CN104734200A - Initiative power distribution network scheduling optimizing method based on virtual power generation - Google Patents

Abstract

The invention discloses an initiative power distribution network scheduling optimizing method based on virtual power generation. The initiative power distribution network scheduling optimizing method includes the following steps: 1, building a scheduling optimization objective function; 2, obtaining constraint conditions of the scheduling optimization objective function, wherein the constraint conditions include system load balance constraint, system spinning reserving constraint, conventional-unit and demand-side resource technology contributing constraint, conventional unit climbing constraint, conventional-unit minimum running time and minimum stopping time constraint, demand-side maximum continuous controlled time constraint and electricity use mode satisfaction constraint; 3, according to the scheduling optimization objective function and the constraint conditions, determining the starting-stopping states of all the units one period by one period with the disperse particle swarm optimization based on a heuristic rule; 4, after the starting-stopping states of all the units in all the periods are calculated, on the basis of the known starting-stopping states, economically distributing the units according to the constraint conditions. In this way, initiative power distribution network scheduling optimization is achieved.

Description

A kind of active distribution network Optimization Scheduling based on virtual generating

Technical field

The invention belongs to active distribution network Optimum Scheduling Technology field, more specifically, relate to a kind of active distribution network Optimization Scheduling based on virtual generating.

Background technology

The extensive access of distributed power source, flexible load and novel energy-storing system, and the extensive use of modern power electronic device in electrical network, make power distribution network all there occurs a series of change in system configuration and operational mode.Active distribution network is as the distribution network system of Comprehensive Control distributed energy (DG, flexible load and energy storage), in whole power distribution network aspect, distributed energy is managed, not only pay close attention to local from master control, the optimization simultaneously also paying close attention to the whole power distribution network overall situation is coordinated, and concentrates access to provide new approaches for solving the extensive of distributed energy.The cooperation of active distribution network source net lotus three, how effectively the schedulable resource of Generation Side, grid side and load side is coordinated in integration, thus obtain the optimum of safety, ecnomics and enviroment benefit, and realize safe and reliable, the high-quality and efficient operation of power distribution network, be the current problem needing solution badly.

Present stage active distribution network Optimized Operation research, be mostly that the scheduling for distributed power source, electric automobile and energy-storage system is studied, or separately for the research of Demand Side Response strategy.Have studied individually the wind electricity digestion model based on Demand Side Response and the mixed-integer programming model that establishes containing wind power system generation schedule a few days ago, and utilize ILOG/CPLEX business software to solve, lack the research of the synthesis optimizing and scheduling method to conventional power unit, distributed power source (mainly comprising wind-powered electricity generation, photovoltaic generation), Demand Side Response and user power utilization satisfaction.The present invention proposes the virtual generating of integrated use and active distribution network technology, and the demand response resource of load side is equivalent to virtual generation assets, and participates in power balance adjustment as virtual robot arm.Simultaneously according to the prediction curve of exerting oneself of wind-powered electricity generation and photovoltaic generation, in conjunction with conventional power unit, establish the Model for Multi-Objective Optimization of comprehensive distributed power source, Demand-side resource and conventional power unit, scheduling problem is converted into Optimization of Unit Commitment By Improved, and utilize the dual particle cluster algorithm after improving to solve.Example shows that this dispatching method can effectively reduce power distribution network generator operation cost and environmental pollution, and can effectively reduce load peak-valley difference on the basis meeting higher user power utilization mode satisfaction.

Summary of the invention

For above defect or the Improvement requirement of prior art, the invention provides a kind of active distribution network Optimization Scheduling based on virtual generating, its object is to use virtual generating thought that the demand response resource of load side is equivalent to virtual generation assets, participate in power balance as virtual robot arm to regulate, and utilize the dual particle swarm optimization of improvement to solve, realize distributing rationally of active distribution network resource, solve in active distribution network thus and consider conventional power unit simultaneously, distributed power source, when Demand Side Response and user power utilization satisfaction, optimize the technical problem that computational speed causes being difficult to carry out scheduling controlling fast more slowly.

The invention provides a kind of active distribution network Optimization Scheduling based on virtual generating, comprise the steps:

(1) Optimized Operation target function is set up; First object function F is set up for target so that systems generate electricity operating cost is minimum ₁, set up the second objective function F so that pollutant emission total yield number is minimum for target ₂;

(2) described Optimized Operation bound for objective function is obtained; Described constraints comprises: system loading Constraints of Equilibrium, system spinning reserve retrains, the technology of conventional power unit and Demand-side resource is exerted oneself and is limited, conventional power unit Climing constant, conventional power unit minimum running time and the constraint of minimum idle time, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction retrains;

(3) Demand Side Response is equivalent to a virtual synchronous generator, according to described Optimized Operation target function and described constraints and by the start and stop state of all units of acquisition that carry out the period one by one based on the discrete particle cluster algorithm of heuristic rule;

(4) according to the start and stop state of all units, and by the continuous particle cluster algorithm based on heuristic rule, on the basis according to described constraints, economic allocation is carried out to each unit; And allocate exerting oneself of each unit according to the result of economic allocation and the final start and stop state of all each periods of unit and size of exerting oneself, realize active distribution network Optimized Operation.

Further, in step (1), described first object function is: $\min F_1=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[f_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\×u_i\left(t\right)+S_i\left(t\right)\×(1-u_i(t-1))\×u_i\left(t\right)]+\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{f}_{\mathrm{DR}}\left(P_{\mathrm{DR}}\left(t\right)\right)\×u_{\mathrm{DR}}\left(t\right);$ Described second target function is: $\min F_2=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{g}_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\×u_i\left(t\right);$

Wherein, F ₁represent systems generate electricity operating cost; T represents time dispatching cycle; N _grepresent the total number of units of conventional power unit; f _i(P _gi(t))=A _i× P _gi(t) ²+ B _i× P _gi(t)+C _i, f _irepresent i-th conventional power unit operating cost; P _girepresent the power output of i-th conventional power unit; S _irepresent i-th conventional power unit start-up and shut-down costs; u _i(t) and u _i(t-1) the start and stop state of i-th unit current time and previous moment is represented respectively; f _dRrepresent that Demand Side Response compensates total amount; f _dR(P _dR(t))=d _dR× P _dR(t), P _dRrepresent Demand-side regulating power, i.e. virtual generated output; u _dRt () represents the start and stop state of virtual generating current time; A _i, B _iand C _irepresent the operational factor of i-th conventional power unit; $S_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{Sh}}_i& T_{i,\mathrm{off},\min }\≤T_{i,\mathrm{off}}\≤T_{i,\mathrm{cs}}\\ {\mathrm{Sc}}_i& T_{i,\mathrm{off}}\≥T_{i,\mathrm{cs}}\end{array}\right.,$ Sh _irepresent i-th conventional power unit warm start cost; Sc _irepresent i-th conventional power unit cold start-up cost; T _i.off.minrepresent the minimum lasting idle time that i-th conventional power unit allows; T _i.offrepresenting that i-th conventional power unit continued the time of shutting down before certain period, if be open state before this unit, is then 0; T _{i, cs}represent the cold start-up time of conventional power unit i; d _dRfor Demand-side reduces the amount of money that every kilowatt hour electricity compensates; F ₂represent pollutant emission total yield number; g _i(P _gi(t))=a _i× P _gi(t) ²+ b _i× P _gi(t)+c _i, g _irepresent the pollutant emission equivalent of i-th conventional power unit; a _i, b _iand c _irepresent the disposal of pollutants coefficient of i-th conventional power unit.

Further, in step (2), described system loading Constraints of Equilibrium is wherein, N _dGrepresent distributed power source species number; P _dGjt () represents that jth kind distributed power source t is exerted oneself; P _lt () represents the load power of t; Described system spinning reserve is constrained to $\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{u}_i\left(t\right)P_{\mathrm{Gi},\max }+P_{\mathrm{DR},\max }\≥P_L\left(t\right)+P_{\mathrm{RL}}\left(t\right)+\underset{j=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{P}_{\mathrm{DGj}}\left(t\right),$ Wherein, P _{gi, max}represent that i-th maximum technology of conventional power unit is exerted oneself; P _{dR, max}represent that maximum available Demand-side regulates load; P _rLexpression system spinning reserve demand; expression system is exert oneself uncertainty and the newly-increased reserve capacity of reply distributed power source, considers the uncertainty of distributed electrical source power 100% probability interval, P herein _dGjbe jth kind distributed power source to exert oneself; The technology of described conventional power unit and Demand-side resource is exerted oneself and is restricted to $\left\{\begin{array}{c}{P}_{\mathrm{Gi},\min }\≤P_{\mathrm{Gi}}\left(t\right)\≤P_{\mathrm{Gi},\max }\\ 0\≤P_{\mathrm{DR}}\left(t\right)\≤P_{\mathrm{DR},\max }\end{array}\right.,$ P _{gi, min}, P _{gi, max}represent that i-th minimum, maximum technology of conventional power unit is exerted oneself respectively; Described conventional power unit Climing constant is $\begin{array}{c}{P}_i\left(t\right)-P_i(t-1)\≤r_{\mathrm{ui}}T\\ P_i(t-1)-P_i\left(t\right)\≤r_{\mathrm{di}}T\end{array},$ Wherein P _i(t) and P _i(t-1) power output of current time and previous moment i-th conventional power unit is represented respectively; r _uiand r _direpresent i-th conventional power unit power climbing speed and fall off rate respectively; Described conventional power unit minimum running time and minimum idle time are constrained to $\begin{array}{c}{T}_{i,\mathrm{on}}\≥T_{i,\mathrm{on},\min }\\ T_{i,\mathrm{off}}\≥T_{i,\mathrm{off},\min }\end{array},$ Wherein, T _{i, on}represent i-th conventional power unit continuous operating time; T _{i, off}represent i-th conventional power unit continuous idle time; T _{i, on, min}represent the minimum continuous working period that i-th conventional power unit allows; The maximum continuous controllable period of time of described Demand-side is constrained to T _dR≤ T _{dR, max}, wherein, T _dRrepresent Demand-side controllable period of time; T _{dR, max}represent the maximum continuous controllable period of time that Demand-side allows; Described power mode satisfaction is constrained to $\left\{\begin{array}{c}{m}_s\≥m_{s,\min }\\ m_s=1-\frac{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\left|\mathrm{\Δ}{q}_t\right|}{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{q}_t}\end{array}\right.;$ Wherein m _srepresent power mode satisfaction; m _{s, min}represent the minimum power mode satisfaction allowed, represent in a dispatching cycle T, before and after optimizing each period electricity knots modification absolute value and; represent in a dispatching cycle T, power consumption total before optimizing.

Further, it is characterized in that, the discrete particle cluster algorithm based on heuristic rule described in step (3) is specially:

(3.1) each conventional power unit of initialization, Demand-side characterisitic parameter, system prediction payload and distributed power source predict size of exerting oneself;

(3.2) each Unit Commitment state of random initializtion, carry out particle rapidity and the location updating of given number of times (desirable 100 times) according to existing discrete particle group velocity renewal formula and location updating formula, complete the calculating of discrete particle cluster algorithm first period start and stop state;

In discrete particle cluster algorithm, required minimum continuous working period constraint to be processed, minimum lasting idle time constraint, the heuristic modification method of maximum continuous controllable period of time constraint employing are: after certain particle position upgrades, if certain unit operation or idle time do not meet given constraint, then the running status of this unit of forcibly changing makes it meet constraint.The heuristic modification method of system spinning reserve constraint is: first sort to bad order from good according to performance driving economy to each unit according to priority method, after certain particle position upgrades, if discontented pedal system spinning reserve constraint, then successively open off-duty unit from good to bad order by performance driving economy, until the constraint of system spinning reserve is satisfied;

Simultaneously, in order to avoid all units all operate near minimum load, adopt following heuristic correction: after certain particle position upgrades, if the minimum load limit value sum of the conventional power unit run all and equivalent virtual synchronous generator is greater than system loading and distributed power source and exerts oneself 0.9 times of difference of summation, then close to the sequence of good order the unit run from bad successively by performance driving economy, until above-mentioned inequality is satisfied, thus ensure the economic allocation of unit output further;

(3.3) when to determine in dispatching cycle total, hop count T (generally getting 24), repeats step (3.2) content, until complete the calculating of discrete particle cluster algorithm whole period start and stop state.Store the final start and stop state of each conventional power unit and equivalent each period of virtual synchronous generator group.

Further, the continuous particle cluster algorithm based on heuristic rule described in step (4) is specially:

(4.1) according to above-mentioned discrete particle cluster algorithm result, each conventional power unit of initialization and each period start and stop state information of equivalent virtual synchronous generator group.

(4.2) output distribution of random initializtion first period each conventional power unit and equivalent virtual synchronous generator group.Adopt existing continuous population speed more new formula and location updating formula carry out particle rapidity and the location updating of given number of times (desirable 100 times), complete continuous particle cluster algorithm first period each unit output economic allocation calculate.

In continuous particle cluster algorithm, the exert oneself heuristic modification method of exerting oneself of restriction and Climing constant of required technology to be processed is: after certain particle position upgrades, if certain unit output size is greater than this unit maximum output limit value and this unit output value of last period and this unit maximum unit time and climbs the smaller value of exerting oneself in sum, then this unit output is forced to be smaller value in above-mentioned two values; If certain unit output size is less than this unit minimum load limit value and this unit output value of last period and this unit maximum unit time and declines higher value in the difference of exerting oneself, then this unit output is forced to be higher value in above-mentioned two values.The heuristic modification method of user power utilization mode satisfaction constraint is: after certain particle position upgrades, if the constraint of user power utilization mode satisfaction does not meet, then forces exerting oneself until meet the constraint of user power utilization mode satisfaction of the equivalent virtual synchronous generator of reduction.

System loading Constraints of Equilibrium adopts Means of Penalty Function Methods process.Processing method is: constraints is converted into penalty function, by penalty factor, penalty function and target function is combined the calculating that the fitness function becoming new participates in algorithm.If constraints does not meet, then the functional value of penalty function is a positive number; If constraints meets, then the functional value of penalty function is 0.

(4.3) repeat step (4.2) content, to exert oneself the calculating of economic allocation until complete each conventional power unit of continuous particle cluster algorithm whole period and equivalent virtual synchronous generator group.Discrete particle cluster algorithm tries to achieve the size of exerting oneself that the final start and stop state of each conventional power unit and equivalent each period of virtual synchronous generator group and continuous particle cluster algorithm try to achieve each conventional power unit and equivalent each period of virtual synchronous generator group, being the final result of Unit Combination, is also Optimized Operation result of the present invention.

In general, the above technical scheme conceived by the present invention compared with prior art, has following beneficial effect:

(1) virtual generating thought is adopted, Demand-side resource is equivalent to virtual generation assets, system power balance adjustment is jointly participated in conventional power unit, distributed power source, traditional Optimal Scheduling is converted into Optimization of Unit Commitment problem, conveniently effectively can carries out unified allocation of resources to Demand-side resource.Example shows that this dispatching method can effectively reduce system cost and environmental pollution, can also meet higher user power utilization satisfaction simultaneously.

(2) the dual particle cluster algorithm of improvement is adopted, compensate for the shortcoming that single continuous particle cluster algorithm is difficult to determine 2 state of value of Unit Commitment, this algorithm is by discrete particle cluster and continuous population decoupling zero simultaneously, adopts the method calculated by the period, solving speed is accelerated greatly.The various heuristic correction added in algorithm can make the solution of gained meet all constraints completely, the introducing of critical operator ensure that particle upgrades towards more excellent direction not losing multifarious while, thus ensureing that algorithm is while meeting high accuracy, solving speed is able to effective raising.

Accompanying drawing explanation

Fig. 1 is the active distribution network Optimized Operation principle schematic of the embodiment of the present invention;

Fig. 2 is that the utilization of the embodiment of the present invention improves dual particle swarm optimization and is optimized scheduling schematic diagram;

Fig. 3 is the discrete particle cluster algorithm flow chart based on heuristic rule of the embodiment of the present invention;

Fig. 4 is the continuous particle cluster algorithm flow chart based on heuristic rule of the embodiment of the present invention;

Fig. 5 is the wind power generation prediction curve that the embodiment of the present invention provides;

Fig. 6 is that desirable photovoltaic that the embodiment of the present invention provides is exerted oneself prediction curve;

Fig. 7 is the system total load curve that the embodiment of the present invention provides;

Fig. 8 is system loading curve comparison figure before and after the optimization that provides of the embodiment of the present invention.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each execution mode of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.

The virtual generating that the embodiment of the present invention proposes refers to using regulatable Demand-side resource as system reserve power supply, when network load value is higher, suitably reduces the electricity consumption of this type of resource, and carries out corresponding valence compensation to user.Be equivalent to increase energy output owing to reducing load, and compensate electricity price and be equivalent to cost of electricity-generating, therefore this type of Demand-side resource can be equivalent to virtual generation assets, active participate power grid regulation.

The active distribution network Optimization Scheduling of the embodiment of the present invention comprises the steps:

(1) Optimized Operation objective function F is set up ₁and F ₂.

S1: set up systems generate electricity operating cost target function:

$\left\{\begin{array}{c}\min F_1=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}[f_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\×u_i\left(t\right)+S_i\left(t\right)\×(1-u_i(t-1))\×u_i\left(t\right)]\\ +\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{f}_{\mathrm{DR}}\left(P_{\mathrm{DR}}\left(t\right)\right)\×u_{\mathrm{DR}}\left(t\right)\\ f_i\left(P_{\mathrm{Gi}}\left(t\right)\right)=A_i\×P_{\mathrm{Gi}}{\left(t\right)}^2+B_i\×P_{\mathrm{Gi}}\left(t\right)+C_i\\ S_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{Sh}}_i& T_{i,\mathrm{off},\min }\≤T_{i,\mathrm{off}}\≤T_{i,\mathrm{cs}}\\ {\mathrm{Sc}}_i& T_{i,\mathrm{off}}\≥T_{i,\mathrm{cs}}\end{array}\right.\\ f_{\mathrm{DR}}\left(P_{\mathrm{DR}}\left(t\right)\right)=d_{\mathrm{DR}}\×P_{\mathrm{DR}}\left(t\right)\end{array}\right.---\left(1\right)$

In formula, F ₁represent systems generate electricity operating cost; T represents time dispatching cycle; N _grepresent the total number of units of conventional power unit; f _irepresent i-th conventional power unit operating cost; P _girepresent the power output of i-th conventional power unit; S _irepresent i-th conventional power unit start-up and shut-down costs; u _i(t) and u _i(t-1) the start and stop state of i-th unit current time and previous moment is represented respectively; f _dRrepresent that Demand Side Response compensates total amount; P _dRrepresent Demand-side regulating power, i.e. virtual generated output; u _dRt () represents the start and stop state of virtual generating current time; A _i, B _iand C _irepresent the operational factor of i-th conventional power unit; Sh _irepresent i-th conventional power unit warm start cost; Sc _irepresent i-th conventional power unit cold start-up cost; T _i.off.minrepresent the minimum lasting idle time that i-th conventional power unit allows; T _i.offrepresenting that i-th conventional power unit continued the time of shutting down before certain period, if be open state before this unit, is then 0; T _{i, cs}represent the cold start-up time of conventional power unit i; d _dRfor Demand-side reduces the amount of money that every kilowatt hour electricity compensates.

S2: set up pollutant emission target function:

$\left\{\begin{array}{c}\min F_2=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{g}_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\×u_i\left(t\right)\\ g_i\left(P_{\mathrm{Gi}}\left(t\right)\right)=a_i\×P_{\mathrm{Gi}}{\left(t\right)}^2+b_i\×P_{\mathrm{Gi}}\left(t\right)+c_i\end{array}\right.---\left(2\right)$

In formula, F ₂represent pollutant emission total yield number; g _irepresent the pollutant emission equivalent of i-th conventional power unit; a _i, b _iand c _irepresent the disposal of pollutants coefficient of i-th conventional power unit.

(2) Optimized Operation constraints is set up, comprise: system loading Constraints of Equilibrium, system spinning reserve retrains, the technology of conventional power unit and Demand-side resource is exerted oneself and is limited, conventional power unit Climing constant, conventional power unit minimum running time and the constraint of minimum idle time, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction retrains.

S3: set up system loading Constraints of Equilibrium:

$\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{P}_{\mathrm{Gi}}\left(t\right)+\underset{j=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{P}_{\mathrm{DGj}}\left(t\right)+P_{\mathrm{DR}}\left(t\right)=P_L\left(t\right)---\left(3\right)$

In formula, N _dGrepresent distributed power source species number; P _dGjt () represents that jth kind distributed power source t is exerted oneself; P _lt () represents the load power of t.

S4: set up the constraint of system spinning reserve:

$\underset{i=1}{\overset{N_G}{\mathrm{\Σ}}}{u}_i\left(t\right)P_{\mathrm{Gi},\max }+P_{\mathrm{DR},\max }\≥P_L\left(t\right)+P_{\mathrm{RL}}\left(t\right)+\underset{j=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{P}_{\mathrm{DGj}}\left(t\right)---\left(4\right)$

In formula, P _{gi, max}represent that i-th maximum technology of conventional power unit is exerted oneself; P _{dR, max}represent that maximum available Demand-side regulates load; P _rLexpression system spinning reserve demand; expression system is exert oneself uncertainty and the newly-increased reserve capacity of reply distributed power source, considers the uncertainty of distributed electrical source power 100% probability interval, P herein _dGjbe jth kind distributed power source to exert oneself.

S5: the technology setting up conventional power unit and Demand-side resource is exerted oneself and limited:

P _Gi,min≤P _Gi(t)≤P _Gi,max(5)

0≤P _DR(t)≤P _DR,max

In formula, P _{gi, min}, P _{gi, max}represent that i-th minimum, maximum technology of conventional power unit is exerted oneself respectively.

S6: set up conventional power unit Climing constant:

P _i(t)-P _i(t-1)≤r _uiT (6)

P _i(t-1)-P _i(t)≤r _diT

In formula, P _i(t) and P _i(t-1) power output of current time and previous moment i-th conventional power unit is represented respectively; r _uiand r _direpresent i-th conventional power unit power climbing speed and fall off rate respectively.

S7: set up the minimum continuous working period of conventional power unit permission and minimum lasting idle time constraint:

T _i,on≥T _i,on,min(7)

T _i,off≥T _i,off,min

In formula, T _{i, on}represent i-th conventional power unit continuous operating time; T _{i, off}represent i-th conventional power unit continuous idle time; T _{i, on, min}represent the minimum continuous working period that i-th conventional power unit allows.

S8: set up the maximum continuous controllable period of time constraint (continuous working period that namely virtual power supply allows retrains) that Demand-side allows:

T _DR≤T _DR，max(8)

In formula, T _dRrepresent Demand-side controllable period of time; T _{dR, max}represent the maximum continuous controllable period of time that Demand-side allows.

S9: set up the constraint of user power utilization mode satisfaction:

$\left\{\begin{array}{c}{m}_s\≥m_{s,\min }\\ m_s=1-\frac{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\left|\mathrm{\Δ}{q}_t\right|}{\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{q}_t}\end{array}\right.---\left(9\right)$

In formula, m _srepresent power mode satisfaction; m _{s, min}represent the minimum power mode satisfaction allowed, represent in a dispatching cycle T, before and after optimizing each period electricity knots modification absolute value and; represent in a dispatching cycle T, power consumption total before optimizing.

(3) Demand Side Response is equivalent to a virtual synchronous generator, by carrying out the start and stop state of all units of determination of period one by one based on the discrete particle cluster algorithm of heuristic rule.

S10: each conventional power unit characterisitic parameter of initialization, comprising: unit output limits, greenhouse gas, minimum start and stop time, cold start-up time, climbing restriction, start-up cost, emission performance coefficient, continuous service/idle time; Initial reguirements side characterisitic parameter, comprising: the maximum technology of Demand-side resource is exerted oneself, the maximum continuous controllable period of time that Demand-side allows; Initialization system prediction payload and distributed power source predict size of exerting oneself.

S11: the start and stop state of certain period conventional power unit of initialization and equivalent virtual synchronous generator group; Be provided with b _popindividual (generally desirable [20,80] integer between) represent the particle of unit in certain period start and stop state, the dimension of each particle is consistent with conventional power unit in system and equivalent virtual synchronous generator group number sum, the start and stop state of corresponding unit in the value expression system of the every one dimension of particle.First the start and stop state of all units of random initializtion is 0 or 1, and wherein 0 represents closedown, and 1 represents unlatching.First judge whether the particle after initialization meets the minimum continuous working period constraint and minimum lasting idle time constraint allowed afterwards, and carry out the correction of particle position, correction formula is as follows:

In formula, represent the value of a kth particle the 0th iteration i-th dimension, i.e. the state of unit after i-th a certain period initialization of conventional power unit.If at initial runtime, be stopped status before i-th conventional power unit, then T _{i, on}=0; If be open state before i-th conventional power unit, then T _i.off=0.

Each particle also needs to judge whether to satisfy the demands the maximum continuous controllable period of time constraint in side, and carries out the correction of particle position, and correction formula is as follows:

In formula, represent a kth particle, the 0th iteration, the value of the n-th dimension, the i.e. state of virtual synchronous generator n unit after a certain period initialization.T _{n, DR}representing that n-th virtual synchronous generator continued the time of start before certain period, if be stopped status before this unit, is then 0.T _{n, DR, max}represent the maximum continuous working period that maximum continuous controllable period of time i.e. equivalence n-th virtual synchronous generator that Demand-side allows allows.

Whether constraint minimum running time is met to each particle, after the detection that minimum idle time constraint and the maximum continuous controllable period of time of Demand-side retrain, correction, judges whether each particle meets the constraint of system spinning reserve.Introduce priority ordering method to sort successively from the order of getting well to differing from according to performance driving economy to each conventional power unit.Performance driving economy is used for judging by average expense AFLC (Average Full-LoadCost) at full capacity, and expression formula is as follows:

$\mathrm{AFLC}=\frac{f_i\left(P_{\mathrm{Gi},\max }\right)}{P_{\mathrm{Gi},\max }}---\left(12\right)$

f _i(P _Gi,max)＝A _i×P _Gi,max ²+B _i×P _Gi,max+C _i

In formula, f _i(P _gi.max) represent operating cost under i-th conventional power unit full load situation.Average expense is less at full capacity, and the performance driving economy of unit is better.

If a certain particle is discontented with the constraint of pedal system spinning reserve, then according to performance driving economy from getting well to the unit do not opened before the order differed from is opened successively and judging whether particle meets the constraint of system spinning reserve, until all particles all meet the constraint of system spinning reserve.

Afterwards heuristic correction is carried out to each particle start and stop state, judges whether each particle start and stop state meets following constraints:

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_k^i\left(0\right)\×P_{\mathrm{Gi},\min }>0.9(P_L\left(t\right)-\underset{j=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\Σ}}}{P}_{\mathrm{DGj}}\left(t\right))---\left(13\right)$

In formula, represent the exert oneself summation of distributed power source in the t period.N represents conventional power unit and equivalent virtual synchronous generator number sum.

When the minimum technology of certain moment all operating units exert oneself sum be all greater than 90% of total load and distributed power source gross capability difference time, in order to meet system loading Constraints of Equilibrium, all units almost have to operate at about minimum load, economic optimum so scarcely, therefore, heuristic correction is added with convergence speedup speed.Equally each conventional power unit is sorted by the good order to difference successively according to performance driving economy, judge that the index of conventional power unit performance driving economy quality is shown in formula (12).If a certain particle does not meet constraint equation (13), then judge whether to meet constraint equation (13), until all particles all meet constraint equation (13) from bad unit to not closing before good order is closed successively according to performance driving economy.

S12: the single step completing discrete particle cluster upgrades.First carry out speed renewal according to the discrete particle cluster algorithm based on heuristic rule, speed more new formula is as follows:

v _k(t+1)＝wv _k(t)+c ₁r ₁(P _k,best(t)-x _k(t))+c ₂r ₂(P _g(t)-x _k(t)) (14)

In formula, v _kt () represents the speed after a kth particle the t time iteration, x _kt () represents the position after a kth particle the t time iteration, P _{k, best}t () represents the history optimal value of a kth particle, P _gt () represents global optimum.W represents inertia weight (generally between [0.4,0.9]), c ₁, c ₂represent Studying factors (generally desirable 2), r ₁, r ₂for the random number between [0,1].

After the speed that completes upgrades, carry out location updating, location updating step is as follows:

First judge whether the start and stop state of unit meets constraint minimum running time, minimum idle time constraint and maximum sustainable operation time-constrain, if meet, then according to the location updating formula of normal scatter population, use critical operator to carry out location updating; If do not meet, use (10), (11) formula upgrades.Wherein, the introducing of critical operator wishes to make the better to dominance of iterative process.Ordinary particle group algorithm adopts a random number λ, and after several s that speed is converted between 0 ~ 1, it is excessive or too small that λ gets, set state be transformed into 0 or 1 probability excessive, then have a strong impact on iteration to dominance.Critical operator 0.1< λ ₁<0.4,0.6< λ ₂<0.9, by the range shorter of random number λ to [λ ₁, λ ₂], like this do not lose particle multifarious simultaneously ensure particle upgrade towards more excellent direction.

The location updating formula of discrete particle cluster is as follows:

$\mathrm{Sigmoid}\left(v\right)=\frac{1}{1+e^{-v}}$

In formula, x _kt () represents the position after a kth particle the t time iteration; Rand is the random number between [0,1]; Sigmoid is SQL.

Next, judge whether particle meets spinning reserve constraint, and upgrade, renewal process is with the renewal process of spinning reserve constraint in S11.

Finally, carry out heuristic correction to each particle start and stop state, makeover process is with makeover process heuristic in S11.

After completing particle position renewal, each particle is brought in fitness function and solves adaptive value, carry out optimal value renewal.In this problem, fitness function is unit starting cost, and formula is as follows:

F＝S _i(t)×(1-u _i(t-1))

$S_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{Sh}}_i& T_{i,\mathrm{off},\min }\&le;T_{i,\mathrm{off}}\&le;T_{i,\mathrm{cs}}\\ {\mathrm{Sc}}_i& T_{i,\mathrm{off}}\&GreaterEqual;T_{i,\mathrm{cs}}\end{array}\right.---\left(16\right)$

In formula, F represents unit starting cost.

The step that optimal value upgrades is: if a kth particle brings the adaptive value P that fitness function calculates into _kt () is less than the history optimal value P of a kth particle _{k, best}t (), then make P _{k, best}(t)=P _k(t), otherwise P _{k, best}t () remains unchanged.The global optimum P of population after if current renewal completes _{g, now}(t) be less than before global optimum P _gt (), then make P _{g, now}(t)=P _g(t).

S13: the calculating completing discrete particle cluster algorithm list period start and stop state; Determine the iterations C that each period needs iteration _b(generally desirable 100 times), first carry out being S11 step, repeat S12 step afterwards until cycle-index reaches C _bsecondary, obtain the start and stop state of certain period final, and store this data.

S14: the calculating completing discrete particle cluster algorithm whole period start and stop state.When to determine in what a total dispatching cycle after hop count T (generally getting 24), just can carry out the calculating of each period start and stop state successively according to the method for S13 step.The final start and stop state outcome result of all each periods of unit is stored.

(4) after the calculating completing each period start and stop state of all units, on the basis of known start and stop state, carrying out economic allocation to each unit according on the basis of constraints by the continuous particle cluster algorithm based on heuristic rule, concrete implementation step is as follows:

S15: the output distribution of a certain each unit of period of initialization.Be provided with c _popindividual (generally desirable [20,80] random integers between) represent the particle that all units were exerted oneself in certain period, the dimension of each particle is consistent with conventional power unit in system and equivalent virtual synchronous generator number sum, the size of exerting oneself of corresponding unit in the value expression system of the every one dimension of particle.In the scope that each unit minimax is exerted oneself, the size of exerting oneself of all units of random initializtion.

S16: complete standard particle group single step and upgrade.First carry out speed renewal according to standard particle group algorithm, speed more new formula, with the speed more new formula of discrete particle cluster, is shown in formula (14), then carries out location updating.Continuous particle cluster algorithm location updating formula is:

x _k(t+1)＝x _k(t)+v _k(t) (17)

After location updating, each particle needs to judge whether the Climing constant meeting conventional power unit, and carries out the correction of particle position, and correction formula is as follows:

In formula, with represent the kth particle value in t period and t-1 period i-th dimension respectively, namely in the size of exerting oneself of t period and t-1 period i-th conventional power unit.UP ⁱand DN ⁱrepresent the maximum output size of the maximum output size that the conventional power unit i unit interval can climb and decline respectively.

The also demand fulfillment user power utilization mode satisfaction constraint of each particle, and carry out the correction of particle position, correction formula is as follows:

In formula, represent the value that a kth particle was tieed up in the t period n-th, namely in the size of exerting oneself of t period n-th virtual synchronous generator.P _lt () represents the system total load of period t.

After completing location updating, each particle is brought in fitness function and solves adaptive value, carry out the renewal of optimal value.The fitness function of this problem by the operating cost of unit, the equivalent price of system blowdown and the penalty composition of system loading Constraints of Equilibrium.Fitness function formula is:

$\begin{array}{c}\min F=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}[f_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\&times;u_i\left(t\right)]+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{f}_{\mathrm{DR}}\left(P_{\mathrm{DR}}\left(t\right)\right)\&times;u_{\mathrm{DR}}\left(t\right)\\ +c\&times;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{g}_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\&times;u_i\left(t\right)\\ +\mathrm{\&lambda;}\&times;\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\max (\left(\right|\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{Gi}}\left(t\right)+\underset{j=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{DGj}}\left(t\right)+P_{\mathrm{DR}}\left(t\right)-P_L\left(t\right)|-\mathrm{\&epsiv;}),0)\end{array}---\left(20\right)$

In formula, c represents blowdown Price factor.λ represents penalty factor.The penalty of last expression system loading Constraints of Equilibrium of multinomial.

The step that optimal value upgrades is identical with the step that optimal value in S12 upgrades.

S17: the economic allocation completing single each unit output of period calculates; Determine the iterations C that each period needs iteration _c(generally desirable 100 times), first carry out S15 step, repeat S16 step afterwards until cycle-index reaches C _csecondary, obtain the situation of final each unit output economic allocation of certain period, and store this data.

S18: the calculating completing standard particle group algorithm each unit output economic allocation of whole period; When determining after hop count T, just can carry out the calculating of each period Unit Economic distribution successively according to the method for S17 step.The size of finally exerting oneself of all each periods of unit is stored.

S19: try to achieve the final start and stop state of all each periods of unit and size of exerting oneself, be the final result of Unit Combination.According to Unit Combination result, control centre in a planned way allocates exerting oneself of each unit, realizes active distribution network Optimized Operation.

In embodiments of the present invention, by checking the validity of proposition Optimization Scheduling, the method for proposition is applied to the calculating of active distribution network Optimized Operation.The technical parameter choosing conventional fired power generating unit is as shown in table 1, and respectively as shown in Figure 5 and Figure 6, system total load as shown in Figure 7 for the prediction curve that wind-powered electricity generation, photovoltaic generation are exerted oneself.In actual electric network, Demand-side resource type has diversity, and user side Demand Side Response need compensate electricity price, and the Demand-side resources such as energy storage only need to consider maintenance cost, without the need to carrying out price benefication.For simplified model calculates, setting the total size of all Demand-side schedulable resources is 80MW, and average every kilowatt hour amount of compensation of equivalence is 0.016 $, and user power utilization mode satisfaction is not less than 90%.

The technical parameter of the conventional fired power generating unit of table 1

The technical parameter of the conventional fired power generating unit of continued 1

Simulation result is as follows:

A () is not optimized scheduling before, only adopt conventional power unit supply workload demand, in now 24 hours, system cloud gray model total cost is 426300 $, and pollutant emission equivalents is 82985kg.

B () adds distributed power source, after being optimized scheduling, can obtain system cloud gray model total cost in system 24 hours is 390660 $, and comparatively situation (a) have dropped 35640 $; Pollutant emission equivalents is 74433kg, and comparatively situation (a) have dropped 8552kg.

If c () considers distributed power source and Demand Side Response simultaneously, after being optimized scheduling, can obtain system cloud gray model total cost in system 24 hours is 370698 $, and comparatively situation (b) have dropped 19962 $; Pollutant emission equivalents is 72487kg, and comparatively situation (b) have dropped 1946kg.Before and after Optimized Operation, load curve contrast as shown in Figure 8, can be found out, after considering Demand Side Response, load peak obviously declines, and effectively reduces the peak-valley difference of load.

As can be seen from example, adopt the Optimization Scheduling that the embodiment of the present invention proposes, distributed power source and Demand-side resource participate in electrical network simultaneously and regulate, systems generate electricity operating cost can be effectively reduced, reduce load peak-valley difference, reduce environmental pollution simultaneously, to the optimizing operation of electrical network, there is positive effect.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (3)

1., based on an active distribution network Optimization Scheduling for virtual generating, it is characterized in that, comprise the steps:

(1) Optimized Operation target function is set up; First object function F is set up for target so that systems generate electricity operating cost is minimum ₁, set up the second objective function F so that pollutant emission total yield number is minimum for target ₂;

(2) described Optimized Operation bound for objective function is obtained; Described constraints comprises: system loading Constraints of Equilibrium, system spinning reserve retrains, the technology of conventional power unit and Demand-side resource is exerted oneself and is limited, conventional power unit Climing constant, conventional power unit minimum running time and the constraint of minimum idle time, the maximum continuous controllable period of time constraint of Demand-side, power mode satisfaction retrains;

(3) Demand Side Response is equivalent to a virtual synchronous generator, according to described Optimized Operation target function and described constraints and by the start and stop state of all units of acquisition that carry out the period one by one based on the discrete particle cluster algorithm of heuristic rule;

(4) according to the start and stop state of all units, and by the continuous particle cluster algorithm based on heuristic rule, on the basis according to described constraints, economic allocation is carried out to each unit; And allocate exerting oneself of each unit according to the result of economic allocation and the final start and stop state of all each periods of unit and size of exerting oneself, realize active distribution network Optimized Operation.

2. active distribution network Optimization Scheduling as claimed in claim 1, it is characterized in that, in step (1), described first object function is: $\min F_1=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}[f_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\&times;u_i\left(t\right)+S_i\left(t\right)\&times;(1-u_i(t-1))\&times;u_i\left(t\right)]+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{f}_{\mathrm{DR}}\left(P_{\mathrm{DR}}\left(t\right)\right)\&times;u_{\mathrm{DR}}\left(t\right);$ Described second target function is: $\min F_2=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{g}_i\left(P_{\mathrm{Gi}}\left(t\right)\right)\&times;u_i\left(t\right);$

Wherein, F ₁represent systems generate electricity operating cost; T represents time dispatching cycle; N _grepresent the total number of units of conventional power unit; f _i(P _gi(t))=A _i× P _gi(t) ²+ B _i× P _gi(t)+C _i, f _irepresent i-th conventional power unit operating cost; P _girepresent the power output of i-th conventional power unit; S _irepresent i-th conventional power unit start-up and shut-down costs; u _i(t) and u _i(t-1) the start and stop state of i-th unit current time and previous moment is represented respectively; f _dRrepresent that Demand Side Response compensates total amount; f _dR(P _dR(t))=d _dR× P _dR(t), P _dRrepresent Demand-side regulating power, i.e. virtual generated output; u _dRt () represents the start and stop state of virtual generating current time; A _i, B _iand C _irepresent the operational factor of i-th conventional power unit; $S_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{Sh}}_i& T_{i,\mathrm{off},\min }\&le;T_{i,\mathrm{off}}\&le;T_{i,\mathrm{cs}}\\ {\mathrm{Sc}}_i& T_{i,\mathrm{off}}\&GreaterEqual;T_{i,\mathrm{cs}}\end{array}\right.,$ Sh _irepresent i-th conventional power unit warm start cost; Sc _irepresent i-th conventional power unit cold start-up cost; T _i.off.minrepresent the minimum lasting idle time that i-th conventional power unit allows; T _i.offrepresenting that i-th conventional power unit continued the time of shutting down before certain period, if be open state before this unit, is then 0; T _{i, cs}represent the cold start-up time of conventional power unit i; d _dRfor Demand-side reduces the amount of money that every kilowatt hour electricity compensates; F ₂represent pollutant emission total yield number; g _i(P _gi(t))=a _i× P _gi(t) ²+ b _i× P _gi(t)+c _i, g _irepresent the pollutant emission equivalent of i-th conventional power unit; a _i, b _iand c _irepresent the disposal of pollutants coefficient of i-th conventional power unit.

3. active distribution network Optimization Scheduling as claimed in claim 1 or 2, it is characterized in that, in step (2), described system loading Constraints of Equilibrium is wherein, N _dGrepresent distributed power source species number; P _dGjt () represents that jth kind distributed power source t is exerted oneself; P _lt () represents the load power of t;

Described system spinning reserve is constrained to $\underset{i=1}{\overset{N_G}{\mathrm{\&Sigma;}}}{u}_i\left(t\right)P_{\mathrm{Gi},\max }+P_{\mathrm{DR},\max }\&GreaterEqual;P_L\left(t\right)+P_{\mathrm{RL}}\left(t\right)+\underset{j=1}{\overset{N_{\mathrm{DG}}}{\mathrm{\&Sigma;}}}{P}_{\mathrm{DGj}}\left(t\right),$ Wherein, P _{gi, max}represent that i-th maximum technology of conventional power unit is exerted oneself; P _{dR, max}represent that maximum available Demand-side regulates load; P _rLexpression system spinning reserve demand; expression system is exert oneself uncertainty and the newly-increased reserve capacity of reply distributed power source, considers the uncertainty of distributed electrical source power 100% probability interval, P herein _dGjbe jth kind distributed power source to exert oneself;

The technology of described conventional power unit and Demand-side resource is exerted oneself and is restricted to $\begin{array}{c}{P}_{\mathrm{Gi},\min }\&le;P_{\mathrm{Gi}}\left(t\right)\text{\&le;}{P}_{\mathrm{Gi},\max }\\ 0\&le;P_{\mathrm{DR}}\left(t\right)\&le;P_{\mathrm{DR},\max }\end{array},$ P _{gi, min}, P _{gi, max}represent that i-th minimum, maximum technology of conventional power unit is exerted oneself respectively;

Described conventional power unit Climing constant is $\begin{array}{c}{P}_i\left(t\right)-P_i(t-1)\&le;r_{\mathrm{ui}}T\\ P_i(t-1)-P_i\left(t\right)\&le;r_{\mathrm{di}}T\end{array},$ Wherein P _i(t) and P _i(t-1) power output of current time and previous moment i-th conventional power unit is represented respectively; r _uiand r _direpresent i-th conventional power unit power climbing speed and fall off rate respectively;

Described conventional power unit minimum running time and minimum idle time are constrained to $\begin{array}{c}{T}_{i,\mathrm{on}}\&GreaterEqual;T_{i,\mathrm{on},\min }\\ T_{i,\mathrm{off}}\&GreaterEqual;T_{i,\mathrm{off},\min }\end{array},$ Wherein, T _{i, on}represent i-th conventional power unit continuous operating time; T _{i, off}represent i-th conventional power unit continuous idle time; T _{i, on, min}represent the minimum continuous working period that i-th conventional power unit allows;

The maximum continuous controllable period of time of described Demand-side is constrained to T _dR≤ T _{dR, max}, wherein, T _dRrepresent Demand-side controllable period of time; T _{dR, max}represent the maximum continuous controllable period of time that Demand-side allows;

Described power mode satisfaction is constrained to $\left\{\begin{array}{c}{m}_s\&GreaterEqual;m_{s,\min }\\ m_s=1-\frac{\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\left|\mathrm{\&Delta;}{q}_t\right|}{\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{q}_t}\end{array}\right.;$ Wherein m _srepresent power mode satisfaction; m _{s, min}represent the minimum power mode satisfaction allowed, represent in a dispatching cycle T, before and after optimizing each period electricity knots modification absolute value and; represent in a dispatching cycle T, power consumption total before optimizing.