Fuzzy chance constraint-based optimal scheduling method for heat-storage combined heat and power generation unit and electric boiler

Abstract

The invention discloses a fuzzy opportunity constraint-based optimal scheduling method for a heat-storage combined heat and power generation unit and an electric boiler, and the method comprises the steps: collecting and analyzing the historical operation data of the combined heat and power generation unit and the electric boiler, and constructing a typical operation condition; uncertain factors such as weather changes, load fluctuations and equipment faults are processed through fuzzy opportunity constraints, an optimal scheduling model is established, and it is ensured that the model has good robustness and adaptability. An optimal operation strategy is solved by selecting a proper optimization algorithm, and thermoelectric balance and energy utilization efficiency are considered at the same time. The optimized scheduling scheme also needs to be subjected to simulation verification, and is adjusted and improved according to actual operation requirements. The method has the advantages that uncertain factors are effectively processed by introducing fuzzy opportunity constraints, and the practicability and reliability of the model are enhanced; by adopting multiple optimization algorithms, the stability and reliability of the optimal operation strategy are ensured, the system cost is effectively reduced, and the energy efficiency ratio is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of joint economic dispatch of power systems and thermal systems, and in particular to an optimization dispatch method of a heat and power cogeneration unit with heat storage and an electric boiler based on fuzzy opportunity constraints. Background Art

[0002] With the continuous growth of energy demand and the transformation of energy structure, combined heat and power (CHP) as an efficient way of energy utilization has received widespread attention and application. The combined heat and power unit can generate electricity and heat energy at the same time, realizing the efficient cascade utilization of energy and significantly improving energy utilization efficiency.

[0003] Traditional methods usually use deterministic models, which fail to effectively deal with uncertainties such as weather changes, load fluctuations, and equipment failures, resulting in poor robustness and adaptability of the model. In actual operation, these uncertainties may lead to system performance degradation or even operational failures.

[0004] Traditional methods usually use simple optimization algorithms, such as linear programming or dynamic programming, which have limited effectiveness in dealing with complex optimization problems and are difficult to find the global optimal solution. In addition, these algorithms are sensitive to initial parameters and are prone to falling into local optimality. Traditional methods often have a small number of iterations when solving optimization models, resulting in insufficient stability and reliability of the results, which affects the implementation effect of the optimization scheduling solution. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the above technical defects and provide an optimization scheduling method for a heat and power cogeneration unit with heat storage and an electric boiler based on fuzzy chance constraints.

[0006] In order to solve the above problems, the technical solution of the present invention comprises the following steps:

[0007] S1. Collect and analyze historical operating data of cogeneration units and electric boilers to construct typical operating conditions;

[0008] S2. Based on the typical operating conditions, an optimization scheduling model is established using fuzzy opportunity constraints, taking into account the impact of uncertain factors on system operation;

[0009] S3. Calculate the optimal operation strategy of the cogeneration unit and electric boiler according to the optimization scheduling model to improve the economy and reliability of the system;

[0010] S4. Simulate and verify the optimized scheduling scheme and evaluate its performance in actual operation, including but not limited to system cost, energy efficiency ratio and thermal power balance:

[0011] S5. According to the simulation verification results, make necessary adjustments and improvements to the optimized scheduling plan to ensure that the optimized scheduling plan can meet the actual operation needs.

[0012] Furthermore, the establishment of the optimization scheduling model in step S2 includes the following steps:

[0013] S2.1, the operating parameters of the cogeneration unit and the electric boiler and their uncertainty ranges;

[0014] S2.2, using fuzzy chance constraints to deal with uncertain factors and establish a fuzzy chance constraint optimization model;

[0015] S2.3, considering the economy and reliability of system operation, setting objective functions and constraints.

[0016] Furthermore, the steps of identifying and removing abnormal data in step S1.2 include the following steps:

[0017] S1.2.1, the application of statistical methods or machine learning algorithms to identify data points outside the normal range;

[0018] S1.2.2. For the identified abnormal data points, interpolation or other appropriate methods are used to correct or delete them to ensure the consistency and reliability of the operating data.

[0019] Furthermore, setting the objective function and constraint conditions in step S2.3 includes the following steps:

[0020] S2.3.1, the objective function is to minimize the total system cost, including fuel cost, operation and maintenance cost and electricity cost;

[0021] S2.3.2. The constraints for setting the system operation include thermal and electrical balance constraints, equipment operation constraints and environmental emission constraints.

[0022] Further, the process of adjusting and improving the optimized scheduling scheme in step S5 includes the following steps:

[0023] S5.1, the simulation verification data is analyzed in detail to identify the problems or deficiencies that may exist in the actual operation of the optimized scheduling scheme;

[0024] S5.2: Based on the identified problems or deficiencies, make targeted adjustments and improvements to the optimized scheduling plan in combination with the actual operation requirements of the system;

[0025] S5.3, the adjusted optimized scheduling scheme is simulated and verified again to ensure that its performance in actual operation meets the expected requirements.

[0026] Furthermore, the construction of the typical operating condition in step S1 includes the following steps:

[0027] S1.1, classifying and counting the historical operation data of cogeneration units and electric boilers;

[0028] S1.2, identify and eliminate abnormal data to ensure the accuracy and representativeness of the data;

[0029] S1.3. Based on the statistical results, typical operating condition curves of the cogeneration unit and the electric boiler are drawn.

[0030] Further, the step S3 of calculating the optimal operation strategy includes the following steps:

[0031] S3.1, using the optimization algorithm to solve the optimization scheduling model to obtain the optimal operation strategy of the cogeneration unit and the electric boiler;

[0032] S3.2, the thermal and electrical balance and energy efficiency of the system are considered to ensure the feasibility and rationality of the optimal operation strategy;

[0033] S3.3. The output optimal operation strategy includes the unit output, the electric boiler operation time and the heat charging and discharging strategy of the heat storage device.

[0034] Furthermore, the use of fuzzy chance constraints to process uncertain factors in step S2.2 includes the following steps:

[0035] S2.2.1, as described above, consider uncertain factors such as weather changes, load fluctuations and equipment failures, and define fuzzy variables;

[0036] S2.2.2. The fuzzy mathematical theory is used to establish fuzzy chance constraints to ensure the robustness and adaptability of the model.

[0037] Furthermore, the step S3.1 of solving the optimization scheduling model using the optimization algorithm includes the following steps:

[0038] S3.1.1, selecting a suitable optimization algorithm, such as a genetic algorithm, a particle swarm optimization algorithm or a simulated annealing algorithm;

[0039] S3.1.2, setting the parameters of the optimization algorithm, such as population size, number of iterations and crossover probability;

[0040] S3.1.3, the optimal operation strategy is solved through multiple iterations to ensure the stability and reliability of the results.

[0041] The advantages of the present invention compared with the prior art are:

[0042] 1. The present invention provides an optimization scheduling method for a heat and power cogeneration unit with heat storage and an electric boiler based on fuzzy chance constraints. Fuzzy chance constraints are used to process uncertain factors, and uncertain factors such as weather changes, load fluctuations and equipment failures are considered to establish a fuzzy chance constraint optimization model. This processing method not only improves the robustness and adaptability of the model, but also ensures the feasibility of the optimization scheduling scheme in actual operation.

[0043] 2. The present invention provides an optimal scheduling method for a heat and power cogeneration unit with heat storage and an electric boiler based on fuzzy chance constraints. When solving the optimal scheduling model, the present invention adopts a variety of optimization algorithms, such as genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm. By setting reasonable algorithm parameters and multiple iterations, the optimal operation strategy is solved to ensure the stability and reliability of the results. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] The optimal scheduling method of a heat and power cogeneration unit with heat storage and an electric boiler based on fuzzy chance constraints includes the following steps:

[0046] S1. Collect and analyze historical operating data of cogeneration units and electric boilers to construct typical operating conditions;

[0047] S2. Based on typical operating conditions, an optimal scheduling model is established using fuzzy opportunity constraints, taking into account the impact of uncertain factors on system operation;

[0048] S3. Calculate the optimal operation strategy of the cogeneration unit and electric boiler according to the optimization scheduling model to improve the economy and reliability of the system;

[0049] S4. Simulate and verify the optimized scheduling scheme and evaluate its performance in actual operation, including but not limited to system cost, energy efficiency ratio and thermal power balance:

[0050] S5. According to the simulation verification results, make necessary adjustments and improvements to the optimized scheduling plan to ensure that the optimized scheduling plan can meet the actual operation needs.

[0051] Furthermore, the construction of the typical operating condition in step S1 includes the following steps:

[0052] S1.1. Classify and compile historical operation data of cogeneration units and electric boilers;

[0053] S1.2. Identify and eliminate abnormal data to ensure the accuracy and representativeness of the data;

[0054] S1.3. Based on the statistical results, draw the typical operating condition curves of the cogeneration unit and electric boiler.

[0055] Furthermore, the establishment of the optimization scheduling model in step S2 includes the following steps:

[0056] S2.1. Determine the operating parameters of the cogeneration unit and electric boiler and their uncertainty ranges;

[0057] S2.2, using fuzzy chance constraints to deal with uncertain factors and establish a fuzzy chance constraint optimization model;

[0058] S2.3. Consider the economy and reliability of system operation and set the objective function and constraints.

[0059] Furthermore, the calculation of the optimal operation strategy in step S3 includes the following steps:

[0060] S3.1. Solve the optimization scheduling model using the optimization algorithm to obtain the optimal operation strategy of the cogeneration unit and the electric boiler;

[0061] S3.2. Consider the thermal and electrical balance and energy efficiency of the system to ensure the feasibility and rationality of the optimal operation strategy;

[0062] S3.3. Output the optimal operation strategy, including unit output, electric boiler operation time and heat storage device charging and discharging strategy.

[0063] Furthermore, the steps of identifying and removing abnormal data in step S1.2 include the following steps:

[0064] S1.2.1. Apply statistical methods or machine learning algorithms to identify data points outside the normal range;

[0065] S1.2.2. For the identified abnormal data points, use interpolation or other appropriate methods to correct or delete them to ensure the consistency and reliability of the operating data.

[0066] Furthermore, in step S2.2, using fuzzy chance constraints to process uncertain factors includes the following steps:

[0067] S2.2.1, considering uncertain factors such as weather changes, load fluctuations and equipment failures, define fuzzy variables;

[0068] S2.2.2. Use fuzzy mathematics theory to establish fuzzy chance constraints to ensure the robustness and adaptability of the model.

[0069] Furthermore, setting the objective function and constraint conditions in step S2.3 includes the following steps:

[0070] S2.3.1. Set the minimization of the total system cost as the objective function, including fuel cost, operation and maintenance cost, and electricity cost;

[0071] S2.3.2. Set constraints for system operation, including thermal and electrical balance constraints, equipment operation constraints, and environmental emission constraints.

[0072] Furthermore, the step S3.1 of solving the optimization scheduling model using the optimization algorithm includes the following steps:

[0073] S3.1.1. Select an appropriate optimization algorithm, such as genetic algorithm, particle swarm optimization algorithm or simulated annealing algorithm;

[0074] S3.1.2. Set the parameters of the optimization algorithm, such as population size, number of iterations, and crossover probability;

[0075] S3.1.3. Through multiple iterations, the optimal operation strategy is solved to ensure the stability and reliability of the results.

[0076] Further, the process of adjusting and improving the optimized scheduling scheme in step S5 includes the following steps:

[0077] S5.1. Analyze the simulation verification data in detail to identify the problems or deficiencies that may exist in the actual operation of the optimized scheduling scheme;

[0078] S5.2. Based on the identified problems or deficiencies and the actual operation requirements of the system, make targeted adjustments and improvements to the optimized scheduling plan;

[0079] S5.3. The adjusted optimized scheduling plan is simulated and verified again to ensure that its performance in actual operation meets the expected requirements.

[0080] In specific use,

[0081] S1.1 Data classification and statistics

[0082] Average load: 5000kW on weekdays, 4500kW on weekends, and 4000kW on holidays;

[0083] Maximum load: 6000kW in summer and 5500kW in winter;

[0084] Minimum load: The minimum load at night is 2000kW;

[0085] S2.1 Operating parameters and their uncertainty ranges

[0086] Unit output: 5000kW to 6000kW, uncertainty range ±10%;

[0087] Electric boiler operation time: 8 hours per day, with an uncertainty range of ±1 hour;

[0088] Heat storage device charging and discharging strategy: charging time is 4 hours, discharging time is 4 hours, and the uncertainty range is ±1 hour;

[0089] S2.3 Objective function and constraints

[0090] Objective function: Minimize the total system cost, including fuel cost (0.5 yuan / kWh), operation and maintenance cost (0.1 yuan / kWh) and electricity cost (0.6 yuan / kWh);

[0091] Constraints: thermal power balance constraint (heat load demand is 5000kW), equipment operation constraint (unit output does not exceed 6000kW), environmental emission constraint (CO2 emissions do not exceed 1000kg / h);

[0092] S3.1 Optimization algorithm solution

[0093] Optimization algorithm: genetic algorithm, population size is 100, number of iterations is 1000, crossover probability is 0.8;

[0094] Optimal strategy: The unit output is 5500kW, the electric boiler operation time is 8 hours, the heat storage device charging time is 4 hours, and the heat release time is 4 hours;

[0095] S4.1 Simulation Verification Results

[0096] System cost: The total cost of the optimized system is 12,000 yuan / day, which is 10% lower than the traditional method.

[0097] Energy efficiency ratio: The optimized energy efficiency ratio is 95%, which is 5% higher than the traditional method.

[0098] Thermal and electrical balance: The optimized thermal load satisfaction rate is 98%, which is 3% higher than the traditional method.

[0099] S5.1 Problem Identification

[0100] Under extreme weather conditions, the system cost increases slightly and the energy storage strategy needs to be optimized.

[0101] S5.2 Scheme Adjustment

[0102] Improvement measures: Increase the charging time of the heat storage device, reduce the operating time of the electric boiler, and improve the flexibility and economy of the system.

[0103] S5.3 Verify again

[0104] Test results: The total cost of the optimized system is 11,800 yuan / day, the energy efficiency ratio is 96%, and the heat load satisfaction rate is 99%, which meets the design requirements.

[0105] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0106] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0107] The present invention and its implementation methods are described above, and such description is not restrictive, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design structures and embodiments similar to the technical solution without creativity, which should all fall within the protection scope of the present invention.

Claims

1. An optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints, characterized in that: The following steps are involved: S1. Collect and analyze historical operation data of cogeneration units and electric boilers to construct typical operating conditions; S2. Based on the typical operating conditions, an optimization scheduling model is established using fuzzy opportunity constraints, taking into account the impact of uncertain factors on system operation; S3. Calculate the optimal operation strategy of the cogeneration unit and electric boiler according to the optimization scheduling model to improve the economy and reliability of the system; S4. Simulate and verify the optimized scheduling scheme and evaluate its performance in actual operation, including but not limited to system cost, energy efficiency ratio and thermal power balance: S5. According to the simulation verification results, make necessary adjustments and improvements to the optimized scheduling plan to ensure that the optimized scheduling plan can meet the actual operation needs.

2. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 1 is characterized in that: The construction of the typical operating condition in step S1 includes the following steps: S1.1, classifying and counting the historical operation data of cogeneration units and electric boilers; S1.2, identify and eliminate abnormal data to ensure the accuracy and representativeness of the data; S1.

3. Based on the statistical results, typical operating condition curves of the cogeneration unit and the electric boiler are drawn.

3. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 1 is characterized in that: The establishment of the optimization scheduling model in step S2 includes the following steps: S2.1, the operating parameters of the cogeneration unit and the electric boiler and their uncertainty ranges; S2.2, using fuzzy chance constraints to deal with uncertain factors and establish a fuzzy chance constraint optimization model; S2.3, considering the economy and reliability of system operation, setting objective functions and constraints.

4. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 1 is characterized in that: The step S3 of calculating the optimal operation strategy includes the following steps: S3.1, using the optimization algorithm to solve the optimization scheduling model to obtain the optimal operation strategy of the cogeneration unit and the electric boiler; S3.2, the thermal and electrical balance and energy efficiency of the system are considered to ensure the feasibility and rationality of the optimal operation strategy; S3.

3. The output optimal operation strategy includes the unit output, the electric boiler operation time and the heat charging and discharging strategy of the heat storage device.

5. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 2 is characterized in that: The step S1.2 of identifying and removing abnormal data includes the following steps: S1.2.1, the application of statistical methods or machine learning algorithms to identify data points outside the normal range; S1.2.

2. For the identified abnormal data points, interpolation or other appropriate methods are used to correct or delete them to ensure the consistency and reliability of the operating data.

6. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 3 is characterized in that: The step S2.2 of using fuzzy chance constraints to process uncertain factors includes the following steps: S2.2.1, as described above, consider uncertain factors such as weather changes, load fluctuations and equipment failures, and define fuzzy variables; S2.2.

2. The fuzzy mathematical theory is used to establish fuzzy chance constraints to ensure the robustness and adaptability of the model.

7. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 3 is characterized in that: Setting the objective function and constraint conditions in step S2.3 includes the following steps: S2.3.1, the objective function is to minimize the total system cost, including fuel cost, operation and maintenance cost and electricity cost; S2.3.

2. The constraints for setting the system operation include thermal and electrical balance constraints, equipment operation constraints and environmental emission constraints.

8. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 4 is characterized in that: The step S3.1 of using the optimization algorithm to solve the optimization scheduling model includes the following steps: S3.1.1, selecting a suitable optimization algorithm, such as a genetic algorithm, a particle swarm optimization algorithm or a simulated annealing algorithm; S3.1.2, setting the parameters of the optimization algorithm, such as population size, number of iterations and crossover probability; S3.1.3, the optimal operation strategy is solved through multiple iterations to ensure the stability and reliability of the results.

9. The optimal scheduling method for combined heat and power units with heat storage and electric boilers based on fuzzy chance constraints according to claim 1 is characterized in that The process of adjusting and improving the optimized scheduling scheme in step S5 includes the following steps: S5.1, the simulation verification data is analyzed in detail to identify the problems or deficiencies that may exist in the actual operation of the optimized scheduling scheme; S5.2: Based on the identified problems or deficiencies, make targeted adjustments and improvements to the optimized scheduling plan in combination with the actual operation requirements of the system; S5.3, the adjusted optimized scheduling scheme is simulated and verified again to ensure that its performance in actual operation meets the expected requirements.