Zone area light storage and charging resource aggregation energy optimization control method

Abstract

The embodiment of the invention provides a zone area light storage and charging resource aggregation energy optimization control method, which comprises the following steps: determining the total installation capacity and position of an energy storage system and an electric vehicle charging station by combining node voltage stability judgment and power grid constraint conditions; considering load response characteristics of an energy storage system and an electric vehicle charging station to form a typical scene of a power distribution area; constructing a photovoltaic power generation daily prediction model and an energy storage system and electric vehicle charging station daily operation model, and selecting operation constraint conditions; in a typical scene of a power distribution area, establishing a target function by taking the lowest comprehensive cost as an optimization target; and solving the model by adopting a genetic algorithm, outputting a daily operation curve of the optical storage and charging resources of the transformer area according to a solving result, and obtaining an energy optimization control strategy giving consideration to optimal economical efficiency. By means of the method, the optimal energy storage system and electric vehicle charging station ratio can be obtained under the same maximum photovoltaic consumption condition of the transformer area, the lowest comprehensive cost is achieved, and the photovoltaic consumption cost of the transformer area is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method for optimizing control of energy aggregation of photovoltaic storage and charging resources in a substation. Background Art

[0002] With the continuous increase in random photovoltaic penetration, the problem of voltage over-limit at distribution network nodes has become increasingly prominent. The grid-side dispatching adopts a "power restriction" method for excess photovoltaic power generation, which leads to large-scale "abandonment" on the distribution network side, causing serious energy waste and losses. The photovoltaic absorption level of the distribution network can be effectively improved by adding energy storage systems. However, the current energy storage system is expensive, and the impact of installation at different nodes on the overall photovoltaic capacity of the distribution network is different. Therefore, how to reasonably plan energy storage or combine other flexible load resources, such as electric vehicle charging stations, microgrids, etc., under the premise of meeting the maximum photovoltaic absorption, to achieve optimal economic performance is an important issue in the development of high-proportion new energy access network technology.

[0003] To this end, the application publication number "CN117895542A" provides a joint optimization method and system for the planning and operation of photovoltaic and multi-type energy storage. On the basis of traditional fixed energy storage, mobile energy storage is considered to improve the economic efficiency of planning. However, it does not define mobile energy storage as fixed energy storage. It only installs backup energy storage at other nodes of the distribution network based on the original fixed energy storage according to different typical scenarios. It still stays at the energy storage system level; the authorization announcement number "CN111900727B" provides a PSO-based photovoltaic storage charging station integrated coordinated optimization scheduling method and device, which balances the spatial load by coordinating the photovoltaic power generation module, energy storage module and electric vehicle charging and swapping module, and obtains each output curve and load curve with the minimum operating cost as the goal. However, its electric vehicle adopts the battery swap mode, and the randomness of user behavior is not considered; in addition, the invention integrates photovoltaic, energy storage and electric vehicle charging stations, and when all nodes of the distribution network may have photovoltaic access, the solution will have great limitations. Therefore, it is of great significance to reasonably explain and analyze the electricity consumption of electric vehicle users and apply it to the energy control of multiple resources in the substation to achieve better economic benefits. Summary of the invention

[0004] The embodiment of the present invention provides a method for optimizing the control of energy aggregation of photovoltaic storage and charging resources in a substation, which realizes the optimal ratio of energy storage system and electric vehicle charging station under the same maximum photovoltaic absorption conditions in the substation, achieves the lowest comprehensive cost, and greatly reduces the photovoltaic absorption cost of the substation.

[0005] In a first aspect, an embodiment of the present invention provides a method for optimizing control of energy aggregation of photovoltaic storage and charging resources in a substation, including:

[0006] Determine the target nodes for installing energy storage systems and electric vehicle charging stations in the substation area, and determine the maximum photovoltaic absorption capacity, the total installed capacity of the energy storage system and the electric vehicle charging station based on the photovoltaic maximum output function and the constraints corresponding to the photovoltaic maximum output function;

[0007] Construct a photovoltaic power generation daily prediction model, an energy storage system daily operation model, an electric vehicle charging station daily operation model under the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, and the corresponding constraints of each model, respectively. The electric vehicle disorderly charging scenario is that the energy storage system participates in the distribution network dispatching of the substation and the electric vehicle is a disorderly load, and the electric vehicle orderly charging scenario is that the energy storage system and the electric vehicle orderly charging participate in the distribution network dispatching of the substation at the same time;

[0008] For the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, respectively, according to the photovoltaic power generation daily prediction model, the energy storage system daily operation model, and the electric vehicle charging station daily operation model, determine the objective function for calculating the comprehensive cost of the substation, and form a cost optimization model with the objective function and the constraints corresponding to each model;

[0009] Respectively in the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, for each model parameter of the photovoltaic storage and charging resources in the cost optimization model, the cost optimization model is calculated based on a set algorithm until the set termination condition is reached, and the target values ​​of each model parameter of the photovoltaic storage and charging resources in the substation are determined to control the photovoltaic storage and charging resources in the substation based on each target value.

[0010] The embodiment of the present invention provides a method for optimizing the control of the energy aggregation of photovoltaic storage and charging resources in a substation. The above technical solution first determines the total installed capacity and location of the energy storage system and the electric vehicle charging station in combination with the node voltage stability judgment and the grid constraints; secondly, the load response characteristics of the energy storage system and the electric vehicle charging station are considered to form a typical scene of the distribution substation; then, a photovoltaic power generation daily prediction model and a daily operation model of the energy storage system and the electric vehicle charging station are constructed, and the operation constraints are selected; then, in the typical scene of the distribution substation, an objective function is established with the lowest comprehensive cost as the optimization goal; finally, a genetic algorithm is used to solve the model, and the solution result outputs the daily operation curve of the photovoltaic storage and charging resources in the substation to obtain an energy optimization control strategy that takes into account the best economy. Under the same maximum photovoltaic absorption condition in the substation, the optimal ratio of the energy storage system and the electric vehicle charging station can be obtained, achieving the lowest comprehensive cost and greatly reducing the photovoltaic absorption cost in the substation. The parameters can be adjusted according to the actual situation of the substation, and it has a broad prospect for application and promotion.

[0011] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a method for optimizing control of energy aggregation of photovoltaic storage and charging resources in a substation provided in the first embodiment of the present invention;

[0014] Figure 2 A topological diagram of photovoltaic power generation, energy storage system and electric vehicle charging station in the area provided in the first embodiment of the present invention;

[0015] Figure 3 A schematic flow chart of another method for optimizing control of energy aggregation of photovoltaic storage and charging resources in a substation provided in the second embodiment of the present invention;

[0016] Figure 4 The distribution of daily mileage of electric vehicles obtained by using the Monte Carlo sampling method provided in the second embodiment of the present invention;

[0017] Figure 5 The distribution of charging time of electric vehicles obtained by using the Monte Carlo sampling method provided in the second embodiment of the present invention;

[0018] Figure 6 The photovoltaic output prediction curve provided in the second embodiment of the present invention;

[0019] Figure 7 A typical daily load curve diagram of the substation provided in the second embodiment of the present invention;

[0020] Figure 8 The control optimization results of energy storage system in typical scenarios are given;

[0021] Fig. 9 The control optimization output results of electric vehicle charging stations in typical scenarios are given;

[0022] Fig.10 A schematic diagram of the structure of a device for optimizing control of photovoltaic storage and charging resource aggregation energy in a substation provided in the third embodiment of the present invention;

[0023] Fig.11 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "original", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] Figure 1 The present invention provides a flow chart of a method for optimizing the control of the aggregation energy of photovoltaic storage and charging resources in a substation, which is applicable to the case of optimizing the control of the aggregation energy of photovoltaic storage and charging resources in a substation. The method can be executed by a device for optimizing the aggregation energy of photovoltaic storage and charging resources in a substation, which can be implemented in the form of hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method for optimizing control of the aggregation energy of photovoltaic storage and charging resources in the substation area provided in the first embodiment may specifically include the following steps:

[0028] S101. Determine the target nodes for installing energy storage systems and electric vehicle charging stations in the substation area, and determine the maximum photovoltaic absorption capacity, the total installed capacity of the energy storage system and the electric vehicle charging station based on the photovoltaic maximum output function and the constraints corresponding to the photovoltaic maximum output function.

[0029] In this embodiment, the photovoltaic storage and charging resources in the substation include photovoltaic power generation, energy storage systems and electric vehicle charging stations. The optimal control of the aggregated energy of photovoltaic storage and charging resources in the substation can be understood as the optimal control of the photovoltaic storage and charging energy. The control principle of this scheme is: the first stage is used to determine the maximum capacity of the energy storage system and the electric vehicle charging station when all nodes in the substation have maximum photovoltaic absorption. In the second stage, based on the maximum capacity of the energy storage system, the dynamic output of the daily energy storage system and the electric vehicle charging station is optimized. It should be clear that the maximum capacity of the first stage is further optimized by the energy storage system and the electric vehicle charging station in the second stage. The maximum capacity obtained in the first stage may not actually be the maximum capacity required for daily operation. The first stage optimizes the maximum capacity set for the worst case. After determining the total installed capacity of the energy storage system and the electric vehicle charging station, the maximum capacity and maximum power limit of the energy storage system and the electric vehicle charging station can be determined.

[0030] Consider installing photovoltaic power generation at all nodes in the area, and find the node with the largest local L index in the area through local L index analysis. The local L index is shown in the following formula (1):

[0031]

[0032] In the above formula, α gen is the generator node set; α load is the load node set; is the complex voltage of the ath generator node; is the complex voltage of the bth load node; F ab is the load participation factor. Local L index L j The larger it is, the worse the stability of the point is.

[0033] Install energy storage systems and electric vehicle charging stations at this node. At the node with the largest local L index, which is equivalent to the node with the worst voltage stability, installing an energy storage system will further improve the global absorption of photovoltaics, so that an optimized maximum photovoltaic absorption capacity and a capacity required by the energy storage system can be obtained. The reason for adding electric vehicle charging stations is to further reduce the cost of the energy storage system. Energy storage installation is very expensive. Here, the orderly and disorderly regulation of electric vehicles is considered, and electric vehicles are added as a variable to further optimize the overall system cost. This optimization is actually to optimize the operating curves of energy storage and electric vehicles to achieve the best economy and obtain a scenario with maximum photovoltaic absorption and electricity demand. Figure 2 The topological diagram of photovoltaic power generation, energy storage system and electric vehicle charging station in the area provided in the first embodiment of the present invention is as follows: Figure 2As shown, an energy storage system and an electric vehicle charging station are installed at node 33. After the location of the energy storage system and the electric vehicle charging station is determined, the maximum photovoltaic absorption capacity and the total installed capacity of the energy storage system and the electric vehicle charging station are determined according to the optimization function of formula (2) in combination with the power flow constraints, node voltage constraints, feeder capacity constraints and transformer capacity constraints. Among them, the photovoltaic maximum output function is expressed as:

[0034]

[0035] In the above formula, F 1 represents the maximum photovoltaic output, N is the total number of nodes; P solar,nom (i) is the photovoltaic output power of the i-th node under the original load, P solar,comp (i) is the increased PV output power of the jth node due to the access of the energy storage system and the electric vehicle charging station, and K is the number of nodes whose PV output power is zero under the original load and whose feeder capacity constraint has been reached.

[0036] Among them, the constraints corresponding to the photovoltaic maximum output function include power flow constraints, node voltage constraints, feeder capacity constraints and transformer capacity constraints, which are specifically expressed as follows:

[0037]

[0038] Where U i and U j is the voltage value of node i and node j; P load (i) and Q load (i) is the original active and reactive values ​​of the load at node i; P s (i) and Q s (i) is the active and reactive values ​​of the flexible load at node i; G ij and B ij are the elements corresponding to the real and imaginary parts in the node admittance matrix; θ ij is the phase angle; U max and U min is the maximum and minimum value of the node voltage; S Lj and S Lj,max is the capacity and upper capacity limit of feeder j; S PV,all and S T,max is the maximum value of the total photovoltaic power generation capacity and transformer capacity, among which the flexible load includes the load of the energy storage system and the electric vehicle charging station.

[0039] Specifically, based on the constraints corresponding to the photovoltaic maximum output function, the photovoltaic maximum output function is solved to determine the maximum photovoltaic absorption capacity, the total installed capacity of the energy storage system and the electric vehicle charging station.

[0040] S102, respectively constructing a photovoltaic power generation daily prediction model, an energy storage system daily operation model, an electric vehicle charging station daily operation model under an electric vehicle disorderly charging scenario and an electric vehicle orderly charging scenario, and the constraints corresponding to each model.

[0041] Among them, the disorderly charging scenario of electric vehicles is that the energy storage system participates in the dispatching of the distribution network in the substation and the electric vehicles act as disorderly loads. The orderly charging scenario of electric vehicles is that the energy storage system and electric vehicles charge in an orderly manner and participate in the dispatching of the distribution network in the substation at the same time.

[0042] Considering the load response characteristics of energy storage systems and electric vehicle charging stations, that is, the energy storage system is a two-way energy exchange type, the electric vehicle charging station is a one-way energy exchange type, and the load response characteristics of the electric vehicle charging station are affected by the electricity consumption behavior of electric vehicle users, a typical scenario of the distribution station area can be formed. Scenario 1: The energy storage system participates in the distribution network dispatching, and the electric vehicle is a disorderly load, which is recorded as the disorderly charging scenario of the electric vehicle; Scenario 2: The energy storage system and the orderly charging of electric vehicles participate in the distribution network dispatching at the same time, which is recorded as the orderly charging scenario of the electric vehicle. In the orderly charging scenario, the pressure of photovoltaic absorption is relieved by changing the starting charging time of the electric vehicle.

[0043] In this embodiment, a photovoltaic power generation daily prediction model and an energy storage system and an electric vehicle charging station daily operation model are constructed. The photovoltaic power generation daily prediction model describes the daily operation status of photovoltaic power generation based on the output power of photovoltaic power generation. The energy storage system daily operation model describes the daily operation status of the energy storage system based on the output characteristics and charge state of the energy storage system. The electric vehicle charging station daily operation model describes the daily operation status of the electric vehicle charging station as a load participating in the distribution network of the substation based on the starting charging time, daily mileage and charging time. Based on the photovoltaic power generation daily prediction model, the energy storage system daily operation model and the electric vehicle charging station daily operation model, the power matching situation of the day-ahead scheduling can be clearly described.

[0044] Considering the charging and discharging power and state of charge of the energy storage system and electric vehicle batteries, flow constraints, node voltage constraints, feeder capacity constraints, transformer capacity constraints, energy storage system power constraints, energy storage system state of charge constraints, electric vehicle charging station power constraints and electric vehicle state of charge constraints are selected as constraints. It should be clear that in this embodiment, corresponding electric vehicle charging station daily operation models are constructed for the orderly charging scenario of electric vehicles and the disorderly charging scenario of electric vehicles.

[0045] S103. For the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, determine the objective function for calculating the comprehensive cost of the substation based on the daily prediction model of photovoltaic power generation, the daily operation model of the energy storage system, and the daily operation model of the electric vehicle charging station, and form a cost optimization model with the objective function and the constraints corresponding to each model.

[0046] In this embodiment, in two typical scenarios of distribution substations, namely, the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, an objective function is established with the lowest comprehensive cost as the optimization goal. Specifically, based on the daily prediction model of photovoltaic power generation, the daily operation model of the energy storage system, and the daily operation model of the electric vehicle charging station, the objective function for calculating the comprehensive cost of the substation is determined, and the operating constraints corresponding to each model are set, including: flow constraints, node voltage constraints, feeder capacity constraints, transformer capacity constraints, energy storage system power constraints, energy storage system state of charge constraints, electric vehicle charging station power constraints, and electric vehicle state of charge constraints. The objective function and the constraints corresponding to each model constitute a cost optimization model. The energy storage system state of charge constraints, electric vehicle charging station power constraints, and electric vehicle state of charge constraints in the operating constraints are shown in the following formula (4):

[0047]

[0048] Where P ess,max and P ess,min The maximum and minimum output power of the energy storage system; SOC ess,max and SOC ess,min The maximum and minimum values ​​of the energy storage system's state of charge; P car,i (t) and SOC car,i (t) is the charging power and state of charge of the i-th electric vehicle at time t; P car,i,max is the maximum charging power of the battery of the i-th electric vehicle; SOC car,i,max and SOC car,i,min are the maximum and minimum state of charge of the battery of the i-th electric vehicle.

[0049] S104. In the scenarios of disordered charging of electric vehicles and orderly charging of electric vehicles, for each model parameter of the photovoltaic, storage and charging resources in the cost optimization model, the cost optimization model is calculated based on a set algorithm until the set termination condition is reached, and the target values ​​of each model parameter of the photovoltaic, storage and charging resources in the substation are determined to control the photovoltaic, storage and charging resources in the substation based on the target values.

[0050] This step iteratively optimizes the cost optimization model by setting an algorithm, so as to change the capacity and power of the energy storage system and the electric vehicle charging station, change the calculation results until the set termination conditions are reached, and thus determine the optimal capacity and power of the energy storage system for optimization control. Exemplarily, the set algorithm can be a genetic algorithm. In this embodiment, a genetic algorithm is used to solve the model, and the solution outputs the daily operation curve of the photovoltaic storage and charging resources in the substation to obtain an energy optimization control strategy that takes into account the best economic performance. For each model parameter of the photovoltaic storage and charging resources in the substation in the cost optimization model, the cost optimization model is calculated based on the set algorithm until the set termination conditions are reached, and the target values ​​of each model parameter of the photovoltaic storage and charging resources in the substation are determined, so as to control the photovoltaic storage and charging resources in the substation based on each target value.

[0051] The above technical scheme first determines the total installation capacity and location of the energy storage system and electric vehicle charging station by combining the node voltage stability judgment and the grid constraints; secondly, the load response characteristics of the energy storage system and electric vehicle charging station are considered to form a typical scenario of the distribution area; then, a photovoltaic power generation daily prediction model and a daily operation model of the energy storage system and electric vehicle charging station are constructed, and the operation constraints are selected; then, in the typical scenario of the distribution area, an objective function is established with the lowest comprehensive cost as the optimization goal; finally, a genetic algorithm is used to solve the model, and the solution outputs the daily operation curve of the photovoltaic storage and charging resources in the substation area, and an energy optimization control strategy that takes into account the best economic performance is obtained. Under the same maximum photovoltaic absorption conditions in the substation area, the optimal ratio of energy storage system and electric vehicle charging station can be obtained, achieving the lowest comprehensive cost and greatly reducing the photovoltaic absorption cost in the substation area. Parameters can be adjusted according to the actual situation of the substation area, and it has a broad prospect for application and promotion.

[0052] Embodiment 2

[0053] Figure 3 A flow chart of another method for optimizing control of energy aggregation of photovoltaic, storage and charging resources in a substation provided in Embodiment 2 of the present invention. This embodiment is a further optimization of the above embodiment. In this embodiment, the "daily prediction model of photovoltaic power generation and the steps for constructing the daily operation model of the energy storage system" are further limited and optimized, and the "daily operation model of the electric vehicle charging station under the disordered charging scenario of electric vehicles and the orderly charging scenario of electric vehicles" are further limited and optimized, and the "objective function for calculating the comprehensive cost of the substation is determined according to the daily prediction model of photovoltaic power generation, the daily operation model of the energy storage system, and the daily operation model of the electric vehicle charging station for the disordered charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, respectively" is limited and optimized, and the "model parameters of the photovoltaic, storage and charging resources in the substation in the cost optimization model are calculated based on the set algorithm until the set termination conditions are reached to determine the target values ​​of the model parameters of the photovoltaic, storage and charging resources in the substation" is further limited and optimized.

[0054] like Figure 3 As shown, this embodiment 2 provides a method for optimizing control of energy aggregation of photovoltaic storage and charging resources in a substation, which specifically includes the following steps:

[0055] S201. Determine the target nodes for installing energy storage systems and electric vehicle charging stations in the substation area, and determine the maximum photovoltaic absorption capacity, the total installed capacity of the energy storage system and the electric vehicle charging station based on the photovoltaic maximum output function and the constraints corresponding to the photovoltaic maximum output function.

[0056] S202: construct a photovoltaic power generation daily prediction model based on the current light intensity, the number of photovoltaic modules, the area of ​​the photovoltaic modules and the photoelectric conversion efficiency.

[0057] Among them, the photovoltaic power generation daily prediction model adopts a probability model, and its output power is shown in the following formula (5):

[0058]

[0059] Where α(t) is the light intensity at time t, and its value can be determined by referring to the historical meteorological data of the distribution station area at that time; N is the number of photovoltaic modules, B n and β n is the area and photoelectric conversion efficiency of the photovoltaic module.

[0060] S203: construct a daily operation model of the energy storage system according to the load of the energy storage system at the previous moment, the output power, the charging efficiency and the discharging efficiency at the current moment.

[0061] In this embodiment, the daily operation model of the energy storage system can be regarded as an adjustable load, and the output characteristics are related to its charge state, as shown in the following formula (6):

[0062]

[0063] Where P ess (t) is the output power of the energy storage system at time t, η + and η - It is the charging efficiency of the energy storage system and the discharging efficiency of the energy storage system.

[0064] S204: Determine a starting charging time function of the electric vehicle in a disorderly charging scenario of the electric vehicle and an orderly charging scenario of the electric vehicle.

[0065] The daily operation model of electric vehicle charging stations considers both disorderly charging of electric vehicles and orderly charging of electric vehicles.

[0066] As a specific implementation method, the steps of optimizing and determining the starting charging time function of an electric vehicle in a disorderly charging scenario of an electric vehicle and an orderly charging scenario of an electric vehicle include:

[0067] a1) In the scenario of disorderly charging of electric vehicles, according to the expectation and standard deviation of the starting charging time of the electric vehicles, the probability density function of the starting charging time of the electric vehicles is determined as the starting charging time function of the electric vehicles in the scenario of disorderly charging of electric vehicles.

[0068] Disordered charging of electric vehicles involves the starting charging time, daily mileage and charging time, where the starting charging time function is shown in the following formula (7):

[0069]

[0070] In the formula, f s (x) is the probability density function of the time when the electric vehicle starts charging, μ s and σ s As its expectation and standard deviation, the probability density function of the starting charging time of the electric vehicle is used as the starting charging time function of the electric vehicle in the disorderly charging scenario of the electric vehicle. For example, Figure 4 The distribution of daily mileage of electric vehicles obtained by using the Monte Carlo sampling method provided in the second embodiment of the present invention is as follows: Figure 4 As shown in Figure 1, the Monte Carlo sampling method is used to obtain the distribution of daily mileage of electric vehicles.

[0071] b1) In the scenario of orderly charging of electric vehicles, an incentive electricity price method is adopted, and the electricity price function is determined according to the incentive electricity price start time, incentive electricity price end time, incentive electricity price and flat electricity price.

[0072] Among them, the orderly charging of electric vehicles is guided by the incentive electricity price policy to guide the starting time of electric vehicle charging, actively respond to photovoltaic consumption and reduce part of the cost of the energy storage system. At this time, the electricity price is shown in the following formula (8):

[0073]

[0074] In the formula, c feed and c flat For incentive electricity price and peace electricity price, t feed1 and t feed2 The starting and ending time of the incentive electricity price.

[0075] c1) Based on the incentive electricity price method, determine the response capacity function of electric vehicles' orderly charging in response to electricity price incentives.

[0076] Considering the degree to which electric vehicles can actually respond to electricity price incentives in an orderly manner, the response capacity is defined as shown in the following formula (9):

[0077]

[0078] Where n car,ordThe number of electric vehicles that responded to the orderly charging; N car is the total number of electric vehicles.

[0079] d1) Determine the starting charging time function of the electric vehicle in the orderly charging scenario of the electric vehicle according to the starting time of the incentive electricity price, the ending time of the incentive electricity price, the charging time and the random function.

[0080] In this embodiment, for the orderly charging scenario of electric vehicles, the orderly charging time changes at this time, as shown in the following formula (10):

[0081]

[0082] Where, t sord,i is the starting charging time of the i-th vehicle under the incentive electricity price condition, and rand is a random number in the interval [0,1].

[0083] S205 . Determine a probability density function of the daily mileage of the electric vehicle according to the expectation and standard deviation of the daily mileage of the electric vehicle.

[0084] In this embodiment, the probability density function of the daily mileage of the electric vehicle is shown in the following formula (11):

[0085]

[0086] In the formula, f m (x) is the probability density function of the daily mileage of electric vehicles, μ m and σ m Its expectation and standard deviation.

[0087] S206: Determine a charging time function of the electric vehicle according to the power consumption per kilometer of the electric vehicle, the daily mileage of the electric vehicle, and the charging power of the electric vehicle.

[0088] The charging time function of an electric vehicle is expressed as:

[0089]

[0090] η car is the power consumption per kilometer of the electric vehicle, where the charging efficiency of the electric vehicle has been converted to η car In, M car,i is the daily mileage of the i-th electric vehicle, P car Charging power for electric vehicles.

[0091] S207, forming a daily operation model of an electric vehicle charging station in a disorderly charging scenario by combining a starting charging time function of the electric vehicle in a disorderly charging scenario, a probability density function of a daily mileage, and a charging time function.

[0092] In this embodiment, disorderly charging of electric vehicles involves the starting charging time, daily mileage and charging time. Therefore, the starting charging time function, the probability density function of the daily mileage and the charging time function of the electric vehicle under the disorderly charging scenario of electric vehicles are used to form a daily operation model of the electric vehicle charging station under the disorderly charging scenario of electric vehicles.

[0093] S208, forming a daily operation model of an electric vehicle charging station in an orderly charging scenario of electric vehicles by combining a starting charging time function of the electric vehicles in an orderly charging scenario, a probability density function of daily mileage, and a charging time function.

[0094] In this embodiment, the orderly charging of electric vehicles guides the starting time of electric vehicle charging with incentive electricity price policies, actively responds to photovoltaic consumption and reduces part of the cost of energy storage systems. Therefore, the starting charging time function of electric vehicles in the orderly charging scenario of electric vehicles, the probability density function of daily mileage, and the charging time function constitute the daily operation model of electric vehicle charging stations in the orderly charging scenario of electric vehicles.

[0095] S209. Determine the investment cost of the energy storage system according to the daily operation model of the energy storage system.

[0096] As a specific implementation method, the investment cost of the energy storage system can be determined by optimizing the daily operation model of the energy storage system, including:

[0097] a2) Determine the rated capacity and power of the energy storage system based on the daily operation model of the energy storage system.

[0098] In this embodiment, the daily operation model of the energy storage system includes the relationship between the output characteristics of the energy storage system and the load state, based on which the rated capacity and power of the energy storage system can be determined.

[0099] b2) Determine the investment cost of the energy storage system based on the rated capacity and power of the energy storage system, the initial construction cost and operation and maintenance cost per unit capacity of the energy storage system, and the initial construction cost and operation and maintenance cost per unit power of the energy storage system.

[0100] In this embodiment, the energy storage system investment cost includes the initial construction cost C of the energy storage system. Sint,ess and the energy storage system operation and maintenance cost C Sop,ess , as shown in the following formula (13):

[0101] C S,ess =C Sint,ess +C Sop,ess =(k E,ess E S,ess +k P,ess P S,ess )+(c E,ess ES,ess +c P,ess P S,ess )(13)

[0102] In the formula, E S,ess and P S,ess Invest in the rated capacity and power of the energy storage system; k E,ess and c E,ess k is the initial construction cost and operation and maintenance cost per unit capacity of the energy storage system; P,ess and c P,ess It is the initial construction cost and operation and maintenance cost per unit power of the energy storage system.

[0103] S210. Determine the investment costs of the electric vehicle charging station in the disorderly charging scenario of the electric vehicle and in the orderly charging scenario of the electric vehicle respectively according to the daily operation models of the electric vehicle charging station in the disorderly charging scenario of the electric vehicle and in the orderly charging scenario of the electric vehicle.

[0104] As a specific implementation method, the steps of respectively determining the investment costs of electric vehicle charging stations in the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles can be optimized according to the daily operation models of electric vehicle charging stations in the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, including:

[0105] a3) According to the daily operation models of electric vehicle charging stations under the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, the rated capacity and power of electric vehicle charging stations under the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles are determined respectively.

[0106] In this embodiment, for two classic scenarios, namely, disorderly charging of electric vehicles and orderly charging of electric vehicles, the daily operation model of the electric vehicle charging station includes the starting charging time, daily mileage and charging time of the electric vehicle. Based on this information, the rated capacity and power of the electric vehicle charging station can be calculated.

[0107] b3) Determine the investment cost of electric vehicle charging stations in the disorderly charging scenario and the orderly charging scenario of electric vehicles based on the rated capacity and power of the electric vehicle charging stations, the initial construction cost and operation and maintenance cost per unit capacity of the electric vehicle charging stations, and the initial construction cost and operation and maintenance cost per unit power of the electric vehicle charging stations.

[0108] Among them, the investment cost of electric vehicle charging station includes the initial construction cost C of electric vehicle charging station. Sint,car and the operation and maintenance cost of the electric vehicle charging station C Sop,car , specifically:

[0109] C S,car =C Sint,car +C Sop,car=(k E,car E S,car +k P,car P S,car )+(c E,car E S,car +c P,car P S,car )(14)

[0110] In the formula, E S,car and P S,car Invest in the rated capacity and power of the energy storage system; k E,car and c E,car k is the initial construction cost and operation and maintenance cost per unit capacity of the energy storage system; P,car and c P,car It is the initial construction cost and operation and maintenance cost per unit power of the energy storage system.

[0111] S211. Determine the coefficient for converting the investment cost of the energy storage system to the equivalent day, and the coefficient for converting the investment cost of the electric vehicle charging station to the equivalent day.

[0112] In this embodiment, the coefficient of converting the energy storage system investment cost to the equivalent day is calculated as shown in the following formula (15):

[0113]

[0114] Where r is the social discounted value; L ess and N day,ess Plan the operating life and design operating days for the energy storage system.

[0115] The coefficient for converting the investment cost of electric vehicle charging stations to equivalent days is shown in the following formula (16):

[0116]

[0117] Where, L car and N day,car Plan the operating life and design operating days for electric vehicle charging stations.

[0118] S212. Construct an objective function for the comprehensive cost of the substation under the disorderly charging scenario and the orderly charging scenario of electric vehicles based on the investment cost of the energy storage system, the coefficient of converting the investment cost of the energy storage system to the equivalent day, the investment cost of the electric vehicle charging station under the disorderly charging scenario and the orderly charging scenario of electric vehicles, the coefficient of converting the investment cost of the electric vehicle charging station to the equivalent day, the power grid's electricity purchase cost, and the cost of electric vehicles selling electricity.

[0119] Among them, the objective function can be expressed as: F = f day,ess C S,ess +f day,car C S,car +Cgrid -C sell , where f day,ess and f day,car are the coefficients for converting the investment costs of energy storage systems and electric vehicle charging stations to equivalent days; C S,ess and C S,car are the investment costs of energy storage system and electric vehicle charging station respectively; C grid The cost of purchasing electricity from the power grid; C sell Profits from selling electricity for electric vehicles.

[0120] S213, initializing the model parameters of the photovoltaic power generation daily prediction model, the energy storage system daily operation model, the electric vehicle charging station daily operation model under the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, and the upper and lower limits of the constraint conditions corresponding to each model.

[0121] In this embodiment, the basic data of the distribution network node system in the substation area is initialized, and the network parameters, photovoltaic power generation, energy storage system and electric vehicle charging station model parameters, as well as the upper and lower limits of each constraint condition are input.

[0122] S214, initializing the genetic algorithm, including encoding method, objective function, population, selection operation, crossover operation, mutation operation, inversion operation and termination criterion.

[0123] This step is used to initialize the genetic algorithm, including encoding, objective function, population, selection operation, crossover operation, mutation operation, inversion operation and termination criterion. In this embodiment, binary encoding is used, and the objective function is F = f day,ess C S,ess +f day,car C S,car +C grid -C sell As shown in the figure, the meaning of each letter in the formula is as mentioned above and will not be repeated here. The initial population size is 500, the selection operation is set to nonlinear ranking selection, the crossover operation is set to multi-point crossover and uniform crossover, the mutation operation is set to basic bit mutation combined with inversion sorting, and the termination condition is set to 100 evolutionary generations.

[0124] S215. Generate an initial population, calculate the objective function value of each individual in the population, and further select the next generation of individuals.

[0125] S216. Perform selection, crossover and mutation operations on the next generation population in sequence, and determine whether the individual objective function value meets the termination condition. When the individual objective function value does not meet the termination condition, return to step S215 to calculate the objective function value of each individual in the population. When the individual objective function value meets the termination condition, execute step S217.

[0126] S217. When the individual objective function value meets the termination condition, the iteration process is stopped, and the optimal solution is output as the target value of the model parameter to control the photovoltaic storage and charging resources in the substation based on the target value.

[0127] In this embodiment, when the individual objective function value does not meet the termination condition, it returns to step S215; otherwise, the iteration process is stopped, and the optimal solution is output, and the daily operation curve of the photovoltaic storage and charging resources in the substation area is output to obtain an energy optimization control strategy that takes into account the best economic performance.

[0128] The above technical solution specifies the steps of constructing each operation model, the steps of constructing the objective function, and the steps of solving the objective function. The above technical solution can obtain the optimal ratio of energy storage system and electric vehicle charging station under the same maximum photovoltaic absorption conditions in the substation, achieve the lowest comprehensive cost, and greatly reduce the photovoltaic absorption cost of the substation. In addition, the proposed substation photovoltaic storage and charging resource aggregation energy optimization control strategy can adjust parameters according to the actual situation of the substation, so as to obtain the optimal photovoltaic storage and charging solution under different complex scenarios.

[0129] The effectiveness of the optimal control method for the aggregation of photovoltaic storage and charging resources in the substation area of ​​the present invention is explained below through a specific example:

[0130] This example uses a photovoltaic storage and charging area distribution network based on the standard IEEE-33 distribution network model, such as Figure 2 The power reference value is selected as 10MVA, the voltage reference value is 12.66KV, the node voltage maximum value Umax is 1.05pu, and the node voltage minimum value Umin is 0.95pu. Table 1 shows the relevant parameters of the energy storage system, Table 2 shows the relevant parameters of the electric vehicle charging station, and the relevant model parameters are shown in Tables 1 and 2.

[0131] Table 1

[0132] Parameter name Value and unit <![CDATA[Initial construction cost k per unit capacity of the energy storage system E,ess > 2250 yuan / kW·h <![CDATA[The operation and maintenance cost c per unit capacity of the energy storage system E,ess > 0.05 yuan / kW·h <![CDATA[Initial construction cost k of unit power of energy storage system P,ess > 4500 yuan / kW <![CDATA[The operation and maintenance cost per unit power c of the energy storage system P,ess > 190 yuan / kW Social Discount Value 8％ <![CDATA[Energy storage system planning operation life L ess > 15 years <![CDATA[Design and operating days N of energy storage system day,ess > 360 days <![CDATA[Charging efficiency η of the energy storage system + > 90％ <![CDATA[Discharge efficiency η of the energy storage system - > 90％ <![CDATA[The maximum output power P of the energy storage system ess,max > 4000kW <![CDATA[Minimum output power P of the energy storage system ess,min > -4000kW <![CDATA[Maximum capacity of energy storage system SOC ess,max > 90％ <![CDATA[Minimum SOC of energy storage system capacity ess,min > 10％

[0133] Table 2

[0134]

[0135]

[0136] According to some data, the average on-grid electricity price is 0.4650 yuan / kW·h, the user peak and valley electricity price is 1.1794 yuan / kW·h during peak hours (8:00-11:00, 17:00-22:00), 0.7054 yuan / kW·h during normal hours (11:00-17:00, 22:00-24:00), 0.3187 yuan / kW·h during valley hours (0:00-8:00), and the incentive electricity price c feed Peace Electricity Price flatThey are 0.3187 yuan / kW·h and 0.7054 yuan / kW·h respectively. The starting time of the incentive electricity price is t feed1 and the incentive electricity price termination time t feed2 12.00 and 17.00 respectively. Figure 5 The distribution of charging time of electric vehicles obtained by using the Monte Carlo sampling method provided in the second embodiment of the present invention is as follows: Figure 5 As shown in Figure 2, the distribution of charging time of electric vehicles is obtained by Monte Carlo sampling method. Referring to the published daily operation data and combining with formula (5), Figure 6 The photovoltaic output prediction curve provided in the second embodiment of the present invention; Figure 7 The typical daily load curve diagram of the substation provided in the second embodiment of the present invention, the photovoltaic output prediction curve is as follows Figure 6 As shown in Figure 2, the daily load of the distribution network is as follows: Figure 7 shown.

[0137] According to the typical scenarios of the above two areas of photovoltaic storage and charging, the results of using the energy optimization control strategy of the present invention are as follows: Figure 8 and 9 shown. Figure 8 The control optimization results of energy storage system in typical scenarios are given; Fig. 9 The control optimization output results of electric vehicle charging stations under typical scenarios are given. It can be seen that the energy storage system absorbs energy during the peak phase of photovoltaic output and implements "low storage and high generation" in accordance with the peak-valley electricity price to achieve the best economic benefits; compared with scenario 1 (i.e., the disorderly charging scenario of electric vehicles) and scenario 2 (i.e., the orderly charging scenario of electric vehicles), under the guidance of incentive electricity prices, the peak load period of electric vehicles shifts to the peak of photovoltaic output, while the overall distribution of the remaining time periods is similar; after electric vehicles join the system dispatch, the demand for energy storage during the peak phase of photovoltaic output is reduced to a certain extent, and the output and power of the energy storage system are significantly reduced, and the orderly charging of electric vehicles has the greatest impact on it.

[0138] Table 3 gives a comparison of the comprehensive costs under the two scenarios. It can be seen that after adopting the energy optimization control strategy of the present invention, the comprehensive cost F of scenario 1 is 80,110 yuan, of which the energy storage system cost is 56,170 yuan, the electric vehicle charging station cost is 15,100 yuan, and the power grid purchase cost C is 2.376 billion yuan. grid The profit cost of electric vehicle electricity sales is C sell In scenario 2, the cost of energy storage system is RMB 21,790, the cost of electric vehicle charging station is RMB 31,160, and the cost of power purchase from the power grid is C grid The profit cost of electric vehicle electricity sales is C sellThe above results show that under scenario 2, although the profit of electricity sales of electric vehicle charging stations is reduced, the cost of energy storage system is reduced by guiding more electric vehicle users to participate in photovoltaic consumption, thereby reducing the overall cost of the system.

[0139] Table 3

[0140]

[0141]

[0142] From the above description, it can be seen that the present invention sets up an energy optimization control strategy for the aggregation of photovoltaic storage and charging resources in the substation. By constructing a demand-side response model and an optimization objective function according to the scenario of disorderly and orderly charging of energy storage systems and electric vehicles, the energy optimization control of photovoltaic storage and charging resources in the substation under the condition of maximum photovoltaic absorption is realized, which has the advantages of strong photovoltaic absorption capacity and low comprehensive cost.

[0143] Embodiment 3

[0144] Fig.10 The schematic diagram of the structure of a device for optimizing the control of the aggregation energy of photovoltaic storage and charging resources in a substation provided in the third embodiment of the present invention is applicable to the case of optimizing the control of the aggregation energy of photovoltaic storage and charging resources in a substation. The device for optimizing the control of the aggregation energy of photovoltaic storage and charging resources in a substation can be configured in an electronic device, such as Fig.10 As shown, the device includes: a capacity determination module 31, a model construction module 32, a cost construction module 33 and a model solution module 34; wherein,

[0145] The capacity determination module 31 is used to determine the target nodes for installing the energy storage system and the electric vehicle charging station in the substation area, and determine the maximum photovoltaic absorption capacity, the total installed capacity of the energy storage system and the electric vehicle charging station according to the photovoltaic maximum output function and the constraints corresponding to the photovoltaic maximum output function;

[0146] The model building module 32 is used to respectively build a photovoltaic power generation daily prediction model, an energy storage system daily operation model, an electric vehicle charging station daily operation model under an electric vehicle disorderly charging scenario and an electric vehicle orderly charging scenario, and the operation constraints corresponding to each model, wherein the electric vehicle disorderly charging scenario is that the energy storage system participates in the distribution network dispatching of the substation and the electric vehicle is a disorderly load, and the electric vehicle orderly charging scenario is that the energy storage system and the electric vehicle orderly charging simultaneously participate in the distribution network dispatching of the substation;

[0147] The function construction module 33 is used to determine the objective function for calculating the comprehensive cost of the substation according to the photovoltaic power generation daily prediction model, the energy storage system daily operation model, and the electric vehicle charging station daily operation model for the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, and to form a cost optimization model with the objective function and the constraints corresponding to each model;

[0148] The model solving module 34 is used to calculate the cost optimization model based on the set algorithm for each model parameter of the photovoltaic storage and charging resources in the substation in the cost optimization model under the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles until the set termination conditions are reached, and determine the target values ​​of each model parameter of the photovoltaic storage and charging resources in the substation, so as to control the photovoltaic storage and charging resources in the substation based on the target values.

[0149] The above technical scheme first determines the total installation capacity and location of the energy storage system and electric vehicle charging station by combining the node voltage stability judgment and the grid constraints; secondly, the load response characteristics of the energy storage system and electric vehicle charging station are considered to form a typical scenario of the distribution area; then, a photovoltaic power generation daily prediction model and a daily operation model of the energy storage system and electric vehicle charging station are constructed, and the operation constraints are selected; then, in the typical scenario of the distribution area, an objective function is established with the lowest comprehensive cost as the optimization goal; finally, a genetic algorithm is used to solve the model, and the solution outputs the daily operation curve of the photovoltaic storage and charging resources in the substation area, and an energy optimization control strategy that takes into account the best economic performance is obtained. Under the same maximum photovoltaic absorption conditions in the substation area, the optimal ratio of energy storage system and electric vehicle charging station can be obtained, achieving the lowest comprehensive cost and greatly reducing the photovoltaic absorption cost in the substation area. Parameters can be adjusted according to the actual situation of the substation area, and it has a broad prospect for application and promotion.

[0150] Optionally, the photovoltaic maximum output function is expressed as:

[0151]

[0152] In the above formula, F 1 represents the maximum photovoltaic output, N is the total number of nodes; P solar,nom (i) is the photovoltaic output power of the i-th node under the original load, P solar,comp (i) is the increased PV output power of the jth node due to the access of the energy storage system and the electric vehicle charging station, and K is the number of nodes whose PV output power is zero under the original load and whose feeder capacity constraint has been reached;

[0153] The constraints corresponding to the photovoltaic maximum output function include power flow constraints, node voltage constraints, feeder capacity constraints and transformer capacity constraints.

[0154] Optionally, the model building module 32 includes:

[0155] A photovoltaic model building unit is used to build a photovoltaic power generation daily prediction model based on the current light intensity, the number of photovoltaic modules, the area of ​​the photovoltaic modules and the photoelectric conversion efficiency;

[0156] The energy storage model building unit builds a daily operation model of the energy storage system according to the load of the energy storage system at the previous moment, the output power at the current moment, the charging efficiency and the discharging efficiency.

[0157] Optionally, the model building module 32 further includes a charging station model building unit, which is specifically used to:

[0158] Determine the starting charging time function of electric vehicles in the disorderly charging scenario and the orderly charging scenario of electric vehicles;

[0159] Determine the probability density function of the daily mileage of the electric vehicle based on the expectation and standard deviation of the daily mileage of the electric vehicle;

[0160] Determine the charging time function of the electric vehicle based on the electric vehicle's power consumption per kilometer, the electric vehicle's daily mileage, and the electric vehicle's charging power;

[0161] The starting charging time function, the probability density function of the daily mileage and the charging time function of the electric vehicle in the disorderly charging scenario of the electric vehicle are used to form a daily operation model of the electric vehicle charging station in the disorderly charging scenario of the electric vehicle;

[0162] The starting charging time function of electric vehicles, the probability density function of daily mileage and the charging time function under the orderly charging scenario of electric vehicles are used to form a daily operation model of electric vehicle charging stations under the orderly charging scenario of electric vehicles.

[0163] Optionally, the charging station model building unit is used to execute the step of determining the starting charging time function of the electric vehicle in the disorderly charging scenario of the electric vehicle and the orderly charging scenario of the electric vehicle, including:

[0164] In the scenario of disorderly charging of electric vehicles, according to the expectation and standard deviation of the starting charging time of electric vehicles, the probability density function of the starting charging time of electric vehicles is determined as the starting charging time function of electric vehicles in the scenario of disorderly charging of electric vehicles;

[0165] In the orderly charging scenario of electric vehicles, the incentive electricity price method is adopted, and the electricity price function is determined according to the incentive electricity price starting time, incentive electricity price ending time, incentive electricity price and flat electricity price;

[0166] Based on the incentive electricity price method, the response capacity function of electric vehicle orderly charging in response to electricity price incentives is determined;

[0167] According to the starting time of the incentive electricity price, the ending time of the incentive electricity price, the charging time and the random function, the starting charging time function of the electric vehicle in the orderly charging scenario of the electric vehicle is determined.

[0168] Optionally, the function construction module 33 is specifically used to:

[0169] Determine the investment cost of the energy storage system based on the daily operation model of the energy storage system;

[0170] According to the daily operation models of electric vehicle charging stations under the scenarios of disorderly charging of electric vehicles and orderly charging of electric vehicles, the investment costs of electric vehicle charging stations under the scenarios of disorderly charging of electric vehicles and orderly charging of electric vehicles are determined respectively;

[0171] Determine the coefficient for converting the investment cost of the energy storage system to the equivalent day, and the coefficient for converting the investment cost of the electric vehicle charging station to the equivalent day;

[0172] Based on the investment cost of the energy storage system, the coefficient of converting the investment cost of the energy storage system to the same day, the investment cost of the electric vehicle charging station in the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, the coefficient of converting the investment cost of the electric vehicle charging station to the same day, the power grid's electricity purchase cost, and the cost of electric vehicles selling electricity, an objective function of the comprehensive cost of the substation under the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles is constructed.

[0173] Optionally, the function construction module 33 is used to execute the step of determining the investment cost of the energy storage system according to the daily operation model of the energy storage system, including:

[0174] Determine the rated capacity and power of the energy storage system based on the daily operation model of the energy storage system;

[0175] The investment cost of the energy storage system is determined based on the rated capacity and power of the energy storage system, the initial construction cost and operation and maintenance cost per unit capacity of the energy storage system, and the initial construction cost and operation and maintenance cost per unit power of the energy storage system.

[0176] Optionally, the function construction module 33 is used to execute the steps of determining the investment cost of the electric vehicle charging station in the disorderly charging scenario of the electric vehicle and the orderly charging scenario of the electric vehicle according to the daily operation model of the electric vehicle charging station in the disorderly charging scenario of the electric vehicle and the orderly charging scenario of the electric vehicle, respectively, including:

[0177] According to the daily operation models of electric vehicle charging stations under the scenarios of disorderly charging of electric vehicles and orderly charging of electric vehicles, the rated capacity and power of electric vehicle charging stations under the scenarios of disorderly charging of electric vehicles and orderly charging of electric vehicles are determined respectively;

[0178] Based on the rated capacity and power of the electric vehicle charging station, the initial construction cost and operation and maintenance cost per unit capacity of the electric vehicle charging station, and the initial construction cost and operation and maintenance cost per unit power of the electric vehicle charging station, the investment cost of the electric vehicle charging station in the disorderly charging scenario and the orderly charging scenario of the electric vehicle are determined respectively.

[0179] Optionally, the model solving module 34 is specifically used for:

[0180] Initialize the model parameters of the photovoltaic power generation daily prediction model, the energy storage system daily operation model, the electric vehicle charging station daily operation model under the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, as well as the upper and lower limits of the constraints corresponding to each model;

[0181] Initialize the genetic algorithm, including encoding method, objective function, population, selection operation, crossover operation, mutation operation, inversion operation and termination criterion;

[0182] Generate an initial population, calculate the objective function value of each individual in the population, and further select the next generation of individuals;

[0183] Perform selection, crossover and mutation operations on the next generation population in sequence, and determine whether the individual objective function value meets the termination condition;

[0184] When the individual objective function value does not meet the termination condition, it returns to continue calculating the objective function value of each individual in the population;

[0185] When the individual objective function value meets the termination condition, the iteration process is stopped and the optimal solution is output as the target value of the model parameter to control the photovoltaic storage and charging resources in the substation based on the target value.

[0186] Optionally, the operating constraints corresponding to each model include: power flow constraints, node voltage constraints, feeder capacity constraints, transformer capacity constraints, energy storage system power constraints, energy storage system state of charge constraints, electric vehicle charging station power constraints and electric vehicle state of charge constraints.

[0187] The device for optimizing the control of the aggregated energy of photovoltaic, storage and charging resources in a substation provided in an embodiment of the present invention can execute the method for optimizing the control of the aggregated energy of photovoltaic, storage and charging resources in a substation provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0188] Embodiment 4

[0189] Fig.11A schematic diagram of the structure of an electronic device provided for Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0190] like Fig.11 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0191] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0192] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 41 executes the various methods and processes described above, such as the energy optimization control method for the aggregation of photovoltaic storage and charging resources in the substation area.

[0193] In some embodiments, the method for optimizing the control of the aggregation energy of the photovoltaic storage and charging resources in the substation may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for optimizing the control of the aggregation energy of the photovoltaic storage and charging resources in the substation described above may be executed. Alternatively, in other embodiments, the processor 41 may be configured to execute the method for optimizing the control of the aggregation energy of the photovoltaic storage and charging resources in the substation by any other appropriate means (for example, by means of firmware).

[0194] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0195] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0196] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage 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. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. 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.

[0197] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0198] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0199] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0200] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for optimizing control of energy aggregation of photovoltaic storage and charging resources in a substation as provided in any embodiment of the present invention.

[0201] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code for the operation of the present disclosure, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0202] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0203] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the control of the energy aggregation of photovoltaic storage and charging resources in a substation, characterized in that: include: Determine the target nodes for installing energy storage systems and electric vehicle charging stations in the substation area, and determine the maximum photovoltaic absorption capacity, the total installed capacity of the energy storage system and the electric vehicle charging station based on the photovoltaic maximum output function and the constraints corresponding to the photovoltaic maximum output function; Construct a photovoltaic power generation daily prediction model, an energy storage system daily operation model, an electric vehicle charging station daily operation model under the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, and the corresponding operation constraints of each model, respectively. The electric vehicle disorderly charging scenario is that the energy storage system participates in the distribution network dispatching of the substation and the electric vehicle is a disorderly load, and the electric vehicle orderly charging scenario is that the energy storage system and the electric vehicle orderly charging participate in the distribution network dispatching of the substation at the same time; For the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, respectively, according to the photovoltaic power generation daily prediction model, the energy storage system daily operation model, and the electric vehicle charging station daily operation model, determine the objective function for calculating the comprehensive cost of the substation, and form a cost optimization model with the objective function and the constraints corresponding to each model; Respectively in the disorderly charging scenario of electric vehicles and the orderly charging scenario of electric vehicles, for each model parameter of the photovoltaic storage and charging resources in the cost optimization model, the cost optimization model is calculated based on a set algorithm until the set termination condition is reached, and the target values ​​of each model parameter of the photovoltaic storage and charging resources in the substation are determined to control the photovoltaic storage and charging resources in the substation based on each target value.

2. The method according to claim 1, characterized in that The photovoltaic maximum output function is expressed as: In the above formula, F1 represents the maximum photovoltaic output, N is the total number of nodes; P solar,nom (i) is the photovoltaic output power of the i-th node under the original load, P solar,comp (i) is the increased PV output power of the jth node due to the access of the energy storage system and the electric vehicle charging station, and K is the number of nodes whose PV output power is zero under the original load and whose feeder capacity constraint has been reached; The constraints corresponding to the photovoltaic maximum output function include power flow constraints, node voltage constraints, feeder capacity constraints and transformer capacity constraints.

3. The method according to claim 1, characterized in that The steps of constructing the photovoltaic power generation daily prediction model and the energy storage system daily operation model include: Constructing the photovoltaic power generation daily prediction model according to the current light intensity, the number of photovoltaic modules, the area of ​​the photovoltaic modules and the photoelectric conversion efficiency; A daily operation model of the energy storage system is constructed according to the load of the energy storage system at the previous moment, the output power, the charging efficiency and the discharging efficiency at the current moment.

4. The method according to claim 1, characterized in that: The steps of constructing the daily operation model of the electric vehicle charging station under the disorderly charging scenario of the electric vehicle and the orderly charging scenario of the electric vehicle include: Determine a starting charging time function of the electric vehicle in a disorderly charging scenario of the electric vehicle and an orderly charging scenario of the electric vehicle; Determining a probability density function of the daily mileage of the electric vehicle based on the expectation and standard deviation of the daily mileage of the electric vehicle; Determine the charging time function of the electric vehicle according to the power consumption per kilometer of the electric vehicle, the daily mileage of the electric vehicle, and the charging power of the electric vehicle; The starting charging time function, the probability density function of the daily mileage and the charging time function of the electric vehicle in the disorderly charging scenario of the electric vehicle are used to form a daily operation model of the electric vehicle charging station in the disorderly charging scenario of the electric vehicle; The starting charging time function, the probability density function of the daily mileage and the charging time function of the electric vehicle in the orderly charging scenario of the electric vehicle are used to form a daily operation model of the electric vehicle charging station in the orderly charging scenario of the electric vehicle.

5. The method according to claim 4, characterized in that The function for determining the starting charging time of the electric vehicle in the disorderly charging scenario and the orderly charging scenario of the electric vehicle includes: In the electric vehicle disorderly charging scenario, according to the expectation and standard deviation of the electric vehicle start charging time, determine the probability density function of the electric vehicle start charging time as the electric vehicle start charging time function in the electric vehicle disorderly charging scenario; In the orderly charging scenario of electric vehicles, an incentive electricity price method is adopted, and an electricity price function is determined according to the incentive electricity price starting time, the incentive electricity price ending time, the incentive electricity price and the flat electricity price; Based on the incentive electricity price method, determining a response capability function of the orderly charging of electric vehicles in response to the electricity price incentive; According to the starting time of the incentive electricity price, the ending time of the incentive electricity price, the charging time and the random function, the starting charging time function of the electric vehicle in the orderly charging scenario of the electric vehicle is determined.

6. The method according to claim 1, characterized in that The objective function for calculating the comprehensive cost of the substation is determined based on the photovoltaic power generation daily prediction model, the energy storage system daily operation model, and the electric vehicle charging station daily operation model for the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, including: Determining the investment cost of the energy storage system according to the daily operation model of the energy storage system; According to the daily operation model of the electric vehicle charging station in the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, respectively determine the investment cost of the electric vehicle charging station in the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario; Determine the coefficient for converting the investment cost of the energy storage system to the equivalent day, and the coefficient for converting the investment cost of the electric vehicle charging station to the equivalent day; According to the investment cost of the energy storage system, the coefficient of converting the investment cost of the energy storage system to the same day, the investment cost of the electric vehicle charging station in the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, the coefficient of converting the investment cost of the electric vehicle charging station to the same day, the power grid's electricity purchase cost, and the electric vehicle's electricity sales fee, an objective function of the comprehensive cost of the substation in the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario is constructed.

7. The method according to claim 6, characterized in that Determining the investment cost of the energy storage system according to the daily operation model of the energy storage system includes: Determine the rated capacity and power of the energy storage system according to the daily operation model of the energy storage system; The investment cost of the energy storage system is determined based on the rated capacity and power of the energy storage system, the initial construction cost and operation and maintenance cost per unit capacity of the energy storage system, and the initial construction cost and operation and maintenance cost per unit power of the energy storage system.

8. The method according to claim 6, characterized in that The investment costs of the electric vehicle charging station in the disorderly charging scenario and the orderly charging scenario of the electric vehicle are determined according to the daily operation model of the electric vehicle charging station in the disorderly charging scenario and the orderly charging scenario of the electric vehicle, respectively, including: According to the daily operation model of the electric vehicle charging station in the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, the rated capacity and power of the electric vehicle charging station in the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario are determined respectively; The investment cost of the electric vehicle charging station in the disorderly charging scenario of electric vehicles and in the orderly charging scenario of electric vehicles are determined respectively according to the rated capacity and power of the electric vehicle charging station, the initial construction cost and operation and maintenance cost per unit capacity of the electric vehicle charging station, and the initial construction cost and operation and maintenance cost per unit power of the electric vehicle charging station.

9. The method according to claim 1, characterized in that: The model parameters of the photovoltaic storage and charging resources in the cost optimization model are calculated based on a set algorithm until a set termination condition is reached to determine the target values ​​of the model parameters of the photovoltaic storage and charging resources in the cost optimization model, including: Initialize the model parameters of the photovoltaic power generation daily prediction model, the energy storage system daily operation model, the electric vehicle charging station daily operation model under the electric vehicle disorderly charging scenario and the electric vehicle orderly charging scenario, as well as the upper and lower limits of the constraints corresponding to each model; Initialize the genetic algorithm, including encoding method, objective function, population, selection operation, crossover operation, mutation operation, inversion operation and termination criterion; Generate an initial population, calculate the objective function value of each individual in the population, and further select the next generation of individuals; Perform selection, crossover and mutation operations on the next generation population in sequence, and determine whether the individual objective function value meets the termination condition; When the individual objective function value does not meet the termination condition, the process returns to continue calculating the objective function value of each individual in the population; When the individual objective function value does not meet the termination condition, the iteration process is stopped, and the optimal solution is output as the target value of the model parameter to control the photovoltaic storage and charging resources in the substation based on the target value.

10. The method according to claim 1, characterized in that The operating constraints corresponding to each model include: power flow constraints, node voltage constraints, feeder capacity constraints, transformer capacity constraints, energy storage system power constraints, energy storage system charge state constraints, electric vehicle charging station power constraints and electric vehicle charge state constraints.

Images