Optimized operation method and system for optical storage charging distribution transformer area considering electric energy quality management

Abstract

The invention provides a power distribution area optimization operation method giving consideration to distributed energy characteristics and electric energy quality management requirements, and the method comprehensively considers the cooperative relationship among a photovoltaic power generation system, an energy storage system and power distribution network nodes, builds an electric energy quality evaluation index according to the voltage deviation and fluctuation of the nodes, proposes a light-storage cooperative operation mechanism, and improves the power quality management efficiency. A power distribution area double-layer optimization model including constraint conditions such as node power balance, energy storage operation, voltage and capacity limitation and power flow constraint is formed; a solving method based on an improved particle swarm algorithm is proposed, the search diversity is enhanced by introducing chaotic Tent mapping and genetic operators, the convergence speed is increased, local optimum is avoided, and the practicability of the proposed method is verified through an actual example. According to the method, the influence of distributed energy access on voltage deviation and fluctuation is fully considered, and the method has important significance in the aspects of optimized operation and electric energy quality control of a power distribution area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network operation optimization, in particular to a photovoltaic-storage-charging power distribution area operation optimization method and system considering power quality management. BACKGROUND

[0002] Power quality management is of great significance in the operation of photovoltaic-storage-charging power distribution areas. The optimization operation goal is to minimize voltage deviation and fluctuation and improve power quality under the premise of ensuring power supply and demand balance and system safety. With the large-scale access of distributed new energy, the operation of photovoltaic-storage-charging power distribution areas not only involves traditional load and power generation scheduling, but also needs to consider the access characteristics of intermittent power sources such as photovoltaic and wind power, the charging and discharging strategies of shared energy storage systems, and the dynamic changes of load response. Through accurate node voltage monitoring, power generation prediction and energy storage operation planning, the optimization operation method can effectively adjust the power flow distribution, reduce network loss, and improve new energy consumption capacity and energy storage utilization efficiency. In this process, the energy coordination and control mechanism within the photovoltaic-storage-charging power distribution area can realize unified scheduling of distributed energy and energy storage systems, thereby improving the power quality of the area and enhancing the reliability of power supply. However, the existing optimization method for power distribution areas still has deficiencies in power quality management and multi-objective coordination, such as insufficient comprehensive consideration of voltage fluctuation, energy storage economy and new energy prediction deviation, and complex models may face problems such as large amount of calculation and poor real-time performance in actual solution. Therefore, a photovoltaic-storage-charging power distribution area optimization operation method considering power quality management is proposed to realize the unified goal of safe, economic and high-quality operation of the area through refined modeling and efficient optimization algorithm. SUMMARY

[0003] In view of the above problems, a photovoltaic-storage-charging power distribution area optimization operation method considering power quality management is proposed.

[0004] The technical solutions of the present application are as follows:

[0005] A photovoltaic-storage-charging power distribution area optimization operation method considering power quality management, comprising:

[0006] Based on the voltage deviation and voltage fluctuation problems caused by the access of distributed energy to the power distribution area, an optimization operation model of the power distribution area considering the characteristics of distributed energy and the demand of power quality management is established;

[0007] Based on the photovoltaic-storage collaborative operation mechanism, a double-layer optimization model of the power distribution area is established, with the upper-layer goal of reducing the abandoned light rate and annual investment and operation cost, and the lower-layer goal of minimizing network loss and power quality evaluation value, and the constraint conditions including node power balance, shared energy storage operation, voltage and capacity limitation, and power flow constraint;

[0008] An optimization model based on an improved particle swarm algorithm is established, and a chaotic Tent mapping and genetic operator are introduced to enhance search diversity, accelerate convergence speed and avoid local optimum, so that efficient global optimization of the light storage capacity is realized.

[0009] As a preferred scheme of the light storage charging and distribution area optimization operation method considering power quality treatment of the application, a power quality index of the distribution area, a photovoltaic power generation system model and a shared energy storage system model are constructed.

[0010] (1) Power quality index of distribution area

[0011] The selection range of the power quality evaluation index of the distribution area is wide, including voltage deviation, voltage fluctuation, harmonic and frequency deviation. Combined with the randomness and volatility characteristics of the distributed photovoltaic grid-connected output, in order to accurately reflect the influence of the distributed photovoltaic grid-connected output on the power quality, the voltage deviation and the voltage fluctuation are selected as the main power quality evaluation indexes.

[0012] Voltage is the core index for evaluating the power quality of the power grid, and the voltage deviation reflects the normal operation state of the system. After the distributed photovoltaic is connected to the distribution network, the voltage at the connection node increases with the increase of the photovoltaic output, and when the output exceeds the consumption capacity of the distribution network, it may lead to voltage out-of-limit. The voltage deviation of the node is defined as the difference between the actual voltage at the photovoltaic grid-connected node and the nominal voltage of the system, and its expression is:

[0013]

[0014] In the formula: is the actual voltage at the distributed photovoltaic connection node; is the nominal voltage of the node. The distributed photovoltaic output is affected by external factors such as sunshine period and cloud change, which not only causes voltage deviation, but also may cause obvious voltage fluctuation, and its expression is:

[0015]

[0016]

[0017] In the formula: is the voltage fluctuation of the nth node caused by the influence of the distributed photovoltaic connection; is the number of the grid node; is the line resistance before the nth node; is the distributed photovoltaic output value; is the ratio of the instantaneous change amplitude of the distributed photovoltaic output power to the rated output power of the photovoltaic.

[0018] ​​​​To evaluate the power quality objectively, voltage deviation, voltage fluctuation and voltage harmonic are selected to construct the power quality comprehensive evaluation function, and the weight coefficients of each evaluation index are determined by AHP method. The evaluation function expression is:

[0019]

[0020] In the formula: is the weight coefficient of the evaluation index.

[0021] (2) Photovoltaic power generation system model

[0022] Distributed photovoltaic power generation is a technology that converts solar energy into electricity directly by photovoltaic components. It can generate electricity near the power consumption site and connect to the power grid. It has the advantages of environmental protection, sustainability, and broad application prospects. The maximum DC output power of the solar cell array is:

[0023]

[0024] In the formula: is the output power of the solar cell array, ; is the light intensity, ; is the power conversion efficiency, which is relatively constant; is the area of the solar cell array, ; is the working temperature of the solar cell array, ; C is a constant.

[0025] Because in a specific photovoltaic power generation system, the area of the solar cell array is fixed. Environmental temperature, atmospheric pressure, wind speed and other factors will affect the actual working temperature of the battery array. The working temperature of the solar cell array is:

[0026]

[0027] In the formula: is the environmental temperature, .

[0028] Therefore, the calculation formula of the output power of the solar cell array is:

[0029]

[0030] (3) Shared energy storage system model

[0031] The shared energy storage system has the characteristics of fast charging and discharging, rapid response and flexible use, and can store electric energy during the low electricity consumption period and release electric energy during the peak electricity consumption period. The charging and discharging characteristics of the energy storage can be generally described by five key parameters: capacity, power, charging and discharging efficiency, self-discharge rate and state of charge, and the charging and discharging model of electric energy can be expressed as:

[0032]

[0033] In the formula: is the energy storage state of electric energy at the i-th moment; , are the charging and discharging power in the t-th period, respectively; is the actual energy stored in the electric energy storage; is the actual energy released by the electric energy storage.

[0034] As a preferred scheme of the photovoltaic energy storage charging power distribution station area optimization operation method considering power quality management, the objective function of the load aggregator optimization scheduling model based on the user peak shaving value includes: in the case of considering the output fluctuation and prediction deviation of photovoltaic, wind power and other distributed new energy, the application realizes the balance between supply and demand of the power distribution station area, reduces the operation cost, and takes into account the economy and reliability by reasonably configuring the energy storage capacity and power, and establishes a double-layer optimization model.

[0035] The upper light storage capacity optimization configuration model optimally plans with the lowest total light abandonment rate of the distribution network and the lowest annual investment and operation cost of the light storage power generation system as the target.

[0036] The total light abandonment rate of the power distribution station area is:

[0037]

[0038] In the formula: is the maximum power that can be generated by the photovoltaic grid-connected node at the t-th moment; is the actual power of the photovoltaic grid-connected node at the t-th moment; is the number of photovoltaic grid-connected nodes. The investment and operation cost of the light storage power generation system is:

[0039]

[0040]

[0041] In the formula: is the annual installation and operation cost of the photovoltaic node; ​​​​​​​​​The annual installation and operation cost of the energy storage; The unit power purchase price of the distribution network at the moment The unit power purchase price of the distribution network at the moment The unit power purchase price of the distribution network at the moment The unit power purchase price of the distribution network at the moment The unit power purchase price of the distribution network at the moment The number of energy storage grid-connected nodes

[0042] The annual investment and operation cost of the photovoltaic is:

[0043]

[0044] In the formula: , , The annual investment, operation and processing cost of the unit capacity photovoltaic are respectively; The installation capacity of the photovoltaic at the node .

[0045] The annual investment and operation cost of the shared energy storage system is:

[0046]

[0047] In the formula: , , The annual investment, operation and processing cost of the unit power capacity energy storage are respectively; , , The annual investment, operation and processing cost of the unit energy capacity energy storage are respectively; , The power installation capacity and the energy installation capacity of the energy storage system at the node .

[0048] The lower layer optimizes operation with the minimum network loss and power quality evaluation value as the target, to realize comprehensive optimization of economic operation and power quality management.

[0049] The network loss is:

[0050]

[0051] In the formula: The number of grid nodes; The conductance of the branch . , The active power and the reactive power flowing through the branch at the moment . The node voltage flowing through the branch at the moment . The node voltage flowing through the branch A set of connected branches.

[0052] The photovoltaic grid-connected point power quality evaluation value is:

[0053]

[0054] In the formula: The photovoltaic grid-connected point At The power quality evaluation value at the moment.

[0055] As a preferred scheme of the photovoltaic storage and charging power distribution area optimal operation method considering power quality management, the constraint conditions of the considered photovoltaic storage grid-connected optimal configuration model include:

[0056] 1) The node power balance constraint is:

[0057]

[0058] In the formula: The node power generation power; The node load power.

[0059] 2) The shared storage constraint is:

[0060]

[0061]

[0062] In the formula: And Respectively, the storage charging and discharging state; And Respectively, the storage charging and discharging power; The storage power state at The moment; And Respectively, the storage charging and discharging efficiency; The maximum capacity power of the node ; , Respectively, the upper and lower limits of the installed storage capacity of the node .

[0063] 3) The node photovoltaic output and capacity constraint:

[0064]

[0065] In the formula: The installed capacity of the distributed photovoltaic at the node ; The maximum capacity that can be installed for the photovoltaic at the node ; The active power of the photovoltaic at the moment of the node , The upper and lower limits of the photovoltaic output at the moment of the node

[0066] 4) System power flow constraints:

[0067]

[0068] In the formula: , The active power of the photovoltaic injection and load consumption at the moment of the node , The reactive power of the photovoltaic injection and load consumption at the moment of the node , The active power of the energy storage injection and the capacitor reactive power at the moment of the node , , The conductance, admittance, and power angle between the nodes and , The actual voltage of the node at the moment The number of branches connected to the node As a preferred scheme of the photovoltaic energy storage charging power distribution area optimal operation method considering power quality treatment, based on the multi-objective nonlinear problem of photovoltaic energy storage capacity configuration, an optimization model based on the improved particle swarm algorithm is established:

[0069]

[0070]

[0071]

[0072] In the formula: The speed of the first particle in the next iteration; The inertia weight; The weights of individual learning and group learning, respectively; The random number between The historical optimal position of the particle The historical optimal position of the particle​​​​​​​​​​​​​​​ This is the globally optimal position for the entire population; For the first The position of each particle in the next iteration.

[0073] Sequence generated by chaotic Tent mapping It can be represented as:

[0074]

[0075] In the iterative process of the particle swarm optimization algorithm, this invention introduces a genetic algorithm. Through crossover and mutation operations, it enhances information exchange between particles, thereby improving the algorithm's convergence speed and effectively preventing it from getting trapped in local optima. The calculation formulas for crossover and mutation are as follows:

[0076]

[0077] In the formula: These are crossover and genetic factors, respectively. for A random number between [a certain number of points].

[0078] An optimized operation system for photovoltaic-storage-charging-distribution radio stations, considering power quality management, includes:

[0079] Storage module: Used to store the optimization model and input data;

[0080] Calculation module: Used to input data into the model for calculation.

[0081] The power quality indicators for the distribution area in the storage module include voltage deviation and voltage fluctuation;

[0082] Voltage deviation includes node voltage deviation and voltage fluctuation. Node voltage deviation is the difference between the actual voltage of the photovoltaic grid-connected node and the nominal voltage of the system, and its expression is:

[0083] ,

[0084] In the formula: Distributed photovoltaic access node The actual voltage; The nominal voltage of the node;

[0085] The voltage fluctuation expression is:

[0086] ,

[0087] In the formula: For the first Voltage fluctuations at individual nodes caused by distributed photovoltaic grid connection; For the power grid Each node is numbered; For the first The resistance of the power grid lines before each node; This refers to the output value of distributed photovoltaic power. It is the ratio of the instantaneous change in the output power of distributed photovoltaic power to the rated output power of photovoltaic power.

[0088] To facilitate an objective evaluation of power quality, a comprehensive power quality assessment function is constructed by comprehensively selecting voltage deviation, voltage fluctuation, and voltage harmonics. The weighting coefficients of each assessment indicator are determined using the analytic hierarchy process (AHP). The expression for the assessment function is as follows:

[0089]

[0090] In the formula: The weighting coefficients for the evaluation indicators.

[0091] The distributed photovoltaic power generation system model stored in the storage module is represented by the output power of the solar cells. The maximum DC output power of the solar cell array is:

[0092] ,

[0093] In the formula: The output power of the solar cell array, in units of ; Light intensity, unit: ; This refers to power conversion efficiency; its value is relatively constant. The area of ​​the solar cell array is expressed in units of... ; The operating temperature of the solar cell array, in units of C is a constant coefficient;

[0094] The operating temperature of the solar cell array is:

[0095] ,

[0096] In the formula: Ambient temperature, unit: ;

[0097] Therefore, the formula for calculating the output power of a solar cell array is:

[0098] .

[0099] The dual-layer optimization model for distribution radio areas stored in the storage module includes an upper-layer optical storage capacity optimization configuration model and a lower-layer model.

[0100] The upper-level photovoltaic and energy storage capacity optimization configuration model aims to achieve the lowest total curtailment rate of the distribution network and the lowest annual investment and operation and maintenance cost of the photovoltaic and energy storage power generation system.

[0101] Total abandoned light ratio of distribution area is:

[0102] ;

[0103] In the formula: is the photovoltaic grid-connected node The maximum power that can be emitted at time; is the photovoltaic grid-connected node The actual power at time; is the number of photovoltaic grid-connected nodes;

[0104] The investment and operation cost of the light storage power generation system is:

[0105]

[0106] In the formula: is the photovoltaic installation and operation cost of node ; is the shared energy installation and operation cost of node ; is the power purchase price of the distribution network per unit power at time; is the required power purchase of node at time; is the number of energy storage grid-connected nodes;

[0107] The annual investment and operation cost of photovoltaic is:

[0108] ,

[0109] In the formula: , , The annual investment, operation, and processing costs of photovoltaic per unit capacity are respectively; is the photovoltaic installation capacity of node ;

[0110] The annual investment and operation cost of shared energy storage system is:

[0111] ,

[0112] In the formula: , , The annual investment, operation, and processing costs of energy storage per unit power capacity are respectively; , , The annual investment, operation, and processing costs of energy storage per unit energy capacity are respectively; , are the power installation capacity and energy installation capacity of the energy storage system respectively; are the power installation capacity and energy installation capacity of the energy storage system respectively;

[0113] The lower layer model is optimized for network loss and power quality evaluation value, and realizes the comprehensive optimization of economic operation and power quality management.

[0114] The network loss is:

[0115]

[0116] In the formula: is the number of grid nodes; is the conductance of the branch; , are the active power and reactive power flowing through the branch during the time period respectively; , are the active power and reactive power flowing through the branch during the time period respectively; is the node voltage flowing through the branch during the time period; is the set of branches connected to the node; The power quality evaluation value of the photovoltaic grid-connected point is:

[0117] In the formula: is the power quality evaluation value of the photovoltaic grid-connected node at the time t.

[0118]

[0119] In the formula: is the power quality evaluation value of the photovoltaic grid-connected node at the time t. The constraint conditions of the storage module photovoltaic storage grid-connected optimization configuration model specifically include:

[0120] The node power balance constraint is specifically:

[0121]

[0122] ,

[0123] In the formula: is the node power generation; is the node load power;

[0124] The shared energy storage constraint is specifically:

[0125]

[0126] ,

[0127] In the formula: and ​​​​respectively the state of charge of the energy storage; and respectively the power of charging and discharging of the energy storage; is the state of energy of the energy storage at time t; and respectively the efficiency of charging and discharging of the energy storage; is the maximum capacity power of the node ; , respectively the minimum and maximum capacity of the node ;

[0128] the photovoltaic output and capacity constraints of the node, in particular:

[0129] ,

[0130] where: is the installed capacity of the distributed photovoltaic at the node ; is the maximum capacity that can be installed of the photovoltaic at the node ; is the active power output of the photovoltaic at the node at time t; , respectively the minimum and maximum photovoltaic output at the node at time t;

[0131] the system flow constraints, in particular:

[0132] ,

[0133] where: , respectively the active power of the photovoltaic injection and the load consumption at the node at time t; , respectively the reactive power of the photovoltaic injection and the load consumption at the node at time t; , respectively the active power of the energy storage injection and the capacitor reactive power at the node at time t; , , , , respectively the conductance, admittance, power angle between the nodes and ; , respectively​​​ Instantaneous node actual voltage; For node The number of connected branches.

[0134] The method can realize full utilization of distributed energy in the power distribution area, improve the local consumption capacity of new energy by optimizing the coordinated operation of photovoltaic, energy storage and load, effectively reduce the light abandonment rate, and at the same time, reasonably configure the system operation mode to reduce the energy loss in the power distribution network and reduce the network loss. The method can also realize economic and efficient operation while effectively controlling the voltage deviation and voltage fluctuation level of the power distribution area, thereby improving the overall power quality level.

[0135] The application provides a power distribution area optimization operation method considering distributed energy characteristics and power quality treatment demand, which takes reducing light abandonment rate and annual investment and operation cost as upper target, minimizes network loss and power quality evaluation value as lower target, comprehensively considers the coordination relationship among photovoltaic power generation system, energy storage system and power distribution network nodes, establishes power quality evaluation index according to node voltage deviation and fluctuation, proposes a photovoltaic and energy storage collaborative operation mechanism, and forms a power distribution area double-layer optimization model containing node power balance, energy storage operation, voltage and capacity limitation and power flow constraint and the like. An improved particle swarm algorithm-based solving method is proposed, the search diversity is enhanced, the convergence speed is accelerated and local optimum is avoided by introducing chaos Tent mapping and genetic operator, and the practicability of the method is verified by an actual example. The method fully considers the influence of distributed energy access on voltage deviation and fluctuation, and has important significance in power distribution area optimization operation and power quality treatment. BRIEF DESCRIPTION OF DRAWINGS

[0136] Figure 1 It is a power quality comprehensive evaluation system diagram.

[0137] Figure 2 It is a particle swarm algorithm flowchart. DETAILED DESCRIPTION

[0138] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0139] Embodiment one:

[0140] The application provides a photovoltaic and energy storage charging and power distribution area optimization operation method considering power quality treatment, characterized in that it comprises:

[0141] Based on the voltage deviation and voltage fluctuation problems caused by the access of distributed energy to the distribution substation, an optimized operation model for the distribution substation is established that takes into account both the characteristics of distributed energy and the needs of power quality management.

[0142] Based on the photovoltaic-storage collaborative operation mechanism, a two-layer optimization model for distribution substations is established, with the upper-level objective of reducing curtailment rate and annual investment and operating costs, and the lower-level objective of minimizing network losses and power quality assessment values. Constraints include node power balance, energy storage operation, voltage and capacity limitations, and power flow constraints.

[0143] An optimization model based on an improved particle swarm optimization algorithm is established, and chaotic Tent mapping and genetic operators are introduced to enhance search diversity, accelerate convergence speed and avoid local optima;

[0144] Example 2:

[0145] To address the voltage deviation and fluctuation issues caused by distributed energy access to distribution transformer substations, an optimized operation model for distribution transformer substations that balances the characteristics of distributed energy resources with the needs of power quality management is established, including:

[0146] (1) Power quality indicators of distribution area

[0147] The selection range of power quality evaluation indicators for distribution substations is relatively broad, including voltage deviation, voltage fluctuation, harmonics, and frequency deviation. Considering the randomness and volatility of distributed photovoltaic grid-connected power output, this invention selects voltage deviation and voltage fluctuation as the main power quality evaluation indicators to accurately reflect their impact on power quality.

[0148] Voltage is a core indicator for evaluating the power quality of a power grid, with voltage deviation reflecting the normal operating status of the system. After distributed photovoltaic (PV) power is connected to the distribution network, the voltage at the connection node increases with the increase in PV output. When the output exceeds the absorption capacity of the distribution network, it may lead to voltage exceeding limits. Node voltage deviation is defined as the difference between the actual voltage at the PV grid-connected node and the nominal voltage of the system, and its expression is:

[0149]

[0150] In the formula: Distributed photovoltaic access node The actual voltage; This is the nominal voltage of the node.

[0151] Distributed photovoltaic power output is affected by external factors such as solar cycle and cloud cover changes, which can cause voltage deviations and even significant voltage fluctuations. The expression for this is:

[0152]

[0153] In the formula: For the first voltage fluctuation caused by the influence of the distributed photovoltaic access on the node; numbering the nodes of the power grid; numbering the nodes of the power grid; numbering the nodes of the power grid; numbering the nodes of the power grid; numbering the nodes of the power grid; numbering the nodes of the power grid;

[0154] In order to objectively evaluate the power quality, voltage deviation, voltage fluctuation and voltage harmonic are comprehensively selected to construct the power quality comprehensive evaluation function, and the analytic hierarchy process is used to determine the weight coefficient of each evaluation index, and the evaluation function expression is:

[0155]

[0156] In the formula: is the weight coefficient of the evaluation index.

[0157] (2) Photovoltaic power generation system model

[0158] Distributed photovoltaic power generation is a technology that directly converts solar energy into electric energy by using photovoltaic components. It can generate electricity near the power consumption site and access the power grid, and has the advantages of environmental protection, sustainability, broad application prospects, etc. The maximum direct current output power of the solar cell array is:

[0159]

[0160] In the formula: is the output power of the solar cell array, ; is the light intensity, ; is the power conversion efficiency, which is relatively constant; is the area of the solar cell array, ; is the working temperature of the solar cell array, ; C is a constant.

[0161] In addition, in a specific photovoltaic power generation system, the area of the solar cell array is fixed. Environmental temperature, atmospheric pressure, wind speed and other factors will affect the actual working temperature of the battery array. The working temperature of the solar cell array is:

[0162]

[0163] In the formula: is the environmental temperature, .

[0164] Therefore, the formula for calculating the output power of a solar cell array is:

[0165]

[0166] (3) Shared energy storage system model

[0167] Shared energy storage systems feature rapid charging and discharging, quick response, and flexible use, storing electrical energy during off-peak hours and releasing it during peak hours. The charging and discharging characteristics of battery energy storage can generally be described by five key parameters: capacity, power, charging and discharging efficiency, self-discharge rate, and state of charge. The charging and discharging model of electrical energy can be expressed as:

[0168]

[0169] In the formula: It is the first The energy storage state of electrical energy at a given moment; , They are in Charging and discharging power during the time period; The energy actually stored in the electrical energy storage system; This refers to the actual energy released by the electrical energy storage system.

[0170] Example 3:

[0171] Based on the above methods and the photovoltaic-storage collaborative operation mechanism, a two-layer optimization model for distribution substations is established, with the upper-level objective of reducing curtailment rate and annual investment and operating costs, and the lower-level objective of minimizing network loss and power quality assessment.

[0172] Considering the power output volatility and prediction bias of distributed new energy sources such as photovoltaics and wind power, this invention achieves supply and demand balance in distribution areas and reduces operating costs by rationally configuring shared energy storage capacity and power, while taking into account both economy and reliability, and establishes a two-layer optimization model.

[0173] The upper-level photovoltaic and energy storage capacity optimization configuration model aims to achieve optimal planning with the goal of minimizing the total curtailment rate of the distribution network and minimizing the annual investment and operation and maintenance costs of the photovoltaic and energy storage power generation system.

[0174] Total curtailment rate of distribution area for:

[0175]

[0176] In the formula: For photovoltaic grid-connected nodes exist The maximum power that can be emitted at any given moment; For photovoltaic grid-connected nodes exist Actual power at any given time; The number of photovoltaic grid-connected points.

[0177] The investment and operation cost of the photovoltaic and storage power generation system is:

[0178]

[0179] In the formula: is the number of photovoltaic grid-connected points. is the annual installation and operation cost of the photovoltaic node is the annual installation and operation cost of the shared energy storage node is the power purchase price per unit of power of the power distribution network at the moment is the required power purchase power of the node at the moment is the number of energy storage grid-connected points. The annual investment and operation cost of the photovoltaic is:

[0180]

[0181]

[0182] In the formula: , , respectively, the annual investment, operation and processing cost of the unit capacity photovoltaic; is the installation capacity of the photovoltaic node .

[0183] The annual investment and operation cost of the shared energy storage system is:

[0184]

[0185] In the formula: , , respectively, the annual investment, operation and processing cost of the unit power capacity energy storage; , , respectively, the annual investment, operation and processing cost of the unit energy capacity energy storage; , respectively, the power installation capacity and energy installation capacity of the energy storage system of the node .

[0186] The lower layer is optimized for operation with the minimum network loss and power quality evaluation value as the target, to realize the comprehensive optimization of economic operation and power quality management.

[0187] The network loss is:

[0188]

[0189] ​​​In the formula: is the number of grid nodes; is the branch conductance; , are the active power and reactive power flowing through the branch in the time period; is the node voltage flowing through the branch in the time period; is the node voltage flowing through the branch in the time period; is the set of branches connected to the node .

[0190] The photovoltaic grid-connected point power quality evaluation value is:

[0191]

[0192] In the formula: is the photovoltaic grid-connected node power quality evaluation value at moment.

[0193] Embodiment four:

[0194] The constraint condition of the photovoltaic and storage grid-connected optimization configuration model considered by the double-layer optimization model is:

[0195] The node power balance constraint is:

[0196]

[0197] In the formula: is the node power generation; is the node load power.

[0198] The shared storage constraint is:

[0199]

[0200]

[0201] In the formula: and are the storage charge and discharge states, respectively; and are the storage charge and discharge powers, respectively; is the storage power state at moment; and are the storage charge and discharge efficiencies, respectively; is the maximum capacity power of the node ; , are the upper and lower limits of the installed storage capacity of the node .

[0202] Node photovoltaic output and capacity constraints:

[0203]

[0204] where: is the installed capacity of distributed photovoltaic at node ; is the maximum capacity of the photovoltaic that can be installed at node ; is the active power output of the photovoltaic at node at time ; , are the upper and lower limits of photovoltaic output at node at time .

[0205] System power flow constraints:

[0206]

[0207] where: , are the active power of photovoltaic injection and load consumption at node at time ; , are the reactive power of photovoltaic injection and load consumption at node at time ; , are the active power of energy storage injection and the reactive power of capacitor at node at time ; , , are the conductance, admittance, power angle between node and ; , are the actual voltage at node at time ; is the number of branches connected to node .

[0208] Example five:

[0209] An optimization model based on improved particle swarm algorithm is established, and chaos Tent mapping and genetic operator are introduced to enhance search diversity, speed up convergence speed and avoid local optimum, so as to realize efficient global optimization of photovoltaic and storage capacity;

[0210] As a preferred scheme of the photovoltaic energy storage charging and power distribution area optimal operation method considering power quality treatment of the present application, based on the multi-objective nonlinear problem of photovoltaic energy storage capacity configuration, an optimization model based on improved particle swarm algorithm is established:

[0211]

[0212]

[0213] In the formula: The velocity of the first particle in the next iteration is The inertia weight is The weights of individual learning and group learning are respectively The random number between 0 and 1 is The historical optimal position of the particle The global optimal position of the whole population is The position of the first particle in the next iteration is The historical optimal position of the particle The global optimal position of the whole population is The position of the first particle in the next iteration is The position of the first particle in the next iteration is

[0214] The sequence generated by the chaotic Tent mapping Can be expressed as:

[0215]

[0216] In the iteration process of the particle swarm algorithm, the genetic algorithm is introduced, the information exchange between particles is enhanced through crossover and mutation operation, so as to improve the convergence speed of the algorithm and effectively prevent falling into local optimum. The calculation formula of crossover and mutation is as follows:

[0217]

[0218] In the formula: The crossover and genetic factors are respectively The random number between 0 and 1 is The random number between 0 and 1 is

[0219] Example 6

[0220] As a preferred scheme of the photovoltaic energy storage charging and power distribution area optimal operation method considering power quality treatment of the present application, an optimization model based on improved particle swarm algorithm is established. The method initializes the particle swarm position and velocity by inputting the upper model parameters, and combines chaotic optimization and genetic algorithm to perform chaotic disturbance, crossover and mutation operation on the global optimal particle, constantly updates the particle state and fitness value, realizes the iteration optimization of individual optimal and global optimal, and finally obtains the optimal configuration result of the whole system.

Claims

1. A method for optimal operation of a photovoltaic energy storage and charging power supply area considering power quality management, characterized in that, The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. 2.The method of claim 1, wherein the method further comprises: determining the power quality treatment based on the power quality treatment cost and the power quality treatment effect. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， wherein: is the actual voltage of the distributed photovoltaic access node ; and is the nominal voltage of the node. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: For the first Voltage fluctuations at individual nodes caused by distributed photovoltaic grid connection; For the power grid Each node is numbered; For the first The resistance of the power grid lines before each node; This refers to the output value of distributed photovoltaic power. It is the ratio of the instantaneous change in the output power of distributed photovoltaic power to the rated output power of photovoltaic power. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: is the weight coefficient of the evaluation index; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: Pout is the solar array output power in W / m2; ; I is the light intensity in W / m2; ; η is the power conversion efficiency; which is relatively constant in value; is the area of the solar array in square meters ; is the operating temperature of the solar array in degrees Celsius ; C is a constant; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: is the ambient temperature, in °C ; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ； The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， wherein: is the state of charge of the electrical energy at the moment; , are the charge and discharge power, respectively, during the time period; is the energy actually stored in the electrical storage; is the energy actually released from the electrical storage. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. Total light rejection of a power distribution zone is: ； wherein: is the photovoltaic grid node at the maximum power that can be emitted at the time instant; is the photovoltaic grid node at the actual power at the time instant; is the number of photovoltaic grid nodes; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: is the annual installation and operation cost of the photovoltaic node ; is the annual installation and operation cost of the shared energy storage node ; is the power purchase price of the power distribution network per unit power at the moment ; is the power purchase price of the power distribution network per unit power at the moment ; is the power to be purchased at the moment is the number of energy storage nodes The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， wherein: , , are the annual investment, operation and maintenance, disposal costs per unit of PV capacity; is the PV installation capacity of node ; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: , , are the annual investment, operation and maintenance, disposal cost per unit power capacity of the energy storage; , , are the annual investment, operation and maintenance, disposal cost per unit energy capacity of the energy storage; , are the power installation capacity and energy installation capacity of the energy storage system of the node . The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， wherein: is the number of grid nodes; is the conductance of branch ; , are the active and reactive power flowing through branch during time period ; are the node voltages during time period flowing through branch ; is the set of branches connected to node ; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formula: photovoltaic grid-connected node In the power quality evaluation value at the moment. 4.The method of claim 1, wherein the power quality management is considered. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， In the formulae: is the power generated by the node; is the power consumed by the node; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， ， In the formula: and are the energy storage charge and discharge states, respectively; and are the energy storage charge and discharge powers, respectively; is the energy storage power state at the moment; and are the energy storage charge and discharge efficiencies, respectively; is the maximum capacity power of the node ; , are the upper and lower limits of the energy storage capacity installed in the node ; The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， wherein: is the installed capacity of distributed photovoltaics at node ; is the maximum capacity of photovoltaics that can be installed at node ; is the active power output of the photovoltaics at node ; at time t; , are the upper and lower bounds of photovoltaic power output at node ; at time t. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， wherein: , are the active power of the photovoltaic injection and the load consumption at the time instant node , are the reactive power of the photovoltaic injection and the load consumption at the time instant node , are the active power of the photovoltaic injection and the load consumption at the time instant node , are the reactive power of the photovoltaic injection and the load consumption at the time instant node , are the active power of the photovoltaic injection and the load consumption at the time instant node , are the active power of the photovoltaic injection and the load consumption at the time instant node , , are the conductance, admittance, power angle between nodes and , , are the actual voltage at the time instant node , is the number of branches connected to node .

5. The method of claim 1, wherein: The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. ， ， In the formula: For the first The velocity of each particle in the next iteration; Inertial weight; These are the weights for individual learning and group learning, respectively. for Random numbers between; For particles The historical best position; This is the globally optimal position for the entire population; For the first The position of each particle in the next iteration; Chaotic tent map generated sequences is represented as: ， The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to a photovoltaic (PV) and energy storage (ES) integrated distribution network optimization operation method considering power quality management. The application relates to ， wherein: respectively, are cross, genetic factor; is a random number between 0 and 1.

6. An optimal operation system of a photovoltaic energy storage and charging power distribution area considering power quality treatment, characterized in that, Comprise: Storage module: to store the optimization model and the input data; Operation module: for the data into the model for calculation. 7.The system of claim 6, wherein, The power quality index of distribution area in the storage module includes voltage deviation and voltage fluctuation; The voltage deviation includes node voltage deviation and voltage fluctuation, and the node voltage deviation is the difference between the actual voltage of photovoltaic grid-connected node and the nominal voltage of the system, and its expression is: ， wherein: is the actual voltage of the distributed photovoltaic access node ; and is the nominal voltage of the node. The expression of voltage fluctuation is: ， In the formula: For the first Voltage fluctuations at individual nodes caused by distributed photovoltaic grid connection; For the power grid Each node is numbered; For the first The resistance of the power grid lines before each node; This refers to the output value of distributed photovoltaic power. It is the ratio of the instantaneous change in the output power of distributed photovoltaic power to the rated output power of photovoltaic power. In order to objectively evaluate the power quality, voltage deviation, voltage fluctuation and voltage harmonic are selected to construct the comprehensive evaluation function of power quality, and the weight coefficients of each evaluation index are determined by using analytic hierarchy process, and the expression of evaluation function is: ， In the formula: is the weight coefficient of the evaluation index. 8.The system for optimal operation of an optical storage and charging power distribution area considering power quality treatment according to claim 6, wherein, The model of distributed photovoltaic power generation system stored in the storage module is embodied by the output power of solar cell, the maximum direct current output power of solar cell array is: ， wherein: Pout is the solar array output power in W / m2; ; I is the irradiance in W / m2; ; η is the power conversion efficiency; whose value is relatively constant; Pmax is the maximum power of the solar cell array, in W / m2 ; T is the operating temperature of the solar cell array, in °C ; C is a constant coefficient; The working temperature of solar cell array is: ， In the formula: is the ambient temperature, in °C ; Therefore, the calculation formula of solar cell array output power is: 。 9.The system for optimal operation of an optical storage and charging power distribution area considering power quality treatment according to claim 6, wherein, The double-layer optimization model of distribution area stored in the storage module includes upper layer photovoltaic storage capacity optimization configuration model and lower layer model; The upper layer photovoltaic storage capacity optimization configuration model optimally plans with the lowest total light rejection rate of distribution network and the lowest annual investment and operation cost of photovoltaic power generation system as the target; Total light rejection of a power distribution zone is: ； wherein: photovoltaic grid point at maximum power that can be emitted at the moment; photovoltaic grid point at actual power at the moment; photovoltaic grid points The investment and operation cost of photovoltaic power generation system is: ， In the formula: is the annual installation and operation cost of the photovoltaic node ; is the annual installation and operation cost of the shared energy storage node ; is the unit power purchase price of the power distribution network at the moment ; is the purchased power of the power distribution network at the moment ; is the purchased power of the power distribution network at the moment ; is the number of energy storage nodes The annual investment and operation cost of photovoltaic is: ， wherein: , , are the annual investment, operation and maintenance, disposal costs per unit of PV capacity, respectively; is the PV installation capacity of node . The annual investment and operation cost of shared energy storage system is: ， wherein: , , are the annual investment, operation and disposal cost per unit of power capacity of the energy storage; , , are the annual investment, operation and disposal cost per unit of energy capacity of the energy storage; , are the power installation capacity and energy installation capacity of the energy storage system at the node , respectively. The lower layer model optimizes the operation with the minimum network loss and power quality evaluation value as the target, so as to realize the comprehensive optimization of economic operation and power quality management; The network loss is: ， wherein: is the number of grid nodes; is the conductance of branch ; , are the active and reactive power flowing through branch during time interval ; are the node voltages of nodes during time interval ; is the set of branches connected to node ; The power quality evaluation value of photovoltaic grid-connected node is: ， In the formula: photovoltaic grid-connected node at power quality evaluation value at the moment 10.The system for optimal operation of an optical storage and charging power distribution area considering power quality treatment according to claim 6, wherein, The constraint conditions of photovoltaic storage grid-connected optimization configuration model in the storage module include: Node power balance constraint, specifically: ， In the formulae: is the power generated by the node; is the power consumed by the node; Shared energy storage constraint, specifically: ， ， In the formula: and are the state of charge and discharge of the energy storage, respectively; and are the power of charge and discharge of the energy storage, respectively; is the state of energy of the energy storage at the moment; and are the efficiency of charge and discharge of the energy storage, respectively; is the maximum capacity power of the node ; , are the upper and lower limits of the installation capacity of the energy storage of the node ; Node photovoltaic output and capacity constraint, specifically: ， wherein: is the installed capacity of distributed photovoltaics at node ; is the maximum capacity of photovoltaics that can be installed at node ; is the active power output of the photovoltaics at node ; at time t; , are the upper and lower bounds of photovoltaic power output at node ; at time t, respectively. System power flow constraint, specifically: ， where: , are the active power of the photovoltaic injection and the load consumption at the time instant node , are the reactive power of the photovoltaic injection and the load consumption at the time instant node , are the active power of the energy storage injection and the capacitor reactive power at the time instant node , are the conductance, admittance, power angle between nodes , are the actual voltage at the time instant node , are the number of branches connected to node , , are the conductance, admittance, power angle between nodes and , , are the actual voltage at the time instant node , is the number of branches connected to node .

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