A virtual power plant and power distribution operator collaborative bidding method and related device

Abstract

This invention belongs to the field of electricity market trading technology and discloses a collaborative bidding method and related apparatus between virtual power plants (VPS) and distribution operators. The collaborative bidding method includes: constructing and solving a DSO-VPP two-layer game model based on acquired market environment characteristics, VPS resource characteristics, and multi-level market trading rules to obtain the DSO's power purchase strategy and the VPP's dispatch strategy. In the DSO-VPP two-layer game model, the optimization objective of the upper-layer model is to maximize the DSO's profit, and the decision variables in the optimization process are the internal power purchase price and power sales price set by the DSO for the VPPs. In the lower-layer model, each VPP, after receiving the power sales price set by the DSO, optimizes its internal resource dispatch strategy with the objective of minimizing its total operating cost. This invention solves the technical problems of fragmented bidding, dispersed dispatch, and lack of collaborative mechanisms with distribution operators in the participation of virtual power plants in multi-level markets.

Description

Technical Field

[0001] This invention belongs to the field of electricity market trading technology, and specifically relates to a collaborative bidding method and related apparatus between virtual power plants and distribution operators. Background Technology

[0002] Virtual power plants (VPPs) serve as a control platform that aggregates distributed energy resources, adjustable loads, and energy storage. They can participate in electricity market transactions as a whole, thereby improving system flexibility and the capacity to absorb new energy sources.

[0003] As the electricity market gradually becomes more segmented, VPPs need to participate in multiple markets, including power generation and ancillary services, and their pricing strategies must comprehensively consider the rules and resource constraints of multiple markets. However, existing research on VPP pricing strategies mostly focuses on single markets and lacks collaborative optimization pricing strategies for multi-level markets. In addition, there is a lack of effective interactive pricing mechanisms between VPPs and distribution system operators (DSOs), which makes it difficult to adapt to the current multi-level evolution trend of the electricity market. Resource allocation efficiency and market profitability need to be improved.

[0004] Explained, DSO, as a key intermediary connecting distributed resources and the main grid, has the ability to coordinate and match. Existing technical solutions lack a systematic characterization of the collaborative game relationship between VPP and DSO, lack an integrated pricing strategy modeling mechanism covering multiple market levels (electricity, ancillary services, carbon market, etc.), and lack adaptive capabilities for dynamic multi-market environments in terms of strategy optimization and prediction methods, making it difficult to meet the needs of large-scale access and market-based trading of distributed resources. Specifically, existing technical solutions have the following significant shortcomings in the field of collaborative bidding in multi-level electricity markets involving virtual power plants (VPPs) and distribution network operators (DSOs): First, they lack a multi-level market collaboration perspective. Existing solutions often focus on single-market bidding, failing to consider the temporal correlation and revenue coupling between different market levels (such as day-ahead and real-time markets, main grid and distribution network markets), making it difficult to maximize the overall revenue of the VPP. Second, they have weak cross-market resource allocation capabilities. The distributed energy resources and energy storage aggregated by VPPs have diverse characteristics, but existing solutions can only achieve local optimization of a single market and cannot dynamically allocate resources according to the needs of multiple market levels, easily leading to idle or over-utilization. Third, there is a lack of collaboration mechanisms between VPPs and DSOs. They often make independent decisions without considering the correlation of objectives (such as potential conflicts between VPP revenue and distribution network security), lacking interest coordination and game optimization models, making it difficult to cope with multi-market interaction scenarios. Fourth, they have poor market rule adaptability. Existing solutions' models are difficult to be compatible with the differentiated rules of multiple market levels (such as bidding periods and settlement methods), and lack standardized information exchange, resulting in low bidding efficiency and fragmented decision-making. Summary of the Invention

[0005] The purpose of this invention is to provide a collaborative bidding method and related apparatus between virtual power plants and distribution operators to solve one or more of the aforementioned technical problems. The technical solution disclosed in this invention, through a collaborative optimization framework and interactive mechanism design, solves the technical problems of fragmented bidding, decentralized dispatching, and lack of collaborative mechanisms with distribution operators in multi-level market participation of virtual power plants. It achieves multi-market bidding linkage and efficient resource dispatching, improving revenue and system stability. To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a collaborative pricing method between virtual power plants and distribution operators, comprising: Based on the selected virtual power plant (VPP) and distribution operator (DSO) collaboration scenario, we obtain market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules. Based on the acquired market environment characteristics, virtual power plant resource characteristics and multi-level market transaction rules, a DSO-VPP two-layer game model is constructed and solved to obtain the DSO power purchase strategy and the VPP dispatch strategy. The DSO-VPP two-layer game model includes an upper-layer model as the leader and a lower-layer model as the follower. The optimization objective of the upper-layer model is to maximize the DSO's profit, which includes the difference between the purchase and sale prices of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The decision variables in the optimization process are the internal purchase price and sale price of electricity set by the DSO to the VPPs. After receiving the sale price set by the DSO, each VPP in the lower-layer model optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost.

[0006] A further improvement of the technical solution of the present invention is that the DSO-VPP two-layer game model is constructed based on the Stackelberg game framework.

[0007] A further improvement to the technical solution of this invention lies in the step of obtaining market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules based on the selected virtual power plant (VPP) and distribution operator (DSO) collaborative scenario. Market environment characteristics include: wholesale electricity purchase and sale prices, and transaction capacity limits between DSOs and VPPs; The resource characteristics of a virtual power plant include: the maximum output and load forecast data of the wind turbines aggregated within each VPP, the cost coefficient and operating constraints of the micro gas turbines, and the technical parameters of the battery energy storage system; Multi-level market trading rules include: price upper and lower limits and trading session constraints.

[0008] A further improvement of the technical solution of the present invention is that the total operating cost of the VPP is the sum of the cost of purchasing electricity from the DSO, the power generation cost of the micro gas turbine, and the cycle loss cost of the battery energy storage system, minus the revenue obtained from selling electricity to the DSO.

[0009] A further improvement of the technical solution of the present invention is that, in the step of constructing and solving the DSO-VPP two-layer game model, the lower-level model problem is first transformed into the upper-level model constraint through the Karush-Kuhn-Tucker condition, and a single-layer mixed integer nonlinear programming problem is obtained by reconstruction. Then, the single-layer mixed integer nonlinear programming problem is solved by using a numerical optimizer.

[0010] A further improvement to the technical solution of the present invention is that the objective function for optimizing the upper-level model is expressed as: ; In the formula, The total profit of the power distribution operator within the dispatching cycle T; , They are respectively t The electricity price and power output of DSO to the wholesale market during specific time periods; , They are respectively t The DSO (Distributed Electricity Sort) purchases electricity from the wholesale market based on the electricity price and power consumption during the specified time period. for t The internal electricity price issued by the DSO to the virtual power plant during the time period; for t The total electricity purchased by all J virtual power plants from the DSO during the time period; for t The internal purchase price issued by the DSO to the virtual power plant during the time period; for t The total electricity sold by all J virtual power plants to the DSO during the time period; The optimization process of the upper-level model satisfies the following constraints: the transaction price set by the DSO is within the wholesale market price range; the total transaction volume between the DSO and the wholesale market is equal to the total electricity volume traded between the DSO and all VPPs.

[0011] A further improvement of the technical solution of the present invention is that the objective function for optimizing the lower-level model is expressed as: ; In the formula, Let be the total operating cost of the j-th virtual power plant during the scheduling period T; and These represent the electricity price sold by the DSO to the VPP and the electricity volume purchased by the VPP during time period t, respectively. and These represent the electricity purchase price from the DSO to the VPP and the electricity volume sold by the VPP during time period t, respectively. Let j be the set of distributed resources aggregated within the j-th VPP; The fuel cost and operation and maintenance cost of micro gas turbines; Cost of charge-discharge cycle losses for battery energy storage systems; In the lower-level model, the optimization process of each VPP satisfies the internal power balance constraints and the physical operation constraints of each device.

[0012] A second aspect of the present invention provides a collaborative pricing system for virtual power plants and distribution operators, comprising: The parameter acquisition module is used to acquire market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules based on the selected virtual power plant (VPP) and distribution operator (DSO) collaborative scenario. The coordinated pricing module is used to construct and solve the DSO-VPP two-layer game model based on the acquired market environment characteristics, virtual power plant resource characteristics and multi-level market transaction rules, so as to obtain the DSO power purchase strategy and the VPP dispatch strategy. The DSO-VPP two-layer game model includes an upper-layer model as the leader and a lower-layer model as the follower. The optimization objective of the upper-layer model is to maximize the DSO's profit, which includes the difference between the purchase and sale prices of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The decision variables in the optimization process are the internal purchase price and sale price of electricity set by the DSO to the VPPs. After receiving the sale price set by the DSO, each VPP in the lower-layer model optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost.

[0013] In a third aspect, the present invention 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, when executing the program, implements a collaborative pricing method for a virtual power plant and a distribution operator as described in any one of the first aspects of the present invention.

[0014] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a collaborative bidding method for virtual power plants and distribution operators as described in any one of the first aspects of the present invention.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The technical solution disclosed in this invention is a collaborative bidding method between virtual power plants (VPPs) and distribution operators (DPPs) in a multi-tiered market. It aims to address technical challenges faced by VPPs when participating in multiple markets such as electricity and ancillary services, including fragmented bidding strategies, low resource scheduling efficiency, and a lack of collaboration mechanisms with distribution operators (DSOs). The disclosed solution constructs a "leader-follower" two-layer optimization model (i.e., a DSO-VPP two-layer game model) with DSOs as the dominant players and VPPs as followers. This model guides collaborative interaction between DSOs and multiple VPPs through price signals, adapts the game theory model to business relationships, and uses mathematical methods to balance the interests of both parties. By achieving synergy between the interests of DSOs and VPPs through the game theory model, it ensures the revenue of distribution operators while reducing the operating costs of virtual power plants, ultimately achieving dual optimization of the system and the main players. This approach has high practical application value.

[0016] In this invention, the distribution network operator is positioned as the pricing leader, guiding multiple virtual power plants to conduct responsive resource scheduling within a unified model, forming an integrated strategy structure that links bidding and resource allocation. By constructing a two-layer game model with feasible KKT conversion characteristics, the transaction price range, resource boundaries, market rules, and decision-making logic are integrated into the unified structure, achieving a solvable mixed integer programming model output. Ultimately, it can generate collaborative bidding results and revenue optimization paths, realizing multi-market bidding linkage and efficient resource scheduling, thereby improving revenue and system stability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a collaborative pricing method between a virtual power plant and a power distribution operator, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the calculation process of VPP and DSO in the Stackelberg game model in an embodiment of the present invention. Figure 3 This is a schematic diagram of the model program flow in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the unit power curve in a specific embodiment of the present invention; Figure 5 This is a schematic diagram of the SOC state curve of the energy storage system in a specific embodiment of the present invention; Figure 6 This is a schematic diagram of the power output curve of the energy storage system in a specific embodiment of the present invention; Figure 7 This is a schematic diagram of wind power curves in a specific embodiment of the present invention; Figure 8 This is a schematic diagram of the trading power of virtual power plant 1 in a specific embodiment of the present invention; Figure 9 This is a schematic diagram of the trading power of the virtual power plant 2 in a specific embodiment of the present invention; Figure 10 This is a schematic diagram of the trading power of the virtual power plant 3 in a specific embodiment of the present invention; Figure 11 This is a schematic diagram of the virtual power plant market quotation in a specific embodiment of the present invention; Figure 12 This is a schematic diagram comparing the operating costs of VPP in a specific embodiment of the present invention; Figure 13 This is a schematic diagram comparing the profits of DSO and wholesale markets in a specific embodiment of the present invention; Figure 14 This is a schematic diagram of the internal optimization model framework of a single entity in VPP in the existing technology; Figure 15 This is a schematic diagram of a data-driven VPP pricing prediction model in the existing technology; Figure 16 This is a schematic diagram of a multi-agent reinforcement learning optimization model within a VPP in the existing technology; Figure 17 This is a schematic diagram of a robustly optimized single VPP pricing model in the existing technology; Figure 18 This is a schematic diagram of a collaborative pricing system between a virtual power plant and a power distribution operator, as described in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Based on the technical solutions disclosed in the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0021] Please see Figure 1 The present invention provides a collaborative pricing method between virtual power plants and distribution operators, comprising the following steps: Step 1: Based on the selected virtual power plant (VPP) and distribution operator (DSO) collaboration scenario, obtain market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules; Step 2: Based on the market environment characteristics, virtual power plant resource characteristics and multi-level market transaction rules obtained in Step 1, construct and solve the DSO-VPP two-layer game model to obtain the DSO power purchase strategy and VPP dispatch strategy. The DSO-VPP two-layer game model includes an upper-layer model as the leader and a lower-layer model as the follower. The optimization objective of the upper-layer model is to maximize the DSO's profit, which includes the difference between the purchase and sale prices of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The decision variables in the optimization process are the internal purchase price and sale price of electricity set by the DSO to the VPPs. After receiving the sale price set by the DSO, each VPP in the lower-layer model optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost.

[0022] Given that existing technical solutions mostly focus on a single market and lack cross-market coordination and DSO interaction mechanisms, resulting in technical problems such as inefficient resource response and insufficient economic efficiency, this invention specifically discloses a collaborative bidding method between virtual power plants and distribution operators. It constructs a collaborative bidding mechanism between VPP and DSO, and introduces technical means such as Stackelberg game modeling, bidding space prediction, and strategy optimization to achieve multi-market bidding linkage and efficient resource scheduling, thereby improving revenue and system stability.

[0023] Please see Figure 2 and Figure 3 In a specific embodiment of the present invention, a collaborative bidding method for virtual power plants (VPPs) and distribution operators (DSOs) in a multi-tiered market is disclosed, the specific process of which is as follows: Step 1: System initialization and parameter input.

[0024] In this step, the system is initialized based on the actual situation, and various input parameters required by the model are defined.

[0025] In a further exemplary technical solution, the aforementioned input parameters include: market environment characteristics, virtual power plant resource characteristics, and multi-level market trading rules; among which, market environment characteristics include wholesale market electricity purchase and sale prices, transaction capacity limits between DSO and VPP, etc.; virtual power plant resource characteristics include the maximum output of wind turbine units (WT) aggregated within each VPP, load forecast data, cost coefficient and operating constraints (maximum output, ramp rate) of micro gas turbines (MT), and technical parameters of battery energy storage (BS) (cost coefficient, maximum power, upper and lower limits of state of charge (SOC), initial capacity, etc.); multi-level market trading rules include price upper and lower limits, trading periods, and other constraints.

[0026] Step 2: Construct and solve the Stackelberg game model, also known as the DSO-VPP two-layer game model, which is based on the Stackelberg game framework.

[0027] This step is the core of the embodiments of the present invention, and the key lies in the construction and solution of the two-layer model; wherein, in the exemplary preferred technical solution, the lower-level problem is transformed into the constraints of the upper-level model through KKT (Karush-Kuhn-Tucker) conditions, thereby reconstructing it into a single-layer mixed integer nonlinear programming problem that can be solved uniformly.

[0028] In a specific exemplary technical solution, the upper-level model (DSO profit maximization) acts as the leader. The DSO's goal is to maximize its own profit, which comes from the price difference between the purchase and sale of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The DSO's decision variables are the internal purchase price and sale price it sets for the VPPs. The lower-level model (VPP operating cost minimization) acts as the follower. After receiving the sale price of electricity from the DSO, each VPP optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost. Its costs include the cost of purchasing electricity from the DSO, the generation cost of the internal MT, the cycle loss cost of the BS, and deduct the revenue obtained from selling electricity to the DSO.

[0029] In this embodiment of the invention, the objective function for optimizing the upper-level model is expressed as: ; In the formula, The total profit of the power distribution operator within the dispatching cycle T; , These represent the electricity price and power output of the DSO to the wholesale market during time period t. , These represent the electricity purchase price and power capacity that DSO purchases from the wholesale market during time period t. This refers to the internal electricity price (i.e., the purchase price of electricity by the VPP) issued by the DSO to the virtual power plant. The total electricity purchased by all J virtual power plants from the DSO; The internal purchase price (i.e., the sales price of electricity by the VPP) issued by the DSO to the virtual power plant. This represents the total electricity sold by all virtual power plants to the DSO.

[0030] Interpretationally, the optimization process of the upper-level model must satisfy constraints such as: the transaction price set by the DSO must be within the wholesale market price range, and its total transaction volume with the wholesale market must be equal to the total electricity volume traded with all VPPs.

[0031] In this embodiment of the invention, the objective function for optimizing the lower-level model is expressed as: ; In the formula, Let be the total operating cost of the j-th virtual power plant during the scheduling period T; , These represent the electricity sales price (i.e., the electricity purchase price of VPP) from DSO to VPP during time period t, and the amount of electricity purchased by VPP. , These represent the electricity purchase price (i.e., the electricity sales price of VPP) from DSO to VPP during time period t, and the electricity volume sold by VPP. Let j be the set of distributed resources aggregated within the j-th VPP; The fuel cost and operation and maintenance cost of micro gas turbines; Cost of charge-discharge cycle losses for battery energy storage systems; Explained, the optimization process of each VPP in the lower-level model must satisfy: internal power balance constraints (i.e., the balance between power generation, energy storage charging and discharging, load, and external transactions), as well as the physical operation constraints of each device itself (such as MT ramp rate, BS SOC range, etc.).

[0032] In this embodiment of the invention, the above steps enable collaborative bidding and resource optimization between VPP and DSO in a multi-level market environment, and ultimately output the optimal bidding strategy, resource scheduling plan and internal transaction electricity price of each party, providing data support for subsequent performance evaluation and visualization analysis.

[0033] Please see Figures 4 to 11 The technical solutions disclosed in the embodiments of the present invention also include: Step 3: Implementation and Results Output.

[0034] In this step, to verify the method of the present invention, a simulation system containing 3 VPPs and 1 DSO was constructed and tested within a 24-hour market cycle; each VPP was configured with a micro gas turbine, an energy storage system and a wind turbine; during the model solving process, the constructed two-layer model was input into a mathematical modeling tool for solving; during the result output process, detailed decision results and scheduling schemes were obtained after the model was solved.

[0035] VPP internal resource scheduling, such as Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown, the output curves of the micro gas turbines within each VPP, the SOC changes of the energy storage system, the charging and discharging power of the energy storage system, and the output of the wind turbine are displayed. The collaborative trading between VPPs and DSOs is as follows: Figure 8 , Figure 9 and Figure 10 As shown, the power purchase and sale curves of three VPPs and DSOs over a 24-hour period are displayed, intuitively reflecting how VPPs respond to price signals to adjust their trading behavior. The DSO guides the price as follows... Figure 11 As shown, this illustrates the dynamic power purchase and sale prices set by the DSO at different times, reflecting its price guidance strategy as a leader.

[0036] Please see Figure 12 and Figure 13 The collaborative bidding method for virtual power plants (VPPs) and distribution operators (DSOs) in a multi-tiered market, constructed in this embodiment of the invention, significantly improves system economics and market adaptability through a two-layer optimization model guided by the DSO. Simulation results quantitatively show that, compared to Mode 1 where VPPs trade directly, the operating costs of VPP1, VPP2, and VPP3 are reduced by 0.13%, 2.94%, and 0.78%, respectively, with a total VPP operating cost reduction of 0.71%. Simultaneously, the DSO, acting as an intermediary, earns a profit of €1.134 kJ in Mode 2, whereas this profit is absent in Mode 1. Although the wholesale market profit decreases in Mode 2 (from €5.370 kJ to €4.152 kJ), this indicates that the collaborative strategy effectively optimizes VPP resource allocation and prompts VPPs to respond to dynamic price signals set by the DSO (such as...). Figure 11 Adjusting trading behavior (as shown) Figure 8 , Figure 9 , Figure 10 As shown in the figure, this achieves maximum benefit and efficiency improvement between VPP and DSO.

[0037] To further explain, in order to achieve the goal of coordinated bidding between virtual power plants and distribution operators in a multi-level market, in addition to the game-theoretic optimization structure adopted in this invention, other alternative methods can be used to achieve similar functions. For example, replacing the two-level model with Nash or evolutionary game theory; constructing collaborative trading relationships using auction mechanisms, incentive contracts, or distributed matching methods; and implementing strategy search and resource scheduling through intelligent optimization algorithms or data-driven models. Furthermore, the method of this invention can also be extended to integrated energy markets or other energy trading scenarios, exhibiting good adaptability and scalability. All related alternative solutions should be covered within the scope of protection of this invention.

[0038] Please see Figures 14 to 17 Currently, some research focuses on the bidding optimization problem of virtual power plants (VPPs) in the electricity market, mainly revolving around resource scheduling modeling, bid range prediction, and reinforcement learning strategies. These technologies often treat the VPP as a single market participant, emphasizing internal optimization and lacking a collaborative mechanism with distribution operators (DSOs). Furthermore, they are mostly applicable to single-market scenarios such as the electricity market. Figure 14 As shown. Some methods are data-driven, using historical bidding data to predict bid ranges, thus improving the accuracy of VPP bidding strategies, such as... Figure 15 As shown; however, such methods are limited to static data analysis, are not integrated with dynamic market interaction mechanisms, and lack system optimization capabilities. Other research utilizes multi-agent reinforcement learning methods to optimize policies for internal VPP devices, improving resource response efficiency and scheduling flexibility, such as... Figure 16 As shown; however, such models generally lack a game-theoretic mechanism with DSOs, fail to consider price coordination and resource competition among multiple markets, and are therefore unable to support regional collaborative trading decisions. Some studies have also introduced robust optimization theory to address market uncertainty and construct risk-oriented pricing decision-making models, such as... Figure 17 As shown, however, these methods are typically only applicable to single-player optimization environments and lack comprehensive modeling capabilities for multiple participants, multiple objectives, and multiple markets. In some existing models, a two-layer game framework is constructed to introduce the interaction between DSO and VPP, enabling a certain degree of guided pricing strategies. However, these methods usually only consider electricity trading scenarios and do not incorporate the linkage structure of multiple markets such as ancillary services and carbon trading, making it difficult to adapt to the current multi-layered evolution trend of the electricity market. In summary, existing technologies generally suffer from the following shortcomings: a lack of systematic characterization of the collaborative game relationship between VPP and DSO; a lack of an integrated pricing strategy modeling mechanism covering multiple market levels (electricity, ancillary services, carbon markets, etc.); and a lack of adaptive capabilities for dynamic multi-market environments in terms of strategy optimization and prediction methods, making it difficult to meet the needs of large-scale distributed resource access and market-based trading development.

[0039] In view of the aforementioned existing problems, the technical solution disclosed in this invention introduces a DSO-led guided pricing mechanism and a VPP responsive scheduling model linked together. This differs from existing VPP one-way bidding strategies by constructing a game-theoretic two-layer collaborative model where distribution operators dynamically set electricity purchase and sale prices, and virtual power plants optimize their multi-energy resource scheduling accordingly. This achieves three-dimensional linkage control of electricity price, power, and revenue. Furthermore, this invention discloses a multi-market scenario nested optimization and logical decision-making modeling fusion mechanism. This mechanism integrates the price coupling and resource compatibility of multiple markets, such as electricity, ancillary services, and carbon markets, into a structured bidding strategy. It integrates logical judgment (such as electricity purchase and sale direction identification) and mathematical programming (KKT optimality transformation) to construct a directly solvable collaborative optimization structure, achieving a coordinated closed loop of strategies between different markets.

[0040] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0041] Please see Figure 18 In this embodiment of the invention, a collaborative pricing system for virtual power plants and distribution operators is provided, comprising: The parameter acquisition module is used to acquire market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules based on the selected virtual power plant (VPP) and distribution operator (DSO) collaborative scenario. The coordinated pricing module is used to construct and solve the DSO-VPP two-layer game model based on the acquired market environment characteristics, virtual power plant resource characteristics and multi-level market transaction rules, so as to obtain the DSO power purchase strategy and the VPP dispatch strategy. The DSO-VPP two-layer game model includes an upper-layer model as the leader and a lower-layer model as the follower. The optimization objective of the upper-layer model is to maximize the DSO's profit, which includes the difference between the purchase and sale prices of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The decision variables in the optimization process are the internal purchase price and sale price of electricity set by the DSO to the VPPs. After receiving the sale price set by the DSO, each VPP in the lower-layer model optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost.

[0042] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to execute the operation of a collaborative bidding method between a virtual power plant and a distribution operator.

[0043] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the collaborative pricing method between virtual power plants and distribution operators in the above embodiments.

[0044] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, etc.) containing computer-usable program code.

[0045] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0046] 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.

[0047] 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.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A collaborative pricing method between virtual power plants and distribution operators, characterized in that, include: Based on the selected virtual power plant (VPP) and distribution operator (DSO) collaboration scenario, we obtain market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules. Based on the acquired market environment characteristics, virtual power plant resource characteristics and multi-level market transaction rules, a DSO-VPP two-layer game model is constructed and solved to obtain the DSO power purchase strategy and the VPP dispatch strategy. The DSO-VPP two-layer game model includes an upper-layer model as the leader and a lower-layer model as the follower. The optimization objective of the upper-layer model is to maximize the DSO's profit, which includes the difference between the purchase and sale prices of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The decision variables in the optimization process are the internal purchase price and sale price of electricity set by the DSO to the VPPs. After receiving the sale price set by the DSO, each VPP in the lower-layer model optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost.

2. The collaborative pricing method between virtual power plants and distribution operators according to claim 1, characterized in that, The DSO-VPP two-layer game model is built on the Stackelberg game framework.

3. The collaborative pricing method between virtual power plants and distribution operators according to claim 1, characterized in that, In the selected scenario of collaboration between a Virtual Power Plant (VPP) and a Distribution Operator (DSO), the steps for obtaining market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules are as follows: Market environment characteristics include: wholesale electricity purchase and sale prices, and transaction capacity limits between DSOs and VPPs; The resource characteristics of a virtual power plant include: the maximum output and load forecast data of the wind turbines aggregated within each VPP, the cost coefficient and operating constraints of the micro gas turbines, and the technical parameters of the battery energy storage system; Multi-level market trading rules include: price upper and lower limits and trading session constraints.

4. The collaborative pricing method between virtual power plants and distribution operators according to claim 3, characterized in that, The total operating cost of a VPP is the sum of the cost of purchasing electricity from a DSO, the cost of generating electricity from a micro gas turbine, and the cycle loss cost of the battery energy storage system, minus the revenue obtained from selling electricity to a DSO.

5. The collaborative pricing method between virtual power plants and distribution operators according to claim 1, characterized in that, In the process of constructing and solving the DSO-VPP two-layer game model, the lower-level model problem is first transformed into the upper-level model constraint through the Karush-Kuhn-Tucker conditions, and a single-layer mixed-integer nonlinear programming problem is obtained by reconstruction. Then, the single-layer mixed-integer nonlinear programming problem is solved using a numerical optimizer.

6. The collaborative pricing method between virtual power plants and distribution operators according to claim 1, characterized in that, The objective function for optimization of the upper-level model is expressed as: ； In the formula, The total profit of the power distribution operator within the dispatching cycle T; , They are respectively t The electricity price and power output of the DSO to the wholesale market during the specified time period; , They are respectively t The DSO (Distributed Electricity Sort) purchases electricity from the wholesale market based on the electricity price and power consumption during the specified time period. for t The internal electricity price issued by the DSO to the virtual power plant during the time period; for t The total electricity purchased by all J virtual power plants from the DSO during the time period; for t The internal power purchase price issued by the DSO to the virtual power plant during the time period; for t The total electricity sold by all J virtual power plants to the DSO during the time period; The optimization process of the upper-level model satisfies the following constraints: the transaction price set by the DSO is within the wholesale market price range; the total transaction volume between the DSO and the wholesale market is equal to the total electricity volume traded between the DSO and all VPPs.

7. The collaborative pricing method between virtual power plants and distribution operators according to claim 1, characterized in that, The objective function for optimizing the lower-level model is expressed as: ； In the formula, Let be the total operating cost of the j-th virtual power plant during the scheduling period T; and These represent the electricity price sold by the DSO to the VPP and the electricity volume purchased by the VPP during time period t, respectively. and These represent the electricity purchase price from the DSO to the VPP and the electricity volume sold by the VPP during time period t, respectively. Let j be the set of distributed resources aggregated within the j-th VPP; The fuel cost and operation and maintenance cost of micro gas turbines; Cost of charge-discharge cycle losses for battery energy storage systems; In the lower-level model, the optimization process of each VPP satisfies the internal power balance constraints and the physical operation constraints of each device.

8. A collaborative pricing system for virtual power plants and distribution operators, characterized in that, include: The parameter acquisition module is used to acquire market environment characteristics, virtual power plant resource characteristics, and multi-level market transaction rules based on the selected virtual power plant (VPP) and distribution operator (DSO) collaborative scenario. The coordinated pricing module is used to construct and solve the DSO-VPP two-layer game model based on the acquired market environment characteristics, virtual power plant resource characteristics and multi-level market transaction rules, so as to obtain the DSO power purchase strategy and the VPP dispatch strategy. The DSO-VPP two-layer game model includes an upper-layer model as the leader and a lower-layer model as the follower. The optimization objective of the upper-layer model is to maximize the DSO's profit, which includes the difference between the purchase and sale prices of electricity in the wholesale market and the revenue generated by each VPP through electricity trading. The decision variables in the optimization process are the internal purchase price and sale price of electricity set by the DSO to the VPPs. After receiving the sale price set by the DSO, each VPP in the lower-layer model optimizes its internal resource scheduling strategy with the goal of minimizing its total operating cost.

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 collaborative pricing method between the virtual power plant and the distribution operator as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the collaborative pricing method between the virtual power plant and the distribution operator as described in any one of claims 1 to 7.

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