CN112348245A - Method and system for evaluating economical efficiency of different operation modes of user side energy storage - Google Patents

Method and system for evaluating economical efficiency of different operation modes of user side energy storage Download PDF

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CN112348245A
CN112348245A CN202011199662.8A CN202011199662A CN112348245A CN 112348245 A CN112348245 A CN 112348245A CN 202011199662 A CN202011199662 A CN 202011199662A CN 112348245 A CN112348245 A CN 112348245A
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energy storage
user side
user
power
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肖静
吴宁
冯玉斌
韩帅
鲍海波
覃斌志
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
Nanning Power Supply Bureau of Guangxi Power Grid Co Ltd
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Nanning Power Supply Bureau of Guangxi Power Grid Co Ltd
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Abstract

The invention discloses a method and a system for evaluating the economical efficiency of different operation modes of energy storage at a user side, wherein the method comprises the following steps: reading historical electricity load data of a user side; constructing a user side energy storage optimization model; importing the historical power load data into the user side energy storage optimization model for optimization, and outputting a state evaluation index after the user side is installed with energy storage; and establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes, and performing sequencing evaluation on the energy storage economic benefits of different power utilization behaviors. In the embodiment of the invention, the economic performance of different power utilization behaviors can be evaluated by using an analytic hierarchy process so as to improve the economic benefit of a user side.

Description

Method and system for evaluating economical efficiency of different operation modes of user side energy storage
Technical Field
The invention relates to the technical field of electric power, in particular to an economic assessment method and system for different operation modes of energy storage at a user side.
Background
In recent years, the frequency of peak loads of a power grid is increased continuously, and the load of a user has the characteristic of large peak-valley difference, so that the supply and demand of a power system are unbalanced in the peak period of power utilization, and the safe and stable operation of the power grid is threatened. Therefore, the power grid company increases the investment of power transmission and distribution equipment, so that the comprehensive utilization rate of the power grid equipment is reduced, and the economical efficiency of the power system is influenced. The energy storage technology has flexible bidirectional regulation capacity on power, can realize space-time translation of electric energy, has the potential of coping with unbalanced power supply and demand, is widely applied to a distributed power supply side, a power grid side and a user side of a power system at present, and has wide attention to the commercial development of energy storage at the user side. The user utilizes the energy storage system to have flexible handling characteristics to the electric power to run a 'low storage high discharge' mode so as to achieve the effects of reducing peak load and smoothing a load curve, and meanwhile, the basic capacity electric charge of the user can be reduced and the peak clipping and valley filling benefits can be obtained. Aiming at the problem that the energy storage is configured on the user side, the problems of high investment cost, low economic benefit, long investment recovery year limit and the like are main factors for restricting the commercial development of the energy storage, so that how to carry out reasonable operation scheme design and evaluation on the energy storage before the energy storage planning construction on the user side so as to obtain higher income becomes the research focus on the present stage.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method and a system for evaluating the economical efficiency of different operation modes of energy storage at a user side.
In order to solve the above problems, the present invention provides an economic assessment method for different operation modes of energy storage at a user side, wherein the method comprises:
reading historical electricity load data of a user side;
constructing a user side energy storage optimization model;
importing the historical power load data into the user side energy storage optimization model for optimization, and outputting a state evaluation index after the user side is installed with energy storage;
and establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes, and performing sequencing evaluation on the energy storage economic benefits of different power utilization behaviors.
Optionally, the building of the user-side energy storage optimization model includes:
determining an objective function of the user side energy storage optimization model, wherein the output value of the objective function is the lowest payment fee in the month after the user side installs the energy storage;
and determining the energy storage constraint conditions of the user side energy storage optimization model.
Optionally, the objective function of the user-side energy storage optimization model is as follows:
Figure BDA0002750737500000021
wherein F is the payment in the month after the user side installs the stored energy, C1For monthly electricity consumption, C2Is the basic electricity charge of the current month, m is the time-of-use electricity price, T is the number of days of the current month, PL，i(t) is the user load power at time t on day i, Pc，i(t) is the energy storage charging power at the ith day at time t, Pd，iAnd (t) the energy storage and discharge power at the time of the ith day, wherein a is the maximum demand value reported by the user, and b is the actual demand value of the user.
Optionally, the energy storage constraint conditions of the user-side energy storage optimization model include energy storage state-of-charge constraint, energy storage charge-discharge state constraint, energy storage power constraint, and energy storage state-of-charge continuity constraint.
Optionally, the state evaluation indexes after the energy storage is installed at the user side include net income, investment recovery years and investment return rate in a full life cycle; wherein,
the net benefit over the life cycle is:
B＝f1+f2-D1-D2
the investment recovery years are as follows:
Figure BDA0002750737500000022
the return on investment is as follows:
Figure BDA0002750737500000023
wherein f is1Arbitrage for operating the energy storage system in the low storage and high discharge mode, f2For reduced user base electricity charge, D1For initial investment costs, D2For operating maintenance costs, T' is the recovery period of investment.
In addition, the embodiment of the invention also provides an economic evaluation system for different operation modes of energy storage at the user side, which comprises:
the data acquisition module is used for reading historical electric load data of a user side;
the model building module is used for building a user side energy storage optimization model;
the index optimization module is used for importing the historical power load data into the user side energy storage optimization model for optimization and outputting a state evaluation index after the energy storage is installed on the user side;
and the behavior evaluation module is used for establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes and carrying out sequencing evaluation on the energy storage economic benefits of different power utilization behaviors.
Optionally, the model building module is configured to determine an objective function of the user-side energy storage optimization model, and an output value of the objective function is a minimum monthly payment fee after the user-side installs the energy storage; and determining the energy storage constraint conditions of the user side energy storage optimization model.
Optionally, the objective function of the user-side energy storage optimization model is as follows:
Figure BDA0002750737500000031
wherein F is the payment in the month after the user side installs the stored energy, C1For monthly electricity consumption, C2Is the basic electricity charge of the current month, m is the time-of-use electricity price, T is the number of days of the current month, PL，i(t) is the user load power at time t on day i, Pc，i(t) is the energy storage charging power at the ith day at time t, Pd，iAnd (t) the energy storage and discharge power at the time of the ith day, wherein a is the maximum demand value reported by the user, and b is the actual demand value of the user.
Optionally, the energy storage constraint conditions of the user-side energy storage optimization model include energy storage state-of-charge constraint, energy storage charge-discharge state constraint, energy storage power constraint, and energy storage state-of-charge continuity constraint.
Optionally, the state evaluation indexes after the energy storage is installed at the user side include net income, investment recovery years and investment return rate in a full life cycle; wherein,
the net benefit over the life cycle is:
B＝f1+f2-D1-D2
the investment recovery years are as follows:
Figure BDA0002750737500000041
the return on investment is as follows:
Figure BDA0002750737500000042
wherein f is1Arbitrage for operating the energy storage system in the low storage and high discharge mode, f2For reduced user base electricity charge, D1For initial investment costs, D2For operating maintenance costs, T' is the recovery period of investment.
In the embodiment of the invention, the user side energy storage profit can be calculated under different energy storage operation modes by setting up the user side energy storage optimization model through peak-valley arbitrage and maximum demand management, and the economic benefits of the different energy storage operation modes can be evaluated by using the analytic hierarchy process, so that the economic benefits of the user side can be improved, the algorithm structure is simple and easy to realize, and the method has certain popularization and application values.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an economic evaluation method for different operation modes of energy storage at a user side in an embodiment of the invention;
fig. 2 is a schematic structural composition diagram of an economic evaluation system for different operation modes of energy storage at a user side in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating an economic evaluation method for different operation modes of energy storage at a user side according to an embodiment of the present invention.
As shown in fig. 1, a method for evaluating the economy of different operation modes of energy storage at a user side includes the following steps:
s101, reading historical electric load data of a user side;
in the implementation process of the invention, a power grid manager utilizes a perfect power utilization information acquisition terminal data platform to acquire data, and acquires historical power utilization data of a user side in a specific time period by taking 1h as a measurement interval, wherein the historical power utilization data in the specific time period is historical power utilization data of the past year.
S102, constructing a user side energy storage optimization model;
in the implementation process of the invention, a power grid manager acquires that the maximum charge-discharge power of the energy storage device is 250kW and the capacity of the energy storage device is 525kWh according to the existing energy storage resources, and constructs a user-side energy storage optimization model by combining the relevant parameters, which is specifically represented as follows:
(1) determining an objective function of the user side energy storage optimization model, wherein the output value of the objective function is the lowest payment fee in the month after the user side installs the energy storage;
specifically, the objective function is:
Figure BDA0002750737500000051
wherein F is the payment in the month after the user side installs the stored energy, C1For monthly electricity consumption, C2Is the basic electricity charge of the current month, m is the time-of-use electricity price, T is the number of days of the current month, PL，i(t) is the user load power at time t on day i, Pc，i(t) is the energy storage charging power at the ith day at time t, Pd，i(t) the energy storage and discharge power at the ith moment, a is the maximum demand value reported by the user, and b is the actual demand value of the user;
(2) determining energy storage constraint conditions of the user side energy storage optimization model, wherein the energy storage constraint conditions comprise energy storage charge state constraint, energy storage charge-discharge state constraint, energy storage power constraint and energy storage charge state continuity constraint, and the energy storage charge state constraint conditions are respectively as follows:
a. the energy storage state of charge constraints are:
Smin≤Si(t)≤Smax
wherein S isi(t) is the energy storage state of charge at time t on day i, SminAt the lower limit of the state of charge, SmaxUpper limit of state of charge;
b. the energy storage charging and discharging state constraint is as follows:
Bd，i(t)+Bc，i(t)≤1
wherein, Bd，i(t) is the energy storage discharge state at the ith day at time t, Bc，i(t) is the energy storage charging state at the time of day i;
c. the energy storage power constraint is as follows:
Figure BDA0002750737500000061
wherein, Pd，i(t) is the energy storage discharge power at the time of day ic，i(t) is the energy storage charging power at the ith day at time t, PdmaxFor storing maximum discharge power, PcmaxStoring the maximum charging power for energy storage;
d. the energy storage state of charge continuity constraint is as follows:
Figure BDA0002750737500000062
wherein S isi(t +1) is the energy storage charge state at the t +1 th day, delta t is the charge-discharge time, etacCharging efficiency for energy storage, ηdFor energy storage discharge efficiency, E is the energy storage capacity.
S103, importing the historical power load data into the user side energy storage optimization model for optimization, and outputting a state evaluation index after the user side is provided with energy storage;
in the implementation process of the invention, in the optimization stage of the user-side energy storage optimization model, a power grid manager firstly sets a basic unit electricity price of 40 yuan/kW, an energy storage operation time period of 10 years and a total cost of 130 ten thousand yuan, and defines related peak-valley time-of-use electricity price and operation control parameter values as shown in tables 1 and 2:
TABLE 1 Peak-valley timesharing tariff
Time horizon Electricity price/yuan kWh-1
Peak hours 08:00--12:00，17:00--21:00 0.9367
Flat time period 12:00--17:00，21:00--24:00 0.6261
Off-peak time period 00:00--08:00 0.3125
TABLE 2 table of relevant operational control parameters
Operation control parameter Value/range
E 525kWh
Pdmax、Pcmax 250kW、250kW
Si(0)、Smin、Smax 0.1、0.1、0.9
ηc、ηd 0.9、0.9
Secondly, importing the historical power load data into the user side energy storage optimization model, performing model internal optimization processing by combining all the parameter values mentioned above, and outputting state evaluation indexes after the user side installs energy storage, wherein the state evaluation indexes comprise net income, investment recovery years and investment return rate in the whole life cycle, and the state evaluation indexes are respectively as follows:
a. the net benefit over the life cycle is:
B＝f1+f2-D1-D2
b. the investment recovery years are as follows:
Figure BDA0002750737500000071
c. the return on investment is as follows:
Figure BDA0002750737500000072
wherein f is1Arbitrage for operating the energy storage system in the low storage and high discharge mode, f2For reduced user base electricity charge, D1For initial investment costs, D2For operating maintenance costs, T' is the recovery period of investment.
On this basis, in step S101, the embodiment of the present invention has collected the power load data of the power consumption behavior a and the power consumption behavior B, where the power consumption behavior a is an operation mode of one charging and one discharging every day, and the power consumption behavior B is an operation mode of two charging and two discharging every day, and at this time, after the optimization of the user-side energy storage optimization model, the evaluation index parameter results of the power consumption behavior a and the power consumption behavior B may be output, as shown in table 3.
TABLE 3 evaluation index parameter results for different user behaviors
Net profit/ten thousand yuan Year of recovery/year Return on investment/%
Behavior of Power consumption A 106.16 5.6 178.57
Electric behavior B 160.30 8.7 114.94
And S104, establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes, and performing sequencing evaluation on the energy storage economic benefits of different power utilization behaviors.
In the implementation process of the invention, the power consumption behavior evaluation model determined based on the analytic hierarchy process can be composed of a target layer, a criterion layer and a scheme layer, and is specifically represented as follows: the target layer is used for decision-making or the problem to be solved, and the comprehensive optimal energy and power utilization behavior of the user side is used as the target layer in the embodiment of the invention; the criterion layer is used as a consideration factor or a decision criterion, and the embodiment of the invention takes the net income of the whole life cycle of the energy storage system, the investment recovery age and the investment return rate as the criterion layer; the scheme layer is used as an alternative scheme in decision making, and different types of user energy storage schemes are used as the scheme layer in the embodiment of the invention. The weighting coefficients for preferentially setting the criterion layers are shown in table 4:
TABLE 4 index weight coefficients for criterion layer
Guidelines Net gain Year of recovery Rate of return on investment
Weight coefficient 0.4171 0.3239 0.2590
At this time, the evaluation index parameter results of the user behavior a and the user behavior B shown in table 3 are input into the power consumption behavior evaluation model, and it can be obtained that the comprehensive weight coefficient of the solution layer aligned with the layer is shown in table 5:
TABLE 5 Integrated weight coefficients of scheme layer versus criteria layer
Net gain Year of recovery Rate of return on investment Weight coefficient sum
Behavior of Power consumption A 0.163375 0.18544 0.131545 0.48036
Electric behavior B 0.263525 0.11966 0.136455 0.51964
As can be seen from table 5, the sum of the weight coefficients of the electricity consumption behavior B is greater than the sum of the weight coefficients of the electricity consumption behavior a, which indicates that the energy storage economic benefit of the electricity consumption behavior B is higher, i.e., the energy storage scheme corresponding to the electricity consumption behavior B is optimal.
In the embodiment of the invention, the user side energy storage profit can be calculated under different energy storage operation modes by setting up the user side energy storage optimization model through peak-valley arbitrage and maximum demand management, and the economic benefits of the different energy storage operation modes can be evaluated by using the analytic hierarchy process, so that the economic benefits of the user side can be improved, the algorithm structure is simple and easy to realize, and the method has certain popularization and application values.
Examples
Referring to fig. 2, fig. 2 is a schematic structural composition diagram of an economic evaluation system for different operation modes of energy storage at a user side according to an embodiment of the present invention.
As shown in fig. 2, an economic evaluation system for different operation modes of energy storage at user side comprises:
the data acquisition module 201 is used for reading historical electricity load data of a user side;
in the implementation process of the invention, a power grid manager utilizes a perfect power utilization information acquisition terminal data platform to acquire data, and acquires historical power utilization data of a user side in a specific time period by taking 1h as a measurement interval, wherein the historical power utilization data in the specific time period is historical power utilization data of the past year.
The model establishing module 202 is used for establishing a user side energy storage optimization model;
in the implementation process of the invention, a power grid manager acquires that the maximum charge-discharge power of the energy storage device is 250kW and the capacity of the energy storage device is 525kWh according to the existing energy storage resources, and constructs a user-side energy storage optimization model by combining the relevant parameters, which is specifically represented as follows:
(1) determining an objective function of the user side energy storage optimization model, wherein the output value of the objective function is the lowest payment fee in the month after the user side installs the energy storage;
specifically, the objective function is:
Figure BDA0002750737500000091
wherein F is the payment in the month after the user side installs the stored energy, C1For monthly electricity consumption, C2Is the basic electricity charge of the current month, m is the time-of-use electricity price, T is the number of days of the current month, PL，i(t) is the user load power at time t on day i, Pc，i(t) is the energy storage charging power at the ith day at time t, Pd，i(t) the energy storage and discharge power at the ith moment, a is the maximum demand value reported by the user, and b is the actual demand value of the user;
(2) determining energy storage constraint conditions of the user side energy storage optimization model, wherein the energy storage constraint conditions comprise energy storage charge state constraint, energy storage charge-discharge state constraint, energy storage power constraint and energy storage charge state continuity constraint, and the energy storage charge state constraint conditions are respectively as follows:
a. the energy storage state of charge constraints are:
Smin≤Si(t)≤Smax
wherein S isi(t) is the energy storage state of charge at time t on day i, SminAt the lower limit of the state of charge, SmaxUpper limit of state of charge;
b. the energy storage charging and discharging state constraint is as follows:
Bd，i(t)+Bc，i(t)≤1
wherein, Bd，i(t) is the energy storage discharge state at the ith day at time t, Bc，i(t) is the energy storage charging state at the time of day i;
c. the energy storage power constraint is as follows:
Figure BDA0002750737500000101
wherein, Pd，i(t) is the energy storage discharge power at the time of day ic，i(t) is the energy storage charging power at the ith day at time t, PdmaxFor storing maximum discharge power, PcmaxStoring the maximum charging power for energy storage;
d. the energy storage state of charge continuity constraint is as follows:
Figure BDA0002750737500000102
wherein S isi(t +1) is the energy storage charge state at the t +1 th day, delta t is the charge-discharge time, etacCharging efficiency for energy storage, ηdFor energy storage discharge efficiency, E is the energy storage capacity.
The index optimization module 203 is configured to import the historical power load data into the user-side energy storage optimization model for optimization, and output a state evaluation index after energy storage is installed on the user side;
in the implementation process of the invention, in the optimization stage of the user-side energy storage optimization model, a power grid manager firstly sets a basic unit electricity price of 40 yuan/kW, an energy storage operation time period of 10 years and a total cost of 130 ten thousand yuan, and defines related peak-valley time-of-use electricity price and operation control parameter values as shown in tables 1 and 2:
TABLE 1 Peak-valley timesharing tariff
Time horizon Electricity price/yuan kWh-1
Peak hours 08:00--12:00，17:00--21:00 0.9367
Flat time period 12:00--17:00，21:00--24:00 0.6261
Off-peak time period 00:00--08:00 0.3125
TABLE 2 table of relevant operational control parameters
Operation control parameter Value/range
E 525kWh
Pdmax、Pcmax 250kW、250kW
Si(0)、Smin、Smax 0.1、0.1、0.9
ηc、ηd 0.9、0.9
Secondly, importing the historical power load data into the user side energy storage optimization model, performing model internal optimization processing by combining all the parameter values mentioned above, and outputting state evaluation indexes after the user side installs energy storage, wherein the state evaluation indexes comprise net income, investment recovery years and investment return rate in the whole life cycle, and the state evaluation indexes are respectively as follows:
a. the net benefit over the life cycle is:
B＝f1+f2-D1-D2
b. the investment recovery years are as follows:
Figure BDA0002750737500000111
c. the return on investment is as follows:
Figure BDA0002750737500000112
wherein f is1Arbitrage for operating the energy storage system in the low storage and high discharge mode, f2For reduced user base electricity charge, D1For initial investment costs, D2For operating maintenance costs, T' is the recovery period of investment.
On this basis, in step S101, the embodiment of the present invention has collected the power load data of the power consumption behavior a and the power consumption behavior B, where the power consumption behavior a is an operation mode of one charging and one discharging every day, and the power consumption behavior B is an operation mode of two charging and two discharging every day, and at this time, after the optimization of the user-side energy storage optimization model, the evaluation index parameter results of the power consumption behavior a and the power consumption behavior B may be output, as shown in table 3.
TABLE 3 evaluation index parameter results for different user behaviors
Net profit/ten thousand yuan Year of recovery/year Return on investment/%
Behavior of Power consumption A 106.16 5.6 178.57
Electric behavior B 160.30 8.7 114.94
And the behavior evaluation module 204 is used for establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes, and performing sequencing evaluation on the energy storage economic benefits of different power utilization behaviors.
In the implementation process of the invention, the power consumption behavior evaluation model determined based on the analytic hierarchy process can be composed of a target layer, a criterion layer and a scheme layer, and is specifically represented as follows: the target layer is used for decision-making or the problem to be solved, and the comprehensive optimal energy and power utilization behavior of the user side is used as the target layer in the embodiment of the invention; the criterion layer is used as a consideration factor or a decision criterion, and the embodiment of the invention takes the net income of the whole life cycle of the energy storage system, the investment recovery age and the investment return rate as the criterion layer; the scheme layer is used as an alternative scheme in decision making, and different types of user energy storage schemes are used as the scheme layer in the embodiment of the invention. The weighting coefficients for preferentially setting the criterion layers are shown in table 4:
TABLE 4 index weight coefficients for criterion layer
Guidelines Net gain Year of recovery Rate of return on investment
Weight coefficient 0.4171 0.3239 0.2590
At this time, the evaluation index parameter results of the user behavior a and the user behavior B shown in table 3 are input into the power consumption behavior evaluation model, and it can be obtained that the comprehensive weight coefficient of the solution layer aligned with the layer is shown in table 5:
TABLE 5 Integrated weight coefficients of scheme layer versus criteria layer
Net gain Year of recovery Rate of return on investment Weight coefficient sum
Behavior of Power consumption A 0.163375 0.18544 0.131545 0.48036
Electric behavior B 0.263525 0.11966 0.136455 0.51964
As can be seen from table 5, the sum of the weight coefficients of the electricity consumption behavior B is greater than the sum of the weight coefficients of the electricity consumption behavior a, which indicates that the energy storage economic benefit of the electricity consumption behavior B is higher, i.e., the energy storage scheme corresponding to the electricity consumption behavior B is optimal.
In the embodiment of the invention, the user side energy storage profit can be calculated under different energy storage operation modes by setting up the user side energy storage optimization model through peak-valley arbitrage and maximum demand management, and the economic benefits of the different energy storage operation modes can be evaluated by using the analytic hierarchy process, so that the economic benefits of the user side can be improved, the algorithm structure is simple and easy to realize, and the method has certain popularization and application values.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
The method and the system for evaluating the economy of different operation modes of the energy storage at the user side provided by the embodiment of the invention are described in detail, a specific example is adopted in the method to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A method for evaluating the economical efficiency of different operation modes of energy storage at a user side is characterized by comprising the following steps:
reading historical electricity load data of a user side;
constructing a user side energy storage optimization model;
importing the historical power load data into the user side energy storage optimization model for optimization, and outputting a state evaluation index after the user side is installed with energy storage;
and establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes, and performing sequencing evaluation on the energy storage economic benefits of different power utilization behaviors.
2. The method for economic assessment of different operation modes of user-side energy storage according to claim 1, wherein the building of the user-side energy storage optimization model comprises:
determining an objective function of the user side energy storage optimization model, wherein the output value of the objective function is the lowest payment fee in the month after the user side installs the energy storage;
and determining the energy storage constraint conditions of the user side energy storage optimization model.
3. The method for estimating the economy of the user-side energy storage different operation modes according to claim 2, wherein an objective function of the user-side energy storage optimization model is as follows:
min F＝C1+C2
Figure FDA0002750737490000011
Figure FDA0002750737490000012
wherein F is the payment in the month after the user side installs the stored energy, C1For monthly electricity consumption, C2Is the basic electricity charge of the current month, m is the time-of-use electricity price, T is the number of days of the current month, PL,i(t) is the user load power at time t on day i, Pc,i(t) is the energy storage charging power at the ith day at time t, Pd,iAnd (t) the energy storage and discharge power at the time of the ith day, wherein a is the maximum demand value reported by the user, and b is the actual demand value of the user.
4. The method for evaluating the economy of the user side energy storage in different running modes is characterized in that the energy storage constraint conditions of the user side energy storage optimization model comprise energy storage state-of-charge constraint, energy storage charge-discharge state constraint, energy storage power constraint and energy storage state-of-charge continuity constraint.
5. The method for assessing the economy of the user side energy storage in different operation modes according to claim 1, wherein the state assessment indexes after the user side installation of the energy storage comprise net income, investment recovery years and investment return rate in a whole life cycle; wherein,
the net benefit over the life cycle is:
B＝f1+f2-D1-D2
the investment recovery years are as follows:
Figure FDA0002750737490000021
the return on investment is as follows:
Figure FDA0002750737490000022
wherein f is1Arbitrage for operating the energy storage system in the low storage and high discharge mode, f2For reduced user base electricity charge, D1For initial investment costs, D2For operating maintenance costs, T' is the recovery period of investment.
6. A user side energy storage different operation mode economy evaluation system is characterized by comprising:
the data acquisition module is used for reading historical electric load data of a user side;
the model building module is used for building a user side energy storage optimization model;
the index optimization module is used for importing the historical power load data into the user side energy storage optimization model for optimization and outputting a state evaluation index after the energy storage is installed on the user side;
and the behavior evaluation module is used for establishing a power utilization behavior evaluation model based on an analytic hierarchy process by combining the state evaluation indexes and carrying out sequencing evaluation on the energy storage economic benefits of different power utilization behaviors.
7. The system for evaluating the economy of the user side energy storage in different operation modes according to claim 6, wherein the model establishing module is used for determining an objective function of the user side energy storage optimization model, and an output value of the objective function is the lowest payment fee in the month after the energy storage is installed on the user side; and determining the energy storage constraint conditions of the user side energy storage optimization model.
8. The system for estimating the economy of different running modes of energy storage at the user side according to claim 7, wherein the objective function of the optimization model of the energy storage at the user side is as follows:
min F＝C1+C2
Figure FDA0002750737490000031
Figure FDA0002750737490000032
wherein F is the payment in the month after the user side installs the stored energy, C1For monthly electricity consumption, C2Is the basic electricity charge of the current month, m is the time-of-use electricity price, T is the number of days of the current month, PL,i(t) is the user load power at time t on day i, Pc,i(t) is the energy storage charging power at the ith day at time t, Pd,iAnd (t) the energy storage and discharge power at the time of the ith day, wherein a is the maximum demand value reported by the user, and b is the actual demand value of the user.
9. The system for evaluating the economy of the user side energy storage in different running modes according to claim 7, wherein the energy storage constraint conditions of the user side energy storage optimization model comprise energy storage state-of-charge constraint, energy storage charge-discharge state constraint, energy storage power constraint and energy storage state-of-charge continuity constraint.
10. The system for assessing the economy of the user side in different running modes of energy storage according to claim 6, wherein the state assessment indexes after the energy storage is installed on the user side comprise net income, investment recovery years and investment return rate in the whole life cycle; wherein,
the net benefit over the life cycle is:
B＝f1+f2-D1-D2
the investment recovery years are as follows:
Figure FDA0002750737490000033
the return on investment is as follows:
Figure FDA0002750737490000034
wherein f is1Arbitrage for operating the energy storage system in the low storage and high discharge mode, f2For reduced user base electricity charge, D1For initial investment costs, D2For operating maintenance costs, T' is the recovery period of investment.
CN202011199662.8A 2020-10-29 2020-10-29 Method and system for evaluating economical efficiency of different operation modes of user side energy storage Pending CN112348245A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113554218A (en) * 2021-07-01 2021-10-26 国网安徽省电力有限公司电力科学研究院 Shared energy storage capacity value evaluation method and device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113554218A (en) * 2021-07-01 2021-10-26 国网安徽省电力有限公司电力科学研究院 Shared energy storage capacity value evaluation method and device
CN113554218B (en) * 2021-07-01 2024-03-22 国网安徽省电力有限公司电力科学研究院 Shared energy storage capacity value evaluation method and device

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