CN116316716A - Multi-stage planning method for energy storage power station, computer equipment and storage medium - Google Patents
Multi-stage planning method for energy storage power station, computer equipment and storage medium Download PDFInfo
- Publication number
- CN116316716A CN116316716A CN202310090227.9A CN202310090227A CN116316716A CN 116316716 A CN116316716 A CN 116316716A CN 202310090227 A CN202310090227 A CN 202310090227A CN 116316716 A CN116316716 A CN 116316716A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power
- power station
- planning
- model
- 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
- 238000004146 energy storage Methods 0.000 title claims abstract description 155
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000003860 storage Methods 0.000 title claims abstract description 9
- 230000002068 genetic effect Effects 0.000 claims abstract description 23
- 238000005265 energy consumption Methods 0.000 claims abstract description 21
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 230000009467 reduction Effects 0.000 claims abstract description 18
- 230000005611 electricity Effects 0.000 claims abstract description 12
- 238000004513 sizing Methods 0.000 claims abstract description 4
- 238000009826 distribution Methods 0.000 claims description 87
- 230000006870 function Effects 0.000 claims description 28
- 238000010276 construction Methods 0.000 claims description 25
- 238000012423 maintenance Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 11
- 230000005540 biological transmission Effects 0.000 claims description 9
- 238000007599 discharging Methods 0.000 claims description 9
- 230000035772 mutation Effects 0.000 claims description 6
- 239000002699 waste material Substances 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 2
- 239000010410 layer Substances 0.000 description 14
- 238000004590 computer program Methods 0.000 description 5
- 230000007774 longterm Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000002355 dual-layer Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- 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
- H02J3/38—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
-
- 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
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic 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
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Power Engineering (AREA)
- Health & Medical Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a multi-stage planning method of an energy storage power station, computer equipment and a storage medium, wherein the multi-stage planning method of the energy storage power station comprises the following steps: step 1: establishing a power supply and demand growth model; step 2: establishing a constant-volume objective function model of the upper energy storage power station; step 3: establishing a constant-volume constraint condition model of an upper energy storage power station; step 4: establishing a lower energy storage electricity locating and sizing objective function model; step 5: establishing a lower energy storage electricity site selection constraint condition model; step 6: and establishing a solving algorithm of the double-layer planning model, optimizing and updating the capacity of the energy storage power station by adopting a genetic algorithm by the upper-layer model, and transmitting the capacity to the lower-layer model. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction can adapt to new energy and user growth conditions in the future multi-stage.
Description
Technical Field
The invention relates to the technical field of power engineering, in particular to a multi-stage planning technology of an energy storage power station.
Background
The scale of the new energy grid-connected capacity under the dual-carbon background is continuously enlarged, the power distribution network is used as an important carrier for receiving new energy and integrating the new energy into a power system, the inherent properties of extremely strong volatility, randomness, reverse peak regulation and the like of the new energy high-permeability output are improved, the stable operation difficulty of the power distribution network is increased, and the problems of increased active loss, insufficient new energy consumption capability and the like of the power distribution network are also caused. The specific electric energy throughput and power control capability of the energy storage technology plays an important role in realizing the adjustment of new energy output fluctuation, the reduction of active loss of a distribution network, the improvement of new energy consumption rate and the like, but the effect is greatly influenced by energy storage constant volume and site selection planning. The main expression is as follows: (1) constant volume: the fluctuation of new energy is difficult to adjust due to the excessively small constant volume, the new energy is improved to be consumed in situ, and the capacity allocation of part of energy storage resources is invalid and wasted due to the excessively large constant volume; (2) site selection: the addressing scheme influences the output of new energy through factors such as line flow constraint, voltage stability constraint and the like, so that the utility function of energy storage configuration capacity is limited, and meanwhile, the situation of increasing the active power loss of the power distribution network exists due to unreasonable addressing.
In the existing research about constant volume and site selection planning of the energy storage power station, new energy output and load demand are often set to constant values, future growth caused by factors such as policy and market are ignored, so that planning results are difficult to adapt to future new energy development, and meanwhile, the planning scheme is mainly used for realizing the economical efficiency of comprehensive cost in the planning of the energy storage power station on the basis of meeting short-term stable operation demands such as different power fluctuation stabilization and voltage stabilization of a power distribution network system, such as: the energy storage investment construction cost, the network loss cost and other cost are minimized, the respective targets and the mutual influence achieved by the long-term planning of the energy storage power station and the short-term operation of the power distribution network are ignored, and the dissipatable degree of the future development scale of new energy is also lack of research.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problem to be solved by the invention is to provide the multi-stage planning method of the energy storage power station, which considers new energy consumption and network loss reduction, is suitable for new energy and user growth conditions in the future multi-stage, and the constant volume scheme can improve the consumption rate of the new energy after growth and reduce the comprehensive cost of the power distribution network construction operation maintenance energy storage power station; the addressing scheme can reduce active loss after the distributed energy sources are connected into the power distribution network, and improves the running economy of the power distribution network.
In order to solve the technical problems, the invention adopts the following technical scheme:
a multi-stage planning method of an energy storage power station considering new energy consumption and network loss reduction comprises the following steps:
step 1: establishing a power supply and demand growth model, wherein the power supply and demand growth model comprises a new energy output growth model and a load growth model;
step 2: establishing an upper energy storage power station constant volume objective function model, minimizing the total comprehensive cost of each planning stage as an objective function, wherein the comprehensive cost comprises: investment construction cost, operation maintenance cost and waste wind and waste light cost of the power distribution network of the energy storage power station;
step 3: establishing a constant-volume constraint condition model of the upper energy storage power station, wherein the constraint condition comprises: power balance constraint, new energy output constraint, energy storage power station charge and discharge power and state of charge constraint;
step 4: establishing a lower energy storage electricity locating and sizing objective function model, and minimizing the active loss of the power distribution network in the total planning stage as an objective function;
step 5: establishing a lower-layer energy storage electricity site selection constraint condition model, wherein the constraint condition comprises: each node allows the energy storage capacity constraint, the power distribution network alternating current power flow constraint, the node voltage constraint and the branch power flow constraint to be installed;
step 6: and establishing a solving algorithm of a double-layer planning model, wherein the upper layer model adopts a genetic algorithm to optimize and update the capacity of the energy storage power station, transmitting the capacity to the lower layer model, solving and obtaining a network loss value and the addressing node and capacity of the energy storage power station by adopting an MOSEK solver, transmitting the network loss value to the upper layer model, and finally obtaining a balanced solution in the information interaction process.
Preferably, the new energy output growth model and the load growth model are respectively:
(1.1) New energy output increase model
The new energy output has obvious seasonality, and the output under various scenes is considered for the reason:
in the method, in the process of the invention,
the method comprises the steps of integrating new energy output, wind power output and photovoltaic output in a t period in a scene s of an nth planning stage; />
The expected output of wind power and photovoltaic in the nth planning stage is compared with that of the wind power and the photovoltaicGrowth rate of the n-1 th planning stage;
(1.2) load growth model
Wherein D is n,s,t For a period t of time in a scene s of an nth planning phase;
the rate of increase of force compared to the n-1 th planning phase is predicted for the load in the n-th planning phase.
Preferably, the constant volume objective function of the energy storage power station in the step 2 is:
the comprehensive cost of the distribution network in the total planning stage is minimized, the configuration capacity of the energy storage power station is optimized, the investment construction cost, the operation maintenance cost and the wind and light discarding cost are reduced,
wherein F is total For the comprehensive cost of the distribution network in the overall planning phase,
respectively the investment construction cost, the operation maintenance cost and the wind and light discarding cost of the energy storage power station of the power distribution network in the nth planning stage, N is the total number of the planning stages,
wherein C is IE 、C IP The investment construction cost of unit capacity and the investment construction cost of unit power of the energy storage power station are respectively; c (C) OC Operating maintenance costs for the energy storage power station; mu (mu) n,s,t For indicating the charge and discharge states of the energy storage power station in the t period in the scene s of the nth planning stage, the state of charge is mu n,s,t =1, μ in discharge state n,s,t = -1, μ in float state n,s,t ＝0；
The online prices of photovoltaic and wind power at the period t are respectively set; />
The proportion coefficients of the total abandoned wind quantity of new energy of the un-consumed photovoltaic and wind power in the t period in the scene s of the nth planning stage are respectively calculated; />
And (5) discarding the wind and discarding the light for the power distribution network of the t period in the scene s of the nth planning stage.
Preferably, the constraint conditions in the step 3 are:
(3.1) Power balance constraint
Wherein L is S The total branch number of the upper layer main network is connected to the power distribution network,
for the active power delivered by the upper main network to the distribution network by way of the branch l in the t-period in the scenario s of the nth planning phase, +.>
The method comprises the steps that the active loss of the power distribution network in a t period in a scene s of an nth planning stage is obtained by solving a lower model;
(3.2) Power constraint by branch of Power distribution network connected by Main network
In the method, in the process of the invention,
the power distribution network branch circuit l is connected with the main network to allow the maximum reverse power to be transmitted;
(3.3) constraints on charging and discharging power and state of charge of energy storage power stations in distribution network
Wherein eta is d 、η c Respectively discharging efficiency and charging efficiency; η (eta) n,s,t 、S n,s,t Respectively charging and discharging efficiency and charge quantity of the energy storage power station at the t period in a scene s of the nth planning stage; s is S min 、S max 、S 0 Respectively a lower limit, an upper limit and an initial value of the charge quantity of the energy storage power station; t is the time period number of the energy storage power station in a short-term operation optimization period;
(3.4) investment construction constraints for energy storage Power stations
Preferably, in the step 4, the addressing objective function of the energy storage power station is:
(4.1) optimizing the grid-connected position of the energy storage power station in the distribution network with the aim of minimizing the network loss cost of the distribution network in the total planning stage so as to reduce the operation cost of the distribution network, wherein the objective function is expressed as the following formula:
wherein F is loss The cost of network loss of the power distribution network in the total planning stage,
For the net loss cost in the nth planning stage, the calculation formula is as follows:
wherein p is loss To reduce unit price of net, P ij,n,s,t 、Q ij,n,s,t Active power, reactive power, resistance value, V on the branch from node i to node j in the t period in the scenario s of the nth planning phase i,n,s,t For the voltage value of the node i of the t period in the scene s of the nth planning phase, R ij The resistance value on the branch from node i to node j.
Preferably, the constraint conditions in the step 5 are:
(5.1) energy storage Capacity constraints that each node allows to install
In the method, in the process of the invention,
the energy storage station capacity value and the upper limit value of the node i are respectively accessed in the n planning stage;
(5.2) AC Power flow constraint of Power distribution network
The whole power distribution network architecture is a radial network, the DistFlow model is adopted to describe the alternating current power flow constraint,
wherein P is j,n,s,t 、Q j,n,s,t 、D j,n,s,t 、Q j,n,s,t 、
The net load active power, the net load reactive power, the load active power, the load reactive power, the new energy active output, the new energy reactive output, the abandoned wind quantity, the reactive power of reactive compensation equipment, the active output of an energy storage power station, the reactive output of the energy storage power station and L of the energy storage power station are respectively the net load active power, the net load reactive power, the load active power, the load reactive power in the scene s of the n planning stage ij,n,s,t Representing the current square value, X, of the branch from node i to node j at time period t in the scene s of the nth planning stage ij For the reactance value of the branch from node i to node j, P jl,n,s,t 、Q jl,n,s,t Respectively the active power and the reactive power from the node j to the branch l in the period t in the scene s of the n planning stage;
(5.3) node voltage constraints
U N (1-ε 1 )≤U k ≤U N (1+ε 2 ) (15)
In U k Is the voltage of node k; u (U) N The voltage amplitude is the outlet voltage amplitude of the transformer substation; epsilon 1 、ε 2 The voltage negative deviation upper limit value and the voltage positive deviation upper limit value are respectively;
(5.4) Branch tidal current constraint
Wherein S is l 、
The transmission power, the transmission power lower limit value and the transmission power upper limit value of the branch circuit l in the power distribution network are respectively.
Preferably, the solution algorithm of the dual-layer planning model in step 6 includes:
(6.1) basic data preparation, initializing various parameters of a power distribution network, new energy and load, and determining various parameters in a genetic algorithm, wherein the parameters comprise mutation probability, crossover probability, total genetic algebra G, population m, genetic algebra count value k and convergence error eta, and the initial value of k is set to be 1;
(6.2) population initialization, randomly generating m groups by genetic algorithm
Transmitting the capacity and power initial value of the energy storage power station to a lower energy storage electricity site selection model;
(6.2) solving a lower energy storage electricity site selection model according to the constant volume condition of the energy storage power station
Is->
Determining grid-connected nodes and capacity of an energy storage power station to minimize grid loss cost F loss Obtaining the capacity of the energy storage power station at the grid-connected node by using a MOSEK solver as an objective function>
And to keep the net cost +.>
(6.3) calculating the constant-volume comprehensive cost of the upper energy storage power station, wherein the lower model transmits a net loss value to the upper model, and calculating the constant-volume comprehensive cost of the energy storage power station in the kth iteration
(6.4) updating the algebraic count value, k=k+1;
(6.5) updating the genetic algebra population data and the constant volume data, and forming new population data by using the selection, crossing and mutation of the genetic algorithm, wherein the new population data comprises m groups
Is->
Repeating the steps (6.2), (6.3) to obtain +.>
(6.6) judging whether the double-layer planning model obtains the equilibrium solution, if so
And->
Indicating that an equilibrium solution is obtained; otherwise, repeating the steps (6.4) and (6.5);
(6.7) outputting the equalization result, and optimizing variables of the upper layer model and the lower layer model in the equalization state
Respective target values +.>
And outputting as a final result.
The invention also provides a computer device comprising at least one processor and a memory; the memory stores computer-executable instructions; the at least one processor executes the computer-executable instructions stored by the memory such that the at least one processor executes the multi-stage planning method of the energy storage power station that takes into account new energy consumption and grid loss reduction.
The invention also provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when a processor executes the computer execution instructions, the multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction is realized.
According to the invention, the optimal granularity and the target of long-term planning of the energy storage power station and short-term operation of the power distribution network in different time scales are comprehensively considered, the thought of hierarchical coordination planning is provided, and the growth condition of new energy output and load demand is considered, a multi-stage energy storage power station constant volume and site selection double-layer coordination planning model is established, wherein the upper model takes the whole power distribution network as an object, and the energy storage power station capacity of each stage is decided from the power distribution network level, so that the investment construction cost economy and new energy consumption of the energy storage power station in multiple stages are realized; the lower model takes each node of the power distribution network as an object, and energy storage power station access nodes in each stage are decided from a grid-connected level, so that the active loss minimization of the short-term operation of the power distribution network is realized.
Therefore, compared with the prior art, the method has the remarkable advantages that:
1) The new energy and load increase in the multi-stage planning time range is considered, and the planning result of the energy storage power station constant volume and site selection can adapt to the future new energy and load increase condition;
2) The capacity of the energy storage power station is optimized in a centralized manner in multiple stages, and the objective function considers multiple long-term comprehensive costs such as investment construction cost, operation maintenance cost, wind and light discarding cost and the like, so that the economical efficiency of the investment construction of the energy storage power station can be realized;
3) And the grid-connected node of the energy storage power station is determined by taking the minimum active loss in the total planning stage as a target, so that the short-term operation economy of the power distribution network is effectively ensured.
The specific technical scheme adopted by the invention and the beneficial effects brought by the technical scheme are disclosed in the following detailed description in combination with the drawings.
Drawings
The invention is further described with reference to the drawings and detailed description which follow:
FIG. 1 is a flow chart of a multi-stage planning method for an energy storage power station according to the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be explained and illustrated below with reference to the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all embodiments. Based on the examples in the implementation manner, other examples obtained by a person skilled in the art without making creative efforts fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, the embodiment provides a multi-stage planning method of an energy storage power station considering new energy consumption and network loss reduction, comprehensively considers optimization granularity and targets of long-term planning of the energy storage power station and short-term operation of a power distribution network in different time scales, proposes a concept of hierarchical coordination planning, and considers the growth condition of new energy output and load demand to establish a multi-stage energy storage power station constant volume and site selection double-layer coordination planning model, wherein an upper layer model takes the whole power distribution network as an object, decides the energy storage power station capacity of each stage from the power distribution network level, and realizes investment construction cost economy and new energy consumption of the energy storage power station in multiple stages; the lower model takes each node of the power distribution network as an object, and energy storage power station access nodes in each stage are decided from a grid-connected level, so that the active loss minimization of the short-term operation of the power distribution network is realized.
The method specifically comprises the following steps:
(1) Establishing an electric power supply and demand growth model, including a new energy output growth model and a load growth model;
(2) Establishing an upper energy storage power station constant volume objective function model by minimizing the total cost of each planning stage as an objective function, wherein the method comprises the following steps: investment construction cost, operation maintenance cost and waste wind and waste light cost of the power distribution network of the energy storage power station;
(3) Establishing an upper energy storage power station constant volume constraint condition model, wherein the constraint condition comprises the following steps: power balance constraint, new energy output constraint, energy storage power station charge and discharge power and state of charge constraint;
(4) Establishing a lower energy storage electricity locating and sizing objective function model, wherein the objective function is to minimize the active loss of the power distribution network in the total planning stage;
(5) Establishing a lower energy storage electric site selection and volume determination objective function model, wherein constraint conditions comprise: each node allows the energy storage capacity constraint, the power distribution network alternating current power flow constraint, the node voltage constraint and the branch power flow constraint to be installed;
(6) The upper model is solved by adopting a genetic algorithm, and the lower model is solved by adopting a solver.
The new energy output increase model and the load increase model in the step (1) are specifically as follows:
(1.1) New energy output increase model
The new energy output has obvious seasonality, and the output under various scenes is considered for this purpose and needs to be reflected in a model:
in the method, in the process of the invention,
the method comprises the steps of integrating new energy output, wind power output and photovoltaic output in a t period in a scene s of an nth planning stage; />
And respectively predicting the growth rate of the force of wind power and photovoltaic in the nth planning stage compared with that of the force of the wind power and the photovoltaic in the (n-1) th planning stage.
(1.2) load growth model
Wherein D is n,s,t For a period t of time in a scene s of an nth planning phase;
the rate of increase of force compared to the n-1 th planning phase is predicted for the load in the n-th planning phase.
The constant volume objective function of the energy storage power station in the step (2) is as follows:
and (2.1) minimizing the comprehensive cost of the power distribution network in the total planning stage, and optimizing the configuration capacity of the energy storage power station so as to reduce investment construction cost, operation maintenance cost and wind and light discarding cost.
Wherein F is total For the comprehensive cost of the distribution network in the overall planning phase,
the energy storage power station investment construction cost, the operation maintenance cost and the wind and light discarding cost of the power distribution network in the nth planning stage are respectively calculated, and N is the total number of the planning stages.
Wherein C is IE 、C IP The investment construction cost of unit capacity and the investment construction cost of unit power of the energy storage power station are respectively; c (C) OC Operating maintenance costs for the energy storage power station; mu (mu) n,s,t For indication of the charge-discharge state (μ in state of charge) of the energy storage plant for a period t in the scenario s of the nth planning phase n,s,t Mu in discharge state =1 n,s,t Mu in the state of = -1 and float n,s,t ＝0)；
The online prices of photovoltaic and wind power at the period t are respectively set; />
The proportion coefficients of the total abandoned wind quantity of new energy of the un-consumed photovoltaic and wind power in the t period in the scene s of the nth planning stage are respectively calculated; />
And (5) discarding the wind and discarding the light for the power distribution network of the t period in the scene s of the nth planning stage.
The detailed constraint model described in step (3), comprising:
(3.1) Power balance constraint
Wherein L is S The total branch number of the upper layer main network is connected to the power distribution network,
for the active power delivered by the upper main network to the distribution network by way of the branch l in the t-period in the scenario s of the nth planning phase, +.>
And solving the active loss of the power distribution network at the t period in the scene s of the nth planning stage by using a lower model.
(3.2) Power constraint by branch of Power distribution network connected by Main network
In the method, in the process of the invention,
the main network is connected with the maximum reverse power allowed to be transmitted by the distribution network branch circuit l.
(3.3) constraints on charging and discharging power and state of charge of energy storage power stations in distribution network
Wherein eta is d 、η c Respectively discharging efficiency and charging efficiency; η (eta) n,s,t 、S n,s,t Respectively charging and discharging efficiency and charge quantity of the energy storage power station at the t period in a scene s of the nth planning stage; s is S min 、S max 、S 0 Respectively a lower limit, an upper limit and an initial value of the charge quantity of the energy storage power station; and T is the time period number of the energy storage power station in a short-term operation optimization period.
(3.4) investment construction constraints for energy storage Power stations
The addressing objective function of the energy storage power station in the step (4) is as follows:
(4.1) optimizing the grid-connected position of the energy storage power station in the distribution network with the aim of minimizing the network loss cost of the distribution network in the total planning stage so as to reduce the operation cost of the distribution network, wherein the objective function is expressed as the following formula:
wherein F is loss The cost of network loss of the power distribution network in the total planning stage,
For the net loss cost in the nth planning stage, the calculation formula is as follows:
wherein p is loss To reduce unit price of net, P ij,n,s,t 、Q ij,n,s,t Active power, reactive power, resistance value, V on the branch from node i to node j in the t period in the scenario s of the nth planning phase i,n,s,t For the voltage value of the node i of the t period in the scene s of the nth planning phase, R ij The resistance value on the branch from node i to node j.
The detailed constraint model described in step (5), comprising:
(5.1) energy storage Capacity constraints that each node allows to install
In the method, in the process of the invention,
and the capacity value and the upper limit value of the energy storage station accessed by the node i in the n planning stage are respectively.
(5.2) AC Power flow constraint of Power distribution network
The whole power distribution network architecture is a radial network, and the alternating current power flow constraint is described by adopting a DistFlow model.
Wherein P is j,n,s,t 、Q j,n,s,t 、D j,n,s,t 、Q j,n,s,t 、
The net load active power, the net load reactive power, the load active power, the load reactive power, the new energy active output, the new energy reactive output, the abandoned wind quantity, the reactive power of reactive compensation equipment, the active output of an energy storage power station, the reactive output of the energy storage power station and L of the energy storage power station are respectively the net load active power, the net load reactive power, the load active power, the load reactive power in the scene s of the n planning stage ij,n,s,t Representing the current square value, X, of the branch from node i to node j at time period t in the scene s of the nth planning stage ij For the reactance value of the branch from node i to node j, P jl,n,s,t 、Q jl,n,s,t Active power and reactive power of the nodes j to the branch l in the period t in the scene s of the n-th planning stage are respectively shown.
(5.3) node voltage constraints
U N (1-ε 1 )≤U k ≤U N (1+ε 2 ) (31)
In U k Is the voltage of node k; u (U) N The voltage amplitude is the outlet voltage amplitude of the transformer substation; epsilon 1 、ε 2 The voltage is respectively a negative deviation upper limit value and a positive deviation upper limit value, and the power is supplied by GB/T12325-2008 electric energy qualityThe power deviation is prescribed.
(5.4) Branch tidal current constraint
Wherein S is l 、
The transmission power, the transmission power lower limit value and the transmission power upper limit value of the branch circuit l in the power distribution network are respectively.
The solving algorithm of the step (6) comprises:
(6.1) basic data preparation. Initializing various parameters of a power distribution network, new energy and load, and determining parameters such as mutation probability, crossover probability, total genetic algebra G, population number m, genetic algebra count value k, convergence error eta and the like in a genetic algorithm, wherein the initial value of k is set to be 1;
(6.2) population initialization. Random generation of m sets using genetic algorithm
Transmitting the capacity and power initial value of the energy storage power station to a lower energy storage electricity site selection model;
and (6.2) solving a lower energy storage electric site selection model. According to the constant volume condition of the energy storage power station
Is->
Determining grid-connected nodes and capacity of an energy storage power station to minimize grid loss cost F loss Obtaining the capacity of the energy storage power station at the grid-connected node by using a MOSEK solver as an objective function>
And to keep the net cost +.>
And (6.3) calculating the constant volume comprehensive cost of the upper energy storage power station. The lower model transmits a net loss value to the upper model, and the constant volume comprehensive cost of the energy storage power station in the kth iteration is calculated
(6.4) updating the genetic algebra count value. k=k+1;
and (6.5) updating the genetic algebra population data and the constant volume data. New population data is formed by using selection, crossover and mutation of genetic algorithm, and m groups are formed
Is->
Repeating the steps (6.2), (6.3) to obtain +.>
And (6.6) judging whether the double-layer planning model obtains an equilibrium solution. If->
And->
Indicating that an equilibrium solution is obtained; otherwise, repeating the steps (6.4) and (6.5);
(6.7) outputting the equalization result. Optimizing variables of the upper model and the lower model in an equilibrium state
Respective target values +.>
And outputting as a final result.
In summary, the multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction can adapt to new energy and user growth conditions in the future multi-stage, and the constant volume scheme can improve the consumption rate of the new energy after growth and reduce the comprehensive cost of the power distribution network construction operation maintenance energy storage power station; the addressing scheme can reduce active loss after the distributed energy sources are connected into the power distribution network, and improves the running economy of the power distribution network.
Example two
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing a multi-stage planning method of an energy storage plant taking into account new energy consumption and network loss reduction as described in embodiment one when executing the computer program.
The computer devices in embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, PDAs (personal digital assistants), PADs (tablet computers), etc., and stationary terminals such as desktop computers, etc.
The computer device may include a processing means (e.g., a central processing unit) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the computer device are also stored. The processing device, ROM and RAM are connected to each other via a bus. An input/output (I/O) interface is also connected to the bus.
A computer program, carried on a computer readable medium, contains program code for executing an algorithm. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from ROM. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by a processing device.
It should be noted that the computer readable medium disclosed in the present invention may be a computer readable signal medium or a computer readable medium or any combination of the two.
The computer readable medium may be embodied in the computer device; or may exist alone without being assembled into the computer device.
While the invention has been described in terms of embodiments, it will be appreciated by those skilled in the art that the invention is not limited thereto but rather includes the drawings and the description of the embodiments above. Any modifications which do not depart from the functional and structural principles of the present invention are intended to be included within the scope of the appended claims.
Claims (9)
1. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction is characterized by comprising the following steps of:
step 1: establishing a power supply and demand growth model, wherein the power supply and demand growth model comprises a new energy output growth model and a load growth model;
step 2: establishing an upper energy storage power station constant volume objective function model, minimizing the total comprehensive cost of each planning stage as an objective function, wherein the comprehensive cost comprises: investment construction cost, operation maintenance cost and waste wind and waste light cost of the power distribution network of the energy storage power station;
step 3: establishing a constant-volume constraint condition model of the upper energy storage power station, wherein the constraint condition comprises: power balance constraint, new energy output constraint, energy storage power station charge and discharge power and state of charge constraint;
step 4: establishing a lower energy storage electricity locating and sizing objective function model, and minimizing the active loss of the power distribution network in the total planning stage as an objective function;
step 5: establishing a lower-layer energy storage electricity site selection constraint condition model, wherein the constraint condition comprises: each node allows the energy storage capacity constraint, the power distribution network alternating current power flow constraint, the node voltage constraint and the branch power flow constraint to be installed;
step 6: and establishing a solving algorithm of a double-layer planning model, wherein the upper layer model adopts a genetic algorithm to optimize and update the capacity of the energy storage power station, transmitting the capacity to the lower layer model, solving and obtaining a network loss value and the addressing node and capacity of the energy storage power station by adopting an MOSEK solver, transmitting the network loss value to the upper layer model, and finally obtaining a balanced solution in the information interaction process.
2. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction according to claim 1, wherein the new energy output growth model and the load growth model are respectively:
(1.1) New energy output increase model
The new energy output has obvious seasonality, and the output under various scenes is considered for the reason:
in the method, in the process of the invention,
the method comprises the steps of integrating new energy output, wind power output and photovoltaic output in a t period in a scene s of an nth planning stage; />
The growth rate of the force is predicted in the nth planning stage compared with the growth rate of the force in the (n-1) th planning stage for wind power and photovoltaic respectively;
(1.2) load growth model
Wherein D is n,s,t For a period t of time in a scene s of an nth planning phase;
the rate of increase of force compared to the n-1 th planning phase is predicted for the load in the n-th planning phase.
3. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction according to claim 2, wherein the constant volume objective function of the energy storage power station in step 2 is as follows:
the comprehensive cost of the distribution network in the total planning stage is minimized, the configuration capacity of the energy storage power station is optimized, the investment construction cost, the operation maintenance cost and the wind and light discarding cost are reduced,
wherein F is total For the comprehensive cost of the distribution network in the overall planning phase,
respectively the investment construction cost, the operation maintenance cost and the wind and light discarding cost of the energy storage power station of the power distribution network in the nth planning stage, N is the total number of the planning stages,
wherein C is IE 、C IP The investment construction cost of unit capacity and the investment construction cost of unit power of the energy storage power station are respectively; c (C) OC Operating maintenance costs for the energy storage power station; mu (mu) n,s,t For indicating the charge and discharge states of the energy storage power station in the t period in the scene s of the nth planning stage, the state of charge is mu n,s,t =1, μ in discharge state n,s,t = -1, μ in float state n,s,t ＝0；
The online prices of photovoltaic and wind power at the period t are respectively set; />
Light of period t in the scene s of the nth planning phase respectivelyThe proportion coefficient of the un-consumed amount of the wind power and the wind power to the total abandoned wind and abandoned light amount of the new energy; />
And (5) discarding the wind and discarding the light for the power distribution network of the t period in the scene s of the nth planning stage.
4. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction according to claim 3, wherein the constraint conditions in the step 3 are as follows:
(3.1) Power balance constraint
Wherein L is S The total branch number of the upper layer main network is connected to the power distribution network,
for the active power delivered by the upper main network to the distribution network by way of the branch l in the t-period in the scenario s of the nth planning phase, +.>
The method comprises the steps that the active loss of the power distribution network in a t period in a scene s of an nth planning stage is obtained by solving a lower model;
(3.2) Power constraint by branch of Power distribution network connected by Main network
Wherein P is l smax The power distribution network branch circuit l is connected with the main network to allow the maximum reverse power to be transmitted;
(3.3) constraints on charging and discharging power and state of charge of energy storage power stations in distribution network
Wherein eta is d 、η c Respectively discharging efficiency and charging efficiency; η (eta) n,s,t 、S n,s,t Respectively charging and discharging efficiency and charge quantity of the energy storage power station at the t period in a scene s of the nth planning stage; s is S min 、S max 、S 0 Respectively a lower limit, an upper limit and an initial value of the charge quantity of the energy storage power station; t is the time period number of the energy storage power station in a short-term operation optimization period;
(3.4) investment construction constraints for energy storage Power stations
5. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction according to claim 4, wherein the addressing objective function of the energy storage power station in the step 4 is:
(4.1) optimizing the grid-connected position of the energy storage power station in the distribution network with the aim of minimizing the network loss cost of the distribution network in the total planning stage so as to reduce the operation cost of the distribution network, wherein the objective function is expressed as the following formula:
wherein F is loss The cost of network loss of the power distribution network in the total planning stage,
For the net loss cost in the nth planning stage, the calculation formula is as follows:
wherein p is loss To reduce unit price of net, P ij,n,s,t 、Q ij,n,s,t Active power, reactive power, resistance value, V on the branch from node i to node j in the t period in the scenario s of the nth planning phase i,n,s,t For the voltage value of the node i of the t period in the scene s of the nth planning phase, R ij The resistance value on the branch from node i to node j.
6. The multi-stage planning method of the energy storage power station considering new energy consumption and network loss reduction according to claim 5, wherein the constraint conditions in the step 5 are as follows:
(5.1) energy storage Capacity constraints that each node allows to install
In the method, in the process of the invention,
the energy storage station capacity value and the upper limit value of the node i are respectively accessed in the n planning stage;
(5.2) AC Power flow constraint of Power distribution network
The whole power distribution network architecture is a radial network, the DistFlow model is adopted to describe the alternating current power flow constraint,
wherein P is j,n,s,t 、Q j,n,s,t 、D j,n,s,t 、Q j,n,s,t 、
The net load active power, the net load reactive power, the load active power, the load reactive power, the new energy active output, the new energy reactive output, the abandoned wind quantity, the reactive power of reactive compensation equipment, the active output of an energy storage power station, the reactive output of the energy storage power station and L of the energy storage power station are respectively the net load active power, the net load reactive power, the load active power, the load reactive power in the scene s of the n planning stage ij,n,s,t Representing the current square value, X, of the branch from node i to node j at time period t in the scene s of the nth planning stage ij For the reactance value of the branch from node i to node j, P jl,n,s,t 、Q jl,n,s,t Respectively the active power and the reactive power from the node j to the branch l in the period t in the scene s of the n planning stage;
(5.3) node voltage constraints
U N (1-ε 1 )≤U k ≤U N (1+ε 2 ) (15)
In U k Is the voltage of node k; u (U) N The voltage amplitude is the outlet voltage amplitude of the transformer substation; epsilon 1 、ε 2 The voltage negative deviation upper limit value and the voltage positive deviation upper limit value are respectively;
(5.4) Branch tidal current constraint
Wherein S is l 、
The transmission power, the transmission power lower limit value and the transmission power upper limit value of the branch circuit l in the power distribution network are respectively.
7. The multi-stage planning method for the energy storage power station considering new energy consumption and network loss reduction according to claim 6, wherein the two-layer planning model solving algorithm in the step 6 comprises:
(6.1) basic data preparation, initializing various parameters of a power distribution network, new energy and load, and determining various parameters in a genetic algorithm, wherein the parameters comprise mutation probability, crossover probability, total genetic algebra G, population m, genetic algebra count value k and convergence error eta, and the initial value of k is set to be 1;
(6.2) population initialization, randomly generating m groups by genetic algorithm
Transmitting the capacity and power initial value of the energy storage power station to a lower energy storage electricity site selection model;
(6.2) solving a lower energy storage electricity site selection model according to the constant volume condition of the energy storage power station
Is->
Determining grid-connected nodes and capacity of an energy storage power station to minimize grid loss cost F loss Obtaining the capacity of the energy storage power station at the grid-connected node by using a MOSEK solver as an objective function>
And retains the net cost F in the kth iteration k loss ；
(6.3) calculating the constant-volume comprehensive cost of the upper energy storage power station, wherein the lower model transmits a net loss value to the upper model, and calculating the constant-volume comprehensive cost of the energy storage power station in the kth iteration
(6.4) updating the algebraic count value, k=k+1;
(6.5) updating the genetic algebra population data and the constant volume data, and forming new population data by using the selection, crossing and mutation of the genetic algorithm, wherein the new population data comprises m groups
Is->
Repeating the steps (6.2), (6.3) to obtain +.>
(6.6) judging whether the double-layer planning model obtains the equilibrium solution, if so
And->
Indicating that an equilibrium solution is obtained; otherwise, repeating the steps (6.4) and (6.5);
(6.7) outputting the equalization result, and optimizing variables of the upper layer model and the lower layer model in the equalization state
Respective target values +.>
And outputting as a final result.
8. A computer device comprising at least one processor and memory; the memory stores computer-executable instructions; the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform a multi-stage planning method of an energy storage plant taking into account new energy consumption and grid loss reduction as claimed in any one of claims 1 to 7.
9. A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, which when executed by a processor, implement a multi-stage planning method of an energy storage plant taking into account new energy consumption and network loss reduction according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310090227.9A CN116316716A (en) | 2023-01-17 | 2023-01-17 | Multi-stage planning method for energy storage power station, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310090227.9A CN116316716A (en) | 2023-01-17 | 2023-01-17 | Multi-stage planning method for energy storage power station, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116316716A true CN116316716A (en) | 2023-06-23 |
Family
ID=86821310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310090227.9A Pending CN116316716A (en) | 2023-01-17 | 2023-01-17 | Multi-stage planning method for energy storage power station, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116316716A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117767369A (en) * | 2023-12-25 | 2024-03-26 | 中国长江电力股份有限公司 | Energy storage site selection and hierarchical configuration method considering medium-long term planning |
-
2023
- 2023-01-17 CN CN202310090227.9A patent/CN116316716A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117767369A (en) * | 2023-12-25 | 2024-03-26 | 中国长江电力股份有限公司 | Energy storage site selection and hierarchical configuration method considering medium-long term planning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107590744B (en) | Active power distribution network distributed power supply planning method considering energy storage and reactive compensation | |
| CN110956324B (en) | Day-ahead high-dimensional target optimization scheduling method for active power distribution network based on improved MOEA/D | |
| Wang et al. | Multi-objective energy management system for DC microgrids based on the maximum membership degree principle | |
| CN111626594B (en) | Power distribution network expansion planning method with cooperation of multiple demand side resources | |
| Wu et al. | Optimal allocation of microgrid considering economic dispatch based on hybrid weighted bilevel planning method and algorithm improvement | |
| Lu et al. | Data-driven stochastic programming for energy storage system planning in high PV-penetrated distribution network | |
| Tian et al. | Coordinated planning with predetermined renewable energy generation targets using extended two-stage robust optimization | |
| CN117526432B (en) | Distribution network regulation and control system and method for source-load interaction | |
| CN108197766A (en) | A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group | |
| CN115147245B (en) | Virtual power plant optimal scheduling method for industrial load participating in peak shaving auxiliary service | |
| Batool et al. | Multi-level supervisory emergency control for operation of remote area microgrid clusters | |
| Wen et al. | Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration | |
| CN112561273A (en) | Active power distribution network renewable DG planning method based on improved PSO | |
| CN116316716A (en) | Multi-stage planning method for energy storage power station, computer equipment and storage medium | |
| CN115313519A (en) | Power distribution network energy storage optimal configuration method, device, equipment and storage medium | |
| Linlin et al. | Research on multi-objective reactive power optimization of power grid with high proportion of new energy | |
| Lu et al. | Two-stage robust scheduling and real-time load control of community microgrid with multiple uncertainties | |
| CN108493943B (en) | Method, system and device for scheduling electric energy of multi-microgrid system and storage medium | |
| Yin et al. | Collaborative decision-making model for capacity allocation of photovoltaics energy storage system under Energy Internet in China | |
| Ebrahimi et al. | Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks | |
| CN116645089A (en) | Energy storage system double-layer optimal configuration method considering capacity degradation of retired battery | |
| CN114825348A (en) | Power distribution network double-layer optimization scheduling method considering wind and light uncertainty | |
| Alam et al. | Energy management by scheduling ESS with active demand response in low voltage grid | |
| CN115411719A (en) | Distributed power supply planning method based on source load uncertainty and voltage stability | |
| Lu et al. | Location and size planning of distributed photovoltaic generation in distribution network system based on K-means clustering analysis |
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 |