CN116205337A - Annual operation evaluation method and device for source network charge storage project planning - Google Patents
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Abstract
The invention discloses a method and a device for evaluating annual operation of source network charge storage project planning, which consider the charge and discharge plan of an energy storage device in each period in the operation process of the source network charge storage project according to the characteristics of combined operation of wind power, photovoltaic, energy storage, a power grid and loads. The method is characterized by taking the total annual electricity purchasing cost, the energy storage device loss cost and the minimum equipment operation and maintenance cost as targets, taking into consideration the operation constraints of wind power, photovoltaic, a power grid and energy storage equipment, the charging rate constraint of energy storage and the like, and providing a source network charging and storage annual 8760-hour operation simulation method based on a particle swarm algorithm. And finally, the method can output the economic index of the source network in any period and throughout the year, the technical indexes such as the new energy electricity discarding rate, the new energy electricity ratio and the like, and provides a favorable support for decision.
Description
Technical Field
The invention belongs to the technical field of power supply system simulation, and particularly relates to a method and a device for evaluating annual operation of source network load storage project planning.
Background
The source network charge storage system is a novel network structure consisting of a distributed power supply, energy storage equipment, electric equipment, a main power grid and a control device. The source network charge storage system is an important means for solving a plurality of problems of the power system, and needs to exert huge economic benefits brought by the source network charge storage system as much as possible. Thus, the economical operation of the source network charge storage system is a key to attract users and enable it to be generalized in the power system. However, as the types of devices in the source network charge storage system increase and the scale is continuously enlarged, the problem of optimizing the economic operation of the micro-grid becomes more and more complex.
The source network load storage integrated project comprises a power supply such as wind power, photovoltaic and the like, a power grid, a load and an energy storage system; the running targets comprise low cost, high reliability, low waste wind and light rate and the like of the whole system life cycle, and the constraint conditions comprise total electric quantity balance, real-time electric power balance, energy storage device power constraint, energy storage device electric quantity constraint, new energy electric quantity duty ratio constraint, adjustable characteristic constraint of load and the like. The source network charge storage economic operation problem is a complex nonlinear mixed integer random programming problem, and has the characteristics of high variable dimension, large calculation scale, continuous variable discrete variable inclusion, multiple constraint conditions, nonlinear objective function and the like. Based on the difficulty of optimizing the problem, the traditional solution is easy to fall into dimension disasters in a limited scheduling period, so that the problem can be solved theoretically, which takes years or cannot be solved at all.
Intelligent optimization algorithms are increasingly accepted as compared to traditional planning algorithms. Intelligent computation is also called soft computation, which is a solution algorithm designed according to the principle of the intelligent computation and imitating the law of the intelligent computation after people are inspired by the development law of the nature or the biology. In recent years, many biomimetic intelligent optimization algorithms, which are quite different from the principles of classical mathematical programming and which are expected to solve complex optimization problems by simulating natural ecosystems, are proposed one after the other, like simulated annealing algorithms, artificial immune algorithms, bacterial foraging algorithms and particle swarm algorithms, genetic algorithms, etc. These algorithms are each advantageous and disadvantageous and are widely used by people with unique flow characteristics and search characteristics.
Among them, the particle swarm algorithm (Particle Swarm Optimization, PSO) is a subject that has emerged in the 90 th century, and is known for its simple concept, convenient implementation, and fast convergence. The basic idea of the particle swarm algorithm is to simulate the predation behavior of a bird swarm randomly searching for food, and the bird swarm adjusts its search path through own experience and communication between the swarms, so as to find the place with the largest food. Where the position/path of each bird is then the combination of arguments, the food density at each arrival location, i.e. the function value. Each search adjusts its own search direction and speed according to its own experience (the optimal place searched by its own history) and population communication (the optimal place searched by its population history), which is called tracking extremum, so as to find the optimal solution.
Referring to fig. 1, the basic steps of the particle swarm algorithm are:
step1, determining the motion state of a particle is described by two parameters of position and speed, and thus the two parameters are initialized;
step2, the result (function value) of each search is particle fitness, and then the individual historical optimal position of each particle and the historical optimal position of the group are recorded;
step3, the historical optimal position of the individual and the historical optimal position of the group are equivalent to generating two forces, and the motion state of the particles is influenced by the combination of the inertia of the particles, so that the position and the speed of the particles are updated.
The particle swarm algorithm has the following characteristics:
(1) compared with the traditional algorithm, the method has the advantages that the calculation speed is very high, and the global searching capability is also very high;
(2) PSO is not very sensitive to the size of the population, so the initial population is set to 500-1000, and the influence of speed is not great;
(3) the particle swarm algorithm is suitable for the problem of continuous function extremum, and has stronger global searching capability for the problem of nonlinearity and multimodal.
The method is very suitable for solving the problems of high variable dimension, large calculation scale, continuous variable discrete variable inclusion, various constraint conditions and nonlinear objective function of the source network charge storage optimization operation based on the characteristics of high calculation speed of a particle swarm algorithm and strong global searching capability for nonlinear and multimodal problems.
Most of the current methods for evaluating the planning schemes adopt 4 typical day methods, which are respectively calculated and verified in spring, summer, autumn and winter in one day, and the typical day methods have higher speed, but because of fewer included scenes, the difference between the calculated result and the real situation is larger, so that investment waste is easily caused, or the running effect cannot be expected.
Disclosure of Invention
The invention provides a method and a device for evaluating annual operation of source network charge storage project planning, so that the planned values of running cost, new energy electric quantity ratio, new energy electricity rejection rate and the like in all periods of the year corresponding to the planned wind power, photovoltaic and energy storage capacity can be counted, and the source network charge storage project planning scheme can be evaluated according to the running cost, the new energy electric quantity ratio and the new energy electricity rejection rate, and the evaluation result is more accurate.
In order to achieve the purpose, the annual operation evaluation method for planning the source network load storage project comprises the following steps:
step1, constructing annual hourly power generation capacity of wind power and photovoltaic according to capacity configuration of wind power and photovoltaic and annual hourly wind power and photovoltaic power output determined by a project planning scheme; according to the load information, constructing the annual hour-by-hour load power consumption;
step2, constructing constraint conditions according to a real-time balance principle of electric power;
step3, constructing an objective function;
and step 7, evaluating the planning scheme of the source network charge storage project according to the technical indexes of the new energy.
Further, in step2, the constraint conditions include electric power and power balance constraint, operation constraint of wind power equipment, operation constraint of photovoltaic equipment, operation constraint of grid equipment and charging rate constraint of energy storage.
Further, in step3, the objective function is the lowest total annual cost.
Further, in step 4, the number of groups is 200, and the number of iterations is 10 to 15.
Further, step 5 includes the steps of:
step 5.1, initializing all particles, wherein each particle is an array formed by the hours of the current month, and each number represents the charge and discharge plan of the energy storage device at the current moment;
step 5.2, calculating an objective function for all particles to obtain objective function values of different particles;
step 5.3, taking the objective function value with the smallest objective function value as the population optimal function value of the round, and taking the particle corresponding to the optimal function value as the optimal particle of the round;
step 5.4, generating N particles of the next round by adopting the following formula:
v f (t+1)＝v f (t)+c 1 ×rand×(pbest f -SOC f )+c 2 ×rand×(gbest-SOC f )
SOC f (t+1)＝SOC f +v f (t+1)
wherein v is f (t+1) represents the speed of the f-th particle at the time of updating, v f (t) represents the speed of the f-th particle at this time, c 1 Represents a self-learning factor, rand is a random number between 0 and 1, pbest f For the f-th particle self-historic optimal position, SOC f An array of the present time for the f-th particle, c 2 Representing a population learning factor, wherein gbest is the historical optimal particle position of the population;
step 5.5, judging the position of each particle, and when the position or the speed of the particle exceeds a limit value, pulling the particle back to the boundary;
step 5.6, repeating the steps 5.2 to 5.5 until the maximum iteration times are reached;
and 5.7, outputting the final population optimal particle position as a result of a particle swarm algorithm, namely an optimal charging and discharging plan of the energy storage device.
Further, in step 6, the electricity purchasing cost of the power grid is as follows: the sum of the product of the main grid power and its electricity price per hour throughout the year.
Further, in step 6, the new energy power consumption P is calculated by hour-by-hour calculation or sum calculation abd,t ，
The calculation formula of the hour-by-hour algorithm is as follows:
P abd,t ＝P WT,t +P PV,t +P BESS,t -P Load,t
wherein P is WT,t For the t hour wind power, P PV,t For the t hour photovoltaic power, P BESS,t Power of energy storage device at t hour, P Load,t The power used for loading at the t hour;
the sum calculation method is as follows:
P abd,sum ＝P WT,sum +P PV,sum +P Grid,sum -P Load,sum
wherein P is abd,sum Represents the new energy waste amount of the whole year, P WT,sum Represents total annual wind power generation amount, P PV,sum Representing total photovoltaic power generation amount, P Grid,sum Representing total power purchase amount from power grid all year around, P Load,sum Representing the total annual load power consumption.
Further, in step 6, the new energy power rejection rate γ abd The calculation method of (1) is as follows:
wherein P is abd,sum Represents the new energy waste amount of the whole year, P WT,sum Represents total annual wind power generation amount, P PV,sum Representing the total amount of photovoltaic power generation throughout the year.
Further, in step 6, the new energy electric quantity is the ratio gamma green The calculation method comprises the following steps:
in the above, P abd,sum Represents the new energy waste amount of the whole year, P WT, Represents total annual wind power generation amount, P PV, Representing total photovoltaic power generation amount, P Load, Representing the total annual load power consumption.
An annual operation evaluation device for planning a source network charge storage project comprises:
the input module is used for acquiring basic data, wherein the basic data comprises capacity configuration of wind power and photovoltaic, and wind power, photovoltaic output and load information of each year and every hour;
the processing module is used for storing an objective function and constraint conditions, and solving the objective function and constraint conditions according to a particle swarm algorithm to obtain an optimal charge-discharge plan of the energy storage device;
and the output module is used for calculating new energy technical indexes according to the basic data and the optimal charging and discharging plan of the energy storage device, wherein the new energy technical indexes comprise the power grid electricity purchasing cost, the new energy electricity discarding quantity, the new energy electricity discarding rate and the new energy electricity occupying ratio.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the characteristics of wind power, photovoltaic, energy storage, power grid and load combined operation, according to the capacity configuration of wind power and photovoltaic determined by a source network charge storage project planning scheme, the charge and discharge plan of an energy storage device in each period of the whole year is considered in the source network charge storage project simulation operation process, new energy technical indexes of the source network charge storage project in the whole year under the charge and discharge plan are calculated, the new energy technical indexes comprise a scene of 365 days of the whole year, the accuracy and the reliability of the new energy technical indexes are obviously higher than those of a method of calculating 4 typical days of data only in 4 days, and the source network charge storage project planning scheme can be effectively evaluated, so that investment is guided, and waste or unexpected operation effects are avoided.
The method is characterized in that the aim of lowest annual operation cost is achieved, real-time balance of electric power, the upper and lower power limits of all equipment, the upper and lower charge rate limits of energy storage and the equal charge rate of energy storage at the initial and end moments are taken as constraint conditions, the full-feasible-area optimization solution based on a particle swarm algorithm is completed, and finally the economic indexes such as annual power grid electricity purchasing cost and the technical indexes such as new energy waste, new energy waste rate and new energy electricity ratio are obtained. Has the following advantages:
first: the ultra-large scale (8760 hours) data can be calculated, and the calculation method section by section is adopted, so that the calculation difficulty is greatly reduced, the calculation speed is improved, and the 365-day source network load storage operation evaluation based on the particle swarm algorithm is realized for the first time under the condition that the result accuracy is not affected.
Second,: in the calculation process and result, the charge and discharge cost of the energy storage device and the power grid electricity purchasing cost are considered, and in the calculation of the power grid electricity purchasing cost, the peak, valley and flat electricity price are fully considered. The cost of calculation is accurate, and the method provides favorable support for decision.
Third,: the electric quantity, the electric quantity and the green electric duty ratio of any time and any time period can be calculated. And a quantitative basis is provided for whether the planning scheme of the project can meet the rigid requirements of the policy.
Drawings
FIG. 1 is a flow chart of a particle swarm algorithm;
FIG. 2 is an hour-by-hour output curve of a month of January photovoltaic;
FIG. 3 is an hour-by-hour output curve of the January wind power;
FIG. 4 is a strategy block diagram of an annual hourly operation evaluation method for source network load storage project planning;
FIG. 5 is an example of a velocity generation process for a particle swarm algorithm;
FIG. 6 is a graph of power grid electricity purchases from year to year;
FIG. 7 is a new energy power ratio year by year;
FIG. 8 shows new energy power rejection rate month by month throughout the year;
fig. 9 is a schematic diagram of an annual 8760 hour operation evaluation device for a source network load storage project.
Detailed Description
In order to make the purpose and technical scheme of the invention clearer and easier to understand. The present invention will now be described in further detail with reference to the drawings and examples, which are given for the purpose of illustration only and are not intended to limit the invention thereto.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more. In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The invention provides a rapid and efficient annual hourly (8760 hours) source network load storage operation simulation method which can cope with various complex constraint conditions, so that the planned annual operation cost, new energy electric quantity ratio, new energy electricity rejection rate and other numerical values corresponding to wind power, photovoltaic and energy storage capacity can be counted. Providing quantized data support for decision makers.
Example 1
The annual operation evaluation method for planning the source network charge storage project comprises the following steps:
and step1, constructing an annual hourly generating capacity curve of the wind power and the photovoltaic according to the capacity configuration of the wind power and the photovoltaic and the annual hourly wind power and photovoltaic generating capacity determined by the project planning scheme. And constructing a load power consumption curve hour by hour all year according to the load information. Fig. 2 is an hour-by-hour power generation curve of the photovoltaic in 1 month, and fig. 3 is an hour-by-hour power generation curve of the wind power in 1 month.
Step2, constructing constraint conditions
According to the real-time balance principle of the power, constructing power balance constraint:
P WT,t +P PV,t +P Grid,t +P BESS,t ＝P Load,t
in the above, P WT,t For the t hour wind power, P PV,t For the t hour photovoltaic power, P Grid,t Main power grid power, P for the t hour BESS,t The power of the energy storage device at the t hour, wherein the power of the energy storage device is either positive or negative, representing power supply to the load, or represents charging. P (P) Load,t And (5) using electric power at t hours as a load.
According to the characteristics of wind power, photovoltaic, power grid and energy storage, constructing operation constraint of each device:
P WT,min ≤P WT,t ≤P WT,max
P PV,min ≤P PV,t ≤P PV,max
P Grid,min ≤P Grid,t ≤P Grid,max
P BESS,min ≤P BESS,t ≤P BESS,max
in the above, P WT,max Representing the upper power output limit of wind power, P WT,min Representing wind powerLower power output limit, P PV,max Representing the upper power output limit of photovoltaic, P PV,min Representing the lower power output limit, P, of the photovoltaic Grid,max Representing the upper power output limit, P, of a large power grid Grid,min The lower limit of the power output of the wind power is represented, and the source network charge storage does not allow power to be transmitted to the power grid according to the policy requirement, so P Grid,min ≥0，P BESS,max Represents the upper power output limit of stored energy, P BESS,min Representing the lower power output limit of the stored energy.
According to the energy storage operation characteristics, constructing the charge rate (SOC) constraint of the energy storage:
SOC BESS,min ≤SOC BESS,t ≤SOC BESS,max
in the above, SOC BESS,t Representing the charge rate (percent of remaining charge) of the energy storage device at t hours, SOC BESS,max Representing the upper limit of the charge rate of the energy storage device, SOC BESS,min Representing the lower limit of the charge rate of the energy storage device.
SOC BESS,start ＝SOC BESS,End
In the above, SOC BESS,start Representing the charge rate and SOC of the energy storage device at the beginning of the scheduling period BESS,End Representing the charge rate of the energy storage device at the end of the scheduling period.
Step3, constructing an objective function
The operation and maintenance cost, loss cost, electricity purchasing cost from a main power grid and the like of the functional equipment are fully considered, and the economic cost is taken as an objective function, as follows:
in the above, minF 1 F is the objective function value Op,t Representing the operation and maintenance cost at the t hour, F Loss,t Represents the electric energy loss cost of the energy storage device at the t hour, F Grid,t Representing the cost of purchasing electricity from the main grid at hour t.
F Op, ＝ WT ×C WT + PV ×C PV + BESS ×C BESS
In the above, gamma WT Representing the operation and maintenance coefficient (unit is yuan/kW) of wind power, C WT Representing the installed capacity of wind power, gamma PV Representing the operation and maintenance coefficient (unit is yuan/kW) of the photovoltaic, C PV Representing the installed capacity of the photovoltaic, S BESS Operation and maintenance coefficient (unit is yuan/kW) representing energy storage, C BESS Representing the installed capacity of the stored energy.
F Loss, ＝ ch, ×E ch, ×Cost t + dis,t ×E dis,t ×Cost t
In the above, eta ch, Represents the efficiency of energy storage and charging at t hours, E ch, Represents the energy of energy storage and charge at t hour, eta dis,t Represents the efficiency of energy storage and discharge at t hours, E dis,t Represents the energy of the energy storage discharge at the t hour, cost t Representing the electricity costs at hour t.
F Grid, ＝rice Buy,t ×E Grid,
In the above formula, price Buy,t Representing the price of electricity purchased from the power grid at the t hour E Grid, Representing the energy purchased from the grid at hour t.
The population number N of the particle swarm, the iteration number Iter of the particle swarm and the self-learning factor c of the particle swarm are required to be set 1 Population learning factor c of particle swarm 2 Upper speed limit v of particle swarm max Lower speed limit v of particle swarm min . The greater the population number and the number of iterations, the greater the accuracy of the final solution, but the longer the computational time spent. The smaller the population number and the number of iterations, the lower the accuracy of the final solution, but the shorter the calculation time taken. The population number proposal is selected as 200, and the iteration number proposal is selected as 10-15.
In order to realize operation acceleration, the annual time is equally divided into 12 sections within 8760 hours, each section is calculated by adopting the following method, and finally the statistics are summed up.
1) Initializing all particles, wherein each particle is an array formed by the hours of the current month, each number represents the charge and discharge plan of the energy storage device at the current moment, and all particles are uniformly and randomly distributed in a solution space in the initialization process, and the solution space is a multidimensional space meeting constraint conditions.
2) Calculating an objective function for all particles to obtain a function value;
3) And selecting the optimal function value as the population optimal function value of the round, and selecting the particle corresponding to the optimal function value as the optimal particle of the round.
4) The following formula is used to generate the next round of N particles:
v f (t+1)＝v f (t)+c 1 ×rand×(pbest f -SOC f )+c 2 ×rand×(gbest-SOC f )
SOC f (t+1)＝SOC f +v f (t+1)
in the above, v f (t+1) represents the speed of the f-th particle at the time of updating, v f (t) represents the speed of the f-th particle at this time, c 1 Represents a self-learning factor, rand is a random number between 0 and 1, pbest f For the f-th particle self-historic optimal position, SOC f An array of the present time for the f-th particle, c 2 Representing a population learning factor, gbest is the historical optimal particle position of the population.
By the above formula, the velocity of each particle can be calculated. SOC (State of Charge) f (t+1) represents the updated array of the f-th particle. For a fixed value point in the initialization process, the value is maintained unchanged. The concept is shown in fig. 4. For each iteration, the optimal value pbest of each particle and the optimal value gbest of the population are updated.
5) Determining the position of each particle
When the position or velocity of the particle exceeds a limit value, it is necessary to pull it back to the boundary.
(1) When the particle velocity v f (t) > particle groupUpper speed limit v of (2) max When let v f (t)＝v max ；
When the particle velocity v f (t) < upper speed limit v of particle swarm min When let v f (t)＝v min ；
(2) When SOC is f ＞SOC max At the time, let SOC f ＝SOC max ；
When SOC is f <SOCmin, let SOC f ＝SOCmin。
Wherein SOC is max The SOCmin is the minimum value of the charge rate of the stored energy; the programming method adopted is as follows:
6) Repeating the iteration until reaching the maximum iteration number
Repeating 2), 3), 4), 5) until the maximum number of iterations Iter is reached.
7) And outputting a final population optimal function value gbest which is an array and represents the optimal charge and discharge plan of the energy storage device as a result of a particle swarm algorithm.
Results P calculated according to particle swarm algorithm BESS,t The formula for real-time power balance can be combined:
P WT,t +P PV,t +P Grid,t +P BESS,t ＝P Load,t
at this time P is known WT,t 、P PV,t 、P Load,t 、P BESS,t The power P of the main power grid at the t hour can be obtained Grid,t 。
And Price Buy,t Is a price that takes into account different periods of peak-to-valley, for example:
price will be Price Buy,t And P Grid,t Multiplying can result in the cost of purchasing electricity from the main grid. The total cost of the annual green electricity can be calculated by flattening the electricity cost. The total cost of annual power supply is the total cost of annual power supply, namely the cost of annual power purchase from a main power grid plus the cost of green power.
According to the charge and discharge quantity of the energy storage device in each period and the charge and discharge efficiency of the energy storage device, the charge and discharge loss of the energy storage device can be calculated, the charge and discharge loss of the energy storage device in each period is multiplied by the electricity consumption cost, and then the energy storage device and the electricity consumption cost are summed together, so that the annual energy storage device loss cost can be obtained.
The operation and maintenance cost of the equipment is generally a fixed proportion multiplied by the investment of the equipment, and wind power, photovoltaic and energy storage equipment respectively have different operation and maintenance cost coefficients.
The annual total cost can be obtained by summing the annual electricity purchasing cost, the energy storage device loss cost and the equipment operation and maintenance cost.
Because permit to sell the electricity to the electric wire netting, consequently when new energy generated energy is greater than load power consumption, and energy storage device also can not absorb, then can cause new energy to abandon the electricity, calculate new energy and abandon the electric quantity all year round and have 2 modes:
the first is to calculate the following formula on an hour-by-hour basis:
P abd,t ＝P WT,t +P PV,t +P BESS,t -P Load,t
in the above, P abd,t Represents tThe new energy waste amount of the hour. For all P's greater than 0 abd,t And adding to obtain the total electric quantity abandoned all the year round.
The second method is a sum-total calculation method,
P abd,sum ＝ WT, + PV, + Grid, -Load,
in the above, P abd,sum Representing the new energy waste amount of the whole year. P (P) WT, Represents total annual wind power generation amount, P PV, Representing total photovoltaic power generation amount, P Grid, Representing total power purchase amount from power grid all year around, P Load, Representing the total annual load power consumption.
Calculating new energy electricity rejection rate:
in the above, gamma abd The power rejection rate is used as new energy.
The annual green duty cycle of the project was calculated:
in the above, gamma green Is annualGreen electrical duty cycle.
The capacity configuration of wind power, photovoltaic and energy storage in the calculation example is as follows:
the photovoltaic power generation amount of 1-12 months is as follows:
the wind power generation amount of 1-12 months is as follows:
referring to fig. 6, the main grid electricity purchases for 1-12 months are as follows:
| month of month | Generating capacity (Yi degree) | Month of month | Generating capacity (Yi degree) |
| 1 | 5.88 | 7 | 5.05 |
| 2 | 6.07 | 8 | 4.98 |
| 3 | 5.91 | 9 | 4.92 |
| 4 | 3.95 | 10 | 5.09 |
| 5 | 4.02 | 11 | 6.02 |
| 6 | 4.97 | 12 | 7.10 |
Referring to fig. 7, the new energy power ratio is 1-12 months
Referring to fig. 8, the new energy electricity rejection rate is 1-12 months
And 8, evaluating the planning scheme of the source network charge storage project according to the technical index of the new energy.
Example 2
Referring to fig. 9, an annual 8760 hour operation evaluation device for a source network load storage item is characterized by comprising:
the input module is used for acquiring basic data, wherein the basic data comprises capacity configuration of wind power and photovoltaic, and wind power, photovoltaic output and load information of each year and every hour;
the calculation module is used for storing an objective function and constraint conditions, and solving the objective function and constraint conditions according to a particle swarm algorithm to obtain an optimal charge-discharge plan of the energy storage device;
and the output module is used for calculating new energy technical indexes according to the basic data and the optimal charging and discharging plan of the energy storage device, wherein the new energy technical indexes comprise the power grid electricity purchasing cost, the new energy electricity discarding quantity, the new energy electricity discarding rate and the new energy electricity occupying ratio.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
According to the characteristics of wind power, photovoltaic, energy storage, power grid and load combined operation, the charge and discharge plan of the energy storage device in each period is considered in the operation process of a source network charge storage project. The annual electricity purchasing cost, the energy storage device loss cost and the equipment operation and maintenance cost are taken as targets, and the annual 8760-hour operation simulation method of the source network charge storage based on the particle swarm algorithm is provided by taking the operation constraints of wind power, photovoltaic, a power grid and energy storage equipment, the charge rate (SOC) constraint of energy storage and the like into consideration. And finally, the method can output the technical indexes such as any period of the charge storage of the source network, the annual economic index, the new energy electricity discarding rate, the new energy electricity ratio and the like.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. The annual operation evaluation method for planning the source network charge storage project is characterized by comprising the following steps of:
step1, constructing annual hourly power generation capacity of wind power and photovoltaic according to capacity configuration of wind power and photovoltaic and annual hourly wind power and photovoltaic output determined by a source network charge storage project planning scheme; according to the load information, constructing the annual hour-by-hour load power consumption;
step2, constructing constraint conditions according to a real-time balance principle of electric power;
step3, constructing an objective function;
step 4, setting the group number and the iteration number of the particle swarm algorithm;
step 5, solving an equation set consisting of constraint conditions and objective functions by using a particle swarm algorithm to obtain an optimal charge-discharge plan of the energy storage device;
step 6, calculating new energy technical indexes including power grid electricity purchasing cost, new energy electricity discarding amount, new energy electricity discarding rate and new energy electricity occupying ratio according to the annual hourly generated energy of wind power and photovoltaic, annual hourly load power consumption and the optimal charging and discharging plan of the energy storage device;
and step 7, evaluating the planning scheme of the source network charge storage project according to the technical indexes of the new energy.
2. The annual operation evaluation method for planning a source network charge storage project according to claim 1, wherein in the step2, the constraint conditions include an electric power balance constraint, an operation constraint of wind power equipment, an operation constraint of photovoltaic equipment, an operation constraint of grid equipment and a charging rate constraint of stored energy.
3. The annual operation evaluation method for planning a source network load storage project according to claim 2, wherein in the step3, the objective function is the minimum annual total cost.
4. The annual operation evaluation method for planning source network load storage projects according to claim 1, wherein in the step 4, the number of groups is 200, and the number of iterations is 10-15.
5. The annual hourly operation evaluation method of source network charge storage project planning according to claim 1, wherein said step 5 comprises the steps of:
step 5.1, initializing all particles, wherein each particle is an array formed by the hours of the current month, and each number represents the charge and discharge plan of the energy storage device at the current moment;
step 5.2, calculating an objective function for all particles to obtain objective function values of different particles;
step 5.3, taking the objective function value with the smallest objective function value as the population optimal function value of the round, and taking the particle corresponding to the optimal function value as the optimal particle of the round;
step 5.4, generating N particles of the next round by adopting the following formula:
v f (t+1)＝V f (t)+c 1 ×rand×(pbest f -SOC f )+c 2 ×rand×(gbest-SOC f )SOC f (t+1)＝SOC f +V f (t+1)
wherein v is f (t+1) represents the speed of the f-th particle at the time of updating, v f (t) represents the speed of the f-th particle at this time, c 1 Represents a self-learning factor, rand is a random number between 0 and 1, pbest f For the f-th particle self-historic optimal position, SOC f For the f-th particle, c2 represents a population learning factor, and gbest is the historical optimal particle position of the population;
step 5.5, judging the position of each particle, and when the position or the speed of the particle exceeds a limit value, pulling the particle back to the boundary;
step 5.6, repeating the steps 5.2 to 5.5 until the maximum iteration times are reached;
and 5.7, outputting the final population optimal particle position as a result of a particle swarm algorithm, namely an optimal charging and discharging plan of the energy storage device.
6. The annual operation evaluation method for planning a source network charge storage project according to claim 1, wherein in the step 6, the power grid purchase cost is as follows: the sum of the product of the main grid power per hour and the electricity price per hour throughout the year.
7. The annual operation evaluation method for planning of source network load storage project according to claim 1, wherein in said step 6, new energy waste amount P is calculated by hour calculation or sum calculation abd，t ，
The calculation formula of the hour-by-hour algorithm is as follows:
P abd，t ＝P WT，t +P PV，t +P BESS，t -P Load，t
wherein P is WT，t For the t hour wind power, P PV，t Is the firstt hours photovoltaic power, P BESS，t Power of energy storage device at t hour, P Load,t The power used for loading at the t hour;
the sum calculation method is as follows:
P abd，sum ＝P WT，sum +P PV，sum+ P Grid，sum -P Load，sum
wherein P is abd ， sum Represents the new energy waste amount of the whole year, P WT，sum Represents total annual wind power generation amount, P PV，sum Representing total photovoltaic power generation amount, P Grid，sum Representing total power purchase amount from power grid throughout the year, PL oad，sum Representing the total annual load power consumption.
8. The annual operation evaluation method for planning a source network charge storage project according to claim 1, wherein in the step 6, the new energy power rejection rate γ is abd The calculation method of (1) is as follows:
wherein P is abd，sum Represents the new energy waste amount of the whole year, P WT，sum Represents total annual wind power generation amount, P PV，sum Representing the total amount of photovoltaic power generation throughout the year.
9. The annual operation evaluation method for planning a source network charge storage project according to claim 1, wherein in the step 6, the new energy electric quantity is a ratio gamma green The calculation method comprises the following steps:
wherein P is abd，sum Represents the new energy waste amount of the whole year, P WT，sum Represents total annual wind power generation amount, P PV，sum Representing total photovoltaic power generation amount, P Load，sum Representing the total annual load power consumption.
10. An annual operation evaluation device for planning a source network load storage project is characterized by comprising:
the input module is used for acquiring basic data, wherein the basic data comprises capacity configuration of wind power and photovoltaic, and wind power, photovoltaic output and load information of each year and every hour;
the processing module is used for storing an objective function and constraint conditions, and solving the objective function and constraint conditions according to a particle swarm algorithm to obtain an optimal charge-discharge plan of the energy storage device;
and the output module is used for calculating new energy technical indexes according to the basic data and the optimal charging and discharging plan of the energy storage device, wherein the new energy technical indexes comprise the power grid electricity purchasing cost, the new energy electricity discarding quantity, the new energy electricity discarding rate and the new energy electricity occupying ratio.
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