Collaborative multi-scene power distribution network low-carbon dispatching system

Abstract

The invention discloses a collaborative multi-scene power distribution network low-carbon dispatching system, and relates to the field of low-carbon dispatching systems, the collaborative multi-scene power distribution network low-carbon dispatching system comprises a resident load scene and a commercial load scene, in the resident load scene, a traditional load is regarded as a rigid reference load, and a charging load of an electric vehicle is set as an adjustable load; the rigid reference load and the adjustable load are integrated through a model to form a total load of the power distribution network in a resident scene, the model takes minimization of system carbon emission as an optimization target, and meanwhile, in combination with dynamic constraint conditions of operation, it is ensured that the low-carbon requirement of the power distribution network and the safety and stability of operation are met; according to the method, the electric vehicle load is regarded as a flexible and adjustable load, and an electric vehicle load uncertainty model based on time distribution is established. The charging modes of the electric vehicle are divided into a resident charging mode and a commercial charging mode according to the difference of the charging behaviors of the vehicle owner, and modeling is carried out respectively so as to accurately describe the load distribution characteristics in different load scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon dispatching systems, and in particular to a coordinated multi-scenario distribution network low-carbon dispatching system. Background Art

[0002] As a core component of modern social infrastructure, the power system plays a vital role in carbon emission reduction and energy transformation. Among them, the distribution network, as the terminal link of the power system, not only directly connects end users, but also has a profound impact on the overall carbon emission level of the system due to its scheduling mode and topological structure. Therefore, optimizing the operation mode of the distribution network has become an important technical path to achieve low-carbonization of the power system.

[0003] According to the carbon emission characteristics of different loads in the distribution network, formulating a scientific and reasonable optimization scheduling plan to reduce carbon emissions and ensure the economical and efficient operation of the distribution network has become a key issue that needs to be solved urgently. In recent years, the focus of research on carbon emissions in power systems has gradually shifted from the power generation side to the load side. Although distributed generation units on the distribution network side produce almost no carbon emissions, their access may cause significant changes in the power grid flow distribution and system stability due to the diversity and uncertainty of the load. For example, load access may cause local node overload or voltage instability. As a phenomenon that accompanies the power flow, the flow characteristics of carbon flow are directly affected by the load type. Therefore, the access of diversified loads will significantly affect the carbon emission intensity of the distribution network.

[0004] In the existing technology,; for this purpose, we provide a collaborative multi-scenario distribution network low-carbon dispatching system to solve the above problems. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a coordinated multi-scenario distribution network low-carbon dispatching system. The present invention proposes a low-carbon optimization dispatching scheme based on the characteristics of the distribution network under different operating scenarios, which can not only effectively reduce carbon emissions, but also provide a theoretical basis and technical path for the economical and efficient operation of the distribution network. This research is of great significance to promoting green and low-carbon development.

[0006] In order to achieve the above-mentioned object, the present invention adopts a coordinated multi-scenario distribution network low-carbon dispatching system, including a residential load scenario and a commercial load scenario. In the residential load scenario, the traditional load is regarded as a rigid benchmark load, while the charging load of the electric vehicle is set as an adjustable load;

[0007] The rigid benchmark load and adjustable load are integrated through the model to form the total load of the distribution network in the residential scenario. The model takes minimizing the carbon emissions of the system as the optimization goal, and combines the dynamic constraints of operation to ensure that the low-carbon requirements of the distribution network and the safety and stability of operation are met;

[0008] The optimization objective function under the residential load scenario is expressed as:

[0009]

[0010] In the formula, F res is the optimization objective function; W res is the total dispatch cost for residential load; is the active power of the traditional load; is the electricity price of traditional load; x j,t Whether the jth electric vehicle is charged during the charging period; is the charging power of the jth electric vehicle, including the power of fast charging and conventional charging; is the electricity price of electric vehicles at time t; is the total carbon emission of residents; C res,t is the dynamic carbon emission factor matrix of the system; T is the total charging time; t is the charging time;

[0011] The dynamic constraints include emergency charging constraints, hydropower generation constraints, distributed photovoltaic constraints, emergency charging constraints, and SOC charging constraints;

[0012] SOC charging constraints:

[0013]

[0014] Represents the lower limit of SOC when the j-th electric vehicle finishes charging; represents the SOC of the jth EV when it is connected to the distribution grid; The upper limit of SOC when the j-th electric vehicle finishes charging;

[0015] Emergency charging restrictions:

[0016]

[0017] In the formula, x j,t Whether the jth electric vehicle needs emergency charging at time t; and represents the charging time interval of the jth electric vehicle. j,t When it is 1, vehicle j is in the charging interval, and when it is 0, it is not in the charging interval; Power for emergency charging of electric vehicles; η EV For charging efficiency; Represents the lower limit of SOC when the jth electric vehicle finishes charging; is the SOC of the jth electric vehicle when it is connected to the distribution grid; is the battery capacity;

[0018] Distributed photovoltaic constraints:

[0019]

[0020] In the formula, Provide active power output for distributed photovoltaics in the distribution network; The active power output of distributed photovoltaic power in the residential distribution network for basic residential electricity consumption; Active power output of distributed photovoltaics to electric vehicle loads in residential distribution networks;

[0021] Distribution network flow constraints:

[0022] P Lj,min ≤P Lj ≤P Lj,max

[0023] Where P Lj is the active power on branch j; P Lj,min With P Lj,max They represent the minimum and maximum active powers that can be output on branch j respectively;

[0024] Hydropower Constraints:

[0025]

[0026] In the formula, is the power of hydroelectric power at time t; P hydro,max is the maximum power output of hydroelectric power;

[0027] Hydropower generation is also subject to water balance constraints:

[0028]

[0029] Where W avail the total amount of water resources available for hydroelectric systems; The power output of hydropower The product of the time interval Δt;

[0030] Optimize the scheduling of electric vehicle loads in residential and commercial loads, and comprehensively consider emergency charging, hydropower generation, distributed photovoltaics, distribution network trends and load reduction constraints to solve the distribution network scheduling results that meet low-carbon goals and economic operation requirements.

[0031] As a further optimization of the above solution, the specific optimization model and its mathematical formula in the commercial load scenario are as follows:

[0032]

[0033] The constraints include load reduction constraint, hydropower generation constraint, distributed photovoltaic constraint, emergency charging constraint and SOC charging constraint.

[0034] As a further optimization of the above scheme, distributed photovoltaic constraints:

[0035]

[0036] is the load size before reduction at time t in the active power output of distributed photovoltaic in the commercial load scenario in the distribution network Subtract the load reduction at time t The resulting value, It is the active load of electric vehicles at time t in the active output of distributed photovoltaics in the commercial load scenario in the distribution network; It is the total active power output of distributed photovoltaic under commercial load scenario in the distribution network;

[0037] Load reduction constraints:

[0038] The load reduction model is expressed as:

[0039]

[0040] The constraints are:

[0041]

[0042] In the formula, represents the load size after the reduction at time t, represents the load size before the reduction at time t, is the load reduction at time t, To reduce the maximum value of load action, is the total amount of commercial load during this period.

[0043] As a further optimization of the above solution, it also includes distributed photovoltaic power generation system, small hydropower system, electric vehicle charging pile, control unit, DC / DC converter, AC / DC converter, DC bus;

[0044] The distributed photovoltaic power generation system is composed of multiple solar panels connected in series or in parallel, which absorb solar energy and convert it into direct current electricity, which is connected to the DC bus via a DC / DC converter and serves as the main power source for charging electric vehicles in the station, providing clean energy support for the charging station;

[0045] The small hydropower system consists of a small reservoir, a turbine and a generator, and uses the potential energy and kinetic energy of water flow to convert into electrical energy. The coordinated optimization of the small hydropower system and photovoltaic power generation provides a stable clean energy supplement during the period of photovoltaic power generation with large fluctuations;

[0046] The DC / DC converter is used to connect the photovoltaic cell and the DC bus, and the DC / DC converter realizes energy transmission between the electric vehicle charging pile and the DC bus;

[0047] The AC / DC converter converts AC power into DC power to provide a stable DC power supply for system operation;

[0048] The control unit is the core management module of the system, responsible for real-time collection of electrical parameters of each component in the system, including voltage, current, and power information, and coordinating the energy flow between components; through dynamic control strategies, it realizes the energy interaction optimization between photovoltaic power generation, electric vehicle charging, and power grid;

[0049] The electric vehicle charging pile is an electric vehicle charging terminal for realizing charging of electric vehicles.

[0050] As a further optimization of the above scheme, the improved IEEE33 node system is used as an example for analysis, and the Yalmip and Cplex tools in Matlab are used to solve and optimize the models of different scenarios;

[0051] In the residential load scenario, distributed photovoltaic power generation, small hydropower generation system and electric vehicle charging facilities are introduced into the improved IEEE33 node system; the charging time of electric vehicles is concentrated between 17:00 and 22:00, and the time of disconnection from the distribution network is concentrated between 6:00 and 8:00;

[0052] In the commercial load scenario, four distributed photovoltaic power generation units with a capacity of 800 kW are installed at nodes 5, 15, 20 and 27 of the improved IEEE33 system respectively; electric vehicle charging piles are configured at nodes 3, 8 and 13, and hydropower facilities are installed at node 24. The electric vehicle charging time is from 8 to 10 o'clock and the time of disconnection from the distribution network is from 17 to 19 o'clock.

[0053] As a further optimization of the above scheme, two different charging modes are adopted, namely, a disordered charging mode that meets the minimum and maximum SOC requirements, and an ordered charging mode that can coordinate SOC; in the residential load scenario, the coordinated and ordered charging mode optimizes the charging behavior of the electric vehicle load; in the 12-24 hour period when the carbon potential of the main grid is high, the residential load power in the coordinated mode is lower than that in the disordered mode; and in the 24-36 hour period when the carbon potential is low, its power is higher than that in the disordered mode.

[0054] As a further optimization of the above scheme, it also includes a calculation method for dynamic carbon emission factors;

[0055] In the formula, C Dis,t represents the dynamic carbon emission factor in the distribution network at time t; P j,t represents the active power of the node on the distribution network at time t; e j,t is the carbon potential of the node at time t; N is the set of all nodes in the distribution network;

[0056] If the system dynamic carbon emission factor matrix C in the given period T T =[C Dis,1 ,C Dis,2 ,…,C Dis,T ], and then with the system active power matrix P L,t =[P L,1 ,P L,2 ,…,P L,T ] to obtain the carbon emissions of the distribution network at this time:

[0057]

[0058] As a further optimization of the above solution, the residential electric vehicle load in the residential load scenario satisfies the following normal distribution:

[0059]

[0060] In the formula and They represent the arrival time and charging completion time of residents’ electric vehicles, respectively; and Represent the mean of arrival time and charging completion time respectively; and Represent the standard deviation of arrival time and charging completion time respectively; exp is an exponential function, indicating the attenuation of probability density; It means that within 12 hours before the mean, the first normal distribution is used; For the time period after the mean, a second normal distribution is used.

[0061] As a further optimization of the above solution, in the residential load scenario, for commercial loads, the charging time period is completely opposite to that of residential loads, satisfying the following normal distribution:

[0062]

[0063] As a further optimization of the above scheme, a day is divided into 24 periods, and the arrival and departure time of each electric vehicle is:

[0064]

[0065] Where N represents the number of electric vehicles; and are the arrival and departure times of the electric vehicles, respectively; ΔT is the length of the time slot; and are the arrival time and departure time of the i-th and j-th electric vehicles respectively;

[0066] Based on the charging period of electric vehicles, the period during which the electric vehicle load is connected to the distribution network is expressed by the following formula:

[0067]

[0068] According to the charging mode of electric vehicles, IND is defined as an emergency directive and used as the standard for electric vehicles to adopt fast charging mode. The formula is as follows:

[0069]

[0070] In the formula, The power for conventional charging of electric vehicles; η EV For charging efficiency; Represents the lower limit of SOC when the i-th electric vehicle finishes charging; is the SOC of the i-th electric vehicle when it is connected to the distribution network; is the battery capacity; emergency charging decision is based on IND value:

[0071] When IND<1, the remaining power of the i-th electric vehicle is insufficient to meet the needs of the next stage of travel, and emergency charging is required;

[0072] When IND>1, the remaining power is sufficient and the normal charging mode is selected.

[0073] A coordinated multi-scenario distribution network low-carbon dispatching system of the present invention has the following beneficial effects:

[0074] The present invention is a coordinated multi-scenario distribution network low-carbon dispatching system. The present invention regards the electric vehicle load as a flexible and adjustable load and establishes an electric vehicle load uncertainty model based on time distribution. According to the differences in charging behaviors of car owners, the charging mode of electric vehicles is divided into residential charging mode and commercial charging mode, and modeling is performed separately to accurately depict the load distribution characteristics under different load scenarios;

[0075] The model takes minimizing the system carbon emissions as the optimization goal, and combines the dynamic constraints of operation to ensure that the low-carbon requirements of the distribution network and the safety and stability of operation are met;

[0076] The coordinated SOC scheduling mode has significant advantages. By adjusting the charging time, this mode effectively transfers the load from the high carbon potential period of the main grid to the low carbon potential period, significantly reducing the carbon emissions of the system. In addition, compared with the minimum SOC and maximum SOC charging modes, the coordinated SOC mode significantly reduces the total load peak and effectively controls the load fluctuation range, thereby further improving the system's operating stability and low-carbon efficiency.

[0077] With reference to the following description and drawings, a specific embodiment of the present invention is disclosed in detail, indicating the manner in which the principles of the present invention can be adopted. It should be understood that the scope of the embodiments of the present invention is not limited thereby, and within the spirit and scope of the appended claims, the embodiments of the present invention include many changes, modifications and equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a structural diagram of the source-load coordination system containing distributed new energy of the present invention;

[0079] Figure 2 A flow chart of the model solving steps of the present invention;

[0080] Figure 3 It is the improved IEEE 33-node system diagram under the residential load scenario of the present invention;

[0081] Figure 4 This is a diagram of an improved IEEE 33-node system in a commercial load scenario of the present invention. DETAILED DESCRIPTION

[0082] Journal 1: Zhou Tianrui, Kang Chongqing. Research on low-carbon optimization operation method of distribution system based on carbon emission flow, 2019, DOI: 10.19705 / j.cnki.issn2096-5125.2019.03.005. The focus is on the distribution characteristics of carbon emission flow in the distribution system, and a low-carbon optimization operation method of the distribution system based on the carbon emission flow calculation model of energy storage elements is proposed. This study assumes that the electric vehicle load can be fully controlled, but in actual scenarios, the user's charging behavior is affected by many factors, such as the availability of charging facilities and personal travel arrangements. The randomness and diversity of user behavior patterns are not fully considered, which may lead to limited applicability of the optimization model in actual scenarios. Therefore, in scenarios where there is a large randomness in the actual charging behavior of users, the applicability of this method may be subject to certain constraints.

[0083] Journal 2, Wu Bin, Yang Chao. Research on orderly charging strategy of electric vehicles based on double-layer optimization, 2019, DOI: CNKI: SUN: DLQB.0.2019-11-008. Taking residential areas as the application scenario, an orderly charging optimization control strategy based on multi-objective and multi-layer partitioning is proposed. Although this strategy has achieved certain results in optimizing the charging mode of residential users, it fails to effectively deal with the challenges brought by the volatility and randomness of photovoltaic power generation to the stability of system operation when dealing with large-scale distributed photovoltaic power generation connected to the distribution network. In addition, in the context of multi-energy collaborative optimization, this study has not fully explored the potential of orderly charging in energy conservation and emission reduction, which limits its scope of application in a diversified energy environment. In addition to distributed photovoltaic power generation, hydropower plays an important role in the low-carbon scheduling of distribution networks due to its stability and high efficiency. Through collaborative optimization with photovoltaic power generation, it can provide stable clean energy supplements during photovoltaic power generation periods with large fluctuations.

[0084] Journal 1 studied the low-carbon optimization operation of the distribution system through the carbon emission flow calculation model of energy storage elements, but it assumed that the electric vehicle load can be fully controlled, ignoring the diversity of users' willingness and behavior in responding to orderly charging in actual scenarios. This assumption leads to the failure of the optimization model to fully consider the randomness and ambiguity of user behavior when applied to real-world scenarios. For example, the study failed to quantify the random characteristics of electric vehicle loads as optimization decision variables, resulting in a low degree of match between the scheduling scheme and actual user needs. This limitation stems from the bias of traditional research on the ability to control user behavior, which generally assumes that user behavior is fully controlled and does not pay attention to the complexity of random modeling.

[0085] A multi-objective partition optimization charging control strategy was proposed in Journal 2, but in the scenario of large-scale distributed photovoltaic power generation access, it failed to effectively deal with the challenges posed by the volatility and randomness of photovoltaic power generation to system stability. In this study, the dynamic volatility of photovoltaic power generation was not effectively coupled with the electric vehicle load, resulting in low utilization efficiency of photovoltaic power generation and failure to achieve dynamic balance on the load side. This problem arises mainly because existing optimization methods usually focus on a single load scenario and do not fully consider the coordinated optimization between multiple loads and distributed power generation. In addition, the technical complexity of dynamic modeling and real-time response has also hindered the solution of similar problems to a certain extent.

[0086] In view of the defects of the prior art, the present invention provides a technical solution: a coordinated multi-scenario distribution network low-carbon dispatching system, please refer to the attached manual Figure 1-4First, the present invention adopts a dynamic carbon emission factor calculation model to quantify the carbon emissions of the distribution network operation in real time, and converts the uncertainty of user charging behavior into optimization decision variables, thereby making up for the lack of response to user behavior in the prior art. Secondly, the present invention introduces the volatility constraints of distributed photovoltaic power generation into the optimization model to achieve collaborative optimization. At the same time, in order to further improve the utilization rate of clean energy in the distribution network, this model also introduces the constraints of small hydropower in the optimization scheduling. Through photovoltaics and small hydropower, the distribution network model is more perfect, and the utilization efficiency of clean energy and the dynamic balance ability of the system are improved.

[0087] In addition, the present invention constructs charging behavior models for residential and commercial users respectively, and guides users' charging loads to shift to low-carbon time periods through a time-of-use electricity price mechanism, which not only enhances the adaptability of the optimization scheme to actual scenarios, but also improves users' acceptance of orderly charging. The reason why the prior art has failed to propose similar improvements is that it is generally subject to the bias of "fully controllable load" in inertial thinking, as well as the technical complexity of dynamic modeling and data processing. Through innovative dynamic factor calculation and collaborative optimization methods, the present invention not only significantly reduces carbon emissions while improving the utilization efficiency of photovoltaic power generation, but also reduces implementation costs and technical complexity. It provides an efficient and feasible solution for the operation optimization of low-carbon distribution networks.

[0088] In the real-time quantification of carbon emissions from the operation of the distribution network, the node active load and carbon potential data are collected in real time, and the carbon emission factor is adjusted dynamically. Specifically, the carbon emission factor reflects the changing pattern of carbon emissions in different time periods and node locations by combining the carbon potential of the node with its load intensity. In combination with this factor, the present invention constructs a stochastic modeling method for electric vehicle loads in multiple scenarios based on the behavioral characteristics of residential users and commercial users, uses a time-of-use electricity price mechanism to guide user behavior, and uses the volatility constraints of distributed photovoltaic power generation as input parameters through an optimization model. Ultimately, the present invention aims to minimize carbon emissions, and realizes the coordinated scheduling of user loads and clean energy power generation in the dynamic optimization process, thereby achieving the purpose of optimizing the low-carbon operation of the distribution network.

[0089] like Figure 1 As shown in the figure, the model consists of a distributed photovoltaic power generation system, a small hydropower system, an electric vehicle charging pile, a control unit, a DC / DC converter, an AC / DC converter, a DC bus and other parts.

[0090] The following are the functions of each component:

[0091] Distributed photovoltaic power generation system: It consists of multiple solar panels connected in series or in parallel, absorbing solar energy and converting it into DC power. It is connected to the system DC bus through a DC / DC converter and serves as the main power source for charging electric vehicles in the station, providing clean energy support for the charging station.

[0092] Small hydropower system: It consists of a small reservoir, a turbine and a generator. Its main function is to convert the potential energy and kinetic energy of water flow into electrical energy. In addition to distributed photovoltaic power generation, hydropower plays an important role in the low-carbon dispatching of distribution networks due to its stability and efficiency. Through coordinated optimization with photovoltaic power generation, it can provide a stable clean energy supplement during the period of photovoltaic power generation with greater volatility.

[0093] DC / DC converter: used to connect photovoltaic cells and DC bus, and to transmit energy between charging piles and DC bus. Through efficient DC voltage conversion function, it ensures that the voltage level of photovoltaic power generation and the load demand of charging piles match, and realizes stable energy transmission.

[0094] AC / DC converter: converts AC power into DC power to provide stable DC power for system operation.

[0095] Control unit: The core management module of the system, responsible for real-time collection of electrical parameters of each component in the system, including voltage, current, power and other information, and coordinating the energy flow between components. Through dynamic control strategies, energy interaction optimization between photovoltaic power generation, electric vehicle charging and power grid is achieved.

[0096] Electric vehicle charging pile: Electric vehicle charging terminal, which enables charging of electric vehicles.

[0097] In order to accurately reflect the impact of different scenarios and user behaviors on carbon emissions, the present invention proposes a method for calculating a dynamic carbon emission factor, the formula of which is as follows:

[0098]

[0099] In the formula, C Dis,t represents the dynamic carbon emission factor in the distribution network at time t; P j,t represents the active power of the node on the distribution network at time t; e j,t is the carbon potential of the node at time t; N is the set of all nodes in the distribution network.

[0100] If the system dynamic carbon emission factor matrix C in the given period T T =[C Dis,1 ,C Dis,2 ,…,C Dis,T ], and then with the system active power matrix P L,t =[PL,1 ,P L,2 ,…,P L,T ], we can get the carbon emissions of the distribution network at this time:

[0101]

[0102] The present invention regards the electric vehicle load as a flexible and adjustable load and establishes an electric vehicle load uncertainty model based on time distribution. According to the differences in charging behaviors of car owners, the charging modes of electric vehicles are divided into residential charging mode and commercial charging mode, and modeled separately to accurately depict the load distribution characteristics under different load scenarios.

[0103] The main period of residential electric vehicle load is at night, usually after get off work, when charging begins. The arrival and charging times satisfy the following normal distribution:

[0104]

[0105] In the formula and They represent the arrival time and charging completion time of residents’ electric vehicles, respectively; and Represent the mean of arrival time and charging completion time respectively; and They represent the standard deviation of arrival time and charging completion time respectively; exp is an exponential function, indicating the attenuation of probability density. It means that within 12 hours before the mean, the first normal distribution is used; For the time period after the mean, a second normal distribution is used.

[0106] It should be noted that the above is the normal distribution formula, a formula commonly used in probability mathematics. The charging time represented here satisfies this law, so there is no need to explain it in detail, and it is safely related to the content of this article.

[0107] For commercial loads, the charging time period is completely opposite to that of residential loads, satisfying the following normal distribution:

[0108]

[0109] In order to improve the execution efficiency, the day is divided into 24 periods, so the arrival and departure time of each electric car is:

[0110]

[0111] Where N represents the number of electric vehicles; and are the arrival and departure times of the electric vehicles, respectively; ΔT is the length of the time slot; and are the arrival time and departure time of the i-th and j-th electric vehicles respectively.

[0112] Based on the charging period of electric vehicles, the period during which the electric vehicle load is connected to the distribution network can be expressed by the following formula:

[0113]

[0114] According to the charging mode of electric vehicles, the present invention defines IND as an emergency instruction and uses it as a standard for electric vehicles to adopt a fast charging mode. The specific formula is as follows:

[0115]

[0116] In the formula, The power for conventional charging of electric vehicles; η EV For charging efficiency; Represents the lower limit of SOC when the i-th electric vehicle finishes charging; is the SOC of the i-th electric vehicle when it is connected to the distribution network; is the battery capacity. Emergency charging is determined based on the IND value: when IND<1, the remaining power of the i-th electric vehicle is insufficient to meet the needs of the next stage of travel, so emergency charging is required; when IND>1, the remaining power is sufficient and the conventional charging mode can be selected.

[0117] According to the operating characteristics of the distribution network in different scenarios, the present invention proposes a low-carbon scheduling optimization model. In the residential load scenario, the traditional load is regarded as a rigid benchmark load, while the charging load of the electric vehicle is set as an adjustable load. These two types of loads are integrated through the model to form the total load of the distribution network in the residential scenario. In order to achieve the low-carbon goal, the model takes minimizing the carbon emissions of the system as the optimization goal, and combines the dynamic constraints of the operation to ensure that the low-carbon requirements of the distribution network and the safety and stability of the operation are met. Specifically, the optimization objective function in the residential load scenario can be expressed as:

[0118]

[0119] In the formula, F res is the optimization objective function; W res is the total dispatch cost for residential load; is the active power of the traditional load; is the electricity price of traditional load; x j,t Whether the jth electric vehicle is charged during the charging period; is the charging power of the jth electric vehicle, including the power of fast charging and conventional charging; is the electricity price of electric vehicles at time t; is the total carbon emission of residents; C res,t is the dynamic carbon emission factor matrix of the system; T is the total charging time; t is the charging time.

[0120] The constraints mainly include emergency charging constraints, hydropower generation constraints, distributed photovoltaic constraints, emergency charging constraints and SOC charging constraints.

[0121] SOC charging constraints:

[0122]

[0123] Represents the lower limit of SOC when the j-th electric vehicle finishes charging; represents the SOC of the jth EV when it is connected to the distribution grid; is the upper limit of SOC when the j-th electric vehicle finishes charging.

[0124] Emergency charging restrictions:

[0125]

[0126] In the formula, x j,t Whether the jth electric vehicle needs emergency charging at time t; and represents the charging time interval of the jth electric vehicle. j,t When it is 1, vehicle j is in the charging interval, and when it is 0, it is not in the charging interval; Power for emergency charging of electric vehicles; η EV For charging efficiency; Represents the lower limit of SOC when the jth electric vehicle finishes charging; is the SOC of the jth electric vehicle when it is connected to the distribution grid; is the battery capacity.

[0127] Distributed photovoltaic constraints:

[0128]

[0129] In the formula, Provide active power output for distributed photovoltaics in the distribution network; The active power output of distributed photovoltaic power in the residential distribution network for basic residential electricity consumption; It is the active power output of distributed photovoltaics to electric vehicle loads in residential distribution networks.

[0130] Distribution network flow constraints:

[0131] PLj,min ≤P Lj ≤P Lj,max

[0132] Where P Lj is the active power on branch j; P Lj,min With P Lj,max They represent the minimum and maximum active powers that can be output on branch j respectively.

[0133] Hydropower Constraints:

[0134]

[0135] In the formula, is the power of hydroelectric power at time t; P hydro,max The maximum power output of hydroelectric power.

[0136] In addition to being constrained by power range, hydroelectric power generation is also constrained by water resource balance:

[0137]

[0138] Where W avail the total amount of water resources available for hydroelectric systems; The power output of hydropower The product of the time interval Δt.

[0139] Commercial loads and residential loads are similar in modeling methods, and their total loads are also divided into two parts: benchmark loads and electric vehicle charging loads. However, unlike residential loads, benchmark loads in commercial scenarios have the characteristic of being reducible. Therefore, for commercial loads, before optimizing the scheduling of electric vehicle loads, it is necessary to prioritize the reduction and optimization of benchmark loads. The optimized benchmark load is used as an input condition to participate in the optimization scheduling of electric vehicle loads in commercial scenarios, thereby ensuring the low carbon and economic efficiency of the overall load. Based on this feature, the specific optimization model and its mathematical formula for the commercial load scenario are as follows:

[0140]

[0141] The constraints are mainly load reduction constraints, hydropower generation constraints, distributed photovoltaic constraints, emergency charging constraints and SOC charging constraints. Since SOC charging constraints, emergency charging constraints and hydropower generation constraints are consistent with residential loads, they are not listed here. The remaining two types of constraints are as follows:

[0142] Distributed photovoltaic constraints:

[0143]

[0144] is the load size before reduction at time t in the active power output of distributed photovoltaic in the commercial load scenario in the distribution network Subtract the load reduction at time t The resulting value, It is the active load of electric vehicles at time t in the active output of distributed photovoltaics in the commercial load scenario in the distribution network; It is the total active power output of distributed photovoltaics under commercial load scenarios in the distribution network.

[0145] Load reduction constraints:

[0146] The load reduction model can be expressed as:

[0147]

[0148] The constraints are:

[0149]

[0150] In the formula, represents the load size after the reduction at time t, represents the load size before the reduction at time t, is the load reduction at time t, To reduce the maximum value of load action, is the total amount of commercial load during this period.

[0151] For the mathematical model given, the residential load is considered as a rigid load because it will hardly change dramatically, and the commercial load can be reduced by load reduction in a certain period of time. The electric vehicle load in the residential load and commercial load is optimized and dispatched. Finally, the distribution network dispatch result that meets the low-carbon goal and economic operation requirements is solved by combining the constraints of emergency charging, hydropower generation, distributed photovoltaics, distribution network flow and load reduction. The specific process is as follows: Figure 2 shown.

[0152] The improved IEEE33 node system is used as an example for analysis, and the Yalmip and Cplex tools in Matlab are used to solve and optimize the models of different scenarios.

[0153] In the residential load scenario, distributed photovoltaic power generation, small hydropower generation systems and electric vehicle charging facilities are introduced into the improved IEEE33 node system. Specifically, three distributed photovoltaic power generation units with a capacity of 400kW are installed at nodes 14, 20 and 27 respectively; at the same time, electric vehicle charging piles are equipped at nodes 5, 8 and 12 to meet the charging needs of residential electric vehicle loads. Since the planning and construction of hydropower in actual construction mainly depends on natural conditions, it is set to be installed at node 24 in this case. The node diagram is shown in the figure below. Figure 3 As shown in the figure, most of its electric vehicles are charged between 17:00 and 22:00, and are disconnected from the power distribution network between 6:00 and 8:00.

[0154] In the commercial load scenario, four distributed photovoltaic generators with a capacity of 800kW are installed at nodes 5, 15, 20 and 27 of the improved IEEE33 system; electric vehicle charging piles are configured at nodes 3, 8 and 13. Hydropower facilities are also installed at node 24. The node diagram is shown in the figure below. Figure 4 Most of its electric vehicles are charged between 8 and 10 a.m. and disconnected from the power distribution network between 17 and 19 p.m.

[0155] The present invention adopts two different charging modes, namely a disordered charging mode that meets the minimum and maximum SOC requirements and an ordered charging mode that can coordinate the SOC.

[0156] In the residential load scenario, the coordinated and orderly charging mode optimizes the charging behavior of electric vehicle loads. In the 12-24 hour period when the carbon potential of the main grid is higher, the power of residential loads in the coordinated mode is lower than that in the disordered mode; while in the 24-36 hour period when the carbon potential is lower, its power is higher than that in the disordered mode. Through this charging scheduling, the residential load has achieved the transfer from the high-carbon emission period to the low-carbon emission period, which not only optimizes the time distribution, but also significantly reduces the overall carbon emissions of the system.

[0157] Further analysis shows that, taking the expected value of disordered SOC electric vehicle load distribution as the benchmark, under the coordinated SOC dispatch mode, the dispatch cost is reduced by 979.68 yuan and the carbon emissions are reduced by 14.82%. It can be seen that in the residential load scenario, the coordinated SOC mode can achieve very good results in optimizing carbon emissions.

[0158] In commercial load scenarios, the coordinated SOC dispatch mode has significant advantages. This mode effectively transfers the load from the high-carbon potential period of the main grid to the low-carbon potential period by adjusting the charging time, significantly reducing the carbon emissions of the system. In addition, compared with the minimum SOC and maximum SOC charging modes, the coordinated SOC mode significantly reduces the total load peak and effectively controls the load fluctuation range, thereby further improving the system's operating stability and low-carbon efficiency.

[0159] According to calculations, in commercial load scenarios, compared with the disordered SOC dispatch mode, the coordinated SOC mode reduces the dispatch cost by 772.41 yuan and reduces carbon emissions by 17.92%. This shows that the coordinated SOC dispatch mode performs better than the disordered SOC dispatch mode in commercial load scenarios and can effectively achieve low-carbon and economic operation goals.

Claims

1. A coordinated multi-scenario distribution network low-carbon dispatching system, characterized in that: It includes residential load scenarios and commercial load scenarios. In the residential load scenario, the traditional load is regarded as a rigid benchmark load, while the charging load of electric vehicles is set as an adjustable load; The rigid benchmark load and adjustable load are integrated through the model to form the total load of the distribution network in the residential scenario. The model takes minimizing the carbon emissions of the system as the optimization goal, and combines the dynamic constraints of operation to ensure that the low-carbon requirements of the distribution network and the safety and stability of operation are met; The optimization objective function under the residential load scenario is expressed as: In the formula, F res is the optimization objective function; W res is the total dispatch cost for residential load; is the active power of the traditional load; is the electricity price of traditional load; x j,t Whether the jth electric vehicle is charged during the charging period; is the charging power of the jth electric vehicle, including the power of fast charging and conventional charging; is the electricity price of electric vehicles at time t; is the total carbon emission of residents; C res,t is the dynamic carbon emission factor matrix of the system; T is the total charging time; t is the charging time; The dynamic constraints include emergency charging constraints, hydropower generation constraints, distributed photovoltaic constraints, emergency charging constraints, and SOC charging constraints; SOC charging constraints: Represents the lower limit of SOC when the j-th electric vehicle finishes charging; represents the SOC of the jth EV when it is connected to the distribution grid; The upper limit of SOC when the j-th electric vehicle finishes charging; Emergency charging restrictions: In the formula, x j,t Whether the jth electric vehicle needs emergency charging at time t; and represents the charging time interval of the jth electric vehicle. j ,When t is 1, vehicle j is in the charging interval, and when it is 0, it is not in the charging interval; Power for emergency charging of electric vehicles; η EV For charging efficiency; Represents the lower limit of SOC when the jth electric vehicle finishes charging; is the SOC of the jth electric vehicle when it is connected to the distribution grid; is the battery capacity; Distributed photovoltaic constraints: In the formula, Provide active power output for distributed photovoltaics in the distribution network; The distributed photovoltaic power generation in the residential distribution network contributes to the active power consumption of basic residents; Active power output of distributed photovoltaics to electric vehicle loads in residential distribution networks; Distribution network flow constraints: P Lj,min ≤P Lj ≤P Lj,max Where P Lj is the active power on branch j; P Lj,min With P Lj,max They represent the minimum and maximum active powers that can be output on branch j respectively; Hydropower Constraints: In the formula, is the power of hydroelectric power at time t; P hydro,max is the maximum power output of hydroelectric power; Hydropower is also subject to water balance constraints: Where W avail the total amount of water resources available for hydroelectric systems; The power output of hydropower The product of the time interval Δt; Optimize the scheduling of electric vehicle loads in residential and commercial loads, and comprehensively consider emergency charging, hydropower generation, distributed photovoltaics, distribution network trends and load reduction constraints to solve the distribution network scheduling results that meet low-carbon goals and economic operation requirements.

2. According to claim 1, a coordinated multi-scenario distribution network low-carbon dispatching system is characterized by: The specific optimization model and its mathematical formula in the commercial load scenario are as follows: The constraints include load reduction constraint, hydropower generation constraint, distributed photovoltaic constraint, emergency charging constraint and SOC charging constraint.

3. According to claim 2, a coordinated multi-scenario distribution network low-carbon dispatching system is characterized by: Distributed photovoltaic constraints: is the load size before reduction at time t in the active power output of distributed photovoltaic in the commercial load scenario in the distribution network Subtract the load reduction at time t The resulting value, It is the active load of electric vehicles at time t in the active output of distributed photovoltaics in the commercial load scenario in the distribution network; It is the total active power output of distributed photovoltaic under commercial load scenario in the distribution network; Load reduction constraints: The load reduction model is expressed as: The constraints are: In the formula, represents the load size after the reduction at time t, represents the load size before the reduction at time t, is the load reduction at time t, To reduce the maximum value of load action, is the total amount of commercial load during this period.

4. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 3, characterized in that: It also includes distributed photovoltaic power generation systems, small hydropower systems, electric vehicle charging piles, control units, DC / DC converters, AC / DC converters, and DC buses; The distributed photovoltaic power generation system is composed of multiple solar panels connected in series or in parallel, which absorb solar energy and convert it into direct current electricity, which is connected to the DC bus via a DC / DC converter and serves as the main power source for charging electric vehicles in the station, providing clean energy support for the charging station; The small hydropower system consists of a small reservoir, a turbine and a generator, and uses the potential energy and kinetic energy of water flow to convert into electrical energy. The coordinated optimization of the small hydropower system and photovoltaic power generation provides a stable clean energy supplement during the period of photovoltaic power generation with large fluctuations; The DC / DC converter is used to connect the photovoltaic cell and the DC bus, and the DC / DC converter realizes energy transmission between the electric vehicle charging pile and the DC bus; The AC / DC converter converts AC power into DC power to provide a stable DC power supply for system operation; The control unit is the core management module of the system, responsible for real-time collection of electrical parameters of each component in the system, including voltage, current, and power information, and coordinating the energy flow between components; through dynamic control strategies, it realizes the energy interaction optimization between photovoltaic power generation, electric vehicle charging, and power grid; The electric vehicle charging pile is an electric vehicle charging terminal for realizing charging of electric vehicles.

5. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 4, characterized in that: The improved IEEE33 node system is used as an example for analysis, and the Yalmip and Cplex tools in Matlab are used to solve and optimize the models of different scenarios. In the residential load scenario, distributed photovoltaic power generation, small hydropower generation system and electric vehicle charging facilities are introduced into the improved IEEE33 node system; the charging time of electric vehicles is concentrated between 17:00 and 22:00, and the time of disconnection from the distribution network is concentrated between 6:00 and 8:00; In the commercial load scenario, four distributed photovoltaic power generation units with a capacity of 800 kW are installed at nodes 5, 15, 20 and 27 of the improved IEEE33 system respectively; electric vehicle charging piles are configured at nodes 3, 8 and 13, and hydropower facilities are installed at node 24. The electric vehicle charging time is from 8 to 10 o'clock and the time of disconnection from the distribution network is from 17 to 19 o'clock.

6. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 5, characterized in that: Two different charging modes are adopted, namely the disordered charging mode that meets the minimum and maximum SOC requirements and the ordered charging mode that can coordinate SOC; in the residential load scenario, the coordinated and ordered charging mode optimizes the charging behavior of the electric vehicle load; in the 12-24 hour period when the carbon potential of the main grid is high, the residential load power in the coordinated mode is lower than that in the disordered mode; and in the 24-36 hour period when the carbon potential is low, its power is higher than that in the disordered mode.

7. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 6, characterized in that: It also includes the calculation method of dynamic carbon emission factors; In the formula, C Dis,t represents the dynamic carbon emission factor in the distribution network at time t; P j,t represents the active power of the node on the distribution network at time t; e j,t is the carbon potential of the node at time t; N is the set of all nodes in the distribution network; If the system dynamic carbon emission factor matrix C in the given period T T =[C Dis,1 , C Dis,2 ,…,C Dis,T ], and then with the system active power matrix P L,t =[P L，1 ,P L,2 ,…,P L，T ] to obtain the carbon emissions of the distribution network at this time:

8. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 7, characterized in that: In the residential load scenario, the residential electric vehicle load satisfies the following normal distribution: In the formula and They represent the arrival time and charging completion time of residents’ electric vehicles, respectively; and Represent the mean of arrival time and charging completion time respectively; and Represent the standard deviation of arrival time and charging completion time respectively; exp is an exponential function, indicating the attenuation of probability density; It means that within 12 hours before the mean, the first normal distribution is used; For the time period after the mean, a second normal distribution is used.

9. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 8, characterized in that: In the residential load scenario, for commercial loads, the charging time period is completely opposite to that of residential loads, satisfying the following normal distribution:

10. A coordinated multi-scenario distribution network low-carbon dispatching system according to claim 9, characterized in that: Divide a day into 24 time periods, and the arrival and departure times of each electric vehicle are: Where N represents the number of electric vehicles; and are the arrival and departure times of the electric vehicles, respectively; ΔT is the length of the time slot; and are the arrival time and departure time of the i-th and j-th electric vehicles respectively; Based on the charging period of electric vehicles, the period during which the electric vehicle load is connected to the distribution network is expressed by the following formula: According to the charging mode of electric vehicles, IND is defined as an emergency directive and used as the standard for electric vehicles to adopt fast charging mode. The formula is as follows: In the formula, Power for conventional charging of electric vehicles; η EV For charging efficiency; Represents the lower limit of SOC when the i-th electric vehicle finishes charging; is the SOC of the i-th electric vehicle when it is connected to the distribution network; is the battery capacity; emergency charging decision is based on IND value: When IND<1, the remaining power of the i-th electric vehicle is insufficient to meet the needs of the next stage of travel, and emergency charging is required; When IND>1, the remaining power is sufficient and the normal charging mode is selected.

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