CN117154778A - Distributed energy storage optimal configuration method and system for power distribution network - Google Patents
Distributed energy storage optimal configuration method and system for power distribution network Download PDFInfo
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Abstract
The invention discloses a distributed energy storage optimal configuration method and a distributed energy storage optimal configuration system for a power distribution network, which relate to the technical field of energy storage utilization, wherein the method comprises the following steps: based on the two-parameter Weibull distribution, determining a wind power output probability distribution model according to the historical wind power output data set, and further determining a wind power output multi-state model; based on Beta distribution, determining a photovoltaic output probability distribution model according to a historical photovoltaic output data set, and further determining a photovoltaic output multi-state model; constructing an energy storage constraint set based on a target power distribution network; based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic output multi-state model, a distributed energy storage optimal configuration model of the power distribution network is built by taking the minimum sum of the power purchase cost, the energy storage operation cost and the network loss of the power distribution network as a target, and the distributed energy storage optimal configuration result of the power distribution network is determined by solving. The invention improves the running stability of the power distribution network.
Description
Technical Field
The invention relates to the technical field of energy storage utilization, in particular to a distributed energy storage optimal configuration method and system for a power distribution network.
Background
The novel power system taking new energy as a main body is an important component of a clean low-carbon and safe high-efficiency energy system, is a power system taking new energy as a main body, taking the premise of ensuring the energy power safety as a basic premise, taking the requirement of developing power in the economical society as a primary target, taking a strong smart grid as a hub platform and taking the interaction of the charge storage of the source network and the complementation of multiple functions as a support, and has the basic characteristics of clean low-carbon, safe, controllable, flexible, efficient, intelligent and friendly and open interaction. The energy storage is used as an important component and a key technology of a smart power grid, a renewable energy high-duty ratio system and an energy internet, and can provide various services such as peak regulation, frequency modulation, standby, black start, demand response support and the like for power grid operation. The application of the energy storage technology can smooth the power generation capacity of the photovoltaic and wind power, shift peaks and fill valleys, improve the utilization rate of the power transmission channel, greatly improve the flexibility and stability of power grid dispatching, and is beneficial to improving the frequency stability of regional power grids, realizing high-proportion access of new energy sources to the power grids and relieving the problem of the consumption of the new energy sources.
At present, the large-scale application of Chinese energy storage is still in the starting stage, and the trend of diversification development is presented in recent years. Energy storage systems with different capacities have different capacities on power fluctuation absorbing capacity of wind and light output, and have certain influence on the economical efficiency of system operation. The capacity of the energy storage system is optimized, the output fluctuation is reduced, the output stability is effectively improved, and the energy storage system is an important break-through for solving the problem of high-proportion new energy consumption.
Disclosure of Invention
The invention aims to provide a distributed energy storage optimal configuration method and system for a power distribution network, and the running stability of the power distribution network containing high-proportion new energy is improved.
In order to achieve the above object, the present invention provides the following solutions:
in a first aspect, the present invention provides a distributed energy storage optimization configuration method for a power distribution network, including:
acquiring a historical wind power output data set of a wind power plant and a historical photovoltaic output data set of a photovoltaic power station in a target power distribution network;
determining a wind power output probability distribution model according to the historical wind power output data set based on the two-parameter Weibull distribution; based on Beta distribution, determining a photovoltaic output probability distribution model according to the historical photovoltaic output data set;
sectional recombination is carried out on the wind power output probability distribution model so as to obtain a wind power output multi-state model; carrying out sectional recombination on the photovoltaic output probability distribution model to obtain a photovoltaic output multi-state model;
constructing an energy storage constraint set based on the target power distribution network; the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint;
based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic output multi-state model, a distributed energy storage optimization configuration model of the power distribution network is built by taking the minimum sum of the power purchase cost, the energy storage operation cost and the power distribution network loss of the power distribution network as a target;
and solving the distributed energy storage optimal configuration model of the power distribution network by adopting a self-adaptive robust optimization algorithm so as to determine the distributed energy storage optimal configuration result of the power distribution network.
In a second aspect, the present invention provides a distributed energy storage optimization configuration system for a power distribution network, including:
the data set acquisition module is used for acquiring a historical wind power output data set of the wind power plant and a historical photovoltaic output data set of the photovoltaic power station in the target power distribution network;
the probability distribution model construction module is used for determining a wind power output probability distribution model according to the historical wind power output data set based on two-parameter Weibull distribution; based on Beta distribution, determining a photovoltaic output probability distribution model according to the historical photovoltaic output data set;
the multi-state model construction module is used for carrying out sectional recombination on the wind power output probability distribution model so as to obtain a wind power output multi-state model; carrying out sectional recombination on the photovoltaic output probability distribution model to obtain a photovoltaic output multi-state model;
the constraint set construction module is used for constructing an energy storage constraint set based on the target power distribution network; the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint;
the energy storage optimization configuration model construction module is used for constructing a distribution network distributed energy storage optimization configuration model based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic output multi-state model and taking the minimum sum of the electricity purchasing cost, the energy storage operation cost and the network loss of the distribution network as a target;
and the optimal configuration result determining module is used for solving the distributed energy storage optimal configuration model of the power distribution network by adopting a self-adaptive robust optimization algorithm so as to determine the distributed energy storage optimal configuration result of the power distribution network.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a distributed energy storage optimal configuration method and a distributed energy storage optimal configuration system for a power distribution network, wherein a wind power output probability distribution model and a photovoltaic output probability distribution model are determined according to a historical wind power output data set of a wind power plant and a historical photovoltaic output data set of a photovoltaic power station in a target power distribution network, so that a wind power output multi-state model and a photovoltaic output multi-state model are determined; then constructing an energy storage constraint set, wherein the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint; based on the constraint and the multi-state model, a distributed energy storage optimal configuration model of the power distribution network is constructed by taking the minimum sum of the power distribution network electricity purchasing cost, the energy storage running cost and the power distribution network loss as a target, and finally, the distributed energy storage optimal configuration result of the power distribution network is determined by solving. According to the invention, the distributed energy storage in the power distribution network is optimally configured, so that the new energy consumption level of the power distribution network can be improved, the running stability of the high-proportion new energy power distribution network is further improved, and the reasonable planning and scientific investment of the novel power system energy storage project are facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a distributed energy storage optimization configuration method of a power distribution network;
FIG. 2 is a schematic diagram of probability density curves for wind speeds according to the present invention;
fig. 3 is a schematic diagram of a distributed energy storage optimization configuration system of the power distribution network.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a distributed energy storage optimal configuration method and a distributed energy storage optimal configuration system for a power distribution network, wherein an equivalent topological structure and a mathematical model of the power distribution network are constructed according to the operation mode of the power distribution network, so that the operation rationality of the power distribution network is ensured; the method comprises the steps of establishing a distribution network wind power plant and photovoltaic power station output probability distribution model, combining the operation mode of the distribution network, taking the investment cost of distributed energy storage and the minimum operation of the whole distribution network as targets, constructing an optimal configuration model of the distributed energy storage, comprehensively considering the operation limit constraint and the power balance constraint of the distribution network equipment containing high-proportion new energy, and solving the optimal configuration scheme of the distributed energy storage by adopting a self-adaptive robust optimization algorithm, so that the function of the distributed energy storage can be fully exerted, and the operation stability of the high-proportion new energy distribution network is improved.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, the present invention provides a distributed energy storage optimization configuration method for a power distribution network, including:
step 100, acquiring a historical wind power output data set of a wind power plant and a historical photovoltaic output data set of a photovoltaic power station in a target power distribution network.
Step 200, determining a wind power output probability distribution model according to the historical wind power output data set based on the two-parameter Weibull distribution; based on Beta distribution, determining a photovoltaic output probability distribution model according to the historical photovoltaic output data set; the historical wind power output data set comprises wind power plant wind speed and wind power unit output power at the same historical moment; the historical photovoltaic output data set comprises illumination intensity and photovoltaic output power at the same historical time; in particular, the wind and light output data is synchronized.
The determining process of the wind power output probability distribution model specifically comprises the following steps:
1) Based on the two-parameter Weibull distribution, determining a wind speed probability distribution model according to wind speeds of the wind power plant at a plurality of historical moments; specifically, through wind speed statistical data of a wind power plant (namely wind power plant wind speeds at a plurality of historical moments), the wind speed probability distribution model is obtained by adopting two-parameter Weibull distribution to fit the wind speed probability distribution of the wind power plant and utilizing a maximum likelihood method to estimate the size parameters and the shape parameters of the Weibull distribution model.
The wind speed probability distribution model is as follows:
wherein f i (v) Probability of wind speed for ith wind farm, v i For the ith wind farm wind speed, k i For the form factor of the wind speed of the ith wind farm c i Is the ith wind farm scale factor.
Fig. 2 is a schematic diagram of probability density curves of wind speeds, a wind speed histogram is obtained according to measured wind speed data of a wind farm for 3 years, and a Weibull distribution wind speed probability density curve is obtained by using a maximum likelihood estimation method. The Weibull distribution parameters are k=10.5 and c=3.94, respectively. From fig. 2, it can be seen that the Weibull distribution based on the maximum likelihood estimation method has higher fitting precision and good stability, and can accurately describe the uncertain characteristics of wind speed of the wind power plant, so as to obtain a probability distribution model of wind power through piecewise linearization relation between wind speed and wind power according to the fitted Weibull model.
2) And determining a wind power output probability distribution model based on the wind speed probability distribution model, the wind speed of the wind power plant at the same historical moment and the output power of the wind turbine generator.
The determining process of the photovoltaic output probability distribution model specifically comprises the following steps:
1) Based on Beta distribution, determining an illumination intensity probability distribution model according to illumination intensities at a plurality of historical moments; specifically, the illumination intensity of the photovoltaic power station is fitted by adopting the Beta distribution, and the shape parameters of the Beta distribution are fitted through the illumination intensity actual measurement data of the photovoltaic power station for 3 years. The illumination intensity probability distribution model is as follows:
wherein f i (α i ,β i ) Probability of ith illumination intensity, α i ，β i All are shape parameters of ith illumination intensity based on Beta distribution fitting, H t Is the standardized illumination intensity.
2) And determining a photovoltaic output probability distribution model based on the illumination intensity probability distribution model, the illumination intensity at the same historical time and the photovoltaic output power.
The invention adopts wind speed and illumination intensity to carry out probability distribution modeling, and mainly aims to improve applicability, and can be particularly used for newly-built wind power stations, photovoltaic power stations and newly-planned power distribution networks.
Step 300, segment recombination is carried out on the wind power output probability distribution model so as to obtain a wind power output multi-state model; and carrying out sectional recombination on the photovoltaic output probability distribution model to obtain a photovoltaic output multi-state model. Specifically, segmenting a probability density function curve of wind power output, and converting expectations in each segment into expectations of actual wind power output, so as to establish a multi-state model of wind power output, wherein the number of wind power output states in the multi-state model of wind power output is consistent with the number of segments of the probability distribution model of wind power output; and similarly, a photovoltaic output multi-state model is obtained, and the number of photovoltaic output states in the photovoltaic output multi-state model is consistent with the number of segments of the photovoltaic output probability distribution model.
The multi-state model of the wind power output is as follows:
wherein P (P kWF ) Is the kth WF Probability of individual wind force output states, P kWF Is the kth WF Wind power expected value f of each wind power output state WF (P kWF ) Is the kth of the wind farm WF A probability density function of the individual wind state forces,is the kth of the wind farm WF Lower limit of the force of the individual wind force states, +.>Is the kth of the wind farm WF Wind power predicted value of each wind power state output, N WF The wind power output state number is the wind power output state number.
The photovoltaic output multi-state model is as follows:
wherein P (P) kPV ) Is the kth PV Probability of individual photovoltaic output states, P kPV Is the kth PV Photovoltaic power expected value f of each photovoltaic output state PV (P kPV ) K is the photovoltaic power station PV Probability density function of individual photovoltaic state output,k is the photovoltaic power station PV Lower output limit of individual photovoltaic output states, < ->K is the photovoltaic power station PV Photovoltaic power predictive value, N, of individual photovoltaic state outputs PT The number of photovoltaic output states.
Step 400, constructing an energy storage constraint set based on the target power distribution network; the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint.
The section output power constraint of the power distribution network is as follows:
P iGmin ≤P iG ≤P iGmax 。
wherein P is iGmin The lower limit of the section output power at the ith node of the power distribution network, P iG Output power for the section of the ith node of the power distribution network, P iGmax And the upper limit of the section output power at the ith node of the power distribution network. The ith node of the distribution network is also the access point of the ith wind power output or photovoltaic output, and the distribution network corresponds to the target distribution network.
The wind power generation output power constraint is as follows:
P iWFmin ≤P iWF ≤P iWFmax 。
wherein P is iWFmin For the lower limit of the output power of the wind turbine generator at the ith node of the power distribution network, P iWF For the output power, P, of the wind turbine generator set at the ith node of the power distribution network iWFmax And the upper limit of the output power of the wind turbine generator at the ith node of the power distribution network.
The photovoltaic power generation output power constraint is as follows:
P iPVmin ≤P iPV ≤P iPVmax 。
wherein P is iPVmin For the lower limit of photovoltaic output power at ith node of power distribution network, P iPV For photovoltaic output power at ith node in power distribution network, P iPVmax And (5) the photovoltaic output power upper limit at the ith node of the power distribution network.
The line transmission power constraint is:
-P lmax ≤P l ≤P lmax 。
wherein P is lmax For the upper limit of the transmission power of the line in the target power distribution network, P l And the transmission power corresponding to the first line of the target power distribution network is obtained.
The power balance constraint is:
wherein N is G For the number of nodes of the input power of the distribution network, N WF For the quantity of grid-connected wind power plants, N PV For the quantity of grid-connected photovoltaic power stations, N b N is the number of the energy storage devices L For the number of load nodes, P ibdis For energy storage and discharge power at ith node of power distribution network, P ibch For energy storage and charging power at ith node of power distribution network, P iL And (5) the node load power at the ith node of the power distribution network.
The power distribution network tide constraint is as follows:
wherein P is i Active power injection at ith node of power distribution networkGo into, Q i Reactive power injection at ith node of power distribution network, U i For the node voltage at the ith node of the power distribution network, U j For the node voltage at the j-th node of the power distribution network, G ij B is the electric conduction between the ith node and the jth node of the power distribution network ij For susceptance, delta, between the ith node and the jth node of the power distribution network ij Is the power angle between the ith node and the jth node of the power distribution network.
The node voltage constraint of the power distribution network is as follows:
U imin ≤U i ≤U imax 。
wherein U is imin U is the lower voltage limit of the ith node of the power distribution network imax Is the upper voltage limit of the ith node of the power distribution network.
The energy storage charging and discharging constraint is as follows:
|S ocb0 -S ocbT |≤σ。
wherein sigma is the maximum deviation amplitude of the initial charge state and the final charge state of the energy storage, S ocb0 For the state of charge at the beginning of energy storage, S ocbT For the state of charge at the end of the energy storage,is the lower limit of the state of charge, S ocbt For the state of charge at time t +.>Is the upper state of charge limit.
And 500, constructing a distributed energy storage optimization configuration model of the power distribution network based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic power output multi-state model by taking the minimum sum of the power purchase cost, the energy storage operation cost and the power distribution network loss of the power distribution network as a target. The power distribution network containing high-proportion new energy has the problems of large voltage fluctuation, increased network loss and the like, meanwhile, the investment cost of distributed energy storage is still higher, and the investment and the operation cost are important factors to be considered during optimal configuration, so that the objective function in the distributed energy storage optimal configuration model of the power distribution network is as follows:
F 0 ＝F 1 +F 2 +F 3 。
N＝k WF k PV 。
wherein F is an objective function value, F 1 F for the purchase cost of the power distribution network 2 F for energy storage operation cost 3 For power distribution network loss, P N For the rated power of the distributed wind and light,wind-light output probability P for kth node state of target power distribution network 0 For the output power of distributed wind and light, +.>To increase the cost after wind and light disturbance, k WF The serial number k of the wind power output state of the wind power plant PV Serial number, e, of output force for photovoltaic state of photovoltaic power station t Is divided intoTime-of-flight power price, T is time-of-flight power price period, P i Connecting node power for power distribution network, N B For the number of the connecting nodes, gamma is bank interest rate, y is the service life of the energy storage device and N b C is the number of the energy storage devices p Investment cost per unit power for energy storage device c e Investment cost per unit capacity for energy storage device, P b For storing energy, E b For the capacity of the energy storage device c op Maintenance costs for the energy storage device; />For the power loss of the distribution network line, < >>For power loss of power distribution network transformer, N l For the total number of lines in the power distribution network, N t The total number of transformers in the power distribution network is calculated; c PV Punishment of cost coefficients for photovoltaic light rejection, c WF Punishment of cost coefficients for wind power waste wind, c L Penalty cost coefficients for load loss.
Wherein, related formula n=k WF k PV Is set up by:
for a power distribution network containing only 1 wind farm or 1 photovoltaic power station, the optimal configuration model only contains N WF Or N PV Probability of occurrence of individual states; if there are a plurality of wind farms and photovoltaic power stations, then the presence state is a combination of all wind farms and photovoltaic power stations, namely:
in order to reduce the computational complexity, reduce the computational complexity of wind power field and photovoltaic power plant, when historical data collection, guarantee that the wind speed and the illumination intensity of collection have the uniformity of time scale, the state number of whole distribution network reduces by a wide margin when carrying out probability calculation like this, namely:
N＝k WF k PV 。
step 600, solving the distributed energy storage optimization configuration model of the power distribution network by adopting a self-adaptive robust optimization algorithm to determine the distributed energy storage optimal configuration result of the power distribution network, wherein the method specifically comprises the steps of locating and sizing the distributed energy storage device.
In one specific example, the parameters in the target distribution network include: the power distribution network topology equivalent circuit, the power distribution network line and the transformer power grid equivalent parameters; the wind power plant and photovoltaic power plant historical data comprise wind power plant wind speed data, wind power plant output data, other meteorological data of the wind power plant, photovoltaic power plant illumination intensity data, photovoltaic power plant output data and other meteorological data of the photovoltaic power plant; the load data includes power data for each load node.
And then, establishing a probability distribution model of the output of the wind power plant and the photovoltaic power station according to the obtained historical data of the wind power plant and the photovoltaic power station. In order to facilitate energy storage optimization configuration modeling and calculation under a probability distribution model, the invention provides a method for carrying out sectional processing on a probability density function curve, so as to obtain probability and mathematical expectation of each distribution state under wind-light output, and establish objective function and constraint of distributed energy storage optimization configuration according to the probability and mathematical expectation. The objective function aims at minimizing the sum of distributed energy storage investment and running cost, whole power distribution network electricity purchasing cost and grid loss and wind-light absorption penalty cost, and the constraints comprise power distribution network section output power constraint, wind power generation output power constraint, photovoltaic power generation output power constraint, line transmission power constraint, power balance constraint, power distribution network tide constraint, power distribution network node voltage constraint and energy storage charging and discharging constraint.
According to the obtained equivalent parameters of the power distribution network, the typical operation mode of the power distribution network is combined, the power flow distribution characteristics of the power distribution network are analyzed, calculation conditions are provided for distributed energy storage optimization configuration of the power distribution network, then according to the established objective function and constraint equation, the adaptive robust optimization algorithm is adopted to solve the objective function, finally, the site selection and the constant volume of the distributed energy storage device are obtained, and the maximized new energy consumption and the economical operation of the whole power distribution network are realized.
According to the invention, the economic, low-carbon and reliable operation of the power distribution network can be realized by optimally configuring the distributed energy storage method, so that the construction process of a novel power system is effectively promoted, and the economic benefits of power supply companies and new energy power generation companies are effectively improved. The invention has clear thought, clear concept, simple implementation scheme and great engineering use value.
Example two
As shown in fig. 3, in order to implement the technical solution in the first embodiment to achieve the corresponding functions and technical effects, this embodiment further provides a distributed energy storage optimization configuration system of a power distribution network, including:
the data set acquisition module is used for acquiring a historical wind power output data set of the wind power plant and a historical photovoltaic output data set of the photovoltaic power station in the target power distribution network.
The probability distribution model construction module is used for determining a wind power output probability distribution model according to the historical wind power output data set based on two-parameter Weibull distribution; and determining a photovoltaic output probability distribution model according to the historical photovoltaic output data set based on Beta distribution.
The multi-state model construction module is used for carrying out sectional recombination on the wind power output probability distribution model so as to obtain a wind power output multi-state model; and carrying out sectional recombination on the photovoltaic output probability distribution model to obtain a photovoltaic output multi-state model.
The constraint set construction module is used for constructing an energy storage constraint set based on the target power distribution network; the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint.
The energy storage optimization configuration model construction module is used for constructing a distributed energy storage optimization configuration model of the power distribution network based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic output multi-state model and taking the minimum sum of the electricity purchasing cost, the energy storage operation cost and the network loss of the power distribution network as a target.
And the optimal configuration result determining module is used for solving the distributed energy storage optimal configuration model of the power distribution network by adopting a self-adaptive robust optimization algorithm so as to determine the distributed energy storage optimal configuration result of the power distribution network.
Example III
The embodiment provides an electronic device, which includes a memory and a processor, where the memory is configured to store a computer program, and the processor runs the computer program to enable the electronic device to execute the distributed energy storage optimization configuration method of the power distribution network of the first embodiment. Alternatively, the electronic device may be a server.
In addition, the embodiment of the invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the distributed energy storage optimization configuration method of the power distribution network of the first embodiment when being executed by a processor.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The distributed energy storage optimal configuration method for the power distribution network is characterized by comprising the following steps of:
acquiring a historical wind power output data set of a wind power plant and a historical photovoltaic output data set of a photovoltaic power station in a target power distribution network;
determining a wind power output probability distribution model according to the historical wind power output data set based on the two-parameter Weibull distribution; based on Beta distribution, determining a photovoltaic output probability distribution model according to the historical photovoltaic output data set;
sectional recombination is carried out on the wind power output probability distribution model so as to obtain a wind power output multi-state model; carrying out sectional recombination on the photovoltaic output probability distribution model to obtain a photovoltaic output multi-state model;
constructing an energy storage constraint set based on the target power distribution network; the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint;
based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic output multi-state model, a distributed energy storage optimization configuration model of the power distribution network is built by taking the minimum sum of the power purchase cost, the energy storage operation cost and the power distribution network loss of the power distribution network as a target;
and solving the distributed energy storage optimal configuration model of the power distribution network by adopting a self-adaptive robust optimization algorithm so as to determine the distributed energy storage optimal configuration result of the power distribution network.
2. The distributed energy storage optimization configuration method of a power distribution network according to claim 1, wherein the historical wind power output data set comprises wind power plant wind speed and wind power unit output power at the same historical moment; the historical photovoltaic output data set comprises illumination intensity and photovoltaic output power at the same historical time;
the determining process of the wind power output probability distribution model specifically comprises the following steps:
based on the two-parameter Weibull distribution, determining a wind speed probability distribution model according to wind speeds of the wind power plant at a plurality of historical moments; determining a wind power output probability distribution model based on the wind speed probability distribution model, the wind speed of the wind power plant and the output power of the wind turbine generator set at the same historical moment;
the determining process of the photovoltaic output probability distribution model specifically comprises the following steps:
based on Beta distribution, determining an illumination intensity probability distribution model according to illumination intensities at a plurality of historical moments; and determining a photovoltaic output probability distribution model based on the illumination intensity probability distribution model, the illumination intensity at the same historical time and the photovoltaic output power.
3. The distributed energy storage optimization configuration method of a power distribution network according to claim 2, wherein the wind speed probability distribution model is:
wherein f i (v) Probability of wind speed for ith wind farm, v i For the ith wind farm wind speed, k i For the form factor of the wind speed of the ith wind farm c i The scale factor of the ith wind power plant;
the illumination intensity probability distribution model is as follows:
wherein f i (α i ,β i ) Probability of ith illumination intensity, α i ，β i All are shape parameters of ith illumination intensity based on Beta distribution fitting, H t Is the standardized illumination intensity.
4. The distributed energy storage optimization configuration method of a power distribution network according to claim 1, wherein the wind power output state number in the wind power output multi-state model is consistent with the segmentation number of the wind power output probability distribution model;
the multi-state model of the wind power output is as follows:
wherein P (P kWF ) Is the kth WF Probability of individual wind force output states, P kWF Is the kth WF Wind power expected value f of each wind power output state WF (P kWF ) Is the kth of the wind farm WF A probability density function of the individual wind state forces,is the kth of the wind farm WF Lower limit of the force of the individual wind force states, +.>Is the kth of the wind farm WF Wind power predicted value of each wind power state output, N WF The wind power output state number is the wind power output state number.
5. The distributed energy storage optimization configuration method of the power distribution network according to claim 1, wherein the number of photovoltaic output states in the photovoltaic output multi-state model is consistent with the number of segments of the photovoltaic output probability distribution model;
the photovoltaic output multi-state model is as follows:
wherein P (P) kPV ) Is the kth PV Probability of individual photovoltaic output states, P kPV Is the kth PV Photovoltaic power expected value f of each photovoltaic output state PV (P kPV ) K is the photovoltaic power station PV Probability density function of individual photovoltaic state output,k is the photovoltaic power station PV Lower output limit of individual photovoltaic output states, < ->K is the photovoltaic power station PV Photovoltaic power predictive value, N, of individual photovoltaic state outputs PT The number of photovoltaic output states.
6. The distributed energy storage optimization configuration method of a power distribution network according to claim 1, wherein the section output power constraint of the power distribution network is as follows:
P iGmin ≤P iG ≤P iGmax ；
wherein P is iGmin The lower limit of the section output power at the ith node of the power distribution network, P iG Output power for the section of the ith node of the power distribution network, P iGmax The upper limit of the power is output for the broken surface at the ith node of the power distribution network;
the wind power generation output power constraint is as follows:
P iWFmin ≤P iWF ≤P iWFmax ；
wherein P is iWFmin For the lower limit of the output power of the wind turbine generator at the ith node of the power distribution network, P iWF For the output power, P, of the wind turbine generator set at the ith node of the power distribution network iWFmax The method comprises the steps that the output power upper limit of a wind turbine generator set at an ith node of a power distribution network is set;
the photovoltaic power generation output power constraint is as follows:
P iPVmin ≤P iPV ≤P iPVmax ；
wherein P is iPVmin For the lower limit of photovoltaic output power at ith node of power distribution network, P iPV For photovoltaic output power at ith node in power distribution network, P iPVmax The photovoltaic output power upper limit is the photovoltaic output power upper limit at the ith node of the power distribution network;
the line transmission power constraint is:
-P lmax ≤P l ≤P lmax ；
wherein P is lmax For the upper limit of the transmission power of the line in the target power distribution network, P l The transmission power corresponding to the first line of the target power distribution network is obtained;
the power balance constraint is:
wherein N is G For the number of nodes of the input power of the distribution network, N WF For the quantity of grid-connected wind power plants, N PV For the quantity of grid-connected photovoltaic power stations, N b N is the number of the energy storage devices L For the number of load nodes, P ibdis For energy storage and discharge power at ith node of power distribution network, P ibch For energy storage and charging power at ith node of power distribution network, P iL Node load power at an ith node of the power distribution network;
the power distribution network tide constraint is as follows:
wherein P is i For active injection at ith node of power distribution network, Q i Reactive power injection at ith node of power distribution network, U i For the node voltage at the ith node of the power distribution network, U j For the node voltage at the j-th node of the power distribution network, G ij B is the electric conduction between the ith node and the jth node of the power distribution network ij For susceptance, delta, between the ith node and the jth node of the power distribution network ij The power angle between the ith node and the jth node of the power distribution network;
the node voltage constraint of the power distribution network is as follows:
U imin ≤U i ≤U imax ；
wherein U is imin U is the lower voltage limit of the ith node of the power distribution network imax The upper voltage limit of the ith node of the power distribution network;
the energy storage charging and discharging constraint is as follows:
|S ocb0 -S ocbT |≤σ；
wherein sigma is the maximum deviation amplitude of the initial charge state and the final charge state of the energy storage, S ocb0 For the state of charge at the beginning of energy storage, S ocbT For the state of charge at the end of the energy storage,is the lower limit of the state of charge, S ocbt For the state of charge at time t +.>Is the upper state of charge limit.
7. The distributed energy storage optimization configuration method of a power distribution network according to claim 1, wherein an objective function in the distributed energy storage optimization configuration model of the power distribution network is:
F 0 ＝F 1 +F 2 +F 3 ；
N＝k WF k PV ；
wherein F is an objective function value, F 1 F for the purchase cost of the power distribution network 2 F for energy storage operation cost 3 For power distribution network loss, P N For the rated power of the distributed wind and light,wind-light output probability P for kth node state of target power distribution network 0 For the output power of distributed wind and light, +.>To increase the cost after wind and light disturbance, k WF The serial number k of the wind power output state of the wind power plant PV Serial number, e, of output force for photovoltaic state of photovoltaic power station t For the time-sharing electricity price, T is the time-sharing electricity price period, P i Connecting node power for power distribution network, N B For the number of the connecting nodes, gamma is bank interest rate, y is the service life of the energy storage device and N b C is the number of the energy storage devices p Investment cost per unit power for energy storage device c e Investment cost per unit capacity for energy storage device, P b For storing energy, E b For the capacity of the energy storage device c op Maintenance costs for the energy storage device; p (P) l loss For the power loss of the distribution network line, < >>For power loss of power distribution network transformer, N l For the total number of lines in the power distribution network, N t The total number of transformers in the power distribution network is calculated; c PV Punishment of cost coefficients for photovoltaic light rejection, c WF Punishment of cost coefficients for wind power waste wind, c L Penalty cost coefficients for load loss.
8. A distributed energy storage optimization configuration system for a power distribution network, the system comprising:
the data set acquisition module is used for acquiring a historical wind power output data set of the wind power plant and a historical photovoltaic output data set of the photovoltaic power station in the target power distribution network;
the probability distribution model construction module is used for determining a wind power output probability distribution model according to the historical wind power output data set based on two-parameter Weibull distribution; based on Beta distribution, determining a photovoltaic output probability distribution model according to the historical photovoltaic output data set;
the multi-state model construction module is used for carrying out sectional recombination on the wind power output probability distribution model so as to obtain a wind power output multi-state model; carrying out sectional recombination on the photovoltaic output probability distribution model to obtain a photovoltaic output multi-state model;
the constraint set construction module is used for constructing an energy storage constraint set based on the target power distribution network; the energy storage constraint set comprises a power distribution network section output power constraint, a wind power generation output power constraint, a photovoltaic power generation output power constraint, a line transmission power constraint, a power balance constraint, a power distribution network tide constraint, a power distribution network node voltage constraint and an energy storage charging and discharging constraint;
the energy storage optimization configuration model construction module is used for constructing a distribution network distributed energy storage optimization configuration model based on the energy storage constraint set, the wind power output multi-state model and the photovoltaic output multi-state model and taking the minimum sum of the electricity purchasing cost, the energy storage operation cost and the network loss of the distribution network as a target;
and the optimal configuration result determining module is used for solving the distributed energy storage optimal configuration model of the power distribution network by adopting a self-adaptive robust optimization algorithm so as to determine the distributed energy storage optimal configuration result of the power distribution network.
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| CN117910836A (en) * | 2024-03-19 | 2024-04-19 | 浙江大学 | Energy storage power station planning method for improving flexibility of large power grid |
| CN117972363A (en) * | 2024-03-29 | 2024-05-03 | 西安热工研究院有限公司 | Wind speed prediction method and system for energy storage auxiliary black start based on stability |
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| CN117910836A (en) * | 2024-03-19 | 2024-04-19 | 浙江大学 | Energy storage power station planning method for improving flexibility of large power grid |
| CN117910836B (en) * | 2024-03-19 | 2024-05-28 | 浙江大学 | Energy storage power station planning method for improving flexibility of large power grid |
| CN117972363A (en) * | 2024-03-29 | 2024-05-03 | 西安热工研究院有限公司 | Wind speed prediction method and system for energy storage auxiliary black start based on stability |
| CN117972363B (en) * | 2024-03-29 | 2024-06-07 | 西安热工研究院有限公司 | Wind speed prediction method and system for energy storage auxiliary black start based on stability |
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