CN116345565A - New energy and energy storage capacity combined optimization method, system, equipment and medium - Google Patents
New energy and energy storage capacity combined optimization method, system, equipment and medium Download PDFInfo
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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
- H02J3/472—For selectively connecting the AC sources in a particular order, e.g. sequential, alternating or subsets of sources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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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
- H02J3/381—Dispersed generators
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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
- 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
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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
- 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]
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
Abstract
The invention provides a new energy and energy storage capacity combined optimization method, a system, equipment and a medium, which comprise the following steps: based on wind power and photovoltaic power generation output historical data, generating a simulated wind power and photovoltaic power generation output time sequence by using a time sequence modeling method; inputting the output time sequence into a new energy and energy storage capacity combined determination model for simulation to obtain the capacities of wind power, photovoltaic power generation and energy storage; converging the capacities of wind power, photovoltaic power generation and energy storage to obtain the optimal capacities of wind power, photovoltaic power generation and energy storage, and performing joint optimization of new energy and energy storage capacity; the capacity joint determination model takes the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and takes the balance of the time sequence power of medium-term wind power, photovoltaic and energy storage as constraint; the invention combines various new energy output time sequence scenes, considers the long-term time sequence power balance in the multiple scenes, and can formulate a new energy and energy storage combined optimization scheme meeting the requirements of economy, new energy consumption weight and utilization rate.
Description
Technical Field
The invention belongs to the technical field of new energy power generation, and particularly relates to a new energy and energy storage capacity combined optimization method, a system, equipment and a medium.
Background
With the further increase of the installation ratio of the new energy, the problems of the new energy and the conventional power supply, such as reduction of the utilization hours, increase of the absorption pressure, low system operation efficiency and the like, are more remarkable, and the mismatch between the power and the electricity of the power supply system is aggravated. The configuration of electrochemical energy storage is an effective way for solving the problem of new energy consumption. The energy storage has multiple functions, and not only can the requirements of new energy enterprises on reducing the waste wind and the waste light electric quantity, but also the requirements of different time scales of the electric power system can be met. Therefore, the new energy and energy storage collaborative planning layout and the optimal configuration research work are necessary to be carried out, and the system operation efficiency and the clean energy consumption capability are effectively improved by reasonably planning the new energy installation and the energy storage device.
However, the random fluctuation of the power output of wind power and photovoltaic power generation makes the new energy and energy storage collaborative planning conditions, operation modes and the like more complex and diversified, the existing planning theory and method based on a deterministic model and oriented to a conventional power supply cannot be completely suitable for new energy and energy storage planning, the traditional planning method based on electric quantity balance and typical electric power balance cannot consider the space-time characteristics and randomness of the new energy output and the new energy absorbing capacity of an electric power system, and in addition, in wind/light/storage collaborative planning, if calculation is performed by only adopting a certain new energy output scene, deviation between a planning result and an actual operation condition is easily caused, and the accuracy of the planning result is affected. Therefore, a joint optimization method capable of adapting to the characteristics of the current new energy and the energy storage capacity needs to be provided.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a new energy and energy storage capacity combined optimization method, which comprises the following steps:
based on pre-acquired wind power and photovoltaic power generation output historical data, respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method;
inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain a plurality of capacities of wind power, photovoltaic power generation and energy storage;
the optimal capacities of wind power, photovoltaic power generation and energy storage are determined to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage;
the new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint.
Preferably, the construction of the new energy and energy storage capacity combined determination model comprises the following steps:
the method comprises the steps of taking a simulated wind power and photovoltaic power generation output time sequence as input, taking the capacity of a plurality of wind power, photovoltaic power generation and energy storage as output, taking the minimum total investment cost of wind power, photovoltaic power and energy storage as an objective function, taking time sequence power balance of medium-and-long-term wind power, photovoltaic power and energy storage as constraint conditions, and constructing a new energy and energy storage capacity combined determination model;
The constraint condition of time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage comprises the following steps: wind power and photovoltaic installed capacity range constraint, wind power and photovoltaic power generation power range constraint, wind power and photovoltaic credible output power constraint, wind power and photovoltaic power generation capacity duty ratio constraint, wind power and photovoltaic utilization ratio constraint, conventional power output range constraint, conventional power starting capacity upper and lower limit constraint, energy storage and electricity storage balance constraint, energy storage and electricity storage range constraint, energy storage charge and discharge power range constraint, energy storage power installation range constraint, energy storage capacity range constraint, regional load balance constraint, full power grid standby capacity constraint, regional standby capacity constraint and inter-regional tie line transmission capacity constraint.
Preferably, the objective function with minimum total investment cost of wind power, photovoltaic and energy storage is expressed as follows:
wherein F is the total investment cost of wind power, photovoltaic and energy storage,
wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Electric generator for storing energy for the b-th energy of the region n, < >>
Capacity for the b-th energy storage of region n, < >>
Investment cost per installed capacity for regional n wind power, < >>
Investment cost per installed capacity for regional n-photovoltaics, < > >
Unit power generation installation investment cost for the b-th energy storage of the region n, < >>
Investment cost per unit capacity for the B-th energy storage of zone N, N being the total number of zones, B n For the amount of stored energy in region n.
Preferably, the wind power and photovoltaic trusted output constraint is represented by the following formula:
in the method, in the process of the invention,
wind power trusted output for zone n, s week, t period, < >>
Photovoltaic credibility for zone n, s, week and t time periodsOutput (I)>
Wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Normalized theoretical output of regional n wind power in the t period of the s week, < >>
Normalized theoretical output of photovoltaic power generation in the t period of the s week for the region n, +.>
For the prediction error of the wind power of region n +.>
A prediction error for the region n photovoltaic;
the wind power generation and photovoltaic power generation amount duty ratio constraint is expressed as follows:
where N is the total number of regions, T is the number of weekly periods, S is the number of weeks,
wind power output for zone n s week t period,/->
Photovoltaic output for zone n, s-th week, t-th period,>
for the power load of the region n, s and t periods, alpha is newThe lowest energy electric quantity duty ratio;
the wind power and photovoltaic utilization constraint is expressed by the following formula:
wherein, beta is the maximum electricity rejection rate of new energy;
The energy storage and electricity storage quantity balance constraint is expressed by the following formula:
in the method, in the process of the invention,
for the energy storage of the region n b in the s-th week t period, +.>
The charging and discharging power of the energy storage of the region n b in the s week t period is optimized by deltat as a unit;
the zone load balancing constraint is expressed by the following formula:
in the method, in the process of the invention,
for the output of the G-th conventional power supply in the region n in the t period of the s-th week, G n For the number of normal power supplies of region n, B n For the stored energy quantity of region n +.>
The transmission power of the interconnecting line between the region n and the region m in the t period of the s week;
the full power grid reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention,
for the power-on capacity of the g-th normal power supply of region n at the s-th week, +.>
Maximum discharge power coefficient of the energy stored for zone n, b in the t period of the s week,/>
Power generator P for storing energy for the nth energy of the region n r (s, t) is the standby requirement of the whole power grid in the t period of the s week;
the area reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention,
is the standby requirement of the region n in the t period of the s week.
Preferably, the wind power and photovoltaic installed capacity range constraint is determined according to the upper limit and the lower limit of regional wind power and photovoltaic installed capacity;
the wind power and photovoltaic power generation power range constraint is determined according to the maximum power generation power of wind power and photovoltaic theory;
The conventional power supply output range constraint is determined by taking the starting-up capacity of the conventional power supply as an upper limit and taking the product of the starting-up capacity of the conventional power supply and the minimum output coefficient as a lower limit;
the upper limit and the lower limit of the starting-up capacity of the conventional power supply are constrained, and are determined according to the upper limit and the lower limit of the starting-up capacity of the conventional power supply;
the energy storage and electric quantity range constraint takes the product of the energy storage capacity and the energy storage maximum electric quantity coefficient as an upper limit and takes the product of the energy storage capacity and the energy storage minimum electric quantity coefficient as a lower limit;
the energy storage charging and discharging power range constraint is determined by taking the product of an energy storage power generator and an energy storage maximum discharging power coefficient as an upper limit and taking the energy storage power generator and the energy storage maximum charging power coefficient as a lower limit;
the range constraint of the energy storage power generation installation machine is determined according to the upper limit and the lower limit of the energy storage power generation installation machine;
the energy storage capacity range constraint is determined according to the upper limit and the lower limit of the energy storage capacity;
and the inter-area interconnecting line transmission capacity constraint is determined according to the upper limit and the lower limit of the inter-area interconnecting line transmission power.
Preferably, the determining the optimal capacity of wind power, photovoltaic power generation and energy storage by converging the capacities of the wind power, photovoltaic power generation and energy storage includes:
Based on the capacities of the wind power, the photovoltaic power generation and the energy storage, calculating variances of the wind power, the photovoltaic power generation and the energy storage capacities respectively, and determining variance coefficients of the wind power, the photovoltaic power generation and the energy storage capacities;
taking variance coefficients of the wind power, photovoltaic power generation and energy storage capacity as convergence criteria, and respectively converging the capacities of the wind power, photovoltaic power generation and energy storage;
and calculating expected values of the wind power, photovoltaic power generation and energy storage capacity as optimal capacities of the wind power, photovoltaic power generation and energy storage based on the capacities of the wind power, photovoltaic power generation and energy storage after convergence.
Preferably, the generating the time series of simulating wind power and photovoltaic power generation output respectively based on the pre-acquired wind power and photovoltaic power generation output historical data by using a time series modeling method includes:
based on the pre-acquired wind power output historical data, generating a simulated wind power output time sequence by using a wind power output time sequence modeling method;
generating a simulated photovoltaic power generation output time sequence by using a photovoltaic power generation output time sequence modeling method based on the photovoltaic power generation output historical data acquired in advance;
according to the wind power output time sequence modeling method, wind power output historical data are taken as input, simulated wind power output time sequences are taken as output, and the simulated wind power output time sequences are obtained by calculating wind fluctuation data and determining the atmospheric fluctuation process transition probability based on SOM clustering;
According to the photovoltaic power generation output time sequence modeling method, photovoltaic power generation output historical data are taken as input, the simulated photovoltaic power generation output time sequence is taken as output, and the simulated photovoltaic power generation output time sequence is generated by using a headroom model based on weather feature uncertainty.
Preferably, the generating the simulated wind power output time sequence based on the pre-acquired wind power output historical data by using a wind power output time sequence modeling method includes:
calculating the pre-acquired wind power output historical data to obtain wind fluctuation data;
and carrying out SOM clustering on the wind fluctuation data, and carrying out atmospheric fluctuation process transition probability calculation by utilizing the clustered wind fluctuation data to obtain a simulated wind power output time sequence.
Preferably, the generating the simulated photovoltaic power generation output time sequence based on the photovoltaic power generation output historical data obtained in advance by using a photovoltaic power generation output time sequence modeling method includes:
dividing pre-acquired photovoltaic power generation output historical data into a deterministic part and a stochastic part based on weather feature uncertainty;
and simulating by utilizing a headroom model based on the photovoltaic power generation force historical data of the deterministic part and the stochastic part to obtain a simulated photovoltaic power generation force time sequence of the deterministic part and the stochastic part.
Preferably, the obtaining of the wind power and photovoltaic power generation output history data includes:
taking a fixed time resolution as an acquisition period, and acquiring wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity of a set time scale;
based on the obtained wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity, the wind power and photovoltaic power generation output data are normalized by calculating the ratio of the wind power output data to the wind power installation capacity and the ratio of the photovoltaic power generation output data to the photovoltaic installation capacity in each period, so that wind power and photovoltaic power generation output historical data are obtained.
Based on the same inventive concept, the invention also provides a new energy and energy storage capacity combined optimization system, which comprises: a time sequence module, a capacity module and an optimization module;
the time sequence module is used for respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method based on the pre-acquired wind power and photovoltaic power generation output historical data;
the capacity module is used for inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain the capacities of a plurality of wind power, photovoltaic power generation and energy storage;
The optimizing module is used for determining the optimal capacity of wind power, photovoltaic power generation and energy storage to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage;
the new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint.
Preferably, the construction of the new energy and energy storage capacity combined determination model in the capacity module includes:
the method comprises the steps of taking a simulated wind power and photovoltaic power generation output time sequence as input, taking the capacity of a plurality of wind power, photovoltaic power generation and energy storage as output, taking the minimum total investment cost of wind power, photovoltaic power and energy storage as an objective function, taking time sequence power balance of medium-and-long-term wind power, photovoltaic power and energy storage as constraint conditions, and constructing a new energy and energy storage capacity combined determination model;
the constraint condition of time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage comprises the following steps: wind power and photovoltaic installed capacity range constraint, wind power and photovoltaic power generation power range constraint, wind power and photovoltaic credible output power constraint, wind power and photovoltaic power generation capacity duty ratio constraint, wind power and photovoltaic utilization ratio constraint, conventional power output range constraint, conventional power starting capacity upper and lower limit constraint, energy storage and electricity storage balance constraint, energy storage and electricity storage range constraint, energy storage charge and discharge power range constraint, energy storage power installation range constraint, energy storage capacity range constraint, regional load balance constraint, full power grid standby capacity constraint, regional standby capacity constraint and inter-regional tie line transmission capacity constraint.
Preferably, the new energy and the energy storage capacity in the capacity module jointly determine an objective function of the model, and the objective function is represented by the following formula:
wherein F is the total investment cost of wind power, photovoltaic and energy storage,
wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Electric generator for storing energy for the b-th energy of the region n, < >>
Capacity for the b-th energy storage of region n, < >>
Investment cost per installed capacity for regional n wind power, < >>
Investment cost per installed capacity for regional n-photovoltaics, < >>
Unit power generation installation investment cost for the b-th energy storage of the region n, < >>
Investment cost per unit capacity for the B-th energy storage of zone N, N being the total number of zones, B n For the amount of stored energy in region n.
Preferably, the new energy and the energy storage capacity in the capacity module jointly determine wind power and photovoltaic credibility constraint of the model, and the constraint is expressed by the following formula:
in the method, in the process of the invention,
wind power trusted output for zone n, s week, t period, < >>
Photovoltaic reliability for zone n, s-th week, t-th period,/for example>
Wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
For region n wind power at the time of the (th) week (t)Normalized theoretical force of segment, ++>
Normalized theoretical output of photovoltaic power generation in the t period of the s week for the region n, +. >
For the prediction error of the wind power of region n +.>
A prediction error for the region n photovoltaic;
the wind power generation and photovoltaic power generation amount duty ratio constraint is expressed as follows:
where N is the total number of regions, T is the number of weekly periods, S is the number of weeks,
wind power output for zone n s week t period,/->
Photovoltaic output for zone n, s-th week, t-th period,>
the power utilization load is the power utilization load of the region n in the t period of the s-th week, and alpha is the lowest electric quantity duty ratio of the new energy;
the wind power and photovoltaic utilization constraint is expressed by the following formula:
wherein, beta is the maximum electricity rejection rate of new energy;
the energy storage and electricity storage quantity balance constraint is expressed by the following formula:
in the method, in the process of the invention,
for the energy storage of the region n b in the s-th week t period, +.>
The charging and discharging power of the energy storage of the region n b in the s week t period is optimized by deltat as a unit;
the zone load balancing constraint is expressed by the following formula:
in the method, in the process of the invention,
for the output of the G-th conventional power supply in the region n in the t period of the s-th week, G n For the number of normal power supplies of region n, B n For the stored energy quantity of region n +.>
The transmission power of the interconnecting line between the region n and the region m in the t period of the s week;
the full power grid reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention, 
For the power-on capacity of the g-th normal power supply of region n at the s-th week, +.>
Maximum discharge power coefficient of the energy stored for zone n, b in the t period of the s week,/>
Power generator P for storing energy for the nth energy of the region n r (s, t) is the standby requirement of the whole power grid in the t period of the s week;
the area reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention,
is the standby requirement of the region n in the t period of the s week.
Preferably, the new energy and the energy storage capacity in the capacity module are combined to determine the wind power and photovoltaic installed capacity range constraint of the model, and the constraint is determined according to the upper limit and the lower limit of regional wind power and photovoltaic installed capacity;
the wind power and photovoltaic power generation power range constraint is determined according to the maximum power generation power of wind power and photovoltaic theory;
the conventional power supply output range constraint is determined by taking the starting-up capacity of the conventional power supply as an upper limit and taking the product of the starting-up capacity of the conventional power supply and the minimum output coefficient as a lower limit;
the upper limit and the lower limit of the starting-up capacity of the conventional power supply are constrained, and are determined according to the upper limit and the lower limit of the starting-up capacity of the conventional power supply;
the energy storage and electric quantity range constraint takes the product of the energy storage capacity and the energy storage maximum electric quantity coefficient as an upper limit and takes the product of the energy storage capacity and the energy storage minimum electric quantity coefficient as a lower limit;
The energy storage charging and discharging power range constraint is determined by taking the product of an energy storage power generator and an energy storage maximum discharging power coefficient as an upper limit and taking the energy storage power generator and the energy storage maximum charging power coefficient as a lower limit;
the range constraint of the energy storage power generation installation machine is determined according to the upper limit and the lower limit of the energy storage power generation installation machine;
the energy storage capacity range constraint is determined according to the upper limit and the lower limit of the energy storage capacity;
and the inter-area interconnecting line transmission capacity constraint is determined according to the upper limit and the lower limit of the inter-area interconnecting line transmission power.
Preferably, the optimization module is specifically configured to:
based on the capacities of the wind power, the photovoltaic power generation and the energy storage, calculating variances of the wind power, the photovoltaic power generation and the energy storage capacities respectively, and determining variance coefficients of the wind power, the photovoltaic power generation and the energy storage capacities;
taking variance coefficients of the wind power, photovoltaic power generation and energy storage capacity as convergence criteria, and respectively converging the capacities of the wind power, photovoltaic power generation and energy storage;
and calculating expected values of the wind power, photovoltaic power generation and energy storage capacity based on the converged capacities of the wind power, photovoltaic power generation and energy storage, and performing joint optimization of new energy and energy storage capacity as the optimal capacities of the wind power, photovoltaic power generation and energy storage.
Preferably, the timing module includes: a wind power timing unit and a photovoltaic timing unit;
the wind power time sequence unit is used for generating a simulated wind power output time sequence by using a wind power output time sequence modeling method based on the pre-acquired wind power output historical data;
the photovoltaic time sequence unit is used for generating a simulated photovoltaic power generation output time sequence by utilizing a photovoltaic power generation output time sequence modeling method based on the photovoltaic power generation output historical data acquired in advance;
according to the wind power output time sequence modeling method, wind power output historical data are taken as input, simulated wind power output time sequences are taken as output, and the simulated wind power output time sequences are obtained by calculating wind fluctuation data and determining the atmospheric fluctuation process transition probability based on SOM clustering;
according to the photovoltaic power generation output time sequence modeling method, photovoltaic power generation output historical data are taken as input, the simulated photovoltaic power generation output time sequence is taken as output, and the simulated photovoltaic power generation output time sequence is generated by using a headroom model based on weather feature uncertainty.
Preferably, the wind power time sequence unit is specifically configured to:
calculating the pre-acquired wind power output historical data to obtain wind fluctuation data;
And carrying out SOM clustering on the wind fluctuation data, and carrying out atmospheric fluctuation process transition probability calculation by utilizing the clustered wind fluctuation data to obtain a simulated wind power output time sequence.
Preferably, the photovoltaic timing unit is specifically configured to:
dividing pre-acquired photovoltaic power generation output historical data into a deterministic part and a stochastic part based on weather feature uncertainty;
and simulating by utilizing a headroom model based on the photovoltaic power generation force historical data of the deterministic part and the stochastic part to obtain a simulated photovoltaic power generation force time sequence of the deterministic part and the stochastic part.
Preferably, the system further comprises: an acquisition module;
the acquisition module is specifically used for acquiring wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity of a set time scale by taking fixed time resolution as an acquisition period before the time sequence module is called;
based on the obtained wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity, the wind power and photovoltaic power generation output data are normalized by calculating the ratio of the wind power output data to the wind power installation capacity and the ratio of the photovoltaic power generation output data to the photovoltaic installation capacity in each period, so that wind power and photovoltaic power generation output historical data are obtained.
Based on the same inventive concept, the present invention also provides a computer device, comprising:
one or more processors;
a memory for storing one or more programs;
when the one or more programs are executed by the one or more processors, the new energy and energy storage capacity combined optimization method is realized.
Based on the same inventive concept, the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed, implements the new energy and energy storage capacity joint optimization method.
Compared with the closest prior art, the invention has the following beneficial effects:
1. the invention provides a new energy and energy storage capacity combined optimization method and system, comprising the following steps: based on pre-acquired wind power and photovoltaic power generation output historical data, respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method; inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain a plurality of capacities of wind power, photovoltaic power generation and energy storage; the optimal capacities of wind power, photovoltaic power generation and energy storage are determined to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage; the new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint; according to the invention, a new energy and energy storage capacity combined determination model is utilized, the output time sequence scene of wind power and photovoltaic is combined, the long-term time sequence power balance in multiple scenes is considered, and the capacities of wind power, photovoltaic power generation and energy storage are determined, so that a new energy and energy storage collaborative planning scheme meeting the requirements of economy, new energy consumption weight and utilization rate can be formulated; the optimal capacities of the wind power, the photovoltaic power generation and the energy storage are determined by converging the obtained capacities of the wind power, the photovoltaic power generation and the energy storage, so that the planning accuracy of the capacities of the wind power, the photovoltaic power generation and the energy storage can be improved;
2. The invention utilizes a time sequence modeling method to obtain a simulated wind power output time sequence and a photovoltaic power generation output time sequence which take the long time scale characteristic and the weather random characteristic of new energy into account; according to the simulated wind power output time sequence and the photovoltaic power generation output time sequence, the accuracy and the authenticity of new energy and energy storage planning can be improved.
Drawings
FIG. 1 is a schematic flow chart of a new energy and energy storage capacity combined optimization method provided by the invention;
FIG. 2 is a schematic diagram of a design idea of a new energy and energy storage capacity combined optimization method flow provided by the invention;
fig. 3 is a schematic diagram of a basic structure of a new energy and energy storage capacity combined optimization system provided by the invention.
Detailed Description
The invention provides a new energy and energy storage capacity combined optimization method, and considers the long-term time sequence power balance in multiple scenes, takes the expected value of the converged calculation result as the final result, considers the long-time scale characteristic and the random characteristic of the new energy in the planning stage, and can obtain a new energy and energy storage collaborative planning scheme meeting the requirements of economy, new energy consumption weight and utilization rate.
The following describes the embodiments of the present invention in further detail with reference to the drawings.
Example 1:
the invention provides a new energy and energy storage capacity combined optimization method, a flow diagram of which is shown in figure 1, comprising the following steps:
step 1: based on pre-acquired wind power and photovoltaic power generation output historical data, respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method;
step 2: inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain a plurality of capacities of wind power, photovoltaic power generation and energy storage;
step 3: the optimal capacities of wind power, photovoltaic power generation and energy storage are determined to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage;
the new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint.
Step 1 to step 3 above, the design concept schematic diagram of the new energy and energy storage capacity combined optimization method provided by the present invention is determined after being simplified, and the design concept schematic diagram is shown in fig. 2, and includes: the method comprises the steps of collecting and normalizing historical new energy output data, generating a plurality of groups of annual wind power/photovoltaic power generation output scenes based on the historical new energy output data, establishing a new energy and energy storage capacity combined optimization planning model (namely a new energy and energy storage capacity combined determination model), and performing multi-scene simulation to determine a wind and light storage planning scheme by utilizing the combined optimization planning model.
According to the design thought of the invention, wind power and photovoltaic power generation output historical data are firstly obtained.
Taking a fixed time resolution as an acquisition period, and acquiring wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity of a set time scale;
according to the invention, the time resolution of 15min is taken as an acquisition period, and the wind power output historical data, the photovoltaic power generation output historical data, the wind power installation capacity and the photovoltaic installation capacity of the provincial power grid with the time scale of 1 year are acquired.
Based on the obtained wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity, calculating the ratio of the wind power output data to the wind power installation capacity and the ratio of the photovoltaic power generation output data to the photovoltaic installation capacity at each moment respectively, and carrying out normalization processing on the wind power and photovoltaic power generation output data to obtain wind power and photovoltaic power generation output historical data.
Step 1, generating a time sequence of wind power and photovoltaic power generation output, specifically comprising the following steps:
based on the pre-acquired wind power output historical data, generating a simulated wind power output time sequence by using a wind power output time sequence modeling method;
according to the wind power output time sequence modeling method, wind power output historical data are taken as input, simulated wind power output time sequences are taken as output, and the wind fluctuation data are calculated, the atmospheric fluctuation process transition probability is determined based on SOM clustering, so that a plurality of groups of annual simulated wind power output time sequences are obtained; the invention relates to a wind power output time sequence modeling method, namely the combination of a wind power output time sequence modeling method based on fluctuation characteristics and a historical wind power output data processing and fluctuation identification method with an authorized bulletin number of CN104182914B, which comprises the following steps: the wind power output historical data are tidied, and the change trend of a wind fluctuation curve is quantitatively described; generating a fluctuation sample clustering feature vector according to the change trend of the wind fluctuation, and automatically clustering the wind fluctuation into three types of large fluctuation, medium fluctuation and small fluctuation by adopting a SOM two-stage clustering method based on visualization; respectively counting the multidimensional joint probability distribution of the statistical parameters of various wind fluctuation according to natural months, and calculating the transition probability of various wind fluctuation; and randomly sampling according to natural months according to the multidimensional joint probability distribution and the transition probability, and calculating the output value of the wind fluctuation output data point to obtain the simulated wind power output time sequence. Because the wind power output time sequence modeling method is the prior art, the detailed process is not described.
Generating a simulated photovoltaic power generation output time sequence by using a photovoltaic power generation output time sequence modeling method based on the photovoltaic power generation output historical data acquired in advance;
according to the photovoltaic power generation output time sequence modeling method, photovoltaic power generation output historical data are taken as input, a simulated photovoltaic power generation output time sequence is taken as output, and a headroom model is utilized to generate a simulated photovoltaic power generation output time sequence based on weather characteristic uncertainty; the invention discloses a photovoltaic power generation output time sequence modeling method, namely an authority bulletin number CN 105184465B-based photovoltaic power station output decomposition method based on a headroom model, which comprises the following steps: the output of the photovoltaic is influenced by a plurality of factors, and under the combined action of the factors, the photovoltaic output has certain regularity and strong randomness, which is called weather feature uncertainty; and decomposing the photovoltaic output into a deterministic part and a stochastic part based on uncertainty of weather features, and simulating the deterministic part and the stochastic part of the photovoltaic output based on a headroom model to obtain a simulated photovoltaic power generation output time sequence of the deterministic part and the stochastic part. Because the photovoltaic power generation output time sequence modeling method is the prior art, the detailed process is not described.
In the step 2, the construction of the combined determination model of the energy source and the energy storage capacity comprises the following steps:
the method comprises the steps of taking a simulated wind power and photovoltaic power generation output time sequence as input, taking the capacity of a plurality of wind power, photovoltaic power generation and energy storage as output, taking the minimum total investment cost of wind power, photovoltaic power and energy storage as an objective function, taking time sequence power balance of wind power, photovoltaic power and energy storage as constraint conditions, and constructing a new energy and energy storage capacity combined determination model.
The objective function is represented by the following formula:
wherein F is the total investment cost of wind power, photovoltaic and energy storage,
wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Electric generator for storing energy for the b-th energy of the region n, < >>
Capacity for the b-th energy storage of region n, < >>
Investment cost per installed capacity for regional n wind power, < >>
Investment cost per installed capacity for regional n-photovoltaics, < >>
Unit power generation installation investment cost for the b-th energy storage of the region n, < >>
Investment cost per unit capacity for the B-th energy storage of zone N, N being the total number of zones, B n For the amount of stored energy in region n.
Constraints for time-sequential power balance of mid-to-long term wind power, photovoltaic and energy storage, comprising:
(1) The wind power and photovoltaic installed capacity range constraint is determined according to the upper limit and the lower limit of regional wind power and photovoltaic installed capacity; the expression is as follows:
In the method, in the process of the invention,
wind power installation capacity lower limit for region n, < ->
Wind power installation capacity upper limit for region n, < ->
Lower photovoltaic installation capacity limit for region n, < ->
The upper limit of the photovoltaic installed capacity of the region n is known; this constraint defines the capacity range of wind power and photovoltaic installation.
(2) The wind power and photovoltaic power generation power range constraint is determined according to the maximum power generation power of wind power and photovoltaic theory; the expression is as follows:
in the method, in the process of the invention,
wind power output in the t period of the s-th week of the region n is a continuous optimization variable; />
Photovoltaic output in the t period of the s-th week of the region n is a continuous optimization variable; />
Normalized theoretical output of the wind power in the region n in the t period of the s week is a known quantity; />
Normalized theoretical output of the region n photovoltaic power generation in the t period of the s week is a known quantity; the constraint indicates that the output of each period of wind power and photovoltaic power is lower than the theoretical maximum power.
(3) Wind power and photovoltaic reliability constraints are expressed as follows:
in the method, in the process of the invention,
wind power trusted output for zone n, s week, t period, < >>
Photovoltaic credible output force of the region n in the t period of the s-th week is a continuous optimization variable; />
For the prediction error of the wind power of region n +.>
The prediction error of the photovoltaic of the region n is known; this constraint is used to calculate the trusted output of wind power and photovoltaic minus the prediction error.
(4) The wind power generation and photovoltaic power generation amount duty ratio constraint has the following expression:
where T is the number of weekly periods, S is the number of weeks,
the power load of the time period t is the s-th week of the region n, and the power load is a known quantity; alpha is the lowest electric quantity duty ratio of the new energy; the constraint ensures that the proportion of the new energy generating capacity to the total generating capacity of the whole network exceeds a certain proportion.
(5) Wind power and photovoltaic utilization constraints are expressed as follows:
wherein, beta is the maximum electricity rejection rate of new energy and is a known quantity; the constraint ensures that the power generation amount of the new energy reaches a certain proportion of the theoretical maximum power generation amount.
(6) The output range constraint of the conventional power supply is determined by taking the starting-up capacity of the conventional power supply as an upper limit and taking the product of the starting-up capacity of the conventional power supply and the minimum output coefficient as a lower limit; the expression is as follows:
in the method, in the process of the invention,
the output of the g-th conventional power supply in the region n in the t period of the s-th week is a continuous optimization variable;
starting capacity of the g-th conventional power supply in the region n at the s-th week is a continuous optimization variable; />
The minimum technical output coefficient at the s-th week for the g-th conventional power supply of the region n is a known quantity; this constraint defines the range of output of the conventional power supply.
(7) The upper limit and the lower limit of the starting-up capacity of the conventional power supply are constrained, and are determined according to the upper limit and the lower limit of the starting-up capacity of the conventional power supply; the expression is as follows:
In the method, in the process of the invention,
starting-up capacity lower limit of the g-th normal power supply in the s-th week for the region n, +.>
The upper limit of the starting capacity of the g-th conventional power supply in the s-th week is the known quantity; this constraint defines the range of conventional power supply turn-on capacities. />
(8) The energy storage and electricity storage quantity balance constraint has the following expression:
in the method, in the process of the invention,
the electricity storage quantity of the energy storage in the t period of the s week for the b th energy storage of the region n is a continuous optimization variable;
charging and discharging power of the energy storage of the region n and the b in the s week and t period is a continuous optimization variable; Δt is the unit of optimization period length in minutes, which is a known quantity.
(9) The energy storage capacity range constraint takes the product of the energy storage capacity and the energy storage maximum capacity coefficient as an upper limit and takes the product of the energy storage capacity and the energy storage minimum capacity coefficient as a lower limit; the expression is as follows:
in the method, in the process of the invention,
the capacity of the energy storage of the b th energy storage of the region n is a continuous optimization variable; />
Maximum charge capacity coefficient for the region n, b-th energy storage in the s-th period t-th period, respectively>
The minimum Chu Dianliang coefficient for the nth energy storage of region n at the nth period of the s-th week is known.
(10) The energy storage charging and discharging power range constraint is determined by taking the product of an energy storage power generator and an energy storage maximum discharging power coefficient as an upper limit and taking the energy storage power generator and the energy storage maximum charging power coefficient as a lower limit; the expression is as follows:
In the method, in the process of the invention,
the b-th energy storage power generation machine of the region n is a continuous optimization variable; />
Maximum discharge power coefficient of the energy stored for zone n, b in the t period of the s week,/>
The maximum charging power coefficient of the energy storage of the region n and the period t of the s week is known; />
Indicating that the stored energy is discharging->
Indicating that the stored energy is charging.
(11) The range constraint of the energy storage power generation installation machine is determined according to the upper limit and the lower limit of the energy storage power generation installation machine; the expression is as follows:
in the method, in the process of the invention,
upper limit of energy storage generator set for zone n b,/->
The lower limit of the energy storage power generation machine for the b-th energy storage power generation of the region n is known.
(12) The energy storage capacity range constraint is determined according to the upper limit and the lower limit of the energy storage capacity; the expression is as follows:
in the method, in the process of the invention,
for the upper limit of the energy storage capacity of zone n b, -/->
The lower limit of the energy storage capacity b of the region n is known.
(13) The regional load balancing constraint is expressed as follows:
wherein G is n For the number of normal power supplies of region n, B n For the amount of stored energy in region n,
the transmission power of a connecting line between the region n and the region m in the t period of the s week is a continuous optimization variable; />
Taking positive representation area n to transmit power to area m, and taking negative representation area m to transmit power to area n; the constraint indicates that the total power generated by all power sources and stored energy in each region is equal to the sum of the load and the delivered power. / >
(14) The full grid backup capacity constraint is expressed as follows:
wherein P is r (s, t) is the standby requirement of the whole power grid in the t period of the s week, and is a known quantity; the constraint indicates that the maximum power generation capacity of the trusted output and the conventional power supply and the stored energy of the new energy source of the whole network exceeds the load and the standby powerAnd, a method for producing the same.
(15) The area spare capacity constraint is expressed as follows:
in the method, in the process of the invention,
the standby requirement for the region n in the t period of the s week is a known quantity; the constraint represents the spare requirement for each region.
(16) The transmission capacity constraint of the inter-area interconnecting lines is determined according to the upper limit and the lower limit of the transmission power of the inter-area interconnecting lines; the expression is as follows:
in the method, in the process of the invention,
for the upper limit of the tie line transmission power between the region n and the region m in the t period of the s-th week,
the lower limit of the transmission power of the connecting line between the region n and the region m in the t period of the s-th week is known.
The step 3 specifically comprises the following steps:
based on the capacities of the wind power, the photovoltaic power generation and the energy storage, calculating variances of the wind power, the photovoltaic power generation and the energy storage capacities respectively, and determining variance coefficients of the wind power, the photovoltaic power generation and the energy storage capacities;
taking variance coefficients of the wind power, photovoltaic power generation and energy storage capacity as Monte Carlo convergence criteria, and respectively converging the capacities of the wind power, the photovoltaic power generation and the energy storage;
And calculating expected values of the wind power, photovoltaic power generation and energy storage capacity as optimal capacities of the wind power, photovoltaic power generation and energy storage based on the capacities of the wind power, photovoltaic power generation and energy storage after convergence.
According to the invention, a new energy and energy storage capacity combined determination model is utilized, the output time sequence scene of wind power and photovoltaic is combined, the long-term time sequence power balance in multiple scenes is considered, and the capacities of wind power, photovoltaic power generation and energy storage are determined, so that a new energy and energy storage collaborative planning scheme meeting the requirements of economy, new energy consumption weight and utilization rate can be formulated; the optimal capacities of the wind power, the photovoltaic power generation and the energy storage are determined by converging the obtained capacities of the wind power, the photovoltaic power generation and the energy storage, so that the planning accuracy of the capacities of the wind power, the photovoltaic power generation and the energy storage can be improved; by using a time sequence modeling method, a simulated wind power output time sequence and a photovoltaic power generation output time sequence which take the long time scale characteristic and the weather random characteristic of the new energy into account can be obtained; according to the simulated wind power output time sequence and the photovoltaic power generation output time sequence, the accuracy and the authenticity of new energy and energy storage planning can be improved.
Example 2:
based on the same inventive concept, the invention also provides a new energy and energy storage capacity combined optimization system, the basic structure schematic diagram of which is shown in fig. 3, comprising: a time sequence module, a capacity module and an optimization module;
The time sequence module is used for respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method based on the pre-acquired wind power and photovoltaic power generation output historical data;
the capacity module is used for inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain the capacities of a plurality of wind power, photovoltaic power generation and energy storage;
the optimizing module is used for determining the optimal capacity of wind power, photovoltaic power generation and energy storage to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage;
the new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint.
Preferably, the construction of the new energy and energy storage capacity combined determination model in the capacity module includes:
the method comprises the steps of taking a simulated wind power and photovoltaic power generation output time sequence as input, taking the capacity of a plurality of wind power, photovoltaic power generation and energy storage as output, taking the minimum total investment cost of wind power, photovoltaic power and energy storage as an objective function, taking time sequence power balance of wind power, photovoltaic power and energy storage as constraint conditions, and constructing a new energy and energy storage capacity combined determination model;
The constraint condition of time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage comprises the following steps: wind power and photovoltaic installed capacity range constraint, wind power and photovoltaic power generation power range constraint, wind power and photovoltaic credible output power constraint, wind power and photovoltaic power generation capacity duty ratio constraint, wind power and photovoltaic utilization ratio constraint, conventional power output range constraint, conventional power starting capacity upper and lower limit constraint, energy storage and electricity storage balance constraint, energy storage and electricity storage range constraint, energy storage charge and discharge power range constraint, energy storage power installation range constraint, energy storage capacity range constraint, regional load balance constraint, full power grid standby capacity constraint, regional standby capacity constraint and inter-regional tie line transmission capacity constraint.
Preferably, the new energy and the energy storage capacity in the capacity module jointly determine an objective function of the model, and the objective function is represented by the following formula:
wherein F is the total investment cost of wind power, photovoltaic and energy storage,
wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Electric generator for storing energy for the b-th energy of the region n, < >>
Capacity for the b-th energy storage of region n, < >>
Investment cost per installed capacity for regional n wind power, < >>
Investment cost per installed capacity for regional n-photovoltaics, < > >
Unit power generation installation investment cost for the b-th energy storage of the region n, < >>
Investment cost per unit capacity for the B-th energy storage of zone N, N being the total number of zones, B n For the amount of stored energy in region n.
Preferably, the new energy and the energy storage capacity in the capacity module jointly determine wind power and photovoltaic credibility constraint of the model, and the constraint is expressed by the following formula:
in the method, in the process of the invention,
wind power trusted output for zone n, s week, t period, < >>
Photovoltaic reliability for zone n, s-th week, t-th period,/for example>
Wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Normalized theoretical output of regional n wind power in the t period of the s week, < >>
Normalized theoretical output of photovoltaic power generation in the t period of the s week for the region n, +.>
For the prediction error of the wind power of region n +.>
A prediction error for the region n photovoltaic;
the wind power generation and photovoltaic power generation amount duty ratio constraint is expressed as follows:
where N is the total number of regions, T is the number of weekly periods, S is the number of weeks,
wind power output for zone n s week t period,/->
For zone n s weekPhotovoltaic output of the t-th period, +.>
The power utilization load is the power utilization load of the region n in the t period of the s-th week, and alpha is the lowest electric quantity duty ratio of the new energy;
the wind power and photovoltaic utilization constraint is expressed by the following formula:
Wherein, beta is the maximum electricity rejection rate of new energy;
the energy storage and electricity storage quantity balance constraint is expressed by the following formula:
in the method, in the process of the invention,
for the energy storage of the region n b in the s-th week t period, +.>
The charging and discharging power of the energy storage of the region n b in the s week t period is optimized by deltat as a unit;
the zone load balancing constraint is expressed by the following formula:
in the method, in the process of the invention,
for the output of the G-th conventional power supply in the region n in the t period of the s-th week, G n For the number of normal power supplies of region n, B n For the stored energy quantity of region n +.>
The transmission power of the interconnecting line between the region n and the region m in the t period of the s week;
the full power grid reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention,
for the power-on capacity of the g-th normal power supply of region n at the s-th week, +.>
Maximum discharge power coefficient of the energy stored for zone n, b in the t period of the s week,/>
Power generator P for storing energy for the nth energy of the region n r (s, t) is the standby requirement of the whole power grid in the t period of the s week;
the area reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention,
is the standby requirement of the region n in the t period of the s week.
Preferably, the new energy and the energy storage capacity in the capacity module are combined to determine the wind power and photovoltaic installed capacity range constraint of the model, and the constraint is determined according to the upper limit and the lower limit of regional wind power and photovoltaic installed capacity;
The wind power and photovoltaic power generation power range constraint is determined according to the maximum power generation power of wind power and photovoltaic theory;
the conventional power supply output range constraint is determined by taking the starting-up capacity of the conventional power supply as an upper limit and taking the product of the starting-up capacity of the conventional power supply and the minimum output coefficient as a lower limit;
the upper limit and the lower limit of the starting-up capacity of the conventional power supply are constrained, and are determined according to the upper limit and the lower limit of the starting-up capacity of the conventional power supply;
the energy storage and electric quantity range constraint takes the product of the energy storage capacity and the energy storage maximum electric quantity coefficient as an upper limit and takes the product of the energy storage capacity and the energy storage minimum electric quantity coefficient as a lower limit;
the energy storage charging and discharging power range constraint is determined by taking the product of an energy storage power generator and an energy storage maximum discharging power coefficient as an upper limit and taking the energy storage power generator and the energy storage maximum charging power coefficient as a lower limit;
the range constraint of the energy storage power generation installation machine is determined according to the upper limit and the lower limit of the energy storage power generation installation machine;
the energy storage capacity range constraint is determined according to the upper limit and the lower limit of the energy storage capacity;
and the inter-area interconnecting line transmission capacity constraint is determined according to the upper limit and the lower limit of the inter-area interconnecting line transmission power.
Preferably, the optimization module is specifically configured to:
Based on the capacities of the wind power, the photovoltaic power generation and the energy storage, calculating variances of the wind power, the photovoltaic power generation and the energy storage capacities respectively, and determining variance coefficients of the wind power, the photovoltaic power generation and the energy storage capacities;
taking variance coefficients of the wind power, photovoltaic power generation and energy storage capacity as convergence criteria, and respectively converging the capacities of the wind power, photovoltaic power generation and energy storage;
and calculating expected values of the wind power, photovoltaic power generation and energy storage capacity based on the converged capacities of the wind power, photovoltaic power generation and energy storage, and performing joint optimization of new energy and energy storage capacity as the optimal capacities of the wind power, photovoltaic power generation and energy storage.
Preferably, the timing module includes: a wind power timing unit and a photovoltaic timing unit;
the wind power time sequence unit is used for generating a simulated wind power output time sequence by using a wind power output time sequence modeling method based on the pre-acquired wind power output historical data;
the photovoltaic time sequence unit is used for generating a simulated photovoltaic power generation output time sequence by utilizing a photovoltaic power generation output time sequence modeling method based on the photovoltaic power generation output historical data acquired in advance;
according to the wind power output time sequence modeling method, wind power output historical data are taken as input, simulated wind power output time sequences are taken as output, and the simulated wind power output time sequences are obtained by calculating wind fluctuation data and determining the atmospheric fluctuation process transition probability based on SOM clustering;
According to the photovoltaic power generation output time sequence modeling method, photovoltaic power generation output historical data are taken as input, the simulated photovoltaic power generation output time sequence is taken as output, and the simulated photovoltaic power generation output time sequence is generated by using a headroom model based on weather feature uncertainty.
Preferably, the wind power time sequence unit is specifically configured to:
calculating the pre-acquired wind power output historical data to obtain wind fluctuation data;
and carrying out SOM clustering on the wind fluctuation data, and carrying out atmospheric fluctuation process transition probability calculation by utilizing the clustered wind fluctuation data to obtain a simulated wind power output time sequence.
Preferably, the photovoltaic timing unit is specifically configured to:
dividing pre-acquired photovoltaic power generation output historical data into a deterministic part and a stochastic part based on weather feature uncertainty;
and simulating by utilizing a headroom model based on the photovoltaic power generation force historical data of the deterministic part and the stochastic part to obtain a simulated photovoltaic power generation force time sequence of the deterministic part and the stochastic part.
Preferably, the system further comprises: an acquisition module;
the acquisition module is specifically used for acquiring wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity of a set time scale by taking fixed time resolution as an acquisition period before the time sequence module is called;
Based on the obtained wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity, the wind power and photovoltaic power generation output data are normalized by calculating the ratio of the wind power output data to the wind power installation capacity and the ratio of the photovoltaic power generation output data to the photovoltaic installation capacity in each period, so that wind power and photovoltaic power generation output historical data are obtained.
According to the invention, a new energy and energy storage capacity combined determination model in the capacity module is utilized, a wind power and photovoltaic output time sequence scene is combined, long-term time sequence power balance in multiple scenes is considered, and the capacities of wind power, photovoltaic power generation and energy storage are determined, so that a new energy and energy storage collaborative planning scheme meeting the requirements of economy, new energy consumption weight and utilization rate can be formulated; the optimization module is used for converging the obtained capacities of the wind power, the photovoltaic power generation and the energy storage, determining the optimal capacities of the wind power, the photovoltaic power generation and the energy storage, and improving the planning accuracy of the capacities of the wind power, the photovoltaic power generation and the energy storage; the invention utilizes a time sequence modeling method in a time sequence module to obtain a simulated wind power output time sequence and a photovoltaic power generation output time sequence which take the long time scale characteristic and the weather random characteristic of new energy into account; according to the simulated wind power output time sequence and the photovoltaic power generation output time sequence, the accuracy and the authenticity of new energy and energy storage planning can be improved.
Example 3:
based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of a new energy and energy storage capacity joint optimization method in the above embodiments.
The computer equipment provided by the embodiment realizes a new energy and energy storage capacity combined optimization method, utilizes a new energy and energy storage capacity combined determination model, combines the output time sequence scene of wind power and photovoltaic, and considers the long-term time sequence power balance in multiple scenes to determine the capacities of wind power, photovoltaic power generation and energy storage, thereby being capable of formulating a new energy and energy storage collaborative planning scheme meeting the requirements of economy, new energy consumption weight and utilization rate; the optimal capacities of the wind power, the photovoltaic power generation and the energy storage are determined by converging the obtained capacities of the wind power, the photovoltaic power generation and the energy storage, so that the planning accuracy of the capacities of the wind power, the photovoltaic power generation and the energy storage can be improved; the invention utilizes a time sequence modeling method to obtain a simulated wind power output time sequence and a photovoltaic power generation output time sequence which take the long time scale characteristic and the weather random characteristic of new energy into account; according to the simulated wind power output time sequence and the photovoltaic power generation output time sequence, the accuracy and the authenticity of new energy and energy storage planning can be improved.
Example 4:
based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and 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 stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a new energy and energy storage capacity joint optimization method in the above embodiments.
The storage medium provided by the embodiment realizes a new energy and energy storage capacity combined optimization method, utilizes a new energy and energy storage capacity combined determination model, combines the output time sequence scene of wind power and photovoltaic, and considers the long-term time sequence power balance in multiple scenes to determine the capacities of wind power, photovoltaic power generation and energy storage, thereby being capable of formulating a new energy and energy storage collaborative planning scheme meeting the requirements of economy, new energy consumption weight and utilization rate; the optimal capacities of the wind power, the photovoltaic power generation and the energy storage are determined by converging the obtained capacities of the wind power, the photovoltaic power generation and the energy storage, so that the planning accuracy of the capacities of the wind power, the photovoltaic power generation and the energy storage can be improved; the invention utilizes a time sequence modeling method to obtain a simulated wind power output time sequence and a photovoltaic power generation output time sequence which take the long time scale characteristic and the weather random characteristic of new energy into account; according to the simulated wind power output time sequence and the photovoltaic power generation output time sequence, the accuracy and the authenticity of new energy and energy storage planning can be improved.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of protection thereof, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: various changes, modifications, or equivalents may be made to the particular embodiments of the invention by those skilled in the art after reading the present disclosure, but such changes, modifications, or equivalents are within the scope of the invention as defined in the appended claims.
Claims (15)
1. The new energy and energy storage capacity combined optimization method is characterized by comprising the following steps of:
based on pre-acquired wind power and photovoltaic power generation output historical data, respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method;
Inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain a plurality of capacities of wind power, photovoltaic power generation and energy storage;
the optimal capacities of wind power, photovoltaic power generation and energy storage are determined to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage;
the new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint.
2. The method of claim 1, wherein the constructing of the new energy and energy storage capacity joint determination model comprises:
the method comprises the steps of taking a simulated wind power and photovoltaic power generation output time sequence as input, taking the capacity of a plurality of wind power, photovoltaic power generation and energy storage as output, taking the minimum total investment cost of wind power, photovoltaic power and energy storage as an objective function, taking time sequence power balance of medium-and-long-term wind power, photovoltaic power and energy storage as constraint conditions, and constructing a new energy and energy storage capacity combined determination model;
the constraint condition of time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage comprises the following steps: wind power and photovoltaic installed capacity range constraint, wind power and photovoltaic power generation power range constraint, wind power and photovoltaic credible output power constraint, wind power and photovoltaic power generation capacity duty ratio constraint, wind power and photovoltaic utilization ratio constraint, conventional power output range constraint, conventional power starting capacity upper and lower limit constraint, energy storage and electricity storage balance constraint, energy storage and electricity storage range constraint, energy storage charge and discharge power range constraint, energy storage power installation range constraint, energy storage capacity range constraint, regional load balance constraint, full power grid standby capacity constraint, regional standby capacity constraint and inter-regional tie line transmission capacity constraint.
3. The method of claim 2, wherein the objective function that minimizes the total investment costs for wind power, photovoltaic and energy storage is expressed by the following equation:
wherein F is the total investment cost of wind power, photovoltaic and energy storage,
wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Electric generator for storing energy for the b-th energy of the region n, < >>
Capacity for the b-th energy storage of region n, < >>
Investment cost per installed capacity for regional n wind power, < >>
Investment cost per installed capacity for regional n-photovoltaics, < >>
Unit power generation installation investment cost for the b-th energy storage of the region n, < >>
For the b th energy storage of the region nIs the investment cost per unit capacity of (a), N is the total number of areas, B n For the amount of stored energy in region n.
4. The method of claim 2, wherein the wind power and photovoltaic trusted force constraints are expressed by the following formula:
in the method, in the process of the invention,
wind power trusted output for zone n, s week, t period, < >>
Photovoltaic reliability for zone n, s-th week, t-th period,/for example>
Wind installation capacity for region n, +.>
For the photovoltaic installation capacity of region n, +.>
Normalized theoretical output of regional n wind power in the t period of the s week, < >>
Normalized theoretical output of photovoltaic power generation in the t period of the s week for the region n, +. >
For the prediction error of the wind power of region n +.>
A prediction error for the region n photovoltaic;
the wind power generation and photovoltaic power generation amount duty ratio constraint is expressed as follows:
where N is the total number of regions, T is the number of weekly periods, S is the number of weeks,
wind power output for zone n s week t period,/->
Photovoltaic output for zone n, s-th week, t-th period,>
the power utilization load is the power utilization load of the region n in the t period of the s-th week, and alpha is the lowest electric quantity duty ratio of the new energy;
the wind power and photovoltaic utilization constraint is expressed by the following formula:
wherein, beta is the maximum electricity rejection rate of new energy;
the energy storage and electricity storage quantity balance constraint is expressed by the following formula:
in the method, in the process of the invention,
for the energy storage of the region n b in the s-th week t period, +.>
The charging and discharging power of the energy storage of the region n b in the s week t period is optimized by deltat as a unit;
the zone load balancing constraint is expressed by the following formula:
in the method, in the process of the invention,
for the output of the G-th conventional power supply in the region n in the t period of the s-th week, G n For the number of normal power supplies of region n, B n For the stored energy quantity of region n +.>
The transmission power of the interconnecting line between the region n and the region m in the t period of the s week;
the full power grid reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention, 
For the power-on capacity of the g-th normal power supply of region n at the s-th week, +.>
Maximum discharge power coefficient of the energy stored for zone n, b in the t period of the s week,/>
Power generator P for storing energy for the nth energy of the region n r (s, t) is the standby requirement of the whole power grid in the t period of the s week;
the area reserve capacity constraint is expressed by the following formula:
in the method, in the process of the invention,
is the standby requirement of the region n in the t period of the s week.
5. The method of claim 2, wherein the wind power and photovoltaic installed capacity range constraint is determined based on upper and lower limits of regional wind power and photovoltaic installed capacity;
the wind power and photovoltaic power generation power range constraint is determined according to the maximum power generation power of wind power and photovoltaic theory;
the conventional power supply output range constraint is determined by taking the starting-up capacity of the conventional power supply as an upper limit and taking the product of the starting-up capacity of the conventional power supply and the minimum output coefficient as a lower limit;
the upper limit and the lower limit of the starting-up capacity of the conventional power supply are constrained, and are determined according to the upper limit and the lower limit of the starting-up capacity of the conventional power supply;
the energy storage and electric quantity range constraint takes the product of the energy storage capacity and the energy storage maximum electric quantity coefficient as an upper limit and takes the product of the energy storage capacity and the energy storage minimum electric quantity coefficient as a lower limit;
The energy storage charging and discharging power range constraint is determined by taking the product of an energy storage power generator and an energy storage maximum discharging power coefficient as an upper limit and taking the energy storage power generator and the energy storage maximum charging power coefficient as a lower limit;
the range constraint of the energy storage power generation installation machine is determined according to the upper limit and the lower limit of the energy storage power generation installation machine;
the energy storage capacity range constraint is determined according to the upper limit and the lower limit of the energy storage capacity;
and the inter-area interconnecting line transmission capacity constraint is determined according to the upper limit and the lower limit of the inter-area interconnecting line transmission power.
6. The method of claim 1, wherein determining the optimal capacity for wind power, photovoltaic power generation, and energy storage by converging the capacities for the number of wind power, photovoltaic power generation, and energy storage comprises:
based on the capacities of the wind power, the photovoltaic power generation and the energy storage, calculating variances of the wind power, the photovoltaic power generation and the energy storage capacities respectively, and determining variance coefficients of the wind power, the photovoltaic power generation and the energy storage capacities;
taking variance coefficients of the wind power, photovoltaic power generation and energy storage capacity as convergence criteria, and respectively converging the capacities of the wind power, photovoltaic power generation and energy storage;
and calculating expected values of the wind power, photovoltaic power generation and energy storage capacity as optimal capacities of the wind power, photovoltaic power generation and energy storage based on the capacities of the wind power, photovoltaic power generation and energy storage after convergence.
7. The method of claim 1, wherein generating the simulated wind power and photovoltaic power generation output time series based on the pre-acquired wind power and photovoltaic power generation output history data using a time series modeling method, respectively, comprises:
based on the pre-acquired wind power output historical data, generating a simulated wind power output time sequence by using a wind power output time sequence modeling method;
generating a simulated photovoltaic power generation output time sequence by using a photovoltaic power generation output time sequence modeling method based on the photovoltaic power generation output historical data acquired in advance;
according to the wind power output time sequence modeling method, wind power output historical data are taken as input, simulated wind power output time sequences are taken as output, and the simulated wind power output time sequences are obtained by calculating wind fluctuation data and determining the atmospheric fluctuation process transition probability based on SOM clustering;
according to the photovoltaic power generation output time sequence modeling method, photovoltaic power generation output historical data are taken as input, the simulated photovoltaic power generation output time sequence is taken as output, and the simulated photovoltaic power generation output time sequence is generated by using a headroom model based on weather feature uncertainty.
8. The method of claim 7, wherein generating a simulated wind power output time series using a wind power output time series modeling method based on pre-acquired wind power output history data comprises:
Calculating the pre-acquired wind power output historical data to obtain wind fluctuation data;
and carrying out SOM clustering on the wind fluctuation data, and carrying out atmospheric fluctuation process transition probability calculation by utilizing the clustered wind fluctuation data to obtain a simulated wind power output time sequence.
9. The method of claim 7, wherein generating the simulated photovoltaic power generation output time series using a photovoltaic power generation output time series modeling method based on the pre-acquired photovoltaic power generation output history data comprises:
dividing pre-acquired photovoltaic power generation output historical data into a deterministic part and a stochastic part based on weather feature uncertainty;
and simulating by utilizing a headroom model based on the photovoltaic power generation force historical data of the deterministic part and the stochastic part to obtain a simulated photovoltaic power generation force time sequence of the deterministic part and the stochastic part.
10. The method of claim 1, wherein the obtaining of wind power and photovoltaic power generation output history data comprises:
taking a fixed time resolution as an acquisition period, and acquiring wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity of a set time scale;
Based on the obtained wind power output data, photovoltaic power generation output data, wind power installation capacity and photovoltaic installation capacity, the wind power and photovoltaic power generation output data are normalized by calculating the ratio of the wind power output data to the wind power installation capacity and the ratio of the photovoltaic power generation output data to the photovoltaic installation capacity in each period, so that wind power and photovoltaic power generation output historical data are obtained.
11. The new energy and energy storage capacity combined optimizing system is characterized by comprising: a time sequence module, a capacity module and an optimization module;
the time sequence module is used for respectively generating simulated wind power and photovoltaic power generation output time sequences by using a time sequence modeling method based on the pre-acquired wind power and photovoltaic power generation output historical data;
the capacity module is used for inputting the simulated wind power and photovoltaic power generation output time sequence into a pre-constructed new energy and energy storage capacity combined determination model for simulation to obtain the capacities of a plurality of wind power, photovoltaic power generation and energy storage;
the optimizing module is used for determining the optimal capacity of wind power, photovoltaic power generation and energy storage to perform joint optimization of new energy and energy storage capacity by converging the capacities of the wind power, photovoltaic power generation and energy storage;
The new energy and energy storage capacity combined determination model is constructed by taking the minimum total investment cost of wind power, photovoltaic and energy storage as an objective function and taking time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage as constraint.
12. The system of claim 11, wherein the constructing of the new energy and energy storage capacity joint determination model in the capacity module comprises:
the method comprises the steps of taking a simulated wind power and photovoltaic power generation output time sequence as input, taking the capacity of a plurality of wind power, photovoltaic power generation and energy storage as output, taking the minimum total investment cost of wind power, photovoltaic power and energy storage as an objective function, taking time sequence power balance of medium-and-long-term wind power, photovoltaic power and energy storage as constraint conditions, and constructing a new energy and energy storage capacity combined determination model;
the constraint condition of time sequence power balance of medium-and-long-term wind power, photovoltaic and energy storage comprises the following steps: wind power and photovoltaic installed capacity range constraint, wind power and photovoltaic power generation power range constraint, wind power and photovoltaic credible output power constraint, wind power and photovoltaic power generation capacity duty ratio constraint, wind power and photovoltaic utilization ratio constraint, conventional power output range constraint, conventional power starting capacity upper and lower limit constraint, energy storage and electricity storage balance constraint, energy storage and electricity storage range constraint, energy storage charge and discharge power range constraint, energy storage power installation range constraint, energy storage capacity range constraint, regional load balance constraint, full power grid standby capacity constraint, regional standby capacity constraint and inter-regional tie line transmission capacity constraint.
13. The system of claim 11, wherein the optimization module is specifically configured to:
based on the capacities of the wind power, the photovoltaic power generation and the energy storage, calculating variances of the wind power, the photovoltaic power generation and the energy storage capacities respectively, and determining variance coefficients of the wind power, the photovoltaic power generation and the energy storage capacities;
taking variance coefficients of the wind power, photovoltaic power generation and energy storage capacity as convergence criteria, and respectively converging the capacities of the wind power, photovoltaic power generation and energy storage;
and calculating expected values of the wind power, photovoltaic power generation and energy storage capacity based on the converged capacities of the wind power, photovoltaic power generation and energy storage, and performing joint optimization of new energy and energy storage capacity as the optimal capacities of the wind power, photovoltaic power generation and energy storage.
14. A computer device, comprising:
one or more processors;
a memory for storing one or more programs;
a new energy source and energy storage capacity joint optimization method according to any one of claims 1 to 10, when said one or more programs are executed by said one or more processors.
15. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements a new energy source and energy storage capacity joint optimization method according to any of claims 1 to 10.
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| CN114156920B (en) * | 2021-11-29 | 2023-12-29 | 国网宁夏电力有限公司经济技术研究院 | Capacity configuration method for electric-thermal energy storage in multi-energy complementary comprehensive energy system |
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