Energy storage system double-layer optimization configuration method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides an energy storage system double-layer optimization configuration method and device, a storage medium and electronic equipment, and relates to the technical field of energy storage system configuration.The method comprises the steps that an energy storage system network structure and parameters of a power distribution network are initialized, network power flow is calculated, and the initial power flow and voltage distribution condition of the power distribution network are obtained; establishing an upper-layer optimization model, determining an objective function of the upper-layer optimization model, and determining constraint conditions of the upper-layer optimization model; solving the upper-layer optimization model, and outputting the access capacity of the energy storage system; establishing a lower-layer optimization model by taking the energy storage system access capacity as a model parameter, and confirming a target function of the lower-layer optimization model and a constraint condition of the lower-layer optimization model; and solving the lower-layer optimization model by using a red-mouth blue-magpie optimization algorithm, and outputting the optimal access position and capacity of the energy storage system. According to the technical scheme, the power grid stability can be effectively improved, the network loss is reduced, and energy supply and demand balance is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage system configuration, and in particular to a method, device, storage medium and electronic device for double-layer optimization configuration of an energy storage system. Background Art

[0002] As the global energy crisis and environmental pollution problems intensify, new energy technology has gradually become one of the key means to solve these problems. However, the power generation of renewable energy is significantly intermittent and volatile, which easily leads to supply and demand imbalance when it is connected to the power grid, thus affecting the stable operation of the power grid. Voltage and load are key reference indicators for the operation and planning of distribution networks. The matching of the two reflects the balance of supply and demand. Effective voltage regulation and load balancing can improve the stability, reliability and economy of the power system.

[0003] Energy storage systems can not only effectively alleviate the instability of renewable energy generation, but also improve the flexibility and reliability of the power grid. However, the construction and operation costs of energy storage systems are high, so how to reasonably configure energy storage equipment in the power system to achieve the best economic benefits and operating effects is the focus of current research.

[0004] The configuration of the energy storage system includes decisions on its installation location in the power grid, capacity, charging and discharging strategies, etc. Reasonable energy storage configuration can improve the operating efficiency of the power system, reduce the operating costs of the power grid, and maximize the absorption of renewable energy. The optimization of the energy storage system involves multiple goals, such as cost, efficiency, reliability, etc. There are often conflicts between these goals. How to balance these goals is the difficulty of current research; in addition, the energy storage system needs to deal with the volatility and unpredictability of renewable energy generation, which increases the complexity of the optimization configuration; the cost and life of energy storage technology (such as battery energy storage) are key factors in determining its economic feasibility. How to find the best balance between technical performance and cost is the key to energy storage configuration. Summary of the invention

[0005] The embodiments of the present application provide a method, device, storage medium and electronic device for optimizing the configuration of a double-layer energy storage system to solve the technical problems existing in the prior art.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0007] According to a first aspect of an embodiment of the present application, a double-layer optimization configuration method for an energy storage system is provided, comprising:

[0008] Initialize the energy storage system network structure and parameters of the distribution network, calculate the network power flow, and obtain the initial power flow and voltage distribution of the distribution network;

[0009] Establishing an upper optimization model, and confirming the objective function of the upper optimization model based on the economic index and the distribution network voltage fluctuation index, and confirming the constraint conditions of the upper optimization model;

[0010] Solving the upper optimization model and outputting the access capacity of the energy storage system;

[0011] Establishing a lower-level optimization model with the access capacity of the energy storage system as a model parameter, and confirming the objective function of the lower-level optimization model based on the distribution network vulnerability index and the distribution network loss index, and confirming the constraint conditions of the lower-level optimization model;

[0012] The red-billed blue magpie optimization algorithm is used to solve the lower optimization model and output the optimal energy storage system access location and capacity.

[0013] In some embodiments of the present application, based on the above-mentioned solution, the objective function of the upper optimization model is determined based on the economic index and the distribution network voltage fluctuation index, including:

[0014] Calculate the average annual investment cost of the energy storage system, the annual operating cost of the distribution network, and the voltage fluctuation index of the distribution network;

[0015] The objective function of the upper optimization model is confirmed based on the annual average investment cost of the energy storage system, the annual operating cost of the distribution network, and the voltage fluctuation index of the distribution network.

[0016] In some embodiments of the present application, based on the above solution, the step of confirming the constraint conditions of the upper-layer optimization model includes:

[0017] According to the initial power flow and voltage distribution of the distribution network, the power flow balance constraints of the distribution network, the node voltage constraints, the energy balance constraints of the energy storage system, and the power and capacity constraints of the energy storage system are confirmed.

[0018] In some embodiments of the present application, based on the above-mentioned solution, the objective function of the lower-level optimization model is confirmed based on the distribution network vulnerability index and the distribution network loss, including:

[0019] Calculating a power grid vulnerability index value as a first objective function of the lower optimization model;

[0020] Active network loss is calculated as the second objective function of the lower optimization model.

[0021] In some embodiments of the present application, based on the above-mentioned solution, the calculation of the power grid vulnerability index value as the first objective function of the lower-layer optimization model includes:

[0022] Use formula (1) to calculate the vulnerability v(i,t) of node i at time t;

[0023]

[0024] In formula (1), U i,t is the voltage amplitude of node i at time t; U i,0 is the rated voltage of node i; V max is the maximum voltage offset;

[0025] Normalize the vulnerability of node i at time t;

[0026]

[0027] In formula (2), v(i max ,t),v(i min ,t) is the maximum and minimum vulnerability value of all nodes at time t before normalization;

[0028] According to the normalized results, the average vulnerability of the power grid at time t, AV(t), is calculated using formula (3);

[0029]

[0030] Formula (4) is used to calculate the ratio of node i vulnerability to the total vulnerability of the current power grid at time t;

[0031]

[0032] Based on the ratio of node i vulnerability to the total vulnerability of the current power grid at time t, the balance degree of power grid vulnerability is calculated using formula (5);

[0033]

[0034] In formula (5), J(t) represents the balance degree of the overall vulnerability of the power grid at time t, p i,t is the ratio of node i vulnerability to the total vulnerability of the current power grid at time t;

[0035] Based on the average fragility of the power grid at time t and the balance degree of the power grid fragility, the power grid fragility index value F1 is calculated using formula (6) as the first objective function of the lower optimization model;

[0036]

[0037] Among them, AV(t) is the average vulnerability of the power grid at time t; J(t) represents the balance degree of power grid vulnerability.

[0038] In some embodiments of the present application, based on the above solution, the calculation of active network loss as the second objective function of the lower layer optimization model includes:

[0039] Use formula (7) to calculate the active network loss;

[0040]

[0041] In formula (7), U i,t with U j,t are the voltages of node i and node j at time t; G ij is the conductance between node i and node j; θ ij,t is the phase difference between nodes i and j at time t.

[0042] In some embodiments of the present application, based on the above solution, the step of confirming the constraint conditions of the lower-level optimization model includes:

[0043] According to the initial power flow and voltage distribution of the distribution network, active power balance constraints, static power flow constraints, node voltage and phase constraints, wind turbine output constraints, photovoltaic generator output constraints, and energy storage charge state and output constraints are confirmed.

[0044] According to a second aspect of an embodiment of the present application, a double-layer optimization configuration device for an energy storage system is provided, comprising:

[0045] The first calculation unit is used to initialize the network structure and parameters of the energy storage system of the distribution network, calculate the network flow, and obtain the initial flow and voltage distribution of the distribution network;

[0046] A first establishing unit, used for establishing an upper layer optimization model;

[0047] A first confirmation unit is used to confirm the objective function of the upper optimization model based on the economic index and the distribution network voltage fluctuation index;

[0048] A second confirmation unit, used to confirm the constraint conditions of the upper optimization model;

[0049] A second calculation unit is used to solve the upper optimization model and output the access capacity of the energy storage system;

[0050] A second establishing unit is used to establish a lower-level optimization model using the access capacity of the energy storage system as a model parameter;

[0051] A third confirmation unit is used to confirm the objective function of the lower optimization model based on the distribution network vulnerability index and the distribution network loss index;

[0052] A fourth confirmation unit, used to confirm the constraint conditions of the lower layer optimization model;

[0053] The third calculation unit is used to solve the lower-level optimization model and output the optimal energy storage system access location and capacity.

[0054] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0055] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including: a memory and a processor;

[0056] The memory is used to store computer instructions;

[0057] The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.

[0058] The technical solution of the present application can effectively improve the stability of the power grid, reduce network losses, and achieve a balance between energy supply and demand.

[0059] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0061] Figure 1 A schematic diagram of a process of a double-layer optimization configuration method for an energy storage system according to an embodiment of the present application is shown;

[0062] Figure 2 A schematic diagram of a set of optimal solution energy storage output strategies in scenario 3 according to an embodiment of the present application is shown;

[0063] Figure 3 A block diagram of a double-layer optimization configuration device for an energy storage system according to an embodiment of the present application is shown;

[0064] Figure 4 A block diagram of an electronic device according to an embodiment of the present application is shown;

[0065] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0066] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0067] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0068] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0069] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0070] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the objects used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those shown or described.

[0071] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0073] See also Figure 1 , showing a flow chart of a double-layer optimization configuration method for an energy storage system according to an embodiment of the present application.

[0074] like Figure 1 As shown, a double-layer optimization configuration method for an energy storage system is presented, including steps S100 to S500.

[0075] refer to Figure 1 , step S100, initialize the network structure and parameters of the energy storage system of the distribution network, calculate the network flow, and obtain the initial flow and voltage distribution of the distribution network.

[0076] It should be noted that the initial power flow and voltage distribution of the distribution network calculated in this step can be used to confirm the constraint conditions of the upper optimization model and the lower optimization model in the subsequent steps.

[0077] Continue to refer Figure 1 , step S200, establishing an upper optimization model, and confirming the objective function of the upper optimization model based on the economic index and the distribution network voltage fluctuation index, and confirming the constraint conditions of the upper optimization model.

[0078] It should be noted that the established upper-level optimization model takes the distribution network topology as the action space and dynamically selects the optimal energy storage access location and capacity.

[0079] In some feasible embodiments, based on the above scheme, the objective function of the upper optimization model is determined based on the economic index and the distribution network voltage fluctuation index, including:

[0080] Calculate the average annual investment cost of the energy storage system, the annual operating cost of the distribution network, and the voltage fluctuation index of the distribution network;

[0081] The objective function of the upper optimization model is confirmed based on the annual average investment cost of the energy storage system, the annual operating cost of the distribution network, and the voltage fluctuation index of the distribution network.

[0082] Exemplarily, the calculation process of the average annual investment cost of the energy storage system is as follows:

[0083] The average annual investment cost of the energy storage system C in Defined as:

[0084]

[0085] Where τ is the discount rate; y is the expected operating life of the energy storage device; n is the number of installed energy storage devices; C e and C p are the investment costs per unit capacity and power of energy storage equipment respectively; E B，kand P B，k are the rated capacity and rated power of the kth energy storage device respectively.

[0086] Distribution network annual operating cost C op The calculation process is as follows:

[0087] C op =C om +C loss ;

[0088]

[0089] Among them, C om is the annual operation and maintenance cost of the energy storage system; C loss is the network loss cost; C ep is the network loss cost per unit branch; N1 is the total number of branches in the distribution network; Δt is the sampling period, which is generally 1h; P loss,l，t is the active power loss of the lth branch at time t.

[0090] Distribution network voltage fluctuation index D dev The calculation process is as follows:

[0091]

[0092] Where μ is the conversion coefficient of unified unit; λ is the conversion coefficient of active power loss and voltage deviation, which is 500kw / pu.

[0093] According to the calculated average annual investment cost of energy storage system C in , annual operating cost of distribution network C op and distribution network voltage fluctuation index D dev , calculate the objective function minF of the upper optimization model out :

[0094] f out =C in +C op +μD dev .

[0095] In some feasible embodiments, based on the above solution, the step of confirming the constraint conditions of the upper optimization model includes:

[0096] According to the initial power flow and voltage distribution of the distribution network, the power flow balance constraints of the distribution network, the node voltage constraints, the energy balance constraints of the energy storage system, and the power and capacity constraints of the energy storage system are confirmed.

[0097] For example, first consider the power flow balance constraint of the distribution network:

[0098]

[0099] Among them, P G,i,t and Q G,i,t are the active and reactive power injected by node i at time t respectively; P B,i,t is the operating power of the energy storage device at node i at time t; P L,i,t With Q L,i,t are the active and reactive loads of node i at time t; U i,t is the voltage amplitude of node i at time t; G ij and B ij are the real and imaginary parts of the mutual admittance between node i and node j respectively; θ ij,t is the voltage phase difference between node i and node j at time t.

[0100] Then there are the node voltage constraints:

[0101]

[0102] in, and are the minimum and maximum voltage amplitudes of node i, respectively.

[0103] Since the energy storage system must maintain a balance of charging and discharging energy within a cycle when the system is operating normally, the energy balance constraint on the energy storage system is expressed as:

[0104]

[0105] Finally, there are the power and capacity constraints of the energy storage system:

[0106]

[0107] Among them, E max With P max are the maximum design capacity and power of the energy storage system respectively.

[0108] Continue to refer Figure 1 , step S300, solving the upper optimization model and outputting the access capacity of the energy storage system.

[0109] It should be noted that, since in the two-layer model, the lower optimization model is solved by the optimization algorithm, each time the lower model is solved, the upper optimization model also needs to be solved. In order to prevent the calculation amount from being too large and the calculation time from being too long, YALMIP and CPLEX solvers are chosen to solve the upper optimization model.

[0110] Continue to refer Figure 1, step S400, establishing a lower-level optimization model with the access capacity of the energy storage system as a model parameter, and confirming the objective function of the lower-level optimization model based on the distribution network vulnerability index and the distribution network loss index, and confirming the constraint conditions of the lower-level optimization model.

[0111] It should be noted that in the lower-level optimization model, it is necessary to consider the distribution network vulnerability index and distribution network loss, and consider five constraints including active power balance constraint, static power flow constraint, node voltage and phase constraint, wind power, photovoltaic generator output constraint, energy storage charge state and output constraint, so as to achieve the energy supply and demand balance of the energy storage system in power supply and demand.

[0112] In some feasible embodiments, based on the above solution, the objective function of the lower optimization model is confirmed based on the distribution network vulnerability index and the distribution network loss, including:

[0113] Calculating a power grid vulnerability index value as a first objective function of the lower optimization model;

[0114] Active network loss is calculated as the second objective function of the lower optimization model.

[0115] It should be noted that the grid vulnerability index is based on the standard value of voltage deviation at each node in the distribution network to measure the stability of the voltage. The higher the vulnerability, the poorer the power quality of the node, the low power supply quality, and the poorer safety and risk resistance.

[0116] It should be noted that the energy storage system can be equivalent to a power source in the power grid and can also participate in regulation as a load. It can effectively reduce the current flow in the line, thereby reducing the active network loss of the power grid.

[0117] In some feasible embodiments, based on the above solution, the calculation of the power grid vulnerability index value as the first objective function of the lower optimization model includes:

[0118] Calculate the vulnerability v(i,t) of node i at time t;

[0119]

[0120] Among them, U i,t is the voltage amplitude of node i at time t; U i,0 is the rated voltage of node i; V max is the maximum voltage offset;

[0121] Normalize the vulnerability of node i at time t;

[0122]

[0123] Among them, v(imax ,t),v(i min ,t) is the maximum and minimum vulnerability value of all nodes at time t before normalization;

[0124] The average vulnerability of the power grid at time t, AV(t), is calculated based on the normalized results;

[0125]

[0126] Where N is the total number of nodes in the distribution network system; V(i,t) is the normalized value of the vulnerability of node i at time section t;

[0127] In an actual power grid, when the voltage at a certain node collapses or jumps, a huge interference signal will be generated and affect other nodes. Therefore, it is necessary not only to consider the vulnerability of each node, but also the mutual influence between them, that is, the balance of distribution.

[0128] The ratio of the vulnerability of node i to the total vulnerability of the current power grid at time t is calculated;

[0129]

[0130] The balance degree of grid vulnerability is calculated based on the ratio of node i vulnerability to the total current grid vulnerability at time t;

[0131]

[0132] Where J(t) represents the balance degree of the overall vulnerability of the power grid at time t. When it is 0, it represents absolute balance, and when it is 1, it represents absolute imbalance. i,t is the ratio of node i vulnerability to the total vulnerability of the current power grid at time t;

[0133] Based on the average fragility of the power grid at time t and the balance degree of the power grid fragility, a power grid fragility index value F1 is calculated as the first objective function of the lower optimization model;

[0134]

[0135] Among them, AV(t) is the average vulnerability of the power grid at time t; J(t) represents the balance degree of power grid vulnerability.

[0136] In some feasible embodiments, based on the above solution, the calculation of active network loss as the second objective function of the lower optimization model includes:

[0137] Calculate active network losses;

[0138]

[0139] Among them, U i,t with Uj,t are the voltages of node i and node j at time t; G ij is the conductance between node i and node j; θ ij,t is the phase difference between nodes i and j at time t.

[0140] In some feasible embodiments, based on the above solution, the step of confirming the constraint conditions of the lower-level optimization model includes:

[0141] According to the initial power flow and voltage distribution of the distribution network, active power balance constraints, static power flow constraints, node voltage and phase constraints, wind turbine output constraints, photovoltaic generator output constraints, and energy storage charge state and output constraints are confirmed.

[0142] Exemplarily, the confirmation process of each constraint condition is as follows:

[0143] The active power balance constraint is:

[0144]

[0145] Where: P load (t) and P loss (t) are the total system load and total network loss at time t; P BT,m and P DG,n are the charging and discharging power of energy storage device m and the active power of distributed power source n at time t; N E and N DG are the number of connected energy storage and distributed generation respectively; P Q (t) is the active power flowing into the network.

[0146] The static power flow constraint is:

[0147] P g,i,t +P WT,i,t +P E,i,t +P PV,i,t -P L,i,t =U i,t ∑U j,t (G ij cosθ ij,t +B ij sinθ ij,t );

[0148] Q g,i,t +Q WT,i,t +Q E,i,t +Q PV,i,t -Q L,i,t =U i,t ∑U j,t (G ij sinθ ij,t -B ij cosθij,t );

[0149] Where: P g,i,t is the active power flowing into node i at time t; P WT,i,t P is the active power output of the wind turbine; E,i,t , P PV,i,t , P L,i,t are the active power output of energy storage system charging and discharging, the active power output of photovoltaic generator, and the active power consumed by node i; Q g,i,t , Q WT,i,t , Q E,i,t , Q PV,i,t , Q L,i,t They are the reactive power flowing into node i at time t, the reactive output of wind turbine, the reactive output of energy storage system charging and discharging, the reactive output of photovoltaic generator, and the reactive power consumed by node i.

[0150] The node voltage and phase constraints are:

[0151]

[0152] Where: and are the upper and lower limits of the active output of the wind turbine respectively, and They are the upper and lower limits of the reactive power output of the wind turbine respectively.

[0153] The output constraint of photovoltaic generator is:

[0154]

[0155] Where: and are the upper and lower limits of the active output of the photovoltaic generator, and They are the upper and lower limits of the reactive power output of the photovoltaic generator respectively.

[0156] The energy storage charge state and output constraints are:

[0157]

[0158] Where: SOC i,t Represents the SOC value of energy storage i in period t; SOC i,min and SOC i,max Respectively represent the upper and lower limits of energy storage SOC; σ i Represents the energy storage self-discharge rate; η cha,i and η dis,i Respectively represent the energy storage charging and discharging efficiency; E E,i is the capacity of energy storage i.

[0159] Continue to refer Figure 1 , step S500, using the red-billed blue magpie optimization algorithm to solve the lower optimization model and output the optimal energy storage system access location and capacity.

[0160] It should be noted that in the specific optimization process, it is necessary to determine whether the solution output by the lower-level optimization model is optimal. If not, return to the upper-level optimization model to continue iterative solution until the optimal energy storage system access location and capacity are obtained.

[0161] Specifically, the principle and solution process of the red-billed blue magpie optimization algorithm are as follows:

[0162] 1) Population initialization, the initial search agent of the algorithm is realized by the following formula:

[0163] x i,j =(ub-lb)×Rand1+lb;

[0164] Where ub and lb are the upper and lower bounds of the problem respectively, and Rand represents a random number between 0 and 1.

[0165] 2) Searching for food: In the process of searching for food, red-billed blue magpies usually act in small groups (2 to 5) or groups (more than 10) to improve search efficiency. Therefore, the following formula is used to iterate when a small group explores food:

[0166]

[0167] Where t represents the current iteration number, X i (t+1) represents the i-th new search agent position, p represents the number of red-billed blue magpies in a small group of 2 to 5 randomly selected from all search individuals, and X m represents the mth individual selected randomly, Xi represents the ith individual, X rs (t) represents the randomly selected search agent in the current iteration.

[0168] When a group is formed, the following formula is used for iteration:

[0169]

[0170] Where q represents the number of search agents in the cluster when exploring for food, ranging from 10 to n. Again, it is randomly selected from the entire population.

[0171] 3) Attacking prey:

[0172] In small group actions, the main target is usually small prey or plants. The corresponding mathematical model is as follows:

[0173]

[0174] When acting in groups, they are able to collectively target large prey, such as large insects or small vertebrates. The mathematical expression of this behavior is shown below:

[0175]

[0176] Where Xfood(t) represents the location of the food, CF = (1-t / T)^(2*(t / T)), and Randn represents the random number used to generate a standard normal distribution (mean 0, standard deviation 1).

[0177] Food Storage:

[0178] In addition to finding and attacking food, the red-billed blue magpie also stores excess food in tree holes or other hidden places for future consumption, ensuring a stable food supply when food is scarce. This process retains information about the solution, making it easier for individuals to find the global optimal value. Its mathematical expression is shown below:

[0179]

[0180] in, and They respectively represent the fitness values ​​of the i-th red-billed blue magpie before and after the position is updated.

[0181] Below, a specific example is provided to verify the effect of the method provided in this application.

[0182] The effect is verified based on the IEEE33-node distribution network model. A 200kW distributed photovoltaic generator is connected at nodes 10 and 30, a 250kW wind turbine is connected at node 16, and the predicted output data of wind power and photovoltaic power and load data in a certain area are selected as the operating data of photovoltaic and wind power.

[0183] Four scenarios were set up to verify the superiority of the method:

[0184] Scenario 1: No energy storage installed;

[0185] Scenario 2: Only a single energy storage system is connected;

[0186] Scenario 3: Energy storage is connected at two nodes;

[0187] Scenario 4: Energy storage is connected at three nodes;

[0188] The proposed two-layer optimization configuration model of energy storage system is used to solve scenarios 2, 3, and 4, and MatPower is used to calculate the distribution network flow in scenario 1 to obtain various index parameters in scenario 1. The results are shown in the following table:

[0189] Table 1 Index parameters of each scenario

[0190]

[0191] From the results in Table 1, we can see that compared with the case where no energy storage equipment is installed, in scenario 2, the average vulnerability index of the distribution network decreased by 5.74%, and the active network loss decreased by 4.96%; but compared with scenario 1, the two indicators of scenario 3 decreased by 15.82% and 14.44%, respectively, and the two indicators of scenario 4 decreased by 21.68% and 27.18%, respectively. Therefore, installing energy storage equipment can effectively improve the stability of the power grid, reduce network losses, and achieve energy supply and demand balance.

[0192] Figure 2 This is a set of optimal energy storage output strategies in scenario 3. The access capacity of energy storage devices in nodes 2 and 13 is 0.959MWh and 0.721MWh respectively. The charging and discharging power and SOC changes of the energy storage devices in one day obtained by the proposed method are shown in the following figure: Figure 2 As shown (positive power means discharging, negative power means charging)

[0193] from Figure 2 It can be seen that at 5:00 the load demand reached a valley value and the wind power output also reached a peak value. At this time, the energy storage device absorbed the excess electric energy and stored it. When the load demand reached a peak value, that is, around 12:00 and 20:00, the electricity was released to ensure the normal power supply of the grid and to make the initial and final charge states of the energy storage device the same so that it can operate normally tomorrow.

[0194] In addition, in order to reflect the superiority of the adopted red-billed blue magpie optimization algorithm in solving the lower-layer energy storage operation output optimization model, the multi-objective optimization particle swarm algorithm and genetic algorithm are selected for solution. The calculation time of the solution, the number of iterations at convergence, and the results of each objective function value (all based on the solution of connecting two energy storage devices in scenario 3) are shown in the following table:

[0195] Table 2 Solution algorithm and calculation results

[0196]

[0197] From the results in Table 2, we can see that although the particle swarm algorithm has a shorter calculation time and a faster convergence speed, its final optimization result is worse than that of other algorithms. The genetic algorithm has a longer calculation time and a slower convergence speed, but the adopted red-billed blue magpie optimization algorithm not only has a short calculation time and a fast convergence speed, but its final optimization result is also the best among the three. It can be better applied to the optimal configuration of energy storage systems, and can obtain the optimal strategy for the operation output of energy storage systems, so that the network loss of the power grid is lower and the stability is improved, so as to better meet the requirements of energy supply and demand balance.

[0198] In summary, at present, in the research on the optimal configuration of energy storage systems, only economic indicators are usually considered, and distribution network safety indicators are ignored. Therefore, the technical solution of the present application takes into account multiple factors when constructing the objective function; in addition, the configuration and optimal operation of the energy storage system are usually carried out separately, but the configuration of energy storage will also have a certain impact on its optimal operation. Therefore, the technical solution of the present application establishes a two-layer optimization model of energy storage configuration and optimal operation, and solves the two problems of configuration and optimal operation of the energy storage system at the same time; finally, in response to the shortcomings of the currently commonly used intelligent optimization algorithms in solving multi-objective optimization problems, the technical solution of the present application introduces the red-billed blue magpie optimization algorithm for solution, and obtains better optimization solution effect.

[0199] The following describes an embodiment of the device of the present application, which can be used to implement a method for optimizing the configuration of a double-layer energy storage system in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the above method of the present application.

[0200] Reference Figure 3 As shown, according to an embodiment of the present application, a double-layer optimization configuration device 300 for an energy storage system includes:

[0201] The first calculation unit 301 is used to initialize the network structure and parameters of the energy storage system of the distribution network, calculate the network flow, and obtain the initial flow and voltage distribution of the distribution network;

[0202] A first establishing unit 302 is used to establish an upper layer optimization model;

[0203] A first confirmation unit 303 is used to confirm the objective function of the upper optimization model based on the economic index and the distribution network voltage fluctuation index;

[0204] A second confirmation unit 304 is used to confirm the constraint conditions of the upper optimization model;

[0205] The second calculation unit 305 is used to solve the upper optimization model and output the access capacity of the energy storage system;

[0206] A second establishing unit 306 is used to establish a lower-level optimization model using the energy storage system access capacity as a model parameter;

[0207] A third confirmation unit 307, configured to confirm the objective function of the lower optimization model based on the distribution network vulnerability index and the distribution network loss index;

[0208] The fourth confirmation unit 308 is used to confirm the constraint conditions of the lower optimization model;

[0209] The third calculation unit 309 is used to solve the lower optimization model using the red-billed blue magpie optimization algorithm, and output the optimal energy storage system access location and capacity.

[0210] like Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor, and when the processor 420 executes the computer program 411, the steps of the above-mentioned method for double-layer optimization configuration of an energy storage system are implemented.

[0211] Since the electronic device introduced in this embodiment is a device used to implement a double-layer optimization configuration device of an energy storage system in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0212] During the specific implementation process, when the computer program 411 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0213] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0214] It should be noted that Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0215] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503, such as executing the method described in the above embodiment. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, the ROM 502 and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0216] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that a computer program read therefrom is installed into the storage section 508 as needed.

[0217] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 509, and / or installed from a removable medium 511. When the computer program is executed by a central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0218] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0219] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0220] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0221] As another aspect, the present application also provides a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes a two-layer optimization configuration method for an energy storage system described in the above embodiment.

[0222] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements a double-layer optimization configuration method for an energy storage system described in the above embodiment.

[0223] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0224] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0225] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A double-layer optimization configuration method for an energy storage system, characterized in that: include: Initialize the energy storage system network structure and parameters of the distribution network, calculate the network power flow, and obtain the initial power flow and voltage distribution of the distribution network; Establishing an upper optimization model, and confirming the objective function of the upper optimization model based on the economic index and the distribution network voltage fluctuation index, and confirming the constraint conditions of the upper optimization model; Solving the upper optimization model and outputting the access capacity of the energy storage system; Establishing a lower-level optimization model with the access capacity of the energy storage system as a model parameter, and confirming the objective function of the lower-level optimization model based on the distribution network vulnerability index and the distribution network loss index, and confirming the constraint conditions of the lower-level optimization model; The red-billed blue magpie optimization algorithm is used to solve the lower optimization model and output the optimal energy storage system access location and capacity.

2. The method according to claim 1, characterized in that The objective function of the upper optimization model is determined based on the economic index and the distribution network voltage fluctuation index, including: Calculate the average annual investment cost of the energy storage system, the annual operating cost of the distribution network, and the voltage fluctuation index of the distribution network; The objective function of the upper optimization model is confirmed based on the annual average investment cost of the energy storage system, the annual operating cost of the distribution network, and the voltage fluctuation index of the distribution network.

3. The method according to claim 1, characterized in that The confirming of the constraint conditions of the upper-level optimization model includes: According to the initial power flow and voltage distribution of the distribution network, the power flow balance constraints of the distribution network, the node voltage constraints, the energy balance constraints of the energy storage system, and the power and capacity constraints of the energy storage system are confirmed.

4. The method according to claim 1, characterized in that: The objective function of the lower optimization model is determined based on the distribution network vulnerability index and the distribution network loss, including: Calculating a power grid vulnerability index value as a first objective function of the lower optimization model; Active network loss is calculated as the second objective function of the lower optimization model.

5. The method according to claim 4, characterized in that The calculating of the grid vulnerability index value as the first objective function of the lower optimization model includes: Use formula (1) to calculate the vulnerability v(i,t) of node i at time t; In formula (1), U i,t is the voltage amplitude of node i at time t; U i,0 is the rated voltage of node i; V max is the maximum voltage offset; Normalize the vulnerability of node i at time t; In formula (2), v(i max ,t),v(i min ,t) is the maximum and minimum vulnerability value of all nodes at time t before normalization; According to the normalized results, the average vulnerability of the power grid at time t, AV(t), is calculated using formula (3); Formula (4) is used to calculate the ratio of node i vulnerability to the total vulnerability of the current power grid at time t; Based on the ratio of node i vulnerability to the total vulnerability of the current power grid at time t, the balance degree of power grid vulnerability is calculated using formula (5); In formula (5), J(t) represents the balance degree of the overall vulnerability of the power grid at time t, p i,t is the ratio of node i vulnerability to the total vulnerability of the current power grid at time t; Based on the average fragility of the power grid at time t and the balance degree of the power grid fragility, the power grid fragility index value F1 is calculated using formula (6) as the first objective function of the lower optimization model; Among them, AV(t) is the average vulnerability of the power grid at time t; J(t) represents the balance degree of power grid vulnerability.

6. The method according to claim 4, characterized in that The calculating of active network loss as a second objective function of the lower optimization model includes: Use formula (7) to calculate the active network loss; In formula (7), U i,t with U j,t are the voltages of node i and node j at time t; G ij is the conductance between node i and node j; θ ij,t is the phase difference between nodes i and j at time t.

7. The method according to claim 1, characterized in that The determining of the constraint conditions of the lower-level optimization model includes: According to the initial power flow and voltage distribution of the distribution network, active power balance constraints, static power flow constraints, node voltage and phase constraints, wind turbine output constraints, photovoltaic generator output constraints, and energy storage charge state and output constraints are confirmed.

8. A double-layer optimization configuration device for an energy storage system, characterized in that: include: The first calculation unit is used to initialize the network structure and parameters of the energy storage system of the distribution network, calculate the network flow, and obtain the initial flow and voltage distribution of the distribution network; A first establishing unit, used for establishing an upper layer optimization model; A first confirmation unit is used to confirm the objective function of the upper optimization model based on the economic index and the distribution network voltage fluctuation index; A second confirmation unit, used to confirm the constraint conditions of the upper optimization model; A second calculation unit is used to solve the upper optimization model and output the access capacity of the energy storage system; A second establishing unit is used to establish a lower-level optimization model using the access capacity of the energy storage system as a model parameter; A third confirmation unit is used to confirm the objective function of the lower optimization model based on the distribution network vulnerability index and the distribution network loss index; A fourth confirmation unit, used to confirm the constraint conditions of the lower layer optimization model; The third calculation unit is used to solve the lower optimization model by using the red-billed blue magpie optimization algorithm, and output the optimal energy storage system access location and capacity.

9. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

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