CN114567006A - Multi-objective optimization operation method and system for power distribution network - Google Patents

Abstract

The invention discloses a multi-objective optimization operation method and system for a power distribution network, and belongs to the technical field of optimization scheduling of the power distribution network. In the prior art, the constraint of the regulation rate of the power regulation resource is not considered comprehensively, so that the optimization result cannot be implemented in the adjustable resource power regulation in the actual engineering. The multi-objective optimization operation method for the power distribution network considering the adjustable resource regulation rate can meet the maximum regulation rate constraint, further enables the optimization result to be more reasonable, accords with the actual constraint of the industry, has the advantages of enabling the simulation calculation of the optimization scheduling of the power distribution network to be more accurate, and meeting the actual constraint requirement of equipment participating in optimization in engineering. The invention can set different optimization objective function expressions and different decision variables and constraint conditions according to different optimization scenes on the aspect of processing the problem that adjustable resources participate in the optimization operation of the power distribution network, flexibly adopts a numerical optimization algorithm and an intelligent algorithm, and has simple, practical and feasible scheme.

Description

Multi-objective optimization operation method and system for power distribution network

Technical Field

The invention relates to a multi-objective optimization operation method and system for a power distribution network, and belongs to the technical field of optimization scheduling of the power distribution network.

Background

In the existing multi-objective optimization problem of a plurality of power distribution networks containing renewable energy, in order to ensure that the renewable energy is consumed as much as possible, the renewable energy is assumed to be fully used, and the situation that under a certain operation scene, the load side demand is not so large, the residual capacity of an energy storage system is insufficient or the regulation rate is insufficient is ignored, so that the generated energy of the renewable energy cannot be completely consumed and cannot be completely stored in the energy storage system at the optimization moment.

In the calculation of the optimal scheduling of the power distribution network, the prior art lacks consideration of the constraint of the adjustment rate of the power adjustment resources, the adjustment capability of the adjustable resources participating in the optimal operation of the power distribution network is often excessively amplified, and the optimization result cannot be applied to the power adjustment of the adjustable resources in the actual engineering. For example, when the optimization is performed according to the prior art, the situation that the rise amplitude of the electrical load at adjacent optimization moments is large may occur, and if the planned generated power of a certain power generation device at the previous scheduling moment is small, and the planned generated power of the certain power generation device at the later scheduling moment is required to be large to meet the power balance, and the difference between the generated powers at the previous and next moments exceeds the maximum adjustment rate, the power supply reliability cannot be met in the actual adjustment.

Further, in the control calculation of the optimal scheduling of the power distribution network, in the prior art, a coping method for the problem that the optimal solution for the lower-layer optimization cannot meet the global optimization of the upper-layer optimization after optimization is rarely considered, so that the final optimization result is not optimal.

Disclosure of Invention

Aiming at the defects of the prior art, the invention aims to provide a power distribution network multi-objective optimization operation method and system which can meet the maximum regulation rate constraint, further enable the optimization result to be more reasonable, meet the actual constraint of the industry, enable the calculation of power distribution network optimization scheduling to be more accurate and meet the actual constraint requirement of equipment participating in optimization in the engineering.

The second purpose of the invention is to provide a method for establishing a sub-objective function of an upper-layer optimization objective function as an objective function of a lower-layer optimization, so that the condition that the final optimization result is not optimal due to optimization is avoided to a certain extent, and the method has the advantage that the final optimization result of the problem that adjustable resources participate in the optimization operation of the power distribution network is more reasonable; in the lower-layer optimization, bilinear objective functions which are restricted with each other are constructed, the situation that the optimal solution of the lower-layer optimization cannot meet the global optimization of the upper-layer optimization possibly caused by the optimization is avoided to a certain extent, and the effect of the lower-layer optimization of peak clipping and valley filling of the active total load of the system by ordered charging of the electric automobile and no new charging peak is ensured is realized.

In order to achieve one of the above objects, a first technical solution of the present invention is:

a multi-objective optimization operation method of a power distribution network considering adjustable resource regulation rate,

the method comprises the following steps:

the method comprises the following steps: acquiring a plurality of targets capable of assisting the power distribution network in optimizing operation;

the plurality of targets comprise renewable energy sources, energy storage systems, temperature control loads and electric vehicle loads;

step two, setting corresponding decision variables and constraint conditions thereof according to the targets in the step one;

when the renewable energy sources participate in regulation, the dispatching plan output of each grid-connected renewable energy source unit after participating in optimization at each optimization moment is selected as a decision variable, regulation rate constraint does not need to be considered in the regulation of the decision variable, and the constraint condition is that the grid-connected power is larger than zero and smaller than the predicted output;

when the energy storage systems participate in regulation, the output power of each energy storage system at each optimization moment is selected as a decision variable, and both regulation rate constraint and energy storage output constraint are considered;

when the temperature control load participates in adjustment, the total output power of all the electric refrigerating units for supplying cold to each cold load at each optimization moment is selected as a decision variable, and the adjustment rate constraint of the temperature control load needs to be considered while the user satisfaction constraint is considered;

when the electric automobile load participates in regulation, the main body participating in power regulation is the output power of the charging device at the network topology node at each optimized moment, and the output power of the charging device at a certain moment is the opposite number of the sum of all the electric automobile charging powers at the certain moment;

step three, constructing system constraints according to the decision variables and the constraint conditions thereof in the step two;

the system constraint comprises power grid power balance constraint and power grid load flow constraint;

the power grid power balance constraint is used for controlling the total power supply power to be equal to the total power utilization power, and is embodied in that the input power and the output power of each node in the network topology are equal, and the power grid power balance constraint needs to be met through load flow calculation; calculating voltage and power corresponding to each node and loss on each line by inputting net injection power, impedance of a distribution network line and communication information of distribution network topology corresponding to each type of node;

the power grid power flow constraint is used for controlling the voltage amplitude to fluctuate within an allowable range and controlling the active power transmission and the reactive power transmission of the line to fluctuate within an allowable range;

step four, constructing an optimization target with lowest daily operation cost and highest comprehensive energy efficiency according to system constraints in the step three;

the lowest daily operation cost is the lowest operation cost of the power distribution network in the whole day scheduling period, and the lowest daily operation cost comprises the electricity purchasing cost of the power distribution network to a superior power distribution network, the operation income of a load aggregator, the online scheduling cost of a distributed power supply and the wind and light abandoning cost;

the highest comprehensive energy efficiency is the highest comprehensive energy efficiency of the power distribution network in the whole day scheduling period, and is used for reflecting the ratio of the total load power consumption and the total power supply quantity in the power distribution network in one day;

step five, an optimization strategy of the adjustable resources is formulated according to the optimization target in the step four;

the optimization strategy comprises the following contents:

for the power generation of renewable energy sources, a downward regulation model is constructed, and the output power of the downward regulation model is controlled;

the downward adjustment model intentionally reduces the output of renewable energy sources at each optimization moment through power electronic elements, and achieves optimization targets such as optimal economy, highest comprehensive energy efficiency and the like on the basis of meeting power balance;

meanwhile, the cost of abandoned wind and abandoned light is increased in the expression of the daily operation cost of the objective function, and the cost is used for reducing the loss of electricity selling income caused by power generation, so that the renewable energy and the resources on the network side, the load side and the storage side play games according to the difference of the cost of abandoned wind and abandoned light and the scheduling cost and the optimal target with the highest comprehensive energy efficiency, and an optimal adjustable resource scheduling plan is obtained;

unified scheduling is carried out on temperature control units which can reduce loads and participate in adjustment;

for the electric automobile load, under the condition that the electric automobile participates in disordered charging before adjustment, the condition that the electric automobile starts charging at the middle time of a certain scheduling time interval is equivalent to charging from the starting time of the time interval, the condition that the electric automobile stops charging at the middle time of the certain scheduling time interval is equivalent to charging stopping at the ending time of the time interval, and the charging power corresponding to the actual charging time in the scheduling time interval of the starting time or the ending time of the charging is converted into the average charging power in the whole time, so that the load size of the electric automobile during disordered charging before the adjustment is participated in is calculated;

obtaining the output of each adjustable resource at each optimization moment; the adjustable resources are distributed on different nodes in the network topology, so that the net injection power of the nodes is calculated by combining the output of the adjustable resources at each optimization moment and the load of each node, and the net injection power is substituted for load flow calculation so as to meet the node power balance constraint and the network constraint;

step six: optimizing the operation process according to the optimization strategy in the fifth step;

and solving the optimal solution of the optimal operation problem of the adjustable resource auxiliary power distribution network by adopting an optimization algorithm, and executing according to the optimal solution to realize multi-objective optimal operation of the power distribution network.

Through continuous exploration and tests, the invention provides the optimization method considering the adjustable resource regulation rate constraint, can meet the maximum regulation rate constraint, further enables the optimization result to be more reasonable, accords with the actual constraint of the industry, and has the advantages of enabling the simulation calculation of the optimization scheduling of the power distribution network to be more accurate and meeting the actual constraint requirement of equipment participating in optimization in the engineering.

The invention fully considers various operation scenes and provides a concept of downward adjustment of output power, namely, the output of renewable energy sources is intentionally reduced at each optimization moment through power electronic elements, and optimization targets of optimal economy, highest comprehensive energy efficiency and the like are achieved on the basis of meeting power balance. The renewable energy output power is adjusted downwards, so that the problems of wind abandon, light abandon and the like are inevitably caused, in order to reflect the interactive relationship between the source network and the load storage and realize that the power balance is met by not depending on the downward adjustment of the renewable energy output power, the wind abandon cost and the light abandon cost are increased in the expression of the daily operation cost of the objective function, so that the renewable energy and the resources on the network side, the load side and the storage side play games according to the difference of the wind abandon cost, the light abandon cost and the scheduling cost and the optimization target with the highest comprehensive energy efficiency, and the optimal adjustable resource scheduling plan is obtained.

Furthermore, the invention can set different optimization objective function expressions and different decision variables and constraint conditions according to different optimization scenes on the aspect of processing the problem that adjustable resources participate in the optimization operation of the power distribution network, flexibly adopts a numerical optimization algorithm and an intelligent algorithm, and has simple, practical and feasible scheme.

Furthermore, after each group of genes (decision variables) of each generation is generated, the output of each generation at each optimization moment is obtained according to each adjustable resource model. Because the adjustable resources are distributed on different nodes in the network topology, the net injection power of the nodes can be calculated by combining the output of the adjustable resources at each optimization moment and the load of each node, and the net injection power is substituted to perform load flow calculation. And then calculating a fitness function value corresponding to the optimization target under the condition of meeting power balance constraint and power flow constraint of the power grid, and screening more excellent genes according to the fitness function value. And finally, according to the screened excellent genes, performing steps of cross mutation and the like to generate offspring genes, and participating in optimization again to serve as a period.

The adjustment rate is the change of the output power of the adjustable resource or equipment in unit time. The rate of adjustment can reflect the magnitude of the change in output power of the device resource over a short period of time. For the power generation equipment, the change of the generated power of the power generation equipment in unit time is reflected; the power consumption load is represented by the change of the power consumption in unit time. The power plant's rate of power regulation cannot exceed its inherent upper regulation rate limit per unit time.

As a preferable technical measure:

in the second step, the grid-connected power is larger than zero and smaller than the predicted output, and the specific calculation formula is as follows:

wherein the content of the first and second substances,

the power output, kW, of the dispatching plan after the d-th renewable energy grid-connected unit participates in optimization at the time t;

predicting output power, kW, of the d-th renewable energy grid-connected unit at the time t;

the calculation formula for the regulation rate constraint of the energy storage system is as follows:

wherein the content of the first and second substances,

the output power (negative during charging and positive during discharging) of the e-th energy storage system at the moment t is kW;

the output power of the e-th energy storage system at the moment t-1 is kW;

the regulation rate upper limit of the e-th energy storage system is kW/h;

the calculation formula of the capacity constraint of the energy storage system is as follows:

wherein the content of the first and second substances,

outputting electric energy, kWh, of the f energy storage system at the moment t;

total capacity of the f-th energy storage system, kWh;

and T is the accumulated charge (discharge) moment number.

As a preferable technical measure:

the calculation formula of the mathematical model of the cooling load is as follows:

wherein the content of the first and second substances,

indoor temperature of the g-th cold load (hall room) at time t, ° c;

indoor temperature of the g-th cold load (hall room) at time t +1, DEG C; c_gEquivalent heat capacity of the g-th cold load (hall room), kW/deg.c; r_gEquivalent thermal resistance of the g-th cold load (hall room), DEG C/kW;

the outdoor ambient temperature of the cold load (hall) at time t is ° c;

the total output power at the moment t of all the electric refrigerating units for supplying cold to the g-th cold load (hall room), namely kW;

the user satisfaction is related to the indoor temperature of the cold load (the hall room), the value range is [0,1], and the adjustability potential of the cold load are embodied; the calculation formula of the user satisfaction degree constraint of the cold load (hall room) is as follows:

wherein, the first and the second end of the pipe are connected with each other,

the indoor temperature, deg.c, desired by the user of the g-th cold load (hall room) at time t; u is user satisfaction; if the lowest boundary value of the user satisfaction degree is specified, the difference value between the reversely deduced indoor temperature value and the user expected temperature is the adjustable range corresponding to the cold load;

the set temperature of the electric refrigerating unit is the same as the expected temperature of a user, but considering that the electric refrigerating unit has a temperature control dead zone, and defining delta as the hysteresis width between the upper limit and the lower limit of the operating temperature of the electric refrigerating unit, the relation between the fluctuation upper limit and the fluctuation lower limit of the actual temperature of the user and the set temperature of the electric refrigerating unit is as follows:

wherein the content of the first and second substances,

the set temperature, DEG C, of all the electric refrigerator sets for cooling the g-th cooling load (hall room) is numerically equal to

Equal; delta is the hysteresis width between the upper limit and the lower limit of the operation temperature of the electric refrigerating unit, and is DEG C;

and

the upper limit and the lower limit of the fluctuation of the temperature expected by the actual user are respectively shown in the specification of

Indoor temperatures within the range all meet the expectations of users, and the adjustable potential of the cold load is also increased to a certain extent;

the mathematical model of the electric refrigerating unit is as follows:

wherein the content of the first and second substances,

the refrigeration coefficients of all the electric refrigeration units for supplying cold to the g-th cold load (hall room);

all the electric refrigerating units for cooling the g-th cold load (hall room) are arranged intotal power consumption at time t, kW;

the adjustment rate constraint for all the electric refrigerator groups for the cooling of the g-th cooling load (hall) is calculated as follows:

wherein the content of the first and second substances,

the total output power at the moment t-1 of all the electric refrigerating units for supplying cold to the g-th cold load (hall room), kW;

the regulation rate upper limit of all electric refrigerating unit for cooling the g-th cold load (hall room), kW/h;

in addition, the constraint of the upper limit of the power consumption is also considered for the electric refrigerator, as shown in the following formula:

wherein the content of the first and second substances,

the total output power upper limit, kW, of all the electric refrigerating units supplying cold to the g-th cold load (hall room);

selecting whether charging of a certain electric automobile belongs to a 0-1 planning problem at a certain charging pile at a certain moment, and defining a decision variable as a charging state matrix of a jth electric automobile at the ith charging pile at the t moment

The elements in the charge control circuit are binary integer variables of 0-1, wherein 1 is selected when charging is selected, and 0 is selected when no charging is selected;

output power of ith charging pile at time t

The formula (c) is shown as follows:

the method is used for reflecting the relation between the output power of a certain charging pile and the charging power of all electric vehicles at a certain optimized moment, and reflecting the existing constraint in a charging state matrix

The value of the element(s) is taken;

wherein the content of the first and second substances,

the output power (negative number) of the ith charging pile at the moment t is kW;

the dimension of a charging state matrix of the jth electric automobile at the ith charging pile at the time t is 1 multiplied by N_jIn which N is_jThe total number of the j-th type electric vehicles;

charging power, kW, of the jth type electric automobile at the ith charging pile at the moment of t, wherein the dimensionality is N_jX 1; j is the total number of types of the electric automobiles participating in the adjustment in the system;

the calculation formula for satisfying the output power limit constraint of each electric pile type at each moment is as follows:

wherein the content of the first and second substances,

the charging power upper limit, kW, of the ith charging pile at the single optimization moment;

each electricityRemaining charging time T for moving automobile until full_chThe calculation formula of (a) is as follows:

wherein, for a certain electric automobile participating in regulation, s is the current driving mileage, km, of the electric automobile; w₁₀₀The electric consumption of the electric automobile is 100km each time, the kWh/100 km; p_chIs the average charging power, kW; eta_chTo the charging efficiency;

the calculation formula of the charging time constraint of the electric automobile is as follows:

wherein the content of the first and second substances,

the maximum charging time length h of the w-th electric automobile from the time t;

and the actual charging time length h of the w-th electric automobile from the time t.

As a preferable technical measure:

in the third step, the method for load flow calculation is as follows:

firstly, judging the node type; node types can be divided into three categories: PV node, PQ node, V delta node; the PQ node is a node with known injected active power and reactive power and unknown voltage amplitude and phase angle; PV nodes are nodes with known voltage amplitude values, active power, unknown node voltage phase angles and reactive power; the V delta node is also called a balance node, the voltage amplitude and the phase angle of the node are known, and the essence of load flow calculation is that power is supplied to other nodes by adjusting the active and reactive outputs of the balance node so as to maintain the power balance of a power grid;

the network has n nodes, wherein the PV nodes are r, the V delta nodes are 1, the PQ nodes are n-r-1, a power flow equation is obtained, and the calculation formula is as follows:

wherein, P^SPAnd Q^SPRespectively injecting an active power vector (W) and a reactive power vector (Var) into the full-node net; u is a full node voltage vector, V; y is a node admittance matrix, S; u shape^SPSetting initial node voltage V for all nodes; Δ P is the active injection deviation, W, of PQ and PV nodes; Δ Q is the PQ node reactive injection deviation, Var; Δ U is the PV node voltage amplitude squared difference, V²；

The voltage is calculated as follows:

wherein, V_gIs the voltage at node g, V;

and

the upper limit and the lower limit of the voltage amplitude of the node g, V, respectively;

the calculation formula of the line active power transmission and reactive power transmission is as follows:

wherein, P_hIs the active power on line h, W;

and

upper and lower limits, W, of active power on line h, respectively; n is_bThe total number of branches in the network;

Q_his the reactive power on line h, Var;

and

respectively, the upper and lower limit of reactive power on the line h, Var.

As a preferable technical measure:

in the fourth step, a calculation formula taking the lowest operation cost of the power distribution network in the whole-day scheduling period as an optimization target is as follows:

wherein the content of the first and second substances,

purchasing electricity from the power distribution network to a superior power grid at the moment t;

the operation income (the charge of the distribution network to the load aggregator) of the load aggregator at the time t is obtained;

scheduling cost for the distributed power supply to access the Internet at the time t;

punishing cost for wind abandon at the time t;

discarding the cost of light at the moment T, wherein T is the total number of the optimized moments;

wherein, the power purchasing cost of the power distribution network to the superior power grid at the time t

As shown in the following formula:

wherein, the first and the second end of the pipe are connected with each other,

the unit price of purchasing electricity to a superior power grid at the moment t, yuan/kWh; p is_PCC(t) is the power exchange value, kW, between the power exchange value and a superior power grid tie line at the moment t; delta t is the duration of a single optimization moment, h;

operating revenue of load aggregator at time t

The calculation formula of (a) is as follows:

wherein, P_ESS,a(t) the output power (positive during discharging; negative during charging) of the a-th energy storage system at the moment t, kW;

scheduling prices, yuan/kWh, for the energy storage of the a-th energy storage system at the moment t; a is the total number of energy storage systems participating in regulation; p_EVA,b(t) is the output power of the b-th electric vehicle (negative during charging, without considering discharging), kW at time t;

the charging price of the b-th electric vehicle at the moment t is yuan/kWh; b is the total number of the electric automobiles participating in the regulation; p_FL,c(t) the response power, kW, of the c-th compliant load participating in the adjustment at time t;

the calling cost, yuan/kWh, of the c-th flexible load participating in the adjustment at the moment t; c is the total number of electric vehicles participating in regulation;

internet surfing scheduling cost of t-time distributed power supply

The calculation formula of (a) is as follows:

wherein, P_DG,m(t) is the output power, kW, of the mth grid-connected distributed energy at the moment t;

maintenance cost, yuan/kWh, of the mth grid-connected distributed energy at the moment t; m is the total number of the grid-connected distributed energy sources;

cost of wind waste at time t

And cost of light rejection

The calculation formula of (a) is as follows:

wherein, pnt^WT(t) the unit cost of the abandoned wind at the moment t, yuan/kWh;

predicting the output, kW, of the kth grid-connected wind turbine generator at the time t;

the output of a dispatching plan after the kth grid-connected wind turbine generator participates in optimization at the moment t, kW; k is the total number of the grid-connected wind turbine generators;

pnt^PV(t) the cost unit price of the light abandon at the time t, yuan/kWh;

predicting the output, kW, of the first grid-connected photovoltaic unit at the moment t in the day ahead;

the output of a dispatching plan after the first grid-connected photovoltaic unit participates in optimization at the moment t, kW; l is the total number of the grid-connected photovoltaic units;

the specific expression taking the highest comprehensive energy efficiency of the power distribution network in the whole-day scheduling period as an optimization target is as follows:

wherein, P_LOAD,r(t) is the load size at the r node in the time t, kW; r is the total number of nodes of the power distribution network; epsilon is the permeability of renewable energy of a superior power grid; eta_reGenerating efficiency of renewable energy sources of a superior power grid; eta_nreThe power generation efficiency of the non-renewable energy of the superior power grid is obtained.

As a preferable technical measure:

in the fifth step, a lower-layer double optimization target model and a linearly superposed total optimization target model are constructed for the electric vehicle load to be optimized;

the lower layer double optimization target model comprises the following contents:

firstly, the electric automobile charged at each charging pile node in the network topology at each optimization moment is subjected to combined optimization, and the optimal charging state matrix is solved by taking the lowest daily charging cost sum and the lowest daily net load increase ratio sum as optimization targets

Namely an optimal combined charging plan of the jth electric automobile at the ith charging pile at the time t; then solving the output power of each charging pile at each optimized moment according to the optimal combined charging plan of the electric automobile obtained by the lower-layer optimization, taking the output power as a constant load, and participating in the solution of the optimal scheduling plan of other adjustable resources in the power distribution network;

the objective function expression of the total daily charging cost of the electric vehicle is as follows:

wherein, C_EVAThe total daily charging cost of the electric automobile is Yuan; p_EVA,b(t) is the output power of the b-th electric vehicle (negative during charging, without considering discharging), kW at time t;

the charging price of the b-th electric vehicle at the moment t is yuan/kWh; b is the total number of the electric automobiles participating in the regulation; delta t is the duration of a single optimization moment, h; t is the total number of the optimization moments;

the expression of the objective function of the daily net load increase ratio sum of the electric vehicle is as follows:

wherein the content of the first and second substances,

the daily net load increase ratio of the electric automobile in the network topology is obtained; p_Load(t) is the active total load size, kW, in the network topology before the electric automobile is charged at the moment t;

two objective functions are processed by first normalization C_EVAAnd R_ΔLoadSuch that each time C calculated is optimized_EVAAnd R_ΔLoadIs at the same value asAn order of magnitude, and then the weights occupied by the two optimization objectives

And

linearly superposing the two objective functions into a single objective function to form a total optimization objective model, wherein the calculation formula is as follows:

wherein f is_sublayerIs the sum of the lower optimized normalized objective functions;

and

the weights of two optimization targets are respectively the daily charging cost sum and the daily net load increase ratio sum;

and

normalization coefficients of two objective function values, namely a daily charging cost sum and a daily net load increase ratio sum respectively;

the definition of the sum of the daily net load increase ratios is that for all the optimization moments in one day, the ratio of the sum of all the electric automobile charging power in each moment to the active total load in the network at the optimization moment is calculated, and then the sum of the net load increase ratios at all the optimization moments is calculated; if there are T optimization moments in a day, then

The value can be 1/T, and the value range of the sum of the daily net load increase ratio of the normalized target function is [0,1]](ii) a Therefore, the temperature of the molten metal is controlled,

can take on the value of

Wherein

The sum of the charging costs of all the electric vehicles charged with the maximum power at the moment of the peak electricity price is defined as the value range of the sum of the daily charging costs of the normalized objective function of 0,1](ii) a The occupied weight of two optimization objectives

And

should be specified according to the emphasis of the actual optimization target, and meet

As a preferable technical measure:

the total optimization objective model is a sum f of normalized objective functions for optimizing the lower layer_sublayerAt a minimum, it is desirable to make C_EVAAs small as possible while making R_ΔLoadThe value of (A) is as large as possible; because the unit price of charging is higher in peak electricity price moment, electric automobile can more tend not to charge, compares the value of each parameter when unordered charged state before the dispatch, can make after the dispatch

Is smaller, thereby making C_EVAIs smaller, and f_sublayerHas the same optimization direction, but at the same time can also make R_ΔLoadIs smaller, and f_sublayerThe optimization directions of the sub-objective functions are opposite, so that contradiction is generated between the two sub-objective functions, and lower-layer optimization is dedicated to searching a compromise solution in the optimal solution; the electric automobile tends to be more prone to charging within the time of low valley electricity price due to the fact that the charging unit price is lowerCharging, compared with the value of each parameter in the unordered charging state before scheduling, the method can lead the parameters to be charged after scheduling

Is greater, thereby making C_EVAGreater value of, and f_sublayerThe optimization direction of (1) is opposite, but R can also be caused at the same time_ΔLoadGreater value of, and f_sublayerThe optimization directions of the two sub-target functions are the same, so that contradiction is generated between the two sub-target functions, and lower-layer optimization is dedicated to searching a compromise solution in the optimal solution, so that peak clipping and valley filling of the active total load (including the charging load) of the system are guaranteed, and a new charging peak cannot be formed.

The method of establishing the sub-objective function of the upper-layer optimization objective function as the objective function of the lower-layer optimization avoids the situation that the final optimization result is not optimal possibly caused by optimization to a certain extent, and has the advantage that the final optimization result of the problem that adjustable resources participate in the optimization operation of the power distribution network is more reasonable.

Compared with the prior art, the invention provides a method adopting layered optimization, in the lower-layer optimization, bilinear objective functions which are restricted with each other are constructed, one of the bilinear objective functions is shown as formula (28) and belongs to a sub-objective function of daily operating cost (shown as formula (18)) of the upper-layer optimization objective function, and the condition that the lower-layer optimization optimal solution cannot meet the global optimization of the upper-layer optimization, which is possibly caused by the layered optimization, is avoided to a certain extent.

Further, the method and the device perform normalization processing on bilinear objective functions which are restricted with each other in the lower-layer optimization, and linearly superpose the bilinear objective functions into a single linear objective function after weights are given, so that the effect of 'not only ensuring that the orderly charging of the electric automobile cuts peaks and fills valleys of the active total load (including the charging load) of the system, but also ensuring that no new charging peak is formed' in the lower-layer optimization is realized.

As a preferable technical measure:

the sixth step, the optimization algorithm is an NSGA-II optimization algorithm, which specifically comprises the following contents:

before optimization, the population size N needs to be set_popAnd number of evolutionsG_pop(ii) a Then, optimization is executed;

after the optimization is finished, N is obtained_popGroup solutions and Pareto grades corresponding to each group solution; the solution with Pareto grade of 1 is completely proposed to obtain

Assembling and solving; then, will

All corresponding to the group solution

The calculated value of the objective function is normalized to obtain all the values

Converting the calculated values of the objective functions into the same order of magnitude; for the m-th_objThe normalization processing method of the calculated value of the objective function is represented as follows:

wherein the content of the first and second substances,

is m at_objCorresponding to an objective function

A vector composed of the normalized values of the objective function;

is m at_objCorresponding to an objective function

A maximum of the objective function calculated values;

is m at_objCorresponding to an objective function

A minimum of the objective function calculated values;

is m at_objCorresponding to an objective function

A vector composed of objective function calculated values;

will M_objAfter normalization processing is carried out on all the objective functions according to the formula (31), M needs to be specified in the next step_objWeight of optimization objective

Satisfy the requirement of

Then, by the following formula, calculation

Group solution of M corresponding to each group_objThe specific calculation formula of the sum of the normalized values of the objective function is shown as the following formula:

wherein the content of the first and second substances,

is composed of

M corresponding to each group solution in group solutions_objA vector consisting of the sum of the normalized values of the objective function,

is m at_objThe weight occupied by each optimization objective;

finally, search for the obtained

And a group of solutions corresponding to the maximum value in the data acquisition is the optimal solution of the optimal operation problem of the adjustable resource auxiliary power distribution network.

As a preferable technical measure:

initializing decision variables;

the size of a population participating in operation needs to be set, and all decision variables participating in optimization are taken as genes; in the process of initializing genes, regulation rate constraint needs to be considered, and initialization methods including a uniform generation method and a special generation method are set for different adjustable resources;

the unified generation method comprises a unified generation method of initial values of genes at the first optimization moment and a unified generation method of initial values of genes at the non-first optimization moment;

the unified generation method of the initial value of the first optimization time gene specifically comprises the following steps:

the generation of the initial values of the genes of different adjustable resources at the first optimization moment is not restricted by the adjustment rate, and is shown as the following formula:

wherein, the first and the second end of the pipe are connected with each other,

is the initial value of the v gene in the population p;

the value lower limit of the v gene;

the upper limit of the value of the v gene; r is a [0,1]]Random real numbers which are uniformly distributed;

the unified generation method of the initial value of the gene at the non-first optimization moment specifically comprises the following steps:

the generation of the initial values of the genes of different adjustable resources at the non-first optimization moment is constrained by the value size and the adjustment rate at the previous moment, as shown in the following formula:

wherein, the expressions of max (A, B) and min (A, B) respectively represent taking the larger element of the element A and the element B and taking the smaller element of the element A and the element B;

is the initial value of the v-1 gene in the population p;

optimizing the upper limit of the regulation rate of the adjustable resource represented by the v-th gene in the population p at the moment for the unit; therefore, the essence of the unified generation method of the initial value of the gene at the non-first optimization moment is to redefine the upper and lower limits of the value of the gene;

the special generation method updates the upper and lower limits of the output power value of the energy storage system at the next optimization time based on the State of Charge (SOC) of the energy storage system at the end time of the previous scheduling period and the regulation rate constraint;

the generation method of the initial value of the gene at the first optimization time of the special generation method is the same as the unified generation method of the initial value of the gene at the first optimization time of the unified generation method;

in the method for generating the initial value of the gene at the non-first optimization moment of the special generation method, the upper limit and the lower limit of the value of the gene are increased for further constraint; the method for generating the initial value of the gene at the non-first optimization time of the energy storage system is shown as the following formula:

wherein, the first and the second end of the pipe are connected with each other,

the accumulated value from the value of the 1 st gene to the value of the v-1 st gene in the population p is kW;

is the initial value of the u-th gene in the population p, kW; e_essCapacity of the energy storage system, kWh; SOC_essIs the initial state of charge of the energy storage system.

In order to achieve one of the above objects, a second technical solution of the present invention is:

a multi-objective hierarchical optimization operation system for a power distribution network considering adjustable resource regulation rate comprises the following steps:

one or more processors;

storage means for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of multi-objective hierarchical optimization for operation of a power distribution grid with consideration of adjustable resource adjustment rates, as described above.

Compared with the prior art, the invention has the following beneficial effects:

through continuous exploration and tests, the invention provides the optimization method considering the adjustable resource regulation rate constraint, can meet the maximum regulation rate constraint, further enables the optimization result to be more reasonable, accords with the actual constraint of the industry, and has the advantages of enabling the simulation calculation of the optimization scheduling of the power distribution network to be more accurate and meeting the actual constraint requirement of equipment participating in optimization in the engineering.

The invention fully considers various operation scenes and provides a concept of downward adjustment of output power, namely, the output of renewable energy sources is intentionally reduced at each optimization moment through power electronic elements, and optimization targets of optimal economy, highest comprehensive energy efficiency and the like are achieved on the basis of meeting power balance; the renewable energy output power is adjusted downwards, so that the problems of wind abandon, light abandon and the like are inevitably caused, in order to reflect the interactive relationship between the source network and the load storage and realize that the power balance is met by not depending on the downward adjustment of the renewable energy output power, the wind abandon cost and the light abandon cost are increased in the expression of the daily operation cost of the objective function, so that the renewable energy and the resources on the network side, the load side and the storage side play games according to the difference of the wind abandon cost, the light abandon cost and the scheduling cost and the optimization target with the highest comprehensive energy efficiency, and the optimal adjustable resource scheduling plan is obtained.

Furthermore, the method of establishing the sub-objective function of the upper-layer optimization objective function as the lower-layer optimization objective function avoids the situation that the final optimization result is not optimal to a certain extent, which is possibly caused by the hierarchical optimization, and has the advantage that the final optimization result of the problem that the adjustable resources participate in the optimization operation of the power distribution network is more reasonable.

Compared with the prior art, the invention provides a method adopting layered optimization, and in the lower-layer optimization, bilinear objective functions which are restricted with each other are constructed and are superposed into one linear objective function, so that the undesirable extreme optimization result possibly caused by single objective optimization is avoided.

According to the invention, bilinear objective functions in the lower-layer optimization are subjected to normalization processing, and are linearly superposed into a single linear objective function after weights are given, so that the effect of 'not only ensuring that the orderly charging of the electric automobile cuts peaks and fills valleys of the active total load (including the charging load) of the system, but also ensuring that no new charging peak is formed' in the lower-layer optimization is realized.

Drawings

FIG. 1 is a block diagram of the branch-and-bound implementation of the present invention;

FIG. 2 is a block diagram of the NSGA-II algorithm implementation of the present invention;

FIG. 3 is a network topology structure diagram of IEEE 33 node of the present invention

FIG. 4 is a view of the ordered charging load vs of the electric vehicle according to the present invention;

FIG. 5 is a diagram showing the peak clipping and valley filling effects of the orderly charging of the invention on the active total load of the power grid;

FIG. 6 is a diagram of a distribution of all feasible solutions of a two-objective optimized feasible region according to the present invention;

FIG. 7 is a diagram of an optimal value of a target function corresponding to a Pareto frontier in the present invention;

FIG. 8 is a plan view of an adjustable daily optimal allocation of resources according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.

A multi-objective optimization operation method for a power distribution network considering adjustable resource regulation rate comprises the following steps:

the method comprises the following steps: and defining decision variables and constraint conditions thereof.

1. Regulation of renewable energy, such as wind, light, does not require consideration of regulation rate constraints. Wind, light and other generated energy have the characteristics of intermittence and fluctuation, and multiple and less generated energy cannot be really controlled, so that an energy storage system is usually arranged to be matched with the power distribution network in the optimized operation of the power distribution network, and redundant generated energy is stored when the generated energy is excessive. However, considering the limitation of the capacity of the energy storage system or the reason that the state of charge (SOC) of the energy storage system at the previous moment is already at a higher level, that is, excessive electric quantity cannot be stored at the moment, under the condition of meeting the power balance of the power distribution network, only wind and light can be abandoned, the grid-connected quantity of renewable energy sources is limited, and a concept of 'power down regulation' is generated. Therefore, for wind and light renewable energy sources, the scheduling plan output of each grid-connected renewable energy source unit after optimization at each optimization time can be selected as a decision variable, and the adjustment of the decision variable does not need to consider the adjustment rate constraint, but only considers that the grid-connected power is greater than zero and smaller than the predicted output, and is represented by formula (1):

in the formula (1), the reaction mixture is,

the power output, kW, of the dispatching plan after the d-th renewable energy grid-connected unit participates in optimization at the time t;

and predicting the output kW of the d-th renewable energy grid-connected unit at the time t.

2. For the energy storage systems participating in regulation, the output power of each energy storage system at each optimization moment can be selected as a decision variable, and both regulation rate constraint and energy storage output constraint are considered. Regulating the rate constraint to limit the upper and lower limits of the discharge power change of the energy storage system at adjacent moments; because the total capacity of the energy storage system is fixed, and the state of charge (SOC) of the energy storage system at the end of each moment represents the residual electric energy at the end of the moment, the upper and lower limits of the charging power value or the upper and lower limits of the discharging power value of the energy storage system at the next optimization moment are limited, and the energy storage output constraint is formed.

The regulation rate constraint of the energy storage system may be represented by equation (2):

in the formula (2), the reaction mixture is,

the output power (negative during charging and positive during discharging) of the e-th energy storage system at the moment t is kW;

the output power of the e-th energy storage system at the moment t-1 is kW;

and the regulation rate upper limit of the e-th energy storage system is kW/h.

The capacity constraint of the energy storage system may be represented by equation (3):

in the formula (3), the reaction mixture is,

outputting electric energy, kWh, of the f energy storage system at the moment t;

total capacity of the f-th energy storage system, kWh;

and T is the initial charge state of the f-th energy storage system, and is the number of accumulated charging (discharging) moments.

3. For the reducible load, when taking the temperature control load (electric refrigerator) of a cinema or a mall as an example to participate in the adjustment, the total output power of all the electric refrigerator sets for supplying cold to each cold load (such as a certain hall of the cinema or the mall) at each optimized moment can be selected as a decision variable (assuming that the models of all the electric refrigerator sets for supplying cold to the same cold load are the same and the working conditions are the same), and the adjustment rate constraint of the temperature control load needs to be considered while the user satisfaction constraint is considered.

The mathematical model of the cooling load (a certain hall of the cooling building) can be represented by equation (4):

in the formula (4), the reaction mixture is,

is the g-th cooling load in time tHall room) indoor temperature, ° c;

indoor temperature of the g-th cold load (hall room) at time t +1, DEG C; c_gEquivalent heat capacity of the g-th cooling load (hall room), kW/deg.c; r_gEquivalent thermal resistance of the g-th cold load (hall room), DEG C/kW;

the outdoor ambient temperature of the cold load (hall) at time t is ° c;

the total output power at time t, kW, of all the electric refrigerator groups that supply cooling to the g-th cooling load (hall room).

The user satisfaction is related to the indoor temperature of the cold load (the hall room), the value range is [0,1], and the adjustability potential of the cold load are embodied. The user satisfaction constraint of the cooling load (hall) can be expressed by equation (5):

in the formula (5), the reaction mixture is,

the indoor temperature, deg.c, desired by the user of the g-th cold load (hall room) at time t; u is the user satisfaction. If a minimum boundary value of the user satisfaction degree is defined, the difference value between the indoor temperature value reversely deduced and the user expected temperature corresponds to the adjustable range of the cooling load.

Ideally, the set temperature of the electric refrigerator set should be the same as the desired temperature of the user, but considering that the electric refrigerator set has a temperature control dead zone, δ is defined as the hysteresis width between the upper and lower limits of the operating temperature of the electric refrigerator set, and then the relationship between the upper and lower limits of the fluctuation of the actual desired temperature of the user and the set temperature of the electric refrigerator set at this time can be expressed as equation (6).

In the formula (6), the reaction mixture is,

the set temperature, DEG C, of all the electric refrigerator groups for cooling the g-th cooling load (hall room) is numerically compared with the set temperature, DEG C, of all the electric refrigerator groups for cooling the g-th cooling load (hall room)

Equal; delta is the hysteresis width between the upper limit and the lower limit of the operation temperature of the electric refrigerating unit, and is DEG C;

and

the upper and lower limits of the fluctuation of the temperature expected by the actual user, DEG C, respectively, indicate that

The indoor temperatures within the range all meet the expectations of the user, and the adjustability potential of the cooling load is also increased to some extent.

The mathematical model of the electric refrigeration unit can be represented by equation (7).

In the formula (7), the reaction mixture is,

the refrigeration coefficients of all the electric refrigeration units for supplying cold to the g-th cold load (hall room);

the total electric power consumption at the time t of all the electric refrigerating units for cooling the g-th cold load (the hall room) is kW.

The regulation rate constraint for all the electric refrigerator groups that supply the g-th cooling load (hall) can be expressed by equation (8):

in the formula (8), the reaction mixture is,

the total output power at the moment t-1 of all the electric refrigerating units for supplying cold to the g-th cold load (hall room), kW;

the upper limit of the regulation rate for all the electric refrigerator groups for cooling the g-th cooling load (hall room), kW/h.

In addition, the constraint of the upper limit of the electric power consumption is also considered for the electric refrigerator, and can be represented by equation (9).

In the formula (9), the reaction mixture is,

the upper limit of the total output power of all the electric refrigerator groups for cooling the g-th cooling load (hall room), kW.

4. For the translatable load, taking an electric vehicle as an example, the main body participating in power regulation is the output power of the charging pile at the network topology node at each optimized moment, and the output power of the charging pile at a certain moment is the opposite number (negative number, regardless of V2G) of the sum of all the electric vehicle charging powers at the moment. Because whether a certain electric automobile is charged or not at a certain charging pile at a certain moment belongs to the 0-1 planning problem, the decision variable can be defined as a charging state matrix of the jth electric automobile at the ith charging pile at the t moment

The elements in (1) are all 0-1 binary discreteThe integer variable is 1 when charging is selected and 0 when no charging is selected. Assuming that all electric vehicles are charged with constant power, the average charging power of a certain electric vehicle at a certain optimized moment is a constant value or 0 (charging is not selected), so that the regulation rate constraint does not need to be considered.

Output power of ith charging pile at t moment

May be represented by formula (10).

In the formula (10), the compound represented by the formula (10),

the output power (negative number) of the ith charging pile at the moment t is kW;

the dimension of a charging state matrix of the jth electric automobile at the ith charging pile at the moment t is 1 multiplied by N_jIn which N is_jThe total number of the jth type electric automobiles;

charging power, kW, of the jth type electric automobile at the ith charging pile at the moment t, and the dimensionality of the charging power is N_jX 1; j is the total number of types of electric vehicles participating in regulation in the system.

Equation (10) describes the relationship between the output power of a certain charging pile and the charging power of all electric vehicles at a certain optimization moment. There are some constraints formed according to practical regulations, embodied in the state of charge matrix

The value of the element(s) is greater. For example, electric buses and electric taxis are provided with fixed charging piles for use; the electric private car is charged by more than charging piles in residential areas, companies and public parking lots; electric driveThe utility vehicle is mainly used for charging the charging pile at the municipal building. Therefore, if the jth electric vehicle is not allowed to be charged in the ith charging pile at the predetermined time t, it is not necessary to charge the jth electric vehicle in that case

All elements in (b) are designated as decision variables, but are instead designated as 0. Therefore, the optimal scheduling of the electric automobile participating in the power distribution network does not need to additionally constrain the upper and lower limits of the output power of each charging pile, but needs to satisfy the constraint of the output power limit of each charging pile at each moment, and can be represented by formula (11).

In the formula (11), the reaction mixture is,

the upper limit of charging power, kW, of the ith charging pile at the moment is optimized for single.

Furthermore, electric vehicles, similar to energy storage systems, require tracking of the state of charge of the electric vehicle battery at the end of each optimization time. However, the charging power of the electric vehicles is constant and does not participate in the optimization as a decision variable, but rather is used in equation (12) to calculate the remaining charging time T until each electric vehicle is full_ch。

In the formula (12), s is the current driving mileage, km, of the electric vehicle participating in the regulation; w₁₀₀The electric consumption of the electric automobile is 100km each time, the kWh/100 km; p_chIs the average charging power, kW; eta_chThe charging efficiency is obtained.

Therefore, the charging period constraint of the electric vehicle may be represented by equation (13).

In the formula (13), the reaction mixture is,

the maximum charging time length h of the w-th electric automobile from the time t;

and the actual charging time length h of the w-th electric automobile from the time t.

Step two: system constraints are defined.

1. Power balance constraint of power grid

The power balance of the power grid means that the total power supply power is equal to the total power consumption power, the input power and the output power of each node in the network topology are equal, and the constraint needs to be met through load flow calculation. By inputting the net injection power (the difference value between the total power generation power of the nodes and the total power load of the nodes) corresponding to each type of node, the impedance of the distribution network line and the communication information of the distribution network topology, the voltage and the power corresponding to each node and the loss on each line can be accurately calculated.

When load flow calculation is performed, firstly, the node type needs to be judged. Node types can be divided into three categories: PV node, PQ node, V δ node. The PQ node is a node with known injected active power and reactive power and unknown voltage amplitude and phase angle; the PV node is a node with known voltage amplitude and active power, unknown node voltage phase angle and reactive power; the V delta node is also called a balance node, the voltage amplitude and the phase angle of the node are known, and the essence of the power flow calculation is to supply power to other nodes by adjusting the active and reactive outputs of the balance node so as to maintain the power balance of the power grid.

Assuming that there are n nodes in the network, where there are r PV nodes, 1V δ node, and n-r-1 PQ nodes, the power flow equation can be obtained as represented by equation (14).

In formula (14), P^SPAnd Q^SPRespectively injecting an active power vector (W) and a reactive power vector (Var) into the full-node net; u is a full node voltage vector, V; y is a node admittance matrix, S; u shape^sPSetting initial node voltage V for all nodes; Δ P is the active injection deviation, W, of PQ and PV nodes; Δ Q is the PQ node reactive injection deviation, Var; Δ U is the PV node voltage amplitude squared difference, V²。

2. Power flow constraint of power grid

When considering the grid current constraint, the required voltage amplitude fluctuates within an allowable range, which can be represented by equation (15).

In the formula (15), V_gIs the voltage at node g, V;

and

respectively, the upper and lower limits, V, of the voltage amplitude at node g.

At the same time, the line active and reactive power transmission is also required to fluctuate within an allowable range, as represented by equations (16) and (17), respectively.

In formula (16), P_hIs the active power on line h, W;

and

upper and lower limits, W, of active power on line h, respectively; n is_bIs the total number of legs in the network.

In the formula (17), Q_hIs the reactive power on line h, Var;

and

respectively, the upper and lower limit of reactive power on the line h, Var.

Step three: an optimization objective is defined.

The problem of the adjustable resource participating in the optimized operation of the power distribution network includes, but is not limited to, the following optimization objectives:

1. the daily running cost is lowest

The method mainly comprises the steps of taking the lowest operation cost of the power distribution network in the whole day scheduling period as an optimization target, and mainly comprising the cost of purchasing electricity from the power distribution network to a superior power distribution network, the operation benefits (including the calling cost of energy storage, electric vehicles and flexible loads) of a load aggregator, the scheduling cost of surfing the Internet of a distributed power supply and the cost of abandoning wind and light.

2. The highest comprehensive energy efficiency

The method is mainly characterized in that the highest comprehensive energy efficiency of the power distribution network in the whole-day scheduling period is taken as an optimization target, and the ratio of the total load power consumption and the total power supply quantity in the power distribution network in one day is mainly reflected. Wherein, the total load in the power distribution network comprises the electrical load of each node, the total load of the energy storage system, the total load of the electric automobile and other adjustable loads (for example, the load can be reduced by temperature control cold); one part of the total power supply quantity is the total photovoltaic and wind power consumption electric quantity in the power distribution network, and the other part of the total power supply quantity is the electricity purchasing quantity from the inside of the power distribution network to a superior power grid, and the quantity is converted by combining the power generation efficiency of the superior power grid. Therefore, the integrated energy efficiency for the distribution grid is a result of integrating both the energy efficiency in the distribution grid and the energy efficiency in the upper grid.

Step four: a decision variable is initialized.

Taking a genetic algorithm in an intelligent algorithm as an example, the size of a population participating in operation needs to be specified, and all decision variables participating in optimization are taken as genes. In the process of initializing genes, although regulation rate constraints are already specified in step two, if all groups of genes of the first generation population cannot meet all constraints required for optimization after initialization, the genetic algorithm cannot continue to operate. Therefore, in the process of initializing genes, regulation rate constraints need to be considered, and initialization methods are specified for different adjustable resources.

Step five: and (5) formulating an optimization strategy.

1. Formulation optimization method

(1) Renewable energy source

In the existing multi-objective optimization problem of a plurality of power distribution networks containing renewable energy, in order to ensure that the renewable energy is consumed as much as possible, the situation that the renewable energy is fully used is assumed, and the situation that under a certain operation scene, the load side demand is not so large and the residual capacity of an energy storage system is insufficient or the regulation rate is insufficient is ignored, so that the generated energy of the renewable energy cannot be completely consumed and cannot be completely stored in the energy storage system at the optimization moment is caused. The invention considers the operation scene and provides a concept of output power downward adjustment, namely, the output of renewable energy sources is intentionally reduced at each optimization moment through power electronic elements, and optimization targets of optimal economy, highest comprehensive energy efficiency and the like are achieved on the basis of meeting power balance. The renewable energy output power is adjusted downwards, so that the problems of wind abandon, light abandon and the like are inevitably caused, in order to reflect the interactive relationship between the source network and the load storage and realize that the power balance is met by not depending on the downward adjustment of the renewable energy output power, the wind abandon cost and the light abandon cost are increased in the expression of the daily operation cost of the objective function, so that the renewable energy and the resources on the network side, the load side and the storage side play games according to the difference of the wind abandon cost, the light abandon cost and the scheduling cost and the optimization target with the highest comprehensive energy efficiency, and the optimal adjustable resource scheduling plan is obtained.

(2) Temperature controlled load

The flexible load can be reduced for a single temperature control class, the self adjustable potential is not large, so the scene that the group control of the temperature control load participates in the adjustment is mainly used in practice. In the optimization scene, the temperature control units participating in adjustment are in the same type and under the same working condition, so that a method for uniformly scheduling all the temperature control units supplying cold for the cold load is provided by taking buildings with larger spaces such as markets, cinemas and the like as main cold loads.

(3) Electric automobile

The maximum charging time of a certain electric vehicle from a certain optimized time can be calculated by the formula (12), but the electric vehicle does not necessarily start charging at the starting time of the scheduling period, does not necessarily stop charging at the ending time of the scheduling period, and is likely to start charging or stop charging at the middle time of the scheduling period. Because the optimized scheduling only aims at the configuration situation of each resource at a certain optimized moment, the configuration situation of the resources in the previous scheduling period can not be changed. Therefore, when the electric automobile participates in the unordered charging before adjustment, the condition that the charging is started at the middle time of a certain scheduling period is equivalent to the charging is started from the starting time of the period, the condition that the charging is stopped at the middle time of the certain scheduling period is equivalent to the charging is stopped at the ending time of the period, and the charging power corresponding to the actual charging time in the scheduling period of the starting time or the ending time of the charging is converted into the average charging power in the whole time, so that the load size when the electric automobile participates in the unordered charging before adjustment is calculated. For the situation of orderly charging after the electric vehicle participates in the optimization adjustment, in order to facilitate the adjustment and configuration of resources, it is assumed that the optimization scheduling result is that the electric vehicle starts to be charged at the starting time of the scheduling period and stops being charged at the ending time of the scheduling period.

After the method for the adjustable resources to participate in the optimization is formulated, the output of each adjustable resource at each optimization moment can be obtained. Since the adjustable resources are distributed on different nodes in the network topology, the net injection power of the nodes can be calculated by combining the output of the adjustable resources at each optimization moment and the load of each node, and the net injection power is substituted to perform load flow calculation so as to satisfy the node power balance constraint and the network constraint represented by the formulas (14) to (17).

2. Formulating an optimization algorithm

Due to the introduction of electricityThe invention provides a method for optimizing and controlling a system by adopting a layered optimization and optimization method, which reduces the complexity of system optimization and avoids dimension disasters. The lower-layer optimization firstly performs combined optimization on the electric vehicles which are possibly charged at each charging pile node in the network topology at each optimization moment, and solves the optimal charging state matrix described in the formula (10) by taking the lowest daily charging cost sum and the lowest daily net load increase ratio sum as optimization targets

The optimal combined charging plan of the jth electric automobile at the ith charging pile at the moment t; and the upper-layer optimization is to solve the output power of each charging pile at each optimization moment according to the optimal combined charging plan of the electric vehicle obtained by the lower-layer optimization, and the output power is used as a constant load to participate in the solution of the optimal scheduling plan of other adjustable resources in the power distribution network.

The reason for adopting the hierarchical optimization is that because the multi-objective optimization problem is mixed with discrete integer decision variables and continuous decision variables (because a charge state matrix is taken as a decision variable and therefore contains 0-1 planning), the formed optimization problem is too complex, if a random optimization algorithm such as a genetic algorithm and the like is used for processing the multi-objective optimization problem of such mixed discrete (integer) and continuous decision variables, a plurality of initial charge state matrixes need to be generated at the stage of decision variable initialization, even a hidden join method with higher efficiency is adopted, because the complexity of a distribution network model is very high (including trend calculation), the time for solving the distribution network optimization target for a plurality of times is huge, and because the genetic algorithm is population optimization and the like, the optimization rate is slower, and when the multi-objective optimization problem of the mixed discrete and continuous decision variables is processed, and the situation of combined explosion is easy to occur, so that the invention introduces a layered optimization method, and the lower layer separately considers the 0-1 planning problem of orderly charging of the electric automobile only containing discrete integer variables.

The invention considers that the lowest sum of daily charging cost is selected as an optimization target in the lower-layer optimizationOne reason for the target is that the electric vehicle load responds to the excitation of the half-time charging electricity price, peak clipping and valley filling are favorably carried out on a daily load curve of the power distribution network, the optimization target belongs to a sub-optimization target (the formula (28) belongs to a part of the formula (18)) with the lowest daily operation cost of an upper-layer optimization target, and the situation that the lower-layer optimization optimal solution cannot meet the global optimization of the upper-layer optimization, which may be caused by hierarchical optimization, can be avoided to a certain extent. However, due to the incentive of time-of-use electricity prices, if only the lowest daily charging cost sum is considered as the only optimization target, the electric vehicles may be concentrated on charging at the time of the low-valley electricity price, and another electricity utilization peak may occur, so that another optimization target which conflicts with the lowest daily charging cost sum, namely the lowest daily net load increase ratio of the electric vehicles needs to be introduced. The net load increase ratio of the electric automobile at the current optimization moment can reflect the relation between the charging power of the electric automobile at the current optimization moment and the active total load of the system, if the net load increase ratio of the electric automobile at the current optimization moment is the minimum optimization direction, the situation that a charging peak is formed at the current optimization moment can be guaranteed to be avoided as much as possible, and the situation that the charging peak is formed at the valley moment of the active total load of the system can be guaranteed to be avoided as much as possible by taking the sum of the net load increase ratios at all the optimization moments as the minimum optimization target. However, if the daily net load increase ratio is the lowest as the only optimization target, the charging load of the electric vehicle at the valley time of the system active total load may be lower than that at the peak time due to the fact that the system active total load at the valley time is lower. Therefore, the two linear objective functions are linearly superimposed to obtain the expression shown in the formula (30). According to equation (30), the sum of normalized objective functions f for the optimization of the lower layer_sublayerAt a minimum, it is necessary to make C_EVAAs small as possible while making R_ΔLoadThe value of (c) is as large as possible. Because the unit price of charging is higher in peak electricity price moment, electric automobile can more tend not to charge, compares the value of each parameter when unordered charged state before the dispatch, can make after the dispatch

Is smaller, and thenTo obtain C_EVAIs smaller, and f_sublayeThe optimized direction of R is the same, but R is also caused_ΔLoadIs smaller, and f_sublayerThe optimization directions of the sub-objective functions are opposite, so that contradiction is generated between the two sub-objective functions, and lower-layer optimization is dedicated to searching a compromise solution in the optimal solution; because the unit price of charging is lower in the low ebb price moment, electric automobile can tend to more charge, compares the value of each parameter when unordered charged state before the dispatch, can make after the dispatch

Is greater, thereby making C_EVAGreater value of, and f_sublayerThe optimization direction of (1) is opposite, but R can also be caused at the same time_ΔLoadGreater value of, and f_sublayerThe optimization directions of the sub-target functions are the same, so that contradiction is generated between the two sub-target functions, and the lower-layer optimization aims to find a compromise solution in the optimal solution. According to the lower-layer double optimization target model and the linearly superposed total optimization target model, peak clipping and valley filling of active total loads (including charging loads) of the system can be guaranteed to a certain extent, and a new charging peak can not be formed.

Because the decision variable of the lower layer optimization is a discrete integer variable with the value range of {0, 1}, the objective function of the lower layer optimization is a linear function, and the lower layer optimization has N in total_SOCVIf the decision variable is adopted, the concept of hierarchical optimization is required to be executed if the 'exhaustion method' is adopted

And (4) performing linear programming, and doubling the solving time for each variable. Therefore, in consideration of the problem of time complexity, the method adopts a branch-and-bound method suitable for solving the mixed integer linear programming problem to solve.

The essence of the branch-and-bound method is an improved exhaustive method, and the core idea is to decompose the original mixed integer linear programming problem into solutions for individual linear programming problems, and continuously update the upper bound (optimal feasible solution) and the lower bound (optimal linear relaxed solution) of the original problem in the solution process, so as to avoid the execution process of suboptimal solutions, and reduce the computation time, the execution block diagram is shown in fig. 1.

(2) Upper layer optimization and algorithm selection

The genetic algorithm is selected as the optimization algorithm in consideration of upper-layer optimization, the genetic algorithm is simulated and researched according to the genetic principle and natural selection in the nature, the random optimization algorithm comprises self-adaptability and random search, and the random optimization algorithm has the advantages that:

I. the optimization is started from a plurality of initial values, and the capability of acquiring a global optimal solution is stronger.

And II, the objective function is not required to be continuous and microminiature, and the robustness is stronger.

And III, the method is convenient for processing the target function nonlinearity and the complex nonlinear programming problem containing a large amount of nonlinear constraints.

For the multi-objective problem of the optimized operation of the adjustable resource auxiliary power distribution network, a genetic algorithm is selected for solving because the multi-objective problem comprises complex nonlinear constraints (power flow constraints of the power grid) and nonlinear objective functions (the power flow calculation result and the exchange power of an external network connecting line participate in the calculation of the objective functions) and also comprises integer decision variables (belonging to a non-convex optimization problem, and a local optimal solution is easily obtained by using a numerical optimization algorithm).

Because the genetic algorithm belongs to a random optimization algorithm and has the defects of large calculation amount, long operation time and the like, the algorithm needs to be improved. Through evaluation of calculation rates of various algorithms, a fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is selected and adopted when a multi-target problem of the optimized operation of the adjustable resource-assisted power distribution network is processed. The algorithm is established on the basis of a classical genetic algorithm, and is different from the traditional genetic algorithm in that:

I. adding a process of fast non-dominated sorting

The NSGA-II algorithm carries out rapid non-domination sequencing in a population formed by a first generation parent, each generation of descendants and a previous generation parent, generates a non-domination solution set and is used for updating a new parent population with the same population size. Is totally M_objAn objective function of a population size N_popWherein the fast non-dominant rowThe maximum temporal complexity of the sequence is O (M)_obj(2N_pop)²)。

Increased estimation of crowding distance

The NSGA-II algorithm defines the concept of crowding distance before the process of updating the new parent population, the diversity of the population is ensured, and the maximum time complexity of the crowding distance estimation process is O (M)_obj(2N_pop)lg(2N_pop))。

Increase congestion ordering with elite reservation mechanism

The NSGA-II algorithm adopts a congestion degree sorting method with an elite reservation mechanism in the process of updating the new parent population, non-dominated solution sets are sequentially sorted according to Pareto grades from low to high and assigned to the new parent population until all individuals of the non-dominated solution sets corresponding to a certain Pareto grade cannot be put into the new parent population (the size of the new parent population reaches N), and then the non-dominated solution sets corresponding to the Pareto grade are sequentially put into the new parent population according to the arrangement of the congestion degree from large to small until the size of the new parent population reaches N.

The congestion degree sequencing with the elite reservation mechanism is added to ensure that the obtained local optimal solution is not lost, the speed of convergence to the Pareto frontier is accelerated while the evolution level of the population is improved by reserving excellent individuals, the time required by algorithm optimization is greatly reduced, and the maximum time complexity of the congestion degree sequencing process with the elite reservation mechanism is O (2N)_poplg(2N_pop))。

The block diagram for the implementation of the NSGA-II algorithm is shown in fig. 2.

Step six: and optimizing the execution of the running process.

The application embodiment of the invention comprises the following steps:

1. example basic parameters

The embodiment employs a network topology of IEEE 33 nodes, as shown in fig. 3.

The network comprises 32 branches, a reference voltage of 12.66kV at the head end of the power supply network, 10MVA for a three-phase power standard value and 6220.5+ j2332.7kVA for a total load of the network. The network of the embodiment comprises five typical adjustable resources of distributed photovoltaic, distributed wind power, energy storage, electric vehicles and temperature control loads.

(1) Distributed photovoltaic

Example distributed photovoltaics in the network are distributed mainly in superstores (node 11), large movie theaters (node 14), and residential rooftops ( nodes 21, 24, 28), with the parameters shown in table 1.

TABLE 1 distributed photovoltaic parameters

At the node	11	14	21	24	28
						Capacity (kWh)	1200	800	300	200	500
Climbing rate (kW/h)	120	80	30	20	50
						Maintenance costs (Yuan/kWh)	0.01	0.01	0.01	0.01	0.01
Abandon light punishment (Yuan/kWh)	0.55	0.55	0.55	0.55	0.55

(2) Distributed wind power

Distributed wind power in the embodiment network is mainly distributed in suburbs ( nodes 1, 17, 32), and the parameters are shown in table 2.

TABLE 2 distributed wind power parameters

At the node	17	32	1
				Capacity (kWh)	1200	700	1100
Climbing rate (kW/h)	120	70	110
				Maintenance costs (Yuan/kWh)	0.035	0.035	0.035
Abandon wind punishment (Yuan/kWh)	0.45	0.45	0.45

(3) Electrical energy storage

In the embodiment, the electric energy storage in the network is mainly close to the distribution of the high-power distributed energy sources, and is set to be in the same node with the high-power distributed photovoltaic and distributed wind power, and the parameters of the electric energy storage are shown in table 3.

TABLE 3 Electrical energy storage parameters

At the node	11	14	17	1	32
						Capacity (kw)h)	1600	1000	1000	1000	1200
Climbing rate (kW/h)	150	150	150	150	150
						Maintenance costs (Yuan/kwh)	0.003	0.003	0.003	0.003	0.003
Initial SOC	0.5	0.5	0.5	0.5	0.5

(4) Electric automobile

The embodiment adopts the electric vehicle data counted in 2019 of Shenzhen city, and covers four electric vehicle types: buses, taxis, private cars and business cars. Wherein, buses are respectively in a Bianddi K9 type, and taxies, private cars and public service cars are respectively in a Bianddi E6 type. The initial charging moments of the four types of electric automobiles are subjected to normal distribution, and the daily average driving mileage is subjected to log-normal distribution. The parameters corresponding to each type of electric vehicle are shown in table 4.

TABLE 4 electric vehicle parameters

For the electric vehicle as an adjustable resource to participate in the optimized operation of the power distribution network, the following assumptions are made in the embodiment:

a. the initial charging time, daily mileage and charging power of each type of electric vehicle are mutually independent random quantities.

b. The charging power of each type of electric vehicle is regarded as a constant power model, and the 2 stages of constant voltage charging and constant current charging do not exist.

c. Each type of electric vehicle will only charge during its main charging period after regulation (with the user's charging requirements as a first constraint).

d. All vehicles are fully charged each time.

e. Let the initial SOC of all vehicles at time 0 be 0.5.

According to equation (10) and the constraint of the charging specification of the electric vehicle, a charging state schematic matrix is obtained and is shown in table 5.

Table 5 state of charge schematic matrix

(5) Temperature controlled load

The temperature control load is adjusted based on group control technology, and typical flexible temperature control loads are concentrated on refrigeration air conditioners in large shopping malls and large movie theaters. Superstores need to maintain low temperatures during business hours and lower temperatures during non-business hours, while large movie theaters require low temperatures throughout the day, representing a difference in adjustable times. The two also differ in terms of user satisfaction factor, representing a difference in tunable potential. The parameters are shown in Table 6.

TABLE 6 temperature control load parameters

	Large-scale market	Large cinema
			At the node	11	14
Number of temperature-controlled loads	100	50
			Minimum coefficient of satisfaction	0.9	0.8
Coefficient of performance COP	3	2.6
			Equivalent thermal resistance (DEG C/kW)	2.5	1.5
Equivalent heat capacity (kW/. degree.C.)	2.5	1.5
			Climbing rate (kw/h)	Maximum output power/3	Maximum output power/3

The upper limit of the temperature control load output power value is calculated, and the boundary condition needs to be considered, namely the maximum output power required at each moment when the comfort level of a user is 1 and the lower boundary of the temperature control load dead zone is reached.

And calculating the lower value limit of the output power of the temperature control load, and considering the boundary condition, namely considering the maximum output power required at each moment when the comfort level of a user is 0.8/0.9 and the upper boundary of the dead zone of the temperature control load is reached.

(6) Others

Example system price parameters are shown in table 7.

TABLE 7 price parameters

2. EXAMPLES results

First, referring to equations (28) to (30), the lower layer optimization is performed with the lowest total daily charging cost and the lowest total daily net load increase ratio of the electric vehicle as optimization targets, and a comparison graph of the disordered charging load and the ordered charging load of the electric vehicle is obtained, as shown in fig. 4. A comparison graph of the total active load of the electric grid during ordered charging and the total active load of the electric grid during unordered charging of the electric vehicle is shown in fig. 5.

As can be seen from fig. 5, under the condition that the charging requirement of the user is taken as the first constraint, that is, the charging of the user in the main charging time is taken as the premise, and two optimization objectives of the lowest daily charging cost sum and the lowest daily net load increase ratio sum of the electric vehicle are considered, the lower-layer optimization method and the optimization algorithm provided by the invention can avoid the occurrence of a new charging load peak while clipping the peak and filling the valley of the active total load of the power grid.

Then, referring to equations (18) to (24), upper-layer optimization is performed with the lowest daily operating cost and the highest comprehensive energy efficiency as the optimization target, wherein the population is measured by 500, and the evolution algebra is calculated by 15, so that all feasible solutions of the dual-target optimization feasible domain are obtained as shown in fig. 6. The optimal value of the objective function corresponding to the Pareto leading edge is shown in fig. 7.

In FIG. 6, there are 474 sets of feasible solutions, 13 Pareto grades, in the feasible domain, with the feasible solutions centered around [ -60, -57.5]Within the fitness function value range of the optimization objective one of [1.330 × 10 ]⁵,1.365×10⁵]And the fitness function value of the second optimization objective is in the range. Because the executed optimization direction is the minimization, the operation cost of the optimization target per day is the same as the value of the fitness function I, and the comprehensive energy efficiency of the optimization target two days is opposite to the value of the fitness function.

In fig. 7, there are 17 sets of solutions on the Pareto frontier, which are all non-dominated solutions with Pareto level 1 in the feasible domain, indicating that other solutions in the feasible domain are dominated by the 17 sets of solutions and the 17 sets of solutions are mutually non-dominated.

When the optimized dual-objective criteria take the same weight, the dual-optimization objective functions are subjected to normalization processing and then are superposed according to the formulas (31) to (32), so that a daily optimal configuration plan of the garden adjustable resource can be obtained, as shown in fig. 8.

Finally, the adjustable resource daily optimal configuration plan and the corresponding optimal daily operation cost are obtained, wherein the optimal daily operation cost is 13.384 ten thousand yuan, and the daily comprehensive energy efficiency is 59.164%.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A multi-objective optimization operation method for a power distribution network considering adjustable resource regulation rate is characterized in that,

the method comprises the following steps:

the method comprises the following steps: acquiring a plurality of targets capable of assisting the power distribution network in optimizing operation;

the plurality of targets comprise renewable energy sources, an energy storage system, a temperature control load and an electric vehicle load;

step two, setting corresponding decision variables and constraint conditions thereof according to the targets in the step one;

when the renewable energy sources participate in regulation, the dispatching plan output of each grid-connected renewable energy source unit after participating in optimization at each optimization moment is selected as a decision variable, regulation rate constraint does not need to be considered in the regulation of the decision variable, and the constraint condition is that the grid-connected power is larger than zero and smaller than the predicted output;

when the energy storage systems participate in regulation, the output power of each energy storage system at each optimization moment is selected as a decision variable, and both regulation rate constraint and energy storage output constraint are considered;

when the temperature control load participates in adjustment, the total output power of all the electric refrigerating units for supplying cold to each cold load at each optimization moment is selected as a decision variable, and the adjustment rate constraint of the temperature control load needs to be considered while the user satisfaction constraint is considered;

when the charging load of the electric automobile participates in regulation, the main body participating in power regulation is the output power of the charging device at the network topology node at each optimized moment, and the output power of the charging device at a certain moment is the opposite number of the sum of all the charging powers of the electric automobile at the certain moment;

step three, constructing system constraints according to the decision variables and the constraint conditions thereof in the step two;

the system constraint comprises power grid power balance constraint and power grid load flow constraint;

the power balance constraint of the power grid is used for controlling the total power supply power to be equal to the total power utilization power, and is embodied in that the input power and the output power of each node in the network topology are equal, and the constraint needs to be met through load flow calculation; calculating voltage and power corresponding to each node and loss on each line by inputting net injection power, distribution network line impedance and communication information of distribution network topology corresponding to each type of node;

the power grid power flow constraint is used for controlling the voltage amplitude to fluctuate within an allowable range and controlling the active power transmission and the reactive power transmission of the line to fluctuate within an allowable range;

step four, constructing an optimization target with lowest daily operation cost and highest comprehensive energy efficiency according to system constraints in the step three;

the lowest daily operation cost is the lowest operation cost of the power distribution network in the whole day scheduling period, and the lowest daily operation cost comprises the electricity purchasing cost of the power distribution network to a superior power distribution network, the operation income of a load aggregator, the online scheduling cost of a distributed power supply and the wind and light abandoning cost;

the highest comprehensive energy efficiency is the highest comprehensive energy efficiency of the power distribution network in the whole day scheduling period, and is used for reflecting the ratio of the total load power consumption and the total power supply quantity in the power distribution network in one day;

step five, an optimization strategy of the adjustable resources is formulated according to the optimization target in the step four;

the optimization strategy comprises the following contents:

for the power generation of renewable energy sources, a downward regulation model is constructed, and the output power of the downward regulation model is controlled;

the downward adjustment model intentionally reduces the output of renewable energy sources at each optimization moment through power electronic elements, and achieves optimization targets such as optimal economy, highest comprehensive energy efficiency and the like on the basis of meeting power balance;

meanwhile, the cost of abandoned wind and abandoned light is increased in the expression of the daily operation cost of the objective function, and the cost is used for reducing the loss of electricity selling income caused by power generation, so that the renewable energy and the resources on the network side, the load side and the storage side play games according to the difference of the cost of abandoned wind and abandoned light and the scheduling cost and the optimal target with the highest comprehensive energy efficiency, and an optimal adjustable resource scheduling plan is obtained;

for the temperature control load, uniformly scheduling the temperature control units participating in the adjustment;

for the electric automobile load, under the condition that the electric automobile participates in the unordered charging before adjustment, the condition that the charging is started at the middle moment of a certain scheduling period is equivalent to the charging is started from the starting moment of the period, the condition that the charging is stopped at the middle moment of the certain scheduling period is equivalent to the charging is stopped at the ending moment of the period, and the charging power corresponding to the actual charging time length in the scheduling period of the starting moment or the ending moment of the charging is converted into the average charging power in the whole moment, so that the load size of the electric automobile participating in the unordered charging before adjustment is calculated;

obtaining the output of each adjustable resource at each optimization moment, calculating the net injection power of the nodes by combining the output of each adjustable resource at each optimization moment and the load of each node, and substituting for carrying out load flow calculation so as to meet node power balance constraint and network constraint;

step six: optimizing the operation process according to the optimization strategy in the fifth step;

and solving the optimal solution of the optimal operation problem of the adjustable resource auxiliary power distribution network by adopting an optimization algorithm, and executing according to the optimal solution to realize multi-objective optimal operation of the power distribution network.

2. The method as claimed in claim 1, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

in the second step, a calculation formula of the regulation rate constraint of the energy storage system is as follows:

wherein the content of the first and second substances,

the output power (negative during charging and positive during discharging) of the e-th energy storage system at the moment t is kW;

the output power of the e-th energy storage system at the moment t-1 is kW;

the regulation rate upper limit of the e-th energy storage system is kW/h;

the calculation formula of the capacity constraint of the energy storage system is as follows:

wherein the content of the first and second substances,

outputting electric energy, kWh, for the f-th energy storage system at the time t;

total capacity of the f-th energy storage system, kWh;

and T is the accumulated charging and discharging time quantity.

3. The method as claimed in claim 2, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

the calculation formula of the cooling load is as follows:

wherein the content of the first and second substances,

indoor temperature of the g-th cooling load in time t is DEG C;

indoor temperature, deg.C, of the g-th cooling load at time t + 1; c_gEquivalent heat capacity of the g-th cooling load, kW/DEG C; r_gEquivalent thermal resistance of the g-th cooling load, DEG C/kW;

the outdoor ambient temperature of the cold load at time t, DEG C;

total output power at the moment t, kW, of all the electric refrigerating units for cooling the g-th cooling load;

the user satisfaction is related to the indoor temperature of the cold load, the value range is [0,1], and the adjustability potential of the cold load are reflected; the calculation formula for the user satisfaction constraint of the cooling load is as follows:

wherein the content of the first and second substances,

the user desired indoor temperature, deg.C, for the g-th cooling load at time t; u is user satisfaction; if the lowest boundary value of the user satisfaction degree is specified, the difference value between the reversely deduced indoor temperature value and the user expected temperature is the adjustable range corresponding to the cold load;

the set temperature of the electric refrigerating unit is the same as the expected temperature of a user, and the relationship between the fluctuation upper limit and the fluctuation lower limit of the actual expected temperature of the user and the set temperature of the electric refrigerating unit is as follows:

wherein the content of the first and second substances,

the set temperature, DEG C, of all the electric refrigerator groups for cooling the g-th cooling load in the time t is numerically equal to

Equal; delta is the hysteresis width between the upper limit and the lower limit of the operation temperature of the electric refrigerating unit, and is DEG C;

and

the upper limit and the lower limit of the fluctuation of the temperature expected by the actual user are respectively shown in the specification of

Indoor temperatures within the range all meet the expectations of users, and the adjustable potential of the cold load is also increased to a certain extent;

the mathematical model of the electric refrigerating unit is as follows:

wherein the content of the first and second substances,

the refrigeration coefficients of all the electric refrigerating units for supplying cold to the g-th cold load;

the total power consumption at the moment t of all the electric refrigerating units for cooling the g-th cold load is kW;

the adjustment rate constraints for all the electric refrigeration chiller units for the g-th cooling load are calculated as follows:

wherein the content of the first and second substances,

all electric refrigeration for cooling the g-th cooling loadThe total output power of the unit at the time t-1, kW;

the upper limit of the adjusting rate of all the electric refrigerating units for cooling the g-th cooling load is kW/h;

the constraint of the upper limit of the power consumption value is also considered for the electric refrigerator, which is shown as the following formula:

wherein the content of the first and second substances,

the total output power upper limit, kW, of all the electric refrigerating units supplying the cooling to the g-th cooling load;

selecting whether charging of a certain electric automobile belongs to a 0-1 planning problem at a certain charging pile at a certain moment, and defining a decision variable as a charging state matrix of a jth electric automobile at the ith charging pile at the t moment

The elements in the charge control circuit are binary integer variables of 0-1, wherein 1 is selected when charging is selected, and 0 is selected when no charging is selected;

output power of ith charging pile at time t

The formula (c) is shown as follows:

the method is used for reflecting the relation between the output power of a certain charging pile and the charging power of all electric vehicles at a certain optimized moment, and reflecting the existing constraint in a charging state matrix

The value of the element(s) is taken;

wherein the content of the first and second substances,

the output power of the ith charging pile at the moment t is kW;

the dimension of a charging state matrix of the jth electric automobile at the ith charging pile at the time t is 1 multiplied by N_jIn which N is_jThe total number of the jth type electric automobiles;

charging power, kW, of the jth type electric automobile at the ith charging pile at the moment t, and the dimensionality of the charging power is N_jX 1; j is the total number of types of the electric automobiles participating in the adjustment in the system;

the calculation formula for satisfying the output power limit constraint of each electric pile type at each moment is as follows:

wherein the content of the first and second substances,

the charging power upper limit, kW, of the ith charging pile at the single optimization moment;

remaining charging time T of each electric automobile until full charge_chThe calculation formula of (a) is as follows:

wherein, for a certain electric automobile participating in regulation, s is the current driving mileage, km, of the electric automobile; w₁₀₀The power consumption of the electric automobile is 100km per drivingAmount, kWh/100 km; p_chIs the average charging power, kW; eta_chTo the charging efficiency;

the calculation formula of the charging time constraint of the electric automobile is as follows:

wherein the content of the first and second substances,

the maximum charging time length h of the w-th electric automobile from the time t;

and h is the actual charging time length of the w-th electric automobile from the time t.

4. The method as claimed in claim 1, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

in the third step, the method for load flow calculation is as follows:

firstly, judging the node type; the node types are divided into three categories: PV node, PQ node, V delta node; the PQ node is a node with known injected active power and reactive power and unknown voltage amplitude and phase angle; PV nodes are nodes with known voltage amplitude values, active power, unknown node voltage phase angles and reactive power; the V delta node is also called a balance node, the voltage amplitude and the phase angle of the node are known, and the essence of load flow calculation is that power is supplied to other nodes by adjusting the active and reactive outputs of the balance node so as to maintain the power balance of a power grid;

the network has n nodes, wherein r PV nodes, 1V delta node and n-r-1 PQ nodes are provided, a power flow equation is obtained, and the calculation formula is as follows:

wherein p is^SPAnd Q^SPRespectively injecting active power vectors and reactive power vectors into the full-node net; u is a full node voltage vector, V; y is a node admittance matrix, S; u shape^SPSetting initial node voltage V for all nodes; Δ P is the active injection deviation, W, of PQ and PV nodes; Δ Q is the PQ node reactive injection deviation, Var; Δ U is the PV node voltage amplitude squared difference, V²；

The voltage is calculated as follows:

wherein, V_gIs the voltage at node g, V;

and

the upper limit and the lower limit of the voltage amplitude of the node g, V, respectively;

the calculation formula of the line active power transmission and reactive power transmission is as follows:

wherein, P_hIs the active power on line h, W;

and

respectively the active power on line hUpper and lower limits of (1), W; n is_bThe total number of branches in the network;

Q_his the reactive power on line h, Var;

and

respectively, the upper and lower limit of reactive power on the line h, Var.

5. The method as claimed in claim 1, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

in the fourth step, a calculation formula taking the lowest operation cost of the power distribution network in the whole-day scheduling period as an optimization target is as follows:

wherein the content of the first and second substances,

purchasing electricity from the power distribution network to a superior power grid at the moment t;

the operation income and element of the load aggregator at the time t;

scheduling cost for the distributed power supply to access the Internet at the time t;

punishing cost for wind abandon at the time t;

the cost of discarding light at time T is, in the first place, TOptimizing the total number of moments;

wherein, the electricity purchasing cost from the power distribution network to the superior power distribution network at the time of t

The formula (c) is shown as follows:

wherein the content of the first and second substances,

the unit price of purchasing electricity to a superior power grid at the moment t, yuan/kWh; p_PCC(t) is the power exchange value, kW, of the tie line of the superior power grid at the moment t; delta t is the duration of a single optimization moment, h;

operating revenue of load aggregator at time t

The calculation formula of (a) is as follows:

wherein, P_ESS，a(t) is the output power of the a-th energy storage system at the moment t, kW;

scheduling prices, yuan/kWh, for the energy storage of the a-th energy storage system at the moment t; a is the total number of energy storage systems participating in regulation; p_EVA，b(t) is the output power, kW, of the b-th electric vehicle at the moment t;

the charging price of the b-th electric vehicle at the moment t is yuan/kWh; b is the total number of the electric vehicles participating in regulation; p_FL，c(t) c-th participation in the regulation at time tThe response power of the compliant load of (a), kW;

the calling cost, yuan/kWh, of the c-th flexible load participating in the adjustment at the moment t; c is the total number of electric vehicles participating in regulation;

internet surfing scheduling cost of t-time distributed power supply

The calculation formula of (a) is as follows:

wherein, P_DG，m(t) is the output power, kW, of the mth grid-connected distributed energy at the moment t;

maintenance cost, yuan/kWh, of the mth grid-connected distributed energy at the moment t; m is the total number of the grid-connected distributed energy sources;

wind curtailment penalty cost at time t

And cost of light rejection

The calculation formula of (a) is as follows:

wherein, pnt^WT(t) the wind curtailment unit price at the time t, yuan/kWh;

predicting the output, kW, of the kth grid-connected wind turbine generator at the time t;

the output of a dispatching plan after the kth grid-connected wind turbine generator participates in optimization at the moment t, kW; k is the total number of the grid-connected wind turbine generators;

p^PV(t) the light abandoning unit price at the time t, yuan/kWh;

the predicted output power, kW, of the grid-connected photovoltaic unit 1 at the moment t in the day ahead;

the power output, kW, of the dispatching plan after the 1 st grid-connected photovoltaic unit participates in optimization at the time t; l is the total number of the grid-connected photovoltaic units;

the specific expression taking the highest comprehensive energy efficiency of the power distribution network in the whole-day scheduling period as an optimization target is as follows:

wherein, P_LOAD，r(t) is the load size at the r node in the time t, kW; r is the total number of nodes of the power distribution network; epsilon is the permeability of renewable energy of a superior power grid; eta_reGenerating efficiency of renewable energy sources of a superior power grid; eta_nreThe power generation efficiency of the non-renewable energy of the superior power grid is obtained.

6. The method as claimed in claim 1, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

in the fifth step, a lower-layer double optimization target model and a linearly superposed total optimization target model are constructed for the electric vehicle load to be optimized;

the lower layer double optimization target model comprises the following contents:

firstly, the electric automobile charged at each charging pile node in the network topology at each optimization moment is subjected to combined optimization, and the optimal charging state matrix is solved by taking the lowest daily charging cost sum and the lowest daily net load increase ratio sum as optimization targets

Namely an optimal combined charging plan of the jth electric automobile at the ith charging pile at the time t; then solving the output power of each charging pile at each optimized moment according to the optimal combined charging plan of the electric automobile obtained by the lower-layer optimization, taking the output power as a constant load, and participating in the solution of the optimal scheduling plan of other adjustable resources in the power distribution network;

the objective function expression of the total daily charging cost of the electric vehicle is as follows:

wherein, C_EVAThe total daily charging cost of the electric automobile is Yuan; p_EVA，b(t) is the output power, kW, of the b-th electric vehicle at the moment t;

the charging price of the b-th electric vehicle at the moment t is yuan/kWh; b is the total number of the electric vehicles participating in regulation; delta t is the duration of a single optimization moment, h; t is the total number of the optimization moments;

the expression of the objective function of the daily net load increase ratio sum of the electric vehicle is as follows:

wherein the content of the first and second substances,

the daily net load increase ratio of the electric automobile in the network topology is obtained; p_Load(t) is the active total load size, kW, in the network topology before the electric automobile is charged at the moment t;

two objective functions are processed by first normalization C_EVAAnd R_ΔLoadSuch that each time C is calculated by optimization_EVAAnd R_ΔLoadIs in the same order of magnitude, and then the weights occupied by the two optimization objectives are specified

And

linearly superposing the two objective functions into a single objective function to form a total optimization objective model, wherein the calculation formula is as follows:

wherein f is_sublayerIs the sum of the lower optimized normalized objective functions;

and

the weights of two optimization targets are respectively the daily charging cost sum and the daily net load increase ratio sum;

and

normalization coefficients of two objective function values, namely a daily charge cost sum and a daily net load increase ratio sum, respectively;

due to sum of daily net load increase ratiosThe method comprises the following steps of calculating the ratio of the sum of all electric automobile charging power at each time to the active total load in a network at the optimization time for all optimization times in a day, and then calculating the sum of net load increase ratios at all the optimization times; if there are T optimization moments in a day, then

The value can be 1/T, and the value range of the sum of the daily net load increase ratio of the normalized target function is [0,1]](ii) a Therefore, the temperature of the molten metal is controlled,

can take on the value of

Wherein

The sum of the charging costs of all the electric vehicles charged with the maximum power at the moment of the peak electricity price is defined as the value range of the sum of the daily charging costs of the normalized objective function of 0,1](ii) a Weight taken of two optimization objectives

And

should be specified according to the emphasis of the actual optimization target, and meet

7. The method as claimed in claim 6, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

the total optimization objective model is a sum f of normalized objective functions for optimizing the lower layer_sublayerAt a minimum, it is necessary to make C_EVAAs small as possibleWhile making R_ΔLoadThe value of (A) is as large as possible; because the unit price of charging is higher in peak electricity price moment, electric automobile can more tend not to charge, compares the value of each parameter when unordered charged state before the dispatch, can make after the dispatch

Is smaller, thereby making C_EVAIs smaller, and f_sublayerHas the same optimization direction, but can also make R_ΔLoadIs smaller, and f_sublayerThe optimization directions of the sub-objective functions are opposite, so that contradiction is generated between the two sub-objective functions, and lower-layer optimization is dedicated to searching a compromise solution in the optimal solution; because the unit price of charging is lower in the low ebb price moment, electric automobile can tend to more charge, compares the value of each parameter when unordered charged state before the dispatch, can make after the dispatch

Is greater, thereby making C_EVAGreater value of, and f_sublayerIn the opposite direction, but at the same time also makes R_ΔLoadGreater value of, and f_sublaverThe optimization directions of the sub-target functions are the same, so that contradiction is generated between the two sub-target functions, and the lower-layer optimization aims to find a compromise solution in the optimal solution.

8. The method as claimed in claim 1, wherein the method for multi-objective optimization of the operation of the power distribution network with consideration of the adjustable resource adjustment rate,

the sixth step, the optimization algorithm is an NSGA-II optimization algorithm, which specifically comprises the following contents:

before optimization, the population size N needs to be set_popAnd the number of evolutions G_pop(ii) a Then, optimization is executed;

after the optimization is finished, N is obtained_popGroup solutions and Pareto grades corresponding to each group solution; the solution with Pareto grade of 1 is completely proposed to obtain

Performing solution; then, will

All corresponding to the group solution

The calculated value of the objective function is normalized to obtain all the values

The calculated values of the objective functions are converted into the same order of magnitude; for the m-th_objThe normalization processing method of the calculated value of the objective function is represented as follows:

wherein the content of the first and second substances,

is m at_objCorresponding to an objective function

A vector composed of the normalized values of the objective function;

is m at_objCorresponding to an objective function

A maximum of the objective function calculated values;

is m at_objCorresponding to an objective function

A minimum of the objective function calculated values;

is m at_objCorresponding to an objective function

A vector composed of objective function calculated values;

will M_objAfter normalization processing is carried out on each target function, M needs to be specified in the next step_objWeight of optimization objective

Satisfy the requirement of

Then, by the following formula, calculation

Group solution of M corresponding to each group_objThe specific calculation formula of the sum of the normalized values of the objective function is shown as the following formula:

wherein the content of the first and second substances,

is composed of

M corresponding to each group solution in group solutions_objA vector consisting of the sum of the normalized values of the objective function,

is m at_objThe weight occupied by each optimization objective;

finally, search for the obtained

And a group of solutions corresponding to the maximum value in the data acquisition is the optimal solution of the optimal operation problem of the adjustable resource auxiliary power distribution network.

9. The method as claimed in any one of claims 1 to 8 for multiobjective optimization of operation of a power distribution network with consideration of adjustable resource regulation rates,

initializing decision variables;

the size of a population participating in operation needs to be set, and all decision variables participating in optimization are taken as genes; in the process of initializing genes, regulation rate constraint needs to be considered, and initialization methods including a uniform generation method and a special generation method are set for different adjustable resources;

the unified generation method comprises a unified generation method of the initial values of the genes at the first optimization moment and a unified generation method of the initial values of the genes at the non-first optimization moment:

the unified generation method of the initial value of the first optimization time gene specifically comprises the following steps:

the generation of the initial values of the genes of different adjustable resources at the first optimization moment is not restricted by the adjustment rate, and is shown as the following formula:

wherein the content of the first and second substances,

is the initial value of the v gene in the population p;

the value lower limit of the v gene;

the upper limit of the value of the v gene; r is a [0,1]]Random real numbers which are uniformly distributed;

the unified generation method of the initial value of the gene at the non-first optimization moment specifically comprises the following steps:

the generation of the initial values of the genes of different adjustable resources at the non-first optimization moment is constrained by the value size and the adjustment rate at the previous moment, as shown in the following formula:

wherein, the expressions of max (A, B) and min (A, B) respectively represent that the larger element of the element A and the element B is taken and the smaller element of the element A and the element B is taken;

is the initial value of the v-1 gene in the population p;

optimizing the upper limit of the regulation rate of the adjustable resource represented by the v-th gene in the population p at the moment for the unit;

the special generation method updates the upper limit and the lower limit of the output power value of the energy storage system at the next optimization moment based on the charge state of the energy storage system at the end moment of the previous scheduling period and the constraint of the regulation rate;

the generation method of the initial value of the gene at the first optimization time of the special generation method is the same as the unified generation method of the initial value of the gene at the first optimization time of the unified generation method;

in the method for generating the initial value of the gene at the non-first optimization moment of the special generation method, the upper limit and the lower limit of the value of the gene are increased for further constraint; the method for generating the initial value of the gene at the non-first optimization time of the energy storage system is shown as the following formula:

wherein the content of the first and second substances,

the accumulated value from the value of the 1 st gene to the value of the v-1 st gene in the population p is kW;

is the initial value of the u-th gene in the population p, kW; e_essCapacity of the energy storage system, kWh; SOC (system on chip)_essIs the initial state of charge of the energy storage system.

10. A multi-objective optimization operation system of a power distribution network considering adjustable resource regulation rate is characterized in that,

it comprises the following steps:

one or more processors;

storage means for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method of multi-objective optimization of operation of a power distribution grid in view of adjustable resource adjustment rates as recited in any of claims 1-9.

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