CN114928054B - Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy - Google Patents
Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy Download PDFInfo
- Publication number
- CN114928054B CN114928054B CN202210838805.8A CN202210838805A CN114928054B CN 114928054 B CN114928054 B CN 114928054B CN 202210838805 A CN202210838805 A CN 202210838805A CN 114928054 B CN114928054 B CN 114928054B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- optimization
- power
- cost
- new energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 216
- 238000005457 optimization Methods 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000005265 energy consumption Methods 0.000 claims abstract description 46
- 238000011084 recovery Methods 0.000 claims abstract description 31
- 230000008901 benefit Effects 0.000 claims abstract description 21
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 48
- 230000005611 electricity Effects 0.000 claims description 38
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 31
- 229910052799 carbon Inorganic materials 0.000 claims description 31
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 24
- 239000001569 carbon dioxide Substances 0.000 claims description 24
- 230000006870 function Effects 0.000 claims description 22
- 230000009467 reduction Effects 0.000 claims description 18
- 239000002803 fossil fuel Substances 0.000 claims description 16
- 238000003860 storage Methods 0.000 claims description 14
- 238000010276 construction Methods 0.000 claims description 12
- 230000007123 defense Effects 0.000 claims description 11
- 238000012937 correction Methods 0.000 claims description 6
- 238000009434 installation Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 239000003245 coal Substances 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 claims description 2
- 230000003647 oxidation Effects 0.000 claims description 2
- 238000007254 oxidation reaction Methods 0.000 claims description 2
- 239000002994 raw material Substances 0.000 claims description 2
- 238000010977 unit operation Methods 0.000 claims description 2
- 238000003723 Smelting Methods 0.000 abstract description 4
- 230000007774 longterm Effects 0.000 abstract description 3
- 238000007599 discharging Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000009826 distribution Methods 0.000 description 3
- 230000035699 permeability Effects 0.000 description 3
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000000418 atomic force spectrum Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- 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
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Power Engineering (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an energy storage multi-target coordination optimization method and system considering new energy uncertainty, wherein the method comprises the following steps: constructing an energy storage configuration optimization model by using the maximum comprehensive benefit of energy storage as a first objective function to obtain optimized rated capacity and rated power; correcting the rated capacity and the rated power to obtain real-time power after energy storage optimization; calculating the new energy consumption rate and the cost recovery age limit after energy storage optimization based on the real-time power, and judging whether the new energy consumption rate is smaller than a first preset threshold value and whether the cost recovery age limit is larger than a second preset threshold value; and if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, correcting the yield coefficient and the cost coefficient according to the difference value of the new energy consumption rate and the first preset threshold to obtain the target rated capacity and rated power. The problem of energy storage configuration under long-term return is effectively solved in the smelting industry of high-proportion new energy access, and benefit maximization of users is achieved.
Description
Technical Field
The invention belongs to the technical field of electrical automation, and particularly relates to an energy storage multi-target coordination optimization method and system considering uncertainty of new energy.
Background
Changing an energy structure, introducing a higher proportion of new energy is a key means for energy conservation and emission reduction, but as the permeability of the new energy in a power grid system is continuously increased, the randomness of the new energy and the unpredictability of load bring huge peak load pressure to the system, so that the consumption rate of the new energy is not high, and the phenomena of wind abandonment and light abandonment are more obvious. The energy storage is the best choice for solving the problem of new energy consumption and improving the peak regulation, frequency regulation and voltage regulation capabilities of a power grid, so the energy storage has attracted extensive attention in recent years.
The existing energy storage power station construction scheme mainly plans the power grid side, most of the energy storage power station construction scheme only researches the peak load regulation, frequency regulation and other capabilities of the power grid system, and influences of distributed energy storage on the income of industrial users and the consumption rate of new energy are ignored.
Disclosure of Invention
The invention provides an energy storage multi-target coordination optimization method and system considering new energy uncertainty, which are used for solving the technical problems of low income of industrial users and low consumption rate of new energy.
In a first aspect, the invention provides an energy storage multi-objective coordination optimization method considering uncertainty of new energy, which comprises the following steps: establishing an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of industrial users as a first objective function to obtain the rated capacity and rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:in the formula (I), wherein,for the peak clipping and valley filling income of the j month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in order to save the construction cost of the energy storage,、respectively a gain factor and a cost factor,the number of the months is the number of the months,the carbon emission reduction benefit for month j; correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization; calculating a new energy consumption rate and a cost recovery year limit based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year limit is larger than a second preset threshold; if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset thresholdAnd according to the differenceAnd correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
In a second aspect, the present invention provides an energy storage multi-objective coordination optimization system considering uncertainty of new energy, including: the building module is configured to build an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of industrial users as a first objective function to obtain the rated capacity and the rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:in the formula (I), wherein,for the peak clipping and valley filling income of the j month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in order to save the construction cost of the energy storage,、respectively a gain factor and a cost factor,for the number of months,the carbon emission reduction benefit for month j; the first correction module is configured to correct the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization; the judging module is configured to calculate a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judge whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold; a second correction module configured to calculate a difference between the new energy consumption rate and a first preset threshold if the new energy consumption rate is less than the first preset threshold and the cost recovery period is greater than a second preset thresholdAnd according to the differenceAnd correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
In a third aspect, an electronic device is provided, comprising: the energy storage multi-objective coordination optimization method comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the steps of the energy storage multi-objective coordination optimization method considering the uncertainty of the new energy source according to any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to perform the steps of the method for energy storage multi-objective coordination optimization considering new energy uncertainty according to any embodiment of the present invention.
The energy storage multi-objective coordination optimization method and system considering the uncertainty of the new energy aim at improving economy and the consumption rate of the new energy and reducing the carbon emission of a user, energy storage is optimized for configuration and operation of energy storage on the user side, during operation optimization, energy storage can be optimized and scheduled according to the electricity price condition and the load condition of the user, energy storage charging time is distributed in the time period of low electricity price and high new energy output, energy storage discharging is mainly concentrated in the late peak period of high electric power load and photovoltaic failure, the maximum energy storage benefit is obtained under the condition of maximum consumption of the new energy, the problem of energy storage configuration under the condition of long-term return in the smelting industry accessed by the high-proportion new energy can be effectively solved, and benefit maximization of the user is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an energy storage multi-objective coordination optimization method considering uncertainty of new energy according to an embodiment of the present invention;
fig. 2 is a structural block diagram of an energy storage multi-objective coordination optimization system considering uncertainty of new energy according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Referring to fig. 1, a flowchart of an energy storage multi-objective coordination optimization method considering uncertainty of new energy according to the present application is shown.
As shown in fig. 1, in step S101, an energy storage configuration optimization model is constructed by using a first objective function with the maximum comprehensive profit of industrial user installation and energy storage, so as to obtain a rated capacity and a rated power of energy storage optimization.
It should be noted that the comprehensive benefit is the sum of the energy storage benefit and the carbon emission reduction benefit minus the energy storage cost, and the expression of the first objective function is as follows:
in the formula,for peak clipping and valley filling gains in the j th month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in order to save the construction cost of the energy storage,、respectively a gain factor and a cost factor,for the number of months,the carbon emission reduction benefit of the j month.
In this embodiment, when the new energy permeability is high, considering the limitation of the energy storage construction cost, in the prior art, considering that the energy storage capacity obtained by optimizing two gains (peak clipping and valley filling and basic electricity price defending gain) is not enough to consume so much new energy, the phenomena of wind abandoning and light abandoning are likely to occur. At the moment, the carbon emission reduction benefit is considered, the game is carried out between the three benefits and the energy storage cost, and the energy storage capacity is properly increased to reduce or avoid the phenomena of wind and light abandonment by the optimization result.
Step S102, correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
step S103, calculating a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold;
step S104, if the new energy consumption rate is less than a first preset threshold and the cost recovery time is greater than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset thresholdAnd according to the differenceAnd correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
According to the method, the power and capacity of the stored energy are obtained through the energy storage configuration optimization modelAnd the energy storage real-time power is used as a boundary constraint condition for operation optimization within an energy storage day, so that the energy storage real-time power of 1000 days is optimized, the cost recovery period of the energy storage in the optimization period (1000 days) is calculated, and the new energy consumption rate is obtained by dividing the actual output of the new energy by the theoretical output. Coefficient in energy storage configuration objective function is judged through new energy consumption rate and energy storage cost recovery age、And (4) whether to perform correction.
In a specific embodiment, the energy storage multi-objective coordination optimization method considering the uncertainty of the new energy specifically comprises the following steps:
step 1: calculating user side energy storage gain
For a large industrial user, the paid electric charge comprises 2 parts of basic electric charge and electric power charge. The basic electricity charge major users select actual demand charging (monthly maximum power demand), namely, the actual demand in the month is multiplied by the basic electricity charge unit price; the electricity rate and the electricity charge are the commonly known electricity quantity actually used by the user and the unit electricity price at the corresponding moment. In order to reduce the load pressure of the power grid, a user is guided to autonomously transfer a part of load in the peak period of the power load, and a power company also provides a power rate and electricity charge pricing rule of time-of-use electricity price.
The energy storage device has the characteristic of peak clipping and valley filling, when the power load is in the valley period or the new energy is abundant, the energy storage is charged to store the electric energy, and when the power load is in the peak period, the energy storage outputs the electric energy, so that the electric power obtained by a user from a power grid is reduced, the electric power charge of the user is reduced, the maximum power of monthly demand is reduced, and the basic electric power charge of the user is reduced. When the new energy is surplus and the user can not consume the new energy, the stored energy is stored and then released in a proper time period, and the electric quantity obtained by the user from the power grid is reduced. When the permeability of the new energy is high, the phenomena of wind abandonment and light abandonment are likely to occur only by considering the limitation of energy storage construction cost and considering that the energy storage capacity obtained by the two kinds of yield optimization is not enough to consume so much new energy. At the moment, the carbon emission reduction benefits are considered, the three benefits and the energy storage cost are in a game, and the energy storage capacity is properly increased to reduce or avoid the phenomena of wind and light abandonment by optimizing the result. Specifically, the method comprises the following steps:
in the formula,the number of days of the j-th month,the electricity rate at time t for the industrial user,is the stored energy power at the time point t,for peak clipping and valley filling gains in the j th month of energy storage,is the maximum value of the load power at month j,for the equivalent load of the simultaneous discontinuity after the optimization of the energy storage month j,in order to be the basic electricity price,in order to be a step of time,for the carbon emission reduction benefit of month j,in order to achieve the unit cost of carbon dioxide emissions,the carbon dioxide amount discharged for one degree of electricity of the replaced coal,for the total carbon dioxide emission of the user before the energy storage optimization at the k day,and (5) storing the optimized total carbon dioxide emission of the user for the k day.
Specifically, the expression for calculating the total carbon dioxide emission of the user before the k-th energy storage optimization is as follows:
in the formula,the total amount of the carbon dioxide emission is,the proportion of carbon emission in the total carbon emission is consumed by power;
calculating the expression of the total carbon dioxide emission of the user after the k-th energy storage optimization is as follows:
in the formula,the amount of power consumed before optimization for the k-th day of energy storage,is as followsThe consumed electric quantity after k days of energy storage optimization;
wherein,,the amount of carbon dioxide emitted to burn fossil fuels,the carbon dioxide amount discharged in the production process of industrial users,the amount of carbon dioxide emitted for net purchased power and heat consumption,the amount of carbon dioxide emitted as a raw material,to the activity level of the ith fossil fuel,is the carbon dioxide emission factor of the ith fossil fuel,average lower calorific value of the i-th fossil fuel,Net consumption of the ith fossil fuel,The carbon content per unit calorific value of the ith fossil fuel,The rate of carbon oxidation for the ith fossil fuel,a unit carbon emission factor for the electricity supplied by the grid,n is the total number of fossil fuels for the net load of electricity.
Step 2: establishing constraint conditions for operation of energy storage battery
In order to ensure that the configured energy storage device and the power grid system can stably operate and achieve the optimal operation effect, the following constraint conditions are given:
1) Constraint of power equation
In order to ensure normal and stable operation of various operations of an enterprise, power balance needs to be met at any moment. The equation is constrained as follows:
in the formula,is the load power at the time point t,the energy storage power at the t moment is charged to be positive, the discharge is negative,is the photovoltaic power at the time t,is the wind power at the t-th moment,and exchanging power between the user and the power grid at the t moment.
2) Energy storage state of charge confinement
The energy storage battery can work for a longer service life only when the state of charge (SOC) of energy storage is within a certain range, and the service life of energy storage can be greatly shortened if the energy storage battery works for a long time in a small state of charge. The constraint inequality is as follows:
3) Energy storage battery performance constraints
The energy storage charging and discharging power cannot exceed the energy storage rated charging and discharging power, and the energy storage battery always runs in a reasonable charge state, so the service life of the energy storage battery is closely related to the electric energy throughput, and the smaller the throughput is, the longer the service life of the energy storage battery is. In order to maximize the benefit of the energy storage device, the daily throughput of the energy storage battery is reasonably limited based on the time-of-use electricity price policy, and meanwhile, the switching times of charging and discharging states of the energy storage battery in one day are reduced, so that the energy storage loss is reduced. The constraint inequality is as follows:
in the formula,is the rated charge-discharge power of the stored energy,the daily throughput of stored battery power for the kth day,is the maximum value of the electric energy throughput, the value of which depends onThe rated capacity is 2 times of the user load, the time-of-use electricity price distribution condition, the PCS (energy storage battery) performance requirement and other factors.
4) Power back-off constraint
The actual load of the user and the running power of the stored energy cannot be smaller than 0, namely, the new energy and the stored energy at the user side are not reversely injected into the power grid and cannot exceed the maximum load value of the user, otherwise, the energy storage device does not play a role of preventing the user from defending the moon and needing the maximum value. The constraint inequality is as follows:
And 3, step 3: building energy storage configuration optimization model
The configuration optimization of the energy storage is based on the load characteristics of a user and the output condition of new energy under the simulation period, the rated power of the wind and light new energy which is installed or planned to be installed is determined, the real output data of the wind and light in the last year is correspondingly selected or the output power of each day in the optimization period (one year) is randomly generated through Weibull distribution and Beta distribution, then one or one typical daily load curve in each season of a smelting enterprise is selected as a load curve in the optimization process, the minimum energy storage cost, the maximum energy storage income (including peak load-off income, basic electricity price defense income) and the maximum carbon emission reduction income of the user for installing the energy storage are multiple targets, and the weighted sum is integrated into a single target functionThe specific calculation formula is as follows. And solving the energy storage configuration model, and optimizing to obtain the rated power and the rated capacity of the energy storage.
In the formula,for peak clipping and valley filling gains in the j th month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in order to save the construction cost of the energy storage,、respectively a gain factor and a cost factor,andand the sum is 1, and the sum is,for the number of months,for the carbon emission reduction benefit of month j,
in the formula,the price per unit of the energy storage device,in order to save the unit operation and maintenance cost of energy,is the rated charge-discharge power of the stored energy,in order to save the unit investment cost of energy storage,is the rated capacity of the stored energy.
And 4, step 4: construction of an intra-day operation optimization model
The optimization principle of the operation of the energy storage device in the day is mainly 3: firstly, peak clipping and valley filling, low storage and high release. The charging is carried out at the valley value of the power load, namely the low-price time period, the discharging is carried out at the peak value of the power load, namely the high-price time period, and the power price difference is obtained. And secondly, high-proportion new energy is consumed, and carbon emission is reduced. Because the new energy has the random fluctuation characteristic, the output condition is random and is not easy to predict, and the power grid system is more inclined to a stable power output device, the stored energy is charged when the new energy meets the load and has surplus, the electric energy is stored, when the output of the new energy is deficient, the energy storage device discharges and supplements in time, the new energy is maximally consumed, and the carbon emission is greatly reduced under the condition of the same load. Third, the maximum value is needed for defending the moon. Because the new energy has volatility, the peak value of the power load of the user is reduced through energy storage, so that the maximum value required by the user in a month is defended, and the power consumption cost of industrial users is reduced.
Based on the above 3 optimization principles, the optimization model is operated in the day to fully consider the uncertainty of the new energy, and the capacity and power of the energy storage obtained in the last step are corrected. First, based on the known rated power of wind and light, a force curve is randomly generated by monte carlo for multiple times (1000 times). And then, selecting a user typical load curve, and carrying out day-to-day operation optimization on the rated capacity obtained in the step 3. And adding a constraint condition of energy storage operation according to the maximum objective function of the sum of the peak clipping and valley filling income of the user and the monthly demand defense income, and optimizing to obtain the real-time power of the stored energy. The objective function is as follows:
in the formula,for the peak clipping and valley filling income of the j month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in parts per month.
And 5: calculating the new energy consumption rate under the optimization period according to the real-time power of the step 4() Judging whether the new energy consumption rate is greater than or equal to a first threshold value or not and judging whether the energy storage cost recovery age limit is larger than or equal to a first threshold value or not under the optimization periodWhether the new energy consumption rate is less than or equal to a second threshold value or not is judged, and if the new energy consumption rate is less than the first threshold value and the cost recovery time is greater than a second preset threshold value, the difference value between the new energy consumption rate and the first threshold value is calculatedModifying the objective functionCoefficient of performanceCoefficient of construction cost of stored energyReduce(The present application takes 0.01),increase of. And jumping to the step 3, and repeatedly running and optimizing until the absorption rate is greater than or equal to the first threshold value to obtain the corrected optimal rated capacity and rated power of the stored energy.
In conclusion, the method of the embodiment aims at improving economy and new energy consumption rate and reducing carbon emission of users, energy storage configuration and operation of the energy storage at the user side are optimized, during operation optimization, energy storage can be optimized and scheduled according to electricity price conditions and load conditions of the users, energy storage charging time is distributed in time periods with low electricity price and high new energy output, energy storage discharging is mainly concentrated in late peak periods with high electric load and photovoltaic failure, the maximum energy storage income is obtained under the condition of maximum new energy consumption, the problem of energy storage configuration under long-term return in the smelting industry with high-proportion new energy access can be effectively solved, and benefit maximization of the users is realized.
Referring to fig. 2, a block diagram of an energy storage multi-objective coordination optimization system considering uncertainty of new energy according to the present application is shown.
As shown in FIG. 2, the energy storage multi-objective coordination optimization system 200 includes a building module 210, a first modification module 220, a judgment module 230, and a second modification module 240.
The building module 210 is configured to build an energy storage configuration optimization model by using a maximum comprehensive profit of energy storage installed by an industrial user as a first objective function to obtain a rated capacity and a rated power of energy storage optimization, wherein the comprehensive profit is the sum of energy storage profit and carbon emission reduction profit minus energy storage cost, and the first profit is the sum of energy storage profit and carbon emission reduction profitThe expression of the objective function is:in the formula (I), wherein,for peak clipping and valley filling gains in the j th month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in order to save the construction cost of the energy storage,、respectively a gain factor and a cost factor,in parts per month; the first correction module 220 is configured to correct the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization; the judging module 230 is configured to calculate a new energy consumption rate and a cost recovery year based on the real-time power after the energy storage optimization, and judge whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold; a second modification module 240 configured to calculate a difference between the new energy consumption rate and a first preset threshold if the new energy consumption rate is less than the first preset threshold and the cost recovery year is greater than a second preset thresholdAnd according to the differenceFor coefficient of profitAnd correcting the cost coefficient to obtain the rated capacity and rated power of the corrected energy storage target.
It should be understood that the modules depicted in fig. 2 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 2, and are not described again here.
In other embodiments, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to execute the method for energy storage multi-objective coordination optimization considering uncertainty of new energy in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
constructing an energy storage configuration optimization model by using the maximum comprehensive profit of industrial user installation energy storage as a first objective function to obtain the rated capacity and the rated power of energy storage optimization;
correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
calculating a new energy consumption rate and a cost recovery year limit based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year limit is larger than a second preset threshold;
if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset thresholdAnd according to the differenceThe gain coefficient and the cost coefficient are corrected to obtain the rated capacity of the corrected energy storage targetAnd a rated power.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the energy storage multi-objective coordinated optimization system considering uncertainty of new energy, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely located from the processor, and the remote memory may be connected to the energy storage multi-objective coordination optimization system over a network that accounts for new energy uncertainty. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 3. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running nonvolatile software programs, instructions and modules stored in the memory 320, namely, implementing the energy storage multi-objective coordination optimization method of the above method embodiment in consideration of uncertainty of new energy. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the energy storage multi-objective coordination optimization system considering uncertainty of new energy. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to an energy storage multi-objective coordination optimization system considering uncertainty of new energy, and is used for a client, and the system includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
constructing an energy storage configuration optimization model by taking the maximum comprehensive profit of the industrial user for installing energy storage as a first objective function to obtain the rated capacity and the rated power of energy storage optimization;
correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
calculating a new energy consumption rate and a cost recovery year limit based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year limit is larger than a second preset threshold;
if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset thresholdAnd according to the differenceAnd correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An energy storage multi-objective coordination optimization method considering new energy uncertainty is characterized by comprising the following steps:
establishing an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of an industrial user as a first objective function to obtain the rated capacity and rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:
in the formula,for peak clipping and valley filling gains in the j th month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,for energy storage constructionThe cost of the manufacture is reduced, and the cost is reduced,、respectively a gain factor and a cost factor,for the number of months,the carbon emission reduction benefit for month j;
correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
calculating a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold;
if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset thresholdAnd according to the differenceAnd correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
2. The method as claimed in claim 1, wherein the energy storage gains comprise peak clipping and valley filling gains and basic electricity price defense gains.
3. The method for energy storage multi-objective coordination optimization considering uncertainty of new energy source according to claim 1, wherein,
,days of month j, k day k,the electricity rate at time t for the industrial user,is the stored energy power at the time point t,is the maximum value of the load power in the j-th month,for the equivalent load of the discontinuity at the same time after the optimization of the energy storage month j,in order to be the basic electricity price,in order to be a step of time,in order to achieve the unit cost of carbon dioxide emissions,the carbon dioxide amount discharged for one degree of electricity of the replaced coal,for the total carbon dioxide emission of the user before the energy storage optimization at the k day,for the total carbon dioxide emission of the user after the k day energy storage optimization,for the unit price of the energy storage device,is the unit operation and maintenance cost of the energy storage,is the rated charge-discharge power of the stored energy,in order to save the unit investment cost of energy storage,is the rated capacity of the stored energy.
4. The energy storage multi-objective coordinated optimization method considering the uncertainty of new energy according to claim 3, wherein the expression for calculating the total carbon dioxide emission of the user before the k-th energy storage optimization is as follows:
in the formula,the total amount of the carbon dioxide emission is,the proportion of carbon emission in the total carbon emission is consumed by power;
calculating the expression of the total carbon dioxide emission of the user after the k-th energy storage optimization as follows:
in the formula,the amount of power consumed before optimization for the kth day of energy storage,the amount of electricity consumed after the energy storage optimization for the kth day;
wherein,,the amount of carbon dioxide emitted to burn fossil fuels,the carbon dioxide amount discharged in the production process of industrial users,the amount of carbon dioxide emitted for net purchased power and heat consumption,the amount of carbon dioxide emitted as a raw material,to the activity level of the ith fossil fuel,is the carbon dioxide emission factor of the ith fossil fuel,average lower calorific value of the i-th fossil fuel,Net consumption of the ith fossil fuel,The carbon content per unit calorific value of the ith fossil fuel,The rate of carbon oxidation for the ith fossil fuel,a unit carbon emission factor for the electricity provided by the grid,n is the total number of fossil fuels for the net load of electricity.
5. The energy storage multi-objective coordination optimization method considering the uncertainty of the new energy source according to claim 1, wherein the step of correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization comprises the steps of:
randomly generating an output curve based on the rated power;
selecting a user typical load curve in the output curve;
and performing day-to-day operation optimization on the rated capacity based on the user typical load curve to obtain real-time power after energy storage optimization, wherein the day-to-day operation optimization on the rated capacity comprises the step of optimizing the rated capacity by adopting constraint conditions of energy storage operation and using a second objective function with the maximum energy storage profit.
6. The method for energy storage multi-objective coordination optimization considering new energy uncertainty according to claim 5, wherein a second objective function of the intra-day operation optimization model is the maximum energy storage profit, and an expression of the second objective function is as follows:
wherein,,the number of days of the j-th month,the electricity rate at time t for the industrial user,is the stored energy power at the t-th moment,for the equivalent load of the discontinuity at the same time after the optimization of the energy storage month j,is the maximum value of the load power in the j-th month,in order to be the basic electricity price,is a time step.
7. The method for energy storage multi-objective coordination optimization considering uncertainty of new energy source as claimed in claim 1, wherein said difference value is used as a basisModifying the cost and gain factors includes increasing the cost and gain factorsDifference of timesAnd reducing the cost factorDifference of times。
8. An energy storage multi-objective coordination optimization system considering uncertainty of new energy sources is characterized by comprising the following steps:
the building module is configured to build an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of industrial users as a first objective function to obtain the rated capacity and the rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:
in the formula,for peak clipping and valley filling gains in the j th month of energy storage,for the defense gains of the basic electricity price in the j month of energy storage,in order to save the construction cost of the energy storage,、respectively a gain factor and a cost factor,the number of the months is the number of the months,the carbon emission reduction benefit for month j;
the first correction module is configured to correct the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
the judging module is configured to calculate a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judge whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold;
a second correction module configured to calculate a difference between the new energy consumption rate and a first preset threshold if the new energy consumption rate is smaller than the first preset threshold and the cost recovery year is greater than a second preset thresholdAnd according to the differenceAnd correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210838805.8A CN114928054B (en) | 2022-07-18 | 2022-07-18 | Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202210838805.8A CN114928054B (en) | 2022-07-18 | 2022-07-18 | Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy |
Publications (2)
Publication Number | Publication Date |
---|---|
CN114928054A CN114928054A (en) | 2022-08-19 |
CN114928054B true CN114928054B (en) | 2022-11-08 |
Family
ID=82815914
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202210838805.8A Active CN114928054B (en) | 2022-07-18 | 2022-07-18 | Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN114928054B (en) |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109687530A (en) * | 2019-01-08 | 2019-04-26 | 南京工程学院 | A kind of power grid mixing rolling scheduling method considering obstruction and energy storage tou power price |
CN110717259A (en) * | 2019-09-25 | 2020-01-21 | 浙江大学 | User side-oriented battery energy storage configuration and operation optimization method |
CN111446711A (en) * | 2020-03-17 | 2020-07-24 | 国网江苏省电力有限公司无锡供电分公司 | Load-storage combined optimization operation method based on demand response |
CN111476412A (en) * | 2020-04-03 | 2020-07-31 | 国网福建省电力有限公司泉州供电公司 | Photovoltaic power generation cost allocation research method considering carbon footprint and transaction |
CN111525601A (en) * | 2020-05-27 | 2020-08-11 | 远景智能国际私人投资有限公司 | Charging and discharging control method and device for user side energy storage equipment and storage medium |
CN111898805A (en) * | 2020-07-14 | 2020-11-06 | 智光研究院(广州)有限公司 | Energy storage system capacity configuration method and device, configuration equipment and storage medium |
CN114741943A (en) * | 2021-12-28 | 2022-07-12 | 南京工程学院 | Energy storage capacity configuration optimization method based on particle swarm optimization |
Family Cites Families (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112072655B (en) * | 2020-09-10 | 2022-05-27 | 天津大学 | Hybrid energy storage optimal configuration method for grid-connected wind energy storage power generation system |
US11949234B2 (en) * | 2020-12-28 | 2024-04-02 | Electric Power Science & Research Institute Of State Grid Tianjin Electric Power Company | Method for making spatio-temporal combined optimal scheduling strategy of mobile energy storage (MES) system |
-
2022
- 2022-07-18 CN CN202210838805.8A patent/CN114928054B/en active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109687530A (en) * | 2019-01-08 | 2019-04-26 | 南京工程学院 | A kind of power grid mixing rolling scheduling method considering obstruction and energy storage tou power price |
CN110717259A (en) * | 2019-09-25 | 2020-01-21 | 浙江大学 | User side-oriented battery energy storage configuration and operation optimization method |
CN111446711A (en) * | 2020-03-17 | 2020-07-24 | 国网江苏省电力有限公司无锡供电分公司 | Load-storage combined optimization operation method based on demand response |
CN111476412A (en) * | 2020-04-03 | 2020-07-31 | 国网福建省电力有限公司泉州供电公司 | Photovoltaic power generation cost allocation research method considering carbon footprint and transaction |
CN111525601A (en) * | 2020-05-27 | 2020-08-11 | 远景智能国际私人投资有限公司 | Charging and discharging control method and device for user side energy storage equipment and storage medium |
CN111898805A (en) * | 2020-07-14 | 2020-11-06 | 智光研究院(广州)有限公司 | Energy storage system capacity configuration method and device, configuration equipment and storage medium |
CN114741943A (en) * | 2021-12-28 | 2022-07-12 | 南京工程学院 | Energy storage capacity configuration optimization method based on particle swarm optimization |
Non-Patent Citations (3)
Title |
---|
"Stacking Battery Energy Storage Revenues in Future Distribution Networks";Ahmed A. Raouf Mohamed;《IEEE Access 》;20220328;全文 * |
"计及用户不确定性的需求响应激励优化策略研究";崔晓昱;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20220115;全文 * |
"面向用户侧的电池储能配置与运行优化策略";赵乙潼 等;《电力系统自动化》;20191022;全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN114928054A (en) | 2022-08-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111969593B (en) | Combined heat and power microgrid model prediction control optimization scheduling method based on hybrid energy storage | |
Li et al. | Improving wind power integration by regenerative electric boiler and battery energy storage device | |
Sun et al. | A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties | |
CN104881716A (en) | Optimization programming and evaluation method of micro-grid power supply | |
CN110633854A (en) | Full life cycle optimization planning method considering energy storage battery multiple segmented services | |
CN110994606B (en) | Multi-energy power supply capacity configuration method based on complex adaptation system theory | |
CN116050637A (en) | Comprehensive energy virtual power plant optimal scheduling method and system based on time-of-use electricity price | |
CN111126675A (en) | Multi-energy complementary microgrid system optimization method | |
CN112541612B (en) | Multi-network coupling-based virtual power plant economic optimization scheduling method | |
CN114201858A (en) | Pricing method for real-time electricity price of new energy | |
Cheng et al. | Optimized configuration of distributed power generation based on multi-stakeholder and energy storage synergy | |
CN110334856A (en) | A kind of wind-light storage method for planning capacity based on carbon transaction mechanism | |
CN112653180B (en) | Wind-fire-storage combined system environment economic dispatching method and system | |
CN117495012A (en) | Double-time-scale low-carbon optimal scheduling method for comprehensive energy system | |
CN117559526A (en) | Router-simulated energy regulation and control method based on optical storage and charging integrated charging station | |
CN114928054B (en) | Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy | |
Zheng et al. | Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy | |
CN116961008A (en) | Micro-grid capacity double-layer optimization method considering power spring and load demand response | |
CN116054250A (en) | Scheduling method of wind-solar-energy-storage-coupled hydrogen production system based on energy storage grouping | |
CN115775049A (en) | Power grid distributed energy carbon emission management method and terminal | |
Rezaeimozafar et al. | Optimal scheduling for behind-the-meter batteries under different tariff structures | |
Jiarui et al. | Research on Demand Response Strategy of Electricity Market Based on Intelligent Power Consumption | |
CN117610848A (en) | Comprehensive energy system optimal scheduling method considering prediction deviation and application thereof | |
Yin et al. | Economic and Technical Optimization Configuration Methods for Energy Storage on the New Energy Side | |
Georgiou et al. | A preliminary design of an intelligent system for the optimal utilization of renewable energy sources in buildings |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |