Quantitative evaluation method for industry-by-industry power regulation capability margin

Abstract

The invention relates to an industry-by-industry power regulation capability margin quantitative evaluation method, which comprises the following steps of 1, firstly, proposing a group feature identification technology of industry-by-industry flexible resources; 2, a data driving method is used, and a flexible resource group feature recognition technology oriented to second and third industry loads is provided; 3, providing a quantitative evaluation method and a group intelligent matching method for the adjustment capability of the electric vehicle load; 4, then, a quantitative evaluation method and a group intelligent matching method for the adjusting capacity of the air conditioner load are put forward; and step 5, finally, providing an electrolytic aluminum load regulation capability quantitative evaluation method and a group intelligent matching method. The method has the advantages that the electric vehicle aggregation model is established, linear processing is carried out, the regulation capability quantitative evaluation method is put forward, and the swarm intelligence method is put forward.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and specifically relates to a quantitative assessment method for electric power regulation capacity margin by industry and sector. Background Art

[0002] In recent years, the proportion of new energy sources represented by wind power and photovoltaics in the power grid has increased year by year, which has put forward higher requirements on the power grid's ability to absorb renewable energy. At the same time, there is huge adjustable potential on the load side. Adjustable residential loads represented by HVAC and electric vehicles and adjustable industrial loads represented by flexible industrial electrolytic aluminum are gradually playing an important role in maintaining the safe and stable operation of the power grid, improving the power grid's ability to absorb renewable energy, reducing the peak-to-valley difference of the power system, and economic dispatch of the power grid. However, air-conditioning loads are distributed and dispersed demand response resources, and the power can only change discretely. The aggregate modeling and coordinated regulation of large-scale air-conditioning loads play a very important role in improving the flexibility of the power system. Existing studies often design aggregate regulation strategies for air-conditioning / electric vehicle loads for a single scenario and a single demand response function, which makes it difficult to fully utilize their flexibility. How to consider the diversity of demand response functions in design There is room for further improvement in the corresponding aggregation control strategy, and based on this, the model interface between large-scale air-conditioning load / electric vehicle and power system optimization scheduling is established to enhance the flexibility of the system; on the other hand, for the research on electrolytic aluminum load modeling, most of the existing models use finite element models. Although the factors considered are very detailed, it will also bring a heavy computational burden, making this method difficult to embed into the power system optimization scheduling problem; and in the research using the energy balance equation and heat calculation formula, only the change of the aluminum electrolytic cell from steady state to another steady state is considered, ignoring the dynamic process in the middle; the existing research lacks an electrolytic aluminum load model that can reflect the dynamic characteristics of electrolytic aluminum temperature and can be directly applied to the power system optimization scheduling problem; therefore, it is very necessary to provide an industry-specific and industrial-specific quantitative evaluation method for the margin of power regulation capacity, which establishes an electric vehicle aggregation model and linearization processing, proposes a quantitative evaluation method for regulation capacity, and proposes a swarm intelligence method. Summary of the invention

[0003] 1. Technical issues

[0004] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0005] 1. Existing studies only design aggregate control strategies for air conditioning / electric vehicle loads for a single scenario and a single demand response function, which makes it difficult to fully utilize their flexibility;

[0006] 2. For electrolytic aluminum load modeling, most existing models use finite element models, which will bring heavy computational burdens, making it difficult to embed this method into the optimization scheduling problem of the power system;

[0007] 3. The research using the energy balance equation and heat calculation formula only considers the change of the aluminum electrolytic cell from a steady state to another steady state, ignoring the dynamic process in the middle;

[0008] 4. Existing research lacks an electrolytic aluminum load model that can reflect the dynamic characteristics of electrolytic aluminum temperature and can be directly applied to the optimal scheduling problem of power systems.

[0009] (II) Technical solution

[0010] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for quantitatively evaluating the margin of power regulation capacity by industry and sector, which establishes an electric vehicle aggregation model and linearization processing, proposes a quantitative evaluation method for regulation capacity, and proposes a swarm intelligence method.

[0011] The object of the present invention is achieved by: a quantitative evaluation method for electric power regulation capacity margin by industry and by sector, the method comprising the following steps:

[0012] Step 1: First, propose a group feature recognition technology for flexible resources by industry and sector;

[0013] Step 2: Then, using data-driven methods, a flexible resource group feature identification technology for the secondary and tertiary industry loads is proposed;

[0014] Step 3: Propose a quantitative evaluation method for the regulation capability of electric vehicle loads and a swarm intelligence matching method;

[0015] Step 4: Furthermore, a quantitative evaluation method of the regulation capacity of air-conditioning load and a group intelligent matching method are proposed;

[0016] Step 5: Finally, a quantitative evaluation method for the regulation capacity of electrolytic aluminum load and a swarm intelligence matching method are proposed.

[0017] (III) Beneficial effects: The present invention aims to solve the following main problems in the prior art

[0018] 1. Lack of quantitative evaluation methods for regulating capacity by industry: At present, the evaluation methods for regulating capacity of power systems usually only start from the perspective of the whole or a single load type, and lack specific analysis for different industries. This makes it difficult for the system to accurately identify the characteristics and potential of adjustable resources during the dispatching process, affecting the flexibility and stability of the power grid. The quantitative evaluation method proposed in this invention can provide more detailed resource information for power grid dispatching by identifying load characteristics by industry and industry;

[0019] 2. Insufficient utilization of the diversity and flexibility of demand response resources: With the increase of distributed power resources, the role of load-side flexibility resources (such as air-conditioning loads, electric vehicles, industrial loads, etc.) in grid regulation has become increasingly prominent. However, existing methods fail to fully consider the regulation characteristics and diversity of different load types when utilizing these resources for demand response, making it difficult to effectively implement load regulation in practical applications. The present invention fully taps the demand response potential of various loads and improves the flexibility of grid dispatching by identifying the characteristics of loads in different industries and evaluating their regulation capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the overall flow chart of the present invention.

[0021] Figure 2 This is a diagram of the HVAC load system architecture of multiple chillers of the present invention.

[0022] Figure 3 It is a power consumption curve diagram of the HVAC system of the present invention.

[0023] Figure 4 It is a schematic diagram of the start and stop states of the chiller of the present invention under different refrigeration requirements.

[0024] Figure 5 A dispatch framework diagram is provided for providing demand response considering HVAC loads of multiple chillers according to the present invention.

[0025] Figure 6 This is the electrolytic aluminum production flow chart of the present invention.

[0026] Figure 7 It is a schematic diagram of heat absorption and release of the aluminum electrolytic cell of the present invention. DETAILED DESCRIPTION

[0027] The present invention relates to the technical field of power system regulation capability assessment, and in particular to a quantitative assessment method for power regulation capability margin for different industries and sectors. This technology is mainly used in power system optimization scheduling, demand response and flexible resource management, and aims to provide support for the reliability, flexibility and renewable energy absorption capacity of the power grid by accurately assessing the load characteristics and regulation potential of industries and sectors.

[0028] The present invention is further described below in conjunction with embodiments and / or drawings.

[0029] Example 1

[0030] like Figure 1-7 As shown, a quantitative evaluation method for the margin of power regulation capacity by industry and sector is provided, and the method comprises the following steps:

[0031] Step 1: First, propose a group feature recognition technology for flexible resources by industry and sector: Analyze the annual electricity load data of a province and propose a group feature recognition technology for flexible resources for residential electricity load;

[0032] In the present invention, the air conditioner on the residential side has great adjustable potential. Specifically, for winter, from 18:00 to 21:00, the heating load of the whole province is about 16 million kilowatts, of which the heating load of residents is about 14 million kilowatts; for summer, from 21:00 to 23:00, the cooling load of the whole province is about 36 million kilowatts, of which the cooling load of residents is about 27 million kilowatts; in general, the winter heating load of low-voltage users is 52% of the summer cooling load, and the winter heating load of users in the whole province is 44% of the summer cooling load. ; Among the eleven major industries in a certain province, the industrial electricity load accounts for about 71.09%, among which "non-ferrous metal smelting and rolling processing industry" has a large adjustable potential, with a typical working load of about 4.8 million kilowatts and an adjustable range of about 1.2 million kilowatts, which is a high-quality candidate adjustable resource.

[0033] Step 2: Then, using data-driven methods, a flexible resource group feature identification technology for the secondary and tertiary industry loads is proposed;

[0034] In the present invention, firstly, the annual electricity load data of the secondary and tertiary industries in a certain province in a certain year are collected, and the electricity load conditions of the eleven major industries are statistically analyzed. The present invention will focus on the calculation of the margin of industrial electricity load regulation capacity. Since the electricity consumption characteristics of various industrial electricity loads are different; the industrial load of "non-ferrous metal smelting and rolling processing industry" represented by electrolytic aluminum has the potential to become a flexible and adjustable resource because its production links have greater adjustable flexibility. For this reason, the present invention will focus on the electricity load of "non-ferrous metal smelting and rolling processing industry" in the future.

[0035] Based on the statistical power load data, the present invention proposes a confidence estimate to make a preliminary estimate of the adjustable potential of the power load, as shown in the following formula: Where i is the node index; t is the time window index; P is the power load power; P max , P min are the upper and lower limits of the power of the power load to be decided respectively; ε reflects the risk acceptance of the decision maker facing the estimation of adjustable potential, which is 5% in the present invention; the meaning of the above formula is: under the goal of estimating the adjustable potential to the greatest extent, the adjustable potential calculation result given has a probability of not violating the power constraint.

[0036] Step 3: Propose a quantitative evaluation method for the regulation capability of electric vehicle loads and a swarm intelligence matching method: Establish a single electric vehicle charging and discharging model based on the physical characteristics of electric vehicles, then use the constraint classification method to build an aggregate model that provides regulation flexibility for large-scale electric vehicles and linearize it. Mathematical derivation proves that the solution accuracy of the aggregate model is the same as that of the original single model.

[0037] In the present invention, electric vehicle load: Before studying the flexibility of electric vehicles in providing power system regulation, it is necessary to model and analyze the physical characteristics of electric vehicle charging and discharging and user behavior characteristics, specifically:

[0038] ① Quantitative evaluation method of regulation capacity based on electricity consumption characteristics of production links: The charging and discharging power range of single electric vehicles should meet the following constraints: In the formula, t is the time period number, each hour is one time period, and there are 24 time periods in a day; r is the number of the electric vehicle; are the charging and discharging power of electric vehicle r in period t respectively; are the maximum charging and discharging power of electric vehicle r, respectively; are the time when the electric vehicle r connects to and leaves the charging pile, respectively.

[0039] In addition, the charging and discharging power of electric vehicles should also meet the following mutually exclusive constraints: Formula (5) indicates that the electric vehicle r cannot be charged and discharged simultaneously in a certain period of time;

[0040] The net charging capacity constraint of electric vehicles is: Where: are the start and end time of the controllable period of the electric vehicle r, respectively; Δt is the time interval; They are the battery power of the electric vehicle at the start and end of the controllable time period respectively.

[0041] In the electric vehicle charging and discharging model, constraint (5) is a nonlinear constraint, which makes it difficult to solve the model. Constraint (6) is the calculation of the net charge amount within the schedulable time range of the electric vehicle, which is only applicable to the case where the electric vehicle has only one controllable time period. In order to linearize the above model and extend it to electric vehicles with multiple controllable time periods, the present invention uses a decision variable P to represent the charging and discharging power of the electric vehicle group. rt Indicates that P rt <0 means that the electric vehicle group discharges to the grid, P rt > 0 means that the electric vehicle group is charged from the grid, constraint (5) is omitted, and a binary parameter U is introduced rt .

[0042] Make a reasonable assumption about the charging and discharging power range of electric vehicles: the maximum charging power is equal to the maximum discharging power, then the power range constraint of electric vehicles can be converted to:r,max U rt ≤P rt ≤P r,max U rt (7) Where: P rt is the power of electric vehicle r in period t; P r,max is the maximum power of electric vehicle r; U rt is the binary state variable of the electric vehicle r, U rt =1 means the electric vehicle has been connected to the charging pile, U rt =0 means the electric vehicle is not connected to a charging station.

[0043] The battery power constraint of electric vehicles at different time periods is: E rt =E rt-1 +P rt U rt Δt(9), In the formula, E rt is the battery power value of electric vehicle r in period t; The initial power value of the electric vehicle when it is connected to the charging pile; are the maximum and minimum values ​​of the battery power of electric vehicle r respectively; the above battery power constraint is applicable to electric vehicles with any controllable time period in a day.

[0044] Each electric vehicle needs to meet the charging requirements set by the electric vehicle user when leaving the charging pile, which is: Where: The charging capacity requirement value set for electric vehicle users.

[0045] ②Swarm intelligent matching method based on flexible scene characteristics: constrain the electric vehicle charging and discharging model and enlarge the feasible domain to establish an electric vehicle charging and discharging aggregation model; constraint classification refers to the total number of N EV Among electric vehicles, electric vehicles with the same model, the same controllable start and end time, and the same charging requirements are classified into one category and regarded as one electric vehicle, and the individual variables of a single electric vehicle are converted into group variables of each category of electric vehicles, thereby reducing the number of variables and shortening the calculation time.

[0046] Let N EV electric vehicles are classified into J categories, and the number of vehicles in each category is v1,v2,...,v J ,but: The power variables of electric vehicles in the first stage can be converted into group variables through the following formula: In the formula, j is the number of the electric vehicle group; r is the number of the vehicle in the electric vehicle group; t is the time period number in a day, each hour is a time period, and there are 24 (T) time periods in a day; is the power of the j-th type of vehicle in period t; is the power of the rth electric vehicle in the jth category of vehicles at time t; is the maximum power of the jth category vehicle; The maximum power of the rth electric vehicle in the jth category of vehicles, and the maximum power of each electric vehicle in each category of vehicles is the same; are the upper and lower frequency regulation capacities of the j-th type of vehicle in period t, respectively; are the upper and lower frequency regulation capacities of the rth electric vehicle in the jth category of vehicles in period t; are the maximum up and down frequency regulation capacities that can be provided for the j-th type of vehicle, respectively; They are the maximum up and down frequency regulation capacities that can be provided by the rth electric vehicle in the jth category of vehicles.

[0047] The energy variables of electric vehicles in the first stage can be converted into group variables through the following formula:

[0048] Where: are the upper and lower limits of the battery power value of the j-th category vehicle respectively; They are the upper and lower limits of the battery power value of the rth electric vehicle in the jth category of vehicles; Charging demand for the jth type of vehicle; Charging demand for the rth electric vehicle in the jth category of vehicles; is the initial power of the jth type of vehicle; is the initial power of the rth electric vehicle in the jth category of vehicles; are the charging and discharging efficiencies of the jth type of vehicle battery respectively; are the time when the jth type of vehicle accesses and leaves the charging pile; They represent the charging efficiency, discharging efficiency, arrival time, and departure time of the rth electric vehicle in the jth category of vehicles respectively.

[0049] The individual variables of the second stage of electric vehicles can be converted into group variables through the following formula:

[0050] Where: τ is the sub-period number within one hour, and there are 12 in total within one hour. Period; P j,t,τ P is the power of the j-th vehicle in the τ-th secondary period within the t period; (j,r)t,τ is the power of the rth electric vehicle in the jth category in the τth secondary period within the tth period; are the charging and discharging power components decomposed from the j-th vehicle in the τ-th secondary period within the t-th period; are the charging and discharging power components decomposed from the rth electric vehicle in the jth category in the τth secondary period within the tth period; is the l-cycle deep discharge power of the j-th vehicle in the τ-th secondary period of period t; E is the l-segment cycle deep discharge power of the r-th electric vehicle in the j-th category of vehicles in the τ-th secondary period of period t; j,t,τ E is the battery power value of the j-th vehicle in the τ-th secondary period of time; (j,r)t,τ It is the battery power value of the rth electric vehicle in the jth category of vehicles in the τth secondary period of period t.

[0051] ③ Proof of aggregation effectiveness: From the aggregation model, we can see that the objective functions of the models before and after the electric vehicle aggregation are equivalent. Let the single model of the optimal bidding strategy for electric vehicle frequency regulation be M1, and the aggregate model of the optimal bidding strategy for electric vehicle frequency regulation be M2. It can be seen that when the constraints in the M1 model are established, the constraints in the M2 model are also established, then the feasible domain of the M1 model is included in the feasible domain of the M2 model, that is: Assume that the maximum value of the objective function of the M2 model is Then there must be: Since the solution of the M2 model The variable solution of a single electric vehicle obtained by solving the simultaneous equations (13)-(25) is All constraints of the M1 model are satisfied, so: According to formula (27), we have: That is, the electric vehicle monomer variable solutions obtained after this set of decomposition are a set of optimal solutions for the M1 model. Therefore, the solution accuracy of the model remains unchanged before and after the electric vehicle aggregation.

[0052] Step 4: Secondly, a quantitative evaluation method for the regulation capacity of air-conditioning loads and a group intelligent matching method are proposed: a modeling method for massive heterogeneous air-conditioning loads is designed, and a method for aggregating massive heterogeneous air-conditioning loads is further constructed, and then a quantitative evaluation model for the regulation capacity of massive heterogeneous air-conditioning loads is proposed, and the effectiveness of aggregation is further verified;

[0053] In the present invention, ① a quantitative evaluation method of regulation capacity based on electricity consumption characteristics of production links: the present invention will consider the load distribution strategy between multiple chillers and linearize the energy conversion of the HVAC system; a simplified architecture of a system using multiple chillers to provide cooling for a building is as follows: Figure 2As shown, normal temperature water is sent to multiple chillers for cooling, and then sent to the building by the delivery pipeline, and the indoor temperature is adjusted by heat exchange. The main goal is to consider the load distribution strategy of the chiller to meet the energy-saving needs of the system. Therefore, the heat loss of cooling water during the transmission process is ignored in the modeling;

[0054] In the HVAC system, the energy conversion characteristics of each chiller vary due to factors such as the manufacturer and age. The power consumed by the i-th chiller is P i c Its cooling capacity The relationship between can be fitted into a cubic function: In the formula, a i,n , i∈I represents the coefficient parameter of the fitting polynomial; Formula (31) represents the cooling capacity range of the i-th chiller; represents the rated cooling capacity of the i-th chiller; when the total cooling demand of the building is Q De Once determined, the HVAC system will allocate cooling loads to each chiller. In the formula, μ i represents the startup of the i-th chiller (μ i =1) / Shutdown(μ i =0) state; at this time, the total power consumption of the HVAC system P Su Equal to the sum of the power consumption of all chillers: The relationship between the power consumption and the total cooling capacity of the HVAC system can be obtained through equations (30)-(33), but this relationship is not unique; when different strategies are adopted to distribute the cooling load of the chiller, the energy conversion efficiency of the entire system will be different; therefore, the present invention obtains the energy conversion efficiency distribution range of the HVAC system by solving the optimal and worst load distribution strategies of multiple chillers, and then uses the least squares method for linear approximation to obtain the optimal linear power consumption curve of the HVAC system within this range.

[0055] The optimal load distribution strategy for multiple chillers aims to minimize the electrical power consumed while meeting the cooling demand of the HVAC system: minP Su (34); Similarly, the worst load allocation strategy for multiple chillers aims to consume the maximum electrical power while meeting the cooling demand of the HVAC system: maxP Su (35), according to equations (34) and (35), the minimum power that the HVAC system can consume while meeting its cooling demand can be obtained: P Su and maximum electrical power Moreover, no matter what load distribution strategy is adopted for multiple chillers, the total power consumption of the system will remain within this range. Since the switching state of the chiller is a discrete variable, the present invention discretely samples the cooling demand from 0 to the maximum value, and obtains the range of electric power consumed by the HVAC system at all sampling points based on equations (34) and (35), respectively, as follows: Figure 3 shown.

[0056] The upper and lower gray solid lines represent the maximum and minimum power consumption of the HVAC system at each sampling point, respectively. The horizontal axis represents the load rate of the HVAC system (the ratio of the actual cooling capacity to the rated cooling capacity), and the vertical axis represents the electric power consumed. Subsequently, the optimal linear power consumption curve within this range is fitted by the least squares method: P Su =k Su Q De +l Su (36), where k Su and l Su represents the slope and intercept of the fitting curve to be solved; the goal of the least squares method is to achieve the power consumption P corresponding to the cooling demand of each sampling point Su The sum of the squares of the distances to its lower boundary is the smallest, where the power consumption of each chiller is P Su It should be kept within the rated power consumption range, and the fitted linear optimal power consumption curve is obtained as follows Figure 3 Shown by the solid black line.

[0057] When the corresponding electric power consumption of the HVAC system when meeting the cooling demand of each sampling point is known, the load distribution strategy of the chiller can be calculated according to equations (30)-(33); Figure 4 The optimized start and stop states of different chillers under the cooling demand at different sampling points are given. The horizontal axis represents the load rate of the HVAC system (the ratio of the actual total cooling capacity to the rated total cooling capacity), and the vertical axis represents the number of the chiller. The solid point indicates that it is in the on state, and the hollow point indicates that it is in the off state. A feasible solution can be obtained for each sampling point on the curve.

[0058] ②Swarm intelligent matching method based on flexible scenario characteristics: Introduce load aggregators to aggregate and dispatch them, so as to achieve the coordinated optimization of HVAC loads of multiple chillers and thermal power units at the system level, and consider the large-scale heterogeneous HVAC loads of multiple chillers to provide demand response for the power system. Figure 5 As shown, a load aggregator is set on each load node, which can regulate the HVAC loads of all multi-chilled water units connected to the node and submit the aggregated standby response model to the dispatch center.

[0059] The dispatch center adopts a random optimization dispatch method to achieve coordinated optimization of thermal power units and aggregators to cope with the randomness of wind power output. This method takes into account the uncertainty of wind power output and makes decisions on energy and reserve capacity simultaneously based on two-stage random optimization. The decision in the first stage corresponds to the day-ahead dispatch plan, and the decision in the second stage is related to the real-time adjustment of power under different wind power output scenarios. The aggregator will reserve reserve capacity for the system in the first stage, and provide deployment reserve in the second stage to achieve real-time power adjustment.

[0060] Based on the proposed linear energy conversion model, the present invention establishes a standby response model for a single multi-chilled water unit HVAC load, and then proposes an aggregate modeling and coordinated control method for large-scale heterogeneous multi-chilled water unit HVAC loads based on a unified dispatch signal; the changes in thermal characteristics inside a building equipped with multi-chilled water unit HVAC loads can be expressed in the following mathematical form: Among them, R b and C b Represents the equivalent thermal resistance and thermal capacity of the building; T o represents the outdoor temperature of the building; unlike the air conditioner, which has only two operating states, 0 and rated power, the electric power of the HVAC load of the multi-chilled water unit can be continuously adjusted. Therefore, when no demand response is provided, the indoor temperature of the building can be maintained at the set temperature value. Combining equations (36) and (37), the baseline electric power consumed by the HVAC load of the multi-chilled water unit can be calculated. Among them, k Su , l Su Represents the slope and intercept of the fitting parameters of the relationship between air conditioning power and temperature.

[0061] Since users have a certain tolerance for indoor temperature comfort, and the thermal inertia of buildings allows chillers to appropriately increase or decrease power consumption while maintaining user thermal comfort, multi-chilled water unit HVAC loads have the potential to provide backup for the power grid. A single HVAC load can provide backup R for the system. u ,Su (t) and the next deployed spare R d,Su (t) can be expressed as follows: R u,Su (t)-R d,Su (t) = P Base (t)-P Su (t),R u,Su (t)≥0,R d,Su (t)≥0(39), T set -ε≤T i (t)≤T set +ε(40), where T set is the temperature dead zone; P SuThe actual power consumption of the HVAC load needs to satisfy the constraint set composed of equations (36), (37) and (40). Equation (39) indicates that if the actual power consumption is greater than the baseline power, the HVAC load of multiple chillers will provide the system with a lower deployment reserve, otherwise it will provide an upper deployment reserve.

[0062] Assuming that there are N(k) HVAC loads with multiple chillers connected to load node k, the available upper deployment reserve that the load aggregator at the node can provide under the sth wind power output scenario can be calculated. and deploy backup for: Where n represents the index of the HVAC load on node k.

[0063] The system operator will settle the bill based on the deployment reserve and reserve capacity provided by the load aggregator. Therefore, the reserve capacity must also be determined. The reserve capacity provided by the load aggregator to the system and lower spare capacity The deployment reserve should be no less than the upper deployment reserve provided in each scenario. and deploy backup If the load aggregator can regulate all loads based on a unified signal, it can not only realize the autonomous allocation of spare capacity and deployed spares among various HVAC loads, but also aggregate the variables and constraints in the above model. Therefore, the present invention defines the indoor temperature state of a building equipped with HVAC as an indicator of its spare provision capability and uses it as a unified signal: In the formula, SOT(t) represents the deviation rate of indoor temperature from the user set temperature, also known as the deviation rate of user comfort. The larger its absolute value, the worse the comfort experienced by the user, and the smaller the backup capacity that can be provided at that moment. When the SOT(t) values ​​of all loads are the same, it can be considered that the comfort experience of all users is the same. From formula (40), it can be seen that the following constraint needs to be satisfied: -1≤SOT(t)≤1(44). Substituting formula (43) into formula (37), the relationship between the cooling capacity demand of a single multi-chilled water unit HVAC load and the indoor temperature state SOT(t) can be derived: α=-ε / R b ,β=-εC b ,γ(t)=(T 0 (t)-T set ) / R b, where dSOT(t) / dt represents the rate of change of the indoor temperature state; Combining equations (45) and (36), the electric power curve consumed by a single multi-chilled water unit HVAC load can be calculated. Node k is connected to N(k) multi-chilled water unit HVAC loads. Therefore, the total power consumption after the load aggregation at this node can be expressed as: In order to ensure the consistency of comfort for different users, the present invention controls the SOT(t) and dSOT(t) / dt of each building to the same value: The load aggregator can send a unified dispatch signal (SOT(t) and dSOT(t) / dt) to each multi-chilled HVAC load, and then each load adjusts its power consumption according to the signal value and equation (14). Therefore, equation (46) can be rewritten as: Accordingly, the load aggregator in formula (41) provides the upper deployment reserve in the sth wind power output scenario: and deploy backup Can be rewritten as: In summary, the reserve response model submitted by the load aggregator to the dispatch center can be expressed as (42), (44), (48) and (49). The reserve capacity and deployed reserve at each load node can be automatically allocated among the heterogeneous multi-chiller HVAC loads through the SOT(t) and dSOT(t) / dt signals uniformly released by the load aggregator.

[0064] ③Verification of aggregation effectiveness: 54 thermal power units are installed in the IEEE standard system with a total capacity of 7220MW. In addition, a wind farm with a capacity of 2000MW is installed at node 59. The historical data of power load and wind power output are from EirGrid and scaled down to match the system. The price of each thermal power unit for providing deployment backup and backup capacity is set to 1.3 times and 0.4 times of its unit power generation price, respectively. The total capacity of multi-chilled water unit HVAC load that can provide backup response for the system is 700MW, which is distributed on 10 different load nodes. Load aggregators are set on the corresponding load nodes to aggregate and regulate the multi-chilled water unit HVAC load with backup response function. The prices of deployment backup and backup capacity provided by load aggregators are set to 13$ / MWh and 4$ / MWh, respectively. The indoor temperature set points and user comfort ranges of the buildings where the multi-chiller HVAC loads are located are randomly distributed in the intervals of [22°C, 24°C] and [0°C, 3°C], respectively. The time step selected for the discretized standby response models of all load aggregators is 1 hour.

[0065] The following two examples are designed for analysis and comparison: Example 1: random optimal dispatch of power system without considering demand response; Example 2: random optimal dispatch of power system considering the aggregated demand response of HVAC loads of multiple chillers; The operating cost results of the above two groups of examples are shown in Table 1. By comparing Example 1 and Example 2, it can be seen that after considering the load reserve response of multiple chillers in the random optimal dispatch of the power system, the total operating cost of the system is reduced by $14092. The results show that purchasing reserve capacity from both thermal power units and multi-chiller load aggregators is more economical than purchasing it only from thermal power units.

[0066] Table 1 Operating cost results

[0067]

[0068] The random optimization scheduling includes the day-ahead scheduling stage and the real-time adjustment stage. The spare capacity reserved by the aggregator in the day-ahead scheduling stage will be deployed in the real-time adjustment stage to ensure power balance under different wind power scenarios. The SOT values ​​of the aggregator in different scenarios in the real-time adjustment stage are maintained in the range of [-1,1] during the scheduling cycle, indicating that the aggregated demand response model can ensure user comfort.

[0069] Based on the SOT value in the real-time adjustment stage obtained by optimization, the load aggregator can realize the automatic allocation of deployment backup demand among heterogeneous multi-chilled water unit HVAC loads. The load aggregator on load node 77 is selected as a representative to analyze and verify the effectiveness of the aggregation method and control strategy proposed in the present invention. Node 77 is connected to 50 multi-chilled water unit HVACs with backup response function, and the capacity of a single load is distributed in the interval [1MW, 2MW]. The aggregation parameters are shown in Table 2.

[0070] Table 2 Related parameters of the aggregate standby response model

[0071]

[0072] The difference between the time-varying parameters in the aggregation model and the aggregate baseline power consumption in the scheduling period always remains a constant value of 6.15MW, which is equal to Since the deployment reserve provided by the multi-chilled water unit load aggregator is equal to the difference between its baseline power consumption and actual power consumption, it can be concluded based on the above data and formula (38): the deployment reserve provided by the multi-chilled water unit load aggregator at a certain moment is equal to the indoor temperature state SOT of the building at that moment t and its rate of change SOT t+1 -SOT t Linear correlation; in a certain wind power output scenario in the real-time adjustment stage, the curve of the aggregator SOT and the deployment reserve it provides for the system changes over time. It can be seen that the maximum value of the upper deployment reserve provided by the aggregator appears in SOTt <0 and SOT t+1 -SOT t When the maximum value is taken; the time when the maximum value of the next deployment reserve occurs corresponds to SOT t ≥0 and SOT t+1 -SOT t When the minimum value is taken; the result shows that SOT t The rapid change of can make more up / down deployment reserves be utilized; the load aggregator issues a unified dispatch signal to the HVAC load of heterogeneous multi-chilled water units according to the obtained SOT curve, and each load automatically allocates deployment reserves according to this signal. From the curve of the indoor temperature T of the heterogeneous load changing with time, and according to formula (12), it can be seen that the indoor temperature T is positively correlated with SOT; when SOT remains unchanged, the indoor temperature T is linearly correlated with the temperature set point and the user's comfort range. In the present invention, the indoor temperature set point and the user's comfort range each have three different values, so the indoor temperature T of the heterogeneous load has 9 different values; at the same time, the sum of the deployment reserves automatically allocated by each multi-chilled water unit load based on the indoor temperature T is equal to the deployment reserve demand allocated to the aggregator by the system. The results show that the method of allocating system deployment reserve demand based on a unified SOT signal is effective.

[0073] In order to verify the role of the proposed multi-chiller load modeling and aggregation method in improving the computing efficiency of the dispatching center, the present invention compares the computing time of two scenarios; the first scenario is to use the proposed method, and the time for solving the random optimization scheduling problem of the power system is about 5 minutes; the second scenario is to directly piecewise linearize the energy conversion model of each chiller, and then consider the scheduling plan of all chillers in the random scheduling of the power system, and the problem solving time exceeds one day; since the unit combination model is mainly used for the day-ahead scheduling of the dispatching center, if the calculation time exceeds 1 day, it will not be able to meet the actual needs, therefore, the necessity of the proposed modeling and aggregation method is verified.

[0074] A multi-chilled water unit HVAC load with a capacity of 1.8MW governed by the aggregator on node 77 is selected as a representative to analyze the energy-saving effect of the proposed linear energy conversion model. The cooling demand after the deployment of the multi-chilled water unit HVAC load is provided, and the proposed model and the optimal load allocation strategy are used for calculation. The results show that the proposed model significantly reduces the calculation and solution time of the optimal scheduling problem containing multi-chilled water unit HVAC load demand response with only a 1.8% loss of energy-saving demand; due to The value of is related to the time step selected when solving equation (45). Therefore, the present invention selects the multi-chilled water unit load aggregator on the load node 77 as a representative to analyze the influence of the time step on the regulation ability of the multi-chilled water unit HVAC load aggregator. At the same time, The value of is related to the outdoor temperature. Therefore, Table 3 gives the demand response model parameters of the load aggregator at different time steps and temperatures. It can be seen from Table 3 that under the same outdoor temperature, as the time step value increases, The value of gradually increases; when the time step value is the same, The value increases with the increase of temperature.

[0075] Table 3. Related parameters of the aggregated demand response model

[0076]

[0077] First, based on the demand response model parameters provided by the load aggregator when the outdoor temperature is 34.7℃, the influence of the selection of the time step on its upper regulation capacity is analyzed. When the time step and the SOT of the load aggregator at the current moment take different values, the selected time step decreases when the model is discretized, and the upper regulation capacity that the aggregator can provide will remain unchanged or increase. This shows that if the time interval selected in the random optimization scheduling is large, it will be unfavorable to tap the regulation potential of the aggregator. As the SOT value of the aggregator at the current moment decreases, the upper regulation capacity will also increase. This is because the SOT change rate between the previous and next moments is positively correlated with the upper regulation capacity. The size of the upper regulation capacity is related to the electric power required to be consumed and its rated electric power when the SOT of the aggregator takes the maximum value at the next moment. The reduction in the time step value means As the value increases, when the SOT value changes between adjacent moments are the same, the smaller the time step selected, the smaller the electric power the aggregator needs to consume, and the larger the upward regulation capacity it can provide for the system; and when the time step is small enough, if the aggregator wants to maximize its SOT at the next moment, the electric power required to be consumed will exceed the minimum value of its rated electric power; therefore, the upward regulation capacity of the load remains unchanged due to the limitation of the rated electric power; when the time step is not greater than 0.3 hours, if the SOT value of the aggregator at the current moment is too small, its SOT value at the next moment will be less than 1; that is, when the time step is small enough, the aggregator can provide greater upward regulation capacity at the expense of a small amount of user comfort.

[0078] Then, the influence of the selection of time step on the down-regulation capacity of the aggregator is analyzed. When the time step and the SOT of the aggregator at the current moment take different values ​​respectively, when the selected time step becomes smaller, the down-regulation capacity that the aggregator can provide will remain unchanged or increase; similarly, it can be concluded that a longer time interval selected in random optimization scheduling will be unfavorable for tapping the regulation potential of the aggregator; the difference is that since the size of the down-regulation capacity is related to the electric power required to be consumed when the SOT of the temperature control load takes the minimum value at the next moment and its rated electric power, the down-regulation capacity value will increase as the SOT value of the aggregator at the current moment decreases; since the SOT change rate between the previous and next moments is negatively correlated with the down-regulation capacity, when the selected time step is small and the SOT value of the aggregator at the current moment is large, the electric power required for the aggregator to reach the minimum SOT at the next moment will exceed the maximum value of its rated electric power. Therefore, the downward regulation capacity of the load remains unchanged due to the limitation of rated electric power; when the time step is not greater than 0.3 hours, if the SOT value of the aggregator at the current moment is large, its SOT value at the next moment will be greater than -1; therefore, when the time step is small enough, the aggregator can provide greater downward regulation capacity at the expense of a small amount of user comfort; finally, the demand response model parameters provided by the aggregator when the outdoor temperature is 37.7℃ are selected to analyze the impact of the selection of the time step on its upward regulation capacity and downward regulation capacity. At this temperature, the impact trend of the time step on the upward regulation capacity and downward regulation capacity is consistent with the analysis described above; in addition, when the temperature is higher, the upward regulation capacity of the aggregator increases, while the downward regulation capacity decreases. The main reason is that the baseline electric power consumed by the aggregator increases.

[0079] Step 5: Finally, a quantitative evaluation method for the regulation capacity of the electrolytic aluminum load and a swarm intelligence matching method are proposed: the characteristics of the flexible industrial electrolytic aluminum load are analyzed to establish a heat conduction model of the aluminum electrolytic cell, and then a quantitative evaluation model for the regulation capacity of the flexible industrial electrolytic aluminum load is proposed to further verify the effectiveness of the model.

[0080] In the present invention, ① a quantitative evaluation method of the regulation capacity based on the electricity consumption characteristics of the production link: the power consumption of the electrolytic aluminum industry production process is very stable, and the production is 24 hours a day. Its load rate is generally above 90%, and the load rate can even reach 95% without maintenance or accidents. The load curve of electrolytic aluminum is relatively stable and is less affected by the seasons; modern electrolytic aluminum industry production generally adopts cryolite-alumina molten salt electrolysis. Alumina is used as a raw material and is melted in cryolite. The anode is a carbon body and the cathode is aluminum liquid. An electrochemical reaction occurs under the action of strong direct current. The chemical reaction formula of electrolytic aluminum: 2Al2O3+3C=4Al+3CO2(50).

[0081] The electrolytic aluminum production process is as follows: Figure 6As shown, the production of electrolytic aluminum is mainly carried out in aluminum electrolytic cells. Direct current passes through molten cryolite containing alumina and other elements conducive to electrolysis, and generates metallic aluminum through electrochemical reactions. The molten metallic aluminum is precipitated at the bottom of the electrolytic cell, sucked out by a vacuum ladle and sent to a foundry for casting. Alumina is not only one of the raw materials for production, but also can absorb harmful substances (fluoride) in the exhaust flue gas, thereby reducing the fluoride content in the flue gas. During the electrolysis process, the anode material will continue to decrease due to the electrochemical reaction, so the anode needs to be replenished in time to ensure the continuity and stability of the production process.

[0082] As a high-energy-consuming load, the electrolytic aluminum load consumes huge amounts of electricity. Among them, the aluminum electrolytic cell is the core equipment of production and also the main power-consuming equipment, accounting for about 90% of the total power consumption of production. Usually, the aluminum electrolytic cell operates under strong direct current and extremely low voltage. The model of the electrolytic cell is generally expressed by the current intensity. The most commonly used electrolytic cell models are the 400kA series and the 500kA series. At present, all industrial electrolytic cells are operated as series loads. Hundreds of electrolytic cells are connected in series to form an electrolytic cell series. The total power of each electrolytic cell series can reach hundreds of megawatts, and an electrolytic aluminum plant may contain multiple electrolytic cell series. If the load power can be reasonably regulated without affecting the safety and stability of electrolytic aluminum production, then the electrolytic aluminum load can make a positive contribution to the low-carbon economic operation of the power system.

[0083] Compared with other decentralized users, electrolytic aluminum load has many advantages in participating in demand response. The first is that electrolytic aluminum load can provide a larger response capacity. An electrolytic aluminum plant can provide a response capacity of several megawatts, while individual user loads can usually only provide a few kilowatts of response capacity. The second is that the electrolytic aluminum load can be adjusted quickly, and the load power can be accurately adjusted within milliseconds. The third is that the electrolytic aluminum load is less sensitive to climate, and seasonal changes have little impact on the electrolytic aluminum load, while the load levels of other users will fluctuate with the change of seasons, which is not conducive to coordinated and unified management. The fourth is that as an industrial load, the electrolytic aluminum load has a high level of automation, which can not only automatically and flexibly control the equipment, but also facilitate centralized management.

[0084] ② Swarm intelligent matching method based on flexible scenario characteristics: Electrolyte temperature is an important factor affecting electrolytic aluminum production, which is generally maintained at 940-960°C. The aluminum electrolytic cell must maintain a suitable temperature so that cryolite remains molten in the center of the electrolytic cell and frozen on the cell wall. If the cell temperature is too low, the electrolyte in the cell will solidify on the side and bottom of the cell, and the cryolite melt will also freeze to the anode carbon block in the center of the cell, greatly reducing the thermal stability of the electrolytic cell; if the cell temperature is too high, the cryolite melt will melt to the cell wall, corroding the cell and shortening the cell life. In order to ensure that the electrolyte temperature of the electrolytic aluminum load is always maintained within a reasonable range when participating in demand response, the present invention proposes a flexible industrial electrolytic aluminum load model that takes into account the thermal conductivity characteristics of the electrolytic cell.

[0085] The heat dissipation of the aluminum electrolytic cell is mainly from the top and the side, of which the top heat dissipation accounts for about 48% of the total heat dissipation, the side heat dissipation accounts for about 43% of the total heat dissipation, and the bottom heat dissipation accounts for only 9%; due to the small amount of heat dissipation at the bottom, the present invention only considers the top and side heat dissipation of the aluminum electrolytic cell; in order to facilitate the analysis of the thermal conductivity characteristics of the aluminum electrolytic cell, the present invention divides the aluminum electrolytic cell into two systems: the electrolyte-aluminum liquid system and the tank shell-anode carbon block system, which correspond to the liquid system and the solid system respectively. The schematic diagram of heat absorption and release of the aluminum electrolytic cell is shown in Figure 7 shown.

[0086] The electrolyte-aluminum liquid system is the place where the electrochemical reaction occurs, and its temperature will directly affect the aluminum electrolysis process. Taking the electrolyte as the research object, considering the heat exchange between it and other media and the internal heat loss, its heat calculation formula is: Q in1 -Q reac -Q h -Q cout1 -Q dout -Q ex =c e m e ΔT e (51), where Q in1 The Joule heat generated by the current absorbed by the electrolyte; Q reac The heat absorbed by the electrochemical reaction process; Q h The heat required to heat the material; Q cout1 Q is the heat dissipated by the electrolyte to the side tank shell; dout Q is the heat dissipated by the electrolyte to the top anode carbon block; ex is the heat exchanged between electrolyte and aluminum liquid; c e is the specific heat capacity of the electrolyte; m e is the mass of the electrolyte; ΔT e is the temperature change of the electrolyte.

[0087] Taking aluminum liquid as the research object, the heat calculation formula is: Q in2+Q ex -Q cout2 =c l m l ΔT l (52), where Q in2 It is the part of Joule heat generated by the current absorbed by the aluminum liquid; Q cout2 is the heat dissipation of the aluminum liquid to the side tank shell; c l is the specific heat capacity of aluminum liquid; m l is the mass of aluminum liquid; ΔT l is the temperature change of the aluminum liquid; since the molten electrolyte and aluminum liquid are in a circulating flow state, the internal temperature gradient is very small, and the temperatures of the electrolyte and aluminum liquid can be considered uniform. Therefore, the electrolyte and aluminum liquid can be combined into one system. At this time, the temperature change of the electrolyte and aluminum liquid is the same, that is, ΔT e =ΔT l Therefore, equations (51) and (52) can be combined to obtain: Q in -Q reac -Q h -Q cout1 -Q cout2 -Q dout =(c e m e +c l m l )ΔT e (53), where Q in =Q in1 +Q in2 It represents the Joule heat generated by the current input into the aluminum electrolytic cell, and its expression is: Q in =(U cell -E)IΔt(54), where, U cell is the cell voltage; E is the back electromotive force of the electrolytic cell; I is the current intensity of the electrolytic cell; Δt is the unit time; according to the non-ferrous metal industry index system, the aluminum output of each electrolytic cell per unit time is: M = 0.3356Iη (55), where η is the current efficiency; the aluminum electrolysis reaction is an endothermic reaction, and the heat absorbed is Q reac It can be expressed as: In the formula, It is the equivalent energy consumption of the electrochemical reaction to produce one ton of aluminum. According to the first law of thermodynamics, when the temperature is 900-1000℃, the thermal enthalpy of aluminum electrolysis reaction is about 1094kJ / mol, which is equivalent to the energy consumption of about 5.63kWh / kg(Al).

[0088] The raw alumina is transported to the electrolytic cell through a super dense phase conveying system. The material needs to absorb heat to heat it to the reaction temperature. This part of the heat Q h It can be expressed as: In the formula, The equivalent electric energy consumption for heating materials to produce one ton of aluminum is about 0.7 kWh / kg (Al); at the solid-liquid phase boundary, heat is transferred at the interface in the form of convection heat transfer. The heat dissipation from the electrolyte and aluminum liquid to the side tank shell and the heat dissipation from the electrolyte to the top anode carbon block can be calculated by the convection heat transfer formula: Q cout1 =h e (T e -T s )A e Δt(58),Q cout2 =h l (T e -T s )A l Δt(59),Q dout =h c (T e -T s )A c Δt(60), where T e is the temperature of the electrolyte-aluminum liquid system; T s is the average temperature of the tank shell-anode carbon block system; h e A is the convection heat transfer coefficient between the electrolyte and the side tank shell; e is the heat exchange area between the electrolyte and the side tank shell; h l A is the convection heat transfer coefficient between the aluminum liquid and the side tank shell; l h is the heat exchange area between the aluminum liquid and the side tank shell; c A is the convection heat transfer coefficient between the electrolyte and the top anode carbon block; c is the heat exchange area between the electrolyte and the top anode carbon block; Substituting equations (54)-(60) into equation (53), the expression of the temperature change of the electrolyte-aluminum liquid system with time can be obtained: The tank shell-anode carbon block system is between the external environment and the electrolyte-aluminum liquid system. The heat in the tank is dissipated to the outside air through the tank shell. Taking the tank shell-anode carbon block system as the research object, its heat calculation formula is: Q conv -Q out =c s m s ΔT s (62), where Q conv Q is the heat transferred from the electrolyte-aluminum liquid system to the tank shell-anode carbon block system; out The heat transferred from the tank shell-anode carbon block system to the environment (air); c s is the specific heat capacity of the tank shell-anode carbon block system; m s is the mass of the tank shell-anode carbon block system; ΔT s It is the change of average temperature of cell shell-anode carbon block system.

[0089] The heat transferred from the electrolyte-aluminum liquid system to the tank shell-anode carbon block system includes the heat dissipated from the electrolyte and aluminum liquid to the side tank shell and the heat dissipated from the electrolyte to the top anode carbon block, which can be expressed as: Q conv =Q cout1 +Q cout2 +Q dout (63) The heat transferred from the cell shell-anode carbon block system to the outside air can be calculated by the convective heat transfer formula: Q out =h s (T s -T a )A s Δt(64), where h s A is the convection heat transfer coefficient between the tank shell-anode carbon block system and the air; s is the heat exchange area between the tank shell-anode carbon block system and the air; T a is the external environment temperature; Substituting equations (58)-(60), (63)-(64) into equation (62), the expression of the average temperature of the tank shell-anode carbon block system changing with time can be obtained: After obtaining the relationship between the temperature of the two systems and time from equations (61) and (65), the time difference of the two equations can be obtained as follows (in simplified form, Δt is taken as 1h): Rearranging formula (66) yields the following expression: e(t+2) =aT e(t+1) -bT et +cI t+1 -dI t +K(67), where

[0090] K=b-a+1, where a, b, c, d, and K are all constant coefficients. Formula (67) describes the dynamic process of the electrolyte temperature in the cell changing with time and current. The electrolyte temperature at time t+2 is not only related to the electrolyte temperature and the current intensity at time t+1, but also to the state of the electrolytic cell at time t.

[0091] In order to ensure the safe operation of aluminum electrolysis production, the electrolyte temperature needs to be maintained within a reasonable range, that is, between the minimum and maximum allowable values: In the formula, and They represent the minimum and maximum allowable values ​​of the electrolyte temperature respectively.

[0092] ③Verification of model validity: In order to verify the rationality of the heat conduction model of aluminum electrolytic cells, the present invention takes a single electrolytic cell as an example for explanation. Since the electrolytic cells are connected in series and the current intensity is the same, the state of a single electrolytic cell can also represent the state of the electrolytic cell series in which it is located; the electrolytic aluminum load can be adjusted in two ways to provide demand response for the aluminum electrolytic cell: the first way is to adjust the current of the electrolytic cell to slightly change the load power. This adjustment method can complete the precise adjustment of the load power within a few milliseconds. It is fast and flexible, and the adjustment of the electrolytic cell current can be considered to be completed instantaneously; the second way is to shut down a certain electrolytic cell or a series of electrolytic cells, thereby achieving a significant reduction in load power; in addition, an aluminum smelter with multiple electrolytic cell series can transfer the interrupted load from one series to another, thereby making the total allowable interruption time longer. It is generally stipulated that the total power outage time of the aluminum electrolytic cell must be less than 45 minutes.

[0093] Although the second method can achieve a larger response capacity, the potential harm of shutting down the electrolytic cell is relatively large. If the liquid in the electrolytic cell solidifies due to power loss, it will cause permanent and serious damage to the electrolytic cell and may even cause the equipment to be scrapped. Therefore, after field research, considering that the second method of shutting down the electrolytic cell will have a significant impact on the normal production of aluminum electrolysis and has poor feasibility, the present invention selects the first method to adjust the aluminum electrolytic cell. The relevant parameters of the aluminum electrolytic cell are shown below.

[0094] Table 4 Electrolyzer parameters

[0095]

[0096] Table 5 Convective heat transfer coefficient and heat transfer area of ​​each part of the electrolytic cell

[0097]

[0098] Assuming that the normal current intensity of the aluminum electrolytic cell is 400kA, when t=1h, the current drops by 1kA, and when t=10h, the current is restored to normal level; when the current drops by 1kA, the electrolyte temperature drops at a rate from fast to slow, and the steady-state temperature is about 2.26℃ lower than the normal temperature, which is in line with the actual production operation situation; when the current returns to normal, the electrolyte temperature will gradually increase with time until it reaches a steady state, and the temperature rise rate will gradually decrease from fast to slow; from the simulation results, it can be seen that the electrolytic cell takes a long time to reach a new steady state, so when the electrolytic aluminum load participates in demand response, the temperature state of the electrolytic cell will always be in a transient state, and the proposed aluminum electrolytic cell heat conduction model can well describe the dynamic changes of the electrolytic cell temperature and ensure the safety of the production process.

[0099] When the current passed into the aluminum electrolytic cell decreases to different degrees, although the change trends are similar, the greater the decrease in the current intensity, the faster the electrolyte temperature decreases; when the current drops by 40kA (a decrease of 10%), it only takes about 3 hours for the electrolyte temperature to drop below 940°C, which will affect the production stability of electrolytic aluminum. It can be seen that the electrolytic cell cannot be maintained at a lower current level for a long time; and when the current drops by 90kA (a decrease of about 22%), it only takes about 1 hour for the electrolyte temperature to drop below 940°C; therefore, when the scheduling time is 1 hour, the limit adjustment ratio of the aluminum electrolytic cell under normal conditions (operating power is the maximum operating power) is about 22%. When the adjustment range is greater than this ratio, the electrolyte temperature in the cell drops too fast, and production safety and stability will be difficult to guarantee.

[0100] In order to illustrate the necessity and effectiveness of the proposed aluminum electrolytic cell heat conduction model in ensuring production safety and stability, the present invention will compare the simulation results of the model that does not take into account the heat conduction characteristics of electrolytic aluminum. In the model that does not take into account the heat conduction characteristics of electrolytic aluminum, the influence of temperature is ignored, and the heat balance constraint is used to ensure the safety and stability of production. The heat balance constraint can be expressed as follows: In the formula, τ represents the time for the electrolytic cell to maintain thermal equilibrium; U represents the voltage of a single electrolytic cell; E τ It represents the minimum energy input required by the electrolytic cell to maintain thermal balance during the continuous τ period; E τ,max k represents the maximum input energy of the electrolytic cell in a continuous τ period; τ is the proportionality coefficient; take τ=4h, E τ =0.95E τ,max , select a set of current values ​​that meet constraint (69). The electrolyte temperature exceeds the lower limit in time period 21-24, which will have an adverse impact on the production safety of aluminum electrolysis. In some scenarios where the load response of aluminum electrolysis is relatively large, this problem will be more prominent. Although the thermal balance constraint ensures the energy supply in the continuous period, as the current decreases for a long time, the temperature decreases over time. The thermal balance constraint (69) cannot guarantee that the temperature in the tank is always maintained within the normal range; take τ = 10h, E τ =0.97E τ,max , a set of current values ​​that satisfy constraint (69) is selected. Although time periods 1-10 satisfy the minimum energy supply for 10 consecutive hours, the electrolyte temperature still exceeds the lower limit in time period 8. It can be seen that the thermal balance constraint has disadvantages for the situation where the current suddenly decreases in a short period of time.

[0101] Compared with the model that takes into account the thermal conductivity characteristics of electrolytic aluminum, the model that does not take into account the thermal conductivity characteristics can only roughly evaluate the response capacity of the electrolytic aluminum load, so that the minimum input energy of the electrolytic cell in a continuous τ period is not less than E τ , once τ or E τIf the set value is not appropriate, it is easy for the electrolyte temperature in the tank to exceed the limit; if you want to avoid this situation, you can only choose a more conservative set value, but the cost will be that the responsiveness of the electrolytic aluminum load may not be fully utilized; in contrast, in the model that takes into account the thermal conductivity characteristics of electrolytic aluminum, the effect of current intensity on the electrolyte temperature in the tank can be fully quantified, which better reflects the responsiveness of the electrolytic aluminum load, not only ensuring the safety and stability of production, but also fully tapping the responsiveness potential of the electrolytic aluminum load.

[0102] In summary, the present invention proposes a quantitative evaluation method and a swarm intelligence method for the regulation capacity of electric vehicle loads. The calculation results show that the establishment and linearization of the electric vehicle aggregation model reduces the number of model variables and the difficulty of solving the model. Through mathematical derivation, it can be proved that the solution accuracy of the aggregation model is the same as that of the original single electric vehicle model.

[0103] The present invention proposes a quantitative evaluation method and a swarm intelligence method for the regulation capacity of air-conditioning loads. The results of the example show that: (1) the proposed modeling and aggregation method can improve the computational efficiency of the optimization scheduling problem while meeting the energy-saving requirements of the HVAC loads of multiple chillers as much as possible. The proposed collaborative control strategy can automatically allocate the deployment reserve of each multiple chiller load while meeting the total deployment reserve demand allocated by the system; (2) the proposed reserve response model can improve the flexibility and economy of the system. At the same time, when the time step selected for solving the differential equations in the reserve response model is small enough, the aggregator can provide greater regulation capacity at the cost of sacrificing less user comfort.

[0104] The present invention proposes a quantitative evaluation method and a swarm intelligence method for the regulation capability of electrolytic aluminum load. The calculation results show that: (1) the proposed model can better describe the dynamic process of the electrolyte temperature in the cell changing with the current intensity and time, and can effectively ensure that the electrolyte temperature is always maintained within a reasonable range when the electrolytic aluminum load participates in demand response, providing a model basis for subsequent research; (2) compared with the model that does not take into account the thermal conductivity characteristics of the electrolytic cell, the proposed model can better reflect the regulation capability of the electrolytic aluminum load, which is conducive to more fully exploring the response potential of the electrolytic aluminum load under the premise of ensuring safe and stable production.

[0105] The present invention is a quantitative assessment method for the margin of electric power regulation capacity by industry and sector. In use, the present invention has the following significant advantages and positive effects compared to the prior art: 1. Improving the accuracy and applicability of regulation capacity assessment: The present invention quantitatively assesses the electric power regulation capacity by industry and sector, making the assessment results more accurate and applicable to the industry. The characteristic differences of the loads of various industries are incorporated into the assessment process, so that the dispatching center can implement more accurate control measures according to the load characteristics of specific industries, thereby improving the stability of the power grid and the efficiency of demand response; 2. Fully tapping the regulation potential of demand response resources: Through the flexibility assessment of air-conditioning loads, electric vehicle loads, and industrial loads such as electrolytic aluminum, the present invention can maximize the utilization of demand response resources while ensuring the safe operation of the system. The flexibility of load-side resources, especially through modeling the diversity of demand response resources, the present invention realizes the maximum utilization of regulation capabilities without affecting the user experience, and provides strong support for the economic dispatch of the power grid and the consumption of new energy; 3. Improve the economy and reliability of the power grid: By accurately quantifying the regulation capabilities of loads in different industries, the present invention not only reduces the operating cost of the power system, but also significantly improves the economy of the power grid. The scientific evaluation and effective utilization of the regulation capabilities enable the system to maintain reliable operation during peak loads and new energy fluctuations, providing support for future power systems with a high proportion of renewable energy access; the present invention has the advantages of establishing an electric vehicle aggregation model and linearization processing, proposing a quantitative evaluation method for regulation capabilities, and proposing a swarm intelligence method.

Claims

1. A quantitative assessment method for the margin of power regulation capacity by industry and sector, characterized by: The method comprises the following steps: Step 1: First, propose a group feature recognition technology for flexible resources by industry and sector; Step 2: Then, using data-driven methods, a flexible resource group feature identification technology for the secondary and tertiary industry loads is proposed; Step 3: Propose a quantitative evaluation method for the regulation capability of electric vehicle loads and a swarm intelligence matching method; Step 4: Furthermore, a quantitative evaluation method of the regulation capacity of air-conditioning load and a group intelligent matching method are proposed; Step 5: Finally, a quantitative evaluation method for the regulation capacity of electrolytic aluminum load and a swarm intelligence matching method are proposed.

2. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 1, characterized in that: The flexible resource group feature identification technology for the secondary and tertiary industry loads in step 2 is specifically as follows: based on the statistical power load data, a confidence estimate is proposed to make a preliminary estimate of the adjustable potential of the power load, as shown in the following formula: Where i is the node index; t is the time window index; P is the power load power; P max , P min are the upper and lower limits of the power of the power load to be decided; ε reflects the risk acceptance of the decision maker facing the estimation of adjustable potential; the meaning of the above formula is: under the goal of estimating the adjustable potential to the greatest extent, the adjustable potential calculation result given has a probability of not violating the power constraint.

3. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 1, characterized in that: The quantitative evaluation method for regulating capacity of electric vehicle load in step 3 is a quantitative evaluation method for regulating capacity based on the electricity consumption characteristics of the production link, specifically: the charging and discharging power range of a single electric vehicle should meet the following constraints: In the formula, are the charging and discharging power of electric vehicle r in period t respectively; are the maximum charging and discharging power of electric vehicle r, respectively; are the time when the electric vehicle r connects to and leaves the charging pile respectively; a reasonable assumption is made about the charging and discharging power range of the electric vehicle: the maximum charging power is equal to the maximum discharging power, then the power range constraint of the electric vehicle can be converted to: r,max U rt ≤P rt ≤P r,max U rt (7) Where: P rt is the power of electric vehicle r in period t; P r,max is the maximum power of electric vehicle r; U rt is the binary state variable of electric vehicle r; the battery power constraint of electric vehicle in each period is: E rt =E rt-1 +P rt U rt Δt(9), In the formula, E rt is the battery power value of electric vehicle r in period t; The initial power value of the electric vehicle when it is connected to the charging pile; are the maximum and minimum values ​​of the battery power of the electric vehicle r respectively; each electric vehicle needs to meet the charging capacity requirements set by the electric vehicle user when leaving the charging pile, then: Where: The charging capacity requirement value set for electric vehicle users.

4. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 3 is characterized by: The group intelligent matching method of electric vehicle loads in step 3 is a group intelligent matching method based on flexible scene characteristics, specifically: constraining the electric vehicle charging and discharging model and enlarging the feasible domain, establishing an electric vehicle charging and discharging aggregation model, and constraining classification means that the total number of N EV Among electric vehicles, electric vehicles with the same model, the same controllable start and end time, and the same charging requirements are classified into one category and regarded as one electric vehicle. The individual variables of a single electric vehicle are converted into group variables of each category of electric vehicles. Let N EV electric vehicles are classified into J categories, and the number of vehicles in each category is v1,v2,...,v J ,but: The power variables of electric vehicles in the first stage can be converted into group variables through the following formula: In the formula, is the power of the j-th type of vehicle in period t; is the power of the rth electric vehicle in the jth category of vehicles at time t; is the maximum power of the jth category vehicle; The maximum power of the rth electric vehicle in the jth category of vehicles, and the maximum power of each electric vehicle in each category of vehicles is the same; are the upper and lower frequency regulation capacities of the j-th type of vehicle in period t, respectively; are the upper and lower frequency regulation capacities of the rth electric vehicle in the jth type of vehicles in period t; are the maximum up and down frequency regulation capacities that can be provided for the j-th type of vehicle, respectively; are the maximum up and down frequency regulation capacities that can be provided by the rth electric vehicle in the jth category of vehicles; The energy variables of electric vehicles in the first stage can be converted into group variables through the following formula: Where: are the upper and lower limits of the battery power value of the j-th category vehicle respectively; They are the upper and lower limits of the battery power value of the rth electric vehicle in the jth category of vehicles; Charging demand for the jth type of vehicle; Charging demand for the rth electric vehicle in the jth category of vehicles; is the initial power of the jth type of vehicle; is the initial power of the rth electric vehicle in the jth category of vehicles; are the charging and discharging efficiencies of the jth type of vehicle battery respectively; are the time when the jth type of vehicle accesses and leaves the charging pile; The individual variables of the second stage of electric vehicles can be converted into group variables through the following formula: Where: τ is the secondary time period number within one hour; P j,t,τ P is the power of the j-th vehicle in the τ-th secondary period within the t period; (j,r)t,τ is the power of the rth electric vehicle in the jth category in the τth secondary period within the tth period; are the charging and discharging power components decomposed from the j-th vehicle in the τ-th secondary period within the t-th period; are the charging and discharging power components decomposed from the rth electric vehicle in the jth category in the τth secondary period within the tth period; is the l-cycle deep discharge power of the j-th vehicle in the τ-th secondary period of period t; E is the l-segment cycle deep discharge power of the r-th electric vehicle in the j-th category of vehicles in the τ-th secondary period of period t; j,t,τ E is the battery power value of the j-th vehicle in the τ-th secondary period of time; (j,r)t,τ It is the battery power value of the rth electric vehicle in the jth category of vehicles in the τth secondary period of period t.

5. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 4, characterized in that: The proof of the aggregation effectiveness of the group intelligent matching method of electric vehicle loads in step 3 is as follows: From the aggregation model, it can be seen that the objective functions of the models before and after the aggregation of electric vehicles are equivalent. The single model of the optimal bidding strategy for electric vehicle frequency regulation is denoted as M1, and the aggregation model of the optimal bidding strategy for electric vehicle frequency regulation is denoted as M2. It can be seen that when each constraint in the M1 model is established, each constraint in the M2 model is also established, and the feasible domain of the M1 model is contained in the feasible domain of the M2 model, that is: Assume that the maximum value of the objective function of the M2 model is Then there must be: Since the solution of the M2 model The variable solution of a single electric vehicle obtained by solving the simultaneous equations (13)-(25) is All constraints of the M1 model are satisfied, so: According to formula (27), we have: That is, the electric vehicle monomer variable solutions obtained after this set of decomposition are a set of optimal solutions for the M1 model. Therefore, the solution accuracy of the model remains unchanged before and after the electric vehicle aggregation.

6. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 1, characterized in that: The quantitative evaluation method for regulating capacity of air conditioning load in step 4 is a quantitative evaluation method for regulating capacity based on the electricity consumption characteristics of the production link, specifically: in the HVAC system, the energy conversion characteristics of each chiller vary due to factors such as the manufacturer and the age of use. The power consumed by the i-th chiller is P i c Its cooling capacity The relationship between can be fitted into a cubic function: In the formula, a i,n , i∈I represents the coefficient parameter of the fitting polynomial; Formula (31) represents the cooling capacity range of the i-th chiller; represents the rated cooling capacity of the i-th chiller; when the total cooling demand of the building is Q De Once determined, the HVAC system will allocate cooling loads to each chiller. In the formula, μ i Indicates the start / stop state of the i-th chiller; the total power consumption of the HVAC system at this time is P Su Equal to the sum of the power consumption of all chillers:

7. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 6, characterized in that: The group intelligent matching method of air conditioning load in step 4 is a group intelligent matching method based on flexible scene characteristics, specifically: introduce load aggregators to aggregate and dispatch them, set a load aggregator on each load node, and regulate all multi-chilled water unit HVAC loads connected to the node, and submit the aggregated standby response model to the dispatch center, and establish a standby response model of a single multi-chilled water unit HVAC load based on the proposed linear energy conversion model, and then propose an aggregation modeling and coordinated regulation method for large-scale heterogeneous multi-chilled water unit HVAC loads based on a unified dispatch signal; the change of thermal characteristics inside a building equipped with multi-chilled water unit HVAC loads can be expressed in the following mathematical form: Among them, R b and C b Represents the equivalent thermal resistance and thermal capacity of the building; T o Represents the outdoor temperature of the building; the baseline electrical power consumed by the HVAC load of multiple chillers can be calculated Among them, k Su , l Su represents the slope and intercept of the fitting parameters of the relationship between air conditioning power and temperature; therefore, the HVAC load of multiple chillers has the potential to provide backup for the power grid, and a single HVAC load can provide the upper deployment backup R for the system. u,Su (t) and the next deployed spare R d,Su (t) can be expressed as follows: R u,Su (t)-R d,Su (t) = P Base (t)-P Su (t),R u,Su (t)≥0,R d,Su (t)≥0(39), T set -ε≤T i (t)≤T set +ε(40), where T set is the temperature dead zone; P Su represents the actual power consumed by the HVAC load; assuming that there are N(k) HVAC loads with multiple chillers connected to the load node k, the load aggregator on the node can provide the upper deployment reserve under the sth wind power output scenario. and deploy backup for: Where n represents the index of the HVAC load on node k.

8. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 1, characterized in that: The quantitative evaluation method for regulating capacity of the electrolytic aluminum load in step 5 is a quantitative evaluation method for regulating capacity based on the electricity consumption characteristics of the production process.

9. The method for quantitatively evaluating the margin of power regulation capacity by industry and by sector as claimed in claim 8, characterized in that: The swarm intelligent matching method for electrolytic aluminum load in step 5 is a swarm intelligent matching method based on flexible scenario characteristics.

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