A method and system for coordinated regulation of multiple sources of renewable energy

Abstract

The present application relates to the technical field of data processing, and particularly relates to a multi-source coordinated regulation method and system of renewable energy. The method of the present application comprises: acquiring multi-dimensional data of a regional comprehensive energy system; determining energy grade coefficients of energy supply equipment and load demand according to the multi-dimensional data; constructing a supply-demand energy potential difference index with the energy grade coefficients; constructing a comprehensive energy utilization efficiency function with the supply-demand energy potential difference index and the economic operation cost of the energy supply equipment; and performing global optimization on the comprehensive energy utilization efficiency function by using a simulated annealing algorithm to obtain optimal scheduling variables. The method of the present application can avoid high energy and low configuration, and improve energy utilization.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for multi-source coordinated regulation of renewable energy. Background Technology

[0002] With the rapid development of distributed energy technologies, regional integrated energy systems, which include photovoltaic power generation units, gas turbines, waste heat recovery boilers, electric chillers, and electrochemical energy storage units, have been widely applied. These systems aim to achieve combined cooling, heating, and power supply within a region through the complementary and synergistic effects of multiple heterogeneous energy sources.

[0003] However, existing regulation of such multi-source energy systems mainly adopts a single-objective following strategy of "electricity-driven heat" or "heat-driven electricity," or optimization scheduling aimed at minimizing operating economic costs. For example, Chinese patent application CN113239607A discloses an economic scheduling optimization method, system, equipment, and storage medium for integrated energy systems. The above scheduling logic mainly follows the law of conservation of energy, focusing on the balance of energy quantity, that is, as long as the total power on the supply side equals the total power on the demand side.

[0004] However, this scheduling method, which only focuses on quantity balance, ignores the differences in energy quality / grade, easily leading to the problem of underutilization of high-energy resources. For example, under traditional scheduling methods, the system may use high-grade electricity or high-temperature exhaust gas from gas turbines to produce low-grade loads that only require 40°C domestic hot water. At this time, a large amount of low-grade waste heat in the system that is originally suited to this demand is idle or not prioritized for use. Although this satisfies the power demand on the user side, it results in a huge loss of work potential and reduces energy utilization efficiency.

[0005] Therefore, how to overcome the drawback of low energy utilization caused by neglecting the differences in energy quality in traditional dispatching methods is a technical problem that urgently needs to be solved. Summary of the Invention

[0006] To address the technical problem of high energy consumption and low utilization in traditional scheduling methods, this invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a multi-source coordinated regulation method for renewable energy, comprising: acquiring multi-dimensional data of a regional integrated energy system; determining energy quality coefficients of energy supply equipment and load demand based on the multi-dimensional data; constructing a supply-demand energy quality potential difference index using the energy quality coefficients; the supply-demand energy quality potential difference index characterizing the degree of matching between the energy quality coefficients of the energy supply equipment and the energy quality coefficients of the load demand; constructing a comprehensive energy quality utilization efficiency function using the supply-demand energy quality potential difference index and the economic operating cost of the energy supply equipment; and using a simulated annealing algorithm to globally optimize the comprehensive energy quality utilization efficiency function to obtain the optimal scheduling variables.

[0008] Beneficial effects: Compared with traditional methods that only focus on the balance of energy quantity, this invention constructs a comprehensive energy quality utilization efficiency function based on the energy quality difference index between supply and demand, and uses a simulated annealing algorithm for global optimization. Under the premise of meeting the needs of the user side, it can ensure the energy quality matching between the supply and demand sides, effectively reduce the downgrading of high-grade energy, and thus improve the effective energy utilization rate.

[0009] Furthermore, the multidimensional data includes the ambient reference temperature, the heat source temperature provided by the energy supply equipment, and the minimum inlet temperature required by each load process. Determining the energy quality coefficients of the energy supply equipment and load demand based on the multidimensional data includes: if the energy supply equipment provides electrical energy, the corresponding energy quality coefficient is 1; if the energy supply equipment provides heat energy, the energy quality coefficient of the energy supply equipment is calculated based on the ratio of the ambient reference temperature to the corresponding heat source temperature; and the energy quality coefficient of the load demand is calculated based on the ratio of the ambient reference temperature to the minimum inlet temperature required by the load process.

[0010] Furthermore, the supply-demand energy quality potential difference index constructed using the energy quality coefficient is as follows:

[0011] In the formula, Indicates the first The power supply equipment supplies the first The energy quality difference index between supply and demand when supplying energy to a given load. Indicates the first Energy grade coefficient of each energy supply device Indicates the first Energy grade coefficient of load demand, Indicates the first The power required by each load.

[0012] Beneficial effects: By calculating the difference in Carnot cycle efficiency, the quality difference between the power supply equipment and the load can be accurately assessed from a thermodynamic perspective; at the same time, power is introduced as a weight to take into account the influence of load scale, and the smoothing process of the square root function prevents high power values ​​from dominating the optimization process.

[0013] Furthermore, the comprehensive energy and quality utilization efficiency function for:

[0014] In the formula, Indicates the number of power supply devices. Indicates the number of load types. Indicates the first The economic cost of operating an individual power supply device Indicates the first The power supply equipment is allocated to the first The power of the load, This represents the energy-quality penalty weighting coefficient. Indicates the first The power supply equipment supplies the first The energy quality difference index between supply and demand when supplying energy to a given load.

[0015] Beneficial effects: By constructing this comprehensive energy quality utilization efficiency function, a nonlinear penalty can be imposed on the behavior of severe grade mismatch, forcing the optimization algorithm to prioritize finding energy with similar grades for matching during the solution process, thereby improving energy utilization efficiency while ensuring lower operating economic costs.

[0016] Furthermore, the simulated annealing algorithm is used to globally optimize the comprehensive energy utilization efficiency function, including: setting an initial simulation temperature, randomly generating an initial scheduling matrix that satisfies energy conservation and / or equipment selection priority rules as the current solution; based on the current solution, randomly selecting the output of the power supply equipment for fine-tuning to obtain a new solution; calculating the energy difference between the new solution and the current solution; if the energy difference is less than zero, accepting the new solution; if the energy difference is less than or equal to zero, calculating the acceptance probability according to the Metropolis criterion, and deciding whether to accept the new solution based on the acceptance probability.

[0017] Beneficial effects: By utilizing the probabilistic jump characteristics of the simulated annealing algorithm, the optimization algorithm is allowed to accept poor solutions with a certain probability, thereby escaping the trap of local optima and finally obtaining the globally optimal control strategy.

[0018] Furthermore, after deciding whether to accept the new solution, the method further includes: multiplying the current simulated annealing temperature by a preset cooling coefficient to obtain the updated simulated annealing temperature; determining whether the updated simulated annealing temperature is lower than a preset termination threshold; if not, returning to the step of generating a new solution until the updated simulated annealing temperature is lower than the termination threshold.

[0019] Furthermore, before performing global optimization, the method further includes: extracting load characteristic indicators from the multidimensional data to construct a feature vector; inputting the feature vector into a clustering algorithm for cluster analysis to identify the energy quality characteristic category of the load; and matching the corresponding equipment selection priority rules according to the energy quality characteristic category.

[0020] Beneficial effects: By clustering load characteristics and matching them with corresponding equipment selection priority rules, during regulation, highly volatile photovoltaic energy is preferentially matched to loads or energy storage units with regulation capabilities, while precision loads are powered by stable sources. This reduces frequent equipment adjustments and ineffective start-ups and shutdowns, thereby extending the service life of key equipment and improving the overall stability of the system.

[0021] Furthermore, the multidimensional data also includes the load's required power and required temperature, and the load characteristic indicators include energy quantity requirement, energy quality requirement, and tolerance to stability; extracting load characteristic indicators from the multidimensional data includes: taking the amplitude of the required power as the energy quantity requirement; calculating the ratio of the required temperature to the ambient reference temperature to obtain the energy quality requirement; and calculating the variance of the required power within a unit time window to obtain the tolerance to stability.

[0022] Furthermore, after acquiring the multidimensional data, the method further includes: preprocessing the multidimensional data.

[0023] In a second aspect, the present invention provides a multi-source coordinated control system for renewable energy, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the multi-source coordinated control method for renewable energy described in the first aspect is implemented. Attached Figure Description

[0024] Figure 1 This is a flowchart of the multi-source coordinated regulation method for renewable energy in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the comparative analysis of energy quality mismatch coefficients on both the supply and demand sides in an embodiment of the present invention. Figure 3 This is a schematic diagram of the cluster distribution of the load in an embodiment of the present invention; Figure 4 This is a schematic diagram comparing the percentage of overall system performance in an embodiment of the present invention; Figure 5 This is a structural block diagram of a multi-source coordinated control system for renewable energy in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0027] Figure 1 This is a flowchart of a multi-source coordinated regulation method for renewable energy in an embodiment of the present invention.

[0028] In a first aspect, the present invention provides a multi-source coordinated control method for renewable energy, applied to a regional integrated energy system comprising a photovoltaic power generation unit, a gas turbine, a waste heat recovery boiler, an electric chiller, and an electrochemical energy storage unit. Specifically, as... Figure 1 As shown, the method of the present invention includes the following steps.

[0029] S1. Obtain multi-dimensional data of the regional integrated energy system.

[0030] In this embodiment, the multidimensional data includes source-side data and load-side data. The source-side data primarily originates from the SCADA system (Supervisory and Control System) or energy management platform configured in the system, and includes: real-time predicted power of the photovoltaic power generation system, grid time-of-use electricity prices, real-time natural gas prices, heat source temperatures provided by the energy supply equipment, and environmental reference temperatures collected by outdoor weather stations, etc.

[0031] The load-side data mainly comes from the smart meters and heat meters at each user terminal, including the required power, required temperature, and specific process requirements of the medium temperature for loads such as electrical load, steam load, domestic hot water load, and cooling load.

[0032] Specifically, multi-dimensional data of the regional integrated energy system can be collected by smart meters and sensors deployed at various nodes in the target area.

[0033] In one embodiment, after collecting multidimensional data, the method of the present invention further includes: preprocessing the multidimensional data. Specifically, all temperature data are uniformly normalized in terms of temperature dimensions, for example, uniformly converted to thermodynamic temperature; similarly, other data that can be converted to uniform dimensions are also converted, for example, all power data are uniformly converted to kilowatts; for non-thermal energy sources, such as electrical energy, since it has the greatest potential for doing work, its equivalent thermodynamic temperature is set to an engineering limit value (set to 5000K in this embodiment) to reflect its high-grade characteristics and make it close to 1 when calculating the energy grade coefficient.

[0034] By standardizing multidimensional data, computational obstacles caused by different units of measurement are eliminated, computational boundary conditions are clarified, and the reliability and accuracy of subsequent calculations are ensured.

[0035] S2. Determine the energy quality coefficient of energy supply equipment and load demand based on multidimensional data.

[0036] The higher the energy quality coefficient of the energy supply equipment, the higher the quality of the energy supplied; the higher the energy quality coefficient of the load demand, the higher the quality of the energy demanded.

[0037] Specifically, for any energy supply device, if the energy supply device provides electrical energy, since electrical energy is 100% exercisable, the energy quality coefficient of the energy supply device is set to 1; if the energy supply device provides heat energy, the ratio of the ambient reference temperature to the heat source temperature provided by the energy supply device is calculated, and then the ratio is subtracted from 1 to obtain the energy quality coefficient of the energy supply device.

[0038] For any given load, calculate the ratio of the ambient reference temperature to the minimum inlet temperature required by the process for that load, and then subtract that ratio from 1 to obtain the energy grade coefficient required by that load.

[0039] By quantifying the energy grade of supply and demand, a calculation basis is provided for subsequently determining the differences in energy grade / quality between supply and demand.

[0040] S3. Construct a supply and demand energy quality potential difference index using the energy quality coefficient.

[0041] In this embodiment, the energy quality difference index represents the degree of matching between the energy quality coefficient of the energy supply equipment and the energy quality coefficient of the load demand. The larger the energy quality difference index, the more mismatched the energy quality of the supply and demand is, that is, the more serious the phenomenon of high energy being used in a low-grade manner or low energy being used in a high-grade manner.

[0042] In one embodiment, the constructed supply-demand energy quality potential difference index is:

[0043] In the formula, Indicates the first The power supply equipment supplies the first The energy quality difference index between supply and demand when supplying energy to a given load. Indicates the first Energy grade coefficient of each energy supply device Indicates the first Energy grade coefficient of load demand, Indicates the first The power required by each load.

[0044] As can be seen from the above formula, the construction of the supply and demand energy quality potential difference index does not simply rely on the difference in energy quality coefficients between the supply and demand sides, but also innovatively introduces the demand power of the load.

[0045] By introducing power variables as weights, the system can prioritize the energy quality matching of high-power loads during scheduling, avoiding high-energy underutilization of large-volume loads. At the same time, by using the nonlinear smoothing characteristics of the square root function to scale the power values, it can effectively prevent extreme high-power values ​​from having an excessively dominant influence on the objective function during subsequent global optimization.

[0046] S4. Construct a comprehensive energy quality utilization efficiency function based on the energy quality potential difference index between supply and demand and the economic operating cost of energy supply equipment.

[0047] The comprehensive energy and mass utilization efficiency function is the objective function of the subsequent optimization algorithm, which aims to find the balance point with the lowest economic cost and the least energy and mass loss.

[0048] In one embodiment, the constructed integrated energy and mass utilization efficiency function It can be:

[0049] In the formula, Indicates the number of power supply devices. Indicates the number of load types. Indicates the first Economic operating cost of an individual power supply device (unit: yuan). Indicates the first The power supply equipment is allocated to the first Power of a load (unit: kW) This represents the energy quality penalty weighting coefficient (unit: yuan / kW). Indicates the first The power supply equipment supplies the first The energy quality difference index between supply and demand when supplying energy to a given load.

[0050] Among them, the operating economic cost of energy supply equipment can be calculated by multiplying the fuel consumption by the unit price, and the energy quality penalty weight coefficient is a constant set according to the system's emphasis on energy-saving indicators.

[0051] By constructing this integrated energy and quality utilization efficiency function, a combination of economic efficiency and energy and quality matching between supply and demand is achieved. Simultaneously, by utilizing the explosive growth characteristic of the exponential function, the system is forced to proactively avoid energy and quality mismatch during scheduling, thereby maximizing the value of energy utilization.

[0052] For example, assume the current ambient reference temperature (25℃), there is a load J (domestic hot water demand) in the system, whose required temperature is... (45℃), required power .

[0053] Then the energy quality coefficient of the load J demand At this point, there are two available energy supply devices, A and B, in the system. A is the power grid, providing electrical energy; therefore, the energy quality coefficient of A is 1. B is a waste heat recovery device, providing heat source temperature... (60℃), then the energy grade coefficient of B is .

[0054] If energy A is used to supply energy to load J, then the corresponding energy quality difference index between supply and demand is... = If energy is supplied to load J using B, then the corresponding energy quality difference index between supply and demand is... , Much larger This indicates that there is a huge difference in grade / energy potential when heating water with electricity, resulting in energy waste.

[0055] Assumption If A and B each allocate 100kW of power to load J, then when using electric heating, the penalty term in the overall energy utilization efficiency function is: When using waste heat for heating, the penalty term in the comprehensive energy utilization efficiency function is: , Much larger It is evident that in subsequent optimization, the optimization algorithm tends to choose waste heat heating.

[0056] like Figure 2 As shown, Figure 2 This diagram illustrates the changes in the energy quality mismatch coefficient (i.e., the supply-demand energy quality potential difference index) between the method of this invention and existing methods over time. The dashed line represents the existing technology, whose supply-demand energy quality potential difference index is high and fluctuates dramatically, indicating a severe energy quality mismatch. The solid line represents the present invention, whose supply-demand energy quality potential difference index remains consistently low, indicating a better match between supply and demand energy quality. The filled area between the two curves represents the energy grade loss recovered by the present invention compared to the existing technology.

[0057] S5. The simulated annealing algorithm is used to perform global optimization of the comprehensive energy and quality utilization efficiency function to obtain the optimal scheduling variable.

[0058] Since the objective function includes an exponential term and discrete constraints for device start-up and shutdown, it is a non-convex nonlinear optimization problem, and conventional gradient algorithms are prone to getting trapped in local optima. To address this, this invention employs a simulated annealing algorithm to perform global optimization by integrating the energy-mass utilization efficiency function.

[0059] Specifically, the global optimization of the comprehensive energy and quality utilization efficiency function using the simulated annealing algorithm includes the following steps.

[0060] S51. Set the initial simulation temperature, and randomly generate a set of initial scheduling matrices that satisfy energy conservation and / or device selection priority rules as the current solution (containing all...). (value).

[0061] It is understood that an initial scheduling matrix that satisfies the energy conservation constraint can be randomly generated as the current solution, or an initial scheduling matrix that simultaneously satisfies energy conservation and the device selection priority rule constraint can be randomly generated as the current solution. Those skilled in the art can set it according to actual needs.

[0062] In one embodiment, a method for determining the device selection priority rule for each load includes the following steps.

[0063] Specifically, historical multidimensional data is cleaned to extract load characteristic indicators, which are then used to construct feature vectors. These load characteristic indicators include energy quantity demand, energy quality demand, and tolerance to stability. In one embodiment, the feature vector construction method includes: using the amplitude of demanded power as the energy quantity demand; calculating the ratio of demanded temperature to ambient reference temperature to obtain the energy quality demand; calculating the variance of load power within a unit time window to obtain the tolerance to stability; and normalizing and concatenating the energy quantity demand, energy quality demand, and tolerance to stability to obtain the feature vectors for each load.

[0064] Furthermore, based on the feature vectors of each load, a clustering algorithm is used to divide all loads into K typical energy quality characteristic categories. In one embodiment, the clustering algorithm is the K-means algorithm, with K set to 3. The categories include high-grade sensitive types (such as electricity for precision equipment), low-grade inert types (such as domestic hot water), and medium-grade fluctuating types (such as industrial drying processes). Since the K-means algorithm is existing technology, it will not be described in detail here.

[0065] like Figure 2 As shown, Figure 2 The clustering results for each load are shown. Among them, the circular data points (loads) belong to the high-grade sensitive type, the square data points belong to the low-grade inert type, and the triangular data points belong to the medium-grade fluctuating type.

[0066] In real-time scheduling, the system constructs a feature vector of the current load based on the collected multidimensional data, and then inputs the feature vector into the clustering algorithm mentioned above to obtain the energy quality characteristic category of the current load.

[0067] Furthermore, based on the preset load-equipment selection priority rules, a corresponding equipment selection priority rule is matched for each load. In one embodiment, the equipment selection priority rule for high-grade sensitive loads is: priority is given to matching gas turbines to ensure high-grade and stable power supply; the equipment selection priority rule for low-grade inert loads is: priority is given to matching waste heat recovery boilers, followed by energy storage units, to maximize the utilization of low-grade waste heat in the system and reduce costs; the equipment selection priority rule for medium-grade fluctuating loads is: priority is given to matching photovoltaic power generation units, followed by electric chillers.

[0068] By constructing multidimensional feature vectors of the loads and performing cluster analysis, the differences in energy quality and stability among different loads can be effectively identified. Based on this, by using equipment selection priority rules as constraints for obtaining the current solution, the invalid search space can be reduced, the convergence speed of the algorithm can be improved, and the final output of the algorithm can be fundamentally ensured to meet the energy quality and stability matching of both supply and demand sides, further avoiding the energy quality mismatch problem of high energy used underutilized or low energy used overutilized.

[0069] S52. Based on the current solution, randomly select the output of a power supply device for further processing. Fine-tuning the (power change) and adjusting the output of the energy storage unit or the power grid according to the power balance principle yields a new solution.

[0070] S53. Substitute the new solution into the above comprehensive energy and mass utilization efficiency function to obtain the objective function value of the new solution; subtract the objective function value of the current solution from the objective function value of the new solution to obtain the energy difference between the new solution and the current solution.

[0071] S54. Determine if the energy difference is less than zero. If yes, it indicates that the new solution is better, and the new solution is accepted. If no, it indicates that the new solution is worse. Calculate the acceptance probability according to the Metropolis criterion, and generate a random number between 0 and 1. Determine if the random number is less than the acceptance probability. If yes, the new solution is accepted; if no, it is rejected.

[0072] In one embodiment, the probability of receiving The calculation expression is:

[0073] In the formula, This represents the energy difference between the new solution and the current solution. Represents the Boltzmann constant. This indicates the current simulated annealing temperature.

[0074] This mechanism allows the algorithm to accept poor solutions in the early stages, thus escaping the trap of local optima.

[0075] S55. Multiply the current simulated annealing temperature by a preset cooling coefficient to obtain the updated simulated annealing temperature; determine whether the updated simulated annealing temperature is lower than a preset termination threshold. If not, return to step S52 until the updated simulated annealing temperature is lower than the termination threshold, ultimately obtaining an optimal solution, i.e., the optimal scheduling variable. The cooling coefficient can be set to 0.98.

[0076] By using the global optimization mechanism of the simulated annealing algorithm, the shortcomings of traditional linear programming in handling non-convex optimization problems are overcome. It can find the optimal control strategy that balances economy and energy quality matching in a complex solution space with multiple variables and strong constraints.

[0077] Furthermore, the optimal scheduling variables are parsed into control commands for each power supply device, such as valve opening degree and converter power setpoint, and then sent to the PLC for execution.

[0078] like Figure 4 As shown, Figure 4 The diagram illustrates the variation in energy efficiency percentage between the control method of this invention and existing control methods over a 24-hour period. The dashed line represents the existing control method, and the solid line represents the control method of this invention. Figure 4 It can be seen that the curve corresponding to the existing control method is always below the curve corresponding to the control method of the present invention, indicating that the present invention has a higher effective energy utilization rate; and, through Figure 4 The double-headed arrows clearly show a significant difference in energy efficiency percentage between the two methods, indicating that the control method of the present invention significantly improves the effective energy utilization rate compared to existing control methods.

[0079] Figure 5 This is a structural block diagram of a multi-source coordinated control system for renewable energy in an embodiment of the present invention.

[0080] In a second aspect, the present invention also provides a multi-source coordinated control system for renewable energy. For example... Figure 5 As shown, the system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the multi-source coordinated control method for renewable energy as described in the first aspect of the present invention.

[0081] The system also includes other components well known to those skilled in the art, such as communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0082] In the description of this specification, the steps of the above method are only for clarity of description. In implementation, they can be combined into one step or some steps can be split into multiple steps, as long as they include the same logical relationship. Furthermore, in all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations.

[0083] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A multi-source coordinated regulation method for renewable energy, characterized in that, include: Acquire multidimensional data of the regional integrated energy system; The energy quality coefficients of the energy supply equipment and load demand are determined based on the multidimensional data. The energy quality coefficient is used to construct a supply-demand energy quality potential difference index; the supply-demand energy quality potential difference index characterizes the degree of matching between the energy quality coefficient of the energy supply equipment and the energy quality coefficient of the load demand. A comprehensive energy quality utilization efficiency function is constructed based on the supply and demand energy quality potential difference index and the economic operating cost of energy supply equipment; The optimal scheduling variable is obtained by globally optimizing the comprehensive energy and quality utilization efficiency function using the simulated annealing algorithm.

2. The multi-source coordinated regulation method for renewable energy according to claim 1, characterized in that, The multidimensional data includes the ambient reference temperature, the heat source temperature provided by the power supply equipment, and the minimum inlet temperature required by each load process. The energy quality coefficients for energy supply equipment and load demand are determined based on the aforementioned multidimensional data, including: If the energy supply equipment provides electrical energy, the corresponding energy quality coefficient is 1; If the energy supply equipment provides heat energy, the energy quality coefficient of the energy supply equipment is calculated based on the ratio of the ambient reference temperature to the corresponding heat source temperature. The energy grade coefficient of the load demand is calculated based on the ratio of the ambient reference temperature to the minimum inlet temperature required by the load process.

3. The multi-source coordinated regulation method for renewable energy according to claim 1, characterized in that, The energy quality difference index constructed using the energy grade coefficient is as follows: In the formula, Indicates the first The power supply equipment supplies the first The energy quality difference index between supply and demand when supplying energy to a given load. Indicates the first Energy grade coefficient of each energy supply device Indicates the first Energy grade coefficient of load demand, Indicates the first The power required by each load.

4. The multi-source coordinated regulation method for renewable energy according to claim 1, characterized in that, The comprehensive energy utilization efficiency function for: In the formula, Indicates the number of power supply devices. Indicates the number of load types. Indicates the first The economic cost of operating an individual power supply device Indicates the first The power supply equipment is allocated to the first The power of the load, This represents the energy-quality penalty weighting coefficient. Indicates the first The power supply equipment supplies the first The energy quality difference index between supply and demand when supplying energy to a given load.

5. The multi-source coordinated regulation method for renewable energy according to claim 1 or 2, characterized in that, The simulated annealing algorithm is used to globally optimize the comprehensive energy and mass utilization efficiency function, including: Set an initial simulation temperature, and randomly generate an initial scheduling matrix that satisfies the constraints of energy conservation and / or device selection priority rules as the current solution; Based on the current solution, the output of the power supply equipment is randomly selected and fine-tuned to obtain a new solution; Calculate the energy difference between the new solution and the current solution; If the energy difference is less than zero, the new solution is accepted; if the energy difference is less than or equal to zero, the acceptance probability is calculated according to the Metropolis criterion, and the decision on whether to accept the new solution is made based on the acceptance probability.

6. The multi-source coordinated regulation method for renewable energy according to claim 5, characterized in that, After deciding whether to accept the new solution, the method further includes: Multiply the current simulated annealing temperature by the preset cooling coefficient to obtain the updated simulated annealing temperature; Determine whether the updated simulated annealing temperature is lower than the preset termination threshold. If not, return to the step of generating a new solution until the updated simulated annealing temperature is lower than the termination threshold.

7. The multi-source coordinated regulation method for renewable energy according to claim 5, characterized in that, Before performing global optimization, the method further includes: Load characteristic indicators are extracted from the multidimensional data to construct a feature vector; The feature vectors are input into a clustering algorithm for cluster analysis to identify the energy quality characteristics of the load. The selection priority rules are matched according to the energy quality characteristics category.

8. The multi-source coordinated regulation method for renewable energy according to claim 7, characterized in that, The multidimensional data also includes the load's required power and required temperature, and the load characteristic indicators include energy quantity requirements, energy quality requirements, and tolerance to stability. The load characteristic indicators are extracted from the multidimensional data, including: The magnitude of the required power is taken as the energy quantity requirement; The energy quality requirement is obtained by calculating the ratio of the required temperature to the ambient reference temperature. Calculate the variance of the required power within a unit time window to obtain the tolerance for stability.

9. The multi-source coordinated regulation method for renewable energy according to claim 1, characterized in that, After acquiring the multidimensional data, the method further includes: preprocessing the multidimensional data.

10. A multi-source coordinated control system for renewable energy, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the multi-source coordinated control method for renewable energy as described in any one of claims 1-9 is implemented.

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