CN116960939A - Multi-target particle swarm algorithm-based wind, solar and diesel storage system optimal scheduling method, equipment and storage medium - Google Patents
Multi-target particle swarm algorithm-based wind, solar and diesel storage system optimal scheduling method, equipment and storage medium Download PDFInfo
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Abstract
A multi-target particle swarm optimization scheduling method, equipment and medium for a wind, light and diesel storage system belong to the field of energy storage, and solve the problems that the efficiency of wind, light and diesel storage system optimal scheduling and the stability of a power system are reduced because the power load prediction and wind, light and diesel generation power prediction are not fully considered in the prior art. The method comprises the following steps: establishing a prediction model; obtaining prediction parameters according to the prediction model; establishing a wind-solar-diesel storage system optimization scheduling model based on a multi-target particle swarm algorithm, and determining an objective function and constraint conditions during energy storage optimization scheduling of the wind-solar-diesel storage system; and optimizing and solving the objective function by adopting a multi-objective particle swarm algorithm to obtain the running capacity ratio of the wind-solar-energy-charge-storage combined system, and realizing the optimized scheduling of the wind-solar-energy-diesel-storage system. The method is suitable for optimizing and scheduling grid connection of the renewable energy source and energy storage system represented by the wind, light and diesel storage system.
Description
Technical Field
The invention belongs to the field of energy storage, and particularly relates to an operation scheduling method of an energy storage power station.
Background
Renewable energy sources represented by wind power and photovoltaic have the characteristics of zero emission and no pollution, the utilization level is gradually improved, and the renewable energy sources become a main body of newly increased power generation. With the large-scale access of renewable energy sources in an electric power system, the randomness and the discontinuity of wind and light resources cause inconsistent output of wind and light in time and space dimensions, and continuous and stable energy is difficult to provide for the system.
The energy storage system can realize energy buffering, peak clipping and valley filling, and improves the utilization efficiency of natural resources such as wind energy, solar energy and the like. When the wind and light output is sufficient, the surplus energy output by the wind and light source is stored in the system, and when the wind and light source is insufficient, the energy is released to supply load. In order to reduce the system operation cost and improve the consumption of new energy, the coordination and coordination among all power supplies are required to be considered, and a proper optimal scheduling method for the wind, light and diesel storage system is found.
At present, the research on the optimal scheduling of the wind, light and diesel storage system does not completely consider the power load prediction and wind and light power generation power prediction, and the benefit of the optimal scheduling of the wind, light and diesel storage system and the stability of the power system are greatly reduced. The particle swarm algorithm adopted in the solving process can only select a single objective function, and cannot simultaneously consider two important standards of economic benefit and energy utilization rate and the problem of being trapped in a local optimal solution.
Disclosure of Invention
The invention provides a wind-solar-diesel storage system optimal scheduling method based on a multi-target particle swarm algorithm, which solves the problems that the efficiency of wind-solar-diesel storage system optimal scheduling and the stability of a power system are reduced because the power load prediction and wind-solar power generation power prediction are not fully considered in the prior art.
A wind, light and diesel storage system optimal scheduling method based on a multi-target particle swarm algorithm comprises the following steps:
selecting an existing prediction model;
obtaining prediction parameters according to the prediction model;
establishing a wind-solar-diesel storage system optimization scheduling model based on a multi-target particle swarm algorithm according to the prediction parameters, and determining an objective function and constraint conditions during energy storage optimization scheduling of the wind-solar-diesel storage system;
and optimizing and solving the objective function by adopting a multi-objective particle swarm algorithm to obtain the running capacity ratio of the wind-solar-energy-charge-storage combined system, and realizing the optimized scheduling of the wind-solar-energy-diesel-storage system.
Further, the prediction model comprises a power load prediction model, a wind power prediction model and a photovoltaic power generation power prediction model;
further, the prediction parameters include: electric load, wind power generation output power and photovoltaic power generation output power;
further, the obtaining the prediction parameters according to the prediction model specifically includes:
carrying out short-term load prediction by adopting a PCA-CNN-LSTM combination model based on a sliding window to obtain power load;
predicting wind power by using a CEEMDAN-LSTM-RF-RBF combined prediction model to obtain wind power generation output power;
taking the generated energy, the temperature and the solar radiation as input quantities of a CNN-GRU model to obtain photovoltaic power generation output power;
further, the objective function comprises the running cost of the system and the consumption of wind-light resources;
further, the constraint includes: power balance constraints, energy storage device constraints, and diesel generator output constraints;
further, the power balance constraint condition is:
P PV +P WT +P DG +P BESS ＝P Load
wherein P is pv For photovoltaic power generation, P WT For wind power generation power, P Load P is the electrical load in the system BESS To store energy and output, P DG The power generated by the diesel generator;
the constraint conditions of the energy storage equipment are as follows:
SOC min ≤SOC t ≤SOC max
in SOC t For the state of charge, SOC, of the energy storage device at time t min SOC as the minimum state of charge of the stored energy max Is the maximum state of charge of the stored energy;
the constraint conditions of the output of the diesel generator are as follows:
wherein P is DG (t) is the actual output power of the diesel generator at the moment t,and->The minimum and maximum output of the diesel generator are respectively.
The invention also provides computer equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the wind-solar-diesel storage system optimal scheduling method based on the multi-target particle swarm algorithm.
The invention also provides a computer readable storage medium for storing a computer program, and the computer program executes the wind-solar-diesel storage system optimal scheduling method based on the multi-target particle swarm algorithm.
The invention also provides a wind-solar-diesel-storage system optimal scheduling system based on the multi-target particle swarm algorithm, which comprises the following steps:
parameter acquisition module: the method comprises the steps of establishing a prediction model, and obtaining prediction parameters according to the prediction model;
and a model building module: the method comprises the steps of establishing a wind-solar-diesel storage system optimization scheduling model based on a multi-target particle swarm algorithm, and determining an objective function and constraint conditions during energy storage optimization scheduling of the wind-solar-diesel storage system;
and a system optimization module: and the method is used for carrying out optimization solution on the objective function by adopting a multi-objective particle swarm algorithm to obtain the running capacity ratio of the wind-solar-energy-load-storage combined system, so as to realize the optimal scheduling of the wind-solar-energy-diesel-storage system.
The invention has the beneficial effects that:
according to the wind-solar-diesel storage system optimal scheduling method based on the multi-target particle swarm algorithm, a prediction model is built to obtain the power load, wind power generation and output power of photovoltaic power generation of a certain day, a model is built for a wind power unit, a photovoltaic power generation unit and an energy storage unit of the wind-solar-diesel storage system on the basis, an objective function is built according to the minimum system operation cost and the maximum wind-solar absorption proportion based on theoretical analysis, the power balance, the energy storage charge state and the diesel generator output are constrained, the multi-target particle swarm algorithm is utilized to solve, the output condition of each unit in the system in a day is obtained, and the coordinated output of each unit in the system is realized.
Compared with the prior art, the invention has the following advantages:
1. before dispatching, the power load and wind-solar power generation power are predicted by using an established model, so that the efficiency of optimizing and dispatching a wind-solar-diesel storage system is greatly improved, and the stability of the power system is improved;
2. compared with the traditional particle swarm algorithm, the method has the advantages that the overall optimal solution is easier to search in the search space, meanwhile, the system has higher economic value by taking the lowest system operation cost and the maximum wind-solar energy consumption as objective functions, clean renewable energy sources can be utilized to the greatest extent, and the wind-discarding and light-discarding quantity is reduced to a certain extent.
The method is suitable for optimizing and scheduling the grid connection of the renewable energy and energy storage system represented by the wind, light and diesel storage system, and has good economic performance while improving the energy utilization rate.
Drawings
Fig. 1 is a structural diagram of a wind, light and diesel storage system according to a sixth embodiment;
fig. 2 is a flowchart of a multi-target particle swarm algorithm according to a fifth embodiment;
fig. 3 is a graph of a load prediction result according to a sixth embodiment;
fig. 4 is a graph of a wind power prediction result according to a sixth embodiment;
fig. 5 shows a photovoltaic power generation power prediction result according to the sixth embodiment;
fig. 6 is a graph of the scheduled photovoltaic power generation power consumption according to the sixth embodiment;
FIG. 7 is a graph of the scheduled wind power consumption according to the sixth embodiment;
fig. 8 is a diagram of a system optimization scheduling result according to the sixth embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one
The wind, solar and diesel storage system optimal scheduling method based on the multi-target particle swarm algorithm is characterized by comprising the following steps:
selecting an existing prediction model;
obtaining prediction parameters according to the prediction model;
establishing a wind-solar-diesel storage system optimization scheduling model based on a multi-target particle swarm algorithm according to the prediction parameters, and determining an objective function and constraint conditions during energy storage optimization scheduling of the wind-solar-diesel storage system;
and optimizing and solving the objective function by adopting a multi-objective particle swarm algorithm to obtain the running capacity ratio of the wind-solar-energy-charge-storage combined system, and realizing the optimized scheduling of the wind-solar-energy-diesel-storage system.
Specifically:
the prediction model comprises an electric load model, a wind power prediction model and a photovoltaic power generation power prediction model.
The prediction parameters include: electric load, wind power generation output power and photovoltaic power generation output power;
the objective function comprises the running cost of the system and the consumption of wind-light resources;
the constraint conditions include: power balance constraints, energy storage device constraints, and diesel generator output constraints.
Second embodiment
The embodiment is a further illustration of obtaining prediction parameters according to the prediction model in the method for optimizing and scheduling a wind-solar-diesel-storage system based on the multi-objective particle swarm algorithm according to the embodiment.
The prediction parameters obtained according to the prediction model described in this embodiment are specifically:
carrying out short-term load prediction by adopting a PCA-CNN-LSTM combination model based on a sliding window to obtain power load;
predicting wind power by using a CEEMDAN-LSTM-RF-RBF combined prediction model to obtain wind power generation output power;
and taking the generated energy, the temperature and the solar radiation as input quantities of a CNN-GRU model to obtain photovoltaic power generation output power.
Embodiment III
The present embodiment is a further illustration of the objective function in the method for optimizing and scheduling a wind-solar-diesel-storage system based on the multi-objective particle swarm algorithm according to the first embodiment.
The objective function described in this embodiment includes the minimum running cost of the system and the maximum amount of wind-light resource consumption.
Specifically:
the running cost of the system mainly surrounds the power generation cost, the running maintenance cost and the environment cost of the system to establish an objective function, and the expression is as follows:
f＝min(C 1 +C 2 +C 3 ) (1)
C 1 the expression of the power generation cost generated by each power generation unit in the system is as follows:
wherein, c PV (i) The photovoltaic power generation cost at the moment i, c WT (i) For wind power generation cost, c DG (i) The power generation cost of the diesel generator is;
C 2 for operation and maintenance cost, specifically, management and maintenance cost generated in the operation process of the system, the expression is as follows:
wherein, c PV.om The operation and maintenance cost of photovoltaic power generation, c WT.om The operation and maintenance cost of wind power generation, c DG.om C is the operation and maintenance cost of the diesel generator bess.om The energy storage operation and maintenance cost is;
C 3 for environmental protection costs, i.e. fuel costs during operation and disposal costs for the production of pollutants, the expression is:
wherein, c DG.F Fuel cost for diesel generator, c DG.en The cost of disposing of the contaminants for the diesel generator;
the wind-light absorption condition in the system is represented by adopting the wind-light absorption proportion, the wind-light absorption quantity corresponding to the system is larger as the absorption proportion is higher, namely the waste wind waste light quantity is smaller, and the specific expression is as follows:
in the Absorb type PV 、Absorb WT The proportion of the photovoltaic power generation and the wind power generation is respectively adopted,is the sum of the predicted values of wind power, +.>For the sum of the actual dispatch output values of wind power, < >>Is the sum of predicted values of photovoltaic power generation power, < >>And (5) dispatching out the sum of the force values for the actual photovoltaic.
Fourth embodiment
The present embodiment is a further illustration of the constraint condition described in the wind-solar-diesel-storage system optimization scheduling method based on the multi-objective particle swarm algorithm in the first embodiment.
The embodiment is described in
In the normal operation process of the system, the power load, wind power, photovoltaic power generation power, the output of a diesel generator and energy storage in the wind-solar-diesel storage system must meet the active power balance, namely:
the power balance constraint conditions are:
P PV +P WT +P DG +P BESS ＝P Load (6)
in order to ensure efficient, accurate and sustainable operation of the power system, it is necessary to strictly control the charging characteristics of the energy storage facilities and their charge and discharge amounts, and to ensure their safe and long-term use, the constraints on the state of charge are:
SOC min ≤SOC t ≤SOC max (7)
secondly, the constraint conditions for the stored energy charge and discharge power are as follows:
0≤P ch ≤P ch-max (8)
0≤P dis ≤P dis-max (9)
wherein P is ch Charging power for energy storage, P ch-max Maximum charge power for energy storage, P dis Discharge power for energy storage, P dis-max Maximum discharge power for energy storage;
output constraint conditions of the diesel generator:
the diesel generator is used as a distributed power supply to restrict the generated power, so that the economy of power generation is ensured, and the resource waste is avoided. The specific force constraint conditions are as follows:
fifth embodiment
The present embodiment will be described with reference to fig. 2.
The embodiment is a further example of the multi-target particle swarm algorithm in the multi-target particle swarm algorithm-based wind-solar-diesel-energy storage system optimization scheduling method of the embodiment.
As shown in fig. 2, the flow of the multi-target particle swarm algorithm according to this embodiment includes:
(1) Initializing a population of microparticles to N and providing each parameter therein, including position, velocity, etc., to construct an initial population;
(2) Evaluating the fitness of each particle and assigning a value;
(3) By comparing the adaptation value of each particle with the optimal position p at which they are located Best If it is better, they can be regarded as the best position p of the current particle Best ；
(4) Calculating particle density information in the population, and calculating the optimum position of each particle in the population, namely the optimum value g of the population Best Comparing, if it is better, then taking it as the current group optimum value g Best ；
(5) Updating the speed and the position of each particle in the population, so that the population searches for an optimal solution under the guidance of the individual optimal value and the population optimal value;
(6) Judging whether the convergence criterion is met, and turning to the step (2) if the convergence criterion is not met, until the convergence criterion is met, and outputting a result.
Embodiment six
The present embodiment will be described with reference to fig. 1, 3, 4, 5, 6, 7, and 8.
The embodiment is a further illustration of the wind-solar-diesel-energy storage system optimizing and scheduling method based on the multi-target particle swarm algorithm in the first embodiment.
FIG. 1 is a block diagram of a wind-solar-diesel storage system.
Parameters of each part in the wind, light and diesel storage system are shown in tables 1-4.
Table 1 stored energy operating parameters
TABLE 2 operating parameters and fuel cost coefficients for diesel generators
| Parameters (parameters) | Numerical value |
| Diesel generator power upper limit/kW | 6000 |
| Lower power limit/kW of diesel generator | 1000 |
| Cost coefficient of diesel fuel/(Yuan/kW.h) | α＝0.0006,β＝0.388,γ＝40 |
TABLE 3 wind and photovoltaic set parameters
TABLE 4 emission coefficients of various pollutants and treatment costs
| Contaminant type | Emission coefficient (g/kW h) | Treatment coefficient (Yuan/kg) |
| CO 2 | 649 | 0.028 |
| CO | 4.143 | 0.875 |
| NO x | 6.108 | 1.015 |
| SO 2 | 3.55 | 1.25 |
1. Firstly, predicting the load and the wind-light power of a certain area in one day according to an established model, and obtaining prediction results shown in figures 3-5.
2. Determining constraints
1) Power balance constraint
In the normal operation process of the system, the power load, wind power, photovoltaic power generation power, the output of the diesel generator and the energy storage in the wind-solar-diesel storage system must meet the active power balance, namely, the formula 6 is shown.
2) Energy storage device restraint
The constraint on the state of charge is shown in the above equation 7, and as can be seen from Table 1, the SOC min At this time, the value was 0.1, and the SOC was obtained max At this time, the value was 0.9.
Next, the constraint condition on the charge and discharge power of the stored energy is shown in the above formula 8 and formula 9, wherein P ch-max Takes the value of 5000kw, P dis-max The value is 5500kw.
3) Diesel generator output constraint
The specific output constraint of the diesel generator is shown in formula 10, and as can be seen from table 2,the value of the water-soluble fiber is 1000kw,the value is 6000kw.
3. Optimizing and solving established objective function by utilizing multi-objective particle swarm algorithm
Firstly, photovoltaic power generation output, wind power output, diesel generator output and energy storage output are assigned (the values are arbitrarily assigned within the range of constraint conditions), the running cost f1 of the system and the wind-light absorption proportion, the absorp PV1 and the absorp WT1 are calculated, the running cost f1 and the wind-light absorption proportion are taken as the optimal solutions of the group at the moment, and each cost coefficient in the calculation example is known from tables 1-4. The four variables are then reassigned, and the running cost f2 of the system is calculated, as well as absorppv 2, absorpwt 2. Comparing the optimal solution with the optimal solution of the current group, if the running cost of the current system is smaller than the optimal solution of the group, taking the current scheduling scheme as the optimal solution of the group, otherwise, keeping the optimal solution of the group unchanged (comparing the running cost of the system preferentially, and comparing the wind-solar energy absorption proportion under the condition that the running cost of the system is the same). And repeating the steps until the optimal solution of the group is kept unchanged, wherein the configuration scheme is the optimal scheduling scheme of the wind-solar-diesel storage system, and the specific configuration is shown in fig. 8.
The simulation results are shown in fig. 6-8. From the graph, it can be concluded that the complementary characteristics of wind power generation and photovoltaic power generation are obvious, 8:00-14: and the peak period of photovoltaic power generation in one day is between 00, 18: the wind power generation system is a peak period of wind power generation after 00 a, and can be well matched with an energy storage system. Daytime is electricity consumption peak period, 6:00-18: the energy storage system between 00 releases energy most of the time, and 18: after 00, the electricity consumption is reduced, the power of wind power generation is increased, and the energy storage system is charged. The system can be flexibly regulated according to the requirements of wind power generation, photovoltaic power generation, a diesel engine and loads, and according to the requirements and obtained through simulation calculation, the running cost of the system is about 477.087 ten thousand yuan, the wind power generation consumption of the area is 72.14%, and the photovoltaic power generation power consumption is 88.04%.
Claims (10)
1. The wind, light and diesel storage system optimal scheduling method based on the multi-target particle swarm algorithm is characterized by comprising the following steps:
selecting an existing prediction model;
obtaining prediction parameters according to the prediction model;
establishing a wind-solar-diesel storage system optimization scheduling model based on a multi-target particle swarm algorithm according to the prediction parameters, and determining an objective function and constraint conditions during energy storage optimization scheduling of the wind-solar-diesel storage system;
and optimizing and solving the objective function by adopting a multi-objective particle swarm algorithm to obtain the running capacity ratio of the wind-solar-energy-charge-storage combined system, and realizing the optimized scheduling of the wind-solar-energy-diesel-storage system.
2. The multi-target particle swarm optimization scheduling method for a wind, solar and diesel storage system according to claim 1, wherein the prediction model comprises a power load prediction model, a wind power prediction model and a photovoltaic power generation prediction model.
3. The optimal scheduling method for the wind, solar and diesel storage system based on the multi-target particle swarm algorithm according to claim 1, wherein the prediction parameters comprise: electric load, wind power generation output power and photovoltaic power generation output power.
4. The optimal scheduling method for the wind, solar and diesel storage system based on the multi-target particle swarm algorithm according to claim 1, wherein the prediction parameters obtained according to the prediction model are specifically:
carrying out short-term load prediction by adopting a PCA-CNN-LSTM combination model based on a sliding window to obtain power load;
predicting wind power by using a CEEMDAN-LSTM-RF-RBF combined prediction model to obtain wind power generation output power;
and taking the generated energy, the temperature and the solar radiation as input quantities of a CNN-GRU model to obtain photovoltaic power generation output power.
5. The optimal scheduling method for the wind, solar and diesel storage system based on the multi-target particle swarm algorithm according to claim 1, wherein the objective function comprises the running cost of the system and the consumption of wind and light resources.
6. The optimal scheduling method for the wind, solar and diesel storage system based on the multi-target particle swarm algorithm according to claim 1, wherein the constraint conditions comprise: power balance constraints, energy storage device constraints, and diesel generator output constraints.
7. The optimal scheduling method for the wind, solar and diesel storage system based on the multi-target particle swarm algorithm according to claim 6, wherein the power balance constraint condition is as follows:
P PV +P WT +P DG +P BESS ＝P Load
wherein P is pv For photovoltaic power generation, P WT For wind power generation power, P Load P is the electrical load in the system BESS To store energy and output, P DG The power generated by the diesel generator;
the constraint conditions of the energy storage equipment are as follows:
SOC min ≤SOC t ≤SOC max
in SOC t For the state of charge, SOC, of the energy storage device at time t min SOC as the minimum state of charge of the stored energy max Is the maximum state of charge of the stored energy;
the constraint conditions of the output of the diesel generator are as follows:
P DGmin ≤P DG (t)≤P DGmax
wherein P is DG (t) is the actual output power of the diesel generator at the moment t,and->The minimum and maximum output of the diesel generator are respectively.
8. A computer device, characterized in that it comprises a memory and a processor, the memory having stored therein a computer program, which when executed by the processor performs the method for optimizing scheduling of a wind, solar and diesel storage system based on a multi-objective particle swarm algorithm according to any of claims 1-7.
9. A computer-readable storage medium, characterized by: the computer readable storage medium is used for storing a computer program, and the computer program executes the wind-solar-diesel storage system optimization scheduling method based on the multi-target particle swarm algorithm according to any one of claims 1-7.
10. Wind, light and diesel storage system optimal scheduling system based on multi-target particle swarm algorithm is characterized in that the system comprises:
parameter acquisition module: the method comprises the steps of establishing a prediction model, and obtaining prediction parameters according to the prediction model;
and a model building module: the method comprises the steps of establishing a wind-solar-diesel storage system optimization scheduling model based on a multi-target particle swarm algorithm, and determining an objective function and constraint conditions during energy storage optimization scheduling of the wind-solar-diesel storage system;
and a system optimization module: and the method is used for carrying out optimization solution on the objective function by adopting a multi-objective particle swarm algorithm to obtain the running capacity ratio of the wind-solar-energy-load-storage combined system, so as to realize the optimal scheduling of the wind-solar-energy-diesel-storage system.
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