CN117096856B - Virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of distribution network - Google Patents
Virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of distribution network Download PDFInfo
- Publication number
- CN117096856B CN117096856B CN202310975011.0A CN202310975011A CN117096856B CN 117096856 B CN117096856 B CN 117096856B CN 202310975011 A CN202310975011 A CN 202310975011A CN 117096856 B CN117096856 B CN 117096856B
- Authority
- CN
- China
- Prior art keywords
- phase
- constraint
- power
- model
- distribution network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 80
- 230000005611 electricity Effects 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000008901 benefit Effects 0.000 claims abstract description 13
- 238000004146 energy storage Methods 0.000 claims description 48
- 238000010438 heat treatment Methods 0.000 claims description 46
- 238000009423 ventilation Methods 0.000 claims description 46
- 239000010410 layer Substances 0.000 claims description 30
- 230000002776 aggregation Effects 0.000 claims description 28
- 238000004220 aggregation Methods 0.000 claims description 28
- 238000004378 air conditioning Methods 0.000 claims description 20
- 238000010248 power generation Methods 0.000 claims description 19
- 238000005457 optimization Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- 229920000642 polymer Polymers 0.000 claims description 7
- 230000009977 dual effect Effects 0.000 claims description 6
- 230000009021 linear effect Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 230000001360 synchronised effect Effects 0.000 claims description 5
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 3
- 238000002347 injection Methods 0.000 claims description 3
- 239000007924 injection Substances 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 239000002356 single layer Substances 0.000 claims description 3
- 238000012725 vapour phase polymerization Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 230000016507 interphase Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 2
- 230000004931 aggregating effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 230000004941 influx Effects 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution 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
- 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]
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of a distribution network, and a physical operation model of different virtual power plants is established; establishing a power distribution network operation model with unbalanced three-phase voltage; establishing a maximum profit scheduling model of the virtual power plant based on the double-layer planning model; and solving the maximum benefit scheduling model. The beneficial effects of the invention are as follows: the virtual power plant output can be more accurately scheduled to achieve its maximized benefits and the voltage imbalance of the distribution network can be improved. A three-phase distribution network model considering the voltage unbalance constraint is established, and three-phase electricity price information considering the shadow price of the voltage unbalance constraint can be provided.
Description
Technical Field
The invention relates to the field of profit maximization scheduling of virtual power plants, in particular to a virtual power plant scheduling method considering unbalanced three-phase electricity prices and inter-phase voltage of a distribution network.
Background
Distributed energy generation is an important power supply form of an active power distribution network, and in recent years, permeability in the power distribution network has shown a continuous rising trend. In the active power distribution network, various resources with adjustable capacity such as electric automobiles, electric heating loads and distributed energy storage are huge in quantity, but because individual adjustment capacity is small and the resources are scattered everywhere in the system, if massive distributed energy individuals directly participate in distribution network scheduling, huge calculation and communication burden is brought to distribution network optimization.
With the rapid development of smart grid technologies, virtual power plant technologies that integrate and aggregate various tunable distributed energy sources are proposed for participating in grid operation and improving system flexibility. The virtual power plant aggregates one or more resources of adjustable loads, distributed energy storage, micro-grids, electric vehicles, distributed power generation and the like in different spaces, and is used as a special power plant to participate in power system operation and power market transaction. Virtual power plants are emerging as business models, on the one hand, which can promote the income of DERs of aggregation; on the other hand, the economical efficiency, the safety and the capability of absorbing renewable energy sources such as wind power and the like of the operation of the power distribution system can be improved.
For a virtual power plant in a power distribution network, the income is mainly influenced by the power price of a node of the power distribution network and the output of the node. The output of the virtual power plant and the power price of the distribution network node have a coupling relation, and the output and the power price of the virtual power plant are mutually influenced. The change in behavior of either party affects the other party. Thus, the maximum revenue scheduling problem for a virtual power plant is the gaming problem of the virtual power plant with the power distribution network operator.
The power price of the distribution network node adopted by the traditional virtual power plant maximum profit scheduling is mainly extracted based on a single-phase power distribution network operation model. The three-phase power generation balance and the three-phase load balance of the power distribution network are considered, so that a simplified single-phase model is adopted to replace the three-phase model. However, with the wide influx of various distributed energy sources and the single-phase access of part of the distributed energy sources, the power generation and load of each phase of the power distribution network gradually show an unbalanced phenomenon. The three-phase imbalance of the distribution network can cause the electricity prices of the same node in different phases to be different, so that the traditional virtual power plant scheduling method based on the single-phase electricity price can not obtain the maximum benefit of the virtual power plant.
In addition, the unbalance of three-phase loads of the power distribution network can further lead to the unbalance of voltages among three phases, and when the unbalance degree of voltages among the phases of the power distribution network exceeds a certain margin, huge damage can be caused to power generation equipment of the power distribution network. The inter-phase voltage unbalance restriction constraint is considered in the three-phase distribution network model, so that the safe operation of the distribution network can be effectively improved, but the three-phase electricity price can be influenced, and the output and the income of the virtual power plant are further influenced.
The existing research on the maximum profit scheduling of the virtual power plant does not consider that a distribution network three-phase model is adopted to extract electricity prices, and how to guide the output of the virtual power plant by using the distribution network node electricity price information, so that the virtual power plant can obtain the maximum profit, and the three-phase imbalance phenomenon of the distribution network cannot be improved.
Disclosure of Invention
In order to solve the problems, the invention provides a virtual power plant scheduling method considering unbalanced three-phase electricity prices and inter-phase voltages of a distribution network, which is used for aggregating residential air conditioner loads, distributed energy storage, distributed wind power and photovoltaics with flexibility into a virtual power plant; in the profit maximization scheduling, the node electricity price information which takes account of the voltage unbalance limitation is utilized to improve the voltage unbalance of the distribution network; and scheduling by using a double-layer planning model, wherein the double-layer planning model is used for reflecting games between the output of the virtual power plant and the power price of the distribution network, and ensuring that the output of the virtual power plant and the power price of the distribution network are balanced. The method mainly comprises the following steps:
S1: establishing physical operation models of different virtual power plants;
S2: establishing a power distribution network operation model with unbalanced three-phase voltage;
s3: establishing a maximum profit scheduling model of a virtual power plant based on a double-layer planning model, wherein the upper layer model of the double-layer planning model is a physical operation model of the virtual power plant, and the lower layer model is a power distribution network operation model;
s4: and solving the maximum benefit scheduling model.
Further, the physical operation model comprises a virtual power plant aggregation model of distributed energy storage, a virtual power plant aggregation model of distributed air conditioner load and a virtual power plant aggregation model of distributed power generation.
Further, the physical operation constraint of the distributed energy storage virtual power plant aggregation model is as follows:
The energy-saving method comprises the following steps of (1) calculating constraint of energy storage, (2) upper and lower limit constraint of energy storage, (3) constraint of charging power and constraint of discharging power of the energy storage respectively, and (5) equal energy at the initial time and the final time of scheduling; wherein, Is the energy storage of the energy storage aggregator at the moment t/>Is the energy storage of the energy storage polymer at the time t+1,/>Is the charge and discharge rate of the energy storage polymer at the moment t,/>Is the minimum and maximum charge state of energy storage,/>Is the energy storage capacity/> Is the maximum charge and discharge rate of the energy storage aggregator, j represents all nodes except the head node.
Further, the physical operation constraint of the virtual power plant aggregation model of the distributed air conditioning load is as follows:
The constraint (6) represents the aggregation temperature characteristic of the heating ventilation air conditioner, the constraint (7) ensures the comfortable temperature range of a user, the constraint (8) limits the range of the synchronicity rate of the heating ventilation air conditioner, the constraint (9) ensures the synchronicity rate difference between two adjacent periods to be within a safe allowable range, the constraint (10) calculates the aggregation power of the heating ventilation air conditioner, the constraint (11) limits the minimum and maximum states of the energy storage of the heating ventilation air conditioner, and the constraint (12) represents the state of the energy storage of the aggregated heating ventilation air conditioner; wherein, Is the equivalent indoor temperature of the heating ventilation air conditioner aggregator at the moment t,/>Is the equivalent indoor temperature of the heating ventilation air conditioning aggregator at the moment t+1/(1)Is the predicted outdoor temperature at the time t, q min、qmax is the maximum and minimum comfortable temperature, respectively,/>Is the synchronous rate of a heating ventilation air conditioner aggregator at the moment t,/>Respectively the minimum and maximum charging stages of the heating ventilation air conditioner aggregator,/>The minimum and maximum synchronous rates of the single heating ventilation air conditioner are respectively,/>Is the ramp rate of a heating, ventilation and air conditioning aggregator,/>Is the active power of a heating, ventilation and air conditioning aggregator at the moment t, and P H is the rated power of a single heating, ventilation and air conditioning unit,/>Is the number of heating, ventilation and air conditioning participated in aggregation,/>The charging state of the heating ventilation air conditioning aggregator at the time t is shown,And/>The three parameters respectively represent fitting parameters of thermodynamic equations of the heating ventilation and air conditioning.
Further, the physical operation constraint of the virtual power plant aggregation model for distributed power generation is as follows:
Wherein constraints (13) and (14) limit the active and reactive power output ranges of the diesel generator, constraints (15) and (16) limit the active and reactive power output ranges of the renewable energy source, wherein, Respectively represent the active power and the reactive power of the diesel generator at the moment t/>The minimum and maximum power of the diesel generator at the moment t,Respectively representing the active power and the reactive power of wind-electricity photovoltaic at the time t, wherein beta is the confidence level,/>Is the daily predicted power generation amount of renewable energy at the moment t, and/>Σ j,t represents the variance of the normal distribution, ψ -1 is the standard normal distribution cumulative inverse function, and a j is the power factor of the distributed generator.
Further, the specific process of step S2 is as follows:
s2.1: conventional operational constraints of the three-phase voltage model of the power distribution network are as follows:
Wherein constraints (17) and (18) are node active and reactive net injection power equations, constraints (19) - (22) are node power balance equations of node 1 and node j e omega N, constraint (23) is a voltage drop constraint, constraint (24) is a voltage limit constraint, constraint (25) is a magnitude calculation of the first node voltage, and constraints (26) and (27) are active and reactive output limits of the first node; wherein, And/>Represents the power of a three-phase line, phi= [ phi 1,φ2,φ3 ] represents the set of ABC three phases,/>Is/>And/>Higher-order terms of/>Respectively represent the active power and the reactive power on the branch ij between the adjacent nodes at the time t,/>Respectively represent active and reactive loads at t time/>Is the cycle efficiency of the energy storage polymerizer,/>Respectively represent the active power and the reactive power of the head node,/>Respectively represent the active power and the reactive power of the line,/>Taylor expansion coefficients, each representing an active net loss,/>Is the square of the node voltage,/>Taylor expansion coefficients, each representing a reactive network loss,/>, ofAll represent constant terms in the Taylor expansion,/>Is the converted branch resistance, reactance,/>Respectively minimum and maximum voltage limit ranges, V ref is the reference voltage of the first node, and P max、Qmax is the maximum active power and reactive power of the transformer substation,/>Duel multiplier representing active and reactive power balance constraint respectively,/>Pair multiplier, respectively representing a voltage lower limit constraint and an upper limit constraint,/>Pair multiplier representing reference voltage constraint,/>The pair multipliers respectively represent the active and reactive output limit constraints; /(I)And/>Representing the pairs of constraints (19) and (20), respectively; omega N represents the set of all nodes except the head node; omega T represents a set of 24 periods; a j represents the set of parent nodes of node j;
s2.2: the three-phase voltage imbalance limit constraints of the distribution network are as follows:
Wherein, A pair multiplier representing a voltage imbalance constraint, K v is a voltage imbalance threshold,And/>Respectively representing squares of three-phase voltages at node j A, B, C at time period t;
s2.3: the optimization targets of the power distribution network are as follows:
Wherein, Representing predicted electricity price before day,/>Representing the active power of the substation.
Further, in step S3, the virtual power plant maximum benefit scheduling model based on the double-layer planning is summarized as follows:
s.t.(1)-(16) (31)
s.t.(17)-(28) (33)
Wherein constraints (30) and (31) are upper-level models, the goal of which is to achieve VPPs aggregate profit maximization in the case of meeting various operating constraints, constraint (30) Representing the cost of power generation of a diesel generator, constraints (32) and (33) are underlying models, with the aim of achieving cost reduction of power generation.
Further, in step S4, the solving process is as follows: taking the complexity of calculation of the double-layer planning model and the linear property of the lower-layer optimization model into consideration, converting the double-layer model into an equivalent single-layer nonlinear model by utilizing the KKT condition of the lower-layer optimization model; and the bilinear term in the objective function is converted into a linear model by utilizing a strong dual theory, so that the problem is easy to solve.
A virtual power plant dispatching system considering unbalanced three-phase electricity price and interphase voltage of a distribution network comprises a processor and a storage device; and the processor loads and executes the instructions and the data in the storage device to realize the virtual power plant scheduling method considering the three-phase electricity price and the phase-to-phase voltage unbalance of the distribution network.
The technical scheme provided by the invention has the beneficial effects that: in the profit calculation of the virtual power plant, the three-phase node marginal electricity price is adopted instead of the single-phase electricity price, so that the profit of the virtual power plant can be reflected more accurately. The adopted three-phase node electricity price is obtained through a three-phase distribution network model, and the three-phase distribution network model considers the phase-to-phase voltage unbalance constraint. Therefore, the shadow price of the constraint of the phase-to-phase voltage unbalance is included in the three-phase electricity price, so that market participants can be guided to improve the phase-to-phase voltage unbalance. And establishing a maximum profit scheduling model of the virtual power plant based on the double-layer planning model, so that games between a virtual power plant main body and a distribution network operator main body can be effectively reflected. The invention provides a virtual power plant maximum benefit scheduling method based on a distribution network three-phase electricity price, which can more accurately schedule the output of a virtual power plant to realize the maximum benefit and can improve the voltage unbalance of the distribution network. A three-phase distribution network model considering the voltage unbalance constraint is established, and three-phase electricity price information considering the shadow price of the voltage unbalance constraint can be provided.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a virtual power plant scheduling method considering three-phase electricity prices and phase-to-phase voltage imbalance of a distribution network in an embodiment of the invention.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present invention, a detailed description of embodiments of the present invention will be made with reference to the accompanying drawings.
The embodiment of the invention provides a virtual power plant scheduling method considering unbalanced three-phase electricity prices and inter-phase voltages of a distribution network.
Referring to fig. 1, fig. 1 is a flowchart of a virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage imbalance of a distribution network according to an embodiment of the present invention, which specifically includes:
S1: establishing physical operation models of different virtual power plants;
s2: establishing a power distribution network operation model considering three-phase voltage unbalance;
s3: establishing a maximum profit scheduling model of a virtual power plant based on a double-layer planning model, wherein the upper layer model of the double-layer planning model is a physical operation model of the virtual power plant, and the lower layer model is a power distribution network operation model;
S4: solving a maximum benefit scheduling model of the virtual power plant.
The physical operation model comprises a distributed energy storage aggregation model, a distributed air conditioner load aggregation model and a distributed power generation aggregation model.
The physical operation constraint of the distributed energy storage virtual power plant aggregation model is as follows:
The energy-saving method comprises the following steps of (1) calculating constraint for energy storage, (2) constraint for upper and lower limits of the energy storage, (3) constraint for charging power and (4) constraint for discharging power of the energy storage respectively, and (5) constraint for energy equalization at the initial time and the final time of scheduling.
Wherein,Is the energy storage of the energy storage polymer,/>Is the charge-discharge rate of the energy storage polymer,Is the minimum/maximum state of charge of the stored energy,/>Is the energy storage capacity/>And/>Is obtained by summing the maximum charge and discharge power and the rated capacity of the individual energy storage devices.
The physical operation constraint of the virtual power plant aggregation model of the distributed air conditioner load is as follows:
the constraint (6) represents the aggregation temperature characteristic of the heating ventilation air conditioner, the constraint (7) ensures the comfortable temperature range of a user, the constraint (8) limits the range of the synchronicity rate of the heating ventilation air conditioner, the constraint (9) ensures the difference of the synchronicity rates of two adjacent periods to be within a safe allowable range, the constraint (10) calculates the aggregation power of the heating ventilation air conditioner, the constraint (11) limits the minimum/large state of the energy storage of the heating ventilation air conditioner, and the constraint (12) represents the state of the energy storage of the aggregated heating ventilation air conditioner; wherein, Is the indoor temperature of a single heating ventilation air conditioner,/>Is the equivalent indoor temperature of the heating ventilation air conditioning polymerizer,Is the predicted outdoor temperature, q min/qmax is the maximum/minimum comfort temperature,/>Is the synchronization rate of the hvac aggregator,Is the minimum/big charge phase of the heating ventilation air conditioning aggregator,/>Is the minimum/large synchronous rate of a single heating ventilation air conditioner,/>Is the ramp rate of a heating, ventilation and air conditioning aggregator,/>Is the active power of a heating, ventilation and air conditioning aggregator, P H is the rated power of a single heating, ventilation and air conditioning unit, and is/areIs the number of heating, ventilation and air conditioning participated in aggregation,/>Indicating the charge state of the heating, ventilation and air conditioning aggregateAnd/>These three parameters can be estimated by least squares parameters.
A virtual power plant aggregation model for distributed power generation has the following physical operation constraints:
Wherein constraints (13) and (14) limit the active and reactive power output ranges of the diesel generator, and constraints (15) and (16) limit the active and reactive power output ranges of the renewable energy source, wherein the renewable energy source comprises wind power volts, wherein, Representing the active power and the reactive power of the diesel generator respectively,/>Is the minimum/large power of diesel generator,/>Representing the active and reactive power of wind-powered photovoltaic, beta being the confidence level,/>Is the predicted power generation of renewable energy before the day, and is assumed/>Psi -1 is a standard normal distribution cumulative inverse function, a j is a power factor of the distributed generator. In this embodiment a j is set to 0.95.
The specific process of step S2 is as follows:
s2.1: conventional operational constraints of a three-phase model of a power distribution network are as follows:
Wherein constraints (17) and (18) are node active and reactive net injection power equations, and constraints (19) - (22) are node power balance equations for node 1 and node j e Ω N. Because of the mutual impedance between the three phases, the network loss of one phase can be affected by the power of the three-phase line And/>Phi= [ phi 1,φ2,φ3 ] represents the set of ABC three phases. The net losses of constraints (21) and (22) are linearized using taylor first order expansion. Constraint (23) is a voltage drop constraint, which is also subject to three-phase line power/>And/>Is a function of (a) and (b). In constraint (23)/>Is/>And/>Is negligible. Constraint (24) is a voltage limiting constraint, constraint (25) accounts for the magnitude of the head node voltage. Constraints (26) and (27) are active reactive output limits of the head node.
Wherein,Representing the active and reactive power on branch ij,/>, respectivelyRepresenting active and reactive load,/>Is the cycle efficiency of the energy storage polymerizer,/>Representing the active and reactive power of the head node,/>Representing the active and reactive power of the line,/>Taylor expansion coefficient representing active network loss,/>Is the square of the node voltage,/>Taylor expansion coefficient representing reactive power network loss,/>Representing constant terms in Taylor expansion,/>Is the converted branch resistance/reactance,Is the minimum/large voltage limit range, V ref is the reference voltage of the head node, P max/Qmax is the maximum active/reactive power of the substation,/>Representing the active and reactive power price,/>Pair multiplier representing a voltage limiting constraint,/>Pair multiplier representing reference voltage constraint,/>And the pair multiplier represents the active and reactive output limit constraint.
S2.2: the three-phase voltage imbalance limit constraints of the distribution network are as follows:
Wherein, A pair multiplier representing a voltage imbalance constraint, K v being a voltage imbalance threshold;
s2.3: the optimization targets of the power distribution network are as follows:
Wherein, Representing predicted electricity price before day,/>Representing the active power of the substation.
The specific process of step S3 is as follows:
the virtual power plant maximum benefit scheduling model based on double-layer planning is summarized as follows:
s.t.(1)-(16) (31)
s.t.(17)-(28) (33)
Wherein constraints (30) and (31) are upper-level models, the goal of which is to achieve VPPs aggregate profit maximization in the case of meeting various operating constraints, constraint (30) Representing the cost of power generation of a diesel generator, constraints (32) and (33) are underlying models, with the aim of achieving cost reduction of power generation.
In step S4, the lower layer model is converted into an equivalent single-layer nonlinear model by using the KKT strong dual theorem in consideration of the complexity of double-layer model calculation and the linear property of the lower layer optimization model. And the bilinear term in the objective function is converted into a linear model by utilizing a strong dual theory, so that the problem is easy to solve. The strong dual theorem of KKT is the prior art, and specifically comprises: 1) A steady equation of the Lagrangian function; 2) Complementary relaxation conditions; 3) Original feasibility; 4) Dual feasibility.
A virtual power plant dispatching system considering unbalanced three-phase electricity price and interphase voltage of a distribution network comprises a processor and a storage device; and the processor loads and executes the instructions and the data in the storage device to realize the virtual power plant scheduling method considering the three-phase electricity price and the phase-to-phase voltage unbalance of the distribution network.
The beneficial effects of the invention are as follows: in the profit calculation of the virtual power plant, the three-phase node marginal electricity price is adopted instead of the single-phase electricity price, so that the profit of the virtual power plant can be reflected more accurately. The adopted three-phase node electricity price is obtained through a three-phase distribution network model, and the three-phase distribution network model considers the phase-to-phase voltage unbalance constraint. Therefore, the shadow price of the constraint of the phase-to-phase voltage unbalance is included in the three-phase electricity price, so that market participants can be guided to improve the phase-to-phase voltage unbalance. And establishing a maximum profit scheduling model of the virtual power plant based on the double-layer planning model, so that games between a virtual power plant main body and a distribution network operator main body can be effectively reflected. The invention provides a virtual power plant maximum benefit scheduling method based on a distribution network three-phase electricity price, which can more accurately schedule the output of a virtual power plant to realize the maximum benefit and can improve the voltage unbalance of the distribution network. A three-phase distribution network model considering the voltage unbalance constraint is established, and three-phase electricity price information considering the shadow price of the voltage unbalance constraint can be provided.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of a distribution network is characterized by comprising the following steps of: comprising the following steps:
S1: establishing physical operation models of different virtual power plants; the physical operation model comprises a virtual power plant aggregation model of distributed energy storage, a virtual power plant aggregation model of distributed air conditioner load and a virtual power plant aggregation model of distributed power generation;
the physical operation constraint of the distributed energy storage virtual power plant aggregation model is as follows:
The energy-saving method comprises the following steps of (1) calculating constraint of energy storage, (2) upper and lower limit constraint of energy storage, (3) constraint of charging power and constraint of discharging power of the energy storage respectively, and (5) equal energy at the initial time and the final time of scheduling; wherein, Is the energy storage of the energy storage aggregator at the moment t/>Is the energy storage of the energy storage polymer at the time t+1,/>Is the charge and discharge rate of the energy storage polymer at the moment t,/>Is the minimum and maximum charge state of energy storage,/>Is the energy storage capacity/>P j ES,D,max,f is the maximum charge and discharge rate of the energy storage aggregator, j represents all nodes except the head node;
The physical operation constraint of the virtual power plant aggregation model of the distributed air conditioner load is as follows:
The constraint (6) represents the aggregation temperature characteristic of the heating ventilation air conditioner, the constraint (7) ensures the comfortable temperature range of a user, the constraint (8) limits the range of the synchronicity rate of the heating ventilation air conditioner, the constraint (9) ensures the synchronicity rate difference between two adjacent periods to be within a safe allowable range, the constraint (10) calculates the aggregation power of the heating ventilation air conditioner, the constraint (11) limits the minimum and maximum states of the energy storage of the heating ventilation air conditioner, and the constraint (12) represents the state of the energy storage of the aggregated heating ventilation air conditioner; wherein, Is the equivalent indoor temperature of the heating ventilation air conditioner aggregator at the moment t,/>Is the equivalent indoor temperature of the heating ventilation air conditioning aggregator at the moment t+1/(1)Is the predicted outdoor temperature at the time t, q min、qmax is the maximum and minimum comfortable temperature, respectively,/>Is the synchronous rate of a heating ventilation air conditioner aggregator at the moment t,/>Respectively the minimum and maximum charging stages of the heating ventilation air conditioner aggregator,/>The minimum and maximum synchronous rates of the single heating ventilation air conditioner are respectively,/>Is the ramp rate of a heating, ventilation and air conditioning aggregator,/>Is the active power of the heating, ventilation and air conditioning aggregator at the moment t, P H is the rated power of a single heating, ventilation and air conditioning unit,Is the number of heating, ventilation and air conditioning participated in aggregation,/>Indicating the charging state of the heating ventilation air conditioner aggregator at the moment t,/>And/>The three parameters respectively represent fitting parameters of a thermodynamic equation of the heating ventilation air conditioner;
S2: establishing a power distribution network operation model with unbalanced three-phase voltage;
s3: establishing a maximum profit scheduling model of a virtual power plant based on a double-layer planning model, wherein the upper layer model of the double-layer planning model is a physical operation model of the virtual power plant, and the lower layer model is a power distribution network operation model;
s4: and solving the maximum benefit scheduling model.
2. The virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of the distribution network according to claim 1, wherein the method comprises the following steps of: a virtual power plant aggregation model for distributed power generation has the following physical operation constraints:
Wherein constraints (13) and (14) limit the active and reactive power output ranges of the diesel generator, constraints (15) and (16) limit the active and reactive power output ranges of the renewable energy source, wherein, Respectively represent the active power and the reactive power of the diesel generator at the moment t/>The minimum and maximum power of the diesel generator at the moment t,Respectively representing the active power and the reactive power of wind-electricity photovoltaic at the time t, b is the confidence level,/>Is the daily predicted power generation amount of renewable energy at the moment t, and/>S j,t represents the variance of the normal distribution, Y -1 is the standard normal distribution cumulative inverse function, and a j is the power factor of the distributed generator.
3. The virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of the distribution network according to claim 2, wherein the method comprises the following steps of: the specific process of step S2 is as follows:
s2.1: conventional operational constraints of the three-phase voltage model of the power distribution network are as follows:
Wherein constraints (17) and (18) are node active and reactive net injection power equations, constraints (19) - (22) are node power balance equations of node 1 and node j e omega N, constraint (23) is a voltage drop constraint, constraint (24) is a voltage limit constraint, constraint (25) is a magnitude calculation of the first node voltage, and constraints (26) and (27) are active and reactive output limits of the first node; wherein, And/>Represents the power of a three-phase line, phi= [ phi 1,φ2,φ3 ] represents the set of ABC three phases,/>Is/>And/>Higher-order terms of/>Respectively represent the active power and the reactive power on the branch ij between the adjacent nodes at the time t,/>Respectively represent active and reactive loads at t time/>Is the cycle efficiency of the energy storage polymerizer,/>Respectively represent the active power and the reactive power of the head node,/>Respectively represent the active power and the reactive power of the line,/>Taylor expansion coefficients, each representing an active net loss,/>Is the square of the node voltage,/>Taylor expansion coefficients, each representing a reactive network loss,/>, ofAll represent constant terms in the Taylor expansion,/>Is the converted branch resistance, reactance,/>Respectively minimum and maximum voltage limit ranges, V ref is the reference voltage of the first node, and P max、Qmax is the maximum active power and reactive power of the transformer substation,/>Duel multiplier representing active and reactive power balance constraint respectively,/>The pairs representing the voltage lower limit constraint and the upper limit constraint respectively,Pair multiplier representing reference voltage constraint,/>The pair multipliers respectively represent the active and reactive output limit constraints; /(I)And/>Representing the pairs of constraints (19) and (20), respectively; w N represents the set of all nodes except the head node; w T denotes a set of 24 periods; a j represents the set of parent nodes of node j;
s2.2: the three-phase voltage imbalance limit constraints of the distribution network are as follows:
Wherein, The pair multiplier representing the voltage imbalance constraint, K v is the voltage imbalance threshold,/>And/>Respectively representing squares of three-phase voltages at node j A, B, C at time period t;
s2.3: the optimization targets of the power distribution network are as follows:
Wherein, Representing predicted electricity price before day,/>Representing the active power of the substation.
4. A virtual power plant scheduling method taking into account three-phase electricity prices and phase-to-phase voltage imbalances of a distribution network as claimed in claim 3, wherein: in step S3, the virtual power plant maximum benefit scheduling model based on the double-layer planning is summarized as follows:
s.t.(1)-(16)(31)
s T. (17) - (28) (33) wherein constraints (30) and (31) are upper level models with the goal of achieving VPPs a polymer's revenue maximization, constraint (30) under conditions that meet various types of operating constraints Representing the cost of power generation of a diesel generator, constraints (32) and (33) are underlying models, with the aim of achieving cost reduction of power generation.
5. The virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of the distribution network according to claim 1, wherein the method comprises the following steps of: in step S4, the solving process is: taking the complexity of calculation of the double-layer planning model and the linear property of the lower-layer optimization model into consideration, converting the double-layer model into an equivalent single-layer nonlinear model by utilizing the KKT condition of the lower-layer optimization model; and the bilinear term in the objective function is converted into a linear model by utilizing a strong dual theory, so that the problem is easy to solve.
6. A virtual power plant dispatching system considering three-phase electricity price and phase-to-phase voltage unbalance of a distribution network is characterized in that: comprises a processor and a storage device; the processor loads and executes instructions and data in the storage device for implementing the virtual power plant scheduling method taking into account three-phase electricity prices and phase-to-phase voltage imbalance of the distribution network according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310975011.0A CN117096856B (en) | 2023-08-02 | 2023-08-02 | Virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310975011.0A CN117096856B (en) | 2023-08-02 | 2023-08-02 | Virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of distribution network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117096856A CN117096856A (en) | 2023-11-21 |
| CN117096856B true CN117096856B (en) | 2024-06-04 |
Family
ID=88776350
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310975011.0A Active CN117096856B (en) | 2023-08-02 | 2023-08-02 | Virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117096856B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117688793B (en) * | 2024-02-04 | 2024-05-10 | 中国地质大学（武汉） | Combined modeling and solving method and equipment for distributed robust unit and storage equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110147907A (en) * | 2019-05-20 | 2019-08-20 | 广西电网有限责任公司电力科学研究院 | A kind of virtual plant bi-level optimal model considering network constraint |
| CN112311017A (en) * | 2020-09-01 | 2021-02-02 | 国网电力科学研究院有限公司 | Optimal collaborative scheduling method for virtual power plant and main network |
| CN113890021A (en) * | 2021-09-29 | 2022-01-04 | 国网综合能源服务集团有限公司 | Multi-virtual power plant distributed transaction method considering constraint of power distribution network |
| CN115564246A (en) * | 2022-10-12 | 2023-01-03 | 国网甘肃省电力公司电力科学研究院 | Method and device for virtual power plant to participate in power market in consideration of new energy |
-
2023
- 2023-08-02 CN CN202310975011.0A patent/CN117096856B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110147907A (en) * | 2019-05-20 | 2019-08-20 | 广西电网有限责任公司电力科学研究院 | A kind of virtual plant bi-level optimal model considering network constraint |
| CN112311017A (en) * | 2020-09-01 | 2021-02-02 | 国网电力科学研究院有限公司 | Optimal collaborative scheduling method for virtual power plant and main network |
| CN113890021A (en) * | 2021-09-29 | 2022-01-04 | 国网综合能源服务集团有限公司 | Multi-virtual power plant distributed transaction method considering constraint of power distribution network |
| CN115564246A (en) * | 2022-10-12 | 2023-01-03 | 国网甘肃省电力公司电力科学研究院 | Method and device for virtual power plant to participate in power market in consideration of new energy |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117096856A (en) | 2023-11-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhou et al. | Optimal sizing of PV and BESS for a smart household considering different price mechanisms | |
| Wang et al. | A near-optimal model-based control algorithm for households equipped with residential photovoltaic power generation and energy storage systems | |
| Calero et al. | A review of modeling and applications of energy storage systems in power grids | |
| Mitra et al. | Optimal deployment of distributed generation using a reliability criterion | |
| Marnay et al. | Policymaking for microgrids | |
| Keyhani | Smart power grids | |
| Leão et al. | The future of low voltage networks: Moving from passive to active | |
| Akindeji et al. | Use of renewable energy sources in university campus microgrid–a review | |
| CN103151797A (en) | Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode | |
| Wei et al. | Coordination optimization of multiple thermostatically controlled load groups in distribution network with renewable energy | |
| Nour et al. | Mitigation of electric vehicles charging impacts on distribution network with photovoltaic generation | |
| CN117096856B (en) | Virtual power plant scheduling method considering three-phase electricity price and phase-to-phase voltage unbalance of distribution network | |
| CN108400593A (en) | Active distribution network electrical model method for building up based on layering multi-agent technology | |
| Masuta et al. | Modeling of a number of heat pump water heaters as control equipment for load frequency control in power systems | |
| Chen et al. | Energy management method applying in integrated energy system | |
| Donnellan et al. | A review of energy storage and its application in power systems | |
| KR20130074045A (en) | A control method for controlling energy of building based ob microgrid and system for same | |
| Ren et al. | Multitime scale coordinated scheduling for electric vehicles considering photovoltaic/wind/battery generation in microgrid | |
| Kang | Generation cost assessment of an isolated power system with a fuzzy wind power generation model | |
| Zhang et al. | Voltage control strategy for distribution network with thermostatically controlled loads equivalent energy storage model considering minimum-on-off time | |
| Hashmi | Optimization and control of storage in smart grids | |
| Mahendru et al. | Reduction in system losses and power demand by combination of optimal power flow and conservation voltage reduction using smart PV inverters | |
| CN116404665A (en) | Active power distribution network voltage three-phase imbalance suppression method based on flexible soft switch | |
| Ntomalis et al. | Optimal siting of BESS in distribution networks under high wind power penetration | |
| van Someren et al. | Impact of Load Simultaneity and Battery Layout on Sizing of Batteries for Preventing Grid Overloading |
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 |