Multi-demand response resource day-ahead market optimal bidding method and device

Abstract

The invention relates to a multi-demand response resource day-ahead market optimal bidding method and device, and the method comprises the steps: defining the structure and operation mechanism of a day-ahead electricity market, considering various demand response resources, and building demand response bidding models corresponding to the various demand response resources; on the basis of the demand response bidding model and contracts of various demand response resources of the signed customer, constructing a load aggregator decision optimization model by taking the maximum total income of a demand response load aggregator as a target; and solving the load aggregator decision optimization model to obtain an optimal bidding strategy. According to the method, the energy use efficiency can be improved, and meanwhile, the demand response utilization efficiency of large-scale data processing facilities with special demand characteristics is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power market demand response, and in particular to a method and device for optimal bidding in a day-ahead market of multiple demand response resources. Background Art

[0002] As the level of global informatization continues to improve, various electronic devices and computing facilities are increasingly used in social economy. These facilities, collectively referred to as data processing facilities, have become a key force in promoting the development of modern society. In traditional electricity markets, transactions are usually centralized and controlled by a few large power generation companies and grid companies. With the rise of distributed energy resources (such as solar energy, wind energy, and energy storage systems), the concept of distributed trading has emerged. Distributed trading allows energy to be traded between multiple small, decentralized participants, improving market flexibility and efficiency.

[0003] The energy consumption of data processing facilities has also become a focus of public attention, especially in terms of electricity consumption, which accounts for an increasing proportion of global energy use. Traditional energy management strategies mainly focus on improving equipment energy efficiency and adopting renewable energy, but these strategies often ignore the temporal and spatial distribution characteristics of electricity demand. During peak periods of electricity demand, data processing facilities often face high electricity costs; while during periods of low demand, energy may be idle and wasted.

[0004] In order to effectively deal with the problem of energy consumption, demand response (DR) technology has been introduced into the management of power systems. Demand response allows power users to voluntarily reduce or delay part of their electricity consumption according to the demand and price signals of the power grid, in order to respond to the regulation needs of the power grid and possibly obtain economic compensation. However, the current demand response technology is mostly concentrated on industrial and household users, and the utilization of large-scale data processing facilities with special demand characteristics is not yet sufficient. Large-scale data processing facilities have the advantages of flexible computing load scheduling, integrated application of energy storage systems, and waste heat recovery. However, demand response technology has not yet fully utilized these advantages, lacks comprehensive management of third-party media, cannot achieve multi-resource coordinated optimization and scheduling, and lacks strategies and mechanisms for effectively combining demand response potential with the power market. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method and device for optimal bidding in the day-ahead market for multiple demand response resources, which can improve energy utilization efficiency and at the same time improve the demand response utilization efficiency of large-scale data processing facilities with special demand characteristics.

[0006] The technical solution adopted by the present invention to solve the technical problem is: to provide an optimal bidding method for a multi-demand response resource day-ahead market, comprising the following steps:

[0007] Clarify the structure and operation mechanism of the day-ahead power market, consider various types of demand response resources, and establish a demand response bidding model corresponding to various types of demand response resources;

[0008] Based on the demand response bidding model and the contracts of various demand response resources of the signed customers, a load aggregator decision optimization model is constructed with the goal of maximizing the total revenue of the demand response load aggregator;

[0009] The load aggregator decision optimization model is solved to obtain the optimal bidding strategy.

[0010] The various types of demand response resources include load reduction demand response resources, load transfer demand response resources, self-provided power supply demand response resources and energy storage demand response resources.

[0011] The demand response bidding model for the load reduction demand response resource is: in, is the load reduction amount, N is the load reduction compensation that the load aggregator needs to pay to the participants. LC For the collection of load shedding contracts, is the status indicator of the i-th load reduction contract in period t, and are the initial cost of the load reduction contract and the fixed cost per period during the load reduction period, respectively. is the starting index of the i-th load reduction contract in period t, is the load reduction amount in the i-th load reduction contract in period t, is the compensation price per unit of load reduction paid to the participant in the i-th load reduction contract.

[0012] The demand response bidding model for the load shifting demand response resource is: in, is the load transfer amount, N is the load transfer fee that the load aggregator needs to pay to the participants. LS is the set of load transfer contracts, is the status indicator of the i-th load transfer contract in period t, and are the initial cost of starting the transfer and the fixed cost of each period during the transfer period stipulated in the i-th load transfer contract, respectively. is the starting indicator of the i-th load transfer contract in period t, is the load transferred in the i-th load transfer contract in period t, is the compensation price per unit of load transferred paid to the participant in the i-th load transfer contract.

[0013] The demand response bidding model for the self-provided power supply demand response resource is: in, The amount of electricity generated for self-generated power, N is the compensation that the load aggregator needs to pay to the participants for the self-generated power generation. OG A collection of self-provided power supply contracts, is the status indicator of the i-th self-provided power supply contract in time period t, and are the initial cost of starting power generation and the fixed cost per period during power generation as specified in the i-th captive power supply contract, is the starting indicator of the ith self-provided power supply contract in period t, is the power generation in the i-th load transfer contract in period t, is the compensation price per unit of electricity generated paid to the participant in the i-th load transfer contract.

[0014] The demand response bidding model for energy storage demand response resources is: in, is the discharge capacity of the energy storage device, N is the discharge remuneration that the load aggregator needs to pay to the participants. ES is the collection of energy storage contracts, and are the start-up cost of the energy storage equipment specified in the i-th energy storage contract and the fixed cost of each period during the operation of the energy storage equipment, is the activation index of the i-th energy storage contract in period t, is the discharge amount in the i-th energy storage contract in period t, is the compensation price per unit of discharge paid to the participant in the i-th energy storage contract.

[0015] The objective function of the load aggregator decision optimization model is: Among them, C LA is the total revenue of the demand response load aggregator, P t market is the day-ahead electricity price in the electricity market, N T is the set of all scheduling periods, is the load reduction amount, is the load transfer amount, The amount of electricity generated for self-generated power, is the discharge capacity of the energy storage device, The load aggregator needs to pay the participants for load reduction. The load aggregator needs to pay the participants for the load transfer. The load aggregator needs to pay the participants for the self-generated power generation. Load aggregators need to pay discharge remuneration to participants.

[0016] The constraints of the load aggregator decision optimization model include:

[0017] The node load transfer and energy storage charging constraints are expressed as:

[0018]

[0019] The DC power flow constraint is expressed as:

[0020] Among them, G b is the set of generators connected to node b, P g,t is the output of generator g during period t, is the load connected to node b during period t, L b is the set of lines connected to node b, P l,t is the active power injected into node b by line l during period t, is the load reduction of node b in period t, is the load transfer amount of node b in period t, is the power generation of the self-provided power supply of node b in period t, is the discharge amount of the energy storage device at node b during period t, is the power injected into node b after load transfer from other nodes in period t, is the charging power of the energy storage device at node b during period t; θ x,t and θ y,t are the phase angles of the line sending node x and receiving node y in period t, respectively. l What does the reactance of line l mean? Please explain. max,l is the maximum transmission power of line l, θ b is the phase angle of node b, θ min,b and θ max,b are the minimum and maximum phase angles of node b respectively.

[0021] The technical solution adopted by the present invention to solve the technical problem is: to provide an optimal bidding device for a multi-demand response resource day-ahead market, comprising:

[0022] Establish a module to clarify the structure and operation mechanism of the day-ahead power market, consider various types of demand response resources, and establish a demand response bidding model corresponding to various types of demand response resources;

[0023] A construction module is used to construct a load aggregator decision optimization model based on the demand response bidding model and the contracts of various demand response resources of the contracted customers, with the goal of maximizing the total revenue of the demand response load aggregator;

[0024] The solution module is used to solve the load aggregator decision optimization model to obtain the optimal bidding strategy.

[0025] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned optimal bidding method for the day-ahead market of multiple demand response resources when executing the computer program.

[0026] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned optimal bidding method for the day-ahead market of multiple demand response resources are implemented.

[0027] Beneficial Effects

[0028] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention clarifies the structure and operation mechanism of the day-ahead power market, considers multiple types of demand response resources and establishes a corresponding demand response bidding model, and considers the power market price at the same time, and establishes a load aggregator decision optimization model based on various types of demand response resources and corresponding contracts of the contracted customers; the optimal bidding strategy is obtained by solving the load aggregator decision optimization model, and the optimal bidding strategy can improve energy utilization efficiency and demand response utilization efficiency of large-scale data processing facilities with special demand characteristics. The present invention participates in the power market through multiple types of resource demand response, saving the investment cost of the power grid; the present invention combines demand response resources, corresponding contracts and the power market to provide load aggregators with the optimal bidding strategy, improve the profitability of aggregators and users, and stimulate the willingness of aggregators and users to participate. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of the optimal bidding method for multiple demand response resources in the day-ahead market according to the first embodiment of the present invention;

[0030] Figure 2 is a schematic diagram of the day-ahead power market structure and operation mechanism in the first embodiment of the present invention;

[0031] Figure 3 is a system time-sharing electricity price diagram in the first embodiment of the present invention;

[0032] Figure 4It is a PJM5 node system topology diagram of large-scale data processing load in the first embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0034] The first embodiment of the present invention relates to a method for optimal bidding in a day-ahead market for multiple demand response resources. Figure 1 As shown, the following steps are included:

[0035] Step 1: Clarify the structure and operation mechanism of the day-ahead electricity market, consider various types of demand response resources, and establish a demand response bidding model corresponding to various types of demand response resources.

[0036] The structure and operation mechanism of the day-ahead power market in this step are as follows: Figure 2 As shown in the figure, there are three major market players: system operators, load aggregators, and end users. Among them, load aggregators are considered to be third-party intermediaries between demanders and suppliers of demand response resources. They can effectively integrate and give full play to the demand response utility of various loads, actively participate in the market bidding process on behalf of users, provide market services and obtain economic benefits from them, and realize interaction from the user level to the system level. Assume that the energy consumption of the i-th participant at time t is L i (t), which can be expressed as:

[0037] L i (t) = P IT (t)+P Cooling (t)+P Other (t);

[0038] Among them, P IT (t) represents the power consumption of IT equipment, P Cooling (t) represents the power consumption of the cooling system, P Other (t) represents the power consumption of other devices.

[0039] In this step, corresponding demand response bidding models are established for load reduction demand response resources, load transfer demand response resources, self-provided power supply demand response resources and energy storage demand response resources respectively.

[0040] Among them, the demand response bidding model of load reduction demand response resources is expressed as:

[0041]

[0042] in, is the load reduction amount, N is the load reduction compensation that the load aggregator needs to pay to the participants. LC For the collection of load shedding contracts, is the status indicator of the i-th load reduction contract in period t, and are the initial cost of starting the load reduction and the fixed cost per period during the load reduction period specified in the i-th load reduction contract, respectively. is the starting index of the i-th load reduction contract in period t, is the load reduction amount in the i-th load reduction contract in period t, is the compensation price per unit of load reduction paid to the participant in the i-th load reduction contract.

[0043] The constraints of the demand response bidding model for load reduction demand response resources are: ensuring that the reduction amount of response in each period must be less than the bid amount of the load reduction contract, and limiting the duration of load reduction within the minimum and maximum reduction time.

[0044] The demand response bidding model for load shifting demand response resources is expressed as:

[0045]

[0046] in, is the load transfer amount, N is the load transfer fee that the load aggregator needs to pay to the participants. LS is the set of load transfer contracts, is the status indicator of the i-th load transfer contract in period t, and are the initial cost of starting the transfer and the fixed cost of each period during the transfer period stipulated in the i-th load transfer contract, respectively. is the starting indicator of the i-th load transfer contract in period t, is the load transferred in the i-th load transfer contract in period t, is the compensation price per unit of load transferred paid to the participant in the i-th load transfer contract.

[0047] The constraints of the demand response bidding model for load transfer demand response resources are similar to those of the demand response bidding model for load reduction demand response resources, namely, it is necessary to ensure that the transfer-out amount of the response in each period must be less than the bid amount of the load transfer contract, and at the same time limit the duration of the load transfer within the minimum and maximum transfer time. At the same time, the transfer balance constraint should be met so that the total transfer-in load is equal to the total transfer-out load, ensuring that the total load during the scheduling period does not change.

[0048] The demand response bidding model for self-provided power demand response resources is expressed as:

[0049]

[0050] in, The amount of electricity generated for self-generated power, N is the compensation that the load aggregator needs to pay to the participants for the self-generated power generation. OG A collection of self-provided power supply contracts, is the status indicator of the i-th self-provided power supply contract in time period t, and are the initial cost of starting power generation and the fixed cost per period during power generation as specified in the i-th captive power supply contract, is the starting indicator of the ith self-provided power supply contract in period t, is the power generation in the i-th load transfer contract in period t, is the compensation price per unit of electricity generated paid to the participant in the i-th load transfer contract.

[0051] The constraints of the demand response bidding model for self-provided power supply demand response resources are: the power generation of the self-provided power supply in each period must be less than the bid amount of the self-provided power supply contract, and at the same time less than the maximum power generation of the generator; the ramp rate of the self-provided power supply within the set working hours must be between the maximum rise and fall rates.

[0052] The demand response bidding model of energy storage demand response resources is expressed as:

[0053]

[0054] in, is the discharge capacity of the energy storage device, N is the discharge remuneration that the load aggregator needs to pay to the participants. ES is the collection of energy storage contracts, and are the start-up cost of the energy storage equipment specified in the i-th energy storage contract and the fixed cost of each period during the operation of the energy storage equipment, is the activation index of the i-th energy storage contract in period t, is the discharge amount in the i-th energy storage contract in period t, is the compensation price per unit of discharge paid to the participant in the i-th energy storage contract.

[0055] The demand response bidding model constraints for energy storage demand response resources are as follows: the energy storage discharge amount in each period must be less than the bid amount of the energy storage contract, and the charging and discharging power must be less than the rated power of the energy storage equipment. According to the equipment requirements, the charging rate and charging efficiency must be within the range specified by the equipment.

[0056] Step 2: Based on the demand response bidding model and the contracts of various demand response resources of the signed customers, a load aggregator decision optimization model is constructed with the goal of maximizing the total revenue of the demand response load aggregator.

[0057] The objective function of the load aggregator decision optimization model constructed in this step is expressed as:

[0058]

[0059] Among them, C LA is the total revenue of the demand response load aggregator, P t market is the day-ahead electricity price in the electricity market, N T The decision variables of this model include the state index of each demand response resource contract, the required response amount of each demand response resource, the start and end index of each demand response resource contract, and the charge amount of the energy storage device.

[0060] When the load aggregator decision optimization model is extended to a multi-node power system, special attention should be paid to load transfer and energy storage charging, namely:

[0061]

[0062] Among them, G b is the set of generators connected to node b, P g,t is the output of generator g during period t, is the load connected to node b during period t, L b is the set of lines connected to node b, P l,t is the active power injected into node b by line l during period t, is the load reduction of node b in period t, is the load transfer amount of node b in period t, is the power generation of the self-provided power supply of node b in period t, is the discharge amount of the energy storage device at node b during period t, is the power injected into node b after load transfer from other nodes in period t, is the charging power of the energy storage device at node b during time period t.

[0063] To ensure the safety of power grid operation, the network must also meet the DC power flow constraints:

[0064]

[0065] Among them, θ x,t and θ y,t are the phase angles of the line sending node x and receiving node y in period t, respectively. l is the reactance of line l, please explain, P max,l is the maximum transmission power of line l, θ b is the phase angle of node b, θ min,b and θ max,b are the minimum and maximum phase angles of node b respectively.

[0066] At this time, the objective function of the model can be changed to maximize the total benefits of the demand response load aggregator at each node, that is:

[0067]

[0068] In the formula, C DR is the total revenue of all demand response load aggregators in the system, and Z is the set of demand response load aggregators.

[0069] Step 3: Solve the load aggregator decision optimization model to obtain the optimal bidding strategy.

[0070] This implementation also verifies the effectiveness of the proposed aggregator's day-ahead market bidding decision optimization model by assuming a specific case.

[0071] This implementation first simulates a PJM5 node system with three power loads (see Figure 4 ), each node is connected to a different number of generators, and each power load is configured with a demand response load aggregator. Then, 12 demand response bidding contracts of four categories are designed for each power load, including load reduction, load transfer, self-provided power supply and energy storage system, using a unified system fixed time-of-use electricity price (see Figure 3 ) to simplify the model. Then, based on the above steps, a load aggregator decision optimization model was constructed. The model is a mixed integer linear programming model. The model is solved by calling Gurobi solver on Matlab's Yalmip platform to maximize the total revenue of the demand response load aggregator. Finally, the results are analyzed to calculate the total revenue of the demand response load aggregator under different scenarios, analyze the response load of each node under different scenarios, and evaluate the impact of adding different demand response resources on system benefits.

[0072] A second embodiment of the present invention relates to an optimal bidding device for a day-ahead market of multiple demand response resources, comprising:

[0073] Establish a module to clarify the structure and operation mechanism of the day-ahead power market, consider various types of demand response resources, and establish a demand response bidding model corresponding to various types of demand response resources;

[0074] A construction module is used to construct a load aggregator decision optimization model based on the demand response bidding model and the contracts of various demand response resources of the contracted customers, with the goal of maximizing the total revenue of the demand response load aggregator;

[0075] The solution module is used to solve the load aggregator decision optimization model to obtain the optimal bidding strategy.

[0076] The various types of demand response resources include load reduction demand response resources, load transfer demand response resources, self-provided power supply demand response resources and energy storage demand response resources.

[0077] The demand response bidding model of the load reduction demand response resource established by the establishment module is: in, is the load reduction amount, N is the load reduction compensation that the load aggregator needs to pay to the participants. LC For the collection of load shedding contracts, is the status indicator of the i-th load reduction contract in period t, and are the initial cost of the load reduction contract and the fixed cost per period during the load reduction period, respectively. is the starting index of the i-th load reduction contract in period t, is the load reduction amount in the i-th load reduction contract in period t, is the compensation price per unit of load reduction paid to the participant in the i-th load reduction contract.

[0078] The demand response bidding model of the load transfer demand response resource established by the establishment module is: in, is the load transfer amount, N is the load transfer fee that the load aggregator needs to pay to the participants. LS is the set of load transfer contracts, is the status indicator of the i-th load transfer contract in period t, and are the initial cost of starting the transfer and the fixed cost of each period during the transfer period stipulated in the i-th load transfer contract, respectively. is the starting indicator of the i-th load transfer contract in period t, is the load transferred in the i-th load transfer contract in period t, is the compensation price per unit of load transferred paid to the participant in the i-th load transfer contract.

[0079] The demand response bidding model of the self-contained power supply demand response resource established by the establishment module is: in, The amount of electricity generated for self-generated power, N is the compensation that the load aggregator needs to pay to the participants for the self-generated power generation. OG A collection of self-provided power supply contracts, is the status indicator of the i-th self-provided power supply contract in time period t, and are the initial cost of starting power generation and the fixed cost per period during power generation as specified in the i-th captive power supply contract, is the starting indicator of the ith self-provided power supply contract in period t, is the power generation in the i-th load transfer contract in period t, is the compensation price per unit of electricity generated paid to the participant in the i-th load transfer contract.

[0080] The demand response bidding model of energy storage demand response resources established by the establishment module is: in, is the discharge capacity of the energy storage device, N is the discharge remuneration that the load aggregator needs to pay to the participants. ES is the collection of energy storage contracts, and are the start-up cost of the energy storage equipment specified in the i-th energy storage contract and the fixed cost of each period during the operation of the energy storage equipment, is the activation index of the i-th energy storage contract in period t, is the discharge amount in the i-th energy storage contract in period t, is the compensation price per unit of discharge paid to the participant in the i-th energy storage contract.

[0081] The objective function of the load aggregator decision optimization model constructed by the building module is: Among them, C LA is the total revenue of the demand response load aggregator, P t market is the day-ahead electricity price in the electricity market, N T is the set of all scheduling periods, is the load reduction amount, is the load transfer amount, The amount of electricity generated for self-generated power, is the discharge capacity of the energy storage device, The load aggregator needs to pay the participants for load reduction. The load aggregator needs to pay the participants for the load transfer. The load aggregator needs to pay the participants for the self-generated power generation. Load aggregators need to pay discharge remuneration to participants.

[0082] The constraints of the load aggregator decision optimization model constructed by the building module include:

[0083] The node load transfer and energy storage charging constraints are expressed as:

[0084]

[0085] The DC power flow constraint is expressed as:

[0086] Among them, G b is the set of generators connected to node b, P g,t is the output of generator g during period t, is the load connected to node b during period t, L b is the set of lines connected to node b, P l,t is the active power injected into node b by line l during period t, is the load reduction of node b in period t, is the load transfer amount of node b in period t, is the power generation of the self-provided power supply of node b in period t, is the discharge amount of the energy storage device at node b during period t, is the power injected into node b after load transfer from other nodes in period t, is the charging power of the energy storage device at node b during period t; θ x,t and θ y,t are the phase angles of the line sending node x and receiving node y in period t, respectively. l is the reactance of line l, please explain, P max,l is the maximum transmission power of line l, θ b is the phase angle of node b, θ min,b and θ max,b are the minimum and maximum phase angles of node b respectively.

[0087] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the optimal bidding method for the day-ahead market of multiple demand response resources of the first embodiment when executing the computer program.

[0088] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the optimal bidding method for multiple demand response resources in the day-ahead market of the first embodiment are implemented.

[0089] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimal bidding in a day-ahead market for multiple demand response resources, characterized in that: The following steps are involved: Clarify the structure and operation mechanism of the day-ahead power market, consider various types of demand response resources, and establish a demand response bidding model corresponding to various types of demand response resources; Based on the demand response bidding model and the contracts of various demand response resources of the signed customers, a load aggregator decision optimization model is constructed with the goal of maximizing the total revenue of the demand response load aggregator; The load aggregator decision optimization model is solved to obtain the optimal bidding strategy.

2. The optimal bidding method for the multi-demand response resource day-ahead market according to claim 1, characterized in that: The various types of demand response resources include load reduction demand response resources, load transfer demand response resources, self-provided power supply demand response resources and energy storage demand response resources.

3. The optimal bidding method for the day-ahead market of multiple demand response resources according to claim 2 is characterized in that: The demand response bidding model for the load reduction demand response resource is: in, is the load reduction amount, N is the load reduction compensation that the load aggregator needs to pay to the participants. LC For the collection of load shedding contracts, is the status indicator of the i-th load reduction contract in period t, and are the initial cost of starting the load reduction and the fixed cost per period during the load reduction period specified in the i-th load reduction contract, respectively. is the starting index of the i-th load reduction contract in period t, is the load reduction amount in the i-th load reduction contract in period t, is the compensation price per unit of load reduction paid to the participant in the i-th load reduction contract.

4. The optimal bidding method for the multi-demand response resource day-ahead market according to claim 2, characterized in that: The demand response bidding model for the load shifting demand response resource is: in, is the load transfer amount, N is the load transfer fee that the load aggregator needs to pay to the participants. LS is the set of load transfer contracts, is the status indicator of the i-th load transfer contract in period t, and are the initial cost of starting the transfer and the fixed cost of each period during the transfer period stipulated in the i-th load transfer contract, respectively. is the starting indicator of the i-th load transfer contract in period t, is the load transferred in the i-th load transfer contract in period t, is the compensation price per unit of load transferred paid to the participant in the i-th load transfer contract.

5. The optimal bidding method for the multi-demand response resource day-ahead market according to claim 2, characterized in that: The demand response bidding model for the self-provided power supply demand response resource is: in, The amount of electricity generated for self-generated power, N is the compensation that the load aggregator needs to pay to the participants for the self-generated power generation. OG A collection of self-provided power supply contracts, is the status indicator of the i-th self-provided power supply contract in time period t, and are the initial cost of starting power generation and the fixed cost per period during power generation as specified in the i-th captive power supply contract, is the starting indicator of the ith self-provided power supply contract in period t, is the power generation in the i-th load transfer contract in period t, is the compensation price per unit of electricity generated paid to the participant in the i-th load transfer contract.

6. The optimal bidding method for the day-ahead market of multiple demand response resources according to claim 2, characterized in that: The demand response bidding model for energy storage demand response resources is: in, is the discharge capacity of the energy storage device, N is the discharge remuneration that the load aggregator needs to pay to the participants. ES is the collection of energy storage contracts, and are the start-up cost of the energy storage equipment specified in the i-th energy storage contract and the fixed cost of each period during the operation of the energy storage equipment, is the activation index of the i-th energy storage contract in period t, is the discharge amount in the i-th energy storage contract in period t, is the compensation price per unit of discharge paid to the participant in the i-th energy storage contract.

7. The optimal bidding method for the day-ahead market of multiple demand response resources according to claim 1, characterized in that: The objective function of the load aggregator decision optimization model is: Among them, C LA is the total revenue of the demand response load aggregator, P t market is the day-ahead electricity price in the electricity market, N T is the set of all scheduling periods, is the load reduction amount, is the load transfer amount, The amount of electricity generated for self-generated power, is the discharge capacity of the energy storage device, The load aggregator needs to pay the participants for load reduction. The load aggregator needs to pay the participants for the load transfer. The load aggregator needs to pay the participants for the self-generated power generation. Load aggregators need to pay discharge remuneration to participants.

8. The optimal bidding method for the day-ahead market of multiple demand response resources according to claim 1, characterized in that: The constraints of the load aggregator decision optimization model include: The node load transfer and energy storage charging constraints are expressed as: The DC power flow constraint is expressed as: Among them, G b is the set of generators connected to node b, P g,t is the output of generator g during period t, is the load connected to node b during period t, L b is the set of lines connected to node b, P l,t is the active power injected into node b by line l during period t, is the load reduction of node b in period t, is the load transfer amount of node b in period t, is the power generation of the self-provided power supply of node b in period t, is the discharge amount of the energy storage device at node b during period t, is the power injected into node b after load transfer from other nodes in period t, is the charging power of the energy storage device at node b during period t; θ x,t and θ y,t are the phase angles of the line sending node x and receiving node y in period t, respectively. l is the reactance of line l, P max,l is the maximum transmission power of line l, θ b is the phase angle of node b, θ min,b and θ max,b are the minimum and maximum phase angles of node b respectively.

9. An optimal bidding device for a multi-demand response resource day-ahead market, characterized in that: include: Establish a module to clarify the structure and operation mechanism of the day-ahead power market, consider various types of demand response resources, and establish a demand response bidding model corresponding to various types of demand response resources; A construction module is used to construct a load aggregator decision optimization model based on the demand response bidding model and the contracts of various demand response resources of the contracted customers, with the goal of maximizing the total revenue of the demand response load aggregator; The solution module is used to solve the load aggregator decision optimization model to obtain the optimal bidding strategy.

10. 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 computer program, the steps of the optimal bidding method for the day-ahead market of multiple demand response resources are implemented as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the optimal bidding method for the day-ahead market of multiple demand response resources are implemented as described in any one of claims 1-8.

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