CN117638944A - Dynamic allocation method and system of power grid capacity margin based on source-load-storage interaction - Google Patents
Dynamic allocation method and system of power grid capacity margin based on source-load-storage interaction Download PDFInfo
- Publication number
- CN117638944A CN117638944A CN202311752184.2A CN202311752184A CN117638944A CN 117638944 A CN117638944 A CN 117638944A CN 202311752184 A CN202311752184 A CN 202311752184A CN 117638944 A CN117638944 A CN 117638944A
- Authority
- CN
- China
- Prior art keywords
- capacity
- time scale
- planning
- capacity margin
- level time
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H3/00—Emergency protective circuit arrangements for automatic disconnection directly responsive to an undesired change from normal electric working condition with or without subsequent reconnection ; integrated protection
- H02H3/26—Emergency protective circuit arrangements for automatic disconnection directly responsive to an undesired change from normal electric working condition with or without subsequent reconnection ; integrated protection responsive to difference between voltages or between currents; responsive to phase angle between voltages or between currents
- H02H3/32—Emergency protective circuit arrangements for automatic disconnection directly responsive to an undesired change from normal electric working condition with or without subsequent reconnection ; integrated protection responsive to difference between voltages or between currents; responsive to phase angle between voltages or between currents involving comparison of the voltage or current values at corresponding points in different conductors of a single system, e.g. of currents in go and return conductors
-
- 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/04—Circuit arrangements for AC mains or AC distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- 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
- H02J3/12—Circuit arrangements for AC mains or AC distribution networks for adjusting voltage in AC networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for AC mains or AC distribution networks for adjusting voltage in AC networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- 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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
- H02J3/1821—Arrangements for adjusting, eliminating or compensating reactive power in networks using shunt compensators
-
- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- 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
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Tourism & Hospitality (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
本发明提供了一种基于源荷储互动的电网容量裕度动态配置方法及系统，属于电力规划调度技术领域。所述方法，包括：在第一级时间尺度下进行容量裕度规划，得到源荷储侧容量配置的规划结果，以年度容量裕度规划结果为初始值，进行第二级时间尺度下网侧容量裕度配置，以及第三级时间尺度下的网侧容量裕度配置；以第三级时间尺度下的网侧容量裕度配置结果修正第二级时间尺度下网侧容量裕度配置结果，以修正后的第二级时间尺度下网侧容量裕度配置结果，结合源荷储侧容量配置的规划结果作为第一级时间尺度下容量裕度规划的初始值，执行滚动优化，得到逐年的网侧设备容量裕度配置结果。本发明极大的提高了配置结果的精确性。
The invention provides a dynamic configuration method and system for power grid capacity margin based on source-load-storage interaction, which belongs to the technical field of power planning and dispatching. The method includes: performing capacity margin planning under the first-level time scale to obtain the planning results of the source-load-storage side capacity allocation, using the annual capacity margin planning results as the initial value, performing grid-side planning under the second-level time scale. Capacity margin configuration, and network-side capacity margin configuration under the third-level time scale; modify the network-side capacity margin configuration result under the second-level time scale with the network-side capacity margin configuration result under the third-level time scale, Based on the revised grid-side capacity margin allocation results under the second-level time scale, combined with the planning results of the source-load-storage-side capacity allocation, as the initial value of the capacity margin planning under the first-level time scale, rolling optimization is performed to obtain year-by-year Network side equipment capacity margin configuration results. The invention greatly improves the accuracy of configuration results.
Description
Technical Field
The invention relates to the technical field of power planning and scheduling, in particular to a power grid capacity margin dynamic configuration method and system based on source load storage interaction.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In a future power system accessed by high-proportion renewable energy sources, randomness, volatility and relativity of wind power and photovoltaic power generation can bring great challenges to the high-proportion new energy source system in the aspects of efficient new energy source consumption, power grid planning decision-making and safe and economic operation, and the operation characteristics of the power system dominated by an uncertainty power source are more complex. The randomness and volatility of new energy power generation are influenced by power grid planning and operation, and higher importance is required.
Under the background of rapid energy transformation, new requirements are put forward for power system planning aiming at the current planning and operation mechanism of a power grid and the continuous development of new energy. The traditional power system planning comprises three parts of planning year power load prediction, power supply side planning and power grid side planning, and the main tasks of the traditional power system planning are to reasonably plan a planning year power supply structure and a power transmission network respectively based on the future power load increment prediction condition, the power load distribution condition or the power load curve change condition. However, the disjoint of the power supply side and the power grid side reduces the matching and coordination of the power generation set and the power transmission grid construction, and the defect of the disjoint planning of the source network is increasingly prominent along with the high-speed development of new energy power generation technology. Therefore, expert scholars start to call for enhanced orchestration planning on the source side and the network side.
The source network coordination planning mode mainly comprises three aspects of source-source complementation, source-network coordination and network-load-storage interaction. The source-source complementation realizes flexible and coordinated output complementation between a conventional thermal power unit and a new energy unit, and the complementation system comprises two aspects: the first is coordination complementation between renewable energy and traditional power generation resources, in the area range, the main body of distributed energy supply and demand is taken as the main body, distributed renewable energy (such as a small fan, distributed photovoltaic, a heat pump and the like) needs to be coordinated with other schedulable distributed energy (such as small distributed fuel gas and the like) and power supply resources of a power grid, and in the wide area range, the centralized renewable energy is coordinated with the conventional power generation unit; and secondly, the demand side resource is regarded as the same supply side resource as the power supply, and the adverse effect of the renewable energy power generation reverse load characteristic on the safe and stable operation of the power system is reduced by using the demand side management technology such as a virtual power plant.
The coordination of the source and the network is based on the force complementation between the conventional thermal power generating unit and the new energy generating unit, and a proper extension strategy is provided for planning the annual net rack so as to improve the capacity of the net rack for receiving diversified power sources. The power grid is required to expand the capacity and level of receiving diversified power supplies, the intelligent regulation and control technology, the optimization technology and the information technology are utilized, the complementary thinking of source-source is used as guidance, complementary coordination among different power supplies and complementary coordination among different combination modes are exerted, supply side resources on two levels of distributed and centralized are optimally combined, meanwhile, the schedulable potential of the resources on the demand side is fully mobilized, and the power grid can maximize the power generation receiving capacity of renewable energy sources on the basis of ensuring the safety, reliability and economy of the system. However, in the background that the permeability of the new energy gradually reaches a medium-high proportion, the severity of the new energy discarding is continuously increased, and the main reasons of the new energy discarding are mainly attributed to the defects of the peak shaving capacity of the system and the power delivery capacity of the net rack. The network-charge-storage interaction is characterized in that energy storage, electric vehicles and user side equipment or user side resources with the characteristic of integration of electric energy supply and demand are regarded as generalized demand side resources, and the demand side electricity load is guided to actively track renewable energy power generation output through the ordered charge and discharge of the energy storage equipment by the aid of intelligent electricity utilization, electricity utilization diagnosis and other technologies, so that the matching degree of the two sides of the system supply and demand in time and space is improved.
The inventor finds that the existing source network charge storage coordination planning mode has certain limitation, the flexibility of the system is insufficient, the coordination among different scheduling modules cannot be effectively considered, and the centralized/distributed new energy cannot be fully consumed; moreover, with the mass access of high-proportion new energy, especially distributed energy, the coupling between different models is obviously enhanced, if the mode of single time scale configuration is still adopted, the connection conflict between different modules is extremely easy to cause, and the improper planning and scheduling connection causes high economic loss.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a dynamic power grid capacity margin configuration method and system based on source load storage interaction, provides a planning operation integrated multi-time scale network side equipment capacity margin configuration strategy, synthesizes the interaction characteristics of source load storage and multi-time scale capacity margin configuration results, forms a planning operation integrated power grid capacity margin configuration scheme based on source load storage interaction, and greatly improves the accuracy of configuration results.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the invention provides a dynamic configuration method for a power grid capacity margin based on source load storage interaction.
A power grid capacity margin dynamic configuration method based on source load storage interaction comprises the following steps:
performing capacity margin planning under a first-stage time scale to obtain a planning result of capacity configuration of a source load storage side, and performing network side capacity margin configuration under a second-stage time scale and network side capacity margin configuration under a third-stage time scale by taking the annual capacity margin planning result as an initial value; wherein, the time granularity of the first stage, the second stage and the third stage time scale is gradually decreased;
correcting the network side capacity margin configuration result under the second-level time scale by using the network side capacity margin configuration result under the third-level time scale, using the corrected network side capacity margin configuration result under the second-level time scale, combining the planning result of the source load storage side capacity configuration as the initial value of the capacity margin planning under the first-level time scale, and executing rolling optimization to obtain the annual network side equipment capacity margin configuration result.
As a further limitation of the first aspect of the present invention, a planning solution of source load storage side capacity configuration under a first level time scale is obtained by taking the minimum total cost of the planning year as a first objective function, and the planning solution is used as a planning result of the source load storage side capacity configuration;
respectively taking the planning solutions as initial values of a second objective function to obtain optimal solutions of network side capacity margin configuration under a second-stage time scale;
taking the planning solution as a third objective function initial value to obtain an optimal solution of network side capacity margin configuration under a third-level time scale, wherein the time granularity of the first-level time scale is annual, the time granularity of the first-level time scale is hour, and the time granularity of the first-level time scale is minute;
correcting the optimal solution of the network side capacity margin configuration under the second-level time scale by using the optimal solution of the network side capacity margin configuration under the third-level time scale;
and performing rolling optimization by combining the optimal solution of the network side capacity margin configuration under the corrected second-stage time scale with the planning solution of the source load storage side capacity configuration under the first-stage time scale to serve as an initial value of a new first objective function, so as to obtain a year-by-year network side equipment capacity margin configuration result.
As a further limitation of the first aspect of the present invention, the total cost of the planning year in the first objective function is: the addition of various power generation technologies, annual initial investment costs of new or modified lines, operational maintenance costs, fuel costs and power transmission costs.
As a further limitation of the first aspect of the invention, the second objective function and the third objective function are: the sum minimum of the grid running cost, the power generation capacity standby cost and the line capacity standby cost;
the operation cost is as follows: the electricity purchasing cost, the energy storage running cost, the controllable distributed power supply running and starting cost, the demand response cost and the reactive compensator running cost are added;
the spare cost of the power generation capacity is the spare cost provided by a power grid, a controllable distributed power supply, energy storage and demand response.
As a further definition of the first aspect of the invention, the second objective function and the third objective function each comprise: node power balance constraint, line power flow constraint, power flow limit constraint, distributed power supply output limit constraint, distributed power supply available reserve limit constraint, distributed power supply climbing limit constraint, power supply output limit constraint, energy storage related constraint, demand response limit constraint, reactive compensation related constraint and reserve demand constraint.
As a further definition of the first aspect of the invention, the first objective function comprises: power demand constraints, actual operating capacity constraints, and power generation installed capacity constraints.
In a second aspect, the invention provides a power grid capacity margin dynamic configuration system based on source load storage interaction.
A power grid capacity margin dynamic configuration system based on source load storage interaction, comprising:
a capacity margin configuration unit configured to: performing capacity margin planning under a first-stage time scale to obtain a planning result of capacity configuration of a source load storage side, and performing network side capacity margin configuration under a second-stage time scale and network side capacity margin configuration under a third-stage time scale by taking the annual capacity margin planning result as an initial value; wherein, the time granularity of the first stage, the second stage and the third stage time scale is gradually decreased;
a scroll optimization configuration unit configured to: correcting the network side capacity margin configuration result under the second-level time scale by using the network side capacity margin configuration result under the third-level time scale, using the corrected network side capacity margin configuration result under the second-level time scale, combining the planning result of the source load storage side capacity configuration as the initial value of the capacity margin planning under the first-level time scale, and executing rolling optimization to obtain the annual network side equipment capacity margin configuration result.
As a further limitation of the second aspect of the present invention, the total cost of the planned year is minimum as the first objective function, so as to obtain a planning solution of the source load storage side capacity configuration under the first level time scale, and the planning solution is used as a planning result of the source load storage side capacity configuration;
respectively taking the planning solutions as initial values of a second objective function to obtain optimal solutions of network side capacity margin configuration under a second-stage time scale;
taking the planning solution as a third objective function initial value to obtain an optimal solution of network side capacity margin configuration under a third-level time scale, wherein the time granularity of the first-level time scale is annual, the time granularity of the first-level time scale is hour, and the time granularity of the first-level time scale is minute;
correcting the optimal solution of the network side capacity margin configuration under the second-level time scale by using the optimal solution of the network side capacity margin configuration under the third-level time scale;
and performing rolling optimization by combining the optimal solution of the network side capacity margin configuration under the corrected second-stage time scale with the planning solution of the source load storage side capacity configuration under the first-stage time scale to serve as an initial value of a new first objective function, so as to obtain a year-by-year network side equipment capacity margin configuration result.
In a third aspect, the present invention is a computer readable storage medium, on which a program is stored, which when executed by a processor implements the steps in the method for dynamically configuring grid capacity margins based on source load storage interactions according to the first aspect of the present invention.
In a fourth aspect, the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the method for dynamically configuring a grid capacity margin based on source load storage interaction according to the first aspect of the present invention when the program is executed by the processor.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention creatively provides a planning and running integrated multi-time-scale network side equipment capacity margin configuration strategy, integrates the interaction characteristics of source load storage and the multi-time-scale capacity margin configuration result, forms a planning and running integrated power grid capacity margin configuration scheme based on source load storage interaction, and greatly improves the accuracy of the configuration result.
2. According to the invention, the network side capacity margin configuration result under the corrected second-stage time scale is combined with the planning result of the source load storage side capacity configuration as the initial value of capacity margin planning under the first-stage time scale, and the rolling optimization is executed, so that the annual network side equipment capacity margin configuration result is obtained, the multi-time scale combined rolling optimization is realized, and the configuration accuracy is further improved.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flow chart of a dynamic configuration method for power grid capacity margin based on source load storage interaction according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of linearization of inscribed circles according to embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of power circle linearization provided in embodiment 1 of the present invention;
fig. 4 is a schematic diagram of a planning operation integrated dynamic configuration method provided in embodiment 1 of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1:
according to the method, under the condition that the operation cost of a conventional unit, the operation cost of an energy storage power station, the new energy electricity discarding cost, the electricity discarding punishment cost of a load side and the line transmission cost are considered, an optimal configuration model of capacity margin of power grid equipment is established, a model simplification method is provided, and a rapid solving method is designed to achieve efficient solving of the network side capacity margin configuration model taking the interaction characteristics of source and load into account; meanwhile, the invention provides a multi-time scale network side equipment capacity margin configuration method of 'planning operation integration'; finally, integrating interaction characteristics of source load storage and multi-time scale capacity margin configuration results to form a planning operation integrated power grid capacity margin configuration strategy based on source load storage interaction, and specifically comprising the following steps:
s1: a network side flexibility margin configuration method based on source load storage interaction.
According to the invention, a quantized relation between active power capacity and reactive power capacity is provided by establishing a power grid voltage and power regulation capacity lifting way, and an optimal capacity margin is configured for power grid transmission equipment, so that a source side power distribution network and a platform region micro-grid can stably and interactively support a power grid accessed by user side adjustable resource energy sources such as a charging facility, an electric automobile and the like.
S1.1: an objective function.
The objective function is to minimize grid operating costs, power generation capacity reserve costs, and line capacity reserve. The running cost comprises electricity purchasing cost, energy storage running cost, controllable distributed power supply running and starting cost, demand response cost and reactive compensator running cost. The spare cost of the power generation capacity comprises the spare cost provided by a power grid, a controllable distributed power supply, energy storage and demand response:
（1）；
wherein,、/>and->The specific expression of (2) is as follows:
（2）；
（3）；
（4）
in the method, in the process of the invention,，/>the total operation cost and the standby cost of the power distribution network are respectively represented; />,/>,/>,/>,/>,/>,/>,/>,/>,/>,/>Respectively are related toA coefficient of running cost; />，/>，/>，/>Coefficients respectively representing the associated standby costs of power generation; />A coefficient representing the spare cost of the associated line capacity; />，/>Representing the active and reactive power purchased to the upper grid; />，/>Respectively representing the charge and discharge power of the stored energy; />，/>Active and reactive power of the distributed power supply are respectively represented; />Is a binary variable, and indicates whether the distributed power supply is started (the starting is 1, otherwise, the starting is 0); />Active power representing demand response; />，/>Representing the reactive power of the capacitor bank and the static var compensator, respectively; />，/>Respectively representing climbing up and climbing down provided by an upper power grid for standby; />，/>Respectively representing climbing up and climbing down provided by the controllable distributed power supply for standby; />，/>Respectively representing the climbing up and climbing down provided by energy storage for standby; />Indicating that the demand response provides redundancy; />Representing the duration of the time interval; />，/>Respectively representing a set of times and a set of grid nodes; />Representation lineijCapacity of the new (retrofit).
S1.2: constraint conditions.
(1) Node power balance type:
（5）；
（6）；
in the method, in the process of the invention,representing slave nodesiTo the nodejIs an active power flow of (a); />Indicating time of daytNodeiAn upper active load; />,,/>Respectively representing a set of energy storage nodes, load nodes and demand response nodes; />Representing reactive load.
(2) Line tide formula:
（7）；
in the method, in the process of the invention,representation oftTime nodeiIs set to the voltage amplitude of (1); />Representing the phase angle difference between the line head end node and the line tail end node; />，/>Respectively represent linesijAnd susceptance.
(3) Load flow restriction:
（8）；
in the method, in the process of the invention,representation lineijMaximum capacity of (2); />Representation lineijCapacity of the new (retrofit).
(4) Distributed power supply output limits.
The following formula limits the distributed power supply output variation to a certain range:
（9）；
（10）；
in the method, in the process of the invention,is a binary variable, representingtTime distributed power supplyiWhether or not to be online; />，/>Respectively represent distributed power suppliesiMaximum and minimum output limits of (2); />Representing distributed power suppliesiMaximum apparent power of (a).
(5) The distributed power supply may provide backup limits:
（11）；
（12）；
in the method, in the process of the invention,，/>representing the up-and-down-climbing rate of the distributed power supply; />Indicating the length of the corresponding time scale at time t.
(6) The climbing limit of the distributed power supply, the output change of the unit is limited by the climbing speed:
（13）；
（14）；
in the method, in the process of the invention,and->Respectively representing the output of the initial state of the unit i and the output of the first moment,/>The start-stop state of the unit i is shown, the start-up time is 1, and the stop time is 0.
(7) Power supply output limit constraints:
（15）；
（16）；
（17）；
in the method, in the process of the invention,representing a predicted value and a maximum output value of the power supply; />Representing the power factor angle of the power supply; />，Representing the maximum and minimum values of the power factor angle, respectively.
(8) The energy storage is related to constraint, the energy stored by the energy storage is in the upper and lower limit ranges, the charging or discharging power of the energy storage is in the upper limit range, and the energy storage can only be charged or discharged at the same time:
（18）；
（19）；
（20）；
（21）；
（22）；
（23）；
（24）；
（25）；
in the method, in the process of the invention,，/>respectively representing the maximum and minimum energy values of the stored energy; />Representation oftStoring energy at any time; />And->Respectively representing an initial value and a final value of the stored energy; />，/>Respectively representing the charge and discharge power of the stored energy; />，/>Is a binary variable, and the values of 1 and 0 respectively indicate that the energy storage is in a charging state and a discharging state;，/>respectively representing an upper limit of charging power and an upper limit of discharging power of the stored energy; />And->Respectively charging and discharging efficiency; />，/>Respectively represent the discharge and charge speeds of the stored energy.
(9) Demand response limits:
（26）；
（27）；
in the method, in the process of the invention,indicating whether the demand response is at a nodeiOn (I)>Representing the maximum proportion of demand response to the total load of the node.
(10) Reactive compensation-related constraints:
（28）；
（29）；
in the method, in the process of the invention,is a binary variable, representingtTime of day (time)iThe first capacitor in the capacitor bank on the individual nodekWhether the capacitors are put into use; />Reactive power for each capacitor; />Representing the number of capacitors in the capacitor bank; />Representing the maximum number of capacitor switching in a certain time.
（30）；
（31）；
In the method, in the process of the invention,，/>representing the minimum and maximum power of the static var compensator, respectively.
(11) Standby demand constraint:
（32）；
（33）；
in the middle of，/>Respectively representing the active climbing standby requirement and the active climbing standby requirement; />，And the reactive climbing standby requirement and the active climbing standby requirement are respectively indicated.
S1.3: model linearization method.
(1) A linearization method for the output cost of a unit.
The quadratic term in the unit output cost function isIt is now necessary to convert it into a piecewise linear function:
（34）；
wherein:
（35）；
in the method, in the process of the invention,the output of the unit i on the mth section is represented; />And->Respectively representing upper and lower boundary values of an mth segmented output section of the unit i; />And->Respectively represent the units iMaximum and minimum force.
(2) And linearizing a line tide formula.
The original line flow formula is an alternating current flow constraint expression form containing quadratic terms and sine quantities, the voltage amplitude of each node is approximately 1 on the premise of neglecting network loss, and the phase angle difference of the nodes at two ends of the line is approximately 0, so that，，/>，/>The method can obtain:
（36）；
(3) And linearizing branch power flow constraint and distributed power supply output limit.
The branch flow constraint is similar to the formula form of the distributed power supply output, and from the geometric point of view, the feasible domains are all with the radius ofS ij The invention carries out equivalent linearization representation on the power circle interior, and according to the basic method of analytic geometry, the area surrounded by inscribed regular polygons can be represented by a series of linear constraints as follows:
（37）；
in the method, in the process of the invention,the coefficient corresponding to the linear power circle constraint varies with the number of sides of the divided regular polygon.
In consideration of the compromise between precision and calculation efficiency, the model equally divides the circumference 12, and sequentially connects the division points to obtain a circular inscribed dodecagon, as shown in fig. 1, that is, the area enclosed by the inscribed dodecagon is approximately replaced by the area enclosed by the circular inscribed dodecagon, and specifically, as shown in fig. 2. Assuming A, B as two adjacent points of regular dodecagon in the circle, the radian angles of A, B are α and β respectively, then:
（38）；
the A point coordinates areThe coordinates of the point B are +.>The straight line equation for any one side AB of a right dodecagon within a circle can be expressed as:
（39）；
based on analytical geometry theory, the formula can be:
（40）；
(4) The Big-M method processes reactive compensation constraint.
The reactive power sent by the capacitor bank is the sum of all switched capacitor reactive power; the number of the switched capacitors at a certain moment is limited by the number of the capacitors in the capacitor bank; the number of times the capacitor bank is switched is limited within a certain time.
（41）；
In the constraint group, the nonlinear term is obtained by multiplying a discrete variable by a continuous variable, and linearizing the discrete variable according to a Big-M method, wherein the following formula is shown as follows:
（42）；
wherein,M 1 is a very large constant.
S2: the planning operation integrated network side equipment capacity margin dynamic configuration method.
The invention provides an integrated decision model integrating three modules of long-term planning, short-term scheduling and real-time scheduling, which considers multi-time granularity in power grid capacity margin configuration from the angles of three time scales of long term, short term and real time, wherein the time granularity of the first-stage time scale is 1 year, and long-term annual capacity margin planning is realized; the time granularity of the second-stage time scale is 1 hour, so that the short-term scheduling optimization of 8760 hours in each planning year is realized; and the time granularity of the residual time scale is 15min, so that the ultra-short-term flexibility margin operation capability is optimized. The model operates dynamically, and decision variables (such as newly-increased capacity and actual output of a generator set, an energy storage power station and a distributed power supply) of each time interval are optimized each time, the capacity of a network side newly-increased (transformed) line and the capacity of the newly-increased line are newly-increased, only the result of a first stage time scale is used as a planning instruction, the strong constraint force is provided, the result of the rest time scales is used as a correction basis, and the planning decision result of the first stage is further perfected, as shown in fig. 3.
The planning and scheduling independent model is converted into an integrated model, and various constraints under different time granularities are required to be changed into constraints under variable time granularities.
S2.1: a multi-time scale dynamic model.
The first-stage time scale model takes the minimum sum of the costs of all years in different stages as an objective function, and comprises all power generation technologies, the initial investment cost of the newly added (reformed) line years, and the operation and maintenance cost.
（43）；
Wherein,TCrepresenting the total cost of planning a year,、/>representing the investment costs of the new additions (modifications) of the generator set and the network-side lines, respectively,/->、/>Representing the operating and maintenance costs of the generator set and the network side line respectively,Nrepresenting a set of power generation technology types,N i representing a set of newly added (rebuilt) lines;
wherein, investment cost:
（44）；
wherein,representing the unit investment cost of newly built (or improved) generator sets or line capacities; />Representing total newly built (or improved) capacity; />Representing an internal rate of return; />Life cycle of the technique, ">"indicates the subscript i or line ij of the unit.
Operation and maintenance costs:
（45）；
wherein,representation ofRunning and maintaining cost coefficients of the unit and the line; />Representing the actual running installed capacitySet to 1); />Indicating the number of actual operating hours.
The constraint conditions of the first-stage time scale model mainly comprise power demand constraint, actual operation capacity constraint (the installed capacity of each generator set in actual operation every year is smaller than the technical stock quantity of the technology in the current year, wherein the technical stock quantity refers to the sum of the installed capacity of the technology in the previous year and the newly built (or improved) capacity under consideration of depreciation, and the installed capacity subjected to forced elimination), power generation installed capacity constraint (according to economic society development situation and environmental resource limitation, upper and lower limits exist on the installed capacities of different generator sets, the installed capacity of new energy power generation technology and distributed power sources popularized by policies is higher than the capacity of the reference year within the allowable range of physical resource capacity, the installed capacity of the technology in the next year is lower than the capacity of the reference year for the obsolete capacity, line capacity limitation constraint and the like.
The second and third time scale models are network side flexibility margin configuration models based on source load storage interaction in S1, and it is noted that the time length T in the models needs to be replaced by a variable time scale parameter T h (h is 1 and 2 times T 1 And T 2 Respectively 1 hour and 15 minutes).
S2.2: and (5) dynamically configuring the capacity margin of the power grid.
According to the regional power system development prediction result, a planning layer model of a first-stage time scale is operated, a planning solution of source load storage side capacity configuration is solved, the solution value is substituted into a second-layer scheduling layer optimization model and a third-layer scheduling layer optimization model as an initial value, and an optimal solution of network side capacity margin configuration is solved; and taking the optimal solution as a correction value of the capacity margin solution of the network side equipment in the first-layer planning model, forming a new planning model initial value together with the planning solution of the capacity configuration of the source load storage side and the like, operating the first-layer planning model, solving to obtain the planning solution of the second year, performing rolling optimization, and finally obtaining the capacity margin configuration optimal solution of the network side equipment year by year.
Example 2:
the embodiment 2 of the invention provides a dynamic configuration system for power grid capacity margin based on source load storage interaction, which comprises the following components:
a capacity margin configuration unit configured to: performing capacity margin planning under a first-stage time scale to obtain a planning result of capacity configuration of a source load storage side, and performing network side capacity margin configuration under a second-stage time scale and network side capacity margin configuration under a third-stage time scale by taking the annual capacity margin planning result as an initial value; wherein, the time granularity of the first stage, the second stage and the third stage time scale is gradually decreased;
a scroll optimization configuration unit configured to: correcting the network side capacity margin configuration result under the second-level time scale by using the network side capacity margin configuration result under the third-level time scale, using the corrected network side capacity margin configuration result under the second-level time scale, combining the planning result of the source load storage side capacity configuration as the initial value of the capacity margin planning under the first-level time scale, and executing rolling optimization to obtain the annual network side equipment capacity margin configuration result.
The working method of the system is the same as the power grid capacity margin dynamic configuration method based on source load storage interaction provided in embodiment 1, and will not be described here again.
Example 3:
embodiment 3 of the present invention provides a computer readable storage medium having a program stored thereon, which when executed by a processor, implements the steps in the method for dynamically configuring a grid capacity margin based on source load storage interaction according to embodiment 1 of the present invention.
Example 4:
the embodiment 4 of the invention provides an electronic device, which comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in the dynamic configuration method of the grid capacity margin based on the source load storage interaction according to the embodiment 1 of the invention when executing the program.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (14)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311752184.2A CN117638944B (en) | 2023-12-19 | 2023-12-19 | Power grid capacity margin dynamic configuration method and system based on source-load-storage interaction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311752184.2A CN117638944B (en) | 2023-12-19 | 2023-12-19 | Power grid capacity margin dynamic configuration method and system based on source-load-storage interaction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117638944A true CN117638944A (en) | 2024-03-01 |
| CN117638944B CN117638944B (en) | 2024-08-27 |
Family
ID=90034017
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311752184.2A Active CN117638944B (en) | 2023-12-19 | 2023-12-19 | Power grid capacity margin dynamic configuration method and system based on source-load-storage interaction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117638944B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119671372A (en) * | 2024-11-28 | 2025-03-21 | 中国电力科学研究院有限公司 | A method, system, device and medium for estimating balance margin of power system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7467291B1 (en) * | 2005-02-28 | 2008-12-16 | Sun Microsystems, Inc. | System and method for calibrating headroom margin |
| CN111555366A (en) * | 2020-04-16 | 2020-08-18 | 杭州市电力设计院有限公司 | Multi-time scale-based microgrid three-layer energy optimization management method |
| CN115345389A (en) * | 2022-10-19 | 2022-11-15 | 广东电网有限责任公司佛山供电局 | Multi-time scale optimization scheduling method for virtual power plant by cluster |
| CN116247737A (en) * | 2022-12-07 | 2023-06-09 | 国网浙江省电力有限公司 | Analysis method based on dynamic load margin of active power distribution network planning |
-
2023
- 2023-12-19 CN CN202311752184.2A patent/CN117638944B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7467291B1 (en) * | 2005-02-28 | 2008-12-16 | Sun Microsystems, Inc. | System and method for calibrating headroom margin |
| CN111555366A (en) * | 2020-04-16 | 2020-08-18 | 杭州市电力设计院有限公司 | Multi-time scale-based microgrid three-layer energy optimization management method |
| CN115345389A (en) * | 2022-10-19 | 2022-11-15 | 广东电网有限责任公司佛山供电局 | Multi-time scale optimization scheduling method for virtual power plant by cluster |
| CN116247737A (en) * | 2022-12-07 | 2023-06-09 | 国网浙江省电力有限公司 | Analysis method based on dynamic load margin of active power distribution network planning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119671372A (en) * | 2024-11-28 | 2025-03-21 | 中国电力科学研究院有限公司 | A method, system, device and medium for estimating balance margin of power system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117638944B (en) | 2024-08-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111064210B (en) | Hierarchical control method for island power grid energy storage system for increasing new energy power generation fluctuation | |
| CN111934362B (en) | Multi-power-supply coordinated optimization peak regulation method for fluctuation characteristics of renewable energy | |
| CN111355265B (en) | Micro-grid energy two-stage robust optimization method and system | |
| WO2018059096A1 (en) | Combined decision method for power generation plans of multiple power sources, and storage medium | |
| CN109948849B (en) | Power distribution network frame planning method considering energy storage access | |
| CN110350589B (en) | Renewable energy and energy storage scheduling model and scheduling method | |
| CN111682531B (en) | PL-IMOCS-based short-term optimal scheduling method and device for wind, water, fire and primary energy complementarity | |
| CN116865270A (en) | Optimal scheduling method and system for flexible interconnection power distribution network containing embedded direct current | |
| CN112039126A (en) | Multi-time scale coordinated scheduling method and system for power distribution network containing distributed power supply | |
| CN118826154A (en) | A multi-stage optimal peak-shaving method for wind power system with adaptive step-size adjustment | |
| CN118889479A (en) | A method, device and equipment for configuring multi-level coordinated energy storage capacity in an offshore wind farm | |
| CN117638944A (en) | Dynamic allocation method and system of power grid capacity margin based on source-load-storage interaction | |
| CN108683211A (en) | A kind of virtual power plant combined optimization method and model considering distributed generation resource fluctuation | |
| Ni et al. | Dynamic Characteristics-Based Capacity Optimization Strategy for Hybrid AA-CAES and Battery Storage Systems in Source-grid-load-storage Integrated Base | |
| Zhu et al. | Optimum power control and coordinate sizing for the stand-alone wind-energy storage integrated hydrogen production system | |
| CN119051100A (en) | New energy and energy storage combined optimization operation method, device and storage medium | |
| CN118378752A (en) | Micro-grid-connected operation optimization scheduling method based on improved beluga optimization algorithm | |
| CN116488264B (en) | Optimized dispatching method, device, equipment and storage medium for distribution network | |
| CN118381002A (en) | Daily electricity quantity distribution method for weekly-regulated pumped storage power station | |
| CN117394301A (en) | A dispatching and operation method of hydrogen-electricity coupled DC microgrid considering the uncertainty of wind and light | |
| Yang et al. | Research on regulation method of energy storage system based on multi-stage robust optimization | |
| CN117335381A (en) | Considering the flexibility of peak shaving, resource optimization configuration methods, systems, media and equipment | |
| Peng et al. | Robust Model Predictive Control based Strategy for Battery Energy Storage System to Mitigate Wind Power Fluctuation | |
| Song et al. | Research on Multi-Source Frequency Regulation Strategies Based on the Stackelberg Game and Deep Adaptive Dynamic Programming | |
| Wang et al. | Research on the power and energy balance scheduling problem in new-type power systems |
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 |