CN114844120A - New energy production simulation operation optimization method and system containing multiple types of energy storage - Google Patents

New energy production simulation operation optimization method and system containing multiple types of energy storage Download PDF

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CN114844120A
CN114844120A CN202210451205.6A CN202210451205A CN114844120A CN 114844120 A CN114844120 A CN 114844120A CN 202210451205 A CN202210451205 A CN 202210451205A CN 114844120 A CN114844120 A CN 114844120A
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energy
power
optimization
new energy
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刘纯
李湃
张金平
礼晓飞
王晓蓉
李驰
刘思扬
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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

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Abstract

The invention provides a new energy production simulation operation optimization method and system containing multi-type energy storage, which comprises the following steps: acquiring new energy operation parameters in each optimization time period according to a time sequence; bringing the new energy operation parameters into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period; making a new energy production operation optimization scheme according to operation parameters of each power supply and each type of stored energy in each optimization time period when a new energy consumption result in the optimization time period is obtained; the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function; to sum up, this patent can avoid the waste of energy storage resource, improves the accuracy of new forms of energy consumption calculation result.

Description

New energy production simulation operation optimization method and system containing multiple types of energy storage
Technical Field
The invention belongs to the technical field of new energy power generation, and particularly relates to a new energy production simulation operation optimization method and system containing multiple types of stored energy.
Background
The randomness and the intermittence of the output of the new energy bring great challenges to the planning and the operation of a power grid, and the new energy is abandoned when influenced by factors such as insufficient peak regulation capacity, limited transmission lines and the like. Energy storage resources such as chemical energy storage and pumped storage power stations have flexible adjusting capacity, and the promotion effect on new energy consumption is mainly embodied in two aspects: the peak power generation function is achieved, peak load requirements can be met, starting capacity of a conventional unit is reduced, and space is provided for new energy consumption; and secondly, aiming at the condition that new energy cannot be sent out due to circuit limitation, the stored energy can store surplus new energy electric quantity, and power generation is carried out when the new energy output is low, so that the electricity abandon caused by circuit limitation is reduced. It should be noted that, energy storage charging and discharging have certain efficiency loss, and the charging and discharging efficiency of the chemical energy storage battery is usually between 80 and 90%, that is, after the battery stores 100MWh of electricity, the electricity generation amount after the battery is completely discharged is 80 to 90 MWh. The efficiency of the pumped storage power station is about 70-75%.
The 8760h new energy production simulation calculation is an important technical means for evaluating the new energy consumption of the power grid, reasonably optimizing the medium and long-term operation modes of the power grid and the power supply and promoting the new energy consumption based on the time sequence production simulation technology. The current new energy production simulation operation optimization method usually takes the maximum consumption of new energy as a target, considers the system load balance, the standby requirement, the line sending limitation, the operation constraints of new energy, conventional power supplies and various energy storage resources and the like, and obtains the optimal operation states of various power supplies and the annual consumption result of the new energy through production simulation calculation.
Disclosure of Invention
With the increase of various energy storage resources in a power grid, the current production simulation optimization method lacks uniform and coordinated operation limitation on the energy storage resources. Because the energy storage charging and discharging efficiency has certain loss, under the condition of taking maximum consumption of new energy as a target, if the new energy is not limited, the phenomena of part of energy storage charging and the other part of energy storage discharging can occur, and the total generated energy of the new energy can be improved from the point of view of mathematical optimization, but the phenomenon is not consistent with the actual operation condition. To avoid this, constraints are usually added to make the operating states of different energy storage devices mutually exclusive.
In order to overcome the defects of the prior art, the invention provides a new energy production simulation operation optimization method containing multiple types of energy storage, which comprises the following steps:
acquiring new energy operation parameters in each optimization time period according to a time sequence;
bringing the new energy operation parameters into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period;
making a new energy production operation optimization scheme according to operation parameters of each power supply and each type of stored energy in each optimization time period when a new energy consumption result in the optimization time period is obtained;
the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function.
Preferably, the construction of the production simulation optimization model includes:
constructing an objective function by taking the maximum value of the new energy generating capacity in all the optimization time periods as a target, and constructing a constraint condition for the objective function;
constructing a production simulation optimization model based on the objective function and the constraint condition;
the constraint conditions include: the system comprises a system power balance constraint, a new energy power generation constraint, an energy storage operation constraint and an energy storage and discharge coordination operation constraint.
Preferably, the bringing the new energy operation parameters into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period includes:
on the basis of the new energy operation parameters and a pre-constructed production simulation optimization model, under the condition that all constraint conditions are met, solving to obtain the maximum value of the total power generation amount of the new energy in all optimization time periods;
and taking the maximum value of the new energy generating capacity in all the optimization time periods as a new energy consumption result.
Preferably, the operating state variables of each power supply and the multiple types of stored energy at least include one or more of the following: the system comprises new energy power generation power, power generation power of electric equipment, received power of a connecting line between a power grid and each region, charging power and discharging power of each stored energy and electric energy storage of each type of stored energy.
Preferably, the objective function is represented by the following formula:
Figure BDA0003617255010000021
in the formula, boj represents the new energy generated energy in the whole optimization time period; p is a radical of w (t) is the new energy power generation power at the moment t; t is the total number of the optimization periods.
Preferably, the system power balance constraint is as follows:
Figure BDA0003617255010000022
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure BDA0003617255010000023
generating power of the ith thermal power generating unit at the moment t;
Figure BDA0003617255010000024
for the kth energy storageCharging power at time t;
Figure BDA0003617255010000025
storing the discharge power of the kth energy at the moment t; i is the total number of the thermal power generating units; k is the total number of stored energy; d (t) is the system load at time t.
Preferably, the new energy power generation constraint is as follows:
Figure BDA0003617255010000031
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure BDA0003617255010000032
and the theoretical maximum generating power of the new energy at the moment t.
Preferably, the energy storage operation constraint is as follows:
Figure BDA0003617255010000033
in the formula (I), the compound is shown in the specification,
Figure BDA0003617255010000034
the discharge state of the kth stored energy at the moment t;
Figure BDA0003617255010000035
the charging state of the kth stored energy at the moment t;
Figure BDA0003617255010000036
the maximum discharge power of the kth stored energy;
Figure BDA0003617255010000037
the maximum charging power for the kth stored energy;
Figure BDA0003617255010000038
for charging the kth stored energyElectrical efficiency;
Figure BDA0003617255010000039
the discharge efficiency of the kth stored energy; e k (t +1) is the electric storage capacity of the kth stored energy at the moment of t + 1; e k (t) is the electric energy storage quantity of the kth stored energy at the moment t;
Figure BDA00036172550100000310
the maximum stored energy amount for the kth stored energy;E k the minimum electric storage capacity for storing the kth energy; k is the total number of stored energy; t is the total number of the optimization periods.
Preferably, the energy storage charge-discharge coordinated operation constraint is as follows:
Figure BDA00036172550100000311
in the formula (I), the compound is shown in the specification,
Figure BDA00036172550100000312
the charging state of the kth stored energy at the moment t;
Figure BDA00036172550100000313
the discharge state of the mth stored energy at the time t; k is the total number of stored energy; t is the total number of the optimization periods.
Based on the same invention concept, the invention also provides a new energy production simulation operation system containing multi-type energy storage, which comprises: the system comprises a data module, a calculation module and an optimization module;
the data module is used for acquiring new energy operation parameters in each optimization time period according to a time sequence;
the calculation module is used for substituting the new energy operation parameters into a pre-constructed production simulation optimization model to carry out optimization solution, and obtaining a new energy consumption result in an optimization time period;
the optimization module is used for formulating a new energy production operation optimization scheme based on the operation parameters of each power supply and each type of stored energy in each optimization time interval when the new energy consumption result is obtained;
the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function.
Preferably, the construction of the production simulation optimization model includes:
constructing an objective function by taking the maximum value of the new energy generating capacity in all the optimization time periods as a target, and constructing a constraint condition for the objective function;
constructing a production simulation optimization model based on the objective function and the constraint condition;
the constraint conditions include: the system comprises a system power balance constraint, a new energy power generation constraint, an energy storage operation constraint and an energy storage and discharge coordination operation constraint.
Preferably, the calculation module is specifically configured to:
on the basis of the new energy operation parameters and a pre-constructed production simulation optimization model, under the condition that all constraint conditions are met, solving to obtain the maximum value of the total power generation amount of the new energy in all optimization time periods;
and taking the maximum value of the new energy generating capacity in all the optimization time periods as a new energy consumption result.
Preferably, the operating state variables of each power supply and the multiple types of stored energy at least include one or more of the following: the power generation power of the new energy, the discovery power of the electric equipment, the power receiving power of the power grid and other parts, the charging power and the discharging power of each stored energy and the electric storage quantity of each type of stored energy.
Preferably, the objective function is represented by the following formula:
Figure BDA0003617255010000041
in the formula, obj is the new energy power generation amount in all the optimization time periods; p is a radical of w (t) is the new energy power generation power at the moment t; t is the total number of the optimization periods.
Preferably, the system power balance constraint is as follows:
Figure BDA0003617255010000042
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure BDA0003617255010000043
generating power of the ith thermal power generating unit at the moment t;
Figure BDA0003617255010000044
storing the charging power of the kth energy at the moment t;
Figure BDA0003617255010000045
storing the discharge power of the kth energy at the moment t; i is the total number of the thermal power generating units; k is the total number of stored energy; d (t) is the system load at time t.
Preferably, the new energy power generation constraint is as follows:
Figure BDA0003617255010000046
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure BDA0003617255010000047
and the theoretical maximum generating power of the new energy at the moment t.
Preferably, the energy storage operation constraint is as follows:
Figure BDA0003617255010000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003617255010000052
the discharge state of the kth stored energy at the moment t;
Figure BDA0003617255010000053
the charging state of the kth stored energy at the moment t;
Figure BDA0003617255010000054
the maximum discharge power of the kth stored energy;
Figure BDA0003617255010000055
the maximum charging power for the kth stored energy;
Figure BDA0003617255010000056
charging efficiency for the kth stored energy;
Figure BDA0003617255010000057
the discharge efficiency of the kth stored energy; e k (t +1) is the electric storage capacity of the kth stored energy at the moment of t + 1; e k (t) is the electric energy storage quantity of the kth stored energy at the moment t;
Figure BDA0003617255010000058
the maximum stored energy amount for the kth stored energy;E k the minimum electric storage capacity for storing the kth energy; k is the total number of stored energy; t is the total number of the optimization periods.
Preferably, the energy storage charge-discharge coordinated operation constraint is as follows:
Figure BDA0003617255010000059
in the formula (I), the compound is shown in the specification,
Figure BDA00036172550100000510
the charging state of the kth stored energy at the moment t;
Figure BDA00036172550100000511
the discharge state of the mth stored energy at the time t; k is the total number of stored energy; t is the total number of the optimization periods.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a new energy production simulation operation optimization method and system containing multi-type energy storage, which comprises the following steps: acquiring new energy operation parameters in each optimization time period according to a time sequence; bringing the new energy operation parameters into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period; formulating a new energy production operation optimization scheme according to operation parameters of each power supply and each type of stored energy in each optimization time period when a new energy consumption result in the optimization time period is obtained; the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function. This patent is through introducing unified energy storage and charging, discharge state variable and charge-discharge coordinated operation restraint, can avoid appearing in the production simulation that the same moment part energy storage charges, the unreasonable phenomenon of partial energy storage discharge takes place, the extravagant use of energy storage resource has been avoided, coordinate the operation restraint for linear form through the polymorphic type energy storage that establishes, the solution difficult problem that nonlinear constraint brought can be avoided, and simultaneously, this patent is applicable to polytype energy storage resource, including but not limited to common electrochemistry energy storage and extraction storage power station etc. to sum up, can know the waste that energy storage resource can be avoided to this patent, improve the accuracy of new forms of energy absorption quantity calculated result.
Drawings
FIG. 1 is a schematic flow chart of a method for optimizing the production simulation operation of new energy containing multiple types of stored energy according to the present invention;
FIG. 2 is a schematic structural diagram of a new energy production simulation operation optimization system with multi-type energy storage provided by the present invention;
FIG. 3 is a schematic diagram of a power generation operation curve of 4 energy storage power stations for 24h without considering the unified coordinated operation constraint;
FIG. 4 is a schematic diagram of a power generation operation curve of 4 energy storage power stations 24h under the consideration of the unified coordinated operation constraint.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
the schematic flow chart of the new energy production simulation operation optimization method containing multi-type energy storage provided by the invention is shown in figure 1, and comprises the following steps:
step 1: acquiring new energy operation parameters in each optimization time period according to a time sequence;
step 2: bringing the new energy operation parameters into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period;
and step 3: making a new energy production operation optimization scheme according to operation parameters of each power supply and each type of stored energy in each optimization time period when a new energy consumption result in the optimization time period is obtained;
the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function.
Specifically, step 1 further comprises:
let T be 1,2, where T denotes the tth period.
Step 1: acquiring new energy operation parameters in each optimization time period according to a time sequence;
the new energy operation parameters comprise: various power sources, energy storage, line parameters, and new energy theoretical maximum generated power and load sequence data.
After step 1, before step 2, further comprising:
establishing a new energy production simulation operation optimization model containing multiple types of stored energy, wherein the mathematical form of the model is as follows:
2-1 objective function
The objective function is that the total power generation amount of the new energy is maximum in all the optimization time periods, and the mathematical expression is as follows:
Figure BDA0003617255010000061
wherein obj is the new energy generation amount p in the whole optimization period w (t) is the new energy power generation power at the moment t; t is the total number of the optimization periods.
The main constraint conditions of the optimization model are as follows:
2-2-1 system power balance constraints
Figure BDA0003617255010000071
In the formula, p w (T) is the new energy power generation power at the moment T, T is the total number of the optimization time periods,
Figure BDA0003617255010000072
for the generated power of the ith thermal power generating unit at the time t,
Figure BDA0003617255010000073
the charging power at time t is stored for the kth,
Figure BDA0003617255010000074
and (d), (t) is the system load at the moment t.
2-2-2 new energy generated power constraint
Figure BDA0003617255010000075
In the formula, p w (T) is the new energy power generation power at the moment T, T is the total number of the optimization time periods,
Figure BDA0003617255010000076
the theoretical maximum generating power of the new energy at the time t is input data required by time series production simulation calculation, and the constraint indicates that the generating power of the new energy at any time is not more than the theoretical maximum generating capacity.
2-2-3 energy storage operation constraints
Figure BDA0003617255010000077
In the formula (I), the compound is shown in the specification,
Figure BDA0003617255010000078
is a variable from 0 to 1 when
Figure BDA0003617255010000079
When the k-th stored energy is in a discharge state at the moment of t
Figure BDA00036172550100000710
The time indicates that the kth stored energy is not in a discharge state at the moment t;
Figure BDA00036172550100000711
is a variable from 0 to 1 when
Figure BDA00036172550100000712
When it indicates that the kth stored energy is in a charging state at time t, when
Figure BDA00036172550100000713
Time indicates that the kth stored energy is not in a charged state at time t,
Figure BDA00036172550100000714
for the maximum discharge power of the kth stored energy,
Figure BDA00036172550100000715
the maximum charging power for the kth stored energy.
Figure BDA00036172550100000716
For the charging efficiency of the kth stored energy,
Figure BDA00036172550100000717
discharge efficiency for the kth stored energy, E k (t +1) is the amount of stored energy at the kth time at t +1, E k And (t) is the electric storage capacity of the kth stored energy at the moment t.
Figure BDA00036172550100000718
The maximum amount of stored energy for the kth stored energy,E k the minimum amount of stored energy for the kth.
In the above formula: the first constraint condition is the discharge power constraint of the stored energy; the second constraint condition is the charging power constraint of the stored energy; the third constraint is the state of charge constraint of the stored energy; the fourth constraint is the energy storage constraint of the stored energy.
It should be noted that the energy storage in the constraint may be different types of energy storage resources such as electrochemical energy storage, pumped storage power station, and the like.
2-2-4 energy storage charge-discharge coordination operation constraint
Figure BDA00036172550100000719
The role of this constraint is as follows: when k is m, the same stored energy can only be in one state of charging or discharging at most at the same time; when k ≠ m, any two different stored energies cannot be charged one and discharged the other.
The constraint is in a linear form, so that the consistency of charging and discharging behaviors of all stored energy at the same moment can be ensured, and the condition that partial stored energy is charged and partial discharged at the same moment can not occur.
Besides the constraints, the model also comprises system standby constraint, thermal power unit operation constraint and tie line transmission power limit constraint, and the invention is not described in detail.
In summary, the optimization target and the constraint condition form a new energy production simulation operation optimization model containing multiple types of energy storage resources, and the model is a mixed integer linear programming model.
In the step 2, the new energy operation parameters are brought into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period, and the method specifically comprises the following steps:
and calling commercial optimization software Cplex to solve the optimization model to obtain the power generation condition of each power supply and energy storage resource and the new energy consumption result.
And step 3: and formulating a new energy production operation optimization scheme according to the operation parameters of each power supply and each type of stored energy in each optimization time period when the new energy consumption result in the optimization time period is obtained.
Example 2:
based on the same inventive concept, the invention also provides a new energy production simulation operation optimization system containing multi-type energy storage, the system structure is shown in fig. 2, and the system comprises: the system comprises a data module, a calculation module and an optimization module;
the data module is used for acquiring new energy operation parameters in each optimization time period according to a time sequence;
the calculation module is used for substituting the new energy operation parameters into a pre-constructed production simulation optimization model to carry out optimization solution, and obtaining a new energy consumption result in an optimization time period;
the optimization module is used for formulating a new energy production operation optimization scheme based on the operation parameters of each power supply and each type of stored energy in each optimization time interval when the new energy consumption result is obtained;
the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function.
Wherein, the construction of the production simulation optimization model comprises the following steps:
constructing an objective function by taking the maximum value of the new energy generating capacity in all the optimization time periods as a target, and constructing a constraint condition for the objective function;
constructing a production simulation optimization model based on the objective function and the constraint condition;
the constraint conditions include: the system comprises a system power balance constraint, a new energy power generation constraint, an energy storage operation constraint and an energy storage and discharge coordination operation constraint.
Wherein, the calculation module is specifically configured to:
on the basis of the new energy operation parameters and a pre-constructed production simulation optimization model, under the condition that all constraint conditions are met, solving to obtain the maximum value of the total power generation amount of the new energy in all optimization time periods;
and taking the maximum value of the new energy generating capacity in all the optimization time periods as a new energy consumption result.
Wherein, the operation state variables of each power supply and the multi-type energy storage at least comprise one or more of the following: the power generation power of the new energy, the discovery power of the electric equipment, the power grid and other receiving power, the charging power and the discharging power of each energy storage and the electric storage quantity of each type of energy storage.
Wherein the objective function is shown as follows:
Figure BDA0003617255010000091
in the formula, obj is the new energy power generation amount in all the optimization time periods; p is a radical of w (t) is the new energy power generation power at the moment t; t is the total number of the optimization periods.
Wherein the system power balance constraint is as follows:
Figure BDA0003617255010000092
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure BDA0003617255010000093
generating power of the ith thermal power generating unit at the moment t;
Figure BDA0003617255010000094
storing the charging power of the kth energy at the moment t;
Figure BDA0003617255010000095
storing the discharge power of the kth energy at the moment t; i is the total number of the thermal power generating units; k is the total number of stored energy; d (t) is the system load at time t.
Wherein, the new energy power generation power constraint is as follows:
Figure BDA0003617255010000096
in the formula, p w (t) is the new energy generated power at the moment t; t is the total number of the optimization time periods;
Figure BDA0003617255010000097
and the theoretical maximum generating power of the new energy at the moment t.
Wherein the stored energy operating constraint is as follows:
Figure BDA0003617255010000098
in the formula (I), the compound is shown in the specification,
Figure BDA0003617255010000099
the discharge state of the kth stored energy at the moment t;
Figure BDA00036172550100000910
charging state at time t for the kth stored energy: and is
Figure BDA00036172550100000911
Is a variable of 0 to 1 when
Figure BDA00036172550100000912
When the k-th stored energy is in a discharge state at the moment of t
Figure BDA00036172550100000913
Time indicates not in a discharge state;
Figure BDA00036172550100000914
is a variable from 0 to 1 when
Figure BDA00036172550100000915
When it indicates that the kth stored energy is in a charging state at time t, when
Figure BDA0003617255010000101
Time indicates not in a charged state;
Figure BDA0003617255010000102
the maximum discharge power of the kth stored energy;
Figure BDA0003617255010000103
the maximum charging power for the kth stored energy;
Figure BDA0003617255010000104
charging efficiency for the kth stored energy;
Figure BDA0003617255010000105
the discharge efficiency of the kth stored energy; e k (t +1) is the electric storage capacity of the kth stored energy at the moment of t + 1; e k (t) is the electric energy storage quantity of the kth stored energy at the moment t;
Figure BDA0003617255010000106
the maximum stored energy amount for the kth stored energy;E k the minimum electric storage capacity for storing the kth energy; k is the total number of stored energy; t is the total number of the optimization periods.
Wherein, the energy storage charge-discharge coordination operation constraint is as follows:
Figure BDA0003617255010000107
in the formula (I), the compound is shown in the specification,
Figure BDA0003617255010000108
the charging state of the kth stored energy at the moment t;
Figure BDA0003617255010000109
the discharge state of the mth stored energy at the time t; k is the total number of stored energy; t is the total number of the optimization periods.
Example 3:
the test is carried out by taking a certain provincial power grid as an example, the power grid comprises 4 energy storage power stations, wherein the installed machines of 3 power stations are 200MW, the installed machines of 1 power station are 250MW, and the energy storage charge-discharge efficiency is 90%. The new energy production simulation operation optimization method in the patent is adopted to carry out annual optimization calculation, and new energy consumption conditions under the condition of unified coordinated operation constraint of energy storage are respectively measured, considered and not considered.
When the unified coordinated operation constraint is not considered, the new energy utilization rate is 95.23 percent after the annual new energy consumption is calculated to be 516.64 hundred million kilowatts. Fig. 3 shows the operation result of 4 power stations 24h in a certain day without considering the unified coordinated operation constraint, and it can be found that some energy storages are in charging in a part of time period, and some energy storages are in discharging, and because there is 10% electric quantity loss in charging and discharging of the energy storages, although the consumption of new energy is indirectly improved, actually only the number of times of operation of the energy storages is increased, which causes waste of energy storage resources, and does not bring gain to the load side.
When the unified coordinated operation constraint is considered, the new energy consumption is calculated to be 508.56 hundred million kilowatt hours all year round, and the new energy utilization rate is 93.75%. Fig. 4 shows the operation result of 24 hours in the same day of 4 energy storage power stations under consideration of the unified coordinated operation constraint, and it can be found that the phenomenon of simultaneous charging and discharging does not occur in different power stations under the condition of introducing the unified coordinated operation constraint. In addition, the annual consumption of the new energy is reduced by 8.08 hundred million kilowatt hours compared with the annual consumption without considering the unified coordinated operation constraint, which shows that the method can avoid the waste of energy storage resources and improve the accuracy of the calculation result of the consumption of the new energy.
Example 4:
based on the same inventive concept, the present invention also provides a computer apparatus comprising a processor and a memory, the memory being configured to store a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to implement one or more instructions, and to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the new energy production simulation operation optimization method including multiple types of energy storage in the foregoing embodiments.
Example 5:
based on the same inventive concept, the present invention further provides a storage medium, in particular, a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium may be loaded and executed by the processor to implement the steps of the new energy production simulation operation optimization method with multi-type energy storage in the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting the protection scope thereof, and although the present invention is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present invention, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the protection scope of the claims of the application.

Claims (18)

1. A new energy production simulation operation method containing multi-type energy storage is characterized by comprising the following steps:
acquiring new energy operation parameters in each optimization time period according to a time sequence;
bringing the new energy operation parameters into a pre-constructed production simulation optimization model for optimization solution to obtain a new energy consumption result in an optimization time period;
making a new energy production operation optimization scheme according to operation parameters of each power supply and each type of stored energy in each optimization time period when a new energy consumption result in the optimization time period is obtained;
the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function.
2. The method of claim 1, wherein the building of the production simulation optimization model comprises:
constructing an objective function by taking the maximum value of the new energy generating capacity in all the optimization time periods as a target, and constructing a constraint condition for the objective function;
constructing a production simulation optimization model based on the objective function and the constraint condition;
the constraint conditions include: the system comprises a system power balance constraint, a new energy power generation constraint, an energy storage operation constraint and an energy storage and discharge coordination operation constraint.
3. The method of claim 1, wherein the bringing the new energy operating parameters into a pre-constructed production simulation optimization model for optimization solution to obtain new energy consumption results within an optimization time period comprises:
on the basis of the new energy operation parameters and a pre-constructed production simulation optimization model, under the condition that all constraint conditions are met, solving to obtain the maximum value of the total power generation amount of the new energy in all optimization time periods;
and taking the maximum value of the new energy generating capacity in all the optimization time periods as a new energy consumption result.
4. The method of claim 2, wherein the operating state variables of the power sources and the multiple types of stored energy include at least one or more of: the system comprises new energy power generation power, power generation power of electric equipment, received power of a connecting line between a power grid and each region, charging power and discharging power of each stored energy and electric energy storage of each type of stored energy.
5. The method of claim 2, wherein the objective function is expressed as:
Figure FDA0003617254000000011
in the formula, obj is the new energy power generation amount in all the optimization time periods; p is a radical of w (t) is the new energy power generation power at the moment t; t is the total number of the optimization periods.
6. The method of claim 2, wherein the system power balance constraint is expressed by:
Figure FDA0003617254000000012
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure FDA0003617254000000021
generating power of the ith thermal power generating unit at the moment t;
Figure FDA0003617254000000022
storing the charging power of the kth energy at the moment t;
Figure FDA0003617254000000023
storing the discharge power of the kth energy at the moment t; i is the total number of the thermal power generating units; k is the total number of stored energy; d (t) is the system load at time t.
7. The method of claim 2, wherein the new energy source power generation constraint is represented by:
Figure FDA0003617254000000024
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure FDA0003617254000000025
and the theoretical maximum generating power of the new energy at the moment t.
8. The method of claim 2, wherein the energy storage operating constraint is represented by:
Figure FDA0003617254000000026
in the formula (I), the compound is shown in the specification,
Figure FDA0003617254000000027
the discharge state of the kth stored energy at the moment t;
Figure FDA0003617254000000028
the charging state of the kth stored energy at the moment t;
Figure FDA0003617254000000029
the maximum discharge power of the kth stored energy;
Figure FDA00036172540000000210
the maximum charging power for the kth stored energy;
Figure FDA00036172540000000211
charging efficiency for the kth stored energy;
Figure FDA00036172540000000212
the discharge efficiency of the kth stored energy; e k (t +1) is the electric storage capacity of the kth stored energy at the moment of t + 1; e k (t) is the electric energy storage quantity of the kth stored energy at the moment t;
Figure FDA00036172540000000213
the maximum stored energy amount for the kth stored energy;E k the minimum electric storage capacity for storing the kth energy; k is the total number of stored energy; t is the total number of the optimization periods.
9. The method of claim 2, wherein the energy storage charge-discharge coordinated operation constraint is expressed by the following equation:
Figure FDA00036172540000000214
in the formula (I), the compound is shown in the specification,
Figure FDA00036172540000000215
the charging state of the kth stored energy at the moment t;
Figure FDA00036172540000000216
the discharge state of the mth stored energy at the time t; k is the total number of stored energy; t is the total number of the optimization periods.
10. A new energy production simulation operation system containing multiple types of energy storage is characterized by comprising: the system comprises a data module, a calculation module and an optimization module;
the data module is used for acquiring new energy operation parameters in each optimization time period according to a time sequence;
the calculation module is used for substituting the new energy operation parameters into a pre-constructed production simulation optimization model to carry out optimization solution, and obtaining a new energy consumption result in an optimization time period;
the optimization module is used for formulating a new energy production operation optimization scheme based on the operation parameters of each power supply and each type of stored energy in each optimization time interval when the new energy consumption result is obtained;
the production simulation optimization model is constructed by an objective function constructed by taking maximum utilization of new energy as a target and a constraint condition set for the objective function.
11. The system of claim 10, wherein the building of the production simulation optimization model comprises:
constructing an objective function by taking the maximum value of the new energy generating capacity in all the optimization time periods as a target, and constructing a constraint condition for the objective function;
constructing a production simulation optimization model based on the objective function and the constraint condition;
the constraint conditions include: the system comprises a system power balance constraint, a new energy power generation constraint, an energy storage operation constraint and an energy storage and discharge coordination operation constraint.
12. The system of claim 10, wherein the calculation module is specifically configured to:
on the basis of the new energy operation parameters and a pre-constructed production simulation optimization model, under the condition that all constraint conditions are met, solving to obtain the maximum value of the total power generation amount of the new energy in all optimization time periods;
and taking the maximum value of the new energy generating capacity in all the optimization time periods as a new energy consumption result.
13. The system of claim 12, wherein the operating state variables of the power sources and the multiple types of stored energy include at least one or more of: the power generation power of the new energy, the discovery power of the electric equipment, the power receiving power of the power grid and other parts, the charging power and the discharging power of each stored energy and the electric storage quantity of each type of stored energy.
14. The system of claim 11, wherein the objective function is expressed as:
Figure FDA0003617254000000031
in the formula, obj is the new energy power generation amount in all the optimization time periods; p is a radical of w (t) is the new energy power generation power at the moment t; t is the total number of the optimization periods.
15. The system of claim 11, wherein the system power balance constraint is represented by:
Figure FDA0003617254000000032
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure FDA0003617254000000033
generating power of the ith thermal power generating unit at the moment t;
Figure FDA0003617254000000034
storing the charging power of the kth energy at the moment t;
Figure FDA0003617254000000035
storing the discharge power of the kth energy at the time t; i is the total number of the thermal power generating units; k is the total number of stored energy; d (t) is the system load at time t.
16. The system of claim 11, wherein the new energy source generated power constraint is represented by:
Figure FDA0003617254000000036
in the formula, p w (t) is the new energy power generation power at the moment t; t is the total number of the optimization time periods;
Figure FDA0003617254000000037
and the theoretical maximum generating power of the new energy at the moment t.
17. The system of claim 11, wherein the energy storage operating constraint is represented by:
Figure FDA0003617254000000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003617254000000042
the discharge state of the kth stored energy at the moment t;
Figure FDA0003617254000000043
the charging state of the kth stored energy at the moment t;
Figure FDA0003617254000000044
the maximum discharge power of the kth stored energy;
Figure FDA0003617254000000045
the maximum charging power for the kth stored energy;
Figure FDA0003617254000000046
charging efficiency for the kth stored energy;
Figure FDA0003617254000000047
the discharge efficiency of the kth stored energy; e k (t +1) is the electric storage capacity of the kth stored energy at the moment of t + 1; e k (t) is the electric energy storage quantity of the kth stored energy at the moment t;
Figure FDA0003617254000000048
the maximum stored energy amount for the kth stored energy;E k the minimum electric storage capacity for storing the kth energy; k is the total number of stored energy; t is the total number of the optimization periods.
18. The system of claim 11, wherein the energy storage charge-discharge coordinated operation constraint is represented by:
Figure FDA0003617254000000049
in the formula (I), the compound is shown in the specification,
Figure FDA00036172540000000410
the charging state of the kth stored energy at the moment t;
Figure FDA00036172540000000411
the discharge state of the mth stored energy at the time t; k is the total number of stored energy; t is the total number of the optimization periods.
CN202210451205.6A 2022-04-26 2022-04-26 New energy production simulation operation optimization method and system containing multiple types of energy storage Pending CN114844120A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116629458A (en) * 2023-07-24 2023-08-22 深圳康普盾科技股份有限公司 Energy storage equipment operation optimization method and system based on data analysis

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116629458A (en) * 2023-07-24 2023-08-22 深圳康普盾科技股份有限公司 Energy storage equipment operation optimization method and system based on data analysis
CN116629458B (en) * 2023-07-24 2024-01-12 深圳康普盾科技股份有限公司 Energy storage equipment operation optimization method and system based on data analysis

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