Energy market energy utilization method and device based on different transaction targets

Abstract

The invention discloses an energy market energy utilization method and device based on different transaction objectives, and relates to the technical field of energy usage, and the method comprises the steps: building a game model framework between an LEMO and a plurality of VPPs through considering the interest pursuit and operation characteristics of a plurality of interest subjects, such as the LEMO and the VPPs; the method comprises the following steps: constructing a VPP bidding model of three different transaction targets based on a multi-VPP transaction model in a finite rationality local energy market; the method comprises the following steps of: selecting a most suitable transaction target through clustering resources of each virtual power plant and a historical bidding strategy of an opponent by considering the strong tendency and autonomy characteristics of each virtual power plant, and constructing a local energy market operator LEMO decision model; and a distributed iterative solution algorithm for protecting the privacy of each subject is developed to obtain the transaction electric quantity and the electricity price between the multiple virtual power plants and the local energy market dealer interest subject, and energy can be optimally utilized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and in particular to an energy utilization method and device in an energy market based on different transaction objectives. Background Art

[0002] With the integration of renewable energy sources (RESs) and electrical development, distributed generation (DG), energy storage (ESS) and responsive load (RL) are widely used in local energy markets (LEMs). The concept of virtual power plant (VPP) has attracted much attention in recent years, because VPP can effectively bring together DGs, ESSs and RLs to form a virtual cluster to participate in the operation of the power system. In actual game analysis, the decision of VPP will be affected by the information obtained, situation, psychology and other factors. In practice, it is unreasonable to describe all the behaviors of VPP with completely deterministic modeling. Due to the incompleteness of information and the unpredictability of the opponent's decision, the rationality of each VPP in decision-making is limited. VPP needs to consider the opponent's bidding strategy and its own cluster resources, and then adjust its own goals to improve the degree of rationality. Therefore, it is very necessary to deeply analyze the transactions between multiple VPPs, especially to consider the transaction behaviors of VPPs with different goals. VPPs with different goals have different transaction goals and transaction behaviors. These differences increase the uncertainty and delay of decision-making, which may lead to a decrease in market response speed and affect the overall resource utilization efficiency. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide an energy market energy utilization method and device based on different transaction objectives.

[0004] In a first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for utilizing energy in an energy market based on different transaction objectives, the method comprising the following steps:

[0005] Obtain the interests and operating characteristics of LEMO and VPP, and generate a game model between LEMO and multiple VPPs based on the interests and operating characteristics of LEMO and VPP;

[0006] Receive historical data, generate three VPP bidding models with different trading objectives based on a pre-established multi-VPP trading model in a local energy market with bounded rationality and combining three different trading objectives; input the historical data into the VPP bidding models with three different trading objectives, and output the optimal trading objective;

[0007] Based on the optimal transaction target, the system power balance and electricity price constraints are obtained, and the system power balance and electricity price constraints are input into the game model between LEMO and multiple VPPs, and the LEMO decision model of the local energy market operator is output;

[0008] Based on the electricity balance and the convergence characteristics of electricity prices, the LEMO decision model of local energy market operators is solved in combination with a distributed iterative solution algorithm, and the transaction electricity volume and electricity price between multiple virtual power plants and local energy market traders are output, thereby improving energy utilization.

[0009] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the three different trading objectives include a conservative trading objective, a stable trading objective, and an aggressive trading objective.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: the three VPP bidding models with different transaction objectives include a conservative VPP bidding model that aims to maximize its own economy, a stable VPP bidding model that aims to minimize fluctuations in renewable energy output and improve the stability of power supply within 24 hours, and an aggressive VPP bidding model that aims to maximize its own electricity purchase share.

[0011] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the conservative VPP model is as follows:

[0012] The operating costs within VPP include the costs of MT, ESS, and RL. The VPP bidding target is defined as follows:

[0013]

[0014]

[0015] In the formula, u I is the objective function of conservative VPP; P is the electricity purchase and sale price of VPP to LEMO; t VPP ,sell , P t VPP,buy Purchase and sale of electricity from LEMO for VPP; They are the operating costs of MT, ESS, and RL respectively; are the subsidy costs of PV and WT respectively; are the MT, ESS, RL, PV, and WT outputs of VPP i at time t, respectively. When discharging α i , βi , γ i is the MT unit cost coefficient; is the dispatch cost coefficient of ESS; is the load compensation electricity price; θ is the renewable energy subsidy electricity price, formula (1) is the objective function of the conservative VPP model, formula (2)-formula (4) are the dispatch cost functions of MT, ESS and RL respectively, and formula (5)-formula (6) are the subsidy cost functions of PV and WT respectively;

[0016] The constraints include: bidding restrictions, VPPs power balance constraints, MTs operation constraints, response load constraints, and energy storage system constraints.

[0017] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the stable VPP model is as follows:

[0018] The stable VPP minimizes the volatility of renewable energy output and smoothes the 24-hour power curve. Renewable energy includes WTs and PVs. The VPP bidding target is defined as follows:

[0019]

[0020]

[0021] In the formula, u II is the objective function of the stable VPP; Subsidies for “stable VPP”; is the combined effective output of WT, PV and ESS; α and β are the weighting coefficients of the objective function; ρ i,t is the grid-connected electricity price of WT, PV and ESS; ε is the subsidy price per unit output; f 0 is the fluctuation variance allowed by the system; f is the power fluctuation variance;

[0022] The constraints include: bidding restrictions, VPPs power balance constraints, MTs operation constraints, response load constraints, and energy storage system constraints.

[0023] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the VPP bidding target of the aggressive VPP model is defined as follows:

[0024]

[0025] In the formula, u III is the objective function of the aggressive VPP; P is the electricity purchase / sale price of LEMO to DSO; t LEMO,buy , P t LEMO,sellThe purchase / sale of electricity from LEMO to DSO;

[0026] The constraints include: bidding restrictions, VPPs power balance constraints, MTs operation constraints, response load constraints, and energy storage system constraints.

[0027] In combination with the first aspect, in some implementations of the first aspect, the method further includes: learning the historical transaction information of the historical data as follows:

[0028]

[0029] In the formula, ξ 0 is the opponent’s bidding strategy coefficient; N t is the total number of transactions.

[0030] After obtaining the opponent's bidding information, the judgment index coefficient of the VPP target is calculated as follows:

[0031]

[0032] In the formula, ξ 1 , 2 , 3 is the judgment index coefficient. lim1 , lim2 , lim3 is the index coefficient constant, which is determined by the historical bidding data of each VPP. 1 ≥ξ lim1 When ξ 2 ≤ξ lim2 When ξ 3 ≤ξ lim3 When selecting VPP, choose conservative VPP.

[0033] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the local energy market operator LEMO decision model is as follows:

[0034] LEMO seeks the best electricity buying and selling price strategy within LEM: and LEMO’s decision variable is the electricity price and After LEMO obtains the bidding rights for each VPP, it determines the selling price in a way that maximizes profits. and the bid price The model looks like this:

[0035] maxu LEMO =B VPP -C LEMO (26)

[0036]

[0037] In the formula, u LEMO is LEMO's profit; B VPP Profits from LEMO and VPPs transactions; C LEMO is the transaction cost between LEMO and DSO. From equations (26)-(28), we can see that LEMO’s transaction objects include the upper-end DSO and the lower-end VPP;

[0038] Set the following constraints:

[0039]

[0040] The electricity price restrictions are as follows:

[0041]

[0042] The electricity price and is fixed. From formula (30), we can see that the power purchase price of the distribution system is the highest, while the power sales price of the distribution system is the lowest. VPPs choose to trade with LEMO, and LEMO's profit is determined by the electricity price. and and with The exchange power is determined by

[0043] The feasible region of LEMO is determined by equations (29)-(30) and is denoted as Ω LEMO .

[0044] In combination with the first aspect, in some implementations of the first aspect, the method further includes: the process of solving the LEMO decision model of the local energy market operator based on the electricity balance and the convergence characteristics of electricity prices and in combination with the distributed iterative solution algorithm is as follows:

[0045] Initialize the bidding strategy of VPP for the entire scheduling period Iteration index g and convergence index δ, set the maximum number of iterations g max ;

[0046] VPP selects the most suitable transaction target through its own clustering resources and the historical bidding strategies of its competitors;

[0047] VPP publishes bidding strategy information to LEMO. After receiving the bidding information, LEMO solves its optimization problem to determine the optimal electricity buying and selling price.

[0048] After receiving LEMO’s decision, VPP once again announces its optimal bidding strategy by solving the optimization problem of its objectives.

[0049] definition

[0050]

[0051] In the formula, is the strategy of VPP i in the gth iteration;

[0052] when or g=g max When , the game reaches an equilibrium solution, otherwise set g = g + 1 and determine the optimal electricity trading price again.

[0053] In a second aspect, in order to achieve the above-mentioned object, the present invention discloses an energy market energy utilization device based on different transaction objectives, comprising:

[0054] A model generation module is used to obtain the interests and operating characteristics of LEMO and VPP, and generate a game model between LEMO and multiple VPPs based on the interests and operating characteristics of LEMO and VPP;

[0055] The target determination module is used to receive historical data, generate three VPP bidding models with different trading objectives based on a pre-established multi-VPP trading model in a local energy market with limited rationality and combining three different trading objectives; input the historical data into the VPP bidding models with three different trading objectives, and output the optimal trading objective;

[0056] The model optimization module is used to obtain the system power balance and electricity price constraints based on the optimal transaction target, input the system power balance and electricity price constraints into the game model between LEMO and multiple VPPs, and output the LEMO decision model of the local energy market operator;

[0057] The model solving module is used to solve the LEMO decision model of the local energy market operator based on the electricity balance and electricity price convergence characteristics, combined with the distributed iterative solving algorithm, and output the transaction electricity volume and electricity price between multiple virtual power plants and local energy market traders, thereby improving energy utilization.

[0058] Beneficial effects of the present invention:

[0059] The present invention provides diversified trading strategies for different types of VPPs by setting three typical trading targets. It promotes energy sharing between VPPs in LEM, can meet the preferences of different market participants, and allows multi-VPP transactions with different trading targets. Each VPP and LEMO can improve economic benefits by participating in LEM. As the scale of transactions expands, the economic benefits will be further improved, and ultimately the optimal utilization of energy will be achieved. Through intelligent scheduling and optimized resource allocation, VPP can maximize the use of renewable energy and reduce waste; through flexible scheduling of energy storage systems and response loads, VPP can reduce grid pressure during peak hours and improve the stability and reliability of local energy systems; VPPs with different targets can not only improve the resource utilization efficiency of the local energy market, but also increase the flexibility and reliability of the device, and ultimately achieve more efficient and sustainable energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0061] Figure 1 It is a schematic diagram of the process of the present invention;

[0062] Figure 2 This is a framework diagram of the game model between LEMO and multiple VPPs;

[0063] Figure 3 Different transaction target diagrams for VPP;

[0064] Figure 4 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Embodiment 1:

[0067] The following is an introduction to the relevant terms involved in the embodiments of the present application:

[0068] Power system: A unified whole consisting of power generation, power supply (transmission, transformation, distribution), power consumption facilities, and secondary facilities such as regulation and control, relay protection and safety automatic devices, metering devices, dispatching automation, power communications, etc. required to ensure their normal operation.

[0069] The power system is an electric energy production and consumption system composed of power plants, transmission and transformation lines, power supply and distribution stations, and power consumption. Its function is to convert primary energy in nature into electric energy through power generation devices, and then supply electric energy to various users through transmission, transformation and distribution. In order to achieve this function, the power system also has corresponding information and control systems at various links and levels to measure, regulate, control, protect, communicate and dispatch the production process of electric energy to ensure that users obtain safe and high-quality electric energy. The main structure of the power system includes power sources (power plants such as hydropower stations, thermal power plants, nuclear power plants, etc.), substations (boosting substations, load center substations, etc.), transmission and distribution lines and load centers. The power supply points are also connected to each other to realize the exchange and regulation of electric energy between different regions, thereby improving the safety and economy of power supply. The network composed of transmission lines and substations is usually called a power network. The information and control system of the power system consists of various detection equipment, communication equipment, safety protection devices, automatic control devices, and monitoring automation and dispatching automation systems. The structure of the power system should ensure the rational coordination of electricity production and consumption based on advanced technical equipment and high economic benefits.

[0070] like Figure 1 As shown, a method for utilizing energy in an energy market based on different transaction objectives comprises the following steps:

[0071] S101: Obtaining the interests and operating characteristics of LEMO and VPP, and generating a game model between LEMO and multiple VPPs based on the interests and operating characteristics of LEMO and VPP;

[0072] Considering the interests and operation characteristics of multiple stakeholders such as LEMO and VPP, a game model framework between LEMO and multiple VPPs is constructed. Under this framework, VPP integrates different types of distributed resources into a coordinated and operable entity to competitively participate in LEM; LEM is market-oriented, all participants submit their bidding information, and then LEMO determines a market clearing result with the optimal electricity price.

[0073] Virtual power plants (VPPs) consist of decentralized and heterogeneous distributed energy resources (DERs). VPPs can integrate DERs to participate in local energy markets (LEMs), which are operated by governments or third-party agencies to facilitate distributed energy transactions between nearby end users. As the proportion of electricity transactions between VPPs in LEMs increases, the flexibility demand pressure on distribution system operators (DSOs) will decrease.

[0074] The network physical structure of VPP can vary depending on how it is controlled, which creates a certain degree of flexibility. The VPP in this article includes micro turbines (MT), wind turbines (WT), photovoltaic generators (PV), energy storage systems, and end users (including responsive loads and non-responsive loads). These DERs are physically connected to the distribution network, and the VPP will trade with LEMO as an agent for these DERs. Therefore, VPPs are subject to market risks and need to optimize their dispatch strategies by considering operational constraints and system state predictions. In addition, VPPs need to sign contracts with RLs to specify caps, costs, and allowed load reduction hours.

[0075] As mentioned earlier, VPP aims to integrate different kinds of DERs into a coordinated and operable entity to competitively participate in LEM. Due to their low capacity and high volatility, it may be challenging for DERs to participate in LEM individually. However, DERs can be integrated into a coordinated VPP entity to provide reliable and economical energy supply. Compared with microgrids dominated by thermal power consumption, VPP aggregation not only improves economic efficiency but also contributes to the utilization of DERs in LEM. LEM is market-oriented, and all participants submit their bidding information, including their willingness to output and expected prices. Then, LEMO decides a market clearing result with the optimal electricity price. VPP needs to simulate the opponent's bidding strategy based on the opponent's historical data, and then adjust its own trading objectives ("conservative target", "stable target" or "aggressive target") to find the most appropriate model and bidding scheme. The game model framework between LEMO and multiple VPPs is as follows: Figure 2 shown.

[0076] S102: receiving historical data, generating three VPP bidding models with different trading objectives based on a pre-established multi-VPP trading model in a local energy market with bounded rationality and combining three different trading objectives; inputting the historical data into the VPP bidding models with three different trading objectives, and outputting the best trading objective;

[0077] The three different trading objectives include a conservative trading objective, a stable trading objective and an aggressive trading objective.

[0078] The three VPP bidding models with different transaction objectives include a conservative VPP bidding model that aims to maximize its own economy, a stable VPP bidding model that aims to minimize the fluctuation of renewable energy output and improve the stability of power supply within 24 hours, and an aggressive VPP bidding model that aims to maximize its own power purchase share.

[0079] The conservative VPP model is as follows:

[0080] The operating costs within VPP include the costs of MT, ESS, and RL. The VPP bidding target is defined as follows:

[0081]

[0082] In the formula, u I is the objective function of conservative VPP; P is the electricity purchase and sale price of VPP to LEMO; t VPP ,sell , P t VPP,buy Purchase and sale of electricity from LEMO for VPP; They are the operating costs of MT, ESS, and RL respectively; are the subsidy costs of PV and WT respectively; are the MT, ESS, RL, PV, and WT outputs of VPP i at time t, respectively. When discharging α i , β i , γ i is the MT unit cost coefficient; is the dispatch cost coefficient of ESS; is the load compensation electricity price; θ is the renewable energy subsidy electricity price, formula (1) is the objective function of the conservative VPP model, formula (2)-formula (4) are the dispatch cost functions of MT, ESS and RL respectively, and formula (5)-formula (6) are the subsidy cost functions of PV and WT respectively;

[0083] Constraints include:

[0084] ① Bidding restrictions:

[0085]

[0086]

[0087] In the formula, A cap on the amount of electricity that VPP can purchase from LEMO; The upper limit of the electricity that VPP can sell to LEMO.

[0088] ②VPPs power balance constraints:

[0089]

[0090] In the formula, is the unresponsive load consumption, the left side of equation (9) is the output power, and the right side is the consumed power.

[0091] ③MTs operation constraints:

[0092]

[0093] In the formula, is the lower / upper limit of MT output at time t; is the output of MT at time t-1; r d,i 、r u,i is the down / up ramp rate; ΔT is the length of the time interval.

[0094] ④Response load constraints:

[0095]

[0096] In the formula, is the maximum value of load that can be transferred / reduced at time t.

[0097] ⑤Energy storage system constraints:

[0098]

[0099] In the formula, The lower / upper limit value for ESS charging / discharging; is the charge state of ESS at time t; is the lower / upper limit of the ESS charge state at time t; is the charge state of ESS at the initial moment; is the charge state of ESS at time T. Equation (13) is the ESS charge / discharge power constraint, and equations (14)-(15) are the ESS capacity constraints.

[0100] The feasible region of VPPs is determined by equations (7)-(15) and is denoted by Ω VPP .

[0101] 2) Stable VPP model

[0102] "Stabilized VPP" hopes to minimize the volatility of renewable energy output and smooth the 24-hour power curve. Renewable energy includes WTs and PVs, and ESS is used to reduce the volatility of renewable energy. The VPP bidding target is defined as follows:

[0103]

[0104] In the formula, u II is the objective function of the stable VPP; Subsidies for “stable VPP”; is the combined effective output of WT, PV and ESS; α and β are the weighting coefficients of the objective function; ρ i,t is the grid-connected electricity price of WT, PV and ESS; ε is the subsidy price per unit output; f 0 is the fluctuation variance allowed by the system; f is the power fluctuation variance. The “stable VPP” balances the internal resources of the VPP through the objective function (16) to ensure the stability of renewable energy output. ESS is used to reduce the volatility of renewable energy. By optimizing the charging and discharging of ESS, the fluctuation of renewable energy output and the amount of wind / solar energy curtailment can also be reduced. Formula (19) represents the subsidy obtained by the “stable VPP” to ensure the stability of renewable energy output. The constraints of the stable VPP are shown in (7)-(15).

[0105] 3) Radical VPP model

[0106] Some VPPs want to maximize market share, especially in the early stages of market development. We call this type of VPP "aggressive VPP". The VPP bidding target is defined as follows:

[0107]

[0108] In the formula, u III is the objective function of the aggressive VPP; P is the electricity purchase / sale price of LEMO to DSO; t LEMO,buy , P t LEMO,sell It is the amount of electricity purchased / sold by LEMO to DSO.

[0109] The technical constraints of the aggressive VPP are the same as those of the conservative and stable VPPs, as shown in (7)-(15).

[0110] VPP needs to learn the opponent's historical strategy and use a judgment indicator system to select the best trading target from conservative targets, stable targets and aggressive targets based on the historical data obtained.

[0111] Due to the incompleteness of the information environment and the unpredictability of the opponent's decision-making, VPP needs to learn the opponent's historical strategies in previous rounds and adjust its own strategies accordingly. This is a continuous game process. As the game progresses, each VPP uses a judgment index coefficient to select the best trading target from conservative targets, stable targets, and aggressive targets based on the historical data obtained. First, VPP learns the opponent's historical trading information to improve its rationality. The historical trading information is learned as follows:

[0112]

[0113] In the formula, ξ 0 is the opponent’s bidding strategy coefficient; N t is the total number of transactions.

[0114] After obtaining the opponent's bidding information, the judgment index coefficient of the VPP target is calculated as follows:

[0115]

[0116] In the formula, ξ 1 , 2 , 3 is the judgment index coefficient. lim1 , lim2 , lim3 is the index coefficient constant, which is determined by the historical bidding data of each VPP. 1 ≥ξ lim1 When ξ 2 ≤ξ lim2 When ξ 3 ≤ξ lim3 Select Conservative VPP.

[0117] S103: obtaining system power balance and electricity price constraints based on the optimal transaction target, inputting the system power balance and electricity price constraints into the game model between LEMO and multiple VPPs, and outputting a LEMO decision model for the local energy market operator;

[0118] In order to promote energy trading among multiple VPPs and reduce power exchange with the distribution grid, a local energy market operator (LEMO) decision model was constructed.

[0119] The role of LEM is to facilitate energy transactions between multiple VPPs and reduce power exchanges with the distribution system. LEMO seeks the optimal power buying / selling price strategy within LEM ( and ), which is better than the traditional DSO price. This pricing strategy is acceptable to both LEMO and VPPs. The decision variable of LEMO is the price ( and ). After obtaining the bidding rights for each VPP, LEMO determines the selling price in a profit-maximizing manner. and the bid price The model looks like this:

[0120] maxu LEMO =B VPP -C LEMO (26)

[0121]

[0122] In the formula, u LEMO is LEMO's profit; B VPP Profits from LEMO and VPPs transactions; C LEMO is the transaction cost between LEMO and DSO. From equations (26)-(28), we can see that LEMO's transaction objects include the upper-end DSO and the lower-end VPP.

[0123] In order to ensure the power balance of the system, the following constraints need to be set:

[0124]

[0125] The electricity price should also be limited to a reasonable range to ensure that VPPs are willing to trade with LEMO, as follows:

[0126]

[0127] The electricity price and is fixed. From formula (30), we can see that the power purchase price of the distribution system is the highest, while the power selling price of the distribution system is the lowest. Therefore, VPPs will choose to trade with LEMO to ensure their own interests are maximized. LEMO's profit is determined by the electricity price ( and ) and with The exchange power is determined by

[0128] The feasible region of LEMO is determined by equations (29)-(30) and is denoted as Ω LEMO .

[0129] S104: Based on the electricity balance and electricity price convergence characteristics, the LEMO decision model of the local energy market operator is solved in combination with a distributed iterative solution algorithm, and the transaction electricity volume and electricity price between multiple virtual power plants and local energy market traders are output, thereby improving energy utilization.

[0130] Specifically, the solution of the present invention is further described below through an embodiment: a distributed iterative solution algorithm is developed to protect the privacy of each subject, and the transaction power and electricity price between multiple virtual power plants and local energy market traders are obtained. The iterative process is similar to a two-layer model including LEMO optimization and VPP scheduling. The iterative solution steps are as follows:

[0131] (1) Initialize the bidding strategy of VPP for the entire scheduling period Iteration index g and convergence index δ, set the maximum number of iterations g max .

[0132] (2) VPP selects the most appropriate transaction target based on its own clustering resources and the historical bidding strategies of its competitors.

[0133] (3) VPP publishes bidding strategy information to LEMO. After receiving the bidding information, LEMO solves its optimization problem to determine the optimal electricity buying and selling price.

[0134] (4) After receiving LEMO’s decision, VPP solves the optimization problem of its objectives and announces its optimal bidding strategy again.

[0135] (5) Definition

[0136]

[0137] In the formula, is the strategy of VPP i in the gth iteration.

[0138] when or g=g max When , the game reaches an equilibrium solution, otherwise set g = g + 1 and return to step (3).

[0139] Embodiment 2: In the second aspect, as Figure 4 As shown, in order to achieve the above-mentioned purpose, the present invention discloses an energy market energy utilization device based on different transaction objectives, including:

[0140] A model generation module 11 is used to obtain the interests and operation characteristics of LEMO and VPP, and generate a game model between LEMO and multiple VPPs based on the interests and operation characteristics of LEMO and VPP;

[0141] The target determination module 12 is used to receive historical data, generate three VPP bidding models with different trading objectives based on a pre-established multi-VPP trading model in a local energy market with limited rationality and in combination with three different trading objectives; input the historical data into the VPP bidding models with three different trading objectives, and output the optimal trading objective;

[0142] The model optimization module 13 is used to obtain the system power balance and electricity price constraints based on the optimal transaction target, input the system power balance and electricity price constraints into the game model between LEMO and multiple VPPs, and output the LEMO decision model of the local energy market operator;

[0143] The model solving module 14 is used to solve the LEMO decision model of the local energy market operator based on the electricity balance and the convergence characteristics of electricity prices, and in combination with a distributed iterative solving algorithm, and output the transaction electricity volume and electricity price between multiple virtual power plants and the stakeholders of the local energy market traders, thereby improving energy utilization.

[0144] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0145] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0146] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0147] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A method for utilizing energy in an energy market based on different transaction objectives, characterized in that: The method comprises the following steps: Obtain the interests and operating characteristics of LEMO and VPP, and generate a game model between LEMO and multiple VPPs based on the interests and operating characteristics of LEMO and VPP; Receive historical data, generate three VPP bidding models with different trading objectives based on a pre-established multi-VPP trading model in a local energy market with bounded rationality and combining three different trading objectives; input the historical data into the VPP bidding models with three different trading objectives, and output the optimal trading objective; Based on the optimal transaction target, the system power balance and electricity price constraints are obtained, and the system power balance and electricity price constraints are input into the game model between LEMO and multiple VPPs, and the LEMO decision model of the local energy market operator is output; Based on the electricity balance and the convergence characteristics of electricity prices, the LEMO decision model of local energy market operators is solved in combination with a distributed iterative solution algorithm, and the transaction electricity volume and electricity price between multiple virtual power plants and local energy market traders are output, thereby improving energy utilization.

2. The energy utilization method of energy market based on different transaction objectives according to claim 1, characterized in that: The three different trading objectives include a conservative trading objective, a stable trading objective and an aggressive trading objective.

3. The energy utilization method of energy market based on different transaction objectives according to claim 1, characterized in that: The three VPP bidding models with different transaction objectives include a conservative VPP bidding model that aims to maximize its own economy, a stable VPP bidding model that aims to minimize the fluctuation of renewable energy output and improve the stability of power supply within 24 hours, and an aggressive VPP bidding model that aims to maximize its own power purchase share.

4. The energy utilization method of energy market based on different transaction objectives according to claim 3, characterized in that: The conservative VPP model is as follows: The operating costs within VPP include the costs of MT, ESS, and RL. The VPP bidding target is defined as follows: In the formula, u I is the objective function of conservative VPP; The price of electricity purchased and sold by VPP to LEMO; Purchase and sale of electricity from LEMO for VPP; They are the operating costs of MT, ESS, and RL respectively; are the subsidy costs of PV and WT respectively; are the MT, ESS, RL, PV, and WT outputs of VPP i at time t, respectively. When discharging α i , β i , γ i is the MT unit cost coefficient; is the dispatch cost coefficient of ESS; is the load compensation electricity price; θ is the renewable energy subsidy electricity price, formula (1) is the objective function of the conservative VPP model, formula (2)-formula (4) are the dispatch cost functions of MT, ESS and RL respectively, and formula (5)-formula (6) are the subsidy cost functions of PV and WT respectively; The constraints include: bidding restrictions, VPPs power balance constraints, MTs operation constraints, response load constraints, and energy storage system constraints.

5. The energy utilization method of energy market based on different transaction objectives according to claim 4, characterized in that: The stable VPP model is as follows: The stable VPP minimizes the volatility of renewable energy output and smoothes the 24-hour power curve. Renewable energy includes WTs and PVs. The VPP bidding target is defined as follows: In the formula, u II is the objective function of the stable VPP; Subsidy for "stable VPP"; is the combined effective output of WT, PV and ESS; α and β are the weighting coefficients of the objective function; ρ i,t is the grid-connected electricity price of WT, PV and ESS; ε is the subsidy price per unit output; f0 is the fluctuation variance allowed by the system; f is the power fluctuation variance; The constraints include: bidding restrictions, VPPs power balance constraints, MTs operation constraints, response load constraints, and energy storage system constraints.

6. The energy utilization method of energy market based on different transaction objectives according to claim 5, characterized in that: The VPP bidding target of the aggressive VPP model is defined as follows: In the formula, u III is the objective function of the aggressive VPP; The price of electricity purchased / sold by LEMO to DSO; The purchase / sale of electricity from LEMO to DSO; The constraints include: bidding restrictions, VPPs power balance constraints, MTs operation constraints, response load constraints, and energy storage system constraints.

7. The energy utilization method of energy market based on different transaction objectives according to claim 1, characterized in that: The historical transaction information of the historical data is learned as follows: Where ξ0 is the opponent’s bidding strategy coefficient; N t is the total number of transactions; After obtaining the opponent's bidding information, the judgment index coefficient of the VPP target is calculated as follows: In the formula, ξ1, ξ2, ξ3 are the judgment index coefficients; lim1 , lim2 , lim3 is the index coefficient constant, which is determined by the historical bidding data of each VPP. lim1 When ξ2≤ξ lim2 When ξ3≤ξ lim3 When selecting VPP, choose conservative VPP.

8. The energy utilization method of energy market based on different transaction objectives according to claim 1, characterized in that: The local energy market operator LEMO decision model is as follows: LEMO seeks the best electricity buying and selling price strategy within LEM: and LEMO's decision variable is the electricity price and After LEMO obtains the bidding rights for each VPP, it determines the selling price in a way that maximizes profits. and the bid price The model looks like this: max LEMO =B VPP -C LEMO (26) In the formula, u LEMO is LEMO's profit; B VPP Profits from LEMO and VPPs transactions; C LEMO is the transaction cost between LEMO and DSO. From equations (26)-(28), we can see that LEMO’s transaction objects include the upper-end DSO and the lower-end VPP; Set the following constraints: The electricity price restrictions are as follows: The electricity price and is fixed. From formula (30), we can see that the power purchase price of the distribution system is the highest, while the power sales price of the distribution system is the lowest. VPPs choose to trade with LEMO, and LEMO's profit is determined by the electricity price. and and with The exchange power is determined by The feasible region of LEMO is determined by equations (29)-(30) and is denoted as Ω LEMO .

9. The energy utilization method of energy market based on different transaction objectives according to claim 1, characterized in that: The process of solving the LEMO decision model of the local energy market operator based on the electricity balance and the convergence characteristics of electricity prices and combined with the distributed iterative solution algorithm is as follows: Initialize the bidding strategy of VPP for the entire scheduling period Iteration index g and convergence index δ, set the maximum number of iterations g max ; VPP selects the most suitable transaction target through its own clustering resources and the historical bidding strategies of its competitors; VPP publishes bidding strategy information to LEMO. After receiving the bidding information, LEMO solves its optimization problem to determine the optimal electricity buying and selling price. After receiving LEMO’s decision, VPP once again announces its optimal bidding strategy by solving the optimization problem of its objectives. definition In the formula, is the strategy of VPP i in the gth iteration; when or g=g max When , the game reaches an equilibrium solution, otherwise set g = g + 1 and determine the optimal electricity trading price again.

10. An energy market energy utilization device based on different transaction objectives, characterized in that: include: A model generation module is used to obtain the interests and operating characteristics of LEMO and VPP, and generate a game model between LEMO and multiple VPPs based on the interests and operating characteristics of LEMO and VPP; The target determination module is used to receive historical data, generate three VPP bidding models with different trading objectives based on a pre-established multi-VPP trading model in a local energy market with limited rationality and combining three different trading objectives; input the historical data into the VPP bidding models with three different trading objectives, and output the optimal trading objective; The model optimization module is used to obtain the system power balance and electricity price constraints based on the optimal transaction target, input the system power balance and electricity price constraints into the game model between LEMO and multiple VPPs, and output the LEMO decision model of the local energy market operator; The model solving module is used to solve the LEMO decision model of the local energy market operator based on the electricity balance and electricity price convergence characteristics, combined with the distributed iterative solving algorithm, and output the transaction electricity volume and electricity price between multiple virtual power plants and local energy market traders, thereby improving energy utilization.

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