Investment strategy method considering energy storage and transformation and extension of related equipment of power distribution network

Abstract

The invention relates to an investment strategy method considering energy storage and transformation and extension of related equipment of a power distribution network. The method comprises the following steps: step 1, energy storage and power grid double-layer collaborative planning; 2, constructing an upper layer planning layer model which comprises an upper layer objective function and an upper layer constraint condition; 3, constructing a lower-layer operation layer model which comprises a lower-layer objective function and a lower-layer constraint condition; step 4, double-layer model solving: aiming at the energy storage and power grid double-layer collaborative planning model, adopting a nested improved particle swarm optimization algorithm to solve the energy storage and power grid double-layer collaborative planning model; 5, example analysis is carried out; the method has the advantages that the nested improved particle swarm double-layer optimization algorithm is constructed, the net rack layout is more reasonable, and the planning reliability is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution networks, and in particular relates to an investment strategy method that takes into account the transformation and expansion of equipment related to energy storage and power distribution networks. Background Art

[0002] As the penetration rate of distributed photovoltaic (PV) in the distribution network continues to increase, the uncertainty of its output is an important problem facing the reliable operation of the distribution network. If the distribution network absorbs too little PV, its utilization rate will decrease and the equipment operation and maintenance costs will increase. At the same time, with the large-scale integration of photovoltaics, the power flow of some lines in the distribution network grid will change frequently, so that the grid line will be blocked, which will not only reduce the absorption rate of distributed photovoltaics, but also affect the rapid response of flexible resources, and indirectly reduce the reliability of the distribution network. During the distribution network expansion planning, preparing sufficient line channel margin for PV absorption can not only respond to distributed energy in a timely manner after planning, but also greatly reduce the investment and construction costs of the expansion planning. Therefore, the distribution network expansion planning needs to focus not only on economy, but also on the planning and layout of the location and capacity of the grid lines and distributed power sources, enhance the transmission capacity of the grid lines, and provide good protection for the safe operation of the distribution network lines. In the power grid, good Configuration strategy can cope with large load and randomness of photovoltaic output, but the existing methods have the following problems: the planning object is relatively single, the solution efficiency and accuracy are low, the impact of energy storage control is ignored, the uncertainty of new energy is not considered, and the coordinated optimization operation of source-grid-load-storage is not comprehensively considered. The construction of the planning model is not perfect, and there is a lack of comprehensive consideration of the distribution network grid structure. The content of the distribution network line expansion planning includes finding the optimal distribution network expansion structure and energy storage equipment configuration plan, the purpose is to improve the distribution system's acceptance of distributed new energy and power supply reliability, but the existing methods have the problem that the genetic algorithm for solving the model will fall into the local optimal solution, sacrifice the accurate range of distributed energy output prediction, and lack consideration of the distribution network line. Therefore, it is very necessary to provide a method for constructing a nested improved particle swarm double-layer optimization algorithm, a more reasonable grid layout, and a high planning reliability to consider the investment strategy method for the transformation and expansion of energy storage and distribution network related equipment. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide an investment strategy method for constructing a nested improved particle swarm double-layer optimization algorithm, a more reasonable grid layout, and high planning reliability, which takes into account the transformation and expansion of energy storage and distribution network related equipment.

[0004] The object of the present invention is achieved by: considering the investment strategy method for the transformation and expansion of energy storage and distribution network related equipment, the method comprises the following steps:

[0005] Step 1: Double-layer coordinated planning of energy storage and power grid;

[0006] Step 2: Construct the upper planning layer model: including the upper objective function and upper constraint conditions;

[0007] Step 3: Construct the lower-level operation layer model: including the lower-level objective function and the lower-level constraints;

[0008] Step 4: Solving the two-layer model: For the two-layer collaborative planning model of energy storage and power grid, a nested improved particle swarm optimization algorithm is used to solve it;

[0009] Step 5: Case analysis.

[0010] The energy storage and power grid dual-layer coordinated planning in step 1 is specifically as follows: The original dual-layer model is: Where: G(·), H(·), g(·), h(·) are the constraints of the upper and lower layers respectively; this model is a two-layer model, the upper layer transmits investment decision variables to the lower layer, and the lower layer simulates the operation variables and affects the optimization of the upper layer; the upper planning layer, with the investment cost C I and operating cost C O The goal is to minimize the comprehensive cost, taking into account the expansion investment of the grid line and the site selection and capacity determination of the ESS, and the constraint condition is the equipment investment constraint; the lower operation layer, with the new energy consumption rate f abs As the goal, active control objects such as DG and ESS are considered, and the constraints are simulation operation constraints; the equipment investment decision variables and operation decision variables of the two-layer model are separable.

[0011] The upper objective function of the upper planning layer model constructed in step 2 is specifically: the upper model takes the investment variable as the decision variable, making the construction cost C I and operating cost C O The lowest, as shown in formula (3), C I Including conversion to grid line investment cost Distributed photovoltaic investment cost Energy storage investment cost C O Including the network loss cost for the whole year Cost of purchasing electricity from the large power grid Energy storage equipment operation and maintenance costs Distributed photovoltaic operation and maintenance costs

[0012] The grid line investment cost In the formula, r is the discount rate; y is the useful life; K L is the sum of the original route and the route to be planned; x k is the state variable; c k is the construction cost of the line; the energy storage investment cost In the formula, are the unit power and capacity investment costs of building energy storage devices at node i, respectively; are the energy storage power and capacity built for node i respectively; the network loss cost Where, T is the total time; R ij is the resistance of line ij; c loss is the unit network loss cost.

[0013] The cost of purchasing electricity from the large power grid In the formula, c pur is the unit electricity purchase cost; p G,t is the power generated by the generator at time t; the operation and maintenance cost of the energy storage equipment In the formula, Λ ESS is the set of nodes where ESS is installed; is the operating cost per unit of charge and discharge capacity; are the charging and discharging power of ESS at node i in period t; the operating cost of distributed photovoltaic is In the formula, λ is the conversion ratio of operation and maintenance cost; is the power abandonment cost per unit capacity of distributed photovoltaics; is the theoretical value of the photovoltaic grid-connected capacity of node i at time t; is the actual value of the photovoltaic grid-connected capacity of node i at time t.

[0014] The upper-level constraints for constructing the upper-level planning model in step 2 include: ① Topological constraints: The distribution network framework should satisfy the radial constraints when planning, and the total number of branches is the difference between the total number of nodes and the number of root nodes, as shown in the following formula: Where D l Represents the set of all lines in the distribution network; x ij is the disconnection state of the line; n s Represents the number of root nodes in the system; ② Reconstruction constraints: To avoid loops or islands, assume that all lines are connected and form loops, disconnect one branch of the overlapping branches between the loops, and also split one branch of the non-overlapping branches. At a node with n branches, at most n-1 lines are split, which can ensure that the distribution network does not produce an island or loop state. The reconstruction constraints are as follows: In the formula, NL k , L k Respectively represent the total number and set of branches in the kth loop; n r Indicates the total number of loops; l k Represents the set of branches connected to the kth node.

[0015] The lower-level constraints for constructing the lower-level operation layer model in step 3 include: system safety constraints, distribution network DistFlow constraints, distributed power output constraints, line power transmission constraints, and battery energy storage constraints.

[0016] The system safety constraint is: to maintain the safe operation of the distribution system, the node voltage and the current flowing through the line must be controlled within a safe range. The mathematical model is: In the formula, are the upper and lower limits of the voltage at node i, respectively; U i,t is the voltage of node i at time t; the distribution network DistFlow constraint is: Where AL(j,:) is the set of branch end nodes with j as the head node; AL(:,j) is the set of branch head node with j as the end node; P ij,t , P jk,t , Q ij,t , Q jk,t are the active and reactive power on lines ij and jk during period t respectively; X ij is the reactance of the line; are respectively the active and reactive power of the original load of node j in period t; U i,t is the voltage at node i.

[0017] The output constraint of the distributed power source is: In the formula, are the upper and lower limits of the actual PV output at time t respectively; the line power transmission constraint is: Where P ij,max , Q ij,max are respectively the upper limits of active and reactive power of line transmission; the battery energy storage constraint is: In the formula, are the state variables of ESS charging and discharging respectively; are the charging and discharging power of ESS at node i at time t; is the maximum value of the ESS charging and discharging power of node i; is the power of ESS at node i at time t; is the upper limit of ESS power of node i; η is the charging and discharging efficiency.

[0018] The two-layer model in step 4 is solved by using a nested improved particle swarm optimization algorithm, which includes the following steps:

[0019] Step 4.1: Import PV output and load data and distribution network structure related parameters;

[0020] Step 4.2: Initialize the upper particle population, perform binary encoding on the new lines and energy storage sites, and generate particle positions and velocities;

[0021] Step 4.3: Calculate the population fitness, obtain the distribution network grid line direction and energy storage site selection location, filter out unreasonable planning results and update the population location;

[0022] Step 4.4: Repeat steps 4.2-4.3 until convergence is achieved, that is, the difference between the upper layer planning cost and the previous and next iterations for 10 consecutive times does not exceed 300 yuan or the upper limit of the number of iterations of the upper layer is reached;

[0023] Step 4.5: Import the planning scheme obtained from the upper layer into the lower layer and initialize the lower layer population, encoding each energy storage capacity integer;

[0024] Step 4.6: Calculate the power grid flow, distributed generation output and other data, execute the lower layer objective function, update the individual and overall extreme values ​​of the energy storage capacity corresponding to all grid planning results in the lower layer, regard the overall extreme value as the optimal energy storage capacity corresponding to each planning result, and update the speed and position of the particles in the lower layer;

[0025] Step 4.7: Determine whether the lower-level convergence standard is met, that is, the difference between the distributed photovoltaic absorption rate before and after 10 consecutive iterations is less than 0.001 and 0.05 respectively, or the upper limit of the number of iterations of the lower level is reached;

[0026] Step 4.8: If the maximum number of iterations is reached, stop searching and output the results; otherwise, return to step 4.2.

[0027] Beneficial effects of the present invention: The present invention is an investment strategy method considering the transformation and expansion of energy storage and distribution network related equipment. The present invention considers the investment strategy for the transformation of energy storage and distribution network related equipment, and studies the coordinated planning and solution method of distribution network grid lines and energy storage devices. In use, the upper planning layer of the model constructed by the present invention takes the investment and construction of grid lines and battery energy storage as decision variables, and takes investment and operating costs as objective functions; the lower operating layer constructed by the present invention takes line reconstruction and distributed power output as decision variables, and the new energy consumption rate as the target. By improving the inertia weight and population variation in the particle swarm algorithm, a nested improved particle swarm two-layer optimization algorithm is constructed; finally, taking the improved 40-node distribution network as an example, the effectiveness of the proposed planning model and solution method is analyzed and verified; the present invention has the advantages of constructing a nested improved particle swarm two-layer optimization algorithm, a more reasonable grid layout, and high planning reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a logic diagram of the dual-layer collaborative planning of energy storage and power grid of the present invention.

[0029] Figure 2This is a flow chart of the improved particle swarm optimization algorithm of the present invention.

[0030] Figure 3 Schematic diagram of five typical results after photovoltaic scene reduction of the present invention.

[0031] Figure 4 Schematic diagram of five typical results after load scenario reduction of the present invention.

[0032] Figure 5 This is a topological diagram of the 40-node distribution network structure to be planned in the present invention.

[0033] Figure 6 This is the distribution network line planning result diagram of Case 1 of the present invention.

[0034] Figure 7 This is the distribution network line planning result diagram of Case 2 of the present invention.

[0035] Figure 8 This is the distribution network line planning result diagram of Case 3 of the present invention. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Example 1

[0038] like Figure 1-8 As shown, an investment strategy method considering the transformation and expansion of energy storage and distribution network related equipment includes the following steps:

[0039] Step 1: Double-layer coordinated planning of energy storage and power grid;

[0040] In this embodiment, specifically: bi-level programming is a system optimization problem with a two-level hierarchical structure, which is divided into an upper and lower model during optimization and solution. The upper and lower models respectively have their own optimization objectives and corresponding constraints. During optimization and solution, there is an interactive relationship between the upper and lower models. After the upper model makes a decision, it transfers the decision variables to the lower model. The lower model obtains the optimal solution based on the decision variables transferred by the upper model under the premise of satisfying various constraints, and returns the optimal decision variables to the upper model. The upper model adjusts its own decision based on the information returned by the lower model.

[0041] In the energy storage and power grid double-layer planning model established by the present invention, the upper model is to determine the optimal installation location and capacity of ESS and the direction of distribution network grid line expansion, considering the minimum comprehensive cost of energy storage and grid lines as the goal, and the decision variables are the installation location and capacity of ESS and the direction of grid lines; the lower model considers the operation of the distribution network system, taking the maximum photovoltaic consumption as the goal, and the decision variables include the power flow distribution of the distribution network and the active output of distributed power sources; the structural block diagram of the energy storage and power grid double-layer collaborative planning model is as follows Figure 1 As shown;

[0042] The original two-layer model is: Where: G(·), H(·), g(·), h(·) are the constraints of the upper and lower layers respectively; this model is a two-layer model, the upper layer transmits investment decision variables to the lower layer, and the lower layer simulates the operation variables and affects the optimization of the upper layer; the upper planning layer, with the investment cost C I and operating cost C O The goal is to minimize the comprehensive cost, taking into account the expansion investment of the grid line and the site selection and capacity determination of the ESS, and the constraint condition is the equipment investment constraint; the lower operation layer, with the new energy consumption rate f abs As the goal, active control objects such as DG and ESS are considered, and the constraints are simulation operation constraints; the equipment investment decision variables and operation decision variables of the two-layer model are separable.

[0043] Step 2: Construct the upper planning layer model: including the upper objective function and upper constraint conditions;

[0044] In this embodiment, the upper objective function is specifically: the upper model takes the investment variable as the decision variable, making the construction cost C I and operating cost C O The lowest, as shown in formula (3), C I Including conversion to grid line investment cost Distributed photovoltaic investment cost Energy storage investment cost C O Including the network loss cost for the whole year Cost of purchasing electricity from the large power grid Energy storage equipment operation and maintenance costs Distributed photovoltaic operation and maintenance costs

[0045] ① Grid line investment cost In the formula, r is the discount rate; y is the useful life; K L is the sum of the original route and the route to be planned; x k is a state variable, its value 0 indicates that the kth line is not under construction, and its value 1 indicates that the kth line is under construction; c k is the construction cost of the first line; ② Energy storage investment cost In the formula, are the unit power and capacity investment costs of building energy storage devices at node i, respectively; are the energy storage power and capacity of node i respectively; ③ network loss cost Where, T is the total time; R ij is the resistance of line ij; c loss The unit network loss cost; ④ The cost of purchasing electricity from the large power grid In the formula, c pur is the unit electricity purchase cost; p G,t is the power generated by the generator at time t; ⑤Operation and maintenance costs of energy storage equipment In the formula, Λ ESS is the set of nodes where ESS is installed; is the operating cost per unit of charge and discharge capacity; are the charging and discharging power of ESS at node i during period t; ⑥ The operating cost of distributed photovoltaic Wherein, λ is the conversion ratio of operation and maintenance cost, which is taken as 0.1 in the present invention; is the power abandonment cost per unit capacity of distributed photovoltaics; is the theoretical value of the photovoltaic grid-connected capacity of node i at time t; is the actual value of the photovoltaic grid-connected capacity of node i at time t.

[0046] The upper-level constraints include: ① Topological constraints: The distribution network framework should satisfy the radial constraints when planning, and the total number of branches is the difference between the total number of nodes and the number of root nodes, as shown in the following formula: Where D l Represents the set of all lines in the distribution network; x ij The circuit is open or closed, 0 means open, 1 means closed; n s Represents the number of root nodes in the system; ② Reconstruction constraints: To avoid loops or islands, assume that all lines are connected and form loops, disconnect one branch of the overlapping branches between the loops, and also split one branch of the non-overlapping branches. At a node with n branches, at most n-1 lines are split, which can ensure that the distribution network does not produce an island or loop state. The reconstruction constraints are as follows: In the formula, NL k , L k Respectively represent the total number and set of branches in the kth loop; n r Indicates the total number of loops; l k Represents the set of branches connected to the kth node.

[0047] Step 3: Construct the lower-level operation layer model: including the lower-level objective function and the lower-level constraints;

[0048] In this embodiment, the lower objective function is specifically as follows: the lower operation layer of the two-layer model is based on the reconstruction scheme of the upper planning layer, and considers the multi-objective problem of photovoltaic absorption rate, as shown in formula (3), the new energy absorption rate is the ratio of the actual photovoltaic output to the predicted output; the lower constraint conditions include: system safety constraint, distribution network DistFlow constraint, distributed power output constraint, line power transmission constraint, battery energy storage constraint;

[0049] ① System safety constraints: To maintain the safe operation of the distribution system, the node voltage and the current flowing through the line must be controlled within a safe range. The mathematical model is: In the formula, are the upper and lower limits of the voltage at node i, respectively; U i,t is the voltage of node i at time t; ② The distribution network DistFlow constraint is: Where AL(j,:) is the set of branch end nodes with j as the head node; AL(:,j) is the set of branch head node with j as the end node; P ij,t , P jk,t , Q ij,t , Q jk,t are the active and reactive power on lines ij and jk during period t respectively; X ij is the reactance of the line; are respectively the active and reactive power of the original load of node j in period t; U i,t is the voltage of node i; ③ The output constraint of distributed power generation is: In the formula, are the upper and lower limits of the actual PV output at time t; ④The line power transmission constraint is: Where P ij,max , Q ij,max are the upper limits of active and reactive power of line transmission respectively; ⑤Battery energy storage constraints are: In the formula, They are the state variables of ESS charging and discharging, which are 0-1 variables, representing whether the ESS is charging or discharging; are the charging and discharging power of ESS at node i at time t; is the maximum value of the ESS charging and discharging power of node i; is the power of ESS at node i at time t; is the upper limit of ESS power of node i; η is the charging and discharging efficiency.

[0050] Step 4: Solving the two-layer model: For the two-layer collaborative planning model of energy storage and power grid, a nested improved particle swarm optimization algorithm is used to solve it;

[0051] Step 5: Case analysis.

[0052] The present invention is an investment strategy method considering the transformation and expansion of energy storage and distribution network related equipment. The present invention considers the investment strategy for the transformation of energy storage and distribution network related equipment, and studies the coordinated planning and solution method of distribution network grid lines and energy storage devices. In use, the upper planning layer of the model constructed by the present invention takes the investment and construction of grid lines and battery energy storage as decision variables, and takes investment and operating costs as objective functions; the lower operating layer constructed by the present invention takes line reconstruction and distributed power output as decision variables, and the new energy consumption rate as the target. By improving the inertia weight and population variation in the particle swarm algorithm, a nested improved particle swarm two-layer optimization algorithm is constructed; finally, taking the improved 40-node distribution network as an example, the effectiveness of the proposed planning model and solution method is analyzed and verified; the present invention has the advantages of constructing a nested improved particle swarm two-layer optimization algorithm, a more reasonable grid layout, and high planning reliability.

[0053] Example 2

[0054] like Figure 1-8 As shown, an investment strategy method considering the transformation and expansion of energy storage and distribution network related equipment includes the following steps:

[0055] Step 1: Double-layer coordinated planning of energy storage and power grid;

[0056] Step 2: Construct the upper planning layer model: including the upper objective function and upper constraint conditions;

[0057] Step 3: Construct the lower-level operation layer model: including the lower-level objective function and the lower-level constraints;

[0058] Step 4: Solving the two-layer model: For the two-layer collaborative planning model of energy storage and power grid, a nested improved particle swarm optimization algorithm is used to solve it;

[0059] In this embodiment, the particle swarm optimization algorithm makes full use of the position information of excellent particles and searches in parallel in the feasible solution space. At the same time, the algorithm makes full use of the probability transfer rule as the internal working mechanism of the population, and there is no need to add derived information. In the particle swarm optimization algorithm, all particles in the population search for the optimal solution in the search space, and each particle can be used as a potential solution to the target problem. The information of the particle is affected by its own historical optimal experience and the global historical optimal experience. The quality of each particle is evaluated by calculating the cost function. In the iterative search process of the particle swarm, all particles learn from the optimal particles in the population, and the learning process is accompanied by random perturbation parameters to finally determine the position information of the next generation of particles.

[0060] In the common particle swarm optimization algorithm, each particle is represented by its own position information x i (x1,x2,...,x D ), its best historical position and particle velocity v i (v1,v2,...,v D ) consists of three D-dimensional vectors. The particle also needs to save the global best historical position information gbest generated by the population during the iteration process (gbest 1 ,gbest 2 ,...,gbest D ), in each iterative search process of the particle swarm algorithm, the position information of each particle will be evaluated once. If the evaluation result is better than its own historical optimal position, the current position information will replace its own historical optimal position; similarly, if the evaluation result is better than the global historical optimal position, the current position information will replace the global historical optimal position; the algorithm continuously updates the current particle position information, particle speed information, particle's own historical optimal information and global optimal historical information during the iterative operation process until the algorithm searches for a feasible satisfactory solution. The speed and position update formula of the particles in the population at time t+1 is as follows:

[0061] In formulas (17) and (18), the dimension of the problem is d = 1, 2, ..., D, c1 and c2 are used to represent the acceleration coefficients, which respectively control the degree to which the particle learns from its own historical best position and the global historical best position; r1 and r2 represent random numbers between the interval [0, 1], which are randomly generated during each iteration of the algorithm; ω is the inertia weight, which is used to control the influence coefficient of the previous particle speed on the current particle speed; among them, the optimization stability of the population is closely related to the inertia weight.

[0062] However, although the conventional PSO algorithm has strong applicability, it is easy to fall into local optimality. Improvements can be made in inertia weight and population variation to enhance the global search efficiency of the PSO algorithm: ① Inertia weight update: The inertia weight can adjust the impact on the current speed. Its increase and decrease are helpful to increase the search range and local precise optimization respectively. In order to balance the conflict between efficient search and fast convergence, the inertia weight is updated using formula (19): In the formula, ω max ,ω min is the maximum and minimum value of inertia weight; k is the number of iterations; k max is the maximum number of iterations; ② Population mutation operation: In order to improve the diversity of particles, the population is mutated by formula (20): In the formula, p m is the mutation probability; ε m is the variation parameter, ε m The attenuation degree of the mutation probability can be adjusted, that is, the impact of the mutation operation can be attenuated as the number of iterations increases.

[0063] For the two-layer collaborative planning model of energy storage and power grid, a nested improved particle swarm optimization algorithm is used to solve it. The two-layer optimization model will interact when searching for the best result, that is, the optimization of the lower-layer energy storage capacity is based on the results of the upper-layer distribution network grid line direction and energy storage installation location, and the distributed power output and other results are substituted back to the upper layer as the basis for the next iterative optimization. The optimal planning scheme is obtained through the iteration of the upper and lower layers. Figure 2 This is the improved double-layer nested particle swarm optimization algorithm flow chart. The specific algorithm solution steps are as follows:

[0064] Step 4.1: Import PV output and load data and distribution network structure related parameters;

[0065] Step 4.2: Initialize the upper particle population, perform binary encoding on the new lines and energy storage sites, and generate particle positions and velocities;

[0066] Step 4.3: Calculate the population fitness, obtain the distribution network grid line direction and energy storage site selection location, filter out unreasonable planning results and update the population location;

[0067] Step 4.4: Repeat steps 4.2-4.3 until convergence is achieved, that is, the difference between the upper layer planning cost and the previous and next iterations for 10 consecutive times does not exceed 300 yuan or the upper limit of the number of iterations of the upper layer is reached;

[0068] Step 4.5: Import the planning scheme obtained from the upper layer into the lower layer and initialize the lower layer population, encoding each energy storage capacity integer;

[0069] Step 4.6: Calculate the power grid flow, distributed generation output and other data, execute the lower layer objective function, update the individual and overall extreme values ​​of the energy storage capacity corresponding to all grid planning results in the lower layer, regard the overall extreme value as the optimal energy storage capacity corresponding to each planning result, and update the speed and position of the particles in the lower layer;

[0070] Step 4.7: Determine whether the lower-level convergence standard is met, that is, the difference between the distributed photovoltaic absorption rate before and after 10 consecutive iterations is less than 0.001 and 0.05 respectively, or the upper limit of the number of iterations of the lower level is reached;

[0071] Step 4.8: If the maximum number of iterations is reached, stop searching and output the results; otherwise, return to step 4.2.

[0072] Step 5: Case analysis.

[0073] In this embodiment, ① model parameter setting: based on the historical data of the distribution network in a certain area throughout the year, the generated load and photovoltaic output scenarios are reduced by using Monte Carlo simulation and synchronous back-substitution method, and finally 5 typical scenarios are generated, such as Figure 3-4As shown; the probability of each typical scenario is shown in Table 1; the present invention adds a node on the basis of the IEEE 33-node power distribution system, and tests the modified 40-node power distribution network system. The modified 40-node power distribution network structure topology is shown in Figure 5 As shown, the dotted line is the line to be constructed. The corresponding new load nodes and the information of the line to be constructed are shown in Table 2. The relevant planning parameters are set as follows:

[0074] Table 1 Typical day scenario probability

[0075]

[0076] Table 2 New node load and branch data

[0077]

[0078] 1) Line data: To ensure power supply reliability, lines 1-32 of the original IEEE 33-node system are retained, and lines 33-44 are to be planned. The maximum current through the line is 0.4kA;

[0079] 2) Photovoltaic data: The number of PV installations is 4, and the installation nodes are 6, 17, 24 and 30. The operation and maintenance cost is 0.35 yuan / (kW·h); the penalty fee for abandonment is 0.50 yuan / (kW·h);

[0080] 3) ESS data: The energy storage type is battery energy storage, the number of nodes to be installed is 2-40, that is, all nodes except the first node can be installed, the unit capacity investment cost is 1000 yuan / (kW·h), the unit power construction cost is 1500 yuan / (kW·h), the unit capacity operation and maintenance cost is 0.35 yuan / (kW·h), the initial and final state of charge in one cycle is 0.5, the upper and lower limits of the state of charge are 0.9 and 0.2 respectively, and the charge and discharge efficiency is 0.95;

[0081] 4) Other parameters: The node voltage range is 0.95pu-1.05pu, the unit penalty cost of carbon emissions is 80 yuan / t, the carbon emissions per unit power generation of the generator set is set to 0.798t / (MW·h); the discount rate is 5%.

[0082] All tests were modeled and solved using the Matlab R2019b platform with the YALMIP toolkit embedded in it and the Gurobi10.0 solver.

[0083] ② The impact of uncertainty on planning results: In order to compare the impact of photovoltaic and load uncertainties on the distribution network expansion planning, three comparison schemes are set up, namely, considering only photovoltaic uncertainty, only load uncertainty, and considering both source and load uncertainty. The comparison with the deterministic planning results under historical data is shown in Table 3. It can be seen from Table 3 that compared with the planning under historical data, the scheme considering uncertainty has a slight increase in the annual comprehensive investment cost, but has improved the new energy consumption rate, and can reduce the cost of purchasing electricity from the large power grid; and the planning results under the consideration of both source and load uncertainty have a higher new energy consumption rate than those under the consideration of only photovoltaic and load uncertainty, which are increased by 3.5% and 3.85% respectively;

[0084] Table 3 Distribution network expansion planning results under different scenarios

[0085]

[0086] ③ Planning results and analysis under different strategies: In order to compare the advantages and disadvantages of the proposed strategy with other strategies, the following three cases are considered:

[0087] Case 1: the proposed collaborative planning model, which takes into account both economic cost and grid line current margin;

[0088] Case 2: Distribution network expansion planning based on Case 1 without considering line current margin;

[0089] Case 3: Based on Case 1, the distribution network expansion planning without considering the energy storage device is the traditional distribution network expansion planning strategy.

[0090] The three methods of case setting all use distributed photovoltaic equipment of the same specifications and consider photovoltaic and load uncertainties.

[0091] Through simulation calculation, the cost comparison of each planning of Case 1 and Case 2 is shown in Table 4. The grid line planning results are as follows: Figure 6 and Figure 7 As shown;

[0092] Table 4 Distribution network expansion planning costs under Cases 1 and 2

[0093]

[0094] As can be seen from Table 4, the distribution network expansion planning scheme obtained in Case 1 is not economically superior to the planning scheme in Case 2. The annual comprehensive cost of the distribution network increased by 704,800 yuan, an increase of 5.62%. This is because the line selected in Case 1 for line expansion is 40-34-35-36-37-38-39, and the capacity configuration results of the two energy storages are 1.03MW and 0.76MW, while the line selected in Case 2 for grid line expansion is 33-34-35-36-37-38-39, and the capacity configuration results of the two energy storages are 0.63MW and 0.54MW. The distribution network grid line layout is The Bureau tends to build low-cost lines and reduce the configuration and operation and maintenance costs of energy storage capacity to reduce investment costs; but Case 1 is better than Case 2 in terms of photovoltaic operation and maintenance costs, power purchase costs, line network loss costs and carbon emission costs. The power purchase costs of the large power grid were reduced by 549,600 yuan, and the line network loss costs were reduced by 545,600 yuan. This shows that after considering the transformation of related equipment, the planning and layout of the grid lines are more reasonable and the power flow distribution is more uniform; at the same time, the capacity configuration and site selection of energy storage further optimize the transmission capacity of the grid channel, promote the consumption of distributed photovoltaics, reduce the purchase of electricity from thermal power units, and reduce carbon emission costs.

[0095] At the same time, the economic comparison between Case 1 and Case 3 is shown in Table 5. The overall grid line planning results of Case 3 are shown in Table 5. Figure 8 As shown;

[0096] Table 5 Distribution network expansion planning costs under Cases 1 and 3

[0097]

[0098] As can be seen from Table 5, the planning scheme of Case 1 after considering energy storage is 2.7197 million yuan higher than that of Case 3. This is because Case 3 does not have the cost of energy storage investment, construction and operation and maintenance, but reduces the large power grid power purchase cost, line network loss cost and carbon emission cost by 782,000 yuan, 180,600 yuan and 168,000 yuan respectively, because energy storage can store low power and generate high power, and participate in the optimization of distribution network power flow distribution. It can be seen that the planning scheme considering energy storage can improve the safety of the distribution network, that is, sacrifice part of the economy in exchange for higher stability to ensure the operation of the distribution network.

[0099] In this embodiment, a field application case is taken as an example: taking the Yichuan Northeast Grid as an example, when the photovoltaic penetration rate is 80%, the distribution network only considers the transformation of transformer capacity. It is necessary to add 18 transformers to expand the capacity and expand 3 lines, with a total transformation cost of 1.88 million yuan. At the same time, the 110kV Lujiang transformer will be started in advance in 2025;

[0100] When the photovoltaic penetration rate is 80%, considering distributed energy storage, a total of 2 transformers need to be added, with a storage capacity of 1276.6kWh and a storage power of 638.3kW, and 1 line needs to be expanded, with a total renovation cost of 958,300 yuan;

[0101] When the photovoltaic penetration rate is 80%, taking into account energy storage, demand-side response and distribution network-related equipment transformation, a total of one transformer needs to be added, with a storage capacity of 1171.2kWh and a storage power of 585.6kW, and one line needs to be expanded, with a total transformation cost of 845,600 yuan.

[0102] Taking the 10kV power grid in the northeast grid of Yiyang County, Luoyang City as an example, combined with MATLAB simulation software for field verification, the investment strategy considering the transformation of energy storage and distribution network related equipment was obtained through simulation calculation. The energy storage capacity was 1200kWh, the energy storage power was 600kW, and one line was expanded, with a total transformation cost of 845,600 yuan. According to the investment in new 10kV and below lines, increased distribution transformers, energy storage and other devices, the cost was classified and calculated. Compared with the traditional plan, the grid investment was delayed by 2.2 million yuan, the network loss was saved by 57,000 yuan, the investment in related equipment transformation was saved by 1.1 million yuan, and the grid's ability to absorb new energy was increased by 6.7 percentage points. The project proposal can achieve the coordinated operation of source, grid, load and storage, and improve economic benefits.

[0103] In summary, the expansion planning model of distributed photovoltaic distribution network with energy storage configuration and related data transformation proposed in this invention is used to simulate actual working conditions, and is solved by nested improved particle swarm optimization algorithm, which provides ideas for the planning of distribution network under distributed power access. Finally, the following conclusions are drawn through the simulation results of the example: ① Compared with the traditional distribution network expansion planning, the expansion planning model considering energy storage equipment is not economically advantageous, but it can effectively reduce network losses, power purchase costs of thermal power units and carbon emissions through the adjustment of flexible resources, and make the entire distribution network system in a safer operating state;

[0104] ② Considering the transformation of distribution network related equipment in the planning model can make the distribution network expansion planning obtain a more reasonable grid layout, so that the lines most prone to congestion can be removed from the high-load operation state, the system flow will be more stable, and the planning results will be more reliable and long-term;

[0105] ③ The strategy that takes into account the uncertainty of photovoltaic output and load fluctuations at the same time has a slightly higher planning cost than the deterministic result, but has achieved a better optimization and improvement in the photovoltaic absorption rate. Compared with the results that only consider the uncertainty on the source side or the load side, it is also more advantageous in the photovoltaic absorption rate, while reducing the cost of purchasing electricity from the large power grid.

[0106] The present invention is an investment strategy method considering the transformation and expansion of energy storage and distribution network related equipment. The present invention considers the investment strategy for the transformation of energy storage and distribution network related equipment, and studies the coordinated planning and solution method of distribution network grid lines and energy storage devices. In use, the upper planning layer of the model constructed by the present invention takes the investment and construction of grid lines and battery energy storage as decision variables, and takes investment and operating costs as objective functions; the lower operating layer constructed by the present invention takes line reconstruction and distributed power output as decision variables, and the new energy consumption rate as the target. By improving the inertia weight and population variation in the particle swarm algorithm, a nested improved particle swarm two-layer optimization algorithm is constructed; finally, taking the improved 40-node distribution network as an example, the effectiveness of the proposed planning model and solution method is analyzed and verified; the present invention has the advantages of constructing a nested improved particle swarm two-layer optimization algorithm, a more reasonable grid layout, and high planning reliability.

Claims

1. An investment strategy method considering the transformation and expansion of energy storage and distribution network related equipment, characterized by: The method comprises the following steps: Step 1: Double-layer coordinated planning of energy storage and power grid; Step 2: Construct the upper planning layer model: including the upper objective function and upper constraint conditions; Step 3: Construct the lower-level operation layer model: including the lower-level objective function and the lower-level constraints; Step 4: Solving the two-layer model: For the two-layer collaborative planning model of energy storage and power grid, a nested improved particle swarm optimization algorithm is used to solve it; Step 5: Case analysis.

2. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 1, characterized in that: The two-layer coordinated planning of energy storage and power grid in step 1 is specifically as follows: The original two-layer model is: Where: G(·), H(·), g(·), h(·) are the constraints of the upper and lower layers respectively; this model is a two-layer model, the upper layer transmits investment decision variables to the lower layer, and the lower layer simulates the operation variables and affects the optimization of the upper layer; the upper planning layer, with the investment cost C I and operating cost C O The goal is to minimize the comprehensive cost, taking into account the expansion investment of the grid line and the site selection and capacity determination of the ESS, and the constraint condition is the equipment investment constraint; the lower operation layer, with the new energy consumption rate f abs As the goal, active control objects such as DG and ESS are considered, and the constraints are simulation operation constraints; the equipment investment decision variables and operation decision variables of the two-layer model are separable.

3. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 1, characterized in that: The upper objective function of the upper planning layer model constructed in step 2 is specifically: the upper model takes the investment variable as the decision variable, making the construction cost C I and operating cost C O The lowest, as shown in formula (3), C I Including conversion to grid line investment cost Distributed photovoltaic investment cost Energy storage investment cost C O Including the network loss cost for the whole year Cost of purchasing electricity from the large power grid Energy storage equipment operation and maintenance costs Distributed photovoltaic operation and maintenance costs 4. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 3, characterized in that: The grid line investment cost In the formula, r is the discount rate; y is the useful life; K L is the sum of the original route and the route to be planned; x k is the state variable; c k is the construction cost of the line; the energy storage investment cost In the formula, are the unit power and capacity investment costs of building energy storage devices at node i, respectively; are the energy storage power and capacity built for node i respectively; the network loss cost Where, T is the total time; R ij is the resistance of line ij; c loss is the unit network loss cost.

5. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 4, characterized in that: The cost of purchasing electricity from the large power grid In the formula, c pur is the unit electricity purchase cost; p G,t is the power generated by the generator at time t; the operation and maintenance cost of the energy storage equipment In the formula, Λ ESS is the set of nodes where ESS is installed; is the operating cost per unit of charge and discharge capacity; are the charging and discharging power of ESS at node i in period t; the operating cost of distributed photovoltaic is In the formula, λ is the conversion ratio of operation and maintenance cost; is the power abandonment cost per unit capacity of distributed photovoltaics; is the theoretical value of the photovoltaic grid-connected capacity of node i at time t; is the actual value of the photovoltaic grid-connected capacity of node i at time t.

6. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 3, characterized in that: The upper-level constraints for constructing the upper-level planning model in step 2 include: ① Topological constraints: The distribution network framework should satisfy the radial constraints when planning, and the total number of branches is the difference between the total number of nodes and the number of root nodes, as shown in the following formula: Where D l Represents the set of all lines in the distribution network; x ij is the disconnection state of the line; n s Represents the number of root nodes in the system; ② Reconstruction constraints: To avoid loops or islands, assume that all lines are connected and form loops, disconnect one branch of the overlapping branches between the loops, and also split one branch of the non-overlapping branches. At a node with n branches, at most n-1 lines are split, which can ensure that the distribution network does not produce an island or loop state. The reconstruction constraints are as follows: In the formula, NL k , L k Respectively represent the total number and set of branches in the kth loop; n r Indicates the total number of loops; l k Represents the set of branches connected to the kth node.

7. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 1, characterized in that: The lower-level constraints for constructing the lower-level operation layer model in step 3 include: system safety constraints, distribution network DistFlow constraints, distributed power output constraints, line power transmission constraints, and battery energy storage constraints.

8. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 7, characterized in that: The system safety constraint is: to maintain the safe operation of the distribution system, the node voltage and the current flowing through the line must be controlled within a safe range. The mathematical model is: In the formula, are the upper and lower limits of the voltage at node i; U i,t is the voltage of node i at time t; the distribution network DistFlow constraint is: Where AL(j,:) is the set of branch end nodes with j as the head node; AL(:,j) is the set of branch head node with j as the end node; P ij,t , P jk,t , Q ij,t , Q jk,t are the active and reactive power on lines ij and jk during period t respectively; X ij is the reactance of the line; are respectively the active and reactive power of the original load of node j in period t; U i,t is the voltage at node i.

9. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 7, characterized in that: The output constraint of the distributed power source is: In the formula, are the upper and lower limits of the actual PV output at time t respectively; the line power transmission constraint is: Where P ij,max , Q ij,max are respectively the upper limits of active and reactive power of line transmission; the battery energy storage constraint is: In the formula, are the state variables of ESS charging and discharging respectively; are the charging and discharging power of ESS at node i at time t; P i ESS,max is the maximum value of the ESS charging and discharging power of node i; is the power of ESS at node i at time t; is the upper limit of ESS power of node i; η is the charge and discharge efficiency.

10. The investment strategy method for considering the transformation and expansion of energy storage and distribution network related equipment as claimed in claim 1, characterized in that: The two-layer model in step 4 is solved by using a nested improved particle swarm optimization algorithm, which includes the following steps: Step 4.1: Import PV output and load data and distribution network structure related parameters; Step 4.2: Initialize the upper particle population, perform binary encoding on the new lines and energy storage sites, and generate particle positions and velocities; Step 4.3: Calculate the population fitness, obtain the distribution network grid line direction and energy storage site selection location, filter out unreasonable planning results and update the population location; Step 4.4: Repeat steps 4.2-4.3 until convergence is achieved, that is, the difference between the upper layer planning cost and the previous and next iterations for 10 consecutive times does not exceed 300 yuan or the upper limit of the number of iterations of the upper layer is reached; Step 4.5: Import the planning scheme obtained from the upper layer into the lower layer and initialize the lower layer population, encoding each energy storage capacity integer; Step 4.6: Calculate the power grid flow, distributed generation output and other data, execute the lower layer objective function, update the individual and overall extreme values ​​of the energy storage capacity corresponding to all grid planning results in the lower layer, regard the overall extreme value as the optimal energy storage capacity corresponding to each planning result, and update the speed and position of the particles in the lower layer; Step 4.7: Determine whether the lower-level convergence standard is met, that is, the difference between the distributed photovoltaic absorption rate before and after 10 consecutive iterations is less than 0.001 and 0.05 respectively, or the upper limit of the number of iterations of the lower level is reached; Step 4.8: If the maximum number of iterations is reached, stop searching and output the results; otherwise, return to step 4.2.

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