Supercritical wet cooling heat supply unit load economic distribution method based on multi-objective intelligent optimization

Abstract

The invention discloses a supercritical wet cooling heat supply unit load economic distribution method based on multi-objective intelligent optimization, relates to the technical field of supercritical wet cooling heat supply units, and remarkably enhances the global search capability of tradeoff between objectives in the load distribution process through the intervention of an intelligent algorithm. Strong support is provided for making an operation strategy in a complex and changeable operation environment, environmental emission control is taken as an optimization target for consideration, the influence of work of a heat supply system on the environment is reduced, the current social requirement for environmental protection is met, through accurate simulation and optimization calculation, unit operation is more refined, energy waste is reduced, and the energy efficiency is improved. Compared with the prior art, the method has the advantages that the environmental protection target is achieved, meanwhile, the maximization of economic benefits is ensured, the capacity of reflecting market changes can be greatly improved, load distribution is higher in adaptability and timeliness, and due to the high intelligence and self-adaptability, the method has important application value and popularization prospects in the current and future energy supply field.

Description

Technical Field

[0001] The invention relates to the technical field of supercritical wet cooling heating units, and in particular to a load economic distribution method for supercritical wet cooling heating units based on multi-objective intelligent optimization. Background Art

[0002] The supercritical wet cooling heating unit is a highly efficient energy device that works under supercritical pressure and is used to generate electricity and provide heat energy. In this unit, the high-temperature and high-pressure steam generated by the boiler can drive the turbine to generate electricity and also supply heat energy, and its operating efficiency is significantly improved compared to traditional equipment.

[0003] However, such heating units need to balance multiple goals during operation, such as meeting heat load demand, improving power generation efficiency, reducing fuel consumption, and taking environmental protection into consideration. Current load allocation methods often fail to take all of the above goals into account at the same time, especially when faced with complex operating conditions and changing market demands, which limits the flexibility and optimization effect of operating strategies.

[0004] Traditional load allocation methods such as linear programming and dynamic programming, although they have achieved load optimization to a certain extent, often perform poorly in solving multi-objective, high-dimensional and nonlinear problems, and are difficult to meet the complexity and variability of supercritical wet cooling and heating unit operation. Based on this, the present invention designs a method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method for economically distributing loads of a supercritical wet cooling and heating unit based on multi-objective intelligent optimization to solve the above-mentioned problems.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for economic load allocation of a supercritical wet cooling heating unit based on multi-objective intelligent optimization, which is characterized in that it includes the following steps: Establish a multi-objective optimization model including fuel cost, operation and maintenance cost and environmental emissions; Set constraints for unit operation, including power generation, heat supply, reheat steam volume, boiler feed water volume, etc.; Applying a multi-objective intelligent optimization algorithm to solve the optimization model to find the optimal trade-off solution among multiple objectives; According to the solution results and the operator's experience, the final solution for load economic allocation is selected. Preferably, in the above-mentioned method for economic load allocation of supercritical wet cooling heating units based on multi-objective intelligent optimization, the multi-objective intelligent optimization algorithm is a multi-objective particle swarm optimization algorithm (MOPSO), that is, the fuel cost objective function f1=Σ(Ci*Pi), where Ci is the unit price of fuel of the i-th unit, and Pi is the power generation power of the i-th unit.

[0007] Preferably, in the above-mentioned method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization, the fuel cost objective function of the multi-objective intelligent optimization model is defined as the weighted sum of the unit price of fuel and the power generation capacity of each unit, which provides an accurate pricing of the fuel utility of individual units.

[0008] Preferably, in the above-mentioned method for economic allocation of loads of supercritical wet cooling and heating units based on multi-objective intelligent optimization, the operation and maintenance cost optimization objective function further considers the start and stop status and fixed operation and maintenance cost of each unit, accurately reflecting the operation economy of the unit; The environmental emission objective function f3=Σ(Ei*Pi+Di), where Ei is the emission coefficient of the i-th unit, Pi is the power generation, and Di is the fixed emission.

[0009] Preferably, in the above-mentioned method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization, the environmental emission objective function includes the product of the unit's pollution emission coefficient and the power generation power and a fixed emission amount, so as to quantify the environmental impact and provide an environmental performance evaluation index.

[0010] Preferably, in the above-mentioned method for economic load distribution of a supercritical wet cooling heating unit based on multi-objective intelligent optimization, the solution result of the multi-objective intelligent optimization algorithm is analyzed by a decision support system, which takes into account the experience and opinions of operators and market dynamic information to assist in selecting the final solution.

[0011] Preferably, in the above-mentioned method for economic load distribution of a supercritical wet cooling heating unit based on multi-objective intelligent optimization, the constraints further include the start and stop logic of the unit, thermal efficiency curve, safe operating limit of the equipment, etc., to ensure that the unit operates in a safe and stable state.

[0012] Preferably, in the above-mentioned method for economic load distribution of supercritical wet cooling and heating units based on multi-objective intelligent optimization, the multi-objective intelligent optimization algorithm further includes a dynamic adjustment mechanism for the weight of each objective function to reflect the changes in preferences for different objectives in different operating periods.

[0013] Preferably, in the above-mentioned method for economic load distribution of supercritical wet cooling and heating units based on multi-objective intelligent optimization, the multi-objective intelligent optimization algorithm also takes into account random factors including external environmental changes, market demand uncertainty, etc., and can adapt to and adjust the impact of these factors during the solution process.

[0014] Preferably, in the above-mentioned method for economic load distribution of a supercritical wet cooling heating unit based on multi-objective intelligent optimization, the multi-objective intelligent optimization algorithm also includes an online monitoring module, which can collect and process unit operation data in real time to provide the latest input parameters for the optimization model.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The method for economic load distribution of supercritical wet cooling heating units based on multi-objective intelligent optimization of the present invention is mainly capable of optimizing the load distribution of the units while ensuring the maximization of energy efficiency and the minimization of operating costs, thus providing a highly efficient and economical operation scheme for the heating system; firstly, the present invention significantly enhances the global search capability of the trade-offs between objectives in the load distribution process through the intervention of intelligent algorithms, thus providing strong support for the formulation of operation strategies under complex and changeable operating environments; 2. Secondly, environmental emission control is also taken into consideration as an optimization target, which helps to reduce the impact of the heating system on the environment and meets the current social demand for green environmental protection. Through precise simulation and optimization calculation, the unit operation is more refined, energy waste is reduced, and environmental protection goals are achieved, while ensuring the maximization of economic benefits. In addition, the decision support system of the present invention combines the precise calculation of the algorithm with operating experience, which can greatly improve the ability to respond to market changes and make load distribution more adaptable and timely. This high degree of intelligence and adaptability makes the present invention have important application value and promotion prospects in the current and future energy supply fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a diagram of the economic load distribution method of the supercritical wet cooling heating unit of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] See also Figure 1 A method for economically distributing loads of a supercritical wet cooling and heating unit based on multi-objective intelligent optimization is provided, which is characterized in that it comprises the following steps: Establish a multi-objective optimization model including fuel cost, operation and maintenance cost and environmental emissions; Set constraints for unit operation, including power generation, heat supply, reheat steam volume, boiler feed water volume, etc.; Applying a multi-objective intelligent optimization algorithm to solve the optimization model to find the optimal trade-off solution among multiple objectives; Based on the solution results and the experience of operators, the final solution for economic load distribution is selected.

[0020] The multi-objective intelligent optimization algorithm is the multi-objective particle swarm optimization algorithm (MOPSO), that is, the fuel cost objective function f1=Σ(Ci*Pi), where Ci is the fuel unit price of the i-th unit and Pi is the power generation power of the i-th unit.

[0021] The fuel cost objective function of the multi-objective intelligent optimization model is defined as the weighted sum of the fuel unit price and the power generation capacity of each unit, which provides an accurate valuation of the fuel utility of individual units.

[0022] The operation and maintenance cost optimization objective function further considers the start and stop status and fixed operation and maintenance costs of each unit to accurately reflect the operation economy of the unit; The environmental emission objective function f3=Σ(Ei*Pi+Di), where Ei is the emission coefficient of the i-th unit, Pi is the power generation, and Di is the fixed emission.

[0023] The environmental emission objective function includes the product of the unit's pollution emission coefficient and the power generation capacity as well as the fixed emission volume to quantify the environmental impact and provide an evaluation index for environmental performance.

[0024] The solution results of the multi-objective intelligent optimization algorithm are analyzed through a decision support system, which takes into account the experience and opinions of operators and market dynamic information to assist in selecting the final solution.

[0025] The constraints further include the start and stop logic of the unit, thermal efficiency curve, safe operating limits of the equipment, etc., to ensure that the unit operates in a safe and stable state.

[0026] The multi-objective intelligent optimization algorithm further includes a dynamic adjustment mechanism for the weight of each objective function to reflect the changes in preferences for different objectives at different operating time periods.

[0027] The multi-objective intelligent optimization algorithm also takes into account random factors including changes in the external environment and uncertainty in market demand, and can adapt and adjust to the impact of these factors during the solution process.

[0028] The multi-objective intelligent optimization algorithm also includes an online monitoring module, which can collect and process unit operation data in real time and provide the latest input parameters for the optimization model.

[0029] Working principle: The load economic allocation method of supercritical wet cooling heating units based on multi-objective intelligent optimization proposed in the present invention is to achieve a more economical and efficient operation of the heating system. The working principle first involves establishing a multi-objective optimization model with fuel cost, operation and maintenance cost and environmental emissions as the core. The model comprehensively considers the operating data and cost information of different units. By setting a reasonable optimization objective function, it can accurately reflect the cost-effectiveness and environmental impact of each unit. On this basis, by setting necessary unit operation constraints, such as power generation, heat supply, reheat steam volume, and boiler feed water volume, the system is guaranteed to not only meet basic energy supply needs, but also operate under the premise of safety and technical feasibility. Afterwards, a multi-objective intelligent optimization algorithm, such as a multi-objective particle swarm optimization algorithm, is used to solve the optimization model. This algorithm can find the optimal or quasi-optimal solution among multiple objectives by simulating the flight and migration behavior of bird flocks, achieve an effective trade-off between objectives, and obtain a Pareto optimal solution set. Ultimately, based on the optimal solution set and the experience of operators combined with market dynamics, after analysis by the decision support system, the load economic distribution plan that best meets the current market and operational requirements can be selected. This process covers the combination of intelligent decision-making and human experience, aiming to provide a more flexible and adaptable operating strategy.

[0030] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0031] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization, which is characterized by comprising the following steps: Establish a multi-objective optimization model including fuel cost, operation and maintenance cost and environmental emissions; Set constraints for unit operation, including power generation, heat supply, reheat steam volume, boiler feed water volume, etc.; Applying a multi-objective intelligent optimization algorithm to solve the optimization model to find the optimal trade-off solution among multiple objectives; Based on the solution results and the experience of operators, the final solution for economic load distribution is selected.

2. According to claim 1, a method for economic load allocation of supercritical wet cooling heating units based on multi-objective intelligent optimization is characterized by: The multi-objective intelligent optimization algorithm is a multi-objective particle swarm optimization algorithm (MOPSO), that is, the fuel cost objective function f1=Σ(Ci*Pi), where Ci is the fuel unit price of the i-th unit, and Pi is the power generation power of the i-th unit.

3. The method for economic load allocation of supercritical wet cooling heating units based on multi-objective intelligent optimization according to claim 2 is characterized by: The fuel cost objective function of the multi-objective intelligent optimization model is defined as the weighted sum of the fuel unit price and the power generation capacity of each unit, which provides an accurate pricing of the fuel utility of individual units.

4. The method for economic load allocation of supercritical wet cooling heating units based on multi-objective intelligent optimization according to claim 3 is characterized by: The operation and maintenance cost optimization objective function further considers the start and stop status and fixed operation and maintenance cost of each unit, accurately reflecting the operation economy of the unit; The environmental emission objective function f3=Σ(Ei*Pi+Di), where Ei is the emission coefficient of the i-th unit, Pi is the power generation, and Di is the fixed emission.

5. The method for economic load allocation of supercritical wet cooling heating units based on multi-objective intelligent optimization according to claim 4 is characterized by: The environmental emission objective function includes the product of the unit pollution emission coefficient and the power generation power and the fixed emission amount, so as to quantify the environmental impact and provide an evaluation index of environmental performance.

6. The method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization according to claim 5 is characterized by: The solution of the multi-objective intelligent optimization algorithm is analyzed by a decision support system, which takes into account the experience and opinions of operators and market dynamic information to assist in selecting the final solution.

7. The method for economic load allocation of supercritical wet cooling heating units based on multi-objective intelligent optimization according to claim 6 is characterized by: The constraints further include the start and stop logic of the unit, thermal efficiency curve, safe operating limit of the equipment, etc., to ensure that the unit operates in a safe and stable state.

8. The method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization according to claim 7 is characterized by: The multi-objective intelligent optimization algorithm further includes a dynamic adjustment mechanism for the weight of each objective function to reflect the change in preference for different objectives at different operating time periods.

9. The method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization according to claim 8, characterized in that: The multi-objective intelligent optimization algorithm also takes into account random factors including changes in the external environment, uncertainty in market demand, etc., and can adapt to and adjust the impact of these factors during the solution process.

10. The method for economic load allocation of supercritical wet cooling and heating units based on multi-objective intelligent optimization according to claim 9, characterized in that: The multi-objective intelligent optimization algorithm also includes an online monitoring module, which can collect and process unit operation data in real time and provide the latest input parameters for the optimization model.

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