CN110717259B - User side-oriented battery energy storage configuration and operation optimization method - Google Patents
User side-oriented battery energy storage configuration and operation optimization method Download PDFInfo
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Abstract
The invention discloses a user side battery energy storage configuration and operation oriented optimization method. The invention constructs an energy storage configuration optimization model combining demand defense and peak clipping and valley filling based on the charging rule of the electric charge of the large industrial user. And aiming at different users, the most appropriate energy storage configuration can be selected by utilizing the return on investment evaluation. Energy storage operation performance constraint is added during construction of an optimization model, so that operation loss can be reduced, and the energy storage service life can be prolonged. When the operation of the energy storage day is optimized, updating the load data in real time, constructing a scheduling optimization model for the day operation, and performing real-time rolling optimization; and secondly, a monthly demand defending value updating model is constructed, monthly demand defending value is updated in time, and the influence of load prediction errors is continuously corrected. The embodiment verifies that the rolling optimization algorithm provided by the invention can more reasonably schedule energy storage and effectively reduce the power consumption cost of users.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a method for optimizing configuration and operation of battery energy storage at a user side.
Background
The electricity consumption cost is an important component of the operation cost of large-power users. The battery energy storage (hereinafter referred to as "energy storage") can be realized by a "low-storage high-discharge" strategy, namely, electric energy is stored when the user power load and the electricity price are low, electric energy is discharged when the user power load and the electricity price are high, the monthly maximum demand value of the user load is reduced under the condition of not changing the electricity consumption behavior of the user, and the peak clipping and valley filling benefits are generated while the basic electricity charges are reduced. And moreover, the user side energy storage service object is clear, the variation factors are fewer, and the operation of energy storage is more facilitated. Meanwhile, the state puts forward a series of policies such as guidance suggestions about promoting energy storage technology and industry development, suggestions about clean energy heating price policies in northern areas and the like, so that the development of energy storage at the user side is greatly supported, the peak-valley price difference at the sale side is appropriately expanded, the peak-valley price difference of the user is expected to be enlarged continuously in the future, and the policies are beneficial to improving the investment benefit of energy storage at the user side. Therefore, the installation of energy storage at the user side is an important means for reducing the operation cost of the large-power user.
The development space of the user-side energy storage market in China is huge, however, at present, the development of the user-side energy storage is not expected, on one hand, a user still cannot bear the investment cost of high energy storage, on the other hand, the energy storage operation efficiency is low, and the economic benefit brought by the energy storage optimization operation cannot be fully played. Therefore, the optimization research on the configuration and the operation of the energy storage at the user side has important significance.
Currently, energy storage configuration and operation optimization research is divided into a power grid side and a user side. The energy storage optimization research of the power grid side mainly aims to ensure safe and reliable operation of the power grid and solve the problems of load balance and the like, and the energy storage optimization research of the user side aims to reasonably transfer load and improve the economy of a user after energy storage installation, so that the energy storage optimization research result of the power grid side is difficult to directly apply to the user side. In the research of the user side energy storage optimization, part of the research only aims at optimizing one aspect of energy storage configuration or operation, and part of the research only considers the economy of peak clipping, valley filling or demand defense. And in addition, energy storage capacity configuration and day-ahead operation planning are performed by means of a predetermined scheduling strategy, the method is difficult to obtain the optimal configuration capacity of energy storage, and poor performance is caused in actual operation within an energy storage day because the influence of power load prediction errors is not considered. Therefore, at present, a research for realizing real-time rolling optimization by simultaneously considering energy storage configuration and operation optimization and considering load prediction error influence during energy storage operation is lacked.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects in the prior art and provide a method for optimizing the configuration and operation of battery energy storage at a user side, wherein the method is used for establishing an energy storage configuration optimization model combining demand defense and peak clipping and valley filling on the basis of a charging rule of the electric charge of a large industrial user; in order to ensure that the optimal economic benefit is realized after the energy storage is put into operation, the charging and discharging state conversion times constraint of the energy storage battery is provided, and then a rolling optimization model before and in the month and day of the energy storage is constructed by combining load prediction data. Compared with the day-ahead operation optimization, the day-in rolling optimization continuously corrects the load prediction error by updating the actual load data and the monthly demand defensive duty in real time, so that a user obtains higher income.
The method comprises the following steps:
step 1: based on the electric charge charging rule of a large industrial user, aiming at the characteristics that the stored energy can be reasonably transferred to the user power load through strategies such as low storage and high discharge and the like, and the monthly maximum demand of the power consumption of the user is reduced, the peak clipping valley filling income and the monthly demand defending income obtained after the user additionally installs the stored energy are calculated by establishing an energy storage income model.
Step 2: establishing physical constraint conditions of the energy storage battery aiming at the hard physical requirements of the energy storage battery on operation; meanwhile, good performance is guaranteed during energy storage operation, and energy storage battery performance constraint conditions are established.
And step 3: and formulating an energy storage configuration optimization and operation optimization strategy, determining a load prediction model, and predicting the load before the month and the load before the day by using the historical load data of the user.
And 4, step 4: and constructing an energy storage configuration optimization model by taking the highest monthly comprehensive benefit of the user after the energy storage is installed as a target, and optimizing the power and capacity of the energy storage configuration.
And 5: and constructing an energy storage operation pre-month optimization model by taking the highest sum of the demand defense profit and the peak load shifting profit of the optimization month as a target.
Step 6: and constructing an energy storage day-ahead operation optimization model by taking the maximum peak clipping and valley filling yield of the user every day as an objective function.
And 7: and calling a CPLEX solver to solve each model in the energy storage optimization based on a YALMIP toolbox in MATLAB software.
The invention aims at the condition of large electric power user load, combines the peak-valley electricity price and the charging rule of the user electricity fee, and carries out optimization research on the configuration and the operation of the energy storage at the user side. The invention can optimize the configuration and the operation of the energy storage simultaneously, and can realize real-time rolling optimization by considering the influence of the load prediction error during the operation of the energy storage.
Drawings
Fig. 1 is a diagram illustrating a user-side energy storage optimization strategy.
Fig. 2 is a flowchart of the energy storage operation scheduling rolling optimization algorithm.
Fig. 3 is a peak-to-valley electricity rate time distribution diagram.
Fig. 4 is a graph of revenue variation under different energy storage configurations.
FIG. 5 is a graph comparing the results of operation within the day with no energy constraint.
FIG. 6 is a graph of the results of the daily operation optimization of the two models.
Fig. 7 is a diagram of the optimization results of the operation before the energy storage day.
FIG. 8 is a graph of the results of the rolling optimization performed during the energy storage day.
Detailed Description
The invention is further described below with reference to the accompanying drawings, comprising the steps of:
step 1: calculating added energy storage benefit of user
For large industrial users, the electric charge collected by the power supply company is divided into a basic electric charge and an electric power charge. The charging forms of the basic electricity fee to be paid by the user are various, and most of the users select to charge according to the actual demand (the maximum demand actually recorded every month), namely the maximum demand in the month is multiplied by the basic electricity fee price (different regions); the electricity consumption rate is obtained by multiplying the real-time electricity consumption of the user by the electricity price at the corresponding moment.
If the user installs the energy storage battery in advance, the electric power handling characteristic with flexible energy storage is utilized, the user electric power load is reasonably transferred through strategies such as low storage and high discharge, the monthly maximum demand value of the user electricity can be reduced, and the monthly demand defense benefit is generated. Meanwhile, a time-of-use electricity price mechanism is adopted in the current electric power market, stored energy is charged in an off-peak electricity price period, and is discharged in an off-peak electricity price period, so that peak clipping and off-peak income can be generated by utilizing an off-peak electricity price difference. The specific calculation formula is as follows:
Rj＝m(PLmaxj-Pmaxj) (1)
in the formula: rjFor the defense gain of the monthly demand of the jth month, m is the basic electricity charge price, PLmaxj、PmaxjThe maximum load of the user in the month when the energy storage is not additionally arranged in the jth month and the maximum demand value which can prevent the user from being arranged after the energy storage is additionally arranged are respectively the monthly demand defending value. RiThe peak clipping and valley filling gains of the day i, k is the total time of the day, mtPeak-to-valley electricity prices for the tth moment of the scheduling day; pcs (personal computer)i,tThe energy storage operation power at the ith moment is the energy storage operation power at the ith day, wherein the charging power is positive, and the discharging power is negative. h is the length of the time window in hours.
Step 2: establishing energy storage operation constraint conditions
(1) Physical restraint of energy storage battery
1) Energy storage charge and discharge power constraint
The energy storage charging and discharging power can not exceed the energy storage rated power:
-PCS,N≤PCS,i,t≤PCS,N (3)
in the formula, PCS,NRated power configured for energy storage.
2) Energy storage battery capacity constraint
The charge in the energy storage battery at each moment should be between the upper and lower limits of the battery charge constraint:
0.17SN≤Si,t≤SN (4)
in the formula, Si,tFor the electricity quantity in the energy storage battery at the ith moment of the day, SNRated capacity configured for energy storage. Since the charge of the energy storage battery can not reach or approach zero in the operation process, the method selectsThe lower limit of the electric quantity of the energy storage battery is 0.17SNAnd selecting the initial charge of the energy storage battery as 0.17SN。
3) Constraint of energy storage rate
PCS,N＝SN/η (5)
In the formula, η is the energy multiplying power of the energy storage battery.
(2) Power constraint
The actual load of the user and the running power of the stored energy cannot be less than 0, the power is transmitted to the power grid in a backward mode, the monthly maximum load value of the user cannot be exceeded, and otherwise, the demand defense function is lost.
0≤PL,i,t+PCS,i,t≤PLmax,j (6)
In the formula, PL,i,tThe actual load of the user at the time of the ith day is shown.
(3) Energy storage battery performance constraints
When the energy storage device operates, the change of the charge and discharge power in the determined state has little influence on the performance of the energy storage, and the conversion between the charge and discharge state and the standby state directly influences the adjustment cost of the energy storage and the battery loss. Secondly, the life loss of the stored energy is closely related to the throughput, and reducing the throughput can prolong the service life of the stored energy. In order to more reasonably utilize the stored energy, the simulation comparison of the operation in the energy storage day is carried out on the load data of the actual user by combining the peak-valley electricity price, and the simulation result shows that the daily throughput of the energy storage battery is limited, so that the throughput of the stored energy is reduced, and the times of charge and discharge state conversion in the energy storage day can be well limited. The daily throughput refers to the sum of the total charge and discharge electric quantity of the energy storage battery in one day, and the constraint is shown as the following formula:
Qi≤Qmax (7)
in the formula: qiDaily throughput, Q, of energy storage for day imaxThe energy storage day throughput upper limit is determined by comprehensively considering factors such as user load, peak-valley electricity price distribution, PCS performance requirements and the like.
And step 3: constructing energy storage optimization strategy and predicting load
The energy storage optimization is divided into two parts of configuration optimization and operation optimization. Firstly, an energy storage configuration optimization model is constructed based on historical load data of a user, and optimal energy storage configuration power and capacity are solved and determined. And then, based on the determined energy storage configuration power and capacity, a predicted monthly demand defense value is determined by constructing a monthly optimization model by utilizing the monthly load prediction data. And on the basis of meeting the requirement of monthly demand defense as much as possible, an energy storage day-to-day operation scheduling optimization model is constructed by combining the day-ahead load prediction data. Meanwhile, considering the influence of load prediction errors, real-time rolling optimization is performed on the operation scheduling in the energy storage day, the monthly demand defense value is updated in time, and finally, an energy storage day scheduling instruction is obtained, wherein the overall strategy of the energy storage optimization is shown in fig. 1.
During energy storage configuration optimization and pre-month optimization, the purpose is to determine appropriate configuration selection and monthly demand defense so as to obtain optimal benefits, and a time window is set as the electric quantity acquisition time of a power supply enterprise for 15min, namely k equals to 96 time points every day. When the energy storage day operation scheduling optimization is carried out, the operation power of each time of energy storage is concerned to realize the demand defense and obtain the peak clipping and valley filling benefits, in order to fully exert the minute-level and even second-level quick response performance of the energy storage, the load power is flexibly adjusted, the time window is set to be 5min, namely k equals to 288 time points each day.
Load prediction is needed during energy storage operation optimization, a machine learning method is adopted for load prediction, in python3.5, a long short-term memory (LSTM) model is built based on a keras framework in Tensorflow, and load prediction before the month and the day is carried out by using historical load data. The average absolute error percentages MAPE between the prediction results of the monthly prediction and the day-ahead prediction and the real load data are 8.756488 and 7.263825 respectively, and the load prediction accuracy can meet the requirement of energy storage operation scheduling optimization.
And 4, step 4: building energy storage configuration optimization model
The energy storage configuration optimization model takes the highest monthly comprehensive benefit of the user after the user installs the energy storage as a target to optimize the power and capacity of the energy storage configuration.
In order to calculate the monthly comprehensive benefit after the user adds the stored energy, the total investment of the user for adding the stored energy is converted to obtain the monthly conversion cost C of each month, and the specific calculation is shown as the following formula:
in the formula: m ispFor storing a purchase price per kW, myThe operation and maintenance cost per kW of stored energy is saved; m isBThe investment unit price per kWh for energy storage; and N is the energy storage life.
Objective function F of energy storage configuration optimization model1The difference value between the energy storage monthly income and the monthly conversion cost is specifically calculated in a formula (9).
Wherein y and d are the total number of months and days in the simulation cycle, respectively.
Therefore, the energy storage configuration optimization model takes the configuration power, the configuration capacity, the demand defense value of each month and the operation power of the energy storage at each time point as independent variables, and the respective variables are constrained by the formulas (3) to (7). The simulation period of the model is a plurality of months, and the model can be set according to the actual load data condition of a user.
And 5: building energy storage operation pre-month optimization model
Based on the energy storage configuration and the monthly load prediction data obtained by optimization, when reasonable monthly demand defense is sought, the objective function F of the optimization model2In order to optimize the sum of the monthly demand defense income and the peak clipping and valley filling income, the specific formula is as follows:
the model is different from the energy storage configuration optimization model in that the configuration of the energy storage battery in the model is a known quantity and is solved and determined by the energy storage configuration optimization model; the independent variables of the model are the monthly demand guard value and the operating power of the energy storage battery at each moment, and the simulation period is one month，djThe constraint conditions are expressed by formulas (3) to (7) for the days corresponding to the jth month.
Step 6: building intra-day operation rolling optimization model
Objective function F of energy storage day-ahead operation optimization model3Generally, the peak clipping and valley filling gains of the user per day are represented by the following specific formula:
the energy storage day-ahead optimization model obtains a monthly demand defensive duty P through monthly optimizationmaxAnd the standard is adopted, the monthly demand defense value cannot be updated, and the final monthly maximum demand value is the user monthly load maximum value after energy storage scheduling. If the energy storage scheduling is completely performed according to the optimization result before the day, when the actual value of the power load is higher, the maximum monthly demand value of the last month is larger than the P obtained by the optimization before the monthmaxHigh, resulting in non-ideal optimization results. If the load prediction error is considered, optimizing the actual maximum demand value before the month to obtain P when controlling the actual operation scheduling of the stored energymaxIn contrast, if the energy storage is not completely scheduled according to the optimization result before the day, at a time point when the true value of the power load is higher, the energy storage system may emit more electric power than the optimization result before the day, which may cause the electric power of the energy storage battery to be exhausted in advance and lose the peak shaving capability to the remaining time. Therefore, to solve the above problem, an intra-day operation rolling optimization model needs to be constructed.
In the process of constructing the rolling optimization model operating in the day, the fact that if the formula (11) is directly adopted as the objective function, the charging time of the stored energy is random in the electricity price valley period, and the situation that the battery can be fully charged but is not fully charged can occur in the first electricity price valley period. This is because the algorithm is a real-time rolling optimization, and if the following electricity price valley period occurs and cannot be filled, the energy storage operation power at the past time point cannot be changed. In order to realize the prior charging of the stored energy at the front moment of the electricity price valley period, the stored energy electric quantity is used as a part of the target function of the electricity price valley period, so that the stored energy can be charged as early as possible in the electricity price valley period, and the maximum of the target function is realized. The objective function of the rolling optimization model is operated in the current day by adopting a piecewise function F4Represents:
in the formula, Si,tAnd w is the electric quantity in the energy storage battery at the time t of the ith day, and the value of w is determined by simulation test.
The simulation period of the intra-day operation optimization model is one day, namely k equals 288; the constraint conditions of the model are similar to those of a monthly optimization model, except that the power constraint in the independent variable constraint conditions is changed from an equation (6) to an equation (13), namely the sum of the operating power of the energy storage battery and the load of a user cannot exceed the currently determined monthly demand defense value.
0≤PL,τ+PCS,τ≤Pmax (13)
In the formula, tau represents the moment of an acquisition point, and as the maximum value of the load average value of each 15min acquired by a power supply enterprise is taken as the maximum demand, namely the acquisition points are fixed at 0 minute, 15 minutes, 30 minutes and 45 minutes per hour, the judgment is only needed to be carried out on the acquisition points in the model; pL,τ、PCS,τAnd the average values of the user load and the stored energy running power 15min before the acquisition point moment are respectively.
If the solution of the model is wrong, the energy storage battery at the moment cannot realize the optimized and determined monthly demand defense, so that the maximum monthly demand optimal value which can prevent the energy storage at the current moment of the scheduling day is searched by taking the highest daily comprehensive benefit of the day as a target, and the monthly demand defense is updated. Optimization objective function F of monthly demand defending value updating model5As shown in the following formula:
the first part of the objective function represents the average value of the daily demand defense profit in the j month, and the second part is the daily peak clipping and valley filling profit. At this time, R is calculatedjIn the formula (1)P ofLmax,jPredicting a maximum value of the data for the known actual load before the tth time of the month and the load before the month at the tth and later times; before the t-th moment PCS,i,tIt is determined that the model takes the demand defense and the scheduled power of the energy storage battery at the t-th and later moments as independent variables, and the constraint conditions are shown in formulas (3), (4), (7) and (13).
And 7: energy storage optimization algorithm based on CPLEX solver
And calling a CPLEX solver to solve each model in the energy storage optimization in MATLAB software based on a YALMIP toolbox. The energy storage operation optimization comprises pre-month optimization and in-day operation scheduling rolling optimization, the algorithm flow is shown in figure 2, and the specific steps are as follows:
1) optimizing to obtain the demand defense value P of the month based on the load forecasting data of the monthmax。
2) For the t-th time of the scheduling day, the day load data used for optimization is the known actual load data before the t-th time, and the t-th and later times are the day-ahead load prediction data. Defending P according to monthly demandmaxConstraint conditions are adopted, and a daily scheduling optimization model is solved; if the solution is successful, obtaining the energy storage operating power at each moment of the day, obtaining the energy storage operating power at the t-th moment from the energy storage operating power, ordering to implement scheduling for energy storage, and continuing to execute the step (4); and (4) if the solution is wrong, executing the step (3).
3) Solving a monthly demand defense value updating model based on the daily load data of the current optimization day to obtain a new demand defense value PmaxAnd obtaining the energy storage operating power of the t-th time point, and ordering to implement scheduling for the energy storage.
4) And (4) judging whether the tth moment belongs to the electric quantity acquisition point tau, if the t belongs to the electric quantity acquisition point tau, performing the step (5), and if not, performing the step (6).
5) Calculating the average value P of the user load counted after the energy storage power is adjusted in the first 15min including the t momentJAnd determining whether it exceeds PmaxIf so, it is replaced, i.e. Pmax＝PJ(ii) a And (6) if the step is not executed.
6) Judging whether t exceeds the total number k of the day time points, if not, updating the load prediction data after the t-th time point, and continuing to execute the step (2); and if k is exceeded, ending the optimization algorithm.
In order to verify the effectiveness and the reasonability of the user side battery energy storage configuration and operation optimization strategy, the energy storage configuration and operation strategy of a certain industrial park is analyzed by taking the industrial park as an example. The power supply voltage class of the park is 10kV, the basic electricity charge price m is 40 yuan/kilowatt, two parts of power generation price of the province and the large industry are adopted for pricing, and the time distribution of the peak-valley electricity price is shown in figure 3. At present, the battery types suitable for user side energy storage optimization in the market mainly comprise a lithium battery, a lead-acid battery and a sodium-sulfur battery, and relevant parameters of the batteries are shown in the following table:
TABLE 1 three types of energy storage cell parameters
Aiming at the historical load data and the peak-valley electricity price distribution characteristics of the park, after simulation test, it is determined that Qmax is 2SN, w is 0.083, energy storage configuration optimization is carried out on the three batteries, evaluation and comparison are carried out by utilizing the return on investment, and the result is shown in table 2.
TABLE 2 energy storage configuration optimization results
| Type of stored energy | Configured power/kW | Configured capacity/kWh | Investment cost/ten thousand yuan | Lunar healdBenefit/ten thousand yuan | Rate of return on investment |
| Lead- |
1200 | 4200 | 411.72 | 3.7987 | 0.5536 |
| Lithium iron battery | 1365 | 2730 | 539.8575 | 4.2786 | 0.7068 |
| Sodium-sulfur battery | 1440 | 10080 | 1489.536 | 5.4035 | 0.5224 |
As can be seen from table 2, the lithium iron battery for the campus users has the highest return on investment, and the lithium iron battery can obtain higher benefits with lower investment cost. Therefore, the lithium iron battery is most suitable to be selected and configured in the park, the optimal configuration power is 1365kW, and the configuration capacity is 2730 kWh.
To further illustrate the influence of the energy storage configuration on the user benefit, taking the lithium iron battery as an example, the monthly comprehensive benefit and the expected benefit of the full life cycle of the user under different energy storage configurations are obtained through simulation, as shown in fig. 4. It can be seen from the figure that as the capacity of the energy storage configuration increases, the monthly combined benefit and the predicted benefit of the full life cycle both increase and decrease, because the increase of the monthly benefit is not large enough to offset the increase of the energy storage cost after the energy storage reaches a certain capacity. When the energy storage power is 1365kW, the monthly comprehensive benefit and the predicted benefit of the whole life cycle are both maximized.
In order to fully verify the rationality and effectiveness of the optimization strategy, the proposed energy storage performance constraint and the daily rolling optimization model objective function are respectively subjected to simulation verification, and the results of the daily rolling optimization method are compared and analyzed with the results of the prior operation optimization method in the existing research:
(1) energy storage performance constraint verification
In order to verify the reasonability of the energy storage performance constraint condition provided by the invention, the optimization result is shown in FIG. 5 by utilizing actual user load data and carrying out simulation comparison on the optimization segmented model which runs in the energy storage day under the constraint condition of the energy storage performance. It can be seen from the figure that, compared with the non-energy-constrained mode, the times of mutual conversion among the throughput of energy storage, the charge-discharge state and the standby state in the optimization result are obviously reduced after the performance constraint condition is added, the energy storage operation loss can be reduced, and the service life is prolonged.
(2) Intra-day rolling optimization model objective function verification
After the energy storage configuration of the park is determined, taking the actual historical load data of a certain month of the park as an example, respectively adopting a single-segment function model formula (11) and a piecewise function model formula (12), carrying out energy storage operation optimization simulation comparison on a certain day of the month, wherein the optimization result is shown in fig. 6. As can be seen from the graph, the charging time of the stored energy in the valley under the single-segment function model is relatively random, and the situation that the stored energy is not fully charged in the first valley period occurs; the energy storage charging under the piecewise function model provided by the invention is concentrated in the valley period, and the valley period starts charging at the previous time point, so that the full charge of the energy storage battery can be ensured, the peak period discharging is met, and higher peak clipping and valley filling benefits are obtained.
(3) Comparison of results of day-ahead operation optimization and day-in rolling optimization
Firstly, monthly load prediction is carried out on a certain month, the maximum monthly load of the month is obtained to be 7732.9kW, and a model of 2.3.1 sections is adopted to optimize and obtain a monthly demand defense value 6006 kW. Then, selecting a certain day in the month, and operating the optimized scheduling result according to the day ahead as shown in FIG. 7, wherein the daily cutting peak-filling profit is 2178.69 yuan; before the dispatching day, the maximum load of the user after energy storage adjustment reaches 6731.33kW, the average demand defense benefit to each day of the dispatching day is 1335.43 yuan, and the daily comprehensive benefit is 3541.12 yuan.
To verify the effectiveness of the rolling optimization algorithm, the results of the rolling optimization performed during the same energy storage day are shown in fig. 8. The monthly demand defense at this time is updated to 6584.96kW, and the daily cutting peak and valley filling income is 2317.59 yuan. The maximum value of the daily load of the daily actual load after energy storage adjustment is 6583.31kW, the monthly demand defense value is not exceeded, the average demand defense benefit from the scheduling day to each day is 1530.59 yuan, and the daily comprehensive benefit is 3848.18 yuan. The energy storage is more concentrated in the valley period charging time and is more reasonable.
The comparison of simulation results of the two methods can find that the daily comprehensive income obtained by operating the rolling optimization algorithm in the day provided by the scheme is 334.06 yuan higher than that obtained by operating the optimization algorithm in the day before in the scheduling day. And respectively carrying out energy storage day operation optimization simulation under two methods for each day of the month, and finding out the following comparison results: under the intra-day operation rolling optimization and the pre-day operation optimization algorithm, the final month maximum demand value of the month is 6631.66kW and 6731.33kW respectively, and the month comprehensive income is 103402.4 yuan and 94400.13 yuan respectively, namely, the intra-day operation rolling optimization is higher than the pre-day operation optimization month comprehensive income by 9002.27 yuan. Particularly, the comprehensive yield of the intra-day operation rolling optimization is higher than that of the intra-day operation rolling optimization by 668.5 yuan compared with the comprehensive yield of the intra-day operation rolling optimization in the day before, so that the intra-day operation rolling optimization algorithm provided by the invention can more reasonably schedule energy storage, bring more economic benefits for users and better reduce the power consumption cost of the users.
The invention simultaneously considers energy storage configuration and operation optimization, and can select the most appropriate energy storage battery type and configuration capacity by utilizing the return on investment rate evaluation aiming at different users. Energy storage operation performance constraint is added when each optimization model is built, energy storage cannot be charged and discharged randomly in each electricity price time period, the times of conversion among energy storage daily throughput, a charging and discharging state and a standby state are obviously reduced, energy storage operation is more reasonable, operation loss can be reduced, and the energy storage service life is prolonged. During operation optimization of the energy storage day, actual load data is adopted for data before each moment point, predicted load data is adopted for data after each moment point, the state of charge of the energy storage is used as a part of a target function, an intra-day operation scheduling optimization model taking a piecewise function as the target function is constructed, and real-time rolling optimization is realized; and secondly, a monthly demand defending value updating model is constructed, and when the stored energy cannot defend the existing monthly maximum demand value, the monthly demand defending value is updated in time, and the influence of the load prediction error is continuously corrected. The embodiment verifies that the rolling optimization algorithm provided by the invention can more reasonably schedule energy storage, bring more economic benefits to users and effectively reduce the power consumption cost of the users.
Claims (4)
1. The method is characterized in that the method simultaneously considers the problems of energy storage configuration and operation optimization, constructs an energy storage configuration optimization model combining demand defense and peak clipping and valley filling and an energy storage monthly and intraday rolling optimization model based on the charging rule of the electric charge of a large industrial user, and adds energy storage operation performance constraints when constructing each optimization model to ensure that the energy storage operation is optimal; through constantly updating load prediction data and a defense value, the load prediction error is corrected, so that the scheduling energy storage is optimal, the power consumption cost of a user is reduced, and the method specifically comprises the following steps:
step 1: calculating the added energy storage income of the user;
based on the electric charge charging rule of a large industrial user, transferring the user power load through a low-storage high-discharge strategy, and calculating the peak clipping valley filling income and the monthly demand defending income obtained after the user additionally installs the energy storage through establishing an energy storage income model;
step 2: establishing an energy storage operation constraint condition;
establishing physical constraint conditions of the energy storage battery aiming at the hard physical requirements of the energy storage battery on operation; limiting daily throughput of the energy storage battery, and establishing energy storage battery performance constraint conditions;
and step 3: constructing an energy storage optimization strategy and predicting load;
an overall strategy of energy storage configuration optimization and operation optimization is formulated, a load prediction model is determined, and load prediction before the month and load prediction before the day are carried out based on historical coincidence data of a user;
and 4, step 4: constructing an energy storage configuration optimization model;
the energy storage configuration optimization model takes the highest monthly comprehensive benefit of the user after the user installs the energy storage as a target, and optimizes the power and the capacity of the energy storage configuration based on the historical load data of the user;
and 5: constructing an energy storage operation monthly optimization model;
based on the energy storage configuration power, capacity and monthly load prediction data obtained by optimization, an energy storage operation monthly optimization model is constructed to obtain monthly demand defense values by taking the sum of the optimized monthly demand defense gains and the peak clipping and valley filling gains as an objective function;
step 6: constructing an intra-day operation rolling optimization model;
constructing an energy storage day-ahead operation optimization model by taking the maximum peak clipping and valley filling yield of a user as an objective function; considering the influence of load prediction errors, adopting actual load data for data before each moment point, adopting predicted load data for the later data, taking the energy storage charge state as a part of a target function, constructing a daily operation scheduling rolling optimization model taking a piecewise function as the target function, and performing real-time rolling optimization;
and 7: energy storage optimization solving method based on CPLEX solver
In the energy storage optimization, each model is used for solving in MATLAB software based on a YALMIP toolbox and a CPLEX solver is called; the energy storage operation optimization comprises pre-month optimization and in-day operation scheduling rolling optimization, when the in-day operation rolling optimization is carried out in the energy storage day, the in-day scheduling optimization model is solved to make errors, and the monthly demand defense value updating model is solved to obtain a new monthly demand defense value.
2. The user side-oriented battery energy storage configuration and operation optimization method according to claim 1, wherein the establishing of the physical constraint conditions of the energy storage battery in the step 2 includes energy storage charging and discharging power constraint, energy storage battery capacity constraint and energy storage rate constraint; the energy storage battery performance constraint condition is that the daily throughput of energy storage in a certain day is less than or equal to the upper limit of the daily throughput of energy storage.
3. The method of claim 2, wherein the daily throughput is the sum of the total charge and discharge capacity of the energy storage battery during a day.
4. The user-side-oriented battery energy storage configuration and operation optimization method according to claim 1, wherein the building of the energy storage optimization strategy in step 3 includes two parts, namely configuration optimization and operation optimization: firstly, constructing an energy storage configuration optimization model based on historical load data of a user, and solving and determining optimal energy storage configuration power and capacity; secondly, based on the determined energy storage configuration power and capacity, a predicted monthly demand defense value is determined by constructing a monthly optimization model by utilizing monthly load prediction data; on the basis of meeting the requirement of monthly demand defense as much as possible, an energy storage day-to-day operation scheduling optimization model is constructed by combining day-ahead load prediction data; meanwhile, the influence of load prediction errors is considered, real-time rolling optimization is carried out on the operation scheduling in the energy storage day, the monthly demand defense value is updated in time, and finally the energy storage day scheduling instruction and the energy storage optimization overall strategy are obtained.
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| CN112086977B (en) * | 2020-08-28 | 2022-08-30 | 华南理工大学 | User energy storage capacity configuration method based on energy storage profit space maximization |
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