A Method for Evaluating the Adjustable Potential of High-Energy Industrial Loads and Demand Response Scheduling

Abstract

The present invention provides a method for evaluating the adjustable potential of high-energy-consuming industrial loads and demand response scheduling, which relates to the technical field of industrial Internet. This method first constructs a high-energy-consuming magnesium oxide industrial Internet system, and clusters the high-energy-consuming load and commercial load data in the high-energy-consuming magnesium oxide industrial Internet system; for different clusters, calculates their load adjustment rate and load volatility respectively, and then evaluates the adjustable potential of the industrial loads in the high-energy-consuming magnesium oxide industrial Internet system; then divides the power consumption period into peak, flat, and valley periods; establishes a segmented demand response load model based on the load transfer rate of each power consumption transition period to determine the load power transferred or reduced during a certain power consumption period; and establishes an optimized scheduling model for demand response participated by the high-energy-consuming magnesium oxide industrial Internet; finally, solves the optimized scheduling model for the demand response to obtain the optimal demand response scheduling scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet, and particularly to a method for evaluating the adjustable potential of high-energy-consuming industrial loads and a demand response scheduling method involving the participation of the high-energy-consuming magnesia industry in the industrial Internet. Background Art

[0002] After decades of development in the domestic magnesia industry, most enterprises have experienced overcapacity, and problems such as high material consumption and high energy consumption are relatively serious. The contradiction between the existing development model and the low-carbon economy is extremely prominent. To promote the high-quality development of traditional industries, integrating the magnesia industry into the industrial Internet platform helps to give play to the advantages of digitalization, networking, and intelligence of the industrial Internet, injecting new vitality into the development of the magnesia industry. By comprehensively integrating the industrial Internet with all aspects of the magnesia industry, the production process can be optimized, carbon emissions can be significantly reduced, and costs can be lowered at the same time. Constructing a high-energy-consuming magnesia industrial Internet system helps to optimize production, monitoring, and management processes, promote the flexible allocation and collaborative scheduling of cross-domain resources, thereby driving industrial clusters and promoting the high-level development of regional economies.

[0003] The access of large-scale renewable energy generating units to the power system has brought greater pressure to the operation and control of the power grid due to the resulting uncertainties. Therefore, it is necessary to explore large-scale rapid regulation resources to improve the flexibility and resource utilization rate of the power system. The high-energy-consuming fused magnesia load has the advantages of a large installed capacity and convenient centralized control. Adjusting the power of the fused magnesia load in a short period of time will not affect the quality of the fused magnesia product, and the output will only be slightly affected. Therefore, its regulation potential is huge. Evaluating the regulation potential of the high-energy-consuming magnesia industrial load can provide certain technical references for load scheduling and demand response.

[0004] Demand-side response refers to the market-oriented participation behavior of power users in response to price signals or incentive mechanisms and changing their own electricity consumption patterns. As a form of load energy storage technology, it has played a positive role in absorbing renewable energy, assisting in peak shaving and frequency modulation, and enhancing the flexibility of the power grid. The electricity cost of the high-energy-consuming magnesia industry is high, so the initiative to participate in demand response is higher. It can not only reduce the electricity cost of industrial users and bring good economic benefits, but also help to achieve peak shaving and valley filling, enhancing the emergency regulation ability and safety and stability performance of the power grid.

[0005] Xu Hui, Jiao Yang, Pu Lei, etc. established a stochastic scheduling optimization model considering the uncertainty of clean energy grid connection in the paper titled "Stochastic Scheduling Optimization Model of Wind-Solar-Biomass-Energy-Storage Integrated Virtual Power Plant Considering Uncertainty and Demand Response" (Power System Technology, 2017, 41(11): 3590-3597.), but there is no research on the participation of industrial Internet in demand response scheduling; Yi Z, Xu Y, Gu W, etc. proposed an economic scheduling method for virtual power plants considering the aggregation and disaggregation of interruptible loads in the paper titled "A multi-time-scale economic scheduling strategy for virtual power plant based on deferrable loads aggregation and disaggregation" (IEEE Transactions on Sustainable Energy, 2019, 11(3): 1332-1346.), but the analysis of the participation of shiftable loads in load regulation is lacking. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for evaluating the adjustable potential of high-energy-consuming industrial loads and demand response scheduling in view of the deficiencies of the above-mentioned prior art, to evaluate the regulation potential of industrial loads in the industrial Internet system of high-energy-consuming magnesium oxide, and to realize the demand response scheduling participated by the industrial Internet of high-energy-consuming magnesium oxide.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: a method for evaluating the adjustable potential of high-energy-consuming industrial loads and demand response scheduling, which includes two parts: evaluating the adjustable potential of high-energy-consuming industrial loads and demand response scheduling participated by the industrial Internet of high-energy-consuming magnesium oxide. Among them, the evaluation of the adjustable potential of high-energy-consuming industrial loads includes the following steps:

[0008] Step 1: Construct an industrial Internet system of high-energy-consuming magnesium oxide, which includes a renewable energy generator set, a self-provided thermal power unit, an energy recovery system, high-energy-consuming loads, commercial loads, and a distributed energy storage system;

[0009] The high-energy-consuming magnesium oxide industrial Internet system is a resource cluster naturally formed based on geographical location, and can aggregate various resources within the cluster in real time and upload them to the terminal cloud platform to uniformly participate in power grid regulation; at the same time, it can also decompose the control signals of the terminal cloud platform and send them to each resource within the cluster; the operation mode of the high-energy-consuming magnesium oxide industrial Internet system is as follows: preferentially consume the electric energy provided by renewable energy generating units. If this part of the electric energy is sufficient to meet the industrial load power demand, the surplus electric energy is stored in the distributed energy storage system and sold to the power grid when the electricity price is greater than the set value; if this part of the power is not sufficient to meet the power demand, the electric energy in the energy storage system is preferentially released, or the self-provided thermal power unit generates electricity, and purchases electricity from the power grid;

[0010] Step 2: Use the K-means clustering algorithm combined with the Canopy algorithm to cluster the high-energy-consuming load and commercial load data in the high-energy-consuming magnesium oxide industrial Internet system;

[0011] Step 2.1: Obtain sample points for all load data at fixed time intervals, and obtain X sample points every day to form a sample data set;

[0012] Step 2.2: For each sample point, calculate the distance from other sample points to this point and obtain the average value ave 1 ,ave 2 ,……,ave X , and then calculate the average value of these X average values as the T 0 value;

[0013] Step 2.3: Randomly select a sample point from the sample set as the initial clustering center of the Canopy algorithm;

[0014] Step 2.4: Then select a sample point from the sample set and calculate the distance from this sample point to all the generated clustering centers; if the distance from this sample point to a certain clustering center is less than T 0 value, then delete this sample point from the sample set; if the distance from this point to all clustering centers is greater than T 0 value, then take this sample point as a new clustering center;

[0015] Step 2.5: Repeat Step 2.4 until the sample set is empty, and obtain the k value and k clustering centers of the K-means clustering;

[0016] Step 2.6: Calculate the distance from each sample point in the sample set to each clustering center, and divide each sample point into the class corresponding to the nearest clustering center;

[0017] Step 2.7: Calculate the average value of each class and use it as the new clustering center;

[0018] Step 2.8: Repeat Step 2.6 to Step 2.7 until the K-means clustering reaches the maximum number of iterations;

[0019] Step 3: For the different clusters divided by the above clustering algorithm, calculate their load adjustment rate and load volatility respectively, and then evaluate the adjustable potential of the industrial load in the high-energy-consuming magnesium oxide industrial Internet system;

[0020] The load adjustment rate R is shown in the following formula:

[0021]

[0022] where N is the number of loads in a certain cluster; P mi is the load power after adjustment of the i-th load; P ni is the baseline load power of the i-th load before adjustment;

[0023] The load volatility V is shown in the following formula:

[0024]

[0025] where σ is the standard deviation of the daily average load; is the average value of the daily average load; P i is the power of the i-th load in a certain cluster; is the average power of the i-th load in a certain cluster;

[0026] The load adjustment rate is an index reflecting the adjustable ability of the load in the magnesium oxide industrial Internet when participating in demand response. The larger the load adjustment rate, the stronger the load adjustment ability; the load volatility is an index reflecting the fluctuation of the load in the magnesium oxide industrial Internet when participating in demand response. The larger the load volatility, the greater the peak-valley difference of the load during this period.

[0027] The specific method of the demand response scheduling participated by the high-energy-consuming magnesium oxide industrial Internet is as follows: First, divide the power consumption period into peak, flat, and valley periods, establish a segmented demand response load model based on the load transfer rates of each power consumption transition period, namely peak-flat, flat-valley, and peak-valley periods, and then determine the load power transferred or reduced during a certain power consumption period. Then, establish an optimal scheduling model for the demand response participated by the high-energy-consuming magnesium oxide industrial Internet. Finally, use the particle swarm optimization algorithm to solve the optimal scheduling model, which specifically includes the following steps:

[0028] Step S1: Divide the response of users to electricity price incentives into a dead zone, a linear zone, and a saturation zone, and construct a segmented demand response load model based on the load transfer rates of these power consumption transition periods, namely peak-flat, flat-valley, and peak-valley;

[0029] The load transfer rate during the peak-to-flat power consumption transition period is shown by the following formula:

[0030]

[0031] Among them, μ p-f is the load transfer rate during the peak-to-flat power consumption transition period, and △p p-f is the electricity price difference during the peak-to-flat power consumption transition period; △p 0,p-f is the peak-to-flat electricity price difference at the dead zone inflection point; △p 1,p-f is the peak-to-flat electricity price difference at the saturation zone inflection point; k p-f is the slope of the load transfer rate in the linear region changing with the peak-to-flat electricity price; is the maximum load transfer rate during the peak-to-flat power consumption transition period;

[0032] The load transfer rate during the flat-to-valley power consumption transition period is shown by the following formula:

[0033]

[0034] Among them, μ f-v is the load transfer rate during the flat-to-valley power consumption transition period, and △p f-v is the electricity price difference during the flat-to-valley power consumption transition period; △p 0,f-v is the flat-to-valley electricity price difference at the dead zone inflection point; △p 1,f-v is the flat-to-valley electricity price difference at the saturation zone inflection point; k f-v is the slope of the load transfer rate in the linear region changing with the flat-to-valley electricity price; is the maximum load transfer rate during the flat-to-valley power consumption transition period;

[0035] The load transfer rate during the peak-to-valley power consumption transition period is shown by the following formula:

[0036]

[0037] Among them, μ p-v is the load transfer rate during the peak-to-valley power consumption transition period, and △p p-v is the electricity price difference during the peak-to-valley power consumption transition period; △p 0,p-v is the peak-to-valley electricity price difference at the dead zone inflection point; △p 1,p-v is the peak-to-valley electricity price difference at the saturation zone inflection point; k p-v is the slope of the load transfer rate in the linear region changing with the peak-to-valley electricity price; is the maximum load transfer rate during the peak-to-valley transition period;

[0038] Construct a segmented demand response load model involving the high-energy-consuming magnesia industrial Internet, as shown by the following formula;

[0039]

[0040] Among them, P(t) is the load during the t-th period after the demand response of the high-energy-consuming magnesium oxide industrial Internet; p, f, and v respectively represent the sets of periods during the peak electricity consumption period, the flat period, and the valley period; P 0,t is the load during the t-th period before the demand response of the high-energy-consuming magnesium oxide industrial Internet; are respectively the average loads during the peak period and the normal period before the demand response of the high-energy-consuming magnesium oxide industrial Internet;

[0041] Then, the load power △P(t) transferred or reduced during the t-th period of the high-energy-consuming magnesium oxide industrial Internet is shown in the following formula:

[0042] △P(t) = P(t) - P 0,t

[0043] Step S2: Based on the load power transferred or reduced during the t-th period of the high-energy-consuming magnesium oxide industrial Internet determined in Step S1, combined with the output of renewable energy generating units, the output of self-provided thermal power units, the charge and discharge power and start-stop status of the energy storage system, and the power purchased from the power grid, establish an optimal dispatching model for the high-energy-consuming magnesium oxide industrial Internet to participate in the demand response;

[0044] Step S2.1: Construct the objective function of the optimal dispatching model for the high-energy-consuming magnesium oxide industrial Internet to participate in the demand response, as shown in the following formula:

[0045]

[0046] Among them, F is the total operating cost of the magnesium oxide industrial Internet during the dispatching period; T is the total number of dispatching periods; F t is the operating cost of the magnesium oxide industrial Internet during the t-th period; M is the total number of power generation devices in the magnesium oxide industrial Internet; are respectively the operating cost and start-stop cost of the m-th power generation device during the t-th period; is the output of the m-th power generation device during the t-th period; is the start-stop status of the m-th power generation device during the t-th period, taking 0 represents the shutdown state, taking 1 represents the operating state; G is the number of energy storage devices in the magnesium oxide industrial Internet; are respectively the operating cost, start-stop cost and start-stop status of the j-th energy storage device during the t-th period; is the charge and discharge power of the j-th energy storage device during the t-th period; K is the number of transferable loads and interruptible loads participating in the demand response dispatching in the magnesium oxide industrial Internet; c t,k is the compensation price of the k-th load participating in the demand response during the t-th period; △P k (t) is the power transferred or reduced by the k-th load during the t-th period; c t,g is the electricity purchase price from the power grid during the t-th period; P t,gThe power purchase from the power grid during period t;

[0047] Step S2.2: Establish the constraint conditions of the demand response optimal scheduling model; the constraint conditions include power balance constraint, energy storage device charge and discharge power constraint, power generation device power constraint and demand response constraint, where:

[0048] The power balance constraint is shown in the following formula:

[0049]

[0050] where, P t,p is the predicted load power of the magnesia industrial Internet that does not participate in demand response;

[0051] The energy storage device charge and discharge power constraint is shown in the following formula:

[0052]

[0053] where, P essc,t , P essd,t are the charging power and discharging power of the energy storage device respectively; P essc,max , P essd,max are the charging and discharging upper limits of the energy storage device respectively;

[0054] The power generation device power constraint is shown in the following formula:

[0055]

[0056] where, are the lower and upper limits of the active power of the m-th power generation device at time t respectively;

[0057] The demand response constraint is shown in the following formula:

[0058] △P(t) min ≤△P(t)≤△P(t) max (13)

[0059] where, △P(t) min , △P(t) max are the minimum and maximum load change amounts allowed by the magnesia industrial Internet at time t respectively;

[0060] Step S3: Use the particle swarm optimization algorithm to solve the optimal scheduling model of the high-energy-consuming magnesia industrial Internet participating in demand response, and obtain the optimal demand response scheduling plan.

[0061] The beneficial effects of adopting the above technical solutions are as follows: A method for evaluating the adjustable potential of high-energy industrial loads and demand response scheduling provided by the present invention. (1) The constructed high-energy-consuming magnesium oxide industrial Internet helps to significantly reduce carbon emissions, reduce the adverse effects brought by the grid connection of clean energy, and at the same time reduce the costs of enterprises and users, having good social and economic benefits. (2) Recycling the high-temperature flue gas and waste heat in the magnesium oxide production process enables the entire industrial Internet to achieve "zero emissions", which can effectively promote the utilization of energy and reduce environmental pollution, having great environmental protection significance. (3) The present invention evaluates the adjustable potential of the load using two parameters, namely the load regulation rate and the load volatility, for the clustered loads, which can provide a reference for load scheduling and demand-side management. (4) An optimized scheduling model for the magnesium oxide industrial Internet to participate in demand response is proposed, mainly considering the consumption of clean energy and the regulation of shiftable loads and interruptible loads, realizing the coordinated management of each controllable unit within the industrial Internet and reducing the operating cost of the magnesium oxide industrial Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is an architecture diagram of the high-energy-consuming magnesium oxide industrial Internet system provided by an embodiment of the present invention;

[0063] Figure 2 It is a flowchart of the K-means clustering algorithm combined with the Canopy algorithm provided by an embodiment of the present invention;

[0064] Figure 3 It is a flowchart of the particle swarm optimization algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The following combines the drawings and embodiments to further describe in detail the specific embodiments of the present invention. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0066] A method for evaluating the adjustable potential of high-energy industrial loads and demand response scheduling includes two parts: evaluating the adjustable potential of high-energy industrial loads and demand response scheduling participated by the high-energy-consuming magnesium oxide industrial Internet. Among them, the evaluation of the adjustable potential of high-energy industrial loads includes the following steps:

[0067] Step 1: Construct a high-energy-consuming magnesium oxide industrial Internet system, which includes a renewable energy generating set, a self-provided thermal power generating set, an energy recovery system, high-energy-consuming loads, commercial loads, and a distributed energy storage system;

[0068] The high-energy-consuming magnesia industrial Internet system is a resource cluster naturally formed based on geographical location, and can aggregate various resources within the cluster in real time and upload them to the terminal cloud platform to uniformly participate in power grid regulation; at the same time, it can also decompose the control signals of the terminal cloud platform and send them to each resource within the cluster. The system architecture of the high-energy-consuming magnesia industrial Internet is as Figure 1 shown; this system can achieve the local consumption of large-scale clean energy. The specific operation mode is as follows: preferentially consume the electric energy provided by renewable energy generating units. If this part of the electric energy is sufficient to meet the industrial load power demand, the surplus electric energy is stored in the distributed energy storage system and sold to the power grid when the electricity price is greater than the set value; if this part of the power is not enough to meet the power demand, the electric energy in the energy storage system is preferentially released, or the self-provided thermal power unit generates electricity, and purchases electricity from the power grid;

[0069] The energy recovery system is used to recover CO 2 generated during the magnesite smelting process and conduct hierarchical recovery and cascade utilization of the waste heat; the high-temperature flue gas generated during the magnesite smelting process enters the heat exchange system. After preheating the waste heat boiler water, the flue gas temperature decreases and then enters the carbon dioxide recovery system; a large amount of heat is generated during the natural cooling process of the smelted magnesite. First, this waste heat is recovered for power generation, heating, energy storage, etc. Then, the smelted magnesite is crushed and transported to the high-temperature heat exchange tower, and the discharged gas can be used for power generation, heating, and preheating the magnesite raw materials. Among them, waste heat recovery is for the heat generated during the natural cooling process of the smelted magnesite. First, the heat above 350°C is used to preheat the waste heat boiler water, and this part of the heat can be used for power generation, heating, and energy storage; then, the heat of 80 - 350°C is used for power generation, heating, and refrigeration, and finally, the heat below 80°C is directly supplied for household use. After each stage of high-temperature waste heat utilization, it can be converted into waste heat of a lower level for continuous utilization to maximize the energy utilization rate;

[0070] Step 2: Use the K-means clustering algorithm combined with the Canopy algorithm to cluster the high-energy-consuming load and commercial load data in the high-energy-consuming magnesia industrial Internet system; the K-means clustering algorithm combined with the Canopy algorithm first roughly clusters the load data using the Canopy algorithm to determine the value of k and k clustering centers, and then uses the K-means algorithm for precise clustering, as Figure 2 shown. The specific steps are as follows:

[0071] Step 2.1: Obtain a sample point for all load data every 15 minutes, and obtain 96 sample points per day to form a sample data set;

[0072] Step 2.2: For each sample point, calculate the distance from other sample points to this point and obtain the average value ave 1,ave 2 ,……,ave 96 , calculate the average value of these 96 average values as the T 0 value;

[0073] Step 2.3: Randomly select a sample point P from the sample set as the initial clustering center of the Canopy algorithm;

[0074] Step 2.4: Then select a sample point from the sample set and calculate the distances from this sample point to all the generated clustering centers; if the distance from this sample point to a certain clustering center is less than the T 0 value, then delete this sample point from the sample set; if the distances from this point to all clustering centers are greater than the T 0 value, then take this sample point as a new clustering center;

[0075] Step 2.5: Repeat Step 2.4 until the sample set is empty, and obtain the k value and k clustering centers of the K-means clustering;

[0076] Step 2.6: Calculate the distances from each sample point in the sample set to each clustering center, and divide each sample point into the class corresponding to the nearest clustering center;

[0077] Step 2.7: Calculate the average value of each class as the new clustering center;

[0078] Step 2.8: Repeat Step 2.6 to Step 2.7 until the K-means clustering reaches the maximum number of iterations;

[0079] Step 3: For the different clusters divided by the above clustering algorithm, calculate their load regulation rates and load volatility rates respectively, and then evaluate the adjustable potential of the industrial load in the high-energy-consuming magnesium oxide industrial Internet system;

[0080] The load regulation rate R is shown in the following formula:

[0081]

[0082] where N is the number of loads in a certain cluster; P mi is the load power after regulation of the i-th load; P ni is the baseline load power before regulation of the i-th load;

[0083] The load volatility rate V is shown in the following formula:

[0084]

[0085] where σ is the standard deviation of the daily average load; is the average value of the daily average load; P iThe power of the i-th load in a certain cluster; The average power of the i-th load in a certain cluster;

[0086] The load regulation rate is an index reflecting the adjustable ability of the load in the magnesia industrial Internet when participating in demand response. The larger the load regulation rate, the stronger the load regulation ability; the load volatility rate is an index reflecting the fluctuation of the load in the magnesia industrial Internet when participating in demand response. The larger the load volatility rate, the greater the peak-valley difference of the load during this period. This kind of difference fluctuation may affect its regulation ability. At this time, peak shaving and valley filling operations should be carried out.

[0087] The specific method of the demand response scheduling participated by the high-energy-consuming magnesia industrial Internet is as follows: First, divide the power consumption period into peak, flat, and valley periods, establish a segmented demand response load model based on the load transfer rates of each power consumption transition period, namely peak-flat, flat-valley, and peak-valley periods, and then determine the load power transferred or reduced during a certain power consumption period. Then, establish an optimal scheduling model for the demand response participated by the high-energy-consuming magnesia industrial Internet. Finally, use the particle swarm optimization algorithm to solve the optimal scheduling model, which specifically includes the following steps:

[0088] Step S1: Divide the user's response to electricity price incentives into a dead zone, a linear zone, and a saturation zone, and construct a segmented demand response load model based on the load transfer rates of these power consumption transition periods, namely peak-flat, flat-valley, and peak-valley;

[0089] The load transfer rate of the peak-flat power consumption transition period is shown in the following formula:

[0090]

[0091] Among them, μ p-f is the load transfer rate of the peak-flat power consumption transition period, △p p-f is the electricity price difference of the peak-flat power consumption transition period; △p 0,p-f is the peak-flat electricity price difference at the inflection point of the dead zone; △p 1,p-f is the peak-flat electricity price difference at the inflection point of the saturation zone; k p-f is the slope of the load transfer rate in the linear zone changing with the peak-flat electricity price; is the maximum load transfer rate of the peak-flat power consumption transition period;

[0092] The load transfer rate of the flat-valley power consumption transition period is shown in the following formula:

[0093]

[0094] Among them, μ f-v is the load transfer rate of the flat-valley power consumption transition period, △p f-v is the electricity price difference of the flat-valley power consumption transition period; △p0,f-v is the flat-valley electricity price difference at the dead zone inflection point; △p 1,f-v is the flat-valley electricity price difference at the saturation zone inflection point; k f-v is the slope of the load transfer rate in the linear region changing with the flat-valley electricity price; is the maximum load transfer rate during the flat-valley electricity consumption transition period;

[0095] The load transfer rate during the peak-valley electricity consumption transition period is shown by the following formula:

[0096]

[0097] where, μ p-v is the load transfer rate during the peak-valley electricity consumption transition period, △p p-v is the electricity price difference during the peak-valley electricity consumption transition period; △p 0,p-v is the peak-valley electricity price difference at the dead zone inflection point; △p 1,p-v is the peak-valley electricity price difference at the saturation zone inflection point; k p-v is the slope of the load transfer rate in the linear region changing with the peak-valley electricity price; is the maximum load transfer rate during the peak-valley transition period;

[0098] Construct a segmented demand response load model involving the high-energy-consuming magnesium oxide industrial Internet, as shown by the following formula;

[0099]

[0100] where, P(t) is the load at time t after the demand response of the high-energy-consuming magnesium oxide industrial Internet; p, f, v respectively represent the sets of time periods of the peak electricity consumption period, the flat electricity consumption period, and the valley electricity consumption period; P 0,t is the load at time t before the demand response of the high-energy-consuming magnesium oxide industrial Internet; are respectively the average load values of the peak period and the flat period before the demand response of the high-energy-consuming magnesium oxide industrial Internet;

[0101] Then, the load power △P(t) transferred or reduced at time t by the high-energy-consuming magnesium oxide industrial Internet is shown by the following formula:

[0102] △P(t) = P(t) - P 0,t

[0103] Step S2: Based on the load power transferred or reduced at time t by the high-energy-consuming magnesium oxide industrial Internet determined in Step S1, and combined with the output of renewable energy generating units, the output of self-provided thermal power units, the charge-discharge power and start-stop status of energy storage systems, and the power purchased from the power grid, establish an optimal scheduling model for the high-energy-consuming magnesium oxide industrial Internet to participate in demand response;

[0104] Step S2.1: Construct the objective function of the optimal scheduling model for the high-energy-consuming magnesium oxide industrial Internet to participate in demand response, as shown in the following formula:

[0105]

[0106] Among them, F is the total operating cost of the magnesium oxide industrial Internet during the scheduling period; T is the total number of scheduling periods; F t is the operating cost of the magnesium oxide industrial Internet at time t; M is the total number of power generation devices in the magnesium oxide industrial Internet; are the operating cost and start-stop cost of the m-th power generation device at time t, respectively; is the output of the m-th power generation device at time t; is the start-stop state of the m-th power generation device at time t, taking 0 represents the shutdown state, taking 1 represents the operating state; G is the number of energy storage devices in the magnesium oxide industrial Internet; are the operating cost, start-stop cost and start-stop state of the j-th energy storage device at time t, respectively; is the charging and discharging power of the j-th energy storage device at time t; K is the number of shiftable loads and interruptible loads participating in demand response scheduling in the magnesium oxide industrial Internet; c t,k is the compensation price for the k-th load to participate in demand response at time t; △P k (t) is the power transferred or reduced by the k-th load at time t; c t,g is the electricity purchase price from the power grid at time t; P t,g is the electricity purchase power from the power grid at time t;

[0107] Step S2.2: Establish the constraint conditions of the demand response optimal scheduling model; the constraint conditions include power balance constraint, energy storage device charging and discharging power constraint, power generation device power constraint and demand response constraint, where:

[0108] The power balance constraint is shown in the following formula:

[0109]

[0110] Among them, P t,p is the predicted load power of the magnesium oxide industrial Internet without participating in demand response;

[0111] The energy storage device charging and discharging power constraint is shown in the following formula:

[0112]

[0113] Among them, P essc,t , P essd,t are the charging power and discharging power of the energy storage device, respectively; Pessc,max and P essd,max are the upper limits of charge and discharge of the energy storage device, respectively;

[0114] The power constraint of the power generation device is shown in the following formula:

[0115]

[0116] where are the lower and upper limits of the active power of the m-th power generation device at time t, respectively;

[0117] The demand response constraint is shown in the following formula:

[0118] △P(t) min ≤△P(t)≤△P(t) max (13)

[0119] where △P(t) min and △P(t) max are the minimum and maximum load change amounts allowed by the magnesia industrial Internet at time t, respectively;

[0120] Step S3: Use the particle swarm optimization algorithm as shown in Figure 3 to solve the optimal dispatching model of the high-energy-consuming magnesia industrial Internet participating in demand response, and obtain the optimal demand response dispatching plan;

[0121] Step S3.1: Initialize the output of the renewable energy generator set, thermal power unit, distributed energy storage, and the parameters of shiftable load and interruptible load in the optimal dispatching model of the high-energy-consuming magnesia industrial Internet participating in demand response, and randomly initialize the velocity and position of the particles;

[0122] Step S3.2: Take the objective function of the optimal dispatching model of the high-energy-consuming magnesia industrial Internet participating in demand response as the fitness function, and calculate the fitness value of each particle according to formula 9;

[0123] Step S3.3: Calculate the individual extreme value and the global extreme value of the particles;

[0124] The individual extreme value is the position with the optimal fitness value that an individual particle can search to, and the global extreme value is the position with the optimal fitness value that all particles in the population can search to;

[0125] Step S3.4: Update the velocity and position of the particles, and update the individual extreme value and the global extreme value according to the fitness value of the new particles;

[0126] Compare the fitness value of the current position of the particle with the fitness value of its historical best position. If the fitness of the current position is better, update the historical best position with the current position. At this time, the fitness value of the particle is used as the individual extreme value. Compare the fitness value of the current position of the particle with the fitness value of the global best position. If the fitness of the current position is better, update the global best position with the current position. At this time, the fitness value of the particle is used as the swarm extreme value.

[0127] The update of the particle velocity is shown in the following formula:

[0128]

[0129] where is the d-dimensional component of the velocity of particle i at the k-th iteration; w is the inertia weight; c 1 , c 2 are acceleration constants; r 1 , r 2 are random parameters with a value range of [0, 1]; pbest id is the d-dimensional component of the historical best position of particle i; is the d-dimensional component of the position of particle i at the (k - 1)-th iteration; gbest d is the d-dimensional component of the global best position in the particle swarm;

[0130] The update of the particle position is shown in the following formula:

[0131]

[0132] Step S3.5: Repeat steps S3.2 - S3.4 for iterative calculation until the number of iterations reaches the maximum value;

[0133] Step S3.6: Output the global optimal solution, that is, the global best position.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for evaluating the adjustable potential of high-energy-consuming industrial loads and demand response scheduling, Characterized in that: It includes two parts: the evaluation of the adjustable potential of high-energy-consuming industrial loads and the demand response scheduling participated by the industrial Internet of high-energy-consuming magnesium oxide; among them, the method for evaluating the adjustable potential of high-energy-consuming industrial loads is: Construct an industrial Internet system of high-energy-consuming magnesium oxide, which includes renewable energy generating units, self-provided thermal power generating units, energy recovery systems, high-energy-consuming loads, commercial loads and distributed energy storage systems; Cluster the data of high-energy-consuming loads and commercial loads in the industrial Internet system of high-energy-consuming magnesium oxide; For different clusters, calculate their load adjustment rates and load volatility respectively, and then evaluate the adjustable potential of industrial loads in the industrial Internet system of high-energy-consuming magnesium oxide; The method for demand response scheduling participated by the industrial Internet of high-energy-consuming magnesium oxide is: Divide the power consumption period into peak, flat and valley periods; Based on the load transfer rates in each power consumption transition period, namely peak-flat, flat-valley, and peak-valley periods, establish a segmented demand response load model to determine the load power transferred or reduced during a certain power consumption period of the industrial Internet of high-energy-consuming magnesium oxide; Establish an optimal scheduling model for demand response participated by the industrial Internet of high-energy-consuming magnesium oxide; Use the particle swarm optimization algorithm to solve the optimal scheduling model for demand response participated by the industrial Internet of high-energy-consuming magnesium oxide to obtain the optimal demand response scheduling plan; The evaluation of the adjustable potential of high-energy-consuming industrial loads includes the following steps: Step 1: Construct an industrial Internet system of high-energy-consuming magnesium oxide, which includes renewable energy generating units, self-provided thermal power generating units, energy recovery systems, high-energy-consuming loads, commercial loads and distributed energy storage systems; Step 2: Use the K-means clustering algorithm combined with the Canopy algorithm to cluster the data of high-energy-consuming loads and commercial loads in the industrial Internet system of high-energy-consuming magnesium oxide; Step 3: For different clusters divided by the above clustering algorithm, calculate their load adjustment rates and load volatility respectively, and then evaluate the adjustable potential of industrial loads; The load adjustment rate is an index reflecting the adjustable ability of the load in the industrial Internet of magnesium oxide when participating in demand response. The larger the load adjustment rate, the stronger the load adjustment ability; the load volatility is an index reflecting the fluctuation of the load in the industrial Internet of magnesium oxide when participating in demand response. The larger the load volatility, the greater the load peak-valley drop during this period; The demand response scheduling participated by the industrial Internet of high-energy-consuming magnesium oxide includes the following steps: Step S1: Divide the response of users to electricity price incentives into a dead zone, a linear zone and a saturation zone, and based on the load transfer rates in the power consumption transition periods of peak-flat, flat-valley, and peak-valley, construct a segmented demand response load model, and then determine the load power transferred or reduced by the industrial Internet of high-energy-consuming magnesium oxide; Step S2: Based on the load power transferred or reduced by the industrial Internet of high-energy-consuming magnesium oxide determined in Step S1, combined with the output of renewable energy generating units, self-provided thermal power generating units, the charge and discharge power and start-stop status of the energy storage system, and the power purchased from the power grid, establish an optimal scheduling model for demand response participated by the industrial Internet of high-energy-consuming magnesium oxide; Step S3: Solve the optimal dispatching scheme of demand response for the optimized dispatching model of the high-energy-consuming magnesium oxide industrial Internet participating in demand response by using the particle swarm optimization algorithm.

2. A method for evaluating the adjustable potential of high-energy-consuming industrial loads and dispatching demand response according to claim 1, characterized in that: The high-energy-consuming magnesium oxide industrial Internet system is a resource cluster formed naturally based on geographical location, and can aggregate various resources within the cluster in real time and upload them to the terminal cloud platform to participate in power grid regulation uniformly; at the same time, it can also decompose the control signals of the terminal cloud platform and send them to each resource within the cluster; the operation mode of the high-energy-consuming magnesium oxide industrial Internet system is: preferentially consume the electric energy provided by renewable energy generating units. If this part of the electric energy is sufficient to meet the industrial load electricity demand, the surplus electric energy is stored in the distributed energy storage system and sold to the power grid when the electricity price is greater than the set value; if this part of the power is not enough to meet the electricity demand, the electric energy in the energy storage system is preferentially released, or the self-provided thermal power unit generates electricity, and electricity is purchased from the power grid.

3. A method for evaluating the adjustable potential of high-energy-consuming industrial loads and dispatching demand response according to claim 1, characterized in that: The specific method of step 2 is as follows: Step 2.1: Obtain sample points at fixed time intervals for all load data, and obtain X sample points every day to form a sample data set; Step 2.2: For each sample point, calculate the distances from other sample points to this point respectively and obtain the average value ave 1 , ave 2 , ……, ave X , then calculate the average of these X average values as T 0 value; Step 2.3: Randomly select a sample point from the sample set as the initial clustering center of the Canopy algorithm; Step 2.4: Then select a sample point from the sample set and calculate the distances from this sample point to all the generated clustering centers; If the distance from the sample point to a certain cluster center is less than the value of T 0 then delete the sample point from the sample set; if the distance from the point to all cluster centers is greater than the value of T 0 then use the sample point as a new cluster center; Step 2.5: Repeat step 2.4 until the sample set is empty to obtain the k value of K-means clustering and k clustering centers; Step 2.6: Calculate the distances from each sample point in the sample set to each clustering center, and divide each sample point into the class corresponding to the nearest clustering center respectively; Step 2.7: Calculate the average value of each class as the new clustering center; Step 2.8: Repeat steps 2.6 to 2.7 until the K-means clustering reaches the maximum number of iterations.

4. A method for evaluating the adjustable potential of high-energy-consuming industrial loads and dispatching demand response according to claim 1, characterized in that: The load regulation rate R in step 3 is shown in the following formula: Among them, N is the load quantity in a certain cluster; P mi is the load power after the adjustment of the i-th load; P ni is the baseline load power of the i-th load before the adjustment; The load volatility V is shown in the following formula: Among them, σ is the standard deviation of the daily average load; is the daily average load mean; P i is the power of the i-th load in a certain cluster; is the average power of the i-th load in a certain cluster.

5. A method for evaluating the adjustable potential of high-energy-consuming industrial loads and dispatching demand response according to claim 1, characterized in that: The load transfer rate during the peak-to-flat electricity transition period is shown in the following formula: Among them, μ p-f is the load transfer rate during the peak-to-flat power consumption transition period, and △p p-f is the electricity price difference during the peak-to-flat power consumption transition period; △p 0,p-f is the peak-to-flat electricity price difference at the dead zone inflection point; △p 1,p-f is the peak-to-flat electricity price difference at the saturation zone inflection point; k p-f is the slope of the load transfer rate in the linear region changing with the peak-to-flat electricity price; is the maximum load transfer rate during the peak-to-flat power consumption transition period; The load transfer rate during the flat-to-valley electricity transition period is shown in the following formula: Among them, μ f-v is the load transfer rate during the flat-valley electricity consumption transition period, and △p f-v is the electricity price difference during the flat-valley electricity consumption transition period; △p 0,f-v is the flat-valley electricity price difference at the dead zone inflection point; △p 1,f-v is the flat-valley electricity price difference at the saturation zone inflection point; k f-v is the slope of the load transfer rate in the linear region changing with the flat-valley electricity price; is the maximum load transfer rate during the flat-valley electricity consumption transition period; The load transfer rate during the peak-to-valley electricity transition period is shown in the following formula: Among them, μ p-v is the load transfer rate during the peak-valley electricity consumption transition period, and △p p-v is the electricity price difference during the peak-valley electricity consumption transition period; △p 0,p-v is the peak-valley electricity price difference at the inflection point of the dead zone; △p 1,p-v is the peak-valley electricity price difference at the inflection point of the saturation zone; k p-v is the slope of the load transfer rate in the linear region changing with the peak-valley electricity price; is the maximum load transfer rate during the peak-valley transition period; Construct a segmented demand response load model participated by the high-energy-consuming magnesium oxide industrial Internet, as shown in the following formula; Among them, P(t) is the load at time t after the demand response of the industrial Internet of high-energy-consuming magnesium oxide; p, f, and v respectively represent the sets of time periods during peak electricity consumption, flat periods, and off-peak periods; P 0,t is the load at time t before the demand response of the industrial Internet of high-energy-consuming magnesium oxide; are respectively the average loads during the peak period and normal period before the demand response of the industrial Internet of high-energy-consuming magnesium oxide; Then the load power △P(t) transferred or reduced by the high-energy-consuming magnesium oxide industrial Internet at time t is shown in the following formula: △P(t) = P(t) - P 0,t .

6. A method for evaluating the adjustable potential of high-energy-consuming industrial loads and dispatching demand response according to claim 5, characterized in that: The specific method of step S2 is as follows: Step S2.1: Construct the objective function of the optimal scheduling model for the high-energy-consuming magnesium oxide industrial Internet to participate in demand response, as shown in the following formula: Among them, F is the total operating cost of the magnesia industrial Internet during the scheduling period; T is the total number of scheduling periods; F t is the operating cost of the magnesia industrial Internet in period t; M is the total number of power generation devices in the magnesia industrial Internet; are respectively the operating cost and start-stop cost of the m-th power generation device in period t; is the output of the m-th power generation device in period t; is the start-stop state of the m-th power generation device in period t, taking 0 represents the shutdown state, taking 1 represents the operating state; G is the number of energy storage devices in the magnesia industrial Internet; are respectively the operating cost, start-stop cost and start-stop state of the j-th energy storage device in period t; is the charging and discharging power of the j-th energy storage device in period t; K is the number of transferable loads and interruptible loads participating in demand response scheduling in the magnesia industrial Internet; c t,k is the compensation price for the k-th load participating in demand response in period t; △P k (t) is the power transferred or reduced by the k-th load in period t; c t,g is the electricity purchase price from the power grid in period t; P t,g is the electricity purchase power from the power grid in period t; Step S2.2: Establish the constraint conditions of the demand response optimal scheduling model; the constraint conditions include power balance constraint, charge and discharge power constraint of energy storage devices, power constraint of power generation devices, and demand response constraint, where: The power balance constraint is as shown in the following formula: Among them, P t,p is the predicted load power of the magnesia industrial Internet that does not participate in demand response; The charge and discharge power constraint of the energy storage device is as shown in the following formula: Among them, P essc,t , P essd,t are the charging power and discharging power of the energy storage device respectively; P essc,max , P essd,max are the upper limits of charging and discharging of the energy storage device respectively. The power constraint of the power generation device is as shown in the following formula: wherein, are respectively the lower limit and the upper limit of the active power of the m-th power generation device in the t-th period; The demand response constraint is as shown in the following formula: △P(t) min ≤△P(t)≤△P(t) max (13) where, △P(t) min and △P(t) max are respectively the minimum and maximum load change amounts allowed by the magnesia industrial Internet in the t period.

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