CN106447216A - Virtual power plant bidding optimization method considering uncertainties - Google Patents
Virtual power plant bidding optimization method considering uncertainties Download PDFInfo
- Publication number
- CN106447216A CN106447216A CN201610889920.2A CN201610889920A CN106447216A CN 106447216 A CN106447216 A CN 106447216A CN 201610889920 A CN201610889920 A CN 201610889920A CN 106447216 A CN106447216 A CN 106447216A
- Authority
- CN
- China
- Prior art keywords
- market
- virtual plant
- competitive bidding
- bidding
- meter
- 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 23
- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 230000005611 electricity Effects 0.000 claims abstract description 24
- 230000002860 competitive effect Effects 0.000 claims description 32
- 238000004146 energy storage Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000001172 regenerating effect Effects 0.000 claims description 2
- 238000000354 decomposition reaction Methods 0.000 abstract description 5
- 230000008569 process Effects 0.000 abstract description 5
- 238000005516 engineering process Methods 0.000 abstract description 4
- 230000009471 action Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- Finance (AREA)
- Marketing (AREA)
- Accounting & Taxation (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a virtual power plant bidding optimization method considering uncertainties. The method includes the following steps that: 1, an interval prediction model of market electricity prices is established based on EEMD (ensemble empirical mode decomposition) and an RVM (relevance vector machine); 2, a virtual power plant bidding optimization model considering market electricity price uncertainties under an electricity market environment is established; and 3, a virtual power plant optimal bidding result participating in market operation is obtained according to a solution obtained in the step 2. According to the method of the invention, the interval prediction of the market electricity prices is realized through adopting the empirical mode decomposition and the relevance vector machine, the uncertainties under the electricity market environment are processed through a robust optimization method, and the virtual power plant robust optimization model is established; a linearization technology is adopted to make the optimization model robust, and a modeling process is simplified, computation time is greatly reduced, computational efficiency is improved, and the robustness of bidding strategies is improved.
Description
Technical field
The invention belongs to virtual plant run field, particularly to a kind of meter and probabilistic virtual plant competitive bidding excellent
Change method.
Background technology
The becoming increasingly conspicuous of increasingly depleted with fossil energies such as oil and problem of environmental pollution, wind-power electricity generation distributed
The ratio of power supply and the active load such as electric automobile access electrical network that can network improves constantly.Reliable, warp that distributed power source has
Ji, flexibly, environmental protection the features such as be conducive to alleviating energy scarcity and problem of environmental pollution.However, distributed power source geographical position is divided
Dissipate, single-machine capacity is less and has significant randomness, fluctuation and uncertainty, and being directly accessed electrical network can be to the safety of electrical network
Stable generation is greatly impacted.
The proposition of virtual plant (virtual power plant, VPP) is that distributed power source and active load etc. access electricity
Net provides new approaches.Virtual plant, on the premise of not changing the existing topological structure of electrical network, controls Consultation Center to be based on and generates electricity
Prediction, load prediction, are polymerized distributed electrical by advanced Coordinated Control, the intelligent metering technology and information communication technology
Source, can network the different types of element such as electric automobile, energy-storage system, realizes many distributed energies by the software algorithm on upper strata
Coordination optimization run, thus promote resource reasonably optimizing configuration and utilize.
When virtual plant participates in market competitive bidding, market guidance directly affects the bidding behavior of virtual plant, due to market electricity
Valency has significant uncertainty, so causing the robustness of Bidding Strategiess that virtual plant carries out providing during competitive bidding low, calculates
Efficiency is low.
Content of the invention
Goal of the invention：For the problem overcoming prior art to exist, the invention provides one kind effectively improves Bidding Strategiess
Robustness and the meter of computational efficiency and the optimization method of probabilistic virtual plant competitive bidding.
Technical scheme：The invention provides a kind of meter and probabilistic virtual plant competitive bidding optimization method, including with
Lower step：
Step 1：Set up the interval prediction model of market guidance based on EEMD-RVM；
Step 2：Set up meter and market guidance probabilistic virtual plant competitive bidding Optimized model under Power Market；Its
In, count and market guidance probabilistic virtual plant competitive bidding Optimized model is：
In formula, G is virtual plant competitive bidding optimization object function；T is the competitive bidding cycle；CtCost for t； λ tRespectively
Represent upper, the lower limit of market guidance；Pk,tCompetitive bidding amount for t；Pt DFor t internal load demand；λt DBear for t
Charged valency；Γ is robust coefficient；v,ηt,ytFor the aid decision variable introducing in equity conversion；
Step 3：Draw the virtual plant optimum competitive bidding result participating in running in market according to the solution of step 2.
Further, described virtual plant includes the traditional energy being made up of wind energy turbine set, energy-storage battery and hydroenergy storage station
The united forms of electricity generation with regenerative resource.
Further, in the meter set up in described step 2 and market guidance probabilistic virtual plant competitive bidding Optimized model
Including controlled gas turbine group constraints, uncontrollable Wind turbines constraints, transmission line of electricity capacity constraints and power
Equilibrium constraint.
Beneficial effect：Compared with prior art, the present invention realizes city using set empirical mode decomposition and Method Using Relevance Vector Machine
The interval prediction of field electricity price, processes the uncertainty of market guidance using robust optimization, sets up the optimization of virtual plant robust competing
Mark model.Linearize Robust Optimization Model with linearization technique, simplify modeling process, greatly reduce the calculating time, improve
Computational efficiency, improves the robustness of Bidding Strategiess.
Brief description
Fig. 1 is a kind of meter of the present invention and the flow chart of probabilistic virtual plant optimization competitive tender method；
Fig. 2 is EEMD-RVM model realization step；
Fig. 3 is the IEEE30 node structure figure containing virtual plant；
Fig. 4 is the interval prediction result of market hotel owner under 90% confidence level；
Fig. 5 is the optimum bidding curve of virtual plant.
Specific embodiment
Below in conjunction with the accompanying drawings the present invention is done and further explain.
As shown in figure 1, the optimization method of a kind of meter of present invention offer and probabilistic virtual plant competitive bidding, main bag
Include following steps：
Step 1), the interval prediction model of market guidance is set up based on EEMD-RVM；
Interval prediction model (hereinafter referred, EEMD-RVM model) based on set empirical mode decomposition and Method Using Relevance Vector Machine
Initially with EEMD (set empirical mode decomposition) method, market guidance data sequence is resolved into multiple characteristic modes functions to divide
Amount (IMF component) and a residual components (RES component), secondly carry out interval prediction using group synkaryon RVM model, implement
Step is as shown in Figure 2.
Step 2), set up meter and the probabilistic virtual plant of market guidance under Power Market and optimize competitive bidding model；
Consider to constitute VPP by Miniature wind electric field, gas turbine, energy-storage battery etc., VPP is accepting wind-power electricity generation to greatest extent
On the basis of, optimize gas turbine, the running and participate in ahead market competitive bidding of energy-storage battery, realize systematic economy in the competitive bidding cycle
Benefit is maximum.Wherein, concrete formula is as follows：
In formula, T is the competitive bidding cycle；F is economic benefit function；K is purchase (selling) electrical nodes, and GSP is purchase (selling) electrical nodes；Pt D
For workload demand amount in t virtual plant, λt DFor load electricity price in t virtual plant；{λtIt is t market guidance,
{λtIt is the result that the interval prediction model that step 1 is set up is predicted out；αkFor external electrical network cost coefficient, αkFor being manually set
Constant value；Pk,tFor competitive bidding amount, it is decision variable, wherein, Pk,tFor just representing virtual plant from external electrical network power purchase, Pk,tFor
Negative indication virtual plant is to external electrical network sale of electricity；CtFor the cost of t, NiFor generating set number, 0-1 variable K can be distributedt,iFor
T unit i action (start or close) variable, action variable be description t whether there is by reach close or by pass to
Open this process；Kt,iFor 1 expression t unit i action, Kt,iIt is failure to actuate for 0 expression t unit i, kiDynamic for unit i
Make cost；By the secondary cost function piece-wise linearization of unit i, njFor segmentation sum, piFor the fixed cost of unit i,For
The slope of j section,For the generated energy of jth section, Pi.tGenerated energy for t unit i.
Constraints：
(1) controlled gas turbine group constraint：
Pi minvi,tΔt≤Pi,t≤Pi maxvi,tΔt (4)
-ri downΔt≤Pi,t-Pi,t-1≤ri upΔt (5)
Ki,t=| vi,t-vi,t-1| (6)
yi,t-zi,t=vi,t-vi,t-1(7)
yi,t+zi,t≤1 (8)
In formula, Pi max(Pi min) be controlled unit i power output upper (lower) limit；Pi,t-1Generating for t-1 moment unit i
Amount；ri up(ri down) it is controlled unit i to upper (lower) creep speed；0-1 variable vi,tFor controlled unit i state variable, state change
The generating of amount description t unit or the specific state of shutdown；0-1 variable yi,t(zi,t) start for t controlled unit i and (stop
Only) variable, TUi,wRepresent starting time variable, TDi,wRepresent dwell time variable, w represents time variable, MUTi(MDTi) being can
Control unit i is minimum to start (stopping) time；Δ t is Period Length.
(2) uncontrollable Wind turbines constraint：
Pt W≤{Pt AW} (13)
Pt W≥0 (14)
In formula：Pt WFor Wind turbines power output uncontrollable in wind energy turbine set, subscript W represents wind energy turbine set, { Pt AWIt is uncontrollable
The predicted value of Wind turbines power output, { Pt AWDrawn according to prediction according to wind-powered electricity generation historical data.
(3) transmission line of electricity capacity-constrained：
In formula：PD, maxRepresent the maximum of t internal load demand, Pi DG, minRepresent controlled unit minimum generating work(
Rate, DG refers to virtual plant controlled unit set,Power transmission line maximum capacity for k-th purchase (selling) electrical nodes limits,Table
Show energy-storage battery peak power, subscript ST represents energy-storage battery.
(4) power-balance constraint：
Wherein, Pt dRepresent the discharge capacity in t virtual plant, Pt cRepresent the charge volume in t virtual plant.
Process the uncertainty of market guidance using robust optimization, meter and probabilistic virtual plant optimize competitive bidding mould
Type is：
In formula：G is virtual plant competitive bidding optimization object function, and object function G is the dual function of economic goal function F,
Γ is robust coefficient, λ tFor the bound of market guidance, v, ηt,ytFor the aid decision variable introducing in equity conversion, auxiliary
Decision variable draws in converting for model equivalency.
Step 3), the model solution according to obtaining in step 2 show that the virtual plant optimum competitive bidding participating in running in market is bent
Line, thus obtain the competitive bidding scheme of the virtual plant of optimum according to the optimum bidding curve obtaining.
Embodiment
As shown in figure 3, choose IEEE30 node system being suitably modified：2 gas turbines, node is accessed at node 26
Access 2 gas turbines and 1 wind energy turbine set at 29, at node 30, access an energy-storage battery, node 26/29/30 collectively constitutes
One virtual plant system.The competitive bidding cycle is 1 day, is divided into 24 periods.Virtual plant can be by node 26/29/30 from outside
Electrical network purchase (selling) electricity is it is assumed that each node purchases the α that (selling) electricity price lattice are ahead market Research on electricity price prediction valuekTimes, αkValue be 0.95,
1.0,1.05}.
Using EEMD-RVM interval prediction model, based on European power trade center EEX historical data, market guidance is entered
Row interval prediction, interval prediction result and real data are as shown in Figure 4.
During robust optimizes, robust coefficient is bigger, that is, think that predicted value is bigger with actual value deviation, optimum results are more conservative.As
Shown in Fig. 5, the Bidding Strategiess for 2 kinds of difference degree of guarding are analyzed comparing, respectively：Tactful A：Γ=24；Tactful B：Γ
=8.T=5 and t=17 corresponding optimum bidding curve under two kinds of strategies.
Low electricity is more than by the interval that the optimum bidding curve of virtual plant can be seen that high rate period bidding curve covering
The valency period.On the other hand, in the same period, increase with robust coefficient Γ numerical value, optimum bidding curve moves right, that is, take
More conservative Bidding Strategiess can reduce the electricity sales amount to external electrical network for the virtual plant.Virtual plant optimizes the robust of Bidding Strategiess
Property strengthen.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvement done etc., should be included within the scope of protection of the invention.
Claims (3)
1. a kind of meter and probabilistic virtual plant competitive bidding optimization method it is characterised in that：Comprise the following steps：
Step 1：Set up the interval prediction model of market guidance based on EEMD-RVM；
Step 2：Set up meter and market guidance probabilistic virtual plant competitive bidding Optimized model under Power Market；Wherein,
Meter and market guidance probabilistic virtual plant competitive bidding Optimized model are：
In formula, G is virtual plant competitive bidding optimization object function；T is the competitive bidding cycle；CtCost for t；Represent respectively
Upper, the lower limit of market guidance；Pk,tCompetitive bidding amount for t；Pt DFor t internal load demand；λt DFor t load electricity
Valency；Γ is robust coefficient；v,ηt,ytFor the aid decision variable introducing in equity conversion；
Step 3：Solved according to step 2 and draw the virtual plant optimum bidding curve participating in running in market.
2. according to claim 1 meter and probabilistic virtual plant competitive bidding optimization method it is characterised in that：Described
Virtual plant includes the regenerative resource forming and traditional energy by wind energy turbine set, energy-storage battery and hydroenergy storage station and sends out in combination
Electric form.
3. according to claim 1 meter and probabilistic virtual plant competitive bidding optimization method it is characterised in that：Described
In step 2, the meter set up and market guidance probabilistic virtual plant competitive bidding Optimized model include controlled gas turbine group about
Bundle condition, uncontrollable Wind turbines constraints, transmission line of electricity capacity constraints and power-balance constraint condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610889920.2A CN106447216A (en) | 2016-10-12 | 2016-10-12 | Virtual power plant bidding optimization method considering uncertainties |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610889920.2A CN106447216A (en) | 2016-10-12 | 2016-10-12 | Virtual power plant bidding optimization method considering uncertainties |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106447216A true CN106447216A (en) | 2017-02-22 |
Family
ID=58173633
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610889920.2A Pending CN106447216A (en) | 2016-10-12 | 2016-10-12 | Virtual power plant bidding optimization method considering uncertainties |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106447216A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110390467A (en) * | 2019-06-25 | 2019-10-29 | 河海大学 | A kind of random ADAPTIVE ROBUST Optimization Scheduling of virtual plant distinguished based on key scenes |
-
2016
- 2016-10-12 CN CN201610889920.2A patent/CN106447216A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110390467A (en) * | 2019-06-25 | 2019-10-29 | 河海大学 | A kind of random ADAPTIVE ROBUST Optimization Scheduling of virtual plant distinguished based on key scenes |
| CN110390467B (en) * | 2019-06-25 | 2022-08-19 | 河海大学 | Virtual power plant random self-adaptive robust optimization scheduling method based on key scene identification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | Optimization model for the short-term joint operation of a grid-connected wind-photovoltaic-hydro hybrid energy system with cascade hydropower plants | |
| Ju et al. | A two-stage optimal coordinated scheduling strategy for micro energy grid integrating intermittent renewable energy sources considering multi-energy flexible conversion | |
| CN102694391B (en) | Day-ahead optimal scheduling method for wind-solar storage integrated power generation system | |
| CN105071389B (en) | The alternating current-direct current mixing micro-capacitance sensor optimizing operation method and device of meter and source net load interaction | |
| CN102097866B (en) | Mid-long-term unit commitment optimizing method | |
| CN103971181A (en) | Day-ahead economic dispatch method for virtual power plant | |
| Agamah et al. | A heuristic combinatorial optimization algorithm for load-leveling and peak demand reduction using energy storage systems | |
| CN103699941A (en) | Method for making annual dispatching operation plan for power system | |
| CN114021390A (en) | Random robust optimization method for urban comprehensive energy system and application thereof | |
| CN105305423A (en) | Determination method for optimal error boundary with uncertainty of intermittent energy resource being considered | |
| CN108053057A (en) | A kind of virtual plant Optimized Operation modeling method based on CVaR | |
| CN107706932A (en) | A kind of energy method for optimizing scheduling based on dynamic self-adapting fuzzy logic controller | |
| CN110417062A (en) | A kind of electrical integrated energy system Optimization Scheduling | |
| CN113988714A (en) | Dynamic planning method, device and medium for park integrated energy system considering multiple uncertainties | |
| Wen et al. | ELCC-based capacity value estimation of combined wind-storage system using IPSO algorithm | |
| CN115882523A (en) | Optimal operation method, system and equipment for power system with distributed energy storage | |
| Li et al. | Energy management model of charging station micro-grid considering random arrival of electric vehicles | |
| CN108667077A (en) | A kind of wind storage association system Optimization Scheduling | |
| Dou et al. | Double‐deck optimal schedule of micro‐grid based on demand‐side response | |
| CN105633950A (en) | Multi-target random, fuzzy and dynamic optimal power flow considering wind power injection uncertainty | |
| Zhang et al. | A double-deck deep reinforcement learning-based energy dispatch strategy for an integrated electricity and district heating system embedded with thermal inertial and operational flexibility | |
| Mercurio et al. | Distributed control approach for community energy management systems | |
| Yang | Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm | |
| CN104112168A (en) | Intelligent home economic optimization method based on multi-agent system | |
| CN104935004B (en) | Based on many microgrids polymerization coordination optimization operation method that panorama is theoretical |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170222 |
|
| RJ01 | Rejection of invention patent application after publication |