CN109687430A - Power distribution network economical operation method based on network reconfiguration and uncertain demand response - Google Patents

Abstract

An economical operation method of distribution network based on network reconfiguration and uncertain demand response, comprising: respectively establishing an electric boiler model and a heat storage device model at the user end; establishing a user demand response uncertainty model, including an actual response capacity model and an incentive model. The cost model of the type response is established; the economic dispatch model of the distribution network is established, including the objective function and constraints; the economic dispatch model of the distribution network is solved based on the particle swarm algorithm. The present invention makes full use of the existing resources of the system, can effectively avoid complex grid capacity construction and feeder construction, and reduce investment costs.

Description

Economic operation method of distribution network based on network reconfiguration and uncertain demand response

technical field

The invention relates to an economical operation method of a distribution network. In particular, it relates to an economical operation method of distribution network based on network reconfiguration and uncertain demand response suitable for optimal operation of distribution network.

Background technique

As the last link connecting electric energy from production to users, distribution network is extremely important for its safe and economical operation. Distribution network reconstruction only needs to change the state of the tie switch or segment switch in the network, and it can achieve the purpose of reducing network loss, improving reliability, economy and power supply efficiency without adding other investment, which is the optimal operation of the distribution network. However, with the continuous growth of multi-type loads on the user side in the future urban distribution network, the peak load increase is particularly obvious, which seriously affects the safety of distribution network operation. Require. Therefore, in order to solve the operation safety problem caused by the continuous growth of multi-energy sources on the user side of the distribution network in the future, it is necessary to introduce a certain demand response strategy to ensure that the distribution network can operate safely and economically after the load increases. Smart grid requires users to be motivated to achieve the purpose of reducing peaks and filling valleys and improving energy utilization. Demand response is an important means of interaction between distribution network and users. There are two types of demand response, price-based load response and incentive-based load response. Price-based load response means that the power grid sets peak and valley electricity prices, and users adjust their electricity consumption according to the price. Therefore, the uncertainty of price-based load response mainly comes from the uncertainty of the price demand curve; The power company signs the contract and the user accepts the signal from the dispatching department to reduce the load. The power company compensates for the load reduced by the user. However, due to the uncertainty of the external environment, the delay of information and the cognition deviation of the decision-making body, the user is not satisfied with the load reduction. There is uncertainty in the response of the signal.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to provide an economical operation method of distribution network based on network reconfiguration and uncertain demand response which can effectively avoid complex grid capacity construction and feeder construction.

The technical scheme adopted by the present invention is: an economical operation method of distribution network based on network reconfiguration and uncertain demand response, comprising the following steps:

1) Establish the electric boiler model and heat storage device model of the user side respectively;

2) Establish the uncertainty model of user demand response, including the actual response capacity model and the cost model of incentive response;

3) Establish the economic dispatch model of distribution network, including objective function and constraints;

4) Based on the particle swarm algorithm, the economic dispatch model of the distribution network is solved.

The electric boiler model described in step 1) refers to the relationship between the electric power consumed by the electric boiler and the thermal power generated as follows:

Q _b =P _b ·η _b (1)

In the formula, Q _b represents the heating power of the electric boiler; η _b represents the thermoelectric power ratio, and P _b represents the electric power required by the electric boiler to produce heat.

The heat storage device model described in step 1) refers to the relationship between heat storage capacity, input and output power, and heat loss as follows:

S(t)=S(t-1)+P _hs (t)Δt-η×S(t-1) (2)

In the formula, S(t) and S(t-1) represent the energy stored by the heat storage device at time t and time t-1, respectively, P _hs represents the output power of the heat storage device at time t, and η represents the heat storage system s efficiency.

The actual response capacity model described in step 2) is as follows:

In the formula, is the actual response capacity; Δp _{I, k} is the planned response capacity, and the constraints are: r _I1 and r _I3 are the response deviation coefficients of the k-th gear allowed by the contract, and k is the response gear. When k=1, it belongs to the reference response gear, and when k>1, it belongs to the elastic response gear.

The cost model of the incentive response described in step 2) is:

In the formula, C _IDR is the cost of the excitation response, c _I,k is the unit compensation standard of the k-th gear, k is the response gear, when k=1, it belongs to the reference response gear, and when k>1, it belongs to the elasticity response file; is the actual response capacity, and _NI is the number of response gears.

The objective function described in step 3) is:

In the formula, M is the total number of branches in the distribution network; T is the total time of economic operation dispatch; P _m and Q _m are the active power and reactive power flowing through the head end of branch m; U _m is the voltage on branch m ; R _m is the impedance on branch m; C _IDR is the cost of the excitation-type response.

The constraints described in step 3) include:

(3.1) Power flow constraints of distribution network:

In the formula, Ω _i is the set of nodes adjacent to node i; V _i , V _j and θ _ij are the voltage amplitude and phase angle difference between node i and node j, respectively; G _ii , B _ii , G _ij and B _ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix, respectively; P _i and Q _i are the active power and reactive power of node i;

(3.2) Safe operation constraints, including current constraints and voltage constraints:

I _l ≤I _{l max} l=1,...L _i (7)

V _Li ≤V _i ≤V _Ui i=1,.....N (8)

In the formula, I _l is the current flowing through element l; I _lmax is the maximum allowable passing current of element l; Li is the number of elements l; V _Li is the lower voltage limit of node _i ; V _Ui is the upper voltage limit of node i , N is the number of nodes;

(3.3) Radial network operation constraints:

g _p ∈ G _p (9)

where g _p represents the current network structure; G _p represents all allowed radial network configurations;

(3.4) Constraints on the number of switch actions:

N _{z ≤N zmax} z∈S (10)

In the formula, N _z is the number of actions of switch z; N _zmax is the upper limit of the number of actions of switch z; S is the switch number;

(3.5) N-1 constraint:

d _w ≥ 0 (11)

where _dw is the distance from the operating point to the safety boundary of the distribution network;

(3.6) Electric boiler constraints:

P _{b min} ≤P _b (t)≤P _{b max} (12)

Q _{b min} ≤Q _b (t)≤Q _{b max} (13)

In the formula, P _b (t) is the electric power required by the electric boiler to produce heat at time t; P _{b min} and P _{b max} are the upper and lower limits of the electric power required for the boiler to produce heat; Q _b (t) is the electric power of the electric boiler at time t. Heating power; Q _{b min} and Q _{b max} are the upper and lower limits of the heating power of the electric boiler;

(3.7) Heat storage device constraints:

P _{hs min} ≤P _hs (t)≤P _{hs max} (14)

S _min ≤S(t)≤S _max (15)

In the formula, P _hs (t) is the output power of the heat storage device at time t; P _{hs min} and P _{hs max} are the upper and lower limits of the output power of the heat storage device at time t; S(t) is the heat storage device at time t stored energy; S _min and S _max are the upper and lower limits of the stored energy of the heat storage device;

(3.8) Incentive demand response cost constraints:

C _IDR ≤ C _max (16)

In the formula, _CIDR is the cost of the stimulus response; _Cmax is the upper limit of the cost of the stimulus response.

Step 4) includes:

(4.1) Input the network structure parameters of the distribution network, the load data of each node and the electricity price information;

(4.2) Judge whether the distribution network satisfies safe operation, if yes, then go to step (4.8), otherwise go to step (4.3);

(4.3) Initialize the quantum particle swarm algorithm, including the parameters of the algorithm and the initial particle swarm;

(4.4) Calculate the objective function and determine the individual fitness value.

(4.5) Update the particle position to obtain the individual optimal solution and the global optimal solution;

(4.6) Judging whether the number of iterations X is exceeded, if yes, go to step (4.7), if no, add one to the number of iterations, and return to step (4.4);

(4.7) Output optimized electricity price, total operating cost and distribution network reconstruction results;

(4.8) End.

The updated particle position described in step (4.5), the individual optimal solution and the global optimal solution are obtained as:

θ _h = (-1+2×rand ₀ )×π/2 (17)

chrom=[θ _h1 ,θ _h2 ,...,θ _hn ] (18)

dangle=[Δθ _h1 ,Δθ _h2 ,...,Δθ _hn ] (19)

where θ _h is the phase angle of the h-th particle; θ _hn is the phase angle between the h-th particle and the n-th particle; Δθ _hn is the rotation angle between the h-th particle and the n-th particle; rand ₀ is a random number between [0, 1]; n is the dimension of the solution space; chrom and dangle are the position and velocity of the particle, respectively, selfchrom _h is the best position of the particle h, and bestchrom is the best position of the group;

dangle(x+1)=ω×dangle(x+1)+c ₁ ×r ₁ ×(selfchrom _h -chrom(t))+c ₁ ×r ₁ ×(bestchrom-chrom(x)) (20)

chrom(x+1)=chrom(x)+dangle(x+1) (21)

In the formula, ω inertia factor; c ₁ and c ₂ are normal numbers, called cognitive factors and social factors; r ₁ and r ₂ are random numbers uniformly distributed between [0,1];

For the chromosome chrom(x) of the population in the x-th iteration, the phase angle of the g-th particle of the x+1-th generation chromosome chrom(x+1) is:

θ _hg (x+1)=θ _hg (x)+sign(θ _bh (x)-θ _hg (x))Δθ _hg (x) (22)

In the formula, Δθ _hg (x) is the rotation angle between h and g particles in the xth iteration; θ _bg (x) is the phase angle of the hth qubit of the chromosome corresponding to the optimal solution in the xth iteration .

The economical operation method of the distribution network based on network reconfiguration and uncertain demand response of the present invention combines two means of combined electric and heat demand response and distribution network reconfiguration to ensure safe and economical operation of the distribution network. Through the comprehensive demand response for electricity and heat, the demand-side resources are enriched, the distribution network scheduling is more flexible, and the peak shaving and valley filling is more effective; the incentive demand response is introduced, the compensation standard is reasonably formulated, and the enthusiasm of users to participate in demand response is mobilized; by considering The distribution network reconfiguration with N-1 security constraints can adjust the network structure, further balance the distribution of power supply and load, and ensure the feasibility of the reconfiguration results; the combination of the two can realize economical operation on the basis of ensuring the safe operation of the distribution network. The method makes full use of the existing resources of the system, can effectively avoid complex grid capacity construction and feeder construction, and reduce investment costs.

Description of drawings

Figure 1 is a schematic diagram of the incentive mechanism combining rigid constraints and elastic constraints;

Figure 2 is a schematic diagram of a quantum revolving gate.

Detailed ways

The economical operation method of a distribution network based on network reconfiguration and uncertain demand response of the present invention will be described in detail below with reference to the embodiments and accompanying drawings.

The economical operation method of distribution network based on network reconfiguration and uncertain demand response of the present invention comprises the following steps:

1) Establish the electric boiler model and the heat storage device model of the user side respectively; wherein,

Electric boiler is a device that realizes electric-heat coupling on the load side, which can realize electric-heat conversion, convert electric energy into heat energy, supply heat load and store more than heat energy in heat storage device. The electric boiler model described refers to the relationship between the electric power consumed by the electric boiler and the thermal power generated as follows:

Q _b =P _b ·η _b (1)

In the formula, Q _b represents the heating power of the electric boiler; η _b represents the thermoelectric power ratio, and P _b represents the electric power required by the electric boiler to produce heat.

Heat storage devices are generally heat storage tanks, heat storage tanks, etc. The heat storage device model refers to the relationship between heat storage capacity, input and output power, and heat loss as follows:

S(t)=S(t-1)+P _hs (t)Δt-η×S(t-1) (2)

In the formula, S(t) and S(t-1) represent the energy stored by the heat storage device at time t and time t-1, respectively, P _hs represents the output power of the heat storage device at time t, and η represents the heat storage system s efficiency.

2) Establish a user demand response uncertainty model

The electric load response is mainly through the interruptible load and the translational load to reduce the peak shaving and valley filling of the electric load, and the thermal load response is mainly realized through the control of the electric boiler and the heat storage device. On the one hand, the uncertainty of the heat load response comes from the fact that the user's requirement for the ambient temperature is not a specific value, but should be a comfort level range, so the uncertainty of the ambient temperature within this range causes the inconsistency of the demand response. The certainty, on the other hand, comes from the uncertainty of the response of the equivalent electrical load after the thermal load demand is converted into the electrical load demand. The uncertainty of the incentive load response is mainly derived from the uncertainty of the response of the user's incentive policy. The actual response capacity of the incentive demand response may deviate from the expectation. The uncertainty of the response mainly comes from the estimation of the baseline load, the user's response execution and the uncertainty of the demand for load reduction.

In the present invention, an incentive mechanism combining rigid constraints and elastic constraints is adopted, and the user's incentive demand response is divided into a reference response file and an elastic response file according to the contract. The reference response is set as a rigid constraint, and its actual response capacity should be equal to Planned response capacity; the elastic response file is set as an elastic constraint, allowing its actual response capacity to fluctuate within a certain range of the planned response capacity. The benchmark response file corresponds to the benchmark subsidy, and the response increment based on this is an elastic response file, corresponding to different subsidy standards.

After considering the impact of response uncertainty, the contract between the power grid and the user should stipulate: ① the upper limit of the response capacity and subsidy standard of the user’s benchmark tranche; ② the upper limit of the response capacity of each flexible tranche of the user, the allowable deviation ratio of the actual implementation, and the subsidy standard .

Users will have different response capacities for different subsidy standards, that is, the user's heat load demand will fluctuate within the comfort range, and the user's electric load demand will also follow the changes in the amount of interruptible and transferable loads. fluctuation. Under different subsidy standards, users need to meet the benchmark response, while the elastic response part depends on the user's subjective factors. When k=1, it belongs to the reference response gear, and when k=2, it belongs to the elastic response gear. As shown in Figure 1, c _I,k represents the unit compensation standard of the k-th gear, and Δp _I,k are the actual response capacity and planned response capacity of k-th gear, respectively.

The user demand response uncertainty model includes an actual response capacity model and an incentive response cost model; wherein,

The actual response capacity model described is as follows:

In the formula, is the actual response capacity; Δp _{I, k} is the planned response capacity, and the constraints are: r _I1 and r _I3 are the response deviation coefficients of the k-th gear allowed by the contract, and k is the response gear. When k=1, it belongs to the reference response gear, and when k>1, it belongs to the elastic response gear.

The cost model of the stimulus-type response described is:

In the formula, C _IDR is the cost of the excitation response, c _I,k is the unit compensation standard of the k-th gear, k is the response gear, when k=1, it belongs to the reference response gear, and when k>1, it belongs to the elasticity response file; is the actual response capacity, and _NI is the number of response gears.

3) Establishment of economic dispatch model of distribution network

During peak electricity consumption, the network loss cost of the distribution network will increase, and it will also bring certain security risks. A certain demand response strategy is introduced in the present invention to reduce network loss and ensure safe and economical operation of the distribution network. At the same time, the introduction of demand response will have a certain cost. Therefore, the present invention aims to minimize the total operating cost of the distribution network within 24 hours a day, that is, the sum of the network loss cost and the demand response cost, by optimizing the on-off state of the distribution network switch and the unit compensation standard of each response file. , to construct the optimal dispatching model of the distribution network, so as to realize the economic dispatch of the distribution network.

The distribution network economic dispatch model includes an objective function and constraints; wherein

The objective function described is:

In the formula, M is the total number of branches in the distribution network; T is the total time of economic operation dispatch; P _m and Q _m are the active power and reactive power flowing through the head end of branch m; U _m is the voltage on branch m ; R _m is the impedance on branch m; C _IDR is the cost of the excitation-type response.

The constraints include:

(3.1) Power flow constraints of distribution network:

In the formula, Ω _i is the set of nodes adjacent to node i; V _i , V _j and θ _ij are the voltage amplitude and phase angle difference between node i and node j, respectively; G _ii , B _ii , G _ij and B _ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix, respectively; P _i and Q _i are the active power and reactive power of node i;

(3.2) Safe operation constraints, including current constraints and voltage constraints:

I _l ≤I _{l max} l=1,...L _i (7)

V _Li ≤V _i ≤V _Ui i=1,.....N (8)

In the formula, I _l is the current flowing through element l; I _lmax is the maximum allowable passing current of element l; Li is the number of elements l; V _Li is the lower voltage limit of node _i ; V _Ui is the upper voltage limit of node i , N is the number of nodes;

(3.3) Radial network operation constraints:

g _p ∈ G _p (9)

where g _p represents the current network structure; G _p represents all allowed radial network configurations;

(3.4) Constraints on the number of switch actions:

N _{z ≤N zmax} z∈S (10)

In the formula, N _z is the number of actions of switch z; N _zmax is the upper limit of the number of actions of switch z; S is the switch number;

(3.5) N-1 constraint:

The safety distance _dw of the distribution network refers to the distance from the working point to the safety boundary of the distribution network, which reflects the position of the working point in the safety domain of the distribution network. When the working point does not meet the N-1 safety, the safety distance is a negative value; When the working point satisfies N-1 safety, the safety distance is a positive value, and the larger the absolute value is, the higher the safety degree of the working point is.

d _w ≥ 0 (11)

where _dw is the distance from the operating point to the safety boundary of the distribution network;

(3.6) Electric boiler constraints:

P _{b min} ≤P _b (t)≤P _{b max} (12)

Q _{b min} ≤Q _b (t)≤Q _{b max} (13)

In the formula, P _b (t) is the electric power required by the electric boiler to produce heat at time t; P _{b min} and P _{b max} are the upper and lower limits of the electric power required for the boiler to produce heat; Q _b (t) is the electric power of the electric boiler at time t. Heating power; Q _{b min} and Q _{b max} are the upper and lower limits of the heating power of the electric boiler;

(3.7) Heat storage device constraints:

P _{hs min} ≤P _hs (t)≤P _{hs max} (14)

S _min ≤S(t)≤S _max (15)

In the formula, P _hs (t) is the output power of the heat storage device at time t; P _{hs min} and P _{hs max} are the upper and lower limits of the output power of the heat storage device at time t; S(t) is the heat storage device at time t stored energy; S _min and S _max are the upper and lower limits of the stored energy of the heat storage device;

(3.8) Incentive demand response cost constraints:

C _IDR ≤ C _max (16)

In the formula, _CIDR is the cost of the stimulus response; _Cmax is the upper limit of the cost of the stimulus response.

4) Based on the particle swarm algorithm, the economic dispatch model of the distribution network is solved. include:

The solution algorithm adopted in the present invention is the quantum particle swarm algorithm (QPSO). Compared with the general particle swarm algorithm, an improvement of QPSO is that the movement and mutation of particles are carried out in two-dimensional quantum space, so that particles The ability to cover unreachable regions within the one-dimensional solution space in a single search or mutation accelerates the convergence of the optimization problem to some extent.

The QPSO algorithm maps the actual solution space to the quantum space according to certain rules. First, the current position of the particle is represented by the probability amplitude of the qubit, and then the solution space is transformed, and each probability amplitude of the qubit is represented as a variable in the solution space. The state updates of particles include position updates and velocity updates. The update of the rotation angle of the quantum revolving door is used to replace the update of the moving speed of the ordinary medium, and the update of the probability amplitude of the qubit is used to replace the update of the particle position. The schematic diagram of the quantum revolving gate is shown in Figure 2.

(4.1) Input the network structure parameters of the distribution network, the load data of each node and the electricity price information;

(4.2) Judge whether the distribution network satisfies safe operation, if yes, then go to step (4.8), otherwise go to step (4.3);

(4.3) Initialize the quantum particle swarm algorithm, including the parameters of the algorithm and the initial particle swarm;

(4.4) Calculate the objective function and determine the individual fitness value.

(4.5) Update the particle position to obtain the individual optimal solution and the global optimal solution; the updated particle position to obtain the individual optimal solution and the global optimal solution are:

θ _h = (-1+2×rand ₀ )×π/2 (17)

chrom=[θ _h1 ,θ _h2 ,...,θ _hn ] (18)

dangle=[Δθ _h1 ,Δθ _h2 ,...,Δθ _hn ] (19)

where θ _h is the phase angle of the h-th particle; θ _hn is the phase angle between the h-th particle and the n-th particle; Δθ _hn is the rotation angle between the h-th particle and the n-th particle; rand ₀ is a random number between [0, 1]; n is the dimension of the solution space; chrom and dangle are the position and velocity of the particle, respectively, selfchrom _h is the best position of the particle h, and bestchrom is the best position of the group;

dangle(x+1)=ω×dangle(x+1)+c ₁ ×r ₁ ×(selfchrom _h -chrom(t))+c ₁ ×r ₁ ×(bestchrom-chrom(x)) (20)

chrom(x+1)=chrom(x)+dangle(x+1) (21)

In the formula, ω inertia factor; c ₁ and c ₂ are normal numbers, called cognitive factors and social factors; r ₁ and r ₂ are random numbers uniformly distributed between [0,1];

For the chromosome chrom(x) of the population in the x-th iteration, the phase angle of the g-th particle of the x+1-th generation chromosome chrom(x+1) is:

θ _hg (x+1)=θ _hg (x)+sign(θ _bh (x)-θ _hg (x))Δθ _hg (x) (22)

In the formula, Δθ _hg (x) is the rotation angle between h and g particles in the xth iteration; θ _bg (x) is the phase angle of the hth qubit of the chromosome corresponding to the optimal solution in the xth iteration .

(4.6) Judging whether the number of iterations X is exceeded, if yes, go to step (4.7), if no, add one to the number of iterations, and return to step (4.4);

(4.7) Output optimized electricity price, total operating cost and distribution network reconstruction results;

(4.8) End.

Claims (9)

1. A power distribution network economic operation method based on network reconstruction and uncertainty demand response is characterized by comprising the following steps:

1) respectively establishing an electric boiler model and a heat storage device model of a user side;

2) establishing a user demand response uncertainty model comprising an actual response capacity model and an incentive response cost model;

3) establishing an economic dispatching model of the power distribution network, wherein the economic dispatching model comprises a target function and a constraint condition;

4) and solving the economic dispatching model of the power distribution network based on the particle swarm algorithm.

2. The method for economic operation of a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein said electric boiler model of step 1) is the relationship between electric power consumed and thermal power generated by the electric boiler as follows:

Q_b＝P_b·η_b(1)

in the formula, Q_bIndicating the heating power of the electric boiler η_bRepresenting the thermoelectric power ratio, P_bRepresenting the electrical power required by the electric boiler to generate heat.

3. The method as claimed in claim 1, wherein the heat storage device model in step 1) is a relationship among heat storage capacity, input/output power and heat loss, and is as follows:

S(t)＝S(t-1)+P_hs(t)Δt-η×S(t-1) (2)

wherein S (t) and S (t-1) represent the energy stored in the heat storage device at time t and time t-1, respectively, and P_hsThe representation represents the output power of the heat storage device at time t, and η represents the efficiency of the heat storage system.

4. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein the actual response capacity model of step 2) is as follows:

in the formula,actual response capacity; Δ p_I,kFor planning response capacity, the constraints are:r_I1and r_I3Response deviation coefficients of a k-th gear allowed by a contract are respectively, k is a response gear, when k is 1, the response gear belongs to a reference response gear, and when k is 1, the response gear belongs to a reference response gear>1, belonging to the elastic response gear.

5. The method for economically operating a power distribution network based on network reconstruction and uncertainty demand response as claimed in claim 1, wherein the cost model of incentive type response in step 2) is:

in the formula, C_IDRCost for stimulus-type response, c_I,kThe unit compensation standard of the k-th gear is that k is a response gear, when k is 1, the unit compensation standard belongs to a reference response gear, and when k is 1>1, belonging to an elastic response gear;for actual response capacity, N_IIn response to the number of gears.

6. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein the objective function of step 3) is:

in the formula, M is the total number of the branch circuits of the power distribution network; t is the total time of economic operation scheduling; p_mAnd Q_mThe active power and the reactive power flowing through the head end of the branch m; u shape_mIs the voltage on branch m; r_mIs the impedance on branch m; c_IDRThe cost of the stimulus-type response.

7. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response according to claim 1, wherein the constraint conditions in step 3) comprise:

(3.1) power flow constraint conditions of the power distribution network:

in the formula, omega_iIs a set of nodes adjacent to node i; v_i、V_jAnd theta_ijThe voltage amplitude and the phase angle difference of the node i and the node j are respectively; g_ii、B_ii、G_ijAnd B_ijRespectively are self conductance, self susceptance, mutual conductance and mutual susceptance in the node admittance matrix; p_iAnd Q_iActive power and reactive power for node i;

(3.2) safe operation constraints including current constraints and voltage constraints:

I_l≤I_lmaxl＝1,......L_i(7)

V_Li≤V_i≤V_Uii＝1,.....N (8)

in the formula I_lIs the current flowing through element l; i is_lmaxMaximum allowed current for element/; l is_iThe number of elements l; v_LiIs the lower voltage limit of node i; v_UiIs the voltage upper limit of the node i, and N is the number of nodes;

(3.3) radial network operation constraints:

g_p∈G_p(9)

in the formula, g_pRepresenting the current network structure; g_pRepresents all allowed radial network configurations;

(3.4) switch action frequency constraint:

N_z≤N_zmaxz∈S(10)

in the formula, N_zIs the number of times of switch z action; n is a radical of_zmaxIs the upper limit of the number of times of the switch z; s is a switch number;

(3.5) N-1 constraint:

d_w≥0 (11)

in the formula d_wThe distance from the working point to the safety boundary of the power distribution network;

(3.6) electric boiler constraint:

P_bmin≤P_b(t)≤P_bmax(12)

Q_bmin≤Q_b(t)≤Q_bmax(13)

in the formula, P_b(t) the electric power required by the electric boiler for generating heat at the moment t; p_bmin、P_bmaxUpper and lower limits of electric power required for the boiler to generate heat; q_b(t) the heating power of the electric boiler at the moment t; q_bmin、Q_bmaxThe upper limit and the lower limit of the heating power of the electric boiler are set;

(3.7) heat storage device restraint:

P_hsmin≤P_hs(t)≤P_hsmax(14)

S_min≤S(t)≤S_max(15)

in the formula, P_hs(t) is the output power of the heat storage device at time t; p_hsmin、P_hsmaxThe upper limit and the lower limit of the output power of the heat storage device at the moment t; s (t) is the energy stored by the heat storage device at the moment t; s_min、S_maxUpper and lower limits of energy stored in the heat storage device;

(3.8) incentive demand response cost constraints:

C_IDR≤C_max(16)

in the formula, C_IDRCost for an excitation-type response; c_maxThe upper limit of the incentive response cost.

8. The method for economically operating a power distribution network based on network reconfiguration and uncertainty demand response as set forth in claim 1, wherein step 4) comprises:

(4.1) inputting network structure parameters of the power distribution network, load data of each node and information of electricity price;

(4.2) judging whether the power distribution network meets the safe operation, if so, entering the step (4.8), otherwise, entering the step (4.3);

(4.3) initializing a quantum particle swarm algorithm, wherein the quantum particle swarm algorithm comprises all parameters of the algorithm and an initial particle swarm;

and (4.4) calculating an objective function and determining an individual fitness value.

(4.5) updating the particle positions to obtain an individual optimal solution and a global optimal solution;

(4.6) judging whether the iteration times X are exceeded, if yes, entering the step (4.7), if not, adding one to the iteration times, and returning to the step (4.4);

(4.7) outputting an optimized electricity price, total running cost and a power distribution network reconstruction result;

and (4.8) finishing.

9. The method for economic operation of a power distribution network based on network reconstruction and uncertainty demand response of claim 8 wherein the updating of particle locations in step (4.5) results in individual optimal solutions and global optimal solutions that are:

θ_h＝(-1+2×rand₀)×π/2 (17)

chrom＝[θ_h1,θ_h2,...,θ_hn](18)

dangle＝[Δθ_h1,Δθ_h2,...,Δθ_hn](19)

in the formula, theta_hIs the phase angle of the h particle; theta_hnIs the phase angle between the h particle and the n particle; delta theta_hnIs the rotation angle between the h particle and the n particle; rand₀Is [0,1 ]]A random number in between; n is the dimension of the solution space; chrom and dangle are the position and velocity of the particle, selfchrom, respectively_hIs the optimal position of the particle h, bestchrom is the optimal position of the population;

dangle(x+1)＝ω×dangle(x+1)+c₁×r₁×(selfchrom_h-chrom(t))+c₁×r₁×(bestchrom-chrom(x))

(20)

chrom(x+1)＝chrom(x)+dangle(x+1) (21)

in the formula, ω is an inertia factor; c. C₁And c₂Normal, known as cognitive and social factors; r is₁And r₂Is [0,1 ]]All areUniformly distributed random numbers;

for chromosome chrom (x) of the population in the x-th iteration, the phase angle of the g-th particle of chromosome chrom (x +1) of the x +1 generation is:

θ_hg(x+1)＝θ_hg(x)+sign(θ_bh(x)-θ_hg(x))Δθ_hg(x) (22)

in the formula,. DELTA.theta._hg(x) Is the rotation angle between h and g particles in the x-th iteration; theta_bg(x) Is the phase angle of the h-th qubit of the chromosome corresponding to the optimal solution in the x-th iteration.