CN113326467B - Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties - Google Patents
Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties Download PDFInfo
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Abstract
The application discloses a multi-target optimization method, a storage medium and an optimization system of a multi-station fusion comprehensive energy system based on multiple uncertainties, and a multi-station fusion comprehensive energy system model is established; multiple uncertainties of a multi-station fusion comprehensive energy system model are analyzed, wherein the multiple uncertainties comprise prediction error uncertainties of new energy stations and prediction error uncertainties of charging stations; setting a multi-objective optimization model, establishing a cost optimization model according to the total daily operation cost of each substation, and establishing a risk optimization model according to the operation fluctuation of the charging station and the new energy station; and solving the multi-objective optimization model. The multi-station fusion comprehensive energy system optimization operation model which takes the uncertainty and the fluctuation of the new energy and the electric automobile into account is established, the coordination and optimization operation capacity of the comprehensive energy system is improved, the economical efficiency of the fusion station system is improved due to the fact that multiple uncertainties influence, meanwhile, the risk factors existing in the system are considered, and the stability and the safety of the system are improved.
Description
Technical Field
The application belongs to the technical field of coordination optimization of a comprehensive energy system, and particularly relates to a multi-station fusion comprehensive energy system multi-objective optimization method based on multiple uncertainties, a storage medium and an optimization system.
Background
The multi-station fusion is used as one of important application of implementing the landing of the electric power Internet of things, resources such as a transformer substation, a data center station, a new energy plant station, a charging station, an energy storage station, a 5G base station, a Beidou navigation station and the like are converged, urban resource allocation is optimized, data perception and analysis operation efficiency is improved, and load is consumed in situ. In the fusion station system, the access of a large-scale new energy station becomes a key for improving the running economy and environmental protection of the system, and meanwhile, the fusion station also provides guarantee for the photovoltaic digestion capability. However, due to the influence of environmental factors such as weather, geography and the like, renewable energy power generation often has uncertainty and volatility, and when the power output prediction of photovoltaic power generation is not accurate enough, the optimal operation economy and safety of the system are seriously affected. Meanwhile, with the continuous development of new capital construction of China, a large number of charging stations are connected into a power grid, charging piles are laid in a large number in a residential community, and random charging and discharging behaviors of electric automobile users can also have great influence on the operation of the comprehensive energy system. The lack of consideration of multiple uncertainties of new energy stations and charging stations in the fusion station system in the research at present increases the complexity of optimal scheduling of the fusion station system due to the uncertainties and intermittence of the new energy stations and the charging stations, so that system operators have difficulty in economic scheduling planning before making days. In addition, the uncertainty factor is accompanied by the fluctuation characteristic, so that impact is caused on the power grid, the operation risk of the comprehensive energy system is increased, the flexibility is reduced, the utilization rate of renewable energy is also reduced, and the safe and stable operation of the comprehensive energy cannot be well realized. In order to effectively reduce the adverse effect of uncertainty factors in the system on the comprehensive energy system, the integral stable and efficient operation of the comprehensive energy system of the fusion station is realized, and the uncertainty factors in the coordination optimization of the comprehensive energy system of the fusion station are necessary to be studied in depth.
Disclosure of Invention
In order to solve the problems, the application provides a multi-target optimization method of a multi-station fusion integrated energy system based on multiple uncertainties, which can improve the economical efficiency and the stability of the integrated energy system, and the multi-target optimization method comprises the following steps:
s1) establishing a multi-station fusion comprehensive energy system model
The multi-station comprehensive energy system model comprises five sub-station models, wherein the five sub-station models are a transformer substation model, a charging station model, an energy storage station model, a data center station model and a new energy station model respectively, and the operation constraint of the multi-station comprehensive energy system model is set;
s2) analyzing uncertainty of multi-station fusion comprehensive energy system model
Analyzing the prediction error uncertainty of the new energy station according to the new energy station model, and analyzing the prediction error uncertainty of the charging station according to the charging station model;
s3) establishing a multi-objective optimization model
Establishing a cost optimization model according to the total daily operation cost of the transformer station, the total daily operation cost of the charging station, the total daily operation cost of the energy storage station, the total daily operation cost of the data center station and the total daily operation cost of the new energy station, and establishing a risk optimization model according to the charging station and the new energy station;
s4) solving the multi-objective optimization model
Further, in the step S1), the substation model is as follows:
substation model
P Tr,t ＝P Tr,c,t +P Tr,l,t +P Tr,d,t (1)
Wherein P is Tr,t For the total load of the transformer substation at the moment t, P Tr,c,t 、P Tr,l,t 、P Tr,d,t Respectively the energy consumption of the air conditioner, the illumination energy consumption and the power transmission and distribution energy consumption of the transformer substation at the moment t,the upper limit and the lower limit of the load of the transformer substation are respectively;
charging station model
Wherein P is C,t The total load of the charging station at the time t of the system; n is the total number of charging piles in the system; t is the total scheduling period; p (P) C,n,t The load of the nth charging pile; delta C Self-loss coefficient of the charging pile battery; e (E) C,n,t (t+1)、E C,n,t (t) the electric energy storage capacity of the charging pile n battery at the time t+1 and t respectively;charging and discharging power of the charging pile n battery at the time t are respectively;Charging and discharging efficiencies of the charging pile battery are respectively; p (P) n,t The charging load of the nth charging pile at the time t is set;the electricity selling power of the nth charging pile at the time t is obtained; alpha C,n The value of the variable 0-1 of the running state of the nth charging pile is 1 time, which represents that the charging pile charges the EV (electric vehicle); p (P) o,n The rated charging power of the electric automobile entering the charging pile n; t is t s 、t e Respectively starting and ending the charging of the automobile;The maximum charging power and the maximum discharging power of the charging pile n at the moment t are respectively;And respectively selling the maximum value and the minimum value of the electric power for the nth charging pile.
Further, in the step S1), the substation model is as follows:
energy storage station model
E bat (0)＝E bat (T) (15)
Wherein E is bat (t+1)、E bat (t) is the real-time capacity of the energy storage battery of the energy storage station at the time t+1 and the time t respectively; delta bat Is the self-loss coefficient of the battery; p (P) bc,t 、P bd,t Charging and discharging power of the energy storage battery at the moment t respectively;respectively charging and discharging power of the energy storage battery;The maximum capacity and the minimum capacity of the energy storage battery are respectively;Respectively the maximum charging power and the maximum discharging power of the energy storage battery; alpha bat The value of the variable is 0-1, which is the running state of the energy storage battery and represents that the energy storage battery is in a charging state when the value is 1;
data center station model
Wherein P is data,t The total power consumption load of the data center station at the moment t; p (P) IT,t The method comprises the steps that the power load of IT equipment of an edge data center at the moment t is used; COP is the energy efficiency ratio of the air conditioning and refrigerating unit; t (T) t Outdoor temperature at a certain moment;maximum load for the data center station;
new energy station model
P PV,t ＝r i Aη (18)
Wherein P is PV (t) is the photovoltaic active power; r is (r) i Is the intensity of solar radiation; a is the total area of the solar cell matrix; η is the overall photoelectric conversion efficiency, taking η=0.965; q (Q) PV Photovoltaic reactive power output; θ Si The power factor angle of the photovoltaic power generation system at the node i is set;the upper limit and the lower limit of the photovoltaic output are respectively set.
Further, in the step S1), the operation constraint of the given multi-station fusion integrated energy system model is as follows:
(1) System electric power constraint
Pr{P g,t +P bd,t +P PV,t +ΔP PV,t ＝P load.t +P Tr,t +P C,t +P data,t +P bc,t }≥β (20)
Wherein P is g,t The electric energy purchased from the power grid at the moment t; ΔP PV,t Is new toThe predicted power error of the energy station at the time t; p (P) loa.d The load is the resident side load at the moment t; beta is a given confidence level;
(2) System and power grid interaction constraint
P line,min ≤P g,t ≤P line,max (21)
Wherein P is linemin 、P linemax KW is the minimum power and the maximum power of the connecting line;
(3) Plant operation constraints
Wherein P is p,i,t The output of the ith controllable unit at the t moment;the upper limit and the lower limit of the output of the ith controllable unit are respectively set.
Further, in the step S2), the prediction error uncertainty formula of the new energy station is as follows:
Γ (·) is a Gamma function; r is (r) i The actual illumination intensity of the ith scene; r is (r) i,max Maximum radiant intensity of sunlight for the ith scene; alpha i 、β i The shape parameters and the scale parameters of Beta distribution of the energy output errors of the ith scene description system are respectively.
Further, in the step S2), the prediction error uncertainty formula of the charging station is as follows:
wherein k represents different types of EVs, each fusion station area comprises 3 types of EVs with different typical behaviors, each EV is an electric private car when k=1, a bus when k=2, and a taxi when k=3; f (f) s,k (x)、f e,k (x) Probability density distribution of the k-th EV starting trip and returning finally; x is x s,k 、x e,k Mu respectively the time when the k-th EV starts traveling and returns last s,k 、μ e,k Expected values, sigma, of the time when the k-th EV starts traveling and the time when the k-th EV returns last, respectively s,k 、σ e,k The variances of the starting trip time and the last return time of the kth EV are respectively.
Further, in the step S3), the cost optimization model and the risk optimization model are formulated as follows:
wherein F is 1 Optimizing a model for cost; f (F) 2 A risk optimization model; f (F) Tr The total daily operation cost of the transformer substation; f (F) C Total cost for daily operation of the charging station; f (F) bat The total daily operation cost of the energy storage power station; f (F) data Total cost of daily operation for the data center station; f (F) PV The total cost of daily operation of the new energy station; omega shape PV 、Ω EV Photovoltaic collection and charging station respectively of new energy stationsAn accessed electric automobile set; u (u) k,t 、u l,t The risk factors are the risk factors of the new energy and the electric vehicle respectively; r is R k,t 、R l,t The absolute value of the difference between the predicted value of the output and the actual output of the new energy station and the charging station at the time t represents the risk of the new energy station and the charging station being transmitted to the multi-station fusion system;
the total daily operation cost of the transformer substation is specifically expressed as follows:
wherein F is Tr The total daily operation cost of the transformer substation; i represents the type of equipment in the substation; p (P) Tr,t Representing the total load of the transformer substation at the t moment, including the load of an air conditioner, lighting equipment and a transformer; r is (r) i The unit power consumption operation cost of the equipment type i of the transformer substation;
the total daily operational cost of the charging station is specifically expressed as follows:
F C ＝F elec +F deg -F V2G (31)
wherein F is C Total cost for daily operation of the charging station; f (F) elec The cost of purchasing electricity from the power grid for the charging station; f (F) deg Battery loss cost for the electric automobile in the station; f (F) V2G Earnings obtained for the charging station to participate in the internet of vehicles (V2G); c t Representing the electricity selling price of the power grid in the period t; p (P) C,n,t For charging pile nCharging power to EV for period t;the charging power and the discharging power of the storage battery of the charging pile n in the period t are respectively; c (C) C,n The total cost in the life cycle of the storage battery of the charging pile n; w (W) C,n The sum of the charge and discharge capacities of the charging station n in the whole life cycle is obtained; alpha C,n The binary variable represents the interaction state of the charging pile n and the power grid, and the value of the binary variable represents that the power grid charges a charging pile battery in the period of 1; c comp The unit electric quantity compensation is provided for the electric network to the electric automobile participating in the V2G;
the total daily operational cost of the energy storage power station is specifically expressed as follows:
wherein F is bat The total daily operation cost of the energy storage power station; n (N) bat The number of the energy storage power stations; c in,t 、c out,t The electricity prices of the energy storage power stations during charging and discharging at the time t are respectively; s is(s) bat The cost of the energy storage power station in a single-time conversion charging and discharging mode is reduced; b is the charge and discharge state of the energy storage power station at the last moment before the day; delta bat (t) a binary variable of charge-discharge state at time t;
the total daily operational costs of the data center station are specifically expressed as follows:
wherein F is data Total cost of daily operation for the data center station; j is the equipment type in the data center station, and mainly comprises IT equipment, air conditioning equipment and lighting equipment; p (P) data,j The total power consumption of j-class equipment; r is (r) data,j And the unit power consumption operation cost corresponding to the j-class equipment is set.
The daily operation assembly cost of the new energy station is specifically expressed as follows:
wherein F is PV Total cost for photovoltaic daily operation; omega shape PV The method comprises the steps of collecting grid-connected photovoltaic cells; c (C) PV,h,t The power generation cost coefficient of the photovoltaic cell h; p (P) PV,h,t The available active force of the photovoltaic cell h at the moment t; c (C) aban,h,t The unit light discarding cost coefficient of the photovoltaic unit h; p (P) PVaban,h,t The optical power of the photovoltaic cell h at the moment t is discarded; c (C) sell,h,t The photovoltaic online electricity price is that of photovoltaic; p (P) PVsell,h,t And the net surfing quantity of the photovoltaic cell h at the moment t.
Further, in the step S4), the multi-objective optimization model is solved by adopting an NSGA-II algorithm and multi-attribute decision, and the specific process is as follows:
setting economic parameters of components in a system, system configuration parameters and a genetic algorithm;
solving a series of solutions under the conditions of cost and risk by using an NSGA-II algorithm;
and further solving an optimal solution in a series of solutions by combining a multi-attribute decision method.
There is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to execute the above-described coordinated control method when run.
The multi-station fusion comprehensive energy system multi-objective optimization system based on multiple uncertainties is also provided, comprising:
the multi-station fusion comprehensive energy system model setting module is used for setting five substation models, wherein the five substation models are respectively a transformer substation model, a charging station model, an energy storage station model, a data center station model and a new energy station model, and setting operation constraint of the multi-station fusion comprehensive energy system model;
the uncertainty analysis module of the multi-station integrated comprehensive energy system model analyzes the prediction error uncertainty of the new energy station according to the new energy station model and analyzes the prediction error uncertainty of the charging station according to the charging station model;
the multi-objective optimization model setting module is used for establishing a cost optimization model according to the total daily operation cost of the transformer substation, the total daily operation cost of the charging station, the total daily operation cost of the energy storage station, the total daily operation cost of the data center station and the total daily operation cost of the new energy station, and establishing a risk optimization model according to the charging station and the new energy station;
and the multi-objective optimization model solving module adopts an NSGA-II algorithm and multi-attribute decision to solve the multi-objective optimization model.
The application has the advantages that:
1) In the aspect of the model, a typical five-station-in-one multi-station fusion comprehensive energy system model is established, and the main energy utilization characteristics and numerous model constraints of each substation are considered, so that the mutual complementation and coordinated operation in the system can be realized; the multi-station integrated comprehensive energy system integrates a plurality of substations, and compared with the traditional transformer substation, the structure is more complex, and the system stability is more difficult to coordinate;
2) The application carries out theoretical analysis aiming at the uncertainty of the new energy station output and charging station charging, considers the system running cost and the risk caused by two fluctuation units of the new energy station and the charging station, greatly improves the coordination and optimization running capacity of the fusion station system through multiple uncertainty analysis of the system, and simultaneously considers the risk factors existing in the system, thereby improving the stability and safety of the system;
3) The method analyzes the energy utilization characteristics of each substation in the multi-station fusion, predicts the future energy utilization trend by adopting historical energy utilization data, establishes an intelligent energy station comprehensive energy system optimization operation model considering the uncertainty and fluctuation of new energy and electric vehicles, improves the coordinated and optimized operation capability of the comprehensive energy system, improves the economical efficiency of the comprehensive energy system under the influence of multiple uncertainties, considers the risk factors existing in the system, and improves the stability and safety of the system.
Drawings
Fig. 1 is a schematic diagram of a multi-station fusion system according to an embodiment of the present application.
FIG. 2 is a schematic diagram of an example charging station charging principle of the present application;
fig. 3 is a daily charge demand load curve of an electric vehicle of an example charging station of the application.
Fig. 4 is a graph showing a change in capacity of an energy storage station during a day in accordance with an example of the present application.
Fig. 5 is a daily electrical load of an example data center station of the present application.
Fig. 6 is a view of the output scenario of the example monte carlo simulated new energy station of the present application.
FIG. 7 is a view showing a reduced K-means method according to an embodiment of the present application.
FIG. 8 is a flow chart of an example multi-objective solution algorithm of the present application.
FIG. 9 is a flow chart of an example multi-attribute decision process of the present application.
FIG. 10 is a diagram of an optimization system of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The multi-station fusion comprehensive energy system of the embodiment is shown in fig. 1, and comprises five sub-stations, wherein the five sub-stations are a transformer station, a charging station, an energy storage station, a data center station and a new energy station respectively. Based on the coupling operation and coordination optimization of transformer substations, energy storage stations, data center stations, charging stations and new energy stations, the power supply load difference, motor policy, power consumption period difference, land resource limitation and the like of each transformer substation are considered, under the condition that the power supply safety of the transformer substations is guaranteed, the data center stations with different scales are built in transformer substations with different levels, the energy storage power stations reserve spare capacity for 30min normal operation for the data center stations, power is supplied for the data center stations when power is off, and meanwhile the energy storage power stations are brought into power scheduling to enhance the flexibility of the intelligent energy stations. The access of the new energy station reduces the electricity cost and brings uncertainty factors and risk factors, and the charging station also has certain risk factors. According to the application, the multi-objective coordination optimization model considering risk and cost is constructed by considering uncertainty and volatility of the fusion station, the optimization model is solved by using an NSGA-II algorithm (multi-objective genetic algorithm), and the optimal solution is obtained by performing performance evaluation on the solved solution by using a multi-attribute decision method. The specific optimization method is as follows:
s1) establishing a multi-station fusion comprehensive energy system model
The multi-station fusion comprehensive energy system model comprises five sub-station models, wherein the five sub-station models are a transformer substation model, a charging station model, an energy storage station model, a data center station model and a new energy station model respectively, the five sub-station models can be selected for use according to actual requirements, one or more of the following five models, such as two or three, can be selected, other well-known model formulas can be selected, and operation constraint of the multi-station comprehensive energy system model is set.
(1) Substation model
P Tr,t ＝P Tr,c,t +P Tr,l,t +P Tr,d,t (1)
Wherein P is Tr,t For the total load of the transformer substation at the moment t, P Tr,c,t 、P Tr,l,t 、P Tr,d,t Respectively the energy consumption of the air conditioner, the illumination energy consumption and the power transmission and distribution energy consumption of the transformer substation at the moment t,the upper limit and the lower limit of the transformer substation load are respectively.
(2) Charging station model
Wherein P is C,t The total load of the charging station at the time t of the system; n is the total number of charging piles in the system; t is the total scheduling period; p (P) C,n,t The load of the nth charging pile; delta C Self-loss coefficient of the charging pile battery; e (E) C,n,t (t+1)、E C,n,t (t) the electric energy storage capacity of the charging pile n battery at the time t+1 and t respectively;respectively charging the n batteries of the charging pile at the time t,Discharge power;Charging and discharging efficiencies of the charging pile battery are respectively; p (P) n,t The charging load of the nth charging pile at the time t is set;the electricity selling power of the nth charging pile at the time t is obtained; alpha C,n The value of the variable 0-1 of the running state of the nth charging pile is 1 time, and the variable represents that the charging pile charges EV (Electric Vehicle); p (P) o,n The rated charging power of the electric automobile entering the charging pile n; t is t s 、t e Respectively starting and ending the charging of the automobile;The maximum charging power and the maximum discharging power of the charging pile n at the moment t are respectively;And respectively selling the maximum value and the minimum value of the electric power for the nth charging pile. The charging principle diagram of the charging station is shown in fig. 2, and the daily charging demand load curve of the electric vehicle is shown in fig. 3.
(3) Energy storage station model
E bat (0)＝E bat (T) (15)
Wherein E is bat (t+1)、E bat (t) is the real-time capacity of the energy storage battery of the energy storage station at the time t+1 and the time t respectively; delta bat Is the self-loss coefficient of the battery; p (P) bc,t 、P bd,t Charging and discharging power of the energy storage battery at the moment t respectively;respectively charging and discharging power of the energy storage battery;The maximum capacity and the minimum capacity of the energy storage battery are respectively;Respectively the maximum charging power and the maximum discharging power of the energy storage battery; alpha bat The value of the variable 0-1 is 1, which represents that the energy storage battery is in a charging state. The energy storage station has the effects of peak clipping and valley filling and maintaining the energy balance of the system, and the daily charge state is shown in fig. 4.
(4) Data center station model
Wherein P is data,t The total power consumption load of the data center station at the moment t; p (P) IT,t The method comprises the steps that the power load of IT equipment of an edge data center at the moment t is used; COP is the energy efficiency ratio of the air conditioning and refrigerating unit; t (T) t Outdoor temperature at a certain moment;maximum load for the data center station. Number of digitsThe daily electricity demand from the central station is shown in fig. 5.
(5) New energy station model
P PV,t ＝r i Aη (18)
Wherein P is PV (t) is the photovoltaic active power; r is (r) i Is the intensity of solar radiation; a is the total area of the solar cell matrix; η is the overall photoelectric conversion efficiency, taking η=0.965; q (Q) PV Photovoltaic reactive power output; θ Si The power factor angle of the photovoltaic power generation system at the node i is set;the upper limit and the lower limit of the photovoltaic output are respectively set.
In addition, for the normal operation of the system model, the operation constraint of the system model is given:
(1) System electric power constraint
Pr{P g,t +P bd,t +P PV,t +ΔP PV,t ＝P load.t +P Tr,t +P C,t +P data,t +P bc,t }≥β (20)
Wherein P is g,t The electric energy purchased from the power grid at the moment t; ΔP PV,t The power error is predicted for the new energy station at the time t; p (P) loa.d The load is the resident side load at the moment t; beta is a given confidence level;
(2) System and power grid interaction constraint
P line,min ≤P g,t ≤P line,max (21)
Wherein P is linemin 、P linemax KW is the minimum power and the maximum power of the connecting line;
(3) Plant operation constraints
Wherein P is p,i,t The output of the ith controllable unit at the t moment;the upper limit and the lower limit of the output of the ith controllable unit are respectively set.
S2) analyzing uncertainty of multi-station fusion comprehensive energy system
As can be seen from the new energy station model and the charging station model in step S1), the new energy station model output is related to the illumination intensity, the charging station model load is related to the class of the vehicle entering the charging station and the start charging time, and therefore, the new energy station output and the charging station load have high uncertainty; for safe and stable operation of the system, it is necessary to analyze it. According to the application, 100 new energy station output scenes are randomly generated by adopting a Monte Carlo simulation method, the generated scenes are shown in figure 6, the generated 100 output scenes are clustered into 3 new energy station output scenes by using a K-means method, and the scene after reduction is shown in figure 7. And (3) representing the prediction errors of the new energy stations in different scenes by using probability density functions:
(1) Prediction error uncertainty of new energy station
Generating 100 new energy station output scenes by adopting a Monte Carlo simulation method, clustering the 100 output scenes into 3 new energy station output scenes by using a K-means method, and representing the prediction error of the new energy station by using a probability density function:
Γ (·) is a Gamma function; r is (r) i The actual illumination intensity of the ith scene; r is (r) i,max Maximum radiant intensity of sunlight for the ith scene; alpha i 、β i Beta distribution of energy output errors of ith scene description system respectivelyShape parameters and dimension parameters of (a).
(2) Prediction error uncertainty of charging station:
charging station uncertainty is related to the starting charging time, the daily charging amount of the automobile and the charging power, and the starting trip time and the last return time are normally distributed, and are expressed as a probability density function:
wherein k represents different types of EVs, each fusion station area comprises 3 types of EVs with different typical behaviors, each EV is an electric private car when k=1, a bus when k=2, and a taxi when k=3; f (f) s,k (x)、f e,k (x) Probability density distribution of the k-th EV starting trip and returning finally; x is x s,k 、x e,k Mu respectively the time when the k-th EV starts traveling and returns last s,k 、μ e,k Expected values, sigma, of the time when the k-th EV starts traveling and the time when the k-th EV returns last, respectively s,k 、σ e,k The variances of the starting trip time and the last return time of the kth EV are respectively.
S3) establishing a multi-objective optimization model;
the system uncertainty can influence the overall economy and safety of the system, in order to embody the superiority of the method, the objective function takes the overall operation economy and the system safety of the system as the core, the objective function is constructed by taking the minimum daily operation cost and the minimum risk degree into consideration, the cost optimization model is set according to the daily operation total cost of the transformer substation, the daily operation total cost of the charging station, the daily operation total cost of the energy storage station, the daily operation total cost of the data center station and the daily operation total cost of the new energy station, and the risk optimization model is set according to the charging station and the new energy station.
Wherein F is 1 Optimizing a model for cost; f (F) 2 A risk optimization model; f (F) Tr The total daily operation cost of the transformer substation; f (F) C Total cost for daily operation of the charging station; f (F) bat The total daily operation cost of the energy storage power station; f (F) data Total cost of daily operation for the data center station; f (F) PV The total cost of daily operation of the new energy station; omega shape PV 、Ω EV The photovoltaic collection of the new energy stations and the electric automobile collection accessed by the charging station are respectively adopted; u (u) k,t 、u l,t The risk factors are the risk factors of the new energy and the electric vehicle respectively; r is R k,t 、R l,t And the absolute value of the difference between the predicted value of the output and the actual output of the new energy station and the charging station at the time t represents the risk of the new energy station and the charging station being transmitted to the multi-station fusion system.
The total daily operation cost of the transformer substation is specifically expressed as follows:
wherein F is Tr The total daily operation cost of the transformer substation; i represents the type of equipment in the substation; p (P) Tr,t Representing the total load of the transformer substation at the t moment, including the load of an air conditioner, lighting equipment and a transformer; r is (r) i The unit power consumption operation cost of the equipment type i of the transformer substation.
The total daily operational cost of the charging station is specifically expressed as follows:
F C ＝F elec +F deg -F V2G (31)
wherein F is C Total cost for daily operation of the charging station; f (F) elec The cost of purchasing electricity from the power grid for the charging station; f (F) deg Battery loss cost for the electric automobile in the station; f (F) V2G Earnings obtained for the charging station to participate in the internet of vehicles (V2G); c t Representing the electricity selling price of the power grid in the period t; p (P) C,n,t Charging power to EV for charging pile n at period t;the charging power and the discharging power of the storage battery of the charging pile n in the period t are respectively; c (C) C,n The total cost in the life cycle of the storage battery of the charging pile n; w (W) C,n The sum of the charge and discharge capacities of the charging station n in the whole life cycle is obtained; alpha C,n The binary variable represents the interaction state of the charging pile n and the power grid, and the value of the binary variable represents that the power grid charges a charging pile battery in the period of 1; c comp And compensating the unit electric quantity provided for the electric power grid to the electric vehicles participating in the V2G.
The total daily operational cost of the energy storage power station is specifically expressed as follows:
wherein F is bat The total daily operation cost of the energy storage power station; n (N) bat The number of the energy storage power stations; c in ,t、c out,t Respectively are energy storage power stationsElectricity price when charging and discharging at time t; s is(s) bat The cost of the energy storage power station in a single-time conversion charging and discharging mode is reduced; b is the charge and discharge state of the energy storage power station at the last moment before the day; delta bat (t) a binary variable of charge-discharge state at time t.
The total daily operational costs of the data center station are specifically expressed as follows:
wherein F is data Total cost of daily operation for the data center station; j is the equipment type in the data center station, and mainly comprises IT equipment, air conditioning equipment and lighting equipment; p (P) data,j The total power consumption of j-class equipment; r is (r) data,j And the unit power consumption operation cost corresponding to the j-class equipment is set.
The daily operation assembly cost of the new energy station is specifically expressed as follows:
wherein F is PV Total cost for photovoltaic daily operation; omega shape PV The method comprises the steps of collecting grid-connected photovoltaic cells; c (C) PV,h,t The power generation cost coefficient of the photovoltaic cell h; p (P) PV,h,t The available active force of the photovoltaic cell h at the moment t; c (C) aban,h,t The unit light discarding cost coefficient of the photovoltaic unit h; p (P) PVaban,h,t The optical power of the photovoltaic cell h at the moment t is discarded; c (C) sell,h,t The photovoltaic online electricity price is that of photovoltaic; p (P) PVsell,h,t And the net surfing quantity of the photovoltaic cell h at the moment t.
S4) solving the multi-objective optimization model by adopting NSGA-II algorithm and multi-attribute decision
The multi-objective optimization model is solved by using NSGA-II and multi-attribute decision, and the multi-attribute decision can further solve the optimal solution in a series of Pareto solutions solved by NSGA-II, so that the multi-objective optimization model is more superior to a single NSGA-II solving method.
The NSGA-II algorithm solving flow chart adopted by the application is shown in fig. 8, the multi-attribute decision method flow chart is shown in fig. 9, and the solving process is as follows:
1) Initializing a system, and reading economic parameters of components in the system, system configuration parameters and a genetic algorithm;
2) Initializing a population, and randomly generating N' possible individuals to serve as an initial population P;
3) Evaluating the fitness of the initial population P;
4) Sorting an initial population P;
(1) calculating fitness functions between individuals
(2) Calculating crowd Congestion degree
5) Selecting, crossing and mutating to obtain new offspring populations P2 and P3;
6) Evaluating the suitability of the P3 population;
7) Sorting an initial population { P.u.P3 };
8) Selecting individuals with Pareto ordering layers from the population { P.u.P3 }, and forming a new population P;
9) If the maximum iteration number indicated by the user is reached, stopping operation, otherwise returning to the step 5).
10 Proceeding to multi-attribute decision for the series of solutions to further find the optimal solution.
As shown in fig. 10, the multi-station fusion integrated energy system multi-objective optimization system based on multiple uncertainties includes:
the multi-station fusion comprehensive energy system model setting module is used for setting five substation models, wherein the five substation models are respectively a transformer substation model, a charging station model, an energy storage station model, a data center station model and a new energy station model, and setting operation constraint of the multi-station fusion comprehensive energy system model;
the uncertainty analysis module of the multi-station integrated comprehensive energy system model analyzes the prediction error uncertainty of the new energy station according to the new energy station model and analyzes the prediction error uncertainty of the charging station according to the charging station model;
the multi-objective optimization model setting module is used for establishing a cost optimization model according to the total daily operation cost of the transformer substation, the total daily operation cost of the charging station, the total daily operation cost of the energy storage station, the total daily operation cost of the data center station and the total daily operation cost of the new energy station, and establishing a risk optimization model according to the charging station and the new energy station;
and the multi-objective optimization model solving module adopts an NSGA-II algorithm and multi-attribute decision to solve the multi-objective optimization model.
The method aims at the running capability of the multi-station fusion integrated energy system, aims at the reduction of the stability, economy and safety of the integrated energy system caused by the fluctuation and intermittence of the new energy station connected with the photovoltaic output in the fusion station, analyzes the energy utilization characteristics of each substation of the multi-station fusion system, predicts the future energy utilization trend by adopting historical energy utilization data, establishes an intelligent energy station integrated energy system optimized running model considering the uncertainty and fluctuation of the new energy and the electric automobile, improves the coordinated optimized running capability of the integrated energy system, improves the economical efficiency of the integrated energy system under the influence of multiple uncertainties, and simultaneously considers the risk factors existing in the system, thereby improving the stability and safety of the system.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (7)
1. A multi-station fusion comprehensive energy system multi-objective optimization method based on multiple uncertainties is characterized by comprising the following steps of: the multi-objective optimization method specifically comprises the following steps:
s1) establishing a multi-station fusion comprehensive energy system model
The multi-station fusion comprehensive energy system model comprises five sub-station models, wherein the five sub-station models are a transformer substation model, a charging station model, an energy storage station model, a data center station model and a new energy station model respectively, and the operation constraint of the multi-station fusion comprehensive energy system model is set;
s2) analyzing uncertainty of multi-station fusion comprehensive energy system
Analyzing the prediction error uncertainty of the new energy station according to the new energy station model, and analyzing the prediction error uncertainty of the charging station according to the charging station model;
s3) establishing a multi-objective optimization model
Establishing a cost optimization model according to the total daily operation cost of the transformer station, the total daily operation cost of the charging station, the total daily operation cost of the energy storage station, the total daily operation cost of the data center station and the total daily operation cost of the new energy station, and establishing a risk optimization model according to the charging station and the new energy station;
s4) solving the multi-objective optimization model;
in the step S1), the substation model is as follows:
substation model
P Tr,t ＝P Tr,c,t +P Tr,l,t +P Tr,d,t (1)
Wherein P is Tr,t For the total load of the transformer substation at the moment t, P Tr,c,t 、P Tr,l,t 、P Tr,d,t Respectively the energy consumption of the air conditioner, the illumination energy consumption and the power transmission and distribution energy consumption of the transformer substation at the moment t,the upper limit and the lower limit of the load of the transformer substation are respectively;
charging station model
Wherein P is C,t The total load of the charging station at the time t of the system; n is the total number of charging piles in the system; t is the total scheduling period; p (P) C,n,t The load of the nth charging pile; delta C Self-loss coefficient of the charging pile battery; e (E) C,n,t (t+1)、E C,n,t (t) the electric energy storage capacity of the charging pile n battery at the time t+1 and t respectively;charging and discharging power of the charging pile n battery at the time t are respectively;charging and discharging efficiencies of the charging pile battery are respectively; p (P) n,t The charging load of the nth charging pile at the time t is set;The electricity selling power of the nth charging pile at the time t is obtained; alpha C,n The value of the variable 0-1 of the running state of the nth charging pile is 1 time, which represents that the charging pile charges the EV; p (P) o,n The rated charging power of the electric automobile entering the charging pile n; t is t s 、t e Respectively starting and ending the charging of the automobile;The maximum charging power and the maximum discharging power of the charging pile n at the moment t are respectively;The maximum value and the minimum value of the electric power sold by the nth charging pile are respectively;
in the step S1), the substation model is as follows:
energy storage station model
E bat (0)＝E bat (T) (15)
Wherein E is bat (t+1)、E bat (t) is the real-time capacity of the energy storage battery of the energy storage station at the time t+1 and the time t respectively; delta bat Is the self-loss coefficient of the battery; p (P) bc,t 、P bd,t Charging and discharging power of the energy storage battery at the moment t respectively;respectively charging and discharging power of the energy storage battery;The maximum capacity and the minimum capacity of the energy storage battery are respectively;Respectively the maximum charging power and the maximum discharging power of the energy storage battery; alpha bat The value of the variable is 0-1, which is the running state of the energy storage battery and represents that the energy storage battery is in a charging state when the value is 1;
data center station model
Wherein P is data,t The total power consumption load of the data center station at the moment t; p (P) IT,t The method comprises the steps that the power load of IT equipment of an edge data center at the moment t is used; COP is the energy efficiency ratio of the air conditioning and refrigerating unit; t (T) t Outdoor temperature at a certain moment;maximum load for the data center station;
new energy station model
P PV,t ＝r i Aη (18)
Wherein P is PV (t) is the photovoltaic active power; r is (r) i Is the intensity of solar radiation; a is the total area of the solar cell matrix; η is the overall photoelectric conversion efficiency, taking η=0.965; q (Q) PV Photovoltaic reactive power output; θ Si The power factor angle of the photovoltaic power generation system at the node i is set;the upper limit and the lower limit of the photovoltaic output are respectively set;
in the step S1), the operation constraint of the given multi-station fusion integrated energy system model is as follows:
(1) System electric power constraint
Pr{P g,t +P bd,t +P PV,t +ΔP PV,t ＝P load.t +P Tr,t +P C,t +P data,t +P bc,t }≥β (20)
Wherein P is g,t The electric energy purchased from the power grid at the moment t; ΔP PV,t The power error is predicted for the new energy station at the time t; p (P) loa.d The load is the resident side load at the moment t; beta is a given confidence level;
(2) System and power grid interaction constraint
P line,min ≤P g,t ≤P line,max (21)
Wherein P is linemin 、P linemax KW is the minimum power and the maximum power of the connecting line;
(3) Plant operation constraints
Wherein P is p,i,t The output of the ith controllable unit at the t moment;the upper limit and the lower limit of the output of the ith controllable unit are respectively set.
2. The multi-station fusion comprehensive energy system multi-objective optimization method based on multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps: in the step S2), the prediction error uncertainty formula of the new energy station is as follows:
Γ (·) is a Gamma function; r is (r) i The actual illumination intensity of the ith scene; r is (r) i,max Maximum radiant intensity of sunlight for the ith scene; alpha i 、β i The shape parameters and the scale parameters of Beta distribution of the energy output errors of the ith scene description system are respectively.
3. The multi-station fusion comprehensive energy system multi-objective optimization method based on multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps: in the step S2), the prediction error uncertainty formula of the charging station is as follows:
wherein k represents different types of EVs, each fusion station area comprises 3 types of EVs with different typical behaviors, each EV is an electric private car when k=1, a bus when k=2, and a taxi when k=3; f (f) s,k (x)、f e,k (x) Probability density distribution of the k-th EV starting trip and returning finally; x is x s,k 、x e,k Mu respectively the time when the k-th EV starts traveling and returns last s,k 、μ e,k Expected values, sigma, of the time when the k-th EV starts traveling and the time when the k-th EV returns last, respectively s,k 、σ e,k The variances of the starting trip time and the last return time of the kth EV are respectively.
4. The multi-station fusion comprehensive energy system multi-objective optimization method based on multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps: in the step S3), the cost optimization model and the risk optimization model are formulated as follows:
wherein F is 1 Optimizing a model for cost; f (F) 2 A risk optimization model; f (F) Tr The total daily operation cost of the transformer substation; f (F) C Total cost for daily operation of the charging station; f (F) bat The total daily operation cost of the energy storage power station; f (F) data Total cost of daily operation for the data center station; f (F) PV The total cost of daily operation of the new energy station; omega shape PV 、Ω EV The photovoltaic collection of the new energy stations and the electric automobile collection accessed by the charging station are respectively adopted; u (u) k,t 、u l,t The risk factors are the risk factors of the new energy and the electric vehicle respectively; r is R k,t 、R l,t The absolute value of the difference between the predicted value of the output and the actual output of the new energy station and the charging station at the time t represents the risk of the new energy station and the charging station being transmitted to the multi-station fusion system;
the total daily operation cost of the transformer substation is specifically expressed as follows:
wherein F is Tr The total daily operation cost of the transformer substation; i represents the type of equipment in the substation; p (P) Tr,t Represents the total load of the transformer substation at the t moment, and comprises an air conditioner,Lighting equipment, transformer loads, etc.; r is (r) i The unit power consumption operation cost of the equipment type i of the transformer substation;
the total daily operational cost of the charging station is specifically expressed as follows:
F C ＝F elec +F deg -F V2G (31)
wherein F is C Total cost for daily operation of the charging station; f (F) elec The cost of purchasing electricity from the power grid for the charging station; f (F) deg Battery loss cost for the electric automobile in the station; f (F) V2G Earnings obtained for the charging station to participate in the internet of vehicles (V2G); c t Representing the electricity selling price of the power grid in the period t; p (P) C,n,t Charging power to EV for charging pile n at period t;the charging power and the discharging power of the storage battery of the charging pile n in the period t are respectively; c (C) C,n The total cost in the life cycle of the storage battery of the charging pile n; w (W) C,n The sum of the charge and discharge capacities of the charging station n in the whole life cycle is obtained; alpha C,n The binary variable represents the interaction state of the charging pile n and the power grid, and the value of the binary variable represents that the power grid charges a charging pile battery in the period of 1; c comp The unit electric quantity compensation is provided for the electric network to the electric automobile participating in the V2G;
the total daily operational cost of the energy storage power station is specifically expressed as follows:
wherein F is bat The total daily operation cost of the energy storage power station; n (N) bat The number of the energy storage power stations; c in,t 、c out,t The electricity prices of the energy storage power stations during charging and discharging at the time t are respectively; s is(s) bat The cost of the energy storage power station in a single-time conversion charging and discharging mode is reduced; b is the charge and discharge state of the energy storage power station at the last moment before the day; delta bat (t) a binary variable of charge-discharge state at time t;
the total daily operational costs of the data center station are specifically expressed as follows:
wherein F is data Total cost of daily operation for the data center station; j is the equipment type in the data center station, and mainly comprises IT equipment, air conditioning equipment and lighting equipment; p (P) data,j The total power consumption of j-class equipment; r is (r) data,j The unit power consumption operation cost corresponding to j-class equipment;
the daily operation assembly cost of the new energy station is specifically expressed as follows:
wherein F is PV Total cost for photovoltaic daily operation; omega shape PV The method comprises the steps of collecting grid-connected photovoltaic cells; c (C) PV,h,t The power generation cost coefficient of the photovoltaic cell h; p (P) PV,h,t The available active force of the photovoltaic cell h at the moment t; c (C) aban,h,t The unit light discarding cost coefficient of the photovoltaic unit h; p (P) PVaban,h,t The optical power of the photovoltaic cell h at the moment t is discarded; c (C) sell,h,t The photovoltaic online electricity price is that of photovoltaic; p (P) PVsell,h,t And the net surfing quantity of the photovoltaic cell h at the moment t.
5. The multi-station fusion comprehensive energy system multi-objective optimization method based on multiple uncertainties according to claim 1, wherein the method is characterized by comprising the following steps: in the step S4), solving the multi-objective optimization model by adopting an NSGA-II algorithm and multi-attribute decision, wherein the specific process is as follows:
setting economic parameters of components in a system, system configuration parameters and a genetic algorithm;
solving a series of solutions under the conditions of cost and risk by using an NSGA-II algorithm;
and further solving an optimal solution in a series of solutions by combining a multi-attribute decision method.
6. A storage medium, characterized by: the storage medium having stored therein a computer program, wherein the computer program is arranged to perform the multi-objective optimization method of any of the claims 1 to 5 when run.
7. A multi-station fusion comprehensive energy system multi-objective optimization system based on multiple uncertainties is characterized in that: comprising the following steps:
the multi-station fusion comprehensive energy system model setting module is used for setting five substation models, wherein the five substation models are respectively a transformer substation model, a charging station model, an energy storage station model, a data center station model and a new energy station model, and setting operation constraint of the multi-station fusion comprehensive energy system model;
substation model
P Tr,t ＝P Tr,c,t +P Tr,l,t +P Tr,d,t (1)
Wherein P is Tr,t For the total load of the transformer substation at the moment t, P Tr,c,t 、P Tr,l,t 、P Tr,d,t Respectively the energy consumption of the air conditioner, the illumination energy consumption and the power transmission and distribution energy consumption of the transformer substation at the moment t,the upper limit and the lower limit of the load of the transformer substation are respectively;
charging station model
Wherein P is C,t The total load of the charging station at the time t of the system; n is the total number of charging piles in the system; t is the total scheduling period; p (P) C,n,t The load of the nth charging pile; delta C Self-loss coefficient of the charging pile battery; e (E) C,n,t (t+1)、E C,n,t (t) the electric energy storage capacity of the charging pile n battery at the time t+1 and t respectively;charging and discharging power of the charging pile n battery at the time t are respectively;charging and discharging efficiencies of the charging pile battery are respectively; p (P) n,t The charging load of the nth charging pile at the time t is set;The electricity selling power of the nth charging pile at the time t is obtained; alpha C,n The value of the variable 0-1 of the running state of the nth charging pile is 1 time, which represents that the charging pile charges the EV; p (P) o,n The rated charging power of the electric automobile entering the charging pile n; t is t s 、t e Respectively starting and ending the charging of the automobile;The maximum charging power and the maximum discharging power of the charging pile n at the moment t are respectively;The maximum value and the minimum value of the electric power sold by the nth charging pile are respectively;
energy storage station model
E bat (0)＝E bat (T) (15)
Wherein E is bat (t+1)、E bat (t) is the real-time capacity of the energy storage battery of the energy storage station at the time t+1 and the time t respectively; delta bat Is the self-loss coefficient of the battery; p (P) bc,t 、P bd,t Charging and discharging power of the energy storage battery at the moment t respectively;respectively charging and discharging power of the energy storage battery;The maximum capacity and the minimum capacity of the energy storage battery are respectively;Respectively the maximum charging power and the maximum discharging power of the energy storage battery; alpha bat The value of the variable is 0-1, which is the running state of the energy storage battery and represents that the energy storage battery is in a charging state when the value is 1;
data center station model
Wherein P is data,t The total power consumption load of the data center station at the moment t; p (P) IT,t The method comprises the steps that the power load of IT equipment of an edge data center at the moment t is used; COP is the energy efficiency ratio of the air conditioning and refrigerating unit; t (T) t Outdoor temperature at a certain moment;maximum load for the data center station;
new energy station model
P PV,t ＝r i Aη (18)
Wherein P is PV (t) is the photovoltaic active power; r is (r) i Is the intensity of solar radiation; a is the total area of the solar cell matrix; η is the overall photoelectric conversion efficiency, taking η=0.965; q (Q) PV Photovoltaic reactive power output; θ Si The power factor angle of the photovoltaic power generation system at the node i is set;the upper limit and the lower limit of the photovoltaic output are respectively set;
the operational constraints for a given multi-site fusion integrated energy system model are as follows:
(1) System electric power constraint
Pr{P g,t +P bd,t +P PV,t +ΔP PV,t ＝P load.t +P Tr,t +P C,t +P data,t +P bc,t }≥β (20)
Wherein P is g,t The electric energy purchased from the power grid at the moment t; ΔP PV,t The power error is predicted for the new energy station at the time t; p (P) loa.d The load is the resident side load at the moment t; beta is a given confidence level;
(2) System and power grid interaction constraint
P line,min ≤P g,t ≤P line,max (21)
Wherein P is linemin 、P linemax KW is the minimum power and the maximum power of the connecting line;
(3) Plant operation constraints
Wherein P is p,i,t The output of the ith controllable unit at the t moment;the upper limit and the lower limit of the output of the ith controllable unit are respectively set;
the uncertainty analysis module of the multi-station integrated comprehensive energy system model analyzes the prediction error uncertainty of the new energy station according to the new energy station model and analyzes the prediction error uncertainty of the charging station according to the charging station model;
the multi-objective optimization model setting module is used for establishing a cost optimization model according to the total daily operation cost of the transformer substation, the total daily operation cost of the charging station, the total daily operation cost of the energy storage station, the total daily operation cost of the data center station and the total daily operation cost of the new energy station, and establishing a risk optimization model according to the charging station and the new energy station;
and the multi-objective optimization model solving module adopts an NSGA-II algorithm and multi-attribute decision to solve the multi-objective optimization model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110175788A (en) * | 2019-05-31 | 2019-08-27 | 国网上海市电力公司 | A kind of smart city energy cloud platform |
| CN110504708A (en) * | 2019-08-09 | 2019-11-26 | 国家电网有限公司 | The power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108376291A (en) * | 2017-11-28 | 2018-08-07 | 国网甘肃省电力公司电力科学研究院 | A kind of electric vehicle electric charging station addressing constant volume method based on micro-capacitance sensor |
| CN110175788A (en) * | 2019-05-31 | 2019-08-27 | 国网上海市电力公司 | A kind of smart city energy cloud platform |
| CN110504708A (en) * | 2019-08-09 | 2019-11-26 | 国家电网有限公司 | The power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource |
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