CN117895547A - Large-industry user new energy allocation and storage decision method considering energy storage dynamic life - Google Patents

Abstract

The invention discloses a large-industry user new energy allocation and storage decision method considering the dynamic life of energy storage. Aiming at the characteristics of large fluctuation and more data of new energy, the invention uses a K-means clustering algorithm to reduce the scene of the new energy power generation data of one year of large industrial user history; the method takes the sum of annual cost of energy storage equipment investment, operation maintenance and the like and trade annual cost of a large power grid as an objective function, estimates the estimated service life by adopting rated charge and discharge times, builds an energy storage optimal configuration model under the condition constraints of system operation power balance constraint, energy storage charge and discharge constraint and the like, and realizes solving. According to the invention, the energy storage life is not a fixed reference value calibrated by a manufacturer, but the energy storage life is dynamically predicted and calculated according to the change of daily throughput of the energy storage, so that the accuracy of annual cost calculation is effectively improved.

Description

Large-industry user new energy allocation and storage decision method considering energy storage dynamic life

Technical Field

The invention belongs to the technical field of power systems, and particularly relates to a large-industry user new energy allocation and storage decision method considering the dynamic life of energy storage.

Background

The duty ratio of new energy power generation in the power grid is continuously improved. The intermittence and randomness of new energy output seriously affect the digestion capacity of the power distribution network. The energy storage is a necessary means for stabilizing new energy power fluctuation, relieving energy waste and realizing peak clipping and valley filling of the system power load by virtue of the rapid power regulation characteristic and the energy supply capacity of the energy storage.

The energy storage system is expensive in cost, and economic benefit is a key factor influencing energy storage configuration. At present, many researches are made on the reliability and economical efficiency of energy storage configuration of new energy systems at home and abroad. The method is mainly focused on stabilizing the energy storage configuration of large wind power stations and photovoltaic power fluctuation, maintaining the energy storage configuration of stable and economical operation of the micro-grid system, peak clipping and valley filling and the like. The research on improving the electricity economy of the new energy system users is relatively less, and the method mainly focuses on the aspects of initial investment, leveling cost, full life cycle cost, daily operation economic benefit, electricity selling benefit, reduction of abandoned wind and abandoned light benefit and the like, namely, partial cost or benefit of the energy storage system is considered. However, for users, the most concerned is that the total cost of construction, operation and maintenance and electricity consumption of new energy distribution and storage is minimum. In the above studies, most of the simplifications consider battery energy storage life as a fixed period, objective function as a linear function, and no consideration is given to the energy storage run time requirements. According to market research, the service life of the existing energy storage is mainly determined by rated charge and discharge times of the energy storage, the service life of the energy storage can be changed along with different service conditions of the energy storage by a user, and the service life of the energy storage is measured by fixed years and is not accurate.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a large-industry user new energy allocation and storage decision method considering the dynamic life of energy storage.

The first aspect of the invention provides a large industrial user new energy allocation and storage decision method considering the dynamic life of energy storage, which comprises the following steps:

step 1: the new energy power generation condition of the large industrial user in one year is collected, and the proper clustering number is found, so that the data is representative and the data volume can be effectively reduced.

Step 2: and (3) clustering the 365-day new energy curve in one year according to the clustering number obtained in the step (1) to obtain the typical day of the clustering curve and the duty ratio of the days of various curves in one year.

Step 3: and under the constraint of the conditions of system power balance, energy storage charge and discharge constraint and the like, the optimal energy storage rated capacity, rated power, annual cost, average daily operation hours of energy storage and predicted dynamic life are determined based on the load electricity utilization curve, the new energy power generation curve and the electricity purchase price by taking the minimum total annual cost as an objective function.

The second aspect of the invention provides a large industrial user new energy allocation and storage decision device, comprising: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the large industrial user new energy allocation and storage decision method when executing the program.

A third aspect of the present invention is a computer-readable storage medium storing a computer program for executing the above-described large industrial user new energy allocation and storage decision method.

The invention has the beneficial effects that: the method is suitable for large industrial users with new energy sources, the energy storage life is not a fixed reference value calibrated by manufacturers, dynamic prediction calculation is carried out on the energy storage life according to the daily throughput change of the energy storage, and the annual cost calculation accuracy is effectively improved. In addition, the validity of the allocation and storage decision method is verified for users with electricity curves with different characteristics, influence factors including the rated charge and discharge cycle times of energy storage, load fluctuation, electricity purchasing price of a power grid and the like are analyzed, and theoretical reference is provided for allocation and storage of new energy sources of large-industry users.

Drawings

Fig. 1 is a schematic diagram of photovoltaic output for a year at a location.

Fig. 2 is a graph of the relation between the output result SSE of the photovoltaic cluster and k.

Fig. 3 is a schematic diagram of a typical solar photovoltaic output curve.

Fig. 4 is a schematic diagram of a user load profile.

Fig. 5 is a peak-to-valley electricity price time distribution.

Fig. 6 is a graph of the load balance curve, the stored charge-discharge power and the state of charge of a bimodal consumer at a known lifetime n=8.

FIG. 7 is a bimodal consumer power load balancing curve.

Fig. 8 is a graph of a unimodal consumer power load balance.

Fig. 9 is a plateau-type user electricity load balancing curve.

Fig. 10 is a graph of the load balance curve, the stored charge and discharge power and the state of charge of the user at a rated cycle number of 2950.

Fig. 11 is a graph of the load balance of the user power consumption, the stored energy charge and discharge power and the state of charge at a rated cycle number of 3050.

Fig. 12 is a graph of the load balance of the user power consumption, the stored energy charge and discharge power and the state of charge at a rated cycle number of 6500.

Fig. 13 is a user power load balance curve and an energy storage charge-discharge power and state of charge curve when the load is 0.2 times the size of the original data.

Fig. 14 is a user power load balance curve and an energy storage charge-discharge power and state of charge curve when the load is 0.4 times the size of the original data.

Fig. 15 is a user power load balance curve and an energy storage charge-discharge power and state of charge curve when the load is 1.4 times the size of the original data.

Fig. 16 is a graph of load balance, stored charge and discharge power and state of charge for the user when the change in the current price of electricity is-0.01.

Fig. 17 is a graph of load balance of user electricity and charge/discharge power and state of charge of stored energy when the change of the original electricity price is 0.1.

FIG. 18 is a graph of load balance of consumer electricity and stored charge and discharge power and state of charge for the primary electricity price change of-1.5%.

Fig. 19 is a graph of load balance, stored charge and discharge power and state of charge of the user when the price of electricity is changed by 2%.

FIG. 20 is a graph of load balance, stored charge and discharge power and state of charge for user electricity at a selling price of 0.31/kWh.

FIG. 21 is a user electricity load balance curve and stored energy charge and discharge power and state of charge curve at a selling price of 0.53/kWh.

Detailed Description

In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.

According to the method, the simulation time and the calculation difficulty of the data are reduced through clustering the new energy curves; the service life year is estimated by using the rated charge and discharge times, the reference service life year is replaced, and the configuration accuracy is improved; and the energy storage optimization configuration is carried out on different kinds of users, the operation characteristics and the economy of the energy storage configuration of different users are analyzed and compared, and the influence factors influencing the electricity consumption cost of the energy storage are provided for the preparation of large-industry new energy users and the preparation of new energy forced preparation and storage.

The invention is further described below with reference to the accompanying drawings and examples, and an embodiment of the application discloses a large-industry user new energy allocation and storage decision method considering the dynamic life of energy storage, which comprises the following steps:

step 1: and (3) according to a new energy power generation curve of one year in history, adopting a K-means clustering algorithm to perform scene reduction. The method specifically comprises the following steps:

the optimum K value for K-means was determined using the elbow method.

The K-means algorithm can divide the feature matrix of the sample into K non-intersecting clusters. According to the experience value, the new energy clustering number is generally between 3-7 types, k=1-10 is set in the embodiment, and the change of the sum of squares of errors SSE (sum of the squared errors, sum of squares of errors) is observed. SSE (secure Shell) _k The error of k types is gathered for all samples, and represents the quality of the clustering effect, specifically, the square sum of the distances from each point in the cluster to the cluster center.

Wherein C is _i Is the i-th cluster; p is C _i Sample points in (a); m is m _i Is C _i Centroid of (C) _i Average of all samples in the sample.

As the clustering number k increases, the sample division becomes finer, the aggregation degree of each cluster is gradually increased, and the error square sum SSE _k Gradually becoming smaller. When K is smaller than a certain cluster number, such as K, the increase of K greatly increases the aggregation degree of each cluster, SSE _k The drop amplitude of (2) is larger; when K is larger than K, the polymerization degree return obtained by increasing K is rapidly reduced, namely SSE _k The decreasing width of the elbow is gradually decreased and gradually flattened along with the continuous increase of the K value, and the k=k value corresponding to the elbow is taken as the clustering number of the embodiment.

In one embodiment: the new energy annual power generation curve is shown in fig. 1, k=1-10 is obtained by using a K-means clustering algorithm in python, a graph of the relation between SSE and K of the photovoltaic clustering output result is shown in fig. 2, and as the clustering number K increases, the square sum of errors SSE becomes smaller gradually, and after K reaches 4, SSE becomes gentle along with the continuous increase of the K value, so that k=4 is selected as the clustering number of the photovoltaic data.

Step 2: use in MATLABK-means clustering algorithm, clustering the new energy output of one year according to the clustering number obtained in the step 1 to obtain K data curves and the proportion alpha of each type of data in the clustering group _k 。

In one embodiment: clustering the photovoltaic data of fig. 1 at k=4 gave a graph of the typical solar photovoltaic output of fig. 3, with the duty cycle and daily power generation of four typical days in one year shown in table 1.

TABLE 1 typical annual occupancy and daily Power production

Typical day	1	2	3	4
					Annual duty cycle	0.213699	0.273973	0.230137	0.282192
Solar energy production	47.49MWh	64.9MWh	30.7MWh	7.77MWh

Step 3: and in MATLAB, the minimum annual cost of the sum of annual cost of energy storage equipment investment, operation maintenance and the like and annual cost of transaction with a large power grid is taken as an objective function, under the constraint of conditions such as system power balance, energy storage charging and discharging constraint and the like, a load electricity utilization curve, a new energy electricity generation curve and electricity purchase price are input into MATLAB software, and a Gurobi solver is called based on a YALMIP tool box to obtain the optimal rated capacity, rated power, annual cost, average daily operation hours of energy storage and predicted dynamic life.

1) The total annual cost is calculated.

After new energy users install energy storage, the annual electricity consumption related cost is mainly C, which is the annual cost of investment, operation and maintenance of energy storage equipment and the like _BESS And trade annual cost with large grid C _net Two parts.

C _year ＝C _BESS +C _net (2)

The objective function is the total annual cost C _year Minimum, min { C _year }。

(1) Annual cost of investment, operation and maintenance of energy storage equipment

The total equivalent annual cost of investment energy storage devices includes the full life cycle reduced to the annual initial investment cost, annual operating and maintenance costs, and installation of energy storage subsidies.

C _BESS ＝C _BESS,init +C _BESS,reg +C _BESS,var -C _BESS,gov (3)

Wherein C is _BESS,init Initial equal annual investment cost for energy storage; c (C) _BESS,reg And C _BESS,var The annual fixed operation and maintenance cost and the variable operation and maintenance cost are respectively; c (C) _BESS,gov And (5) an equivalent annual value of subsidy income obtained for the energy storage system.

The initial total investment is converted into the initial equal annual investment cost of each year:

wherein lambda is ₁₁ Investment cost per unit energy storage power capacity (yuan/kW-yr); lambda (lambda) ₁₂ Investment cost per stored energy capacity (yuan/kWh-yr); p (P) _BESS,N Rated power (kW) for an energy storage system; s is S _BESS,N Energy storage system rated capacity (kWh); i is interest rate; n is the predicted life year which changes with different energy storage running states;the energy storage operation power at the t moment of the kth new energy curve is discharged when the value is positive, and is charged when the value is negative; u is the rated charge and discharge times of the energy storage, which is provided by manufacturers and converted into the times of full charge and discharge operation according to rated capacity; t represents the total number of time points of the scheduling day, and in this embodiment, one point is taken every 5 minutes, so the value of T is 288; h is the time window length; k is the kth clustering curve, K is the clustering number, alpha _k Is the duty cycle of the kth cluster curve in one year.

C _BESS,reg ＝λ ₂₁ P _BESS,N (6)

Wherein lambda is ₂₁ The operation and maintenance cost (Yuan/kW-yr) is fixed for each power year.

Wherein lambda is ₂₂ Variable operation and maintenance cost (Yuan/kWh-yr) for unit charge and discharge power.

C _BESS,gov Including patches related to discharge amount, disposable patches for important individual items, disposable patches related to installed capacity, etc., which are generally one of them and provided with a year and a highest rating depending on the unit under jurisdiction of the item. The subsidy can reduce necessary reserved funds in the initial stage of purchase or in the using process of the energy storage, so that the investment cost of the energy storage can be effectively reduced.

Wherein lambda is ₃ For subsidy (meta/kWh-yr) related to the charge and discharge of the stored energy,other disposable patches are provided.

(2) Trade cost with large electric network C _net

The trade cost of the large power grid can reflect the income and load expenditure of new energy power generation and also can reflect the energy transfer effect of stored energy.

Wherein m is _net The purchase price is the electricity price for trading with the power grid; m is m _net,new Price sold to the power grid for new energy power generation; m is m _net,t The peak-valley electricity price of the power grid at the t-th moment of the dispatching day is the purchase price;and the electricity purchasing power of the user to the power grid at the t moment of the kth new energy power generation curve is shown, the electricity purchasing power is shown in positive and the electricity selling power is shown in negative.

The operation constraint of the new energy storage system comprises a system operation power balance constraint, an energy storage charge-discharge constraint and an energy storage multiplying power constraint.

(1) System operating power balance constraints

Wherein:generating power for new energy at the t moment of the kth new energy clustering curve; p (P) _load And using electric power for load power, namely, user.

(2) Energy storage charge-discharge constraint

The service life of the energy storage battery is related to the charge and discharge depth, the service life loss of the storage battery can be increased due to over-charge and over-discharge, the service life of the storage battery can be prolonged due to shallow cyclic discharge, and the energy storage battery is subjected to charge and discharge quantity constraint and charge and discharge power constraint according to actual operation experience.

In the method, in the process of the invention,the electric quantity in the energy storage battery at the t moment of the kth new energy curve; />And->The charge and discharge state variables of the energy storage are respectively 1 or 0.

The equation (14) and the equation (15) are continuous constraints of electric quantity, the equation (16) is a constraint that does not allow simultaneous charge and discharge, and the equation (17) is a constraint of inverted electric quantity. The inverted power supply constraint is used for the effective operation of a self-power-consumption and allowance surfing mode, and the power sold to the power grid by a user is ensured to be sent by a photovoltaic and consumed by the user, namely, the power sold in the power selling state does not exceed the difference between the new energy power generation amount and the user load at the current moment.

In the formula, h _BESS H is the charge and discharge efficiency of the battery _dis H is the discharge efficiency of the battery _cha Which is the charge efficiency of the battery.

(3) Energy storage multiplying power constraint:

in the method, in the process of the invention,is the energy multiplying power of the energy storage battery.

In one embodiment: the large industrial users are classified into three types of users of a double-peak type, a single-peak type and a stable type according to the shape of the load curve, and the daily electricity consumption curves of the users are similar, so that the final curve is an average value daily curve at each time point. For convenience of comparison, the power consumption of the other two large industrial users at each time point is multiplied by the same coefficient by taking the bimodal users as a reference, so that the total daily power consumption of the three users is equal to about 105.61MWh, as shown in fig. 4. The trade price of electricity adopts a certain time-saving electricity price, as shown in fig. 5.

For the energy storage proportion requirement, the photovoltaic and wind power are not clearly different. In the embodiment, photovoltaic is taken as an example, the capacity is 9MW, a common iron-lithium battery for energy storage of large industrial users is adopted, and the optimal configuration of storage batteries of different types of users is solved by using the model and the method provided by the embodiment. Other parameters are as follows: the interest rate is 2.75%, the purchase price of unit power is 1700 yuan/kW, the purchase price of unit capacity is 2200 yuan/kWh, the energy multiplying power is 0.5, the operation and maintenance cost of unit power is 70 yuan/kW, the charge and discharge efficiency is 0.95, the rated charge and discharge times are 3000, the photovoltaic selling electricity price is the reference price of local coal-fired power generation, and the national average coal-electricity reference price is 0.4335 yuan/kWh.

The method comprises the steps of programming and solving three types of users on MATLAB by adopting a hybrid optimization algorithm provided by the text to obtain the optimal energy storage rated power and rated capacity configuration of the three types of users, and predicting the life year, annual cost and average daily operation hours of energy storage; and comparing the annual costs of three types of users with 5% and 30% of the upper limit and the lower limit of the distribution and storage specified in each province and the non-configuration and the upper limit double configuration. In addition, for the purpose of comparing the accuracy of the dynamic life years, the rated charge and discharge times are changed into 3000 times, and the calculation is carried out according to the energy storage reference life year n=8. The results are shown in Table 2.

Table 2 three types of user energy storage configuration results

Parameters (parameters)	Bimodal type	Unimodal type	Stable type
				Configuring power/MW	0.792	1.286	1.315
Configuration capacity/(MW. H)	1.583	2.572	2.629
				Dynamic life year/year	12.171	12.110	11.920
Energy storage average number of operating hours per day/h	3.76	3.79	4.21
				Energy storage ratio	17.59％	28.58％	29.22％
Dynamic annual cost/ten thousand yuan	2641.01	2374.46	2297.91
				Dynamic life year/year of 5% proportion	11.259	11.259	11.259
Annual cost/ten thousand yuan according to 5 percent proportion	2641.67	2375.67	2299.30
				Dynamic life year/year at 30% ratio	13.810	12.186	11.965
Annual cost/ten thousand yuan according to 30 percent proportion	2641.62	2374.46	2297.92
				Annual cost of non-matched storage/ten thousand yuan	2642.19	2376.19	2299.82
Annual cost/ten thousand yuan according to 60 percent proportion	2644.28	2374.85	2298.30
				Dynamic life year/year of 60% proportion	16.287	13.208	13.077
n=8 annual cost/ten thousand yuan	2639.55	2375.14	2299.24
				n=8 configuration capacity/(MW. H)	1.010	0.738	0.450
Actual charge and discharge times of n=8 years	18537	16543	16563

From table 2 the following rules and conclusions can be drawn: 1) Under the same new energy and load daily electricity quantity, different load characteristic curves and optimal allocation and storage ratios are different, and the allocation and storage ratio of a knife is unscientific from the aspect. 2) From the viewpoint of single user characteristics, from different distribution and storage to different distribution and storage ratios, the annual cost of users is not great, and the cost of additionally installing energy storage is balanced with the low-storage and high-release benefits; however, if the proportion is too high, for example, 60%, the cost is obviously increased for the users with double peaks and without installing too much energy storage. Therefore, from the perspective of one-cutter cutting and storing, the proportion of 5% -30% is reasonable, the influence on the economy of users is small, and if the proportion is increased on the basis, the influence on users with smaller optimal energy storage proportion is larger. 3) The average daily operation hours and the energy storage life of the three users configured energy storage can meet the requirements of each province, and the working life of the energy storage system is regulated for 10 years if a certain province. And with the improvement of the proportion, the energy storage life is obviously improved, which is beneficial to saving resources. 4) If the optimal configuration is performed according to the reference lifetime n=8, the annual cost is not quite similar, but under the condition of fixed year and unlimited charge and discharge times, the user tends to install smaller capacity energy storage and frequent charge and discharge to reduce the electricity purchasing cost of the power grid, as shown in fig. 6. This can result in the consumer's energy storage battery having been excessively depleted not until the reference lifetime. Therefore, the optimal configuration of energy storage economy is not accurate only with reference to the life years.

Because the influence of the user load curve type on the new energy allocation and storage is large, the double-peak type user has poor economical efficiency and the stable user has optimal economical efficiency. The characteristics of each user type, the energy storage configuration and the operation mode are analyzed.

(1) Bimodal users. The bimodal user type mainly uses common industrial users, the daytime production time is the electricity consumption peak period, and the non-working time is the electricity consumption valley. As shown in fig. 7, in four typical days of photovoltaic, the first two types and the double peak type power consumption curves have similar trend, the second two types are often covered by the power consumption curves due to lower power generation capacity, and the double peak type users use less power in the late peak and 'peak' periods, so the required energy storage proportion is relatively lower compared with the other two types of users.

(2) Single peak users. The peak duration of the unimodal user is similar to the peak duration of the bimodal user group, but the overall power usage varies relatively little. As shown in fig. 8, in the last two typical days with weak illumination, the energy stored in the solar cell is charged in the valley period, and the peak time is discharged; in the first two typical days with strong illumination, the stored energy is charged in the electricity price valley period and the flat price period when the photovoltaic power generation power is larger than the load power. The power capacity configuration size of the required energy storage is centered among the three classes of users.

(3) Smooth users. The smooth type of users is mainly large industrial users in park users, load power changes are small throughout the day, and compared with other two users, more electricity is used at night and at peak time, and less electricity is used at flat price time. As shown in fig. 9, there is a sufficient margin of photovoltaic available for storage in the afternoon, while the spike period is the period where the energy storage discharge is most needed, so the energy storage power capacity needed for the stationary user is the largest among the three types of users. Meanwhile, more electricity consumption is balanced due to low price at night, so that the smooth user is the user with optimal economical efficiency among three types of users.

Taking a stable user as an example, the influence of rated cycle times, load power consumption, time-sharing electricity purchase price and new energy electricity selling price on the total cost of electricity consumption and the allocation and storage will of the user is further analyzed:

(1) Rated cycle number of stored energy

For the stability user, different rated energy storage circulation times are set during energy storage configuration, and the change conditions of energy storage configuration power, capacity, service life, average daily operation hours, energy storage proportion and annual cost are studied, and the results are shown in Table 3. As shown in the table, when the cycle number is 2900, the energy storage life is too short to be cost-effectively matched, so that the optimal result is that no energy storage is matched; when the cycle number is 3050, the energy storage configuration has obvious advantages, and the configuration capacity is mainly related to the electricity consumption in the peak period; and after 6500, the peak period energy storage also begins to discharge in addition to the peak period. As the number of cycles increases, the annual cost decreases.

As can be seen from fig. 10-12, when the rated cycle times are smaller, the lifetime of the stored energy is lower, and otherwise the initial cost of occupying most of the cost of stored energy is on average to be relatively more per year, at which time the user tends to install less stored energy and sell rather than store as much as possible when the photovoltaic is abundant; when the rated cycle times are larger, the service life is prolonged, the initial investment cost from average to each year is reduced, the energy storage is relatively more economical, and users tend to install larger-capacity energy storage, and even redundant photovoltaic power generation is fully stored. Under otherwise similar conditions, battery products with a greater number of rated cycles should therefore be considered as much as possible.

TABLE 3 energy storage configuration Power, capacity, life, average number of hours per day operation, energy storage ratio and annual cost at rated cycle times

(2) Load electric quantity

Under the condition that other conditions are unchanged, the load electric power of the stationarity user is multiplied by a certain coefficient, and the influence of the change on annual cost, energy storage proportion and the like is studied, and the results are shown in table 4. Because the photovoltaic capacity is unchanged, when the load ratio is smaller, the annual cost ratio is reduced faster, and the annual cost after optimal allocation is lower when the new energy capacity is larger under the condition of load determination, large industrial users are encouraged to build new energy according to local conditions and configure energy storage.

Under different loads, as can be seen from fig. 13 to 15, when the load power is smaller, enough residual photovoltaic is available daily and a longer period of 'photovoltaic power > load power' can be used for storing energy, the energy storage configuration capacity mainly depends on the electricity consumption in the peak period, and when the load is small to a certain extent, a user realizes that the annual cost of electricity consumption is negative, namely profit, through photovoltaic electricity selling and timely charging and discharging by utilizing the energy storage. When the load is large, available residual photovoltaic is reduced, and the influence of load fluctuation on the energy storage proportion is small.

TABLE 4 energy storage configuration Power, capacity, life, average number of hours per day of operation, energy storage ratio and annual cost at different load sizes

(3) Market price of electricity

As the overall electricity price decreases, the annual cost to the user must be reduced. However, too high peak-to-valley electricity price difference can lead users to obtain benefits from the price difference, so that excessive energy storage capacity is configured, and the problems of resource waste and the like are caused. Therefore, it is necessary to study the influence of the variation in peak-to-valley electricity price difference in market electricity price on annual cost. The peak-to-valley electricity prices were adjusted in two ways of adjusting the same amount and the same ratio, and the results are shown in tables 5 and 6, respectively.

In Table 5, the change of 0.02 element of the electricity price of the original purchase electricity is defined as the constant price of the flat period, the decrease of 0.02 element of the valley price, and the increase of 0.02 element of the peak and peak; -0.02 element is the constant price of the "flat" period, the increase of the "valley" price by 0.02 element, the decrease of the "peak" and "peak" by 0.02 element; and so on.

As can be seen from table 5 and fig. 16 to 17, in the case that the low-peak price is changed to the same amount price, when the peak-valley electricity price difference is small, the "low-storage high-discharge" benefit is insufficient to compensate for the high energy storage cost, the polarity of the user's low-valley electricity deposit is not high, the user tends to sell the photovoltaic directly when the photovoltaic is excessive, the user's profit is less and the configuration capacity is smaller, and even the excessively small peak-valley electricity price difference can cause the user to not install energy storage; with the increase of peak-valley gap, the gain of the user through the gap increases more than the increase of the installation cost of the energy storage, and the gain of the user also tends to install more energy storage. Further, since the total time period at "valley" is longer than the total time period of electricity prices, that is, the total electricity prices decrease with an increase in price difference, this is one of the reasons for the gradual decrease in annual cost.

TABLE 5 energy storage configuration Power, capacity, life, average number of hours per day operation, energy storage ratio and annual cost at different electricity prices

In table 6, the price is unchanged in the period when the electricity price of the original purchase is changed by 1%, the price of the electricity price of the original purchase is reduced to 99%, and the price of the electricity price of the original purchase is increased to 101%; -1% is the constant price of the 'flat' period, the 'valley' price is increased to 101% and the 'peak' and 'peak' are reduced to 99% respectively; and so on.

As can be seen from table 6 and fig. 18 to 19, when the peak-valley price difference is small, the user will not tend to perform low-storage high-discharge actions, and the installation of energy storage is not active; as the peak-valley difference is increased, the energy storage capacity is gradually increased by the user; when the peak-to-valley electricity price is large, the energy storage capacity is mainly determined by the discharge amount required in the peak period. When the electricity prices of the valley and the peak are changed in proportion, the price difference is increased, but the peak increment is larger than the valley decrement in the same proportion, namely the sum of the electricity prices in each period is increased, and the cost is increased along with the increase of the price difference. When the peak-valley difference is too large, the user can be caused to install too much energy storage, but the annual cost change is small. Therefore, the electricity price difference is not only the electricity price difference, but also the electricity price sum of each period is a non-negligible factor, namely, the excessive electricity price difference is not necessarily beneficial to the development of energy storage, but under the condition that the total electricity price change is smaller at each moment, the annual electricity cost can be effectively reduced by pulling the peak-valley price difference.

TABLE 6 energy storage configuration Power, capacity, life, average number of hours per day operation, energy storage ratio and annual cost at different electricity prices

(3) New energy electricity selling price

Compared with the common energy storage users, the new energy storage users can increase a income source, namely selling new energy power generation electricity quantity, and the new energy surfing has various modes of full surfing, spontaneous self-use, residual surfing and the like, and the embodiment mainly considers the latter. The online electricity price, i.e. the selling electricity price, can influence the scheduling behavior of the stored energy, such as whether the stored energy is mainly derived from the electric energy purchased from the power grid at the off-peak electricity price or the new energy balance electric energy.

As shown in table 7 and fig. 20-21, when the electricity selling price is low, particularly below the grid electricity selling price at each moment, the user tends to prefer the surplus photovoltaic as the energy stored in the energy storage and will store as much energy as possible for the peak moment discharge. Along with the increase of electricity selling price, especially when the electricity selling price is higher than the off-peak electricity price, the energy storage capacity required by a user is drastically reduced, and the contribution of direct electricity selling to electricity saving cost is gradually larger than the electricity saving cost of low-energy storage and high-energy discharge. Therefore, the annual cost of the users is gradually reduced along with the increase of the electricity selling price, but the electricity selling price which is too high can hit the enthusiasm of the users for installing energy storage.

TABLE 7 energy storage configuration Power, capacity, life, average number of hours per day operation, energy storage ratio and annual cost at different electricity prices

The embodiment of the application also discloses a large industrial user new energy allocation and storage decision device, which comprises: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the large industrial user new energy allocation and storage decision method when executing the program.

The embodiment of the application also discloses a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is used for executing the large-industry user new energy allocation and storage decision method.

Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

A processor in the present application may include one or more processing cores. The processor performs the various functions of the present application and processes the data by executing or executing instructions, programs, code sets, or instruction sets stored in memory, calling data stored in memory. The processor may be at least one of an application specific integrated circuit (Application Specific IntegratedCircuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), a Programmable logic device (Programmable LogicDevice, PLD), a field Programmable gate array (Field Programmable Gate Array, FPGA), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, and a microprocessor. It will be appreciated that the electronic device for implementing the above-mentioned processor function may be other for different apparatuses, and embodiments of the present application are not specifically limited.

In summary, the application provides a technical scheme for large industrial user new energy allocation and storage decision, the objective function of the model is that annual electricity cost is minimum, namely, the sum of annual cost of energy storage equipment investment, operation maintenance and the like and annual cost of transaction with a large power grid is minimum, and the reference life year for measuring life in other models in the past is replaced by the dynamic life year predicted by rated charge and discharge times, so that the accuracy of the model and the referenceability for users are increased. In addition, the model not only can help large industrial users to realize new energy configuration energy storage decision, but also can be used for analyzing the influence of the development of parameters such as rated cycle times, load electricity consumption, time-sharing electricity purchase price, new energy electricity price and the like on the users, and provides references for the large industrial users to configure new energy and store energy according to the load characteristics of the large industrial users.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. The specification and examples are to be regarded in an illustrative manner only.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention to the precise form disclosed, and any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. A large industrial user new energy allocation and storage decision method considering the dynamic life of energy storage is characterized by comprising the following steps:

step 1: collecting the power generation condition of new energy of one year of large industrial user history, and adopting a K-means clustering algorithm to perform scene reduction to obtain a clustering number;

step 2: clustering 365 days of new energy curves in one year according to the clustering number obtained in the step 1 to obtain typical days of the clustering curves and the duty ratio of the days of various curves in one year;

step 3: and under the constraint of system power balance and energy storage charge and discharge constraint conditions, solving the optimal energy storage rated capacity, rated power, annual cost, average daily operation hours of energy storage and prediction of dynamic life based on a large industrial user load electricity utilization curve, a new energy power generation curve and a selling electricity price by taking the minimum total annual cost as an objective function.

2. The large industrial consumer new energy allocation and storage decision-making method according to claim 1, wherein the method comprises the following steps: in the step 1, if the large industrial user does not have new energy power generation data, the historical data of other users with the same type of new energy in the same area are used for replacing the new energy, and only capacity parameter conversion is carried out.

3. The large industrial consumer new energy allocation and storage decision-making method according to claim 1, wherein the method comprises the following steps: the elbow method is used to determine the optimal K value for the K-means clustering algorithm.

4. The large industrial user new energy allocation and storage decision-making method based on the large industrial user new energy allocation and storage decision-making method is characterized by comprising the following steps of: the step 3 specifically comprises the following steps:

1) Establishing an objective function with minimum annual electricity cost after new energy allocation and storage of large industrial users; the annual electricity cost mainly comprises annual cost such as energy storage equipment investment, operation maintenance and the like and annual cost of trade with a large power grid;

2) Establishing constraint conditions of system operation after large industrial user new energy is matched and stored, wherein the constraint conditions comprise system operation power balance constraint, energy storage charge-discharge constraint and energy storage multiplying power constraint;

3) And calculating the optimal configuration power, capacity, dynamic life year, annual cost, average daily operation hours of the energy storage and a scheduling mode by adopting a Gurobi solver.

5. The large industrial consumer new energy allocation and storage decision-making method according to claim 4, wherein the method comprises the following steps: the annual cost of the objective function of the energy storage device comprises the initial annual investment cost of energy storage, the annual fixed operation and maintenance cost, the annual variable operation and maintenance cost, the annual value of subsidy income obtained by the energy storage system and the like.

6. The large industrial consumer new energy allocation and storage decision-making method according to claim 5, wherein the method comprises the following steps: the service life of the energy storage device is the dynamic service life year which is predicted by using the rated charge and discharge times of the energy storage device when the annual cost is reduced, so that the calculation result is more in line with the actual service life of the energy storage device.

7. The large industrial consumer new energy allocation and storage decision-making method according to claim 4, wherein the method comprises the following steps: the energy storage charging and discharging constraint also comprises an electric quantity continuous constraint, a constraint which does not allow simultaneous charging and discharging and a constraint which does not allow inverted electric quantity.

8. The large industrial consumer new energy allocation and storage decision-making method according to claim 1, wherein the method comprises the following steps: further comprising step 4: for large industrial users with new energy, the energy storage optimization configuration is carried out by adopting the steps 1-3, and the annual cost, the service life and the average daily operation hours of the energy storage after the energy storage is installed by the users can be estimated.

9. Large industrial user new energy allocation and storage decision-making equipment is characterized by comprising the following steps: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the large industrial consumer new energy allocation decision method of any one of the preceding claims 1-8 when executing the program.

10. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the large industrial consumer new energy allocation and storage decision method according to any of the preceding claims 1-8.