CN117039962A - Comprehensive energy service general calculation evaluation method and system - Google Patents
Comprehensive energy service general calculation evaluation method and system Download PDFInfo
- Publication number
- CN117039962A CN117039962A CN202310438126.6A CN202310438126A CN117039962A CN 117039962 A CN117039962 A CN 117039962A CN 202310438126 A CN202310438126 A CN 202310438126A CN 117039962 A CN117039962 A CN 117039962A
- Authority
- CN
- China
- Prior art keywords
- user
- battery
- power
- battery capacity
- configuration
- 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.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 39
- 238000011156 evaluation Methods 0.000 title claims abstract description 24
- 230000005611 electricity Effects 0.000 claims abstract description 58
- 238000004146 energy storage Methods 0.000 claims abstract description 46
- 238000000034 method Methods 0.000 claims abstract description 34
- 230000008901 benefit Effects 0.000 claims abstract description 29
- 230000009286 beneficial effect Effects 0.000 claims abstract description 10
- 230000008569 process Effects 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 8
- 230000032683 aging Effects 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- 101100353161 Drosophila melanogaster prel gene Proteins 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000007599 discharging Methods 0.000 abstract description 3
- 238000005265 energy consumption Methods 0.000 abstract description 2
- 238000004590 computer program Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000004075 alteration Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- 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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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
- H02J3/007—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
-
- 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
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Power Engineering (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention belongs to the technical field of comprehensive energy service, and particularly provides a comprehensive energy service general calculation evaluation method and a system, wherein the method comprises the following steps: judging whether the user is a peak-valley difference user or not according to the power data of the power load of the enterprise user; estimating annual electricity charge of enterprise users before energy storage configuration according to the calculated parameters; determining the maximum configurable battery capacity of an enterprise user, presetting calculation accuracy, and traversing all battery capacity configuration conditions; and traversing the beneficial conditions in the service life of all the battery capacity configuration schemes, and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage. According to the characteristic of a typical daily load power curve of an enterprise user, the method comprises the steps of firstly judging the user type, providing economic optimal configuration of the capacity of the energy storage system through peak clipping and valley filling, and providing a battery charging and discharging scheme based on the economic optimal configuration. The comprehensive energy supply investment risk is greatly reduced, and the energy consumption cost of enterprise users is maximally reduced.
Description
Technical Field
The invention relates to the technical field of comprehensive energy service, in particular to a comprehensive energy service general calculation evaluation method and a comprehensive energy service general calculation evaluation system.
Background
The method is mainly used for the comprehensive energy service project budget assessment, and has the advantages that the current domestic research and application are more in project cost, the application scene and assessment model are relatively mature due to the early popularization of photovoltaic power generation, the cost budget is more, the project cost assessment software tool is mainly used for generating capacity prediction, installation projects, building projects and other various project overviews, annual investment plans and the like, the project field is emphasized at multiple sides, but the overviews in various industries are lack of exploration and research due to standardization of overviews in different scenes.
The distributed photovoltaic power generation is a novel power supply mode, renewable solar energy is used as energy, and consumption of non-renewable resources is reduced. The energy storage project at the user side becomes a key method for solving the intermittent defect of clean energy and ensuring the safe operation of the power grid, and the peak-valley electricity price system is utilized to cut peaks and fill valleys so as to help the user side reduce the cost. However, for different industries, different types of customers and different energy power supply modes, different solutions need to be formulated, project cost benefit and evaluation of user electricity cost are affected by various factors, customer managers often need to cooperate with a plurality of departments to formulate reasonable benefit schemes, and a rapid standardized comprehensive energy service estimation evaluation means and method based on a user side are lacking at present.
Disclosure of Invention
The invention aims at the technical problem that the general energy service estimation based on the user side is lacking in the prior art.
The invention provides a comprehensive energy service approximate assessment method, which comprises the following steps:
s1, judging whether the user is a peak-valley difference user or not according to the power data of the electric load of the enterprise user;
s2, estimating annual electricity charge of the enterprise user before energy storage configuration according to the calculated parameters;
s3, determining the maximum configurable battery capacity of the enterprise user, presetting calculation accuracy, and traversing all battery capacity configuration conditions;
and S4, traversing the beneficial conditions in the service life of all the battery capacity configuration schemes, and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
Preferably, the S1 specifically includes: and inputting a 9-point to 6-point daily load power curve and a 6-point to 9-point morning night load power curve of the enterprise user, and judging the enterprise user as a peak-valley difference user when the daily load power is far greater than the night load power.
Preferably, the calculation parameters in S2 include:
the method comprises the steps of transformer capacity, capacity basic electricity charge, demand basic electricity charge, peak electricity price, off-peak electricity price, flat section electricity price and market electricity price information of charging time periods corresponding to the electricity prices;
battery physical constraints of battery power and capacity proportion, charge-discharge depth, battery capacity coefficient considering aging, discharge efficiency and charge-discharge times;
the investment economic cost constraints of enterprise's primary investment budget, battery purchase cost, auxiliary facility construction cost, converter investment cost, equipment annual maintenance cost, depreciation coefficient and discount rate.
Preferably, the step S3 specifically includes:
s31, obtaining the maximum value Pmax of the user power consumption when the peak electricity price is obtained before the energy storage configuration, namely the maximum configurable battery capacity of the enterprise user;
s32, calculating the current cycle demand management target power P= (rt-i) multiplied by Pmax/rt, wherein rt is the preset precision, and i is the traversal times;
s33, calculating the required battery capacity V1= (Pmax-P)/K when corresponding power support is provided, wherein K1 is the ratio of battery power to capacity;
s34, calculating a required battery capacity v2= (Prel-P) ×t when providing corresponding power support; prel is the maximum power corresponding to the electric energy required by the corresponding electric energy support; wherein t is the length of the demand management time;
s35, taking v=max (V1, V2) as the battery capacity required for the calculation of the demand management in this traversal;
s36, calculating battery discharge power in the process of no-demand management task, namely peak clipping and valley filling discharge power Psp= (V1-V2)/t ', wherein t' is the non-demand management time;
s37, modifying a user power curve, and calculating month electricity charge of the enterprise user after energy storage configuration.
Preferably, between S35 and S36, further includes:
judging whether the capacity V is larger than the calculated maximum capacity or not according to the formula V > Vmax/(K2X K3X eta); wherein, K2 is the depth of charge and discharge of the battery, K3 is the aging coefficient of the battery, eta is the charge and discharge efficiency of the battery;
if the above formula is satisfied, the process goes to step S36, otherwise, the peak clipping and valley filling discharge power psp=0 is made, and the process goes to step S37.
Preferably, the S4 specifically includes:
traversing the one-time investment cost Crel, the annual investment cost Ca and the annual investment cost CRE accounting for the discount rate of all the battery capacity allocation, monthly demand management benefits Cneed, monthly peak clipping and valley filling benefits Cpeak, and finding out the most net benefits in the battery capacity allocation scheme as the energy storage economic optimal allocation scheme;
the calculation formula of the net benefit in the battery capacity configuration scheme is as follows:
Ctotal＝-Crel-Ca×N+CRE+12N×(Cneed+Cpeak)
ctotal is net benefit, N is battery service life, and whether the optimal configuration scheme is profitable or not is judged.
Preferably, the step S4 further includes:
the method comprises the steps of outputting annual maintenance cost, disposable investment cost, annual demand management income, annual peak clipping and valley filling income, disposable investment cost recovery period, net profit total amount, battery configuration capacity, and outputting enterprise power consumption curve, battery charge and discharge curve and charge quantity change curve after power management.
The invention also provides a comprehensive energy service general estimation evaluation system, which is used for realizing a comprehensive energy service general estimation evaluation method, comprising the following steps:
the peak-valley difference user judging module is used for judging whether the user is a peak-valley difference user or not according to the power data of the power load of the enterprise user;
the annual electricity charge calculation module is used for estimating annual electricity charges of enterprise users before energy storage configuration according to calculation parameters;
the battery capacity configuration module is used for determining the maximum configurable battery capacity of the enterprise user, presetting calculation precision and traversing all battery capacity configuration conditions;
and the general calculation evaluation module is used for traversing the beneficial conditions in the service life of all the battery capacity configuration schemes and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
The invention also provides an electronic device, which comprises a memory and a processor, wherein the processor is used for realizing the steps of the comprehensive energy service approximate evaluation method when executing the computer management program stored in the memory.
The invention also provides a computer readable storage medium having stored thereon a computer management class program which when executed by a processor implements the steps of the integrated energy service profile assessment method.
The beneficial effects are that: the invention provides a comprehensive energy service approximate evaluation method and a system, wherein the method comprises the following steps: judging whether the user is a peak-valley difference user or not according to the power data of the power load of the enterprise user; estimating annual electricity charge of enterprise users before energy storage configuration according to the calculated parameters; determining the maximum configurable battery capacity of an enterprise user, presetting calculation accuracy, and traversing all battery capacity configuration conditions; and traversing the beneficial conditions in the service life of all the battery capacity configuration schemes, and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage. According to the characteristic of the typical daily load power curve of the enterprise user, the method comprises the steps of judging the user type, performing traversing on all energy storage configuration conditions to judge the economy, and finally calculating the configured user power optimization curve and cost benefit result according to the economic optimal energy storage, so that the economy of the enterprise configured battery is ensured, and the investment risk is reduced. By peak clipping and valley filling, the economic optimal configuration of the capacity of the energy storage system is provided, and a battery charging and discharging scheme is provided on the basis of the economic optimal configuration. The comprehensive energy supply investment risk is greatly reduced, and the energy consumption cost of enterprise users is maximally reduced.
Drawings
FIG. 1 is a flow chart of a comprehensive energy service general calculation evaluation method provided by the invention;
fig. 2 is a schematic hardware structure of one possible electronic device according to the present invention;
FIG. 3 is a schematic diagram of a possible hardware configuration of a computer readable storage medium according to the present invention;
fig. 4 is a graph of electricity consumption before and after energy storage configuration provided by the invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
FIG. 1 is a schematic view of an integrated energy service assessment method according to the present invention, including:
and step 1, judging whether the user is a peak-valley difference user or not according to the type of the enterprise user load curve.
Specifically, a 9-point to 6-point daily load power curve and a 6-point to 9-point night load power curve of an enterprise user are input, the daily load power is far larger than the night load power, the fact that the enterprise user uses electricity in the daytime and does not use electricity at night is indicated, and the enterprise user is judged to be a peak-valley difference user. The starting point or the length of the time period of the daily load power curve can be floated, for example, 7 a.m. to 4 a.m. or 7 a.m. to 9 a.m. or 10 a.m. to 5 a.m. and so on, so long as the power consumption can be significantly higher than that of the other time period, the enterprise user is indicated to be a peak-valley difference user.
If the user is not the peak-valley difference user, outputting a non-target user, outputting a non-profit, and ending without energy storage configuration. Because only peak-valley difference users need to perform energy storage configuration, otherwise the input income ratio is too large to be cost-effective.
Step 2: estimating annual electric charge of enterprises of peak-valley difference users,
the method comprises the steps of inputting calculation parameters, and estimating annual electric charge of enterprise users before energy storage configuration, wherein the calculation parameters comprise transformer capacity, capacity basic electric charge, demand basic electric charge, peak electric charge, off-peak electric charge, flat electric charge and market electric charge information of charging time periods corresponding to the electric charges; battery physical constraints such as battery power and capacity proportion, charge and discharge depth, battery capacity coefficient considering aging, discharge efficiency, design charge and discharge times and the like; investment economic cost constraints such as enterprise one-time investment budget, battery purchase cost, auxiliary facility construction cost, converter investment cost, equipment annual maintenance cost, depreciation coefficient, discount rate and the like.
And 3, determining the maximum configurable battery capacity of the enterprise user, determining the calculation accuracy, taking the maximum configurable battery capacity of the enterprise user as an upper limit, taking the calculation accuracy as a traversing condition, and traversing all battery capacity configuration conditions. The one-time traversal process is as follows:
s31, obtaining the maximum value Pmax of the user power consumption when the peak electricity price is obtained before the energy storage configuration, namely the maximum configurable battery capacity of the enterprise user;
s32, calculating the current cycle demand management target power P= (rt-i) multiplied by Pmax/rt, wherein rt is a preset precision value, and i is the traversal times. So-called demand management, i.e. during peak periods of electricity consumption; no management is required, i.e. during the power consumption valley period. The battery is required to be configured in the electricity utilization peak period, and is not required to be configured in the electricity utilization valley period, so that the electricity charge cost of the enterprise user side can be reduced. Because electricity is expensive during peak hours and there is also a problem of disconnection or limitation of the supply. By configuring the battery during the peak period of electricity consumption and utilizing the battery to supply electricity, the risk of unsustainable supply during the peak period of electricity consumption and the electricity consumption cost can be greatly reduced.
S33, calculating a required battery capacity v1= (Pmax-P)/K when providing corresponding power support, where K1 is a battery power and capacity ratio.
S34, calculating a required battery capacity v2= (Prel-P) ×t when providing corresponding power support; prel is the maximum power corresponding to the power required for the corresponding power support. Wherein t is the required management time length, namely the electricity consumption peak period time period.
S35, taking vmax=max (V1, V2) as the battery capacity required for the calculation of the demand management in this traversal; the battery capacity is ensured to meet the support under the conditions of power support and demand management electric quantity support.
Judging whether the capacity V is larger than the calculated allowable maximum capacity or not, wherein the formula is as follows: v > Vmax/(k2×k3×η); wherein, K2 is the depth of charge and discharge of the battery, K3 is the aging coefficient of the battery, and eta is the charge and discharge efficiency of the battery. If the inequality is true, the process is directly finished. When the inequality is not established, continuously judging whether V is equal to V1, if V is not equal to V1, enabling psp=0, and forming a user power curve after energy storage configuration, namely monthly electric charge. And starting the next traversal cycle after the traversal is finished, and enabling the traversal times i=i+1 until the traversal times i > the preset precision rt.
S36, calculating the battery discharge power during the unnecessary management task, namely peak clipping and valley filling discharge power Psp= (V1-V2)/t ', wherein t' is the unnecessary management time.
S37, modifying a user power curve, and calculating month electricity charge of the enterprise user after energy storage configuration.
Starting the next traversing cycle, and enabling the traversing times i=i+1, and repeating the steps S31-S37 until the traversing times i > the preset precision rt.
Step 4: traversing all the service life-time benefit conditions of the battery capacity configuration, such as the disposable investment cost Crel, the annual investment cost Ca and the annual investment cost CRE accounting for the discount rate, monthly demand management benefit Cneed, monthly peak clipping and valley filling benefit Cpeak and the like, and finding out the net benefit maximum in the battery capacity configuration scheme to obtain the energy storage economy optimal configuration scheme. Finally, the total income of the energy storage economic optimal allocation scheme is obtained:
Ctotal＝-Crel-Ca×N+CRE+12N×(Cneed+Cpeak)
and N is the service life of the battery, and whether the optimal configuration scheme is profitable or not is judged.
And 5, calculating and outputting annual maintenance cost, one-time investment cost, annual demand management income, annual peak clipping and valley filling income, one-time investment cost recovery period, net profit total amount and battery configuration capacity, wherein an output power managed enterprise power consumption curve, a battery charge and discharge curve and a charge quantity change curve are shown in figure 4. The electricity utilization curve before and after the energy storage configuration can be intuitively seen through the curve, and as can be seen from fig. 4, the electricity utilization curve is smoother after the energy storage configuration, peak clipping and valley filling are completed, the electricity utilization in the daytime electricity utilization high stage is reduced, and the electricity consumption in the evening electricity utilization low stage is increased. By comparing enterprise electricity utilization curves before and after energy storage configuration, the energy storage configuration method and the charging and discharging strategy are adopted, so that electricity utilization peaks of peak-valley difference users in high electricity prices can be remarkably reduced, electricity consumption of the enterprise users is reduced, and payment of the enterprise electricity consumption is reduced in a peak-clipping and valley-filling mode.
According to the characteristic of a typical daily load power curve of an enterprise user, the method is provided, the user type is judged firstly, then all energy storage configuration conditions are traversed to judge the economy, finally the user power optimization curve and the cost benefit result method after the economic optimal energy storage calculation configuration are aimed at, the economy of the enterprise configuration battery is guaranteed, and the investment risk is reduced.
Through the economic analysis of the energy storage system at the user side, the energy storage configuration algorithm combining the factors such as photovoltaic maximum absorption, peak clipping and valley filling, demand management and the like is researched by combining the two aspects of investment cost and income from different angles such as demand management, peak clipping and valley filling, power failure loss avoidance and the like, so that the aim of realizing the optimal economic benefit is achieved, the standardized configuration method of the general calculation process is researched, and the general calculation standardized service flow is established.
The embodiment of the invention also provides a comprehensive energy service general estimation evaluation system, which is used for realizing the comprehensive energy service general estimation evaluation method, and comprises the following steps:
the peak-valley difference user judging module is used for judging whether the user is a peak-valley difference user or not according to the power data of the power load of the enterprise user;
the annual electricity charge calculation module is used for estimating annual electricity charges of enterprise users before energy storage configuration according to calculation parameters;
the battery capacity configuration module is used for determining the maximum configurable battery capacity of the enterprise user, presetting calculation precision and traversing all battery capacity configuration conditions;
and the general calculation evaluation module is used for traversing the beneficial conditions in the service life of all the battery capacity configuration schemes and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
Fig. 2 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the present invention. As shown in fig. 2, an embodiment of the present invention provides an electronic device, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320, wherein the processor 1320 executes the computer program 1311 to implement the following steps: s1, judging whether the user is a peak-valley difference user or not according to the power data of the electric load of the enterprise user;
s2, estimating annual electricity charge of the enterprise user before energy storage configuration according to the calculated parameters;
s3, determining the maximum configurable battery capacity of the enterprise user, presetting calculation accuracy, and traversing all battery capacity configuration conditions;
and S4, traversing the beneficial conditions in the service life of all the battery capacity configuration schemes, and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
Fig. 3 is a schematic diagram of an embodiment of a computer readable storage medium according to the present invention. As shown in fig. 3, the present embodiment provides a computer-readable storage medium 1400 having stored thereon a computer program 1411, which computer program 1411, when executed by a processor, performs the steps of: s1, judging whether the user is a peak-valley difference user or not according to the power data of the electric load of the enterprise user;
s2, estimating annual electricity charge of the enterprise user before energy storage configuration according to the calculated parameters;
s3, determining the maximum configurable battery capacity of the enterprise user, presetting calculation accuracy, and traversing all battery capacity configuration conditions;
and S4, traversing the beneficial conditions in the service life of all the battery capacity configuration schemes, and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
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 computer, 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The comprehensive energy service general calculation evaluation method is characterized by comprising the following steps of:
s1, judging whether the user is a peak-valley difference user or not according to the power data of the electric load of the enterprise user;
s2, estimating annual electricity charge of the enterprise user before energy storage configuration according to the calculated parameters;
s3, determining the maximum configurable battery capacity of the enterprise user, presetting calculation accuracy, and traversing all battery capacity configuration conditions;
and S4, traversing the beneficial conditions in the service life of all the battery capacity configuration schemes, and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
2. The comprehensive energy service overview evaluation method according to claim 1, wherein S1 specifically comprises: and inputting a 9-point to 6-point daily load power curve and a 6-point to 9-point morning night load power curve of the enterprise user, and judging the enterprise user as a peak-valley difference user when the daily load power is far greater than the night load power.
3. The integrated energy service profile evaluation method according to claim 1, wherein the calculation parameters in S2 include:
the method comprises the steps of transformer capacity, capacity basic electricity charge, demand basic electricity charge, peak electricity price, off-peak electricity price, flat section electricity price and market electricity price information of charging time periods corresponding to the electricity prices;
battery physical constraints of battery power and capacity proportion, charge-discharge depth, battery capacity coefficient considering aging, discharge efficiency and charge-discharge times;
the investment economic cost constraints of enterprise's primary investment budget, battery purchase cost, auxiliary facility construction cost, converter investment cost, equipment annual maintenance cost, depreciation coefficient and discount rate.
4. The comprehensive energy service overview evaluation method according to claim 1, wherein the step S3 specifically includes:
s31, obtaining the maximum value Pmax of the user power consumption when the peak electricity price is obtained before the energy storage configuration, namely the maximum configurable battery capacity of the enterprise user;
s32, calculating the current cycle demand management target power P= (rt-i) multiplied by Pmax/rt, wherein rt is the preset precision, and i is the traversal times;
s33, calculating the required battery capacity V1= (Pmax-P)/K when corresponding power support is provided, wherein K1 is the ratio of battery power to capacity;
s34, calculating a required battery capacity v2= (Prel-P) ×t when providing corresponding power support; prel is the maximum power corresponding to the electric energy required by the corresponding electric energy support; wherein t is the length of the demand management time;
s35, taking v=max (V1, V2) as the battery capacity required for the calculation of the demand management in this traversal;
s36, calculating battery discharge power in the process of no-demand management task, namely peak clipping and valley filling discharge power Psp= (V1-V2)/t ', wherein t' is the non-demand management time;
s37, modifying a user power curve, and calculating month electricity charge of the enterprise user after energy storage configuration.
5. The integrated energy service profile evaluation method of claim 4, further comprising between S35 and S36:
judging whether the capacity V is larger than the calculated maximum capacity or not according to the formula V > Vmax/(K2X K3X eta); wherein, K2 is the depth of charge and discharge of the battery, K3 is the aging coefficient of the battery, eta is the charge and discharge efficiency of the battery;
if the above formula is satisfied, the process goes to step S36, otherwise, the peak clipping and valley filling discharge power psp=0 is made, and the process goes to step S37.
6. The comprehensive energy service overview evaluation method according to claim 1, wherein S4 specifically comprises:
traversing the one-time investment cost Crel, the annual investment cost Ca and the annual investment cost CRE accounting for the discount rate of all the battery capacity allocation, monthly demand management benefits Cneed, monthly peak clipping and valley filling benefits Cpeak, and finding out the most net benefits in the battery capacity allocation scheme as the energy storage economic optimal allocation scheme;
the calculation formula of the net benefit in the battery capacity configuration scheme is as follows:
Ctotal＝-Crel-Ca×N+CRE+12N×(Cneed+Cpeak)
ctotal is net benefit, N is battery service life, and whether the optimal configuration scheme is profitable or not is judged.
7. The integrated energy service profile evaluation method according to claim 1, wherein after S4, further comprising:
the method comprises the steps of outputting annual maintenance cost, disposable investment cost, annual demand management income, annual peak clipping and valley filling income, disposable investment cost recovery period, net profit total amount, battery configuration capacity, and outputting enterprise power consumption curve, battery charge and discharge curve and charge quantity change curve after power management.
8. An integrated energy service profile assessment system, wherein the system is configured to implement the integrated energy service profile assessment method of any one of claims 1-7, comprising:
the peak-valley difference user judging module is used for judging whether the user is a peak-valley difference user or not according to the power data of the power load of the enterprise user;
the annual electricity charge calculation module is used for estimating annual electricity charges of enterprise users before energy storage configuration according to calculation parameters;
the battery capacity configuration module is used for determining the maximum configurable battery capacity of the enterprise user, presetting calculation precision and traversing all battery capacity configuration conditions;
and the general calculation evaluation module is used for traversing the beneficial conditions in the service life of all the battery capacity configuration schemes and finding out the economic optimal configuration scheme with the largest net benefit, namely the energy storage.
9. An electronic device comprising a memory, a processor for implementing the steps of the integrated energy service profile assessment method according to any one of claims 1-7 when executing a computer management class program stored in the memory.
10. A computer-readable storage medium, having stored thereon a computer-management-class program which, when executed by a processor, implements the steps of the integrated energy service profile assessment method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310438126.6A CN117039962A (en) | 2023-04-23 | 2023-04-23 | Comprehensive energy service general calculation evaluation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310438126.6A CN117039962A (en) | 2023-04-23 | 2023-04-23 | Comprehensive energy service general calculation evaluation method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117039962A true CN117039962A (en) | 2023-11-10 |
Family
ID=88637824
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310438126.6A Pending CN117039962A (en) | 2023-04-23 | 2023-04-23 | Comprehensive energy service general calculation evaluation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117039962A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117728472A (en) * | 2023-12-29 | 2024-03-19 | 日新鸿晟智慧能源(上海)有限公司 | User side energy storage working day fine calculation method and fine calculation model |
-
2023
- 2023-04-23 CN CN202310438126.6A patent/CN117039962A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117728472A (en) * | 2023-12-29 | 2024-03-19 | 日新鸿晟智慧能源(上海)有限公司 | User side energy storage working day fine calculation method and fine calculation model |
| CN117728472B (en) * | 2023-12-29 | 2024-05-28 | 日新鸿晟智慧能源(上海)有限公司 | User side energy storage working day fine calculation method and fine calculation model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109193720B (en) | User side energy storage capacity configuration method based on typical daily load curve of enterprise user | |
| CN107370170B (en) | Energy storage system capacity configuration method considering capacity electricity price and load prediction error | |
| CN109217290B (en) | Microgrid energy optimization management method considering electric vehicle charging and discharging | |
| WO2022193794A1 (en) | Multi-objective energy management method for smart community microgrid that takes into consideration decommissioned batteries | |
| CN104376385A (en) | Microgrid power price optimizing method | |
| CN109149651A (en) | It is a kind of meter and pressure regulation ancillary service income light-preserved system optimizing operation method | |
| CN108448567B (en) | Intelligent control system for electric vehicle agent participating in energy and frequency modulation market | |
| CN107565585A (en) | Energy storage device peak regulation report-back time Forecasting Methodology and its model creation method | |
| CN112688328B (en) | Time coordination energy optimal configuration method for AC/DC hybrid micro-grid | |
| CN110555607A (en) | method, terminal and storage medium for calculating and prompting peak load shifting index of power consumer | |
| CN117039962A (en) | Comprehensive energy service general calculation evaluation method and system | |
| CN110932296B (en) | Energy storage control method and device and virtual power plant | |
| CN114154790A (en) | Industrial park light storage capacity configuration method based on demand management and flexible load | |
| CN116663933A (en) | Method and system for determining charge and discharge strategy of energy storage system | |
| CN115864376A (en) | Multi-time scale power supply conservation analysis method and system | |
| Martinez-Rico et al. | Energy storage sizing based on automatic frequency restoration reserve market participation of hybrid renewable power plants | |
| Chen et al. | Retracted: Research on Consumer Side Energy Storage Optimization Configuration Based on Cloud Energy Storage | |
| CN104112168A (en) | Intelligent home economic optimization method based on multi-agent system | |
| Ritchie et al. | The impact of the increasing residential battery backup systems on load shedding | |
| CN109119988B (en) | Photovoltaic-battery microgrid energy scheduling management method based on dynamic wholesale market price | |
| CN111064188A (en) | Light storage system capacity configuration method based on net present value calculation | |
| Zhang et al. | Optimization Method of User-Side Energy Storage Capacity Considering Typical Daily Load Characteristics | |
| Ren et al. | Decision-making approach in charging mode for electric vehicle based on cumulative prospect theory | |
| Witharama et al. | Optimal Scheduling of a Solar-Powered Microgrid Using ML-Based Solar and Load Forecasting | |
| Yan et al. | Mitigating load forecast errors for suppliers by utilizing energy storage at a substation level |
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 |