CN103632205A - Optimized electric-vehicle dispatching method considering wind-electricity and load uncertainty - Google Patents
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Abstract
The invention discloses an optimized electric-vehicle dispatching method considering wind-electricity and load uncertainty, which comprises the following steps of constructing an energy-saving emission-reducing dispatching model with uncertainty by using an electrical power system with a PHEV (Plug-in Hybrid Electric Vehicle), wind electricity and fire electricity as a researching object; firstly decomposing the random process of the uncertainty into a plurality of typical discrete probability scenes by adopting a multi-scene simulating technology, decomposing optimized dispatching taking energy saving and CO2 emission as targets into 24 working agents by adopting a multi-agent system technology, calculating a solution set by adopting a genetic algorithm, taking charge of the dynamic coupling dispatching among the working agents by cooperative agents so as to meet the dynamic balance constraint of the system, and finally realizing the effective coordination between carbon emission and energy saving through weight adjustment. The model established by the invention is effective and feasible, peak load shifting can be effectively realized through the PHEV, the absorption of the wind electricity is promoted, the action of loading dispatching is exerted, and the effective compromise between energy saving and emission reduction can be realized by reasonably selecting the weights of energy-saving and carbon-emitting targets.
Description
Technical field
The invention belongs to power system optimal dispatch field, relate to wind-powered electricity generation and process with load is uncertain, electric automobile discharge and recharge control, relate in particular to probabilistic processing.
Background technology
Along with energy scarcity and environmental pollution contradiction, clean reproducible energy is subject to extensive concern.Electric automobile is at energy-saving and emission-reduction, containment climate warming and ensure that oil supply safely etc. has the incomparable advantage of orthodox car in aspect, is subject to the extensive concern of national governments, car manufactures and energy enterprise.The technical report that a American National regenerative resource laboratory provides shows that PHEV can reduce the discharge of CO2 in a large number.Just because of PHEV, have huge energy-saving and emission-reduction potentiality, its research and extension has become the focus that various countries pay close attention to.Can PHEV effectively use the utilization that is somewhat dependent upon regenerative resource.Carry out positive charging (V2G) and control, the charge capacity of electric automobile is provided by regenerative resource completely, forms regenerative resource and PHEV effective supplement.Can realize the tracking that charging load is exerted oneself and changed regenerative resource, promote regenerative resource to receive.
Under traditional mode, containing the Optimized Operation of wind-powered electricity generation and electric automobile, do not consider the uncertainty of wind-powered electricity generation and load, all adopt Deterministic Methods to solve, do not conform to the randomness characteristic of wind-powered electricity generation, load, can not truly reflect actual schedule situation.Adopt many scenario simulations technology that wind-powered electricity generation, probabilistic stochastic process of loading are decomposed into some typical probability scenes, make it accurately reflect the electric power Optimized Operation stochastic process containing electric automobile and wind-powered electricity generation.Then, adopt multi-agent system technology that 1 day 24 period was corresponded to 24 work agencies, be responsible for the static scheduling between thermoelectricity, wind-powered electricity generation and electric automobile, coordinating agent is responsible for 24 dynamic coordinates between work agency, thereby make energy-optimised wind-powered electricity generation and the electric automobile of utilizing, make the more approaching reality of scheduling result.
Summary of the invention
In order to make up the defect of traditional mode, the present invention proposes a kind of consideration wind-powered electricity generation and load is probabilistic containing electric automobile Optimized Operation strategy, take containing the electric system of hybrid-electric car (hereinafter to be referred as PHEV), wind-powered electricity generation and thermoelectricity of can networking is research object, built and taken into account probabilistic energy-saving and emission-reduction scheduling model, considered the uncertainty of wind-powered electricity generation and load, PHEV discharges and recharges control, the harmonizing of PHEV and wind-powered electricity generation.First adopt many scenario simulations technology that probabilistic stochastic process is decomposed into some typical discrete probability scenes, adopt on this basis multi-agent system technology (hereinafter to be referred as MAS) technology that Optimized Operation is divided into 24 work agencies, work agency is responsible for the static scheduling of each period, coordinating agent is responsible for Dynamic Coupling scheduling between work agency, finally by weight regulate realized carbon emission and energy-conservation between effective coordination.
The object of the invention is to be achieved through the following technical solutions:
Consideration wind-powered electricity generation and load are probabilistic containing an electric automobile Optimization Scheduling, comprise the steps:
(1) accept the following 24 hours workload demand data of system that electrical network machine unit scheduling center draws; Receive wind energy turbine set to the wind-powered electricity generation big or small predicted data of exerting oneself, comprise the bound interval that prediction wind-powered electricity generation size and wind-powered electricity generation are exerted oneself; Receive the correlation properties data of PHEV; The machine unit characteristic data that report according to each generating plant draw the restrain condition of each unit;
(2) the uncertain information of exerting oneself, loading according to Large Scale Wind Farm Integration, adopt many scenario simulations technology to choose some typical system running states and use and carbon emission Optimized Operation for systematic running cost, can more accurately reflect the electric power Optimized Operation containing PHEV and wind-powered electricity generation.
(3) data and the some typical scenes that according to first and second step, receive, power system optimal dispatch is carried out to modeling, according to service requirement select target function and constraint condition, comprise equality constraint and inequality constrain condition, form mixed integer nonlinear programming problem;
(4) the mixed integer nonlinear programming problem producing according to previous step, draws and considers that wind-powered electricity generation and the probabilistic electric automobile energy saving that contains of load reduce discharging multiple objective function and corresponding constraint condition.Constraint condition is: containing the system power Constraints of Equilibrium of PHEV, the Constraints of Equilibrium of PHEV, containing spinning reserve constraint, the PHEV of PHEV discharge and recharge the exerting oneself of total amount constraint, fired power generating unit self, climb, the constraint such as minimum startup-shutdown.
(5) the energy-saving and emission-reduction multiple goal of previous step is passed through to compose weight to each target, multi-objective problem is converted into new single goal problem, and regulates the importance of each target in energy-saving and emission-reduction general objective by weight.
(6) whole day is divided into 24 periods, it within 1 hour, is 1 scheduling slot, by 24 work, acting on behalf of A1-A24 is responsible for, be to correspond to 1 work agency each period, each work is acted on behalf of inner utilization genetic algorithm and is solved, then by Collaborative Agent, 24 work agencies' solution is coordinated, finally obtained the solution of Optimized Operation in a day.
The invention has the beneficial effects as follows: the present invention studies considering the uncertain lower energy-saving and emission-reduction scheduling strategy containing PHEV and wind-powered electricity generation, has set up and has contained PHEV and the probabilistic energy-saving and emission-reduction model of wind-powered electricity generation.And for the randomness of loading and wind-powered electricity generation is exerted oneself, adopting many scenario simulations technology will load discrete with wind-powered electricity generation variable is some typical scenes, then adopts MAS technology to solve the scheduling problem of discretize.The agency that works in MAS is responsible in each scheduling slot, the adjusting that thermoelectricity is exerted oneself, the complementation scheduling of wind-powered electricity generation and PHEV, the coordination of wind-powered electricity generation, thermoelectricity and PHEV.Coordinating agent is responsible for coordinating 24 work agencies, realizes dynamic Optimized Operation.Example shows, the model of setting up is effective and feasible, and PHEV can effectively realize the peak load that disappears of load, promotes receiving of wind-powered electricity generation, the effect of performance load scheduling.The weight of and carbon emission target energy-conservation by Rational choice, can realize energy-conservation with reduce discharging between effectively trade off.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention;
Fig. 2 is the scene distribution figure of wind-powered electricity generation;
Fig. 3 is the scene distribution figure of load;
Fig. 4 is the MAS system assumption diagram of Optimized Operation strategy;
Fig. 5 is work agency's synergy schematic diagram.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in further detail.
Consideration wind-powered electricity generation and load are probabilistic containing an electric automobile Optimized Operation strategy, comprise the steps:
(1) accept the following 24 hours workload demand data of system that electrical network machine unit scheduling center draws; Receive wind energy turbine set to the wind-powered electricity generation big or small predicted data of exerting oneself, comprise the bound interval that prediction wind-powered electricity generation size and wind-powered electricity generation are exerted oneself; Receive the correlation properties data of PHEV; The machine unit characteristic data that report according to each generating plant draw the restrain condition of each unit;
(2) the uncertain information of exerting oneself, loading according to Large Scale Wind Farm Integration, adopt many scenario simulations technology to choose some typical system running states and use and carbon emission Optimized Operation for systematic running cost, can more accurately reflect the electric power Optimized Operation containing PHEV and wind-powered electricity generation.
(3) data and the some typical scenes that according to first and second step, receive, power system optimal dispatch is carried out to modeling, according to service requirement select target function and constraint condition, comprise equality constraint and inequality constrain condition, form mixed integer nonlinear programming problem;
(4) the mixed integer nonlinear programming problem producing according to previous step, draws and considers that wind-powered electricity generation and the probabilistic electric automobile energy saving that contains of load reduce discharging multiple objective function and corresponding constraint condition.Constraint condition is: containing the system power Constraints of Equilibrium of PHEV, the Constraints of Equilibrium of PHEV, containing spinning reserve constraint, the PHEV of PHEV discharge and recharge the exerting oneself of total amount constraint, fired power generating unit self, climb, the constraint such as minimum startup-shutdown.
(5) the energy-saving and emission-reduction multiple goal of previous step is passed through to compose weight to each target, multi-objective problem is converted into new single goal problem, and regulates the importance of each target in energy-saving and emission-reduction general objective by weight.
(6) whole day is divided into 24 periods, it within 1 hour, is 1 scheduling slot, by 24 work, acting on behalf of A1-A24 is responsible for, be to correspond to 1 work agency each period, each work is acted on behalf of inner utilization genetic algorithm and is solved, then by Collaborative Agent, 24 work agencies' solution is coordinated, finally obtained the solution of Optimized Operation in a day.
Below in conjunction with accompanying drawing, embodiments of the invention are elaborated, process flow diagram of the present invention is as shown in Figure 1.
The discrete probability distribution collection that load and wind-powered electricity generation are exerted oneself can be expressed as:
The set of load and all scenes of wind-powered electricity generation is used
represent,
Suppose wind-powered electricity generation, load error obedience standardized normal distribution, and according to statistics, the probability distribution of wind-powered electricity generation and load is divided into 5 scenes, the scene distribution of wind-powered electricity generation and load is shown in Fig. 2,3.
Corresponding objective function is:
Adopt many scenario simulations technology, consider that probabilistic fuel cost function of load and wind-powered electricity generation develops into:
Fired power generating unit carbon emission scale is shown the quadratic function form of unit output,
Therefore consider energy-saving and emission-reduction and uncertain Optimal Operation Model is:
In formula
=1/0 represents that unit is in operation/stopped status;
start-up cost for unit;
for time hop count,
for unit number.
with
it is the corresponding weight of operating cost and carbon emission.
Corresponding constraint condition is:
1) contain the system power Constraints of Equilibrium of PHEV
PHEV electric discharge
PHEV charging
2) Constraints of Equilibrium of PHEV
3) spinning reserve containing PHEV retrains
PHEV electric discharge
PHEV charging
the maximal value of exerting oneself for fired power generating unit,
capacity for each PHEV.
4) PHEV discharges and recharges total amount constraint
Because all PHEV can not discharge and recharge at synchronization simultaneously, in order to guarantee the safe and reliable operation of system, the quantity that discharges and recharges that controls PHEV is necessary,
for
the sum of period PHEV maximum charge-discharge.
5) unit output bound constraint
6) fired power generating unit minimax units limits
7) fired power generating unit climbing constraint
During unit emersion power
Unit falls while exerting oneself
8) the minimum startup-shutdown time-constrain of fired power generating unit
In formula:
,
for fired power generating unit
maximum, minimum load;
for
period system spinning reserve capacity;
with
be respectively unit
the limit value of active power ascending amount and slippage;
for unit
arrive
period move continuously (
for on the occasion of) or shut down continuously (
for negative value) time hop count;
,
be respectively unit
minimum working time and minimum stop time.Fig. 4 is the MAS system assumption diagram of Optimized Operation strategy; Fig. 5 is work agency's synergy schematic diagram.
In sum, the consideration wind-powered electricity generation that we propose and load are probabilistic containing electric automobile Optimized Operation strategy, adopt many scenario simulations technology and multi-agent system technology, in wind-powered electricity generation and the probabilistic situation of load, realized and significantly reduced carbon emission amount, and effectively realize the peak load that disappears of load, promote receiving of wind-powered electricity generation, the effect of performance load scheduling.
It should be noted that, in process flow diagram or any process of otherwise describing at this or method describe and can be understood to, represent to comprise that one or more is for realizing the module of code of executable instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiments of the invention comprises other realization, wherein can be not according to order shown or that discuss, comprise according to related function by the mode of basic while or by contrary order, carry out function, this should be understood by embodiments of the invention person of ordinary skill in the field.
In sum, the present invention is that a kind of many scenario simulations technology and multi-agent system technology of adopting realizes considering the probabilistic Optimized Operation containing electric automobile of wind-powered electricity generation and load.Present technique can be used for the processing of other uncertain problems, as the uncertainty optimization scheduling containing other new forms of energy such as sun power, the electric system that the present invention be take containing PHEV, wind-powered electricity generation and thermoelectricity is research object, built and taken into account probabilistic energy-saving and emission-reduction scheduling model, considered the uncertainty of wind-powered electricity generation and load, can the network control that discharges and recharges of hybrid-electric car (PHEV), the harmonizing of PHEV and wind-powered electricity generation.Adopt many scenes technology that stochastic process is decomposed into some typical discrete scene more accurately.By multi-agent system technology, realize wind-powered electricity generation subsequently, the harmonizing of electric automobile and thermoelectricity, realizes energy saving of system and reduction of discharging and social benefit and maximizes, and more tallies with the actual situation.
More than show and described ultimate principle of the present invention, principal character and advantage.The technician of the industry should understand; the present invention is not restricted to the described embodiments; that in above-described embodiment and instructions, describes just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.The claimed scope of the present invention is defined by appending claims and equivalent thereof.
Claims (8)
1. consider wind-powered electricity generation and the probabilistic electric automobile Optimization Scheduling that contains of load for one kind, it is characterized in that: take containing the electric system of hybrid-electric car, wind-powered electricity generation and thermoelectricity of can networking is research object, structure is taken into account probabilistic energy-saving and emission-reduction scheduling model, consider the uncertainty of wind-powered electricity generation and load, the hybrid-electric car that can network discharge and recharge control, the harmonizing of can network hybrid-electric car and wind-powered electricity generation; First adopt many scenario simulations technology that probabilistic stochastic process is decomposed into some typical discrete probability scenes, on this basis, employing multi-agent system technology will be with energy-conservation and CO
2discharge is acted on behalf of for the Optimized Operation of target is decomposed into 24 work, and work agency is responsible for the static scheduling of each period, adopts genetic algorithm for solving collection; Collaborative Agent is responsible for Dynamic Coupling scheduling between work agency, makes system meet mobile equilibrium constraint; Finally by weight regulate realize carbon emission and energy-conservation between effective coordination; Specifically comprise the steps:
(1) accept the following 24 hours workload demand data of system that electrical network machine unit scheduling center draws; Receive wind energy turbine set to the wind-powered electricity generation big or small predicted data of exerting oneself, comprise the bound interval that prediction wind-powered electricity generation size and wind-powered electricity generation are exerted oneself; Can the network correlation properties data of hybrid-electric car of reception; The machine unit characteristic data that report according to each generating plant draw the restrain condition of each unit;
(2) the uncertain information of exerting oneself, loading according to Large Scale Wind Farm Integration, adopts many scenario simulations technology to choose some typical system running states and uses and carbon emission Optimized Operation for systematic running cost;
(3) data and the some typical scenes that according to first and second step, receive, power system optimal dispatch is carried out to modeling, according to service requirement select target function and constraint condition, comprise equality constraint and inequality constrain condition, form mixed integer nonlinear programming problem;
(4) the mixed integer nonlinear programming problem producing according to previous step, draws and considers that wind-powered electricity generation and the probabilistic electric automobile energy saving that contains of load reduce discharging multiple objective function and corresponding constraint condition;
(5) the energy-saving and emission-reduction multiple objective function of previous step is passed through to compose weight to each target, multi-objective problem is converted into new single goal problem, and regulates the importance of each target in energy-saving and emission-reduction general objective by weight;
(6) whole day is divided into 24 periods, it within 1 hour, is 1 scheduling slot, by 24 work, acting on behalf of A1-A24 is responsible for, be to correspond to 1 work agency each period, each work is acted on behalf of inner utilization genetic algorithm and is solved, then by Collaborative Agent, 24 work agencies' solution is coordinated, finally obtained the solution of Optimized Operation in a day.
2. a kind of consideration wind-powered electricity generation according to claim 1 and load are probabilistic containing electric automobile Optimization Scheduling, it is characterized in that: described many scenario simulations technology adopts discrete probability distribution to replace the uncertainty of stochastic variable, the generation of scene is through 2 steps: the probability distribution that 1) obtains stochastic variable by probabilistic method; 2) adopt approximate method, reducing as far as possible under the prerequisite of information loss, by the former probability distribution discretize of stochastic variable;
The discrete probability distribution set representations that load and wind-powered electricity generation are exerted oneself is:
(1)
for uncertain wind-powered electricity generation is in scene
under the value of exerting oneself and corresponding probability,
scene sum for wind-powered electricity generation;
The set of load and all scenes of wind-powered electricity generation is used
represent,
(6)
3. probabilistic containing electric automobile Optimization Scheduling according to a kind of consideration wind-powered electricity generation described in claim 1 and load, it is characterized in that: described consideration wind-powered electricity generation and load are probabilistic adopts many scenario simulations technology containing electric automobile energy saving reduction of discharging multiple objective function, considers that probabilistic fuel cost function of load and wind-powered electricity generation develops into:
4. probabilistic containing electric automobile Optimization Scheduling according to a kind of consideration wind-powered electricity generation described in claim 1 and load, it is characterized in that: considering that wind-powered electricity generation and load are probabilistic reduces discharging constraint condition containing electric automobile energy saving and is: containing can network the system power Constraints of Equilibrium of hybrid-electric car, the Constraints of Equilibrium of the hybrid-electric car that can network, containing the spinning reserve constraint of the hybrid-electric car that can network, the hybrid-electric car that can network discharge and recharge the exerting oneself of total amount constraint, fired power generating unit self, climb, the constraint of minimum startup-shutdown.
5. probabilistic containing electric automobile Optimization Scheduling according to a kind of consideration wind-powered electricity generation described in claim 1 and load, it is characterized in that: in described step (5), consider energy-saving and emission-reduction and uncertain Optimal Operation Model is:
In formula
=1/0 represents that unit is in operation/stopped status;
start-up cost for unit;
for time hop count,
for unit number,
with
the corresponding weight of operating cost and carbon emission,
6. probabilistic containing electric automobile Optimization Scheduling according to a kind of consideration wind-powered electricity generation described in claim 1 and load, it is characterized in that: each work agency of described multi-agent system technology is except existing contact with Collaborative Agent, also carry out information interchange with its adjacent agency in front and back, for ease of difference, claim the forerunner that the agency of last period is current agency, the agency of a rear period is the follow-up of current agency, and Collaborative Agent reaches the target that systematic collaboration is evolved.
7. a kind of consideration wind-powered electricity generation according to claim 1 and load are probabilistic containing electric automobile Optimization Scheduling, it is characterized in that: the agency that works described in each is responsible for the static scheduling of coordinating wind-powered electricity generation, thermoelectricity and can networking between hybrid-electric car, it is minimum that its target is the general objective of operating cost in this period and gas emissions, constraint condition is the static constraint condition under corresponding load section, and the Dynamic Coupling such as start-stop time of not considering unit retrain, adopt genetic algorithm to try to achieve a disaggregation.
8. probabilistic containing electric automobile Optimization Scheduling according to a kind of consideration wind-powered electricity generation described in claims 1 and load, it is characterized in that: the target of described Collaborative Agent is that fuel consumption and the gas total emission volumn in whole dispatching cycle is minimum, constraint condition is the Dynamic Coupling constraint in whole dispatching cycle.
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