Power market-oriented multi-virtual power plant game transaction method, system and equipment and medium

Abstract

The invention discloses a multi-virtual power plant game transaction method, system, equipment and medium for an electric power market, and belongs to the technical field of electric power system optimization and electric power market transaction.The method comprises the steps that a coordination center initializes the electricity price and guides virtual power plants to report plans, and cluster bidding strategies are optimized through master-slave game iteration; and forming a comprehensive plan to participate in day-ahead market clearing. And the transaction center performs security check and unified clearing, and issues a scheduling plan. And dealing with supply and demand fluctuation through a deviation balance mechanism, performing intraday adjustment, and completing financial settlement according to an actual execution result. According to the invention, the profit of the energy market and the frequency modulation market is collaboratively optimized by applying the master-slave game and a unified price mechanism; dynamic coupling of price formulation and resource scheduling is realized through bidirectional iteration, and transaction flexibility and convergence efficiency are improved; and new energy and load uncertainty is quantitatively processed by adopting opportunity-constrained programming, so that economic risks and system fluctuation caused by prediction deviation are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimization and power market transaction, and particularly relates to a multi-virtual power plant game transaction method, system, equipment and medium for a power market. BACKGROUND

[0002] With the continuous advancement of power market reform, the large-scale access of distributed energy, energy storage devices and flexible loads to the traditional centralized power system poses a challenge. As a coordination platform for multiple types of distributed resources, virtual power plant (VPP) has become an important technical means to promote the market-oriented participation of distributed resources. Through unified regulation and optimal scheduling, VPP can enhance the adjustability and market response capability of resources, improve the flexibility and stability of the system, and enhance the economic value of user-side resources.

[0003] However, in the actual power market, a single VPP faces a series of challenges in participating in transactions: its resource scale and regulation capacity are relatively limited, and it is difficult to form equal competition with large traditional market participants in price games; its transaction strategy is difficult to take into account the dual goals of the energy market and the ancillary service market, especially in the utilization of income channels such as capacity compensation and frequency modulation incentives. In addition, due to market access costs, information asymmetry and complex bidding mechanisms, a single VPP generally lacks the ability to fully mobilize its internal resources to participate in multiple markets, and the overall revenue is limited.

[0004] At the same time, with the widespread deployment of multiple VPPs in a regional range, the potential of coordinated operation of virtual power plant clusters gradually emerges. If a certain coordination mechanism can be used to realize collective market transaction, not only can the resource scale advantage be amplified, but also the overall scheduling flexibility and bargaining power can be improved. Therefore, it is urgent to build a multi-virtual power plant cluster coordinated transaction method for the power market (including the energy market and the ancillary service market such as frequency modulation) to overcome the limitations of the single VPP mode and realize optimal resource allocation and multi-market revenue coordination within the cluster. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that the existing virtual power plant market transaction mode mainly takes a single VPP as the participant, and generally has the following defects and deficiencies:

[0007] (1) A single VPP usually has a small resource scale and limited scheduling range, lacks the ability to effectively influence market prices, and is difficult to form a fair competition with large power companies or aggregation subjects.

[0008] (2) Current most VPPs only face the day-ahead electricity market to carry out bidding and transaction, and it is difficult to consider the frequency modulation, standby and other auxiliary service markets, so that the resource potential is not fully activated, and the overall benefit level is limited.

[0009] (3) When there are multiple VPPs in a region, there is a lack of unified coordination mechanism to integrate resources, share market information or develop collective transaction strategies, resulting in scattered resources, information silos and low operation efficiency.

[0010] (4) In the existing mode, the price signal is mostly directly issued by the market, and an effective feedback mechanism between VPPs or between VPPs and operators has not been formed, so there is a disconnection between the price and the dispatching behavior, affecting the optimization and stability of the transaction.

[0011] To solve the above technical problems, the present application provides the following technical solutions: A multi-virtual power plant game transaction method for power market, comprising,

[0012] The coordination center initializes the electricity price parameter through external data and internal prediction, guides each virtual power plant to report the optimization plan, iteratively updates the electricity price strategy based on the reported optimization plan, and stops until the game converges to the maximum iteration number. After the game converges, the coordination center submits the cluster comprehensive bidding plan to the market to participate in the day-ahead market clearing to complete the bidding stage. The transaction center performs the whole network safety check based on the bidding results of each market subject, clears the day-ahead energy and auxiliary service market according to the rules, forms and releases the next day's dispatching plan, and distributes it to each execution unit. In real-time operation, the transaction center monitors the whole network operation and identifies the supply-demand deviation, and adjusts the intraday compensation through the deviation balancing mechanism. According to the difference between the actual execution result and the day-ahead plan, the financial settlement and clearing of all market subjects are completed, and a complete market closed loop is formed.

[0013] As a preferred scheme of the multi-virtual power plant game transaction method for power market, each virtual power plant independently solves the optimization problem based on the internal aggregated distributed resources under the given electricity price signal.

[0014] Based on the result of solving the optimization problem, the participation plan in the energy market and the frequency modulation market is determined.

[0015] As a preferred scheme of the multi-virtual power plant game transaction method for power market, the iterative updating of the electricity price strategy comprises,

[0016] The coordination center reevaluates the cluster overall benefit based on the optimization plan reported by each virtual power plant.

[0017] The internal electricity price strategy is updated based on the result of reevaluating the cluster overall benefit to promote the adjustment of the cluster overall benefit to the maximum direction.

[0018] As a preferred scheme of the multi-virtual power plant game transaction method for the electricity market, the execution of the whole network safety check comprises,

[0019] The transaction center verifies the feasibility of each bidding scheme in terms of grid safety constraints.

[0020] After verification, the transaction center includes each bidding scheme in the safety constraint unit commitment for unified clearing, and generates a preliminary market clearing result.

[0021] As a preferred scheme of the multi-virtual power plant game transaction method for the electricity market, the independent solving of the optimization problem comprises,

[0022] When each virtual power plant receives the transaction price issued by the coordination center, it combines its own resource structure and operation constraints, wherein the operation objective function is:

[0023] minC i =C CG,i +C ES,i +C in,i

[0024] Wherein, C i is the operation cost of the i-th virtual power plant, C CG,i , C ES,i are the operation costs of controllable units and energy storage in the i-th virtual power plant, and C in,i represents the cost of the i-th virtual power plant and the coordination center transaction.

[0025] The operation cost of controllable units is represented as,

[0026] C CG,i =c CG,i,E P CG,i +c CG,i,F R CG,i

[0027] Wherein, c CG,i,E , c CG,i,F are the unit costs of controllable units providing electric energy in the electric energy market and providing frequency modulation services in the frequency modulation market, P CG,i , R CG,i are the power provided by controllable units in the electric energy market and the frequency modulation capacity in the frequency modulation market.

[0028] The calling cost of energy storage is represented as,

[0029] C ES,i =c ES,i,E (P ES,i,c η ES,i,c +PES,i,d / η ES,i,d )+c ES,i,F R ES,i

[0030] wherein, c ES,i,E , c ES,i,F are the unit cost of providing electricity in the electricity market and providing frequency modulation service in the frequency modulation market for the energy storage; P ES,i,c , P ES,i,d are the charging and discharging power provided by the energy storage in the electricity market, R ES,i is the frequency modulation capacity of the energy storage in the frequency modulation market, η ES,i，c , η ES，i，d represent the charging and discharging efficiency of the energy storage, and R ES，i represents the frequency modulation capacity of the energy storage in the frequency modulation market.

[0031] The cost of the virtual power plant and the coordinating center transaction is represented as,

[0032] C in,i = p in,buy P in,i,buy -p in,sell P in,i,sell -(p in,cap +p in,mail λ i )R in,i

[0033] wherein, p in,buy , p in,sell are the electricity market purchase and sale prices formulated by the coordinating center within the virtual power plant cluster, p in,cap , p in,mail are the frequency modulation market transaction capacity price and mileage price formulated by the coordinating center within the virtual power plant cluster, λ i is the average mileage calling rate of the i-th virtual power plant, R in,i is the frequency modulation capacity provided by the i-th virtual power plant, p in,i，buy represents the purchased electricity, and p in,i，sell represents the sold electricity.

[0034] The beneficial effects of the preferred technical solution are that the present application constructs an operation objective function containing controllable units, energy storage, and transaction cost with the coordinating center, and finely models the income and cost relationship of each unit in the market, so that the virtual power plant can autonomously optimize the dispatching strategy based on the electricity price signal, effectively improving the internal resource coordination efficiency and market income of the cluster.

[0035] As a preferred scheme of the multi-virtual power plant game transaction method for the electricity market, the re-evaluation of the overall income of the cluster includes,

[0036] The coordination center guides the allocation and scheduling of resources by formulating and issuing price signals among its internal virtual power plants. The optimization objective function of the coordination center can be expressed as follows:

[0037]

[0038] Where F represents the overall revenue of the coordination center, F out,E F out,F These represent the coordination center's revenue in the external electricity market and the frequency regulation market, respectively. Revenue from transactions involving virtual power plants and coordination centers.

[0039] The coordination center's revenue from the external electricity market is expressed as follows:

[0040] F out,E =p out,buy P out,buy -p out,sell P out,sell

[0041]

[0042] Where, p out,cap p out,mail These represent the capacity tariff and mileage tariff for the coordination center's participation in the external frequency regulation market, respectively, where λ is the average mileage dispatch rate of the coordination center, and R is... out Frequency regulation capacity provided by the coordination center in the external electricity market.

[0043] The revenue of the Coordination Center in the external FM market is expressed as follows:

[0044] F out,F =(p out,cap +p out,mail λ)R out

[0045]

[0046] Where, p out,cap p out,mail These represent the capacity tariff and mileage tariff for the coordination center's participation in the external frequency regulation market, respectively, where λ is the average mileage dispatch rate of the coordination center, and R is... out Frequency regulation capacity provided by the coordination center in the external electricity market.

[0047] The electricity price set by the coordination center for the virtual power plant cluster should meet the following constraints:

[0048] p in,buy,min ≤p in,buy ≤p in,buy,max

[0049] p in,sell,min ≤pin,sell ≤p in,sell,max

[0050] p in,cap,min ≤p in,cap ≤p in,cap,max

[0051] p in,mail,min ≤p in,mail ≤p in,mail,max

[0052] Where, p in，buy，min p in，buy，max p represents the upper and lower limits of the electricity purchase price, respectively. in，sell，min p in，sell，max The upper and lower limits of electricity sales prices, p in，cap，min p in，cap，max p represents the upper and lower limits of the capacity-based electricity price, respectively. in，mail，min p in，mail，max These represent the upper and lower limits of the mileage-based electricity price, respectively.

[0053] The beneficial effects of this preferred technical solution are that the present invention optimizes the external market revenue and internal electricity pricing strategy through a coordination center, thereby maximizing the overall revenue of the virtual power plant cluster while satisfying internal and external market constraints. At the same time, it guides the efficient allocation of resources through reasonable internal price signals, thereby improving the cluster's collaborative response capability and market competitiveness.

[0054] This invention provides a system for a multi-virtual power plant game trading method for the electricity market.

[0055] To address the aforementioned technical problems, this invention provides the following technical solution: a system for a multi-virtual power plant game trading method for the electricity market, comprising: a coordination center module, a virtual power plant module, a trading center module, a market interface module, and an optimization solution module.

[0056] The coordination center module initializes electricity price parameters using external data and internal predictions, guides each virtual power plant to report optimization plans, and iteratively updates the electricity price strategy based on the reported optimization plans with the overall revenue as the objective, until the game converges and the maximum number of iterations is reached.

[0057] The virtual power plant module receives electricity price signals from the coordination center, independently solves optimization problems, determines its participation plan in the energy market and frequency regulation market, and reports the optimization plan.

[0058] The trading center module receives bidding results, performs network-wide security checks, clears the energy and ancillary service markets before the deadline, generates scheduling plans, and distributes them to the execution units.

[0059] The market interface module interacts with the external market, including submitting bids and receiving clearing results.

[0060] The optimization solution module is used to solve optimization problems in the virtual power plant and coordination center.

[0061] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a multi-virtual power plant game trading method for the electricity market.

[0062] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a multi-virtual power plant game trading method for the electricity market.

[0063] The beneficial effects of this invention are as follows: Compared with most existing models that only support a single VPP to participate in the market independently, this invention organizes multiple VPPs into a cluster to participate in the energy market and frequency regulation market as a whole through a coordination center, which effectively expands the scale of resources and significantly enhances the competitiveness of VPPs in various types of electricity markets.

[0064] This invention, through master-slave game theory and a unified pricing mechanism, comprehensively considers the revenue from energy market purchases and sales with the capacity and mileage revenue of the frequency regulation market, thereby achieving dynamic coordination and optimal revenue for VPP resources in different markets.

[0065] This invention is based on the master-slave game iteration between the coordination center and the VPP to achieve bidirectional coupling feedback between price setting and resource scheduling, thereby improving the flexibility and convergence of the transaction mechanism.

[0066] This invention introduces an opportunity-constrained programming model to quantitatively describe the dual uncertainties of new energy sources and loads, and incorporates them into VPP scheduling optimization, effectively reducing economic losses and system risks caused by prediction bias. Attached Figure Description

[0067] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart illustrating a multi-virtual power plant game trading method for the electricity market, as provided in one embodiment of the present invention.

[0069] Figure 2This is a system framework diagram of a multi-virtual power plant game trading method for the electricity market, provided as an embodiment of the present invention. Detailed Implementation

[0070] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0071] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a multi-virtual power plant game trading method for the electricity market, including:

[0072] S1. The coordination center initializes electricity price parameters using external data and internal forecasts, and guides each virtual power plant to report optimization plans.

[0073] S2. Based on the reported optimization plan, iteratively update the electricity price strategy with the overall revenue as the objective until the game converges and the maximum number of iterations is reached.

[0074] S3. After the game convergence, the coordination center submits the cluster comprehensive bidding plan to the market and participates in the bidding stage after the day-ahead market clearing is completed.

[0075] S4. Based on the bidding results of each market participant, the trading center performs a full-network security check, clears the energy and ancillary service markets according to the rules, and forms and publishes the next day's time-segmented scheduling plan, which is then distributed to each implementing unit.

[0076] S5. In real-time operation, the trading center monitors the entire network operation and identifies supply and demand deviations, and performs intraday compensation and adjustment by activating the deviation balancing mechanism.

[0077] S6. Based on the difference between the actual execution results and the current plan, complete the financial settlement and liquidation of all market entities to form a complete market closed loop.

[0078] This invention constructs a closed-loop market transaction process encompassing "day-ahead collaborative bidding - unified market clearing - real-time balance adjustment - post-event financial settlement," and utilizes the cluster game optimization and electricity price guidance mechanism of the coordination center to effectively integrate decentralized resources to participate in multiple electricity market transactions. This improves the overall revenue of virtual power plant clusters and the level of renewable energy consumption, thereby forming a scalable and efficient market transaction model.

[0079] Example 2, an embodiment of the present invention, provides a multi-virtual power plant game trading method for the electricity market based on the previous embodiment, including:

[0080] Furthermore, the external data and internal forecast initialization electricity price parameters in S1 are based on preliminary external electricity market prices and internal VPP resource forecast information.

[0081] Furthermore, the process of guiding each virtual power plant to report its optimization plan in S1 includes the following steps A1-A2:

[0082] A1. Under a given electricity price signal, each virtual power plant independently solves the optimization problem based on internally aggregated distributed resources.

[0083] A2. Based on the results of solving the optimization problem, determine the participation plan in the energy market and frequency regulation market.

[0084] In this embodiment, the distributed resources aggregated internally in A1 initialize key parameters such as internal power purchase and sale price, capacity price, and mileage price. After receiving these price signals, each virtual power plant independently solves the problem of minimizing operating costs (comprehensively considering the call costs of controllable units and energy storage, as well as transaction costs with the central authority) to generate an optimal power dispatch scheme. Based on this, it determines its specific participation plan in the energy market and frequency regulation market, including time-of-use power bidding, frequency regulation capacity bidding, and corresponding mileage service capabilities, thereby forming a market response strategy that matches the price signals.

[0085] In one alternative implementation, the internally aggregated distributed resources can be traditional distributed generation units, such as controllable power generation resources like gas turbines, diesel generators, small hydropower stations, and biomass power generation. Their power generation cost curves and frequency regulation capabilities are relatively clear, serving as a core basis for constructing initial electricity price parameters and providing stable price expectations for the virtual power plant.

[0086] In another alternative implementation, the internally aggregated distributed resources can also be novel flexible load and energy storage systems, including industrial and commercial interruptible loads, electric vehicle charging stations, user-side energy storage batteries, ice storage air conditioning, and demand response resources. These resources have significant bidirectional power regulation capabilities and timing flexibility, enabling them to provide rapid frequency regulation services and energy time-shift value, thereby supporting the initialization of more granular and dynamic time-of-use pricing and capacity pricing parameters.

[0087] This invention constructs a market trading architecture involving virtual power plant clusters, which mainly consists of an electricity market trading center, a virtual power plant cluster coordination center, and multiple virtual power plants.

[0088] The responsibilities of the power market trading center include centrally collecting bidding information from various power market participants (including large power plants and operators), and conducting security verification and system constraint testing to ensure the feasibility and safety of power grid operation. After completing the security verification, the power market trading center executes the day-ahead market clearing based on the bidding results, and settles and clears transactions based on the transaction results and actual transaction volume.

[0089] As a coordination and transaction intermediary between the virtual power plant cluster and the external electricity market, the coordination center's main responsibilities include: collecting and analyzing key information such as prices, demand, and system constraints in the external electricity market in real time; formulating internal purchase and sale tariff, capacity tariff, and mileage tariff strategies for the energy market and ancillary service markets such as frequency regulation, based on the resource characteristics and status of each VPP within the cluster; and formulating bidding strategies with the goal of maximizing the overall revenue of the cluster, and submitting the cluster's bidding information to the electricity market trading center to participate in market clearing.

[0090] Each virtual power plant, as a rational and autonomous market participant, receives electricity price signals from the coordination center and, in combination with its own resource status and operational constraints, independently optimizes its output plans and resource allocation strategies in various markets to maximize its individual profits.

[0091] Based on the aforementioned hierarchical game process, this invention establishes a master-slave game model between the coordination center and each VPP: the coordination center, as the master player, formulates pricing strategies and bidding plans; each VPP, as a slave player, makes autonomous decisions based on given price signals. Through multiple rounds of game iteration, the two parties achieve a dynamic balance between the optimal cluster return pursued by the coordination center and the optimal individual return pursued by each VPP, forming a market trading mechanism with strong convergence and high feasibility.

[0092] Furthermore, the independent optimization problems in A2 include,

[0093] In the master-slave game structure, the lower layer consists of various Virtual Power Utilities (VPPs) acting as slaves in the game. After receiving the transaction price published by the coordination center, each VPP independently makes optimization scheduling decisions based on its own resource structure and operational constraints. It is assumed that the distributed resources contained in each VPP include controllable generating units (CG), energy storage (ES), photovoltaic (PV), and wind power (WT).

[0094] After each virtual power plant receives the transaction price published by the coordination center, it considers its own resource structure and operational constraints. The operational objective function is as follows:

[0095] minC i =C CG,i +C ES,i +C in,i

[0096] Among them, C i Let C be the operating cost of the i-th virtual power plant. CG,i C ES,i C represents the operating costs of the controllable generating units and energy storage within the i-th virtual power plant, respectively. in,i This represents the cost of the transaction between the i-th virtual power plant and the coordination center;

[0097] The operating cost of a controllable unit is expressed as follows:

[0098] C CG,i =c CG,i,E P CG,i +c CG,i,F R CG,i

[0099] Among them, c CG,i,E c CG,i,F P represents the unit cost, respectively, of providing electricity to the controllable generating unit in the electricity market and providing frequency regulation services in the frequency regulation market. CG,i R CG,i These refer to the power and frequency regulation capacity provided by controllable generating units in the electricity market and frequency regulation market, respectively.

[0100] The cost of accessing energy storage is expressed as follows:

[0101] C ES,i =c ES,i,E (P ES,i,c η ES,i,c +P ES,i,d / η ES,i,d )+c ES,i,F R ES,i

[0102] Among them, c ES,i,E c ES,i,F The unit costs of energy storage providing electricity in the electricity market and frequency regulation services in the frequency regulation market, respectively; P ES,i,c P ES,i,d R represents the charging and discharging power provided by energy storage in the electric energy market. ES,i To increase the frequency regulation capacity of the frequency regulation market, η ES,i，c η ES，i，d R represents the charge / discharge efficiency of energy storage, respectively. ES，i This represents the frequency regulation capacity of energy storage in the frequency regulation market.

[0103] The cost of transactions between virtual power plants and coordination centers is expressed as follows:

[0104] C in,i =p in,buy P in,i,buy -p in,sell P in,i,sell -(p in,cap +pin,mail λ i )R in,i

[0105] Where, p in,buy p in,sell The respective electricity purchase and sale prices within the virtual power plant cluster set by the coordination center, p in,cap p in,mail These are the capacity electricity price and mileage electricity price for the frequency regulation market within the virtual power plant cluster, respectively, set by the coordination center. i Let R be the average mileage call rate of the i-th virtual power plant. in,i For the frequency regulation capacity provided by the i-th virtual power plant, p in,i，buy p represents the amount of electricity purchased. in,i，sell This represents the amount of electricity sold.

[0106] Each VPP should also satisfy the following constraints:

[0107] Controllable unit CG operation constraints

[0108]

[0109] in, These represent the upper and lower limits of the output of the controllable generator unit (CG).

[0110] Energy Storage (ES) Operational Constraints:

[0111] 0≤P ES,i,d ≤u i P ES,max

[0112] 0≤P ES,i,c ≤(1-u i )P ES,max

[0113] 0≤P ES,i,d +R ES,i ≤P ES,max

[0114] 0≤P ES,i,c +R ES,i ≤P ES,max

[0115] E i (t+1)=E i (t)+P ES,i,c (t)η ES,i,c - P ES,i,d (t) / η ES,i,d

[0116] E i,min ≤E i ≤E i,max

[0117] Among them, P ES,i,max The upper limit of storage charge and discharge, u i In the energy storage charging and discharging state, E i For energy storage capacity, E i,min E i,max These represent the upper and lower limits of energy storage capacity, respectively.

[0118] Electricity purchase and sale constraints:

[0119] 0≤P in,i,buy ≤s i P trade,max

[0120] 0≤P in,i,sell ≤(1-s i )P trade,max

[0121] 0≤R in,i ≤R trade,max

[0122] Among them, P trade,max The upper limit for purchasing and selling electricity, s i For electricity purchase and sale status, R trade,max This is the maximum frequency regulation capacity that can be declared.

[0123] Power balance constraints:

[0124] P PV,i +P WT,i +P CG,i +P ES,i,d +P in,i,buy =P L,i +P ES,i,c +P in,i,sell

[0125] R in,i =R CG,i +R ES,i

[0126] Among them, P L,i P PV,i P WT,i These represent the load, photovoltaic, and wind power output, respectively.

[0127] This allows us to construct a day-ahead trading model under conditions of deterministic new energy sources and load.

[0128] At this point, the output of new energy sources and the load demand are considered known and fixed parameters. However, in actual operation, these two factors generally have significant uncertainties, and their predicted values ​​often deviate from the actual situation, thus posing challenges to system scheduling and operational safety.

[0129] Specifically, the instability of renewable energy output and the unpredictability of load fluctuations can lead to frequent imbalances in power supply and demand, resulting in frequent grid adjustments, increased operational pressure and dispatch complexity, and impacting system stability. To address these imbalances, VPPs typically need to utilize energy storage systems, activate adjustable loads, or utilize backup resources for regulation. However, these measures usually come at a high economic cost, such as frequent charging and discharging losses of energy storage devices, the configuration cost of backup capacity, and real-time market adjustment fees, thus increasing overall operating costs.

[0130] Therefore, effectively addressing the dual uncertainties of power generation and load within a VPP becomes a key issue in ensuring the system's economy, stability, and reliability. To this end, this invention introduces the Chance-Constrained Programming (CHP) method to model the stochasticity of renewable energy output and load demand, enabling robust scheduling and optimal decision-making under uncertain conditions.

[0131] The difference between the actual output of new energy sources and the actual output of the load is defined as net load P. net That is, P net =P L -P PV +P WT The net load P net Consider it as the sum of the predicted value and the random error, and assume that the random error of the net load follows a normal distribution with a mean of 0 and a variance of . The net load can then be expressed in the following form:

[0132]

[0133] Among them, P net,pre The predicted value of net load is derived from the predicted power of new energy sources and the load.

[0134] Based on this, the power balance equation can be expressed in the following chance-constrained form:

[0135]

[0136] Where α represents the confidence probability level.

[0137] The above-mentioned chance constraint form is difficult to solve directly, so it can be transformed into a deterministic form for calculation:

[0138]

[0139] Where, Φ -1 (α) is the inverse function of the standard normal distribution.

[0140] By transforming the above formula, the deterministic model can be converted into an opportunity-constrained model that considers the dual uncertainties of new energy sources and load sources.

[0141] In the implementation of this application, the iterative update of the electricity price strategy in S2 is that the coordination center re-evaluates the overall revenue of the cluster based on the optimization plans reported by each virtual power plant; and updates the internal electricity price strategy based on the result of the re-evaluation of the overall revenue of the cluster, so as to promote the adjustment of the overall revenue of the cluster towards maximization.

[0142] In one alternative implementation, the iterative update strategy for electricity prices can be a gradient optimization algorithm based on sensitivity analysis. That is, the coordination center determines the adjustment direction and step size of each price parameter by calculating the partial derivatives of the overall revenue of the cluster with respect to various types of electricity prices.

[0143] In another alternative implementation, the iterative update of the electricity price strategy can also be based on a distributed optimization method of Lagrange dual decomposition, which decomposes the overall optimization problem of the cluster into multiple subproblems. Through the alternating iteration between the coordination center updating the Lagrange multipliers (i.e., the electricity price signal) and the local optimization of the virtual power plant, the overall optimal goal of the cluster is achieved while ensuring the autonomous decision-making of each subject.

[0144] Furthermore, the iterative electricity price update strategy in S2 includes the following steps B1-B2:

[0145] B1. The coordination center reassesses the overall benefits of the cluster based on the optimization plans reported by each virtual power plant.

[0146] B2. Update the internal electricity pricing strategy based on the results of the reassessment of the overall benefits of the cluster, so as to adjust the overall benefits of the cluster towards maximization.

[0147] Furthermore, B1 and B2 are executed repeatedly to form a multi-round game process between the coordination center and each VPP until the maximum number of convergences is reached.

[0148] Furthermore, the overall benefit of the evaluation cluster in B1 includes the coordination center guiding the allocation and scheduling of resources by each virtual power plant within it through the formulation and dissemination of price signals. The optimization objective function of the coordination center can be expressed as follows:

[0149]

[0150] Where F represents the overall revenue of the coordination center, F out,E F out,F These represent the coordination center's revenue in the external electricity market and the frequency regulation market, respectively. This parameter represents the revenue generated from transactions between virtual power plants and the coordination center. It represents a cost for the virtual power plant but a revenue for the coordination center. The same parameter has different meanings for the buyer and seller.

[0151] The coordination center's revenue from the external electricity market is expressed as follows:

[0152] F out,E =p out,buy P out,buy -p out,sell P out,sell

[0153]

[0154] Where, p out,buy p out,sell These represent the purchase and sale prices of electricity in the external energy market, P out,buy P out,sell These refer to the electricity volume purchased and sold by the coordination center in the external electricity market.

[0155] The revenue of the Coordination Center in the external FM market is expressed as follows:

[0156] F out,F =(p out,cap +p out,mail λ)R out

[0157]

[0158] Where, p out,cap p out,mail These represent the capacity tariff and mileage tariff for the coordination center's participation in the external frequency regulation market, respectively, where λ is the average mileage dispatch rate of the coordination center, and R is... out Frequency regulation capacity provided by the coordination center in the external electricity market;

[0159] The electricity price set by the coordination center for the virtual power plant cluster should meet the following constraints:

[0160] p in,buy,min ≤p in,buy ≤p in,buy,max

[0161] p in,sell,min ≤p in,sell ≤p in,sell,max

[0162] p in,cap,min ≤p in,cap ≤p in,cap,max

[0163] p in,mail,min ≤p in,mail ≤p in,mail,max

[0164] Where, p in，buy，min p in，buy，max p represents the upper and lower limits of the electricity purchase price, respectively.in，sell，min p in，sell，max The upper and lower limits of electricity sales prices, p in，cap，min p in，cap，max p represents the upper and lower limits of the capacity-based electricity price, respectively. in，mail，min p in，mail，max These represent the upper and lower limits of the mileage-based electricity price, respectively.

[0165] Furthermore, in S3, participation in the day-ahead market clearing and bidding phase includes, after game convergence, the coordination center submits the cluster's comprehensive bidding plan to the power market trading center to participate in the day-ahead market bidding and clearing process and complete the bidding phase.

[0166] Furthermore, performing a full network security check in S4 includes the following steps C1-C2:

[0167] C1. The trading center verifies the feasibility of each bidding proposal in terms of power grid security constraints;

[0168] C2. After verification, the trading center will include all bidding proposals into the safety-constrained unit combination for unified clearing and generate preliminary market clearing results.

[0169] In the implementation of this application, the grid security constraints in C1 are to verify the feasibility of each bidding scheme in terms of power balance, transmission line power flow limits, and node voltage stability. Specifically, these include: real-time power balance between generation and load at the basic level to maintain system frequency stability; power flow limit constraints on transmission lines, transformers, and other components at the critical network level to prevent equipment overload and network congestion; and deeper static security and stability constraints, such as node voltage upper and lower limits and N-1 fault verification, to ensure that the daily plan does not cause systemic risks.

[0170] In one alternative implementation, grid safety constraints can be system power balance and line transmission capacity constraints, including real-time power balance between generation and load, power flow limits for key transmission sections, transformer capacity limits, etc., to ensure that the system operates within equipment safety limits and prevent overload or blockage.

[0171] In another optional implementation, power grid security constraints can also be system static and dynamic stability constraints, including node voltage upper and lower limits, N-1 fault verification, frequency stability requirements, transient stability margins, etc. These constraints ensure that the system can maintain stable operation under normal and fault conditions, thereby improving power supply reliability.

[0172] Furthermore, the unified clearing of the day-ahead energy and ancillary services market in S4 according to the rules includes the following steps D1-D2:

[0173] D1. The trading center, in accordance with market rules, sorts bids by electricity price and capacity priority, and executes a unified competitive bidding and clearing process for the day-ahead energy market and ancillary services market to determine the winning electricity volume, capacity, and corresponding settlement price for each market participant.

[0174] D2. Release the pre-schedule scheduling plan. Based on the clearing results, formulate a scheduling plan (including energy output and frequency regulation service capacity) for all winning bidders within the next 24 hours, and distribute the plan to the coordination center and other market participants as the basis for execution of the next day's operations.

[0175] In this embodiment, the deviation balancing mechanism in S5 allows the trading center to activate either a real-time market or an intraday market. The coordination center can allocate flexible resources such as energy storage and adjustable loads of the virtual power plants (VPPs) within the cluster for deviation adjustment, reducing system regulation pressure. When the trading center detects a supply-demand deviation between real-time operation and the day-ahead plan, it initiates multi-level adjustment measures to maintain system stability. The core of this mechanism lies in the trading center's ability to activate standardized mechanisms such as real-time or intraday markets, while the coordination center can allocate flexible resources such as energy storage and adjustable loads of the virtual power plants (VPPs) within the cluster for rapid response, thereby effectively reducing system regulation pressure and improving economic efficiency.

[0176] In one alternative implementation, the deviation balancing mechanism can be an economic incentive model based on real-time electricity price signals. For example, the trading center publishes real-time deviation adjustment prices, and the coordination center guides the distributed energy storage system within the cluster to adjust its charging and discharging power and interruptible loads to change their electricity consumption plans based on this price signal, so as to efficiently smooth out deviations in a market-oriented manner.

[0177] In another alternative implementation, the deviation balancing mechanism can also be a targeted call mode based on predefined rules. For example, the coordination center can prioritize the use of energy storage resources or flexible loads with fast response speed and high adjustment accuracy according to the adjustment service agreement pre-signed by the virtual power plant, and execute the deviation adjustment task in an instructive manner to ensure the safe and stable operation of the system during critical periods.

[0178] Furthermore, in S5, the trading center monitors the entire network operation and identifies supply and demand discrepancies. During the actual operation phase of the following day, the trading center continuously collects the actual output, load, and renewable energy power of each entity, compares the deviation between the previous day's plan and the actual execution in real time, and identifies situations that may lead to supply and demand imbalances.

[0179] Furthermore, S6 completes the financial settlement and clearing for all market participants, including calculating the electricity trading volume, ancillary service performance, and deviation electricity volume of each market participant based on the difference between actual execution and day-ahead plan, and settling various economic benefits or penalty fees according to market rules, thus completing the financial clearing and forming a closed loop of transactions.

[0180] Example 3, referring toFigure 2 This is one embodiment of the present invention, which provides a system for improving the efficiency of short-term high-frequency energy storage, including: a coordination center module, a virtual power plant module, a trading center module, a market interface module, and an optimization solution module.

[0181] The coordination center module initializes electricity price parameters using external data and internal forecasts, guides each virtual power plant to report optimization plans, and iteratively updates the electricity price strategy based on the reported optimization plans with the overall revenue as the objective, until the game converges and the maximum number of iterations is reached.

[0182] The virtual power plant module receives electricity price signals from the coordination center, independently solves optimization problems, determines its participation plan in the energy market and frequency regulation market, and reports its optimization plan.

[0183] The trading center module receives bidding results, performs network-wide security checks, clears the energy and ancillary service markets before the deadline, generates scheduling plans, and distributes them to the implementing units.

[0184] The market interface module interacts with external markets, including submitting bids and receiving clearing results;

[0185] An optimization solution module is used to solve optimization problems in virtual power plants and coordination centers.

[0186] This embodiment also provides an electronic device applicable to a multi-virtual power plant game trading method for the electricity market, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the multi-virtual power plant game trading method for the electricity market as proposed in the above embodiment.

[0187] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a multi-virtual power plant game trading method for the electricity market as proposed in the above embodiments.

[0188] The storage medium proposed in this embodiment and the method for implementing a multi-virtual power plant game trading in the electricity market proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0189] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0190] 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-virtual power plant game trading method for the electricity market, characterized in that: include, The coordination center initializes electricity price parameters using external data and internal forecasts, guiding each virtual power plant to submit its optimization plan. Based on the reported optimization plan, the electricity pricing strategy is iteratively updated with the overall revenue as the objective until the game converges and the maximum number of iterations is reached. After the game convergence, the coordination center submits the cluster comprehensive bidding plan to the market and participates in the bidding stage of the day-ahead market clearing. Based on the bidding results of each market participant, the trading center performs a full-network security check, clears the energy and ancillary service markets according to the rules, and forms and publishes the next day's time-segmented scheduling plan, which is then distributed to each implementing unit. In real-time operation, the trading center monitors the entire network and identifies supply and demand discrepancies, and adjusts them intraday by activating the discrepancy balancing mechanism. Based on the differences between the actual implementation results and the previous plan, complete the financial settlement and liquidation of all market entities to form a complete market closed loop.

2. The multi-virtual power plant game trading method for the electricity market as described in claim 1, characterized in that: The process of guiding each virtual power plant to report its optimization plan includes... Each virtual power plant independently solves the optimization problem based on its internally aggregated distributed resources, given a given electricity price signal. Based on the results of solving the optimization problem, participation plans in the energy market and frequency regulation market are determined.

3. The multi-virtual power plant game trading method for the electricity market as described in claim 2, characterized in that: The iterative electricity price update strategy includes, Based on the optimization plans reported by each virtual power plant, the coordination center reassesses the overall benefits of the cluster. The internal electricity pricing strategy is updated based on the results of the reassessment of the overall cluster revenue, in order to adjust the overall cluster revenue towards maximization.

4. The multi-virtual power plant game trading method for the electricity market as described in claim 3, characterized in that: The execution of the full network security check includes, The trading center verifies the feasibility of each bidding proposal in terms of power grid security constraints; After verification, the trading center will include all bidding proposals into the safety-constrained unit combination for unified clearing, generating preliminary market clearing results.

5. A multi-virtual power plant game trading method for the electricity market as described in claim 4, characterized in that: The independent optimization problems include After each virtual power plant receives the transaction price published by the coordination center, it considers its own resource structure and operational constraints. The operational objective function is as follows: minC i =C CG,i +C ES,i +C in,i Among them, C i Let C be the operating cost of the i-th virtual power plant. CG,i C ES,i C represents the operating costs of the controllable generating units and energy storage within the i-th virtual power plant, respectively. in,i This represents the cost of the transaction between the i-th virtual power plant and the coordination center; The operating cost of a controllable unit is expressed as follows: C CG,i =c CG,i,E P CG,i +c CG,i,F R CG,i Among them, c CG,i,E c CG,i,F P represents the unit cost, respectively, of providing electricity to the controllable generating unit in the electricity market and providing frequency regulation services in the frequency regulation market. CG,i R CG,i These refer to the power and frequency regulation capacity provided by controllable generating units in the electricity market and frequency regulation market, respectively. The cost of accessing energy storage is expressed as follows: C ES,i (c ES,i,E (P ES,i,c η ES,i,c +P ES,i,d / η ES,i,d )+c ES,i,F R ES,i Among them, c ES,i,E c ES,i,F The unit costs of energy storage providing electricity in the electricity market and frequency regulation services in the frequency regulation market, respectively; P ES,i,c P ES,i,d R represents the charging and discharging power provided by energy storage in the electric energy market. ES,i To increase the frequency regulation capacity of the frequency regulation market, η ES,i，c η ES，i，d R represents the charge / discharge efficiency of energy storage, respectively. ES，i This represents the frequency regulation capacity of energy storage in the frequency regulation market. The cost of transactions between virtual power plants and coordination centers is expressed as follows: C in,i =p in,buy P in,i,buy -p in,sell P in,i,sell -(p in,cap +p in,mail λ i )R in,i Where, p in,buy p in,sell The respective electricity purchase and sale prices within the virtual power plant cluster set by the coordination center, p in,cap p in,mail These are the capacity electricity price and mileage electricity price for the frequency regulation market within the virtual power plant cluster, respectively, set by the coordination center. i Let R be the average mileage call rate of the i-th virtual power plant. in,i For the frequency regulation capacity provided by the i-th virtual power plant, p in,i，buy p represents the amount of electricity purchased. in,i，sell This represents the amount of electricity sold.

6. A multi-virtual power plant game trading method for the electricity market as described in claim 5, characterized in that: The reassessment of the overall cluster benefits includes, The coordination center guides the allocation and scheduling of resources by formulating and issuing price signals among its internal virtual power plants. The optimization objective function of the coordination center can be expressed as follows: Where F represents the overall revenue of the coordination center, F out,E F out,F The respective revenues of the coordination center in the external electricity market and the frequency regulation market, ∑C cn，i Revenue from transactions involving virtual power plants and coordination centers; The coordination center's revenue from the external electricity market is expressed as follows: F out,E =p out,buy P out,buy -p out,sell P out,sell Where, p out,buy p out,sell These represent the purchase and sale prices of electricity in the external energy market, P out,buy P out,sell These refer to the electricity purchased and sold by the coordination center in the external electricity market; The revenue of the Coordination Center in the external FM market is expressed as follows: F out,F =(p out,cap +p out,mail λ)R out Where, p out,cap p out,mail These represent the capacity tariff and mileage tariff for the coordination center's participation in the external frequency regulation market, respectively, where λ is the average mileage dispatch rate of the coordination center, and R is... out Frequency regulation capacity provided by the coordination center in the external electricity market; The electricity price set by the coordination center for the virtual power plant cluster should meet the following constraints: p in,buy,min ≤p in,buy ≤p in,buy,max p in,sell,min ≤p in,sell ≤p in,sell,max p in,cap,min ≤p in,cap ≤p in,cap,max p in,mail,min ≤p in,mail ≤p in,mail,max Where, p in，buy，min p in，buy，max p represents the upper and lower limits of the electricity purchase price, respectively. in，sell，min p in，sell，max The upper and lower limits of electricity sales prices, p in，cap，min p in，cap，max p represents the upper and lower limits of the capacity-based electricity price, respectively. in，mail，min p in，mail，max These represent the upper and lower limits of the mileage-based electricity price, respectively.

7. A system for improving the efficiency of short-term high-frequency energy storage, employing a multi-virtual power plant game trading method for the electricity market as described in any one of claims 1 to 6, characterized in that... It includes: a coordination center module, a virtual power plant module, a trading center module, a market interface module, and an optimization solution module; The coordination center module initializes the electricity price parameters through external data and internal predictions, guides each virtual power plant to report optimization plans, and iteratively updates the electricity price strategy based on the reported optimization plans with the overall revenue as the objective, until the game converges and the maximum number of iterations is reached. The virtual power plant module receives electricity price signals from the coordination center, independently solves optimization problems, determines its participation plan in the energy market and frequency regulation market, and reports the optimization plan. The trading center module receives bidding results, performs network-wide security checks, clears the energy and ancillary service markets before the deadline, generates scheduling plans, and distributes them to the execution units. The market interface module interacts with the external market, including submitting bids and receiving clearing results; The optimization solution module is used to solve optimization problems in the virtual power plant and coordination center.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-virtual power plant game trading method for the electricity market as described in any one of claims 1 to 6.

9. A 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 steps of the multi-virtual power plant game trading method for the electricity market as described in any one of claims 1 to 6.

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