corrected cost learning curve;
Photovoltaic installation capacity for node i within stage T h; And The method comprises the steps of setting energy storage capacity and power installation capacity of a node i in a stage T h, setting R as a fund recovery coefficient, setting R as a cash register, setting T h as a planning stage, setting omega pv as a photovoltaic installation point set, and setting omega ESS as an energy storage installation point set; representing a set of planning phases; Is a 0-1 decision variable when When the photovoltaic device is installed on the i node of the T h stage,When, representing that the photovoltaic equipment is not installed at the i node in the stage T h; a decision variable of 0-1 is used for indicating whether energy storage is installed at a node i in a stage T h;
C operation is the operation and maintenance cost of the system, expressed as:
In the formula, For the operational and maintenance costs of the photovoltaic,The photovoltaic only considers the operation and maintenance cost, and the energy storage also considers the replacement cost of the battery while considering the operation and maintenance cost, and the specific formula is as follows:
In the formula, Maintenance costs per unit capacity for photovoltaic at stage T h; the maintenance cost is the unit capacity of the energy storage in the stage T h;
Wherein, C t,buy is the electricity price at the time t, P t,buy is the main network purchase power at the time t; The photovoltaic power generation patch in the stage T h is carried out; The photovoltaic power at the time T in the stage T h;
S2.2, the operation layer aims at the minimum active loss and node voltage offset of the system in the total planning stage, and the objective function is as follows:
Wherein f 1 is a first sub-objective function of an operation layer, f 2 is a second sub-objective function of the operation layer, and P CL is the active loss of the system in a total planning stage; Active power at branch ij at time T in phase T h; Reactive power at branch ij at time T in phase T h; The voltage at a node i at T in a stage T h, r ij as a resistor at a line ij, U offest as a node voltage offset of the system in a total planning stage, N as the number of nodes, U k,min as the minimum rated voltage of a node k; the voltage amplitude of the k node at the moment T in the planning stage T h is set;
Overall objective function of the run layer:
Wherein ω 1 and ω 2 are the weighting coefficients of the system active loss and the node voltage offset, ω 1+ω2＝1;f1 * and ω Is the sub objective function value after normalization processing.
And S3, solving the optical storage multi-stage double-layer optimization configuration model by adopting a particle swarm algorithm, and taking constraint conditions into consideration when solving to obtain an optimal solution as an optimal power distribution network planning scheme.
The constraint conditions include:
(1) Tidal current constraint condition
Wherein: And The voltages of nodes i and j at the moment T in the planning stage T h are respectively; And Active power and reactive power of a node i in the planning stage T h respectively; Is the voltage phase angle difference between nodes i, j at time T in phase T h. G ij and B ij are the conductance, susceptance, respectively, of branch ij, Ω Th,ij is the set of system nodes within phase T h;
(2) Power balance constraint
Wherein P load,t is the power of the power distribution network at the time t; The output power of the photovoltaic at the moment T in the phase T h is obtained; To store the charge and discharge power at time T in the phase T h, Represents energy storage discharge; Representing energy storage charging;
(3) Node voltage constraint
Wherein U min is the minimum voltage allowed during system operation and U max is the maximum voltage allowed during system operation;
(4) Main network purchase electric power constraint
Pt,buy≥0
(5) Photovoltaic mounting capacity constraints
Is the maximum photovoltaic installed quantity installable at the node i;
(6) Photovoltaic active power output constraint
In the formula,Representing the photovoltaic output of node j;
(7) Line capacity constraints
S ij,max is the capacity of the line ij;
(8) Multi-stage coordination constraints
When planning the photovoltaic and the energy storage, the capacity of the energy storage and the photovoltaic equipment is constrained to ensure the engagement of the schemes in each stage, and the formula is expressed as follows:
(9) Energy storage device restraint
Constraint conditions to be met when the energy storage device operates include charge and discharge constraint, residual capacity constraint and storage battery capacity constraint, and the formula is expressed as follows:
Is the maximum installed capacity of the energy storage device at the inode. AndThe energy storage charging and discharging state at the point T of the node i in the stage T h is 0-1 variable, whenWhen the energy storage device is in a charging state,When the energy storage device is in a non-charging state; when the energy storage device is in a discharge state, When the energy storage device is in a non-discharge state; The charging power at the moment T at the node i in the energy storage equipment stage T h; And The upper limit and the lower limit of the charging power at the node i in the stage T h are respectively; The discharge power at the moment T at the node i in the energy storage equipment stage T h; And The upper limit and the lower limit of the discharge power at the node i in the stage T h are respectively; the residual electric quantity of the energy storage device in the moment T at the node i in the stage T h; And Charging and discharging efficiency of the energy storage device at node i; rated capacity of the energy storage device for node i in stage T h; And The minimum and maximum proportions of the residual capacity of the energy storage equipment at the node i to the rated maximum capacity of the energy storage equipment are respectively; represents the energy storage operating state constraint at time T at node i in phase T h, Representing the residual electric quantity of the energy storage device in the moment T at the node i in the stage T h; the residual electric quantity of the energy storage device in the time T-1 at the node i in the stage T h is represented, and the delta T represents the charge and discharge time.
In another embodiment, the present invention provides a power distribution network source-storage collaborative planning system accounting for driving factors corresponding to the method of the first embodiment, including:
The driving factor modeling module is used for establishing mathematical models of technical driving factors, policy driving factors and market-oriented driving factors;
The optical storage multi-stage double-layer optimization configuration model building module is used for building an optical storage multi-stage double-layer optimization configuration model based on a mathematical model of a technical driving factor, a policy driving factor and a market guiding driving factor, wherein the optical storage multi-stage double-layer optimization configuration model comprises an upper planning layer and a lower running layer, the planning layer aims at the lowest total cost in the planning period of a power distribution network, decision variables of the planning layer are photovoltaic and energy storage site selection and volume determination, the running layer aims at the minimum active loss and node voltage offset of the system in the total planning period, and decision variables of the running layer are energy storage scheduling strategies;
And the solving module is used for adopting a particle swarm algorithm to solve the optical storage multi-stage double-layer optimization configuration model, and taking constraint conditions into consideration when solving to obtain an optimal solution as an optimal power distribution network planning scheme.
The implementation manner of each module and the function of the module in the system are completely consistent with each step of the method in the first embodiment, so that a detailed description is omitted here.
In another embodiment, the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the power distribution network source-storage collaborative planning method according to the first embodiment taking into account driving factors when executing the computer program.
In another embodiment, the present invention provides a computer readable storage medium storing a computer program for causing a computer to execute the power distribution network source-storage collaborative planning method according to the first embodiment, wherein the power distribution network source-storage collaborative planning method includes a first storage device and a second storage device.
The method provided by the invention is subjected to simulation analysis by adopting an IEEE33 node system. The capacity of Shan Tai photovoltaic grid-connected power generation equipment is assumed to be 100KW, the upper limit of the capacity of a single node is 3MW, the capacity of a single energy storage equipment is 10KW (40 kwh), and in order to further study the planning method provided by the invention, the whole system is supplied by an upper power grid under the assumption that the system does not contain photovoltaic and energy storage equipment in the initial stage. The number of planning stages is 3, the stage growth rate of the load demand is 5% for 5 years as one planning stage, the electricity prices of each year are consistent, and the initial permeability of the 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, and a Gurobi solver is adopted for optimization solving.
Because the output of the photovoltaic has stronger seasonal characteristics, the planning stage in the planning period is divided into two typical seasons of winter and summer according to seasons.
TABLE 1 model simulation parameters
The statistics of the installation capacity, number, net loss cost and investment operation cost of each device in each planning stage obtained finally are shown in table 2.
Table 2 planning results
As can be seen from table 2, the method according to the present invention is more prone to increase the installed photovoltaic amount in the first planning stage, while the installed photovoltaic amount gradually decreases in the second and third stages, but the total photovoltaic permeability continuously increases, with the decrease of the policy driving factors and the photovoltaic patch mechanism, taking into consideration the influence of the evolution driving factors. The energy storage loading is approximately the same in the three planning stages, but with the continuous development of technology, the energy storage cost is continuously reduced.
Through the analysis, the influence of the evolution driving factors of the power system in the power distribution network planning is considered, and the reasonable planning of the photovoltaic and the energy storage is realized.
In the disclosed embodiments, a computer storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer storage medium would include one or more wire-based electrical connections, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
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