Load side virtual control unit self-scheduling optimal framework construction method

Abstract

The invention belongs to the technical field of power system dispatching, and particularly relates to a load side virtual control unit self-dispatching optimal framework construction method. The method comprises the following steps: constructing a virtual control unit; aiming at the virtual control unit, constructing a day-ahead stage self-scheduling optimal model; performing optimal allocation on instructions sent by the day-ahead scheduling optimal model; and performing system optimization and scheduling on the virtual control unit. And constructing a load side virtual control unit self-scheduling optimal framework based on a system optimization and scheduling result performed on the virtual control unit. According to the method, the optimal deaggregation allocation of the main network day-ahead scheduling instruction by the VCU can be realized, the distributed frequency control resources of the power distribution side are effectively aggregated by constructing the load side virtual control unit and designing the day-ahead self-scheduling optimal model, and the optimal allocation of the main network scheduling instruction is realized. The frequency stability of the power system is improved, and the utilization rate and economical efficiency of power distribution side resources in the novel power system can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and in particular relates to a method for constructing a self-dispatching optimal framework for a load-side virtual control unit. Background Art

[0002] In the construction of new power systems, with the continuous increase in the proportion of new energy, conventional thermal power units, which are the main frequency control resources, are constantly being replaced, resulting in a lack of frequency stability control resources and serious problems in the stability of system operation frequency. There are abundant distributed frequency control resources on the distribution network side, which have great potential if they can be utilized; but due to their small size, they cannot be directly called by the grid dispatch in an individual form. It is possible to consider using local dispatch as a unit, using virtual power plant technology to aggregate distributed loads with frequency response capabilities, and constructing a virtual control unit that can be directly dispatched by the provincial dispatch to participate in provincial network frequency control.

[0003] The regulation capability and characteristics of distributed controllable loads with active regulation capability will change over time, and the fluctuation range is large and cannot be ignored. In addition, the regulation cost is also different due to the differences in regulation methods among users. Since the regulation capability of distributed resources is constantly changing, when the virtual control unit is required to provide services after the system is disturbed, it is necessary to reasonably allocate the regulation tasks to each distributed resource in the best way.

[0004] There are many dispatching studies that aggregate resources on the same side of the system into virtual power plants, such as aggregating wind farms and carbon capture power plants into virtual power plants. However, the load-side resources involved in the study are not included in the virtual power plant. There are also studies that integrate thermal power plants, carbon capture units, wind power, photovoltaics, etc. into virtual power plants, and propose optimization dispatching models. However, these studies currently focus on combining source-side resources into virtual power plants.

[0005] After the massive flexible and adjustable resources on the load side are aggregated into virtual power plants, their own adjustment capabilities and adjustment rates are different and time-varying, and there are differences among the stakeholders, and the market is also diversified. In addition, there are multiple uncertainties in the mathematical model of each component. Therefore, whether from the level of the virtual power plant itself or the optimization scheduling at the global level of the system, the theoretical core is quite different from the existing research, and it is necessary to use fuzzy theory, random optimization, risk analysis, probability scheduling and other mathematical methods at the same time. From the current time node survey, there are few results in the research of these directions. Therefore, it is necessary to provide an effective method for constructing the optimal framework of self-dispatching of the load-side virtual control unit in response to the main grid's day-ahead scheduling instructions. Summary of the invention

[0006] In view of the shortcomings of the above-mentioned prior art, the present invention provides a method for constructing a self-dispatching optimal framework of a load-side virtual control unit. Its purpose is to solve the problem of lack of frequency stability control resources caused by the increase in the proportion of new energy in the new power system, and to achieve the invention purpose of optimal allocation of the main grid's day-ahead dispatching instructions by effectively aggregating the distributed frequency control resources on the distribution side.

[0007] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0008] A method for constructing a self-dispatching optimal framework of a load-side virtual control unit comprises the following steps:

[0009] Constructing a virtual control unit;

[0010] For the virtual control unit, an optimal self-dispatching model for the day-ahead phase is constructed;

[0011] Optimally allocate the instructions issued by the optimal day-ahead scheduling model;

[0012] Solve the optimal mathematical model for the optimal allocation of virtual control units in response to the main network dispatching instructions, and obtain the optimal strategy for virtual control unit system optimization and dispatching;

[0013] Based on the system optimization and scheduling results of the virtual control unit, an optimal self-scheduling framework for the load-side virtual control unit is constructed.

[0014] Furthermore, the construction of the virtual control unit includes: the virtual control unit VCU is composed of a load-side distributed battery energy storage power station BESS cluster and a charging and swapping station BSCS, wherein each charging and swapping station BSCS contains a power battery EVB for multiple electric vehicles EV on the load side to swap power and a retired battery energy storage system RBESS.

[0015] Furthermore, for the virtual control unit, the optimal self-dispatching model for the day-ahead stage is constructed, including: the distributed BESS realizes the operation control of the multi-battery energy storage power station BESS by managing the BESS cluster, and the decision variables of the virtual control unit VCU in the day-ahead stage include: the operation plan of the battery energy storage power station BESS cluster and each charging and swapping station BSCS in each period of the scheduling day;

[0016] The virtual control unit VCU includes: station self-sufficiency and inter-station mutual assistance coordination mode, that is, the retired battery energy storage system RBESS and the battery energy storage power station BESS cluster charge the power battery EVB through discharge, so that It represents the charging power of EVB in BSCS-m to meet the EV demand in the period ξ, which is expressed as:

[0017] Among them, PR2P，m，t represents the power of RBESS charging EVB in BSCS-m, P BESS2P，m，t represents the charging power provided by the distributed BESS cluster to the EVB in BSCS-m, P G2P，m，t represents the amount of electricity purchased from the grid by BSCS-m during period t;

[0018] Part of the power discharged by the EVB charging in the mth charging and swapping station BSCS of the distributed BESS cluster, the retired battery energy storage system RBESS, and the battery energy storage station BESS cluster P R2P，m，t , and P BESS2P，m，t It is used to meet the battery replacement needs of multiple electric vehicles (EV) on the load side, and the remaining electricity is used to respond to the active power regulation requirements of the power grid; the electricity from the discharge of the power battery EVB, the charging of the RBESS, and the charging of the battery energy storage station BESS is all used to provide frequency control auxiliary services FCAS backup for the power grid.

[0019] Furthermore, the optimal allocation of the instructions issued by the optimal model for day-ahead scheduling includes: the frequency control auxiliary service FCAS standby reservation required to be responded to by the virtual control unit VCU in the day-ahead stage, including the standby reserved for participating in the fast frequency response FFR in each period of the second day of operation; Reserved for participating in primary frequency regulation PFR Reserved for the upper / lower frequency for SFR and and charging / discharging operation plan for PR participating in peak load regulation and

[0020] The mathematical representation of the virtual control unit VCU regulation task allocation is:

[0021]

[0022] in, It indicates the spare capacity allocated by VCU to the distributed BESS cluster for participating in various frequency control auxiliary services FCAS; It indicates the spare capacity of participating in various frequency control auxiliary services FCAS issued by the virtual control unit VCU to BSCS-m a few days ago;

[0023] The reserve capacity of the FCAS that the virtual control unit VCU needs to reserve for participating in the frequency control auxiliary services at various time scales is determined by the virtual control unit VCU and the power grid through bidding or negotiation during the day-ahead clearing process at the main grid level; the virtual control unit VCU allocates the parameters to the distributed resources being regulated in an optimal way;

[0024] The power capacity of the charging and swapping station BSCS to provide regulation services for the power grid comes from its internal power battery EVB and RBESS;

[0025] The objective function of the virtual control unit VCU control center is the daily operating income I VCU maximize: I G2V，t Provided by the power grid, it represents the income obtained by VCU from reserving spare capacity for participating in the active power regulation service of the power grid during period t; I BSCS2V，t It indicates that VCU arranges the battery energy storage station BESS cluster to charge the charging and swapping station BSCS, and the fee that the charging and swapping station BSCS needs to pay; C V2D，t The cost of allocating the output and reserve plan agreed with the grid to each distributed resource by the virtual control unit VCU in the previous stage needs to include the reserve reservation price C paid by the virtual control unit VCU to the charging and swapping station BSCS and the battery energy storage station BESS cluster. V2BSCS，t ; C V2G，t It represents the charging cost generated by the charging and swapping station BSCS in the virtual control unit VCU to meet the needs of multiple electric vehicles EV on the load side.

[0026] Furthermore, the constraints of the construction of the self-dispatching optimal model include: constraints of the battery energy storage power station BESS cluster, constraints of the retired battery energy storage system RBESS cluster and constraints of the power battery EVB.

[0027] Furthermore, the constraints of the battery energy storage power station BESS cluster include:

[0028] The virtual control unit VCU needs to consider the power limit, function-energy relationship, time period coupling, power limit, daily operation cycle and spare capacity limit constraints in the actual operation of the battery energy storage power station BESS cluster. The constraints are as follows:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] The constraints of the retired battery energy storage system RBESS cluster include:

[0041] For the retired battery energy storage system RBESS in each charging and swapping station BSCS, its operation constraints are similar to those of the battery energy storage station BESS;

[0042] The constraints of the power battery EVB include:

[0043] Considering the power battery EVB, let the number of power batteries EVB charged in BSCS-m in period t to meet the needs of multiple electric vehicles EV on the load side be The number of power batteries EVB charged and discharged to provide active power regulation for the grid is With N Ding，m，t , so we have: Among them, P CB It is the rated charging / discharging power of the charging / discharging pile in the charging / discharging station BSCS;

[0044] Assume that there are N Cing，m，t The power battery EVB is being charged, so there are: N Cing，m，t +N Ding，m，t ≤N CB，m ; Among them, N CB，m Indicates the number of charging and discharging piles in the mth charging and swapping station BSCS;

[0045] The EVB state transition relationship is as follows:

[0046]

[0047] A device for constructing a self-dispatching optimal framework of a load-side virtual control unit, comprising:

[0048] A virtual control unit building module, used for building a virtual control unit;

[0049] A module for constructing an optimal model for self-dispatching in the day-ahead phase, used to construct an optimal model for self-dispatching in the day-ahead phase for a virtual control unit;

[0050] The optimal allocation module is used to optimally allocate the instructions issued by the optimal model for day-ahead scheduling;

[0051] The optimization and scheduling module is used to solve the optimal mathematical model for the virtual control unit's optimal allocation in response to the main network's scheduling instructions, and obtain the optimal strategy for virtual control unit system optimization and scheduling;

[0052] The optimal framework building module is used to build the optimal framework for self-scheduling of the load-side virtual control unit based on the system optimization and scheduling results of the virtual control unit.

[0053] Furthermore, the device is used to implement any of the steps of a method for constructing a self-scheduling optimal framework for a load-side virtual control unit.

[0054] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the methods for constructing a self-scheduling optimal framework for a load-side virtual control unit are implemented.

[0055] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods for constructing a self-dispatching optimal framework for a load-side virtual control unit are implemented.

[0056] The present invention has the following beneficial effects and advantages:

[0057] The present invention takes into account that the optimal control strategy made by the control center of the virtual control unit VCU is a sequential decision, and it manages a large number of control units, and the operating characteristics of each unit are differentiated. Facing such a large-scale and overlapping optimization problem, it requires not only sophisticated algorithm improvements to avoid the "curse of dimensionality", but also sufficient solution time to obtain the optimal solution. This is difficult to achieve within the decision-making time range of intraday and real-time control. To this end, the present invention first formulates and allocates plans for each control unit, and then follows the intraday and real-time timeline to make decision corrections step by step. From fuzzy to certain, the decisions made at the three time scales are connected and inherited, and together complete the optimal formulation and allocation of control decisions of the control center of the virtual control unit VCU.

[0058] The present invention proposes to conduct research on the three aspects involved in the theory and practical application of the virtual control unit participating in the provincial grid frequency response control on the distribution side: the first is the research on the construction technology of the virtual control unit with frequency response function, the second is the research on the key technology of the virtual control unit participating in the provincial grid frequency response control, and the third is the preliminary research on the practical application of the virtual control unit participating in the provincial grid frequency response control. It takes the combination of multiple charging and swapping stations BSCS and battery energy storage station BESS clusters on the load side as a virtual control unit VCU as the research object, conducts the optimal day-ahead self-dispatching analysis of the internal regulating elements by the virtual control unit VCU control center, and realizes the optimal de-aggregation allocation of the day-ahead dispatching instructions of the main grid by the virtual control unit VCU.

[0059] Based on the consideration of the combination of distributed small-scale battery energy storage clusters and charging and swapping station aggregates on the distribution side as a virtual control unit, the present invention combines the existence of virtual control units operated by pure middlemen in actual power system operation to construct an optimal self-dispatching model for virtual control units in the day-ahead stage. The constructed model carefully depicts the operating mechanism of the virtual control unit and the dynamic time-varying characteristics of the daily operation regulation capabilities of distributed components, and proposes a number of flexible resources. The coordinated operation mechanism of intra-station self-sufficiency and inter-station mutual assistance is proposed to complete the optimal allocation of the day-ahead dispatching instructions issued by the main grid while meeting the charging and swapping station's own operating tasks. The main contribution of the present invention is to propose a method for constructing an optimal model for the day-ahead self-dispatching of virtual control units on the distribution side, which can better assist the distribution side resources in the new power system to participate in the active operation of the power grid.

[0060] By constructing a virtual control unit on the load side and designing an optimal model for day-ahead self-dispatching, the present invention can also effectively aggregate the distributed frequency control resources on the distribution side and achieve optimal allocation of main grid dispatching instructions. This not only improves the frequency stability of the power system, but also helps to improve the utilization and economy of distribution side resources in the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0062] Figure 1 It is a schematic diagram of the physical structure of a virtual control unit VCU provided by the present invention;

[0063] Figure 2 It is a schematic diagram of operation control of a virtual control unit VCU provided by the present invention. DETAILED DESCRIPTION

[0064] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0066] Refer to the following Figure 1 and Figure 2 The technical solutions of some embodiments of the present invention are described.

[0067] Example 1

[0068] The present invention provides an embodiment, which is a method for constructing an optimal framework for self-dispatching of a virtual control unit on the load side. The present invention takes a cluster of multiple electric vehicle (EV) charging and swapping stations (BSCS) and battery energy storage stations (BESS) on the load side as a virtual control unit (VCU) as a research object, conducts an optimal day-ahead self-dispatching analysis of the internal regulating elements by the virtual control unit (VCU) control center, and realizes the optimal de-aggregation allocation of the virtual control unit (VCU) to the main grid day-ahead dispatching instructions.

[0069] A method for constructing a self-dispatching optimal framework of a load-side virtual control unit comprises the following steps:

[0070] Step 1. Build a virtual control unit.

[0071] Considering the distributed small battery energy storage cluster and charging and swapping station aggregation on the distribution side, virtual control units are constructed. These virtual control units are operated by pure intermediaries and can achieve flexible self-dispatching in actual power system operation.

[0072] like Figure 1 As shown, Figure 1 The present invention provides a physical structure diagram of a virtual control unit VCU.

[0073] The virtual control unit VCU is composed of a distributed battery energy storage station BESS cluster on the load side and a charging and swapping station BSCS. Each charging and swapping station BSCS contains power batteries EVB that can be used to swap power for multiple electric vehicles EV on the load side, as well as a retired battery energy storage system RBESS.

[0074] Step 2. For the virtual control unit, build the optimal self-scheduling model in the day-ahead phase.

[0075] The day-ahead self-dispatching optimal model describes in detail the operation mechanism of the virtual control unit and the dynamic time-varying characteristics of the daily operation regulation capability of the distributed components.

[0076] like Figure 2 As shown, Figure 2 This is a schematic diagram of the operation control of a virtual control unit VCU provided by the present invention. In the figure, the dotted arrows represent the power flow of each component in response to the needs of grid dispatching, and the solid arrows represent the power flow of charging the power battery EVB to meet the power replacement needs of multiple electric vehicles EV on the load side.

[0077] Distributed BESS is a fully controlled component that can directly control the operation of BESS in multi-battery energy storage power stations by managing the BESS cluster.

[0078] The decision variables of the virtual control unit VCU in the day-ahead phase include: the operation plan of the battery energy storage power station BESS cluster and each charging and swapping station BSCS in each period of the dispatching day. Its internal multi-resource design "self-sufficiency within the station" and "mutual assistance between stations" coordination mode, that is, the retired battery energy storage system RBESS and the battery energy storage power station BESS cluster can charge the power battery EVB through discharge, so as to save the cost of charging the power battery EVB to meet the battery swapping needs of multiple electric vehicles EV on the load side.

[0079] The virtual control unit VCU needs to respond to the active power regulation requirements of the main grid and ensure the service quality for multiple electric vehicle EV users on the load side.

[0080] The "self-sufficiency within the station" and "mutual assistance between stations" coordination modes are designed for multiple resources within the virtual control unit VCU, that is, the retired battery energy storage system RBESS and the battery energy storage power station BESS cluster can charge the power battery EVB through discharge, so as to save the cost of charging the power battery EVB to meet the battery replacement needs of multiple electric vehicles EV on the load side. It represents the charging power of EVB in BSCS-m to meet the EV demand in period t, which can be expressed as:

[0081] Among them, P R2P，m，t represents the power of RBESS charging EVB in BSCS-m, P BESS2P，m，t represents the charging power provided by the distributed BESS cluster to the EVB in BSCS-m, P G2P，m，t It represents the amount of electricity purchased by BSCS-m from the power grid during period t.

[0082] Through the above analysis, it can be seen that the EVB charging in the mth charging and swapping station BSCS of the distributed BESS cluster, the discharge of the retired battery energy storage system RBESS, and the partial discharge of the battery energy storage station BESS cluster With P BESS2P，m，t It is used to meet the battery replacement needs of multiple electric vehicles (EV) on the load side, and the remaining power is used to respond to the active power regulation requirements of the power grid. As for the power battery EVB discharge, RBESS charging and battery energy storage station BESS charging, all the power is used to provide frequency control auxiliary service FCAS backup for the power grid.

[0083] Step 3. Optimally allocate the instructions issued by the optimal day-ahead scheduling model.

[0084] Flexible resource coordination operation mechanism. Under the premise of meeting the operation tasks of the charging and swapping station itself, the present invention proposes a number of flexible resource coordination operation mechanisms for self-sufficiency within the station and mutual assistance between stations, so as to achieve the optimal allocation of the day-ahead dispatching instructions issued by the main grid.

[0085] The frequency control auxiliary service FCAS reserve that the virtual control unit VCU needs to respond to during the day-ahead phase, including the reserve reserved for participating in the fast frequency response FFR during each period of the second day of operation Reserved for participating in primary frequency regulation PFR Reserved for the upper / lower frequency for SFR and and charging / discharging operation plan for PR participating in peak load regulation and The mathematical representation of the virtual control unit VCU regulation task allocation is:

[0086] in, It indicates the spare capacity allocated by VCU to the distributed BESS cluster for participating in various frequency control auxiliary services FCAS; It indicates the spare capacity of various frequency control auxiliary services FCAS issued by the virtual control unit VCU to BSCS-m.

[0087] The reserve capacity that the virtual control unit VCU needs to reserve for participating in the frequency control auxiliary service FCAS at various time scales has been determined by the virtual control unit VCU and the power grid through bidding or negotiation during the day-ahead clearing process at the main grid level. Therefore, when the virtual control unit VCU makes self-dispatching decisions, they are known quantities. The task of the virtual control unit VCU is to allocate these parameters to the distributed resources it regulates in an optimal way.

[0088] The power capacity of the charging and swapping station BSCS to provide regulation services to the power grid comes from its internal power battery EVB and RBESS.

[0089] The objective function of the virtual control unit VCU control center is the daily operating income I VCU maximize: I G2V，t Provided by the power grid, it represents the income obtained by VCU from reserving spare capacity for participating in the active power regulation service of the power grid during period t. BSCS2V，t It indicates that VCU arranges the battery energy storage station BESS cluster to charge the charging and swapping station BSCS, and the fee that the charging and swapping station BSCS needs to pay. The present invention sets the charging power of the charging and swapping station BSCS to come from the battery energy storage station BESS cluster, and the charging fee has a 95% discount compared to charging from the power grid. V2D，t The cost of allocating the output and reserve plan agreed with the grid to each distributed resource by the virtual control unit VCU in the previous stage needs to include the reserve reservation price C paid by the virtual control unit VCU to the charging and swapping station BSCS and the battery energy storage station BESS cluster. V2BSCS，t . C V2G，t It represents the charging cost generated by the charging and swapping station BSCS in the virtual control unit VCU to meet the needs of multiple electric vehicles EV on the load side.

[0090] Step 4. Perform system optimization and scheduling on the virtual control unit.

[0091] Step 3 of the present invention aims to construct an optimal mathematical model for the optimal allocation problem of the virtual control unit responding to the main network scheduling instructions, and this step 4 is based on step 3 to solve the optimization model built in step 3 to obtain the virtual control unit system optimization and scheduling optimal strategy.

[0092] The present invention adopts a variety of mathematical methods such as fuzzy theory, random optimization, risk analysis, probability scheduling, etc. to optimize the scheduling of virtual control units to meet the needs of frequency stability control in the power system.

[0093] The construction of the optimal self-dispatching model of the virtual control unit needs to consider multiple constraints, including: the constraints of the battery energy storage power station BESS cluster, the constraints of the retired battery energy storage system RBESS cluster, and the constraints of the power battery EVB.

[0094] (1) Constraints of battery energy storage power station BESS cluster.

[0095] The virtual control unit VCU needs to consider the power limit, function-energy relationship, time period coupling, power limit, daily operation cycle and spare capacity limit constraints in the actual operation of the battery energy storage power station BESS cluster. The constraints are as follows:

[0096]

[0097]

[0098]

[0099]

[0100] (2) Constraints of retired battery energy storage system (RBESS) clusters.

[0101] For the retired battery energy storage system RBESS in each charging and swapping station BSCS, its operating constraints are similar to those of the battery energy storage station BESS.

[0102] (3) Constraints of power battery EVB.

[0103] Considering the power battery EVB, let the number of power batteries EVB charged in BSCS-m in period t to meet the needs of multiple electric vehicles EV on the load side be The number of power batteries EVB charged and discharged to provide active power regulation for the grid is With N Ding，m，t , so we have: Among them, P CB It is the rated charging / discharging power of the charging / discharging pile in the charging / discharging station BSCS.

[0104] Assume that there are N Cing，m，t The power battery EVB is being charged, so there are: N Cing，m，t +N Ding，m，t ≤N CB，m Among them, N CB，m Indicates the number of charging and discharging piles in the mth charging and swapping station BSCS.

[0105] The EVB state transition relationship is as follows:

[0106]

[0107]

[0108] Step 5. Based on the system optimization and scheduling results of the virtual control unit in step 4, build the optimal self-scheduling framework of the load-side virtual control unit.

[0109] The so-called load-side virtual control unit self-dispatching optimal framework in the present invention refers to a macroscopic framework for solving the problem as a whole after summarizing the above steps 1 to 4. It includes the construction of virtual control units, construction of optimization models, and solving of optimization models as mentioned above.

[0110] The method for constructing an optimal framework for self-dispatching of a load-side virtual control unit in the present invention is a method for constructing an optimal framework for self-dispatching of a load-side virtual control unit in response to a day-ahead dispatch instruction of a main network. The objective function of the VCU control center is the daily operating benefit I VCU Maximize. The objective function of the virtual control unit VCU control center is the daily operating income I VCU By solving the mathematical optimization model composed of the maximization and constraints of the BESS cluster, the RBESS and the EVB, the optimal allocation method of the frequency response control and regulation responsibility in the distributed resources of the present invention can be obtained.

[0111] The optimal allocation of frequency response control and regulation responsibilities in distributed resources, namely, the virtual control unit on the power distribution side, needs to optimally deaggregate the frequency response control and regulation responsibilities issued by the main grid to achieve optimal self-scheduling of the virtual control unit.

[0112] Example 2

[0113] The present invention further provides an embodiment, which is a device for constructing a self-dispatching optimal framework of a load-side virtual control unit, comprising:

[0114] A virtual control unit building module, used for building a virtual control unit;

[0115] A module for constructing an optimal model for self-dispatching in the day-ahead phase, used to construct an optimal model for self-dispatching in the day-ahead phase for a virtual control unit;

[0116] The optimal allocation module is used to optimally allocate the instructions issued by the optimal model for day-ahead scheduling. It aims to construct an optimal mathematical model for the optimal allocation problem of the virtual control unit responding to the main network scheduling instructions.

[0117] The optimization and scheduling module is used to optimize and schedule the virtual control unit. Specifically, the built optimization model is solved to obtain the optimal strategy for virtual control unit system optimization and scheduling.

[0118] The optimal framework construction module is used to construct the load-side virtual control unit self-dispatching optimal framework based on the system optimization and scheduling results of the virtual control unit. The load-side virtual control unit self-dispatching optimal framework in the present invention refers to the macro framework for solving the problem as a whole after summarizing the above steps. It includes the virtual control unit construction, optimization model construction, optimization model solution, etc.

[0119] Example 3

[0120] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of the method for constructing a self-dispatching optimal framework for a load-side virtual control unit described in Embodiment 1 are implemented.

[0121] Example 4

[0122] Based on the same inventive concept, an embodiment of the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for constructing a self-scheduling optimal framework of a load-side virtual control unit described in Example 1 are implemented.

[0123] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for constructing an optimal framework for self-dispatching of a load-side virtual control unit, characterized by: The following steps are involved: Constructing a virtual control unit; For the virtual control unit, an optimal self-dispatching model for the day-ahead phase is constructed; Optimally allocate the instructions issued by the optimal day-ahead scheduling model; Solve the optimal mathematical model for the optimal allocation of virtual control units in response to the main network dispatching instructions, and obtain the optimal strategy for virtual control unit system optimization and dispatching; Based on the system optimization and scheduling results of the virtual control unit, an optimal self-scheduling framework for the load-side virtual control unit is constructed.

2. The method for constructing a self-dispatching optimal framework of a load-side virtual control unit according to claim 1 is characterized in that: The construction of the virtual control unit includes: the virtual control unit VCU is composed of a load-side distributed battery energy storage power station BESS cluster and a charging and swapping station BSCS, wherein each charging and swapping station BSCS contains a power battery EVB for multiple electric vehicles EV on the load side to swap power and a retired battery energy storage system RBESS.

3. The method for constructing a self-dispatching optimal framework of a load-side virtual control unit according to claim 1 is characterized in that: The virtual control unit is used to construct an optimal self-dispatching model for the day-ahead stage, including: the distributed BESS realizes the operation control of the multi-battery energy storage power station BESS by managing the BESS cluster, and the decision variables of the virtual control unit VCU in the day-ahead stage include: the operation plan of the battery energy storage power station BESS cluster and each charging and swapping station BSCS in each period of the scheduling day; The virtual control unit VCU includes: station self-sufficiency and inter-station mutual assistance coordination mode, that is, the retired battery energy storage system RBESS and the battery energy storage power station BESS cluster charge the power battery EVB through discharge, so that It represents the charging power of EVB in BSCS-m to meet the EV demand in period t, which is expressed as: Among them, P R2P,m,t represents the power of RBESS charging EVB in BSCS-m, P BESS2P,m,t represents the charging power provided by the distributed BESS cluster to the EVB in BSCS-m, P G2P,m,t represents the amount of electricity purchased from the grid by BSCS-m during period t; Part of the power discharged by the EVB charging in the mth charging and swapping station BSCS of the distributed BESS cluster, the retired battery energy storage system RBESS, and the battery energy storage station BESS cluster P R2P,m,t , and P BESS2P,m,t It is used to meet the battery replacement needs of multiple electric vehicles (EV) on the load side, and the remaining electricity is used to respond to the active power regulation requirements of the power grid; the electricity from the discharge of the power battery EVB, the charging of the RBESS, and the charging of the battery energy storage station BESS is all used to provide frequency control auxiliary services FCAS backup for the power grid.

4. The method for constructing a self-dispatching optimal framework of a load-side virtual control unit according to claim 1 is characterized in that: The optimal allocation of the instructions issued by the optimal model for day-ahead scheduling includes: the frequency control auxiliary service FCAS standby reservation required by the virtual control unit VCU in the day-ahead stage, including the standby reserved for participating in the fast frequency response FFR in each period of the second day of operation; Reserved for participating in primary frequency regulation PFR Reserved for the upper / lower frequency for SFR and and charging / discharging operation plan for PR participating in peak load regulation and The mathematical representation of the virtual control unit VCU regulation task allocation is: in, It indicates the spare capacity allocated by VCU to the distributed BESS cluster for participating in various frequency control auxiliary services FCAS; It indicates the spare capacity of participating in various frequency control auxiliary services FCAS issued by the virtual control unit VCU to BSCS-m a few days ago; The reserve capacity of the FCAS that the virtual control unit VCU needs to reserve for participating in the frequency control auxiliary services at various time scales is determined by the virtual control unit VCU and the power grid through bidding or negotiation during the day-ahead clearing process at the main grid level; the virtual control unit VCU allocates the parameters to the distributed resources being regulated in an optimal way; The power capacity of the charging and swapping station BSCS to provide regulation services for the power grid comes from its internal power battery EVB and RBESS; The objective function of the virtual control unit VCU control center is the daily operating income I VCU maximize: I G2V,t Provided by the power grid, it represents the income obtained by VCU from reserving spare capacity for participating in the active power regulation service of the power grid during period t; I BSCS2V,t It indicates that VCU arranges the battery energy storage station BESS cluster to charge the charging and swapping station BSCS, and the fee that the charging and swapping station BSCS needs to pay; C V2D,t The cost of allocating the output and reserve plan agreed with the grid to each distributed resource by the virtual control unit VCU in the previous stage needs to include the reserve reservation price C paid by the virtual control unit VCU to the charging and swapping station BSCS and the battery energy storage station BESS cluster. V2BSCS,t ; C V2G,t It represents the charging cost generated by the charging and swapping station BSCS in the virtual control unit VCU to meet the needs of multiple electric vehicles EV on the load side.

5. The method for constructing a self-dispatching optimal framework of a load-side virtual control unit according to claim 1 is characterized in that: The construction of the self-dispatching optimal model has constraints including: constraints on the battery energy storage power station BESS cluster, constraints on the retired battery energy storage system RBESS cluster and constraints on the power battery EVB.

6. A method for constructing a self-dispatching optimal framework for a load-side virtual control unit according to claim 5, characterized in that: The constraints of the BESS cluster include: The virtual control unit VCU needs to consider the power limit, function-energy relationship, time period coupling, power limit, daily operation cycle and spare capacity limit constraints in the actual operation of the battery energy storage power station BESS cluster. The constraints are as follows: The constraints of the retired battery energy storage system RBESS cluster include: For the retired battery energy storage system RBESS in each charging and swapping station BSCS, its operation constraints are similar to those of the battery energy storage station BESS; The constraints of the power battery EVB include: Considering the power battery EVB, let the number of power batteries EVB charged in BSCS-m in period t to meet the needs of multiple electric vehicles EV on the load side be The number of power batteries EVB charged and discharged to provide active power regulation for the grid is With N Ding,m,t , so we have: Among them, P CB It is the rated charging / discharging power of the charging / discharging pile in the charging / discharging station BSCS; Assume that there are N Cing,m,t The power battery EVB is being charged, so there are: Among them, N CB,m Indicates the number of charging and discharging piles in the mth charging and swapping station BSCS; The EVB state transition relationship is as follows:

7. A device for constructing a self-dispatching optimal framework for a load-side virtual control unit, characterized in that: include: A virtual control unit building module, used for building a virtual control unit; A module for constructing an optimal model for self-dispatching in the day-ahead phase, used to construct an optimal model for self-dispatching in the day-ahead phase for a virtual control unit; The optimal allocation module is used to optimally allocate the instructions issued by the optimal model for day-ahead scheduling; The optimization and scheduling module is used to solve the optimal mathematical model for the optimal allocation of virtual control units in response to the main network scheduling instructions, and obtain the optimal strategy for virtual control unit system optimization and scheduling; The optimal framework building module is used to build the optimal framework for self-scheduling of the load-side virtual control unit based on the system optimization and scheduling results of the virtual control unit.

8. A device for constructing a self-dispatching optimal framework for a load-side virtual control unit according to claim 7, characterized in that: The device is used to implement the steps of a method for constructing a self-scheduling optimal framework for a load-side virtual control unit as described in any one of claims 1 to 6.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the processor implements the steps of a method for constructing a self-scheduling optimal framework for a load-side virtual control unit as described in any one of claims 1 to 6.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for constructing a self-dispatching optimal framework for a load-side virtual control unit as described in any one of claims 1 to 6.

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