A method and system for processing day-ahead information of aggregated load based on adjustable robust optimization

Abstract

The present invention provides a method and system for processing information of aggregated load participating in the day-ahead based on adjustable robust optimization, which belongs to the technical field of power load calculation, obtains the losses of the users caused by participating in the market regulation process, the compensation of the unit regulated load to the users, and the local quotation coefficient, and the power system operator terminal obtains the quotation information, and generates the clearing price and load regulation amount with the goal of minimizing the system regulation cost; after the load aggregator terminal obtains the clearing price and load regulation amount information, it calculates the current profit level; executes a loop iteration until a certain profit level satisfies the parameters preset by the load aggregator terminal and is greater than or equal to, the iteration is completed, and the current quotation coefficient, clearing price and load regulation amount are output. The present invention not only ensures the feasibility of the results that meet the worst case scenario that may occur in the future, but also can adjust the strategy in real time according to the existing information, so as to take into account the flexibility and achieve the optimization of the profit.

Description

Background Art

[0002] With the increase in electricity demand, the deregulation of the electricity market, and the increasing penetration of intermittent renewable energy in the power supply structure, the uncertainty level of the power grid has increased dramatically, and the challenges faced by the stable and reliable operation of the power grid are becoming increasingly greater. The access of flexible and adjustable resources on the load side is a major help in coping with the grid stability problems brought about by renewable energy, but the geographical dispersion and lack of effective profit and market competition means are key issues that restrict the promotion and utilization of adjustable loads.

[0003] Integrating the existing adjustable load resources in an aggregated form is an effective solution, but how to give full play to the market initiative of aggregated loads and help increase market participation benefits and thus the degree of market participation through bidding is still a problem that needs to be solved.

[0004] In order to solve the above problems, robust optimization is currently used as an optimization method to solve uncertainty characteristics. Robust optimization expresses the fluctuation range of uncertain parameters by establishing uncertainty sets. When the uncertain parameters are within the range of the uncertainty set, it can ensure the existence of feasible solutions, that is, it guarantees the optimal solution under the acceptable worst case. However, the results of robust optimization have the disadvantage of being too conservative. When used for profit maximization decisions, it is easy to lose some profit opportunities, causing potential losses, and the accuracy of data processing cannot be guaranteed. Summary of the invention

[0005] The present invention provides a method for processing information of aggregated load participating in day-ahead based on adjustable robust optimization. The method can adjust the strategy in real time according to the existing information, thereby taking into account the flexibility and achieving the optimization of the benefits.

[0006] The method comprises: Step 1, obtaining the user's losses caused by participating in the market adjustment process , and the compensation of the unit regulation load to its users , and the local quote coefficient , and pass the above three parameters to the power system operator terminal;

[0007] Step 2: The power system operator terminal obtains the quotation information, and generates the quotation coefficient based on the system topology information and power balance constraints, energy and reserve constraints, and load constraints, with the goal of minimizing the system regulation cost. The clearing price and load regulation ;

[0008] Step 3: The load aggregator terminal obtains the clearing price and load regulation After receiving the information, calculate the current profit level , and update the next quotation parameters ;

[0009] Step 4: Repeat steps 2 and 3 in order until the profit level meets the load aggregator terminal preset parameters greater than or equal to , the iteration is completed, and the current quotation coefficient is output , Clearing Price and load regulation The value of .

[0010] Preferably, step 1 also includes:

[0011] The information obtained by the load aggregator terminal is expressed by the following expression:

[0012] (3)

[0013] In the formula, is the price declared by the load aggregator, is the load aggregator's bid coefficient, is a parameter The initial value to use for iteration; It is the loss of the users caused by the adjustable load participating in the market regulation process; The unit adjusts the load to compensate its users.

[0014] Preferably, step 2 specifically includes the following steps:

[0015] Step 2.1 The power system operator terminal receives the quotation strategy information from the load aggregator terminal and completes the power system balance with the goal of minimizing cost. The specific expression is as follows:

[0016] (4)

[0017] In the formula, is the index of the generator in the power system, is the variable cost of each generator, , and Corresponding to The generation capacity, up-reserve and down-reserve of each generator;

[0018] Step 2.2 The power system operator terminal takes the DC power flow model as the basis and considers the constraints of node power balance, line transmission power constraint, energy and reserve constraint and load constraint. The specific expressions are as follows:

[0019] (5)

[0020] (6)

[0021] (7)

[0022] (8)

[0023] (9)

[0024] (10)

[0025] (11)

[0026] (12)

[0027] (13)

[0028] Where, formula (5) is the power balance equation for each node, is the node index, is the load index, is the line index, is a node set, is a collection of lines, and They exist at the nodes The generator and load collection on and Nodes where power flows to and from A collection of lines, For the flow line The power on the line; Equations (6) to (7) are the line transmission power limits;

[0029] is the line reactance, and Inflow and outflow lines The voltage phase angle of the directional power, For Line The maximum transmission power of; Equations (8) to (12) are the power generation and reserve limits;

[0030] and Generator The minimum and maximum power generation, and are the maximum values ​​of the upward and downward reserves respectively; Formula (13) uses a binary variable to represent the on / off state of the generator;

[0031] 0 means the generator is in shutdown state. A value of 1 indicates that the generator is in the on state.

[0032] Step 2.3 Assume that the bus load is at time It conforms to the normal distribution and satisfies the following expression:

[0033] (14)

[0034] (15)

[0035] In the formula, is a set of parameters representing the normal distribution, and denote the expectation and standard deviation of the normal distribution, respectively. is the shift parameter of the normal distribution;

[0036] Step 2.4 After the power system operator terminal obtains the aggregate load demand change, it is expressed in a robust equivalent form. The specific expression is as follows:

[0037] (16)

[0038] (17)

[0039] (18)

[0040] (19)

[0041] (20)

[0042] (twenty one)

[0043] In the formula, is an adjustable robust optimization parameter, The predicted value is based on the historical changes in load demand; is the load output control variable, is an intermediate parameter, with a value range of (0,1);

[0044] and are the maximum and minimum values ​​of the current demand and predicted demand set of the aggregated load, respectively; Aggregate load demand for the current moment, To forecast demand for the current moment based on historical experience.

[0045] Preferably, the costs in achieving power system balancing with the goal of cost minimization include power generation costs, backup costs, system imbalance costs and compensation costs.

[0046] Preferably, step 3 also includes the following steps:

[0047] The load aggregator terminal obtains the price and load regulation information calculated by the power system operator terminal, calculates the load aggregator's current profit according to the following expression, and sets the next quotation parameter Pass it to the next layer for the next iteration;

[0048] (twenty two)

[0049] In the formula, To provide load aggregator terminals with benefits from participating in the electricity market. Price information calculated for the power system operator terminal and delivered to the load aggregator terminal, The aggregated load to be adjusted is calculated for the power system operator terminal and delivered to the load aggregator terminal.

[0050] Preferably, step 4 also includes the following steps:

[0051] Repeat the iterative process of steps 2 to 3, and calculate the revenue of each iteration. and expected returns Make comparisons;

[0052] If satisfied , the iteration process is terminated and the last quotation parameters are output , price information , Aggregation load to be adjusted and the resulting profit , and finally form a complete aggregate load bidding plan.

[0053] The present invention also provides a system for aggregated load participating in day-ahead information processing based on adjustable robust optimization, the system comprising: a power system operator terminal and a load aggregator terminal;

[0054] The load aggregator terminal is used to obtain the losses of its users caused by participating in the market regulation process. , and the compensation of the unit regulation load to its users , and the local quote coefficient , and pass the above three parameters to the power system operator terminal;

[0055] The power system operator terminal obtains the quotation information and generates the quotation coefficient based on the system topology information and power balance constraints, energy and reserve constraints, and load constraints with the goal of minimizing the system regulation cost. The clearing price and load regulation ;

[0056] The load aggregator terminal is also used to obtain the clearing price and load regulation After receiving the information, calculate the current profit level , and update the next quotation parameters ; Then execute the loop iteration until a certain profit level meets the parameters preset by the load aggregator terminal and is greater than or equal to , the iteration is completed, and the current quotation coefficient is output , Clearing Price and load regulation The value of .

[0057] Preferably, the power system operator terminal and the load aggregator terminal are connected in communication via wired or wireless means.

[0058] It can be seen from the above technical solutions that the present invention has the following advantages:

[0059] The aggregated load participation day-ahead information processing method based on adjustable robust optimization provided by the present invention can perform intensive management of power load information, facilitate access and use by power users and monitoring personnel, and effectively improve the efficiency of using power load information. It can also efficiently collect, store, and process power load information, and provide data support for the stable operation of the power grid. In order to improve the management level and efficiency of power load information and control the risks of the power operation process, the timeliness and scientificity of the whole process management and control of the power system operation can be achieved. It ensures the feasibility of the results that meet the worst possible scenario in the future, and can also adjust the strategy in real time according to the existing information, so as to take into account flexibility and achieve the optimization of benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0061] Figure 1 It is a flow chart of the method for processing the day-ahead information of aggregated load participation based on adjustable robust optimization;

[0062] Figure 2 Schematic diagram of the aggregated load participating in the day-ahead information processing system based on adjustable robust optimization. DETAILED DESCRIPTION

[0063] The method for processing information based on adjustable robust optimization for aggregated load participation provided by the present invention is to solve the problem that the result of robust optimization is too conservative, which is easy to lose some profit opportunities when used for profit maximization decision-making, causing potential losses and failing to guarantee the accuracy of data processing.

[0064] The method for processing information based on adjustable robust optimization for aggregated load participation provided by the present invention can acquire and process the associated power load resource data based on intelligent technology. The method for aggregated load participation in day-ahead bidding utilizes the machine simulation of digital computers, extends and expands human intelligence, and perceives the environment, acquires knowledge, and uses knowledge to obtain the best results. The theory, method, technology and application device.

[0065] The present invention relates to a method for processing information based on adjustable robust optimization for aggregated load participation on the day-ahead, which has both hardware-level technology and software-level technology. Figure 2 As shown, the system includes the power system operator terminal and the load aggregator terminal.

[0066] Among them, the load aggregator terminal is mainly used for load aggregators, which are service providers that aggregate the power loads on the customer side to achieve more efficient power operation and utilization. It mainly includes functions such as load forecasting, scheduling, and control. It aggregates scattered small power loads through technical means to achieve large-scale power transactions and assist users in participating in power market competition.

[0067] The load aggregator terminal of the present invention can realize the intensive management of power load, aggregate the small power load on the customer side, so that the originally dispersed power load can form a larger total load, so that it can be operated and managed more efficiently. It can realize the optimized dispatching of the power market, and can predict, dispatch and control the total load to achieve more optimized emergency regulation services. It can also use the market mechanism to aggregate distributed new energy according to customer needs and provide related services.

[0068] Load aggregator terminals can be applied to industrial production, commercial electricity use, new energy applications and other fields to meet the usage needs of the corresponding fields.

[0069] Among them, the power system operator terminal and the load aggregator terminal may both include: a wireless communication unit, a user input unit, a sensing unit, an output unit, a memory, an interface unit, a controller and a power supply unit, etc.

[0070] The input unit can receive input digital or character information, and generate key signal input related to the method for implementing the aggregated load participating in the day-ahead bidding in this embodiment, such as a touch screen, a keypad, a mouse, a touch pad, one or more mouse buttons, etc. The output unit may include a display device, an auxiliary lighting device, a tactile feedback device, etc.

[0071] The power system operator terminal and the load aggregator terminal can implement the systems and techniques described herein in a computing system including a backend component, for example, as a data server, or in a computing system including a middleware component, for example, an application server, or in a computing system including a frontend component, for example, a user computer with a graphically displayed power load resource data interface or a power load resource data browser, through which the user can participate in the day-ahead bidding method with the aggregated load described herein. Examples of the communication network between the power system operator terminal and the load aggregator terminal include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0072] The present invention relates to a method for processing information on aggregated loads participating in day-ahead based on adjustable robust optimization. The method utilizes adjustable robust optimization technology and establishes a two-layer model to place load aggregator terminals and power system operator terminals in the upper and lower layers, respectively. The upper layer maximizes the benefits of aggregated loads, and the lower layer realizes the adjustment requirements of the power system at the minimum cost. The upper and lower layers are connected by transmitting decision variables and decision results, thereby realizing information interaction between the upper and lower layers. In this way, the feasibility of the results that meet the worst-case scenario that may occur in the future is guaranteed, and the strategy can be adjusted in real time according to the existing information, thereby taking into account flexibility and achieving the optimization of benefits.

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] See also Figure 1 FIG. 1 is a flowchart of a method for processing information of aggregated load participating in day-ahead based on adjustable robust optimization in a specific embodiment, the method comprising:

[0075] Step S1: Obtain the user's losses caused by participating in the market adjustment process , and the compensation of the unit regulation load to its users , and the local quote coefficient , and pass the above three parameters to the power system operator terminal;

[0076] In this embodiment, when renewable energy causes increased instability in the power system, the adjustable aggregated load participates in the day-ahead power market, with maximizing the load aggregator's revenue as the upper-level goal and minimizing the system regulation cost as the lower-level goal. The lower-level model here refers to the power system operator terminal, and the upper-level model refers to the load aggregator terminal.

[0077] First, for the upper model, the revenue of the load aggregator is determined by the clearing price and load regulation The product of is obtained, and its expression is as follows

[0078] (1)

[0079] (2)

[0080] In the formula, is the price declared by the load aggregator, It is the bid coefficient of the load aggregator, which directly affects the profit level of participating in the power market and is a direct reflection of the bidding strategy; It is the loss of the users caused by the adjustable load participating in the market regulation process, which is defined as the inherent cost; The compensation for the unit's load regulation to its users is generally determined by the power operating unit or the relevant national policies.

[0081] The decision variable of the upper model is the clearing price and load regulation , which is optimized by the middle-level model, and the upper-level model converts the quotation coefficient Determined declared price Input to the middle layer for the next iteration.

[0082] Step S2: The power system operator terminal obtains the quotation information, and generates the quotation coefficient based on the system topology information and power balance constraints, energy and reserve constraints, and load limit constraints, with the goal of minimizing the system regulation cost. The clearing price and load regulation ;

[0083] According to an embodiment of the present application, step S2 further includes the following steps:

[0084] Step 2.1 The lower model receives the bidding strategy information from the upper model and completes the power system balance with the goal of minimizing costs, including power generation costs, backup costs, system imbalance costs, and compensation costs. The specific expressions are as follows:

[0085] (4)

[0086] In the formula, is the index of the generator in the power system, is the variable cost of each generator, , and Corresponding to The power generation, upward reserve and downward reserve of each generator.

[0087] Step 2.2 The lower model is based on the DC power flow model, taking into account constraints such as node power balance, line transmission power constraints, energy and reserve constraints, and load constraints. The specific expressions are as follows:

[0088] (5)

[0089] (6)

[0090] (7)

[0091]

[0092] (9)

[0093]

[0094] (11)

[0095] (12)

[0096] (13)

[0097] Where, formula (5) is the power balance equation for each node, is the node index, is the load index, is the line index, is a node set, is a collection of lines, and They exist at the nodes The generator and load collection on and Nodes where power flows to and from A collection of lines, For the flow line The power on the line; Equations (6) to (7) are the line transmission power limits.

[0098] is the line reactance, and Inflow and outflow lines The voltage phase angle of the directional power, For Line The maximum transmission power of; Equations (8)~(12) are the power generation and reserve constraints.

[0099] and Generator The minimum and maximum power generation, and are the maximum values ​​of the upward and downward reserves respectively; Formula (13) uses binary variables to represent the on / off state of the generator, where 0 means the generator is in shutdown state. A value of 1 indicates that the generator is in the on state.

[0100] Step 2.3 The bus load demand in the lower model only considers active power, assuming that it is It conforms to the normal distribution and satisfies the following expression:

[0101] (14)

[0102] (15)

[0103] In the formula, is a set of normal distribution parameters, and denote the expectation and standard deviation of the normal distribution, respectively. is the shift parameter of the normal distribution.

[0104] Step 2.4 After obtaining the aggregate load demand change, the lower-level model needs to express it in a robust equivalent form. The specific expression is as follows:

[0105] (16)

[0106] (17)

[0107] (18)

[0108] (19)

[0109] (20)

[0110] (twenty one)

[0111] In the formula, is an adjustable robust optimization parameter, The predicted value is based on the historical changes in load demand; is the load output control variable, is an intermediate parameter, with a value range of (0,1); and are the maximum and minimum values ​​of the current demand and predicted demand set of the aggregated load, respectively; Aggregate load demand for the current moment, To forecast demand for the current moment based on historical experience.

[0112] Step S3: The load aggregator terminal obtains the clearing price and load regulation After receiving the information, calculate the current profit level , and update the next quotation parameters ;

[0113] In step S3 of this embodiment, the upper model obtains the price and load adjustment information calculated by the lower model, calculates the current revenue of the load aggregator according to the following expression, and sets the next quotation parameter Passed to the next layer for the next iteration.

[0114] (twenty two)

[0115] In the formula, To provide load aggregators with benefits from participating in the electricity market, The price information calculated by the lower model and passed to the upper model, The aggregate load to be adjusted is calculated for the lower model and transferred to the upper model.

[0116] Step S4: Iterate in the order of steps 2 to 3 until a certain profit level satisfies the parameter preset by the load aggregator terminal and is greater than or equal to , the iteration is completed, and the current quotation coefficient is output , Clearing Price and load regulation The value of .

[0117] Repeat the iterative process of steps 2 to 3, and calculate the revenue of each iteration. and expected returns Compare, if satisfied , the iteration process is terminated and the last quotation parameters are output , price information , Aggregation load to be adjusted and the resulting profit , and finally form a complete aggregate load bidding plan.

[0118] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0119] The present invention can carry out intensive management of power load information, making it convenient for power users and monitoring personnel to consult and use it, and effectively improving the efficiency of using power load information. It can also efficiently collect, store, and process power load information, and provide data support for the stable operation of the power grid. In order to improve the management level and efficiency of power load information and control the risks of the power operation process, the timeliness and scientificity of the whole process management and control of the power system operation can be achieved. It ensures the feasibility of the results that meet the worst possible situation in the future, and can also adjust the strategy in real time according to the existing information, so as to take into account flexibility and achieve the optimization of benefits.

[0120] The method for processing information based on adjustable robust optimization for aggregated load participation provided by the present invention is a combination of the units and algorithm steps of each example described in the embodiments disclosed herein, and can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0121] Those skilled in the art will appreciate that the various aspects of the method for processing information based on adjustable robust optimization for aggregated load participation in the day-ahead information provided by the present invention can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which can be collectively referred to as "circuit", "module" or "system" here.

[0122] In an embodiment of the present invention, the method for aggregated load to participate in day-ahead bidding is written in one or more programming languages ​​or a combination thereof to write computer program codes for performing the operations of the present disclosure, and the above-mentioned programming languages ​​include but are not limited to object-oriented programming languages ​​- such as Java, Smalltalk, C++, and also include conventional procedural programming languages ​​- such as "C" language or similar programming languages.

[0123] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing information of aggregated load participation based on adjustable robust optimization, characterized in that the method include: Step 1 The information obtained by the load aggregator terminal is expressed by the following expression: C bid =k×(C fix +C com ) (1) In the formula, C bid is the declared price of the load aggregator, k is the quotation coefficient of the load aggregator, k0 is the initial value of parameter k used for iteration; C fix It is the loss of the user caused by the adjustable load participating in the market regulation process; C com To compensate the users for the load adjustment of the unit; Obtain the user's loss C caused by participating in the market regulation process fix , and the compensation C of the unit regulation load to its users com , and the local quotation coefficient k0, and transmit the above three parameters to the power system operator terminal; Step 2: The power system operator terminal obtains the quotation information and generates the clearing price C for the quotation coefficient k0 based on the system topology information and power balance constraints, energy and reserve constraints, and load limit constraints with the goal of minimizing the system regulation cost. cle and load regulation P use ; Step 2.1 The power system operator terminal receives the quotation strategy information from the load aggregator terminal and completes the power system balance with the goal of minimizing cost. The specific expression is as follows: Where i is the index of the generator in the power system, C i is the variable cost of each generator, p i , and They correspond to the power generation, upward reserve and downward reserve of the i-th generator respectively; Step 2.2 The power system operator terminal takes the DC power flow model as the basis, and considers the node power balance, line transmission power constraint, energy and reserve constraint, and load limit constraint respectively. The specific expressions are as follows: Where, formula (5) is the power balance equation of each node, b is the node index, j is the load index, l is the line index, B is the node set, L is the line set, I b and J b are the generator and load sets existing on node b, and is the set of lines through which power flows to and from node b, and f l is the power flowing through line l; equations (6) to (7) are the line transmission power limits; x l is the line reactance, and are the voltage phase angles of the power flowing into and out of line l, is the maximum transmission power of line l; equations (8) to (12) are the generation and reserve limits; P i and are the minimum and maximum power generation of generator i, and are the maximum values ​​of the upward and downward reserves respectively; Formula (13) uses a binary variable to represent the on / off state of the generator; v i 0 means the generator is in shutdown state, v i 1 means the generator is in the on state; Step 2.3 Assume that the bus load at time t conforms to the normal distribution, specifically satisfying the following expression: L j，t ~N b (a, p j，t ) (14) a=[μ j，t ;s j，t ] (15) In the formula, a is the set of normal distribution parameters, μ j，t and σ j，t They represent the expectation and standard deviation of the normal distribution, ρ j，t is the shift parameter of the normal distribution; Step 2.4 After the power system operator terminal obtains the aggregate load demand change, it is expressed in a robust equivalent form. The specific expression is as follows: γ=λ×η (18) Where γ is an adjustable robust optimization parameter, E j，t is the predicted value based on the historical change of load demand; λ is the load output control variable, η is the intermediate parameter, and its value range is (0, 1); and L are the maximum and minimum values ​​of the current demand and predicted demand set of the aggregated load, respectively; Aggregate load demand for the current moment, To forecast demand for the current moment based on historical experience; Step 3: The load aggregator terminal obtains the clearing price C cle and load regulation P use After receiving the information, calculate the current profit level W and update the next quotation parameter k1; The load aggregator terminal obtains the price and load regulation information calculated by the power system operator terminal, calculates the load aggregator's current revenue according to the following expression, and passes the next quotation parameter k1 to the lower layer for the next iteration; W=C cle ×P use (2) Where W is the revenue of load aggregator terminal participating in the electricity market, C cle The price information calculated for the power system operator terminal and delivered to the load aggregator terminal, P use The aggregated load to be adjusted is calculated for the power system operator terminal and transmitted to the load aggregator terminal; Step 4: Iterate in the order of steps 2 to 3 until a certain profit level satisfies the parameter preset by the load aggregator terminal and is greater than or equal to W. exp , the iteration is completed, and the current quotation coefficient k and clearing price C are output cle and load regulation P use The value of Repeat the iterative process of steps 2 to 3, and compare the revenue W of each iteration with the expected revenue W exp Make comparisons; If W ≥ W exp , the iteration process is terminated and the last quotation parameter k is output n 、Price information C cle 、Polymerization load to be adjusted P use And the profit W calculated thereby finally forms a perfect aggregate load bidding scheme.

2. The method for processing day-ahead information based on adjustable robust optimization for aggregated load participation according to claim 1 is characterized in that: The costs of achieving power system balancing with the goal of cost minimization include power generation costs, reserve costs, system imbalance costs and compensation costs.

3. A day-ahead information processing system for aggregated load participation based on adjustable robust optimization, characterized in that: The system adopts the method for processing information based on adjustable robust optimization for aggregated load participation in the day-ahead according to any one of claims 1 to 2; The system includes: power system operator terminals and load aggregator terminals; The load aggregator terminal is used to obtain the user losses C caused by participating in the market regulation process. fix , and the compensation C of the unit regulation load to its users com , and the local quotation coefficient k0, and transmit the above three parameters to the power system operator terminal; The power system operator terminal obtains the quotation information and generates the clearing price C for the quotation coefficient k0 based on the system topology information and power balance constraints, energy and reserve constraints, and load limit constraints with the goal of minimizing the system regulation cost. cle and load regulation P use ; The load aggregator terminal is also used to obtain the clearing price C cle and load regulation P use After receiving the information, calculate the current profit level W and update the next quotation parameter k1; then execute the loop iteration until a certain profit level meets the parameter preset by the load aggregator terminal and is greater than or equal to W exp , the iteration is completed, and the current quotation coefficient k and clearing price C are output cle and load regulation P use The value of .

4. The aggregated load participating in the day-ahead information processing system based on adjustable robust optimization according to claim 3 is characterized in that: The power system operator terminal and the load aggregator terminal are connected by wired or wireless communication.

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