Distributed photovoltaic access energy storage micro-grid optimization scheduling method and system

Abstract

The invention is suitable for the technical field of micro-grid optimization scheduling, and provides a distributed photovoltaic access energy storage micro-grid optimization scheduling method and system, and the method comprises the steps: building a first scheduling model with the minimization of the operation cost of a micro-grid as a target, the target parameters of the first scheduling model comprise fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost; constructing a second scheduling model by taking the maximization of the utilization rate of distributed photovoltaic as a target, wherein the target parameters of the second scheduling model comprise the conversion efficiency of a photovoltaic module, the output power caused by shadow shielding, the output power influenced by installation and configuration and the output power influenced by an inverter; determining constraint conditions of the first scheduling model and the second scheduling model respectively; and solving the first scheduling model and the second scheduling model by using an efficacy coefficient method, and performing optimization based on the obtained comprehensive efficacy coefficient, thereby effectively improving the comprehensiveness and stability of microgrid scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization and scheduling, and in particular to a distributed photovoltaic access energy storage microgrid optimization and scheduling method and system. Background Art

[0002] As a new type of energy supply system, microgrid can effectively integrate multiple energy resources, realize the local consumption and optimal configuration of distributed energy, and improve energy utilization efficiency and power supply reliability. Since the power demand of users varies greatly at different stages, the output of distributed power sources will also be limited by natural conditions. Therefore, it is necessary to balance the output of distributed power in different time periods through effective control and management means to match it with the power demand of users.

[0003] In the prior art, microgrid dispatching based on distributed photovoltaic output usually considers factors such as temperature, light intensity and light incident angle, but lacks consideration of some additional factors. For example, the characteristics of photovoltaic modules may result in lower power generation than expected under the same lighting conditions due to the use of low conversion efficiency modules or the lack of attention to the influence of temperature coefficients; ignoring shadows, obstructions caused by buildings, trees, etc., and accumulation of pollutants such as dust will reduce the light receiving area and affect photovoltaic output; not considering inverter performance will lead to low efficiency or inaccurate maximum power tracking, which will lead to insufficient conversion of DC to AC and reduce the effective utilization of photovoltaic output. In addition, there are other influencing factors. If all influencing factors are not comprehensively considered, it will affect the absorption of photovoltaic energy and even affect the performance and benefits of the entire photovoltaic system, resulting in the problem that microgrid dispatching is not comprehensive and unreasonable.

[0004] In view of this, a method and system for optimizing the scheduling of a distributed photovoltaic-connected energy storage microgrid is needed. Summary of the invention

[0005] The embodiments of the present application provide a method and system for optimizing the scheduling of a distributed photovoltaic-connected energy storage microgrid, which is used to solve the problem that the microgrid scheduling is not comprehensive and reasonable.

[0006] The first aspect of the embodiment of the present application provides a method for optimizing the scheduling of a distributed photovoltaic access energy storage microgrid, including:

[0007] A first dispatching model is constructed with the goal of minimizing the operating cost of the microgrid, wherein the target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost;

[0008] A second scheduling model is constructed with the goal of maximizing the utilization rate of distributed photovoltaics, wherein the target parameters of the second scheduling model include conversion efficiency of photovoltaic modules, output power caused by shadow shading, output power affected by installation and configuration, and output power affected by inverters;

[0009] Determining constraints of the first scheduling model and the second scheduling model respectively;

[0010] The first scheduling model and the second scheduling model are solved using the efficiency coefficient method, and are optimized based on the obtained comprehensive efficiency coefficients.

[0011] Furthermore, the first dispatching model is constructed with the goal of minimizing the operating cost of the microgrid, and the target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost, including:

[0012] minC=C F +C B +C S +C M

[0013] Where: C F is the fuel cost, C B is the electricity purchase cost, C S is the energy storage loss cost, C M For operation and maintenance costs.

[0014] Furthermore, the first dispatching model is constructed with the goal of minimizing the operating cost of the microgrid, and the target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost, including:

[0015]

[0016]

[0017] Among them: a F is the fuel cost coefficient, P F (t) is the fuel consumption power at time t, a G is the price of electricity purchased from the external power grid, P G (t) is the interaction power between the microgrid and the external grid at time t, (0, P G (t)) max is the maximum interaction power, a c and a d are the charge and discharge coefficients of the energy storage device, P C (t) and P D (t) are the charging and discharging power of the energy storage device at time t, a m 、b m and c m are the operation and maintenance cost coefficients of distributed photovoltaic, energy storage devices and other distributed power sources, P PV (t), P S (t) and P DG(t) are the equipment capacity or power corresponding to distributed photovoltaic, energy storage devices and other distributed power sources at time t.

[0018] Furthermore, the second scheduling model is constructed with the goal of maximizing the utilization rate of distributed photovoltaics. The target parameters of the second scheduling model include the conversion efficiency of photovoltaic modules, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter, including:

[0019]

[0020] Where: ηpv(t) is the utilization rate of distributed photovoltaic at time t, P PV (t) is the output power of distributed photovoltaic at time t, P PV-max (t) is the maximum output power of distributed photovoltaic at time t, is the maximum conversion efficiency of the photovoltaic module, is the maximum power reduction caused by shadowing, is the maximum output power after uneven shadow distribution, The maximum output power of the photovoltaic array is the installation inclination and azimuth angle. is the maximum output power of the photovoltaic array in series-parallel configuration, is the maximum output power affected by the inverter efficiency, The maximum output power affected by the inverter tracking accuracy.

[0021] Furthermore, the second scheduling model is constructed with the goal of maximizing the utilization rate of distributed photovoltaics. The target parameters of the second scheduling model include the conversion efficiency of photovoltaic modules, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter, including:

[0022]

[0023] Where: η eff is the conversion efficiency of the photovoltaic module, S is the area of ​​the photovoltaic module, G STC is the standard light intensity, N s is the number of components connected in series, N p is the number of components connected in parallel;

[0024]

[0025] in: is the maximum output power of the unshaded photovoltaic array, γ is the ratio of the shadow area to the total area of ​​the photovoltaic array;

[0026]

[0027] Where: k is the shadow unevenness coefficient;

[0028]

[0029] Where: P STC is the rated power of a single photovoltaic module, G is the actual light intensity, cosθ is the solar incident angle, α is the azimuth angle, β is the installation inclination angle, δ is the solar declination angle, is the local latitude, ω is the hour angle;

[0030]

[0031] Where: V mp is the maximum power point voltage of a single component, I mp is the maximum power point current of a single component;

[0032]

[0033] in: is the maximum DC power output by the photovoltaic array, η inv is the inverter efficiency;

[0034]

[0035] Where: P mppt is the theoretical maximum power point power of the inverter, P actual It is the maximum power actually tracked by the inverter.

[0036] Furthermore, the determining the constraint conditions of the first scheduling model and the second scheduling model respectively includes:

[0037] The constraints include power balance constraints, capacity constraints of energy storage devices, charging and discharging power constraints of energy storage devices, power constraints for interaction with external power grids, distributed photovoltaic power constraints, and distributed photovoltaic utilization constraints.

[0038] Furthermore, the constraints include power balance constraints, capacity constraints of energy storage devices, charging and discharging power constraints of energy storage devices, power constraints for interaction with external power grids, distributed photovoltaic power constraints, and distributed photovoltaic utilization constraints, including:

[0039] Power balance constraints:

[0040] P PV (t)+P D (t)+P G (t) = P L (t)+P C (t)

[0041] Where: P L(t) is the load power in the microgrid at time t;

[0042] Capacity constraints of energy storage devices:

[0043] SOC min ≤SOC(t)≤SOC max

[0044]

[0045] Among them: SOC min , SOC max are the minimum and maximum state of charge of the energy storage device, E r is the rated capacity of the energy storage device, Δt is the time interval;

[0046] Charging and discharging power constraints of energy storage devices:

[0047] 0≤P C (t)≤P C-max (t)

[0048] 0≤P D (t)≤P D-max (t)

[0049] Where: P C-max (t) and P D-max (t) is the maximum charging power and maximum discharging power of the energy storage device;

[0050] Interaction power constraints with external grid:

[0051] P G-min (t)≤P G (t)≤P G-max (t)

[0052] Where: P G-min (t) and P G-max (t) are the minimum and maximum values ​​of the interaction power between the microgrid and the external grid, respectively;

[0053] Distributed photovoltaic power constraints:

[0054] 0≤P PV (t)≤P PV-max (t)

[0055] Distributed photovoltaic utilization constraints:

[0056]

[0057] in: It is the minimum utilization rate of distributed photovoltaics.

[0058] Furthermore, solving the first scheduling model and the second scheduling model by using the efficiency coefficient method and optimizing based on the obtained comprehensive efficiency coefficient includes:

[0059] Set the satisfaction value C of the first scheduling model 0 and the unacceptable value C 1 , set the satisfaction value of the second scheduling model and unacceptable values

[0060] Calculate the efficiency coefficient of microgrid operation cost and distributed photovoltaic utilization rate respectively;

[0061] A comprehensive efficiency coefficient is determined based on the efficiency coefficient of the microgrid operating cost and the efficiency coefficient of the distributed photovoltaic utilization rate.

[0062] Furthermore, the comprehensive efficiency coefficient is determined based on the efficiency coefficient of the microgrid operation cost and the efficiency coefficient of the distributed photovoltaic utilization rate, including:

[0063] The efficiency coefficient d1 of the microgrid operation cost:

[0064]

[0065] The efficiency coefficient d2 of distributed photovoltaic utilization rate:

[0066]

[0067] Comprehensive efficacy coefficient D:

[0068] D=w1d1+w2d2

[0069] Among them: w1 and w2 are the weight coefficients of the efficiency coefficient of the microgrid operation cost and the efficiency coefficient of the distributed photovoltaic utilization rate respectively.

[0070] A second aspect of the embodiment of the present application provides a distributed photovoltaic access energy storage microgrid optimization scheduling system, including:

[0071] A first scheduling model building unit, used to build a first scheduling model with the goal of minimizing the operating cost of the microgrid, wherein the target parameters of the first scheduling model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost;

[0072] A second scheduling model building unit is used to build a second scheduling model with the goal of maximizing the utilization rate of distributed photovoltaics, wherein the target parameters of the second scheduling model include the conversion efficiency of photovoltaic components, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter;

[0073] A constraint condition determination unit, used to determine the constraint conditions of the first scheduling model and the second scheduling model respectively;

[0074] The model optimization unit is used to solve the first scheduling model and the second scheduling model by using the efficiency coefficient method, and optimize based on the obtained comprehensive efficiency coefficient.

[0075] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0076] 1. The present invention can reduce fuel costs, electricity purchase costs, energy storage losses and operation and maintenance costs by minimizing the cost of microgrid operation and reasonably arranging distributed power sources, energy storage devices and interactive power with external power grids, thereby avoiding unnecessary energy expenditures and improving the economy of microgrids.

[0077] 2. The present invention can promote the photovoltaic system to accurately configure resources by considering the maximization of the utilization rate of distributed photovoltaic access, so that each link can operate efficiently and reduce energy waste. In terms of distributed photovoltaic utilization, optimized scheduling can ensure that photovoltaics can output as much electricity as possible under various environmental conditions, reducing idle power generation capacity caused by factors such as light changes and shadows. It can not only make full use of clean energy and reduce dependence on traditional energy, but also improve the ability of microgrids to cope with energy supply uncertainty and enhance power supply stability and reliability.

[0078] Other advantages, objectives, and features of the present invention will be set forth in part in the following description, and in part will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 The present invention is a schematic diagram of an embodiment of a method for optimizing the scheduling of a distributed photovoltaic-connected energy storage microgrid. DETAILED DESCRIPTION

[0080] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "corresponding to" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0081] Embodiment 1

[0082] The implementation method in this embodiment can be implemented in the system, in the server, or in the terminal, and is not specifically limited. The following will introduce the distributed photovoltaic access energy storage microgrid optimization scheduling method in this application from the perspective of system implementation. Figure 1 , the method provided in the embodiment of the present application comprises the following steps:

[0083] S11. Constructing a first dispatching model with the goal of minimizing the operating cost of the microgrid, the target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost;

[0084] The operating costs of the energy storage microgrid mainly include fuel costs, electricity purchase costs, energy storage loss costs, and operation and maintenance costs. The objective function generates the first scheduling model, and the expression is as follows:

[0085] minC=C F +C B +C S +C M

[0086] Where: C F is the fuel cost, C B is the electricity purchase cost, C S is the energy storage loss cost, C M For operation and maintenance costs.

[0087] Specific:

[0088]

[0089] Among them: a F is the fuel cost coefficient, P F (t) is the fuel consumption power at time t, a G is the price of electricity purchased from the external power grid, P G(t) is the interaction power between the microgrid and the external grid at time t, (0, P G (t)) max is the maximum interaction power, a c and a d are the charge and discharge coefficients of the energy storage device, P C (t) and P D (t) are the charging and discharging power of the energy storage device at time t, a m 、b m and c m are the operation and maintenance cost coefficients of distributed photovoltaic, energy storage devices and other distributed power sources, P PV (t), P S (t) and P DG (t) are the equipment capacity or power corresponding to distributed photovoltaic, energy storage devices and other distributed power sources at time t.

[0090] S12. Constructing a second scheduling model with the goal of maximizing the utilization rate of distributed photovoltaics, the target parameters of the second scheduling model include the conversion efficiency of photovoltaic modules, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter;

[0091] In this embodiment, the utilization rate of distributed photovoltaics is maximized. Here, the conversion efficiency of photovoltaic modules, the output power caused by shadowing, the output power affected by installation and configuration, and the output power affected by the inverter are considered. The second scheduling model is determined by the objective function, and the expression is as follows:

[0092]

[0093] Where: ηpv(t) is the utilization rate of distributed photovoltaic at time t, P PV (t) is the output power of distributed photovoltaic at time t, P PV-max (t) is the maximum output power of distributed photovoltaic at time t, is the maximum conversion efficiency of the photovoltaic module, is the maximum power reduction caused by shadowing, is the maximum output power after uneven shadow distribution, The maximum output power of the photovoltaic array is the installation inclination and azimuth angle. is the maximum output power of the photovoltaic array in series-parallel configuration, is the maximum output power affected by the inverter efficiency, The maximum output power affected by the inverter tracking accuracy.

[0094] Different types and models of photovoltaic modules have different conversion efficiencies, which are calculated as follows:

[0095]

[0096] Where: η eff is the conversion efficiency of the photovoltaic module, S is the area of ​​the photovoltaic module, G STC is the standard light intensity, N s is the number of components connected in series, N p is the number of components connected in parallel.

[0097] When a photovoltaic module is shaded by buildings, trees, or other objects, the photovoltaic cells in the shadowed area cannot generate electricity normally, thereby reducing the output power of the entire photovoltaic module. The maximum output power reduction due to shadowing is:

[0098]

[0099] in: is the maximum output power of the photovoltaic array without shadows, and γ is the ratio of the shadow area to the total area of ​​the photovoltaic array. The calculation of γ requires detailed analysis based on factors such as the layout of the array, the size of the components, the installation angle, and the position of the sun at different times. For example, for a regularly arranged photovoltaic array, the number and area of ​​the components that are shaded at a certain moment can be calculated through geometric relationships, and then the shadow area ratio can be obtained.

[0100] The influence of uneven shadow distribution on the maximum output power is relatively complex. Here, the calculation formula of the maximum output power is corrected by introducing the uneven shadow coefficient:

[0101]

[0102] Where: k is the shadow unevenness coefficient. The value range of the shadow unevenness coefficient k is to, and its specific value needs to be evaluated and determined based on the actual distribution of the shadow and the degree of impact on the performance of the component. Generally speaking, the more uneven the shadow distribution is, the larger the value of k is, and the more significant the impact on the maximum output power is.

[0103] The installation inclination and azimuth can be determined based on the influence of the installation position and orientation. Assuming the solar incident angle is θ, the maximum output power of the photovoltaic array under different installation inclination and azimuth can be calculated by the following formula:

[0104]

[0105] Where: P STC is the rated power of a single photovoltaic module, G is the actual light intensity, cosθ is the solar incident angle, α is the azimuth angle, β is the installation inclination angle, δ is the solar declination angle, is the local latitude, and ω is the hour angle. The solar incidence angle θ can be calculated through solar geometry based on factors such as the local latitude, season, time, and the installation inclination and azimuth of the photovoltaic array.

[0106] Taking into account the selection and matching of components and the series-parallel configuration, the maximum output power calculation formula of the photovoltaic array is:

[0107]

[0108] Where: V mp is the maximum power point voltage of a single component, I mp is the maximum power point current of a single component.

[0109] The inverter efficiency is related to factors such as input voltage, input power, and load rate:

[0110]

[0111] in: is the maximum DC power output by the photovoltaic array, η inv is the inverter efficiency.

[0112] In actual calculation, it is necessary to select the appropriate efficiency value for calculation according to the efficiency curve of the inverter and different input power and working conditions. Generally speaking, the efficiency of the inverter is higher near the rated load, and the efficiency will decrease at low or high load.

[0113] The maximum power tracking accuracy indicates the difference between the maximum power actually tracked by the inverter and the theoretical maximum power point power. Then the maximum output power of the photovoltaic system after the maximum power tracking accuracy is:

[0114]

[0115] Where: P mppt is the theoretical maximum power point power of the inverter, P actual The maximum power actually tracked by the inverter. By improving the maximum power tracking accuracy of the inverter, the photovoltaic modules can work closer to the maximum power point, thereby improving the power generation efficiency and maximum output power of the photovoltaic system.

[0116] S13. Determine the constraints of the first scheduling model and the second scheduling model respectively;

[0117] In this embodiment, the constraints include power balance constraints, capacity constraints of energy storage devices, charging and discharging power constraints of energy storage devices, power constraints for interaction with external power grids, distributed photovoltaic power constraints, and distributed photovoltaic utilization constraints.

[0118] Specifically:

[0119] Power balance constraints:

[0120] P PV (t)+P D (t)+P G (t) = P L (t)+P C (t)

[0121] Where: P L (t) is the load power in the microgrid at time t;

[0122] Capacity constraints of energy storage devices:

[0123] SOC min ≤SOC(t)≤SOC max

[0124]

[0125] Among them: SOC min , SOC max are the minimum and maximum state of charge of the energy storage device, E r is the rated capacity of the energy storage device, Δt is the time interval;

[0126] Charging and discharging power constraints of energy storage devices:

[0127] 0≤P C (t)≤P C-max (t)

[0128] 0≤P D (t)≤P D-max (t)

[0129] Where: P C-max (t) and P D-max (t) is the maximum charging power and maximum discharging power of the energy storage device;

[0130] Interaction power constraints with external grid:

[0131] P G-min (t)≤P G (t)≤P G-max (t)

[0132] Where: P G-min (t) and P G-max (t) are the minimum and maximum values ​​of the interaction power between the microgrid and the external grid, respectively;

[0133] Distributed photovoltaic power constraints:

[0134] 0≤P PV (t)≤P PV-max(t)

[0135] Distributed photovoltaic utilization constraints:

[0136]

[0137] in: It is the minimum utilization rate of distributed photovoltaics.

[0138] S14. Solve the first scheduling model and the second scheduling model using the efficiency coefficient method, and optimize based on the obtained comprehensive efficiency coefficient.

[0139] Step S14 also includes the following:

[0140] 1. Set the satisfaction value C of the first scheduling model 0 and the unacceptable value C 1 , set the satisfaction value of the second scheduling model and unacceptable values

[0141] 2. Calculate the efficiency coefficient of microgrid operation cost and distributed photovoltaic utilization efficiency respectively;

[0142] 3. Determine the comprehensive efficiency coefficient based on the efficiency coefficient of the microgrid operating cost and the efficiency coefficient of the distributed photovoltaic utilization rate.

[0143] The efficiency coefficient method is an evaluation method that converts multiple objectives into a single objective. For the two objectives of minimizing the operating cost of the microgrid and maximizing the utilization rate of distributed photovoltaics, efficiency coefficient functions are established respectively, and the actual value of each objective is mapped to an efficiency coefficient between 0 and 1. Then, these efficiency coefficients are synthesized into a comprehensive efficiency coefficient through a certain synthesis method, and the comprehensive efficiency coefficient is used as the optimization target. The larger its value, the better the solution.

[0144] Here, the satisfaction value C of the first scheduling model is set 0 and the unacceptable value C 1 , set the satisfaction value of the second scheduling model and unacceptable values Then the efficiency coefficient d1 of the microgrid operation cost and the efficiency coefficient d2 of the distributed photovoltaic utilization rate can be expressed as:

[0145]

[0146] Specifically, the comprehensive efficacy coefficient is calculated according to the weighted arithmetic mean method:

[0147] D=w1d1+w2d2

[0148] Among them: w1 and w2 are the weight coefficients of the efficiency coefficient of the microgrid operation cost and the efficiency coefficient of the distributed photovoltaic utilization rate respectively.

[0149] Finally, the first scheduling model and the second scheduling model are optimized with the comprehensive efficiency coefficient as the optimization target to obtain the optimal energy storage microgrid scheduling plan.

[0150] Embodiment 2

[0151] An embodiment of a distributed photovoltaic access energy storage microgrid optimization dispatching system of the present invention comprises the following steps:

[0152] A first dispatch model building unit is used to build a first dispatch model with the goal of minimizing the operating cost of the microgrid, and the target parameters of the first dispatch model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost;

[0153] A second scheduling model building unit is used to build a second scheduling model with the goal of maximizing the utilization rate of distributed photovoltaics, and the target parameters of the second scheduling model include the conversion efficiency of photovoltaic components, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter;

[0154] A constraint condition determination unit, used to determine the constraint conditions of the first scheduling model and the second scheduling model respectively;

[0155] The model optimization unit is used to solve the first scheduling model and the second scheduling model by using the efficiency coefficient method, and optimize based on the obtained comprehensive efficiency coefficient.

[0156] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0157] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0158] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0159] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0160] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A distributed photovoltaic access energy storage microgrid optimization scheduling method, characterized in that: include: A first dispatching model is constructed with the goal of minimizing the operating cost of the microgrid, wherein the target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost; A second scheduling model is constructed with the goal of maximizing the utilization rate of distributed photovoltaics, wherein the target parameters of the second scheduling model include conversion efficiency of photovoltaic modules, output power caused by shadow shading, output power affected by installation and configuration, and output power affected by inverters; Determining constraints of the first scheduling model and the second scheduling model respectively; The first scheduling model and the second scheduling model are solved using the efficiency coefficient method, and are optimized based on the obtained comprehensive efficiency coefficients.

2. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 1, characterized in that: The first dispatching model is constructed with the goal of minimizing the operating cost of the microgrid. The target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost, including: minC=C F +C B +C S +C M Where: C F is the fuel cost, C B is the electricity purchase cost, C S is the energy storage loss cost, C M For operation and maintenance costs.

3. The distributed photovoltaic access energy storage microgrid optimization scheduling method according to claim 2 is characterized in that: The first dispatching model is constructed with the goal of minimizing the operating cost of the microgrid. The target parameters of the first dispatching model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost, including: Among them: a F is the fuel cost coefficient, P F (t) is the fuel consumption power at time t, a G is the price of electricity purchased from the external power grid, P G (t) is the interaction power between the microgrid and the external grid at time t, (0,P G (t)) max is the maximum interaction power, a c and a d are the charge and discharge coefficients of the energy storage device, P C (t) and P D (t) are the charging and discharging power of the energy storage device at time t, a m 、b m and c m are the operation and maintenance cost coefficients of distributed photovoltaic, energy storage devices and other distributed power sources, P PV (t), P S (t) and P DG (t) are the equipment capacity or power corresponding to distributed photovoltaic, energy storage devices and other distributed power sources at time t.

4. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 1, characterized in that: The second scheduling model is constructed with the goal of maximizing the utilization rate of distributed photovoltaics. The target parameters of the second scheduling model include the conversion efficiency of photovoltaic components, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter, including: Where: ηpv(t) is the utilization rate of distributed photovoltaic at time t, P PV (t) is the output power of distributed photovoltaic at time t, P PV―max (t) is the maximum output power of distributed photovoltaic at time t, is the maximum conversion efficiency of the photovoltaic module, is the maximum power reduction caused by shadowing, is the maximum output power after uneven shadow distribution, The maximum output power of the photovoltaic array for the installation inclination and azimuth angles is: is the maximum output power of the photovoltaic array in series-parallel configuration, is the maximum output power affected by the inverter efficiency, The maximum output power affected by the inverter tracking accuracy.

5. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 4 is characterized in that: The second scheduling model is constructed with the goal of maximizing the utilization rate of distributed photovoltaics. The target parameters of the second scheduling model include the conversion efficiency of photovoltaic components, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter, including: Where: η eff is the conversion efficiency of the photovoltaic module, S is the area of ​​the photovoltaic module, G STC is the standard light intensity, N s is the number of components connected in series, N p is the number of components connected in parallel; in: is the maximum output power of the unshaded photovoltaic array, γ is the ratio of the shadow area to the total area of ​​the photovoltaic array; Where: k is the shadow unevenness coefficient; Where: P STC is the rated power of a single photovoltaic module, G is the actual light intensity, cosθ is the solar incident angle, α is the azimuth angle, β is the installation inclination angle, δ is the solar declination angle, is the local latitude, ω is the hour angle; Where: V mp is the maximum power point voltage of a single component, I mp is the maximum power point current of a single component; in: is the maximum DC power output by the photovoltaic array, η inv is the inverter efficiency; Where: P mppt is the theoretical maximum power point power of the inverter, P actual It is the maximum power actually tracked by the inverter.

6. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 1, characterized in that: The determining the constraint conditions of the first scheduling model and the second scheduling model respectively includes: The constraints include power balance constraints, capacity constraints of energy storage devices, charging and discharging power constraints of energy storage devices, power constraints for interaction with external power grids, distributed photovoltaic power constraints, and distributed photovoltaic utilization constraints.

7. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 6, characterized in that: The constraints include power balance constraints, capacity constraints of energy storage devices, charging and discharging power constraints of energy storage devices, power constraints for interaction with external power grids, distributed photovoltaic power constraints, and distributed photovoltaic utilization constraints, including: Power balance constraints: P PV (t)+P D (t)+P G (t)=P L (t)+P C (t) Where: P L (t) is the load power in the microgrid at time t; Capacity constraints of energy storage devices: SOC min ≤SOC(t)≤SOC max Among them: SOC min , SOC max are the minimum and maximum state of charge of the energy storage device, E r is the rated capacity of the energy storage device, Δt is the time interval; Charging and discharging power constraints of energy storage devices: 0≤P C (t)≤P C―max (t) 0≤P D (t)≤P D―max (t) Where: P C―max (t) and P D―max (t) is the maximum charging power and maximum discharging power of the energy storage device; Interaction power constraints with external grid: P G―min (t)≤P G (t)≤P G―max (t) Where: P G―min (t) and P G―max (t) are the minimum and maximum values ​​of the interaction power between the microgrid and the external grid, respectively; Distributed photovoltaic power constraints: 0≤P PV (t)≤P PV―max (t) Distributed photovoltaic utilization constraints: in: It is the minimum utilization rate of distributed photovoltaics.

8. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 1, characterized in that: The method of solving the first scheduling model and the second scheduling model by using the efficiency coefficient method and optimizing based on the obtained comprehensive efficiency coefficient includes: Set the satisfaction value C of the first scheduling model 0 and the unacceptable value C 1 , set the satisfaction value of the second scheduling model and unacceptable values Calculate the efficiency coefficient of microgrid operation cost and distributed photovoltaic utilization rate respectively; A comprehensive efficiency coefficient is determined based on the efficiency coefficient of the microgrid operating cost and the efficiency coefficient of the distributed photovoltaic utilization rate.

9. The method for optimizing and dispatching distributed photovoltaic access energy storage microgrid according to claim 8, characterized in that: The determining of the comprehensive efficiency coefficient based on the efficiency coefficient of the microgrid operation cost and the efficiency coefficient of the distributed photovoltaic utilization rate includes: The efficiency coefficient d1 of the microgrid operation cost: The efficiency coefficient d2 of distributed photovoltaic utilization rate: Comprehensive efficacy coefficient D: D=w1d1+w2d2 Among them: w1 and w2 are the weight coefficients of the efficiency coefficient of the microgrid operation cost and the efficiency coefficient of the distributed photovoltaic utilization rate respectively.

10. A distributed photovoltaic access energy storage microgrid optimization dispatching system, characterized in that: include: A first scheduling model building unit, used to build a first scheduling model with the goal of minimizing the operating cost of the microgrid, wherein the target parameters of the first scheduling model include fuel cost, electricity purchase cost, energy storage loss cost and operation and maintenance cost; A second scheduling model building unit is used to build a second scheduling model with the goal of maximizing the utilization rate of distributed photovoltaics, wherein the target parameters of the second scheduling model include the conversion efficiency of photovoltaic components, the output power caused by shadow shading, the output power affected by installation and configuration, and the output power affected by the inverter; A constraint condition determination unit, used to determine the constraint conditions of the first scheduling model and the second scheduling model respectively; The model optimization unit is used to solve the first scheduling model and the second scheduling model by using the efficiency coefficient method, and optimize based on the obtained comprehensive efficiency coefficient.

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