CN109606198B - Intelligent power distribution network electric automobile charging method considering user behavior uncertainty - Google Patents
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Abstract
An intelligent power distribution network electric vehicle charging method considering uncertainty of user behaviors comprises the following steps: initializing the total electric energy required by the electric automobile and the number of the electric automobiles connected to the power grid in each time period according to the input data, and respectively calculating the total charging requirement of the electric automobile at the starting moment of each time period; judging whether the total electric energy required by the electric automobile in the next time period is equal to the total charging requirement of the electric automobile at the starting moment or not; judging whether the current time point is in the set of all time points or not, and if not, calculating a utility function of load fluctuation in the next 24 hours; if the electric vehicle is connected to the grid at time t, let the decision function be 1; otherwise, judging the function to be zero; judging the value of the battery residual capacity of the ith electric automobile at the time t; and calculating a charging strategy of the ith electric vehicle, and updating the residual capacity of the battery of the electric vehicle. The invention can automatically and actively reduce the access of the electric automobile in the time period of higher electricity consumption of residents, and disperse the electricity consumption demand of electricity consumption peak.
Description
Technical Field
The invention relates to an electric automobile charging method. In particular to an intelligent power distribution network electric automobile charging method considering uncertainty of user behaviors.
Background
In recent years, with the rapid development of science and technology, smart power grids are more generally applied by virtue of the characteristics of flexibility, safety and cleanness. A traditional power grid has a lot of information islands, certain information sharing property is lacked, the intelligentization ratio of the whole power grid is low, and when a power grid fault occurs, a large amount of manpower and material resources are needed to determine a fault area. The intelligent power grid can utilize the characteristics of observability, intelligence, self-adaptability, controllability, automatic analysis and the like of the intelligent power grid, timely obtain more complete information through a certain technical means, and improve the comprehensive utilization of energy.
The development of the smart grid is still in a starting stage all over the world, and the technology of the smart grid can be roughly divided into four fields: advanced smart grid measurement systems, advanced distribution operations, advanced transmission operations, and advanced asset management. The advanced measurement system is mainly used for authorizing a user, so that the system is in contact with a load, and the user can support the operation of a power grid; the core of the high-grade power distribution operation is online real-time decision command, the goal is catastrophe prevention and control, and the prevention of large-area cascading failure is realized; the main function of the high-grade transmission operation is to emphasize the blocking management and reduce the risk of large-scale outage; advanced asset management is to install a large number of advanced sensors in the system, which can provide system parameters and stable operation conditions of equipment, and integrate the collected real-time information with processes of resource management, simulation and the like, so as to improve the operation and efficiency of the power grid.
Electric Vehicle (Electric Vehicle EV) technology is one of the most important means for promoting clean energy consumption and green traffic in all countries in the world at present.
In recent years, the EV has a plurality of benefits such as energy conservation and emission reduction, so that the EV is widely applied, and EV technical research and development and market competition are added in various countries in the world. Vehicle-to-grid technology (Vehicle-to-GridV2G) is an important application in EV technology. Through V2G, the EV may provide power and other supplementary services to the smart grid. By optimizing the charge-discharge strategy of the EV, the smart grid can better achieve a series of aims of peak clipping, valley filling, frequency management and the like. The charging standard for EV in the world at present has three systems, namely a daily system, an European and American system and a Chinese system. The difference is mainly in the shape of the charging interface and the communication mode. The charging modes of all systems are different, and the problems of incomplete networking and control schemes generally exist.
Currently, charging strategy research for participation of EVs in the electric power market is mainly developed from two aspects, namely, a power grid side and a user side. For the user side, through research and research, the charging strategy is a charging strategy based on theoretical calculation and parameter estimation, and the research is strong in theory and cannot completely reflect the inherent logicality of the charging behavior. By conclusion, the key bottleneck restricting the development of charging facilities in residential areas (areas below distribution transformers) is the problem of distribution capacity of the areas, and the problem of insufficient planning of the capacity of a power transmission and distribution channel is found behind the key bottleneck.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent power distribution network electric vehicle charging method which can fully meet load requirements and has better user experience and considers user behavior uncertainty.
The technical scheme adopted by the invention is as follows: the utility model provides a consider uncertain intelligent power distribution network electric automobile charging method of user's action, includes the following step:
1) according to inputData initialization of total electric energy required by electric vehicles and number N of electric vehicles connected to a power grid in each time periodcar(t), then respectively calculating the total charging demand P of the electric automobile at the starting moment of each time periodini；
2) Judging the total electric energy P required by the electric automobile in the next time periodEV(t) whether it is equal to the total charging demand P of the electric vehicle at the starting momentiniIf the two are equal, ending, and if the two are not equal, entering the next step;
3) judging whether the current time point is in the set T of all time points, if not, calculating the utility function of load fluctuation in the next 24 hours, if the utility function of load fluctuation is less than or equal to the utility function of load fluctuation in the current 24 hours, returning to the step 1) of recalculating the total charging demand P of the electric automobile at the starting moment of the current time periodiniIf not, the utility function of the load fluctuation in the next 24 hours is equal to the utility function of the load fluctuation in the current 24 hours, and the step 2) is returned for judging again; if the current time point is in the set T of all time points, entering the next step;
4) if the electric vehicle is connected to the grid at time t, let decision function Xi(t) is 1; otherwise, the function X is determinedi(t) is zero; and determining the residual electric quantity SOC of the battery of the ith electric automobile at the time tini(i, t) if Xi(t) 1 and SOCini(i,t)<1, entering the next step, otherwise, making t equal to t +1, and returning to the step 3);
5) calculating the charging strategy of the ith electric vehicle and updating the residual capacity SOC of the battery of the electric vehicleini(i, t), let t be t +1, return to step 3) and make a determination again.
Step 2) total electric energy P required by the electric automobile in the next time periodEV(t) is derived from the formula:
therein, SOCexp(i, t) represents the required electric quantity of the ith electric automobile; SOCini(i, t) represents the residual electric quantity of the battery of the ith electric automobile at the time t; i is the total number of the electric automobiles; EV (electric vehicle)iIs the ith electric automobile.
The utility function P of the load fluctuation within 24 hours in the step 3)PFLThe calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the maximum load value of the residential area corresponding to the time of the maximum load;
the minimum load value of the residential area corresponding to the minimum load occurrence time is represented; t represents the set of all time points; ptotal(t) is the total load of the residential area, which is obtained by the following equation:
Ptotal(t)＝Phome(t)+PEV(t),t∈T
in the formula, Phome(t) is the total load of the residences in the residential area.
The charging strategy of the ith electric vehicle in the step 5) comprises the following steps: total electric energy P required by electric automobileEV(t), utility function P of load fluctuation in 24 hoursPFLAnd total load P of residential areatotal(t)。
The intelligent power distribution network electric vehicle charging method considering the uncertainty of the user behavior can automatically and actively reduce the access of electric vehicles in the time period of higher residential electricity consumption. The peak clipping and valley filling are performed while the power consumption requirement of the peak power consumption is dispersed. The simulation analysis is carried out through modeling to obtain the invention which has the following advantages:
1. after reuse of the algorithm proposed herein, the utility function PPFLCompared with the traditional algorithm, the method has larger reduction.
2. By increasing access to the EV at the location on the timeline where power usage is minimal, the method presented herein achieves better load-filling than conventional algorithms.
3. At the position with the largest power consumption on the time axis, the method provided by the invention does not completely stop the access of the electric automobile, so that a certain requirement on the intervention rigidity of the electric automobile is met, and the user experience is improved.
Drawings
FIG. 1 is a flow chart of a smart distribution grid electric vehicle charging method of the present invention that accounts for user behavior uncertainty;
FIG. 2 is a power consumption model of a residential area without using the EV-optimized charging strategy in the present invention;
FIG. 3 is a power consumption model of a residential area in a conventional EV-optimized charging strategy according to the present invention;
fig. 4 is a power consumption model of the novel EV charging strategy optimization method of the present invention.
Detailed Description
The intelligent power distribution network electric vehicle charging method considering the uncertainty of the user behavior is described in detail below with reference to the embodiments and the accompanying drawings.
As shown in fig. 1, the intelligent power distribution network electric vehicle charging method considering uncertainty of user behavior of the invention includes the following steps:
1) initializing the total electric energy required by the electric vehicle and the number of Electric Vehicles (EV) connected to the grid N per time period according to the input datacar(t), then respectively calculating the total charging demand P of the electric automobile at the starting moment of each time periodini；
2) Judging the total electric energy P required by the electric automobile in the next time periodEV(t) whether it is equal to the total charging demand P of the electric vehicle at the starting momentiniIf the two are equal, ending, and if the two are not equal, entering the next step;
the next time periodTotal electric energy P required by electric automobileEV(t) is derived from the formula:
therein, SOCexp(i, t) represents the required electric quantity of the ith electric automobile; SOCini(i, t) represents the residual electric quantity of the battery of the ith electric automobile at the time t; i is the total number of the electric automobiles; EV (electric vehicle)iIs the ith electric automobile.
3) Judging whether the current time point is in the set T of all time points, if not, calculating the utility function of load fluctuation in the next 24 hours, if the utility function of load fluctuation is less than or equal to the utility function of load fluctuation in the current 24 hours, returning to the step 1) of recalculating the total charging demand P of the electric automobile at the starting moment of the current time periodiniIf not, the utility function of the load fluctuation in the next 24 hours is equal to the utility function of the load fluctuation in the current 24 hours, and the step 2) is returned for judging again; if the current time point is in the set T of all time points, entering the next step;
the utility function P of the load fluctuation in 24 hoursPFLThe calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
the maximum load value of the residential area corresponding to the time of the maximum load;
the minimum residential area corresponding to the minimum load occurrence time is representedA load value; t represents the set of all time points; ptotal(t) is the total load of the residential area, which is obtained by the following equation:
Ptotal(t)＝Phome(t)+PEV(t),t∈T
in the formula, Phome(t) is the total load of the residences in the residential area.
The invention herein utilizes a method of taking log of statistics to speed up the convergence of the algorithm. Meanwhile, the method can also improve the stability of the algorithm.
4) If the electric vehicle is connected to the grid at time t, let decision function Xi(t) is 1; otherwise, the function X is determinedi(t) is zero; and determining the residual electric quantity SOC of the battery of the ith electric automobile at the time tini(i, t) if Xi(t) 1 and SOCini(i,t)<1, entering the next step, otherwise, making t equal to t +1, and returning to the step 3);
5) calculating the charging strategy of the ith electric vehicle and updating the residual capacity SOC of the battery of the electric vehicleini(i, t), let t be t +1, return to step 3) and make a determination again. The charging strategy of the ith electric automobile comprises the following steps: total electric energy P required by electric automobileEV(t), utility function P of load fluctuation in 24 hoursPFLAnd total load P of residential areatotal(t)。
Fig. 2 and 3 compare the electricity consumption models of residential areas when the conventional EV-optimized charging strategy is applied and when the EV-optimized charging strategy is not used. The present invention utilizes existing data during the simulation process. The total demand for residential electricity is shown in dashed lines in fig. 2 and 3. From fig. 2 we can see that without the EV optimized charging strategy, there are two periods where the power usage demand exceeds the carrying capacity of the grid. Meanwhile, the peak value of the power demand is 329.5KVA which is higher than the highest peak value P which can be borne by the power gridmaxThe height is 31.8 percent. Also we note that the electricity demand at 5:00 is 58.5 KVA. It can be seen that access to an EV without an optimized charging strategy would make fluctuations in residential electricity demand more pronounced. At the same time, the invention calculates the corresponding PPFL0.56. Whereas in fig. 3, a conventional charger is appliedIn the case of an electrical strategy. The power demand does not exceed the carrying capacity of the power grid as a whole, and PPFL0.20. However, the conventional method does not take into account modeling of the user charging behavior and therefore has room for further improvement.
Fig. 4 shows the EV charging strategy optimization method proposed by the present invention and the electricity consumption model of the residential district when the EV optimized charging strategy is not used. We found by calculation that P is in this casePFL0.20, which is superior to the methods proposed in fig. 2 and 3. In one aspect, the efficiency of the algorithm of the present invention is for reducing PPFLA contribution is made.
Yet another important reason is PPFLThe log function presented in (a) largely averages the distribution of EV charging strategies over the access time points. Meanwhile, the method automatically reduces the access of the EV in a period with higher resident electricity consumption. The peak clipping and valley filling are performed while the power consumption requirement of the peak power consumption is dispersed. In conclusion, the method provided by the invention does not completely stop the access of the EV, thereby ensuring a certain EV intervention rigidity requirement and improving the user experience.
Claims (3)
1. An intelligent power distribution network electric automobile charging method considering uncertainty of user behaviors is characterized by comprising the following steps:
1) initializing the total electric energy required by the electric vehicles according to the input data and the number N of the electric vehicles connected to the power grid in each time periodcar(t), then respectively calculating the total charging demand P of the electric automobile at the starting moment of each time periodini；
2) Judging the total electric energy P required by the electric automobile in the next time periodEV(t) whether it is equal to the total charging demand P of the electric vehicle at the beginning of the next time periodiniIf the two are equal, ending, and if the two are not equal, entering the next step;
3) judging whether the current time point is in the set T of all time points, if not, calculating the utility function of the load fluctuation in the next 24 hours, and if the utility function of the load fluctuation is less than or equal to the effect of the load fluctuation in the current 24 hoursUsing the function, returning to the step 1) to calculate the total charging demand P of the electric automobile again at the starting moment of the current time periodiniIf not, the utility function of the load fluctuation in the next 24 hours is equal to the utility function of the load fluctuation in the current 24 hours, and the step 2) is returned to for judging again; if the current time point is in the set T of all time points, entering the next step;
the utility function P of the load fluctuation in 24 hoursPFLThe calculation formula is as follows:
in the formula, maxt Ptotal(t) a residential maximum load value corresponding to the maximum load occurrence time; mint Ptotal(t) a minimum load value of the residential area corresponding to the minimum load occurrence time; t represents the set of all time points; ptotal(t) is the total load of the residential area, which is obtained by the following equation:
Ptotal(t)＝Phome(t)+PEV(t),t∈T
in the formula, Phome(t) is the total load of the residences in the residential area;
4) if the electric vehicle is connected to the grid at time t, let decision function Xi(t) is 1; otherwise, the function X is determinedi(t) is zero; and determining the residual electric quantity SOC of the battery of the ith electric automobile at the time tini(i, t) if Xi(t) 1 and SOCini(i,t)<1, entering the next step, otherwise, making t equal to t +1, and returning to the step 3);
5) calculating the charging strategy of the ith electric vehicle and updating the residual capacity SOC of the battery of the electric vehicleini(i, t), let t be t +1, return to step 3) and make a determination again.
2. The method for charging the electric vehicle of the intelligent distribution network in consideration of the uncertainty of the user behavior according to claim 1, wherein the total power required by the electric vehicle in the next time period in the step 2) isCan PEV(t) is derived from the formula:
function(s)
Therein, SOCexp(i, t) represents the required electric quantity of the ith electric automobile; SOCini(i, t) represents the residual electric quantity of the battery of the ith electric automobile at the time t; i is the total number of the electric automobiles; EV (electric vehicle)iIs the ith electric automobile.
3. The intelligent power distribution network electric vehicle charging method considering uncertainty of user behavior according to claim 1, wherein the charging strategy of the ith electric vehicle in step 5) comprises: total electric energy P required by electric automobileEV(t), utility function P of load fluctuation in 24 hoursPFLAnd total load P of residential areatotal(t)。
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