An optimization method and device for the load response capacity of electrolytic aluminum

Abstract

The present invention provides a method and device for optimizing the response capacity of an electrolytic aluminum load, and relates to the technical field of power system operation and control. The method comprises: 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's responsive 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 interests of the electrolytic aluminum enterprise.

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 absorbing new energy. 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 be affected.

[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:

[0008] 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;

[0009] Step 2: Obtain the declared capacity of the electrolytic aluminum load according to the comprehensive electricity cost model;

[0010] 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;

[0011] Step 4: Obtain the optimal participation response capacity of the electrolytic aluminum load according to the response power consumption model.

[0012] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, the operating parameters include direct current, on-load tap-changer ratio, and high-voltage bus voltage.

[0013] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, calculate the power of the electrolytic aluminum load before participating in the response and the range of the response capacity of the electrolytic aluminum load, including:

[0014] Based on the variation relationship between the DC side voltage and the direct current, construct the equation as:

[0015]

[0016] In the formula, is the DC side voltage, is the direct current, and E and R are intermediate parameters;

[0017] Based on the least squares principle, find the values of the intermediate parameters E and R;

[0018] Calculate the power of the electrolytic aluminum load before participating in the response as:

[0019]

[0020] By adjusting the saturable reactor for power regulation, obtain the DC side voltage regulation range as:

[0021]

[0022]

[0023] In the formula, and are respectively the minimum and maximum values allowed for the DC side voltage; and are respectively the minimum and maximum values allowed for the high-voltage bus; k is the on-load tap-changer ratio; and are respectively the minimum and maximum values of the voltage regulation; is the voltage regulation boundary;

[0024] According to the DC side voltage regulation range, solve for the minimum and maximum values of the capacity allowed for the electrolytic aluminum load to participate in the response when adjusting the saturable reactor as:

[0025]

[0026]

[0027] In the formula, , are respectively the minimum and maximum values of the capacity allowing the electrolytic aluminum load to participate in the response when adjusting the saturated reactor;

[0028] It is determined that the range of the capacity that the electrolytic aluminum load can respond to is:

[0029]

[0030] In the formula, is the capacity of the electrolytic aluminum load participating in the response.

[0031] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, the comprehensive power consumption cost model is:

[0032]

[0033]

[0034]

[0035] In the formula, is the power purchase cost of the electrolytic aluminum load participating in the response; is the electricity price during the period when the electrolytic aluminum load participates in the response; is the time required for the electrolytic aluminum load to participate in the response; is the subsidy obtained by the electrolytic aluminum load participating in the response is the clearing price of the demand response capacity; is the comprehensive power consumption cost after the electrolytic aluminum load participates in the response.

[0036] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, step 2 specifically includes: solving

[0037]

[0038]

[0039] The capacity of the electrolytic aluminum load participating in the response at this time is obtained and declared, which becomes the declared capacity of the electrolytic aluminum load.

[0040] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, the response power consumption cost model is:

[0041]

[0042]

[0043]

[0044] In the formula, Let \(\alpha\) be the assessment factor, and \(S\) be the capacity of the electrolytic aluminum load actually participating in the response. Let \(\beta\) be the assessment factor. Let \(P\) be the subsidy obtained by the electrolytic aluminum load participating in the response after introducing the assessment factor. Let \(C\) be the electricity purchase cost of the electrolytic aluminum load participating in the response after introducing the assessment factor.

[0045] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, the expression of the assessment factor is:

[0046]

[0047] In the formula, \(Q\) is the declared capacity of the electrolytic aluminum load.

[0048] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, step 4 specifically includes: solving

[0049]

[0050]

[0051] to obtain the capacity of the electrolytic aluminum load actually participating in the response at this time, that is, the optimal participation response capacity.

[0052] According to an optimization method for the response capacity of an electrolytic aluminum load provided by the present invention, the particle swarm optimization algorithm is used for solving in step 4.

[0053] In a second aspect, the present invention provides an optimization device for the response capacity of an electrolytic aluminum load, including:

[0054] A construction unit, configured to collect the operation parameters of the electrolytic aluminum load, calculate the power before the electrolytic aluminum load does not participate in the response and the range of the response capacity of the electrolytic aluminum load, and construct a comprehensive electricity consumption cost model for the electrolytic aluminum load to participate in the response;

[0055] A processing unit, configured to obtain the declared capacity of the electrolytic aluminum load according to the comprehensive electricity consumption cost model;

[0056] A modification unit, configured to introduce an assessment factor based on a predetermined response rule and modify the comprehensive electricity consumption cost model to obtain a response electricity consumption cost model;

[0057] An optimization unit, configured to obtain the optimal participation response capacity of the electrolytic aluminum load according to the response electricity consumption cost model.

[0058] The technical solution of the present invention at least has the following technical effects:

[0059] 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

[0060] 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.

[0061] In the attached picture:

[0062] Figure 1 This is a flow chart of the method for optimizing the load response capacity of electrolytic aluminum according to the present invention;

[0063] Figure 2 This is a result diagram of a specific embodiment of the present invention using a particle swarm algorithm to solve the problem;

[0064] 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

[0065] 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.

[0066] 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.

[0067] The present invention deeply studies the electricity demand response plans of each province and finds that there are assessment fees for the electricity demand response in each province, 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.

[0068] Please refer to Figure 1 , an embodiment of the present invention provides an optimization method for the load response capacity of electrolytic aluminum, including the following steps:

[0069] 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 range of the load response capacity of the electrolytic aluminum, and construct a comprehensive electricity consumption cost model for the electrolytic aluminum load to participate in the response;

[0070] Specifically, the operating parameters of the electrolytic aluminum load, that is, the existing production parameters of the electrolytic aluminum plant, include: direct current , the tap-changing ratio k of the on-load tap-changer transformer, and the high-voltage bus voltage .

[0071] Calculating the power before the electrolytic aluminum load participates in the response and the range of the load response capacity of the electrolytic aluminum includes:

[0072] Adjust the DC side voltage , monitor the change of the direct current , and construct an equation according to the change relationship between the DC side voltage and the direct current:

[0073]

[0074] Collect n groups of data. According to the least squares principle, that is, make the objective function:

[0075]

[0076] Reach the minimum value. At this time, the coupling effect is the best, and the corresponding E and R values of the electrolytic aluminum load can be obtained.

[0077] In the formula, is the actual value, is the corresponding observed value.

[0078] Collect the parameters of the electrolytic aluminum load at this time , k, , and the power before the electrolytic aluminum load participates in the response can be calculated as follows:

[0079]

[0080] Since the regulation of the saturable reactor is of great significance for suppressing the anode effect of the electrolytic cell, the power regulation method selected in the present invention is to regulate the saturable reactor. The voltage regulation depth is set to , under normal circumstances, that is, 、 are the minimum and maximum values of the voltage regulation respectively. And in order to ensure its regulation effect, the regulation boundary is taken as here, but other values can also be taken according to specific situations. Then the DC-side voltage regulation range equation can be obtained as follows:

[0081]

[0082]

[0083] In the formula, 、 are the minimum and maximum values allowed for the DC-side voltage respectively; while 、 are the minimum and maximum values allowed for the high-voltage bus of the electrolytic aluminum load respectively. According to the operation rules of the power system, the minimum value is generally 90% of the rated value, and the maximum value is 110% of the rated value; k is the tap-changing ratio of the on-load tap-changer transformer, which is obtained by collecting data.

[0084] According to this condition, the minimum and maximum values of the capacity of the electrolytic aluminum load participating in the response can be solved.

[0085]

[0086]

[0087] Among them 、 are the minimum and maximum values of the capacity of the electrolytic aluminum load allowed to participate in the response when regulating the saturable reactor.

[0088] Among them, the capacity of the electrolytic aluminum load participating in the response has constraint conditions, that is, it is not allowed to exceed the upper and lower limits of the response capacity range, otherwise it will affect the normal production of the factory. The constraint equation is as follows:

[0089]

[0090] Construct a comprehensive electricity consumption cost model for the electrolytic aluminum load participating in the response, including:

[0091] The electrolytic aluminum industry consumes a large amount of electricity, so its electricity purchase cost is:

[0092]

[0093] Its 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.

[0094] 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.

[0095] The subsidies for the participation of electrolytic aluminum load in response are:

[0096]

[0097] That Subsidy for aluminum smelter load participation response Clearing prices for demand response capacity.

[0098] 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:

[0099]

[0100] in It is the comprehensive electricity cost after the electrolytic aluminum load participates in the response.

[0101] Step 2: Obtain the declared capacity of the electrolytic aluminum load according to the comprehensive electricity cost model;

[0102] Specifically, the specific implementation steps of step 2 are as follows:

[0103] Confirm that the solution formula is as follows:

[0104]

[0105]

[0106] 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.

[0107] 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;

[0108] Specifically, the specific implementation steps of step 3 are as follows:

[0109] 3.1. Introducing Assessment Factors .

[0110] According to the predetermined response rules, if it does not fully participate in the response, there will be an assessment factor, which will reduce the response profit of the enterprise's participation 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 at this time is S, and the declared capacity is P. The specific assessment cost can be obtained according to the size relationship between S and P, and the effective capacity ratio at this time can be obtained from the relationship between the two. It is:

[0111]

[0112] According to this ratio, various calculation methods are formulated according to the predetermined response rules of each province. Therefore, according to the relationship value between the assessment price and the clearing price at this time can be obtained:

[0113]

[0114] is the relationship value between the assessment price and the clearing price, which is hereinafter referred to as the assessment factor.

[0115] 3.2. Modify the comprehensive electricity consumption cost model at this time to obtain the response electricity consumption cost model, including:

[0116] Introduce After that, the subsidy obtained by the electrolytic aluminum load participating in the response has changed, and the equation is as follows:

[0117]

[0118] Among them is the assessment factor, which is formulated by each province and is a constant less than 1; S is the actual capacity of the electrolytic aluminum load participating in the response.

[0119] And its electricity purchase cost has also changed, and the equation is as follows:

[0120]

[0121] Furthermore, the comprehensive electricity consumption cost after the electrolytic aluminum load participates in the response is:

[0122]

[0123] Step 4. According to the response electricity consumption cost model, obtain the optimal participation response capacity of the electrolytic aluminum load.

[0124] Specifically, the specific implementation steps of Step 4 are as follows:

[0125] 4.1. Modify the comprehensive electricity consumption cost and its limiting conditions for the electrolytic aluminum load to participate in the response

[0126] Modify the comprehensive electricity cost at this time, with the variable being the capacity S of the electrolytic aluminum load actually participating in the response. The objective function is to minimize it, and the equation is as follows:

[0127]

[0128] Since the actually participating response capacity is not allowed to exceed its maximum and minimum participating response capacities, there are the following constraint conditions:

[0129]

[0130] 4.2. Solve the optimal value at this time

[0131] This model is a non-linear model and is difficult to solve by ordinary methods. Therefore, the present invention introduces a particle swarm algorithm to obtain the final optimal solution and the optimal participating response capacity. The specific steps are as follows:

[0132] Assume that the independent variable values of several points of the function (i.e., the actually participating response capacity S to be obtained) are a group of particles, and the corresponding function results are the global optimal solutions of the particles. Then the particle swarm algorithm is introduced. When the actually participating response capacity S is continuously substituted into the particle values for iteration, if the global optimal solution of the particle is better than the previous particle, then use this particle value to replace the previous particle value. After multiple (50 in the present invention) iterations, the optimal solution of the function and its corresponding optimal participating response capacity can be obtained.

[0133] In the first step, assume that a group of particles are randomly generated, corresponding to the independent variable values of the function (i.e., the actually participating response capacity S to be obtained). It is a population composed of nPop particles in the target space (Dim dimensions), and this population should be represented as a vector of nPop×Dim dimensions. The i-th particle is represented as

[0134]

[0135] In the formula, is the initial position of each particle, and is a random value.

[0136] In the second step, the particle swarm generates random solutions through the initialization stage and then finds the optimal solution through iteration. In each iteration, the particle updates itself by tracking its own experience and the group experience. The specific algorithm is as follows.

[0137]

[0138]

[0139] In the formula, the first term is the memory term, the second term is the self-cognition term, and the third term is the group-cognition term. is the velocity 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 .

[0140] 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.

[0141] 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 .

[0142] 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.

[0143] 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:

[0144] 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;

[0145] A processing unit, used for obtaining the declared capacity of the electrolytic aluminum load according to a comprehensive electricity cost model;

[0146] 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;

[0147] Optimization unit, used to obtain the optimal participation response capacity of the electrolytic aluminum load according to the response electricity cost model.

[0148] The following is a specific embodiment of the present invention.

[0149] Adjust the DC side voltage of a certain aluminum plant , and monitor the corresponding DC current , as shown in Table 1.

[0150] Table 1. DC side voltage and the corresponding DC current data

[0151]

[0152] Based on the least squares principle, the intermediate parameters , . Therefore, the DC current can be collected again , and the power at its current operation can be obtained as follows:

[0153]

[0154] The selected power regulation method is to adjust the saturable reactor, and its voltage regulation depth is set to [0, 70] V under normal circumstances. And to ensure its regulation effect, the regulation boundary is taken here as = 5V, then the DC side voltage regulation range equation can be obtained as follows:

[0155]

[0156]

[0157] , are 90% and 110% of the rated value of the high-voltage bus voltage respectively. Set the rated value of the high-voltage bus voltage at this time to be 10 kV; k is 1.2.

[0158] According to this condition, the minimum and maximum values of the capacity of the electrolytic aluminum load participating in the response can be solved as:

[0159]

[0160]

[0161] The electricity purchase cost at this time can be obtained through the formula as follows:

[0162]

[0163] Among them, take 1.8; is 1 h.

[0164] The subsidy obtained by the electrolytic aluminum load participating in the response is:

[0165]

[0166] Among them, Take 3 yuan / kW.

[0167] Through the above formula calculation, the comprehensive electricity cost after the electrolytic aluminum load participates in the response can be obtained as:

[0168]

[0169] Among them, is a monotonic function, and it can be obtained that when = the comprehensive electricity cost is the lowest.

[0170] According to the local billing rules, when the ratio of the actual capacity participating in the response to the declared capacity < 50%, it is regarded as an invalid response; when 50% ≤ the ratio of the actual capacity participating in the response to the declared capacity < 80%, 60% of the actual capacity participating in the response is included in the effective capacity; when 80% ≤ the ratio of the actual capacity participating in the response to the declared capacity < 120%, all of the actual capacity participating in the response is included in the effective capacity; when the ratio of the actual capacity participating in the response to the declared capacity > 120%, 120% of the actual capacity participating in the response is included in the effective capacity. Therefore, the following relational expressions can be constructed:

[0171]

[0172] In the formula, is the declared capacity of the electrolytic aluminum load, and S is the actual capacity of the electrolytic aluminum load participating in the response.

[0173] Therefore, the comprehensive electricity cost formula can be modified. Among them, the subsidy obtained by the modified participation in the response is:

[0174]

[0175] Among them, Take 0.6.

[0176] And its electricity purchase cost has also changed. The equation is as follows:

[0177]

[0178] Therefore, it is necessary to solve:

[0179]

[0180]

[0181] The actual response capacity of the electrolytic aluminum load at this time is obtained, that is, the optimal response capacity.

[0182] Using the particle swarm algorithm, the specific algorithm process is:

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Other embodiments of the present invention will be readily apparent to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the invention following the general principles of the invention and including known common general knowledge or conventional technical means in the technical field not disclosed by the present invention. It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. An optimization method for 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; The comprehensive electricity cost model is: Wherein, is the electricity purchase cost for the electrolytic aluminum load to participate in the response; is the electricity price during the period when the electrolytic aluminum load participates in the response; is the time required for the electrolytic aluminum load to participate in the response; is the subsidy obtained by the electrolytic aluminum load for participating in the response; is the clearing price of the demand response capacity; is the comprehensive electricity consumption cost after the electrolytic aluminum load participates in the response; P f is the power of the electrolytic aluminum load before it participates in the response; is the capacity of the electrolytic aluminum load to participate in the response; The response electricity cost model is: Wherein, is the assessment factor, S is the capacity of the actual participation in the response of the electrolytic aluminum load, is the assessment factor, is the subsidy obtained by the electrolytic aluminum load participating in the response after introducing the assessment factor, is the power purchase cost of the electrolytic aluminum load participating in the response after introducing the assessment factor.

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

3. The optimization method for the load response capacity of electrolytic aluminum according to claim 2, wherein 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: Wherein, is the DC side voltage, is the DC current, and 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: Wherein, and are respectively the minimum and maximum values allowed for the DC-side voltage; and are respectively the minimum and maximum values allowed for the high-voltage bus; k is the tap ratio of the on-load tap-changer transformer; and are respectively the minimum and maximum values of the voltage regulation; 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: Wherein, and are respectively the minimum and maximum values of the capacity allowing the electrolytic aluminum load to participate in the response when adjusting the saturation reactor; The responsive capacity range of electrolytic aluminum load is determined to be: In the formula, is the capacity of the electrolytic aluminum load participating in the response.

4. The optimization method for the load response capacity of electrolytic aluminum according to claim 1, 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.

5. The optimization method of the load response capacity of electrolytic aluminum according to claim 1, wherein The expression of the assessment factor is: In the formula, is the declared capacity of the electrolytic aluminum load.

6. The optimization method for the load response capacity of electrolytic aluminum according to claim 1, 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.

7. The optimization method for the load response capacity of electrolytic aluminum according to claim 6, characterized in that In step 4, a particle swarm algorithm is used for solving.

8. An optimization device for the load response capacity of electrolytic aluminum, 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; An optimization unit, used for obtaining the optimal participating response capacity of the electrolytic aluminum load according to the response electricity cost model; The comprehensive electricity cost model is: Wherein, is the electricity purchase cost for the electrolytic aluminum load to participate in the response; is the electricity price during the period when the electrolytic aluminum load participates in the response; is the time required for the electrolytic aluminum load to participate in the response; is the subsidy obtained by the electrolytic aluminum load for participating in the response; is the clearing price of the demand response capacity; is the comprehensive electricity consumption cost after the electrolytic aluminum load participates in the response; P f is the power of the electrolytic aluminum load before it participates in the response; is the capacity of the electrolytic aluminum load to participate in the response; The response electricity cost model is: In the formula, is the assessment factor, S is the capacity of the actual participation in the response of the electrolytic aluminum load, is the assessment factor, is the subsidy obtained by the electrolytic aluminum load participating in the response after introducing the assessment factor, is the electricity purchase cost of the electrolytic aluminum load participating in the response after introducing the assessment factor.

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