Load aggregator bidding method based on two-dimensional power optimization and related equipment

Abstract

The invention discloses a bidding method for a load aggregator based on two-dimensional power optimization and related equipment, and relates to the field of electricity markets. The method comprises the following steps: constructing a double-layer model of upper-layer load aggregator bidding optimization and lower-layer retail market operator clearing optimization: an upper layer submits active-reactive simultaneous two-dimensional bidding parameters according to nodes and time periods by taking income maximization as a target; and the lower layer carries out clearing and feeds back a result at the minimum total operation cost under the constraint of a power distribution network structure and operation safety. And the load aggregator performs internal optimization on the flexible resources and updates the bidding, and completes bidding-clearing-feedback-closed loop updating by adopting iterative solution. The method realizes P / Q combined declaration and clearing, improves clearing feasibility and convergence efficiency, reduces voltage out-of-limit and network loss, clarifies reactive power value, improves aggregator income stability, and is suitable for a power distribution retail market containing distributed power supplies and demand response.

Description

Technical Field

[0001] This invention relates to the field of electricity markets, specifically to a load aggregator bidding method and related equipment based on two-dimensional power optimization. Background Technology

[0002] Load aggregators (AGGs), as key players in the retail market, serve to connect electricity supply and demand. They encompass flexible resources such as distributed generation, renewable energy plants, active users, and demand response resources, and can connect to the grid from multiple nodes across regions. They are applied in scenarios such as electricity market operation, renewable energy integration, and frequency regulation. However, current issues in the retail market, such as the lack of active-reactive power coordinated pricing and the limitations of a single bidding model, constrain market efficiency and system security.

[0003] There are two main types of existing implementation schemes for load aggregator bidding: The first is a single active power bidding scheme, which designs bidding strategies only for the active power market, completely ignoring the role of reactive power in the retail market. It does not consider the voltage and power flow safety constraints of the distribution system, which can easily lead to potential safety hazards in the power grid during actual operation and fails to tap the market value of reactive power. The second is a single type of bidding scheme, which adopts a single mode of "only quoting prices" or "only quoting quantities," without involving the design of quotation and pricing that combines active and reactive power. This cannot adapt to the dual needs of the retail market for power price and output range, and lacks flexibility. Summary of the Invention

[0004] The technical problem this invention aims to solve is that existing load aggregators mainly use active power-based bidding, failing to coordinate the modeling and pricing of reactive power capacity and distribution network voltage operation safety constraints. This leads to a disconnect between the clearing results and the actual feasible domain, difficulty in effectively settling reactive power support, and a tendency for voltage exceedances and increased network losses. The purpose is to provide a two-dimensional power-optimized load aggregator bidding method that solves the problem of coordinating and resolving the difficulty in unifying the economics and distribution network feasibility of bidding application and operator clearing.

[0005] This invention is achieved through the following technical solution:

[0006] A load aggregator bidding method based on two-dimensional power optimization includes:

[0007] A two-layer optimization model is constructed, consisting of upper-layer load aggregator bidding optimization and lower-layer retail market operator clearing optimization. The upper layer formulates bidding strategies with the goal of maximizing the revenue of load aggregators, while the lower layer aims to minimize the total operating cost of the retail market and clears the market under the constraints of distribution network structure and operational safety.

[0008] The load aggregator submits two-dimensional bidding parameters for active and reactive power combined by node and time period. The two-dimensional bidding parameters use active and reactive power as coupled decision quantities and submit price declarations.

[0009] Retail market operators generate clearing results based on the aforementioned two-dimensional bidding parameters and feed them back to the load aggregator;

[0010] Based on the clearing results, the load aggregator internally optimizes its flexibility resources and updates its bidding strategy;

[0011] The two-layer optimization model is solved using an iterative solution process, which includes initialization, fitness evaluation, iterative update, and termination determination; the optimal bidding scheme is output when the termination condition is met.

[0012] Furthermore, the two-dimensional bidding parameters include price information and output range information set separately for active power and reactive power, and are submitted according to nodes and time periods; the flexible resources include at least distributed power sources, renewable energy power plants, adjustable loads, and demand response resources.

[0013] Furthermore, the iterative solution process is implemented using a genetic algorithm. The initialization of the genetic algorithm includes: acquiring and inputting retail market distribution network parameters, load aggregator flexibility resource parameters and capacity boundary data, renewable energy forecast data, and generating a two-dimensional initial bidding population covering each node and time period, wherein each initial individual at least determines the type of bidding product and sets the bid price and output range.

[0014] The fitness assessment is based on the revenue of the load aggregator under the current clearing results;

[0015] The iterative update sequentially performs sorting, selection, crossover, and mutation, and after each generation is completed, it calls the lower-level clearing to update the upper-level bidding decision.

[0016] Furthermore, the termination determination of the iterative solution process includes one or a combination of the following: reaching a preset maximum number of generations; the improvement of the optimal return for several consecutive generations being less than a threshold; or the difference between the optimal bidding vectors of adjacent generations being less than a threshold, and the optimal bidding scheme is output.

[0017] Furthermore, the bid prices submitted by load aggregators meet the following price range constraints: the active power bid price submitted by load aggregators at any node and at any time is limited to the active power bid price lower limit and upper limit preset by the retail market operator, and the reactive power bid price is limited to the reactive power bid price lower limit and upper limit preset.

[0018] And the following output range constraints must be met: the active power output of the load aggregator in any time period shall not be lower than the minimum output of that time period and shall not exceed the maximum output of that time period; the reactive power output shall not be lower than the minimum output of that time period and shall not exceed the maximum output of that time period.

[0019] Furthermore, the clearing out of retail market operators aims to minimize the total operating costs of the retail market, which include at least: the operating costs of directly controlled equipment, the active power cost and reactive power cost purchased from the wholesale market, and the active power cost and reactive power cost purchased from load aggregators, and are included according to the duration of the time period.

[0020] Furthermore, the clearing results in the retail market include at least the active power and reactive power that won bids at each node, as well as the corresponding settlement prices, and are fed back to the upper level for revenue calculation and bidding strategy updates.

[0021] This invention also provides a load aggregator bidding system based on two-dimensional power optimization, used to implement the load aggregator bidding method based on two-dimensional power optimization as described above, including:

[0022] The data acquisition unit is used to acquire parameters of the retail market distribution network, renewable energy power forecasts, and load aggregator flexibility resource parameters.

[0023] The two-dimensional bid generation unit is used to generate two-dimensional bid parameters that combine active and reactive power according to nodes and time periods, and submit active and reactive power outputs as coupled decision quantities.

[0024] The upper-level optimization unit is used to internally optimize the flexibility resources and update the bidding strategy based on the clearing results, with the goal of maximizing the revenue of the load aggregator.

[0025] The lower-level clearing unit is used to clear the two-dimensional bidding parameters under the constraints of distribution network structure and operation safety, with the goal of minimizing the total operating cost of the retail market, generate clearing results and feed them back to the upper-level optimization unit.

[0026] The iterative solution unit is communicatively connected to the upper-level optimization unit and the lower-level clearing unit. It is used to execute the iterative solution process, including initialization, fitness evaluation, iterative update and termination determination, and outputs the optimal bidding scheme when the termination condition is met.

[0027] The communication interface unit is used to transmit the two-dimensional bidding parameters and the clearing results between the load aggregator and the retail market operator.

[0028] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the load aggregator bidding method based on two-dimensional power optimization as described above.

[0029] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the load aggregator bidding method based on two-dimensional power optimization as described above.

[0030] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0031] By treating active and reactive power as coupled decision-making quantities and submitting them together, the inherent safety constraints such as voltage / power flow in the distribution network are addressed during clearing, avoiding the inconsistency between the clearing result and the physical feasible region caused by single-dimensional bidding. This effectively reduces voltage exceedances and network losses. Maximizing the revenue of upper-level aggregators and minimizing the total cost of lower-level operators are solved in a closed loop within the same two-layer framework, balancing individual profits with system operational safety and reducing local optima and constraint violations caused by focusing solely on economic efficiency.

[0032] Two-dimensional bidding incorporates reactive power support into the pricing and output scope, ensuring that reactive power services receive clear settlement and scheduling priorities, thereby improving the availability and allocation efficiency of reactive power resources. Unified modeling and internal optimization of distributed power sources, renewable energy, electricity price response, and adjustable loads enhance resource utilization and bidding strategy stability, while reducing deviation and penalty risks.

[0033] Employing an iterative solution process (such as a genetic algorithm) coupled with clearing feedback, it is adaptable to large-scale optimization under non-convex, discrete, and uncertain conditions, and possesses convergent, scalable, and online rolling update capabilities for engineering implementation. Submission and clearing by node and by time period can implement differentiated guidance for local voltage-sensitive areas and time-specific congestion, improving the voltage quality and reactive power supply-demand matching of the local grid structure. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0035] Figure 1 Here is a flowchart of the load aggregator bidding method based on two-dimensional power optimization in Example 1;

[0036] Figure 2The flowchart shows the genetic algorithm calculation process of the load aggregator bidding method based on two-dimensional power optimization in Example 1. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0038] Example 1

[0039] A load aggregator bidding method based on two-dimensional power optimization, such as Figure 1 As shown, it includes:

[0040] A two-layer optimization model is constructed, consisting of upper-layer load aggregator bidding optimization and lower-layer retail market operator clearing optimization. The upper layer formulates bidding strategies with the goal of maximizing the revenue of load aggregators, while the lower layer aims to minimize the total operating cost of the retail market and clears the market under the constraints of distribution network structure and operational safety.

[0041] The load aggregator submits two-dimensional bidding parameters for active and reactive power combined by node and time period. The two-dimensional bidding parameters use active and reactive power as coupled decision quantities and submit price declarations.

[0042] Retail market operators generate clearing results based on the aforementioned two-dimensional bidding parameters and feed them back to the load aggregator;

[0043] Based on the clearing results, the load aggregator internally optimizes its flexibility resources and updates its bidding strategy;

[0044] The two-layer optimization model is solved using an iterative solution process, which includes initialization, fitness evaluation, iterative update, and termination determination; the optimal bidding scheme is output when the termination condition is met.

[0045] The model is divided into a load aggregator bidding optimization layer (upper layer) and a retail market operator clearing optimization layer (lower layer), which interact dynamically through "bidding parameter reporting - clearing result feedback".

[0046] The upper layer aims to maximize the profits of load aggregators, and, in combination with the characteristics of internal flexible resources, formulates bidding strategies for active and reactive power, and reports the bidding parameters to the lower layer.

[0047] After receiving the bidding parameters, the lower level simulates the retail market clearing process, takes into account system operation constraints and economic requirements, determines the winning bid volume and settlement price, and feeds it back to the upper level.

[0048] The upper management optimizes internal resource allocation based on feedback, makes a final decision, and evaluates expected benefits.

[0049] In terms of power coordination, existing technologies often consider active power in isolation, neglecting the crucial role of reactive power in the voltage stability and power flow security of the distribution system. This invention optimizes active and reactive power coordination in a two-dimensional manner, ensuring the safety of retail market operations while fully exploring the market value of reactive power and improving overall resource utilization efficiency. Regarding bidding models, existing technologies are limited to a single form of "only quoting prices" or "only quoting quantities," which is difficult to adapt to the diverse transaction needs of the retail market. This invention innovatively adopts a comprehensive bidding model of "quoting quantities + quoting prices," which covers the price and output range of both active and reactive power, making it more flexible and universal.

[0050] To avoid the disconnect between single-dimensional bidding and the physical feasible domain, this embodiment integrates the definition of bidding products, node capability domains, and settlement mechanisms.

[0051] Product and capability domain binding: At node i and time period t, the load aggregator submits two-dimensional bidding parameters that combine active and reactive power; the two-dimensional bidding parameters are constrained by the node capability domain, which is determined by a combination of the equipment's rated apparent capacity, power factor boundary, grid-connected inverter / compensation device capacity, and voltage compliance range.

[0052] Constrained endogenous clearing: Under the constraints of distribution network structure and operational safety (including voltage, power flow, branch capacity, etc.), retail market operators simultaneously settle the clearing prices and winning bids for active and reactive power by node and time period, obtain the clearing results, and feed them back to the upper level.

[0053] Deviation Handling and Penalties: When a bid application or execution deviates from the node's capacity domain, or causes operational risks such as voltage exceeding limits or crossing lines, the operator will impose heavier penalties on the relevant application / execution results. The penalty intensity will not be lower than the preset coefficient (different tolerance bands and coefficients can be set for positive and negative deviations), and may trigger local reactive power priority consumption or rescheduling processes.

[0054] Process implementation: The upper layer completes internal resource optimization → submits two-dimensional bids → the lower layer constrains internal clearing → feedback → the upper layer updates the bids based on the feedback; forming a closed loop of bidding-clearing-feedback-updating.

[0055] To reduce clearing failures and repeated attempts due to infeasible bids, this embodiment performs feasibility processing on candidate bid vectors during iterative solution. This includes:

[0056] Feasible domain definition: Construct a feasible domain defined by network constraints and capacity constraints. The feasible domain shall include at least: node capacity domain, upper and lower limits of bid price, upper and lower limits of output, and time coupling across time periods (such as minimum start-up and shutdown, ramp-up / recovery, and energy storage SOC boundary).

[0057] Mapping strategy: For any candidate bid vector, perform a feasible mapping before entering the next layer clearing, mapping the bid vector to the nearest feasible point in the feasible region.

[0058] Integration Location: Feasibility processing is a mandatory step after iterative updates. Each new generation of individuals must go through this process before calling the next layer clearing.

[0059] Engineering Implementation: Prioritize the use of linear / quadratic programming approximations with local closed-loop pruning and time consistency repair to ensure computational efficiency; use block or incremental repair for cross-node network coupling constraints to avoid global solution overhead.

[0060] To improve convergence efficiency and reduce constraint violations, this embodiment introduces voltage / power flow sensitivity information during the iterative update phase to guide the fine-tuning direction of the bid vector. This includes:

[0061] Sensitivity acquisition: Based on the lower-level clearing power flow Jacobian or equivalent linearization model, calculate the sensitivity of node voltage to active / reactive injection and the sensitivity of network loss to injection; the sensitivity is updated by node and time period.

[0062] Guiding principle: When voltage deviation, branch constraint approaching, or network loss is high, small-step corrections are applied to the price / output components of candidate bidding vectors in the directions of "reducing default degree" and "reducing network loss". The correction magnitude is limited by the price / output boundary and capacity domain constraints and is coordinated with the feasibility mapping.

[0063] In conjunction with heuristics: In genetic algorithms, this information is used to bias mutation / crossover: increase the probability of exploring compliant directions for high-sensitivity nodes and time periods, and shrink the mutation scale for directions that may lead to default; retain a small amount of random exploration to avoid getting trapped in local optima.

[0064] Triggering and update frequency: Sensitivity is updated incrementally at fixed algebraic intervals or when the default rate exceeds the threshold; when the network running point changes little, the sensitivity of the previous round can be reused to reduce the amount of computation.

[0065] In this embodiment, the two-dimensional bidding parameters include price information and output range information set for active power and reactive power respectively, and are submitted according to nodes and time periods; the flexible resources include at least distributed power sources, renewable energy power plants, adjustable loads and demand response resources.

[0066] The bid prices submitted by load aggregators must meet the following price range constraints: The active power bid price submitted by load aggregators at any node and at any time period is limited to the active power bid price lower and upper limits preset by the retail market operator; the reactive power bid price is limited to the reactive power bid price lower and upper limits preset; expressed as:

[0067]

[0068]

[0069] In the formula: This is the lower limit of the active power bid price for load aggregators; The bid price for the active power of the load aggregator at time t; This represents the upper limit of the active power bidding price for load aggregators; This is the lower limit of the reactive power bidding price for load aggregators; Let t be the bid price for reactive power of the load aggregator; This is the upper limit of the reactive power bidding price for load aggregators.

[0070] And it must meet the following output range constraints: the active power output of the load aggregator in any given time period shall not be lower than the minimum output and shall not exceed the maximum output in that time period; the reactive power output shall not be lower than the minimum output and shall not exceed the maximum output in that time period. This is expressed as:

[0071]

[0072]

[0073] In the formula: This is the lower limit of the active power of the load aggregator; Let be the minimum output of the load aggregator active power at time t; This represents the maximum active power output of the load aggregator at time t. This represents the upper limit of the active power of the load aggregator; This is the lower limit of the reactive power of the load aggregator; Let be the minimum reactive power output of the load aggregator at time t; This represents the maximum reactive power output of the load aggregator at time t. This is the upper limit of reactive power for load aggregators.

[0074] In this embodiment, the clearing of retail market operators aims to minimize the total operating cost of the retail market. The operating cost includes at least the operating cost of directly controlled equipment, the cost of active power purchased from the wholesale market, the cost of reactive power, and the cost of active power purchased from load aggregators, and is calculated according to the duration of time periods.

[0075]

[0076] In the formula: The total operating cost for the retail market is represented by T, where T represents the total number of time periods. This refers to the number of directly controlled devices. The duration of the time period; The cost coefficient related to the active power of the nth directly controlled device at time t; Let t be the active power purchased from the wholesale market by the nth directly controlled device; Let be the cost coefficient related to the reactive power of the nth directly controlled device at time t; Let t be the reactive power purchased from the wholesale market by the nth directly controlled device; The cost factor related to the active power of the nth load aggregator (AGG) at time t; Let be the active power purchased from the nth load aggregator at time t; Let be the cost coefficient related to reactive power of the nth load aggregator at time t; Let be the reactive power purchased from the nth load aggregator at time t.

[0077] The results of the retail market clearing process include at least the active power and reactive power that won bids at each node, as well as the corresponding settlement prices, and are fed back to the upper level for revenue calculation and bidding strategy updates.

[0078] Because the retail market optimization layer contains nonlinear constraints, and the revenue of the upper-layer load aggregator is related to the clearing result of the lower layer, and the decision variables and objective function lack explicit analytical expressions, traditional linearization methods cannot effectively solve the problem. Therefore, the iterative solution process adopts a genetic algorithm, such as... Figure 2 As shown, the steps are as follows:

[0079] Initialization: Acquire and input retail market distribution network parameters, load aggregator flexibility resource parameters and capacity boundary data, renewable energy forecast data, and generate a two-dimensional initial bidding population covering each node and time period, wherein each initial individual at least determines the bidding product type and sets the bid price and output range;

[0080] Fitness assessment is based on the revenue of load aggregators under the current clearing results; it includes: solving the clearing results based on the bidding information of load aggregators and the cost function of directly controlled equipment, and obtaining the winning bid electricity and settlement price of each node (the sub-optimization problem is solved using Cplex); load aggregators perform internal optimization based on the clearing information to obtain the optimal revenue, and use the revenue as the fitness value of individual populations.

[0081] The iterative update sequentially performs sorting, selection, crossover, and mutation, and after each generation, it calls the lower-level purging to update the upper-level bidding decisions. The population bidding strategy is updated through the "sort-selection-crossover-mutation" operation.

[0082] The termination criteria for the iterative solution process include one or a combination of the following: reaching the preset maximum number of generations; the improvement of the optimal return for several consecutive generations being less than a threshold; or the difference between the optimal bidding vectors of adjacent generations being less than a threshold, and the optimal bidding scheme is output.

[0083] Represented as:

[0084]

[0085] In the formula, The index of the current iteration; For the first Objective function value of the time-load aggregator; To determine the threshold.

[0086] This invention adopts a two-layer optimization framework for active and reactive power coordination, taking into account the interests of retail market managers and load aggregators, considering the active and reactive power coupling relationship at the power distribution system level, and realizing two-dimensional power collaborative planning through dynamic interaction between the upper and lower layers, thus breaking through the limitations of traditional single active power strategy.

[0087] The bidding content of load aggregators includes the price and output range of both active and reactive power. Compared with the "only price quote" or "only quantity quote" model, it is more suitable for the diversified transaction needs of the retail market and has greater universality.

[0088] It clearly covers distributed power sources, renewable energy power plants, active users, and demand response resources within load aggregators, and is compatible with various forms of existence such as electricity sales companies, renewable energy cluster power plants, virtual power plants, and grid-connected microgrids, which can fully leverage the value of resource synergy.

[0089] To address the issues of nonlinear constraints and lack of explicit analytical relationships in bilayer models, a genetic algorithm is employed to achieve efficient solutions, ensuring model convergence and result feasibility. Sub-optimization problems are combined with Cplex to improve solution accuracy.

[0090] Example 2

[0091] A load aggregator bidding system based on two-dimensional power optimization, used to implement the load aggregator bidding method based on two-dimensional power optimization as described above, includes:

[0092] The data acquisition unit is used to acquire parameters of the retail market distribution network, renewable energy power forecasts, and load aggregator flexibility resource parameters.

[0093] The two-dimensional bid generation unit is used to generate two-dimensional bid parameters that combine active and reactive power according to nodes and time periods, and submit active and reactive power outputs as coupled decision quantities.

[0094] The upper-level optimization unit is used to internally optimize the flexibility resources and update the bidding strategy based on the clearing results, with the goal of maximizing the revenue of the load aggregator.

[0095] The lower-level clearing unit is used to clear the two-dimensional bidding parameters under the constraints of distribution network structure and operation safety, with the goal of minimizing the total operating cost of the retail market, generate clearing results and feed them back to the upper-level optimization unit.

[0096] The iterative solution unit is communicatively connected to the upper-level optimization unit and the lower-level clearing unit. It is used to execute the iterative solution process, including initialization, fitness evaluation, iterative update and termination determination, and outputs the optimal bidding scheme when the termination condition is met.

[0097] The communication interface unit is used to transmit the two-dimensional bidding parameters and the clearing results between the load aggregator and the retail market operator.

[0098] Example 3

[0099] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the load aggregator bidding method based on two-dimensional power optimization as described above.

[0100] Example 4

[0101] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the load aggregator bidding method based on two-dimensional power optimization as described above.

[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A load aggregator bidding method based on two-dimensional power optimization, characterized in that, include: A two-layer optimization model is constructed, consisting of upper-layer load aggregator bidding optimization and lower-layer retail market operator clearing optimization. The upper layer formulates bidding strategies with the goal of maximizing the revenue of load aggregators, while the lower layer aims to minimize the total operating cost of the retail market and clears the market under the constraints of distribution network structure and operational safety. The load aggregator submits two-dimensional bidding parameters for active and reactive power combined by node and time period. The two-dimensional bidding parameters use active and reactive power as coupled decision quantities and submit price declarations. Retail market operators generate clearing results based on the aforementioned two-dimensional bidding parameters and feed them back to the load aggregator; Based on the clearing results, the load aggregator internally optimizes its flexibility resources and updates its bidding strategy; The two-layer optimization model is solved using an iterative solution process, which includes initialization, fitness evaluation, iterative update, and termination determination; the optimal bidding scheme is output when the termination condition is met.

2. The load aggregator bidding method based on two-dimensional power optimization according to claim 1, characterized in that, The two-dimensional bidding parameters include price information and output range information set separately for active power and reactive power, and are submitted according to nodes and time periods; the flexible resources include at least distributed power sources, renewable energy power plants, adjustable loads, and demand response resources.

3. The load aggregator bidding method based on two-dimensional power optimization according to claim 1, characterized in that, The iterative solution process is implemented using a genetic algorithm. The initialization of the genetic algorithm includes: acquiring and inputting retail market distribution network parameters, load aggregator flexibility resource parameters and capacity boundary data, renewable energy forecast data, and generating a two-dimensional initial bidding population covering each node and time period. Each initial individual at least determines the type of bidding product and sets the bid price and output range. The fitness assessment is based on the revenue of the load aggregator under the current clearing results; The iterative update sequentially performs sorting, selection, crossover, and mutation, and after each generation is completed, it calls the lower-level clearing to update the upper-level bidding decision.

4. The load aggregator bidding method based on two-dimensional power optimization according to claim 3, characterized in that, The termination criteria for the iterative solution process include one or a combination of the following: reaching the preset maximum number of generations; the improvement of the optimal return for several consecutive generations being less than a threshold; or the difference between the optimal bidding vectors of adjacent generations being less than a threshold, and the optimal bidding scheme is output.

5. The load aggregator bidding method based on two-dimensional power optimization according to claim 2, characterized in that, The bid prices submitted by load aggregators must meet the following price range constraints: the active power bid price submitted by load aggregators at any node and at any time period is limited to the active power bid price lower limit and upper limit preset by the retail market operator, and the reactive power bid price is limited to the reactive power bid price lower limit and upper limit preset. And the following output range constraints must be met: the active power output of the load aggregator in any time period shall not be lower than the minimum output of that time period and shall not exceed the maximum output of that time period; the reactive power output shall not be lower than the minimum output of that time period and shall not exceed the maximum output of that time period.

6. The load aggregator bidding method based on two-dimensional power optimization according to claim 1, characterized in that, The clearing out of the retail market aims to minimize the total operating costs of the retail market. These operating costs include, at a minimum, the operating costs of directly controlled equipment, the active power costs and reactive power costs purchased from the wholesale market, and the active power costs and reactive power costs purchased from load aggregators, and are calculated by time period.

7. The load aggregator bidding method based on two-dimensional power optimization according to claim 1, characterized in that, The results of the retail market clearing process include at least the active power and reactive power that won bids at each node, as well as the corresponding settlement prices, and are fed back to the upper level for revenue calculation and bidding strategy updates.

8. A load aggregator bidding system based on two-dimensional power optimization, characterized in that, To implement the load aggregator bidding method based on two-dimensional power optimization as described in any one of claims 1 to 7, comprising: The data acquisition unit is used to acquire parameters of the retail market distribution network, renewable energy power forecasts, and load aggregator flexibility resource parameters. The two-dimensional bid generation unit is used to generate two-dimensional bid parameters that combine active and reactive power according to nodes and time periods, and submit active and reactive power outputs as coupled decision quantities. The upper-level optimization unit is used to internally optimize the flexibility resources and update the bidding strategy based on the clearing results, with the goal of maximizing the revenue of the load aggregator. The lower-level clearing unit is used to clear the two-dimensional bidding parameters under the constraints of distribution network structure and operation safety, with the goal of minimizing the total operating cost of the retail market, generate clearing results and feed them back to the upper-level optimization unit. The iterative solution unit is communicatively connected to the upper-level optimization unit and the lower-level clearing unit. It is used to execute the iterative solution process, including initialization, fitness evaluation, iterative update and termination determination, and outputs the optimal bidding scheme when the termination condition is met. The communication interface unit is used to transmit the two-dimensional bidding parameters and the clearing results between the load aggregator and the retail market operator.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the load aggregator bidding method based on two-dimensional power optimization as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the load aggregator bidding method based on two-dimensional power optimization as described in any one of claims 1 to 7.

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