Optimized operation method for power distribution network containing distributed photovoltaic and temperature control loads

Abstract

The invention discloses a distributed photovoltaic and temperature control load-containing power distribution network optimization operation method. The method comprises the steps of constructing an electric heating load model and a refrigeration house model; establishing a dynamic optimal power flow model of the power distribution network of the distributed photovoltaic and temperature control load, and setting an objective function and constraint conditions; converting the dynamic optimal power flow model of the power distribution network into a second-order cone relaxation model through second-order cone relaxation; and solving the second-order cone relaxation model by adopting an improved dwarf weasel optimization algorithm to obtain a photovoltaic and temperature control load regulation and control plan. According to the invention, the problem of unbalance between photovoltaic output and load is improved, the reliability and economical efficiency of operation of the power distribution network after distributed photovoltaic access are improved, and the heating cost of users is effectively reduced. And the optimal power flow model is converted into a second-order cone programming problem, so that the solving speed of the model is improved. By improving the cat weasel algorithm, the search capability of the optimization algorithm is enhanced, and better optimization performance is achieved.

Description

Technical Field

[0001] The present invention relates to the field of clean heating and optimized operation technology, and in particular to a method and device for optimizing operation of a distribution network containing distributed photovoltaic and temperature control loads. Background Art

[0003] At present, in the interaction between electric heating and clean energy, the research focus tends to be on the consumption of wind power. However, the spatial matching between wind power generation and electric heating is relatively poor, and new transmission lines need to be planned, which has a relatively high investment cost. Distributed photovoltaics have the characteristics of self-generation and self-use, local consumption, and cleanliness. They can alleviate the problem of electricity shortage in some power-deficient areas to a certain extent, and can also alleviate the power supply pressure during peak hours, promote the green transformation of energy in rural areas, optimize energy production and consumption patterns, and achieve high-quality development of the energy industry. In addition, most of the existing studies assume that the power of electric heating equipment is constant, which seriously affects the potential of electric heating equipment to consume clean energy. Therefore, the joint optimization operation of the "photovoltaic + electric heating" system is crucial. Summary of the invention

[0004] In view of the above problems, the present invention provides a method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads, so as to obtain a control plan for photovoltaic and temperature control loads and realize orderly control of distributed photovoltaic and electric heating.

[0005] According to one aspect of the present invention, there is provided a method for optimizing operation of a distribution network including distributed photovoltaic and temperature control loads, comprising:

[0006] Construct an electric heating load model and a cold storage model, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by a lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage;

[0007] Based on the room temperature change process simulated by the electric heating load model and the refrigeration process simulated by the cold storage model, a dynamic optimal power flow model of the distribution network of distributed photovoltaic and temperature control loads is established, and the objective function and constraint conditions are set; wherein the objective function is to minimize grid loss in the distribution network area connected to distributed photovoltaics, optimize user comfort and maximize distributed photovoltaic absorption; the constraint conditions include branch power flow constraint conditions, photovoltaic operation regulation constraint conditions, node voltage constraint conditions, electric heating constraint conditions and cold storage constraint conditions;

[0008] The distribution network dynamic optimal power flow model is converted into a second-order cone relaxation model through second-order cone relaxation;

[0009] The improved dwarf mongoose optimization algorithm is used to solve the second-order cone relaxation model to obtain the control plan of photovoltaic and temperature control loads.

[0010] In an optional manner, the second-order model of the electric heating equipment is:

[0011]

[0012] Among them, C in , C wall are the equivalent heat capacity of indoor air and the equivalent heat capacity of wall respectively; R 1 , R 2 are the equivalent thermal resistance between indoor air and the inner side of the wall, and the equivalent thermal resistance between the outer side of the wall and the outdoor air; θ in ,θ wall and θ out are indoor temperature, wall temperature, and outdoor temperature respectively; P heat is the heating power of the electric heating equipment, P heat =S(t)P N , P N is the rated power of the electric heating equipment, S(t) is the switch state of the electric heating equipment, which is 0 when it is off and 1 when it is on; θ in (t+1) is the indoor temperature at time t+1; θ wall (t+1) is the wall temperature at time t+1; θ out (t) is the outdoor temperature at time t; Δt is the step size of the optimal scheduling.

[0013] In an optional manner, the first-order equivalent thermal parameter model is:

[0014] T in (t+1)=T out (t+1)-η F P F (t)R-(T out (t+1)-η F P F (t)RT in (t))e -Δt / RC

[0015] Among them, η F is the energy efficiency ratio of the cold storage; P F (t) is the power consumed by the cold storage at time t; Δt is the step size of the optimal scheduling; T in (t) and T out (t) are the temperature inside and outside the cold storage at time t, both in °C; R is the equivalent thermal resistance, in °C / W; C is the equivalent heat capacity, in J / °C.

[0016] In an optional manner, the calculation formula for the grid loss in the distribution network area is:

[0017]

[0018] Among them, P loss is the sum of the active power loss of the branches throughout the day, ij represents the branch connected between node i and node j; Q is the set of distribution network branches; T is the total dispatching time; R ij is the resistance of branch ij; t is the time period mark; I ij (t) is the branch current of branch ij at time t;

[0019] The calculation formula of the user comfort is:

[0020] E comf =αF F +βF min

[0021] Among them, E comf F is the electricity comfort index; F and F min are the variance of temperature utility coefficient and the minimum value of temperature utility coefficient respectively; α and β are the weight distribution coefficients of variance and minimum value;

[0022] The calculation formula for the distributed photovoltaic consumption is:

[0023]

[0024] Among them, pv 1 、pv 2 ...pv n The actual power generation of each distributed photovoltaic user.

[0025] In an optional manner, the branch power flow constraint condition is:

[0026]

[0027] Where i and j represent the node numbers of the beginning and end of the line respectively; U i (t), U j (t) are the voltages of nodes i and j at time t; p i (t), p j (t) is the active power injected into nodes i and j at time t; q i (t), q j (t) are the reactive power injected into nodes i and j at time t; P ij (t) and Q ij (t) are respectively the active power and reactive power at the head end of branch ij at time t; R ij is the resistance of branch ij; X ij is the reactance of branch ij; P jk (t) and Qjk (t) are respectively the active power and reactive power of the head end of branch jk at time t; k:j→k is the set of child nodes with node j as the parent node; I ij (t) is the branch current of branch ij at time t.

[0028] In an optional manner, converting the distribution network dynamic optimal power flow model into a second-order cone relaxation model by second-order cone relaxation further includes:

[0029] Introduce the square of the voltage of node i in period t as variable α i (t) and the quadratic power of the branch ij current is the variable β ij (t), where U i (t) is the node voltage value at time t, I ij (t) is the branch current of branch ij at time t;

[0030] The objective function of minimizing the grid loss in the distribution network area connected to distributed photovoltaics, the branch flow constraints and the node voltage constraints are transformed respectively to obtain the dynamic optimal power flow model β of the distribution network ij (t); where P ij (t) and Q ij (t) are respectively the active power and reactive power at the head end of branch ij at time t;

[0031] After being processed by SCOR and then equivalently transformed, the dynamic optimal power flow model of the distribution network is converted into a second-order cone relaxation model; the second-order cone relaxation model is: Among them, ||.|| 2 is the two-norm.

[0032] In an optional manner, before using the improved dwarf mongoose optimization algorithm to solve the dynamic optimal power flow model of the distribution network, the method further includes:

[0033] According to the current number of iterations and the maximum number of iterations, the peeping behavior of the meerkat is modified; the calculation formula of the peeping behavior of the meerkat is: Among them, it represents the current number of iterations, and Maxit represents the maximum number of iterations;

[0034] According to the randomly selected solution and the current optimal solution in the iterative process, a new candidate solution is generated. The generation formula of the new candidate solution is: Among them, X k is a randomly selected solution during the iteration process, X best is the optimal solution obtained by iteration so far, X i Indicates the current position, X i+1The new location after the update.

[0035] In an optional manner, the using of the improved dwarf mongoose optimization algorithm to solve the second-order cone relaxation model further comprises:

[0036] Step S41, initializing the population and calculating its fitness value, establishing a time parameter C, wherein the population includes the power of heating and cold storage in each time period;

[0037] Step S42, according to the formula Select the Alpha, the female leader of the group;

[0038] Step S43: Update the next position X according to the generation formula of the new candidate solution i+1 ;

[0039] Step S44, calculate the next position X i+1 The fitness value of

[0040] Step S45, calculating the average sleeping time of the mound;

[0041] Step S46, updating time parameter C=C+1;

[0042] Step S47, determine whether C is greater than or equal to the nanny exchange parameter L, if C>=L, initialize the population and calculate its fitness value;

[0043] Step S48, update the scout group position X i+1 , update and iterate to the current optimal solution X * And the optimal fitness value f(X * );

[0044] Step S49, output X * and f(X * ), where X * represents the optimal combination of photovoltaic power output, electric heating and cold storage load power in each period, f(X * ) represents the optimal fitness function value of the objective function corresponding to the optimal solution.

[0045] In an optional manner, after the improved dwarf mongoose optimization algorithm is used to solve the second-order cone relaxation model to obtain the optimization strategy of photovoltaic power generation power and temperature control load power, the method further includes:

[0046] When the photovoltaic power generation is sufficient within a preset time period, the power of electric heating and cold storage loads is increased, that is, within the comfortable temperature range acceptable to residents, the indoor temperature is increased and the freezing and refrigeration temperatures in the cold storage are appropriately lowered; if the indoor temperature has reached the highest comfortable temperature acceptable to residents and the cold storage has also reached the lowest temperature, and there is still surplus photovoltaic power, the surplus power is connected to the grid to achieve full photovoltaic consumption;

[0047] When the photovoltaic power generation power is insufficient within a preset time period, the power of electric heating and cold storage loads is reduced within the comfortable temperature range acceptable to residents, that is, the indoor temperature is reduced and the temperature in the cold storage is appropriately increased.

[0048] According to another aspect of the present invention, there is provided a distribution network optimization operation device for distributed photovoltaic and temperature control loads, comprising:

[0049] The electric heating load and cold storage model construction module is used to construct the electric heating load model and the cold storage model, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by the lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage;

[0050] A dynamic optimal power flow model building module is used to establish a dynamic optimal power flow model of the distribution network of distributed photovoltaic and temperature control loads based on the room temperature change process of the electric heating load model and the refrigeration process of the cold storage model, and set the objective function and constraint conditions; wherein the objective function is to minimize the grid loss in the distribution network area connected to the distributed photovoltaic, optimize the user comfort and maximize the distributed photovoltaic consumption; the constraint conditions include branch power flow constraint conditions, photovoltaic operation regulation constraint conditions, node voltage constraint conditions, electric heating constraint conditions and cold storage constraint conditions;

[0051] An optimal power flow model conversion module, used for converting the distribution network dynamic optimal power flow model into a second-order cone relaxation model through second-order cone relaxation;

[0052] The optimal power flow model solving module is used to solve the second-order cone relaxation model by using an improved dwarf mongoose optimization algorithm to obtain a control plan for photovoltaic and temperature control loads.

[0053] According to the solution provided by the present invention, an electric heating load model and a cold storage model are constructed, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by a lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage; based on the room temperature change process of the electric heating load model and the refrigeration process of the cold storage model, a dynamic optimal power flow model of the distribution network for distributed photovoltaic and temperature control loads is established, and an objective function and constraints are set; wherein the objective function is to minimize grid loss in the distribution network area connected to distributed photovoltaics, optimize user comfort and maximize distributed photovoltaic absorption; the constraints include branch power flow constraints, photovoltaic operation regulation constraints, node voltage constraints, electric heating constraints and cold storage constraints; the dynamic optimal power flow model of the distribution network is converted into a second-order cone relaxation model through second-order cone relaxation; the improved dwarf mongoose optimization algorithm is used to solve the second-order cone relaxation model to obtain a control plan for photovoltaic and temperature control loads. The present invention establishes a dynamic optimal power flow model of a distribution network containing distributed photovoltaic and temperature control loads based on branch power flow, takes network loss minimization, user comfort optimization and distributed photovoltaic absorption maximization as objective functions, and solves the output plan of distributed photovoltaic and temperature control loads through an improved dwarf mongoose optimization algorithm, effectively improves the imbalance problem between photovoltaic output and load, realizes efficient absorption and flexible and friendly grid connection of distributed photovoltaic, effectively improves the reliability and economy of distribution network operation after distributed photovoltaic access, improves the reliability of user heating and effectively reduces the heating cost of users. In view of the non-convex constraints in the optimal power flow model, SOCR is used to transform the model, and the optimal power flow model is relaxed by a second-order cone to a second-order cone programming problem containing integer variables, which effectively improves the speed of solution. In view of the problem of solving the model, the WI of the DMO algorithm is improved to a nonlinear decrease of the peep value from 1 to 0 with linear decrease with iteration; and the exploration and development of the mongoose is considered when updating the candidate solution, which is beneficial to the global search of the model and the search of local information, so that the candidate solution update mechanism can enhance the search ability of the optimization algorithm to achieve better optimization performance.

[0054] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 A schematic diagram of a flow chart of a method for optimizing operation of a distribution network including distributed photovoltaic and temperature control loads according to an embodiment of the present invention is shown;

[0057] Figure 2 A schematic diagram of an equivalent model of a room temperature change process according to an embodiment of the present invention is shown;

[0058] Figure 3 The first-order equivalent thermal parameter model of the cold storage in the embodiment of the present invention is shown;

[0059] Figure 4 A schematic diagram of the process of improving the dwarf mongoose optimization algorithm according to an embodiment of the present invention is shown;

[0060] Figure 5 The frame structure diagram of the distribution network optimization operation device for distributed photovoltaic and temperature control loads according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0061] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0062] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0063] Figure 1 A schematic flow chart of a method for optimizing operation of a distribution network including distributed photovoltaic and temperature control loads according to an embodiment of the present invention is shown, comprising the following steps:

[0064] Step S1, constructing an electric heating load model and a cold storage model, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by a lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage.

[0065] For the electric heating load model, the temperature change process of the building room is determined by the heating power of the electric heating equipment and the outdoor temperature. The room temperature change process can be Figure 2 The lumped parameter equivalent model shown is an approximate expression. Among them: C in , C wall are the equivalent heat capacity of indoor air and the equivalent heat capacity of wall respectively; R 1 , R 2are the equivalent thermal resistance between indoor air and the inner side of the wall, and the equivalent thermal resistance between the outer side of the wall and the outdoor air; θ in ,θ wall and θ out are indoor temperature, wall temperature, and outdoor temperature respectively; P heat (t) is the heating power of the electric heating equipment, P heat (t) = S(t)P N , where P N is the rated power of the electric heating equipment, S(t) is the switch state of the electric heating equipment, which is 0 when closed and 1 when opened.

[0066] The lumped parameter ordinary differential equation describing the room temperature change process is:

[0067]

[0068] Its difference equation is:

[0069]

[0070] Among them, Δt is the step size of the optimal scheduling.

[0071] After simplification, the second-order model of the electric heating equipment is obtained:

[0072]

[0073] Among them, C in , C wall are the equivalent heat capacity of indoor air and the equivalent heat capacity of wall respectively; R 1 , R 2 are the equivalent thermal resistance between indoor air and the inner side of the wall, and the equivalent thermal resistance between the outer side of the wall and the outdoor air; θ in ,θ wall and θ out are indoor temperature, wall temperature, and outdoor temperature respectively; P heat is the heating power of the electric heating equipment, P heat =S(t)P N , P N is the rated power of the electric heating equipment, S(t) is the switch state of the electric heating equipment, which is 0 when it is off and 1 when it is on; θ in (t+1) is the indoor temperature at time t+1; θ wall (t+1) is the wall temperature at time t+1; θ out (t) is the outdoor temperature at time t; Δt is the step size of the optimal scheduling.

[0074] For the cold storage model, the cold storage has two functions: freezing and refrigeration. Different temperature levels have different functions. For example, the freezing temperature range of the cold storage is generally controlled at -20℃ to -10℃, which is used to preserve aquatic products and poultry meat; the refrigeration temperature range of the cold storage is generally controlled at -5℃ to 5℃, which is used to preserve fruits and vegetables. The refrigeration process of the cold storage is similar to the refrigeration process of the air conditioner, such as Figure 3 As shown, in order to simplify the simulation model, in this embodiment, a first-order equivalent thermal parameter model is used to simulate the refrigeration process of the cold storage, and the first-order equivalent thermal parameter model is:

[0075] T in (t+1)=T out (t+1)-η F P F (t)R-(T out (t+1)-η F P F (t)RT in (t))e -Δt / RC

[0076] Among them, η F is the energy efficiency ratio of the cold storage; P F (t) is the power consumed by the cold storage at time t; Δt is the step size of the optimal scheduling; T in (t) and T out (t) are the temperature inside and outside the cold storage at time t, both in °C; R is the equivalent thermal resistance, in °C / W; C is the equivalent heat capacity, in J / °C.

[0077] Step S2, based on the room temperature change process of the electric heating load model and the refrigeration process of the cold storage model, establish a dynamic optimal power flow model of the distribution network of distributed photovoltaic and temperature control loads, and set the objective function and constraints; wherein the objective function is to minimize grid loss in the distribution network area connected to distributed photovoltaics, optimize user comfort and maximize distributed photovoltaic absorption; the constraints include branch power flow constraints, photovoltaic operation regulation constraints, node voltage constraints, electric heating constraints and cold storage constraints.

[0078] In this embodiment, the electric heating constraint conditions and the cold storage constraint conditions are determined based on the electric heating load model and the cold storage model, which not only ensures the safe and stable operation of the distribution network, but also coordinates and optimizes the relationship between distributed photovoltaics and temperature control loads.

[0079] Specifically, combined with the actual scenarios of distributed photovoltaic and temperature control loads in rural areas of Beijing, the objective function is set as the minimum network loss in the distribution network area connected to distributed photovoltaic, the best user comfort and the maximum distributed photovoltaic consumption. Among them, for the network loss in the distribution network area, the distribution system with a high proportion of distributed photovoltaic is a system with multiple power sources inside. The disordered power flow inside may increase network loss and damage economic benefits. Therefore, the minimum network loss is taken as the optimization goal, and its calculation formula is:

[0080]

[0081] Among them, P loss is the sum of the active power loss of the branches throughout the day, ij represents the branch connected between node i and node j; Q is the set of distribution network branches; T is the total dispatching time; R ij is the resistance of branch ij; t is the time period mark; I ij (t) is the branch current of branch ij at time t.

[0082] For the optimization goal of best user comfort, user comfort refers to the effect of changes in user living comfort caused by different room temperatures. In this embodiment, a temperature utility function is used to calculate the comfort level of heating based on the relationship between the actual indoor temperature and the user's comfort temperature.

[0083]

[0084] Where F(t) is the temperature utility coefficient at time t, θ in (t) is the indoor temperature during heating, θ min and θ max are the minimum and maximum values ​​of user comfort temperature respectively, γ is the comfort level decrease adjustment coefficient, and its value range is (0,1).

[0085] In this embodiment, the variance and minimum value of the temperature utility coefficient are used to measure the heating comfort of the user under temperature changes. The calculation formula of the user comfort is:

[0086] E comf =αF F +βF min

[0087] Among them, E comf F is the electricity comfort index; F and F min are the variance of the temperature utility coefficient and the minimum value of the temperature utility coefficient respectively; α and β are the weight distribution coefficients of the variance and the minimum value. The larger the value of the comfort index, the higher the heating comfort of the user.

[0088] For the maximum optimization target of distributed photovoltaic consumption, the calculation formula is:

[0089]

[0090] Among them, pv 1 、pv 2 ...pv n The actual power generation of each distributed photovoltaic user.

[0091] The above objective functions are weighted combined:

[0092] f=min(λ 1 P loss -λ 2 E comf -λ 3 P PV )

[0093] In the formula, λ 1 +λ 2 +λ 3 =1, representing the weight coefficient of the objective function respectively.

[0094] For branch flow constraints, the operating state of a branch of the distribution network at time t is selected to establish the branch ij flow model. The constraints satisfied by the branch flow are:

[0095]

[0096] Where i and j represent the node numbers of the beginning and end of the line respectively; U i (t), U j (t) are the voltages of nodes i and j at time t; p i (t), p j (t) is the active power injected into nodes i and j at time t; q i (t), q j (t) are the reactive power injected into nodes i and j at time t; P ij (t) and Q ij (t) are respectively the active power and reactive power at the head end of branch ij at time t; R ij is the resistance of branch ij; X ij is the reactance of branch ij; R ij +jX ij is the impedance of branch ij; P jk (t) and Q jk (t) are respectively the active power and reactive power of the head end of branch jk at time t; k:j→k is the set of child nodes with node j as the parent node; I ij (t) is the branch current of branch ij at time t.

[0097] For the photovoltaic operation regulation constraints, if distributed photovoltaic is connected at a certain node i, the constraints satisfied by the photovoltaic output range should be:

[0098]

[0099] Where P i pv (t) is the actual output of the photovoltaic power added at node i in period t, is the maximum value of the photovoltaic output allowed. It is assumed here that the photovoltaic output is continuously adjustable.

[0100] For the node voltage constraint conditions, in order to ensure that the system node voltage can operate within a reasonable range in each time period and prevent the voltage from exceeding the upper and lower limits, the node voltage constraint is:

[0101] U min ≤U i (t)≤U max

[0102] Where U max and U min are the maximum and minimum values ​​of the node voltage respectively, U i (t) is the node voltage value at time t.

[0103] The electric heating constraints include electric heating load regulation constraints and electric heating temperature constraints. For the electric heating load regulation constraints, the electric heating load is controlled in real time, and the constraints satisfied by the electric heating load output range should be:

[0104] P heat,min ≤P heat (t)≤P heat,max

[0105] Where P heat (t) is the actual value of the electric heating load, P heat,min To ensure the minimum temperature control load required for the minimum heating temperature; P heat,max To ensure the maximum value of the temperature control load to meet the highest heating temperature demand.

[0106] For the electric heating temperature constraint, according to the relevant content of the "Design Code for Heating, Ventilation and Air Conditioning of Civil Buildings", the heating temperature range is within [18,22]℃, and the user's comfort level is good. Therefore, the heating temperature can be used to constrain the range of indoor temperature changes, as shown in the following formula:

[0107] θ min ≤θ in (t)≤θ max

[0108] In the formula, θ min and θ maxThey are the lower limit and upper limit of indoor temperature obtained by the operation settings of the electric heating equipment.

[0109] For the cold storage constraints, there are cold storage load adjustment constraints and cold storage temperature constraints. For the cold storage load adjustment constraints, the cold storage load is controlled in real time. The constraints satisfied by the cold storage load output range should be:

[0110] P F,min ≤P F (t)≤P F,max

[0111] Where P F (t) is the actual value of the cold storage load, P F,min To ensure the minimum cold storage load for the minimum refrigeration demand; P F,max To ensure the maximum value of cold storage load for the highest refrigeration demand.

[0112] For cold storage temperature constraints:

[0113] T in,min <T in (t) <T in,max

[0114] Among them, T in,min and T in,max They are respectively the lower and upper limits of the internal temperature of the cold storage when it is in operation.

[0115] Step S3: converting the distribution network dynamic optimal power flow model into a second-order cone relaxation model through second-order cone relaxation.

[0116] The objective function P in step S2 loss The branch power flow constraints and node voltage constraints contain quadratic terms and integer terms. The optimal power flow problem is a mixed integer nonlinear programming problem. Since the conventional algorithms and intelligent algorithms do not perform well, the second order cone relaxation (SOCR) is used to convert the model into a standard second order cone programming problem for solution.

[0117] In an optional manner, converting the distribution network dynamic optimal power flow model into a second-order cone relaxation model by second-order cone relaxation further includes:

[0118] Introduce the square of the voltage of node i in period t as variable α i (t) and the quadratic power of the branch ij current is the variable β ij (t), where U i (t) is the node voltage value at time t, I ij (t) is the branch current of branch ij at time t;

[0119] The objective function of minimizing the grid loss in the distribution network area connected to distributed photovoltaics, the branch flow constraints and the node voltage constraints are transformed respectively to obtain the dynamic optimal power flow model β of the distribution network ij (t); where P ij (t) and Q ij (t) are respectively the active power and reactive power at the head end of branch ij at time t;

[0120] After being processed by SCOR and then equivalently transformed, the dynamic optimal power flow model of the distribution network is converted into a second-order cone relaxation model; the second-order cone relaxation model is: Among them, ||.|| 2 is the two-norm.

[0121] In this embodiment, the objective function P in step S2 loss The branch power flow constraints and node voltage constraints contain quadratic terms and integer terms. The optimal power flow problem belongs to a mixed integer nonlinear programming problem. Since the conventional algorithm and intelligent algorithm have poor solving effect, the second order cone relaxation (SOCR) is used to convert the model into a standard second order cone programming problem for solution.

[0122] First introduce the variables:

[0123]

[0124]

[0125] In the formula, α i (t) and β ij (t) are the square of the voltage at node i and the square of the current at branch ij during time period t, respectively.

[0126] At this time, the objective function P loss And the branch flow constraints and node voltage constraints can be transformed into:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] At this time, using SCOR to process, we can get:

[0134]

[0135] After equivalent transformation, the above formula can be written in standard second-order cone form, that is:

[0136]

[0137] In the formula, |||| 2 is the second norm. Through the above changes, the optimal power flow model can be transformed into a second-order cone programming problem.

[0138] Step S4, using an improved dwarf mongoose optimization algorithm to solve the second-order cone relaxation model to obtain a control plan for photovoltaic and temperature control loads.

[0139] This embodiment uses an improved dwarf mongoose optimization algorithm (PCDMO) to solve the model. Compared with the dwarf mongoose optimization algorithm, this optimization algorithm improves it in two aspects. First, based on the living habits of dwarf mongoose, the peeping behavior in the algorithm is modified to better simulate the foraging behavior of dwarf mongoose; second, a candidate solution update mechanism is used to enhance the exploration ability of the algorithm and improve the algorithm's optimization performance.

[0140] To better understand the PCDMO algorithm, the following is a brief introduction to the PCDMO algorithm.

[0141] (1) Basic principles of the dwarf mongoose optimization algorithm

[0142] Dwarf Mongoose Optimization (DMO) is an optimization algorithm based on the foraging behavior of meerkats. The algorithm uses three social groups of meerkats to imitate the semi-nomadic behavior of meerkats. The DMO algorithm has the advantages of only one adjustable parameter, simple structure, and easy implementation. Compared with other optimization algorithms such as particle swarm optimization, gray wolf algorithm, and ant colony algorithm, it has excellent performance in solving optimization problems (Constrained Optimization Problems, COPs). However, when solving optimization problems, it has shortcomings such as slow convergence speed and low convergence accuracy. In view of the shortcomings of the DMO algorithm, an improved dwarf mongoose optimization algorithm is proposed. After a full analysis of the DMO algorithm, when the meerkats change their mobile position, their call parameters also change. Based on the peep behavior of meerkats, the algorithm constructs a nonlinear decreasing peep value to better cooperate with companions in preying on prey; in the DMO algorithm, only the previous iteration process is considered when updating the candidate solution, and the exploration and development in the candidate solution updating process are not considered. Therefore, this algorithm proposes a new candidate solution updating mechanism to enhance the exploration ability of the DMO algorithm.

[0143] The DMO algorithm divides the population into the Alpha group, the Nanny group, and the Scout group. The Alpha group is responsible for foraging, the Nanny group is responsible for taking care of the cubs, and the Scout group is responsible for finding foraging locations. The occurrence of the Nanny exchange behavior marks the beginning of a new exploration phase.

[0144] a) Alpha Group Stage

[0145] Calculate the fitness value of each individual and select the leader female meerkat Alpha based on the probability of the fitness value.

[0146]

[0147] Among them, fit i is the fitness value of the i-th individual, and α is the probability of being selected as the leader. The Alpha group keeps the entire family within a fixed range through peeking behavior, and the generation of candidate solutions is shown in the following formula:

[0148] X i+1 =X i +U r *peep

[0149] Among them, U r ∈[-1,1], peep is its call parameter, X i Indicates the current position, X i+1 The new location after the update.

[0150] The value of the sleeping hill (the place where the mongoose rests) after each iteration is calculated as:

[0151]

[0152] The average values ​​for this sleep hill are:

[0153]

[0154] In the formula, mo i is the value of the sleep hill, and θ is the average value of the sleep hill.

[0155] b) Nanny Group Stage

[0156] The main responsibility of the nanny group is to take care of the pups. They rotate regularly and are members of the clade of the dwarf mongoose population. The algorithm is affected by reducing the number of nannies. When the Alpha group receives the signal from the nanny, the scout group begins to look for a new mound and the reconnaissance phase begins.

[0157] c) Scout Group Stage

[0158] The scout group quickly searches for the next sleeping mound. The behavior of the scouts can be simulated by the following formula:

[0159]

[0160] Where:

[0161]

[0162]

[0163] Among them, rand()∈[0,1], WI decreases linearly with the increase of iteration number, and is the parameter that controls the movement of the mongoose position. It controls the direction of the mongoose's movement; it represents the current iteration number, and Maxit represents the maximum iteration number.

[0164] In an optional manner, before solving the second-order cone relaxation model using the improved dwarf mongoose optimization algorithm, the method further includes:

[0165] According to the current number of iterations and the maximum number of iterations, the peeping behavior of the meerkat is modified; the calculation formula of the peeping behavior of the meerkat is: Among them, it represents the current number of iterations, and Maxit represents the maximum number of iterations;

[0166] According to the randomly selected solution and the current optimal solution in the iterative process, a new candidate solution is generated. The generation formula of the new candidate solution is: Among them, X k is a randomly selected solution during the iteration process, X best is the optimal solution obtained by iteration so far, X i Indicates the current position, X i+1 The new location after the update.

[0167] Usually, as the meerkat's moving position changes, its call parameters also change. Since the meerkat's speed gradually increases during the running process, but as it gets closer to the target, it begins to gradually reduce its speed until it reaches the destination and reduces its speed to 0, so as to better cooperate with its companions to hunt prey, the meerkat's PeepBehavior (PB) is modified as shown in the following formula:

[0168]

[0169] Where it represents the current number of iterations, and Maxit represents the maximum number of iterations.

[0170] In the DMO algorithm, only the solution obtained in the previous iteration is considered when updating the candidate solution, and its exploration and development are not effectively considered, which leads to a decrease in the convergence accuracy of the algorithm. Therefore, this algorithm uses a new candidate solution updating mechanism (Candidate Solution Updating Mechanism, CSUM) to improve the shortcomings of the DMO algorithm.

[0171]

[0172] Among them, X k is a randomly selected solution during the iteration process, X best is the optimal solution obtained by iteration so far. When updating the candidate solution, if the value of rand() is in the range of [0,0.5), use X best With X i Update X i+1 This process is conducive to global search and further ensures the peeping behavior of the mongoose. When the rand() value range is [0.5,1], X is used. i With X k Update X together i+1 ,This process is conducive to local search and prevents the omission of excellent individuals. Therefore, the candidate solution update mechanism can enhance the exploration ability to achieve better optimization performance.

[0173] In this embodiment, Figure 4 As shown, the PCDMO algorithm mainly includes the following steps:

[0174] Step S41, initializing the population and calculating its fitness value, establishing a time parameter C, wherein the population includes the power of heating and cold storage in each time period;

[0175] Step S42, start a for loop (it=1:1:Maxit), according to the formula Select the Alpha, the female leader of the group;

[0176] Step S43: Update the next position X according to the generation formula of the new candidate solution i+1 ;

[0177] in,

[0178] Step S44, calculate the next position X i+1 The fitness value of

[0179] Step S45, calculating the average sleeping time of the mound;

[0180] Step S46, updating time parameter C=C+1;

[0181] Step S47, determine whether C is greater than or equal to the nanny exchange parameter L, if C>=L, initialize the population and calculate its fitness value;

[0182] Step S48, update the scout group position X i+1 , update and iterate to the current optimal solution X * And the optimal fitness value f(X * );

[0183] Step S49, end the for loop and output X * and f(X * ), where X * represents the optimal combination of photovoltaic power output, electric heating and cold storage load power in each period, f(X * ) represents the optimal fitness function value of the objective function corresponding to the optimal solution, and X represents the decision variables, including the output of photovoltaic power in each period, and the power of electric heating and cold storage load in each period.

[0184] This embodiment uses the improved dwarf mongoose optimization algorithm (PCDMO) to solve the model. Compared with the dwarf mongoose optimization algorithm, this optimization algorithm improves it in two aspects. First, based on the living habits of dwarf mongoose, the peeping behavior in the algorithm is modified to better simulate the foraging behavior of dwarf mongoose; second, a candidate solution update mechanism is used to enhance the exploration ability of the algorithm and improve the algorithm's optimization performance.

[0185] In an optional manner, after the improved dwarf mongoose optimization algorithm is used to solve the second-order cone relaxation model to obtain the optimization strategy of photovoltaic power generation power and temperature control load power, the method further includes:

[0186] When the photovoltaic power generation is sufficient within a preset time period, the power of electric heating and cold storage loads is increased, that is, within the comfortable temperature range acceptable to residents, the indoor temperature is increased and the freezing and refrigeration temperatures in the cold storage are appropriately lowered; if the indoor temperature has reached the highest comfortable temperature acceptable to residents and the cold storage has also reached the lowest temperature, and there is still surplus photovoltaic power, the surplus power is connected to the grid to achieve full photovoltaic consumption;

[0187] When the photovoltaic power generation power is insufficient within a preset time period, the power of electric heating and cold storage loads is reduced within the comfortable temperature range acceptable to residents, that is, the indoor temperature is reduced and the temperature in the cold storage is appropriately increased.

[0188] In this embodiment, after solving the model, the optimization strategy of photovoltaic power generation and temperature control load power can be obtained. The model is mainly used to achieve the reasonable allocation of photovoltaic and temperature control loads, improve the imbalance between photovoltaic output and load, make distributed photovoltaic efficient and ensure safe and reliable heating for users. The specific application can be divided into the following two cases:

[0189] (1) When the photovoltaic power generation capacity is sufficient within a certain period of time, the power of electric heating and cold storage load can be appropriately increased, that is, within the comfortable temperature range acceptable to residents, the indoor temperature can be appropriately increased and the freezing and refrigeration temperature in the cold storage can be appropriately reduced. This can not only ensure the heating needs of residents, but also increase the absorption of photovoltaic power. If the indoor temperature has reached the highest comfortable temperature acceptable to residents and the cold storage has reached the lowest temperature, and there is still surplus photovoltaic power, the surplus power can be connected to the grid to achieve full absorption of photovoltaic power.

[0190] (2) When the photovoltaic power generation is insufficient within a certain period of time, the power of electric heating and cold storage loads can be appropriately reduced within the comfortable temperature range acceptable to residents, that is, the indoor temperature can be appropriately reduced and the temperature in the cold storage can be appropriately increased. In special weather conditions such as rain, snow, etc., the photovoltaic output may be small or even zero. At this time, the power grid is used to purchase electricity to meet the heating needs of residents and the cooling needs of cold storage, and the system operates at a lower indoor temperature to ensure that the user's power purchase amount is minimized.

[0191] According to the scheme provided by the present invention, the present invention establishes a dynamic optimal power flow model of a distribution network containing distributed photovoltaic and temperature control loads based on branch power flow, takes network loss minimization, user comfort optimization and distributed photovoltaic absorption maximization as the objective function, and solves the output plan of distributed photovoltaic and temperature control loads through the improved dwarf mongoose optimization algorithm, effectively improves the imbalance problem between photovoltaic output and load, realizes efficient absorption and flexible and friendly grid connection of distributed photovoltaic, effectively improves the reliability and economy of distribution network operation after distributed photovoltaic access, improves the reliability of user heating and effectively reduces the heating cost of users. In view of the non-convex constraints in the optimal power flow model, SOCR is used to transform the model, and the optimal power flow model is subjected to second-order cone relaxation to become a second-order cone programming problem containing integer variables, which effectively improves the speed of solution. Aiming at the problem of solving the model, the WI of the DMO algorithm is improved from linearly decreasing with iteration to nonlinearly decreasing the peep value from 1 to 0; and the exploration and development of the mongoose is considered when updating the candidate solution, which is beneficial to both the global search of the model and the search of local information, so that the candidate solution update mechanism can enhance the search ability of the optimization algorithm to achieve better optimization performance.

[0192] Figure 5 The framework structure diagram of the distribution network optimization operation device for distributed photovoltaic and temperature control loads according to an embodiment of the present invention is shown, including:

[0193] The electric heating load and cold storage model construction module 510 is used to construct an electric heating load model and a cold storage model, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by a lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage;

[0194] The dynamic optimal power flow model building module 520 is used to establish a dynamic optimal power flow model of the distribution network of distributed photovoltaic and temperature control loads based on the room temperature change process of the electric heating load model and the refrigeration process of the cold storage model, and set the objective function and constraint conditions; wherein the objective function is to minimize the grid loss in the distribution network area connected to the distributed photovoltaic, optimize the user comfort and maximize the distributed photovoltaic consumption; the constraint conditions include branch power flow constraint conditions, photovoltaic operation regulation constraint conditions, node voltage constraint conditions, electric heating constraint conditions and cold storage constraint conditions;

[0195] An optimal power flow model conversion module 530, configured to convert the distribution network dynamic optimal power flow model into a second-order cone relaxation model through second-order cone relaxation;

[0196] The optimal power flow model solving module 540 is used to solve the second-order cone relaxation model using an improved dwarf mongoose optimization algorithm to obtain a control plan for photovoltaic and temperature control loads.

[0197] According to the scheme provided by the present invention, the present invention establishes a dynamic optimal power flow model of a distribution network containing distributed photovoltaic and temperature control loads based on branch power flow, takes network loss minimization, user comfort optimization and distributed photovoltaic absorption maximization as the objective function, and solves the output plan of distributed photovoltaic and temperature control loads through the improved dwarf mongoose optimization algorithm, effectively improves the imbalance problem between photovoltaic output and load, realizes efficient absorption and flexible and friendly grid connection of distributed photovoltaic, effectively improves the reliability and economy of distribution network operation after distributed photovoltaic access, improves the reliability of user heating and effectively reduces the heating cost of users. In view of the non-convex constraints in the optimal power flow model, SOCR is used to transform the model, and the optimal power flow model is subjected to second-order cone relaxation to become a second-order cone programming problem containing integer variables, which effectively improves the speed of solution. Aiming at the problem of solving the model, the WI of the DMO algorithm is improved from linearly decreasing with iteration to nonlinearly decreasing the peep value from 1 to 0; and the exploration and development of the mongoose is considered when updating the candidate solution, which is beneficial to both the global search of the model and the search of local information, so that the candidate solution update mechanism can enhance the search ability of the optimization algorithm to achieve better optimization performance.

[0198] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0199] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0201] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0202] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0203] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads, characterized in that: include: Construct an electric heating load model and a cold storage model, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by a lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage; Based on the room temperature change process of the electric heating load model and the refrigeration process of the cold storage model, a dynamic optimal power flow model of the distribution network of distributed photovoltaic and temperature control loads is established, and the objective function and constraint conditions are set; wherein the objective function is to minimize grid loss in the distribution network area connected to distributed photovoltaics, optimize user comfort and maximize distributed photovoltaic absorption; the constraint conditions include branch power flow constraint conditions, photovoltaic operation regulation constraint conditions, node voltage constraint conditions, electric heating constraint conditions and cold storage constraint conditions; The distribution network dynamic optimal power flow model is converted into a second-order cone relaxation model through second-order cone relaxation; The improved dwarf mongoose optimization algorithm is used to solve the second-order cone relaxation model to obtain the control plan of photovoltaic and temperature control loads.

2. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: The second-order model of the electric heating equipment is: Among them, C in , C wall are the equivalent heat capacity of indoor air and the equivalent heat capacity of wall respectively; R1 and R2 are the equivalent thermal resistance between indoor air and the inner side of wall and the equivalent thermal resistance between the outer side of wall and outdoor air respectively; θ in ,θ wall and θ out are indoor temperature, wall temperature, and outdoor temperature respectively; P heat is the heating power of the electric heating equipment, P heat =S(t)P N , P N is the rated power of the electric heating equipment, S(t) is the switch state of the electric heating equipment, which is 0 when it is off and 1 when it is on; θ in (t+1) is the indoor temperature at time t+1; θ wall (t+1) is the wall temperature at time t+1; θ out (t) is the outdoor temperature at time t; Δt is the step size of the optimal scheduling.

3. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: The first-order equivalent thermal parameter model is: T in (t+1)=T out (t+1)-η F P F (t)R-(T out (t+1)-η F P F (t)R-T in (t))e -Δt / RC Among them, η F is the energy efficiency ratio of the cold storage; P F (t) is the power consumed by the cold storage at time t; Δt is the step size of the optimal scheduling; T in (t) and T out (t) are the temperature inside and outside the cold storage at time t, both in °C; R is the equivalent thermal resistance, in °C / W; C is the equivalent heat capacity, in J / °C.

4. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: The calculation formula for the grid loss in the distribution network area is: Among them, P loss is the sum of the active power loss of the branches throughout the day, ij represents the branch connected between node i and node j; Q is the set of distribution network branches; T is the total dispatching time; R ij is the resistance of branch ij; t is the time period mark; I ij (t) is the branch current of branch ij at time t; The calculation formula of the user comfort is: E comf =αF F +βF min Among them, E comf F is the electricity comfort index; F and F min are the variance of temperature utility coefficient and the minimum value of temperature utility coefficient respectively; α and β are the weight distribution coefficients of variance and minimum value; The calculation formula for the distributed photovoltaic consumption is: Among them, pv1, pv2...pv n The actual power generation of each distributed photovoltaic user.

5. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: The branch power flow constraint condition is: Where i and j represent the node numbers of the beginning and end of the line respectively; U i (t), U j (t) are the voltages of nodes i and j at time t; p i (t), p j (t) is the active power injected into nodes i and j at time t; q i (t), q j (t) are the reactive power injected into nodes i and j at time t; P ij (t) and Q ij (t) are respectively the active power and reactive power at the head end of branch ij at time t; R ij is the resistance of branch ij; X ij is the reactance of branch ij; P jk (t) and Q jk (t) are respectively the active power and reactive power of the head end of branch jk at time t; k:j→k is the set of child nodes with node j as the parent node; I ij (t) is the branch current of branch ij at time t.

6. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: The converting the distribution network dynamic optimal power flow model into a second-order cone relaxation model by second-order cone relaxation further comprises: Introduce the square of the voltage of node i in time period t as variable α i (t) and the quadratic power of the branch ij current is the variable β ij (t), where U i (t) is the node voltage value at time t, I ij (t) is the branch current of branch ij at time t; The objective function of minimizing the grid loss in the distribution network area connected to distributed photovoltaics, the branch flow constraints and the node voltage constraints are transformed respectively to obtain the dynamic optimal power flow model β of the distribution network ij (t); where P ij (t) and Q ij (t) are respectively the active power and reactive power at the head end of branch ij at time t; After being processed by SCOR and then equivalently transformed, the dynamic optimal power flow model of the distribution network is converted into a second-order cone relaxation model; the second-order cone relaxation model is: Among them, ||.||2 is the two-norm.

7. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: Before solving the second-order cone relaxation model using the improved dwarf mongoose optimization algorithm, the method further includes: According to the current number of iterations and the maximum number of iterations, the peeping behavior of the meerkat is modified; the calculation formula of the peeping behavior of the meerkat is: Among them, it represents the current number of iterations, and Maxit represents the maximum number of iterations; According to the randomly selected solution and the current optimal solution in the iterative process, a new candidate solution is generated. The generation formula of the new candidate solution is: Among them, X k is a randomly selected solution during the iteration process, X best is the optimal solution obtained by iteration so far, X i Indicates the current position, X i+1 The new location after the update.

8. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 7, characterized in that: The adopting of the improved dwarf mongoose optimization algorithm to solve the second-order cone relaxation model further comprises: Step S41, initializing the population and calculating its fitness value, establishing a time parameter C, wherein the population includes the power of heating and cold storage in each time period; Step S42, according to the formula Select the Alpha, the female leader of the group; Step S43: Update the next position X according to the generation formula of the new candidate solution i+1 ; Step S44, calculate the next position X i+1 The fitness value of Step S45, calculating the average sleeping time of the mound; Step S46, updating time parameter C=C+1; Step S47, determine whether C is greater than or equal to the nanny exchange parameter L, if C>=L, initialize the population and calculate its fitness value; Step S48, update the scout group position X i+1 , update and iterate to the current optimal solution X * And the optimal fitness value f(X * ); Step S49, output X * and f(X * ), where X * represents the optimal combination of photovoltaic power output, electric heating and cold storage load power in each period, f(X * ) represents the optimal fitness function value of the objective function corresponding to the optimal solution.

9. The method for optimizing the operation of a distribution network containing distributed photovoltaic and temperature control loads according to claim 1, characterized in that: After the improved dwarf mongoose optimization algorithm is used to solve the second-order cone relaxation model to obtain the optimization strategy of photovoltaic power generation power and temperature control load power, the method further includes: When the photovoltaic power generation is sufficient within a preset time period, the power of electric heating and cold storage loads is increased, that is, within the comfortable temperature range acceptable to residents, the indoor temperature is increased and the freezing and refrigeration temperatures in the cold storage are appropriately lowered; if the indoor temperature has reached the highest comfortable temperature acceptable to residents and the cold storage has also reached the lowest temperature, and there is still surplus photovoltaic power, the surplus power is connected to the grid to achieve full photovoltaic consumption; When the photovoltaic power generation power is insufficient within a preset time period, the power of electric heating and cold storage loads is reduced within the comfortable temperature range acceptable to residents, that is, the indoor temperature is reduced and the temperature in the cold storage is appropriately increased.

10. A distribution network optimization operation device for distributed photovoltaic and temperature control loads, characterized in that: The device comprises: The electric heating load and cold storage model construction module is used to construct the electric heating load model and the cold storage model, wherein the room temperature change process simulated by the electric heating load model is jointly determined by the heating power of the electric heating equipment and the outdoor temperature, and is approximately expressed by the lumped parameter equivalent model; the cold storage model uses a first-order equivalent thermal parameter model to describe the refrigeration process of the cold storage; A dynamic optimal power flow model building module is used to establish a dynamic optimal power flow model of the distribution network of distributed photovoltaic and temperature control loads based on the room temperature change process of the electric heating load model and the refrigeration process of the cold storage model, and set the objective function and constraint conditions; wherein the objective function is to minimize the grid loss in the distribution network area connected to the distributed photovoltaic, optimize the user comfort and maximize the distributed photovoltaic consumption; the constraint conditions include branch power flow constraint conditions, photovoltaic operation regulation constraint conditions, node voltage constraint conditions, electric heating constraint conditions and cold storage constraint conditions; An optimal power flow model conversion module, used for converting the distribution network dynamic optimal power flow model into a second-order cone relaxation model through second-order cone relaxation; The optimal power flow model solving module is used to solve the second-order cone relaxation model by using an improved dwarf mongoose optimization algorithm to obtain a control plan for photovoltaic and temperature control loads.

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