CN106339778A - Optical storage microgrid operation optimization method considering multiple objectives - Google Patents

Abstract

The invention relates to an optical storage microgrid operation optimization method considering multiple objectives. According to the optimization method, a microgrid containing electric vehicle charging and demand response is taken as a research object; and an optimization objective function is established by establishing a mathematical model; and an optimal distribution scheme is obtained through the optimization of the objective function. The optimal distribution method includes the following steps that: step 1, the objective function of the operation of the microgrid is determined, an optimized objective function is determined according to the composition structure of the microgrid; step 2, the constraint conditions of the objective function are determined, and the constraint conditions of the operation of the microgrid are determined according to the objective function and the operation environment of the microgrid; and step 3, an optimization algorithm is determined, and the objective function is optimized under the optimization algorithm, so that the optimal configuration scheme can be obtained. With the method of the invention adopted, the actual operation condition of the optical storage microgrid can be reflected, and a decision-maker can select the scheduling and operation optimal schemes of the optical storage microgrid according to data.

Description

One kind considers that multiobject light stores micro-capacitance sensor running optimizatin method

Technical field

The present invention relates to a kind of consider that multiobject light stores micro-capacitance sensor running optimizatin method.

Background technology

In recent years, under the pressure of the dual-pressure of environment and the energy, the distributed generation technology using regenerative resource is just subject to Increasing concern.But, because some distributed power sources have intermittence, randomness, if directly by it simultaneously It is linked in bulk power grid, the safe and stable operation of bulk power grid can be impacted.Therefore, in order to solve distributed power source and power distribution network Between contradiction, to greatest extent improve clean reproducible energy utilization rate, by distributed power source, energy storage device, monitoring and Protection device, load etc. integrate operation in the form of micro-capacitance sensor.

At present, micro-capacitance sensor demonstration projects quite a lot, the research controlling for its energy management and operation are had both at home and abroad Also achieve more achievement.

With going deep into of microgrid energy Optimized Operation correlational study, researcher focus of interest is also distributed from Generation Side The economy of formula power supply, gradually transfers to dsm and the effect improving micro-capacitance sensor economical operation is come up.In addition, with electricity The developing rapidly of electrical automobile, ev also gradually participates in the scheduling research of micro-capacitance sensor economic optimization, becomes and realizes micro-capacitance sensor economy fortune The important means that row controls.Based on the multiobject light storage micro-capacitance sensor optimization operation comprising charging electric vehicle and Demand Side Response Scheduling problem, generally there are multiple object functions simultaneously, each target is related to one group of decision variable of identical, and mutually restricts. For a multi-objective optimization question, it is generally concerned with the compromise set after considering each target, i.e. pareto optimal solution set.20 Since the 80s and 90s in century, in boundary, scholar proposes different multi-objective optimization algorithms in succession, nsga-ii as wherein it One.

However, be added simultaneously to the research of the traffic control of micro-capacitance sensor currently for charging electric vehicle and Demand Side Response Still there is no open source literature.

Content of the invention

The purpose of the present invention is for being added simultaneously to micro-capacitance sensor with regard to charging electric vehicle and Demand Side Response in existing The optimization method of traffic control disappearance, a kind of optimization method is provided, considered power constraint balance, energy storage charged The constraintss such as state (soc), the transferable time range of load, the charging interval of electric automobile are it is also considered that timesharing is electric Valency mechanism, dsm mechanism, energy storage depreciable cost, can more really react the practical operation situation that light stores micro-capacitance sensor.

In order to achieve the above object, technical solution of the present invention is as follows:

The present invention with comprise charging electric vehicle and demand response micro-capacitance sensor as object of study, by founding mathematical models Mode set up optimization object function, by configuration scheme is obtained to the optimization of object function, its Optimal Configuration Method bag Include following steps:

Step 1: object function when determining that micro-capacitance sensor runs, according to the composition structure of micro-capacitance sensor, determine the target optimizing Function；

Step 2: determine bound for objective function, micro- electricity is determined according to the running environment of object function and micro-capacitance sensor The constraints of network operation；

Step 3: determine optimized algorithm, and optimization object function under optimized algorithm, draw configuration scheme.

Described object function is: runs the minimum object function of total cost and with micro-capacitance sensor and bulk power grid friendship with micro-capacitance sensor Change the minimum object function of electricity.Micro-capacitance sensor operation total cost is included micro-capacitance sensor and is set with the power trade cost of bulk power grid, energy storage The electric discharge penalty function of standby depreciable cost and energy storage device, runs the expression formula of object function of total cost minimum such as with micro-capacitance sensor Under:

Its In,

In formula, δ t is unit time interval, and t is the when hop count optimizing；p_gT () is in unit time interval and power distribution network The mean power (being just during power purchase, be negative during sale of electricity) exchanging, p_rT () is average transaction value in unit time interval；p_b(t) And p_bT () is respectively the discharge power of t period energy-storage system and discharge and recharge exchanges power, wherein p_bIt is just p when () is charged t_b(t) It is negative during electric discharge；σ is penalty coefficient；Soc (t) is the state-of-charge of t period energy storage；soc_minLower limit for energy storage charge state.

Described object function also includes the exchange electricity between micro-capacitance sensor and bulk power grid, and it exchanges the minimum target of electricity The optimization direction of function, exchanges electricity minimum, then the impact to bulk power grid for the micro-capacitance sensor is also little, exchanges electricity expression formula and is:

$\min g=\mathrm{\δ}t\underset{t=1}{\overset{t}{\σ}}\left|p_g\left(t\right)\right|$

In formula, p_gT () is the mean power exchanging with power distribution network in unit time interval, be just, during sale of electricity be during power purchase Negative.

In described step 2, constraints includes:

1. power-balance constraint

According to the internal realtime power balance and between power distribution network of micro-capacitance sensor, obtain:

p_b(t)+p_l(t)=p_pv(t)+p_g(t)

In formula, p_pvT () is photovoltaic generation power, p_lT () is the total load power of t period load side, it includes fixing negative Transferable load after lotus and transfer and the power of electric automobile consumption, that is,

$p_l\left(t\right)=p_{fl}\left(t\right)+\underset{k=1}{\overset{m}{\σ}}{p}_k^{{\mathrm{tl}}^{\′}}\left(t\right)+p_{ev}\left(t\right)$

In formula, p_flT () is the firm demand power of t period；Transferable negative for the kth class of t period after transfer Lotus amount, p_evT () is the charging general power of electric automobile t period, wherein,

$\begin{array}{c}{p}_k^{{\mathrm{tl}}^{\′}}\left(t\right)=p_k^{tl}\left(t\right)+...\\ ...\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\mathrm{\σ}}}\underset{t_s=1}{\overset{t}{\mathrm{\σ}}}(d_k^{tl}(t_s,t_b)-d_k^{tl}(t_b,t_s))\end{array}$

In formula,For transfer before the t period transferable loading, lk be the transferable load of kth class continuous firing when Between；

$p_{ev}\left(t\right)=\underset{n=1}{\overset{n}{\mathrm{\σ}}}{p}_{evn}\left(t\right)$

In formula, p_evnCharge power for n-th electric automobile；N is the quantity of electric automobile.Again with invariable power chargometer Calculate the charge requirement that substantially can complete client, as shown in formula (10).

$p_{evrg\_n}=\frac{e_{evn}}{t_2-t_1}$

$e_{evn}={\&integral;}_{t_1}^{t_2}{p}_{ev\_n}\left(t\right)dt$

Wherein, p_{avrg_n}For average charge power in charge period for n-th car；t_evnInitiation of charge for n-th car In the moment, be one of decision variable；p_{ev_n}T () is the charge power in t for n-th ev；e_evnFor n-th car in t₁～t₂Moment Be filled with electricity, by electrical demand amount e of n-th electric automobile_{req_n}Determine；

2. charging electric vehicle power constraint and charging interval constraint；

p_evn≤|p_{ev_rat}|

$\left\{\begin{array}{cc}{p}_{evn}=0,& t_{evn}\&notelement;s_{ev}\\ p_{evn}>0,& t_{evn}\&element;s_{ev}\end{array}\right.$

In formula, p_{ev_rat}Specified charge-discharge electric power for electric automobile rechargeable battery；s_evRest on charging station for electric automobile Time set；

3. the charge-discharge electric power of energy-storage system and soc constraint；

Being constrained to of the charge-discharge electric power of energy-storage system: | p_b(t)|≤p_{b_rat}

In formula, p_{b_rat}Rated power for energy-storage system；η_bIt is the charge/discharge efficiency of energy-storage system；

The current carrying capacity e of energy-storage system_bT () can be obtained by following formula recursion, that is,

$e_b\left(t\right)=\left\{\begin{array}{cc}{e}_b(t-1)+p_b\left(t\right){\mathrm{\&delta;t\&eta;}}_b,& p_b\left(t\right)\&greaterequal;0\\ e_b(t-1)+\frac{p_b\left(t\right)\mathrm{\&delta;}t}{{\mathrm{\&eta;}}_b},& p_b\left(t\right)<0\end{array}\right.$

In formula, energy storage carrying capacity e_bT () should meet

e_b__ratsoc_min≤e_b(t)≤e_b__ratsoc_max

In formula, soc_maxAnd soc_minIt is the upper and lower bound of energy-storage system soc respectively.

Because the cycle optimizes, there is seriality, the initial soc of energy-storage system of each optimization cycle should be made to be consistent, that is, have

e_b(0)=e_b(t)

4. transferable load related constraint；

For transferable load, the load capacity constraint that the t period proceeds to/produces is represented by

$\underset{t_b=max(t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}{d}_k^{tl}(t_s,t_b)<d_{k\_max}^i\left(t\right)$

$\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}{d}_k^{tl}(t_b,t_s)<d_{k\_\max }^o\left(t\right)=p_k^{tl}\left(t\right)$

In formula,WithIt is respectively the maximum amount of proceeding to of t period kth class transferable load permission and produce Amount.

Transferable load allows constraint transfer time

$\left\{\begin{array}{cc}{d}_k^{tl}(t_b,t_s)=0,& t_s\&notelement;s_k\\ d_k^{tl}(t_b,t_s)\&greaterequal;0,& t_s\&element;s_k\end{array}\right.$

In formula, s_kAllow the time range of transfer for the transferable load of kth class.

5. interconnector power constraint；

The power that exchanges between micro-capacitance sensor and bulk power grid is constrained by interconnector, that is,

p_g(t)≤min{p_{line_max},p_t}

In formula, p_tFor the rated power of distribution transformer, p_{line_max}The maximum transmission power allowing for interconnector.

Optimized algorithm adopts nsga-ii algorithm, and its algorithm steps is as follows:

Step one: data initialization, including distributed photovoltaic power generated output, energy storage charge-discharge electric power, power load distributing, Tou power price and the initialization of nsga-ii algorithm parameter；

Step 2: nsga-ii algorithm initialization, set and produce n individual initial population, each decision variable is set Scope and population algebraically；

Step 3: call the desired value of the population at individual calculating gained；

Step 4: little by order, the little principle of crowding distance carries out non-dominated ranking to initialization population；According to each individuality Non-dominant level is to population layer sorting；Based on the result of non-dominated ranking, to the pareto disaggregation of same layer according to micro-capacitance sensor Target function value calculate crowding distance；

Step 5: select, intersect and mutation operator；Calculated according to binary prize match rule and select 2 individualities, and handed over Fork, variation obtain new population；

Step 6: merge parent population and the new progeny population producing, carry out non-dominated ranking, and give to individual values Order；

Step 7: select the population of n best bodily form Cheng Xin by the little principle of the little crowding of order；

Step 8: repeat step five to step 7, stop when maximum iteration time.

Desired value is calculated by following steps:

1): read in photovoltaic generation power p of prediction_pv(t), firm demand power p_flThe demand electricity of (t) and electric automobile Amount e_{req_n}, and decision variable p_b(t)、t_s、And t_evnValue, the initial value of wherein decision variable is by respective Randomly generate in span；

2): obtain energy storage charge-discharge electric power p_b(t)；

Randomly generate p under constraints_b(t), and calculate current carrying capacity e_bT (), checks whether satisfaction currently charged shape Modal constraint, if being unsatisfactory for, is processed using penalty function；

3): calculate the transfer general power of the t period of tl.

Under tl constrains transfer time, randomly generate t_s, and it is transferable to calculate the kth class of t period after transfer according to formula (8) Loading, then obtains t period all transferable loading sums after transfer, and checks whether the constraint meeting transferable load Condition；

4): calculate the charging general power of electric automobile

It it is the time that it is parked and leaves according to the dump energy of electric automobile, in the constraint bar of charging electric vehicle time The Initial charge time t of electric automobile is randomly generated under part_evn, thus calculating the charge power of single electric automobile, check simultaneously Whether meet the constraints of charging electric vehicle power, finally calculate the charging general power of electric automobile t period；

5): calculate the exchange power p between photovoltaic microgrid and power distribution network_g(t)

Try to achieve the overall power requirement in the t period of load side first, further according to power-balance equality constraint p_b(t)+p_l(t) =p_pv(t)+p_gT () asks for p_gT (), checks whether simultaneously and meets corresponding constraints；

6): calculation optimization object function, draw output result: operation total cost f of micro-capacitance sensor and micro-capacitance sensor and bulk power grid Exchange electricity g, i.e. desired value.

The present invention has considered power constraint balance, the state-of-charge (soc) of energy storage, the turning of load The constraintss such as the time range of shifting, the charging interval of electric automobile；And consider tou power price mechanism, dsm mechanism, energy storage Depreciable cost and penalty term, can more really react the practical operation situation that light stores micro-capacitance sensor, policymaker can be according to data Light is selected to store the management and running prioritization scheme of micro-capacitance sensor.

Brief description

Fig. 1 is the light storage micro-capacitance sensor structured flowchart comprising charging electric vehicle and Demand Side Response；

Fig. 2 is the graph of a relation of energy storage life consumption coefficient and state-of-charge

Fig. 3 is the nsga-ii algorithm flow chart that the present invention provides

Fig. 4 is the flow chart that the object function that the present invention provides calculates

The light storage micro-capacitance sensor optimization fortune that electric automobile and dsm are comprised based on multiple target that Fig. 5 provides for the present invention The flow chart of the dispatching method of row

Specific embodiment

Below in conjunction with accompanying drawing, the present invention is described further.

In the present invention, the composition structure of photovoltaic micro is: light stores micro-grid system topological structure as shown in figure 1, comprising point The assemblies such as cloth photo-voltaic power supply, bess, firm demand, transferable load, charging system for electric automobile and ems.Wherein, photovoltaic System is incorporated to ac bus by dc/ac inverter, and adopts maximal power tracing control strategy；Energy-storage system then passes through voltage Source type bidirectional power converter is connected in parallel on ac bus, realizes the regulation of energy and power；Ac bus then pass through quickly to cut Change switch and access public power distribution network, realize the energy exchange between micro-capacitance sensor and bulk power grid.Between ems is by other assemblies Communication connects, and realizes the coordination between modules and control in micro-capacitance sensor, is that micro-capacitance sensor optimization runs the key controlling.

Optimization method step is as follows: with comprise charging electric vehicle and demand response micro-capacitance sensor as object of study, pass through The mode of founding mathematical models sets up optimization object function, and by obtaining configuration scheme to the optimization of object function, it is excellent Change collocation method to comprise the steps:

Step 1: object function when determining that micro-capacitance sensor runs, according to the composition structure of micro-capacitance sensor, determine the target optimizing Function；

Step 2: determine bound for objective function, micro- electricity is determined according to the running environment of object function and micro-capacitance sensor The constraints of network operation；

Step 3: determine optimized algorithm, and optimization object function under optimized algorithm, draw configuration scheme.

In its whole life cycle, total cycle charge-discharge total amount is held essentially constant energy storage device.Therefore, if The replacement cost of energy storage device is fixed, then may be assumed that the alternative costs caused by the discharge and recharge of the every kilowatt hour of energy storage are also substantially solid Fixed, that is,

In formula, c_{b_wear}For the depreciable cost of energy storage device accumulated discharge 1kwh, rmb/kwh；c_{b_pur}Purchase for energy storage device Buy basis, rmb；q_{b_all}For the total electricity of energy storage device life-cycle output, kwh.

The service life of energy storage device topmost influence factor in economic load dispatching is the big of its state-of-charge.Therefore, If being in higher state-of-charge during energy storage device electric discharge, its life consumption can be reduced, increase the service life.If but in lotus Discharge when electricity condition is relatively low, the loss to its life-span is larger, as shown in Figure 2.By the energy storage life consumption coefficient of Fig. 2 and charged shape State relation is represented by mathematic(al) representation, as follows:

$f\left(soc\right(t\left)\right)=\left\{\begin{array}{cc}1.3& 0\&le;soc\left(t\right)\&le;0.5\\ -1.5soc\left(t\right)+2.05& 0.5<soc\left(t\right)\&le;1\end{array}\right.$

In formula, soc (t) is the state-of-charge of energy storage t period.

The minimum object function of total cost is run and with the micro-capacitance sensor target minimum with bulk power grid exchange electricity with micro-capacitance sensor Function.Micro-capacitance sensor runs power trade cost, the depreciable cost of energy storage device and the storage that total cost includes micro-capacitance sensor and bulk power grid The electric discharge penalty function of energy equipment, the expression formula running the minimum object function of total cost with micro-capacitance sensor is as follows:

Wherein,

$p_b\left(t\right)=\left\{\begin{array}{cc}\left|p_b\left(t\right)\right|& p_b\left(t\right)<0\\ 0& p_b\left(t\right)\&greaterequal;0\end{array}\right.,$

In formula, δ t is unit time interval, and t is the when hop count optimizing；p_gT () is in unit time interval and power distribution network The mean power (being just during power purchase, be negative during sale of electricity) exchanging, p_rT () is average transaction value in unit time interval；p_b(t) And p_bT () is respectively the discharge power of t period energy-storage system and discharge and recharge exchanges power, wherein p_bIt is just p when () is charged t_b(t) It is negative during electric discharge；σ is penalty coefficient；Soc (t) is the state-of-charge of t period energy storage；soc_minLower limit for energy storage charge state.

Exchange electricity between micro-capacitance sensor and bulk power grid is less, then the impact to bulk power grid for the micro-capacitance sensor is also little, exchanges electricity Minimum expression formula is:

$\min g=\mathrm{\&delta;}t\underset{t=1}{\overset{t}{\&sigma;}}\left|p_g\left(t\right)\right|$

In formula, p_gT () is the mean power exchanging with power distribution network in unit time interval, be just, during sale of electricity be during power purchase Negative.

In step 2, constraints includes:

1. power-balance constraint

According to the internal realtime power balance and between power distribution network of micro-capacitance sensor, obtain:

p_b(t)+p_l(t)=p_pv(t)+p_g(t)

If this formula is formula (6), in formula (6), p_pvT () is photovoltaic generation power, p_lT () is the total of t period load side Load power, it includes the power that the transferable load after firm demand and transfer and electric automobile consume, that is,

$p_l\left(t\right)=p_{fl}\left(t\right)+\underset{k=1}{\overset{m}{\&sigma;}}{p}_k^{{\mathrm{tl}}^{\&prime;}}\left(t\right)+p_{ev}\left(t\right)$

If this formula is formula (7), in formula (7), p_flT () is the firm demand power of t period；For t after transfer The transferable loading of kth class of period, p_evT () is the charging general power of electric automobile t period, wherein,

$\begin{array}{c}{p}_k^{{\mathrm{tl}}^{\&prime;}}\left(t\right)=p_k^{tl}\left(t\right)+...\\ ...\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\mathrm{\&sigma;}}}\underset{t_s=1}{\overset{t}{\mathrm{\&sigma;}}}(d_k^{tl}(t_s,t_b)-d_k^{tl}(t_b,t_s))\end{array}$

In formula,For transfer before the t period transferable loading, lk be the transferable load of kth class continuous firing when Between；

$p_{ev}\left(t\right)=\underset{n=1}{\overset{n}{\mathrm{\&sigma;}}}{p}_{evn}\left(t\right)$

In formula, p_evnCharge power for n-th electric automobile；N is the quantity of electric automobile.Again with invariable power chargometer Calculate the charge requirement that substantially can complete client, as shown in formula:

$p_{evrg\_n}=\frac{e_{evn}}{t_2-t_1}$

$e_{evn}={\&integral;}_{t_1}^{t_2}{p}_{ev\_n}\left(t\right)dt$

Wherein, p_{avrg_n}For average charge power in charge period for n-th car；t_evnInitiation of charge for n-th car In the moment, be one of decision variable；p_{ev_n}T () is the charge power in t for n-th ev；e_evnFor n-th car in t₁～t₂Moment Be filled with electricity, by electrical demand amount e of n-th electric automobile_{req_n}Determine；

2. charging electric vehicle power constraint and charging interval constraint；

p_evn≤|p_{ev_rat}|

$\left\{\begin{array}{cc}{p}_{evn}=0,& t_{evn}\&notelement;s_{ev}\\ p_{evn}>0,& t_{evn}\&element;s_{ev}\end{array}\right.$

In formula, p_{ev_rat}Specified charge-discharge electric power for electric automobile rechargeable battery；s_evRest on charging station for electric automobile Time set；

3. the charge-discharge electric power of energy-storage system and soc constraint；

Being constrained to of the charge-discharge electric power of energy-storage system: | p_b(t)|≤p_{b_rat}

In formula, p_{b_rat}Rated power for energy-storage system；η_bIt is the charge/discharge efficiency of energy-storage system；

The current carrying capacity e of energy-storage system_bT () can be obtained by following formula recursion, that is,

$e_b\left(t\right)=\left\{\begin{array}{cc}{e}_b(t-1)+p_b\left(t\right){\mathrm{\&delta;t\&eta;}}_b,& p_b\left(t\right)\&greaterequal;0\\ e_b(t-1)+\frac{p_b\left(t\right)\mathrm{\&delta;}t}{{\mathrm{\&eta;}}_b},& p_b\left(t\right)<0\end{array}\right.$

In formula, energy storage carrying capacity e_bT () should meet

e_{b_rat}soc_min≤e_b(t)≤e_{b_rat}soc_max

In formula, soc_maxAnd soc_minIt is the upper and lower bound of energy-storage system soc respectively.

Because the cycle optimizes, there is seriality, the initial soc of energy-storage system of each optimization cycle should be made to be consistent, that is, have

e_b(0)=e_b(t)

4. transferable load related constraint；

For transferable load, the load capacity constraint that the t period proceeds to/produces is represented by

$\underset{t_b=max(t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}{d}_k^{tl}(t_s,t_b)<d_{k\_max}^i\left(t\right)$

$\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}{d}_k^{tl}(t_b,t_s)<d_{k\_\max }^o\left(t\right)=p_k^{tl}\left(t\right)$

In formula,WithIt is respectively the maximum amount of proceeding to of t period kth class transferable load permission and produce Amount.

Transferable load allows constraint transfer time

$\left\{\begin{array}{cc}{d}_k^{tl}(t_b,t_s)=0,& t_s\&notelement;s_k\\ d_k^{tl}(t_b,t_s)\&greaterequal;0,& t_s\&element;s_k\end{array}\right.$

In formula, s_kAllow the time range of transfer for the transferable load of kth class.

5. interconnector power constraint；

The power that exchanges between micro-capacitance sensor and bulk power grid is constrained by interconnector, that is,

p_g(t)≤min{p_{line_max},pt}

In formula, p_tFor the rated power of distribution transformer, p_{line_max}The maximum transmission power allowing for interconnector.

Above-mentioned mathematical model is carried out with the design of solution strategies, and this model is optimized.Nsga-ii algorithm is applied In the solution of Multiobjective Optimal Operation mathematical model, the flow chart being illustrated in figure 3 this algorithm, the optimized algorithm in step 2 is adopted Use nsga-ii algorithm, its algorithm steps is as follows:

Step one: data initialization, including distributed photovoltaic power generated output, energy storage charge-discharge electric power, power load distributing, Tou power price and the initialization of nsga-ii algorithm parameter；

Step 2: nsga-ii algorithm initialization, set and produce n individual initial population, each decision variable is set Scope and population algebraically；

Step 3: call the desired value of the population at individual calculating gained；

Step 4: little by order, the little principle of crowding distance carries out non-dominated ranking to initialization population；According to each individuality Non-dominant level is to population layer sorting；Based on the result of non-dominated ranking, to the pareto disaggregation of same layer according to micro-capacitance sensor Target function value calculate crowding distance；

Step 5: select, intersect and mutation operator；Calculated according to binary prize match rule and select 2 individualities, and handed over Fork, variation obtain new population；

Step 6: merge parent population and the new progeny population producing, carry out non-dominated ranking, and give to individual values Order；

Step 7: select the population of n best bodily form Cheng Xin by the little principle of the little crowding of order；

Step 8: repeat step five to step 7, stop when maximum iteration time.

Desired value in step 3 is calculated by following steps:

1): read in photovoltaic generation power p of prediction_pv(t), firm demand power p_flThe demand electricity of (t) and electric automobile Amount e_{req_n}, and decision variable p_b(t)、t_s、And t_evnValue, the initial value of wherein decision variable is by respective Randomly generate in span；

2): obtain energy storage charge-discharge electric power p_b(t)；

Randomly generate p under constraints_b(t), and calculate current carrying capacity e_bT (), checks whether satisfaction currently charged shape Modal constraint, if being unsatisfactory for, is processed using penalty function；

3): calculate the transfer general power of the t period of tl.

Under tl constrains transfer time, randomly generate t_s, and according to formula

The kth class calculating the t period after shifting can Transfer load amount, then obtains t period all transferable loading sums after transfer, and checks whether and meet transferable load Constraints；

4): calculate the charging general power of electric automobile

It it is the time that it is parked and leaves according to the dump energy of electric automobile, in the constraint bar of charging electric vehicle time The Initial charge time t of electric automobile is randomly generated under part_evn, thus calculating the charge power of single electric automobile, check simultaneously Whether meet the constraints of charging electric vehicle power, finally calculate the charging general power of electric automobile t period；

5): calculate the exchange power p between photovoltaic microgrid and power distribution network_g(t)

Try to achieve the overall power requirement in the t period of load side first, further according to power-balance equality constraint p_b(t)+p_l(t) =p_pv(t)+p_gT () asks for p_gT (), checks whether simultaneously and meets corresponding constraints；

6): calculation optimization object function, draw output result: operation total cost f of micro-capacitance sensor and micro-capacitance sensor and bulk power grid Exchange electricity g, i.e. desired value.

The overall principle of the present invention and assumed condition, and set up Multiobjective Optimal Operation mathematical model determine its constrain bar Part；

1. the load of user side of the present invention mainly includes firm demand, transferable load and charging electric vehicle load, And known to the distribution character of hypothesis load；2. present invention assumes that light storage micro-capacitance sensor is consistent with load side interests, do not count photovoltaic Send out 3. due to current electric automobile commonly used be constant voltage after first constant current charging modes, and generally, constant-current charge Stage is the Main Stage that soc increases, and charge power remains unchanged substantially, so the present invention is charged with invariable power calculates its charging Power；4. in order to reduce the impact that distributed photovoltaic power generates electricity to bulk power grid, the positive energy exchange with electrical network will be minimized herein As one of optimization aim, realize the purpose that photovoltaic generation is dissolved as far as possible inside micro-capacitance sensor；5. due to energy-storage system Depth of discharge can be used for the life-span and have a direct impact, with the increase of depth of discharge, the service life of energy storage reduces, and enters And indirectly increased the total cost of system operation.Therefore, the replacement cost of energy storage device is converted micro-capacitance sensor and is run by the present invention Total cost in, build another target of multi-objective optimization question together with purchases strategies.

Above in conjunction with accompanying drawing, the present invention is exemplarily described it is clear that the present invention implements is not subject to aforesaid way Restriction, as long as employing the improvement of the various unsubstantialities that technical solution of the present invention is carried out, or not improved by the present invention's Design and technical scheme directly apply to other occasions, all within protection scope of the present invention.

Claims (7)

1. a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: with comprise charging electric vehicle and The micro-capacitance sensor of demand response is object of study, sets up optimization object function, by target by way of founding mathematical models The optimization of function obtains configuration scheme, and its Optimal Configuration Method comprises the steps:

Step 1: object function when determining that micro-capacitance sensor runs, according to the composition structure of micro-capacitance sensor, determine the object function optimizing；

Step 2: determine bound for objective function, determine that micro-capacitance sensor is transported according to the running environment of object function and micro-capacitance sensor The constraints of row；

Step 3: determine optimized algorithm, and optimization object function under optimized algorithm, draw configuration scheme.

2. as claimed in claim 1 a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: described Object function be: the minimum object function of total cost is run with micro-capacitance sensor and to exchange electricity with micro-capacitance sensor and bulk power grid minimum Object function.

3. as claimed in claim 2 a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: micro- electricity Network operation total cost includes putting of power trade cost, the depreciable cost of energy storage device and the energy storage device of micro-capacitance sensor and bulk power grid Electric penalty function, the expression formula running the minimum object function of total cost with micro-capacitance sensor is as follows:

Wherein,

In formula, δ t is unit time interval, and t is the when hop count optimizing；p_gT () is to exchange with power distribution network in unit time interval Mean power (being just during power purchase, be negative during sale of electricity), p_rT () is average transaction value in unit time interval；p_b(t) and p_b(t) It is respectively the discharge power of t period energy-storage system and discharge and recharge exchanges power, wherein p_bIt is just p when () is charged t_bDuring (t) electric discharge It is negative；σ is penalty coefficient；Soc (t) is the state-of-charge of t period energy storage；soc_minLower limit for energy storage charge state.

4. as claimed in claim 2 a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: micro- electricity Exchange electricity between net and bulk power grid is less, then the impact to bulk power grid for the micro-capacitance sensor is also little, exchanges electricity minimum expression formula and is:

$\min g=\mathrm{\&delta;}t\underset{t=1}{\overset{t}{\&sigma;}}\left|p_g\left(t\right)\right|$

In formula, p_gT () is the mean power exchanging with power distribution network in unit time interval, be just, be negative during sale of electricity during power purchase.

5. as claimed in claim 1 a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: described Step 2 in constraints include:

1. power-balance constraint

According to the internal realtime power balance and between power distribution network of micro-capacitance sensor, obtain:

p_b(t)+p_l(t)=p_pv(t)+p_g(t)

In formula, p_pvT () is photovoltaic generation power, p_l(t) be t period load side total load power, it include firm demand and Transferable load after transfer and the power of electric automobile consumption, that is,

$p_l\left(t\right)=p_{fl}\left(t\right)+\underset{k=1}{\overset{m}{\&sigma;}}{p}_k^{{\mathrm{tl}}^{\&prime;}}\left(t\right)+p_{ev}\left(t\right)$

In formula, p_flT () is the firm demand power of t period；The transferable load of kth class for the t period after transfer Amount, p_evT () is the charging general power of electric automobile t period, wherein,

$\begin{array}{c}{p}_k^{{\mathrm{tl}}^{\&prime;}}\left(t\right)=p_k^{tl}\left(t\right)+\mathrm{...}\\ \mathrm{...}\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}\left(d_k^{tl}\right(t_s,t_b)-d_k^{tl}(t_b,t_s\left)\right)\end{array}$

In formula,For the transferable loading of t period before transfer, lk is the continuous working period of the transferable load of kth class；

$p_{ev}\left(t\right)=\underset{n=1}{\overset{n}{\&sigma;}}{p}_{evn}\left(t\right)$

In formula, p_evnCharge power for n-th electric automobile；N is the quantity of electric automobile, and calculates base with invariable power charging Originally the charge requirement of client can be completed, constraint formula is:

$p_{avrg\_n}=\frac{e_{evn}}{t_2-t_1}$

$e_{evn}={\&integral;}_{t_1}^{t_2}{p}_{ev\_n}\left(t\right)dt$

Wherein, p_{avrg_n}For average charge power in charge period for n-th car；t_evnFor the initiation of charge moment of n-th car, For one of decision variable；p_{ev_n}T () is the charge power in t for n-th ev；e_evnFor n-th car in t₁～t₂The filling of moment Enter electricity, by electrical demand amount e of n-th electric automobile_{req_n}Determine；

2. charging electric vehicle power constraint and charging interval constraint；

p_evn≤|p_{ev_rat}|

$\left\{\begin{array}{cc}{p}_{evn}=0,& t_{evn}\&notelement;s_{ev}\\ p_{evn}>0,& t_{evn}\&element;s_{ev}\end{array}\right.$

In formula, p_{ev_rat}Specified charge-discharge electric power for electric automobile rechargeable battery；s_evFor electric automobile rest on charging station when Between gather；

3. the charge-discharge electric power of energy-storage system and soc constraint；

Being constrained to of the charge-discharge electric power of energy-storage system: | p_b(t)|≤p_{b_rat}

In formula, p_{b_rat}Rated power for energy-storage system；η_bIt is the charge/discharge efficiency of energy-storage system；

The current carrying capacity e of energy-storage system_bT () can be obtained by following formula recursion, that is,

$e_b\left(t\right)=\left\{\begin{array}{cc}{e}_b(t-1)+p_b\left(t\right){\mathrm{\&delta;t\&eta;}}_b,& p_b\left(t\right)\&greaterequal;0\\ e_b(t-1)+\frac{p_b\left(t\right)\mathrm{\&delta;}t}{{\mathrm{\&eta;}}_b},& p_b\left(t\right)<0\end{array}\right.$

In formula, energy storage carrying capacity e_bT () should meet

e_b__ratsoc_min≤e_b(t)≤e_b__ratsoc_max

In formula, soc_maxAnd soc_minIt is the upper and lower bound of energy-storage system soc respectively；

Because the cycle optimizes, there is seriality, the initial soc of energy-storage system of each optimization cycle should be made to be consistent, that is, have

e_b(0)=e_b(t)

4. transferable load related constraint；

For transferable load, the load capacity constraint that the t period proceeds to/produces is represented by

$\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}{d}_k^{tl}(t_s,t_b)\&le;d_{k\_max}^i\left(t\right)$

$\underset{t_b=\max (t-l_k+1,1)}{\overset{t}{\&sigma;}}\underset{t_s=1}{\overset{t}{\&sigma;}}{d}_k^{tl}(t_b,t_s)\&le;d_{k\_\max }^o\left(t\right)=p_k^{tl}\left(t\right)$

In formula,WithIt is respectively the maximum amount of proceeding to and the amount of producing that the transferable load of t period kth class allows；

Transferable load allows constraint transfer time

$\left\{\begin{array}{cc}{d}_k^{tl}(t_b,t_s)=0,& t_s\&notelement;s_k\\ d_k^{tl}(t_b,t_s)\&greaterequal;0,& t_s\&element;s_k\end{array}\right.$

In formula, s_kAllow the time range of transfer for the transferable load of kth class；

5. interconnector power constraint；

The power that exchanges between micro-capacitance sensor and bulk power grid is constrained by interconnector, that is,

p_g(t)≤min{p_{line_max},p_t}

In formula, p_tFor the rated power of distribution transformer, p_{line_max}The maximum transmission power allowing for interconnector.

6. as claimed in claim 1 a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: described Step 2 in optimized algorithm adopt nsga-ii algorithm, its algorithm steps is as follows:

Step one: data initialization, including distributed photovoltaic power generated output, energy storage charge-discharge electric power, power load distributing, timesharing Electricity price and the initialization of nsga-ii algorithm parameter；

Step 2: nsga-ii algorithm initialization, set and produce n individual initial population, the scope of each decision variable is set And population algebraically；

Step 3: call the desired value of the population at individual calculating gained；

Step 4: little by order, the little principle of crowding distance carries out non-dominated ranking to initialization population；According to each individuality non- Water distribution is put down to population layer sorting；Based on the result of non-dominated ranking, to the pareto disaggregation of same layer according to the mesh of micro-capacitance sensor Offer of tender numerical computations crowding distance；

Step 5: select, intersect and mutation operator；Calculated according to binary prize match rule and select 2 individualities, and carry out intersecting, become Different obtain new population；

Step 6: merge parent population and the new progeny population producing, carry out non-dominated ranking, and give order to individual values；

Step 7: select the population of n best bodily form Cheng Xin by the little principle of the little crowding of order；

Step 8: repeat step five to step 7, stop when maximum iteration time.

7. as claimed in claim 5 a kind of consider multiobject light store micro-capacitance sensor running optimizatin method it is characterised in that: described Step 3 in desired value calculated by following steps:

1): read in photovoltaic generation power p of prediction_pv(t), firm demand power p_flThe demand electricity of (t) and electric automobile e_{req_n}, and decision variable p_b(t)、t_s、And t_evnValue, the initial value of wherein decision variable is by respectively asking for Randomly generate in the range of value；

2): obtain energy storage charge-discharge electric power p_b(t)；

Randomly generate p under constraints_b(t), and calculate current carrying capacity e_bT (), checks whether and meets current state-of-charge about Bundle, if being unsatisfactory for, is processed using penalty function；

3): calculate the transfer general power of the t period of tl；

Under tl constrains transfer time, randomly generate t_s, and according to formula

Calculate the kth of t period after shifting The transferable loading of class, then obtains t period all transferable loading sums after transfer, and check whether meet transferable negative The constraints of lotus；

4): calculate the charging general power of electric automobile

It it is the time that it is parked and leaves according to the dump energy of electric automobile, under the constraints of charging electric vehicle time Randomly generate the Initial charge time t of electric automobile_evn, thus calculating the charge power of single electric automobile, check whether simultaneously Meet the constraints of charging electric vehicle power, finally calculate the charging general power of electric automobile t period；

5): calculate the exchange power p between photovoltaic microgrid and power distribution network_g(t)

Try to achieve the overall power requirement in the t period of load side first, further according to power-balance equality constraint p_b(t)+p_l(t)=p_pv (t)+p_gT () asks for p_gT (), checks whether simultaneously and meets corresponding constraints；

6): calculation optimization object function, draw output result: the friendship of operation total cost f of micro-capacitance sensor and micro-capacitance sensor and bulk power grid Change electricity g, i.e. desired value.