Virtual power plant multi-type resource aggregation optimization operation method and device

Abstract

The invention discloses a virtual power plant multi-type resource aggregation optimization operation method and device. The method comprises the following steps: respectively establishing adjustable electric quantity models of various flexible adjustable resources on a power supply side and a demand side in a jurisdiction range of a virtual power plant; carrying out aggregation modeling on the adjustable electric quantity models of the various flexible adjustable resources to obtain an output electric quantity aggregation model and a flexible adjustable electric quantity range of the virtual power plant; a next-day available output curve of the virtual power plant is generated according to the output electric quantity aggregation model, and the power grid makes a next-day total output plan based on the available output curve of the virtual power plant and the flexible adjustable electric quantity range; constructing a virtual power plant optimization operation model taking the minimum comprehensive cost as a target; and based on a next-day total output plan issued by the power grid, in combination with operation constraint conditions of the virtual power plant, solving the virtual power plant optimization operation model to obtain scheduling operation optimization results of various flexible adjustable resources in the virtual power plant. According to the invention, the operation economy and stability of the virtual power plant are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource aggregation, and in particular, to a method and device for optimizing the operation of multi-type resource aggregation in a virtual power plant. Background Art

[0002] With the continuous development and popularization of renewable energy technologies, the operation mode of traditional power systems is facing more and more challenges. The intermittent, random, and volatile characteristics of renewable energy (such as wind energy, solar energy, etc.) increase the difficulty of power dispatch; to solve this problem, the virtual power plant, as a new type of energy management mode, has gradually become an effective solution to the problem of distributed energy dispatch.

[0003] By integrating various types of distributed energy resources, energy storage devices, load responses, and other flexible adjustable resources, the virtual power plant can perform flexible dispatch and optimized operation at different times and under different power demands, thereby improving the operation efficiency and response ability of the power system; however, the existing virtual power plant technologies have not been able to fully utilize the characteristics of various adjustable flexible resources on the power supply side and the demand side, and there are still certain limitations in the aspect of resource optimization configuration, which are specifically reflected in the following two aspects: First, the existing research on virtual power plants mainly focuses on optimizing dispatch and trading bidding mechanisms, lacking relevant research on the optimization configuration of distributed resources in virtual power plants. Coordinating various energy demands on the power supply side and the load side to achieve the optimal configuration of various resources is the key foundation for improving the optimized operation of virtual power plants. Second, for the optimization configuration of multi-type resources in virtual power plants, the flexibility and randomness of the power supply side and the demand side are rarely considered simultaneously; due to renewable energy generation resources such as wind power and photovoltaic power, the output power of these resources is greatly affected by environmental factors (such as weather, light intensity, wind speed, etc.), and has strong intermittency and volatility; however, the existing research lacks a quantitative description of random factors and does not consider the random influence of energy such as wind power and photovoltaic power. Therefore, due to factors such as the fluctuation of the power system load and the unstable output of renewable energy, the existing technology faces great challenges in flexible response. Especially in the face of emergencies and extreme weather conditions, the dispatch response ability is limited, and the system stability and economy are affected. Summary of the Invention

[0004] To solve the deficiencies in the existing technology, the present invention provides a method and device for optimizing the operation of multi-type resource aggregation in a virtual power plant, which can, on the basis of considering the characteristics of flexible adjustable resources on the power supply side and the demand side, give full play to the synergistic effects of various resources, achieve the efficient aggregation and optimized operation of multi-type adjustable resources, improve the operation efficiency of the virtual power plant, reduce costs, ensure the stability of the system, and promote the wider application of renewable energy.

[0005] The present invention adopts the following technical solutions.

[0006] In a first aspect, the present invention provides a method for aggregating and optimizing the operation of multiple types of resources in a virtual power plant. The method includes:

[0007] Step 1: Establish adjustable power quantity models for various types of flexible adjustable resources on the power supply side and the demand side within the scope of the virtual power plant respectively;

[0008] Step 2: Aggregate and model the adjustable power quantity models of various types of flexible adjustable resources to obtain the aggregated output power quantity model and the flexible adjustable power quantity range of the virtual power plant;

[0009] Step 3: Generate the available output power curve of the virtual power plant for the next day according to the aggregated output power quantity model, and the power grid formulates the total output plan for the next day based on the available output power curve of the virtual power plant and its flexible adjustable power quantity range;

[0010] Step 4: Construct an optimization operation model of the virtual power plant with the goal of minimizing the comprehensive cost;

[0011] Step 5: Based on the total output plan for the next day issued by the power grid and combined with the operation constraint conditions of the virtual power plant, solve the optimization operation model of the virtual power plant to obtain the optimized scheduling operation results of various types of flexible adjustable resources in the virtual power plant.

[0012] Optionally, in Step 1, the various types of flexible adjustable resources on the demand side include interruptible loads, energy storage units, and electric vehicle clusters; the various types of flexible adjustable resources on the power supply side include controllable power generation resources, wind turbines, and photovoltaic power generation units;

[0013] Among them, the interruptible loads include industrial and commercial user loads; the controllable power generation resources include thermal power generation units, micro gas turbines, and / or hydraulic power generation units.

[0014] Optionally, Step 1 includes: establishing an adjustment power quantity model for interruptible loads, and its expression is as follows:

[0015]

[0016] In the formula, μ EL (P L (t)) represents the probability density function of the interruptible power P L at different times t, μ and σ 2 represent the mean and variance of P L , P L,max is the maximum power value of the interruptible load; Δt is the interruptible time period.

[0017] Optionally, Step 1 includes: establishing an adjustment power quantity model for an electric vehicle cluster, and its expression is as follows:

[0018]

[0019] Wherein, P EV (t) represents the power output of the electric vehicle at different times t; P EV ord represents the power command value of the electric vehicle issued by the virtual power plant; T EV and T ev are respectively the communication delay time and the control response time constant of the electric vehicle from the occurrence of the fault to the control response.

[0020] Optionally, in step 2, the expression of the output power aggregation model of the virtual power plant is as follows:

[0021]

[0022] Wherein, E VPP (Δt, P VPP (t)) represents the output power of the virtual power plant in the current time period Δt, P VPP (t), P a,i (t), P wind,i (t), P pv,i (t), P ess,i (t) and P EV,i (t) are respectively the output values of the virtual power plant, the i-th controllable power generation resource, the i-th wind turbine, the i-th photovoltaic unit, the i-th energy storage unit, and the i-th electric vehicle at time t; N1, N2, N3, N4, and N5 are respectively the numbers of controllable power generation resources, wind turbines, photovoltaic units, energy storage units, and electric vehicles within the scope of the virtual power plant; P L (t) is the load value at time t, and k is the proportion of interruptible demand resources in the total load.

[0023] Optionally, in step 2, the expression of the flexible adjustable power range is as follows:

[0024]

[0025] Wherein, are respectively the power values that the virtual power plant can flexibly adjust upward and downward within the Δt time period; are respectively the power values that the i-th controllable power generation resource can flexibly adjust upward and downward; are respectively the power values that the i-th energy storage unit can flexibly adjust upward and downward; are respectively the power values that the i-th electric vehicle can flexibly adjust upward and downward.

[0026] Optionally, in step 4, the expression of the objective function of the virtual power plant optimal operation model is as follows:

[0027]

[0028] In the formula, F represents the objective function with the minimum comprehensive cost; and respectively represent the total cost of resource aggregation, the total operation and maintenance cost, and the risk cost, and R represents the risk preference coefficient.

[0029] Optionally, the calculation formula of the risk cost is as follows:

[0030]

[0031] In the formula, and are the probability density functions of the errors of the wind turbine, the photovoltaic unit, and the interruptible load respectively; α is the boundary value of the value at risk VaR; β is the set confidence level; M≥0 is a virtual coefficient representing the loss exceeding the value at risk, and t = 1, 2…24 represents the 24 hours within the dispatching day.

[0032] Optionally, the operation constraint conditions of the virtual power plant include: the output constraints of various flexible adjustable resources, the energy storage capacity constraint, the interruptible load upper limit constraint, the reserve constraint, and / or the opportunity planning constraint.

[0033] Optionally, the expression of the reserve constraint is as follows:

[0034]

[0035] In the formula, P a,min and P a,max are the maximum and minimum values of the output power of the controllable power generation resources respectively; P a (t) represents the operating power of the controllable power generation resources at time t; R a (t), R ess (t), and R EV (t) are the reserve capacities of the controllable power generation resources, the energy storage device, and the electric vehicle at time t respectively; Q ess (t) is the energy storage capacity of the energy storage unit at time t, Q ess,min represents the minimum capacity of the energy storage unit, and η is the discharge efficiency of the energy storage device; P ess,max and P EV,max represent the maximum output powers of the energy storage unit and the electric vehicle respectively, and P ess (t) and P EV (t) represent the operating powers of the energy storage unit and the electric vehicle at time t respectively.

[0036] Optionally, the expression of the opportunity planning constraint is as follows:

[0037] P rob [Evpp ≥EP RGs (t) - P wind (t) - P pv (t)] ≥ β

[0038] Wherein, EP RGs (t) is the combined output power P RGs (t) of the wind turbine and photovoltaic unit at time t, and the expected value of P rob [] is the probability operator; E vpp is the adjustable power of the virtual power plant, which is between the upward and downward flexible adjustable power values That is

[0039] Second, the present invention provides a virtual power plant multi-type resource aggregation and optimization operation device, which operates according to the steps of any one of the first aspects of the present invention. The device includes:

[0040] A building module, configured to respectively establish adjustable power models of various flexible adjustable resources on the power supply side and the demand side within the scope of the virtual power plant

[0041] An aggregation module, configured to perform aggregation modeling on the adjustable power models of various flexible adjustable resources to obtain an output power aggregation model and a flexible adjustable power range of the virtual power plant

[0042] A formulation module, configured to generate the next-day available output curve of the virtual power plant according to the output power aggregation model, and the power grid formulates the next-day total output plan based on the available output curve of the virtual power plant and its flexible adjustable power range

[0043] A construction module, configured to construct an optimization operation model of the virtual power plant with the goal of minimizing the comprehensive cost

[0044] An optimization module, configured to solve the optimization operation model of the virtual power plant based on the next-day total output plan issued by the power grid and in combination with the operation constraint conditions of the virtual power plant to obtain the scheduling operation optimization results of various flexible adjustable resources in the virtual power plant

[0045] Third, the present invention provides a terminal, including a processor and a storage medium

[0046] The storage medium is used to store instructions

[0047] The processor is configured to operate according to the instructions to execute the steps of any one of the first aspects of the present invention

[0048] Fourth, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of any one of the first aspects of the present invention

[0049] The beneficial effects of the present invention are as follows. Compared with the prior art:

[0050] 1. By comprehensively considering the characteristics of various flexible resources on the power supply side and the demand side, the present invention proposes a new optimization operation scheme for multiple types of resources. Different from the prior art that mostly focuses on the optimization scheduling of single resources, the present invention can coordinate and integrate multiple resources, such as controllable power generation resources, wind turbines, photovoltaic power generation units, interruptible loads, energy storage units, and electric vehicle groups, realizing the collaborative optimization of the power supply side and the demand side. It not only improves the resource utilization rate but also effectively reduces resource waste, further improving the operation efficiency of the virtual power plant.

[0051] 2. The present invention fully considers the intermittent, random, and volatile characteristics of renewable energy resources such as wind power and photovoltaic power. The optimized operation model of the virtual power plant constructed has a risk cost in its objective function. It not only considers the conditional risk value brought by the randomness of wind power and photovoltaic power generation but also dynamically adjusts the power generation resources through the adjustable flexibility of controllable power generation resources and the role of reserve capacity, making the power output more stable, reducing the burden on the power system caused by the fluctuations of renewable energy, and solving the problems of volatility and intermittency of renewable energy.

[0052] 3. The present invention proposes to optimize the scheduling of various flexible adjustable resources under the formulated reserve constraints and opportunity planning constraints in the case of grid load fluctuations, uncertain output of renewable energy, etc. This optimization not only considers economy but also takes into account factors such as system stability, gives full play to the synergistic effect of various resources, realizes the efficient aggregation and optimized operation of multiple types of adjustable resources, maximizes the utilization efficiency of renewable energy, and further promotes the development of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of the method for aggregating and optimizing the operation of multiple types of resources in the virtual power plant of the present invention;

[0054] Figure 2 It is a structural principle block diagram of the device for aggregating and optimizing the operation of multiple types of resources in the virtual power plant of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the spirit of the present invention belong to the protection scope of the present invention.

[0056] Embodiment 1:

[0057] Refer to Figure 1 , an embodiment of the present invention provides a method for aggregating and optimizing the operation of multiple types of resources in a virtual power plant, specifically including the following steps:

[0058] Step 1: Establish adjustable power quantity models for various types of flexible adjustable resources on the power supply side and the demand side within the scope of the virtual power plant respectively;

[0059] Step 2: Aggregate and model the adjustable power quantity models of various types of flexible adjustable resources to obtain the aggregated output power quantity model and the flexible adjustable power quantity range of the virtual power plant;

[0060] Step 3: Generate the available output curve of the virtual power plant for the next day according to the aggregated output power quantity model, and the power grid formulates the total output plan for the next day based on the available output curve of the virtual power plant and its flexible adjustable power quantity range;

[0061] Step 4: Construct an optimization operation model of the virtual power plant with the goal of minimizing the comprehensive cost;

[0062] Step 5: Based on the total output plan for the next day issued by the power grid, combined with the operation constraint conditions of the virtual power plant, solve the optimization operation model of the virtual power plant to obtain the optimized scheduling operation results of various types of flexible adjustable resources in the virtual power plant.

[0063] In some embodiments, various types of flexible adjustable resources on the demand side within the virtual power plant include interruptible loads, energy storage units, and electric vehicles; various types of flexible adjustable resources on the power supply side include controllable power generation resources, wind turbine generators, and photovoltaic generator sets. Among them, interruptible loads include industrial and commercial user loads; controllable power generation resources include thermal power generating units, micro gas turbines, and / or hydraulic generating units.

[0064] Further, the adjustable power quantity models of various types of flexible adjustable resources established in Step 1 are specifically as follows:

[0065] (1) Adjustable power quantity model of controllable power generation resources

[0066] The adjustable power of the controllable power generation resources is a determined value, and the adjustable power quantity model of the controllable power generation resources is:

[0067]

[0068] In the formula, E + (Δt, P a ) represents the maximum increased power quantity of the controllable power generation unit within the time period Δτ, and E - (Δt, P a ) represents the maximum decreased power quantity of the controllable power generation unit within the time period Δτ; P ais the current output value of the controllable power generation unit; and are the upward ramp rate and downward ramp rate of the controllable power generation unit respectively; P a,min and P a,min are the upper and lower limits of the power regulation of the controllable power generation unit respectively.

[0069] (2) Adjustable power model of wind turbines

[0070] The output of wind turbines is mainly affected and limited by wind speed and wind direction, and has a certain randomness; the output power of wind turbines is:

[0071]

[0072] In the formula, P wind (t) is the output power of wind power at time t, v t represents the actual wind speed at time t, v ci is the cut-in wind speed, v co is the cut-out wind speed, v R is the rated wind speed, P R is the rated power.

[0073] Therefore, the adjustable power of wind turbines is:

[0074]

[0075] In the formula, μ E (P wind (t)) represents the probability density function of the output power value P wind of wind power at different times t; k and c are the shape parameter and speed parameter of the Weibull distribution; ρ = (P R + hP wind (t))v ci , P R is the probability density function of the wind turbine, h = v R - v ci - 1, v R is the rated wind speed of the wind turbine, v ci is the cut-in wind speed

[0076] (3) Adjustable power model of photovoltaic power generation units:

[0077]

[0078] In the formula, μ E (P pv (t)) represents the probability density function of the output power value P pv of the wind turbine at different times t; P pv= ξAη, where ξ is the solar irradiation intensity; A is the area of the photovoltaic panel, and η is the energy conversion coefficient; Γ is the beta distribution operator, and a and b are the location parameter and shape parameter of the beta distribution, respectively; P pv,max represents the maximum output power of the wind turbine.

[0079] (4) Adjustable power model of interruptible load

[0080] Interruptible load refers to a load unit whose power demand can be adjusted through demand response technology; in some embodiments, the interruptible load includes industrial load regulation, and the load demand of large industrial power equipment during peak electricity consumption periods can be reduced or delayed through means such as electricity use agreements; the adjustable power of the interruptible load is:

[0081] E L (P L (Δt)) = Δt(P L,max - P L )(5)

[0082] In the formula, E L (P L (Δt)) is the adjustable power of the interruptible load during the Δt time period, P L is the interruptible load power, P L,max is the maximum load power value. Considering that the interruptible load also has a certain randomness, it is assumed that P L obeys a normal distribution, and thus its adjustable flexibility is also represented in the form of a probability density function. The adjustable power model of the interruptible load is expressed as:

[0083]

[0084] In the formula, μ EL (P L (t)) represents the probability density function of the interruptible power P L at different times t, μ and σ 2 represent the mean and variance of P L , P L,max is the maximum power value of the interruptible load; Δt is the interruptible time period.

[0085] (5) Adjustable power model of energy storage unit

[0086] The virtual power plant includes various energy storage units. In some embodiments, the energy storage units include battery energy storage systems and thermal energy storage systems, etc. The adjustable power model of the energy storage unit is expressed as:

[0087]

[0088] In the formula, Indicates the upper and lower limits of the adjustable power of the energy storage unit during the Δt time period when discharging as a power generation side resource. Indicates the upper and lower limits of the adjustable power of the energy storage unit during the Δτ time period when charging as a demand side; making the maximum charging and discharging power of the energy storage equal, uniformly represented by P ess,max Indicates; E ess Is the current energy storage level; P ess Is the current charging or discharging power of the energy storage; E ess,max And E ess,min Are respectively the upper and lower limits of the energy storage level.

[0089] (6) Regulation power model of electric vehicle cluster

[0090] In some embodiments, the electric vehicle cluster can be equivalent to an energy storage power station, and reverse charging can be achieved through vehicle-to-grid interaction technology, adjusting the charging time and power during peak hours or low electricity price periods; the regulation power model of the electric vehicle cluster:

[0091]

[0092] In the formula, P EV (t) represents the power output of electric vehicles at different times t; P EV ord Represents the power command value of electric vehicles issued by the virtual power plant; T EV And T ev Are respectively the communication delay time and the control response time constant of electric vehicles from the occurrence of a fault to the control response.

[0093] As an embodiment of the present invention, in step 2, aggregating and modeling various types of flexible adjustable resources of the virtual power plant can obtain the power output of the virtual power plant participating in the electricity market, and obtain the flexibility of the adjustable power in the subsequent time period, helping to make up for the losses caused by randomness in the electricity market and participating in the system ancillary service market; according to the basic composition of various types of adjustable flexible power generation resources of the virtual power plant, aggregating and modeling multiple resources, assuming that the virtual power plant includes N1 controllable power generation resources, N2 wind turbines, N3 photovoltaic units, N4 energy storage units, N5 electric vehicle clusters, and the proportion of interruptible demand resources in the total load is k, the following power output aggregation model of the virtual power plant is obtained:

[0094]

[0095] In the formula, E VPP (Δt, P VPP (t)) represents the power output of the virtual power plant in the current time period Δt, P VPP (t), P a,i (t), Pwind,i (t), P pv,i (t), P ess,i (t) and P EV,i (t) are the output values of the virtual power plant, the i-th controllable power generation resource, the i-th wind turbine, the i-th photovoltaic unit, the i-th energy storage unit, and the i-th electric vehicle at time t; P L (t) is the load value at time t, and k is the proportion of interruptible demand resources in the total load.

[0096] Considering the randomness of the output power of wind turbines and photovoltaic units in the virtual power plant, it can be compensated by the regulation power flexibility of the adjustable resources within the virtual power plant itself. Thus, the flexible adjustable power range of the virtual power plant is expressed as follows:

[0097]

[0098] In the formula, are the power values that the virtual power plant can flexibly adjust upward and downward within the Δt time period respectively; are the power values that the i-th controllable power generation resource can flexibly adjust upward and downward respectively; are the power values that the i-th energy storage unit can flexibly adjust upward and downward respectively; are the power values that the i-th electric vehicle can flexibly adjust upward and downward respectively.

[0099] Furthermore, it should be noted that the power E of the virtual power plant obtained by aggregating the above-mentioned multiple flexibility resources in step 3 vpp participates in the electricity market; in the subsequent time period, the upward and downward adjustable flexibility of the virtual power plant is and which helps to make up for the losses caused by randomness in the electricity market and participates in the system ancillary service market.

[0100] As an embodiment of the present invention, the specific process of constructing an optimal operation model of the virtual power plant with the goal of minimizing the comprehensive cost in step 4 is as follows:

[0101] During the construction, operation, and trading stages of the virtual power plant, it not only participates in the electricity purchase and sale transactions in the electricity energy market but also participates in the peak shaving and frequency modulation ancillary service markets, and jointly optimizes the energy market and the ancillary service market; the optimization objectives of the present invention for the virtual power plant mainly include the resource aggregation cost, the operation and maintenance cost, and the risk cost brought by the randomness of multiple types of controllable power generation resources participating in the ancillary service market;

[0102] (1) Investment planning cost

[0103] The investment planning cost is mainly related to the planned construction power of each power generation resource, and the total investment planning cost can be expressed as:

[0104]

[0105] In the formula: C 0 vpp represents the investment and construction costs of various types of power generation resources. C a , C wind , C pv , C EV , and C ess,1 and C ess,2 are the costs of unit investment planning for controllable power generation resources, wind turbines, photovoltaic power generation units, and electric vehicles respectively. C a,c and P wind,c , P pv,c , P ess,c and P EV,c are the powers of investment planning for controllable power generation resources, wind turbines, photovoltaic power generation units, energy storage units, and electric vehicles respectively. Q ess,c is the capacity of the energy storage unit planned; δ ess,in and δ ess,ou represent the state variables of energy storage charging and discharging respectively. Taking 1 means charging and discharging, and taking 0 means no charging and discharging; Taking the controllable power generation resource P a,c as an example, P a,c is the product of the number of construction N1 and the rated power of the unit.

[0106] Using the present value method, the resource aggregation cost within a day is converted through the equal annual value function to obtain the expression of the investment planning cost C 1 vpp as follows:

[0107]

[0108] In the formula, C 1 vpp is the cost of investment and construction of various types of power generation resources within a day, T VPP is the annual operating hours of the virtual power plant; r is the discount rate; m is the operating years of the virtual power plant.

[0109] (2) Operation and maintenance costs

[0110] The operation and maintenance costs of the virtual power plant are:

[0111]

[0112] In the formula, represents the operation and maintenance costs of the virtual power plant; and They are the operation and maintenance costs per unit power of controllable power generation resources, wind turbines, photovoltaic power generation units, energy storage units, and electric vehicles, respectively.

[0113] (3) Risk cost

[0114] In some embodiments, the equivalent power generation of wind power, photovoltaic power, and interruptible load in the virtual power plant will pose certain risks due to randomness when participating in the day-ahead market. The above embodiments use conditional value at risk (CvaR) to represent the risk function caused by randomness in the virtual power plant, which is expressed as:

[0115]

[0116] In the formula, represents the risk function in the virtual power plant and are the error probability density functions of wind turbines, photovoltaic units, and interruptible load, respectively; α is the boundary value of value at risk (VaR); β is the set confidence level; M≥0 is a virtual coefficient representing the loss exceeding the value at risk, and t = 1, 2... 24 represents 24 hours within the scheduling day.

[0117] In summary, the expression of the objective function of the virtual power plant optimal operation model is as follows:

[0118]

[0119] In the formula, F represents the objective function with the minimum comprehensive cost; and represent the total cost of resource aggregation, the total operation and maintenance cost, and the risk cost, respectively, and R represents the risk preference coefficient. Among them, a coefficient is multiplied in front of the risk cost sub-function to describe the investor's preference for risk. R represents the investor's risk preference. The smaller the value, the greater the risk-taking of the investor, but the optimal potential net present value. On the contrary, the larger the value of R, the smaller the risk, but the investment tends to be conservative.

[0120] As an embodiment of the invention, the operation constraint conditions of the virtual power plant in step 5 mainly include the output constraints of various flexible adjustable resources, energy storage capacity constraints, interruptible load upper limit constraints, reserve constraints, and opportunity planning constraints, etc.

[0121] The output constraints of multi-type adjustable resources and energy storage capacity constraints are expressed as follows:

[0122]

[0123] In the formula, P a,min and P a,max are the maximum and minimum values of the output power of the controllable power generation resources, respectively; P wind,max 、P pv,max 、Pess,max and P EV,max respectively represent the maximum output powers of the wind turbine generator set, photovoltaic generator set, energy storage unit, and electric vehicle; Q ess,min and Q ess,max respectively represent the minimum capacity and maximum capacity of the energy storage unit.

[0124] The interruptible load constraint is:

[0125] 0 ≤ kP L (t) ≤ P L,max (17)

[0126] In the formula, P L,max represents the maximum adjustable power load of the interruptible load.

[0127] In this embodiment, the reserve power of the entire virtual power plant system is provided by controllable power generation resources, energy storage devices, and electric vehicles; its reserve constraint can be expressed as:

[0128]

[0129] In the formula, P a,min and P a,max respectively represent the maximum value and minimum value of the output power of the controllable power generation resources; R a (t), R ess (t), and R EV (t) respectively represent the reserve capacities of the controllable power generation resources, energy storage devices, and electric vehicles at time t; Q ess (t) is the energy storage capacity of the energy storage unit at time t, and η is the discharge efficiency of the energy storage device; P EV,max is the maximum value of the discharge power of the electric vehicle, and the reserve capacity provided by the electric vehicle does not exceed its available capacity.

[0130] Since the output of the wind turbine generator set and the photovoltaic generator set is random, resulting in an uncertain reserve capacity constraint condition, this embodiment uses chance-constrained programming to handle the randomness, and the chance-constrained programming can be expressed as:

[0131] P rob [E vpp ≥ E P RGs (t) - P wind (t) - P pv (t)] ≥ β (19)

[0132] In the formula, E P RGs (t) is the expected value of the combined output power P RGs (t) of the wind turbine generator set and the photovoltaic generator set at time t, β is the preset confidence level; P rob [] is the probability operator; E vppIt is the adjustable power of the virtual power plant, which is the power value that can be flexibly adjusted upward and downward between, that is

[0133] Furthermore, when solving the optimal operation model of the virtual power plant in step 5, this model is a mixed-integer linear programming problem, and the YALMIP toolbox in MATLAB can be used for solving. Since the solving algorithm is not the focus of this embodiment, it will not be elaborated here.

[0134] The beneficial effects of the present invention are as follows. Compared with the prior art

[0135] 1. By comprehensively considering the characteristics of various flexible resources on the power supply side and the demand side, the present invention proposes a new multi-type resource optimal operation scheme. Different from the prior art which mostly focuses on the optimal scheduling of single resources, the present invention can coordinate and integrate multiple resources, such as controllable power generation resources, wind turbines, photovoltaic power generation units, interruptible loads, energy storage units and electric vehicle groups, realizing the coordinated optimization of the power supply side and the demand side, not only improving the resource utilization rate, but also effectively reducing resource waste and further improving the operation efficiency of the virtual power plant.

[0136] 2. The present invention fully considers the intermittent, random and volatile characteristics of renewable energy resources such as wind power and photovoltaic power. The constructed optimal operation model of the virtual power plant takes into account the risk cost in its objective function, not only considering the conditional risk value brought by the randomness of wind power and photovoltaic power generation, but also dynamically adjusting the power generation resources through the adjustable flexibility of controllable power generation resources and the role of reserve capacity, making the power output more stable, reducing the burden on the power system caused by the fluctuations of renewable energy, and solving the problems of volatility and intermittency of renewable energy.

[0137] 3. The present invention proposes to optimize the scheduling of various flexibility-adjustable resources under the formulated reserve constraints and opportunity planning constraints in the case of grid load fluctuations, uncertain output of renewable energy, etc. This optimization not only considers economy, but also takes into account factors such as system stability, gives full play to the synergy of various resources, realizes the efficient aggregation and optimal operation of multi-type adjustable resources, maximizes the utilization efficiency of renewable energy, and further promotes the development of green energy.

[0138] Embodiment 2

[0139] As Figure 2 shown, the present invention provides a virtual power plant multi-type resource aggregation and optimal operation device, which is used to implement the steps of the method in Embodiment 1 above. The device specifically includes

[0140] A building module, which is used to respectively build adjustable power models of various flexibility-adjustable resources on the power supply side and the demand side within the scope under the jurisdiction of the virtual power plant

[0141] An aggregation module for aggregating and modeling the adjustable power models of various flexibility-adjustable resources to obtain the aggregated power output model of the virtual power plant and the flexible adjustable power range;

[0142] A formulation module for generating the available output curve of the virtual power plant for the next day according to the aggregated power output model, and the power grid formulates the total output plan for the next day based on the available output curve of the virtual power plant and its flexible adjustable power range;

[0143] A construction module for constructing an optimal operation model of the virtual power plant with the goal of minimizing the comprehensive cost;

[0144] An optimization module for solving the optimal operation model of the virtual power plant based on the total output plan for the next day issued by the power grid and combining the operation constraint conditions of the virtual power plant to obtain the scheduling operation optimization results of various flexibility-adjustable resources in the virtual power plant.

[0145] The virtual power plant multi-type resource aggregation and optimization operation device provided by the embodiments of the present invention and the virtual power plant multi-type resource aggregation and optimization operation method provided by Embodiment 1 are based on the same technical concept, and can produce beneficial effects as described in Embodiment 1. The content not described in detail in this embodiment can be referred to Embodiment 1.

[0146] Embodiment 3:

[0147] A terminal provided by an embodiment of the present invention includes a processor and a storage medium;

[0148] The storage medium is used to store instructions;

[0149] The processor is used to operate according to the instructions to execute the steps of the method according to any one of Embodiment 1.

[0150] Embodiment 4:

[0151] A computer-readable storage medium provided by an embodiment of the present invention stores a computer program, and when the program is executed by a processor, it implements the steps of the method according to any one of Embodiment 1.

[0152] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0153] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed as being a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0154] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0155] Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A virtual power plant multi-type resource aggregation optimization operation method, characterized in that: Methods include: Step 1: Establish adjustable power models for various flexible adjustable resources on the power supply side and demand side within the jurisdiction of the virtual power plant; Step 2: Aggregate the adjustable power models of various flexible and adjustable resources to obtain the output power aggregation model and flexible and adjustable power range of the virtual power plant; Step 3: Generate the available output curve of the virtual power plant for the next day according to the output power aggregation model, and the power grid formulates the total output plan for the next day based on the available output curve of the virtual power plant and its flexible and adjustable power range; Step 4: Construct a virtual power plant optimization operation model with the goal of minimizing comprehensive costs; Step 5: Based on the next-day total output plan issued by the power grid and combined with the operating constraints of the virtual power plant, the virtual power plant optimization operation model is solved to obtain the dispatching and operation optimization results of various flexible and adjustable resources in the virtual power plant.

2. The virtual power plant multi-type resource aggregation optimization operation method according to claim 1 is characterized in that: In step 1, the various flexible and adjustable resources on the demand side include interruptible loads, energy storage units and electric vehicle clusters; the various flexible and adjustable resources on the power supply side include controllable power generation resources, wind turbines and photovoltaic generators; Among them, the interruptible load includes industrial and commercial user load; the controllable power generation resources include thermal power generating sets, micro-turbines and / or hydropower generating sets.

3. The virtual power plant multi-type resource aggregation optimization operation method according to claim 1 is characterized in that: The step 1 includes: establishing a regulation power model of the interruptible load, the expression of which is as follows: In the formula, μ EL (P L (t)) represents the interruptible power P of the interruptible load at different times t L The probability density function, μ and σ 2 Indicates P L The mean and variance of P L,max is the maximum power value of the interruptible load; Δt is the interruptible time period.

4. The virtual power plant multi-type resource aggregation optimization operation method according to claim 1 is characterized in that: The step 1 includes: establishing a power regulation model for the electric vehicle cluster, the expression of which is as follows: Where P EV (t) represents the output power of the electric vehicle at different times t; P EV ord Represents the electric vehicle power command value issued by the virtual power plant; T EV With T ev They are the communication delay time from fault occurrence to control response and the control response time constant of the electric vehicle, respectively.

5. The virtual power plant multi-type resource aggregation optimization operation method according to claim 1 is characterized in that: In step 2, the expression of the output power aggregation model of the virtual power plant is as follows: In the formula, E VPP (Δt,P VPP (t)) represents the output power of the virtual power plant in the current period Δt, P VPP (t), P a,i (t), P wind,i (t), P pv,i (t), P ess,i (t) and P EV,i (t) are the output values ​​of the virtual power plant, the ith controllable power generation resource, the ith wind turbine, the ith photovoltaic unit, the ith energy storage unit, and the ith electric vehicle at time t; N1, N2, N3, N4, and N5 are the numbers of controllable power generation resources, wind turbines, photovoltaic units, energy storage units, and electric vehicles within the jurisdiction of the virtual power plant; P L (t) is the load value at time t, and k is the proportion of interruptible demand resources to the total load.

6. The virtual power plant multi-type resource aggregation optimization operation method according to claim 5 is characterized in that: In step 2, the expression of the flexible adjustable power range is as follows: In the formula, They are the flexibly adjustable upward and downward power values ​​of the virtual power plant within the Δt time period; are the flexibly adjustable power values ​​of the i-th controllable power generation resource upward and downward respectively; are the flexibly adjustable power values ​​of the i-th energy storage unit upward and downward respectively; They are the flexibly adjustable power values ​​of the i-th electric car upward and downward respectively.

7. The virtual power plant multi-type resource aggregation optimization operation method according to claim 5 is characterized in that: In step 4, the objective function of the virtual power plant optimization operation model is expressed as follows: In the formula, F represents the minimum comprehensive cost as the objective function; and They represent the total cost of resource aggregation, the total cost of operation and maintenance, and the risk cost respectively, and R represents the risk preference coefficient.

8. The virtual power plant multi-type resource aggregation optimization operation method according to claim 7 is characterized in that: The risk cost The calculation formula is as follows: In the formula, and are the error probability density functions of wind turbines, photovoltaic units and interruptible loads respectively; α is the boundary value of risk value VaR; β is the set confidence level; M ≥ 0 is a virtual coefficient representing the loss exceeding the risk value, and t = 1, 2…24 represents 24 hours within the scheduling day.

9. The virtual power plant multi-type resource aggregation optimization operation method according to claim 1 is characterized in that: The operating constraints of the virtual power plant include: output constraints and energy storage capacity constraints of various types of flexible and adjustable resources, interruptible load upper limit constraints, backup constraints and / or opportunity planning constraints.

10. The virtual power plant multi-type resource aggregation optimization operation method according to claim 9 is characterized in that: The expression of the backup constraint is as follows: Where P a,min and P a,max are the maximum and minimum output power of controllable power generation resources respectively; P a (t) represents the operating power of the controllable power generation resource at time t; R a (t), R ess (t) and R EV (t) are the reserve capacities of controllable power generation resources, energy storage equipment and electric vehicles at time t; Q ess (t) is the energy storage capacity of the energy storage unit at time t, Q ess,min represents the minimum capacity of the energy storage unit, η is the discharge efficiency of the energy storage device; P ess,max and P EV,max Represent the maximum output power of the energy storage unit and electric vehicle, P ess (t) and P EV (t) represent the operating power of the energy storage unit and the electric vehicle at time t respectively.

11. The virtual power plant multi-type resource aggregation optimization operation method according to claim 9 is characterized in that: The expression of the opportunity planning constraint is as follows: P rob [E vpp ≥E P RGs (t)-P wind (t)-P pv (t)]≥β In the formula, EP RGs (t) is the combined output power P of the wind turbine and photovoltaic unit at time t RGs (t), β is the preset confidence level; P rob [] is a probability operator; E vpp The adjustable power of the virtual power plant is in a flexible and adjustable upward and downward power value. Between 12. A virtual power plant multi-type resource aggregation optimization operation device, which runs the virtual power plant multi-type resource aggregation optimization operation method according to any one of claims 1 to 11, characterized in that: The device includes: Establishing modules for respectively establishing adjustable power models of various flexible and adjustable resources on the power supply side and the demand side within the jurisdiction of the virtual power plant; Aggregation module, which is used to aggregate and model the adjustable power models of various flexible and adjustable resources to obtain the output power aggregation model and flexible and adjustable power range of the virtual power plant; A formulation module, used to generate the available output curve of the virtual power plant for the next day according to the output power aggregation model, and the power grid formulates the total output plan for the next day based on the available output curve of the virtual power plant and its flexible and adjustable power range; A construction module is used to construct an optimal operation model of a virtual power plant with the goal of minimizing comprehensive costs; The optimization module is used to solve the optimization operation model of the virtual power plant based on the next day's total output plan issued by the power grid and the operation constraints of the virtual power plant, so as to obtain the scheduling and operation optimization results of various flexible and adjustable resources in the virtual power plant.

13. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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