Multi-type virtual power plant participation market regulation capacity optimization distribution method and system

Abstract

The invention discloses a multi-type virtual power plant participation market regulation capacity optimization distribution method and system, and the method comprises the steps: constructing a power supply type, load type and energy storage type virtual power plant participation market operation characteristic model, and then combining a value-at-risk assessment CvaR model to obtain a value-at-risk assessment model; a refined virtual power plant capacity distribution model is established by taking maximum pumped storage income and minimum risk when the virtual power plant participates in capacity distribution of main and auxiliary markets as targets, constraint conditions are set for modeling, and finally, the virtual power plant is combined with an electric energy market demand, a peak regulation market demand and a frequency modulation market demand for joint optimization. And continuously detecting and optimizing through the virtual power plant capacity distribution model. According to the method, on one hand, execution of a power generation plan is guaranteed along with wind and light output, on the other hand, a reasonable capacity distribution mechanism in a multi-element market is designed according to different mechanisms of the current power market, services such as peak regulation, frequency modulation and standby are provided, and the optimal configuration problem of a virtual power plant in the power market is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and specifically relates to a method and system for optimizing allocation of capacity of multiple types of virtual power plants participating in market regulation. Background Art

[0002] Under the current development background of my country's power system, with the proposal of the "dual carbon" goal, my country's power system is gradually transforming into a new power system with new energy as the main body, facing a new supply and demand balance situation. Affected by natural conditions, the actual output of new energy power generation such as wind power and photovoltaic power generation has obvious randomness and volatility, which has led to a sharp increase in the demand for flexible resources in the power system. However, the power system dominated by thermal power units has insufficient operational flexibility, which has brought many challenges to the absorption of wind power and photovoltaic power and the safe and stable operation of the power system. Energy storage and flexible regulation are important means to smooth the power supply fluctuations of the power system. Therefore, the reasonable addition of flexible regulation resources to the new power system is an effective means to achieve the stable operation of the current power system.

[0003] As a new type of energy management and dispatching technology, virtual power plants can provide flexible regulation capabilities and support the safe and economic operation of power systems by aggregating distributed energy, controllable loads, energy storage systems and other resources. By collaboratively managing different types of energy resources, virtual power plants can not only participate in power transactions in the power market, but also provide flexibility services in multiple time scales such as frequency regulation, peak regulation, and standby, effectively filling the flexibility gap in the power system and promoting the consumption of new energy. With the advancement of power market reform, the market position of virtual power plants has gradually improved, especially in participating in capacity markets and power spot markets, showing great potential.

[0004] In recent years, the country has introduced a number of policies to support the participation and development of virtual power plants. In 2021, the "Guiding Opinions on Promoting the Development of Virtual Power Plants" proposed that virtual power plants should obtain reasonable returns through market-oriented means and stimulate the participation of various market players. However, the current revenue mechanism of virtual power plants is still imperfect, and the market value has not been fully reflected, especially in terms of pricing and capacity allocation of its flexible adjustment capabilities. Virtual power plants still lack effective market strategies, resulting in low investor willingness to invest, which limits the further development of virtual power plants.

[0005] Therefore, in view of the new situation of my country's power market construction under the background of dual carbon, it is urgent to construct a capacity allocation strategy for virtual power plants in a diversified market to solve the current market participation benefit problem of virtual power plants and further promote their widespread application and development in the power system. Summary of the invention

[0006] The purpose of this invention is to propose a method and system for optimizing the allocation of market regulation capacity for multiple types of virtual power plants, by establishing operating characteristic models of power supply type, load type, energy storage type, and hybrid virtual power plants, and formulating trading strategies for different virtual power plants to participate in the electric energy and ancillary service market according to the operating characteristics. Under this strategy, considering the different proportions of "source-load-storage" capabilities provided by different types of virtual power plants, with the goal of maximizing the joint benefits of multiple types of virtual power plants, the optimal regulation capacity plan for multiple types of virtual power plants is constructed, and the conditional value at risk CvaR is used for risk assessment and decision optimization to maximize benefits and reduce risks.

[0007] The present invention proposes a method for optimizing the allocation of capacity of multiple types of virtual power plants participating in market regulation, including:

[0008] Construct operation characteristic models of power supply type, load type, energy storage type and hybrid virtual power plants participating in the market;

[0009] According to the risk value assessment CvaR model, the virtual power plant capacity allocation model is established by taking the minimum risk and maximum benefit when the virtual power plant participates in the primary and auxiliary markets for capacity allocation as the objective function, and setting modeling constraints;

[0010] The power supply type, load type, energy storage type, and hybrid virtual power plants are jointly optimized in combination with the electricity market demand, peak-shaving market demand, and frequency regulation market demand. The capacity allocation model of the virtual power plant is used to determine whether the optimal target value of the joint operation optimization and whether the constraints are met. If so, the optimization allocation ends; if not, the joint optimization process continues.

[0011] Furthermore, the operational characteristic model of the power supply type virtual power plant participating in the market is specifically as follows:

[0012] For photovoltaic power generation output, the expected value of photovoltaic power generation output of the virtual power plant in the region is calculated by calculating the probability density of photovoltaic power generation points in the area covered by the virtual power plant:

[0013]

[0014]

[0015] Where: B(m t ,n t ) represents the Beta distribution function of solar radiation; m and n represent the corresponding Beta distribution parameters; is the predicted value of photovoltaic output in period t; μ is the average value of solar radiation; σ is the variance of solar radiation;

[0016] For wind power output, calculate its probability density and then find its expected output value:

[0017]

[0018] Where: k and l are the scale parameter and shape parameter of the Weibull distribution respectively.

[0019] Furthermore, the operation characteristic model of the load-based virtual power plant participating in the market includes a transferable load mathematical model and an interruptible load mathematical model:

[0020] The specific mathematical model of transferable load is:

[0021] D t =d t +d t,up -d t,down (30)

[0022] Where: D t Represents the load demand value in period t after time-of-use electricity price incentive; d t Represents the load demand value in period t before time-of-use electricity price incentive; d t,up Characterizes the increase in load during period t; d t,down Characterizes the reduction in load during period t, d t,up With d t,down The two systems are mutually exclusive in time period t, and the total amount of load remains unchanged during the entire scheduling cycle, that is:

[0023]

[0024] The constraints are as follows:

[0025]

[0026]

[0027] In the formula, B t,up Characterizes the maximum increase in load during period t, B t,down Characterizes the maximum reduction ratio of load during period t; Pr t Represents the actual electricity price during period t, Pr ref Characterizes the reference electricity price during period t; ε up and ε down Characterize the elasticity coefficients of load increase and load reduction with respect to electricity price respectively;

[0028] The interruptible load mathematical model includes a compensation capacity cost model and an interruption capacity cost model, wherein the compensation capacity represents the interruptible load on the user side, and the compensation capacity cost model is as follows:

[0029]

[0030] In the formula, represents the compensation capacity cost during period t, represents the compensation capacity during period t, β t,j represents the compensation electricity price during period t;

[0031] Interruption capacity represents the actual interrupted load. The interruption capacity fee model is as follows:

[0032]

[0033] In the formula, represents the interruption capacity cost during period t, represents the interruption capacity during period t, γ t,j It represents the interruption price during period t.

[0034] Furthermore, the operational characteristic model of the energy storage virtual power plant participating in the market is specifically as follows:

[0035] During the battery discharge period, the estimation model of the energy storage unit charge state is as follows:

[0036]

[0037] In the formula, SOC 0 Indicates the initial state of charge of the battery, SOC t Indicates the state of charge of the battery after discharging for Δt time, I d Represents the discharge current, η d represents the discharge efficiency, and C represents the total capacity of the battery;

[0038] During the battery charging period, the estimation model of the energy storage unit SOC is as follows:

[0039]

[0040] In the formula, I c Represents the charging current, η c Indicates charging efficiency.

[0041] Furthermore, the operating characteristic model of the hybrid virtual power plant participating in the market includes a combination of at least two operating characteristic models of power supply type, load type, and energy storage type virtual power plant participating in the market.

[0042] Furthermore, the risk minimization objective function of the virtual power plant capacity allocation model is specifically:

[0043] Assuming x is the decision variable of the controllable power output, y is the random variable of the load and the uncontrollable power output of wind and solar, then the risk value η under the confidence level α is α The calculation formula is as follows:

[0044]

[0045] Where: is the probability that the profit function is not greater than the boundary value g(x,y).

[0046] Furthermore, the profit maximization objective function of the virtual power plant capacity allocation model includes the following three parts: 1) the profit of VPP participating in the purchase and sale of electricity in the multi-market 2) the expected profit of VPP in various scenarios in real-time operation, expressed as the utility of interruptible load plus imbalance penalty, minus the total cost of VPP; 3) the risk return based on CVaR, expressed as the product of risk coefficient and CVaR; the specific formula is:

[0047]

[0048] Where: β is the risk coefficient, 0≤β≤1, and the β value depends on the decision maker's trade-off between risk and return. The larger the β value, the more risk-averse the VPP operation decision maker is, the more conservative its operation strategy is, and the more inclined it is to avoid the risks brought by uncertain factors; T is the total time; Q is the total number of scenarios; p q is the probability of scene q occurring; represents the day-ahead electricity price at time t and scenario q and the power supply price of VPP to internal loads; δ t Represents the unit time interval, the unit is h; Indicates the power purchased and sold by VPP in the day-ahead market. If the value is positive, VPP will sell power to the market. If it is negative, VPP will purchase power from the market. Indicates the internal load of VPP; Indicates interruptible load power; U qt is the imbalance penalty at time t and scenario q; is the total cost of VPP; η is the VaR value; α is the confidence level; ξ q is an auxiliary variable;

[0049] Among them, the total cost of VPP is as follows:

[0050]

[0051]

[0052] Where: are the operating costs of each virtual power plant when participating in the market; α PV , α WT are the operation and maintenance cost coefficients of photovoltaic and wind power generation respectively; a G , b G 、c G is the cost coefficient of thermal power unit; α L , β L is the compensation coefficient of interruptible load; α essis the operation and maintenance cost coefficient of the energy storage equipment; They are respectively the photovoltaic output, wind power output, thermal power output, interruptible load, energy storage discharge and charging capacity in the corresponding time period of the corresponding scenario.

[0053] Furthermore, the setting of modeling constraints specifically includes:

[0054] 1) VPP active power balance constraints

[0055]

[0056] Where: Indicates the deviation between the actual output of VPP and the bid output. At any time, one of them is positive and the other is zero;

[0057] 2) Constraints on thermal power units

[0058]

[0059] Where: μ qt It is a 0-1 variable, indicating the operating status of the unit; is the upper and lower limits of output; ΔP up , ΔP down is the up and down climbing rate; The minimum start and stop time allowed; To accumulate the start and stop time;

[0060] 3) Energy storage constraints

[0061]

[0062] Where: is the maximum charge and discharge power;

[0063] 4) Interruptible load constraints

[0064]

[0065] Where: γ qt It is a 0-1 variable, indicating the call status of the interruptible load; The upper and lower limits of the interruptible load;

[0066] 5) Bidding value constraints

[0067] The upper and lower limits of the multiple market bidding output are as follows:

[0068]

[0069] 6) Conditional risk constraints

[0070] The CVaR used measures the relationship between return and risk, and the conditional risk constraints are as follows:

[0071]

[0072] The present invention also proposes a system for optimizing the allocation of capacity by participating in market regulation of multiple types of virtual power plants, including:

[0073] Operation characteristic model building module, used to build operation characteristic models of power supply type, load type, energy storage type, and hybrid virtual power plants participating in the market;

[0074] The capacity allocation model building module is used to evaluate the CvaR model based on the risk value, take the minimum risk and maximum benefit when the virtual power plant participates in the primary and auxiliary markets for capacity allocation as the objective function, set modeling constraints, and establish the capacity allocation model of the virtual power plant;

[0075] The optimization allocation module is used to jointly optimize power supply type, load type, energy storage type, and hybrid virtual power plants in combination with the electric energy market demand, peak load market demand, and frequency regulation market demand. According to the virtual power plant capacity allocation model, it is determined whether the optimal target value of the joint operation optimization and whether the constraints are met. If so, the optimization allocation ends; if not, the joint optimization process continues.

[0076] The beneficial effects of the present invention are: while giving full play to its own advantages, the virtual power plant, on the one hand, follows the wind and solar power output to ensure the execution of the power generation plan, and on the other hand, designs a reasonable capacity allocation mechanism under the diversified market according to the different mechanisms of the current electricity market, so as to achieve more accurate consideration of internal and external influencing factors and reflect them through simulation results, which is conducive to revealing the impact of various factors on the operation of the virtual power plant, and can also timely judge the response strategy in the event of changes in external mechanisms, policies, system operating conditions, etc., which can help virtual power plants and operating units better plan their way of participating in the market and promote the sound and healthy development of the new power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0078] Figure 1 It is a structural diagram of a multi-type virtual power plant model proposed by the present invention;

[0079] Figure 2 This is a process diagram of the virtual power plant participating in market capacity allocation proposed by the present invention. DETAILED DESCRIPTION

[0080] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0081] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0082] The technical solution adopted by the present invention is to establish a capacity allocation strategy based on pumped storage participating in multiple market operations, including: an operating characteristic model of multiple types of virtual power plants under multiple markets, and a virtual power plant capacity allocation model considering CvaR risk value assessment.

[0083] Among them, the multi-type virtual power plant operation characteristic model under the diversified market refers to the virtual power plant as a new type of power management system, which aggregates various distributed resources through information technology and software systems to participate in power system operation and power market transactions. The virtual power plant is not a real physical power plant, but by aggregating massive distributed resources, it can appear as a controllable power source to the outside world, playing the role of a power plant such as providing electricity, peak load regulation and frequency regulation. It is divided into four types of virtual power plants: power supply type, load type, energy storage type and hybrid type. Different types of virtual power plants participate in different proportions in the diversified market, so it is necessary to analyze their characteristics and establish a suitable market operation model.

[0084] The capacity allocation model that considers CvaR risk value assessment means that virtual power plants, as independent entities, participate in multiple markets such as capacity trading and ancillary services, which can simultaneously increase the revenue of themselves and other power generation resources, play a flexible regulatory role, promote peak shaving and valley filling, and reduce wind and solar power abandonment. In order to promote the reasonable participation of virtual power plants in multiple markets, the market operation mode of virtual power plants is divided into two stages: participation in the electric energy market and the ancillary service market, where the ancillary service market includes frequency regulation and standby. The total revenue obtained by the virtual power plant is the sum of the revenue from the synergy and complementarity with new energy and the revenue from its participation in the main market and the ancillary service market. And on the basis of optimizing the allocation strategy, the risk and strategy optimization model of the proposed scheme are fully considered, and the scheme is evaluated and optimized, so that the risk level of the optimization scheme is limited to an acceptable level, and the system operation cost is minimized.

[0085] 1. The specific analysis of the operating characteristic model of multi-type virtual power plants under multi-market is as follows:

[0086] 1. Multiple types of virtual power plants participate in multiple market characteristics

[0087] (1) Power supply type virtual power plant

[0088] This type of virtual power plant mainly aggregates the main resources on the controllable power generation side, such as wind power and photovoltaic power, including wind power generation, photovoltaic power generation, biomass power generation, gas turbine power generation, fuel cell power generation, wind and solar integrated coordinated power generation and other forms. From the perspective of virtual power plants, the definition of power resources lies in the scheduling relationship. All power generation resources that are not in the existing public system or can be separated from the public system can be included in virtual power generation resources. In this sense, all traditional self-contained power plants are potential resources for power-type virtual power plants. Power-type virtual power plants have the ability to sell energy, can participate in the energy market, and participate in the ancillary service market depending on actual conditions.

[0089] (2) Load-based virtual power plant

[0090] This type of virtual power plant mainly aggregates the main resources on the load side of the power grid, including industrial loads, commercial loads, charging piles, air-conditioning loads and other forms. Load-type resources can be divided into adjustable load resources and interruptible load resources. Different adjustable load resources vary greatly in both quality and quantity. In terms of quality, there are factors such as adjustment willingness, adjustment ability and aggregation cost-effectiveness. Among them, non-continuous industrial and commercial loads are the preferred high-quality resources, followed by charging piles and air-conditioning loads. In terms of quantity, the development of adjustment and aggregation technology and the reduction of costs are constantly increasing the amount of adjustable load resources. Load-type virtual power plants have power adjustment capabilities and can participate in the ancillary service market, but their ability to sell energy is insufficient.

[0091] (3) Energy storage virtual power plant

[0092] This type of virtual power plant mainly aggregates the main resources related to energy storage, including mechanical energy storage, chemical energy storage, electromagnetic energy storage, phase change energy storage and other forms. With the rapid improvement of the economic efficiency of energy storage technology in recent years, electric energy has broken through the limitation of large-scale economic storage, and has also changed the industry's control and optimization mechanism. Energy storage equipment can be roughly divided into four categories: mechanical energy storage, such as pumped storage, flywheel energy storage, etc.; chemical energy storage, such as lead-acid batteries, sodium-sulfur batteries, etc.; electromagnetic energy storage, such as supercapacitors, superconducting energy storage, etc.; phase change energy storage. Energy storage virtual power plants can participate in the ancillary service market, and can also discharge in certain periods to sell electricity.

[0093] (4) Hybrid virtual power plant

[0094] A hybrid virtual power plant is an all-rounder that has the characteristics of multiple types of virtual power plants. It has two or more types of control resources on the power supply side, load side, and energy storage side. The hybrid resource type is composed of distributed generation, energy storage, and adjustable load resources. Through the optimization control of the energy management system, it maximizes the overall benefits of energy utilization and power supply and use.

[0095] 2. Multi-type virtual power plant operation characteristic model

[0096] (1) Power supply type

[0097] Different output models are established according to different types of distributed energy within the power-based virtual power plant.

[0098] For photovoltaic power generation output, by calculating the probability density of photovoltaic power generation points in the area covered by the virtual power plant, the expected value of photovoltaic power generation output of the virtual power plant in the area can be calculated:

[0099]

[0100]

[0101] Where: B(m t ,n t ) represents the Beta distribution function of solar radiation; m and n represent the corresponding Beta distribution parameters; is the predicted value of photovoltaic output in period t; μ is the average value of solar radiation; σ is the variance of solar radiation.

[0102] For wind power generation output, similar to photovoltaic power generation, its probability density can also be calculated to find its expected output value:

[0103]

[0104] Where: k and l are the scale parameter and shape parameter of the Weibull distribution respectively.

[0105] (2) Load type

[0106] In addition to aggregating flexible and controllable distributed resources on the power generation side, virtual power plants can also indirectly increase the output of the power generation side by regulating adjustable loads. Adjustable loads include transferable loads and interruptible loads, which are an effective way to solve the shortage of power generation side output and sustainable development of energy:

[0107] Transferable loads and interruptible loads are essentially price-based and incentive-based in demand response, respectively. Price-based (transferable loads) refers to the behavior of users changing their electricity usage patterns based on time-of-use electricity prices. Time-of-use electricity prices mainly include the following three types: time-of-use electricity prices for different time periods in units of years, months or days; real-time electricity prices for different time periods at the hourly or minutely level; and peak electricity prices for peak periods added to real-time electricity prices. Users can transfer part of their loads between time periods based on time-of-use electricity prices, thereby reducing electricity costs.

[0108] Incentive type (interruptible load) means that the power grid reduces part of the load in the case of power shortage based on the overall supply and demand of the power system, and directly or indirectly gives users electricity price incentives, including direct load control, interruptible load, demand-side bidding, emergency demand response, etc.

[0109] For transferable loads, by setting peak, valley, and average time-sharing electricity prices, users are guided to transfer part of the load during peak hours to valley and average hours to achieve peak shaving and valley filling. The transferable load model is as follows:

[0110] D t =d t +d t,up -d t,down (54) Where: D t Represents the load demand value in period t after time-of-use electricity price incentive; d t Represents the load demand value in period t before time-of-use electricity price incentive; d t,up Characterizes the increase in load during period t; d t,down Characterizes the reduction in load during period t, d t,up With d t,down The two systems are mutually exclusive in time period t, and the total amount of load remains unchanged during the entire scheduling cycle, that is:

[0111]

[0112] The constraints are as follows:

[0113]

[0114] In the formula, B t,up Characterizes the maximum increase in load during period t, B t,down Characterizes the maximum reduction ratio of load during period t; Pr t Represents the actual electricity price during period t, Pr ref Characterizes the reference electricity price during period t; ε up and ε down Characterize the elasticity coefficients of load increase and load reduction with respect to electricity price respectively.

[0115] For interruptible loads, by reducing part of the load during peak hours, the backup resources of the power grid are increased, and the optimal allocation of power grid resources is achieved. The interruptible load model includes a compensation capacity fee model and an interruption capacity fee model, where the compensation capacity represents the interruptible load on the user side, and the compensation capacity fee model is as follows:

[0116]

[0117] In the formula, represents the compensation capacity cost during period t, represents the compensation capacity during period t, βt,j Represents the compensation electricity price during period t.

[0118] Interruption capacity represents the actual interrupted load. The interruption capacity fee model is as follows:

[0119]

[0120] In the formula, represents the interruption capacity cost during period t, represents the interruption capacity during period t, γ t,j It represents the interruption price during period t.

[0121] (3) Energy storage type

[0122] Wind and solar power generation in virtual power plants is random and intermittent, and electricity consumption by different types of loads is also random. As a bridge between the power generation side and the power consumption side, energy storage units play a key role in suppressing power fluctuations, achieving peak shaving and valley filling on the user side, and maintaining the dynamic balance of the system.

[0123] During the battery discharge period, the estimation model of the energy storage unit charge state is as follows:

[0124]

[0125] In the formula, SOC 0 Indicates the initial state of charge of the battery, SOC t Indicates the state of charge of the battery after discharging for Δt time, I d Represents the discharge current, η d represents the discharge efficiency, and C represents the total capacity of the battery.

[0126] During the battery charging period, the estimation model of the energy storage unit SOC is as follows:

[0127]

[0128] In the formula, I c Represents the charging current, η c Indicates charging efficiency.

[0129] (4) The operating characteristic model of the above-mentioned hybrid virtual power plant participating in the market includes a combination of at least two operating characteristic models of the power supply type, load type, and energy storage type virtual power plant participating in the market. The specific content will not be repeated here.

[0130] 2. Virtual Power Plant Capacity Allocation Model Considering CvaR Risk Value Assessment

[0131] (1) Conditional Value at Risk Analysis

[0132] Value at risk (VaR) is a measure of the maximum loss that may be incurred within a specific period of time in the future under normal market conditions and a certain confidence level. The conditional value at risk (CVaR) that reflects the information on the tail end of the loss is a convex risk measurement indicator. This value represents the expected value of the loss after the VaR quantile, taking into account the conditional expectation of losses exceeding the VaR value, better solving the "tail" problem of VaR in dealing with loss distribution, and better reflecting potential risks. Assuming that is a decision variable such as controllable power output, is a random variable of uncontrollable power output such as load and wind and solar, then the value at risk η at the confidence level α is α The calculation formula is as follows:

[0133]

[0134] Where: is the probability that the profit function g(x,y) is not greater than the boundary value η.

[0135] (2) Objective function

[0136] In order to evaluate the risks and impacts brought by the participation of various types of virtual power plants (VPPs) in multiple market operations, CVaR is introduced on the basic capacity allocation model to evaluate the relationship between the benefits and risks of VPPs in market competition. Therefore, a VPP optimization operation model is constructed with the goal of maximizing benefits considering risk factors. The model objective function consists of three parts: 1) The benefits of VPPs participating in the purchase and sale of electricity in multiple markets 2) The expected benefits of VPPs in various scenarios during real-time operation, expressed as the utility of interruptible loads plus imbalance penalties, minus the total cost of VPPs; 3) Risk benefits based on CVaR, expressed as the product of the risk coefficient and CVaR. The specific formula is as follows:

[0137]

[0138] Where: β is the risk coefficient, 0≤β≤1, and its value depends on the decision maker's trade-off between risk and return. The larger the β value, the more risk-averse the VPP operation decision maker is, the more conservative its operation strategy is, and the more inclined it is to avoid the risks brought by uncertain factors; T is the total time; Q is the total number of scenarios; p q is the probability of scene q occurring; represents the day-ahead electricity price at time t and scenario q and the power supply price of VPP to internal loads; δ t Represents the unit time interval, the unit is h; Indicates the power purchased and sold by VPP in the day-ahead market. If the value is positive, VPP will sell power to the market. If it is negative, VPP will purchase power from the market. Indicates the internal load of VPP; Indicates interruptible load power; U qtis the imbalance penalty at time t and scenario q; is the total cost of VPP; η is the VaR value; α is the confidence level; ξ q is an auxiliary variable.

[0139] Among them, the total cost of VPP is as follows:

[0140]

[0141] Where: are the operating costs of each virtual power plant when participating in the market; α PV , α WT are the operation and maintenance cost coefficients of photovoltaic and wind power generation respectively; a G , b G 、c G is the cost coefficient of thermal power unit; α L , β L is the compensation coefficient of interruptible load; α ess is the operation and maintenance cost coefficient of the energy storage equipment; They are respectively the photovoltaic output, wind power output, thermal power output, interruptible load, energy storage discharge and charging capacity in the corresponding time period of the corresponding scenario.

[0142] (3) Constraints

[0143] 1) VPP active power balance constraints

[0144]

[0145] Where: Represents the deviation between the actual output of VPP and the bid output. At any moment, one of them is positive and the other is zero.

[0146] 2) Constraints on thermal power units

[0147]

[0148] Where: μ qt It is a 0-1 variable, indicating the operating status of the unit; is the upper and lower limits of output; ΔP up , ΔP down is the up and down climbing rate; The minimum start and stop time allowed; It is the cumulative start and stop time.

[0149] 3) Energy storage constraints

[0150]

[0151] Where: is the maximum charge and discharge power.

[0152] 4) Interruptible load constraints

[0153]

[0154] Where: γ qt It is a 0-1 variable, indicating the call status of the interruptible load; The upper and lower limits of the interruptible load.

[0155] 5) Bidding value constraints

[0156] The upper and lower limits of the multiple market bidding output are as follows:

[0157]

[0158] 6) Conditional risk constraints

[0159] The CVaR used measures the relationship between return and risk, and the conditional risk constraints are as follows:

[0160]

[0161] In summary, the present invention enables the virtual power plant to fully utilize its own advantages while, on the one hand, following the wind and solar power output to ensure the execution of the power generation plan; on the other hand, according to the different mechanisms of the current electricity market, it designs a reasonable capacity allocation mechanism under the diversified market, achieves more accurate consideration of internal and external influencing factors and reflects them through simulation results, which is conducive to revealing the impact of various factors on the operation of the virtual power plant, and can also timely judge the response strategy when the external mechanism, policy, system operating conditions and other changes occur, which can help virtual power plants and operating units better plan their way of participating in the market and promote the sound and healthy development of the new power industry.

[0162] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0163] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0166] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the allocation of capacity by participating in market regulation of multiple types of virtual power plants, characterized in that: include: Construct operation characteristic models of power supply type, load type, energy storage type and hybrid virtual power plants participating in the market; According to the risk value assessment CvaR model, the virtual power plant capacity allocation model is established by taking the minimum risk and maximum benefit when the virtual power plant participates in the primary and auxiliary markets for capacity allocation as the objective function, and setting modeling constraints; The power supply type, load type, energy storage type, and hybrid virtual power plants are jointly optimized in combination with the electricity market demand, peak-shaving market demand, and frequency regulation market demand. The capacity allocation model of the virtual power plant is used to determine whether the optimal target value of the joint operation optimization and whether the constraints are met. If so, the optimization allocation ends; if not, the joint optimization process continues.

2. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The operational characteristic model of the power supply type virtual power plant participating in the market is specifically as follows: For photovoltaic power generation output, the expected value of photovoltaic power generation output of the virtual power plant in the region is calculated by calculating the probability density of photovoltaic power generation points in the area covered by the virtual power plant: Where: B(m t ,n t ) represents the Beta distribution function of solar radiation; m and n represent the corresponding Beta distribution parameters; P t PV is the predicted value of photovoltaic output in period t; μ is the average value of solar radiation; σ is the variance of solar radiation; For wind power output, calculate its probability density and then find its expected output value: Where: k and l are the scale parameter and shape parameter of the Weibull distribution respectively.

3. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The operation characteristic model of the load-based virtual power plant participating in the market includes a transferable load mathematical model and an interruptible load mathematical model: The specific mathematical model of transferable load is: D t =d t +d t,up -d t,down (6) Where: D t Represents the load demand value in period t after time-of-use electricity price incentive; d t Represents the load demand value in period t before time-of-use electricity price incentive; d t,up Characterizes the increase in load during period t; d t,down Characterizes the reduction in load during period t, d t,up With d t,down The two systems are mutually exclusive in time period t, and the total amount of load remains unchanged during the entire scheduling cycle, that is: The constraints are as follows: In the formula, B t,up Characterizes the maximum increase in load during period t, B t,down Characterizes the maximum reduction ratio of load during period t; Pr t Represents the actual electricity price during period t, Pr ref Characterizes the reference electricity price during period t; ε up and ε down Characterize the elasticity coefficients of load increase and load reduction with respect to electricity price respectively; The interruptible load mathematical model includes a compensation capacity cost model and an interruption capacity cost model, wherein the compensation capacity represents the interruptible load on the user side, and the compensation capacity cost model is as follows: In the formula, represents the compensation capacity cost during period t, represents the compensation capacity during period t, β t,j represents the compensation electricity price during period t; Interruption capacity represents the actual interrupted load. The interruption capacity fee model is as follows: In the formula, represents the interruption capacity cost during period t, represents the interruption capacity during period t, γ t,j It represents the interruption price during period t.

4. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The operational characteristic model of the energy storage virtual power plant participating in the market is specifically as follows: During the battery discharge period, the estimation model of the energy storage unit charge state is as follows: In the formula, SOC0 represents the initial state of charge of the battery, SOC t Indicates the state of charge of the battery after discharging for Δt time, I d Represents the discharge current, η d represents the discharge efficiency, and C represents the total capacity of the battery; During the battery charging period, the estimation model of the energy storage unit SOC is as follows: In the formula, I c Represents the charging current, η c Indicates charging efficiency.

5. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The operation characteristic model of the hybrid virtual power plant participating in the market includes a combination of at least two operation characteristic models of power supply type, load type, and energy storage type virtual power plant participating in the market.

6. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The risk minimization objective function of the virtual power plant capacity allocation model is specifically: Assuming x is the decision variable of the controllable power output, y is the random variable of the load and the uncontrollable power output of wind and solar, then the risk value η under the confidence level α is α The calculation formula is as follows: Where: is the probability that the profit function is not greater than the boundary value g(x,y).

7. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The profit maximization objective function of the virtual power plant capacity allocation model includes the following three parts: 1) The revenue of VPP from participating in the purchase and sale of electricity in the multi-market 2) The expected revenue of VPP in various scenarios in real-time operation, expressed as the utility of interruptible load plus imbalance penalty minus the total cost of VPP; 3) The risk-benefit based on CVaR, expressed as the product of risk factor and CVaR; The specific formula is: Where: β is the risk coefficient, 0≤β≤1, and the β value depends on the decision maker's trade-off between risk and return; T is the total time; Q is the total number of scenarios; p q is the probability of scene q occurring; represents the day-ahead electricity price at time t and scenario q, as well as the power supply price of the VPP to the internal load; δ t Represents the unit time interval, the unit is h; It indicates the power purchased and sold by VPP in the day-ahead market. If the value is positive, VPP will sell power to the market. If it is negative, VPP will buy power from the market. t load Indicates the internal load of VPP; P t IL Indicates interruptible load power; U qt is the imbalance penalty at time t and scenario q; is the total cost of VPP; η is the VaR value; α is the confidence level; ξ q is an auxiliary variable; Among them, the total cost of VPP is as follows: Where: are the operating costs of each virtual power plant when participating in the market; α PV , α WT are the operation and maintenance cost coefficients of photovoltaic and wind power generation respectively; a G 、b G 、c G is the cost coefficient of thermal power unit; α L , β L is the compensation coefficient of interruptible load; α ess is the operation and maintenance cost coefficient of the energy storage equipment; They are respectively the photovoltaic output, wind power output, thermal power output, interruptible load, energy storage discharge and charging capacity in the corresponding time period of the corresponding scenario.

8. The method for optimizing the allocation of capacity of multi-type virtual power plants participating in market regulation according to claim 1 is characterized in that: The setting of modeling constraints specifically includes: 1) VPP active power balance constraints Where: Indicates the deviation between the actual output of VPP and the bid output. At any time, one of them is positive and the other is zero; 2) Constraints on thermal power units Where: μ qt It is a 0-1 variable, indicating the operating status of the unit; is the upper and lower limits of output; ΔP up , ΔP down is the up and down climbing rate; The minimum start and stop time allowed; To accumulate the start and stop time; 3) Energy storage constraints Where: is the maximum charge and discharge power; 4) Interruptible load constraints Where: γ qt It is a 0-1 variable, indicating the call status of the interruptible load; The upper and lower limits of the interruptible load; 5) Bidding value constraints The upper and lower limits of the multiple market bidding output are as follows: 6) Conditional risk constraints The CVaR used measures the relationship between return and risk, and the conditional risk constraints are as follows:

9. Multi-type virtual power plants participate in the market regulation capacity optimization allocation system, characterized by: include: Operation characteristic model building module, used to build operation characteristic models of power supply type, load type, energy storage type, and hybrid virtual power plants participating in the market; The capacity allocation model building module is used to evaluate the CvaR model based on the risk value, take the minimum risk and maximum benefit when the virtual power plant participates in the primary and auxiliary markets for capacity allocation as the objective function, set modeling constraints, and establish the capacity allocation model of the virtual power plant; The optimization allocation module is used to jointly optimize power supply type, load type, energy storage type, and hybrid virtual power plants in combination with the electric energy market demand, peak load market demand, and frequency regulation market demand. According to the virtual power plant capacity allocation model, it is determined whether the optimal target value of the joint operation optimization and whether the constraints are met. If so, the optimization allocation ends; if not, the joint optimization process continues.

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