Thermal power generating unit coordination optimization method

Abstract

The invention relates to the technical field of thermal power generating unit optimization, and discloses a thermal power generating unit coordinated optimization method. According to the method, by collecting and analyzing various data in real time, the deviation between the load demand and the unit output is accurately recognized, whether the current power generation capacity can meet the actual load demand or not is effectively checked, and possible gaps and redundancy are recognized in time; according to the method, targets of power generation cost minimization and environmental emission minimization are converted into target functions in a mathematical form, corresponding constraint conditions are set according to unit performance and market regulations, a scientific basis is provided for the scheduling process, a particle swarm optimization algorithm is used for calculation, and under the condition that multi-target optimization is considered, the scheduling efficiency is improved. According to the method, an efficient operation scheduling plan is generated, reasonable start-stop and output configuration of each unit is ensured, the problem that a traditional scheduling mechanism only focuses on a single target is solved, and the overall efficiency and flexibility of thermal power unit scheduling are improved.

Description

Technical Field

[0001] The invention relates to the technical field of thermal power unit optimization, in particular to a thermal power unit coordination optimization method. Background Art

[0002] With the continuous growth of global energy demand and the continuous development of renewable energy technology, the coordinated optimization of thermal power units is particularly important. Thermal power units play an important role in the power grid, especially in regulating load changes, providing basic load and emergency standby. However, the operating efficiency, environmental emissions and economy of thermal power units have always been the focus of attention in the industry. Therefore, how to reasonably configure and dispatch thermal power units in the context of volatile electricity market and increasing proportion of renewable energy has become the key to improving the overall operating efficiency and safety of the power system.

[0003] The coordinated optimization of thermal power units mainly includes the output scheduling of multiple units, the formulation of start-stop strategies and the economic operation plan. Through scientific and reasonable coordinated optimization methods, the operating costs can be effectively reduced and the responsiveness and stability of the units can be improved. At the same time, with the increasingly stringent environmental protection regulations, the requirements for thermal power units to reduce harmful substance emissions are also getting higher and higher. Therefore, it has become an urgent task to develop new optimization methods to achieve the dual goals of economic benefits and environmental protection.

[0004] Traditional scheduling methods usually only focus on a single objective without considering multi-objective optimization issues, resulting in low scheduling efficiency of thermal power units. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a method for coordinated optimization of thermal power units. The method can accurately identify the deviation between load demand and unit output by real-time collection and analysis of various data, thereby effectively testing whether the current power generation capacity can meet the actual load demand, timely identifying possible gaps and redundancies, and secondly converting the goals of minimizing power generation costs and minimizing environmental emissions into mathematical objective functions, and setting corresponding constraints based on unit performance and market regulations, thereby providing a scientific basis for the scheduling process, and using a particle swarm optimization algorithm for calculation. Under the condition of considering multi-objective optimization, an efficient operation scheduling plan can be generated to ensure that the start and stop and output configuration of each unit are reasonable. This method improves the problem that the traditional scheduling mechanism only focuses on a single goal, and improves the overall efficiency and flexibility of thermal power unit scheduling, thereby effectively reducing environmental pollution while ensuring power supply, achieving both economic and environmental benefits, and solving the above-mentioned problems.

[0007] (II) Technical solution

[0008] To achieve the above object, the present invention provides the following technical solution: a method for coordinating and optimizing a thermal power unit, comprising the following steps:

[0009] S1. Real-time collection of thermal power unit operation data, load data, market data and environmental data;

[0010] S2. Use statistical analysis tools to calculate the deviation between the predicted load and the actual load, calculate the output efficiency and load of the thermal power units, verify whether the current power generation capacity meets the load demand, and identify gaps and redundancies;

[0011] S3. Determine the optimization target of power generation cost and environmental emission, and convert the economic cost minimization and emission minimization into the objective function in mathematical form;

[0012] S4. Determine the constraints according to the performance of the generator set and the market regulations, convert the identified constraints into mathematical equations, and select the particle swarm optimization algorithm according to the objective function and the constraints;

[0013] S5. Use the selected particle swarm optimization algorithm to calculate the particle fitness value to obtain the operation scheduling plan, and convert the plan generated by the algorithm into scheduling instructions for starting and stopping and outputting each unit;

[0014] S6. After the dispatch is completed, a dispatch optimization report for the thermal power unit is generated and sent to the central control display screen.

[0015] Preferably, the formula for calculating the deviation between the predicted load and the actual load in S2 is as follows:

[0016]

[0017] In the formula, Fhpc represents the deviation between the predicted load and the actual load, Fhyc represents the predicted load, and Sjfh represents the actual load, that is, the actual amount of electricity consumed.

[0018] Preferably, the formula for calculating the output efficiency of the thermal power unit in S2 is as follows:

[0019]

[0020] In the formula, Ccxl represents the output efficiency of the thermal power unit, Yxsc represents the effective output power, Yxdj represents the actual number of hours of power generation operation of the unit, Rlrz represents the heat energy contained in unit mass of fuel, and Rlxh represents the amount of fuel consumed in a specific time.

[0021] Preferably, the formula for calculating the load of the thermal power unit in S2 is as follows:

[0022] Hdfz=Dysz*Dlsz*Gyls

[0023] In the formula, Hdfz represents the load of the thermal power unit, Dysz represents the voltage level of power transmission in the system, Dlsz represents the current intensity of power flow, and Gyls represents the power factor, which ranges from 0 to 1.

[0024] Preferably, the formula for S2 to verify whether the current power generation capacity meets the load demand is as follows:

[0025] Puff=Yxsc-Sjfh

[0026] In the formula, Puff represents the verified load demand value. If the calculated result is a positive value, it means that the power generation capacity exceeds the demand. If it is a negative value, it means that there is a power generation gap.

[0027] Preferably, the economic cost minimization objective function in S3 is as follows:

[0028] min C=(Rdpc*Rtca)+C om +C ap

[0029] In the formula, min C represents the objective function of minimizing economic cost, Rdpc represents fuel consumption, Rtca represents operation and maintenance cost, and C ap Represents the capital cost.

[0030] Preferably, the emission minimization conversion objective function in S3 is as follows:

[0031] min E=(Rdpc*Tpyz)+dyhp+Lyhp

[0032] In the formula, min E represents the emission minimization conversion objective function, Rdpc represents fuel consumption, Tpyz represents carbon dioxide emission factor, Dyhp represents nitrogen oxide emissions, and Lyhp represents sulfur oxide emissions.

[0033] Preferably, the S4 particle swarm optimization algorithm steps are as follows:

[0034] A1. Determine the number of particles N, set the maximum number of iterations, and select the learning factor and inertia weight;

[0035] A2, randomly generate the initial position and velocity of each particle, and set the position of the particle within the range of decision variables;

[0036] A3. Calculate the initial fitness value of each particle and set the individual best position as the current particle position;

[0037] A4. Initialize the global best position as the one with the best fitness among the individual best positions of all particles.

[0038] Preferably, the formula for calculating the particle fitness value in S5 is as follows:

[0039] F(X)=ts1*C+ts2*E

[0040] In the formula, F(X) represents the particle fitness function, C represents the economic cost, E represents the emission, and ts1 and ts2 represent the weights of the algorithm selection.

[0041] Preferably, the step of obtaining the operation scheduling plan in S5 is as follows:

[0042] B1. For each unit, check whether its output power Pi is greater than its minimum output Xi. If Pi>Xi, set the unit to be enabled and record it as the running state. If Pi=0, set the unit to be disabled and record it as the stopped state.

[0043] B2. Record the output and status of each unit into the dispatch plan;

[0044] B3. Generate dispatch instructions according to the generated operation dispatch plan. For each unit, format the instructions, with the start instruction as ON and the stop instruction as OFF;

[0045] B4. Output scheduling instructions and execute them, including setting output.

[0046] Compared with the prior art, the present invention provides a method for coordinated optimization of thermal power units, which has the following beneficial effects:

[0047] The present invention can accurately identify the deviation between load demand and unit output by real-time collection and analysis of various data, thereby effectively checking whether the current power generation capacity can meet the actual load demand, timely identifying possible gaps and redundancies, and secondly converting the goals of minimizing power generation costs and environmental emissions into mathematical objective functions, and setting corresponding constraints based on unit performance and market regulations, providing a scientific basis for the scheduling process, and using the particle swarm optimization algorithm for calculation, it can generate an efficient operation scheduling plan under the condition of considering multi-objective optimization, ensuring that the start and stop and output configuration of each unit are reasonable. This method improves the problem of traditional scheduling mechanisms focusing on only a single goal, and improves the overall efficiency and flexibility of thermal power unit scheduling, thereby effectively reducing environmental pollution while ensuring power supply, and achieving both economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In view of the problem that traditional dispatching methods usually only focus on a single objective and do not consider multi-objective optimization, resulting in low dispatching efficiency of thermal power units, a coordinated optimization method for thermal power units is proposed. Figure 1 , the method comprises the following steps:

[0051] S1. Real-time collection of thermal power unit operation data, load data, market data and environmental data;

[0052] Real-time collection of thermal power unit operation data, load data, market data, and environmental data is a key step in achieving efficient scheduling and optimized management. This process usually relies on advanced sensor networks and Internet of Things technologies. Through sensors distributed in various key parts of the unit, operating parameters such as temperature, pressure, vibration, and speed are monitored in real time. At the same time, the system integrates intelligent measuring instruments to obtain important data such as grid load, power generation capacity, and market price. Data collection is initially analyzed by edge computing devices to ensure fast response and low latency during data stream transmission. The massive amount of data collected is uploaded to the central data platform through a secure communication protocol, and is stored, processed, and analyzed using big data technology. Combined with machine learning and statistical analysis tools, these data can not only evaluate the performance of thermal power units in real time, but also optimize load forecasting, monitor environmental pollutant emissions, and provide data basis for decision support systems, thereby achieving dynamic adjustment of power generation and scheduling;

[0053] S2. Use statistical analysis tools to calculate the deviation between the predicted load and the actual load, calculate the output efficiency and load of the thermal power units, verify whether the current power generation capacity meets the load demand, and identify gaps and redundancies;

[0054] The calculation formula for the deviation between the predicted load and the actual load is as follows:

[0055]

[0056] By regularly comparing the predicted load with the actual load, the gap between the dispatch plan and the actual operation can be identified in time, so as to quickly adjust the operation strategy of the unit to ensure the adequacy and stability of power supply. In the formula, Fhpc represents the deviation between the predicted load and the actual load, Fhyc represents the predicted load, and Sjfh represents the actual load, that is, the actual amount of electricity consumed. This deviation analysis helps to discover systematic errors in load forecasting, and then optimize the dispatch strategy to make more efficient use of existing power generation resources and reduce redundant production and operating costs;

[0057] The formula for calculating the output efficiency of thermal power units is as follows:

[0058]

[0059] By analyzing the output efficiency of thermal power units, we can identify the reasons for inefficient operation and adjust the unit operating parameters to improve efficiency, thereby reducing fuel consumption and production costs and increasing economic benefits. In the formula, Ccxl represents the output efficiency of the thermal power unit, Yxsc represents the effective output power, Yxdj represents the actual number of hours of power generation operation of the unit, Rlrz represents the heat energy contained in a unit mass of fuel, and Rlxh represents the amount of fuel consumed in a specific time. Keeping the unit operating within the optimal working efficiency range helps to reduce wear and failure rate, extend the service life of the equipment, and reduce maintenance and repair costs;

[0060] The formula for calculating the load of thermal power units is as follows:

[0061] Hdfz=Dysz*Dlsz*Gyls

[0062] Accurately calculate the load status of thermal power units, reasonably arrange the output and start-stop sequence of the units, ensure that each unit operates within a reasonable load range, avoid overload or no-load, and improve the economy and safety of the overall operation. In the formula, Hdfz represents the load of the thermal power unit, Dysz represents the voltage level of power transmission in the system, Dlsz represents the current intensity of power flow, and Gyls represents the power factor, which is between 0 and 1. By real-time monitoring of the load of the unit, the power generation capacity can be quickly adjusted according to market demand and changes in electricity prices, the system's adaptability to load fluctuations can be enhanced, and the dispatching flexibility of the entire power grid can be improved;

[0063] The formula to verify whether the current power generation capacity meets the load demand is as follows:

[0064] Puff=Yxsc-Sjfh

[0065] In the formula, Puff represents the verification load demand value. If the calculated result is a positive value, it means that the power generation capacity exceeds the demand. If it is a negative value, it means that there is a power generation gap.

[0066] S3. Determine the optimization target of power generation cost and environmental emission, and convert the economic cost minimization and emission minimization into the objective function in mathematical form;

[0067] The economic cost minimization objective function is as follows:

[0068] min C=(Rdpc*Rtca)+C om +C ap

[0069] By optimizing the scheduling and operating parameters, the economic cost minimization objective function can significantly reduce fuel, maintenance and other related operating costs, thereby improving the economic benefits of the unit. In the formula, min C represents the economic cost minimization objective function, Rdpc represents fuel consumption, Rtca represents operating and maintenance costs, and C ap Representing capital cost, setting the economic cost minimization objective function helps to allocate resources rationally and ensure that each unit works within its optimal economic operation range, thereby reducing unnecessary waste of resources;

[0070] The emission minimization conversion objective function is as follows:

[0071] min E=(Rdpc*Tpyz)+Dyhp+Lyhp

[0072] By setting the emission minimization objective function, thermal power units can effectively reduce the emission of harmful gases such as carbon dioxide, sulfur oxides and nitrogen oxides, thereby improving air quality and promoting the protection of the ecological environment. In the formula, min E represents the emission minimization conversion objective function, Pdpc represents fuel consumption, Tpyz represents carbon dioxide emission factor, Dyhp represents nitrogen oxide emissions, and Lyhp represents sulfur oxide emissions. The setting of the emission minimization objective function encourages enterprises to invest in cleaner technologies and equipment, such as improving combustion technology and upgrading environmental protection facilities, and promotes technological progress and sustainable development of the entire industry.

[0073] S4. Determine the constraints according to the performance of the generator set and the market regulations, convert the identified constraints into mathematical equations, and select the particle swarm optimization algorithm according to the objective function and the constraints;

[0074] The specific constraints are as follows:

[0075] Generator output constraints:

[0076] P min ≤P output ≤P max

[0077] P output Indicates the actual output power of the generator set, P minIndicates the minimum output of the generator set, which is usually determined by the design and technical performance of the unit. max Indicates the maximum output of the generator set;

[0078] Load demand constraints:

[0079] P output ≥P load

[0080] P load It represents the actual load demand, which is the demand for electricity from users in the market and may change over time;

[0081] Emission Limits:

[0082] E CO2 ≤E max,CO2

[0083] E NOx ≤E max,NOx

[0084] E SO2 ≤E max,SO2

[0085] E CO2 Indicates the emission of carbon dioxide, E NOx Indicates the emission of nitrogen oxides, E SO2 Indicates the emission of sulfur oxides, E max,CO2 Indicates the upper limit of carbon dioxide emissions, E max,NOx Indicates the upper limit of nitrogen oxide emissions, E max,SO2 Indicates the upper limit of sulfur oxide emissions;

[0086] Run time constraints:

[0087] T min ≤T opt ≤T max

[0088] T min Indicates the minimum operating time of the unit, T max Indicates the maximum operating time of the unit, T opt Indicates the actual running hours;

[0089] By integrating these constraints together, we can form a more comprehensive optimization model. On this basis, we can use the particle swarm optimization algorithm to solve the optimal solution for the economic cost of power generation and environmental emission targets.

[0090] The steps of particle swarm optimization algorithm are as follows:

[0091] A1. Determine the number of particles N, set the maximum number of iterations, and select the learning factor and inertia weight;

[0092] A2, randomly generate the initial position and velocity of each particle, and set the position of the particle within the range of decision variables;

[0093] A3. Calculate the initial fitness value of each particle and set the individual best position as the current particle position;

[0094] A4. Initialize the global best position to the best fitness among the individual best positions of all particles;

[0095] S5. Use the selected particle swarm optimization algorithm to calculate the particle fitness value to obtain the operation scheduling plan, and convert the plan generated by the algorithm into scheduling instructions for starting and stopping and outputting each unit;

[0096] The formula for calculating the particle fitness value is as follows:

[0097] F(X)=ts1*C+ts2*E

[0098] The particle fitness value reflects the pros and cons of a specific solution in meeting the optimization objectives (such as economic cost, emission control, etc.). By comparing the fitness values ​​of different particles, the optimal solution or approximate optimal solution can be effectively found. In the formula, F(X) represents the particle fitness function, C represents the economic cost, E represents the emission, and ts1 and ts2 represent the weights of the algorithm selection. In the case of multiple objectives, different fitness values ​​can be weighted and fused to comprehensively consider factors such as economic benefits and environmental protection requirements to help decision makers make more optimized scheduling choices.

[0099] The steps to get the running schedule are as follows:

[0100] B1. For each unit, check whether its output power Pi is greater than its minimum output Xi. If Pi>Xi, set the unit to be enabled and record it as the running state. If Pi=0, set the unit to be disabled and record it as the stopped state.

[0101] B2. Record the output and status of each unit into the dispatch plan;

[0102] B3. Generate dispatch instructions according to the generated operation dispatch plan. For each unit, format the instructions, with the start instruction as ON and the stop instruction as OFF;

[0103] B4. Output dispatch instructions and execute them, including setting output;

[0104] S6. After the dispatch is completed, a dispatch optimization report for the thermal power unit is generated and sent to the central control display screen;

[0105] After the scheduling is completed, the system will automatically generate a detailed thermal power unit scheduling optimization report, which includes key indicators such as unit output plan, load forecast, fuel consumption, emission data and economic benefit analysis. Through data analysis and visualization technology, the information in the report will be formatted and transmitted to the central control display screen in real time, so that operators can quickly obtain and evaluate the effectiveness of the current optimization plan. The report uses a variety of forms of expression such as charts, curves and numerical indicators to intuitively display the operating status of each unit and the expected environmental emission level, helping central control personnel to quickly identify potential problems and make corresponding adjustments. At the same time, the system also integrates real-time data monitoring and alarm mechanisms, which can provide early warning prompts for abnormal situations, ensure the safety and efficiency of the scheduling process, and thus promote the balance between economic benefits and environmental protection goals of thermal power units.

[0106] Through the application of the above methods, the problem of the traditional dispatching mechanism focusing on only a single goal has been improved, and the overall efficiency and flexibility of the dispatching of thermal power units have been improved. This effectively reduces environmental pollution while ensuring power supply, achieving both economic and environmental benefits.

[0107] 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.

Claims

1. A method for coordinated optimization of thermal power units, characterized in that: The following steps are involved: S1. Real-time collection of thermal power unit operation data, load data, market data and environmental data; S2. Use statistical analysis tools to calculate the deviation between the predicted load and the actual load, calculate the output efficiency and load of the thermal power units, verify whether the current power generation capacity meets the load demand, and identify gaps and redundancies; S3. Determine the optimization target of power generation cost and environmental emission, and convert the economic cost minimization and emission minimization into the objective function in mathematical form; S4. Determine the constraints according to the performance of the generator set and the market regulations, convert the identified constraints into mathematical equations, and select the particle swarm optimization algorithm according to the objective function and the constraints; S5. Use the selected particle swarm optimization algorithm to calculate the particle fitness value to obtain the operation scheduling plan, and convert the plan generated by the algorithm into scheduling instructions for starting and stopping and outputting each unit; S6. After the dispatch is completed, a dispatch optimization report for the thermal power unit is generated and sent to the central control display screen.

2. A method for coordinated optimization of thermal power units according to claim 1, characterized in that: The formula for calculating the deviation between the predicted load and the actual load is as follows: In the formula, Fhpc represents the deviation between the predicted load and the actual load, Fhyc represents the predicted load, and Sjfh represents the actual load, that is, the actual amount of electricity consumed.

3. A method for coordinated optimization of thermal power units according to claim 2, characterized in that: The formula for calculating the output efficiency of the thermal power unit by S2 is as follows: In the formula, Ccxl represents the output efficiency of the thermal power unit, Yxsc represents the effective output power, Yxdj represents the actual number of hours of power generation operation of the unit, Rlrz represents the heat energy contained in unit mass of fuel, and Rlxh represents the amount of fuel consumed in a specific time.

4. A method for coordinated optimization of thermal power units according to claim 3, characterized in that: The formula for calculating the load of the thermal power unit by S2 is as follows: Hdfz=Dysz*Dlsz*Gyls In the formula, Hdfz represents the load of the thermal power unit, Dysz represents the voltage level of power transmission in the system, Dlsz represents the current intensity of power flow, and Gyls represents the power factor, which ranges from 0 to 1.

5. A method for coordinated optimization of thermal power units according to claim 4, characterized in that: The formula for S2 to verify whether the current power generation capacity meets the load demand is as follows: Puff=Yxsc-Sjfh In the formula, Puff represents the verified load demand value. If the calculated result is a positive value, it means that the power generation capacity exceeds the demand. If it is a negative value, it means that there is a power generation gap.

6. A method for coordinated optimization of thermal power units according to claim 5, characterized in that: The economic cost minimization objective function in S3 is as follows: minC=(Rdpc*Rtca)+C om +C ap In the formula, minC represents the objective function of minimizing economic cost, Rdpc represents fuel consumption, Rtca represents operation and maintenance cost, and C ap Represents the capital cost.

7. A method for coordinated optimization of thermal power units according to claim 6, characterized in that: The emission minimization conversion objective function in S3 is as follows: minE=(Rdpc*Tpyz)+Dyhp+Lyhp In the formula, minE represents the emission minimization conversion objective function, Rdpc represents fuel consumption, Tpyz represents carbon dioxide emission factor, Dyhp represents nitrogen oxide emissions, and Lyhp represents sulfur oxide emissions.

8. A method for coordinated optimization of thermal power units according to claim 7, characterized in that: The steps of the S4 particle swarm optimization algorithm are as follows: A1. Determine the number of particles N, set the maximum number of iterations, and select the learning factor and inertia weight; A2, randomly generate the initial position and velocity of each particle, and set the position of the particle within the range of decision variables; A3. Calculate the initial fitness value of each particle and set the individual best position as the current particle position; A4. Initialize the global best position as the one with the best fitness among the individual best positions of all particles.

9. A method for coordinated optimization of thermal power units according to claim 8, characterized in that: The formula for calculating the particle fitness value in S5 is as follows: F(X)=ts1*C+ts2*E In the formula, F(X) represents the particle fitness function, C represents the economic cost, E represents the emission, and ts1 and ts2 represent the weights of the algorithm selection.

10. A method for coordinated optimization of thermal power units according to claim 9, characterized in that: The steps of obtaining the operation scheduling plan in S5 are as follows: B1. For each unit, check whether its output power Pi is greater than its minimum output Xi. If Pi>Xi, set the unit to be enabled and record it as the running state. If Pi=0, set the unit to be disabled and record it as the stopped state. B2. Record the output and status of each unit into the dispatch plan; B3. Generate dispatch instructions according to the generated operation dispatch plan. For each unit, format the instructions, with the start instruction as ON and the stop instruction as OFF; B4. Output scheduling instructions and execute them, including setting output.

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