Industrial green low-carbon energy cooperative control system and method based on multi-energy complementation

Abstract

The invention relates to the field of energy management and technology, in particular to an industrial green low-carbon energy cooperative control system and method based on multi-energy complementation, and the system comprises an energy input unit, an energy management control center, a load dispatching unit and an energy storage unit. By combining wind power, photovoltaic power, waste heat recovery and other energy forms, the system can utilize energy in the industrial production process to the maximum extent, energy waste is avoided, an ACE frequency modulation optimization control method is adopted, supply conditions and demand fluctuation of different energy sources are evaluated in real time, energy source supply and demand matching is achieved, and the system operation efficiency and reliability are improved; through multi-energy complementation and optimal scheduling, the system can select an optimal energy combination under different electric load situations, reduce the energy purchase cost and improve the energy utilization rate, and through reasonable scheduling of various low-carbon energy, the dependence on fossil fuel is reduced, the carbon emission in the industrial production process is reduced, and the green and low-carbon target is promoted to be realized. Meanwhile, the invention also provides a corresponding energy scheduling control method.

Description

Technical Field

[0001] The present invention relates to the field of energy management and technology, and specifically to an industrial green low-carbon energy collaborative control system and method based on multi-energy complementarity. Background Art

[0002] As the world pays more attention to sustainable development, the industrial sector is gradually transforming towards green and low-carbon. Industrial production usually has high energy consumption, and there is a certain amount of inevitable energy waste. The traditional energy supply model that relies on fossil fuels will not only lead to increased operating costs, but also bring about a series of problems such as environmental pollution and greenhouse gas emissions. Therefore, how to improve energy efficiency and reduce carbon emissions in the industrial production process has become an urgent problem to be solved.

[0003] In recent years, the development of renewable energy technologies such as wind power generation and photovoltaic power generation has provided technical support for the green and low-carbon transformation of industry. However, due to the volatility and instability of wind power and photovoltaic power generation, the utilization effect of a single energy form is often limited. Therefore, how to achieve complementarity and coordinated scheduling between different energy sources has become the key to improving energy utilization efficiency and reducing dependence on fossil energy.

[0004] Traditional industrial energy management systems mostly adopt demand-based scheduling strategies, ignoring the coordinated optimization between different energy forms. Especially when multiple energy sources such as wind power, photovoltaics, and waste heat are involved, how to maximize energy utilization and reduce fossil energy consumption has become a hot research issue. Summary of the invention

[0005] In response to the above technical problems, the present invention provides an industrial green and low-carbon energy collaborative control system based on multi-energy complementarity, which can improve the economy and efficiency of the system through reasonable energy scheduling strategies, achieve efficient energy utilization and green and low-carbon goals. At the same time, the present invention also provides a corresponding energy scheduling control method.

[0006] The technical solution is as follows: an industrial green low-carbon energy collaborative control system based on multi-energy complementarity, characterized in that it includes an energy input unit, an energy management control center, a load dispatching unit and an energy storage unit;

[0007] Energy input unit: including wind power generation device, photovoltaic power generation device, waste heat power generation device, waste pressure power generation device, fuel cell. Each energy input unit monitors the energy generation data in real time through sensors and feeds the data back to the energy management control center;

[0008] Energy Management Control Center: Responsible for coordinating and dispatching different energy input units, judging energy supply, load demand and environmental conditions through optimization algorithms and real-time data analysis, and making optimal dispatch decisions.

[0009] Load dispatching unit: According to the instructions of the energy management control center, it adjusts the output of each energy in the energy input unit to meet different load requirements. In the case of fluctuations in energy supply and demand, it adjusts the input ratio of each energy input unit to ensure load balance.

[0010] Energy storage unit: Stores energy from renewable energy sources with high volatility, including wind and photovoltaic power. When demand is high, the energy storage unit releases the stored electricity to ensure the stability and reliability of the system.

[0011] An energy dispatching control method for the above system, characterized in that it includes:

[0012] System model establishment and load forecasting: Use historical data and real-time monitoring data to build a load forecasting model to predict power load demand in different time periods;

[0013] Energy supply assessment: Based on the power generation of wind power, photovoltaic power, waste heat, waste pressure, and fuel cells, combined with the supply and demand data of various energy sources, the current energy supply and volatility are assessed;

[0014] ACE frequency optimization: By real-time monitoring and analyzing the frequency deviation of the power system, the ACE frequency method determines whether the system energy supply is balanced. If a certain type of energy is in excess, the system stores the energy through the energy storage unit. If the supply is insufficient, it is supplemented through other energy input units.

[0015] Multi-energy complementary dispatching: adopting a multi-energy complementary strategy to combine the advantages of wind power, photovoltaic power, waste heat and waste pressure recovery to form a synergistic and complementary energy supply structure;

[0016] System dynamic adjustment and optimization: Based on real-time data and feedback information, the control system dynamically adjusts the scheduling plan.

[0017] Its further feature is that the ACE frequency modulation method is as follows: a mathematical model of a multi-energy complementary system is established, assuming that there are multiple energy sources in the system, including wind power generation P wind 、Photovoltaic power generation pv 、Waste heat and waste pressure power generation waste and fuel cell P fuel , assuming the total load demand of the system is P load , the total energy output is P total =P wind +P pv +P waste +P fuel +P storage , where P storageis the output power of the energy storage system, which can be adjusted according to the actual situation; assuming that there is a certain error between the load demand and the power generation capacity of the system, that is, ΔP = P load -P total , where ΔP is the load gap or excess (corresponding to wind power generation P wind 、Photovoltaic power generation pv 、Waste heat and waste pressure power generation waste and fuel cell P fuel ), the frequency modulation coefficient k is introduced f and the scheduling coefficient k s To optimize system scheduling,

[0018]

[0019] in and are the predicted power of wind power, photovoltaic power, waste heat and waste pressure, and fuel cell respectively. The objective function is expressed as Among them, C i is the unit cost of energy type i, P i is the output power of energy type i, λ is the penalty factor of frequency regulation error, which controls the frequency regulation accuracy in the optimization process. The constraints include the upper and lower limits of the output power of each energy source and the total load demand:

[0020] P wind,min ≤P wind ≤P wind,max

[0021] P pv,min ≤P pv ≤P pv,max

[0022] P waste,min ≤P waste ≤P waste,max

[0023] P fuel,min ≤P fuel ≤P fuel,max

[0024] P storage,min ≤P storage ≤P storage,max ;

[0025] Use the particle swarm optimization algorithm to solve the optimization problem:

[0026] Initialize the particle swarm: randomly generate multiple particles, each particle represents a possible energy output configuration;

[0027] Calculate fitness: According to the objective function J, calculate the fitness of each particle. The fitness value represents the optimization degree of the system. The higher the fitness, the better the running effect of the system.

[0028] Update the particle position and velocity: The particle adjusts the energy output power according to historical experience and group experience:

[0029] v i (t + 1)= wv i (t)+ c 1 r 1 (p i - x i (t))+ c 2 r 2 (g - x i (t))

[0030] x i (t + 1)= x i (t)+ v i (t + 1)

[0031] Among them, v i is the velocity of the particle, x i is the position of the particle, p i is the historical best position of the particle, g is the global best position, w is the global best position, c 1 , c 2 is the acceleration factor, r 1 , r 2 is a random number. When the fitness meets a certain set threshold or the maximum number of iterations is reached, the algorithm stops and outputs the best solution.

[0032] After multiple iterations, the particle swarm algorithm gives a set of optimal energy output power configurations, that is, the power that each energy module should output. These configurations will minimize the operating cost and meet the load demand.

[0033] After adopting the present invention, by combining energy forms such as wind power, photovoltaic power, and waste heat recovery, the system can maximize the utilization of energy in the industrial production process, avoid energy waste, adopt the ACE frequency modulation optimization control method, evaluate the supply situation and demand fluctuations of different energies in real time, realize the matching of energy supply and demand, improve the operating efficiency and reliability of the system. Through multi - energy complementarity and optimal scheduling, the system can select the optimal energy combination under different electrical load scenarios, reduce the energy procurement cost and improve the energy utilization rate. By reasonably scheduling various low - carbon energies, reduce the dependence on fossil fuels, reduce carbon emissions in the industrial production process, and promote the realization of green and low - carbon goals. Brief Description of the Drawings

[0034] Figure 1 is the system schematic diagram of the present invention;

[0035] Figure 2 This is a flow chart of the control method of the present invention. DETAILED DESCRIPTION

[0036] See Figure 1 As shown, an industrial green low-carbon energy collaborative control system based on multi-energy complementarity includes an energy input unit 1, an energy management control center 2, a load scheduling unit 3 and an energy storage unit 4;

[0037] Energy input unit 1: including wind power generation device, photovoltaic power generation device, waste heat power generation device, waste pressure power generation device, and fuel cell. Each energy input unit monitors the energy generation data in real time through sensors and feeds the data back to the energy management control center;

[0038] Energy Management Control Center 2: Responsible for coordinating and dispatching different energy input units, judging energy supply, load demand and environmental conditions through optimization algorithms and real-time data analysis, and making optimal dispatching decisions.

[0039] Load dispatching unit 3: According to the instructions of the energy management control center, adjust the output of each energy in the energy input unit to meet different load requirements. In the case of fluctuations in energy supply and demand, adjust the input ratio of each energy input unit to ensure load balance.

[0040] Energy storage unit 4: stores energy from renewable energy sources with large fluctuations, including wind power and photovoltaic power. When demand is high, the energy storage unit releases the stored electricity to ensure the stability and reliability of the system.

[0041] An energy dispatching control method for the above system comprises:

[0042] System model establishment and load forecasting: Use historical data and real-time monitoring data to build a load forecasting model to predict power load demand in different time periods. Load forecasting takes into account fluctuation factors such as regular load, sudden load, and equipment failure;

[0043] Energy supply assessment: Based on the power generation of wind power, photovoltaic power, waste heat, waste pressure, and fuel cells, combined with the supply and demand data of various energy sources, the current energy supply and volatility are assessed; for example, photovoltaic power generation mostly generates electricity during the day, while wind power generation is greatly affected by the weather, so a reasonable assessment should be conducted based on different environmental conditions;

[0044] ACE frequency optimization: By real-time monitoring and analyzing the frequency deviation of the power system, the ACE frequency method determines whether the system energy supply is balanced. If a certain type of energy is in excess, the system stores the energy through the energy storage unit. If the supply is insufficient, it is supplemented by other energy input units. In addition, the energy ratio can be adjusted in real time to avoid over-reliance on a single energy source, ensuring the efficient and stable operation of the system.

[0045] Multi-energy complementary scheduling: A multi-energy complementary strategy is adopted to combine the advantages of wind power, photovoltaic power generation, waste heat, and waste pressure recovery energy to form a synergistic and complementary energy supply structure; for example, when the wind power and photovoltaic power generation capacity are strong, the load of the waste heat and / or waste pressure power generation device can be reduced; when the wind power and photovoltaic power supply are insufficient, the waste heat and / or waste pressure power generation device plays a supplementary role to maintain the stability and continuous supply of the system;

[0046] Dynamic system adjustment and optimization: Based on real-time data and feedback information, the control system dynamically adjusts the dispatching plan. If the system load demand suddenly increases, the control system will prioritize the use of electricity stored in the energy storage system for adjustment. If wind or photovoltaic power generation generates excess energy, the electricity will be stored or adjusted to non-peak hours. Through dynamic adjustment, it ensures maximum energy utilization and minimum cost in different operating scenarios.

[0047] Combine the following Figure 2 To explain in detail: When the system starts working, the energy management control center receives real-time data from each energy input unit and analyzes the data, including energy output, load demand, environmental conditions, etc. If the data is invalid, it will perform error processing and then collect the data again for analysis. If the data is valid, it will enter algorithm optimization or direct feedback. During algorithm optimization, environmental condition data is input, and the ACE optimization algorithm is used to generate scheduling decisions. The scheduling command is output to the load scheduling unit, the input ratio of each type of energy is adjusted to ensure load balance, and the data of each energy input unit is received again for analysis; during direct feedback, the load demand is evaluated, the environmental condition data is input, the scheduling strategy is directly optimized, and finally the scheduling instruction is output.

Claims

1. An industrial green low-carbon energy collaborative control system based on multi-energy complementarity, characterized in that: It includes an energy input unit, an energy management control center, a load dispatching unit and an energy storage unit; Energy input unit: including wind power generation device, photovoltaic power generation device, waste heat power generation device, waste pressure power generation device, fuel cell. Each energy input unit monitors the energy generation data in real time through sensors and feeds the data back to the energy management control center; Energy management control center: responsible for coordinating and dispatching different energy input units, judging energy supply, load demand and environmental conditions through optimization algorithms and real-time data analysis, and making optimal dispatching decisions; Load dispatching unit: According to the instructions of the energy management and control center, it adjusts the output of each energy in the energy input unit to meet different load requirements. In the case of fluctuations in energy supply and demand, it adjusts the input ratio of each energy input unit to ensure load balance; Energy storage unit: Stores energy from renewable energy sources with high volatility, including wind and photovoltaic power. When demand is high, the energy storage unit releases the stored electricity to ensure the stability and reliability of the system.

2. An energy scheduling control method for the above system, characterized in that: It includes: System model establishment and load forecasting: Use historical data and real-time monitoring data to build a load forecasting model to predict power load demand in different time periods; Energy supply assessment: Based on the power generation of wind power, photovoltaic power, waste heat, waste pressure, and fuel cells, combined with the supply and demand data of various energy sources, the current energy supply and volatility are assessed; ACE frequency optimization: By real-time monitoring and analyzing the frequency deviation of the power system, the ACE frequency method determines whether the system energy supply is balanced. If a certain type of energy is in excess, the system stores the energy through the energy storage unit. If the supply is insufficient, it is supplemented through other energy input units. Multi-energy complementary dispatching: adopting a multi-energy complementary strategy to combine the advantages of wind power, photovoltaic power, waste heat and waste pressure recovery to form a synergistic and complementary energy supply structure; Dynamic adjustment and optimization of the system: Based on real-time data and feedback information, the control system dynamically adjusts the scheduling plan.

3. The energy dispatching control method of the system according to claim 2 is characterized in that: The ACE frequency modulation method is as follows: a mathematical model of a multi-energy complementary system is established, assuming that there are multiple energy sources in the system, including wind power generation P wind 、Photovoltaic power generation pv 、Waste heat and waste pressure power generation waste and fuel cell P fuel , assuming the total load demand of the system is P load , the total energy output is P total =P wind +P pv +P waste +P fuel +P storage , where P storage is the output power of the energy storage system, which can be adjusted according to the actual situation; assuming that there is a certain error between the load demand and the power generation capacity of the system, that is, ΔP = P load -P total , where ΔP is the load gap or excess (corresponding to wind power generation P wind 、Photovoltaic power generation pv 、Waste heat and waste pressure power generation waste and fuel cell P fuel ), the frequency modulation coefficient k is introduced f and the scheduling coefficient k s To optimize system scheduling, in and are the predicted power of wind power, photovoltaic power, waste heat and waste pressure, and fuel cell respectively. The objective function is expressed as Among them, C i is the unit cost of energy type i, P i is the output power of energy type i, λ is the penalty factor of frequency regulation error, which controls the frequency regulation accuracy in the optimization process. The constraints include the upper and lower limits of the output power of each energy source and the total load demand: P wind,min ≤P wind ≤P wind,max P pv,min ≤P pv ≤P pv,max P waste,min ≤P waste ≤P waste,max P fuel,min ≤P fuel ≤P fuel,max P storage,min ≤P storage ≤P storage,max ; Use the particle swarm optimization algorithm to solve the optimization problem: Initialize the particle swarm: randomly generate multiple particles, each particle represents a possible energy output configuration; Calculate fitness: According to the objective function J, calculate the fitness of each particle. The fitness value indicates the degree of optimization of the system. The higher the fitness, the better the system operation effect. Update particle position and velocity: Particles adjust energy output power based on historical experience and group experience: v i (t+1)=wv i (t)+c1r1(p i -x i (t))+c2r2(g-x i (t)) x i (t+1)=x i (t)+v i (t+1) Among them, v i is the velocity of the particle, x i is the position of the particle, p i is the historical best position of the particle, g is the global best position, w is the global best position, c1, c2 are acceleration factors, r1, r2 are random numbers. When the fitness meets a certain set threshold or the maximum number of iterations is reached, the algorithm stops and outputs the best solution; After multiple iterations, the particle swarm algorithm gives a set of optimal energy output power configurations, that is, the power that each energy module should output, which will minimize the operating cost and meet the load demand.

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