CN113285490B - Power system scheduling method, device, computer equipment and storage medium - Google Patents
Power system scheduling method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a power system scheduling method, a power system scheduling device, computer equipment and a storage medium. The method comprises the following steps: according to the obtained wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, a preset system operation cost objective function is solved with the minimum total operation cost of the power system as a target, and the optimal output power of the wind power unit, the optimal output power of the thermal power unit, the optimal output power of the battery energy storage system and the minimum total operation cost of the power system are obtained, and according to the minimum total operation cost of the power system, the power system considering wind power grid connection and the battery energy storage system is scheduled; wherein the objective function includes a carbon emission cost function, a wind power government subsidy function, and a battery energy storage system operating cost function. By adopting the method, the power system can be reasonably planned and scheduled, the total running cost of the system is minimized, and the method is more environment-friendly.
Description
Technical Field
The present disclosure relates to the field of power systems, and in particular, to a power system scheduling method, apparatus, computer device, and storage medium.
Background
Because of concerns about air pollution and global warming, there is a great deal of attention in establishing a low-carbon society, and the use of clean energy is becoming increasingly common. The consumption of renewable energy sources is a key factor in reducing air pollution and reducing clean energy environment.
Wind power has important roles in reducing global greenhouse gas emission and relieving global energy shortage due to the characteristics of cleanliness and reproducibility. Wind power generation has been increasingly used in recent years because it does not produce harmful emissions and is an effective measure for reducing carbon emissions.
According to the traditional power system scheduling scheme considering wind power grid connection, uncertain factors are caused by wind power intermittence and fluctuation, difficulty is brought to power grid scheduling and planning, the power system scheduling is unreasonable, and the operation cost of the power system is high.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a power system scheduling method, apparatus, computer device, and storage medium that can reduce the running cost of a power system.
A power system scheduling method suitable for a power system considering wind power grid connection and a battery energy storage system, the method comprising:
Acquiring a wind power predicted value, wind turbine generator parameters, thermal power generating unit parameters, power grid unit load demand data and battery energy storage system parameters;
based on the wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, a preset system operation cost objective function is solved by taking the minimum total operation cost of the power system as an objective, and the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system are obtained;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system;
scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system;
the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
In one embodiment, based on a wind power predicted value, a wind power unit parameter, a thermal power unit parameter, power grid unit load demand data, a battery energy storage system parameter and a preset constraint condition, and with the minimum total operation cost of the power system as a target, solving a preset system operation cost objective function to obtain an optimal output power of the wind power unit, an optimal output power of the thermal power unit and an optimal output power of the battery energy storage system comprises:
Based on wind power predicted values, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, taking the minimum total running cost of the power system as a target, solving a preset system running cost objective function by adopting a quantum particle swarm optimization algorithm, so as to optimize wind power unit output power, thermal power unit output power and battery energy storage system output power, and obtain wind power unit optimal output power, thermal power unit optimal output power and battery energy storage system optimal output power.
In one embodiment, based on a wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, taking the minimum total running cost of a power system as a target, solving a preset system running cost objective function by adopting a quantum particle swarm optimization algorithm, so as to optimize wind power unit output power, thermal power unit output power and battery energy storage system output power, and obtaining wind power unit optimal output power, thermal power unit optimal output power and battery energy storage system optimal output power comprises:
Initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of a battery energy storage system, and constructing an initial random population;
counting the iterative times of the algorithm and the population scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm;
when the initial random population does not meet the operation suspension condition of the preset algorithm, continuing iteration, and acquiring and updating a local optimal solution and a global optimal solution of quanta, wherein the local optimal solution and the global optimal solution comprise the optimal output power of an initial wind turbine generator set, the optimal output power of an initial thermal power generating unit and the optimal output power of an initial battery energy storage system;
if the current global optimal solution is consistent with the previous global optimal solution, calculating quantum affinity and concentration;
selecting quanta and mutating the sequence by using a self-adaptive probability selection algorithm and chaotic sequence variation;
updating quantum bit and angle of quanta through a quanta revolving door algorithm;
adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm or not until the initial random population meets the operation suspension condition of the preset algorithm, so as to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system.
In one embodiment, obtaining the wind power prediction value includes:
acquiring historical wind speed data;
fitting the historical wind speed data by using a Weibull probability distribution function, and obtaining a probability density function of wind power by combining the relation between wind speed and output power;
and obtaining a wind power predicted value according to the probability density function of the wind power.
In one embodiment, fitting the historical wind speed data by using a Weibull probability distribution function, and combining the relation between the wind speed and the output power to obtain a probability density function of the wind power comprises:
fitting the historical wind speed data by using a Weibull probability distribution function to construct a cumulative distribution function of wind speed;
obtaining a probability density function of the wind speed based on the cumulative distribution function of the wind speed;
according to the cumulative distribution function of the wind speed and the probability density function of the wind speed, combining the relation between the wind speed and the output power to obtain the cumulative distribution function of the wind power;
and obtaining a probability density function of the wind power based on the cumulative distribution function of the wind power.
In one embodiment, the preset constraints include: at least one of a system active load constraint condition, a thermal power generating unit output power constraint condition, a fan output power constraint condition, a battery energy storage system charge and discharge power constraint condition and a battery energy storage system storage capacity constraint condition.
In one embodiment, the carbon emission cost function is derived based on the following:
acquiring a carbon emission tax price and an emission function of greenhouse gases of a thermal power unit;
and constructing a carbon emission cost function according to the carbon emission tax price and the emission function of greenhouse gases of the thermal power generating unit.
A power system scheduling apparatus adapted for a power system that accounts for wind grid integration and a battery energy storage system, the apparatus comprising:
the data acquisition module is used for acquiring wind power predicted values, wind turbine generator set parameters, thermal power unit parameters, power grid unit load demand data and battery energy storage system parameters;
the cost optimization module is used for solving a preset system running cost objective function by taking the minimum total running cost of the electric power system as a target based on the wind power predicted value, wind turbine generator parameters, thermal power generator parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
the cost calculation module is used for obtaining the total running cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system;
The system scheduling module is used for scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system;
the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring a wind power predicted value, wind turbine generator parameters, thermal power generating unit parameters, power grid unit load demand data and battery energy storage system parameters;
based on the wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, a preset system operation cost objective function is solved by taking the minimum total operation cost of the power system as an objective, and the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system are obtained;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system;
Scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system;
the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a wind power predicted value, wind turbine generator parameters, thermal power generating unit parameters, power grid unit load demand data and battery energy storage system parameters;
based on the wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, a preset system operation cost objective function is solved by taking the minimum total operation cost of the power system as an objective, and the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system are obtained;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system;
Scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system;
the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
According to the power system scheduling method, the device, the computer equipment and the storage medium, the power system of the wind power grid connection and the battery energy storage system is taken into consideration as a research object, the randomness of wind power can be stabilized by using the battery energy storage system, the fluctuation influence caused by wind power grid connection is relieved, the objective function comprising the carbon emission cost function, the wind power government patch function and the battery energy storage system operation cost function is designed, the objective function is solved with the aim of minimizing the total operation cost of the power system, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system are obtained, the minimum total operation cost of the power system is further obtained, the scheduling power system can be reasonably planned according to the minimum total operation cost of the power system, the total operation cost of the system is minimized, the consumption of new energy output is enhanced, the carbon emission is effectively reduced, and the power system scheduling method is more environment-friendly.
Drawings
FIG. 1-1 is an application environment diagram of a power system scheduling method in one embodiment;
FIGS. 1-2 are system block diagrams of a power system in one embodiment;
FIG. 2 is a flow chart of a power system scheduling method in one embodiment;
FIG. 3-1 is a schematic diagram of conventional units and parameters related to wind turbines in an embodiment;
FIG. 3-2 is a schematic diagram of three cases of predicted load and predicted wind power in one embodiment;
3-3 are diagrams of parameters associated with a battery energy storage system according to one embodiment;
FIG. 4 is a schematic diagram of an output power curve of a blower according to another embodiment;
FIG. 5 is a schematic diagram of the result of an embodiment of the power system to consider carbon emission tax and include wind farm;
FIG. 6 is a schematic diagram of the result of an electrical power system considering different capacity battery energy storage systems under wind-containing power and carbon emission tax in one embodiment;
FIG. 7 is a block diagram of a power system scheduler in one embodiment;
FIG. 8 is a block diagram of a power system scheduler in another embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The power system scheduling method provided by the application can be applied to an application environment shown in the figure 1-1. Wherein the terminal 102 communicates with the server 104 via a network. Specifically, a manager logs in a service management system at a terminal 102, and sends a system cost analysis message to a server 104 by operating at a system operation interface, and the server 102 receives the system cost analysis message to obtain an existing wind power predicted value, wind turbine generator parameters, thermal power generator parameters, power grid unit load demand data and battery energy storage system parameters; then, based on wind power predicted values, wind power set parameters, thermal power set parameters, power grid set load demand data, battery energy storage system parameters and preset constraint conditions, a preset system operation cost objective function is solved with the minimum total operation cost of the power system as a target, so that the optimal output power of the wind power set, the optimal output power of the thermal power set and the optimal output power of the battery energy storage system are obtained, and the minimum total operation cost of the power system is obtained according to the optimal output power of the wind power set, the optimal output power of the thermal power set and the optimal output power of the battery energy storage system; scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system; the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
1-2, the application takes an electric power system comprising a conventional unit, a wind turbine and a battery energy storage system as research objects, considers carbon emission constraint in the system, and establishes an environment-economy combined dispatching unit combination model aiming at minimum system operation cost and highest renewable energy consumption rate so as to reduce operation cost and carbon emission. The conventional unit and the wind turbine unit are used for supplying energy, the battery energy storage device is connected with a power grid through a power converter, and when the wind power predicted value is smaller than the actual power output (underestimation), the redundant electric quantity can be stored in the energy storage system. If the wind power actual value is smaller than the predicted value (overestimated), the stored electric quantity can meet the system load requirement. The minimum electric quantity and the maximum electric quantity stored by the storage battery are specified, the lower limit of the SOC is 20% of the full capacity of the battery, and the upper limit of the SOC is 80% of the full capacity of the battery. By limiting the SOC to between 20% and 80%, the deep charge-discharge cycle has been minimized to extend battery life. In the charge and discharge process, only one state is provided, namely, the charge and the discharge cannot be performed simultaneously.
In one embodiment, as shown in fig. 2, a power system scheduling method is provided, where the method is applied to a server for illustration, it is understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
The wind power generation set parameters are set characteristic parameters reported by each wind power plant, and specifically include a proportion parameter, a shape parameter, a rated wind speed, an on wind speed, an off wind speed, a rated output power, a production cost coefficient of a fan, a government patch coefficient of wind power, a cost coefficient of purchasing standby power from other operators due to wind power overestimation, a cost coefficient of not fully using wind power due to wind power underestimation, and the like, and can be seen in fig. 3-1. The thermal power unit parameters are unit characteristic parameters reported by each thermal power plant, and specifically comprise fuel cost coefficients, fuel consumption coefficients, upper and lower limit constraints of unit output, fuel emission coefficients of carbon dioxide, carbon emission tax prices and the like, and can be seen in fig. 3-1. The power grid unit load demand data is system future 24-hour load demand data obtained by a power grid unit dispatching center, and specifically comprises predicted system load demand, wind power installed capacity and the like, and can be seen in fig. 3-2. The parameters of the battery energy storage system are related characteristic data of the network-accessible battery energy storage system, and specifically include upper and lower limits of battery energy storage capacity, initial energy storage capacity, upper and lower limits of battery charge state, maximum charge and discharge power, cost coefficient consumed by the energy storage system and charge and discharge efficiency of the battery, and can be seen in fig. 3-3. The battery energy storage system can be used for relieving fluctuation of wind power randomness to a power grid, and can store electric energy when wind power is overlarge and discharge the electric energy to the power grid through the electric energy converter when the wind power is overlarge.
In one embodiment, obtaining the wind power prediction value includes:
step 220, acquiring historical wind speed data;
step 222, fitting the historical wind speed data by using a Weibull probability distribution function, and obtaining a probability density function of wind power by combining the relation between wind speed and output power;
and 224, obtaining a wind power predicted value according to the probability density function of the wind power.
In this embodiment, the wind power prediction value is obtained according to a probability density function of wind power. Wind power is also an uncertainty because wind speed is uncertain. The probability density function of wind power can be obtained by assuming that the wind speed obeys the Weibull probability density function. In this embodiment, the wind speed uncertainty is modeled using a weibull probability distribution, and a cumulative distribution function of the wind speed is obtained as follows:
where c is a scale parameter, k is a shape parameter, c >0, k >0. The probability density function of wind speed can be obtained from the cumulative distribution function of wind speed as follows:
mathematical modeling of wind speed and output power is as follows, see fig. 4:
wherein,,
v in is cut-in wind speed v r Is rated wind speed v out Is the cut-out wind speed, w r Is the rated output power. From the formulas (1) and (2), the cumulative distribution function of the wind power is as follows:
If w=0:
if 0 < W < W r ：
If W=w r ：
The probability density function of the wind power obtainable from equations (1) - (6) is as follows:
after the probability density function of the wind power is obtained, the wind power predicted value can be obtained through analysis. In the embodiment, uncertainty of wind speed and wind power can be accurately described by using the Weibull distribution function, and accuracy of a wind power predicted value is further improved.
In this embodiment, the system running cost objective function is:
wherein M, N is the number of thermal power generating units and wind power generating units, and the system operation is known from the functional expressionThe running cost objective function is an objective function comprising 7 sub-functions, and specifically comprises the generating cost functions of the wind turbine generator set respectively 
Thermal power generating unit power generation cost function>
Carbon emission cost function->
Wind power government subsidy function->
Wind power overestimation penalty cost function>
Wind power underestimation penalty cost function>
Battery energy storage system operation cost function>
Wherein, the cost coefficient of the thermal power generating unit
The quadratic function is used to represent:
wherein p (i, t) is the output power of the thermal power unit i at the moment t, a i,t 、b i,t And c i,t Is the fuel cost coefficient of the thermal power unit i at the time t.
Generating cost coefficient of wind turbine generator
The method comprises the following steps:
wherein alpha is w,j And Q (j, t) represents the start-stop state of the fan j at the moment t. W (W) av (j, t) is the actual output power of the wind power (i.e. the actual output power of the wind power), if the wind farm is owned by the grid, then W av And (j, t) takes on a value of 0.
The carbon emission cost function is:
wherein EM is i (p i ) Represents the emission of carbon dioxide, ef, of a thermal power unit i i The fuel emission coefficient of carbon dioxide of the thermal power plant i is represented. f (f) i 、g i And h i Representing the fuel consumption coefficient, where C Tax Representing the carbon emission tax price.
Wherein alpha is s,j The government patch coefficient for wind power is shown.
According to the probability density function of wind power, constructing a wind power overestimation penalty cost function as follows:
Wherein w is j Is the predicted wind power of the fan j, f W (w) is wind power C o,j Representing a cost factor for purchasing backup power from other operators due to wind power overestimation.
Likewise, wind power underestimation penalty costs are as follows:
wherein w is r,j Indicating the rated power of the fan j. C (C) u,j Representing the cost factor of not fully using wind power due to wind power underestimation.
The last term is the cost of operation of the battery energy storage system, which can be expressed as:
wherein pi is BESS Representing the cost factor consumed by the battery energy storage system. P (P) BESS Is the charge and discharge power of the battery.
In specific implementation, the objective function can be optimized by taking the minimum total running cost of the power system (namely the power generation cost of the power system) as a target to obtain the optimal output power of the wind turbine generator set, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system, wherein the total running cost of the power system is minimum.
And 206, obtaining the total running cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system.
And substituting the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system into a formula (8) after obtaining the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system, so as to obtain the minimum total running cost of the power system.
And step 208, scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system.
After the minimum cost of the power system is obtained, according to the cost, the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system are combined, the power system of the wind power grid connection and the battery energy storage system is considered in scheduling, and relevant parameters of the power system, such as the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of the battery energy storage system, are adjusted, so that the running cost of the power system is minimum.
According to the power system scheduling method, the power system of the wind power grid connection and the battery energy storage system is taken into consideration as a research object, the randomness of wind power can be stabilized by using the battery energy storage system, the fluctuation influence caused by wind power grid connection is relieved, the objective function comprising the carbon emission cost function, the wind power government subsidy function and the battery energy storage system operation cost function is designed, the objective function is solved by taking the total operation cost of the power system as a target, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system are obtained, the minimum total operation cost of the power system is further obtained, the scheduling power system can be reasonably planned according to the minimum total operation cost of the power system, the minimization of the total power generation cost of the system is realized, the consumption of new energy output is enhanced, the carbon emission is effectively reduced, and the power system scheduling method is more environment-friendly.
In one embodiment, the preset constraints include: at least one of a system active load constraint condition, a thermal power generating unit output power constraint condition, a fan output power constraint condition, a battery energy storage system charge and discharge power constraint condition and a battery energy storage system storage capacity constraint condition.
And summarizing the established system operation cost objective function and the corresponding model to obtain corresponding constraint conditions. Specifically, the constraint conditions are as follows:
the system active load constraint conditions are as follows:
wherein P is d Is the total system demand. The upper and lower limits of the thermal power generating unit are constrained as follows:
p i,min ≤p(i,t)≤p i,max (18)
the fan output power constraint conditions are as follows:
0≤w j ≤w r,j (19)
battery energy storage system charge-discharge power constraint:
wherein the method comprises the steps of
And->
Respectively, the maximum charge and discharge rates. This constraint indicates that the battery cannot be charged and discharged at the same time.
The storage capacity constraints of the battery energy storage system are as follows:
SOC L ≤SOC(t)≤SOC U (22)
where SOC is the state of charge of the battery energy storage system at time t. SOC (State of Charge) L And SOC (System on chip) U Respectively upper and lower limits of the state of charge of the battery. Stored electricity C of battery energy storage system BESS The expression is as follows:
wherein C is ini Is the initial value of the stored electric quantity of the battery energy storage system, eta ch And eta dis Respectively, the charge and discharge efficiency of the battery.
In practical applications, the constraint conditions may be specifically set according to practical situations.
In one embodiment, step 204 includes: step 224, based on the wind power predicted value, the wind power set parameter, the thermal power set parameter, the power grid set load demand data, the battery energy storage system parameter and the preset constraint condition, a quantum particle swarm optimization algorithm is adopted to solve a preset system running cost objective function so as to optimize the wind power set output power, the thermal power set output power and the battery energy storage system output power, and obtain the wind power set optimal output power, the thermal power set optimal output power and the battery energy storage system optimal output power.
The quantum particle swarm optimization algorithm (Quantum Particle Swarm Optimization, hereinafter referred to as QPSO) is a probability search algorithm, and quantum behaviors are added to the particle swarm optimization algorithm. In this algorithm, the state of the particles is described by qubits and angles, rather than using position and velocity as in classical particle swarm algorithms. In the embodiment, the quantum particle swarm optimization algorithm is adopted to simulate the objective function, so that the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system can be obtained quickly and accurately, and the total running cost of the power system is minimized.
In one embodiment, step 224 includes:
step 240, initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of a battery energy storage system, and constructing an initial random population;
step 241, counting the iterative times of the algorithm and the population scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm;
step 242, when the initial random population does not meet the operation suspension condition of the preset algorithm, continuing iteration, and obtaining and updating a local optimal solution and a global optimal solution of quanta, wherein the local optimal solution and the global optimal solution comprise the optimal output power of the initial wind turbine generator, the optimal output power of the initial thermal power generating unit and the optimal output power of an initial battery energy storage system;
step 243, if the current global optimal solution is consistent with the previous global optimal solution, calculating quantum affinity and concentration;
step 244, selecting quanta and mutating the sequence by using an adaptive probability selection algorithm and chaotic sequence variation;
step 245, updating the quantum bit and angle of the quantum through a quantum rotation gate algorithm;
and step 246, adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm or not until the initial random population meets the operation suspension condition of the preset algorithm, so as to obtain the optimal output power of the wind turbine generator set, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system.
In specific implementation, the specific flow of the quantum particle swarm optimization algorithm is as follows:
1) And (5) quantum initialization. Based on the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of the battery energy storage system, n parameter vectors with m dimensions are defined and used as a population for each iteration, and each individual in the population can be expressed as a qubit. Qubits (the smallest unit in a quantum heuristic particle swarm optimization algorithm) are defined as a pair of m-dimensional parameter vectors:
modulus |alpha ji (t)| 2 And |beta ji (t)| 2 The probabilities that the qubits are in states "0" and "1" respectively are given, and satisfy:
|α ji (t)| 2 +|β ji (t)| 2 ＝1 (26)
a string of qubits consists of n quantum units, which can be expressed as:
the quantum angle can be expressed as:
in this embodiment, the value of m is 3, that is, a three-dimensional parameter vector is defined, and the initialization parameter setting is performed on the qubit and the angle of each quantum according to formulas (25) - (28), so as to obtain an initial random population. The final objective of the algorithm is to search the global optimal quantum, namely the optimal power supply output of the combined operation of the system (comprising the optimal output power of the wind turbine, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system), and calculate the minimum power generation cost according to the formula (8). The iteration number Run and population size Gen are counted, both initial values being 0. The number of suspension iterations is set to N.
2) It is determined whether to abort the iteration. If the initial random population meets the operation suspension condition, namely, the random population is updated 100 times, the globally optimal solution is kept unchanged, the iteration times are counted by run+1, the iteration is continued until the times reach N, and the program is ended; if not, go to step 3).
3) And obtaining a local optimal solution and a global optimal solution of the quantum. And (3) evaluating the fitness of quanta in the population, optimizing with the aim of minimizing the total running cost of the power system, solving the formula (8), updating the local optimal solution of quanta and the global optimal solution of the population to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system, and substituting the optimal output power into the formula (8) to calculate the total running cost of the power system.
4) And judging whether the global optimal solution is kept unchanged. If the optimal output power of the wind turbine, the optimal output power of the thermal power unit, the optimal output power of the battery energy storage system and the total running cost of the power system obtained by the current iteration calculation are consistent with the last iteration result, step 5 is carried out; otherwise, go to step 8).
5) Quantum affinity and concentration were calculated. The individual affinity and individual concentration can be expressed as:
where r is a random number in the range of (0, 1).
6) Roulette selection and chaotic sequence variation are performed on quanta. In the embodiment, the problem of premature convergence is solved by adopting self-adaptive probability selection and chaotic sequence variation, and the defects of premature convergence and easy sinking into local optimum of an original particle swarm algorithm are effectively overcome. Sequentially performing selection and sequence mutation according to a formula (32):
g(t+1)＝μg(t)[1-g(t)],μ∈[0,4] (32)
small differences in initial values can lead to significant differences in long-term behavior. Here μ=4, mutation operation is defined as:
7) The qubits and angles are updated. The basic update mechanism of the quantum particle swarm algorithm is the evolution of the quantum bit and the angle, so that the update of the quantum bit still meets the normalization condition. The updated equation for the quantum revolving door is:
wherein,,
is angle change +.>
Is the current angle +.>
Is a local optimum angle, & lt & gt>
Is a globally optimal angle.
8) And (5) evolving a new quantum unit. After the qubit and the angle are updated, new quantum individuals are added into the initial random population, the population scale is counted into Gen+1, and the step 2) is returned to iterate. Wherein, new quantum individuals are evolved through qubits and turnstiles.
The quantum particle swarm optimization algorithm has quick convergence and strong optimal solution searching capability. In the embodiment, the optimal output power of the thermal power, the optimal output power of wind power and the optimal output power of battery energy storage are obtained by utilizing a quantum particle swarm optimization algorithm, the minimum power generation cost of the system is further calculated, the minimum total power generation cost of the joint scheduling system is realized, the consumption of new energy output is enhanced, and the carbon emission is effectively reduced.
In one embodiment, assume that the carbon emission tax is $23/ton, pi BESS =0.1 dollar/Kwh. Carbon emission costs are added to the total cost of system operation, making the schedule environmentally friendly-economical. Solving the quantum particle swarm optimization algorithm by setting a series of constraintsReferring to fig. 5, it can be seen that in the objective function, a portion of the power provided by the highly contaminated coal-fired units (G1-G6) will be replaced by less contaminated gas generators (G7-G10) and petroleum generators (G11, G12). Although the electricity generation cost of the carbon tax model is far higher than that of the electricity generation cost without carbon tax; however, the carbon-containing tax model can reduce the carbon emission, and the scheduling is more environment-friendly.
In one embodiment, a quantum particle swarm optimization algorithm can be used to solve the set combination problem, and several scene simulation simulations can be performed on whether a wind farm is contained or not, whether carbon emission cost is contained or not and different battery capacities are contained or not. It can be seen from fig. 5 that when the model contains the wind turbine, part of the conventional turbine output is reduced, and the wind turbine is charged by wind power, so that the total scheduling cost is reduced even if the wind power output cost is more expensive. The wind power output reduces the output of a conventional unit, thereby reducing carbon emission. In case a, G13 produces less wind than predicted. In CaseC, the generated wind force is greater than the predicted value. The overestimated cost and the underestimated cost of wind power increase the scheduling cost, so that the predicted deviation of wind power needs to be compensated, and a battery energy storage system is added. The capacity of the battery energy storage system is different from the capacity of the battery energy storage system to the wind power consumption rate, and considering that the battery energy storage system can improve the wind power consumption capacity of the power grid, in this embodiment, three battery energy storage systems with different capacity levels are added, namely 15%, 20% and 25%, respectively, and the influence of the capacities of the different battery energy storage systems on the adjustment result can be seen in fig. 6. It can be seen from fig. 6 that the 20% capacity battery energy storage system participates in the wind-fire coordination scheduling, so that the total scheduling cost is the lowest, the wind power consumption rate is the highest, and the output of a conventional unit is reduced. Compared with battery energy storage systems with other capacity levels, the battery energy storage system with 20% of capacity has less carbon emission, and the scheduling is more environment-friendly.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a part of the steps in the flowcharts related to the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages performed is not necessarily sequential, but may be performed alternately or alternately with at least a part of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 7, there is provided a power system scheduling apparatus including: a data acquisition module 510, a cost optimization module 520, a cost calculation module 530, and a system scheduling module 540, wherein:
the data acquisition module 510 is configured to acquire a wind power predicted value, a wind turbine generator set parameter, a thermal power generator set parameter, power grid unit load demand data, and a battery energy storage system parameter.
The cost optimization module 520 is configured to solve a preset system operation cost objective function with a minimum total operation cost of the electric power system as a target based on the wind power predicted value, the wind power unit parameter, the thermal power unit parameter, the power grid unit load demand data, the battery energy storage system parameter and the preset constraint condition, so as to obtain an optimal output power of the wind power unit, an optimal output power of the thermal power unit and an optimal output power of the battery energy storage system, wherein the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
The cost calculation module 530 is configured to obtain a total running cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit, and the optimal output power of the battery energy storage system;
the system scheduling module 540 is configured to schedule the power system considering the wind power grid connection and the battery energy storage system according to the minimum total running cost of the power system.
In one embodiment, the cost optimization module 520 is further configured to solve a preset system operation cost objective function by adopting a quantum particle swarm optimization algorithm with the minimum total operation cost of the electric power system as a target based on the wind power prediction value, the wind power generation parameter, the thermal power generation parameter, the power grid load demand data, the battery energy storage system parameter and the preset constraint condition, so as to optimize the wind power generation output power, the thermal power generation output power and the battery energy storage system output power, and obtain the wind power generation optimal output power, the thermal power generation optimal output power and the battery energy storage system optimal output power.
In one embodiment, the cost optimization module 520 is further configured to:
initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of a battery energy storage system, and constructing an initial random population;
counting the iterative times of the algorithm and the population scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm;
when the initial random population does not meet the operation suspension condition of the preset algorithm, continuing iteration, and acquiring and updating a local optimal solution and a global optimal solution of quanta, wherein the local optimal solution and the global optimal solution comprise the optimal output power of an initial wind turbine generator set, the optimal output power of an initial thermal power generating unit and the optimal output power of an initial battery energy storage system;
if the current global optimal solution is consistent with the previous global optimal solution, calculating quantum affinity and concentration;
selecting quanta and mutating the sequence by using a self-adaptive probability selection algorithm and chaotic sequence variation;
updating quantum bit and angle of quanta through a quanta revolving door algorithm;
adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm or not until the initial random population meets the operation suspension condition of the preset algorithm, so as to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system.
In one embodiment, the data obtaining module 510 is further configured to obtain historical wind speed data, fit the historical wind speed data with a weibull probability distribution function, obtain a probability density function of wind power by combining a relationship between wind speed and output power, and obtain a wind power prediction value according to the probability density function of wind power.
In one embodiment, the data obtaining module 510 is further configured to fit the historical wind speed data using a weibull probability distribution function, construct a cumulative distribution function of wind speed, obtain a probability density function of wind speed based on the cumulative distribution function of wind speed, and obtain a cumulative distribution function of wind power according to the cumulative distribution function of wind speed and the probability density function of wind speed and the relationship between wind speed and output power, and obtain a probability density function of wind power.
As shown in FIG. 8, in one embodiment, the system further includes a carbon emission cost function construction module 550 for obtaining a carbon emission tax price and an emission function of greenhouse gases of the thermal power plant, and constructing the carbon emission cost function based on the carbon emission tax price and the emission function of greenhouse gases of the thermal power plant.
For specific embodiments of the power system scheduling apparatus, reference may be made to the above embodiments of the power system scheduling method, which are not described herein. The various modules in the power system scheduling apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing wind power predicted values, wind turbine generator set parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a power system scheduling method.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps in the power system scheduling method described above when the computer program is executed.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the power system scheduling method described above. Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A power system scheduling method, suitable for a power system considering wind power grid connection and a battery energy storage system, comprising:
acquiring a wind power predicted value, wind turbine generator parameters, thermal power generating unit parameters, power grid unit load demand data and battery energy storage system parameters;
based on the wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, taking the minimum total running cost of the power system as a target, adopting a quantum particle swarm optimization algorithm to solve a preset system running cost objective function so as to optimize the wind power unit output power, the thermal power unit output power and the battery energy storage system output power to obtain the wind power unit optimal output power, the thermal power unit optimal output power and the battery energy storage system optimal output power;
Obtaining the total running cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system;
scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system;
the system comprises a preset system running total cost objective function, a wind power overestimated penalty cost function, a wind power underestimated penalty cost function, a carbon emission cost function, a wind power government subsidy function and a battery energy storage system running cost function, wherein the preset system running total cost objective function comprises a wind power overestimated penalty cost function, a wind power underestimated penalty cost function, a carbon emission cost function, a wind power government subsidy function and a battery energy storage system running cost function;
based on the wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, taking the minimum total running cost of the power system as a target, solving a preset system running cost objective function by adopting a quantum particle swarm optimization algorithm, and optimizing the wind power unit output power, the thermal power unit output power and the battery energy storage system output power to obtain the wind power unit optimal output power, the thermal power unit optimal output power and the battery energy storage system optimal output power, wherein the method comprises the following steps:
Based on the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of a battery energy storage system, initializing the qubit and angle of quanta, constructing an initial random population, counting algorithm iteration times and population scale, judging whether the initial random population meets preset algorithm operation suspension conditions, continuing iteration when the initial random population does not meet the preset algorithm operation suspension conditions, acquiring and updating a local optimal solution and a global optimal solution of quanta, wherein the local optimal solution and the global optimal solution comprise the optimal output power of the initial wind turbine generator, the optimal output power of the initial thermal power generating unit and the optimal output power of an initial battery energy storage system, calculating quantum affinity and concentration if the current global optimal solution is consistent with the last global optimal solution, utilizing a self-adaptive probability selection algorithm and chaotic sequence mutation, carrying out roulette selection and chaotic sequence mutation on quanta, updating the qubit and angle of quanta through a quantum rotation gate algorithm, adding new quanta individuals into the initial random population, returning to count the preset iteration times and the preset population, and judging whether the initial random population meets preset operation conditions or not until the initial random population meets the preset operation conditions, and the optimal output power of the thermal power generating unit is obtained.
2. The power system scheduling method of claim 1, wherein obtaining a wind power prediction value comprises:
acquiring historical wind speed data;
fitting the historical wind speed data by using Weibull probability distribution, and obtaining a probability density function of wind power by combining the relation between wind speed and output power;
and obtaining a wind power predicted value according to the probability density function of the wind power.
3. The power system scheduling method according to claim 2, wherein the fitting the historical wind speed data using a weibull probability distribution, and combining the relationship between wind speed and output power, the obtaining the probability density function of wind power comprises:
fitting the historical wind speed data by using a Weibull probability distribution function, analyzing wind speed and wind power uncertainty, and constructing a cumulative distribution function of wind speed;
obtaining a probability density function of the wind speed based on the cumulative distribution function of the wind speed;
according to the cumulative distribution function of the wind speed and the probability density function of the wind speed, combining the relation between the wind speed and the output power to obtain the cumulative distribution function of the wind power;
and obtaining a probability density function of the wind power based on the cumulative distribution function of the wind power.
4. The power system scheduling method according to claim 1, wherein the preset constraint condition includes: at least one of a system active load constraint condition, a thermal power generating unit output power constraint condition, a fan output power constraint condition, a battery energy storage system charge and discharge power constraint condition and a battery energy storage system storage capacity constraint condition.
5. The power system scheduling method of claim 1, wherein the carbon emission cost function is derived based on:
acquiring a carbon emission tax price and an emission function of greenhouse gases of a thermal power unit;
and constructing a carbon emission cost function according to the carbon emission tax price and the emission function of greenhouse gases of the thermal power generating unit.
6. An electrical power system scheduling apparatus adapted to consider an electrical power system for a wind power grid connection and a battery energy storage system, the apparatus comprising:
the data acquisition module is used for acquiring wind power predicted values, wind turbine generator set parameters, thermal power unit parameters, power grid unit load demand data and battery energy storage system parameters;
the cost optimization module is used for solving a preset system running cost objective function by taking the minimum total running cost of the power system as a target and adopting a quantum particle swarm optimization algorithm based on the wind power predicted value, the wind power set parameters, the thermal power set parameters, the power grid set load demand data, the battery energy storage system parameters and the preset constraint conditions so as to optimize the wind power set output power, the thermal power set output power and the battery energy storage system output power to obtain the optimal output power of the wind power set, the optimal output power of the thermal power set and the optimal output power of the battery energy storage system;
The cost calculation module is used for obtaining the total running cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system;
the system scheduling module is used for scheduling the power system considering the wind power grid connection and the battery energy storage system according to the total running cost of the minimum power system;
the system comprises a preset system running total cost objective function, a wind power overestimated penalty cost function, a wind power underestimated penalty cost function, a carbon emission cost function, a wind power government subsidy function and a battery energy storage system running cost function, wherein the preset system running total cost objective function comprises a wind power overestimated penalty cost function, a wind power underestimated penalty cost function, a carbon emission cost function, a wind power government subsidy function and a battery energy storage system running cost function;
the cost optimization module is further used for constructing an initial random population based on the output power of the wind turbine generator, the output power of the thermal power generating unit and the output power of the battery energy storage system, initializing the quantum bit and angle of quanta, counting the iterative times of the algorithm and the population scale, judging whether the initial random population meets the operation suspension condition of the preset algorithm, continuing iterating when the initial random population does not meet the operation suspension condition of the preset algorithm, acquiring and updating the locally optimal solution and the globally optimal solution of quanta, wherein the locally optimal solution and the globally optimal solution comprise the optimal output power of the initial wind turbine generator, the optimal output power of the initial thermal power generating unit and the optimal output power of the initial battery energy storage system, calculating the quantum affinity and concentration, performing roulette selection and chaotic sequence mutation on quanta by utilizing the self-adaptive probability selection algorithm and the chaotic sequence variation, updating the quantum bit and angle of quanta through the quantum rotating gate algorithm, adding new quanta into the initial random population, returning to count the iterative times and the preset scale, and judging whether the initial random population meets the operation suspension condition of the preset algorithm until the initial random population meets the operation suspension condition of the preset algorithm, and the optimal output power of the battery energy storage system is obtained.
7. The power system dispatching device of claim 6, wherein the data acquisition module is further configured to acquire historical wind speed data, fit the historical wind speed data by using a weibull probability distribution, combine a relationship between wind speed and output power, obtain a probability density function of wind power, and acquire a wind power predicted value according to the probability density function of wind power.
8. The power system dispatching device of claim 6, further comprising a carbon emission cost function construction module for obtaining a carbon emission tax price and a greenhouse gas emission function of a thermal power plant, and constructing the carbon emission cost function according to the carbon emission tax price and the greenhouse gas emission function of the thermal power plant.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the following steps of the method of any one of claims 1 to 5 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
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