CN109636056A - A kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology - Google Patents
A kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology Download PDFInfo
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Abstract
A kind of multiple-energy-source microgrid optimizing operation method based on multiple agent, consider the uncertainty of energy internet system operation, it is proposed Regional Energy internet optimization operation control strategy, the continuous time is subjected to sliding-model control, various energy resources equipment in energy internet is modeled, is run minimized cost inside each energy local area network based on Model Predictive Control, introduce non-cooperative game, novel Price Mechanisms are established for Regional Energy interaction, by iterative calculation, game reaches Nash Equilibrium.Optimization operation control strategy of the invention can effectively reduce system net load fluctuation rate, reduce economical operation cost, improve energy internet system reliability and economy.
Description
Technical field
The invention belongs to multiple-energy-source microgrid energy running optimizatin fields, and in particular to a kind of based on the more of multi-agent Technology
Energy microgrid decentralization Optimization Scheduling.
Background technique
Production, transport, the processing of global fossil energy are converted and have had resulted in serious pollution using to ecological environment
And destruction, it constitutes a serious threat to human survival.Using new energy technology and Internet technology as the third time industrial revolution of representative
It is rising, the construction of energy internet (EI) can push industry technology upgrade and the structural adjustment of China's energy industry.The energy
Internet is a flattening, " source-net-lotus-storage " system, including multiple energy local net units, Independent Power Generation unit, independence
Power unit, independent energy-storage units etc..These energy internet subelements are each due to its owner, mission requirements and regulation goal
It is different, and have and pursue the maximized ability of number one and driving force, the diversification of borne forms in energy internet,
Renewable energy (renewable energy source, RES) is uncertain, and traditional centralized Optimized Operation mode is difficult
Applied in the optimization operation of energy internet system, how to cope with as the energy management of energy internet and optimize in operation urgently
Problem to be solved.
Multi-agent system (multi-agent system, MAS) has huge in distributed AC servo system and management aspect
Potentiality are concerned in energy internet area at present.Internet is merged with the deep of traditional energy, that is, renewable energy can be improved
The networking ratio in source realizes the diversification of energy resource supply mode, promotes energy structure optimizing, it is on-demand that energy resources also may be implemented
Flowing, promotion is resource-effective, efficiently utilizes, and realizing reduces total energy consumption, reduction disposal of pollutants.With energy internet
Development, equipment coupled relation will be increasingly complex with energy resource structure in energy internet system, on the other hand, workload demand side and
Relationship is more versatile and flexible between energy internet, and the operation of energy internet and management difficulty is caused to greatly increase.From optimization
Operation angle is seen, guarantees production of energy and the safe and efficient transmission of transmission process information flow is control multi-energy system production run
Key.
Consider that energy internet-based control runs all kinds of controllable devices differences and the factors such as operational mode diversification, to region energy
Influence of the individual to optimisation strategy is studied in source interconnection net, is focused on the flexibility of energy conversion link in netting, is considered to more
Whole description and the characteristic feature extraction of energy internet system structure are current hot spots and hardly possible for the modeling of energy internet
Point.In the electricity transaction market that energy internet system is constituted, the energy local area network with high degree of autonomy ability, behavior tool
Have stronger subjectivity with it is intelligent, therefore how to take into account the game row on the basis of stability and economy to energy local area network
To carry out Accurate Model, it should be the following needle that benefit to realize energy local area network individual is optimal and the coordinated control of system entirety
Carry out the emphasis direction of further investigation to energy internet system.
Summary of the invention
For overcome the deficiencies in the prior art, the invention proposes a kind of, and the multiple-energy-source microgrid based on multi-agent Technology is gone
Centralization Optimization Scheduling applies the energy source interconnection between Regional Energy internet to enjoy together, wherein Regional Energy internet
Including non-renewable energy survey, all kinds of energy conversions, energy storage device, intelligent load etc..Non-renewable energy side (distributed energy, day
Right gas, coal etc.) pass through energy conversion, to meet, user side is hot and cold, electrical load requirement, can effectively improve multipotency
Source microgrid improves the economy of Regional Energy internet and weakens uncertainty to the multizone energy to the digestion capability of new energy
The influence of internet operation.
To achieve the goals above, the technical solution of the present invention is as follows:
One kind being based on multiple agent decentralization multiple-energy-source microgrid optimizing operation method, the described method comprises the following steps:
S1: system initialization considers discrete time model, sets Best Times for 24 hours, to carry out sliding-model control, respectively
Have k ∈ { 1,2 ..., T } for T period for any kth time, and the when a length of Δ t of kth time period；
S2: defining multiple agent (MAS) system, and including load management Agent, the energy measures Agent, nets interior electricity price
Agent, as follows:
(1) a kind of load management Agent, intelligent body for Demand-side load flow direction and service condition, predicts user side
Multipotency load uses, and obtains region internal loading service condition in time, makes Rational Decision distribution load flow direction；
(2) energy measures Agent: a kind of corresponding to non-renewable energy side distributed energy power generation, cooling heating and power generation system
Intelligent body, all kinds of workload demands of real-time monitoring user, Regional Energy measure Agent and receive distributed energy power output prediction, monitoring
Each micro- source power output situation, adjusts energy storage and power supply device power generation/heat supply/refrigerating state in real time, mutual to meet Regional Energy
On-line customer side energy requirement；
(3) electricity price Agent in net: being a kind of energetic interaction Price Mechanisms intelligence between Regional Energy internet
Body；For the information exchange and energetic interaction between different zones, hold the local information that indigenous energy measures Agent transmission,
Based on non-cooperation Dynamic Game Model, the Price Mechanisms between a kind of multizone energy are established；
S3: to "flop-out" method after use, RESs output and user's base load prediction model are established；
Reduce representation method based on typical scene to simulate the uncertainty of load and renewable energy power generation, using illiteracy spy
Carlow simulation generates several scenes, according to wind speed value, solar radiation angle predicted value, is simulated using profile samples method practical
The fluctuation situation of middle predicted value generates random scene；Prediction probability of error distribution is determined according to historical data, obtains random distribution
RES stochastic variable is converted to output power according to output characteristic curve by error, indicates that distribution goes out using Normal probability distribution
Power predicts error；New energy power output is shown in predicted value time series table of contributing of the following T period, sets out field of force scape Power generating value for scene i in moment T, and scene ωiProbability of happening be Pi, the contracting of scene set
Probability metrics minimum before subtracting between final reservation scene subclass is expressed as follows:
In formula, α indicates the scene set finally deleted after scene reduction, and number of scenes is 3000.Initialization retains collection S
={ ω0, ω1, ωi,ωs, addition and the smallest another scene of its probability metrics are concentrated actually abandoning, wherein changing and abandoning
With the nearest scene ω of collectionlProbability is expressed as p (ωl')=p (ωl)+p(θk), it is wanted until abandoning to collect contained number of scenes and reach
It asks；
MPPT maximum power point tracking method (MTTP) is applied to new energy to go out in Force system, makes its work in maximum power point,
Based on prediction result, active output power and the base load prediction of RES is shown as:
Distributed generation resource power output total amount are as follows:
Pres,i=Ppv,i+Pw,i (5)
S4: the electricity price of current k period is calculated by Price Mechanisms for building multi-agent system；
The energy-storage system aspect of model is as follows:
In formula,For energy-storage system initial state of charge,It is expected charging capacity for energy-storage system,For energy-storage system electricity
Tankage,For the charge power of Regional Energy internet i energy-storage system,For putting for Regional Energy internet i energy-storage system
Electrical power；
Assuming that all energy-storage system Li-ion batteries piles having the same, and the charge/discharge power in the single period
Be considered as it is constant, therefore, the model of energy-storage system battery and constraint are established as follows:
In formula,It is illustrated respectively in the SOC state of time period t+1 and t moment,Indicate the energy storage electricity in t moment
Pond power, Mi,Bi,Ci and DiRespectively indicate sytem matrix, input matrix, output matrix and feedforward matrix；
In formula,It is illustrated respectively in the charge-discharge electric power of time t energy-storage system, ηchAnd ηdchIt respectively indicates and fills
Electricity/discharging efficiency；
Gas Turbine Generating Units are higher for energy internet system efficiency, natural gas energy resource made full use of, to environment
Less pollution is expressed as follows Gas Turbine Output:
In formulaFor the generated output of t period energy internet i gas turbine；For the maximum generation function of gas turbine
Rate；For the waste heat regenerative power of t period energy internet i gas turbine；ηcAnd ηrFor the generating efficiency of gas turbine and remaining
Heat recovery efficiency；λgtFor gas consumption rate, λgasFor heating value of natural gas, 9.7kWh/m is taken3；
It is transaction core with electric power, in energy Internet market, the purpose for participating in electricity price Agent in the net bidded is logical
The Bidding Strategiess for crossing rationality obtain maximum benefit；
Wherein electricity price Agent optimization problem in netting is indicated are as follows:
In formula, P is electricity price Agent optimization aim in rolling time horizon in netting, interaction electricity price, r as in netb, rsRespectively
The inside power purchase price and internal sale of electricity price being in a few days arranged；a1And a2It is power-balance with reference to electricity price, respectively corresponds sale of electricity region
Electricity price when energy internet and power purchase energy internet net load are zero；In addition, in formulaFour variables
Expression-form it is as follows:
In formula, PLoadLoad, U are adjusted for energy internet igridFor energy internet i interaction power；
S5: building energy local area network Power Balance Model guarantees the equilibrium of supply and demand in energy local area network；
Energy local area network Power Balance Model is constructed, each composition portion of supply side and Demand-side is in energy local area network
Point, obtain the electrical power balance model of i-th of energy local area network:
Wherein,For energy local area network i and net the interaction power between interior energy local area network；To convey linear heat generation rate
Constraint；
S6: being based on Model Predictive Control, minimizes single energy local area network operating cost, repeats step S2~S5；
Single energy local area network runs minimized totle drilling cost, optimization problem table of the single energy local area network in rolling time horizon
It is shown as a quadratic programming problem:
In formula: τ is optimization rolling time horizon length；ugrid, Qs, QHX, QACRespectively energy interaction power, energy storage residue are held
Amount, heating gas turbine waste heat, cooling gas turbine waste heat；A, B, C, D are respectively energy storage residual capacity, gas turbine hair
Electrical power heats the flexible constraint coefficient for using gas turbine waste heat, cooling gas turbine waste heat；
S7: electricity price Agent receives load management Agent in netting and the energy measures Agent scheduling information, dynamic by non-cooperation
State betting model maximizes each participant's income, determines that itself electricity price obtains strategy set in real time, process is as follows:
S71: establishing betting model, and according to energy internet, optimization problem establishes mixed integer model in rolling time horizon,
Betting model will be described:
Participant is electricity price Agent in the net in set N+, and each participant contains distributed energy, energy-storage system；
Strategy: for any i ∈ N+, within the k period,For all participant's action collections；The strategy taken: including
The strategy that distributed energy power output, all kinds of demand loads and other participants are taken.Optimal Operation Strategies maximize each
Participant i (i ∈ N+) income, be expressed as ρi, it is assumed that ρiFor possible strategy collection；
Income: for measuring each participant's gross profit, the income of each participant i is maximized, U is expressed asi；
Set of strategies A is given from the abovei={ A1,A2,…AN, when following situations is set up:
Wherein A* is to update the later set of set of strategies, and strategic vector A* is referred to as Nash Equilibrium point (Nash
Equilibrium, NE), any Regional Energy internet is unable to improve respective income by unilaterally changing strategy；
S72: tactful PiFor i-th of energy internet interaction electricity price, the Electricity Price Strategy collection of energy internet i is combined into Ai, and Ai
=P | 0≤P≤Pmax, PmaxInteractive electricity price can be reported for maximum, therefore AiFor a compact convex subset, and participant is in gambling process
In, sale of electricity strategy P there will necessarily be, therefore set AiNon-empty；
Prove UiIt (A) is concave function, then there are a Pure strategy nash equilibria points by S；To Ui(A) secondary derivation is carried out, secondly
Subderivative is as follows:
Due to rb,λb,PLoad,i(i=1,2,3 ..., N) is non-negative, i.e.,Therefore UiIt is recessed, non-cooperative game
There are Pure strategy nash equilibria points by problem S；
S8: after the completion of optimization, strategy set is obtained, and calculate whether reach Nash Equilibrium；Step S3~S7 is repeated, when
Determine that termination game obtains multiple-energy-source microgrid and optimizes the method for operation when obtaining preferred plan.
The beneficial effects of the present invention are:
1, in technical solution of the present invention, representation method is reduced based on typical scene to simulate load and renewable energy power generation
Uncertainty, several scenes are generated using Monte Carlo simulation, using the wave of profile samples method simulation predicted value in practice
Emotionally condition, so that distributed energy prediction is more accurate.
2, the novel Price Mechanisms for proposing a kind of multiple-energy-source microgrid based on multiple agent, by mutually rich between intelligent body
The electricity consumption behavior that can effectively guide active load is played chess, peak load shifting is played the role of, to reduce operation of power networks pressure.
3, by the single energy local area network physical characteristic of analysis, various energy resources equipment in energy internet is modeled,
It is run minimized cost inside each energy local area network based on Model Predictive Control, thus guarantee system high efficiency stable operation,
Reduce systematic economy cost.
4, non-cooperative game is introduced, each energy local area network internal control operation reserve is formulated, system net load is effectively reduced
Stability bandwidth reduces economical operation cost, improves energy internet system reliability and economy.
Detailed description of the invention
Fig. 1 is energy internet system schematic diagram.
Fig. 2 is RES power curve figure.
Fig. 3 is hot and cold load conversion curve.
Fig. 4 is electricity price change curve.
Fig. 5 is net load curve graph.
Fig. 6 is energy local network optimization operation curve figure.
Fig. 7 is a kind of flow chart of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings.
Referring to Fig.1~Fig. 7, a kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology, packet
Include following steps:
S1: system initialization considers discrete time model, sets Best Times for 24 hours, to carry out sliding-model control, respectively
Have k ∈ { 1,2 ..., T } for T period for any kth time, and the when a length of Δ t of kth time period；
S2: building energy internet MAS system structure defines the intelligent body of three types, it may be assumed that
(1) a kind of load management Agent, intelligent body for Demand-side load flow direction and service condition, predicts user side
Multipotency load uses, and obtains region internal loading service condition in time, makes Rational Decision distribution load flow direction；
(2) energy measures Agent: a kind of corresponding to non-renewable energy side distributed energy power generation, cooling heating and power generation system
Intelligent body, all kinds of workload demands of real-time monitoring user, Regional Energy measure Agent and receive distributed energy power output prediction, monitoring
Each micro- source power output situation, adjusts energy storage and power supply device power generation/heat supply/refrigerating state in real time, mutual to meet Regional Energy
On-line customer side energy requirement；
(3) electricity price Agent in net: being a kind of energetic interaction Price Mechanisms intelligence between Regional Energy internet
Body；For the information exchange and energetic interaction between different zones, hold the local information that indigenous energy measures Agent transmission,
Based on non-cooperation Dynamic Game Model, the Price Mechanisms between a kind of multizone energy are established；
S3: to "flop-out" method after use, RESs output and user's base load prediction model are established；
Reduce representation method based on typical scene to simulate the uncertainty of load and renewable energy power generation；It is special using covering
Carlow simulation generates several scenes, according to wind speed value, solar radiation angle predicted value, is simulated using profile samples method practical
The fluctuation situation of middle predicted value generates random scene.Prediction probability of error distribution is determined according to historical data, obtains random distribution
RES stochastic variable is converted to output power according to output characteristic curve by error, indicates that distribution goes out using Normal probability distribution
Power predicts error；New energy power output is shown in predicted value time series table of contributing of the following T period, sets out field of force scape Power generating value for scene i in moment T, and scene ωiProbability of happening be Pi, the reduction of scene set
The probability metrics minimum between final reservation scene subclass is expressed as follows before:
In formula, α indicates the scene set finally deleted after scene reduction, and number of scenes is 3000.Initialization retains collection S
={ ω0, ω1, ωi,ωs, addition and the smallest another scene of its probability metrics are concentrated actually abandoning, wherein changing and abandoning
With the nearest scene ω of collectionlProbability is expressed as p (ωl')=p (ωl)+p(θk), it is wanted until abandoning to collect contained number of scenes and reach
It asks；
For preferably analyzing system performance, MPPT maximum power point tracking method (MTTP) is applied to new energy and goes out Force system
In, make its work in maximum power point.Based on prediction result, active output power and the base load prediction of RES is shown as:
Distributed generation resource power output total amount are as follows:
Pres,i=Ppv,i+Pw,i (5)
S4: the electricity price of current k period is calculated by Price Mechanisms for building multi-agent system；
The energy-storage system aspect of model is as follows:
In formula,For energy-storage system initial state of charge,It is expected charging capacity for energy-storage system,For energy-storage system electricity
Tankage,For the charge power of Regional Energy internet i energy-storage system,For putting for Regional Energy internet i energy-storage system
Electrical power；
Assuming that all energy-storage system Li-ion batteries piles having the same, and the charge/discharge power in the single period
Be considered as it is constant, therefore, the model of energy-storage system battery and constraint are established as follows:
In formula,It is illustrated respectively in the SOC state of time period t+1 and t moment,Indicate the energy storage electricity in t moment
Pond power, Mi,Bi,Ci and DiRespectively indicate sytem matrix, input matrix, output matrix and feedforward matrix；
In formula,It is illustrated respectively in the charge-discharge electric power of time t energy-storage system, ηchAnd ηdchRespectively indicate charging/
Discharging efficiency；
Gas Turbine Generating Units are higher for energy internet system efficiency, natural gas energy resource made full use of, to environment
Less pollution is expressed as follows Gas Turbine Output:
In formulaFor the generated output of t period energy internet i gas turbine；For the maximum generation function of gas turbine
Rate；For the waste heat regenerative power of t period energy internet i gas turbine；ηcAnd ηrFor the generating efficiency of gas turbine and remaining
Heat recovery efficiency；λgtFor gas consumption rate, λgasFor heating value of natural gas, 9.7kWh/m is taken3；
It is transaction core with electric power, in energy Internet market, the purpose for participating in electricity price Agent in the net bidded is logical
The Bidding Strategiess for crossing rationality obtain maximum benefit；
Wherein electricity price Agent optimization problem in netting is indicated are as follows:
In formula, P is electricity price Agent optimization aim in rolling time horizon in netting, interaction electricity price, r as in netb, rsRespectively
The inside power purchase price and internal sale of electricity price being in a few days arranged；a1And a2It is power-balance with reference to electricity price, respectively corresponds sale of electricity region
Electricity price when energy internet and power purchase energy internet net load are zero；In addition, in formulaFour variables
Expression-form it is as follows:
In formula, PLoadLoad, U are adjusted for energy internet igridFor energy internet i interaction power；
S5: building energy local area network Power Balance Model guarantees that the equilibrium of supply and demand in energy local area network, process are as follows:
Energy local area network Power Balance Model is constructed, each composition portion of supply side and Demand-side is in energy local area network
Point, the electrical power balance model of available i-th of energy local area network:
Wherein,For energy local area network i and net the interaction power between interior energy local area network；To convey linear heat generation rate
Constraint；
S6: being based on Model Predictive Control, minimizes single energy local area network operating cost, repeats step S2~S5 process such as
Under:
Single energy local area network runs minimized totle drilling cost, and optimization problem of the single energy local area network in rolling time horizon can
To be expressed as a quadratic programming problem:
In formula: τ is optimization rolling time horizon length；ugrid, Qs, QHX, QACRespectively energy interaction power, energy storage residue are held
Amount, heating gas turbine waste heat, cooling gas turbine waste heat；A, B, C, D are respectively energy storage residual capacity, gas turbine hair
Electrical power heats the flexible constraint coefficient for using gas turbine waste heat, cooling gas turbine waste heat；
S7: electricity price Agent receives load management Agent in netting and the energy measures Agent scheduling information, dynamic by non-cooperation
State betting model maximizes each participant's income, determines that itself electricity price obtains strategy set in real time；
Further, the step S7 process is as follows:
S71: establishing betting model, and according to energy internet, optimization problem establishes mixed integer model in rolling time horizon,
Betting model will be described below:
Participant is electricity price Agent in the net in set N+, and each participant contains distributed energy, energy-storage system；
Strategy: for any i ∈ N+, within the k period,For all participant's action collections；The strategy taken: including
The strategy that distributed energy power output, all kinds of demand loads and other participants are taken.Optimal Operation Strategies maximize each
Participant i (i ∈ N+) income, be expressed as ρi, it is assumed that ρiFor possible strategy collection；
Income: for measuring each participant's gross profit, the income of each participant i is maximized, U is expressed asi；
Set of strategies A is given from the abovei={ A1,A2,…AN, when following situations is set up:
Wherein A* is to update the later set of set of strategies, and strategic vector A* is referred to as Nash Equilibrium point (Nash
Equilibrium, NE), any Regional Energy internet is unable to improve respective income by unilaterally changing strategy；
S72: tactful PiFor i-th of energy internet interaction electricity price, the Electricity Price Strategy collection of energy internet i is combined into Ai, and Ai
=P | 0≤P≤Pmax, PmaxInteractive electricity price can be reported for maximum, therefore AiFor a compact convex subset, and participant is in gambling process
In, sale of electricity strategy P there will necessarily be, therefore set AiNon-empty；
Prove UiIt (A) is concave function, then there are a Pure strategy nash equilibria points by S；To Ui(A) secondary derivation is carried out, secondly
Subderivative is as follows:
Due to rb,λb,PLoad,i(i=1,2,3 ..., N) is non-negative, i.e.,Therefore UiIt is recessed, non-cooperative game
There are Pure strategy nash equilibria points by problem S；
S8: after the completion of optimization, strategy set is obtained, and calculate whether reach Nash Equilibrium；Step S3~S7 is repeated, when
Determine that termination game obtains multiple-energy-source microgrid and optimizes the method for operation when obtaining preferred plan；
Optimum results comparative analysis:
In order to intuitively verify the effect that invention mentions strategy, the following 3 kinds of modes of emulation:
Case 1: being not optimized mode, and each energy local area network generates electricity at full capacity without power interaction, generating equipment.
Case 2: not considering that game optimizes, only considers the interaction of energy local area network power.
Case 3: the proposed optimal operation strategy based on non-cooperative game.
Scene analysis is designed as 4 different energy local area network structures, and the supply side of each energy LAN system is by light
Volt, blower, gas turbine, energy storage and other power grids composition, Demand-side are supplied by base load and electric refrigerating machine, wherein each
Energetic interaction between energy internet is completed by a single busbar, and the energy internet net load after interaction passes through single mother again
Line is interacted with external electrical network.Each energy local area network system nominal photovoltaic, wind power output power, stored energy capacitance and combustion gas wheel
Machine capacity is as shown in table 1 below, and energy conversion parameter is as shown in table 2.Wherein energy local area network bus power transmission capacity is
4000kW, energy storage maximum charge-discharge electric power are 2000kW.
| Project | Photovoltaic/kW | Wind-powered electricity generation/kW | Energy storage/kWh | Gas turbine capacity W |
| Energy local area network 1 | 3000 | 2500 | 13000 | 2000 |
| Energy local area network 2 | 3300 | 3550 | 13000 | 2000 |
| Energy local area network 3 | 4000 | 3900 | 13000 | 1000 |
| Energy local area network 4 | 3500 | 3750 | 13000 | 1000 |
Table 1
Table 2
Each energy local area network scene power output is as shown in Fig. 2, Fig. 2 (a) is that 4 energy local area network photovoltaic power outputs described in case are bent
Line, Fig. 2 (b) are the blower power curve of 4 energy local area networks.Fig. 3 is hot and cold load curve.
As shown in figure 4, interaction electricity price is determined by mutual game between multizone energy internet, by successive ignition,
Electricity price finally tends towards stability value, and all participants select not changing itself strategy, and respective interests reach maximization.Each ginseng
Optimal policy is obtained by game with person, this convergence process is repeated and is finally reached Nash Equilibrium.
As shown in Figure 5, case 3 reduces 82.44% and 29.22% relative to case 1,2 respectively in terms of peak-valley difference,
Reduce 80.05% and 27.08% on stability bandwidth, reduces the power difference of energy internet, improve the stability of system.
In addition, mode 2,3, relative to mode 1, energy utilization rate, which has, to be obviously improved.
It will be appreciated from fig. 6 that energy-storage system is actively contributed with gas turbine when new energy undercapacity, and each energy is mutual
Networking actively carries out power interaction, thus systems stabilisation load fluctuation.Wherein energy-storage system plays particularly significant with gas turbine
Effect.When Gas Turbine Output is smaller, UTILIZATION OF VESIDUAL HEAT IN amount is also smaller, and thermic load is mainly provided by gas fired-boiler at this time, cold negative
Lotus is mainly provided by electric refrigerating machine；When Gas Turbine Output is higher, thermic load is mainly provided by gas turbine, and utilizes remaining
Refrigeration heat meets refrigeration duty demand, and system overall operation efficiency and fuel availability all get a promotion.
Table 3
The data of analytical table 3 are available to draw a conclusion:
1) case 2,3 is significantly improved relative to the case 1 being not optimised, distributed energy utilization rate, abandonment light
Close to 0, photoelectricity subsidy improves 4.85% and 4.82% respectively for loss.In addition, the charge and discharge electrical loss of the opposite case 2 of case 3,
Electric energy loss, O&M expense are all slightly promoted, and illustrate that optimization process actively dispatches energy-storage system, and to economic influence very little,
Optimized Operation scheme is more reliable.
2) for case 3 relative to case 1,2, total revenue improves 290.06% and 123.31% respectively, illustrates that non-cooperation is rich
Rolling optimization process is played chess to increase significantly to the economy of energy internet.
3) compared with case 1,11.01% and 4.52% has been respectively increased in case 2,3 natural gas expenses, and gas turbine is flat
19.92% and 15.11% has been respectively increased in equal cost of electricity-generating, shows that case 2,3 in optimization process, not only relies on combustion gas wheel
Machine, but select other modes heat supply, cooling supply.Case 3 and case 2 compare, and the increase expense of natural gas is less, illustrates to pass through net
Interior electricity price Agent can adjust operation reserve in time, and multiple-energy-source is promoted to play an active part in system operation.
Consider the uncertainty of energy internet system operation, propose Regional Energy internet optimization operation control strategy,
Various energy resources equipment in energy internet is modeled.It is minimized inside each energy local area network based on Model Predictive Control
Operating cost introduces non-cooperative game, establishes novel Price Mechanisms for energy interaction, by iterative calculation, game, which reaches, to be received
Assorted equilibrium shows that system net load fluctuation rate can be effectively reduced by mentioning optimization operation control strategy, reduce fortune by case
Row economic cost improves energy internet system reliability and economy.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, those skilled in the art can be by this specification
Described in different embodiments or examples be combined.
Although having shown and having described the embodiment of the present invention above, it is to be understood that this specification embodiment
The content is only enumerating to the way of realization of inventive concept, and protection scope of the present invention is not construed as being only limitted to
The concrete form that embodiment is stated, protection scope of the present invention also include that those skilled in the art conceive institute's energy according to the present invention
The equivalent technologies mean enough expected.
Claims (7)
1. a kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology, which is characterized in that the side
Method the following steps are included:
S1: system initialization considers discrete time model, sets Best Times for 24 hours, to carry out sliding-model control, is divided into T
There is k ∈ { 1,2 ..., T } any kth time in period, and the when a length of Δ t of kth time period；
S2: defining multiple agent MAS system, including load management Agent, the energy measure Agent and nets interior electricity price Agent；
S3: to "flop-out" method after use, RESs output and user's base load prediction model are established；
S4: multi-agent system information is obtained, the electricity price of current k period is calculated by Price Mechanisms；
S5: building energy local area network Power Balance Model guarantees the equilibrium of supply and demand in energy local area network；
S6: being based on Model Predictive Control, minimizes single energy local area network operating cost, repeats step S1~S4；
S7: electricity price Agent receives load management Agent in netting and the energy measures Agent scheduling information, rich by non-cooperation dynamic
Model is played chess, each participant's income is maximized, determines that itself electricity price obtains strategy set in real time；
S8: after the completion of optimization, strategy set is obtained, and calculate whether reach Nash Equilibrium；Step S2~S6 is repeated, when having determined that
When obtaining preferred plan, game is terminated, multiple-energy-source microgrid is obtained and optimizes the method for operation.
2. a kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology as described in claim 1,
It is characterized in that, constructing energy internet MAS system structure in the step S2, defining the intelligent body of three types, it may be assumed that
(1) a kind of load management Agent, intelligent body for Demand-side load flow direction and service condition, predicts user side multipotency
Load uses, and obtains region internal loading service condition in time, makes Rational Decision distribution load flow direction；
(2) energy measures Agent: a kind of corresponding to non-renewable energy side distributed energy power generation, the intelligence of cooling heating and power generation system
Body, all kinds of workload demands of real-time monitoring user, Regional Energy measure Agent and receive distributed energy power output prediction, monitor each
Micro- source power output situation, adjusts energy storage and power supply device power generation/heat supply/refrigerating state, in real time to meet Regional Energy internet
User side energy requirement；
(3) electricity price Agent in net: being a kind of energetic interaction Price Mechanisms intelligent body between Regional Energy internet；Needle
To the information exchange and energetic interaction between different zones, hold the local information that indigenous energy measures Agent transmission, based on non-
Cooperate Dynamic Game Model, establishes the Price Mechanisms between a kind of multizone energy.
3. it is as claimed in claim 1 or 2 a kind of based on multiple agent decentralization multiple-energy-source microgrid optimizing operation method, it is special
Sign is, in the step S3, determines prediction probability of error distribution according to historical data, random distribution error is obtained, according to defeated
RES stochastic variable is converted to output power by characteristic curve out, indicates distributed power output prediction error using Normal probability distribution；
New energy power output is shown in predicted value time series table of contributing of the following T period, sets out field of force scape For
Power generating value of the scene i in moment T, and scene ωiProbability of happening be Pi, retain scene with final before the reduction of scene set
Probability metrics minimum between set is expressed as follows:
In formula, α indicates the scene set finally deleted after scene reduction, and number of scenes is 3000, and initialization retains collection S=
{ω0, ω1, ωi,ωs, addition and the smallest another scene of its probability metrics are concentrated actually abandoning, wherein changing and abandoning
Collect nearest scene ωlProbability is expressed as p (ω 'l)=p (ωl)+p(θk), reach requirement until abandoning the contained number of scenes of collection；
For preferably analyzing system performance, MPPT maximum power point tracking method (MTTP) is applied to new energy and is gone out in Force system, is made
Its work is based on prediction result in maximum power point, and active output power and the base load prediction of RES is shown as:
Distributed generation resource power output total amount are as follows:
Pres,i=Ppv,i+Pw,i (5)。
4. a kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology as claimed in claim 3,
It is characterized in that, the step S4 process is as follows:
The energy-storage system aspect of model is as follows:
In formula,For energy-storage system initial state of charge,It is expected charging capacity for energy-storage system,For energy-storage system battery appearance
Amount, Pi cFor the charge power of Regional Energy internet i energy-storage system, Pi dFor the electric discharge function of Regional Energy internet i energy-storage system
Rate；
Assuming that all energy-storage system Li-ion batteries piles having the same, and the charge/discharge power in the single period is recognized
For be it is constant, therefore, the model of energy-storage system battery and constraint are established as follows:
In formula,It is illustrated respectively in the SOC state of time period t+1 and t moment, Pi tIndicate the energy-storage battery function in t moment
Rate, Mi,Bi,Ciand DiRespectively indicate sytem matrix, input matrix, output matrix and feedforward matrix；
In formula,It is illustrated respectively in the charge-discharge electric power of time t energy-storage system, ηchAnd ηdchRespectively indicate charge/discharge
Efficiency；
Gas Turbine Generating Units are higher for energy internet system efficiency, make full use of natural gas energy resource, environmental pollution
It is smaller, Gas Turbine Output is expressed as follows:
In formulaFor the generated output of t period energy internet i gas turbine；For the maximum power generation of gas turbine；For the waste heat regenerative power of t period energy internet i gas turbine；ηcAnd ηrIt is returned for the generating efficiency and waste heat of gas turbine
It produces effects rate；λgtFor gas consumption rate, λgasFor heating value of natural gas, 9.7kWh/m is taken3；
It is transaction core with electric power, in energy Internet market, the purpose for participating in electricity price Agent in the net bidded is to pass through reason
Property Bidding Strategiess obtain maximum benefit；
Wherein electricity price Agent optimization problem in netting is indicated are as follows:
In formula, P is electricity price Agent optimization aim in rolling time horizon in netting, interaction electricity price, r as in netb, rsRespectively in a few days
The inside power purchase price of setting and internal sale of electricity price；a1And a2It is power-balance with reference to electricity price, respectively corresponds sale of electricity Regional Energy
Electricity price when internet and power purchase energy internet net load are zero；In addition, in formulaThe table of four variables
It is as follows up to form:
In formula, PLoadLoad, U are adjusted for energy internet igridFor energy internet i interaction power.
5. a kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology as claimed in claim 4,
It is characterized in that, the step S5 process is as follows:
Energy local area network Power Balance Model is constructed, each component part of supply side and Demand-side is in energy local area network,
The electrical power balance model of available i-th of energy local area network:
Wherein,For energy local area network i and net the interaction power between interior energy local area network；For pipeline power constraint.
6. a kind of multiple-energy-source microgrid decentralization Optimization Scheduling based on multi-agent Technology as claimed in claim 5,
It is characterized in that, the step S6 process is as follows:
S61: single energy local area network runs minimized totle drilling cost, and optimization problem of the single energy local area network in rolling time horizon can
To be expressed as a quadratic programming problem:
In formula: τ is optimization rolling time horizon length；ugrid, Qs, QHX, QACRespectively energy interaction power, energy storage residual capacity, system
Heat gas turbine waste heat, cooling gas turbine waste heat；A, B, C, D are respectively energy storage residual capacity, gas turbine power generation function
Rate heats the flexible constraint coefficient for using gas turbine waste heat, cooling gas turbine waste heat.
7. a kind of multiple-energy-source microgrid decentralization Optimized Operation side based on multi-agent Technology as claimed in claim 1 or 2
Method, which is characterized in that the step S7 process is as follows:
S71: establishing betting model, and according to energy internet, optimization problem establishes mixed integer model in rolling time horizon, below
Betting model will be described:
Participant is electricity price Agent in the net in set N+, and each participant contains distributed energy, energy-storage system；
Strategy: for any i ∈ N+, within the k period,For all participant's action collections；The strategy taken: including distribution
The strategy that formula energy power output, all kinds of demand loads and other participants are taken, Optimal Operation Strategies maximize each participation
Person i (i ∈ N+) income, be expressed as ρi, it is assumed that ρiFor possible strategy collection；
Income: for measuring each participant's gross profit, the income of each participant i is maximized, U is expressed asi；
Set of strategies A is given from the abovei={ A1,A2,…AN, when following situations is set up:
Wherein A* is to update the later set of set of strategies, and strategic vector A* is referred to as Nash Equilibrium point, any Regional Energy internet
It is unable to improve respective income by unilaterally changing strategy；
S72: tactful PiFor i-th of energy internet interaction electricity price, the Electricity Price Strategy collection of energy internet i is combined into Ai, and Ai={ P
|0≤P≤Pmax, PmaxInteractive electricity price can be reported for maximum, therefore AiFor a compact convex subset, and participant is in gambling process,
Sale of electricity strategy P there will necessarily be, therefore set AiNon-empty；
Prove UiIt (A) is concave function, then there are a Pure strategy nash equilibria points by S；To Ui(A) secondary derivation is carried out, it is secondary to lead
Number is as follows:
Due to rb,λb,PLoad,i(i=1,2,3 ..., N) is non-negative, i.e.,Therefore UiIt is recessed, non-cooperative game problem S
There are Pure strategy nash equilibria points.
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