Thermal power plant auxiliary power system optimized dispatching method and system considering wind-solar-storage system, and device and storage medium

Abstract

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

Description

Optimal scheduling method, system, equipment and storage medium for auxiliary system of thermal power plant considering wind, light and storage

[0001] The present application claims priority to the Chinese patent application No. 202410450030.6, filed on April 15, 2024, and entitled "Optimal scheduling method, system, equipment and storage medium for auxiliary system of thermal power plant considering wind, light and storage", the entire content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of power plant optimization, in particular to an optimal scheduling method, system, equipment and storage medium for auxiliary system of thermal power plant considering wind, light and storage. BACKGROUND

[0003] With the development of new energy field, the thermal power plant on the power generation side needs to be equipped with a certain capacity of new energy. This part of new energy capacity is generally connected to the auxiliary power system of the power plant. The auxiliary power rate during the operation of the thermal power plant is about 5% to 10%. It can be seen that the auxiliary power load is relatively high for large units. At present, in the power generation mode of connecting more wind power and photovoltaic to the auxiliary power system, non-unified control is adopted, and the demand for auxiliary power is not considered, which will result in a high amount of abandoned wind and light, leading to uneconomical operation of the auxiliary power system. Therefore, it has become an important research direction to exert the support ability of thermal power units and jointly cope with power dispatching with energy storage to promote new energy consumption. Therefore, it has become an important research direction to exert the support ability of thermal power units and jointly cope with power dispatching with energy storage to promote new energy consumption.

[0004] At present, for the above-mentioned multi-distributed energy operation optimization problem, the existing research has established a wind, light and storage joint operation system, which effectively improves the new energy consumption. However, the consumption method of the above-mentioned wind, light and storage joint operation system based on multi-energy complementation regards renewable energy and thermal power units as source side to supply power to network side load, and most multi-energy complementary operation models emphasize the network side structure, ignoring the special scene of auxiliary power load of the thermal power plant itself, especially lacking the analysis of deep fusion mechanism of new energy and auxiliary load of thermal power units, and is not suitable for the dispatching mode in the auxiliary system of the power plant.

[0005] SUMMARY

[0006] The present application aims to provide an optimal scheduling method, system, equipment and storage medium for auxiliary system of thermal power plant considering wind, light and storage, to solve the problem that the prior art lacks fusion analysis and dispatching of new energy and auxiliary load of thermal power plant.

[0007] To achieve the above-mentioned purpose, the following technical solutions are adopted in the present application:

[0008] An optimal scheduling method for auxiliary system of thermal power plant considering wind, light and storage, comprising the following steps:

[0009] constructing a wind and solar power generation cluster model in the auxiliary power system;

[0010] collecting auxiliary system data of the thermal power plant, and establishing a multi-objective function of the auxiliary system according to the auxiliary system data of the thermal power plant and the wind and solar power generation cluster model in the auxiliary power system;

[0011] introducing constraint penalty and constraint conditions into the multi-objective function of the auxiliary system to construct an optimal dispatching model of the auxiliary system of the thermal power plant;

[0012] processing the optimal dispatching model of the auxiliary system of the thermal power plant by using a particle swarm algorithm based on a dynamic learning factor to obtain an optimal dispatching result, and completing the optimal dispatching of the auxiliary system of the thermal power plant according to the optimal dispatching result.

[0013] In some embodiments, the step of constructing the wind and solar power generation cluster model in the auxiliary power system specifically comprises:

[0014] After collecting the interactive data of the auxiliary system, the data of the energy storage device, the data of the wind and solar power source and the data of the auxiliary load, the wind and solar power generation cluster model in the auxiliary power system is constructed in combination with the condition that the power generated by the wind power generator of the auxiliary system and the power generated by the photovoltaic power generation of the auxiliary system are balanced.

[0015] In some embodiments, the multi-objective function of the auxiliary system is composed of a running cost objective function and an environmental cost objective function;

[0016] The multi-objective function comprises the following formula: M = min (f + g)

[0017] wherein M is the total running cost of the auxiliary system of the thermal power plant, f is the running cost of the auxiliary system of the thermal power plant, and g is the environmental cost of the auxiliary system of the thermal power plant;

[0018] The running cost objective function comprises the following formula: f = min (f1 + f2 + f3 + f4 + f5 + f6)

[0019] wherein f1 is the cost of the interaction between the auxiliary system and the upper-level power grid, f2 is the running and maintenance cost of the energy storage, f3 is the constraint penalty cost of the energy storage, f4 is the constraint penalty cost of the power balance, f5 is the constraint penalty cost of the output of the photovoltaic power, and f6 is the constraint penalty cost of the output of the wind power;

[0020] The environmental cost objective function comprises the following formula:

[0021] wherein C k is the cost coefficient of the kth pollutant, β g,k is the emission amount of the kth pollutant generated by the running of the upper-level power grid, is the power of the interactive connection line between the auxiliary system and the unit.

[0022] In some embodiments, the step of introducing a constraint penalty and a constraint condition to the auxiliary system multi-objective function of the power plant comprises:

[0023] The constraint penalty comprises a storage constraint penalty, an electrical balance constraint penalty, a photovoltaic output constraint penalty, and a wind turbine output constraint penalty.

[0024] The constraint condition comprises an electrical power balance constraint, a wind-solar output constraint, a storage device constraint, and an auxiliary system and unit interactive tie-line power constraint.

[0025] In some embodiments, the storage constraint penalty comprises the following formula:

[0026] wherein k p1 is a storage constraint penalty factor, N ES is the number of installed storages, is the amount of exceeding or falling below the maximum state of charge of the battery,

[0027] The electrical balance constraint penalty comprises the following formula:

[0028] wherein k p2 is an electrical balance constraint penalty factor, ΔP t is an electrical power imbalance, Δt is a unit scheduling time, and T is a scheduling period.

[0029] The photovoltaic output constraint penalty comprises the following formula:

[0030] wherein k p3 is a photovoltaic output constraint penalty factor, is a photovoltaic output difference,

[0031] The wind turbine output constraint penalty comprises the following formula:

[0032] wherein k p4 is a wind turbine output constraint penalty factor, is a wind turbine output difference.

[0033] The electrical power balance constraint comprises the following formula:

[0034] wherein is a photovoltaic actual power at time t, is a wind turbine actual power at time t, is an auxiliary system and unit interactive tie-line power at time t, is a storage charge and discharge power at time t, is an auxiliary power system load at time t.

[0035] The wind and light output constraint includes the following formula:

[0036] Wherein, Ppv is the maximum photovoltaic output, Pw is the maximum wind turbine output;

[0037] The energy storage device constraint includes the following formula: SOC min ≤ SOC t ≤ SOC max

[0038] Wherein, SOC min is the upper limit of energy storage energy, SOC max is the lower limit of energy storage energy, is the lower limit of energy storage power, is the upper limit of energy storage power;

[0039] The power constraint of the auxiliary system and the unit interconnection line includes the following formula:

[0040] Wherein, is the power of the auxiliary system and the unit interconnection line, is the lower limit of the unit interconnection line power, is the upper limit of the auxiliary system and the unit interconnection line power.

[0041] In some embodiments, the power plant auxiliary system optimization scheduling model is processed by a particle swarm algorithm based on a dynamic learning factor to obtain an optimized scheduling result, and the step specifically includes:

[0042] Initialize the population, each individual in the population corresponds to an optimized scheduling scheme;

[0043] Input the initialization state of the individual into the auxiliary system multi-objective function to obtain the individual fitness;

[0044] The individual fitness is used as the best position value of the individual, and the population best position value is obtained through the best position value of each individual;

[0045] Update the best position value of the individual and the population best position value;

[0046] According to the best position value of the individual and the population best position value, the particle swarm algorithm is updated to obtain the speed and position of the individual by introducing an inertia weight factor, adjusting a learning factor and a dynamic learning factor;

[0047] when the speed and position of the individual satisfy a preset convergence termination condition, obtaining an optimized scheduling result according to the speed and position of the individual;

[0048] when the speed and position of the individual do not satisfy the preset convergence termination condition, continuing to update the optimal position value of the individual and the population optimal position value, and obtaining the speed and position of the individual by using a particle swarm algorithm with an introduced inertia weight factor, an adjusted learning factor and a dynamic learning factor according to the optimal position value of the individual and the population optimal position value, repeating the above steps until the speed and position of the individual satisfy the preset convergence termination condition.

[0049] In some embodiments, the step of obtaining the speed and position of the individual by using the particle swarm algorithm with the introduced inertia weight factor, the adjusted learning factor and the dynamic learning factor is implemented by the following formula:

[0050] wherein, is a velocity vector of the individual i in the kth iteration, is a position vector of the individual i in the kth iteration, pb i is a historical optimal position value of the individual i, gb n is a population historical optimal position value, ti n is a total iteration number, ti m is a current iteration number, w s is an initial value of the inertia weight factor, w e is a termination value of the inertia weight factor, c 1s is an initial value of the adjustment learning factor c1, c 1e is a stop value of the adjustment learning factor c1, c 2s is an initial value of the adjustment learning factor c2, c 2e is a stop value of the adjustment learning factor c2, c 3e is an initial value of the dynamic learning factor c3, c 3s is a stop value of the dynamic learning factor c3, mb j is a historical optimal position value of the individual j randomly searched by the current individual i, r1, r2 and r3 are random functions for increasing search randomness, is a current iteration progress.

[0051] An optimization scheduling system for a power plant auxiliary system considering wind and solar power storage, comprising:

[0052] a wind and solar power cluster model construction module configured to construct a wind and solar power cluster model in the auxiliary power system;

[0053] The multi-objective function establishing module is configured to collect data of the auxiliary system of the thermal power plant, and establish a multi-objective function of the auxiliary system according to the data of the auxiliary system of the thermal power plant and the wind-solar generation cluster model in the auxiliary power system.

[0054] The optimal scheduling model constructing module is configured to introduce constraint penalties and constraint conditions into the multi-objective function of the auxiliary system to construct an optimal scheduling model of the auxiliary system of the thermal power plant.

[0055] The optimal scheduling iteration module is configured to process the optimal scheduling model of the auxiliary system of the thermal power plant by using a particle swarm algorithm based on a dynamic learning factor, obtain an optimal scheduling result, and complete the optimal scheduling of the auxiliary system of the thermal power plant according to the optimal scheduling result.

[0056] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, and the processor implements the steps of the method for optimal scheduling of an auxiliary system of a thermal power plant considering wind-solar storage according to any one of claims 1-7 when executing the computer program.

[0057] A computer readable storage medium stores a computer program, and the computer program implements the steps of the method for optimal scheduling of an auxiliary system of a thermal power plant considering wind-solar storage when executed by a processor.

[0058] Compared with the prior art, the present application has the following beneficial effects:

[0059] The present application provides a method for optimal scheduling of an auxiliary system of a thermal power plant considering wind-solar storage, which constructs a wind-solar generation cluster model in an auxiliary power system, establishes a multi-objective function of the auxiliary system in an optimal scheduling model of the auxiliary system of the thermal power plant, introduces constraint penalties and constraint conditions, and finally solves the optimal scheduling model of the auxiliary system of the thermal power plant by using a particle swarm algorithm based on a dynamic learning factor (DLF) to obtain an optimal scheduling result. The method has good convergence effect, and can determine optimal outputs of interactions between wind turbines, photovoltaic devices, energy storage devices, and units according to the optimal scheduling result, realize low-carbon optimal scheduling of the auxiliary power system, and solve the problem of lack of scheduling of fusion analysis of new energy and auxiliary loads of the thermal power plant in the prior art.

[0060] Optionally, the application collects plant system interaction data, energy storage device data, wind and light power supply data and plant load data, combines the conditions that the plant system wind turbine generator power and the plant system photovoltaic power generation power remain balanced, to construct a wind and light power generation cluster model in the plant power system. Considering the non-unified control in the current more wind power and photovoltaic power generation mode connected to the plant power system, the problem of high abandoned wind and light power caused by not considering the demand of plant power in the plant power system, and the problem of uneconomic operation of the plant system, the support ability of the thermal power unit is played, and the energy storage is used to jointly respond to power dispatching.

[0061] Optionally, the application introduces the constraint penalty and the constraint condition into the multi-objective function of the plant system, which can further enhance the optimization effect of the multi-objective function, limit the solution space of the optimization scheduling problem, and negatively motivate the optimization scheduling scheme that does not meet the constraint, so that the multi-objective function can more comprehensively consider various factors in the solving process.

[0062] Optionally, the application introduces inertia weight factor, adjustment learning factor and dynamic learning factor to the particle swarm algorithm for improvement. The adjustment learning factor is assigned different weights according to the early and late stages of iteration, and the dynamic learning factor is integrated. When the dynamic learning factor is positive, the learning ability of the optimization scheduling scheme corresponding to the individual is improved. When the dynamic learning factor is negative, the optimization scheduling scheme corresponding to the individual will move in the opposite direction of the learning target individual position, avoiding invalid search, so that the performance of the algorithm used in the application is greatly improved, and the convergence ability is improved. BRIEF DESCRIPTION OF DRAWINGS

[0063] FIG. 1 is a flowchart of the method for optimizing the plant system of the thermal power plant considering wind, light and storage according to the embodiment of the application;

[0064] FIG. 2 is a Pareto convergence comparison result diagram of the plant system optimization scheduling target space of the particle swarm algorithm based on the dynamic learning factor DLF and the traditional particle swarm algorithm according to the embodiment of the application;

[0065] FIG. 3 is a scheduling result diagram of the scheduling result with the lowest total cost as the target according to the embodiment of the application;

[0066] FIG. 4 is a scheduling result diagram of the scheduling result with the lowest running cost as the target according to the embodiment of the application;

[0067] FIG. 5 is a scheduling result diagram of the scheduling result with the lowest environmental cost as the target according to the embodiment of the application;

[0068] FIG. 6 is a Pareto solution set diagram in the no storage scenario according to the embodiment of the application;

[0069] FIG. 7 is a scheduling result diagram in the no storage scenario according to the embodiment of the application;

[0070] FIG. 8 is a schematic diagram of the structure of the plant system of the thermal power plant according to the embodiment of the application;

[0071] Fig. 9 is a structural schematic diagram of the optimization scheduling system of the auxiliary system of the thermal power plant considering wind-solar storage provided by the embodiment;

[0072] Fig. 10 is a structural schematic diagram of the electronic device adopted by the embodiment.

[0073] In the figure, pg_BES is the energy storage charging and discharging power value, pg_PG is the power value of the auxiliary system and the unit interconnection line, pg_PG is the photovoltaic power value, and pg_WT is the wind turbine power value. DETAILED DESCRIPTION

[0074] In order for those skilled in the art to better understand the scheme of the present application, the technical scheme of the present application will be further described in detail below in combination with the drawings, and the content is an explanation of the present application and not a limitation.

[0075] It should be noted that the terms "include" and "have" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device containing a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, systems, products or devices.

[0076] The auxiliary system of the thermal power plant involved in the embodiment is shown in Fig. 8, which integrates renewable energy technology, including photovoltaic units, wind turbine units and energy storage units. The photovoltaic units use solar energy to generate electricity, the wind turbine units use wind energy to generate electricity, and the energy storage units store electricity during low power demand peaks and release electricity during high power demand peaks to balance the power grid load. The power generation equipment units such as photovoltaic units, wind turbine units and energy storage units are connected through the auxiliary bus to realize power transmission and distribution. The upper power grid and the generator transmit power to the auxiliary system through high-voltage transmission lines and convert the power into low-voltage power required by the power generation equipment through high-voltage auxiliary transformers (abbreviated as high auxiliary transformers).

[0077] The embodiment provides a kind of considering wind and light storage power plant auxiliary system optimization scheduling method, as shown in Figure 1, wind, light generation group model and plant auxiliary system load characteristics in the power plant auxiliary power system are combined, the multi-objective function of the optimization scheduling model of the power plant auxiliary system is established and the system constraint condition is introduced;Multi-objective function includes operating cost and environmental cost, operating cost introduces storage constraint penalty, power balance constraint penalty, photovoltaic output constraint penalty and wind turbine output constraint penalty;Constraint condition introduces electric power balance constraint, wind and light output constraint, storage device constraint and plant auxiliary system and unit interactive intertie power constraint.The particle swarm algorithm based on dynamic learning factor DLF is used to solve the optimization scheduling model of the power plant auxiliary system, and the optimal output of wind, light, storage and unit interaction is determined according to the solution result, to realize the low-carbon optimization scheduling of auxiliary power system. It specifically includes the following steps:

[0078] Step 1: combined with plant auxiliary system interaction data, storage device data, wind and light power data and plant load data, according to the balance principle of wind turbine generator power and photovoltaic power generation unit in plant auxiliary power system region, wind and light generation group model in plant auxiliary system is constructed;

[0079] Specifically, although the capacity of wind and light power in plant auxiliary power system is small, but it is scattered, if the dispersed wind power, photovoltaic and load are modeled respectively, more optimization variables and higher calculation dimension will be brought to optimization scheduling problem, which causes the difficulty of solution. Here, to simplify model solution, all wind turbine units and photovoltaic units in plant auxiliary system are aggregated respectively to form a single wind and light generation group model, as shown in the following formula:

[0080] Wherein, P is the predicted net load power of plant auxiliary power at t moment, P t,load P is the predicted load power of plant auxiliary power at t moment, P wt,t,i P is the power of the i th wind turbine at t moment, P pv,t,i P is the power of the i th photovoltaic unit at t moment, m is the number of wind turbine installed in plant auxiliary system, and n is the number of photovoltaic unit installed in plant auxiliary system.

[0081] Step 2: according to the data of power plant auxiliary system, combined with wind and light generation group model, the multi-objective function of the optimization scheduling model of the power plant auxiliary system is established, and the formula of the multi-objective function of plant auxiliary system is as follows: M= min (f+g)

[0082] Wherein, M is the total operating cost of the power plant auxiliary system, f is the operating cost of the power plant auxiliary system, and g is the environmental cost of the power plant auxiliary system.

[0083] Step 3: Establish the operation cost objective function of the minimum operation cost of the plant system, mainly considering the cost of power interaction between the plant system and the generator set and the operation and maintenance cost of the energy storage. At the same time, in order to better play the effect of energy storage in absorbing new energy, while considering the system power balance, the energy storage constraint penalty, the power balance constraint penalty, the photovoltaic output constraint penalty and the wind turbine output constraint penalty are introduced into the operation cost objective function. The formula of the operation cost objective function f of the plant system of the thermal power plant is as follows: f = min (f1 + f2 + f3 + f4 + f5 + f6)

[0084] Among them, f1 is the cost of interaction between the plant system and the upper grid, f2 is the operation and maintenance cost of the energy storage, f3 is the cost of the energy storage constraint penalty, f4 is the cost of the power balance constraint penalty, f5 is the cost of the photovoltaic output constraint penalty, and f6 is the cost of the wind turbine output constraint penalty.

[0085] Among them, is the power of the plant system interacting with the unit interconnection line at time t, is the price of the power interaction between the plant system and the unit at time t, N ES is the installation number of the energy storage, is the operation and maintenance unit cost of the i th energy storage unit, is the charge and discharge power of the i th energy storage, k p1 is the energy storage constraint penalty factor, is the amount exceeding or below the maximum state of charge of the battery, k p2 is the power balance constraint penalty factor, ΔP t is the degree of power imbalance, Δt is the unit scheduling time, T is the scheduling time, k p3 is the photovoltaic output constraint penalty factor, is the photovoltaic output difference, k p4 is the wind turbine output constraint penalty factor, is the wind turbine output difference.

[0086] Step 4: Establish the environmental cost objective function of the plant system of the thermal power plant. The environmental cost of the plant system of the thermal power plant mainly considers the power interaction with the unit, which involves the problem of pollutant treatment. The cost generated by this part is the environmental cost. The environmental cost objective function g is as follows:

[0087] Among them, C k is the cost coefficient of k types of pollutants, β g,kThe emission amount of the k type pollutants generated for the superior power grid operation, The power of the interconnection line between the auxiliary system and the unit.

[0088] Step 5: Introduce the constraints of the auxiliary system of the thermal power plant, including the power balance constraint, the wind and light output constraint, the energy storage device constraint and the power constraint of the interconnection line between the auxiliary system and the unit.

[0089] (1) The power balance constraint is:

[0090] Wherein, Ppv(t) is the actual power of the photovoltaic at time t, Pinter(t) is the power of the interconnection line between the auxiliary system and the unit at time t, PES(t) is the load of the auxiliary power system at time t;

[0091] (2) The wind and light output constraint is:

[0092] Wherein,

[0093] (3) The energy storage device constraint is: min t max

[0094] Wherein, SOC min max is the upper limit of the energy storage energy, SOC is the lower limit of the energy storage power, P

[0095] (4) The power constraint of the interconnection line between the auxiliary system and the unit is:

[0096] Wherein,

[0097] ​​​​​​​​​​​Step 6: According to the wind and light electric power generation cluster model, the multi-objective function of the auxiliary system of the power plant, and the introduced constraint penalty and constraint condition involved in steps 1-5 above, an optimization scheduling model of the auxiliary system of the thermal power plant is constructed, and the optimization scheduling model of the auxiliary system of the thermal power plant is solved. The optimization scheduling problem described above is a typical power system optimization problem, which is a multi-element linear programming model with many constraints and is difficult to solve. Therefore, the particle swarm algorithm based on dynamic learning factor DLF is used to optimize and solve the actual power of the wind turbine actual power of photovoltaic charging and discharging power of energy storage and power of the interconnection line between the auxiliary system and the unit The four variables are optimized and solved, and the specific steps are as follows:

[0098] Step 6.1: According to the data of the auxiliary system of the thermal power plant, the parameters are initialized.

[0099] Step 6.2: Initialize the population, and the state of each individual in the population corresponds to a scheduling scheme.

[0100] Step 6.3: Substitute the initialized state of each individual into the multi-objective function of the auxiliary system of the thermal power plant, and calculate the fitness of each individual.

[0101] Step 6.4: Take the current individual fitness as the best position value of the individual, and calculate the best position value of the population through the best position value of the individual;

[0102] Step 6.5: Iterate to update the current best position value of each individual and the best position value of the population.

[0103] Step 6.6: Update the speed and position of the individual using the formula in the particle swarm algorithm based on dynamic learning factor DLF.

[0104] Step 6.7: If the speed and position of the individual reach the preset convergence termination condition, return to step 6.5 to continue iteration.

[0105] Step 6.8: Finally, according to the speed and position of the individual that reaches the preset convergence termination condition, the optimization scheduling result is obtained, and the optimization scheduling of the auxiliary system of the thermal power plant is performed.

[0106] Specifically, the formula in the particle swarm algorithm based on dynamic learning factor DLF includes the following:

[0107] wherein, is the velocity vector of the individual i in the kth iteration, is the position vector of the individual i in the kth iteration, pb igb is a historical best position value of the individual i n ti is a population historical best position value n ti is the total number of iterations m w is the current iteration number s w is the initial value of the inertia weight factor e c is the final value of the inertia weight factor 1s c is the initial value of the adjustment learning factor c1 1e c is the final value of the adjustment learning factor c1 2s c is the initial value of the adjustment learning factor c2 2e c is the final value of the adjustment learning factor c2 3e c is the initial value of the dynamic learning factor c3 3s mb is the final value of the dynamic learning factor c3 j r1, r2, and r3 are random functions for increasing search randomness, the value range of r1, r2, and r3 is [0, 1], The current iteration progress. In the early stage of iteration, the larger inertia weight factor w makes the algorithm not prone to fall into local minimum value, facilitating global search, in the later stage of iteration, the smaller inertia weight factor w is conducive to local search and convergence of the algorithm. In the early stage of iteration, the larger adjustment learning factor c1 and the smaller adjustment learning factor c2 make the individual have better self-learning ability and poorer social learning ability, which is conducive to global search. In the later stage of iteration, the smaller adjustment learning factor c1 and the larger adjustment learning factor c2 make the individual have stronger social learning ability and poorer self-learning ability, which is conducive to convergence of the algorithm. When the dynamic learning factor c3 is positive, the learning ability of the individual is improved, when the dynamic learning factor c3 is negative, the individual moves in the opposite direction of the individual position of the learning target, avoiding invalid search.

[0108] The detailed example analysis of the optimal scheduling method of the application is as follows:

[0109] (1) Taking a plant auxiliary system of a 660 WM thermal power unit of a certain thermal power plant as an example, the plant auxiliary system contains distributed power sources such as photovoltaic power generation unit PV, fan WT, and energy storage device, the operation parameters and costs of each distributed power source in the plant auxiliary system are shown in Table 1, the energy storage parameters are shown in Table 2, the scheduling period T is taken as 24 h, the time scale is taken as 1 h, the typical day plant auxiliary load, wind and light prediction curves of the plant are selected for simulation, the optimal output of each power source in the plant auxiliary system is determined, the total operation cost of the whole system is minimized, and thus the economic and low-carbon optimal operation of the plant auxiliary system is realized.

[0110] Table 1 Distributed power source operation parameters

[0111] Table 2 Energy storage parameters

[0112] (2) As shown in Figure 2, the convergence of the Pareto solution of the plant system optimization scheduling target space of the particle swarm algorithm based on the dynamic learning factor DLF (improved type in Figure 2) proposed in the application and the traditional particle swarm algorithm is compared. As can be seen from Figure 2, the improved algorithm of the application adopts an adaptive adjustment and learning strategy, greatly improving the performance of the algorithm, and thus showing good convergence in the optimization scheduling of the plant system.

[0113] (3) Table 3 is a comparison of the iteration and economy of the improved particle swarm algorithm and the traditional particle swarm algorithm of the application;

[0114] Table 3 Comparison of the performance of the two algorithms

[0115] As can be seen from Table 3, the particle swarm algorithm based on the dynamic learning factor DLF converges faster than the traditional particle swarm algorithm, with a convergence time difference of 11.3 seconds in 100 iterations. Because the traditional particle swarm algorithm is prone to local optimal values, the particle swarm algorithm based on the dynamic learning factor DLF has better global exploration capability. In the optimization scheduling of the plant system, the particle swarm algorithm based on the dynamic learning factor DLF has more non-dominated solutions, and thus can effectively reduce the operation cost and environmental cost of the plant system.

[0116] (4) As shown in Figures 3, 4 and 5, when the total cost is selected as the objective function, the electric power balance needs to meet the load demand basically after considering the operation cost and environmental protection cost. At the same time, the fan and photovoltaic consumption amount is the largest. When the total cost is the lowest as the objective function, compared with the two objectives of the lowest operation cost and the lowest environmental cost, the low-carbon economic operation of the plant system is realized.

[0117] (5) As can be seen from Figure 6, in the no energy storage scenario, the convergence of the Pareto solution is poor, and the operation cost is also not low. As can be seen from the comparison of Figure 7 and Figure 3, the wind and light consumption level in the no energy storage scenario is not as good as that in the energy storage scenario.

[0118] The application also provides a thermal power plant plant system optimization scheduling system considering wind and light storage, which comprises a wind and light power cluster model construction module, a multi-objective function establishment module, an optimization scheduling model construction module and an optimization scheduling iteration module.

[0119] The wind and light power cluster model construction module combines plant system interaction data, energy storage device data, wind and light power supply data and plant load data, constructs a wind and light power cluster model in the plant system according to the principle of keeping balance of the power generation of wind turbine generators and the power generation of photovoltaic power generation units in the plant system region, and inputs the wind and light power cluster model into the multi-objective function establishment module, as shown in the following formula:

[0120] The multi-objective function establishment module establishes a multi-objective function of the optimization scheduling model of the auxiliary system of the thermal power plant according to the auxiliary system data of the thermal power plant, in combination with the wind-solar power generation cluster model, as shown in the following formula: M = min (f + g)

[0121] The multi-objective function includes an operation cost objective function and an environmental cost objective function, wherein the operation cost objective function mainly considers the cost of power interaction between the auxiliary system and the generator unit and the operation and maintenance cost of the energy storage, and the formula of the operation cost objective function f of the auxiliary system of the thermal power plant is as follows: f = min (f1 + f2 + f3 + f4 + f5 + f6)

[0122] The environmental cost objective function mainly considers the power interaction with the unit, which involves the problem of pollutant treatment. The cost generated by this part is the environmental cost, and the formula of the environmental cost objective function g is as follows:

[0123] The optimization scheduling model construction module introduces the energy storage constraint penalty, the power balance constraint penalty, the photovoltaic output constraint penalty and the wind turbine output constraint penalty in the operation cost objective function in order to better play the effect of energy storage on new energy consumption and at the same time consider the system power balance. Meanwhile, the auxiliary system constraint conditions of the thermal power plant are introduced in the multi-objective function, including the power balance constraint, the wind-solar output constraint, the energy storage device constraint and the power constraint of the auxiliary system and the unit interconnection line. Finally, the optimization scheduling model of the auxiliary system of the thermal power plant is constructed according to the wind-solar power generation cluster model, the auxiliary system multi-objective function and the introduced constraint penalty and constraint conditions, and the optimization scheduling model of the auxiliary system of the thermal power plant is input into the optimization scheduling iteration module.

[0124] The optimization scheduling iteration module solves the optimization scheduling model of the auxiliary system of the thermal power plant. The above optimization scheduling problem is a typical power system optimization problem, which is a multi-element linear programming model with many constraints and is difficult to solve. Therefore, the particle swarm algorithm based on dynamic learning factor DLF is adopted to optimize and solve the four variables, i.e. the actual power of the photovoltaic the charging and discharging power of the energy storage and the power of the auxiliary system and the unit interconnection line The optimization scheduling result is obtained, and the optimization scheduling of the auxiliary system of the thermal power plant is performed.

[0125] The division of the modules in the embodiments of the present application is illustrative, and is merely a logical function division. In actual implementation, another division manner can be used. In addition, each function module in each embodiment of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software function module.

[0126] In the embodiment, an electronic device is also provided, as shown in FIG. 10, which includes a processor and a memory. The memory is configured to store a computer program, and the computer program includes program instructions. The processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be another general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or another programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and the like. The processor is a computing core and a control core of the terminal, and is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function. The processor in the embodiment of the present application can be used for the operation of the method for optimizing dispatching of a power plant auxiliary system of a thermal power plant considering wind, light and storage.

[0127] The embodiment further provides a storage medium, specifically a computer readable storage medium (Memory). The computer readable storage medium is a memory device in a computer device, and is configured to store programs and data. It can be understood that the computer readable storage medium can include an internal storage medium in the computer device, and can also include an extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the terminal. In the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for optimizing dispatching of a power plant auxiliary system of a thermal power plant considering wind, light and storage in the above embodiments.

[0128] Those skilled in the art will appreciate that embodiments of the application can be readily used as a method, apparatus, or computer program product. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk memory, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0129] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks or in combination with the flowchart block or blocks.

[0130] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks or combination thereof.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks or in combination with the flowchart block or blocks.

[0132] Finally, it should be noted that the above-described embodiments are merely given as examples of the application and are not intended to limit the present application in any manner. Although the present application has been described in detail with reference to the above embodiments, it should be understood that the inventive concept can be implemented in many other ways. Therefore, modifications and / or additions to the above-described embodiments can occur to those skilled in the art without departing from the spirit and scope of the application. Accordingly, the scope of the application should be determined by the following claims and their legal equivalents rather than by the above description.

Claims

1. A method for optimal dispatch of a power plant auxiliary system considering wind-photovoltaic storage, characterized in that, The method comprises the following steps: constructing a wind and light power generation cluster model in a plant power system; collecting data of the plant system, and establishing a plant system multi-objective function according to the data of the plant system and the wind and light power generation cluster model in the plant power system; introducing constraint penalties and constraint conditions into the plant system multi-objective function to construct an optimization scheduling model of the plant system of the thermal power plant; processing the optimization scheduling model of the plant system of the thermal power plant by using a particle swarm algorithm based on a dynamic learning factor to obtain an optimization scheduling result, and completing the optimization scheduling of the plant system of the thermal power plant according to the optimization scheduling result. 2.The method of claim 1, wherein The step of constructing the wind and light power generation cluster model in the plant power system specifically comprises: After collecting plant system interaction data, energy storage device data, wind and light power source data and plant system load data, a wind and light power generation cluster model in the plant power system is constructed in combination with the condition that the power generated by the wind power generator of the plant system and the power generated by the photovoltaic power generation of the plant system are balanced. 3.The method of claim 1, wherein The plant system multi-objective function is composed of an operation cost objective function and an environmental cost objective function; The multi-objective function comprises the following formula: M = min (f + g) wherein M is the total operation cost of the plant system of the thermal power plant, f is the operation cost of the plant system of the thermal power plant, and g is the environmental cost of the plant system of the thermal power plant; The operation cost objective function comprises the following formula: f = min (f1 + f2 + f3 + f4 + f5 + f6) wherein f1 is the cost of interaction between the plant system and the upper-level power grid, f2 is the operation and maintenance cost of the energy storage, f3 is the constraint penalty cost of the energy storage, f4 is the constraint penalty cost of power balance, f5 is the constraint penalty cost of photovoltaic output, and f6 is the constraint penalty cost of wind turbine output; The environmental cost objective function includes the following: Wherein, C k is the cost coefficient of k type pollutants, β g,k is the emission of k type pollutants generated by the superior power grid operation, is the power of the plant system interacting with the unit interconnection line.

4. The method of Claim 3, wherein, In the step of introducing constraint penalties and constraint conditions into the plant system multi-objective function, The constraint penalties comprise energy storage constraint penalties, power balance constraint penalties, photovoltaic output constraint penalties and wind turbine output constraint penalties; The constraint conditions comprise power balance constraints, wind and light output constraints, energy storage device constraints and plant system and unit interconnection line power constraints.

5. The method of claim 4, wherein the method further comprises: The energy storage constraint penalty includes the following: wherein k p1 is the energy storage constraint penalty factor, N ES is the installed number of energy storages, is the amount exceeding or below the maximum state of charge of the battery, The electrical balance constraint penalty comprises the following equation: where k p2 is the level balancing penalty factor, ΔP t is the electrical power imbalance, Δt is the unit scheduling time, T is the scheduling period; The photovoltaic power output constraint penalty comprises the following formula: where k p3 is a photovoltaic power output constraint penalty factor, is the photovoltaic output difference value, The wind farm output constraint penalty comprises the following equation: where k p4 is a fan output constraint penalty factor, is the wind turbine output difference value; The electrical power balance constraint comprises the following equation: wherein is the actual photovoltaic power at time t, is the actual power of the fan at time t, is the power of the interconnection line between the plant service system and the unit at time t, Pcharge(t) = Pdischarge(t) = Pstorage(t) (1) is the plant power system load at time t; The wind and solar power output constraint includes the following formula: wherein, for a photovoltaic power output maximum, is the maximum wind turbine output; The energy storage device constraints comprise the following formula: SOC min ≤ SOC t ≤ SOC max wherein SOC min is the upper limit of the energy storage energy, SOC max is the lower limit of the energy storage energy, for the lower limit of the energy storage power, is the upper limit of the energy storage power; The power constraint of the plant system and the unit intertie line includes the following formula: wherein for the plant system and the unit intertie line power, To interact with the unit tie-line power lower limit, is the upper limit of the plant system and unit interconnection line power.

6. The method of Claim 3, wherein, The step of processing the optimization scheduling model of the plant system of the thermal power plant by using the particle swarm algorithm based on the dynamic learning factor to obtain the optimization scheduling result specifically comprises: initializing a population, each individual in the population corresponding to an optimization scheduling scheme; inputting the initialized state of the individual into the plant system multi-objective function to obtain individual fitness; taking the individual fitness as the best position value of the individual, and obtaining a population best position value through the best position value of each individual; updating the best position value of the individual and the population best position value; According to the individual optimal position value and the population optimal position value, the velocity and the position of the individual are updated by using a particle swarm algorithm with an introduced inertia weight factor, an adjusted learning factor and a dynamic learning factor; When the velocity and the position of the individual satisfy a preset convergence termination condition, an optimized scheduling result is obtained according to the velocity and the position of the individual; When the velocity and the position of the individual do not satisfy the preset convergence termination condition, the individual optimal position value and the population optimal position value are continuously updated, the velocity and the position of the individual are updated by using a particle swarm algorithm with an introduced inertia weight factor, an adjusted learning factor and a dynamic learning factor according to the individual optimal position value and the population optimal position value, and the above steps are repeated until the velocity and the position of the individual satisfy the preset convergence termination condition.

7. The method of Claim 6, wherein, The step of updating the speed and position of the individual by using the particle swarm algorithm with an introduced inertia weight factor, an adjusted learning factor and a dynamic learning factor is implemented by the following formula: wherein, for the kth iteration of the velocity vector of individual i, pbis the position vector of individual i for the kth iteration i gbis the historical best position value of individual i n tibis the population historical best position value n tis the total number of iterations m wis the current iteration number s wis the initial value of the inertia weight factor e cis the final value of the inertia weight factor 1s cis the initial value of the adjusting learning factor c1 1e cis the final value of the adjusting learning factor c1 2s cis the initial value of the adjusting learning factor c2 2e cis the final value of the adjusting learning factor c2 3e cis the initial value of the dynamic learning factor c3 3s mbis the final value of the dynamic learning factor c3 j r1, r2, and r3 are random functions that increase the randomness of the search, and is the current iteration progress.

8. A system for optimal dispatching of auxiliary system of thermal power plant considering wind and light storage, characterized in that, The method comprises the following steps: a wind-solar power cluster model construction module is configured to construct a wind-solar power cluster model in a plant auxiliary power system; a multi-objective function establishment module is configured to collect data of a plant auxiliary system, and establish a plant auxiliary system multi-objective function according to the data of the plant auxiliary system and the wind-solar power cluster model in the plant auxiliary power system; an optimized scheduling model construction module is configured to introduce a constraint penalty and a constraint condition to the plant auxiliary system multi-objective function, so as to construct an optimized scheduling model of the plant auxiliary system of a thermal power plant; an optimized scheduling iteration module is configured to process the optimized scheduling model of the plant auxiliary system of the thermal power plant by using a particle swarm algorithm based on a dynamic learning factor, so as to obtain an optimized scheduling result, and complete the optimized scheduling of the plant auxiliary system of the thermal power plant according to the optimized scheduling result.

9. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the method for optimized scheduling of a plant auxiliary system of a thermal power plant considering wind-solar storage according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the method for optimized scheduling of a plant auxiliary system of a thermal power plant considering wind-solar storage according to any one of claims 1-7.

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