Distributed control system based on cloud computing

Abstract

The invention discloses a distributed control system based on cloud computing, which relates to the technical field of energy management and comprises an acquisition module, an analysis module, a prediction module and an output module. And dynamically adjusting analysis weights of the carbon emission intensity, the clean energy utilization rate and the price fluctuation parameters according to the collaborative coefficient deviation, and constructing a stepped carbon emission factor association rule and optimizing an objective function according to prediction to output regulation and control parameters. According to the method, dynamic balance of carbon emission reduction, clean energy utilization and cost control is realized through stepped carbon emission factor association rules and dynamic weight regulation and control and focusing on low-carbon emission reduction and cost optimization, an energy system is promoted to be transformed to low-carbon high-efficiency, intelligent collaborative optimization of a carbon emission reduction target and energy cost control is effectively promoted, and the energy utilization efficiency is improved. And the economical efficiency of energy system operation and the regulation and control efficiency of carbon emission reduction are obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and particularly relates to a cloud computing-based distributed control system. BACKGROUND

[0002] In recent years, under the background of global energy conservation and emission reduction, the high energy-consuming industry urgently needs carbon efficiency optimization, and the distributed control system plays a prominent role. The power and chemical industries are the key sources of carbon emissions and face great pressure to reduce emissions. Early industrial control was extensive, and the tightening of environmental protection regulations has promoted the distributed control system to expand from simple automation control to carbon efficiency optimization. At present, it is a trend to use distributed control systems and Internet of Things, cloud computing, and other technologies to tap energy-saving and carbon-reducing opportunities.

[0003] At present, the Chinese invention with the application number CN201910952260.1 discloses an optimization energy-saving and carbon-reducing method for a DCS system for papermaking. The DCS control system is used to reasonably control the pulp concentration, beating current, optimal pulp concentration of the disc mill, core layer ratio, surface layer and bottom layer ratio according to the raw material type, product performance characteristics and paper machine speed, etc. Then, the DCS controls different pulp power distribution and disc mill series connection modes. The results after pulp beating are comprehensively analyzed, and relevant data are obtained. The data are fed back to the DCS control for comprehensive processing to obtain optimized data. Finally, the DCS adjusts according to the optimized data to control different pulp power distribution and disc mill series connection modes. Although the optimization and energy-saving and carbon-reducing of the DCS system for papermaking can be realized, the related technology cannot realize the dynamic balance of clean energy and carbon emissions by using the synergy coefficient, realize the quantitative synergy of environmental protection goals and energy efficiency, cannot realize the reduction of cost by using the price signal by using the regulation and control logic of the energy price fluctuation advantage, and lacks completeness. SUMMARY

[0004] The technical problem solved by the present application is that the related technology cannot realize the dynamic balance of clean energy and carbon emissions by using the synergy coefficient, realize the quantitative synergy of environmental protection goals and energy efficiency, cannot realize the reduction of cost by using the price signal by using the regulation and control logic of the energy price fluctuation trend, and lacks completeness.

[0005] To solve the above technical problems, the present application provides the following technical solutions: The cloud computing-based distributed control system comprises a collection module, an analysis module, a prediction module and an output module: The collection module is used to receive collected energy data and calculate a synergy coefficient through the energy data; The analysis module is used to calculate a synergy coefficient deviation, a carbon emission intensity parameter, a clean energy utilization rate parameter and an energy price fluctuation parameter through the synergy coefficient; The prediction module is configured to predict an energy price fluctuation trend in a future preset time period based on the synergy coefficient and the energy price fluctuation parameter, and establish a stepwise carbon emission factor correlation rule. The output module is configured to construct an optimization objective function by using the synergy coefficient deviation, the carbon emission intensity parameter, the clean energy utilization rate parameter and the energy price fluctuation parameter, and output an energy regulation parameter by optimizing the optimization objective function and the stepwise carbon emission factor correlation rule.

[0006] As a preferred scheme of the cloud computing-based distributed control system, the collection module comprises a sequence collection unit and a sequence calculation unit. The sequence collection unit is configured to receive collected energy data, the energy data comprising carbon emission data, energy consumption data, energy price data and production output value data. The carbon emission data comprises carbon emission amounts of various fossil energy combustion, direct carbon emission amounts of production processes, real-time monitoring data of carbon emission concentration and implicit carbon emission amounts of purchased energy. The energy consumption data comprises fossil energy consumption amounts, clean energy consumption amounts, energy conversion link loss data and energy distribution data of various production links. The energy price data comprises real-time purchase prices of various energy, energy transportation and storage costs, carbon-related derivative costs and energy price historical fluctuation data. The production output value data comprises production output values in unit time and energy consumption ratios of the production output values. The sequence calculation unit is configured to calculate a synergy coefficient, the calculation expression of which is as follows: K current =α×(E clean / E total )+β×(1-E carbon / E standard ) Wherein, K current is the synergy coefficient, E clean is the clean energy consumption amount, E total is the total energy consumption amount, E carbon is the actual carbon emission amount, E standard is a carbon emission reference value, and α and β are weight coefficients, and α+β=1. The total energy consumption amount is the sum of the fossil energy consumption amount and the clean energy consumption amount.

[0007] As a preferred scheme of the cloud computing-based distributed control system, the analysis module comprises a deviation calculation unit and a parameter analysis unit. The deviation calculation unit is configured to calculate a synergy coefficient deviation, the calculation expression of which is as follows: ΔK=K currentK target ; wherein, ΔK is the deviation of the synergy coefficient, K current is the synergy coefficient, K target is the preset target synergy coefficient, the target synergy coefficient is set based on the carbon emission reduction target and the clean energy utilization planning; The parameter analysis unit is configured to perform time series analysis on the energy price data in the historical period, calculate the ratio of the standard deviation to the mean of the energy price data in the historical period, obtain the energy price fluctuation trend parameter and the price fluctuation rate, and calculate the carbon emission intensity parameter CI, the clean energy utilization rate parameter CE, and the energy price fluctuation parameter PR by the energy price fluctuation trend parameter, the price fluctuation rate, and the energy data.

[0008] As a preferred scheme of the cloud computing-based distributed control system, the carbon emission intensity parameter CI is used to obtain by calculating the proportional relationship between the actual carbon emission amount and the production output data; The clean energy utilization rate parameter CE is used to obtain by calculating the proportional relationship between the clean energy consumption amount and the clean energy supplyable amount, the clean energy supplyable amount being the maximum amount of clean energy that can be absorbed by the system; The energy price fluctuation parameter PR is used to obtain by multiplying the price fluctuation rate by the corresponding planned purchase amount and then performing accumulation calculation, and the calculation expression is: PR=Σ(V i ×P i ); wherein, V i is the price fluctuation rate of the i-th type of energy, and P i is the planned purchase amount of the i-th type of energy, the planned purchase amount being the preset purchase scale of the i-th type of energy; Based on the numerical range of the synergy coefficient deviation ΔK, the analysis weights of the carbon emission intensity parameter CI, the clean energy utilization rate parameter CE, and the energy price fluctuation parameter PR are adjusted, including: When ΔK<0, the carbon emission intensity parameter analysis weight ω1 and the clean energy utilization rate parameter analysis weight ω2 are increased; When ΔK≥0, the energy price fluctuation parameter analysis weight ω3 is increased.

[0009] As a preferred scheme of the cloud computing-based distributed control system, the prediction module includes a parameter prediction unit and a rule construction unit; The parameter prediction unit includes energy price trend prediction and synergy coefficient prediction; The energy price trend prediction analyzes historical energy price fluctuation data, inputs price fluctuation rates and macroeconomic indicators in a historical period, predicts various energy price fluctuation trends in a future preset time period, and outputs a price fluctuation rate prediction value, which is used to correct the energy price fluctuation parameter PR. The synergy coefficient prediction is used to predict a synergy coefficient change trend in a future preset time period according to historical synergy coefficient, stepwise carbon emission factor, and alpha weight dynamic adjustment records, and output a synergy coefficient prediction value, which is used to correct the synergy coefficient deviation ΔK. The rule construction unit is used to divide clean energy into three steps to obtain stepwise carbon emission factors, and construct stepwise carbon emission factor association rules according to the stepwise carbon emission factors.

[0010] As a preferred scheme of the cloud computing-based distributed control system, the stepwise carbon emission factor includes first-step energy, second-step energy, and third-step energy. The first-step energy is zero-carbon energy, the first-step energy consumption is E1, the carbon emission factor f1, f1=0tCO2 / MWh. The second-step energy is low-carbon energy, the second-step energy consumption is E2, the carbon emission factor f2, 0tCO2 / MWh<f2≤0.2tCO2 / MWh. The third-step energy is low-carbon fossil energy, the third-step energy consumption is E3, the carbon emission factor f3, 0.2tCO2 / MWh<f3≤0.5tCO2 / MWh.

[0011] As a preferred scheme of the cloud computing-based distributed control system, the stepwise carbon emission factor association rule includes a first association rule and a second association rule. The first association rule is an association rule between the stepwise carbon emission factor and the synergy coefficient, specifically: the first-step energy is allocated the highest basic alpha weight, the second-step energy is allocated the second-highest basic alpha weight, and the third-step energy is allocated the lowest basic alpha weight. When the synergy coefficient deviation ΔK<0, the alpha weight of the first-step energy is temporarily increased by 10% on the basis value, the alpha weights of the second-step energy and the third-step energy remain unchanged, and the corrected alpha weight is substituted into the synergy coefficient calculation formula. The second association rule is an association rule between the stepwise carbon emission factor and the optimization objective function, specifically: The corresponding carbon emission factor of the energy belonging to the ladder is matched, the first ladder energy consumption, the second ladder energy consumption, the third ladder energy consumption and the high-carbon energy consumption are multiplied by the carbon emission factor corresponding to the ladder to which the energy belongs respectively, and the product is obtained, and the sum obtained by adding the product is the actual carbon emission amount, the corrected carbon emission intensity parameter CI' is obtained through the actual carbon emission amount, and the corrected carbon emission intensity parameter CI' is input as a variable into an optimization objective function.

[0012] As a preferred scheme of the cloud computing-based distributed control system, the output module comprises a function optimization unit and a parameter output unit. The function optimization unit is used to form an optimization objective function based on the carbon emission intensity parameter, the clean energy utilization rate parameter, the energy price fluctuation parameter and the synergy coefficient deviation, and the calculation expression is: MinF=ω1×CI'+ω2×(1-CE)+ω3×PR+ω4×max(0,-ΔK)(ω1+ω2+ω3+ω4=1) Wherein ω4 is the synergy coefficient deviation weight, the synergy coefficient deviation weight is determined based on the analysis of the influence of the synergy coefficient deviation on the overall optimization objective of the system, when ΔK<0, it means that the synergy state deviates greatly, and the synergy coefficient deviation weight needs to be increased for priority correction; The optimization objective function is corrected according to the ladder carbon emission factor to obtain the energy regulation parameter; The parameter output unit is used to output the energy regulation parameter, comprising: When ΔK<0: If the first ladder energy price shows a downward trend, the first ladder energy supply increase parameter is output, the increase is positively correlated with |ΔK|, and the upper limit of the increase is 50% of the first ladder energy supply; If the high-carbon energy price shows an upward trend, the high-carbon energy consumption decrease parameter is output, the decrease is min(|ΔK|×1.2,1), that is, the maximum decrease does not exceed 100% of the high-carbon energy consumption, and the high-carbon energy is the energy with a carbon emission factor fhigh>0.5tCO2 / MWh; When ΔK≥0: Based on the energy price fluctuation parameter PR, the low-cost energy preferential procurement parameter is output, the clean energy proportion is adjusted, and the synergy coefficient is maintained within the interval [K target ,K target +δ], wherein δ is a dynamic threshold, which is set to 0.05-0.15.

[0013] As a preferred embodiment of the distributed control system based on cloud computing described in this invention, the energy regulation parameters are obtained by modifying the optimization objective function according to the tiered carbon emission factor as follows: For the first-stage energy, add E1≥0.2×E to the optimization objective function. total The constraint term applies a penalty coefficient of 1.2 to the (1-CE) term when E1 is below this value; For the third-tier energy, when E3×f3>E standard When the percentage is 30%, a penalty coefficient of 1.5 is applied to the PR term; Energy regulation parameters are obtained by solving the constrained optimization objective function.

[0014] As a preferred embodiment of the distributed control system based on cloud computing described in this invention, the output energy regulation parameters are compared with preset constraints, which include total available energy, carbon emission limits, and production load requirements. If the energy control parameters do not meet the constraints, they will be reallocated according to tiered priority, where the tiered priority is the first tier of energy, the second tier of energy, and the third tier of energy. When the available energy supply of the first tier is less than the production load demand, priority is given to ensuring the basic production load, and any shortfall is supplemented starting from the first tier of energy supply in tiered order. When the available energy supply of the first tier is greater than or equal to the production load demand, priority is given to ensuring the consumption of the first tier energy, and the remaining load is allocated to the second tier energy in tier order.

[0015] The beneficial effects of this invention are as follows: The acquisition module receives data on carbon emissions and energy consumption, calculates the synergy coefficient integrating the proportion of clean energy and the carbon emission compliance rate, and accurately quantifies the comprehensive performance of the energy system in terms of low carbon and efficiency. The analysis module calculates the synergy coefficient deviation and dynamically adjusts the analysis weights of carbon emission intensity, clean energy utilization rate, and price fluctuation parameters to achieve flexible adaptation to multiple objectives. The prediction module combines historical data to predict energy price trends, constructs a tiered carbon emission factor correlation rule, and forms a tiered differentiated regulation according to zero carbon and low carbon, guiding energy structure optimization through dynamic weight allocation. The output module optimizes the objective function and outputs energy regulation parameters based on the synergy deviation state, ensuring production load while promoting the reduction of high-carbon energy and the increase of clean energy, and reducing procurement costs through price fluctuation prediction, ultimately achieving a dynamic balance between carbon emission reduction and economic feasibility. Attached Figure Description

[0016] Figure 1 This is a basic flowchart of a cloud computing-based distributed control system provided in one embodiment of the present invention. Detailed Implementation

[0017] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all embodiments.

[0018] Embodiments, with reference to Figure 1 For an embodiment of the present application, a cloud computing-based distributed control system is provided, comprising a collection module, an analysis module, a prediction module and an output module: The collection module is used to receive collected energy data and calculate a synergy coefficient through the energy data; The analysis module is used to calculate a synergy coefficient deviation, a carbon emission intensity parameter, a clean energy utilization rate parameter and an energy price fluctuation parameter through the synergy coefficient; The prediction module is used to predict the energy price fluctuation trend in a future preset time period based on the synergy coefficient and the energy price fluctuation parameter, and establish a stepwise carbon emission factor correlation rule; The output module is used to construct an optimization objective function using the synergy coefficient deviation, the carbon emission intensity parameter, the clean energy utilization rate parameter and the energy price fluctuation parameter, and output energy regulation parameters through the optimization objective function and the stepwise carbon emission factor correlation rule.

[0019] The collection module comprises a sequence collection unit and a sequence calculation unit; The sequence collection unit is used to receive collected energy data, which includes carbon emission data, energy consumption data, energy price data and production output value data; The carbon emission data includes carbon emissions from burning various fossil fuels, direct carbon emissions from production processes, real-time monitoring data of carbon emission concentration, and implicit carbon emissions from purchased energy; The energy consumption data includes fossil energy consumption, clean energy consumption, energy conversion link loss data, and energy distribution data for each production link; The energy price data includes real-time purchase prices of various types of energy, energy transportation and storage costs, carbon-related derivative costs, and energy price historical fluctuation data; The production output value data includes production output value per unit of time and output value energy consumption ratio; The sequence calculation unit is used to calculate the synergy coefficient, and the calculation expression is: K current =α×(E clean / E total )+β×(1-E carbon / E standard ) Wherein, K current is the synergy coefficient, E clean is the clean energy consumption, E totalE is the total energy consumption carbon E is the actual carbon emission standard E is the total energy consumption, and a and β are weight coefficients, and a + β = 1. The total energy consumption is the sum of the fossil energy consumption and the clean energy consumption.

[0020] In specific embodiments, the panoramic capture of the system operation state is realized by comprehensively receiving and integrating multiple types of energy data. The carbon emission data covers all-chain carbon emission sources such as fossil energy combustion, direct emission in production process, real-time concentration monitoring, and implicit emission of purchased energy. The energy consumption data can clearly present the energy flow direction and utilization efficiency. The energy price data quantifies economic influencing factors. The production value data establishes the correlation between energy consumption and production value. And the dispersed data is converted into a unified coordination coefficient through a specific formula. The coefficient integrates the two core dimensions of clean energy proportion and carbon emission compliance rate. The importance of the two dimensions is dynamically balanced through the weight coefficients a and β. The comprehensive coordination state of the system under the clean energy utilization and carbon emission reduction target is directly quantified, providing a standardized evaluation basis for the analysis, prediction and regulation of subsequent modules.

[0021] The analysis module includes a deviation calculation unit and a parameter analysis unit. The deviation calculation unit is used to calculate the coordination coefficient deviation, and the calculation expression is: ΔK = K current - K target ; Where ΔK is the coordination coefficient deviation, K current is the coordination coefficient, K target is the preset target coordination coefficient, which is set based on the carbon emission reduction target and the clean energy utilization plan; The parameter analysis unit is used to perform time series analysis on the energy price data in the historical period, calculate the ratio of the standard deviation to the mean of the energy price data in the historical period, obtain the energy price fluctuation trend parameter and the price fluctuation rate, and calculate the carbon emission intensity parameter CI, the clean energy utilization rate parameter CE, and the energy price fluctuation parameter PR through the energy price fluctuation trend parameter, the price fluctuation rate, and the energy data.

[0022] In specific embodiments, the deviation between the current energy system state and the target is quantitatively analyzed, and the key parameter characteristics are mined combined with historical data, to provide accurate basis for subsequent regulation. For example, a factory sets the target coordination coefficient to 0.7, and the current coordination coefficient is calculated to be 0.5. Then the deviation calculation unit obtains ΔK = 0.5-0.7 =-0.2, indicating that the current state of the system is not up to standard, and low-carbon measures need to be strengthened.

[0023] The carbon emission intensity parameter CI is used to obtain the proportional relationship between the actual carbon emission and the production value data. A clean energy utilization rate parameter CE is used to calculate the ratio between the clean energy consumption and the clean energy supplyable amount, which is the maximum amount of clean energy that can be accommodated by the system; An energy price fluctuation parameter PR is used to calculate the sum of the product of the price fluctuation rate and the corresponding planned purchase amount, and the calculation expression is: PR=∑(Vi i ×P i ); Wherein Vi i is the price fluctuation rate of the i-th energy, and Pi i is the planned purchase amount of the i-th energy, which is the preset purchase scale for the i-th energy; Based on the numerical range of the synergy coefficient deviation ΔK, the analysis weights of the carbon emission intensity parameter CI, the clean energy utilization rate parameter CE and the energy price fluctuation parameter PR are adjusted, including: When ΔK<0, the analysis weight ω1 of the carbon emission intensity parameter and the analysis weight ω2 of the clean energy utilization rate parameter are increased; When ΔK≥0, the analysis weight ω3 of the energy price fluctuation parameter is increased.

[0024] In specific embodiments, the low-carbon nature, clean energy utilization efficiency and cost risk of the energy system are evaluated by quantifying the three key parameters (CI, CE, PR), and the importance of the parameters is dynamically adjusted according to the synergy state, for example, the carbon emission intensity parameter is 50 tons of carbon dioxide per 1 million yuan of output value, the clean energy supplyable amount is 100 million kWh per month, the actual consumption is 60 million kWh, so the clean energy utilization rate parameter CE=60%; The planned purchase power (Vi i =8%) is 50 million kWh, the natural gas (Vi=5%) is 30 million m³, and the energy price fluctuation parameter PR=50×8%+30×5%=5.5 (ten thousand yuan). If the target synergy coefficient is 0.8 and the current synergy coefficient is 0.6, ΔK=-0.2 (<0), which means that the low-carbon and clean energy synergy state is not up to standard, at this time ω1 and ω2 are increased, and the carbon emission intensity and the clean energy utilization rate are preferentially focused on; If the synergy coefficient is =0.9, ΔK=0.1 (≥0), then ω3 is increased, and the cost risk is reduced by optimizing the purchase strategy. This dynamic weight mechanism accurately focuses on the core optimization target in different scenarios, and balances the low-carbon development and economic feasibility.

[0025] The prediction module includes a parameter prediction unit and a rule construction unit; The parameter prediction unit includes energy price trend prediction and synergy coefficient prediction; The energy price trend prediction analyzes historical energy price fluctuation data, inputs price fluctuation rate and macroeconomic indicators in a historical period, predicts various energy price fluctuation trends in a future preset time period, and outputs a price fluctuation rate prediction value, which is used to correct the energy price fluctuation parameter PR. The synergy coefficient prediction is used to predict the change trend of the synergy coefficient in a future preset time period according to the synergy coefficient, the stepwise carbon emission factor, and the dynamic adjustment record of the a weight in the historical period, and output a synergy coefficient prediction value, which is used to correct the synergy coefficient deviation ΔK. The rule construction unit is used to divide clean energy into three steps to obtain a stepwise carbon emission factor, and construct a stepwise carbon emission factor association rule according to the stepwise carbon emission factor.

[0026] In specific embodiments, through forward-looking analysis and rule setting, the system provides prediction ability, improving the timeliness and accuracy of optimization. The parameter prediction unit analyzes historical data and macro factors to predict the change trend of energy prices and synergy status in advance, providing a basis for parameter correction. For example, when predicting that the winter power price fluctuation rate will rise from 5% to 15% in the future, the predicted value can be used to correct the energy price fluctuation parameter PR, so that the system can avoid high price risks in advance when formulating procurement plans. If it is predicted that the synergy coefficient will drop from 0.82 to 0.78 (lower than the target value 0.8) next month, the predicted value can be used to correct the synergy coefficient deviation ΔK, so that the system can start low-carbon regulation measures in advance. The rule construction unit establishes a clear regulation direction through step division, ensuring that the prediction results can be effectively converted into specific strategies. For example, after setting photovoltaic (zero carbon) as the first step and natural gas (low carbon) as the second step, when the predicted synergy coefficient is about to be substandard, the system can automatically increase the a weight of photovoltaic according to the association rule, preferentially increasing the procurement amount of photovoltaic, locking clean energy supply before price fluctuation, which not only guarantees the carbon reduction target, but also controls cost fluctuation.

[0027] The stepwise carbon emission factor includes a first step energy, a second step energy, and a third step energy: The first step energy is zero-carbon energy, the first step energy consumption is E1, and the carbon emission factor f1, f1=0tCO2 / MWh; The second step energy is low-carbon energy, the second step energy consumption is E2, and the carbon emission factor f2, 0tCO2 / MWh<f2≤0.2tCO2 / MWh; The third step energy is low-carbon fossil energy, the third step energy consumption is E3, and the carbon emission factor f3, 0.2tCO2 / MWh<f3≤0.5tCO2 / MWh.

[0028] In specific embodiments, the stepped carbon emission factor classifies the low-carbon properties of energy to provide clear low-carbon orientation standards for the system, making the optimization of energy structure more operable, and providing quantitative basis for subsequent correlation rules and optimization targets. This classification method can accurately distinguish the carbon emission impact of different energies, and facilitate the system to develop targeted control strategies. For example, photovoltaic and wind power are classified into the first step, biomass energy is classified into the second step, and natural gas is classified into the third step. When the system needs to strengthen carbon emission reduction, the procurement amount of photovoltaic and wind power in the first step can be preferentially increased according to the priority of the steps, and the use proportion of natural gas in the third step is limited; when the cost needs to be balanced, the ratio of the second and third step energies can be flexibly adjusted on the premise of ensuring the basic use amount of the first step energy.

[0029] The stepped carbon emission factor correlation rule includes a first correlation rule and a second correlation rule. The first correlation rule is the correlation rule between the stepped carbon emission factor and the synergy coefficient, specifically: the first step energy is allocated the highest basic a weight, the second step energy is allocated the second highest basic a weight, and the third step energy is allocated the lowest basic a weight. When the synergy coefficient deviation ΔK < 0, the a weight of the first step energy is temporarily increased by 10% based on the basic value, and the a weights of the second step energy and the third step energy remain unchanged. The corrected a weight is substituted into the synergy coefficient calculation formula. The second correlation rule is the correlation rule between the stepped carbon emission factor and the optimization target function, specifically: Based on the matching of the energy belonging to the step and the corresponding carbon emission factor, the first step energy consumption, the second step energy consumption, the third step energy consumption, and the high-carbon energy consumption are multiplied by the carbon emission factor corresponding to the step to which the energy belongs, respectively, to obtain the multiplication result. The sum of the product results is the actual carbon emission amount. The corrected carbon emission intensity parameter CI' is obtained through the actual carbon emission amount, and the corrected carbon emission intensity parameter CI' is input as a variable into the optimization target function.

[0030] In specific embodiments, the stepwise carbon emission factor correlation rule guides the energy structure to tilt towards low carbonization through rule constraints, and the regulation strategy more accurately serves the carbon reduction and system optimization goals. The first correlation rule enhances the priority of zero-carbon energy through differentiated weight allocation. For example, set the first step photovoltaic base a weight to 0.6, the second step wind power to 0.3, and the third step natural gas to 0.1. When the synergy coefficient deviation ΔK=-0.15 (not up to standard), the photovoltaic a weight is temporarily increased to 0.66, and after substituting into the synergy coefficient formula, its contribution to the synergy coefficient is increased, which promotes the system to preferentially increase the use of photovoltaic power, and quickly improves the synergy state. The second correlation rule quantifies the influence of energy on carbon intensity through carbon emission factors, ensuring that the optimization objective function accurately reflects the actual carbon emission level. For example, in a certain period, photovoltaic power (E1=100 MWh, f1=0), wind power (E2=50 MWh, f2=0.1), natural gas (E3=30 MWh, f3=0.4), and high-carbon coal power (Ehigh=20 MWh, fhigh=0.8) are consumed, and the actual carbon emission is 100×0+50×0.1+30×0.4+20×0.8=0+5+12+16=33 tCO2. Based on this, the corrected CI' is calculated as the input variable of the optimization objective function, ensuring that the function accurately reflects the carbon emission influence of the energy structure and that subsequent regulation measures can effectively reduce carbon intensity.

[0031] The output module includes a function optimization unit and a parameter output unit. The function optimization unit is used to form an optimization objective function based on the carbon emission intensity parameter, the clean energy utilization rate parameter, the energy price fluctuation parameter, and the synergy coefficient deviation. The calculation expression is: MinF=ω1×CI'+ω2×(1-CE)+ω3×PR+ω4×max(0,-ΔK) (ω1+ω2+ω3+ω4=1) Where ω4 is the synergy coefficient deviation weight, which is determined based on the influence of the synergy coefficient deviation on the overall optimization objective of the system. When ΔK<0, it indicates that the synergy state deviates greatly, and the synergy coefficient deviation weight is then prioritized for correction. The optimization objective function is corrected according to the stepwise carbon emission factor to obtain energy regulation parameters. The parameter output unit is used to output energy regulation parameters, including: When ΔK<0: If the first step energy price shows a downward trend, the first step energy supply increase parameter is outputted. The increase is positively correlated with |ΔK|, and the upper limit of the increase is 50% of the first step energy supply. If the high-carbon energy price is on the rise, output the high-carbon energy consumption reduction parameter, with a reduction of min(|AK| x 1.2, 1), i.e. the maximum reduction does not exceed 100% of the high-carbon energy consumption. High-carbon energy is the carbon emission factor f 高 >0.5 tCO2 / MWh of energy; When AK≥0: Based on the energy price fluctuation parameter PR, output the low-cost energy preferential procurement parameter, adjust the clean energy proportion, and maintain the synergy coefficient within the interval [K target ,K target +δ], where δ is a dynamic threshold, set to 0.05-0.15.

[0032] In specific embodiments, by constructing a multi-objective optimization function and outputting targeted regulation parameters, a dynamic balance between carbon emission reduction, clean energy utilization and cost control is achieved, ensuring that the system can output optimal decisions in different synergy states. The function optimization unit integrates key parameters through formulas to form optimization objectives and focuses on core demands through weight distribution. For example, when the synergy state is not up to standard, increase ω1, ω2 and ω4 to prioritize carbon intensity reduction and clean energy utilization rate improvement; if the synergy state is up to standard, increase ω3 to 0.4 to weaken other weights, so that the function pays more attention to the price fluctuation parameter PR and prioritizes cost control. At the same time, the function is modified by a stepwise factor, such as setting E1≥0.2 x E total as a constraint for the first step energy to ensure the basic proportion of low-carbon energy. The parameter output unit outputs specific measures according to the synergy state, such as when AK=-0.2 and the first step photovoltaic price decreases, output a 20% increase in photovoltaic (not exceeding the 50% upper limit) in positive correlation with |AK|; if the high-carbon coal power price rises, output a 24% reduction in coal power, which is min(0.2 x 1.2, 1). When AK=0.1, if PR shows that the natural gas price fluctuation is small and the cost is low, output the natural gas preferential procurement parameter, and at the same time maintain the clean energy proportion within the target synergy coefficient interval [0.8, 0.9].

[0033] The optimization objective function is modified according to the stepwise carbon emission factor, and the energy regulation parameters are specifically: For the first step energy, add a constraint term E1≥0.2 x E total in the optimization objective function, and apply a penalty coefficient of 1.2 to the (1-CE) term when E1 is below this value; For the third step energy, when E3 x f3 > E standard x 30%, apply a penalty coefficient of 1.5 to the PR term; The energy regulation parameters are obtained by solving the optimization objective function with constraints.

[0034] In specific embodiments, by setting a stepped hard constraint and a dynamic penalty mechanism, a low-carbon-oriented bottom line rule is injected into the optimization objective function, ensuring that the system does not deviate from the core requirements of carbon emission reduction and clean energy utilization when pursuing multi-objective balance, and strengthening the low-carbon nature of the regulation strategy.

[0035] The output energy regulation parameter is compared with the preset constraint condition, and the constraint condition includes the total amount of energy supply, the carbon emission limit and the production load demand; If the energy regulation parameter does not meet the constraint condition, it is redistributed according to the stepped priority, and the stepped priority is the first stepped energy, the second stepped energy and the third stepped energy: When the first stepped energy supply is less than the production load demand, the basic load of production is preferentially guaranteed, and the insufficient part is supplemented in the stepped order from the first stepped energy; When the first stepped energy supply is greater than or equal to the production load demand, the first stepped energy consumption is preferentially guaranteed, and the remaining load is distributed to the second stepped energy in the stepped order.

[0036] In specific embodiments, by the constraint condition verification and the stepped priority distribution mechanism, it is ensured that the energy regulation parameter has feasibility and low-carbon orientation in actual execution, and the dynamic adaptation of the optimization objective and the actual condition is realized. It compares the output regulation parameter with the total amount of energy supply, the carbon emission limit, the production load demand and other core constraint conditions, and selects the execution scheme that meets the actual scene. When the regulation parameter exceeds the constraint range, the energy is redistributed according to the priority of “first stepped energy> second stepped energy> third stepped energy”.

[0037] The present application receives full-dimensional data such as carbon emissions, energy consumption, price and production value through the acquisition module, calculates the synergy coefficient of the clean energy proportion and the carbon emission standard rate, dynamically adjusts the analysis weight of the carbon emission intensity, the clean energy utilization rate and the price fluctuation parameter through the analysis module by using the synergy coefficient deviation, predicts the energy price trend in combination with the historical data through the prediction module, and constructs the ladderization carbon emission factor correlation rule; the output module outputs the energy regulation and control parameter according to the synergy deviation state through the optimization objective function, and ensures the landing feasibility through the constraint check and the ladder priority distribution. The clean energy proportion and the carbon emission standard rate are integrated into a single synergy coefficient through the formula, the double targets are dynamically balanced by using the weight, the carbon index and the energy structure optimization are separated, the energy is divided into three levels of zero carbon, low carbon and low carbon fossil energy, the low carbon oriented precise regulation and control is realized through the weight dynamic adjustment and the constraint punishment mechanism, the parameter weight is dynamically distributed based on the synergy deviation state, and the advance correction strategy is combined with the price and the synergy state prediction. The dynamic balance of carbon emission reduction, clean energy utilization and cost control is realized, the regulation and control focus on the core target by using the ladderization carbon emission factor correlation rule and the dynamic weight, the low carbon orientation is strengthened when the synergy is not up to the standard, the cost optimization is focused on when the synergy is up to the standard, the energy system is promoted to low carbon and efficient transformation, and the intelligent synergy optimization of the carbon emission reduction target and the energy cost control is realized.

[0038] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (media) having computer-usable program code embodied thereon. The media can be any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices, which realize the processes described in the flowcharts and / or the block diagrams.Figure 1 one or more processes and / or functions specified in one or more blocks Figure 1 one or more blocks or multiple instances of a block

[0039] It should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A cloud computing based distributed control system, characterized by, The system comprises a collection module, an analysis module, a prediction module and an output module. The collection module is configured to receive collected energy data, and calculate a synergy coefficient based on the energy data. The analysis module is configured to calculate a synergy coefficient deviation, a carbon emission intensity parameter, a clean energy utilization rate parameter and an energy price fluctuation parameter based on the synergy coefficient. The prediction module is configured to predict an energy price fluctuation trend in a future preset time period based on the synergy coefficient and the energy price fluctuation parameter, and establish a stepwise carbon emission factor correlation rule. The output module is configured to construct an optimization objective function based on the synergy coefficient deviation, the carbon emission intensity parameter, the clean energy utilization rate parameter and the energy price fluctuation parameter, and output an energy regulation parameter based on the optimization objective function and the stepwise carbon emission factor correlation rule.

2. The cloud computing based distributed control system as claimed in claim 1, wherein, The collection module comprises a sequence collection unit and a sequence calculation unit. The sequence collection unit is configured to receive collected energy data, wherein the energy data comprises carbon emission data, energy consumption data, energy price data and production output data. The carbon emission data comprises carbon emission amounts of various types of fossil energy, direct carbon emission amounts of production processes, real-time monitoring data of carbon emission concentrations and implicit carbon emission amounts of purchased energy. The energy consumption data comprises fossil energy consumption amounts, clean energy consumption amounts, energy conversion link loss data and energy distribution data of various production links. The energy price data comprises real-time purchase prices of various types of energy, energy transportation and storage costs, carbon-related derivative costs and energy price historical fluctuation data. The production output data comprises production outputs per unit of time and energy consumption per unit of output. The sequence calculation unit is configured to calculate the synergy coefficient, and the calculation expression is as follows: K current = α x (E clean / E total ) + β x (1 - E carbon / E standard ); Wherein, K current is a synergy coefficient, E clean is a clean energy consumption, E total is a total energy consumption, E carbon is an actual carbon emission, E standard is a carbon emission benchmark value, and α, β are weight coefficients, and α+β=1, and the total energy consumption is the sum of the fossil energy consumption and the clean energy consumption.

3. The cloud computing based distributed control system as claimed in claim 2, wherein, The analysis module comprises a deviation calculation unit and a parameter analysis unit. The deviation calculation unit is configured to calculate the synergy coefficient deviation, and the calculation expression is as follows: AK = K current - K target ; Wherein, AK is the deviation of the synergy coefficient, K current is the synergy coefficient, K target is the preset target synergy coefficient, which is set based on the carbon emission reduction target and the clean energy utilization plan. The parameter analysis unit is configured to perform time series analysis on the energy price data in a historical period, calculate a ratio of a standard deviation to a mean value of the energy price data in the historical period, obtain an energy price fluctuation trend parameter and a price fluctuation rate, and calculate a carbon emission intensity parameter CI, a clean energy utilization rate parameter CE and an energy price fluctuation parameter PR based on the energy price fluctuation trend parameter, the price fluctuation rate and the energy data.

4. The cloud computing-based distributed control system of claim 3, wherein: The carbon emission intensity parameter CI is obtained by calculating a proportional relationship between the actual carbon emission amount and the production output data. The clean energy utilization rate parameter CE is obtained by calculating a proportional relationship between the clean energy consumption amount and a maximum amount of clean energy that can be consumed by the system. The energy price fluctuation parameter PR is obtained by multiplying the price fluctuation rate by a corresponding planned purchase amount and then performing accumulation calculation, and the calculation expression is as follows: PR =∑(V i x P i ); wherein V i is the price fluctuation rate of the i-th energy source, P i is the planned purchase quantity of the i-th energy source, the planned purchase quantity being a preset purchase scale for the i-th energy source; Based on a numerical range of the synergy coefficient deviation ΔK, the analysis weights of the carbon emission intensity parameter CI, the clean energy utilization rate parameter CE and the energy price fluctuation parameter PR are adjusted, including: When ΔK<0, the carbon emission intensity parameter analysis weight ω1 and the clean energy utilization rate parameter analysis weight ω2 are increased; When ΔK≥0, the energy price fluctuation parameter analysis weight ω3 is increased.

5. The cloud computing based distributed control system as claimed in claim 4, wherein, The prediction module comprises a parameter prediction unit and a rule construction unit; The parameter prediction unit comprises energy price trend prediction and synergy coefficient prediction; The energy price trend prediction analyzes historical energy price fluctuation data, inputs price fluctuation rate and macroeconomic indicators in a historical period, predicts various energy price fluctuation trends in a future preset time period, and outputs a price fluctuation rate prediction value, which is used to correct the energy price fluctuation parameter PR; The synergy coefficient prediction is used to predict the change trend of the synergy coefficient in a future preset time period according to the synergy coefficient, the stepwise carbon emission factor and the α weight dynamic adjustment record in a historical period, and output a synergy coefficient prediction value, which is used to correct the synergy coefficient deviation ΔK; The rule construction unit is used to divide clean energy into three steps to obtain a stepwise carbon emission factor, and construct a stepwise carbon emission factor association rule according to the stepwise carbon emission factor.

6. The cloud computing based distributed control system as claimed in claim 5, wherein, The stepwise carbon emission factor comprises first-step energy, second-step energy and third-step energy: The first-step energy is zero-carbon energy, the first-step energy consumption is E1, the carbon emission factor f1 is f1=0tCO2 / MWh; The second-step energy is low-carbon energy, the second-step energy consumption is E2, the carbon emission factor f2 is 0tCO2 / MWh<f2≤0.2tCO2 / MWh; The third-step energy is low-carbon fossil energy, the third-step energy consumption is E3, the carbon emission factor f3 is 0.2tCO2 / MWh<f3≤0.5tCO2 / MWh.

7. The cloud computing-based distributed control system according to claim 6, wherein: The stepwise carbon emission factor association rule comprises a first association rule and a second association rule; The first association rule is an association rule between the stepwise carbon emission factor and the synergy coefficient, specifically: the first-step energy is allocated the highest basic α weight, the second-step energy is allocated the second-highest basic α weight, and the third-step energy is allocated the lowest basic α weight; When the synergy coefficient deviation ΔK<0, the α weight of the first-step energy is temporarily increased by 10% based on the basic value, and the α weights of the second-step energy and the third-step energy remain unchanged, and the corrected α weight is substituted into the synergy coefficient calculation formula; The second association rule is an association rule between the stepwise carbon emission factor and the optimization objective function, specifically: Based on the matching of the energy belonging to the corresponding carbon emission factor, the first-step energy consumption, the second-step energy consumption, the third-step energy consumption and the high-carbon energy consumption are multiplied by the carbon emission factor corresponding to the energy belonging to the corresponding carbon emission factor, to obtain a multiplication result, and the sum obtained by adding the multiplication result is the actual carbon emission amount, the corrected carbon emission intensity parameter CI' is obtained through the actual carbon emission amount, and the corrected carbon emission intensity parameter CI' is input as a variable into the optimization objective function.

8. The cloud computing based distributed control system as claimed in claim 7, wherein, The output module comprises a function optimization unit and a parameter output unit; The function optimization unit is configured to form an optimization objective function based on the carbon emission intensity parameter, the clean energy utilization rate parameter, the energy price fluctuation parameter and the synergy coefficient deviation, and the calculation expression of the optimization objective function is: MinF=ω1×CI'+ω2×(1-CE)+ω3×PR+ω4×max(0,-ΔK) (ω1+ω2+ω3+ω4=1) Wherein ω4 is a synergy coefficient deviation weight, and the synergy coefficient deviation weight is determined based on an analysis of the influence of the synergy coefficient deviation on the overall optimization objective of the system; when ΔK<0, it indicates that the synergy state deviates greatly, and the synergy coefficient deviation weight needs to be increased for priority correction. The optimization objective function is modified according to the stepped carbon emission factor to obtain an energy regulation parameter. The parameter output unit is configured to output the energy regulation parameter, comprising: When ΔK<0: If the first-step energy price shows a downward trend, the first-step energy supply amount increase parameter is outputted, and the increase is positively correlated with |ΔK|, and the upper limit of the increase is 50% of the first-step energy supply amount. If the high-carbon energy price is in an upward trend, output the high-carbon energy consumption reduction parameter, and the reduction is min(|ΔK|*1.2, 1), that is, the maximum reduction does not exceed 100% of the high-carbon energy consumption, and the high-carbon energy is the carbon emission factor f 高 >0.5tCO2 / MWh of energy; When ΔK≥0: Based on the energy price fluctuation parameter PR, the low-cost energy priority procurement parameter is output, the proportion of clean energy is adjusted, and the synergy coefficient is maintained in the [K target ,K target + δ] interval, where δ is a dynamic threshold value, set to 0.05-0.

15.

9. The cloud computing-based distributed control system according to claim 8, wherein: The optimization objective function is modified according to the stepped carbon emission factor to obtain an energy regulation parameter, and the modification comprises: adding a constraint term E1≥ 0.2 x E total in the optimization objective function for the first step energy, imposing a penalty factor 1.2 on the (1-CE) term when E1 is below this value; For the third step energy when E3xf3> E standard × 30%, a penalty factor of 1.5 is applied to the PR term; The energy regulation parameter is obtained by solving the optimization objective function with constraints.

10. The cloud computing-based distributed control system according to claim 9, wherein: The output energy regulation parameter is compared with a preset constraint condition, and the constraint condition comprises a total energy supply amount, a carbon emission limit and a production load demand; If the energy regulation parameter does not meet the constraint condition, the energy regulation parameter is re-distributed according to a stepped priority, and the stepped priority comprises a first-step energy, a second-step energy and a third-step energy: When the first-step energy supply amount is less than the production load demand, the basic production load is guaranteed first, and the insufficient part is supplemented from the first-step energy according to the stepped order; When the first-step energy supply amount is greater than or equal to the production load demand, the first-step energy is consumed first, and the remaining load is distributed to the second-step energy according to the stepped order.

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