CN116579455A - Optimized bidding method of virtual power plant in multi-element power market - Google Patents
Optimized bidding method of virtual power plant in multi-element power market Download PDFInfo
- Publication number
- CN116579455A CN116579455A CN202310315874.5A CN202310315874A CN116579455A CN 116579455 A CN116579455 A CN 116579455A CN 202310315874 A CN202310315874 A CN 202310315874A CN 116579455 A CN116579455 A CN 116579455A
- Authority
- CN
- China
- Prior art keywords
- bidding
- power plant
- market
- energy storage
- virtual power
- 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
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000004146 energy storage Methods 0.000 claims abstract description 73
- 230000008901 benefit Effects 0.000 claims abstract description 30
- 238000005457 optimization Methods 0.000 claims abstract description 27
- 230000005611 electricity Effects 0.000 claims description 57
- 230000007246 mechanism Effects 0.000 claims description 7
- 238000012725 vapour phase polymerization Methods 0.000 description 80
- 230000002860 competitive effect Effects 0.000 description 15
- 238000009826 distribution Methods 0.000 description 13
- 239000010410 layer Substances 0.000 description 9
- 230000002829 reductive effect Effects 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 230000001105 regulatory effect Effects 0.000 description 4
- 238000007599 discharging Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 230000009977 dual effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- SLXKOJJOQWFEFD-UHFFFAOYSA-N 6-aminohexanoic acid Chemical compound NCCCCCC(O)=O SLXKOJJOQWFEFD-UHFFFAOYSA-N 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 239000002356 single layer Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
-
- 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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0611—Request for offers or quotes
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of power grid operation planning, and discloses an optimization bidding method of a virtual power plant in a multi-element power market, wherein a virtual power plant operator firstly acquires external market information, wind-solar power output prediction information and energy storage information; the virtual power plant operators update price signals of the controllable distributed power supply and the flexible load with the aim of maximizing benefits; and the flexible load and the controllable distributed power supply aim at the maximum profit, and the bidding electric quantity is obtained according to the price signal. And the virtual power plant operator formulates the price of wind, light and energy storage and the bidding electric quantity according to the flexible load and the bidding result of the controllable distributed power supply. Robust optimization is adopted for a double-layer optimization model of the internal and external coordination bidding decision of the virtual power plant. The invention can compensate the operation of the energy storage, can improve the energy storage income, attracts the energy storage to participate in and accept the output plan formulated by the virtual power plant, and ensures that the VPP effectively participates in peak regulation bidding, thereby obtaining the peak regulation income and improving the income of the virtual power plant.
Description
Technical Field
The invention relates to the technical field of power grid operation planning, in particular to an optimized bidding method of a virtual power plant in a multi-element power market.
Background
The distributed energy has the characteristics of dispersed positions, strong randomness, difficult management and the like, a large number of grid connection brings difficulty to power balance and peak regulation of the power system, and the virtual power plant (virtual power plant, VPP) is used as a unique distributed energy aggregation and management technology, has high-efficiency, flexible and friendly grid connection characteristics, and becomes an important means for solving the problem of accessing the distributed energy into the power system.
Along with the continuous and deep research of the electric power spot market in China, each region issues the rule of electric power spot market test points successively. The VPP can not only aggregate the 'source network load' to participate in the problem of the uncertainty of the renewable energy source treatment of the electric energy market, but also excavate flexible peak shaving resources to participate in peak shaving auxiliary service of the spot market. The VPP is taken as an independent market body, the aggregate distributed energy participates in the electric energy market in a price receiver mode to purchase and sell electricity, the maximum income is taken as a target, and a bidding plan is formulated according to the constraint of each member. At present, partial regions in China carry out trial-and-spot research on the participation of VPPs in auxiliary service markets, and the North China, east China and China sequentially issue trial-and-spot rules of the participation of VPPs in peak shaving markets, so that policy research on the participation of VPPs in peak shaving becomes a hotspot. When each distributed energy source in the VPP belongs to owners of different property rights, the direct management mode of the VPP to the inner members is not suitable any more, and the problems of coordinated operation and bidding of multiple main bodies in the VPP need to be studied. In the VPP with multiple principals, a virtual power plant operator (virtual power plant operator, virtual power plant) and each distributed energy source are multiple benefit principals, and the internal multi-principal cooperative coordination is the key of the virtual power plant to make market bidding decisions and coordinate operation with each member. The virtual power plant can effectively manage the internal members through the master-slave game theory, and the price signals are utilized to guide the internal members to participate in bidding decisions. In the existing VPP research, the coordination relation between the VPP and the external market and the internal resources is less comprehensively considered.
The invention aims to solve the problem of daily bidding of the multi-main-body-containing VPP participating in the electric energy market and the peak shaving market, so as to realize the coordinated bidding decision of the multi-main-body VPP participating in the market and increase the income of each member in the virtual power plant, and provides an internal and external coordinated bidding strategy of the virtual power plant which participates in the electric energy market and the peak shaving market from outside to inside and cooperates with each member from inside to outside. There is a need for an optimized bidding methodology for virtual power plants in a multi-power market.
Disclosure of Invention
The invention aims to provide an optimized bidding method of a virtual power plant in a multi-element electric power market, and provides an internal-external coordinated bidding strategy of the virtual power plant which participates in an electric energy market and a peak shaving market, and is coordinated and matched with each member in the virtual power plant. According to the characteristics of each internal distributed energy source, the virtual power plant performs operation compensation on stored energy, performs full-scale absorption on wind and light, and performs CDG and flexible load price dynamic gaming through master-slave gaming. And establishing a multi-main-body double-layer bidding model of the virtual power plant and the internal members, and solving by adopting robust optimization. The operation compensation mechanism for the energy storage can improve the energy storage income, attract the energy storage to participate in and accept the output plan formulated by the virtual power plant, and enable the VPP to effectively participate in peak shaving bidding, thereby obtaining peak shaving income and further improving the income of the virtual power plant.
The invention is realized in the following way:
the invention provides an optimized bidding method of a virtual power plant in a multi-element power market, which is implemented by the following steps:
S 1 the virtual power plant operator obtains external market information, wind and light output prediction information and energy storage information;
S 2 the virtual power plant operators update price signals of the controllable distributed power supply and the flexible load with the maximum benefit as a target;
S 3 the flexible load and the controllable distributed power supply aim at the maximum profit, and the bidding electric quantity is obtained according to the price signal.
S 4 The virtual power plant operator formulates the price of wind, light and energy storage and the bidding electric quantity according to the flexible load and the bidding result of the controllable distributed power supply.
S 5 Robust optimization is adopted for a double-layer optimization model of internal and external coordination bidding decisions of a virtual power plant to achieveAfter balancing, the external market bidding schedule and the internal member output schedule of the final virtual power plant are determined.
Further, the virtual power plant includes, but is not limited to, wind power, photovoltaic, controllable distributed power, energy storage, and flexible load multi-benefit agents.
Further, the energy storage operation compensation mechanism is used for selecting whether to participate in the virtual power plant according to an output plan and a compensation price made by the virtual power plant operator, corresponding electricity purchasing cost is born by the virtual power plant operator when the energy storage is charged in the electric energy market, and compensation of the virtual power plant operator is obtained when the energy storage is discharged; peak shaving compensation is obtained when peak shaving is participated.
Further, the double-layer optimization model of the internal and external coordination bidding decision of the virtual power plant adopts robust optimization to carry out uncertainty characterization, so that the virtual power plant market bidding strategy is optimized.
Compared with the prior art, the invention has the beneficial effects that:
1. the domestic research on the optimized bidding technology of the virtual power plant in the multi-element power market at present is less in comprehensive consideration of the coordination relation of the virtual power plant to the external market and the internal resource, and the operation compensation mechanism of the method for the energy storage can improve the energy storage income, attract the energy storage to participate in and accept the output plan formulated by the VPPO, so that the VPP effectively participates in peak shaving bidding, and peak shaving income is obtained, thereby improving the income of the VPPO. The VPPO performs price dynamic gaming of the CDG and the flexible load through master-slave gaming, can exert the guiding function of the VPPO in advance making price, and realizes effective management of the CDG and the flexible load. The VPPO fully absorbs the wind and light, and the uncertainty is processed through standby. The internal mechanism of the VPPO can improve the income of each internal distributed energy source, improve the enthusiasm of each member to participate in the VPP, and realize the coordination management of the internal members.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of bidding results for VPPs under scenarios 1 and 6 of the present invention;
FIG. 3 is a graph of VPP bidding results under scenarios 1 and 7 of the present invention;
FIG. 4 is a graph of the internal membership optimization results of a virtual power plant under scenario 1 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
Referring to fig. 1, an optimization bidding method of a virtual power plant in a multi-element power market;
in this embodiment, peak shaving compensation is obtained when the virtual power plant participates in peak shaving. The benefit of energy storage in the compensation mode is shown in a formula (1);
wherein: f (F) ES The benefit after the energy storage participates in the VPP is greater than the benefit when the energy storage is directly traded with the power grid; p (P) ES,e,d,t To participate in electric energy market for energy storageTotal discharge power of the field; p (P) ES,vf,t And P ES,pf,t The competitive bidding electric quantity is used for storing energy and participating in valley filling peak shaving and peak shaving; lambda (lambda) ES,e Compensating price, lambda for energy storage discharge ES,f And compensating the price for energy storage peak shaving.
For single energy storage, the charge and discharge power constraint of the energy storage i in the electric energy market is as shown in formula (2);
-P ES,i,max ≤P ES,e,i,t ≤P ES,i,max (2)
Wherein: p (P) ES,e,i,t The power of the energy storage i in the electric energy market is stored for a period t; p (P) ES,i,max The maximum power of charge and discharge of the energy storage i.
The peak-shaving competitive power of the energy storage i in the peak-shaving period meets the peak-shaving period requirement and the upper limit constraint, and the peak-shaving competitive power of other periods is zero, as shown in the formulas (3) and (4);
0≤P ES,vf,i,t ≤u vf P ES,i,max (3)
0≤P ES,pf,i,t ≤u pf P ES,i,max (4)
Wherein: p (P) ES,vf,i,t And P ES,pf,i,t The energy storage i is filled with the valley peak shaving electric quantity and the peak shaving electric quantity; u (u) vf For the valley filling peak shaving mark, a value of 1 indicates that the valley filling peak shaving mark is allowed to participate, otherwise, the valley filling peak shaving mark is not allowed; u (u) pf A peak clipping and peak shaving flag of 1 indicates that participation in peak clipping and peak shaving is allowed, otherwise, the two flags are not allowed to be 1 at the same time.
In this embodiment, in order to ensure that the sum of bidding power of the energy storage i does not exceed the limit value of the energy storage capacity when participating in two markets at the same time, the constraint is as shown in formula (5).
-P ES,i,max ≤P ES,e,i,t +P ES,pf,i,t -P ES,vf,i,t ≤P ES,i,max (5)
The energy of the energy storage i is constrained according to the bidding electric quantity of the energy storage i participating in two markets, as shown in formulas (6) - (8);
E ES,i,t0 -E ES,i,T =0 (6)
E ES,i,min ≤E ES,i,t ≤E ES,i,max (7)
E ES,i,t ＝E ES,i,t-1 +P ES,c,i,t η c -P ES,d,i,t /η d +P ES,vf,i,t η c -P ES,pf,i,t /η d (8)
Wherein: p (P) ES,c,i,t And P ES,d,i,t Charging power and discharging power of the energy storage i in the electric energy market in the t period; e (E) ES,i,t Energy in t period is stored as energy i; e (E) ES,i,max And E is ES,i,min The maximum and minimum energy values of the energy storage i are respectively.
In this embodiment, in the virtual power plant, N is contained ES When the energy is stored, the integral operation constraint is the same as that of the monomer, and the charge and discharge states of the energy storage in the same time period are required to be kept consistent, namely the energy storage is in a charge or discharge state at the same time, and the constraint is shown as a formula (9);
P ES,c,i,t P ES,d,j,t ＝0,i＝1,K,N ES ,j＝1,K,N ES (9)
In order to realize effective management of the VPPO on the CDG and the flexible load, a master-slave game mechanism with the VPPO as a leader and the CDG and the flexible load as followers is established. The VPPO is taken as a leader, takes the maximum profit as a target, and establishes price signals of the CDG and the flexible load according to market information and internal member information, wherein the target of the VPPO is shown as a formula (10).
max F VPP (λ CDG,n ，λ FL,n ) (10)
Wherein: lambda (lambda) CDG,n And lambda (lambda) FL,n The CDG price signal and the flexible load price signal formulated by the VPPO at the nth iteration are respectively.
CDG receives F from it CDG Maximum target, according to CDG price signal lambda issued by VPPO CDG Setting bidding electric quantity P CDG,t The target is shown as a formula (11);
max F CDG (P CDG,n )|λ CDG ＝λ CDG,n (11)
The flexible load yields F in its entirety FL The maximum of which is the target of the present invention,according to the price signal lambda issued by VPPO FL ＝{λ load ,λ FL,vf ,λ FL,pf Competitive power P for setting electric energy market and peak shaving market FL ＝{P FL,e ,P FL,vf ,P FL,pf The target is shown as a formula (12);
maxF FL (P FL,n )|λ FL ＝λ FL,n (12)
Wherein P is FL,n Bidding electric quantity formulated according to price signal for flexible load in nth iteration
In this embodiment, the VPPO updates its price signal to pursue a larger profit based on the bidding power information of the CDG and the flexible load, and the CDG and the flexible load formulate the bidding power according to the VPPO price signal. And (3) obtaining the balanced solution of price signals and bidding electric quantity through price-quantity dynamic gaming, and completing the master-slave gaming process of VPPO, CDG and flexible load.
The VPPO is a full-rate absorption mechanism of wind power and photovoltaic; the VPPO predicts and formulates a day-ahead bidding result according to the day-ahead output of wind power and photovoltaic, calculates the output uncertainty, determines positive and negative spares required to be provided by CDG and flexible load according to the prediction deviation, realizes the full consumption of wind power and photovoltaic, improves the income, and enhances the enthusiasm of participating in the VPP. VPPO mainly aims at maximizing the overall gain of inside and outside, and plays a role in guiding CDG and flexible load decision by making price in advance. And (3) completing a master-slave game process of VPPO, CDG and flexible load to obtain a price balance solution, then making price of wind, light and energy storage by the VPPO and bidding electric quantity, and finally determining a bidding plan of the VPP in an external market and an output plan of each member in the interior to complete an internal-external coordination bidding decision-making process.
In this embodiment: the bidding model of the VPPO and each member is a double-layer optimization model, the upper layer is a bidding model of the VPPO, the lower layer is a bidding model of the CDG and flexible load, and the upper layer is a bidding model of the VPPO:
the objective function, VPPO, targets its profit maximum as shown in equation (13).
maxF VPP,operator ＝B f +B energy -C VPP (13)
Wherein: f (F) VPP,operator Is the total benefit of VPPO; b (B) f Benefits of participating in the peak shaving market for VPPs; b (B) energy Costs for participating in the electric energy market for VPP; c (C) VPP Is the internal cost of VPP.
The benefits of VPP in the peak shaver market are shown in formulas (14) - (16).
P vfb,t ＝P ES,vf,t +P FL,vf,t (15)
P pfb,t ＝P ES,pf,t +P FL,pf,t (16)
Wherein: ρ vf,t And ρ pf,t The price of filling the valley and peak shaving are respectively; p (P) vfb,t Filling the valleys, regulating the peak and competing for the bidding capacity of the VPP; p (P) ES,vf,t The energy storage is charged electricity quantity which participates in valley filling and peak shaving; p (P) FL,vf,t The flexible load participates in the increase of valley filling peak shaving; p (P) pfb,t Peak clipping, peak shaving and bidding capacity for VPP; p (P) ES,pf,t The energy storage is the discharge electric quantity participating in peak clipping and peak shaving; p (P) FL,pf,t The soft load takes part in peak clipping and peak shaving. The cost of VPP in the electric energy market is shown in formula (17).
Wherein: b (B) energy Positive values indicate benefit and negative values indicate cost; p (P) s,t And P b,t The power selling power and the power purchasing power of the VPP participating in the electric energy market in the t period are respectively; ρ s,t And ρ b,t The price is corresponding to the electricity selling price and the electricity purchasing price. The internal costs of the VPPs are shown in formulas (18) - (22).
C VPP ＝C CDG +F FL +F W +F PV +F ES (18)
B CDG,R ＝λ R,up,t R CDG,up,t +λ R,down,t R CDG,down,t (20)
Wherein: c (C) VPP Representing cost positively and benefit negatively; c (C) CDG The cost paid for VPPO to CDG; f (F) FL The cost paid for VPPO to flexible load; f (F) W The cost of purchasing power from VPPO to wind power is set; p (P) W,t The predicted value of the wind power day-ahead output is obtained; lambda (lambda) W Distributing electricity purchasing price of wind power to VPPO; f (F) PV The cost of purchasing power from VPPO to photovoltaic; p (P) PV,t The predicted value of the photovoltaic day-ahead output is obtained; lambda (lambda) PV The electricity purchase price for the VPPO is published to the photovoltaic.
In this embodiment, constraint conditions, power balance constraint.
The power balance constraint of the VPP is shown as a formula (23), and the sum of bidding plans of the VPP in the electric energy market and the output of the internal power supply is equal to the internal equivalent load. The internal equivalent load is determined by the load value after flexible load adjustment and the charge-discharge plan of energy storage in the energy market.
P b,t -P s,t +P W,t +P PV,t +P CDG,t ＝P load,t +P ES,e,t (23)
Wherein: p (P) load,t The load value after the flexible load adjustment.
In this embodiment, the peak shaving constraint. The peak shaving constraint is shown in formula (24), and the VPP meets the peak shaving market admission condition when participating in peak shaving market competitive bidding, and the peak shaving competitive bidding quantity is larger than the minimum bidding capacity allowed by the peak shaving market.
P fbmin,t u(t)≤P fb,t ≤Mu(t) (24)
Wherein: u (t) is a variable of 0-1, is a mark of the VPP participating in peak shaving market, and has a value of 1 when participating in peak shaving, or 0; p (P) fbmin,t Minimum bid capacity for peak shaving market; m is an infinitely large positive number.
In this embodiment, price constraints. The CDG price constraint is shown in a formula (25), and the CDG electricity purchasing price formulated by the VPPO is not more than the electricity purchasing price of the electric energy market, so that the VPPO is ensured to purchase electricity through the CDG preferentially; and the electricity purchasing price is not less than the lowest price, so that the CDG income is ensured.
λ min,t ≤λ CDG,t ≤ρ b,t (25)
Wherein: lambda (lambda) CDG,t CDG electricity purchase price formulated for VPP; lambda (lambda) min,t Is the lowest price.
In this embodiment, the load price constraints of the flexible load are as shown in formulas (26) - (28).
In order to ensure the VPPO income, the load electricity price is not less than the electricity selling price of the electric energy market; in order to reduce the flexible load electricity purchasing cost and enhance the participation VPP enthusiasm, the load electricity price is not more than the electricity purchasing price of the electric energy market, and the average value is not more than the maximum average load price.
ρ s,t ≤λ load,t ≤ρ b,t (26)
ρ f,min ≤λ FL,f,t ≤ρ f,max (28)
Wherein: lambda (lambda) load,t Load electricity prices issued to flexible loads for VPPs; lambda (lambda) load,max The maximum average price is the load; lambda (lambda) FL,f,t The price is compensated for flexible load peak regulation issued by VPPO; ρ f,max Compensating the price for maximum peak shaving; ρ f,min The price is compensated for minimum peak shaving.
In this embodiment, the price constraint of the energy storage is shown in formulas (29) and (30), and the peak shaving compensation price is limited according to the peak shaving market price in order to ensure the income of the energy storage in the peak shaving market.
ρ f,min ≤λ ES,f,t ≤ρ f,max (29)
ρ b,min ≤λ ES,e,t ≤ρ b,max (30)
Wherein: ρ b,min And ρ b,max Compensating the upper and lower limits of price for the discharge of energy storage in the electric energy market.
The price constraint of wind power and photovoltaic is shown as formulas (31) and (32), so that the electricity price of participating in VPP is ensured to be larger than the average electricity price of directly surfing the Internet.
In this embodiment, the constraint is standby. The backup constraint is shown in equations (33) and (34). In order to realize full consumption of wind power and photovoltaic, the VPP reserves corresponding positive and negative standby through CDG and flexible load to cope with the output deviation of wind power and photovoltaic.
R CDG,up,t +R FL,up,t ≥ΔP W-,t +ΔP PV-,t (33)
R CDG,down,t +R FL,down,t ≥ΔP W+,t +ΔP PV+,t (34)
Wherein: ΔP W+,t And DeltaP PV+,t Positive deviation of wind power and photovoltaic output; ΔP W-,t And DeltaP PV-,t Negative deviation of wind power and photovoltaic output; r is R CDG,up,t And R is CDG,down,t Positive and negative spares that can be provided for the CDG during period t; r is R FL,up,t And R is FL,down,t Positive and negative redundancy for the flexible load provided during period t. And (5) energy storage constraint.
In this embodiment, the operation constraint of the stored energy is as shown in formulas (2) - (9). A CDG and a flexible load bidding model of the lower layer, wherein the CDG bidding model is as follows:
after the VPPO issues electricity purchase price and standby requirement of the CDG, the CDG obtains bidding electric quantity of each period reported to the VPPO by taking the benefit of the CDG as an optimization target to the maximum.
An objective function. The CDG targets its maximum gain and the objective function is shown in equations (35) - (36).
Wherein: f (F) CDG Is the benefit of CDG; p (P) CDG,t The power generated by CDG; lambda (lambda) CDG,t CDG electricity purchase price issued for VPPO; b (B) CDG,R Benefits gained by providing redundancy for the CDG; lambda (lambda) R,up,t And lambda (lambda) R,down,t Compensating price for corresponding positive and negative standby; f (f) CDG,C The power generation cost of the CDG; a. b and c are cost coefficients of the CDG. Constraint conditions.
The CDG power needs to meet the upper and lower limit constraints as shown in equation (37).
P CDG,min ≤P CDG,t ≤P CDG,max (37)
Wherein: p (P) CDG,max Is the maximum power; p (P) CDG,min Is the minimum power.
In this embodiment, the reserve constraint of the CDG is as shown in equations (38) and (39) to provide a positive reserve less than its maximum up-regulated power and a negative reserve less than its maximum down-regulated power.
0≤R CDG,up,t ≤P CDG,max -P CDG,t (38)
0≤R CDG,down,t ≤P CDG,t -P CDG,min (39)
In this embodiment, the flexible load bidding model: the flexible load takes the maximum overall benefit as an optimization target, and after the VPPO issues the load electricity price, peak shaving compensation price and positive and negative standby requirements of the flexible load, the bidding electric quantity reported to the VPPO by the flexible load is optimized, and the target function is achieved. The objective function of the flexural load is shown in equations (40) - (44).
C load ＝λ load,t P load,t (41)
P load,t ＝P load0,t -P FL,e,t -P FL,pf,t (42)
B FL,f ＝λ FL,vf,t P FL,vf,t +λ FL,pf,t P FL,pf,t (43)
B FL,R ＝λ FL,up,t P FL,up,t +λ FL,down,t P FL,down,t (44)
Wherein: f (F) FL The overall benefit of the flexible load is that; c (C) load The electricity purchasing cost is realized; p (P) load,t The electricity purchasing quantity is the electricity purchasing quantity of the flexible load in VPP electricity purchasing; equal to the overall load initial value P load0,t Subtracting the total load adjustment P of the day-ahead electric energy market FL,e,t Peak clipping and peak shaving total competitive bidding quantity P FL,pf,t ；λ load,t Flexible load electricity purchase price issued for VPPO; b (B) FL,f The peak regulation income is obtained; p (P) FL,vf,t And P FL,pf,t The method comprises the steps of respectively filling the valley, peak shaving and bidding quantity and clipping the peak shaving and bidding quantity of the flexible load in the t period; b (B) FL,R Spare benefit for flexible loads; lambda (lambda) FL,up,t And lambda (lambda) FL,down,t The compensation prices are respectively positive and negative standby compensation prices.
In this embodiment, the constraint condition. The adjustment constraint of the flexible load i in the electric energy market is shown in formula (45).
-P FL,i,max ≤P FL,e,i,t ≤P FL,i,max (45)
Wherein: p (P) FL,i,max Adjusting the maximum allowable value for the flexible load i before the day; p (P) FL,e,i,t The amount of electricity in the electric energy market is adjusted for the flexible load i.
When the flexible load i participates in the peak shaving market, load adjustment is required according to the time period requirement of the peak shaving market, and the peak shaving competitive power of other time periods is zero, as shown in formulas (46) - (47).
0≤P FL,vf,i,t ≤u tfc P FL,i,max (46)
0≤P FL,pf,i,t ≤u tfd P FL,i,max (47)
Wherein: p (P) FL,vf,i,t And P FL,pf,i,t And the competitive power for the flexible load i to participate in valley filling peak shaving and peak clipping peak shaving.
In this embodiment, when the flexible load i participates in both markets at the same time, the total adjustment amount per time period is required to satisfy the upper and lower limit constraints of the load adjustment, as shown in the formula (48).
-P FL,i,max ≤P FL,e,t +P FL,pf,t -P FL,vf,t ≤P FL,i,max (48)
To ensure that the total amount of load for 24 periods is unchanged, the total adjustment amount of the flexible load i satisfies zero as shown in equation (49).
When the VPP contains N FL When the flexible loads are used, the operation constraint of the whole flexible load in the electric energy market and the peak shaving market is similar to that of a single body, and the condition that the load adjustment states of the flexible loads in the same period are consistent is met, namely, the flexible loads are simultaneously in a cut-down or increase state, and the constraint is shown as a formula (50).
P FL,e,i,t P FL,e,j,t ＝0,i＝1,K,N FL ,j＝1,K,N FL (50)
The reserve constraint of the flexible load is shown in equations (51) - (52) and provides less reserve than the allowable regulated power.
0≤R DR,up,t ≤P DR,up,max (51)
0≤R DR,down,t ≤P DR,down,max (52)
Wherein: p (P) DR,up,max And P DR,down,max Is portable for flexible loadMaximum value for positive and negative standby.
In this embodiment, the robust bidding optimization model, the VPP bidding strategy not only considers the influence of other market participants, but also needs to consider the influence of uncertain factors such as wind power output, load, and the like. The invention adopts robust optimization to carry out uncertainty characterization so as to optimize the VPP market bidding strategy. Mainly consider the influence of uncertainty of wind power output on bidding strategy, and for this purpose, boolean variable is introducedCharacterizing a wind power output uncertain parameter scene, wherein the wind power output uncertain parameter scene is specifically shown in formulas (53) - (55);
wherein:the predicted output and the upper limit value and the lower limit value of wind power under the condition of uncertainty are considered respectively; Γ is an uncertainty adjustment parameter whose value affects the robustness and economy of the strategy.
The robust optimization utilizes the uncertain set to formulate an optimal bidding strategy under the condition of fusion severe scenes. For ease of understanding, all of the variables described above are divided into 3 groups, i.e
Andthereby obtaining a min-max-min robust optimization model,as shown in formulas (56) - (59);
wherein: z epsilon S z And (5) collecting all scenes of the uncertain parameters of the VPP wind power output.
The robust optimization general model is as in formula (59):
wherein: x and y are phase 2 and phase 1 decision variables respectively; a. b and c are coefficient column vectors of the objective function respectively; h. m and d are constraint condition constant column vectors respectively; A. b, C, D, H, E, M are coefficient matrices of constraints, respectively.
The robust optimization general model is first divided into a Main Problem (MP) and a sub-problem (SP) as shown in equations (60) and (61), respectively.
The optimal solution is obtained by alternately solving the main problem and the sub-problem, namely: worst scene generated by main problem according to sub problemSolving; sub-problem optimization decision y based on main problem * . And because the inner layer optimization model of the sub-problem is convex, the method can apply strongThe dual theory is converted into a single-layer solution, lambda, gamma, κ As a dual variable of the constraint condition; as in formula (62);
in this embodiment, parameters and scene settings: the VPP aggregates flexible resource energy storage and flexible load to participate in peak shaving according to the requirements of the power dispatching mechanism, can participate in valley filling peak shaving in time periods 1-8, can participate in peak shaving in time periods 9-12 and 18-21, and can participate in the electric energy market in 24 time periods. The prediction error before the wind power and photovoltaic output day is 10%. The maximum adjustment of each flexible load during each period is 25% of the load during that period. The VPP performs electricity purchasing and selling in the electric energy market through the distribution network, and the peak-valley period division and the electricity price are shown in table 1. The admission condition of peak shaving market is that the competitive bidding power is not less than 2.5 MW.h.
The following 7 scenarios were set for example analysis. When the VPPs participate in the electric energy market and the peak shaving market at the same time, the scenes 1-5 are the scenes with the same information of the electric energy market and different information of the peak shaving market, and specific parameters are shown in the table 2.
TABLE 1 distribution network price of electricity
Table 2 peak shaver market information table
In table 2: scene 1 is a peak shaving market which issues two demands of filling, peak clipping and peak shaving; scene 2 is a price for releasing valley filling peak shaving demands for peak shaving markets; scene 3 is a price for releasing peak clipping and peak shaving demands for peak shaving markets; scene 4 is a peak shaving market which issues a valley filling peak shaving requirement, and two prices; scene 5 is a peak clipping and peak shaving demand issued by a peak shaving market, and two prices are obtained; scenario 6 is not participating in the peak shaving market, but only the electric energy market (the information of the electric energy market is the same as that of scenario 1); scenario 7 is where there is no energy storage in the VPP, while participating in the electric energy market and the peak shaving market (market information is the same as scenario 1).
Analysis of results:
in this embodiment, the VPP market bidding result is analyzed, the peak shaving market information affects the peak shaving bidding result, and the benefits of VPPO in various scenarios are shown in table 3.
TABLE 3 virtual Power plant operator revenue case for different scenarios
As can be seen from table 3, the peak shaving market demand is the maximum gain of VPPO in scene 1 of two peak shaving modes of valley filling and peak shaving. The VPPO obtains the maximum peak shaving income through formulating the maximum peak shaving competitive power, and meanwhile, the distribution network electricity purchasing cost is lower, and the internal income is less damaged. When the peak shaving market demands are the same and the peak shaving prices are different, the VPPO can increase the peak shaving competitive power in the high price period to increase the income. When only filling the valley and peak shaving is performed, compared with the scene 2, the peak shaving gain of the scene 4 is increased by 8.6%, and the total gain of the VPPO is increased by 0.2%; when only peak clipping and peak shaving are performed, compared with scene 3, the VPP peak shaving gain of scene 5 of two prices is increased by 1.2%, and the gain of VPPO is increased by 0.2%.
As shown in fig. 2, fig. 2 (a) is an optimization result of flexible resources and peak shaving bidding power under scenario 1; FIG. 2 (b) is an optimized result of flexible resources when VPPs do not participate in peak shaver in scenario 6; fig. 2 (c) shows the results of the electric energy market bidding of VPP in two scenarios. The VPP of scenario 6 can only participate in the electrical energy market, reducing the electricity purchase cost of the VPP through flexible resources. The flexible load reduces the load in peak period and increases the load in valley period to realize peak load transfer of the load. The energy storage is charged in the valley period, and the discharge is carried out in the normal period when the electricity buying is required, so that the electricity buying amount in the high-price period is reduced. The price of the flexible load in the peak period is higher than that in the normal period, and the flexible load can only select the flat period to cut down the load so as to reduce the electricity purchasing quantity in the peak period. Compared with scenario 6, the VPP of scenario 1 participates in both the peak shaving market and the electric energy market, and needs to plan bidding for both markets with maximum interest, the flexible resources will participate in the market with higher profit preferentially.
In the valley filling period of 1-8, the flexible resources participate in valley filling peak shaving, so that peak shaving income is obtained, and the electricity purchasing quantity of the electric energy market can be reduced. The energy storage in fig. 2 (a) takes part in peak clipping and peak shaving, and charging at ordinary times (unlike charging only at ordinary times in scene 6), because the benefits obtained when the flexible resource takes part in peak clipping and peak shaving are higher than the cost reduced when the flexible resource takes part in electric energy market, VPP needs to make as much peak clipping and peak shaving competitive power as possible to obtain higher benefits. The energy storage 1, 2 and the energy storage whole in the scene 1 can meet respective capacity constraint and charge state constraint in each period, and can ensure that capacity conflict does not occur in bidding electric quantity when participating in two markets at the same time. The flexible loads 1 and 2 and the flexible load in the scene 1 can meet respective capacity constraint in each time period, and capacity conflict of bidding electric quantity can be avoided when two markets are participated simultaneously.
Compared with the scene 6, the VPPO income of the scene 1 is increased by 39.2%, and the distribution network electricity purchasing cost is reduced by 0.8%. The VPPO after participating in peak shaving reduces the internal income, but obtains high peak shaving income and reduces the distribution network electricity purchasing cost, and finally improves the income of the VPPO to a great extent.
Flexible resource role analysis of VPPs
1) Influence of Flexible resources on market bidding results
When the sum of the peak-shaving competitive power of the energy storage and the flexible load reaches the peak-shaving market admittance condition of 2.5MW & h, the peak-shaving market competitive power of the VPP is effective. The result pairs for scenarios 1 and 7 are shown in fig. 3, for example. Wherein, the energy storage charging and the flexible load increase represent participation in valley filling peak shaving, and the value of the energy storage charging and the flexible load increase is negative; the energy storage discharge and the flexible load reduction represent the participation of peak clipping and peak shaving, and the value of the energy storage discharge and the flexible load reduction is positive. Fig. 3 (a) shows the flexible load optimization result and peak shaving bidding power of scenario 7, when the stored energy does not participate in VPP, the load adjustment amount of the flexible load in any period cannot reach the peak shaving admittance condition, but cannot participate in peak shaving, but only participate in the electric energy market, and the peak shaving bidding power of VPP is 0. Fig. 3 (b) shows the electric energy market bidding results of scenario 1 and scenario 7, wherein the electric energy market bidding results of scenario 7 are increased in the electricity purchase amount in the valley period, decreased in the electricity sales amount in the peak period, and increased in the flat period, as compared with those of scenario 1.
In this embodiment, the operation compensation strategy of the energy storage is analyzed, and the energy storage income condition is shown in table 4.
TABLE 4 profits of energy storage in different scenarios
The total gain of the energy storage in the scene 1, the scene 2 and the scene 3 is equal to the peak shaving compensation, because the discharging capacity of the energy storage is totally used for peak shaving and peak shaving, and the discharging compensation of the electric energy market cannot be obtained. In the scene 1, the energy storage obtains the biggest benefit under the condition that both valley filling peak shaving and peak clipping peak shaving can be participated. The compensation price formulated by the VPPO can improve the benefit of energy storage while guaranteeing the benefit of the VPPO.
Price-quantity relationship analysis of VPPO and CDG, flexible load:
taking scenario 1 as an example, analysis is performed, and the calculation result is shown in fig. 4. In fig. 4 (a), the load represents an initial value of the flexible load, and the equivalent load is an internal equivalent load of the VPP, which represents an internal load value that the VPPO needs to purchase electricity through a power supply or a distribution network to satisfy. Compared with the initial value, the equivalent load is increased by the energy storage charging and the flexible load increasing load in normal times 12:00-17:00 and 21:00-24:00, is reduced by the energy storage discharging and the flexible load reducing load in peak periods 08:00-12:00 and 17:00-21:00, and is unchanged in valley periods.
As can be seen from fig. 4, when the wind-light output is smaller than the equivalent load in the valley period, the VPPO guides the CDG to make the power generation power through a power purchase price lower than the power purchase price of the distribution network, so that the power purchased from the distribution network is reduced, and the lowest power purchase cost is obtained; when the wind-light output in the peak period is larger than the equivalent load, the VPPO guides the CDG to make the power generation power through a motor purchasing price lower than the power selling price of the power distribution network, so that the power selling to the power distribution network is increased, and the maximum power selling income is obtained. The load price and flexible load peak shaver price in fig. 4 are determined for VPPO according to the goal that its profit is maximum. The VPPO determined load electricity price is close to the distribution network electricity selling price in the valley period 00:00-08:00, the peak period 08:00-12:00 and the peak period 17:00-21:00 are close to the distribution network electricity purchasing price, and the flexible load is guided to transfer the high-price peak period load to the low-price valley period; the flexible load peak regulation price issued by the VPPO is half of the peak regulation market price, and the flexible load is guided to participate in peak regulation; in normal times, the flexible load increases the load according to the principle that the total amount of transferable load is unchanged.
In this embodiment, the profit situation of energy storage is analyzed, and the energy storage is stimulated to participate in VPP in a price compensation manner, and the profit situation of energy storage in various scenes is shown in table 4. Compared with the scene 6, the energy storage income after participating in peak shaving is improved by 1.24 times. When the energy storage is directly transacted with the power grid without participating in the VPP, the income is obtained in a mode of purchasing electricity price difference of valley period charging and peak period electricity selling, and is 618 yuan, and compared with the income of the energy storage after participating in the VPP, the income is improved by 2.34 times. The benefits of CDG and flexible load were analyzed. Revenue condition analysis of each distributed energy participation VPP:
the revenue for CDG in various scenarios is shown in table 5. The total income of the CDG after participating in the VPP is increased, the minimum is 3650 yuan, the maximum is 4368 yuan, and the income is 2257 yuan after directly trading with the power grid. The yield of the flexible load in various scenarios is shown in table 6. In 5 peak shaving scenes, the total cost is lower than the result of not participating in peak shaving because peak shaving income is obtained. Compared with the flexible load which does not participate in VPP to directly purchase electricity from the distribution network, the electricity price of the load formulated by the VPP is lower than the electricity selling price of the power grid, and the electricity purchasing cost of the flexible load after participating in the VPP is reduced.
TABLE 5 benefit of controllable distributed power supply
| Different scenes | CDG revenue/meta |
| Scene 1 | 3675 |
| Scene 2 | 4319 |
| Scene 3 | 3665 |
| Scene 4 | 4368 |
| Scene 5 | 3650 |
| Scene 6 | 4371 |
| Not involving in VPP | 2257 |
TABLE 6 benefit of Flexible load
| Different scenes | Cost of electricity purchase/yuan | Peak-shaving and standby benefits/primes | Total cost/meta |
| Scene 1 | 97386 | 5689 | 91697 |
| Scene 2 | 97632 | 1394 | 96238 |
| Scene 3 | 98875 | 4350 | 94525 |
| Scene 4 | 97838 | 1366 | 96472 |
| Scene 5 | 98847 | 4410 | 94437 |
| Scene 6 | 98628 | 55 | 98573 |
| Not involving in VPP | 106787 | 0 | 106787 |
In the embodiment, the income conditions of wind power and photovoltaic are analyzed;
the electricity selling price and the income situation of the two clean energy sources of wind power and photovoltaic are shown in table 7. The electricity purchasing price of VPPO to wind power is 338.08 yuan/(MW.h), the electricity purchasing price of photovoltaic is 368.64 yuan/(MW.h), and compared with the situation of directly trading with a power grid, the income of wind power and photovoltaic after participating in VPP is increased by 10%.
TABLE 7 benefit of wind and photovoltaic
/>
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The optimizing bidding method of the virtual power plant in the multi-element electric power market is characterized by comprising the following steps:
S 1 the virtual power plant operator obtains external market information, wind and light output prediction information and energy storage information;
S 2 the virtual power plant operators update price signals of the controllable distributed power supply and the flexible load with the maximum benefit as a target;
S 3 the flexible load and the controllable distributed power supply aim at the maximum profit, and the bidding electric quantity is obtained according to the price signal.
S 4 The virtual power plant operator formulates the price of wind, light and energy storage and the bidding electric quantity according to the flexible load and the bidding result of the controllable distributed power supply.
S 5 Robust optimization is adopted for a double-layer optimization model of internal and external coordination bidding decisions of the virtual power plant, and after balance is achieved, an external market bidding plan and an internal member output plan of the final virtual power plant are determined.
2. The method of claim 1, wherein the virtual power plant includes but is not limited to wind power, photovoltaic, controllable distributed power, energy storage and flexible load multi-benefit agents.
3. The method for optimizing bidding of a virtual power plant in a multi-element power market according to claim 1, wherein the energy storage running compensation mechanism is used for selecting whether to participate in the virtual power plant according to an output plan and a compensation price made by an operator of the virtual power plant, and the corresponding electricity purchasing cost of the energy storage is born by the operator of the virtual power plant when the energy storage is charged in the electric energy market, and the compensation of the operator of the virtual power plant is obtained when the energy storage is discharged; peak shaving compensation is obtained when peak shaving is participated.
4. The method for optimizing bidding of a virtual power plant in a multi-element power market according to claim 1, wherein the double-layer optimization model of the virtual power plant internal and external coordination bidding decision is characterized by adopting robust optimization, so that the virtual power plant market bidding strategy is optimized.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310315874.5A CN116579455A (en) | 2023-03-24 | 2023-03-24 | Optimized bidding method of virtual power plant in multi-element power market |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310315874.5A CN116579455A (en) | 2023-03-24 | 2023-03-24 | Optimized bidding method of virtual power plant in multi-element power market |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116579455A true CN116579455A (en) | 2023-08-11 |
Family
ID=87542022
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310315874.5A Pending CN116579455A (en) | 2023-03-24 | 2023-03-24 | Optimized bidding method of virtual power plant in multi-element power market |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579455A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541300A (en) * | 2024-01-08 | 2024-02-09 | 国网浙江省电力有限公司宁波供电公司 | Virtual power plant transaction management method, system, equipment and storage medium |
-
2023
- 2023-03-24 CN CN202310315874.5A patent/CN116579455A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541300A (en) * | 2024-01-08 | 2024-02-09 | 国网浙江省电力有限公司宁波供电公司 | Virtual power plant transaction management method, system, equipment and storage medium |
| CN117541300B (en) * | 2024-01-08 | 2024-06-04 | 国网浙江省电力有限公司宁波供电公司 | Virtual power plant transaction management method, system, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108599373B (en) | Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system | |
| CN113688567B (en) | Virtual power plant two-stage optimization scheduling method considering impact load | |
| Varkani et al. | A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets | |
| Chen et al. | Research on day-ahead transactions between multi-microgrid based on cooperative game model | |
| Zhang et al. | Energy trading with demand response in a community-based P2P energy market | |
| CN113904385A (en) | New energy power generation side virtual power plant shared energy storage method and system and storage medium | |
| Zhu et al. | Peer-to-peer energy trading in smart energy communities: A lyapunov-based energy control and trading system | |
| CN111682526A (en) | Virtual power plant energy management method based on cooperative game | |
| CN112529271B (en) | Optimized scheduling method for providing standby auxiliary service of power grid by controllable load cluster | |
| Gao et al. | Combined provision of economic dispatch and frequency regulation by aggregated EVs considering electricity market interaction | |
| Ghosh et al. | Strategic prosumers: How to set the prices in a tiered market? | |
| CN114971899A (en) | Day-ahead, day-in and real-time market electric energy trading optimization method with new energy participation | |
| CN116914818A (en) | Virtual power plant operation management and optimal scheduling measurement and analysis method based on game | |
| CN116579455A (en) | Optimized bidding method of virtual power plant in multi-element power market | |
| Lee et al. | Optimal operation strategy for community-based prosumers through cooperative P2P trading | |
| Chen et al. | Improved market mechanism for energy storage based on flexible state of energy | |
| Ghaemi et al. | Local energy communities with strategic behavior of multi-energy players for peer-to-peer trading: A techno-economic assessment | |
| CN112232716B (en) | Intelligent park optimization decision-making method considering peak shaving auxiliary service | |
| CN113659573A (en) | New energy and energy storage cooperative working method | |
| CN116706920A (en) | Optimal strategy and optimal scheduling method for virtual power plant to participate in electric energy peak regulation market | |
| CN116885714A (en) | Electric power market balancing method, system, equipment and storage medium | |
| CN117332937A (en) | Multi-energy complementary virtual power plant economic dispatching method considering demand response | |
| CN116307029A (en) | Double-layer optimal scheduling method and system for promoting coordination of source storage among multiple virtual grids | |
| Datta et al. | Energy management study of interconnected microgrids considering pricing strategy under the stochastic impacts of correlated renewables | |
| Bao et al. | Efficient virtual power plant management strategy and Leontief-game pricing mechanism towards real-time economic dispatch support: A case study of large-scale 5G base stations |
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 |