A hierarchical penalty method for default volume based on load aggregator classification

Abstract

This invention relates to the field of smart grid dispatching methods, specifically a graded penalty method for default amounts based on load aggregator classification. The method includes classifying load aggregators according to the different types of flexible loads they aggregate, formulating a graded reward and punishment strategy for default amounts based on the load aggregator classification, determining the objective function and constraints, and solving the model using a hybrid coding genetic algorithm. This invention, by gradedly penalizing the default amounts generated by load aggregators participating in smart grid dispatching based on their different types, can effectively improve the resource priority of load aggregators, better cultivate high-quality load aggregators to participate in grid dispatching, enhance the grid's wind curtailment absorption capacity, stabilize load aggregator dispatching costs, and simultaneously increase the grid's overall revenue, demonstrating the effectiveness of the classification penalty mechanism.

Description

Technical Field

[0001] The invention relates to the technical field of smart grid dispatching methods, and is a method for grading default penalties based on load aggregator classification. Background Art

[0002] With the two-way advancement of the energy Internet, the two-way communication technology between sources and loads in the energy Internet has been continuously improved. At present, power grid companies have been able to basically monitor and control various small and medium-sized loads through monitoring equipment and communication technology, making demand-side management possible. In the context of the rapid development of communication technology and the continuous improvement of the power market, the smart grid can use the advanced communication technology to better apply the demand-side management technology to the "source-load" coordinated optimization dispatching mode through demand-side management means, and effectively alleviate the contradiction between supply and demand. However, the load elasticity level of a single user is low, and it is relatively difficult to participate in the dispatching of the smart grid, and it is difficult to meet the dispatching needs. If each flexible load participates in the demand response as a separate individual, it will cause a waste of resources. Therefore, load aggregators have received great attention as a new type of organization that serves small and medium-sized users to participate in the dispatching of the power grid. Load aggregators can classify and aggregate small and medium-sized users according to the prediction of various loads by the smart grid, and establish a response model based on load groups to participate in the dispatching of the smart grid. It is a convenient way for small loads to participate in demand response more flexibly. At the same time, it can also be defined as a load control department in the smart grid to help the smart grid alleviate the contradiction between supply and demand. However, while load aggregators aggregate small and medium-sized loads to participate in scheduling, they may also cause electricity default problems due to users' own reasons, affecting the balance of the power grid.

[0003] To sum up, in order to alleviate the contradiction between power supply and demand, ensure the safe and stable operation of the power grid, and thus improve the efficiency of resource utilization on the demand side, this paper, under the background of the two-way development of the supply and demand sides of smart grid technology, aims at the default problem caused by the diversification of decision-making subjects on the demand side, and conducts research on the hierarchical reward and punishment method for the classification of negative aggregators, cultivates high-quality individual aggregators, and improves the resource level to reduce the dispatch risk caused by default electricity, better maintain the security of the power grid, and optimize the power grid revenue. Summary of the invention

[0004] The present invention provides a method for grading the amount of default based on the classification of load aggregators, which overcomes the shortcomings of the above-mentioned prior art. Aiming at the problem that load aggregators will have default risks when participating in power grid dispatching due to aggregating a large number of small and medium-sized loads, a graded penalty rule for the classification of load aggregators is established, and a penalty method for evaluating the default level according to the ratio of the actual default amount of each type of aggregator to the total number of successful bids of the load aggregator class is proposed; the method is helpful to maintain fair competition in the market, optimize resource levels, and effectively improve the income of the power grid and load aggregators and the utilization rate of wind energy.

[0005] The technical solution of the present invention is achieved by the following measures: a method for grading the default amount based on load aggregator classification, comprising the following steps:

[0006] Step (1), determining the smart grid infrastructure in which the load aggregator participates;

[0007] Step (2), establishing a load aggregator classification model, first classifying the flexible loads, and then classifying the load aggregators according to the different types of aggregated flexible loads;

[0008] Step (3), establishing a supply-side power output model, the supply-side power output model includes a thermal power unit output model and a wind power output model;

[0009] Step (4), according to the load aggregator classification, formulate a default classification reward and punishment strategy for the load aggregator classification, and determine the objective function and constraint conditions;

[0010] Step (5) uses a hybrid coding genetic algorithm to solve the model. Each chromosome represents a power grid dispatching strategy. The dispatching goal is to maximize the profit of the power grid company throughout the day. With one hour as the minimum time interval, the smart grid dispatching strategy within 24 hours is optimized.

[0011] The following are further optimizations and / or improvements to the above technical solutions:

[0012] In the above step (1), the smart grid infrastructure includes two parts: distributed power sources and loads. The distributed power sources take into account renewable energy generation units (wind power generation) and fossil energy (thermal power generation) to participate in grid dispatching at the same time, and the loads take into account flexible loads. Flexible loads are classified and aggregated by load aggregators to participate in grid dispatching.

[0013] The grid control layer formulates a source-load coordination scheduling plan based on the operating status of thermal power units, the output of wind turbines, conventional load forecasts, and the participation of load aggregators, and sends instructions to power generators and load aggregators. Finally, the load aggregators complete the transfer and interruption of flexible loads according to the plan formulated by the grid, optimize the economic benefits of the entire grid, and form an economic scheduling plan for load aggregators to participate in the smart grid.

[0014] In the above step (2), in order to reflect the differences between user loads and comprehensively consider the diversity of flexible composites, flexible loads are divided into three categories according to load response capabilities and physical characteristics. Different types of flexible loads are modeled and analyzed. Based on the classification of flexible loads, load aggregators are also classified, and the multidimensionality and complexity of various types of flexible loads participating in load aggregator scheduling are studied.

[0015] In the above step (2), based on the load response capability and physical characteristics, the flexible loads are divided into three categories: Class A loads, Class B loads and Class C loads. Different types of flexible loads are modeled and analyzed. Based on the classification of flexible loads, load aggregators are divided into two categories: commercial and residential. The multidimensionality and complexity of various types of flexible loads participating in the load aggregator scheduling are studied.

[0016] Furthermore, the specific steps of step (2) are as follows:

[0017] (21) Class A load can be transferred or interrupted. Assuming H1 is the entire working period of Class A load, the power required by the load must be completed during the working period, that is, Where: Q A is the daily power required by Class A load; Pload1 is the rated power of the load, H1={1,2,...,H};

[0018] (22) Class B loads can be transferred but not interrupted. Assuming that the participation period of Class B loads is H2, the load aggregator needs to ensure that Class B loads complete all required electricity within the H2 period and cannot interrupt this type of load during the dispatch process, that is,

[0019] Where: H2={1,2,...,H}; Q B is the daily power requirement of class B load; arg(Sch2(h)=1) means that during the h period, the load is in the on state and can participate in the scheduling. and α represent the minimum time and maximum time in the on period, respectively;

[0020] (23) Class C loads cannot be transferred but can be interrupted intermittently, that is:

[0021]

[0022] The constraint condition in this formula means that if the C load is turned off in the h-1 period and does not participate in the dispatch, it is turned on in the h period, and then the load is turned off in the h+β period, then the total dispatch period of the C load in the entire dispatch process is min(β).

[0023]

[0024] In the formula, Q C is the daily power requirement of Class C load, t on,3 is the shortest opening time of Class C load, t off,3 Maximum shutdown time;

[0025] (24) Based on the classification of flexible loads, load aggregators are also classified into two categories: commercial and residential. The daily load curve of commercial buildings is mainly single-peak, and the electricity consumption is higher during the day on weekdays. The load of commercial users is mainly rigid load, and interruption will cause huge losses. Therefore, commercial load aggregators only consider air-conditioning loads as Class A loads to participate in load aggregator scheduling. To represent commercial load aggregator n b Aggregated Class A load, then commercial load aggregator n b The load volume planned for dispatching in the day-ahead dispatching phase during period t is: Residential load is a comprehensive load with a bimodal load curve. The power consumption is high in the early morning and evening, and low at night. r There are three types of loads involved in dispatching in the jurisdiction, each type of load has I r Users participated, using To represent the load aggregator n r Aggregate Class A load, with To represent the load aggregator n r Aggregate Class B load, with To represent the load aggregator n r Aggregated Class C load, then the residential load aggregator n r The load volume planned for dispatching in the day-ahead dispatching phase during period t is:

[0026] In the above step (3), the wind power output model includes the wind turbine output constraint model and the wind abandonment cost. The operation of wind power units is subject to certain constraints during the power generation process. The maximum output of a single wind turbine is the rated power P r , when exiting the power grid, the minimum output is 0, and the wind turbine output constraint model is: In order to reduce wind curtailment and increase grid revenue, the cost of wind curtailment is also taken into account when calculating revenue. The expression is: In the formula, is the wind curtailment cost, λ wf is the wind curtailment cost coefficient, is the abandoned wind power of the wind farm at time t;

[0027] The output model of thermal power units includes the upper and lower output constraints of the thermal power units, the number of start and stop times of the thermal power units, the unit climbing constraints and the total operating cost of the thermal power units. The operation of the thermal power units must meet the upper and lower output constraints: Constraints on the number of starts and stops of thermal power units: And the unit climbing constraint: There are two ways to regulate thermal power units: variable load and start-stop. Variable load regulation refers to regulating the output of thermal power units within the upper and lower limits of thermal power output. Start-stop regulation refers to regulating the output of units by starting and stopping units within the constraints of the number of starts and stops. The strategy of jointly regulating the two methods is adopted to regulate the output of thermal power units.

[0028] The operating cost of thermal power units includes coal consumption cost and start-up and shutdown cost: the coal consumption cost can describe the coal-to-electricity conversion efficiency of thermal power units. Considering the impact of grid cost on thermal power unit power dispatch, the coal consumption cost of thermal power units is expressed as a quadratic function of unit power: in, is the output of thermal power unit i at time t, a i , b i 、c i is the coal consumption cost coefficient of unit i during power generation; in the actual power generation process of thermal power units, the start and stop of thermal power units will also bring huge losses, and frequent start and stop of units will also affect the life of the units. Therefore, the start and stop costs of thermal power units are also considered in the operating costs: where λ nf is the start-up and shutdown cost coefficient of thermal power units, n t,i is the start and stop status of thermal power unit i at time t. The total operating cost of the thermal power unit is obtained from the above formula: is the total operating cost of the thermal power unit, F1 is the coal consumption cost of the thermal power unit, and F2 is the start-up and shutdown cost of the thermal power unit.

[0029] In the above step (4), according to the default percentage in each time period and the different types of aggregators, the default amount generated by the load aggregators when participating in the smart grid dispatch is classified and graded. This is to encourage the load aggregators to improve the resource level and encourage various types of load aggregators to actively participate in grid dispatch.

[0030] Furthermore, in step (4), a default grading reward and punishment strategy for the load aggregator classification is formulated according to the load aggregator classification. The specific steps are as follows:

[0031] (41) Modeling of load aggregator default quantity: The load aggregator’s planned load reduction quantity L in the bidding st,n , winning bid

[0032] Actual reduction There will be a certain deviation. Then the deviation between the bid amount of load aggregator n participating in the dispatch during period t and the actual bid amount, that is, the default amount of load aggregator participating in the grid dispatch is:

[0033] (42) Load aggregator grading penalty rule: Calculate the default percentage of load aggregator n in period t, that is, the percentage of the deviation between the load aggregator's bid and the winning bid as a percentage of the total bids of all load aggregators in that period: According to the different default percentages of load aggregators in different time periods, formulate corresponding graded compensation standards for different time periods;

[0034] (43) A graded reward and punishment mechanism for load aggregators: The default level is evaluated based on the ratio of each aggregator’s actual default volume to the total number of successful bids for the load aggregator category, and a penalty mechanism of different levels is implemented.

[0035] In the above step (4), the output of thermal power units, wind curtailment, and the winning bid quantity and winning bid status of load aggregators are dispatched with the goal of maximizing the grid revenue throughout the day. The objective function is expressed as follows: in, The revenue from electricity sales to the power grid, They are the operating cost of thermal power units, the cost of wind curtailment and the cost of load aggregators; is the total load at time t, e t The electricity price at time t is set at different electricity price levels during peak, flat and valley periods. Through the mechanism of time-of-use electricity prices, users are guided to use electricity during periods with lower electricity prices, so as to achieve the effect of load peak shaving and valley filling, increase wind power consumption, and weaken the characteristics of reverse distribution of sources and loads. In order to reduce the load dispatching costs of power companies and cultivate high-quality aggregators to participate in dispatching, when load aggregators participate in dispatching and the output of load aggregators is greater than the dispatching plan, settlement is made according to the dispatching plan. When the output is less than the dispatching plan, settlement is made according to the actual reduction amount. That is, the income of load aggregator n in period t is: Where ei(t) is the load aggregator compensation price set by the power grid company during this period, λ n,t is the penalty unit price for the excess part of load aggregator n during period t, and the total revenue of load aggregator at time t is:

[0036] In the above step (5), the specific steps are as follows:

[0037] (51) The model is solved using a hybrid coding genetic algorithm. Each chromosome represents a grid dispatching strategy. The output and status of each unit, the load aggregator's bid quantity and status, and the amount of wind abandoned are used as decision variables. Real number coding is used for discrete variables, and binary coding is used for status. The dispatching goal is to maximize the profit of the power grid company throughout the day. With one hour as the minimum time interval, the smart grid dispatching strategy within 24 hours is optimized.

[0038] Assume that there are N = 6 load aggregators in the constructed scenario, and analyze on a typical day T = 24 hours. According to the power load level, power consumption can be divided into three periods: valley period (0:00-7:00 and 23:00-24:00); normal period (12:00-19:00); peak period (7:00-12:00 and 19:00-22:00), the electricity price is set at 0.300 yuan / MW.h in the valley period, 0.500 yuan / MW.h in the normal period, and 0.700 yuan / MW.h in the peak period;

[0039] (52) Different scenarios are set according to the different situations in which flexible loads participate in scheduling, and the optimization strategies under different scenarios are analyzed:

[0040] Scenario 1: No penalty will be imposed on the default electricity generated by the load aggregator dispatch;

[0041] Scenario 2: The default electricity generated by the load aggregator is punished at a uniform level;

[0042] Scenario 3: The default electricity generated by the dispatch of load aggregators is punished according to different default levels based on the type of load aggregators.

[0043] The method of the present invention, which performs graded punishment on the amount of default generated by load aggregators when participating in smart grid dispatching according to the different types of aggregators, can effectively improve the resource level of load aggregators, better cultivate high-quality load aggregators to participate in grid dispatching, and improve the grid's wind abandonment and absorption capacity, stabilize the dispatching costs of load aggregators, and at the same time increase the total revenue of the grid, proving the effectiveness of the classified punishment mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Attached Figure 1 It is the overall structure diagram of the method of the present invention.

[0045] Attached Figure 2 This is the flexible load classification diagram.

[0046] Attached Figure 3 Flowchart of the hierarchical penalty classification for load aggregators.

[0047] Attached Figure 4 Solve the flow chart for the coordinated scheduling model.

[0048] Attached Figure 5 This is the optimization result of source-load dual side in scenario 1.

[0049] Attached Figure 6 This is the optimization result of source-load bilaterally for scenario 2.

[0050] Attached Figure 7 This is the optimization result of the three-source-load bilateral side in the scenario. DETAILED DESCRIPTION

[0051] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0052] The present invention will be further described below in conjunction with embodiments:

[0053] Embodiment: The method for classifying the penalty for breach of contract based on load aggregator classification comprises the following steps:

[0054] Step (1), determining the smart grid infrastructure in which the load aggregator participates;

[0055] As attached Figure 1 As shown, the smart grid infrastructure includes two parts: distributed power sources and loads. Distributed power sources take into account wind power generation and thermal power generation and participate in grid dispatching at the same time. Loads take into account flexible loads, which are classified and aggregated by load aggregators to participate in grid dispatching.

[0056] The grid control layer formulates a source-load coordination scheduling plan based on the operating status of thermal power units, the output of wind turbines, conventional load forecasts, and the participation of load aggregators, and sends instructions to power generators and load aggregators. Finally, the load aggregators complete the transfer and interruption of flexible loads according to the plan formulated by the grid, optimize the economic benefits of the entire grid, and form an economic scheduling plan for load aggregators to participate in the smart grid.

[0057] Step (2), establishing a load aggregator classification model, first classifying the flexible loads, and then classifying the load aggregators according to the different types of aggregated flexible loads;

[0058] like Figure 2 As shown, flexible loads are divided into three categories: A, B, and C. Class A loads can be transferred and interrupted, Class B loads can be transferred but not interrupted, and Class C loads cannot be transferred but can be interrupted intermittently. Load aggregators in different regions generally aggregate a variety of flexible loads. Different aggregators contain different types of loads and have different functional positioning, which have different impacts on the power grid. Based on the classification of flexible loads, load aggregators are divided into commercial and residential types. Each type of load aggregator will be dispatched in different time periods according to the physical characteristics of different types of flexible loads.

[0059] The specific steps of step (2) are as follows:

[0060] (21) Class A load can be transferred or interrupted. Assuming H1 is the entire working period of Class A load, the power required by the load must be completed during the working period, that is, Where: Q A is the daily power required by Class A load; Pload1 is the rated power of the load, H1={1,2,...,H};

[0061] (22) Class B loads can be transferred but not interrupted. Assuming that the participation period of Class B loads is H2, the load aggregator needs to ensure that Class B loads complete all required electricity within the H2 period and cannot interrupt this type of load during the dispatch process, that is,

[0062] Where: H2={1,2,...,H}; Q B is the daily power requirement of class B load; arg(Sch2(h)=1) means that during the h period, the load is in the on state and can participate in the scheduling. and α represent the minimum time and maximum time in the on period, respectively;

[0063] (23) Class C loads cannot be transferred but can be interrupted intermittently, that is:

[0064]

[0065] The constraint condition in this formula means that if the C load is turned off in the h-1 period and does not participate in the dispatch, it is turned on in the h period, and then the load is turned off in the h+β period, then the total dispatch period of the C load in the entire dispatch process is min(β).

[0066]

[0067] In the formula, Q C is the daily power requirement of Class C load, t on,3 is the shortest opening time of Class C load, t off,3 Maximum shutdown time;

[0068] (24) Based on the classification of flexible loads, load aggregators are also classified into two categories: commercial and residential. Commercial load aggregators only consider air-conditioning loads as Class A loads to participate in load aggregator scheduling. To represent commercial load aggregator n b Aggregated Class A load, then commercial load aggregator n b The load volume planned for dispatching in the day-ahead dispatching phase during period t is: Assume that in the residential load aggregator n r There are three types of loads involved in dispatching in the jurisdiction, each type of load has I r Users participated, using To represent the load aggregator n r Aggregate Class A load, with To represent the load aggregator n r Aggregate Class B load, with To represent the load aggregator n r Aggregated Class C load, then the residential load aggregator nr The load volume planned for dispatching in the day-ahead dispatching phase during period t is:

[0069] Step (3), establishing a supply-side power output model, the supply-side power output model includes a thermal power unit output model and a wind power output model;

[0070] The wind power output model includes the wind turbine output constraint model and the wind abandonment cost. The operation of wind power units is subject to certain constraints during the power generation process. The maximum output of a single wind turbine is the rated power P r , when exiting the power grid, the minimum output is 0, and the wind turbine output constraint model is: In order to reduce wind curtailment and increase grid revenue, the cost of wind curtailment is also taken into account when calculating revenue. The expression is: In the formula, is the wind curtailment cost, λ wf is the wind curtailment cost coefficient, is the abandoned wind power of the wind farm at time t;

[0071] The output model of thermal power units includes the upper and lower output constraints of the thermal power units, the number of start and stop times of the thermal power units, the unit climbing constraints and the total operating cost of the thermal power units. The operation of the thermal power units must meet the upper and lower output constraints: Constraints on the number of starts and stops of thermal power units: And the unit climbing constraint: There are two ways to regulate thermal power units: variable load and start-stop. Variable load regulation refers to regulating the output of thermal power units within the upper and lower limits of thermal power output. Start-stop regulation refers to regulating the output of units by starting and stopping units within the constraints of the number of starts and stops. The strategy of jointly regulating the two methods is adopted to regulate the output of thermal power units.

[0072] The operating cost of thermal power units includes coal consumption cost and start-up and shutdown cost: the coal consumption cost can describe the coal-to-electricity conversion efficiency of thermal power units. Considering the impact of grid cost on thermal power unit power dispatch, the coal consumption cost of thermal power units is expressed as a quadratic function of unit power: in, is the output of thermal power unit i at time t, a i , b i 、c i is the coal consumption cost coefficient of unit i during power generation; in the actual power generation process of thermal power units, the start and stop of thermal power units will also bring huge losses, and frequent start and stop of units will also affect the life of the units. Therefore, the start and stop costs of thermal power units are also considered in the operating costs: where λ nf is the start-up and shutdown cost coefficient of thermal power units, n t,i is the start and stop status of thermal power unit i at time t. The total operating cost of the thermal power unit is obtained from the above formula: is the total operating cost of the thermal power unit, F1 is the coal consumption cost of the thermal power unit, and F2 is the start-up and shutdown cost of the thermal power unit.

[0073] Step (4), according to the load aggregator classification, formulate a default classification reward and punishment strategy for the load aggregator classification, and determine the objective function and constraint conditions;

[0074] like Figure 3 As shown in the figure, it is assumed that there are M (M∈{A,B,C}) types of loads participating in the area under the jurisdiction of load aggregator n, and each type of load includes I m Users, using Q m,n,i (i=1,2,...,I m ), m∈M{A,B,C} to represent the m types of load I in the area under the jurisdiction of load aggregator n m The load aggregator realizes the transfer by controlling the on / off state of the load. Let sch(t,i) = 1, which means that load i participates in the scheduling, and sch(t,i) = 0, which means that load i does not participate in the scheduling. Then the load actually reduced by the aggregator n in the winning period t is: set up For class m load I m The actual load reduction of each user in time period t. Load aggregators will participate in power grid dispatching with their own interests as the bidding strategy in the bidding stage, but due to the load reduction amount L planned by load aggregators in the bidding st,n , and the actual reduction amount of the winning bid There will be a certain deviation, then the default amount of load aggregator n participating in power grid dispatch during period t is:

[0075] In the above step (4), the output of thermal power units, wind curtailment, and the winning bid quantity and winning bid status of load aggregators are dispatched with the goal of maximizing the grid revenue throughout the day. The objective function is expressed as follows: in, The revenue from electricity sales to the power grid, They are the operating cost of thermal power units, the cost of wind curtailment and the cost of load aggregators; is the total load at time t, e t The electricity price at time t is set at different electricity price levels during peak, flat and valley periods. Through the mechanism of time-of-use electricity prices, users are guided to use electricity during periods with lower electricity prices, so as to achieve the effect of load peak shaving and valley filling, increase wind power consumption, and weaken the characteristics of reverse distribution of sources and loads. In order to reduce the load dispatching costs of power companies and cultivate high-quality aggregators to participate in dispatching, when load aggregators participate in dispatching and the output of load aggregators is greater than the dispatching plan, settlement is made according to the dispatching plan. When the output is less than the dispatching plan, settlement is made according to the actual reduction amount. That is, the income of load aggregator n in period t is: Where ei(t) is the load aggregator compensation price set by the power grid company during this period, λ n,t is the penalty unit price for the excess part of load aggregator n during period t, and the total revenue of load aggregator at time t is:

[0076] Step (5) uses a hybrid coding genetic algorithm to solve the model. Each chromosome represents a power grid dispatching strategy. The dispatching goal is to maximize the profit of the power grid company throughout the day. With one hour as the minimum time interval, the smart grid dispatching strategy within 24 hours is optimized.

[0077] Different scenarios are set according to the different situations in which flexible loads participate in scheduling, and the optimization strategies under different scenarios are analyzed:

[0078] Scenario 1: No penalty will be imposed on the default electricity generated by the load aggregator dispatch;

[0079] Scenario 2: The default electricity generated by the load aggregator is punished at a uniform level;

[0080] Scenario 3: The default electricity generated by the dispatch of load aggregators is punished according to different default levels based on the type of load aggregators.

[0081] like Figure 4 As shown in the figure, the solution steps of the hybrid coding genetic algorithm are as follows:

[0082] First, input the 24-hour wind power output and load forecast, and initialize the population according to the upper and lower limits of the unit output, the maximum bid amount of the load aggregator, and the upper and lower limits of the abandoned wind volume. Since the control variables are both continuous variables and discrete variables, a hybrid encoding method combining binary encoding and real number encoding is adopted. The start and stop status of the thermal power unit and the participation status of the load aggregator need to be represented by binary encoding. Set the maximum number of iterations G and the termination condition of the algorithm parameters; secondly, it is necessary to adjust the unit output and state to meet the start and stop constraints and climbing constraints, and then adjust the load aggregator output and state, and modify the initial population until the electric load balance is met; then perform crossover, mutation, and selection operations on each parent individual to obtain a new offspring population, re-correct the variables of the offspring population so that the offspring individuals meet the constraints, select the best individual from the offspring individuals, and retain its optimization strategy; finally, determine whether the termination condition is met. If it is met, jump out of the loop to obtain the optimal scheduling plan. If it is not met, return to the crossover and mutation steps to generate new offspring individuals and iterate again.

[0083] For load aggregators of different capacities, when the amount of default is the same, the percentage of their total bid amount is different. The default of aggregators with small capacity is relatively large. For example, there are three load aggregators with bid amounts of 20MW, 40MW, and 50MW respectively. When the amount of default is 5MW, the amount of default accounts for 25%, 12.5%, and 10% of the bid amount respectively. If a unified default level is used to punish the amount of default, it will be unfair to load aggregators with small capacity. Therefore, the method of the present invention calculates the default ratio by comparing the amount of default with the total bid amount of all aggregators to ensure that the amount of default of load aggregators of different capacities is equal at the same default level, that is, in the above case, when the amount of default is 5MW, the default ratio of all load aggregators is 4.5%. Assuming that there are N load aggregators participating in the dispatch in time period t, the total bid amount of the load aggregators in time period t is:

[0084] The percentage of the deviation between the load aggregator's bid and the winning bid as a percentage of the total bids of all load aggregators during the period is: That is, the default ratio of load aggregator n in period t. f,t is the entry threshold value of the load aggregator in period t, γ q,t is the threshold value of qualified resources in period t, γ g,t is the threshold value of high-quality resources in period t, the market is divided into the following levels: First, the load aggregator whose default percentage exceeds the entry threshold, i.e., γ t >γ f,t , it should be prohibited from participating in the scheduling; secondly, the default amount γ t <γ g,t resources, and no penalty is imposed on its lower default amount; finally, the second-level qualified resources are set, γ g,t <γ t <γ q,t , and the third level is the restricted resource, γ g,t <γ t <γ f,t For the second and third level resources, the power company imposes corresponding penalties on their default amount according to different levels. The penalty price is calculated by multiplying the penalty price of each period set by the power grid by the penalty multiple of each level. According to the above rules, the unit price of penalty for the part of load aggregator n exceeding the limit in period t is λ n,t for:

[0085] Among them, λ1 is the penalty multiple of the second-level qualified resources, and λ2 is the penalty multiple of the third-level restricted resources. In order to enable the load aggregator to improve the resource level and reduce the default amount, λ1<λ2 should be set.

[0086] Different types of load aggregators have different functional positioning, different time periods for participating in dispatch, and different dispatchable capacities. If they are punished according to the same level, assuming that during the same peak period, the high-quality threshold for load aggregators is set at 5%, and the bids of commercial and residential load aggregators are 30MW and 50MW respectively, then only when the commercial default amount is less than 1.5MW can it be rated as a high-quality resource, and the residential load aggregator can avoid punishment when the default amount is less than 2.5MW. Therefore, the default level should be set more carefully. Taking commercial load aggregators as an example, suppose there are N total number of commercial load aggregators in period t. b load aggregators participate in the dispatch, then the total bid amount of commercial load aggregators in period t is: Commercial load aggregator n b The default percentage in period t is: Accordingly, the entry threshold of commercial load aggregators in period t can be set as γ ft,b , the qualification threshold is γ qt,b , the quality threshold is γ gt,b .

[0087] The output of thermal power units, wind power output and load aggregator participation in each period of a day in three scenarios calculated according to the method described in the embodiment are as follows: Figures 5 to 7 The participation of each load aggregator in the three scenarios is shown in Tables 1 to 3. In Tables 1 to 3, Business 1, Business 2, and Business 3 represent business aggregator 1, business aggregator 2, and business aggregator 3, respectively.

[0088] Compare Figures 5 to 7 It can be seen that after the time-sharing penalty was implemented, the proportion of load aggregators participating increased, and the load demand in residential communities began to increase during off-peak hours.

[0089] It can be seen from Tables 1 to 3 that due to the time-based penalty strategy of load aggregators, load aggregators with large defaults no longer participate in the dispatch of smart grids, and commercial load aggregators with smaller capacity are encouraged to increase their participation during peak and off-peak periods. At the same time, it also improves the efficiency of wind energy utilization and optimizes grid revenue.

[0090] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

[0091] Table 1 Participation of load aggregators in scenario 1

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[0094] Table 2 Participation of load aggregators in scenario 2

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[0097] Table 3 Participation of load aggregators in scenario 3

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[0099] .

Claims

1. A method for classifying default penalties based on load aggregator classification, characterized in that The steps include: Step (1), determining the smart grid infrastructure in which the load aggregator participates; Step (2), establishing a load aggregator classification model, first classifying the flexible loads, and then classifying the load aggregators according to the different types of aggregated flexible loads; Step (3), establishing a supply-side power output model, the supply-side power output model includes a thermal power unit output model and a wind power output model; Step (4), according to the load aggregator classification, formulate a default classification reward and punishment strategy for the load aggregator classification, and determine the objective function and constraint conditions; Step (5), using a hybrid coding genetic algorithm to solve the model, each chromosome represents a power grid dispatching strategy, taking the maximum profit of the power grid company throughout the day as the dispatching goal, and taking one hour as the minimum time interval to optimize the smart grid dispatching strategy within 24 hours; Wherein, in step (1), the smart grid infrastructure includes two parts: distributed power sources and loads. The distributed power sources consider wind power generation and thermal power generation to participate in grid dispatching at the same time, and the load considers flexible loads. Flexible loads are classified and aggregated by load aggregators to participate in grid dispatching. Flexible loads are divided into three categories: Class A loads, Class B loads, and Class C loads. Different types of flexible loads are modeled and analyzed. Based on the classification of flexible loads, load aggregators are divided into commercial and residential types. The specific steps are as follows: (21) Class A load can be transferred or interrupted. Assuming H1 is the entire working period of Class A load, the power required by the load must be completed during the working period, that is, Where: Q A is the daily power required by Class A load; Pload1 is the rated power of the load, H1={1,2,...,H}; (22) Class B loads can be transferred but not interrupted. Assuming that the participation period of Class B loads is H2, the load aggregator needs to ensure that Class B loads complete all required electricity within the H2 period and cannot interrupt this type of load during the dispatch process, that is, Where: H2={1,2,...,H}; Q B is the daily power requirement of class B load; arg(Sch2(h)=1) means that during the h period, the load is in the on state and can participate in the scheduling. and α represent the minimum time and maximum time in the on period, respectively; (23) Class C loads cannot be transferred but can be interrupted intermittently, that is: The constraint condition in this formula means that if the C load is turned off in the h-1 period and does not participate in the dispatch, it is turned on in the h period, and then the load is turned off in the h+β period, then the total dispatch period of the C load in the entire dispatch process is min(β). In the formula, Q C is the daily power requirement of Class C load, t on,3 is the shortest on time of Class C load, t off,3 Maximum shutdown time; (24) Based on the classification of flexible loads, load aggregators are also classified into commercial and residential types. Commercial load aggregators only consider air-conditioning loads as Class A loads to participate in load aggregator scheduling. To represent commercial load aggregator n b Aggregate class A load, i=1,2...,I b ; then commercial load aggregator n b The load volume planned for dispatching in the day-ahead dispatching phase during period t is: Assume that in the residential load aggregator n r There are three types of loads involved in dispatching in the jurisdiction, each type of load has I r Users participated, using To represent the load aggregator n r Aggregate class A load, i=1,2...,I r ;use To represent the load aggregator n r Aggregate Class B load, i=1,2...,I r ;use To represent the load aggregator n r Aggregate Class C load, i=1,2...,I r ; then the residential load aggregator n r The load volume planned for dispatching in the day-ahead dispatching phase during period t is: In step (3), the wind power output model includes the wind turbine output constraint model and the wind abandonment cost. The maximum output of a single wind turbine is the rated power P r , when exiting the power grid, the minimum output is 0, and the wind turbine output constraint model is: In order to reduce wind curtailment and increase grid revenue, the cost of wind curtailment is also taken into account when calculating revenue. The expression is: In the formula, is the wind curtailment cost, λ wf is the wind curtailment cost coefficient, is the abandoned wind power of the wind farm at time t; The output model of thermal power units includes the upper and lower output constraints of the thermal power units, the number of start and stop times of the thermal power units, the unit climbing constraints and the total operating cost of the thermal power units. The operation of the thermal power units must meet the upper and lower output constraints: Constraints on the number of starts and stops of thermal power units: And the unit climbing constraint: There are two ways to regulate thermal power units: variable load and start-stop. Variable load regulation refers to regulating the output of thermal power units within the upper and lower limits of thermal power output. Start-stop regulation refers to regulating the output of units by starting and stopping units within the constraints of the number of starts and stops. The strategy of jointly regulating the two methods is adopted to regulate the output of thermal power units. The operating cost of thermal power units includes coal consumption cost and start-up and shutdown cost. Considering the impact of power grid cost on thermal power unit power dispatch, the coal consumption cost of thermal power units is expressed as a quadratic function of unit power: in, is the output of thermal power unit i at time t, a i , b i 、c i is the coal consumption cost coefficient of unit i during power generation; in the actual power generation process of thermal power units, the start and stop of thermal power units will also bring huge losses, and frequent start and stop of units will also affect the life of the units. Therefore, the start and stop costs of thermal power units are also considered in the operating costs: where λ nf is the start-up and shutdown cost coefficient of thermal power units, n t,i is the start and stop status of thermal power unit i at time t. The total operating cost of the thermal power unit is obtained from the above formula: is the total operating cost of the thermal power unit, F1 is the coal consumption cost of the thermal power unit, and F2 is the start-up and shutdown cost of the thermal power unit; In step (4), according to the load aggregator classification, a default classification reward and punishment strategy for the load aggregator classification is formulated. The specific steps are as follows: (41) Modeling of load aggregator default quantity: The load aggregator’s planned load reduction quantity L in the bidding st,n , the actual reduction amount of the winning bid There will be a certain deviation. Then the deviation between the bid amount of load aggregator n participating in the dispatch during period t and the actual bid amount, that is, the default amount of load aggregator participating in the grid dispatch is: (42) Load aggregator grading penalty rule: Calculate the default percentage of load aggregator n in period t, that is, the percentage of the deviation between the load aggregator's bid and the winning bid as a percentage of the total bids of all load aggregators in that period: According to the different default percentages of load aggregators in different time periods, formulate corresponding graded compensation standards for different time periods; (43) A graded reward and punishment mechanism for load aggregator classification: The default level is evaluated based on the ratio of each aggregator’s actual default volume to the total number of successful bids for each load aggregator class, and a penalty mechanism of different levels is implemented; In step (4), the output of thermal power units, wind curtailment, and the winning bids and winning states of load aggregators are dispatched with the goal of maximizing the grid revenue throughout the day. The objective function is expressed as follows: in, The revenue from electricity sales to the power grid, They are the operating cost of thermal power units, the cost of wind curtailment and the cost of load aggregators; is the total load at time t, e t is the electricity price at time t. When the load aggregator participates in the dispatch and the output of the load aggregator is greater than the dispatch plan, the settlement is made according to the dispatch plan. When the output is less than the dispatch plan, the settlement is made according to the actual reduction amount. That is, the income of the load aggregator n in time period t is: Where ei(t) is the load aggregator compensation price set by the power grid company during this period, λ n,t is the penalty unit price for the excess portion of load aggregator n during period t, and the total revenue of the load aggregator at time t is: In step (5), the specific steps are as follows: (51) The model is solved using a hybrid coding genetic algorithm. Each chromosome represents a grid dispatching strategy. The output and status of each unit, the load aggregator's bid quantity and status, and the amount of wind abandoned are used as decision variables. Real number coding is used for discrete variables, and binary coding is used for status. The dispatching goal is to maximize the profit of the power grid company throughout the day. With one hour as the minimum time interval, the smart grid dispatching strategy within 24 hours is optimized. (52) Different scenarios are set according to the different situations in which flexible loads participate in scheduling, and the optimization strategies under different scenarios are analyzed: Scenario 1: No penalty will be imposed on the default electricity generated by the load aggregator dispatch; Scenario 2: The default electricity generated by the load aggregator is punished at a uniform level; Scenario 3: The default electricity generated by the load aggregator’s dispatch is punished according to different default levels based on the type of load aggregator.

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