CN114362219B - Full life cycle investment optimization method for battery energy storage at user side - Google Patents
Full life cycle investment optimization method for battery energy storage at user side Download PDFInfo
- Publication number
- CN114362219B CN114362219B CN202210022142.2A CN202210022142A CN114362219B CN 114362219 B CN114362219 B CN 114362219B CN 202210022142 A CN202210022142 A CN 202210022142A CN 114362219 B CN114362219 B CN 114362219B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- storage system
- opt
- load
- user side
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 135
- 238000005457 optimization Methods 0.000 title claims abstract description 48
- 238000000034 method Methods 0.000 title claims abstract description 12
- 230000005611 electricity Effects 0.000 claims abstract description 39
- 238000007599 discharging Methods 0.000 claims abstract description 29
- 238000004364 calculation method Methods 0.000 claims abstract description 19
- 238000004088 simulation Methods 0.000 claims abstract description 18
- 238000009826 distribution Methods 0.000 claims abstract description 11
- 230000006870 function Effects 0.000 claims description 27
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 description 9
- 230000002354 daily effect Effects 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/10—Flexible AC transmission systems [FACTS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a full life cycle investment optimization method for battery energy storage at a user side, which comprises the following steps: firstly, determining a profit mode of a battery energy storage system at a user side, and then optimizing energy storage investment at the user side; the optimization is divided into two aspects of load prediction, fitting and directory electricity price determination; then optimally modeling the energy storage investment of the user side; determining an objective function and carrying out modeling solving on the objective function, wherein the determining of the objective function comprises two aspects of net present value calculation and the objective function, and the objective function is the minimum integrated cost present value of the energy storage system in the whole life cycle; determining an energy storage system constraint equation; the constraint equation of the energy storage system comprises a charging and discharging rated power constraint, a rated capacity constraint, a power distribution capacity constraint and an energy time sequence constraint of charging and discharging actions: and simulating the mixed integer linear programming problem, and obtaining the energy storage investment optimization parameters and the energy storage investment optimization results of the user side through simulation calculation.
Description
Technical Field
The invention relates to the technical field of battery energy storage system construction, in particular to a full life cycle investment optimization method for battery energy storage at a user side.
Background
The construction of an energy power system based on new energy is the basic consensus for solving the global temperature rise problem at home and abroad at present, and the domestic working center of gravity comprises industrial structure adjustment and construction of a novel power system taking new energy as a main body. The predictable energy supply structure of the energy power system is determined, and the battery energy storage system is required to be built in a large scale in consideration of the running stability of the system and the optimization of the life cycle investment. At present, the domestic battery energy storage system is built and mainly divided into three types, namely a source side, a network side and a user side. Along with the large-scale access of new energy sources to the power grid, the combination of the energy storage system and the new energy sources becomes an effective solution for the on-site consumption of the new energy sources, and meanwhile, the construction of the energy storage at the user side provides a new thought for the power user to improve the power supply reliability and reduce the electricity consumption cost, and how to use a deterministic method to construct the optimal configuration of the energy storage system is paid attention to by a plurality of researchers.
Disclosure of Invention
The invention aims to solve the technical problems, and provides a full life cycle investment optimization method for the battery energy storage of the user side, which aims to overcome the defects in the prior art, has more accurate result and can effectively reduce the construction cost and investment risk of the battery energy storage system of the user side.
The invention aims to accomplish the following technical scheme, and discloses a full life cycle investment optimization method for battery energy storage at a user side, which comprises the following steps:
1) Determining a profit mode of the battery energy storage system at the user side; the profit mode is mainly based on peak Gu Jiacha and depends on peak-to-off electricity price in the catalogue electricity price, difference value between peak-to-off electricity price, time width of peak-to-off period and chargeable and dischargeable times caused by daily cycle times of peak-to-off period;
2) Optimizing energy storage investment of a user side; the optimization is divided into two aspects of load prediction, fitting and catalogue electricity price determination, wherein the catalogue electricity price determination is to adopt electricity price of a user as energy storage and charge in a low electricity price period according to a profit mode of an energy storage system at the user side, release electric quantity to the user in a peak or peak electricity price period and acquire electricity price or electricity price after preferential treatment; the price mechanism is formulated by the power supply department and is defined in the catalogue price list;
3) Optimizing and modeling the energy storage investment of the user side; determining an objective function and modeling and solving the objective function, wherein determining the objective function comprises the following two aspects:
a) Calculating a net present value; investment optimization of project full life cycle relates to time value calculation of investment, income and cost, and generally reduces the entry and exit of funds at different moments to the same moment, and reduces the net present value and reduces the funds at all moments to the present moment in a unified way; the present value formula is as follows:
P＝V/(1+i) n (1)；
wherein P is the present value, V is the fund value, i is the reference discount rate, and n is the financial year number;
b) An objective function; the objective function is the minimum current value of the comprehensive cost of the energy storage system in the whole life cycle:
minf＝P_nom_opt·C_p+E_nom_opt·C_e-∑ t (P_dis_opt t ·T t - P_cha_opt t ·T t )/(1+i) t//8760-1 (2)；
wherein P_nom_opt is the optimal rated power of the energy storage system, E_nom_opt is the optimal rated capacity of the energy storage system, C_p is the power unit cost of the energy storage system, C_e is the capacity unit cost of the energy storage system, P_dis_optt is the optimal discharging time sequence of the energy storage system, P_cha_opt is the optimal charging time sequence of the energy storage system, tt is the catalogue electricity price at a certain moment, i is the reference discount rate, and t is the hour number;
4) Determining an energy storage system constraint equation; the constraint equation of the energy storage system comprises a charging and discharging rated power constraint, a rated capacity constraint, a power distribution capacity constraint and an energy time sequence constraint of charging and discharging actions:
0≤P_cha_opt t ≤P_nom_opt (3)；
0≤P_dis_opt t ≤P_nom_opt (4)；
0≤E_opt t ≤E_nom_opt (5)；
Load t +P_cha_opt t ≤Load_nom (6)；
P_dis_opt t -Load t ≤0 (7)；
wherein P_cha_optt is an energy storage optimal charging power sequence, P_dis_optt is an energy storage optimal discharging power sequence, P_nom_opt is an energy storage system optimal rated power, E_optt is an energy storage optimal energy sequence, E_nom_opt is an energy storage system optimal rated capacity, load is a Load sequence, and load_nom is a distribution capacity of a Load;
E_opt t+1 ＝Charge·P_cha_opt t ·Effi_cha-Discharge·P_dis_opt t /Effi_dis+ E_opt t (8)；
Charge+Discharge＝1 (9)；
Charge,Discharge∈{0,1} (10)；
regarding the energy time sequence constraint of the charging and discharging actions of the energy storage system in the formulas (8) to (10), the principle is that the energy increment of the energy storage system in a certain period is equal to the sum of the charging and discharging electric quantity in the period; however, because the energy loss of elements such as an energy storage system frequency converter and the like causes that the charging and discharging functions are not continuous functions, the energy storage system frequency converter and the like need to be subjected to sectional processing, and 0-1 integer variable is introduced, namely the problem of energy storage system investment optimization is a mixed integer linear programming problem;
5) Simulating the mixed integer linear programming problem; because the mixed integer linear programming problem is the NP-hard problem, when the simulation period scale of the energy storage system is larger, such as 20 years of simulation, the simulation period is 8760 x 20h, the number of 0-1 variables exceeds 35 ten thousand, the number of decision variables exceeds 50 ten thousand, the constraint equation exceeds 150 ten thousand, and the full life cycle investment optimization problem can be combined and simplified according to the actual calculation force of a computer;
6) And obtaining the energy storage investment optimization parameters and the energy storage investment optimization results of the user side through simulation calculation.
Further, in step 2), the load prediction in the load prediction and fitting includes two cases, namely, the historical time-by-time data of 8760h can be obtained, and at this time, the historical data can be directly scaled according to the load development trend to be used as a predicted annual time-by-time value; in another case, only the annual, monthly and typical daily and time-by-time data of the load can be obtained, and in this case, the typical daily characteristic needs to be convolved with the annual, monthly and Zhou Texing characteristics to fit and form a 8760h load sequence, and the numerical distribution of the load sequence is adjusted based on the discrete level of the constant peak value by using the empirical constraint of the number of hours for the annual load.
The beneficial technical effects of the invention are as follows: according to the invention, full life cycle investment optimization is carried out on the user side energy storage through the steps of optimizing modeling, simulating calculation and the like on the user side energy storage investment, so that the construction of the user side energy storage system is more scientific and reasonable.
Drawings
FIG. 1 is a graph of the energy storage investment optimization result (intercepting 0-87600 h) of the invention;
FIG. 2 is a graph of the energy storage investment optimization result (intercepting 0-8760 h) of the invention;
FIG. 3 is an energy storage investment optimization result graph (intercepting 0-720 h);
FIG. 4 is a graph of energy storage investment optimization results (cut-out 0-24 h).
Detailed Description
The present invention will be further described with reference to the drawings and examples below in order to more clearly understand the objects, technical solutions and advantages of the present invention to those skilled in the art.
The invention relates to a full life cycle investment optimization method for battery energy storage at a user side, which comprises the following steps:
1) Determining a profit mode of the battery energy storage system at the user side; the profit mode is mainly based on peak Gu Jiacha and depends on peak-to-off electricity price in the catalogue electricity price, difference value between peak-to-off electricity price, time width of peak-to-off period and chargeable and dischargeable times caused by daily cycle times of peak-to-off period.
2) Optimizing energy storage investment of a user side; the optimization is divided into two aspects of load prediction and fitting and catalog electricity price determination, wherein the load prediction in the load prediction and fitting comprises the following two cases, namely, the historical time-by-time data of 8760h can be obtained, and the historical data can be directly subjected to proportional adjustment according to the load development trend to serve as a predicted annual time-by-time value; in another case, only the annual, monthly and typical daily and time-by-time data of the load can be obtained, and in this case, the typical daily characteristic needs to be convolved with the annual, monthly and Zhou Texing characteristics to fit and form a 8760h load sequence, and the numerical distribution of the load sequence is adjusted based on the discrete level of the constant peak value by using the empirical constraint of the number of hours for the annual load.
The catalogue electricity price determination is to adopt electricity price of a user as energy storage and charging in a low electricity price period according to a profit mode of an energy storage system at the user side, release electric quantity to the user in a peak or high electricity price period and acquire electricity price or preferential electricity price; the price mechanism is formulated by the power supply department and specified in the catalogue price list. For Zhejiang, the peak electricity price period is 19:00-21:00, the peak electricity price period is 8:00-11:00, 13:00-19:00, 21:00-22:00, and the low-peak electricity price period is 11:00-13:00, 22:00-8:00. Typically large industrial users and general industrial and commercial users have different peak, peak and valley prices.
3) Optimizing and modeling the energy storage investment of the user side; determining an objective function and modeling and solving the objective function, wherein determining the objective function comprises the following two aspects:
a) Calculating a net present value; investment optimization of project full life cycle relates to time value calculation of investment, income and cost, and generally reduces the entry and exit of funds at different moments to the same moment, and reduces the net present value and reduces the funds at all moments to the present moment in a unified way; the present value formula is as follows:
P＝V/(1+i) n (1)；
wherein P is the present value, V is the fund value, i is the reference discount rate, and n is the financial year number;
b) An objective function; the objective function is the minimum current value of the comprehensive cost of the energy storage system in the whole life cycle:
minf＝P_nom_opt·C_p+E_nom_opt·C_e-∑ t (P_dis_opt t ·T t - P_cha_opt t ·T t )/(1+i) t//8760-1 (2)；
wherein P_nom_opt is the optimal rated power of the energy storage system, E_nom_opt is the optimal rated capacity of the energy storage system, C_p is the power unit cost of the energy storage system, C_e is the capacity unit cost of the energy storage system, P_dis_optt is the optimal discharging time sequence of the energy storage system, P_cha_opt is the optimal charging time sequence of the energy storage system, tt is the catalogue electricity price at a certain moment, i is the reference discount rate, and t is the hour number;
4) Determining an energy storage system constraint equation; the constraint equation of the energy storage system comprises a charging and discharging rated power constraint, a rated capacity constraint, a power distribution capacity constraint and an energy time sequence constraint of charging and discharging actions:
0≤P_cha_opt t ≤P_nom_opt (3)；
0≤P_dis_opt t ≤P_nom_opt (4)；
0≤E_opt t ≤E_nom_opt (5)；
Load t +P_cha_opt t ≤Load_nom (6)；
P_dis_opt t -Load t ≤0 (7)；
wherein P_cha_optt is an energy storage optimal charging power sequence, P_dis_optt is an energy storage optimal discharging power sequence, P_nom_opt is an energy storage system optimal rated power, E_optt is an energy storage optimal energy sequence, E_nom_opt is an energy storage system optimal rated capacity, load is a Load sequence, and load_nom is a distribution capacity of a Load;
E_opt t+1 ＝Charge·P_cha_opt t ·Effi_cha-Discharge·P_dis_opt t /Effi_dis+ E_opt t (8)；
Charge+Discharge＝1 (9)；
Charge,Discharge∈{0,1} (10)；
regarding the energy time sequence constraint of the charging and discharging actions of the energy storage system in the formulas (8) to (10), the principle is that the energy increment of the energy storage system in a certain period is equal to the sum of the charging and discharging electric quantity in the period; however, because the energy loss of elements such as an energy storage system frequency converter and the like causes that the charging and discharging functions are not continuous functions, the energy storage system frequency converter and the like need to be subjected to sectional processing, and 0-1 integer variable is introduced, namely the problem of energy storage system investment optimization is a mixed integer linear programming problem;
5) Simulating the mixed integer linear programming problem; because the mixed integer linear programming problem is the NP-hard problem, when the simulation period scale of the energy storage system is larger, such as 20 years of simulation, the simulation period is 8760 x 20h, the number of 0-1 variables exceeds 35 ten thousand, the number of decision variables exceeds 50 ten thousand, the constraint equation exceeds 150 ten thousand, and the full life cycle investment optimization problem can be combined and simplified according to the actual calculation force of a computer;
6) And obtaining the energy storage investment optimization parameters and the energy storage investment optimization results of the user side through simulation calculation.
And (3) carrying out calculation analysis:
taking the energy storage project of the general industrial and commercial user side in a certain area as an example for carrying out calculation and analysis. The user is general industry and commerce, the highest load is 2000kW, and the rated capacity of the user distribution transformer is 6000kW. The charging efficiency and the discharging efficiency of the battery energy storage system are both 0.95, the power cost of the energy storage system is 0.4 yuan/W, and the capacity cost is 1.5 yuan/Wh. The life cycle of the energy storage system is 20 years, the battery of the energy storage system is replaced after 10 years, and the replacement capacity cost of the energy storage system is 1.0 yuan/Wh.
TABLE 1 user side energy storage investment optimization parameters
The computer used was Intel (R) Core (TM) i7-8850H [email protected] (12 CPUs), 16GB memory. And solving the MILP model of the investment optimization problem through PYOMO optimization modeling language under PYTHON3, wherein the solver is GLPK.
In order to observe the optimized output result of the energy storage system within 20 years of the full life cycle, interception results of 0-87600h, 0-8760h, 0-720h and 0-24h in the simulation results of 20 years are calculated and output respectively, and the method is specifically shown as follows. The optimized result graphs are all optimal result graphs of energy storage charging and discharging power, and four curves in the graphs are load time sequence, energy storage charging optimal time sequence, energy storage discharging optimal time sequence and energy storage energy time sequence.
Referring to fig. 1 to 4, in each energy storage investment optimization result diagram, fig. 1 is taken for 0-87600h, fig. 2 is taken for 0-8760h, fig. 3 is taken for 0-720h, and fig. 4 is taken for 0-24h. It can be seen that due to the characteristics of the Zhejiang catalog electricity price structure, the optimal charging and discharging mode of the energy storage system is to perform 'two charging and two discharging' every day, namely, perform one charging before 7:00, perform discharging between 7:00 and 11:00, perform the second charging between 11:00 and 13:00, and perform the second discharging between 13:00 and 21:00, especially between 19:00 and 21:00.
The calculation result shows that under the 20-year life cycle scheme, the optimal construction scheme of the energy storage system of the user is 2364kW/4492kWh, the investment in the present period is 768 ten thousand yuan, and the net present value of operation is 535 ten thousand yuan; under the 10-year life cycle scheme, the optimal construction scheme of the energy storage system of the user is 2047kW/3889kWh, the investment in the present period is 590 ten thousand yuan, and the net present value of operation is 298 ten thousand yuan.
TABLE 2 energy storage investment optimization results
The invention establishes a Mixed Integer Linear Programming (MILP) model for full life cycle investment optimization of battery energy storage at the user side. The following conclusions were reached by the example analysis:
1) The investment optimization problem is different from the unit combination or production simulation problem, and the core of the investment optimization problem is that the construction scale and the optimal solution of the production simulation are solved in a combined mode, continuous simplification is usually needed for the production model, and the calculation result can be used as quantitative reference input of the unit combination or production simulation problem.
2) Because the charge and discharge constraint equation of the energy storage system is not a continuous function and needs to be expressed in a segmented way, the problem of investment optimization of the energy storage system is a mixed integer linear programming problem. If the computer is not sufficiently powerful, the piecewise function can be considered to be combined and simplified into a continuous function, and the mixed integer linear programming problem is weakened into a linear programming problem.
3) In a typical investment optimization problem of an energy storage system at a user side, full life cycle simulation needs to be carried out on the system for 10-20 years, even the simplest single energy storage system has constraint equations of more than 150 ten thousand, the time calculated by a GLPK open source solver under PYTHON is usually several hours, the calculation result is a plurality of time series lists up to 17.5 ten thousand, and the rationality of parameters and calculation results needs to be verified through a large amount of front and back processing work.
4) Because the financial investment optimization problem of the energy storage system at the user side is different from the investment optimization problem of the whole social benefit level of the power supply planning class, the objective function cannot be unified into minimum cost and is related to specific financial revenues, so that the objective function can be set into the net present value of investment and peak Gu Jiacha, and can be considered to be set into financial indexes such as investment yield, investment recovery period and the like, but the problem is to raise the grid into a nonlinear programming problem, and a proper solver is required to be selected for calculation in combination with the specific problem.
The specific embodiments described herein are merely illustrative of the principles and functions of the present invention, and are not meant to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. It is therefore intended that all equivalent modifications and changes made by those skilled in the art without departing from the spirit and technical spirit of the present invention shall be covered by the appended claims.
Claims (2)
1. A full life cycle investment optimization method for battery energy storage at a user side is characterized in that: the method comprises the following steps:
1) Determining a profit mode of the battery energy storage system at the user side; the profit mode is mainly based on peak Gu Jiacha and depends on peak-to-off electricity price in the catalogue electricity price, difference value between peak-to-off electricity price, time width of peak-to-off period and chargeable and dischargeable times caused by daily cycle times of peak-to-off period;
2) Optimizing energy storage investment of a user side; the optimization is divided into two aspects of load prediction, fitting and catalogue electricity price determination, wherein the catalogue electricity price determination is to adopt electricity price of a user as energy storage and charge in a low electricity price period according to a profit mode of an energy storage system at the user side, release electric quantity to the user in a peak or peak electricity price period and acquire electricity price or electricity price after preferential treatment; the price mechanism is formulated by the power supply department and is defined in the catalogue price list;
3) Optimizing and modeling the energy storage investment of the user side; determining an objective function and modeling and solving the objective function, wherein determining the objective function comprises the following two aspects:
a) Calculating a net present value; investment optimization of project full life cycle relates to time value calculation of investment, income and cost, reduces the entry and exit of funds at different moments to the same moment, and reduces the net present value and the funds at all moments to the present moment in a unified way; the present value formula is as follows:
P＝V/(1+i) n (1)；
wherein P is the present value, V is the fund value, i is the reference discount rate, and n is the financial year number;
b) An objective function; the objective function is the minimum current value of the comprehensive cost of the energy storage system in the whole life cycle:
min f＝P_nom_opt·C_p+E_nom_opt·C_e-∑ t (P_dis_opt t ·T t -P_cha_opt t ·T t )/(1+i) t//8760-1 (2)；
wherein P_nom_opt is the optimal rated power of the energy storage system, E_nom_opt is the optimal rated capacity of the energy storage system, C_p is the power unit cost of the energy storage system, ce is the capacity unit cost of the energy storage system, P_dis_optt is the optimal discharge time sequence of the energy storage system, P_cha_opt is the optimal charge time sequence of the energy storage system, tt is the catalogue price of electricity at a certain moment, i is the reference discount rate, and t is the hour number;
4) Determining an energy storage system constraint equation; the constraint equation of the energy storage system comprises a charging and discharging rated power constraint, a rated capacity constraint, a power distribution capacity constraint and an energy time sequence constraint of charging and discharging actions:
0≤P_cha_opt t ≤P_nom_opt (3)；
0≤P_dis_opt t ≤P_nom_opt (4)；
0≤E_opt t ≤E_nom_opt (5)；
Load t +P_cha_opt t ≤Load_nom (6)；
P_dis_opt t -Load t ≤0 (7)；
wherein P_cha_optt is an energy storage optimal charging power sequence, P_dis_optt is an energy storage optimal discharging power sequence, P_nom_opt is an energy storage system optimal rated power, E_optt is an energy storage optimal energy sequence, E_nom_opt is an energy storage system optimal rated capacity, load is a Load sequence, and load_nom is a distribution capacity of a Load;
E_opt t+1 ＝Charge·P_cha_opt t ·Effi_cha-Discharge·P_dis_opt t /Effi_dis+E_opt t (8)；
Charge+Discharge＝1 (9)；
Charge，Discharge∈{0，1} (10)；
regarding the energy time sequence constraint of the charging and discharging actions of the energy storage system in the formulas (8) to (10), the principle is that the energy increment of the energy storage system in a certain period is equal to the sum of the charging and discharging electric quantity in the period; however, because the energy loss of elements such as an energy storage system frequency converter and the like causes that the charging and discharging functions are not continuous functions, the energy storage system frequency converter and the like need to be subjected to sectional processing, and 0-1 integer variable is introduced, namely the problem of energy storage system investment optimization is a mixed integer linear programming problem;
5) Simulating the mixed integer linear programming problem; because the mixed integer linear programming problem is the NP-hard problem, when the simulation period of the energy storage system is large in scale and simulation is carried out for 20 years, the simulation period is 8760 x 20h, the number of 0-1 variables exceeds 35 ten thousand, the number of decision variables exceeds 50 ten thousand, the constraint equation exceeds 150 ten thousand, and the full life cycle investment optimization problem can be combined and simplified according to the actual calculation force of a computer;
6) And obtaining the energy storage investment optimization parameters and the energy storage investment optimization results of the user side through simulation calculation.
2. The full life cycle investment optimization method of battery energy storage at the user side according to claim 1, wherein: in the step 2), the load prediction in the load prediction and fitting includes two cases, namely, the historical time-by-time data of 8760h can be obtained, and the historical data can be directly scaled according to the load development trend to be used as a predicted annual time-by-time value; in another case, only the annual, monthly and typical daily and time-by-time data of the load can be obtained, and in this case, the typical daily characteristic needs to be convolved with the annual, monthly and Zhou Texing characteristics to fit and form a 8760h load sequence, and the numerical distribution of the load sequence is adjusted based on the discrete level of the constant peak value by using the empirical constraint of the number of hours for the annual load.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210022142.2A CN114362219B (en) | 2022-01-10 | 2022-01-10 | Full life cycle investment optimization method for battery energy storage at user side |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210022142.2A CN114362219B (en) | 2022-01-10 | 2022-01-10 | Full life cycle investment optimization method for battery energy storage at user side |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114362219A CN114362219A (en) | 2022-04-15 |
| CN114362219B true CN114362219B (en) | 2024-04-16 |
Family
ID=81109156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210022142.2A Active CN114362219B (en) | 2022-01-10 | 2022-01-10 | Full life cycle investment optimization method for battery energy storage at user side |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114362219B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117807364B (en) * | 2024-02-26 | 2024-06-04 | 国网浙江综合能源服务有限公司 | Lamp power setting method, system and medium based on full life cycle benefit |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110570047A (en) * | 2019-07-18 | 2019-12-13 | 广东电网有限责任公司 | user side energy storage capacity configuration system and method |
| CN110797872A (en) * | 2019-11-18 | 2020-02-14 | 华润智慧能源有限公司 | User side energy storage capacity configuration method, device, equipment and storage medium |
| CN111241463A (en) * | 2020-03-03 | 2020-06-05 | 国网江苏省电力有限公司镇江供电分公司 | User side energy storage device capacity configuration method based on double-layer optimization model |
| CN111697601A (en) * | 2020-05-15 | 2020-09-22 | 西安联创分布式可再生能源研究院有限公司 | Configuration and strategy making method suitable for electrochemical energy storage system of large-scale industrial user |
| AU2020102245A4 (en) * | 2019-01-08 | 2020-10-29 | Nanjing Institute Of Technology | A grid hybrid rolling dispatching method considering congestion and energy storage tou price |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10234886B2 (en) * | 2015-08-04 | 2019-03-19 | Nec Corporation | Management of grid-scale energy storage systems |
-
2022
- 2022-01-10 CN CN202210022142.2A patent/CN114362219B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2020102245A4 (en) * | 2019-01-08 | 2020-10-29 | Nanjing Institute Of Technology | A grid hybrid rolling dispatching method considering congestion and energy storage tou price |
| CN110570047A (en) * | 2019-07-18 | 2019-12-13 | 广东电网有限责任公司 | user side energy storage capacity configuration system and method |
| CN110797872A (en) * | 2019-11-18 | 2020-02-14 | 华润智慧能源有限公司 | User side energy storage capacity configuration method, device, equipment and storage medium |
| CN111241463A (en) * | 2020-03-03 | 2020-06-05 | 国网江苏省电力有限公司镇江供电分公司 | User side energy storage device capacity configuration method based on double-layer optimization model |
| CN111697601A (en) * | 2020-05-15 | 2020-09-22 | 西安联创分布式可再生能源研究院有限公司 | Configuration and strategy making method suitable for electrochemical energy storage system of large-scale industrial user |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114362219A (en) | 2022-04-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11681264B2 (en) | Building energy storage system with planning tool | |
| US11699903B2 (en) | Building energy optimization system with economic load demand response (ELDR) optimization and ELDR user interfaces | |
| Gu et al. | Bi-level optimal low-carbon economic dispatch for an industrial park with consideration of multi-energy price incentives | |
| US10873209B2 (en) | System and method for dynamic energy storage system control | |
| Akbari-Dibavar et al. | A hybrid stochastic-robust optimization approach for energy storage arbitrage in day-ahead and real-time markets | |
| Joskow et al. | The future of the US nuclear energy industry | |
| US20190228481A1 (en) | Intelligent energy management system for distributed energy resources and energy storage systems using machine learning | |
| Chen et al. | Electricity demand response schemes in China: Pilot study and future outlook | |
| Golmohamadi et al. | Retail energy management in electricity markets: Structure, challenges and economic aspects-a review | |
| CN114362219B (en) | Full life cycle investment optimization method for battery energy storage at user side | |
| CN116362584A (en) | Economic analysis method based on user side energy storage capacity configuration | |
| Kia et al. | Coordination of heat and power scheduling in micro-grid considering inter-zonal power exchanges | |
| Shen et al. | Risk-constrained optimal bidding and scheduling for load aggregators jointly considering customer responsiveness and PV output uncertainty | |
| CN115081891A (en) | Value-added service decision method for power consumer | |
| Wang et al. | Distributionally robust optimal scheduling of integrated energy systems including hydrogen fuel cells considering uncertainties | |
| Song et al. | A low-carbon peak-load regulation trading strategy for large-scale wind power integration using information gap decision theory | |
| Helman et al. | Development of long-duration energy storage projects in electric power systems in the United States: A survey of factors which are shaping the market | |
| CN116883064A (en) | Energy storage transaction operation management method and system based on subsection price prediction | |
| CN117010715A (en) | Method and system for formulating medium-long-term bilateral transaction strategy of electric energy storage and new energy | |
| Vaka et al. | Prediction-based optimal sizing of battery energy storage systems in PV integrated microgrids for electricity bill minimization | |
| Zhang et al. | Economic dispatch of generation load aggregators based on two-stage robust optimization | |
| Song et al. | Research on demand response strategy of the electricity deviation assessment | |
| Shen et al. | Two stage robust economic dispatching of microgrid considering uncertainty of wind, solar and electricity load along with carbon emission predicted by neural network model | |
| Mamphogoro et al. | The efficacy of battery energy-storage systems installed in electricity generation and distribution plants in South Africa | |
| Pu et al. | Optimal Planning of Energy Storage for Wind Farm GENCO in Power Spot Market |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |