Electrolytic aluminum load response capacity optimization method and device

Abstract

The invention provides an electrolytic aluminum load response capacity optimization method and device, and relates to the technical field of power system operation and control. The method comprises the following steps: collecting operation parameters of an electrolytic aluminum load, calculating the power of the electrolytic aluminum load before the electrolytic aluminum load participates in response and the response capacity range of the electrolytic aluminum load, and constructing a comprehensive power utilization cost model of the electrolytic aluminum load participating in response; obtaining the declaration capacity of the electrolytic aluminum load according to the comprehensive power utilization cost model; based on a predetermined response rule, introducing an assessment factor, and modifying the comprehensive power consumption cost model to obtain a response power consumption cost model; and obtaining the optimal participation response capacity of the electrolytic aluminum load according to the response power consumption cost model. According to the method, the optimal participation response capacity of the electrolytic aluminum load can be determined, and the benefit maximization of an electrolytic aluminum enterprise is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and control, and in particular to a method and device for optimizing the load response capacity of electrolytic aluminum. Background Art

[0002] At present, it is urgent to deal with the problem of new energy consumption. The country has continuously emphasized green and sustainable development. In order to further deepen the management of power demand, actively respond to the difficulties of power grid peak regulation, promote the consumption of clean energy, play an important role in regulating the balance of power supply and demand, pay attention to the transformation and upgrading of various industries, and add environmental protection pressure on the supply side, various provinces have issued power demand response plans to encourage high-energy-consuming enterprises to actively participate in power grid regulation. Many high-energy-consuming enterprises are facing the risk of elimination due to their inability to change their original production methods, and high-energy-consuming and high-load industries represented by electrolytic aluminum are the first to bear the brunt.

[0003] At the same time, how to solve the problem of local consumption of clean energy has become one of the main factors hindering the development of clean energy. Since wind power itself is highly random, intermittent and uncontrollable, the clean energy supply cannot match the energy consumption at the load end, so it cannot be directly connected to power-consuming equipment. If it is connected to the power grid, it will seriously affect the stability of the power grid. In addition, many areas are severely affected by wind power curtailment and power rationing, and there are a large number of wind energy resources that cannot be used.

[0004] Therefore, aluminum smelters urgently need to accelerate transformation and upgrading, reduce energy consumption, and achieve local consumption of large amounts of clean energy. However, aluminum smelters are not clear about how much capacity they can participate in when participating in grid interactive regulation during transformation and upgrading, and how to maximize their own profits.

[0005] The traditional method is to calculate based on the company's pre-participation situation and output its optimal participation response capacity, but this method is not the best solution. Summary of the invention

[0006] The purpose of the present invention is to provide a method and device for optimizing the load response capacity of electrolytic aluminum, which is used to solve the problems of the prior art that electrolytic aluminum enterprises are unable to determine the capacity participating in the response and how to maximize profits, and can determine the optimal participating response capacity and achieve profit maximization.

[0007] In order to achieve the above objectives, in a first aspect, the present invention provides a method for optimizing the load response capacity of electrolytic aluminum, comprising: Step 1: Collect the operating parameters of the electrolytic aluminum load, calculate the power before the electrolytic aluminum load participates in the response and the responsive capacity range of the electrolytic aluminum load, and construct a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; Step 2: Obtain the declared capacity of the electrolytic aluminum load according to the comprehensive electricity cost model; Step 3: Based on the predetermined response rules, the assessment factors are introduced to modify the comprehensive electricity cost model to obtain the response electricity cost model; Step 4: According to the response electricity cost model, the optimal participating response capacity of the electrolytic aluminum load is obtained.

[0008] According to a method for optimizing the load response capacity of electrolytic aluminum provided by the present invention, the operating parameters include direct current, on-load tap-changing transformer ratio, and high-voltage bus voltage.

[0009] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, the power of the electrolytic aluminum load before it participates in the response and the response capacity range of the electrolytic aluminum load are calculated, including: According to the changing relationship between DC side voltage and DC current, the equation is constructed as follows:

[0010] In the formula, is the DC side voltage, is the DC current, E and R are intermediate parameters; Based on the principle of least squares method, the values ​​of intermediate parameters E and R are obtained; The power before the electrolytic aluminum load participates in the response is calculated as:

[0011] By adjusting the saturated reactor to adjust the power, the DC side voltage adjustment range is:

[0012]

[0013] In the formula, , They are the minimum and maximum values ​​allowed for the DC side voltage respectively; , are the minimum and maximum values ​​allowed by the high-voltage busbar respectively; k is the transformation ratio of the on-load tap-changing transformer; , They are the minimum and maximum values ​​of voltage regulation respectively; is the voltage regulation boundary; According to the DC side voltage adjustment range, the minimum and maximum capacity of the electrolytic aluminum load that is allowed to participate in the response when adjusting the saturated reactor are solved as follows:

[0014]

[0015] In the formula, , They are respectively the minimum and maximum capacity of the electrolytic aluminum load that is allowed to participate in the response when adjusting the saturated reactor; The responsive capacity range of electrolytic aluminum load is determined to be:

[0016] In the formula, The capacity of the electrolytic aluminum load participating in the response.

[0017] According to an optimization method for electrolytic aluminum load response capacity provided by the present invention, the comprehensive electricity cost model is:

[0018]

[0019]

[0020] In the formula, The cost of purchasing electricity for the participation of electrolytic aluminum load in response; The electricity price for the electrolytic aluminum load during the response period; The time required for the electrolytic aluminum load to participate in the response; Subsidy for aluminum smelter load participation response clearing prices for demand response capacity; It is the comprehensive electricity cost after the electrolytic aluminum load participates in the response.

[0021] According to an optimization method for electrolytic aluminum load response capacity provided by the present invention, step 2 specifically includes: solving

[0022]

[0023] The capacity of the electrolytic aluminum load participating in the response at this time is obtained and reported as the reported capacity of the electrolytic aluminum load.

[0024] According to an optimization method for electrolytic aluminum load response capacity provided by the present invention, the response electricity cost model is:

[0025]

[0026]

[0027] In the formula, is the assessment factor, S is the capacity of the electrolytic aluminum load actually participating in the response, As the assessment factor, Subsidy for the electrolytic aluminum load participation response after the introduction of assessment factors. It is the electricity purchase cost of the electrolytic aluminum load participation response after the assessment factors are introduced.

[0028] According to an optimization method for electrolytic aluminum load response capacity provided by the present invention, the expression of the assessment factor is:

[0029] In the formula, The declared capacity of electrolytic aluminum load.

[0030] According to an optimization method for electrolytic aluminum load response capacity provided by the present invention, step 4 specifically includes: solving

[0031]

[0032] The actual response capacity of the electrolytic aluminum load at this time is obtained, that is, the optimal response capacity.

[0033] According to the optimization method of electrolytic aluminum load response capacity provided by the present invention, a particle swarm algorithm is used for solving in step 4.

[0034] In a second aspect, the present invention provides an optimization device for electrolytic aluminum load response capacity, comprising: A construction unit is used to collect the operating parameters of the electrolytic aluminum load, calculate the power of the electrolytic aluminum load before it participates in the response and the responsive capacity range of the electrolytic aluminum load, and construct a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; A processing unit, used for obtaining the declared capacity of the electrolytic aluminum load according to a comprehensive electricity cost model; A modification unit, used for introducing an assessment factor based on a predetermined response rule, modifying the comprehensive electricity cost model, and obtaining a response electricity cost model; The optimization unit is used to obtain the optimal participating response capacity of the electrolytic aluminum load according to the response electricity cost model.

[0035] The technical solution of the present invention has at least the following technical effects: The present invention provides an optimization method and device for the response capacity of an electrolytic aluminum load, the method comprising: collecting the operating parameters of the electrolytic aluminum load, calculating the power of the electrolytic aluminum load before it participates in the response and the range of the electrolytic aluminum load response capacity, and constructing a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; according to the comprehensive electricity cost model, obtaining the declared capacity of the electrolytic aluminum load; based on a predetermined response rule, introducing an assessment factor, modifying the comprehensive electricity cost model, and obtaining a response electricity cost model; according to the response electricity cost model, obtaining the optimal response capacity of the electrolytic aluminum load. The present invention can determine the optimal response capacity of the electrolytic aluminum load and maximize the benefits of the electrolytic aluminum enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] In the attached picture: Figure 1 This is a flow chart of the method for optimizing the load response capacity of electrolytic aluminum according to the present invention; Figure 2 This is a result diagram of a specific embodiment of the present invention using a particle swarm algorithm to solve the problem; Figure 3 This is a graph showing the relationship between the minimum electricity purchase cost and the number of iterations using a particle swarm algorithm according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Some embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0040] The present invention deeply studies the power demand response schemes of various provinces and finds that there are assessment fees for power demand response in various provinces, that is, additional fees need to be paid or obtained when the actual response capacity is different from the declared capacity. Therefore, the actual optimal response capacity is obtained by studying this relationship.

[0041] See also Figure 1 The embodiment of the present invention provides a method for optimizing the load response capacity of electrolytic aluminum, comprising the following steps: Step 1: Collect the operating parameters of the electrolytic aluminum load, calculate the power before the electrolytic aluminum load participates in the response and the responsive capacity range of the electrolytic aluminum load, and construct a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; Specifically, the operating parameters of the electrolytic aluminum load, that is, the existing production parameters of the electrolytic aluminum plant, include: DC current , on-load tap-changing transformer ratio k, high-voltage bus voltage .

[0042] Calculate the power before the electrolytic aluminum load participates in the response and the responsive capacity range of the electrolytic aluminum load, including: Regulate DC voltage , monitor the DC current According to the change relationship between the DC side voltage and DC current, the equation is constructed as follows:

[0043] Collect n groups of data and according to the principle of least squares method, let the objective function be:

[0044] When the minimum value is reached, the coupling effect is optimal, and the E and R values ​​corresponding to the electrolytic aluminum load can be obtained.

[0045] In the formula, is the actual value, is the corresponding observed value.

[0046] Collect the parameters of electrolytic aluminum load at this time , k, , the power before the electrolytic aluminum load participates in the response can be calculated as follows:

[0047] Since the regulation of saturated reactor is of great significance for smoothing the anode effect of electrolytic cell, the power regulation method selected in the present invention is to regulate the saturated reactor. The voltage regulation depth is normally set to [ , ],Right now , are the minimum and maximum values ​​of voltage regulation respectively. In order to ensure its regulation effect, the regulation boundary is taken as , but other values ​​can also be taken according to specific circumstances. Then the DC side voltage regulation range equation can be obtained as follows:

[0048]

[0049] In the formula, , are the minimum and maximum values ​​allowed for the DC side voltage respectively; and , They are the minimum and maximum values ​​allowed for the electrolytic aluminum load high-voltage busbar, which can be obtained according to the power system operation rules. The minimum value is generally 90% of the rated value, and the maximum value is 110% of the rated value; k is the transformation ratio of the on-load tap-changing transformer, which is obtained by collecting data.

[0050] Based on this condition, the minimum and maximum capacity of the electrolytic aluminum load participating in the response can be solved.

[0051]

[0052]

[0053] in , The minimum and maximum capacity values ​​that the electrolytic aluminum load is allowed to participate in when adjusting the saturated reactor.

[0054] Among them, the capacity of electrolytic aluminum load participating in the response There are constraints, that is, the upper and lower limits of the response capacity range are not allowed to be exceeded, otherwise it will affect the normal production of the factory. The constraint equation is as follows:

[0055] Construct a comprehensive electricity cost model for electrolytic aluminum load participation response, including: The electrolytic aluminum industry consumes a lot of electricity, so its electricity purchase cost is:

[0056] That The electricity purchase cost for the electrolytic aluminum load to participate in the response, is the electricity price during the response period of the electrolytic aluminum load. Since the regulation period is not long and relatively concentrated, it is approximately regarded as a constant; The time required for the electrolytic aluminum load to respond.

[0057] Since the output of electrolytic aluminum should not be affected when the load participates in the response, the cost of electrolytic aluminum materials is ignored here.

[0058] The subsidies for the participation of electrolytic aluminum load in response are:

[0059] That Subsidy for aluminum smelter load participation response Clearing prices for demand response capacity.

[0060] Then, the above formula can be used to calculate the comprehensive electricity cost of the electrolytic aluminum load participating in the joint demand response payment:

[0061] in It is the comprehensive electricity cost after the electrolytic aluminum load participates in the response.

[0062] Step 2: Obtain the declared capacity of the electrolytic aluminum load according to the comprehensive electricity cost model; Specifically, the specific implementation steps of step 2 are as follows: Confirm that the solution formula is as follows:

[0063]

[0064] Since the objective function is a monotonic linear function, the linear programming model can be used to simply solve it to obtain the capacity of the electrolytic aluminum load participating in the response at this time. , so it is reported and becomes the reported capacity. Step 3: Based on the predetermined response rules, the assessment factors are introduced to modify the comprehensive electricity cost model to obtain the response electricity cost model; Specifically, the specific implementation steps of step 3 are as follows: 3.1. Introducing Assessment Factors .

[0065] According to the predetermined response rules, if the enterprise does not fully participate in the response, there will be an assessment factor, which will reduce the profit of the enterprise participating in the response. At this time, the profit of participating in the response has changed. Assume that the actual capacity of the electrolytic aluminum load participating in the response is S, and the declared capacity is P. According to the relationship between S and P, the specific assessment fee can be obtained, and the effective capacity ratio at this time can be obtained from the relationship between the two. for:

[0066] Based on this ratio, various calculation methods are formulated according to the response rules predetermined by each province, so Obtain the relationship between the assessment price and the clearing price at this time:

[0067] It is the relationship between the assessment price and the clearing price, referred to here as the assessment factor.

[0068] 3.2. Modify the comprehensive electricity cost model at this time to obtain the response electricity cost model, including: Introduction After that, the subsidy received by the electrolytic aluminum load participation response changes, and the equation is as follows:

[0069] in It is an assessment factor, which is formulated by each province and is a constant less than 1; S is the capacity of the electrolytic aluminum load that actually participates in the response.

[0070] And its electricity purchase cost has also changed, the equation is as follows:

[0071] Furthermore, the comprehensive electricity cost after the electrolytic aluminum load participates in the response is:

[0072] Step 4: According to the response electricity cost model, the optimal participating response capacity of the electrolytic aluminum load is obtained.

[0073] Specifically, the specific implementation steps of step 4 are as follows: 4.1. Modify the comprehensive electricity cost and its restriction conditions for the electrolytic aluminum load to participate in the response The comprehensive electricity cost at this time is modified, and the variable is the capacity S of the electrolytic aluminum load that actually participates in the response. The objective function is to obtain its minimum value, and the equation is as follows:

[0074] Since the actual response capacity is not allowed to exceed the maximum and minimum response capacity, the following restrictions apply:

[0075] 4.2. Find the optimal value at this time This model is a nonlinear model and is difficult to solve using common methods. Therefore, the present invention introduces a particle swarm algorithm to obtain the final optimal solution and the optimal participant response capacity. The specific steps are: Assume that the values ​​of the function's independent variables (i.e., the actual participating response capacity S) are a group of particles, and the corresponding function result is the global optimal solution of the particles. Therefore, the particle swarm algorithm is introduced. When the actual participating response capacity S is continuously substituted into the particle value for iteration, if the global optimal solution of the particle is better than the previous particle, the particle value replaces the previous particle value. After multiple iterations (50 in this invention), the optimal solution of the function and its corresponding optimal participating response capacity can be obtained.

[0076] In the first step, assume that a group of particles are randomly generated. The corresponding function's independent variable value (i.e., the actual participating response capacity S) is a population consisting of nPop particles in the target space (Dim dimension). Then the population should be represented as a vector of nPop×Dim dimension, and the i-th particle is represented as

[0077] In the formula, is the initial position of each particle, which is a random value.

[0078] In the second step, the particle swarm generates random solutions through the initialization phase, and then finds the optimal solution through iteration. In each iteration, the particles update themselves by tracking their own experience and group experience. The specific algorithm is as follows.

[0079]

[0080] In the formula, the first item is the memory item, the second item is the self-cognition item, and the third item is the group cognition item. is the speed of the particle, rand() is a random number between (0, 1), is the current position of the particle, that is, the value of the optimal participation response capacity at this time. and It is a learning factor, which takes different values ​​according to different situations to achieve the best effect. is the optimal solution for the ith particle, is the optimal solution for the entire population, which corresponds to the function value being solved .

[0081] In the third step, the output result of the second step (the optimal participating response capacity and the corresponding function value at this time) is output. If the optimal solution of the entire population at this time is better than the previous value, the speed and position of each particle (i.e. the optimal participating response capacity) are updated, and the fitness of each particle is calculated. Then the individual and group historical optimal fitness value and position of each particle are updated.

[0082] The fourth step is to continuously iterate and update (i.e. repeat the second and third steps) until the maximum number of iterations or the minimum difference between the fitness values ​​of two iterations is reached, that is, the end condition is met, and the optimal solution, i.e. the optimal participating response capacity and the minimum .

[0083] According to the specific value of the capacity of the electrolytic aluminum load participating in the response, the particle has a certain limited range, but the principle is the same. The optimal participating response capacity to be solved is the optimal fitness in the algorithm. It is necessary to first set the range of the particle population and then randomly generate a particle population, input the learning factor of the response, the number of iterations and other parameters, and then use the previously constructed comprehensive electricity cost formula to substitute it for the solution. It is worth noting that the algorithm solves the maximum value, so when using it, a negative sign should be added before the function to be sought, and the absolute value of the negative result is the minimum electricity purchase cost sought, and at this time the global best position is the optimal participating response capacity.

[0084] Based on the same inventive concept, another embodiment of the present invention provides an optimization device for electrolytic aluminum load response capacity, which corresponds to the method of the above embodiment, and includes: A construction unit is used to collect the operating parameters of the electrolytic aluminum load, calculate the power of the electrolytic aluminum load before it participates in the response and the responsive capacity range of the electrolytic aluminum load, and construct a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; A processing unit, used for obtaining the declared capacity of the electrolytic aluminum load according to a comprehensive electricity cost model; A modification unit, used for introducing an assessment factor based on a predetermined response rule, modifying the comprehensive electricity cost model, and obtaining a response electricity cost model; The optimization unit is used to obtain the optimal participating response capacity of the electrolytic aluminum load according to the response electricity cost model.

[0085] The following is a specific embodiment of the present invention.

[0086] Regulating the DC voltage of an aluminum plant , monitor the corresponding DC current , as shown in Table 1.

[0087] Table 1. DC side voltage The corresponding DC current Data

[0088] Based on the principle of least squares method, the intermediate parameters can be obtained , . So the DC current can be collected again , and find its current operating power, as follows:

[0089] The selected power regulation method is to adjust the saturated reactor. The voltage regulation depth is normally set to [0, 70] V. In order to ensure the regulation effect, the regulation boundary is taken as =5V, the DC side voltage regulation range equation is as follows:

[0090]

[0091] , The rated voltage of the high-voltage bus is 90% and 110% respectively. is 10kV; k is 1.2.

[0092] According to this condition, the minimum and maximum capacity of the electrolytic aluminum load participating in the response can be solved as follows:

[0093]

[0094] The electricity purchase cost at this time can be calculated through the formula as follows:

[0095] in, Take 1.8; For 1 hour.

[0096] The subsidies for electrolytic aluminum load participation response are:

[0097] in, Take 3 yuan / kW.

[0098] The above formula can be used to calculate the comprehensive electricity cost after the electrolytic aluminum load participates in the response:

[0099] in, is a monotonic function, we can get = When the power consumption is low, the comprehensive electricity cost is the lowest.

[0100] According to local billing rules, when the ratio of the actual response capacity to the declared capacity is <50%, it is considered an invalid response; when 50%≤the ratio of the actual response capacity to the declared capacity<80%, 60% of the actual response capacity is included in the effective capacity; when 80%≤the ratio of the actual response capacity to the declared capacity<120%, the entire actual response capacity is included in the effective capacity; when the ratio of the actual response capacity to the declared capacity>120%, 120% of the actual response capacity is included in the effective capacity. Therefore, the following relationship can be constructed:

[0101] In the formula, is the declared capacity of the electrolytic aluminum load, and S is the capacity of the electrolytic aluminum load that actually participates in the response.

[0102] Therefore, the comprehensive electricity cost formula can be modified, where the modified subsidy for participating in the response is:

[0103] in, Take 0.6.

[0104] And its electricity purchase cost has also changed, the equation is as follows:

[0105] Therefore, we need to solve:

[0106]

[0107] The actual response capacity of the electrolytic aluminum load at this time is obtained, that is, the optimal response capacity.

[0108] Using the particle swarm algorithm, the specific algorithm process is: Enter specific initial values: number of particles is 10, number of iterations is 50, individual and social learning factors are 2, maximum particle speed is 1.2, and upper and lower bounds of particles are set. After that, the particle position and velocity are initialized, and then its fitness is calculated and each position is continuously marked. Then it enters the iteration process, iterates 50 times, and continuously compares its fitness. If its fitness is higher than the previous fitness, its position and fitness value are updated until the iteration is completed and the optimal value is obtained. The positions of the particles in the iteration process are represented as follows Figure 2 As the number of iterations increases, the optimal solution changes as follows Figure 3 As shown by Figure 3 From the changing curve of , we can see that when the number of iterations increases, the optimal solution remains approximately unchanged. Therefore, the optimization effect here is better and the robustness is stronger.

[0109] Thus, the optimal position and the optimal fitness can be obtained, that is, the optimal participation response capacity of the variable desired in this embodiment is 7.1905×10 4 kilowatts, and its minimum electricity purchase cost is 2.3227×10 9 Yuan.

[0110] In summary, the optimization method and device for the electrolytic aluminum load response capacity provided by the present invention collects the operating parameters of the electrolytic aluminum load, and first calculates its current working power and the upper and lower limits of the capacity that can participate in the response. Then, a comprehensive electricity cost model for enterprises to participate in the joint demand response payment is constructed to measure the pros and cons of enterprises participating in the response and calculate the declared capacity. Then, according to the response rules predetermined by each province, the assessment factors are introduced to modify the above-mentioned comprehensive electricity cost model. Since the objective function of the model at this time is a nonlinear function, the particle swarm algorithm is introduced to solve it, and the capacity that the electrolytic aluminum enterprise should actually respond to can be obtained. The present invention is based on further optimization of the declared capacity, which can maximize the interests of the electrolytic aluminum enterprise.

[0111] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the embodiments disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing the load response capacity of electrolytic aluminum, characterized in that: include: Step 1: Collect the operating parameters of the electrolytic aluminum load, calculate the power before the electrolytic aluminum load participates in the response and the responsive capacity range of the electrolytic aluminum load, and construct a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; Step 2: According to the comprehensive electricity cost model, the declared capacity of the electrolytic aluminum load is obtained; Step 3: Based on the predetermined response rules, the assessment factors are introduced to modify the comprehensive electricity cost model to obtain a response electricity cost model; Step 4: According to the response electricity cost model, the optimal participating response capacity of the electrolytic aluminum load is obtained.

2. The method for optimizing the electrolytic aluminum load response capacity according to claim 1, characterized in that: The operating parameters include direct current, on-load tap-changing transformer ratio, and high-voltage bus voltage.

3. The method for optimizing the electrolytic aluminum load response capacity according to claim 2, characterized in that: The calculation of the power before the electrolytic aluminum load participates in the response and the responsive capacity range of the electrolytic aluminum load includes: According to the changing relationship between DC side voltage and DC current, the equation is constructed as follows: In the formula, is the DC side voltage, is the DC current, E and R are intermediate parameters; Based on the principle of least squares method, the values ​​of intermediate parameters E and R are obtained; The power before the electrolytic aluminum load participates in the response is calculated as: By adjusting the saturated reactor to adjust the power, the DC side voltage adjustment range is: In the formula, , They are the minimum and maximum values ​​allowed for the DC side voltage respectively; , are the minimum and maximum values ​​allowed by the high-voltage busbar respectively; k is the transformation ratio of the on-load tap-changing transformer; , They are the minimum and maximum values ​​of voltage regulation respectively; is the voltage regulation boundary; According to the DC side voltage adjustment range, the minimum and maximum capacity of the electrolytic aluminum load that is allowed to participate in the response when adjusting the saturated reactor are solved as follows: In the formula, , They are respectively the minimum and maximum capacity of the electrolytic aluminum load that is allowed to participate in the response when adjusting the saturated reactor; The responsive capacity range of electrolytic aluminum load is determined to be: In the formula, The capacity of the electrolytic aluminum load participating in the response.

4. The method for optimizing the electrolytic aluminum load response capacity according to claim 3, characterized in that: The comprehensive electricity cost model is: In the formula, The cost of purchasing electricity for the participation of electrolytic aluminum load in response; The electricity price for the electrolytic aluminum load during the response period; The time required for the electrolytic aluminum load to participate in the response; Subsidy for aluminum electrolytic load participation response clearing prices for demand response capacity; It is the comprehensive electricity cost after the electrolytic aluminum load participates in the response.

5. The method for optimizing the electrolytic aluminum load response capacity according to claim 4, characterized in that: The step 2 specifically includes: solving The capacity of the electrolytic aluminum load participating in the response at this time is obtained and reported as the reported capacity of the electrolytic aluminum load.

6. The method for optimizing the electrolytic aluminum load response capacity according to claim 5, characterized in that: The response electricity cost model is: In the formula, is the assessment factor, S is the capacity of the electrolytic aluminum load actually participating in the response, As the assessment factor, Subsidy for the electrolytic aluminum load participation response after the introduction of assessment factors. It is the electricity purchase cost of the electrolytic aluminum load participation response after the assessment factors are introduced.

7. The method for optimizing the electrolytic aluminum load response capacity according to claim 6, characterized in that: The expression of the assessment factor is: In the formula, The declared capacity of electrolytic aluminum load.

8. The method for optimizing the electrolytic aluminum load response capacity according to claim 6, characterized in that: The step 4 specifically includes: solving The actual response capacity of the electrolytic aluminum load at this time is obtained, that is, the optimal response capacity.

9. The method for optimizing the electrolytic aluminum load response capacity according to claim 8, characterized in that: In step 4, a particle swarm algorithm is used for solving.

10. An optimization device for electrolytic aluminum load response capacity, characterized in that: include: A construction unit is used to collect the operating parameters of the electrolytic aluminum load, calculate the power of the electrolytic aluminum load before it participates in the response and the responsive capacity range of the electrolytic aluminum load, and construct a comprehensive electricity cost model for the electrolytic aluminum load to participate in the response; A processing unit, used to obtain the declared capacity of the electrolytic aluminum load according to the comprehensive electricity cost model; A modification unit, configured to introduce an assessment factor based on a predetermined response rule, modify the comprehensive electricity cost model, and obtain a response electricity cost model; The optimization unit is used to obtain the optimal participating response capacity of the electrolytic aluminum load according to the response electricity cost model.

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