Multi-virtual power plant electricity-to-gas transaction method containing wind and light energy storage and demand side response

Abstract

The invention discloses a multi-virtual power plant power-to-gas transaction method containing wind and light energy storage and demand side response, and belongs to the field of power system optimization scheduling. The multi-virtual power plant power-to-gas transaction method containing wind and light energy storage and demand side response comprises the following steps: establishing a wind power and photovoltaic output model according to the characteristic of uncertainty of wind and light output; based on the price signal and an excitation mechanism, excitation and regulation are carried out on the user side load, and a demand side response model is established; constructing a multi-virtual power plant model on the basis of considering energy storage and power balance; constructing a target function of the economic dispatching model of the power system by taking the minimum cost in the dispatching period as a target; quantizing the power-to-gas transaction risk of the multiple virtual power plants by using a CvaR method; and establishing a multi-virtual power plant power-to-gas transaction model containing wind and light energy storage and demand side response, and solving the model to obtain a transaction scheme.

Description

Technical Field

[0001] The present invention belongs to the field of power system optimization and dispatching, and specifically relates to a multi-virtual power plant power-to-gas trading method including wind and solar energy storage and demand-side response. Background Art

[0002] With the rapid development of renewable energy, the proportion of distributed energy such as wind power and photovoltaic power generation in the energy structure is increasing. These energy sources are clean and low-carbon, but they also have problems of intermittency and uncertainty, which bring challenges to the stable operation of the power grid and the dispatch of the power market. In order to solve these problems, the virtual power plant (VPP) technology came into being. It integrates decentralized power generation resources, energy storage equipment and controllable loads through advanced information and communication technology, control technology and energy management technology to achieve unified dispatch and management.

[0003] The core advantage of virtual power plants lies in their flexibility and adjustability. They can dynamically adjust the operating status of internal resources and optimize the matching of power generation and load according to the needs of the power grid and market price signals. In addition, virtual power plants can also participate in power market transactions and maximize economic benefits by providing power, backup services, and participating in demand response.

[0004] However, the optimal scheduling of virtual power plants is a complex decision-making problem that requires consideration of multiple uncertainties, such as the randomness of wind speed and photovoltaic radiation, the volatility of load demand, etc. These uncertainties bring risks to the operation of virtual power plants. Therefore, how to reasonably evaluate and manage these risks is one of the key issues in the optimization of virtual power plant scheduling. Summary of the invention

[0005] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a multi-virtual power plant power-to-gas trading method including wind and solar energy storage and demand-side response, which solves the problems in the prior art.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A multi-virtual power plant power-to-gas trading method including wind, solar, energy storage and demand-side response includes the following steps:

[0008] According to the uncertainty of wind and solar power output, wind and photovoltaic output models are established;

[0009] Based on price signals and incentive mechanisms, the user-side load is stimulated and regulated, and a demand-side response model is established;

[0010] Based on the wind and photovoltaic output models, a multi-virtual power plant model is constructed taking into account energy storage and power balance;

[0011] Based on the multi-virtual power plant model, an objective function of the power system economic dispatch model is constructed with the goal of minimizing the cost within the dispatch cycle;

[0012] Based on the power system economic dispatch model, the CvaR method is used to quantify the risk of power-to-gas transactions of multiple virtual power plants;

[0013] According to the objective functions of the wind and photovoltaic output models, demand-side response model, multi-virtual power plant model, power system economic dispatch model and the quantified multi-virtual power plant power-to-gas trading risks, a multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is established, and the model is solved to obtain a trading plan.

[0014] Furthermore, the wind power output model is:

[0015]

[0016] Where: is the wind force output at time t; v t is the natural wind speed at time t; v in and v out are the cut-in wind speed and cut-out wind speed, v r is the rated wind speed; g r is the rated output power; the natural wind is described by the Weibull function:

[0017]

[0018] Where: v is wind speed; is the shape parameter; g is the scale parameter; f(ν) represents the probability density function;

[0019] The photovoltaic output model is:

[0020]

[0021] Where: is the output of the photovoltaic generator set at time t; η PV is the photovoltaic panel photoelectric conversion efficiency; S PV is the photovoltaic panel area; θ i,t is the photovoltaic radiation intensity of the i-th unit at time t; the photovoltaic radiation intensity is described by the Beta function:

[0022]

[0023]

[0024]

[0025] Where: ξ, ψ are the shape parameters of the Beta function; μ and σ are the mean and standard deviation of the photovoltaic radiation intensity.

[0026] Furthermore, the demand-side response model is:

[0027]

[0028]

[0029]

[0030]

[0031] Where: is the reserve output of the i-th responsive load at time t; is the load reduction of the ith responsive load at time t; and are the backup costs of the i-th responsive load at time t, respectively; Costs incurred to compensate for responsive loads; and are the reserve outputs of the i-th responsive load at time t, respectively; is the developable load reduction of the i-th DRP at time t and step j; is the minimum acceptable load reduction of the ith DRP at step j; is the load reduction of the ith DRP at time t; is the load reduction of the i-th DRP at time t and step j.

[0032] Furthermore, the multi-virtual power plant model includes:

[0033] 1) Fuel cell constraints

[0034]

[0035]

[0036]

[0037]

[0038] Where: is the power generation of the fuel cell in VPPi; and are the minimum and maximum output power of the fuel cell in VPPi, respectively; Spare capacity provided for the fuel cells within the VPPi; and are the lower and upper limits of the fuel cell reserve capacity within VPPi, respectively; and are the upward and downward ramp rates of the fuel cell in VPPi, respectively;

[0039] 2) Energy storage constraints

[0040]

[0041] Where: is the maximum single charge and discharge power of the i-th energy storage device; are binary variables, representing the charging and discharging state parameters of the i-th energy storage device during period t. Indicates that it is in charging state; is the final output power of the nth energy storage device during period t; S i (t) is the capacity of the i-th energy storage device during period t; is the rated capacity of the i-th energy storage device; are the upper and lower limits of the capacity of the i-th energy storage device respectively;

[0042] 3) Balance constraints of VPPi:

[0043]

[0044]

[0045] In the formula, is the output of the energy storage element in VPPi; and are the amount of electricity purchased and sold by VPPi from the electricity market, respectively; is the power consumption of demand-side response; is the reserve demand of VPPi; and are the reserve capacity purchased and sold by VPPi from the reserve market, respectively.

[0046] Furthermore, the objective function of the power system economic dispatch model is:

[0047]

[0048]

[0049] Where: s is the photovoltaic output scenario; t is the trading period; S is the total number of photovoltaic output scenarios; T is the total number of trading periods; ρ s is the probability of PV output scenario s; ρ w is the probability of wind power output scenario w; are the transaction costs of VPPi power and reserve markets in period t, respectively; is the energy storage cost within VPPi; Costs incurred to compensate for responsive loads; is the fuel cell operating cost in VPPi; C i,t is the power-to-gas transaction cost of VPPi; and are the prices for purchasing and selling electricity in the electricity market, and VPP i The amount of electricity purchased and sold from the electricity market; and are the purchase and sale prices of the reserve services in the reserve market, respectively; and VPP i reserve capacity purchased and sold from the reserve market; and VPP i Dissipation coefficient of charging and discharging of internal energy storage; and VPP i The charge and discharge capacity of internal energy storage; For VPP i Unit power generation cost of internal fuel cells; For VPP i The power generated by the fuel cell; C i,t For VPP i The power-to-gas transaction cost; ij ,d ij , is the bilateral transaction coefficient, which reflects the product difference; P i,j,t , R i,j,t VPP i and VPP j The amount of electricity and spare transactions between i The total amount of VPPs involved in the transaction.

[0050] Furthermore, the calculation formula for quantifying the risk of power-to-gas transactions of multiple virtual power plants using the CvaR method is:

[0051]

[0052]

[0053] Where: i is the CVaR value of VPPi cost; i is the VaR value of VPPi cost; i,s is the value by which VPPi costs exceed VaR; ρ s is the probability density function of the random variable.

[0054] Furthermore, the multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is:

[0055]

[0056] In the formula, C all is the overall total cost of multiple VPPs based on CVaR; L is the risk preference coefficient, which represents the investor’s attitude toward risk, and its value range is [0, 1].

[0057] A multi-virtual power plant power-to-gas trading system with wind, solar, energy storage and demand-side response, including:

[0058] Output model building module: According to the uncertainty characteristics of wind and solar output, wind and photovoltaic output models are established;

[0059] Demand response model solving module: Incentivize and regulate user-side loads based on price signals and incentive mechanisms, and establish a demand-side response model;

[0060] Virtual power plant model building module: based on the wind power and photovoltaic output models, multiple virtual power plant models are built on the basis of considering energy storage and power balance;

[0061] Objective function construction module: based on the multi-virtual power plant model, the objective function of the power system economic dispatch model is constructed with the minimum cost within the dispatch cycle as the goal;

[0062] Transaction risk quantification module: Based on the power system economic dispatch model, the CvaR method is used to quantify the power-to-gas transaction risks of multiple virtual power plants;

[0063] Trading model construction and solution module: Based on the objective functions of the wind and photovoltaic output models, demand-side response model, multi-virtual power plant model, power system economic dispatch model and the quantified multi-virtual power plant power-to-gas trading risks, a multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is established, and the model is solved to obtain a trading plan.

[0064] A computer storage medium stores a readable program, which, when running, can execute the above-mentioned multi-virtual power plant power-to-gas trading method including wind and solar energy storage and demand-side response.

[0065] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0066] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned multi-virtual power plant power-to-gas trading method including wind, solar, energy storage and demand-side response.

[0067] Beneficial effects of the present invention:

[0068] The present invention realizes the effective integration and management of wind, photovoltaic, energy storage and demand-side response by establishing wind and photovoltaic output models and a demand-side response model, and combining them with a multi-virtual power plant model; by constructing an economic dispatch model for the power system and using the CvaR method to quantify transaction risks, the dual goals of cost minimization and risk control are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0070] Figure 1 is the actual load demand diagram of the VPP in Example 2 of the present invention;

[0071] Figure 2 It is the main market transaction price chart in Example 2 of the present invention;

[0072] Figure 3 This is a wind power output scene diagram in Embodiment 2 of the present invention;

[0073] Figure 4 This is a photovoltaic output scene diagram in Example 2 of the present invention;

[0074] Figure 5 It is the electric energy transaction volume diagram in Example 2 of the present invention;

[0075] Figure 6 It is the overall transaction block diagram of the present invention. DETAILED DESCRIPTION

[0076] 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.

[0077] Example 1

[0078] like Figure 6 As shown, a multi-virtual power plant power-to-gas trading method including wind, solar, energy storage and demand-side response includes the following steps:

[0079] S1, based on the uncertainty of wind and solar power output, establish wind and photovoltaic output models;

[0080] 1) Wind power output model

[0081] The uncertainty of wind power output is caused by the uncertainty of wind speed. The natural wind can be described by Weibull function:

[0082]

[0083] Where: v is wind speed; is the shape parameter; g is the scale parameter; f(ν) represents the probability density function.

[0084] According to the wind speed distribution function in the formula, the wind power output model expression can be obtained:

[0085]

[0086] Where: is the wind force output at time t; v t is the natural wind speed at time t; v in and v out are the cut-in wind speed and cut-out wind speed, v r is the rated wind speed; g r is the rated output power.

[0087] 2) Photovoltaic output model

[0088] Photovoltaic power generation depends on the intensity of photovoltaic radiation, which can be described by the Beta function:

[0089]

[0090] Where: ξ, ψ are the shape parameters of the Beta function; let μ and σ be the mean and standard deviation of the photovoltaic radiation intensity, then

[0091]

[0092]

[0093] Photovoltaic power generation output can be determined by the area of ​​solar photovoltaic panels and the efficiency of photovoltaic power conversion. The photovoltaic output model expression is as follows:

[0094]

[0095] Where: is the output of the photovoltaic generator set at time t; η PV is the photovoltaic panel photoelectric conversion efficiency; S PV is the photovoltaic panel area; θ i,t is the photovoltaic radiation intensity of the i-th unit at time t.

[0096] S2, based on price signals and incentive mechanisms, stimulate and regulate user-side loads and establish a demand-side response model;

[0097] IBDR (Incentive-Based Demand Response) load implementation incentive compensation policy promotes user participation in system scheduling, which is mainly implemented by demand response providers (DRPs). The IBDR implementation cost model (i.e., the demand-side response model) is as follows:

[0098]

[0099] Where: is the reserve output of the i-th responsive load at time t; is the load reduction of the ith responsive load at time t; and are the backup costs of the i-th responsive load at time t, respectively; Costs incurred to compensate for responsive loads; and are the downward and upward reserve outputs of the i-th responsive load at time t respectively.

[0100] At the same time, IBDR must also meet the maximum reduction constraint. At the same time, if the equivalent output generated by IBDR is regarded as a "virtual generator set", the start-stop state constraint must be met:

[0101]

[0102]

[0103]

[0104] Where: is the developable load reduction of the i-th DRP at time t and step j; is the minimum acceptable load reduction of the ith DRP at step j; is the load reduction of the ith DRP at time t; is the load reduction of the i-th DRP at time t and step j.

[0105] S3, based on the wind and photovoltaic output models constructed in S1, builds a multi-virtual power plant model taking into account energy storage and power balance;

[0106] The multi-virtual power plant model includes: fuel cell constraints, energy storage constraints and balancing constraints of VPPi (the i-th virtual power plant);

[0107] 1) Fuel cell constraints

[0108]

[0109]

[0110]

[0111]

[0112] Where: and are the minimum and maximum output power of the fuel cell in VPPi, respectively; Spare capacity provided for the fuel cells within the VPPi; and are the lower and upper limits of the fuel cell reserve capacity within VPPi, respectively; and are the upward and downward ramp rates of the fuel cell in VPPi, respectively.

[0113] 2) Energy storage constraints

[0114]

[0115] Where: is the maximum single charge and discharge power of the i-th energy storage device; are binary variables, representing the charging and discharging state parameters of the i-th energy storage device during period t. Indicates that it is in charging state; is the final output power of the nth energy storage device during period t; S i (t) is the capacity of the i-th energy storage device during period t; is the rated capacity of the i-th energy storage device; are the upper and lower limits of the capacity of the i-th energy storage device respectively.

[0116] 3) The balance constraints of VPPi include power and reserve balance constraints, which are expressed as:

[0117]

[0118]

[0119] In the formula, is the output of the energy storage element in VPPi; and are the amount of electricity purchased and sold by VPPi from the electricity market, respectively. is the power consumption of demand-side response; is the reserve requirement of VPPi. and are the reserve capacity purchased and sold by VPPi from the reserve market, respectively.

[0120] S4, based on the multi-virtual power plant model constructed in S3, takes the minimum cost within the dispatch cycle as the goal and constructs the objective function of the power system economic dispatch model;

[0121] The virtual power plant includes distributed photovoltaic, distributed wind power, fuel cells, chemical energy storage, and responsive loads. For the i-th virtual power plant (VPPi), this study aims to achieve the optimization goal of minimizing cost. Specifically, the optimization problem aims to minimize the cost of the entire virtual power plant system by reasonably configuring and scheduling the above-mentioned distributed energy resources and demand responsive loads. The objective function of the power system economic dispatch model is:

[0122]

[0123] Where: s is the photovoltaic output scenario; t is the trading period; S is the total number of photovoltaic output scenarios; T is the total number of trading periods;

[0124] ρ s is the probability of PV output scenario s; ρ w is the probability of wind power output scenario w; are the transaction costs of VPPi power and reserve markets in period t, respectively; is the energy storage cost within VPPi; Costs incurred to compensate for responsive loads; is the fuel cell operating cost in VPPi; C i,t is the power-to-gas transaction cost of VPPi.

[0125] The specific calculation formulas for each cost are as follows:

[0126] 1) Electricity market transaction costs:

[0127]

[0128] Where: and are the prices for purchasing and selling electricity in the electricity market, and VPP i The amount of electricity purchased and sold from the electricity market.

[0129] 2) Alternative market transaction costs:

[0130]

[0131] Where: and are the purchase and sale prices of the reserve services in the reserve market, respectively; and VPP iReserve capacity purchased and sold from the reserve market.

[0132] 3) Energy storage cost:

[0133]

[0134] Where: and VPP i Dissipation coefficient of charging and discharging of internal energy storage; and VPP i The charging and discharging amount of internal energy storage.

[0135] 4) Fuel cell cost:

[0136]

[0137] Where: For VPP i Unit power generation cost of internal fuel cells; For VPP i The power generated by the fuel cell inside.

[0138] 5) Power-to-gas trading across multiple VPPs

[0139] This method takes into account the product differences in power-to-gas transactions (such as transmission distance, carbon emissions, etc.). In this case, the transaction cost of power-to-gas can be approximately expressed as a linear function of the transaction volume between VPPs:

[0140]

[0141] Where: C i,t For VPP i The power-to-gas transaction cost; ij d ij , is the bilateral transaction coefficient, which reflects the product difference; P i,j,t , R i,j,t VPP i and VPP j The amount of electricity and spare transactions between i The total amount of VPPs involved in the transaction.

[0142] S5, based on the power system economic dispatch model in S4, uses the CvaR method to quantify the risk of power-to-gas transactions of multiple virtual power plants and achieve a trade-off between VPP risks and benefits;

[0143] The value at risk (VaR) quantifies and analyzes risk characteristics by measuring the maximum possible loss of a portfolio within a specific period of time under normal market conditions and a given confidence level. However, it can only measure the risk situation under a certain confidence level and cannot consider the risk tail situation. The conditional value at risk (CVaR) can describe the risk distribution outside the confidence level. CVaR not only considers the potential maximum loss (VaR) at a given confidence level, but also measures the average loss exceeding this threshold, thereby providing more information about extreme losses. This means that CVaR can more comprehensively capture the risk exposure in extreme market conditions, while VaR only provides a loss threshold and there is no further quantification of losses exceeding this threshold. The calculation method is:

[0144]

[0145] Where: ξ is a specific confidence level; the function f(x, y) is not greater than the distribution function of the boundary value α; ρ(y) is the probability density function of the random variable y; V CVaR is the CvaR value under confidence level ξ.

[0146] In order to reduce the risks brought by the uncertainty of photovoltaic and wind power output in VPP, the CVaR method is used to achieve the trade-off between VPP risks and benefits. i , the CVaR value considering the uncertainty of photovoltaic output is expressed as follows:

[0147]

[0148]

[0149] Where: i is the CVaR value of VPPi cost; i is the VaR value of VPPi cost; i,s is the value by which VPPi costs exceed VaR; ρ s is the probability density function of the random variable, which specifically refers to wind power and photovoltaic power in this embodiment.

[0150] S6, based on the wind and photovoltaic output models constructed by S1, the demand-side response model constructed by S2, the multi-virtual power plant model constructed by S3, the objective function of the power system economic dispatch model constructed by S4, and the transaction risks quantified by S5, establish a multi-virtual power plant power-to-gas transaction model including wind, solar, energy storage and demand-side response, and solve the model to obtain a transaction plan.

[0151] The multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is:

[0152]

[0153] In the formula, C all is the overall total cost of multiple VPPs based on CVaR; L is the risk preference coefficient, which indicates the investor’s attitude toward risk, and its value range is [0, 1]; the larger the L value, the more risk-averse the VPP is.

[0154] The above model is a mixed integer linear programming (MILP) problem. By calling the CPLEX solver in the MATLAB2023b environment to directly solve it, the trading plan can be obtained.

[0155] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program, which, when the program is running, can execute the above-mentioned multi-virtual power plant power-to-gas trading method including wind and solar energy storage and demand-side response.

[0156] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0157] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned multi-virtual power plant power-to-gas trading method including wind, solar, energy storage and demand-side response.

[0158] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned multi-virtual power plant power-to-gas trading method including wind, solar, energy storage and demand-side response.

[0159] Example 2

[0160] In this embodiment, a simulation example consisting of four VPPs is used to verify the effectiveness of the proposed method.

[0161] Each VPP contains photovoltaic, wind power, energy storage, fuel cells and responsive loads. The scenario method is used to randomly generate ten groups of wind power output scenarios and photovoltaic output scenarios. Figure 3 and Figure 4 The electricity price and reserve price of VPP to the main network are shown as Figure 2 The actual load demand of VPP is as shown in Figure 1 As shown. In order to prevent VPP arbitrage, the purchase price of VPP in the main market is set to 1.5 times the selling price. In order to verify the beneficial effect of this method. In this embodiment, two trading schemes are set as follows:

[0162] Option 1: VPP conducts power-reserve transactions with the primary market.

[0163] Option 2: VPP conducts power-reserve transactions with the primary market and other VPPs.

[0164] The costs of the four VPPs participating in the power-reserve transaction are shown in Table 1.

[0165] Table 1 Parameters of thermal power units

[0166]

[0167] Compared with Scheme 2, although the operating cost and standby transaction cost of Scheme 1 increase slightly, the introduction of mutual transactions between VPPs has led to the electricity transaction volume being completely traded in the main market to traded between VPPs. Power-to-gas transactions provide VPPs with additional trading channels. When the power-to-gas transaction price between VPPs is lower than the price of purchasing products from the main market or higher than the price of selling products to the main market, each VPP will change its trading strategy and tend to conduct power-to-gas transactions to reduce transaction costs, ultimately reducing the total cost of the four VPPs. Therefore, considering VPP power-to-gas transactions can effectively reduce total costs.

[0168] Compared with Scheme 1 and Scheme 2, the impact of power-to-gas trading on main market trading is shown in Appendix Figure 5 As shown in the figure. Positive values ​​in the figure indicate that VPP purchases products from the main market, and negative values ​​indicate that VPP sells products to the main market. Scheme 1 purchases electricity from the main market during the period of low photovoltaic output (0:00-9:00, 18:00-24:00); and sells electricity to the grid during the period of high photovoltaic output (10:00-17:00). Under Scheme 2, VPP only purchases electricity from the main market and sells excess electricity through power-to-gas transactions from 10:00 to 17:00. In general, the amount of electricity and standby transactions between VPP and the main market has decreased, which shows that power-to-gas transactions help VPP realize resource sharing, promote local consumption of new energy, and alleviate the tension in power supply of the power grid.

[0169] Example 3

[0170] In this embodiment, a multi-virtual power plant power-to-gas trading system including wind, solar, energy storage and demand-side response is proposed, specifically including:

[0171] Output model building module: According to the uncertainty characteristics of wind and solar output, wind and photovoltaic output models are established;

[0172] Demand response model solving module: Incentivize and regulate user-side loads based on price signals and incentive mechanisms, and establish a demand-side response model;

[0173] Virtual power plant model building module: based on the wind power and photovoltaic output models, multiple virtual power plant models are built on the basis of considering energy storage and power balance;

[0174] Objective function construction module: based on the multi-virtual power plant model, the objective function of the power system economic dispatch model is constructed with the minimum cost within the dispatch cycle as the goal;

[0175] Transaction risk quantification module: Based on the power system economic dispatch model, the CvaR method is used to quantify the power-to-gas transaction risks of multiple virtual power plants;

[0176] Trading model construction and solution module: Based on the objective functions of the wind and photovoltaic output models, demand-side response model, multi-virtual power plant model, power system economic dispatch model and the quantified multi-virtual power plant power-to-gas trading risks, a multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is established, and the model is solved to obtain a trading plan.

[0177] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0178] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A multi-virtual power plant power-to-gas trading method including wind and solar energy storage and demand-side response, characterized in that: The following steps are involved: According to the uncertainty of wind and solar power output, wind and photovoltaic output models are established; Based on price signals and incentive mechanisms, the user-side load is stimulated and regulated, and a demand-side response model is established; Based on the wind and photovoltaic output models, a multi-virtual power plant model is constructed taking into account energy storage and power balance; Based on the multi-virtual power plant model, an objective function of the power system economic dispatch model is constructed with the goal of minimizing the cost within the dispatch cycle; Based on the power system economic dispatch model, the CvaR method is used to quantify the risk of power-to-gas transactions of multiple virtual power plants; According to the objective functions of the wind and photovoltaic output models, demand-side response model, multi-virtual power plant model, power system economic dispatch model and the quantified multi-virtual power plant power-to-gas trading risks, a multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is established, and the model is solved to obtain a trading plan.

2. The multi-virtual power plant power-to-gas trading method with wind and solar energy storage and demand-side response according to claim 1 is characterized in that: The wind power output model is: Where: is the wind force output at time t; v t is the natural wind speed at time t; v in and v out are the cut-in wind speed and cut-out wind speed, v r is the rated wind speed; g r is the rated output power; the natural wind is described by the Weibull function: Where: v is wind speed; is the shape parameter; g is the scale parameter; f(ν) represents the probability density function; The photovoltaic output model is: Where: The output of the photovoltaic generator set at time t; η PV is the photovoltaic panel photoelectric conversion efficiency; S PV is the photovoltaic panel area; θ i,t is the photovoltaic radiation intensity of the i-th unit at time t; the photovoltaic radiation intensity is described by the Beta function: Where: ξ, ψ are the shape parameters of the Beta function; μ and σ are the mean and standard deviation of the photovoltaic radiation intensity.

3. The multi-virtual power plant power-to-gas trading method with wind and solar energy storage and demand-side response according to claim 2 is characterized in that: The demand side response model is: Where: is the reserve output of the i-th responsive load at time t; is the load reduction of the ith responsive load at time t; and are the backup costs of the i-th responsive load at time t, respectively; Costs incurred to compensate for responsive loads; and are the reserve outputs of the i-th responsive load at time t, respectively; is the developable load reduction of the i-th DRP at time t and step j; is the minimum acceptable load reduction of the ith DRP at step j; is the load reduction of the ith DRP at time t; is the load reduction of the i-th DRP at time t and step j.

4. The multi-virtual power plant power-to-gas trading method with wind and solar energy storage and demand-side response according to claim 3 is characterized in that: The multi-virtual power plant model includes: 1) Fuel cell constraints Where: is the power generation of the fuel cell in VPPi; P i min and P i max are the minimum and maximum output power of the fuel cell in VPPi, respectively; Spare capacity provided for the fuel cells within the VPPi; and R i max are the lower and upper limits of the fuel cell reserve capacity within VPPi, respectively; and are the upward and downward ramp rates of the fuel cell in VPPi, respectively; 2) Energy storage constraints Where: P i max is the maximum single charge and discharge power of the i-th energy storage device; are binary variables, representing the charging and discharging state parameters of the i-th energy storage device during period t. Indicates that it is in charging state; is the final output power of the nth energy storage device during period t; S i (t) is the capacity of the i-th energy storage device during period t; is the rated capacity of the i-th energy storage device; are the upper and lower limits of the capacity of the i-th energy storage device respectively; 3) Balance constraints of VPPi: In the formula, is the output of the energy storage element in VPPi; and are the amount of electricity purchased and sold by VPPi from the electricity market, respectively; is the power consumption of demand-side response; is the reserve demand of VPPi; and are the reserve capacity purchased and sold by VPPi from the reserve market, respectively.

5. The multi-virtual power plant power-to-gas trading method with wind and solar energy storage and demand-side response according to claim 4 is characterized in that: The objective function of the power system economic dispatch model is: Where: s is the photovoltaic output scenario; t is the trading period; S is the total number of photovoltaic output scenarios; T is the total number of trading periods; ρ s is the probability of PV output scenario s; ρ w is the probability of wind power output scenario w; are the transaction costs of VPPi power and reserve markets in period t, respectively; is the energy storage cost within VPPi; Costs incurred to compensate for responsive loads; is the fuel cell operating cost in VPPi; C i,t is the power-to-gas transaction cost of VPPi; and are the prices for purchasing and selling electricity in the electricity market, and VPP i The amount of electricity purchased and sold from the electricity market; and are the purchase and sale prices of the reserve services in the reserve market, respectively; and VPP i reserve capacity purchased and sold from the reserve market; and VPP i Dissipation coefficient of charging and discharging of internal energy storage; and VPP i The charge and discharge capacity of internal energy storage; For VPP i Unit power generation cost of internal fuel cells; For VPP i The power generated by the fuel cell; C i,t For VPP i The power-to-gas transaction cost; ij ,d ij , is the bilateral transaction coefficient, which reflects the product difference; P i,j,t , R i,j,t VPP i and VPP j The amount of electricity and spare transactions between i The total amount of VPP involved in the transaction.

6. The multi-virtual power plant power-to-gas trading method with wind and solar energy storage and demand-side response according to claim 5 is characterized in that: The calculation formula for quantifying the risk of power-to-gas transactions of multiple virtual power plants using the CvaR method is: Where: i is the CVaR value of VPPi cost; i is the VaR value of VPPi cost; i,s is the value by which VPPi costs exceed VaR; ρ s is the probability density function of the random variable.

7. The multi-virtual power plant power-to-gas trading method with wind and solar energy storage and demand-side response according to claim 6 is characterized in that: The multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is: In the formula, C all is the overall total cost of multiple VPPs based on CVaR; L is the risk preference coefficient, which represents the investor’s attitude toward risk, and its value range is [0, 1].

8. A multi-virtual power plant power-to-gas trading system with wind, solar, energy storage and demand-side response, characterized in that: include: Output model building module: According to the uncertainty characteristics of wind and solar output, wind and photovoltaic output models are established; Demand response model solving module: Incentivize and regulate user-side loads based on price signals and incentive mechanisms, and establish a demand-side response model; Virtual power plant model building module: based on the wind power and photovoltaic output models, multiple virtual power plant models are built on the basis of considering energy storage and power balance; Objective function construction module: based on the multi-virtual power plant model, the objective function of the power system economic dispatch model is constructed with the minimum cost within the dispatch cycle as the goal; Transaction risk quantification module: Based on the power system economic dispatch model, the CvaR method is used to quantify the power-to-gas transaction risks of multiple virtual power plants; Trading model construction and solution module: Based on the objective functions of the wind and photovoltaic output models, demand-side response model, multi-virtual power plant model, power system economic dispatch model and the quantified multi-virtual power plant power-to-gas trading risks, a multi-virtual power plant power-to-gas trading model including wind, solar, energy storage and demand-side response is established, and the model is solved to obtain a trading plan.

9. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the multi-virtual power plant power-to-gas trading method containing wind, solar, energy storage and demand-side response as described in any one of claims 1-7.

10. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the multi-virtual power plant power-to-gas trading method containing wind, solar, energy storage and demand-side response as described in any one of claims 1-7.

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