Virtual power plant source load resource aggregation regulation and control method and device based on flexibility quantification

Abstract

The invention relates to the field of virtual power plant resource aggregation, in particular to a virtual power plant source load resource aggregation regulation and control method and device based on flexibility quantization, and the method comprises the steps: carrying out the data processing of obtained resource historical operation data based on Monte Carlo simulation and scene reduction technology, and obtaining a typical operation scene; constructing a source-load multi-element resource flexibility model, and quantifying system flexibility requirements and supply; defining a system flexibility margin and a flexible adjustment factor; constructing a virtual power plant source load resource aggregation regulation and control model, and setting an objective function and constraint conditions of the model; setting that wind and light abandoning and wind and light full absorption are allowed, comparing and analyzing the effect of different source load resources participating in aggregation regulation and control, and obtaining an optimal aggregation regulation and control scheme considering a target function and constraint conditions. Compared with the prior art, the method has the advantages that the optimal resource aggregation regulation and control scheme considering economy, flexibility and new energy consumption is achieved, and efficient utilization of resources on the two sides of the source and the load is improved.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant resource aggregation, and in particular to a virtual power plant source-load resource aggregation control method and device based on flexibility quantification. Background Art

[0002] Energy is the foundation and driving force of economic and social development. It has become a consensus of the international community to build a new power system with new energy as the main body and diversified power load development. With the continuous access of a high proportion of renewable energy, thermal power units are operating close to the technical lower limit and will gradually be replaced by wind power and photovoltaic power stations. The net load fluctuation of the system has increased, and the uncertainty characteristics of the source side have become prominent. The phenomenon of wind and light abandonment is serious. The core of the problem lies in improving the flexibility of the system. Therefore, it is urgent to mobilize flexible resources on both the source and load sides to participate in the optimization of the system operation.

[0003] The characteristics of source-load multi-resources, such as large quantity, diverse entities, dispersed distribution, and different operating characteristics, bring possibilities and ways to improve the flexibility of the system. In order to meet the high flexibility requirements under the high proportion of renewable energy, virtual power plant technology can be used to achieve the effective integration and reasonable allocation of massive distributed controllable resources. However, the existing research on flexibility description and virtual power plant modeling has the following shortcomings: (1) Existing research mostly focuses on the power supply side or the demand side, and lacks a virtual power plant model that takes into account the adjustable resources on both the source and load sides; (2) There is a lack of virtual power plant resource aggregation and control methods from the perspective of flexibility; (3) Most of them assume that new energy is fully absorbed and does not participate in system operation, and lack flexibility regulation that allows wind and solar power to be abandoned.

[0004] After searching, China Invention Patent Publication No. CN115456242A discloses a virtual power plant market-oriented optimization scheduling method based on multiple uncertainty representations, using a scenario method based on Monte Carlo and Manhattan probability distance to model wind and solar output and electricity prices, and using the interval method to model carbon prices and demand response; with the goal of minimizing the operating cost of the virtual power plant, comprehensive consideration of electricity market transactions, carbon rights transactions and incentive-based demand response mechanisms, a virtual power plant optimization scheduling model based on interval linear programming was established, and the two-stage decomposition algorithm in the strong interval linear programming method was used to solve the model to obtain the optimized scheduling plan. This existing patent has problems such as lack of flexibility and failure to allow wind and solar power abandonment.

[0005] How to achieve flexible and quantified aggregation and regulation of source and load resources in virtual power plants has become a technical problem that needs to be solved. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a virtual power plant source and load resource aggregation control method and equipment based on flexibility quantification.

[0007] The purpose of the present invention can be achieved by the following technical solutions: According to one aspect of the present invention, a virtual power plant source and load resource aggregation control method based on flexibility quantification is provided, the method comprising the following steps: Step S1, based on Monte Carlo simulation and scenario reduction algorithm, data processing is performed on the acquired resource historical operation data to obtain a typical operation scenario, wherein the resource historical operation data includes output power data of wind power, photovoltaic power, net load, interruptible load and transferable load within a period of time; Step S2, based on typical operation scenarios, by quantifying system flexibility requirements, constructing a source-load multi-resource flexibility model to characterize upward flexibility and downward flexibility of resources; Step S3, defining the system flexibility margin and the flexibility adjustment factor, when the system flexibility margin is less than 0, a flexibility deficit is generated; Step S4, using the result of step S2 and the definition of step S3, construct a virtual power plant source-load resource aggregation control model, set the objective function and constraints of the model, wherein the constraints include flexibility constraints, power balance constraints and output constraints of various resources, and the objective function is to minimize the sum of wind power and photovoltaic combined output power, system total operating cost and flexibility deficiency penalty cost within an operating cycle T; Step S5, respectively solving the schemes of the virtual power plant source-load resource aggregation control model that meets the constraints in step S4 under the settings of wind and solar power abandonment and wind and solar power full absorption, and selecting the scheme with the best objective function as the optimal aggregation control method; The process of obtaining the typical operation scenario in step S1 includes: Step S1.1, select the benchmark scene, set the standard deviation and the number of samples, use Monte Carlo simulation scene generation to obtain the scene set S; Step S1.2, determine the number of scenes after reduction, and calculate the probability distance of each pair of scenes in the scene set S; Step S1.3, selecting the scene d with the smallest sum of probability distances in the scene set S; Step S1.4, replace scene d with scene r that is closest to scene d in scene set S, and add the probability of scene d to the probability of scene r, eliminate scene d, and form a new scene set S'; Step S1.5, determine whether the number of scenarios in the new scenario set S' is equal to the number of scenarios set in step S1.1; if not, return to step S1.2; if yes, output the reduced typical operation scenario set and corresponding probability; The process of step S2 includes: Step S2.1, quantify the system flexibility demand, and obtain the upward and downward flexibility demands of wind power WT, photovoltaic PV and net load L; Step S2.2, building a source-load resource flexibility model, which includes the upward and downward flexibility supply of wind power WT, photovoltaic PV, cogeneration CHP, interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV at time t; wherein the source-load resources include source-side flexibility resources and load-side flexibility resources, the source-side flexibility resources are wind power WT, photovoltaic PV, cogeneration CHP, and the load-side flexibility resources include interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV; Step S2.3, integrating system flexibility provision, specifically: , in, and are the upward and downward flexibility supply of the system at time t, respectively; and are the upward and downward flexibility supply of CHP at time t, respectively; and are the upward and downward flexibility supply of wind power WT and photovoltaic PV at time t, respectively; and are the upward and downward flexibility supplies of the interruptible load IL, the transferable load SL, the energy storage ESS and the electric vehicle EV at time t, respectively; The upward and downward flexibility requirements of wind power WT, photovoltaic PV and net load L are calculated as follows: , , in, are the output power and upper and lower limits of wind power WT, photovoltaic PV and net load L at time t under the kth typical operation scenario; and They are the upward and downward flexibility requirements of the system at time t under the kth typical operation scenario; is the upward and downward flexibility demand of wind power WT, photovoltaic PV and net load L at time t under the kth typical operation scenario; The wind power WT and photovoltaic PV are uncontrollable units, and the upward and downward flexibility supply is provided by allowing wind and light abandonment. The upward and downward flexibility supply of the interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV is specifically as follows: , , , in, are the upward and downward flexibility supplies of the interruptible load IL, the transferable load SL, the energy storage ESS and the electric vehicle EV at time t, respectively; and are the output power and upper and lower limits of the interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV at time t respectively; and are the energy storage ESS capacity and its upper and lower limits at time t respectively; and are the electric energy and its upper and lower limits at time t respectively; is the unit time scale; The upward and downward flexibility of the CHP is specifically provided as follows: , in, and are the upward and downward flexibility supply of CHP at time t, respectively; and They are the upward and downward ramping powers of CHP respectively; and They are respectively the power generation capacity of the combined heat and power (CHP) at time t and its upper and lower limits; The process of step S3 includes: Step S3.1, define the system flexibility margin as the difference between flexibility supply and flexibility demand; Step S3.2, define the flexibility adjustment factor as the proportion of various flexibility resources to bear the total flexibility demand of the system; The virtual power plant source and load resource aggregation control model in step S4 is specifically: , Where: and They respectively represent the output power of the virtual power plant VPP, wind power WT, photovoltaic PV, combined heat and power CHP, transferable load SL, interruptible load IL, energy storage ESS and electric vehicle EV at time t.

[0008] Preferably, the process of step S4 includes: Step S4.1, constructing a virtual power plant source and load resource aggregation control model; Step S4.2, setting the objective function of virtual power plant resource aggregation regulation; Step S4.3, setting the constraints that virtual power plant resource aggregation and regulation must meet.

[0009] Preferably, the objective function F is specifically: , in, is the combined output power of wind power and photovoltaic power in an operation cycle T; is the total operating cost of the system within an operating cycle T; Penalize costs for flexibility shortfalls; F The calculation formula for 1 is: , in and are the output power of wind power WT and photovoltaic PV at time t respectively; is the total operating cost of the system within an operating cycle T, and the calculation formula is: , , in, For operation and maintenance costs; Penalty costs for power curtailment, including wind and solar curtailment costs; is the CHP fuel cost; is the total load compensation cost; Compensate for EV discharge losses; and are wind power WT and photovoltaic PV penalty coefficients respectively; and are the amount of wind and solar power abandoned at time t respectively; The energy provided by the fuel; is the fuel cost coefficient; and are the compensation cost coefficients of interruptible load IL and transferable load SL respectively; and Expected electricity demand of users; and are the output powers of the transferable load SL and the interruptible load IL at time t respectively; is the compensation cost coefficient; is the discharge power of the electric vehicle EV at time t; They are the operation and maintenance cost coefficients of wind power WT, photovoltaic PV, combined heat and power CHP and energy storage ESS respectively; Provide heating power for CHP unit; , , are the output power of WT, PV and CHP at time t, and is the charging power and discharging power of ESS at time t; The operation and maintenance costs of electric vehicles (EVs); The calculation formula is: , in, Penalize costs for flexibility shortfalls; is the deficiency penalty coefficient, and are the upward and downward flexibility requirements of the system at time t respectively.

[0010] Preferably, the flexibility constraints are specifically: , in, and are the upward and downward flexibility margins of the system at time t, respectively; The power balance constraints are specifically: , in, , , and are the output powers of the interruptible load IL, transferable load SL, electric vehicle EV and net load L at time t respectively; , , and They are the output power of wind power WT, photovoltaic PV, combined heat and power CHP and energy storage ESS at time t respectively.

[0011] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1) The present invention effectively handles the uncertainty of wind power and photovoltaic output by performing Monte Carlo simulation and scenario reduction on the acquired historical operation data of resources, quantifies the system flexibility demand on the source side, and uses flexibility margin and flexible adjustment factor on the load side to characterize the flexibility supply capacity of source and load resources, which can effectively recognize the source-load flexibility supply and demand relationship and adjustment capacity; in order to compare the effects of different source and load resources participating in aggregation regulation, the allowable wind and solar power abandonment situations and the full wind and solar power absorption plan are set to obtain the optimal resource aggregation regulation plan that meets the objective function and constraints, and takes into account economy, flexibility and new energy absorption, thereby improving the efficient utilization of resources on both the source and load sides.

[0013] 2) The present invention deals with the uncertainty of wind power and photovoltaic output and typical operating scenarios based on Monte Carlo simulation and scenario reduction technology, and constructs a source-load multi-resource flexibility model based on typical operating scenarios to characterize the flexibility adjustment capabilities of various resources, solve the problems of system net load fluctuations and source-side uncertainty, and aggregate and regulate virtual power plant resources from the perspective of flexibility accuracy. It also provides a theoretical basis for subsequent research on the participation of source-load multi-resources in virtual power plant aggregation regulation and system flexibility operation optimization.

[0014] 3) The present invention defines the system flexibility margin and introduces a flexible adjustment factor to quantify the flexibility adjustment capability of the system flexibility resources, so as to characterize the contribution of different flexibility resources to the system flexibility balance, which can effectively understand the source-load flexibility supply and demand relationship and adjustment capability.

[0015] 4) The constraints of the present invention include flexibility constraints, power balance constraints and other constraints, allowing for wind and solar power abandonment and full wind and solar power absorption plans. The objective function takes into account economy, flexibility and new energy absorption, and obtains the optimal resource aggregation and control plan. Compared with the existing methods that lack flexibility and do not allow wind and solar power abandonment, it can improve the utilization rate of various resources.

[0016] 5) The present invention generates scenarios based on historical operating data, and then uses the rapid predecessor elimination technology based on probability distance to reduce scenarios. Compared with the existing patents that generate random scenarios based on known probability distribution, it is more authentic and reliable, and there are certain differences between the typical operating scenarios obtained, which can basically cover the fluctuation range of actual output. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the source-load resource aggregation control method of the virtual power plant in the present invention; Figure 2 A schematic diagram of the flexibility adjustment capability of the system of the optimal resource aggregation solution in the present invention; Figure 3 A schematic diagram of the flexibility adjustment capability of the system of the optimal resource aggregation solution in the present invention; Figure 4 A schematic diagram of the flexibility balance of the optimal resource aggregation solution in this aspect; Figure 5 Schematic diagram of the virtual power plant resource aggregation and control results of the optimal aggregation scheme in the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0019] Example 1 The present embodiment relates to a virtual power plant source and load resource aggregation and control method based on flexibility quantification, wherein the virtual power plant source and load resources include wind power (wind turbine, WT), photovoltaic (photovoltaic, PV), combined heat and power (combined heat and power, CHP), interruptible load (interruptible load, IL), shiftable load (shiftable load, SL), energy storage system (energy storage system, ESS) and electric vehicle (electric vehicle, EV).

[0020] A virtual power plant source and load resource aggregation control method based on flexibility quantification, such as Figure 1 , including the following steps: Step S1: Collect historical resource operation data, including output power data of wind power, photovoltaic power, net load, interruptible load and transferable load in a historical period, perform data cleaning, and obtain a source-load multi-resource data set; perform data processing based on Monte Carlo simulation and scenario reduction technology to obtain typical operation scenarios, including: Step S1.1, select the benchmark scene, set the standard deviation and the number of samples, use the Monte Carlo method to generate the scene, and obtain the scene set S.

[0021] Step S1.2, determine the number of scenes after reduction, and calculate the probability distance of each pair of scenes in the scene set S.

[0022] Step S1.3, select the scene d with the smallest sum of probability distances.

[0023] Step S1.4, replace scene d with scene r that is closest to scene d in scene set S, and add the probability of scene d to the probability of scene r, eliminate scene d, and form a new scene set S'.

[0024] Step S1.5, determine whether the number of scenarios in the new scenario set S' meets the requirements, that is, whether it is equal to the number of scenarios set in step S1.1. If not, return to step S1.2; if satisfied, output the reduced typical operation scenario set and corresponding probability.

[0025] In this embodiment, the historical operation data of wind power, photovoltaic power and load on a typical day are selected as the benchmark scenario, the standard deviation is set to 0.1, the number of samples is 500, and the number of scenarios after reduction is 5, and the scenario generation and reduction are performed. A typical day refers to a day when the resource data of wind power, photovoltaic power, net load, interruptible load and transferable load are complete. The scenario is generated based on the historical operation data, and then the scenario is reduced by using the fast previous generation elimination technology based on the probability distance. Compared with the existing patents that generate random scenarios based on known probability distributions, it is more authentic and reliable, and there is a certain difference between the typical operation scenarios obtained, which can basically cover the fluctuation range of the actual output.

[0026] Step S2: Construct a source-load multi-resource flexibility model, that is, from the perspective of flexibility, by quantifying the system flexibility requirements, a model is created to describe the upward flexibility and downward flexibility of resources, specifically including: Step S2.1, quantify the system flexibility demand, including the flexibility demand of wind power, photovoltaic power and net load. The system flexibility supply comes from wind power, photovoltaic power, cogeneration, interruptible load, transferable load, energy storage and electric vehicles. Among them, wind power and photovoltaic power are uncontrollable units. By allowing wind power and photovoltaic power to be abandoned (i.e. abandoning wind power and photovoltaic power), the upward and downward flexibility supply of flexibility is provided. The flexibility supply capacity of other resources except wind power and photovoltaic power is quantified by the flexible adjustment factor, and the calculation formula is:

[0027]

[0028]

[0029] in, They are the output power and upper and lower limits of wind power WT at time t under the kth typical operation scenario; are the output power of photovoltaic PV at time t and its upper and lower limits under the kth typical operation scenario; are the output power and upper and lower limits of the net load L at time t under the kth typical operation scenario; and They are the upward and downward flexibility requirements of the system at time t under the kth typical operation scenario; , They are the upward and downward flexibility requirements of the system at time t under the kth typical operation scenario; They are the upward and downward flexibility demands of wind power WT at time t under the kth typical operation scenario; They are the upward and downward flexibility demands of photovoltaic PV at time t under the kth typical operation scenario; They are respectively the upward and downward flexibility requirements of the net load L at time t in the kth typical operation scenario. The net load L is the system net load obtained based on the historical operation data of wind and solar loads.

[0030] Step S2.2, build a source-load resource flexibility model. The source-side flexibility resources are wind power, photovoltaic power and cogeneration. Wind power and photovoltaic power are uncontrollable units. They provide upward and downward flexibility supply by allowing wind and solar power abandonment. The flexibility model of cogeneration includes an upward flexibility model and a downward flexibility model, as follows:

[0031] in, and are the upward and downward flexibility supply of CHP at time t, respectively; and They are the upward and downward ramping powers of CHP respectively; and are the power generation power of CHP at time t and its upper and lower limits respectively.

[0032] The load-side flexibility resources include interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV. The upward flexibility and downward flexibility models are as follows:

[0033]

[0034]

[0035] in, are the upward and downward flexibility supplies of IL, SL, ESS and EV at time t, respectively; and are the output power of IL, SL, ESS and EV at time t and their upper and lower limits; and are the ESS capacity and its upper and lower limits at time t respectively; and are the electric energy and its upper and lower limits at time t respectively; The unit time scale is 1h.

[0036] Step S2.3, integrating system flexibility supply, the model of system upward flexibility and downward flexibility is as follows:

[0037] in, and are the upward and downward flexibility supply of the system at time t, and They are the upward and downward flexibility supply of wind power WT at time t, respectively; and They are the upward and downward flexibility supply of photovoltaic PV at time t respectively.

[0038] Step S3: define the system flexibility margin and flexible adjustment factor to characterize the flexibility adjustment capability of source and load resources, as follows: Step S3.1, define the system flexibility margin as the difference between flexibility supply and flexibility demand, including upward and downward directions, corresponding to the system upward flexibility margin and downward flexibility margin respectively:

[0039] In the formula, and are the upward and downward flexibility margins of the system at time t, and are the upward and downward flexibility requirements of the system at time t, and are the upward and downward flexibility supplies of the system at time t respectively.

[0040] When the margin is less than 0, there will be a flexibility deficit, that is:

[0041]

[0042] Where: and They are the upward and downward flexibility deficits of the system at time t respectively.

[0043] Step S3.2, define the flexible adjustment factor as the proportion of various flexible resources to bear the total flexibility demand of the system, which also has two directions, upward and downward, corresponding to the upward flexible adjustment factor and downward flexible adjustment factor of various flexible resources respectively:

[0044] Where: and are the upward and downward flexible adjustment factors of each flexible resource at time t, and are the upward and downward flexibility requirements of the system at time t, and They are the upward and downward flexibility supply of the mth resource at time t respectively.

[0045] Step S4: construct a virtual power plant source-load resource aggregation and control model, that is, aggregate and control the source-load flexibility resources involved through the virtual power plant. The model can be built based on the MATLAB platform using the YALMIP toolbox, and the solver (such as but not limited to the CPLEX solver) is called for solving. The objective function and constraints need to be set in the solution algorithm, as follows: Step S4.1, construct a virtual power plant source and load resource aggregation control model:

[0046] Where: and They represent the output power of VPP, WT, PV, CHP, SL, IL, ESS and EV at time t, in kW. VPP (Virtual Power Plant) is a virtual power plant.

[0047] Step S4.2, setting the objective function F of the virtual power plant resource aggregation control, specifically:

[0048] The combined output of wind power and photovoltaic power in an operation cycle T is calculated as follows:

[0049] is the total operating cost of the system within an operating cycle T, and the calculation formula is:

[0050] in, is the operation and maintenance cost, unit: yuan; is the penalty cost for power abandonment, unit: yuan, including the cost of wind and solar power abandonment; is the CHP fuel cost, unit: yuan; is the total load compensation cost, unit: yuan; is the EV discharge loss compensation cost, unit: yuan; and is the penalty coefficient, unit: yuan / kW; and is the amount of wind and solar power abandoned at time t, unit: kW; The energy provided by the fuel, in kW; is the fuel cost coefficient, unit: yuan / (kW·h); and is the compensation cost coefficient, unit: yuan / kW; and is the user's expected electricity demand, unit: kW; and are the output power of SL and IL at time t, in kW; is the compensation cost coefficient, unit: yuan / kW; is the EV discharge power at time t; It is the operation and maintenance cost of electric vehicles EV.

[0051] Among them, the unit operation and maintenance cost can be expressed as:

[0052] in, are the operation and maintenance cost coefficients of WT, PV, CHP and ESS, unit: yuan / kW; Heating power for CHP units; , , are the output power of WT, PV and CHP at time t, and are the charging power and discharging power of ESS at time t.

[0053] is the flexibility deficiency penalty cost, and its calculation formula is:

[0054] in, is the flexibility deficiency penalty cost, unit: yuan; It is the shortage penalty coefficient, unit: Yuan / kW.

[0055] Step S4.3, setting the constraints that the virtual power plant resource aggregation control needs to meet, as follows: Flexibility constraints:

[0056] in, and are the upward and downward flexibility margins of the system at time t respectively.

[0057] Power balance constraints:

[0058] in, , , and are the output powers of interruptible load IL, transferable load SL, electric vehicle EV and net load L at time t, , , and They are the output power of wind power WT, photovoltaic PV, combined heat and power CHP and energy storage ESS at time t respectively.

[0059] Wind power and photovoltaic constraints:

[0060] Combined heat and power output constraints:

[0061] in, and are the power generation and heating efficiency of the CHP unit, , and are the power generation capacity of CHP at time t and its upper and lower limits, and They are the climbing power for upward and downward slope, respectively.

[0062] Output constraints of interruptible loads and transferable loads:

[0063] in, is the total demand of IL and SL, unit: kW; and is an auxiliary variable, unit: kW; , and are the IL or SL output power and its upper and lower limits at time t respectively; is the expected electricity demand of the user of IL or SL at time t.

[0064] Energy storage output constraints:

[0065] in, and are the charging power and discharging power of ESS at time t, in kW; is the charge and discharge flag of the energy storage ESS at time t, expressed as 0 or 1; is the ESS output power at time t; and are the upper and lower limits of ESS output power at time t respectively; and are the ESS charge states at time t and time t-1, respectively. and are the upper and lower limits of SOC respectively, is the initial SOC; is the self-damage rate; and are charging efficiency and discharging efficiency respectively; is the unit time scale, which is 1h; It is the SOC at T at the end of an operation cycle.

[0066] Electric vehicle output constraints:

[0067] in, and are the charging power and discharging power of the electric vehicle EV at time t, in kW; It is the charge and discharge flag of electric vehicle EV, which is expressed as 0 or 1; , and They are the output power of the electric vehicle EV at time t and its upper and lower limits; , and are the electric energy and its upper and lower limits at time t respectively.

[0068] Step S5: Set the allowable wind and solar power abandonment situations and the wind and solar power full absorption plan, and compare and analyze to obtain the optimal aggregation control method that takes into account economy, flexibility and new energy absorption.

[0069] Step S5.1, setting the permitted wind and solar power abandonment conditions and the wind and solar power full absorption plan.

[0070] Step S5.2, respectively solve the schemes of the virtual power plant source-load resource aggregation control model that meets the constraints in step S4 under the conditions of wind and solar power abandonment and wind and solar power full absorption, and select the scheme with the best objective function as the optimal aggregation control method. The scheme includes the total operating cost, the penalty cost of the flexibility shortage, and the wind and solar power combined output power, which respectively reflect the system economy, flexibility and new energy absorption.

[0071] When wind and solar power are allowed to be abandoned, the output power of wind power and photovoltaic power meets the wind power and photovoltaic power constraints. When wind and solar power are fully absorbed, the output power of wind power and photovoltaic power is the upper limit of their respective output power.

[0072] Example 2 This embodiment also relates to a method for aggregating and controlling source and load resources of a virtual power plant based on flexibility quantification.

[0073] The virtual power plant is configured with WT, PV and CHP units with capacities of 2000kW, 3000kW and 1500kW respectively; the minimum output power of the CHP unit is 300kW, the upward and downward climbing powers are both 500kW, the power generation efficiency and heating efficiency are 0.8 and 0.5 respectively; the maximum energy storage of EES is 1000kW·h, the upper and lower limits of charging and discharging power are 500kW and 0, the initial SOC is 0.1, the upper and lower limits of SOC are 0.9 and 0.1 respectively, the self-loss rate is 0.001, and the charging efficiency and discharging efficiency are both 0.9; the total interruptible load demand is 500kW, and the upper and lower limits of power are 380kW and 0 respectively; the total transferable load demand is 3600kW, and the upper and lower limits of power are 80kW and 0 respectively; the operation cycle T is 24h, with hours as the unit time period.

[0074] In this embodiment, the parameter penalty coefficient and The fuel cost coefficient is 0.6. Take 0.87 as the compensation cost coefficient and The compensation cost coefficient is 0.32. Take 0.64 as the operation and maintenance cost coefficient The penalty coefficients for shortage are 0.015, 0.025, 0.021 and 0.083 respectively. Take 0.5.

[0075] Set the conditions for wind and solar power abandonment and the full absorption plan of wind and solar power as follows: Option 1: Allowing wind and solar power to be abandoned, the source and load resources participating in VPP aggregation are WT, PV, CHP, and ESS.

[0076] Option 2: Based on Option 1, add EV.

[0077] Solution 3: Based on Solution 1, add IL and SL.

[0078] Plan 4: Based on Plan 1, add EV, IL and SL.

[0079] Option 5: Based on Option 4, it is assumed that wind power and photovoltaic power are fully absorbed.

[0080] S5.2 is based on the MATLAB 2018b platform, uses the YALMIP toolbox to build the model, calls CPLEX12.10.0 for solving, and sets five schemes for comparison. The flexibility resource configuration, system flexibility balance, and resource aggregation control effect of each scheme are shown in Table 1.

[0081] Table 1 plan Flexibility Resources Wind and solar combined output power / kW Total operating cost / yuan Cost of power abandonment penalty / yuan Flexibility deficiency penalty cost / yuan 1 WT, PV, CHP, ESS 44981.08 25365.72 2609.11 8299.31 2 WT, PV, CHP, ESS, EV 45714.98 25989.35 2168.77 3875.78 3 WT, PV, CHP, ESS, IL, SL 47290.05 25986.48 1223.73 4164.41 4 WT,PV,CHP,ESS,EV,IL,SL 48048.61 27175.69 768.59 1952.91 5 WT,PV,CHP,ESS,EV,IL,SL 49329.60 24861.91 0 3233.89 By comparison, Scheme 4 is the best aggregation control method, and the quantitative results of its source-load resource flexibility adjustment capability are as follows: Figure 2 and Figure 3 As shown in the figure, the system flexibility balance and the virtual power plant aggregation control results are shown in Figure 4 and Figure 5 shown.

[0082] Depend on Figure 4-5 It can be seen that under Scheme 4, WT, PV and CHP units played a leading role and undertook most of the flexibility adjustment tasks. In one dispatch cycle, the combined output of wind and solar power was 48048.61kW, and the penalty cost for power abandonment was 768.59 yuan. In addition, the system only had a situation in which the flexibility supply could not meet the flexibility demand in certain periods. For example, at 12:00, the system had a shortage of both upward and downward flexibility, and there was a shortage of downward flexibility at 16:00 and 24:00. The system's flexibility supply capacity was basically able to track the flexibility demand, and the penalty cost for the flexibility shortage was 1952.91 yuan.

[0083] By judging whether the sum of the upward and downward flexible adjustment factors is equal to 1, we can get the situation of the system flexibility supply capacity tracking the system flexibility demand in each period. In addition, the larger the flexible adjustment factor, the stronger the flexibility adjustment capacity of the resource. Figure 2-3 It can be seen that the flexibility of cogeneration is subject to the capacity and ramping capability of the unit, and the downward flexibility supply capacity is more prominent than the upward flexibility supply capacity on the whole. Energy storage can directly store wind power and photovoltaic power, and can participate in flexibility regulation through the maximum power charging and discharging plan. Electric vehicles can also flexibly adjust the charging and discharging state to provide two-way flexibility. They have high flexibility during the period of net load fluctuation, which can make up for the lack of cogeneration regulation capacity to a certain extent, and promote the consumption of wind and solar power. The upward flexibility supply capacity of interruptible and transferable loads is slightly insufficient. The interruptible load is subject to capacity constraints, and the flexibility supply capacity is limited. The electricity demand of the transferable load is relatively large. It can interrupt unnecessary loads or transfer part of the load to other time periods. The downward flexibility supply capacity is slightly higher than the upward flexibility supply capacity.

[0084] Therefore, the present invention is based on the virtual power plant source-load resource aggregation control method based on flexibility quantification, which takes into account the flexibility resources on both sides of the source and load, and handles the uncertainty of wind power and photovoltaic output, so as to take into account the economy, flexibility and new energy absorption capacity of the virtual power plant aggregation control. By defining the system flexibility margin and introducing the flexible adjustment factor, the flexibility adjustment capacity of the system flexibility resources is quantified to characterize the contribution of different flexibility resources to the system flexibility balance, which can effectively understand the source-load flexibility supply and demand relationship and adjustment capacity. By setting the conditions for allowing wind and solar power abandonment and the full absorption plan of wind and solar power, the optimal resource aggregation control method that meets the objective function and constraints and takes into account economy, flexibility and new energy absorption is obtained. Thereby, an effective strategy for improving the economy and flexibility of source-load resources participating in the operation of virtual power plants is provided, which provides a theoretical basis for the subsequent analysis of the source-load flexibility supply and demand relationship and adjustment capacity, and the optimal allocation of limited resources on a certain time scale.

[0085] Example 3 The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0086] Multiple components in the device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0087] The processing unit performs the various methods and processes described above. For example, in some embodiments, the method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the method in any other appropriate manner (e.g., by means of firmware).

[0088] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0089] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0090] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0091] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A virtual power plant source and load resource aggregation control method based on flexibility quantification, characterized in that: The method comprises the following steps: Step S1, based on Monte Carlo simulation and scenario reduction algorithm, data processing is performed on the acquired resource historical operation data to obtain a typical operation scenario, wherein the resource historical operation data includes output power data of wind power, photovoltaic power, net load, interruptible load and transferable load within a period of time; Step S2, based on typical operation scenarios, by quantifying system flexibility requirements, constructing a source-load multi-resource flexibility model to characterize upward flexibility and downward flexibility of resources; Step S3, defining the system flexibility margin and the flexibility adjustment factor, when the system flexibility margin is less than 0, a flexibility deficit is generated; Step S4, using the result of step S2 and the definition of step S3, construct a virtual power plant source-load resource aggregation control model, set the objective function and constraints of the model, wherein the constraints include flexibility constraints, power balance constraints and output constraints of various resources, and the objective function is to minimize the sum of wind power and photovoltaic combined output power, system total operating cost and flexibility deficiency penalty cost within an operating cycle T; Step S5, respectively solving the schemes of the virtual power plant source-load resource aggregation control model that meets the constraints in step S4 under the settings of wind and solar power abandonment and wind and solar power full absorption, and selecting the scheme with the best objective function as the optimal aggregation control method; The process of obtaining the typical operation scenario in step S1 includes: Step S1.1, select the benchmark scene, set the standard deviation and the number of samples, use Monte Carlo simulation scene generation to obtain the scene set S; Step S1.2, determine the number of scenes after reduction, and calculate the probability distance of each pair of scenes in the scene set S; Step S1.3, selecting the scene d with the smallest sum of probability distances in the scene set S; Step S1.4, replace scene d with scene r that is closest to scene d in scene set S, and add the probability of scene d to the probability of scene r, eliminate scene d, and form a new scene set S'; Step S1.5, determine whether the number of scenarios in the new scenario set S' is equal to the number of scenarios set in step S1.1; if not, return to step S1.2; if yes, output the reduced typical operation scenario set and corresponding probability; The process of step S2 includes: Step S2.1, quantify the system flexibility demand, and obtain the upward and downward flexibility demands of wind power WT, photovoltaic PV and net load L; Step S2.2, building a source-load resource flexibility model, which includes the upward and downward flexibility supply of wind power WT, photovoltaic PV, cogeneration CHP, interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV at time t; wherein the source-load resources include source-side flexibility resources and load-side flexibility resources, the source-side flexibility resources are wind power WT, photovoltaic PV, cogeneration CHP, and the load-side flexibility resources include interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV; Step S2.3, integrating system flexibility provision, specifically: ， in, and are the upward and downward flexibility supply of the system at time t, respectively; and are the upward and downward flexibility supply of CHP at time t, respectively; and are the upward and downward flexibility supply of wind power WT and photovoltaic PV at time t, respectively; and are the upward and downward flexibility supplies of the interruptible load IL, the transferable load SL, the energy storage ESS and the electric vehicle EV at time t, respectively; The upward and downward flexibility requirements of wind power WT, photovoltaic PV and net load L are calculated as follows: ， ， in, are the output power and upper and lower limits of wind power WT, photovoltaic PV and net load L at time t under the kth typical operation scenario; and They are the upward and downward flexibility requirements of the system at time t under the kth typical operation scenario; is the upward and downward flexibility demand of wind power WT, photovoltaic PV and net load L at time t under the kth typical operation scenario; The wind power WT and photovoltaic PV are uncontrollable units, and the upward and downward flexibility supply is provided by allowing wind and light abandonment. The upward and downward flexibility supply of the interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV is specifically as follows: ， ， ， in, are the upward and downward flexibility supplies of the interruptible load IL, the transferable load SL, the energy storage ESS and the electric vehicle EV at time t, respectively; and are the output power and upper and lower limits of the interruptible load IL, transferable load SL, energy storage ESS and electric vehicle EV at time t respectively; and are the energy storage ESS capacity and its upper and lower limits at time t respectively; and are the electric energy and its upper and lower limits at time t respectively; is the unit time scale; The upward and downward flexibility of the CHP is specifically provided as follows: ， in, and are the upward and downward flexibility supply of CHP at time t, respectively; and They are the upward and downward ramping powers of CHP respectively; and They are respectively the power generation capacity of the combined heat and power (CHP) at time t and its upper and lower limits; The process of step S3 includes: Step S3.1, define the system flexibility margin as the difference between flexibility supply and flexibility demand; Step S3.2, define the flexibility adjustment factor as the proportion of various flexibility resources to bear the total flexibility demand of the system; The virtual power plant source and load resource aggregation control model in step S4 is specifically: ， Where: and They respectively represent the output power of the virtual power plant VPP, wind power WT, photovoltaic PV, combined heat and power CHP, transferable load SL, interruptible load IL, energy storage ESS and electric vehicle EV at time t.

2. According to claim 1, a virtual power plant source and load resource aggregation control method based on flexibility quantification is characterized in that: The process of step S4 includes: Step S4.1, constructing a virtual power plant source and load resource aggregation control model; Step S4.2, setting the objective function of virtual power plant resource aggregation regulation; Step S4.3, setting the constraints that virtual power plant resource aggregation and regulation must meet.

3. According to claim 1, a virtual power plant source and load resource aggregation control method based on flexibility quantification is characterized in that: The objective function F is specifically: ， in, is the combined output power of wind power and photovoltaic power in an operation cycle T; is the total operating cost of the system within an operating cycle T; Penalize costs for flexibility shortfalls; F The calculation formula for 1 is: ， in and are the output power of wind power WT and photovoltaic PV at time t respectively; is the total operating cost of the system within an operating cycle T, and the calculation formula is: ， ， in, For operation and maintenance costs; Penalty costs for power curtailment, including wind and solar curtailment costs; is the CHP fuel cost; is the total load compensation cost; Compensate for EV discharge losses; and are wind power WT and photovoltaic PV penalty coefficients respectively; and are the amount of wind and solar power abandoned at time t respectively; The energy provided by the fuel; is the fuel cost coefficient; and are the compensation cost coefficients of interruptible load IL and transferable load SL respectively; and Expected electricity demand of users; and are the output powers of the transferable load SL and the interruptible load IL at time t respectively; is the compensation cost coefficient; is the discharge power of the electric vehicle EV at time t; are the operation and maintenance cost coefficients of wind power WT, photovoltaic PV, combined heat and power CHP and energy storage ESS respectively; Provide heating power for CHP unit; , , are the output power of WT, PV and CHP at time t, and is the charging power and discharging power of ESS at time t; The operation and maintenance costs of electric vehicles (EVs); The calculation formula is: ， in, Penalize costs for flexibility shortfalls; is the deficiency penalty coefficient, and are the upward and downward flexibility requirements of the system at time t respectively.

4. According to claim 1, a virtual power plant source and load resource aggregation control method based on flexibility quantification is characterized in that: The flexibility constraints are specifically: ， in, and are the upward and downward flexibility margins of the system at time t, respectively; The power balance constraints are specifically: ， in, , , and are the output powers of the interruptible load IL, transferable load SL, electric vehicle EV and net load L at time t respectively; , , and They are the output power of wind power WT, photovoltaic PV, combined heat and power CHP and energy storage ESS at time t respectively.

5. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

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