Distributed resource scheduling method and system considering safe operation of power distribution network, and medium

Abstract

The invention relates to a distributed resource scheduling method and system considering safe operation of a power distribution network, and a medium. The method specifically comprises the following steps: establishing a schedulable model of distributed adjustable resources including distributed photovoltaic output, an energy storage system, an air conditioner and an electric vehicle; and solving the optimal power flow model of the power distribution network to obtain a scheduling value of the distributed resources by taking the minimum sum of the voltage deviation and the operation cost of the power distribution network as a target and meeting power flow constraints, node voltage constraints, line capacity constraints and related constraints of the distributed resources. According to the method provided by the invention, safe and economical operation of the power distribution network is ensured while a distributed resource optimization scheduling strategy is realized, the possibility that the node voltage of the power distribution network is out of limit is reduced, and the scheduling safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed resource optimization scheduling, and in particular to a distributed resource scheduling method, system and medium taking into account the safe operation of a distribution network. Background Art

[0002] With the development of social economy, the scale of commercial loads represented by electric vehicles (EV) and air conditioners (AIR) is getting larger and larger. At the same time, due to the intensification of environmental pollution and the shortage of electricity, distributed new energy represented by photovoltaics has been used on a large scale. However, the large-scale grid connection of distributed new energy has brought volatility and uncertainty to the power grid. In order to solve this problem, energy storage technology has been widely used in distribution networks. However, the challenges brought by the disorderly charging of large-scale electric vehicles to the peak and frequency regulation of the power grid and the overvoltage caused by photovoltaics to the power grid have posed a threat to the safe and stable operation of the distribution network. It will not only bring voltage fluctuations and flickers to the distribution network, but also cause power quality problems such as voltage over-limit at the distribution network nodes. Since air conditioners and energy storage have large regulation potential and low scheduling costs, the coordinated optimization of distributed resources such as air conditioners, energy storage, electric vehicles, and photovoltaics can ensure the large-scale safe access of commercial loads such as electric vehicles and air conditioners to the distribution network. Therefore, the coordinated optimization of air conditioners, energy storage, electric vehicles, and photovoltaics has become the focus of research. In order to ensure the safe access of electric vehicles, air conditioners, photovoltaics, and energy storage to the distribution network, the modeling of distributed resources is studied. On the basis of establishing a distributed resource model, a distributed resource coordinated optimization scheduling method considering the safe operation of the distribution network is studied.

[0003] As a special type of load, the charging or discharging process of electric vehicles (EVs) has great uncertainty and randomness when interacting with the power grid. With the increase in the number of electric vehicles, the operational risks brought by this volatility to the power grid are becoming more and more obvious. Studies have shown that the battery charging characteristics of electric vehicles are nonlinear. If the charging time is not properly arranged, centralized charging may lead to problems such as harmonic pollution, which in turn affects the power supply quality. More importantly, the high cost of electric vehicle charging facilities requires a large number of charging piles to meet user needs without coordination, which not only increases the investment in the power grid, but also may cause operational problems such as three-phase imbalance and voltage fluctuations. At present, domestic and foreign scholars have conducted relevant research and proposed relevant strategies to alleviate the threat posed by large-scale disordered charging of electric vehicles to the safe operation of distribution networks, mainly including two methods: adjustment from the power supply side and guidance from charging behavior. However, with the increase in the proportion of renewable energy, the volatility of energy such as wind power and photovoltaic power makes it difficult to achieve a stable supply and demand balance by relying solely on the adjustment of the power supply side. Summary of the invention

[0004] The present invention provides a distributed resource scheduling method, system and medium that take into account the safe operation of the distribution network, establishes an adjustable model of distributed resources such as electric vehicles, air conditioners, photovoltaics and energy storage, designs a distributed resource collaborative optimization scheduling method that takes into account the safe operation of the distribution network, and solves the problem of how to ensure the continuous economic and stable operation of the distribution network when commercial loads such as electric vehicles are connected to the distribution network on a large scale.

[0005] The present invention solves the above technical problems in the following way: A distributed resource scheduling method considering the safe operation of a distribution network comprises the following steps:

[0006] Based on the electrical conversion efficiency and application scenarios of distributed resources, an adjustable capacity model of distributed resource clusters is established;

[0007] Based on the adjustable capacity model of distributed resource clusters, a distributed resource coordination and optimization scheduling model considering the safe and economical operation of the distribution network is established;

[0008] The distributed collaborative optimization scheduling model is solved to obtain a day-ahead scheduling plan for the distributed resource cluster, and the day-ahead scheduling plan is decomposed to obtain a single distributed resource optimization scheduling plan.

[0009] Preferably, the adjustable capacity model of the distributed resource cluster is established based on the electrical conversion efficiency and application scenarios of the distributed resources, specifically including:

[0010] Based on the photoelectric conversion efficiency of distributed photovoltaics, a distributed photovoltaic output model is established, and based on the charging and discharging efficiency of the energy storage device, a charging and discharging model of the energy storage system is established;

[0011] Based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, an adjustable capacity model of the charging load of the electric vehicle cluster is established; based on the air conditioner load thermal path model, an adjustable capacity model of the air conditioner cluster is established.

[0012] Preferably, the distributed photovoltaic output model is as follows:

[0013] P PV =LSλ

[0014]

[0015] Among them, P pv Produce power for photovoltaics; The photovoltaic power prediction L is the solar radiation intensity; S is the solar power plant sheet light contact area; λ is the photoelectric conversion efficiency;

[0016] The energy storage system charging and discharging model is as follows:

[0017]

[0018] Where ESS(t) is the amount of electricity stored in the energy storage device; η cha , η dis are the charging and discharging efficiency of the energy storage device; P cha (t), P dis (t) are the charging and discharging power of the energy storage device during period t; ESS min ESS max is the minimum and maximum capacity of the energy storage device; u cha (t),u dis (t) are the charge and discharge status symbols of the energy storage device during time period t; P dis,min , P dis,max It is the minimum and maximum charging and discharging power of the energy storage device.

[0019] Preferably, the charging load adjustable capacity model of the electric vehicle cluster is established based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, and specifically includes:

[0020] Through the historical data of daily driving of electric vehicles, the probability density function of the daily driving mileage s and the starting charging time t of electric vehicles is established, as follows:

[0021]

[0022]

[0023] Among them, f T (t) is the time of electric grid access, μ D and σ D are the expected value and standard deviation of daily mileage, μ T and σ T are the expected value and standard deviation of the starting charging time;

[0024] The charging time of electric vehicles is:

[0025]

[0026] Among them, T c is the charging time, W 100 is the power consumption of electric vehicles per 100 kilometers, P c is the charging power;

[0027] Through Monte Carlo simulation analysis of the charging power of the charging pile, the charging power of the electric vehicle cluster is:

[0028]

[0029] in, is the charging and discharging power of electric vehicle n during period t, N e is the total number of electric vehicles.

[0030] Preferably, the adjustable capacity model of the air conditioning cluster is established based on the air conditioning load heat path model, which is specifically as follows:

[0031]

[0032] Among them, T in (t+1) is the indoor temperature at time t+1, T in is the indoor temperature, iout is the outdoor temperature, Q is the rated cooling capacity, R e is the thermal resistance of the exterior wall surface, C a represents the heat capacity of indoor air, S represents the binary variable of air conditioning state, S=1 represents the air conditioning is turned on, S=0 represents the air conditioning is turned off; Δt is the time interval;

[0033]

[0034] Among them, h on is the boot time, h off is the shutdown time, h is the air conditioner control cycle, T max is the maximum indoor temperature, T min is the lowest indoor temperature;

[0035]

[0036] Among them, P T is the total adjustable capacity, P i is the rated power of the i-th air conditioner, N is the total number of air conditioners, h off,i is the off time of the i-th air conditioner, h i is the control period of the i-th air conditioner.

[0037] Preferably, the adjustable capacity model based on the distributed resource cluster is established to consider the safe and economic operation of the distribution network, including building an objective function with the minimum sum of the distribution network operating costs and the voltage node deviation of the distribution network, and the constraints of the trend network and the distribution network, as follows:

[0038] The expression of the objective function of minimizing the sum of the distributed resource operation cost and the distribution network voltage node deviation is as follows:

[0039] minF=min(f1+f2)

[0040]

[0041]

[0042] Among them, f1 represents the voltage deviation of the distribution network, f2 represents the distributed resource scheduling cost, and λ is the unit wind abandonment penalty coefficient. is the predicted photovoltaic power, P pv is the actual photovoltaic power, γ represents the charging and discharging cost of the energy storage system, ω(t) represents the electricity purchase and sales price of the distribution network during period t, P grid (t) represents the power purchased and sold by the distribution network during period t, N AIR is the number of air conditioners, s i (t) is the on / off state of air conditioner i at time t, P i (t) is the operating power of air conditioner i in time period t, γ0 is the compensation electricity price, N is the number of distribution network nodes, and T is the total dispatch period;

[0043] The power flow network constraints are as follows:

[0044]

[0045] in, is a subset of j, representing the set of lines that outflow power from node j; P jk represents the active power transmitted on line jk; Q jk is the reactive power transmitted on line jk; P ij is the active power transmitted on line ij; Q ij is the reactive power transmitted on line ij, r ij is the resistance of line ij; x ij is the reactance of line ij, is the load active power of node j; are the load reactive power of node j respectively; is the active power produced by the relevant equipment of node j; is the reactive power produced by the relevant equipment at node j, V i is the voltage amplitude of node i; V j is the voltage amplitude at node j;

[0046] The distribution network voltage constraints are as follows:

[0047]

[0048] Among them, V min and V max are the minimum and maximum values ​​of the node voltage, is the set of distribution network nodes.

[0049] Preferably, the distributed collaborative optimization scheduling model is solved to obtain a day-to-day scheduling scheme of the distributed resource cluster, and the day-to-day scheduling scheme is decomposed to obtain a single distributed resource optimization scheduling scheme, which specifically includes:

[0050] Based on the distribution network structure, the distributed resource coordinated optimization scheduling model is solved through cplex to obtain the day-ahead scheduling strategy, which includes the day-ahead power value of the air-conditioning cluster and the electric cluster, the photovoltaic output value, and the energy storage charging and discharging value;

[0051] Based on the day-ahead power values ​​of the air-conditioning cluster and the electric vehicle cluster, the switching time of a single air-conditioner and the number of charging and discharging electric vehicles in the electric vehicle cluster are obtained.

[0052] The present invention also provides a distributed resource scheduling system considering the safe operation of the distribution network, comprising:

[0053] A distributed resource cluster adjustable capacity model building module is used to build a distributed resource cluster adjustable capacity model based on the electrical conversion efficiency and application scenarios of distributed resources;

[0054] Distributed resource coordination optimization scheduling model building module, which is used to establish a distributed resource coordination optimization scheduling model that takes into account the safe and economic operation of the distribution network based on the distributed resource adjustable capacity model;

[0055] The distributed resource optimization scheduling scheme acquisition module is used to solve the distributed collaborative optimization scheduling model, obtain the recent scheduling scheme of the distributed resource cluster, decompose the recent scheduling scheme, and obtain a single distributed resource optimization scheduling scheme.

[0056] Preferably, the adjustable capacity model of the distributed resource cluster is established based on the electrical conversion efficiency and application scenarios of the distributed resources, specifically including:

[0057] Based on the photoelectric conversion efficiency of distributed photovoltaics, a distributed photovoltaic output model is established, and based on the charging and discharging efficiency of the energy storage device, a charging and discharging model of the energy storage system is established;

[0058] Based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, an adjustable capacity model of the charging load of the electric vehicle cluster is established; based on the air conditioner load thermal path model, an adjustable capacity model of the air conditioner cluster is established.

[0059] Preferably, the distributed photovoltaic output model is as follows:

[0060] P PV =LSλ

[0061]

[0062] Among them, P pv Produce power for photovoltaics; The photovoltaic power prediction L is the solar radiation intensity; S is the solar power plant sheet light contact area; λ is the photoelectric conversion efficiency;

[0063] The energy storage system charging and discharging model is as follows:

[0064]

[0065] Where ESS(t) is the amount of electricity stored in the energy storage device; η cha , η dis are the charging and discharging efficiency of the energy storage device; P cha (t), P dis (t) are the charging and discharging power of the energy storage device during period t; ESS min ESS max is the minimum and maximum capacity of the energy storage device; u cha (t),u dis (t) are the charge and discharge status symbols of the energy storage device during time period t; P dis,min , P dis,max It is the minimum and maximum charging and discharging power of the energy storage device.

[0066] Preferably, the charging load adjustable capacity model of the electric vehicle cluster is established based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, and specifically includes:

[0067] Through the historical data of daily driving of electric vehicles, the probability density function of the daily driving mileage s and the starting charging time t of electric vehicles is established, as follows:

[0068]

[0069]

[0070] Among them, f T (t) is the time of electric grid access, μ D and σ D are the expected value and standard deviation of daily mileage, μ T and σ T are the expected value and standard deviation of the starting charging time;

[0071] The charging time of electric vehicles is:

[0072]

[0073] Among them, T c is the charging time, W 100 is the power consumption of electric vehicles per 100 kilometers, Pc is the charging power;

[0074] Through Monte Carlo simulation analysis of the charging power of the charging pile, the charging power of the electric vehicle cluster is:

[0075]

[0076] in, is the charging and discharging power of electric vehicle n during period t, N e is the total number of electric vehicles.

[0077] Preferably, the adjustable capacity model of the air conditioning cluster is established based on the air conditioning load heat path model, which is specifically as follows:

[0078]

[0079] Among them, T in (t+1) is the indoor temperature at time t+1, T in is the indoor temperature, iout is the outdoor temperature, Q is the rated cooling capacity, R e is the thermal resistance of the exterior wall surface, C a represents the heat capacity of indoor air, S represents the binary variable of air conditioning state, S=1 represents the air conditioning is turned on, S=0 represents the air conditioning is turned off; Δt is the time interval;

[0080]

[0081] Among them, h on is the boot time, h off is the shutdown time, h is the air conditioner control cycle, T max is the maximum indoor temperature, T min is the lowest indoor temperature;

[0082]

[0083] Among them, P T is the total adjustable capacity, P i is the rated power of the i-th air conditioner, N is the total number of air conditioners, h off,i is the off time of the i-th air conditioner, h i is the control period of the i-th air conditioner.

[0084] Preferably, the adjustable capacity model based on the distributed resource cluster is established to consider the safe and economic operation of the distribution network, including building an objective function with the minimum sum of the distribution network operating costs and the voltage node deviation of the distribution network, and the constraints of the trend network and the distribution network, as follows:

[0085] The expression of the objective function of minimizing the sum of the distributed resource operation cost and the distribution network voltage node deviation is as follows:

[0086] minF=min(f1+f2)

[0087]

[0088]

[0089] Among them, f1 represents the voltage deviation of the distribution network, f2 represents the distributed resource scheduling cost, and λ is the unit wind abandonment penalty coefficient. is the predicted photovoltaic power, P pv is the actual photovoltaic power, γ represents the charging and discharging cost of the energy storage system, ω(t) represents the electricity purchase and sales price of the distribution network during period t, P grid (t) represents the power purchased and sold by the distribution network during period t, N AIR is the number of air conditioners, s i (t) is the on / off state of air conditioner i at time t, P i (t) is the operating power of air conditioner i in time period t, γ0 is the compensation electricity price, N is the number of distribution network nodes, and T is the total dispatch period;

[0090] The power flow network constraints are as follows:

[0091]

[0092] in, is a subset of j, representing the set of lines that outflow power from node j; P jk represents the active power transmitted on line jk; Q jk is the reactive power transmitted on line jk; P ij is the active power transmitted on line ij; Q ij is the reactive power transmitted on line ij, r ij is the resistance of line ij; x ij is the reactance of line ij, is the load active power of node j; are the load reactive power of node j respectively; is the active power produced by the relevant equipment of node j; is the reactive power produced by the relevant equipment at node j, V i is the voltage amplitude of node i; V j is the voltage amplitude at node j;

[0093] The distribution network voltage constraints are as follows:

[0094]

[0095] Among them, Vmin and V max are the minimum and maximum values ​​of the node voltage, is the set of distribution network nodes.

[0096] Preferably, the distributed collaborative optimization scheduling model is solved to obtain a day-to-day scheduling scheme of the distributed resource cluster, and the day-to-day scheduling scheme is decomposed to obtain a single distributed resource optimization scheduling scheme, which specifically includes:

[0097] Based on the distribution network structure, the distributed resource coordinated optimization scheduling model is solved through cplex to obtain the day-ahead scheduling strategy, which includes the day-ahead power value of the air-conditioning cluster and the electric cluster, the photovoltaic output value, and the energy storage charging and discharging value;

[0098] Based on the day-ahead power values ​​of the air-conditioning cluster and the electric vehicle cluster, the switching time of a single air-conditioner and the number of charging and discharging electric vehicles in the electric vehicle cluster are obtained.

[0099] Another object of the present invention is to provide a computer storage medium, the computer readable storage medium stores a computer program, and when executed by a processor, the steps of implementing the above-mentioned electrolytic cell array power distribution method based on hydrogen production efficiency.

[0100] Another object of the present invention is to provide an electronic device, including a memory and a processor, which is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of implementing the above-mentioned electrolytic cell array power distribution method based on hydrogen production efficiency.

[0101] The beneficial effects of the present invention are as follows: the present invention analyzes the electrical conversion efficiency and application scenarios of typical distributed resources, studies the scheduling model of distributed resources such as electric vehicle charging and discharging and air-conditioning loads, energy storage, and photovoltaics, considers the safe operation and economic efficiency of the distribution network to establish an objective function and set constraints, and obtains a scheduling plan by solving the model, which effectively solves the safety problems such as voltage fluctuations and three-phase imbalance caused by the large-scale access of commercial loads such as electric vehicles and air conditioners to the distribution network, thereby enhancing the stability and operational safety of the power grid, and realizing the safe access of distributed resources such as air conditioners, electric vehicles, energy storage, and photovoltaics to the distribution network, thereby improving the absorption capacity of renewable energy and promoting the effective use of green energy.

[0102] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0104] Figure 1 A schematic flow chart of a distributed resource scheduling method considering the safe operation of a distribution network provided in Example 1;

[0105] Figure 2 This is a schematic diagram of the distributed resource coordination optimization model in Example 1;

[0106] Figure 3 Schematic diagram of the heat path model of air conditioning load in Example 1;

[0107] Figure 4 is the photovoltaic output prediction value in Example 1;

[0108] Figure 5 is the load prediction value of the energy storage system in Example 1;

[0109] Figure 6 This is a schematic diagram of the distribution network structure in Example 1;

[0110] Figure 7 The power values ​​of the air conditioner and the electric vehicle after coordinated optimization in Example 1;

[0111] Figure 8 is the voltage value of the node before and after the coordinated optimization of the distribution network in Example 1;

[0112] Fig. 9 A system module diagram of a distributed resource scheduling method that takes into account the safe operation of the distribution network provided in Example 2. DETAILED DESCRIPTION

[0113] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0114] Example 1

[0115] like Figure 1 As shown, this embodiment provides a distributed resource scheduling method considering the safe operation of the distribution network, including the following steps:

[0116] S1, such as Figure 2 As shown in the figure, based on the electrical conversion efficiency and application scenarios of distributed resources, an adjustable capacity model of distributed resource cluster is established, as follows:

[0117] S1.1 Based on the photoelectric conversion efficiency of distributed photovoltaics, a distributed photovoltaic output model is established:

[0118] P PV =LSλ

[0119]

[0120] Among them, P pv Produce power for photovoltaics; The predicted power of photovoltaic is the solar radiation intensity; S is the contact area of the solar power plant sheet; λ is the photoelectric conversion efficiency. In this embodiment, solar radiation L satisfies beta distribution and uses a photovoltaic inverter to perform reactive adjustment. In order to deal with the uncertainty of photoelectricity, multiple deterministic wind power scene models are generated using the Latin supercube-based scene generation method.

[0121] S1.2 Establish a charging and discharging model of the energy storage system based on the charging and discharging efficiency of the energy storage device;

[0122]

[0123] Where ESS(t) is the amount of electricity stored in the energy storage device; η cha , η dis are the charging and discharging efficiency of the energy storage device; P cha (t), P dis (t) are the charging and discharging power of the energy storage device during period t; ESS min ESS max is the minimum and maximum capacity of the energy storage device; u cha (t),u dis (t) are the charge and discharge status symbols of the energy storage device during time period t; P dis,min , P dis,max It is the minimum and maximum charging and discharging power of the energy storage device. In the current calculation of this embodiment, the energy storage model is simplified. When the energy storage device absorbs electric energy from the power grid, it can be processed as a PQ type load; when the energy storage device releases electric energy to the power grid, it can be processed as a PV type power source.

[0124] S1.3 Based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, the charging load adjustable capacity model of the electric vehicle cluster is established, as follows:

[0125] Through the historical data of daily driving of electric vehicles, the probability density function of the daily driving mileage s and the starting charging time t of electric vehicles is established, as follows:

[0126]

[0127]

[0128] Among them, fT (t) is the time of electric grid access, μ D and σ D are the expected value and standard deviation of daily mileage, μ T and σ T are the expected value and standard deviation of the starting charging time;

[0129] The charging time of electric vehicles is:

[0130]

[0131] Among them, T c is the charging time, W 100 is the power consumption of electric vehicles per 100 kilometers, P c is the charging power;

[0132] Through Monte Carlo simulation analysis of the charging power of the charging pile, the charging power of the electric vehicle is:

[0133]

[0134] in, is the charging and discharging power of electric vehicle n during period t, N e is the total number of electric vehicles.

[0135] S1.4 is based on the heat path model of air conditioning load, such as Figure 3 As shown, an adjustable capacity model of the air conditioning cluster is established, as follows:

[0136]

[0137] Among them, T in (t+1) is the indoor temperature at time t+1, T in is the indoor temperature, iout is the outdoor temperature, Q is the rated cooling capacity, R e is the thermal resistance of the exterior wall surface, C a represents the heat capacity of indoor air, S represents the binary variable of air conditioning state, S=1 represents the air conditioning is turned on, S=0 represents the air conditioning is turned off; Δt is the time interval;

[0138]

[0139] Among them, h on is the boot time, h off is the shutdown time, h is the air conditioner control cycle, T max is the maximum indoor temperature, T min is the lowest indoor temperature;

[0140]

[0141] Among them, P T is the total adjustable capacity, P i is the rated power of the i-th air conditioner, N is the total number of air conditioners, h off,i is the off time of the i-th air conditioner, h i is the control period of the i-th air conditioner.

[0142] S2. Based on the adjustable capacity model of distributed resource clusters, a distributed resource coordination and optimization scheduling model considering the safe and economic operation of the distribution network is established, as follows;

[0143] Based on the adjustable capacity model of distributed resource clusters, an objective function is constructed to minimize the sum of the distributed resource operation cost and the distribution network voltage node deviation. Its expression is as follows:

[0144] minF=min(f1+f2)

[0145]

[0146]

[0147] Among them, f1 represents the voltage deviation of the distribution network, f2 represents the distributed resource scheduling cost, and λ is the unit wind abandonment penalty coefficient. is the predicted photovoltaic power, P pv is the actual photovoltaic power, γ represents the charging and discharging cost of the energy storage system, ω(t) represents the electricity purchase and sales price of the distribution network during period t, P grid (t) represents the power purchased and sold by the distribution network during period t, N AIR is the number of air conditioners, s i (t) is the on / off state of air conditioner i at time t, P i (t) is the operating power of air conditioner i in time period t, γ0 is the compensation electricity price, N is the number of distribution network nodes, and T is the total dispatch period;

[0148] Establish constraints on the power flow network and distribution network. The power flow network constraints are as follows:

[0149]

[0150] in, is a subset of j, representing the set of lines that outflow power from node j; P jk represents the active power transmitted on line jk; Q jk is the reactive power transmitted on line jk; P ij is the active power transmitted on line ij; Q ij is the reactive power transmitted on line ij, r ij is the resistance of line ij; x ij is the reactance of line ij, is the load active power of node j; are the load reactive power of node j respectively; is the active power produced by the relevant equipment of node j; is the reactive power produced by the relevant equipment at node j, V i is the voltage amplitude of node i; V j is the voltage amplitude at node j;

[0151] The distribution network voltage constraints are as follows:

[0152]

[0153] Among them, V min and V max are the minimum and maximum values ​​of the node voltage, is the set of distribution network nodes.

[0154] S3. Solve the distributed collaborative optimization scheduling model to obtain the recent scheduling scheme of the distributed resource cluster, decompose the recent scheduling scheme to obtain a single distributed resource optimization scheduling scheme, including:

[0155] This embodiment obtains the photovoltaic output and electric vehicle load forecast curve based on the historical data of sunlight and electric vehicle travel data on a certain day in summer in Xiamen, such as Figure 4-5 As shown, based on Figure 4-5 The curve, combined with the distribution network structure, such as Figure 4 As shown, the distribution network used in this embodiment is an improved IEEE33-node distribution network model, the line voltage reference value of the distribution system is 12.66kV, the capacity reference value is 100MVA, and node 1 is connected to the upper grid. In the simulation model, the 22nd node among the 33 nodes is selected to connect to the electric vehicle charging pile, and the 10th, 15th, 18th and 22nd nodes are connected to the energy storage system, air conditioning cluster, distributed photovoltaic and electric vehicle cluster respectively. Outdoor temperature T out The average temperature is 37℃, based on the human comfort theory, and T is set max =27℃, T min =23℃. Scheduling period Δt=15min. Air conditioning power is 1.5kw, there are 100 air conditioners in the air conditioning cluster, R e =4.72℃ / KW, C a =0.15kWh / ℃, η=2.8. The number of electric vehicles in the electric vehicle cluster is 100. Only private cars are considered in this invention. μ D =3.2,σ D =0.88, μ T =17.6,σ T=3.4. Based on the above data, the distributed resource coordination optimization scheduling model is solved through cplex to obtain a recent scheduling strategy. The recent scheduling strategy includes the daily power values of air-conditioning aggregates and electric vehicle aggregates ( Figure 7 ), photovoltaic output ( Figure 8 ), energy storage charge and discharge value;

[0156] Based on the day-ahead power values ​​of the air-conditioning cluster and the electric vehicle cluster, the switching time of a single air-conditioner and the number of charging and discharging electric vehicles in the electric vehicle cluster are obtained.

[0157] After collaborative optimization, the node voltages of the distribution network are collected and compared with the node voltages before collaborative optimization. The results are as follows: Figure 8 In most time periods, the node voltage after collaborative optimization is higher than that before collaborative optimization and is within the safe range, which may mean that the node voltage deviation of the distribution network is reduced after collaborative optimization.

[0158] After collaborative optimization, the objective function values ​​of the operating cost of distributed resources and the sum of the voltage node deviations of the distribution network are calculated and compared with those before collaborative optimization. The results are shown in Table 1.

[0159] Table 1 Comparison of objective function values ​​before and after collaborative optimization

[0160] <![CDATA[f1 / yuan]]> Before collaborative optimization 2425.9 After collaborative optimization 1517.2

[0161] Example 2

[0162] like Fig. 9 As shown, this embodiment provides a distributed resource scheduling system considering the safe operation of the distribution network, including:

[0163] A distributed resource cluster adjustable capacity model building module is used to build a distributed resource cluster adjustable capacity model based on the electrical conversion efficiency and application scenarios of distributed resources;

[0164] Distributed resource coordination optimization scheduling model building module, which is used to establish a distributed resource coordination optimization scheduling model that takes into account the safe and economic operation of the distribution network based on the distributed resource adjustable capacity model;

[0165] The distributed resource optimization scheduling scheme acquisition module is used to solve the distributed collaborative optimization scheduling model to obtain the day-ahead scheduling scheme of the distributed resource cluster, and decompose the day-ahead scheduling scheme to obtain a single distributed resource optimization scheduling scheme.

[0166] The adjustable capacity model of the distributed resource cluster is established based on the electrical conversion efficiency and application scenarios of the distributed resources, specifically including:

[0167] Based on the photoelectric conversion efficiency of distributed photovoltaics, a distributed photovoltaic output model is established, and based on the charging and discharging efficiency of the energy storage device, a charging and discharging model of the energy storage system is established;

[0168] Based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, an adjustable capacity model of the charging load of the electric vehicle cluster is established; based on the air conditioner load thermal path model, an adjustable capacity model of the air conditioner cluster is established.

[0169] The distributed photovoltaic output model is as follows:

[0170] P PV =LSλ

[0171]

[0172] Among them, P pv Produce power for photovoltaics; The photovoltaic power prediction L is the solar radiation intensity; S is the solar power plant sheet light contact area; λ is the photoelectric conversion efficiency;

[0173] The energy storage system charging and discharging model is as follows:

[0174]

[0175] Where ESS(t) is the amount of electricity stored in the energy storage device; η cha , η dis are the charging and discharging efficiency of the energy storage device; P cha (t), P dis (t) are the charging and discharging power of the energy storage device during period t; ESS min ESS max is the minimum and maximum capacity of the energy storage device; u cha (t),u dis (t) are the charge and discharge status symbols of the energy storage device during time period t; P dis,min , P dis,max It is the minimum and maximum charging and discharging power of the energy storage device.

[0176] The method of establishing a charging load adjustable capacity model for an electric vehicle cluster based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance specifically includes:

[0177] Through the historical data of daily driving of electric vehicles, the probability density function of the daily driving mileage s and the starting charging time t of electric vehicles is established, as follows:

[0178]

[0179]

[0180] Among them, f T (t) is the time of electric grid access, μ D and σ D are the expected value and standard deviation of daily mileage, μ T and σ T are the expected value and standard deviation of the starting charging time;

[0181] The charging time of electric vehicles is:

[0182]

[0183] Among them, T c is the charging time, W 100 is the power consumption of electric vehicles per 100 kilometers, P c is the charging power;

[0184] Through Monte Carlo simulation analysis of the charging power of the charging pile, the charging power of the electric vehicle cluster is:

[0185]

[0186] in, is the charging and discharging power of electric vehicle n during period t, N e is the total number of electric vehicles.

[0187] Based on the air conditioning load heat path model, an adjustable capacity model of the air conditioning cluster is established, which is as follows:

[0188]

[0189] Among them, T in (t+1) is the indoor temperature at time t+1, T in is the indoor temperature, iout is the outdoor temperature, Q represents the rated cooling capacity, R e is the thermal resistance of the exterior wall surface, C a represents the heat capacity of indoor air, S represents the binary variable of air conditioning state, S=1 represents the air conditioning is turned on, S=0 represents the air conditioning is turned off; Δt is the time interval;

[0190]

[0191] Among them, h on is the boot time, h off is the shutdown time, h is the air conditioner control cycle, T max is the maximum indoor temperature, T min is the lowest indoor temperature;

[0192]

[0193] Among them, P T is the total adjustable capacity, P i is the rated power of the i-th air conditioner, N is the total number of air conditioners, h off,i is the off time of the i-th air conditioner, h i is the control period of the i-th air conditioner.

[0194] The adjustable capacity model based on the distributed resource cluster is established to consider the safe and economic operation of the distribution network, including building an objective function with the minimum sum of the distribution network's operating costs and the voltage node deviation of the distribution network, and the constraints of the trend network and the distribution network are as follows:

[0195] The expression of the objective function of minimizing the sum of the distributed resource operation cost and the distribution network voltage node deviation is as follows:

[0196] minF=min(f1+f2)

[0197]

[0198]

[0199] Among them, f1 represents the voltage deviation of the distribution network, f2 represents the distributed resource scheduling cost, and λ is the unit wind abandonment penalty coefficient. is the predicted photovoltaic power, P pv is the actual photovoltaic power, γ represents the charging and discharging cost of the energy storage system, ω(t) represents the electricity purchase and sales price of the distribution network during period t, P grid (t) represents the power purchased and sold by the distribution network during period t, N AIR is the number of air conditioners, s i (t) is the on / off state of air conditioner i at time t, P i (t) is the operating power of air conditioner i in time period t, γ0 is the compensation electricity price, N is the number of distribution network nodes, and T is the total dispatch period;

[0200] The power flow network constraints are as follows:

[0201]

[0202] in, is a subset of j, representing the set of lines that outflow power from node j; P jk represents the active power transmitted on line jk; Q jk is the reactive power transmitted on line jk; P ij is the active power transmitted on line ij; Q ij is the reactive power transmitted on line ij, r ij is the resistance of line ij; xij is the reactance of line ij, is the load active power of node j; are the load reactive power of node j respectively; is the active power produced by the relevant equipment of node j; is the reactive power produced by the relevant equipment at node j, V i is the voltage amplitude of node i; V j is the voltage amplitude at node j;

[0203] The distribution network voltage constraints are as follows:

[0204]

[0205] Among them, V min and V max are the minimum and maximum values ​​of the node voltage, is the set of distribution network nodes.

[0206] The distributed collaborative optimization scheduling model is solved to obtain a day-to-day scheduling scheme of the distributed resource cluster, and decompose the day-to-day scheduling scheme to obtain a single distributed resource optimization scheduling scheme, which specifically includes:

[0207] Based on the distribution network structure, the distributed resource coordinated optimization scheduling model is solved through cplex to obtain the day-ahead scheduling strategy, which includes the day-ahead power value of the air-conditioning cluster and the electric cluster, the photovoltaic output value, and the energy storage charging and discharging value;

[0208] Based on the day-ahead power values ​​of the air-conditioning cluster and the electric vehicle cluster, the switching time of a single air-conditioner and the number of charging and discharging electric vehicles in the electric vehicle cluster are obtained.

[0209] Example 3

[0210] This embodiment provides a computer storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of implementing the electrolytic cell array power distribution method based on hydrogen production efficiency as described in Embodiment 1.

[0211] Example 4

[0212] This embodiment provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the electrolytic cell array power distribution method based on hydrogen production efficiency as described in Embodiment 1.

[0213] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0214] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0215] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0217] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A distributed resource scheduling method considering the safe operation of a distribution network, characterized in that: The following steps are involved: Based on the electrical conversion efficiency and application scenarios of distributed resources, an adjustable capacity model of distributed resource clusters is established; Based on the adjustable capacity model of distributed resource clusters, a distributed resource coordination and optimization scheduling model considering the safe and economical operation of the distribution network is established; The distributed collaborative optimization scheduling model is solved to obtain a day-ahead scheduling plan for the distributed resource cluster, and the day-ahead scheduling plan is decomposed to obtain a single distributed resource optimization scheduling plan.

2. A distributed resource scheduling method considering the safe operation of the distribution network according to claim 1, characterized in that: The adjustable capacity model of the distributed resource cluster is established based on the electrical conversion efficiency and application scenarios of the distributed resources, specifically including: Based on the photoelectric conversion efficiency of distributed photovoltaics, a distributed photovoltaic output model is established, and based on the charging and discharging efficiency of the energy storage device, a charging and discharging model of the energy storage system is established; Based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, an adjustable capacity model of the charging load of the electric vehicle cluster is established; based on the air conditioning load thermal circuit model, an adjustable capacity model of the air conditioning cluster is established.

3. A distributed resource scheduling method considering the safe operation of the distribution network according to claim 2, characterized in that: The distributed photovoltaic output model is as follows: P PV =LSλ Among them, P pv Produce power for photovoltaics; The photovoltaic power prediction L is the solar radiation intensity; S is the solar power plant sheet light contact area; λ is the photoelectric conversion efficiency; The energy storage system charging and discharging model is as follows: Where ESS(t) is the amount of electricity stored in the energy storage device; η cha , η dis are the charging and discharging efficiency of the energy storage device; P cha (t), P dis (t) are the charging and discharging power of the energy storage device during period t; ESS min ESS max is the minimum and maximum capacity of the energy storage device; u cha (t),u dis (t) are the charge and discharge status symbols of the energy storage device during time period t; P dis,min , P dis,max It is the minimum and maximum charging and discharging power of the energy storage device.

4. A distributed resource scheduling method considering the safe operation of the distribution network according to claim 2, characterized in that: The method of establishing a charging load adjustable capacity model for an electric vehicle cluster based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance specifically includes: Through the historical data of daily driving of electric vehicles, the probability density function of the daily driving mileage s and the starting charging time t of electric vehicles is established, as follows: Among them, f T (t) is the time of electric grid access, μ D and σ D are the expected value and standard deviation of daily mileage, μ T and σ T are the expected value and standard deviation of the starting charging time; The charging time of electric vehicles is: Among them, T c is the charging time, W 100 is the power consumption of electric vehicles per 100 kilometers, P c is the charging power; Through Monte Carlo simulation analysis of the charging power of the charging pile, the charging power of the electric vehicle cluster is: in, is the charging and discharging power of electric vehicle n during period t, N e is the total number of electric vehicles.

5. A distributed resource scheduling method considering the safe operation of the distribution network according to claim 2, characterized in that: Based on the air conditioning load heat path model, an adjustable capacity model of the air conditioning cluster is established, which is as follows: Among them, T in (t+1) is the indoor temperature at time t+1, T in is the indoor temperature, iout is the outdoor temperature, Q is the rated cooling capacity, R e is the thermal resistance of the exterior wall surface, C a represents the heat capacity of indoor air, S represents the binary variable of air conditioning state, S=1 represents the air conditioning is turned on, S=0 represents the air conditioning is turned off; Δt is the time interval; Among them, h on is the boot time, h off is the shutdown time, h is the control cycle of the air conditioner, T max is the maximum indoor temperature, T min is the lowest indoor temperature; Among them, P T is the total adjustable capacity, P i is the rated power of the i-th air conditioner, N is the total number of air conditioners, h off,i is the off time of the i-th air conditioner, h i is the control period of the i-th air conditioner.

6. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 1, characterized in that: The adjustable capacity model based on distributed resource clusters establishes a distributed resource coordination optimization scheduling model that considers the safe and economical operation of the distribution network, including constructing an objective function that minimizes the sum of the distributed resource operation cost and the distribution network voltage node deviation, and the constraints of the power flow network and the distribution network, as follows: The expression of the objective function of minimizing the sum of the distributed resource operation cost and the distribution network voltage node deviation is as follows: minF=min(f1+f2) Among them, f1 represents the voltage deviation of the distribution network, f2 represents the distributed resource scheduling cost, and λ is the unit wind abandonment penalty coefficient. is the predicted photovoltaic power, P pv is the actual photovoltaic power, γ represents the charging and discharging cost of the energy storage system, ω(t) represents the electricity purchase and sales price of the distribution network during period t, P grid (t) represents the power purchased and sold by the distribution network during period t, N AIR is the number of air conditioners, s i (t) is the on / off state of air conditioner i at time t, P i (t) is the operating power of air conditioner i in time period t, γ0 is the compensation electricity price, N is the number of distribution network nodes, and T is the total dispatch period; The power flow network constraints are as follows: in, is a subset of j, representing the set of lines that outflow power from node j; P jk represents the active power transmitted on line jk; Q jk is the reactive power transmitted on line jk; P ij is the active power transmitted on line ij; Q ij is the reactive power transmitted on line ij, r ij is the resistance of line ij; x ij is the reactance of line ij, is the load active power of node j; are the load reactive power of node j respectively; is the active power produced by the relevant equipment of node j; is the reactive power produced by the relevant equipment at node j, V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; The distribution network voltage constraints are as follows: Among them, V min and V max are the minimum and maximum values ​​of the node voltage, is the set of distribution network nodes.

7. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 1, characterized in that: The distributed collaborative optimization scheduling model is solved to obtain a day-ahead scheduling solution for a distributed resource cluster, and the day-ahead scheduling solution is decomposed to obtain a single distributed resource optimization scheduling solution, specifically including: Based on the distribution network structure, the distributed resource coordinated optimization scheduling model is solved through cplex to obtain the day-ahead scheduling strategy, which includes the day-ahead power value of the air conditioning cluster and the electric cluster, the photovoltaic output value, and the energy storage charging and discharging value; Based on the day-ahead power values ​​of the air-conditioning cluster and the electric vehicle cluster, the switching time of a single air-conditioner and the number of charging and discharging electric vehicles in the electric vehicle cluster are obtained.

8. A distributed resource scheduling system considering the safe operation of distribution network, characterized in that: include: A distributed resource cluster adjustable capacity model building module is used to build a distributed resource cluster adjustable capacity model based on the electrical conversion efficiency and application scenarios of distributed resources; Distributed resource coordination optimization scheduling model building module, which is used to establish a distributed resource coordination optimization scheduling model that takes into account the safe and economic operation of the distribution network based on the distributed resource adjustable capacity model; The distributed resource optimization scheduling scheme acquisition module is used to solve the distributed collaborative optimization scheduling model to obtain the day-ahead scheduling scheme of the distributed resource cluster, and decompose the day-ahead scheduling scheme to obtain a single distributed resource optimization scheduling scheme.

9. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 1, characterized in that: The adjustable capacity model of the distributed resource cluster is established based on the electrical conversion efficiency and application scenarios of the distributed resources, specifically including: Based on the photoelectric conversion efficiency of distributed photovoltaics, a distributed photovoltaic output model is established, and based on the charging and discharging efficiency of the energy storage device, a charging and discharging model of the energy storage system is established; Based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance, an adjustable capacity model of the charging load of the electric vehicle cluster is established; based on the air conditioning load thermal circuit model, an adjustable capacity model of the air conditioning cluster is established.

10. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 2, characterized in that: The distributed photovoltaic output model is as follows: P PV =LSλ Among them, P pv Produce power for photovoltaics; The photovoltaic power prediction L is the solar radiation intensity; S is the solar power plant sheet light contact area; λ is the photoelectric conversion efficiency; The energy storage system charging and discharging model is as follows: Where ESS(t) is the amount of electricity stored in the energy storage device; η cha , η dis are the charging and discharging efficiency of the energy storage device; P cha (t), P dis (t) are the charging and discharging power of the energy storage device during period t; ESS min ESS max is the minimum and maximum capacity of the energy storage device; u cha (t),u dis (t) are the charge and discharge status symbols of the energy storage device during time period t; P dis,min , P dis,max It is the minimum and maximum charging and discharging power of the energy storage device.

11. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 2, characterized in that: The method of establishing a charging load adjustable capacity model for an electric vehicle cluster based on the probability density function of the electric vehicle charging start time and the probability density function of the electric vehicle daily driving distance specifically includes: Through the historical data of daily driving of electric vehicles, the probability density function of the daily driving mileage s and the starting charging time t of electric vehicles is established, as follows: Among them, f T (t) is the time of electric grid access, μ D and σ D are the expected value and standard deviation of daily mileage, μ T and σ T are the expected value and standard deviation of the starting charging time; The charging time of electric vehicles is: Among them, T c is the charging time, W 100 is the power consumption of electric vehicles per 100 kilometers, P c is the charging power; Through Monte Carlo simulation analysis of the charging power of the charging pile, the charging power of the electric vehicle cluster is: in, is the charging and discharging power of electric vehicle n during period t, N e is the total number of electric vehicles.

12. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 2, characterized in that: Based on the air conditioning load heat path model, an adjustable capacity model of the air conditioning cluster is established, which is as follows: Among them, T in (t+1) is the indoor temperature at time t+1, T in is the indoor temperature, iout is the outdoor temperature, Q is the rated cooling capacity, R e is the thermal resistance of the exterior wall surface, C a represents the heat capacity of indoor air, S represents the binary variable of air conditioning state, S=1 represents the air conditioning is turned on, S=0 represents the air conditioning is turned off; Δt is the time interval; Among them, h on is the boot time, h off is the shutdown time, h is the control cycle of the air conditioner, T max is the maximum indoor temperature, T min is the lowest indoor temperature; Among them, P T is the total adjustable capacity, P i is the rated power of the i-th air conditioner, N is the total number of air conditioners, h off,i is the off time of the i-th air conditioner, h i is the control period of the i-th air conditioner.

13. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 1, characterized in that: The adjustable capacity model based on distributed resource clusters establishes a distributed resource coordination optimization scheduling model that considers the safe and economical operation of the distribution network, including constructing an objective function that minimizes the sum of the distributed resource operation cost and the distribution network voltage node deviation, and the constraints of the power flow network and the distribution network, as follows: The expression of the objective function of minimizing the sum of the distributed resource operation cost and the distribution network voltage node deviation is as follows: minF=min(f1+f2) Among them, f1 represents the voltage deviation of the distribution network, f2 represents the distributed resource scheduling cost, and λ is the unit wind abandonment penalty coefficient. is the predicted photovoltaic power, P pv is the actual photovoltaic power, γ represents the charging and discharging cost of the energy storage system, ω(t) represents the electricity purchase and sales price of the distribution network during period t, P grid (t) represents the power purchased and sold by the distribution network during period t, N AIR is the number of air conditioners, s i (t) is the on / off state of air conditioner i at time t, P i (t) is the operating power of air conditioner i in time period t, γ0 is the compensation electricity price, N is the number of distribution network nodes, and T is the total dispatch period; The power flow network constraints are as follows: in, is a subset of j, representing the set of lines that outflow power from node j; P jk is the active power transmitted on line jk; Q jk is the reactive power transmitted on line jk; P ij is the active power transmitted on line ij; Q ij is the reactive power transmitted on line ij, r ij is the resistance of line ij; x ij is the reactance of line ij, is the load active power of node j; are the load reactive power of node j respectively; is the active power produced by the relevant equipment of node j; is the reactive power produced by the relevant equipment at node j, V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; The distribution network voltage constraints are as follows: Among them, V min and V max are the minimum and maximum values ​​of the node voltage, is the set of distribution network nodes.

14. A distributed resource scheduling method considering the safe operation of a distribution network according to claim 1, characterized in that: The distributed collaborative optimization scheduling model is solved to obtain a day-ahead scheduling solution for a distributed resource cluster, and the day-ahead scheduling solution is decomposed to obtain a single distributed resource optimization scheduling solution, specifically including: Based on the distribution network structure, the distributed resource coordinated optimization scheduling model is solved through cplex to obtain the day-ahead scheduling strategy, which includes the day-ahead power value of the air conditioning cluster and the electric cluster, the photovoltaic output value, and the energy storage charging and discharging value; Based on the day-ahead power values ​​of the air-conditioning cluster and the electric vehicle cluster, the switching time of a single air-conditioner and the number of charging and discharging electric vehicles in the electric vehicle cluster are obtained.

15. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for allocating power of an electrolyzer array based on taking hydrogen production efficiency into account as described in any one of claims 1 to 7 are implemented.

16. An electronic device, characterized in that: It comprises a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the electrolyzer array power allocation method based on taking into account the hydrogen production efficiency as described in any one of claims 1 to 7 are implemented.

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