Two-stage optimization scheduling method and system for virtual power plant to participate in joint market

Abstract

The invention discloses a two-stage optimization scheduling method and system for a virtual power plant to participate in a joint market, and the method comprises the steps: obtaining first data, and constructing a two-stage optimization scheduling model for the virtual power plant to participate in the joint market based on the first data; target functions and decision variables of the first stage and the second stage of the two-stage optimization scheduling model are determined; according to the objective function and the decision variable, determining boundary conditions of a first stage and a second stage of the two-stage optimization scheduling model; and based on a typical scene, solving the two-stage optimization scheduling model to obtain an optimization solving result. According to the two-stage optimization scheduling model, the electric energy value of the virtual power plant and the value of providing standby capacity for the system are mined, and the reliability of the system is improved; the two-stage optimization scheduling model effectively guides the virtual power plant to aggregate internal resources to participate in the electric energy and standby auxiliary service market, and the cost of a virtual power plant operator is effectively reduced through scientific and reasonable scheduling.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a two-stage optimization scheduling method and system for a virtual power plant participating in a joint market. Background Art

[0002] Under the guidance of the "dual carbon" goal, the penetration rate of new energy sources such as distributed photovoltaics and wind power, as well as new loads such as electric vehicles in the distribution network is continuously and steadily increasing. This trend not only brings a richer form of energy supply and demand to the distribution network, but also poses new challenges to the stable operation and efficient management of the power grid. The massive user-side resources on the distribution network side have become the key to solving this problem because of their flexible adjustment capabilities for power generation, energy storage, and electricity consumption. How to fully tap and give full play to the potential of these resources is a technical problem that researchers are currently in urgent need of overcoming.

[0003] At the same time, the vigorous development of the new generation of intelligent control technology and modern communication technology has provided us with new solutions. By effectively aggregating these distributed power sources, energy storage systems, and adjustable loads, a powerful hybrid virtual power plant can be formed. The power plant can coordinate and optimize the management of various distributed resources within it, actively participate in power market transactions, and while achieving its own economic benefits, it also provides electric energy and backup auxiliary services for the stable operation of the large power grid, thereby promoting the green, low-carbon, and sustainable development of the entire power system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of the present invention to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a two-stage optimization scheduling method for a virtual power plant to participate in a joint market to solve the problem of how to effectively realize the two-stage optimization scheduling of a virtual power plant to participate in electric energy and backup auxiliary services in a joint market to achieve cost-effectiveness and social welfare maximization.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a two-stage optimization scheduling method for a virtual power plant to participate in a joint market, comprising:

[0008] Acquire first data, and construct a two-stage optimization scheduling model for virtual power plants to participate in a joint market based on the first data;

[0009] Determine the objective function and decision variables of the first stage and the second stage of the two-stage optimization scheduling model;

[0010] Determining the boundary conditions of the first stage and the second stage of the two-stage optimization scheduling model according to the objective function and the decision variables;

[0011] Based on typical scenarios, the two-stage optimization scheduling model is solved to obtain the optimization solution results.

[0012] As a preferred solution of the two-stage optimization scheduling method for virtual power plants participating in the joint market described in the present invention, the objective function of the first stage includes:

[0013] The objective function of the first stage is to minimize the expected operating cost of the virtual power plant participating in the electricity energy and reserve auxiliary market.

[0014] As a preferred solution of the two-stage optimization scheduling method for virtual power plants participating in the joint market described in the present invention, the objective function of the second stage includes:

[0015] The objective function of the second stage is to minimize the market operating costs, which include the revenue or costs generated by power generation companies and virtual power plants participating in the electric energy market to provide or purchase electric energy and the costs of participating in the reserve ancillary service market to provide reserve.

[0016] As a preferred solution of the two-stage optimization scheduling method for virtual power plants to participate in the joint market described in the present invention, the boundary conditions of the first stage include system reserve constraints, distributed new energy output and reserve constraints, electric vehicle charging station constraints, energy storage charging and discharging constraints and market quotation constraints;

[0017] The boundary conditions of the second stage include the operating constraints of power generation enterprises and the operating constraints of virtual power plants.

[0018] As a preferred solution of the two-stage optimal scheduling method for virtual power plants participating in the joint market described in the present invention, the feasible solution range of the first stage and the second stage of the two-stage optimal scheduling model is determined based on the boundary conditions.

[0019] As a preferred solution of the two-stage optimization scheduling method for virtual power plants participating in the joint market described in the present invention, the classic scenarios include:

[0020] Scenario 1: The virtual power plant only participates in the electricity market to buy and sell electricity, and obtains the price difference between the purchase and sale of electricity;

[0021] Scenario 2: The virtual power plant participates in electricity purchase and sale transactions to obtain the price difference between the purchase and sale of electricity and participates in the standby ancillary service market to provide standby services and obtain standby service fees.

[0022] As a preferred solution of the two-stage optimization scheduling method for virtual power plants participating in the joint market described in the present invention, solving the two-stage optimization scheduling model and obtaining the optimization solution results include:

[0023] An improved particle swarm optimization algorithm is used to solve the two-stage optimization scheduling model and output the optimization scheduling result.

[0024] In a second aspect, the present invention provides a system for two-stage optimal dispatch of a virtual power plant participating in a joint market, comprising:

[0025] A data acquisition module, used for acquiring first data;

[0026] A model building module, used to build a two-stage optimization scheduling model for virtual power plants to participate in the joint market based on the first data;

[0027] A model determination module, used to determine the objective function and decision variables of the first and second stages of the two-stage optimization scheduling model; and determine the boundary conditions of the first and second stages of the two-stage optimization scheduling model according to the objective function and decision variables;

[0028] A model solving module is used to solve the two-stage optimization scheduling model based on typical scenarios and obtain optimization solution results;

[0029] The model determination module specifically includes: the objective function of the first stage is to minimize the expected operating cost of the virtual power plant participating in the electric energy and reserve auxiliary market;

[0030] The objective function of the second stage is to minimize the market operation cost, which includes the revenue or cost generated by power generation enterprises and virtual power plants participating in the electric energy market to provide or purchase electric energy, and the cost of participating in the reserve ancillary service market to provide reserve;

[0031] The boundary conditions of the first stage include system reserve constraints, distributed new energy output and reserve constraints, electric vehicle charging station constraints, energy storage charging and discharging constraints, and market quotation constraints;

[0032] The boundary conditions in the second stage include the operating constraints of power generation enterprises and virtual power plants;

[0033] Based on the boundary conditions, the feasible solution ranges of the first stage and the second stage of the two-stage optimization scheduling model are determined.

[0034] The model solving module specifically includes: using an improved particle swarm optimization algorithm to solve the two-stage optimization scheduling model, and outputting the optimization scheduling result.

[0035] In a third aspect, the present invention provides a computing device, comprising:

[0036] Memory and processor;

[0037] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the two-stage optimization scheduling method for the virtual power plant to participate in the joint market are implemented.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, can implement the steps of the two-stage optimization scheduling method for the virtual power plant to participate in a joint market.

[0039] Compared with the prior art, the present invention has the following beneficial effects: the present invention proposes a two-stage optimization scheduling model for a hybrid virtual power plant under the electric energy and standby auxiliary service market. The two-stage optimization scheduling model explores the electric energy value of the virtual power plant and the value of providing backup capacity to the system, thereby improving system reliability; the two-stage optimization scheduling model provides a reference for the scientific bidding and quotation of virtual power plants in the process of participating in the electric energy and standby auxiliary service market; the two-stage optimization scheduling model effectively guides virtual power plants to aggregate internal resources to participate in the electric energy and standby auxiliary service market, and effectively reduces the cost of virtual power plant operators through scientific and reasonable scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 It is a schematic diagram of the overall process of a two-stage optimization scheduling method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of the clearing electricity price of the electric energy market in scenario 1 of a two-stage optimization scheduling method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the optimal dispatching result of a virtual power plant bidding in the electric energy market in a scenario of a two-stage optimization dispatching method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of clearing prices of the electric energy market and the standby ancillary service market in scenario 2 of a two-stage optimization scheduling method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the optimal scheduling result of a virtual power plant bidding in the electric energy market in scenario 2 of the two-stage optimization scheduling method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0046] Figure 6 A schematic diagram of supply and demand balance in a standby ancillary service market in scenario 2 of a two-stage optimization scheduling method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0047] Figure 7 A schematic diagram of the optimal dispatch result of a virtual power plant bidding in a standby ancillary service market in scenario 2 of a two-stage optimization dispatch method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0048] Figure 8 A schematic diagram of the supply and demand balance in the electric energy market in scenario 1 of the two-stage optimization scheduling method for a virtual power plant participating in a joint market according to an embodiment of the present invention;

[0049] Fig. 9 This is a schematic diagram of the supply and demand balance in the electric energy market in scenario 2 of the two-stage optimization scheduling method for a virtual power plant participating in a joint market described in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0053] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0054] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0055] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Example 1

[0057] Reference Figure 1 , which is an embodiment of the present invention, provides a two-stage optimization scheduling method for a virtual power plant to participate in a joint market, comprising:

[0058] S100: Acquire first data;

[0059] In the embodiment of the present invention, the first data includes virtual power plant operation data, power generation enterprise output data, and various equipment parameters, etc.;

[0060] Specifically, the first data may be parameters such as the charging and discharging efficiency of electric vehicle charging stations and energy storage power stations during operation, unit electricity loss cost, range of charge states, distributed photovoltaic installed capacity and output data, marginal costs of various power generation companies, incentive prices for providing backup in the backup market, and the probability of the system calling on backup demand at various times.

[0061] S102: Based on the first data, construct a two-stage optimization scheduling model for virtual power plants to participate in the joint market; determine the objective function and decision variables of the first and second stages of the two-stage optimization scheduling model;

[0062] Preferably, the objective function of the first stage is to minimize the expected operating cost of the virtual power plant participating in the electric energy and reserve auxiliary market;

[0063] In the embodiment of the present invention, the objective function of the first stage is expressed as follows:

[0064]

[0065] in, is the probability of the system calling the backup at time t; and They are the output power of distributed new energy q and the reserved spare capacity; and are the charging and discharging power of energy storage e respectively; and are the equivalent charging and discharging power of the electric vehicle charging station c, and They are the reserved spare capacity for energy storage e and electric vehicle charging station c respectively; and are the clearing prices of the electric energy market and the reserve ancillary service market, is the incentive price for the virtual power plant to provide backup services, It is the bid price of the virtual power plant for buying and selling electricity in the electricity market. Bids for virtual power plants to provide reserve in the reserve ancillary services market; and The power purchased and sold by the virtual power plant in the electric energy market and the reserve capacity provided in the reserve ancillary services market respectively; and is the unit cost coefficient; and They are energy storage charging and discharging efficiency; and are the equivalent charging and discharging efficiencies of electric vehicle charging stations respectively; Δt is the unit time interval;

[0066] In the embodiment of the present invention, the decision variables in the first stage include:

[0067]

[0068] in, and They are the output power of distributed new energy q and the reserved spare capacity; and are the charging and discharging power of energy storage e respectively; and are the equivalent charging and discharging power of the electric vehicle charging station c, and They are the reserved spare capacity for energy storage e and electric vehicle charging station c respectively; and are the clearing prices of the electric energy market and the reserve ancillary service market, is the incentive price for the virtual power plant to provide backup services, It is the bid price of the virtual power plant for buying and selling electricity in the electricity market. Bids for virtual power plants to provide reserve in the reserve ancillary services market; and The power purchased and sold by the virtual power plant in the electric energy market and the reserve capacity provided in the reserve ancillary service market respectively.

[0069] Preferably, the objective function of the second stage is to minimize the market operation cost, which includes the income or cost generated by power generation enterprises and virtual power plants participating in the electric energy market to provide or purchase electric energy and the cost of participating in the reserve ancillary service market to provide reserve;

[0070] It should be noted that in the second stage, the electricity and reserve ancillary service markets are managed by independent system operators, and market members include power generation companies and virtual power plants, both of which can participate in the electricity and reserve ancillary service markets at the same time; independent system operators carry out joint clearing of the electricity and reserve ancillary service markets based on the quotations submitted by each market member with the goal of maximizing social welfare;

[0071] In the embodiment of the present invention, the objective function of the second stage is expressed as follows:

[0072]

[0073] in, and They are the bids of power generation enterprises participating in the electric energy market and the reserve ancillary service market respectively; and The power and spare capacity provided to power generation enterprises respectively;

[0074] In the embodiment of the present invention, the decision variables in the first stage include:

[0075]

[0076] in, and The power and spare capacity provided to power generation enterprises respectively; and They are the clearing prices for the electric energy market and the reserve ancillary service market respectively; and The power purchased and sold by the virtual power plant in the electric energy market and the reserve capacity provided in the reserve ancillary service market respectively.

[0077] It should be noted that the present invention constructs a two-stage optimization scheduling model for virtual power plants to participate in the joint market. In the first stage, the model realizes the minimization of the expected operating costs of virtual power plants participating in the electric energy and standby auxiliary market, effectively balances the charging and discharging power of distributed new energy, energy storage and electric vehicle charging stations and their reserved standby capacity, and optimizes the bidding and quotation strategy of virtual power plants in the electricity market; in the second stage, the model further pursues the minimization of market operating costs, and coordinates the trading behaviors of power generation enterprises and virtual power plants in the electric energy and standby auxiliary service markets through the joint clearing mechanism of independent system operators, ensuring the maximization of social welfare. The innovative two-stage optimization scheduling strategy of the present invention not only improves the economic benefits of virtual power plants, but also provides a strong guarantee for the stable operation of the electricity market and the efficiency of resource allocation.

[0078] S104: Determine the boundary conditions of the first stage and the second stage of the two-stage optimization scheduling model according to the objective function and the decision variables;

[0079] Preferably, the boundary conditions of the first stage include system reserve constraints, distributed new energy output and reserve constraints, electric vehicle charging station constraints, energy storage charging and discharging constraints, and market quotation constraints;

[0080] In the embodiment of the present invention, the boundary conditions of the first stage model are specifically as follows:

[0081] 1) System reserve constraints: The reserve capacity provided by the virtual power plant in the reserve ancillary service market is provided by distributed new energy, electric vehicle charging stations and energy storage, and the constraints are as follows:

[0082]

[0083] in, Reserve capacity provided to virtual power plants in the reserve ancillary services market; Reserve spare capacity for distributed renewable energy q; Reserved spare capacity for energy storage e; Reserved spare capacity for electric vehicle charging station c;

[0084] 2) Distributed renewable energy output and reserve capacity constraints: The distributed renewable energy active output and reserved reserve capacity should be within the permitted range, and the constraints are as follows:

[0085]

[0086] in, is the maximum output of distributed renewable energy q; and They are the output power of distributed new energy q and the reserved spare capacity respectively.

[0087] 3) Constraints on electric vehicle charging stations: Since the number of electric vehicles connected to the charging station at different times is random, the electric vehicle charging station can be regarded as a generalized energy storage device whose capacity varies with the on-grid and off-grid status of electric vehicles in the jurisdiction. Its charging and discharging power and equivalent electric energy are the Minkowski sum of the corresponding quantities of electric vehicles in the jurisdiction, expressed as:

[0088]

[0089] in, The parameter set for the charging station to be equivalent to a generalized energy storage device; and A 0-1 variable is introduced to avoid simultaneous charging and discharging of the charging station; The upper limit of charging and discharging power for electric vehicle charging stations; is the equivalent power of the charging station; It is the equivalent power change caused by the change of the electric vehicle's on-grid and off-grid status at the charging station; is the equivalent discharge power of the electric vehicle charging station c, and Reserved spare capacity for electric vehicle charging station c;

[0090] Calculate according to the following formula:

[0091]

[0092] in, It is a 0-1 variable, and 1 indicates and The time when electric vehicle n arrives at and leaves the charging station respectively; and are the initial power of electric vehicle n when it arrives at the charging station and the expected power when it leaves; The electric vehicles are gathered in the charging station c.

[0093] 4) Energy storage charging and discharging constraints: Energy storage constraints are similar to those of electric vehicle charging stations, except that their capacity is fixed, and their charging and discharging power and power constraints, as well as the discharge power and reserved backup constraints are as follows:

[0094]

[0095] in, is the upper limit of charge and discharge of energy storage e; and A 0-1 variable is introduced to avoid simultaneous charging and discharging of energy storage; The amount of energy stored; and The upper and lower limits of its power; and are the charging and discharging power of energy storage e respectively; Reserved spare capacity for energy storage e.

[0096] 5) Quotation constraints: In order to effectively curb the market power of virtual power plants, the market stipulates that market quotation limits should be set for spot electricity and ancillary service transactions, that is, the quotations of virtual power plants in the electricity and standby ancillary service markets should meet the following limit constraints:

[0097]

[0098] in, and The maximum bids allowed for virtual power plants in the energy and reserve ancillary service market are and They are the output power of distributed new energy q and the reserved spare capacity; It is the bid price of the virtual power plant for buying and selling electricity in the electricity market. Bids for virtual power plants to provide reserve in the reserve ancillary services market; and The power purchased and sold by the virtual power plant in the electric energy market and the reserve capacity provided in the reserve ancillary service market respectively;.

[0099] Preferably, the boundary conditions of the second stage include power generation enterprise operation constraints and virtual power plant operation constraints;

[0100] In this embodiment of the present invention, the boundary conditions of the second stage model are as follows:

[0101] 1) Operation constraints of power generation enterprises. The formula is as follows:

[0102]

[0103] in, and They are respectively the upper limit of power generation enterprises' output and the upper limit of their backup provision.

[0104] 2) The operating constraints of the virtual power plant are as follows:

[0105]

[0106] in, and The power purchased and sold by the virtual power plant in the electric energy market and the reserve capacity provided in the reserve ancillary services market respectively;

[0107] Preferably, based on the boundary conditions, a feasible solution range of the first stage and the second stage of the two-stage optimization scheduling model is determined;

[0108] It should be noted that the range of feasible solutions refers to all possible solutions that the model can solve while meeting the boundary conditions set at each stage, that is, the set of optimized dispatching strategies; these boundary conditions are designed to ensure that the dispatching plan meets both the physical limitations of the power system operation and the trading rules and economic requirements of the power market.

[0109] S106: Based on a typical scenario, solve a two-stage optimization scheduling model and obtain an optimization solution result;

[0110] Preferably, classic scenarios include:

[0111] Scenario 1: The virtual power plant only participates in the electricity market to buy and sell electricity, and obtains the price difference between the purchase and sale of electricity;

[0112] Scenario 2: The virtual power plant participates in electricity purchase and sale transactions to obtain the price difference between the purchase and sale of electricity and participates in the standby ancillary service market to provide standby services and obtain standby service fees.

[0113] Preferably, an improved particle swarm optimization algorithm is used to solve the two-stage optimization scheduling model, and the optimization scheduling result is output;

[0114] In the embodiment of the present invention, the two-stage optimization scheduling model is solved by combining the improved particle swarm optimization algorithm and the Yalmip optimization solver in Matlab software;

[0115] In the embodiment of the present invention, the output optimization scheduling result is the optimal operation strategy of the virtual power plant in different scenarios;

[0116] It should be noted that the present invention adopts a two-stage optimization scheduling model based on typical scenarios, and uses an improved particle swarm optimization algorithm and the Yalmip optimization solver in Matlab software to solve the problem. It can accurately output the optimal operation strategy of the virtual power plant under different scenarios, effectively improve the power purchase and sales efficiency and standby service income of the virtual power plant, and achieve optimal allocation of resources and maximization of economic benefits.

[0117] The above is a schematic scheme of a two-stage optimal scheduling method for a virtual power plant to participate in a joint market in this embodiment. It should be noted that the technical scheme of the system for the two-stage optimal scheduling of the virtual power plant to participate in the joint market and the technical scheme of the two-stage optimal scheduling method for the virtual power plant to participate in the joint market belong to the same concept. For details not described in detail in the technical scheme of the two-stage optimal scheduling system for the virtual power plant to participate in the joint market in this embodiment, please refer to the description of the technical scheme of the two-stage optimal scheduling method for the virtual power plant to participate in the joint market.

[0118] In this embodiment, the two-stage optimization dispatching system of the virtual power plant participating in the joint market includes:

[0119] A data acquisition module, used for acquiring first data;

[0120] A model building module, used to build a two-stage optimization scheduling model for virtual power plants to participate in the joint market based on the first data;

[0121] A model determination module is used to determine the objective function and decision variables of the first and second stages of the two-stage optimization scheduling model; based on the objective function and decision variables, the boundary conditions of the first and second stages of the two-stage optimization scheduling model are determined;

[0122] The model solving module is used to solve the two-stage optimization scheduling model based on typical scenarios and obtain the optimization solution results;

[0123] The model determination module specifically includes: the objective function of the first stage is to minimize the expected operating cost of the virtual power plant participating in the electric energy and reserve auxiliary market;

[0124] The objective function of the second stage is to minimize the market operation cost, which includes the revenue or cost generated by power generation enterprises and virtual power plants participating in the electric energy market to provide or purchase electric energy, and the cost of participating in the reserve ancillary service market to provide reserve;

[0125] The boundary conditions of the first stage include system reserve constraints, distributed new energy output and reserve constraints, electric vehicle charging station constraints, energy storage charging and discharging constraints, and market quotation constraints;

[0126] The boundary conditions in the second stage include the operating constraints of power generation enterprises and virtual power plants;

[0127] Based on the boundary conditions, the feasible solution ranges of the first stage and the second stage of the two-stage optimization scheduling model are determined.

[0128] The model solving module specifically includes: using an improved particle swarm optimization algorithm to solve the two-stage optimization scheduling model, and outputting the optimization scheduling result.

[0129] This embodiment further provides a computing device, which is applicable to a situation where a virtual power plant participates in a two-stage optimization dispatch in a joint market, including:

[0130] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the two-stage optimization scheduling method for realizing virtual power plants participating in the joint market as proposed in the above embodiment.

[0131] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the two-stage optimization scheduling method for realizing the participation of virtual power plants in a joint market as proposed in the above embodiment.

[0132] The storage medium proposed in this embodiment and the two-stage optimization scheduling method for realizing the participation of virtual power plants in the joint market proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0133] Through the above description of the implementation methods, the technicians in the field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0134] Example 2

[0135] Reference Figure 2-9 Tables 1-2 and 3 are an embodiment of the present invention, which provide a two-stage optimization scheduling method for a virtual power plant to participate in a joint market. In order to verify its beneficial effects, comparison results of two schemes are provided.

[0136] There are 3,000 households in a village, and the average installed capacity of distributed photovoltaics is 4kW. The scenario method is used to predict the output of distributed photovoltaics on a typical day in summer, and the expected output of 3,000 distributed photovoltaic groups in 24 time periods is output. Distributed photovoltaic systems generate almost no additional operation and maintenance costs, so this embodiment assumes that the unit electricity operation cost is set to 0.

[0137] (2) The village has a centralized energy storage power station with a power of 1MW and a capacity of 2.5MWh and an electric vehicle charging station. Assume that there are 3,000 electric private cars that need to be charged every day. The battery capacity of each car is 60kWh, the rated conventional charging power is 6.6kW, the initial state of charge follows the normal distribution N(0.2,0.4), the start charging time of the electric vehicle follows the normal distribution N(16,1), and the stop charging time of the electric vehicle follows the normal distribution (23,1). The unit loss costs of the electric vehicle charging pile and the energy storage equipment are 0.246 yuan / kWh and 0.211 yuan / kWh respectively, the charging and discharging efficiency is 95%, and the range of the state of charge of the energy storage equipment is 0.05 to 0.95.

[0138] (3) The virtual power plant operator aggregates all distributed photovoltaic systems, electric vehicle charging piles, centralized energy storage power stations and basic loads in the village to participate in the market. Three power generation companies are connected to the transmission grid, and the marginal cost of each power generation company is shown in Table 1. The incentive price for providing backup in the backup market is 0.01 yuan / kWh.

[0139] Table 1 Marginal cost and maximum and minimum output of each power generation enterprise

[0140]

[0141] Based on the method of the present invention, an objective function is constructed, decision variables are clarified, and model boundary conditions are determined according to the objective function and the decision variables;

[0142] Based on scenario 1 and scenario 2, the proposed model is solved by using the improved particle swarm optimization algorithm and the Yalmip optimization solver in Matlab software, and the expected operating costs and load balancing effects of virtual power plant operators under different scenarios are compared and analyzed. As shown in Table 2:

[0143] Table 2 Comparison of virtual power plant market participation results under two scenarios

[0144] Typical scenarios Virtual power plant dispatching cost (10,000 yuan) Power system load standard deviation (MW) Peak load (MW) Scene 1 36.57 43.92 69.35 Scene 2 30.54 39.38 64.78

[0145] As shown in Table 2, when the virtual power plant only participates in the electric energy market, the expected cost of dispatching internal distributed resources is 365,700 yuan;

[0146] The clearing price of the electricity market in scenario 1 and the optimal dispatch results of the virtual power plant participating in the market bidding are as follows: Figure 2 and Figure 3 As shown in the figure; in scenario 2, the clearing prices of the electric energy market and the reserve market are as follows Figure 4 As shown in the figure; the optimal dispatch result of the virtual power plant in the electricity market is as follows Figure 5 As shown in the figure; the supply and demand balance in the backup ancillary service market in scenario 2 is as follows Figure 6 As shown in the figure; the optimal dispatch result of the virtual power plant backup auxiliary service market in the bidding is as follows Figure 7 As shown; when the virtual power plant participates in the joint market for electric energy and reserve ancillary services, the expected cost of dispatching internal distributed resources is 305,400 yuan, which is a 16.49% reduction compared with scenario one.

[0147] In addition, the supply and demand balance in the electricity market in scenario 1 is as follows: Figure 8 The supply and demand balance of the electric energy market in scenario 2 is shown in Fig. 9 As shown, compared Figure 8 and Fig. 9It can be found that when the virtual power plant participates in the joint market, the load peak in the electric energy market is reduced, and the load peak-to-valley difference is reduced. The specific values ​​are as follows: When the virtual power plant participates in the electric energy market, the load peak is 69.35MW, and the load standard deviation of the power system is 43.92MW. When the virtual power plant participates in the electric energy and reserve joint market, by reasonably dispatching internal distributed resources, the load peak can be reduced from 69.35MW to 64.78MW, and the load standard deviation can be reduced from 43.92MW to 39.38MW, with a reduction ratio of 10.34%, which effectively reduces the fluctuations caused by photovoltaic power generation to the power system, making the load curve smoother, thereby improving the reliability of the power system.

[0148] In summary, the present invention provides a two-stage optimization scheduling method for a virtual power plant to participate in a joint market. In order to give full play to the flexibility value of virtual power plants, a two-stage optimization scheduling model for hybrid virtual power plants to participate in the electric energy and standby ancillary service markets is proposed. In the first stage, a distributed resource optimization scheduling model is established with the goal of minimizing the expected operating cost of the virtual power plant; in the second stage, after the bidding information of each market player is known, a joint clearing of the electric energy market and the standby ancillary service market is carried out. Simulation analysis shows that the proposed method can effectively reduce the scheduling cost of virtual power plants, improve their own economic benefits, and at the same time smooth out load fluctuations and improve the reliability of the power system.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A two-stage optimization scheduling method for virtual power plants participating in a joint market, characterized in that: include: Acquire first data, and construct a two-stage optimization scheduling model for virtual power plants to participate in a joint market based on the first data; Determine the objective function and decision variables of the first stage and the second stage of the two-stage optimization scheduling model; Determining the boundary conditions of the first stage and the second stage of the two-stage optimization scheduling model according to the objective function and the decision variables; Based on typical scenarios, the two-stage optimization scheduling model is solved to obtain the optimization solution results.

2. The two-stage optimization scheduling method for virtual power plants participating in the joint market according to claim 1 is characterized in that: The objective function of the first stage includes: The objective function of the first stage is to minimize the expected operating cost of the virtual power plant participating in the electricity energy and reserve auxiliary market.

3. The two-stage optimization scheduling method for virtual power plants participating in the joint market according to claim 2 is characterized in that: The objective function of the second stage includes: The objective function of the second stage is to minimize the market operating costs, which include the revenue or costs generated by power generation companies and virtual power plants participating in the electric energy market to provide or purchase electric energy and the costs of participating in the reserve ancillary service market to provide reserve.

4. The two-stage optimization scheduling method for virtual power plants participating in the joint market according to claim 3 is characterized in that: The boundary conditions of the first stage include system reserve constraints, distributed new energy output and reserve constraints, electric vehicle charging station constraints, energy storage charging and discharging constraints, and market quotation constraints; The boundary conditions of the second stage include the operating constraints of power generation enterprises and the operating constraints of virtual power plants.

5. The two-stage optimization scheduling method for virtual power plants participating in the joint market according to claim 4 is characterized in that: Based on the boundary conditions, the feasible solution ranges of the first stage and the second stage of the two-stage optimization scheduling model are determined.

6. The two-stage optimization scheduling method for virtual power plants participating in the joint market according to claim 5 is characterized in that: Classic scenes include: Scenario 1: The virtual power plant only participates in the electricity market to buy and sell electricity, and obtains the price difference between the purchase and sale of electricity; Scenario 2: The virtual power plant participates in electricity purchase and sale transactions to obtain the price difference between the purchase and sale of electricity and participates in the standby ancillary service market to provide standby services and obtain standby service fees.

7. The two-stage optimization scheduling method for virtual power plants participating in the joint market according to claim 6 is characterized in that: Solving the two-stage optimization scheduling model and obtaining the optimization solution results include: An improved particle swarm optimization algorithm is used to solve the two-stage optimization scheduling model and output the optimization scheduling result.

8. A system for two-stage optimal dispatch of virtual power plants participating in a joint market, characterized in that: include, A data acquisition module, used for acquiring first data; A model building module, used to build a two-stage optimization scheduling model for virtual power plants to participate in the joint market based on the first data; A model determination module, used to determine the objective function and decision variables of the first and second stages of the two-stage optimization scheduling model; and determine the boundary conditions of the first and second stages of the two-stage optimization scheduling model according to the objective function and decision variables; The model solving module is used to solve the two-stage optimization scheduling model based on typical scenarios and obtain optimization solution results.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the two-stage optimization scheduling method for the virtual power plant participating in the joint market as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the two-stage optimization scheduling method for a virtual power plant participating in a joint market as described in any one of claims 1 to 7.

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