Microgrid mobile energy storage configuration method and system

Abstract

The invention discloses a micro-grid mobile energy storage configuration method and a micro-grid mobile energy storage configuration system, which are used for solving the problem of rural micro-grid mobile energy storage constant volume site selection. The method comprises the following steps: taking minimization of a voltage deviation coefficient, energy storage comprehensive cost and active loss of a power grid as a target, considering an active balance constraint and an energy storage battery constraint, establishing an energy storage optimal configuration target function, and combining an Euclidean distance formula to construct an energy storage optimal configuration model; and based on a crisscross algorithm, performing crisscross operation on particles, solving the energy storage optimal configuration model, and finally outputting an optimal scheme of energy storage configuration. The system comprises an objective function construction module, an optimal configuration model construction module and a model solving module. According to the invention, the system operation cost and the network loss can be reduced while the voltage stability is ensured. The method can be widely applied to the field of energy storage configuration.

Description

Technical Field

[0001] The present invention relates to the field of power grid energy storage configuration, and particularly to a method and system for configuring mobile energy storage in a microgrid. Background Art

[0002] With the rapid development of distributed energy and energy storage technologies, the energy structure of rural power grids is undergoing a transformation. The widespread application of distributed photovoltaics and the diversity of load characteristics in rural areas have posed many challenges to the operation mode of traditional power grids, such as increasingly prominent problems like voltage deviation, three-phase imbalance, and active power loss. Especially in rural microgrids, the intermittency of loads and the uncertainty of distributed energy have significantly increased the complexity of the system, and traditional power grid planning and scheduling methods are difficult to meet the requirements of high efficiency and economy for the new rural energy system.

[0003] Energy storage technology has gradually become a key means to solve the operation bottleneck of rural microgrids. By reasonably locating and sizing energy storage, load fluctuations can be effectively smoothed, power quality can be improved, and energy loss can be reduced. However, current energy storage configuration methods usually focus on a single objective, such as reducing investment costs or smoothing new energy fluctuations, and it is difficult to take into account the multi-objective optimization requirements. Summary of the Invention

[0004] In view of this, to solve the technical problem that the existing power grid energy storage configuration methods cannot meet the multi-objective optimization requirements, on the one hand, the present invention proposes a method for configuring mobile energy storage in a microgrid, and the method includes the following steps:

[0005] Taking the minimization of voltage deviation coefficient, comprehensive energy storage cost, and active power loss of the power grid as the objective, considering active power balance constraints and energy storage battery constraints, an energy storage optimization configuration objective function is established, and an energy storage optimization configuration model is constructed in combination with the Euclidean distance formula;

[0006] Based on the crisscross optimization algorithm (CSO), by performing crisscross operations on particles, the energy storage optimization configuration model is solved, and finally the optimal energy storage configuration scheme is output.

[0007] In some embodiments, the step of solving the energy storage optimization configuration model based on the crisscross optimization algorithm specifically includes:

[0008] Obtain power grid data and perform preprocessing to obtain the capacity parameter constraints for energy storage configuration;

[0009] Based on the crisscross optimization algorithm, initialize the particle population in combination with the energy storage optimization configuration model, and set the upper and lower limits of the two values representing the capacity in the particles according to the capacity parameter constraints;

[0010] The fitness function corresponding to each particle is obtained through power flow calculation, and the particle with the optimal fitness is saved;

[0011] Based on the CSO algorithm, crosswise and longitudinal operations are performed on the particles to obtain a new particle population;

[0012] Iteratively calculate the fitness function of the population particles until the maximum number of iterations is reached, and output the optimal energy storage configuration plan and the charge and discharge conditions of the energy storage within 24 hours.

[0013] In a second aspect, the present invention also proposes a mobile energy storage configuration system for a microgrid, and the system includes:

[0014] An objective function construction module, aiming to minimize the voltage deviation coefficient, the comprehensive cost of the energy storage, and the active power loss of the power grid, and considering the active power balance constraint and the energy storage battery constraint, to establish an energy storage optimal configuration objective function;

[0015] An optimal configuration model construction module, combining the Euclidean distance formula, to construct an energy storage optimal configuration model;

[0016] A model solving module, based on the crosswise and longitudinal algorithm, solves the energy storage optimal configuration model and outputs an energy storage configuration plan.

[0017] Based on the above solution, the present invention provides a mobile energy storage configuration method and system for a microgrid. Aiming at the problem of optimal configuration of energy storage in rural microgrids, a multi-objective optimization method based on the CSO algorithm is proposed. Combining the objective function of reducing the voltage deviation coefficient, the comprehensive cost of the energy storage, and the active power loss, as well as dynamic constraint conditions, an energy storage optimal configuration model is constructed, which can provide theoretical support and engineering practice solutions for the efficient operation of the energy storage system in rural power grids. Compared with the prior art, this solution can improve the diversity of the population and enhance the optimization ability of the algorithm by performing crosswise and longitudinal operations on the particle population; further, by longitudinally operating to recombine all individuals of the population, it helps the algorithm to jump out of the local optimum and has strong global optimization ability. The present invention can reduce the system operation cost and network loss while ensuring voltage stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of the steps of a mobile energy storage configuration method for a microgrid according to the present invention;

[0019] Figure 2 is a diagram of the IEEE14-node model of an embodiment of the present invention;

[0020] Figure 3 is a typical daily photovoltaic output curve graph.

[0021] Figure 4 is a typical daily load curve graph.

[0022] Figure 5 is the objective function value of the particle population in the embodiment of the present invention.

[0023] Figure 6 is the charge and discharge condition diagram of the energy storage at node 13 in 24 hours in the embodiment of the present invention.

[0024] Figure 7 is the charge and discharge condition diagram of the energy storage at node 6 in 24 hours in the embodiment of the present invention. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] It should be noted that for the convenience of description, only the parts related to the relevant invention are shown in the accompanying drawings. Without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.

[0027] It should be understood that the "system", "device", "unit" and / or "module" used in the present application is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the word can be replaced by other expressions.

[0028] As shown in the present application and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements. The element defined by the statement "including one..." does not exclude the existence of another same element in the process, method, commodity or device including the element.

[0029] In the description of the embodiments of the present application, "a plurality of" means two or more than two. The following terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0030] In addition, flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0031] Referring to Figure 1 , which is a schematic flowchart of an optional example of the microgrid mobile energy storage configuration method proposed by the present invention. This method can be applied to computer devices. The energy storage configuration method proposed in this embodiment may include but is not limited to the following steps:

[0032] Step S1: Construct a voltage deviation coefficient objective function, an energy storage comprehensive cost objective function, and a grid active power loss objective function;

[0033] Step S2: Considering the active power balance constraint and the energy storage battery constraint, and combining with the Euclidean distance formula, construct an energy storage optimization configuration model with the minimization of the voltage deviation coefficient, the energy storage comprehensive cost, and the grid active power loss as the objectives;

[0034] Step S3: Solve the energy storage optimization configuration model based on the cross-layer and cross-layer algorithm, and output the energy storage configuration plan.

[0035] In some feasible embodiments, in step S1, the proposed objective functions include:

[0036] The voltage deviation coefficient objective function is:

[0037]

[0038] where n N is the total number of system nodes; T is the number of time instants; U i,t is the voltage of node i at time t;; U e is the expected voltage value of each node; U p is the maximum allowable voltage deviation; f1 is the system voltage deviation coefficient index.

[0039] The comprehensive cost of the energy storage includes the investment cost and the operation and maintenance cost, and its objective function is:

[0040] f2 = C1 + C2

[0041] C e = k1·P ess + k2·E ess

[0042] C d = k m ·C i

[0043]

[0044] Among them, C1 is the daily investment cost of energy storage; C2 is the daily operation and maintenance cost; C e is the investment cost of the energy storage device; and C d is the equipment depreciation cost; P ess is the total rated power of the energy storage battery; E ess is the rated capacity of the energy storage battery; k1 is the power cost coefficient of the energy storage battery; k2 is the capacity cost coefficient of the energy storage battery; k m is the equipment depreciation cost coefficient of the energy storage battery capacity attenuation; η is the capital recovery factor; r is the discount rate; y is the battery service life; C y is the annual operation and maintenance cost of the energy storage device per unit capacity; f2 is the comprehensive cost of the system accessing energy storage.

[0045] The objective function of the active power loss of the power grid is:

[0046]

[0047] Among them, L is the set of branches of the system; Y ij,t is the line admittance between node i and node j; θ ij is the line impedance angle; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t.

[0048] In some feasible embodiments, in step S2, it specifically includes:

[0049] The active power balance constraint of the microgrid is:

[0050]

[0051] Among them, P G,u (t) is the power generation power of the u-th traditional generator set at time t, n G is the number of traditional generator sets; P PV,v (t) is the power generation power of the v-th photovoltaic generator set at time t, n PV is the number of photovoltaic generator sets; P ess,w (t) is the charge and discharge power of the w-th energy storage device at time t, n ess is the number of energy storage devices; P Load,x (t) is the load power of the x-th node at time t, n N is the number of system nodes; P Loss,y (t) is the power loss of the y-th branch at time t, n L is the number of system branches;

[0052] The constraints of the energy storage battery are:

[0053]

[0054] E ess E(t + 1) = ess E(t) - P ess (t)Δt·η ch , P ess (t) < 0

[0055] E ess E(t + 1) = ess E(t) - P ess (t)Δt·η dh , P ess (t) ≥ 0

[0056] S SOC,min ≤ E ess (t) / E ess ≤ S SOC,max

[0057] Among them, P ess (t) is the charge and discharge power of the energy storage battery at time t; P ess min is the minimum value of the charge and discharge power of the energy storage battery; P ess max is the maximum value of the charge and discharge power of the energy storage battery; E ess (t) is the battery capacity of the energy storage battery at time t; η ch is the discharge efficiency of the battery; η dh is the charge efficiency of the battery; S SOC,min is the minimum value of the state of charge of the energy storage battery; S SOC,max is the maximum value of the state of charge of the energy storage battery.

[0058] Based on the values of the three objective functions in the ideal state, in the three-dimensional Cartesian coordinate system, the distance between the objective function value and the ideal value is calculated. The smaller the distance, the closer it is to the ideal state and the better the effect. Combining with the Euclidean distance formula, an energy storage optimization configuration model is constructed:

[0059]

[0060] Among them, f1 * , f2 * , f3 * are the values of the three objective functions in the most ideal state; f1 is the system voltage deviation coefficient index, f2 is the comprehensive cost of the system connected to the energy storage, and f3 is the active power loss of the system. The combined objective characterizes the health status of the system operation.

[0061] In some feasible embodiments, step S2 specifically includes:

[0062] S2.1. Preprocess the load data and photovoltaic data of the rural microgrid to obtain the capacity parameter constraints for energy storage configuration.

[0063] Use the k-means clustering algorithm to cluster the daily load data and photovoltaic data, obtain the typical daily load curve and typical daily photovoltaic curve and compare them. Take half of the sum of the parts where the typical daily load curve is greater than the photovoltaic curve as the minimum constraint for the energy storage capacity, and control the maximum constraint to be twice the minimum value.

[0064] This embodiment uses the IEEE 14-node system as shown in Figure 2 for example verification. Among them, 5MW photovoltaic units are connected to nodes 2 and 3 respectively, and a 5MW diesel generator is connected to node 8. The typical daily photovoltaic curve and daily load curve are refined as shown in Figure 3 、 Figure 4 shown.

[0065] S2.2. Based on the CSO algorithm, initialize the particle population in combination with the energy storage optimization configuration model.

[0066] Initialize two matrix variables as X and Y respectively. Each row of X represents a particle, indicating the energy storage access power and access capacity, and each row of Y represents a particle, indicating the position where the energy storage is connected.

[0067] The particle population size N is set to 50. The dimension of variable X is 2*24 + 2, representing the power accessed by the energy storage in 24 hours and the energy storage capacity, and the dimension of variable Y is 2, representing the node position where the energy storage is connected.

[0068] The capacity parameter constraints mainly limit the change range of the two values representing the capacity. Specifically: take half of the sum of the parts where the typical daily load curve is greater than the photovoltaic curve as the minimum constraint for the energy storage capacity, and control the maximum constraint to be twice the minimum value.

[0069] S2.3. Obtain the fitness function corresponding to each particle through power flow calculation, and save the particle with the optimal fitness.

[0070] Construct a node model based on the node parameter matrix, perform power flow calculation in combination with each access scheme of the energy storage (the combination of matrices X and Y) to obtain the voltage and power of each node, and solve the fitness function value corresponding to each scheme according to the objective function formula.

[0071] S2.4. Based on the CSO algorithm, perform horizontal and vertical cross operations on the particles to obtain a new particle population.

[0072] Perform horizontal and vertical cross operations on the parent particles to obtain the offspring particle population. In this embodiment, the horizontal cross probability is set to 1, and the vertical cross probability is set to 0.8.

[0073] The specific steps of the vertical and horizontal cross-operation include:

[0074] S2.4.1. Normalize the total population variables and set the probabilities of vertical operation and horizontal operation;

[0075] S2.4.2. Vertical operation: For all particle individuals in the population, perform cross-calculation on the parameters between different dimensions to generate a new generation of offspring individuals as:

[0076] LE l (i,m1) = c r ·X(i,m1)+(1 - c r )·X(i,m2)

[0077] +r1·(X(i,m1)-X(i,m2))

[0078] i ∈ (1,N), m1,m2 ∈ (1,m)

[0079] where c r and r1 are random numbers between [0,1] and [-1,1] respectively; N is the population size, and m is the dimension of the particle.

[0080] S2.4.3. Horizontal operation: Randomly select two particles from the parental population to perform parameter cross between the same dimensions, and the two new offspring individuals generated are:

[0081] LE k (i,m) = c1·X(i,m)+(1 - c1)·X(j,m)

[0082] +s1·(X(i,m)-X(j,m))

[0083] LE k (j,m) = c2·X(j,m)+(1 - c2)·X(i,m)

[0084] +s2·(X(j,m)-X(i,m))

[0085] where c1, c2 and s1, s2 are random numbers between [0,1] and [-1,1] respectively.

[0086] S2.4.4. Perform anti-normalization processing on the new offspring individuals to obtain new population particles.

[0087] S2.5. Repeatedly iterate and calculate the fitness function of the population particles until the maximum number of iterations is reached, and output the optimal energy storage configuration plan and the charge and discharge conditions of the energy storage within 24 hours.

[0088] The maximum number of iterations is set to 200, and the optimal solution set is obtained as Figure 5 shown. The optimal energy storage configuration plan is as follows: when the capacities of 1.1979 MW·h and 1.3365 MW·h are configured at node 13 and node 6 respectively, the objective functions f1, f2, and f3 are 1.9720, 0.0247, and 5.1172 respectively, making the comprehensive objective function f optimal. The charge and discharge conditions of the energy storage at node 13 and node 6 for 24 hours are as Figure 6 and Figure 7 shown.

[0089] A microgrid mobile energy storage configuration system includes:

[0090] An objective function construction module for constructing an objective function of voltage deviation coefficient, an objective function of comprehensive energy storage cost, and an objective function of grid active power loss;

[0091] An optimal configuration model construction module, considering active power balance constraints and energy storage battery constraints, and combining with the Euclidean distance formula, constructs an energy storage optimal configuration model with the minimization of voltage deviation coefficient, comprehensive energy storage cost, and grid active power loss as the objectives;

[0092] A model solving module, which solves the energy storage optimal configuration model based on the cross-layer and cross-layer algorithm and outputs an energy storage configuration plan.

[0093] The content in the above method embodiments is applicable to the present system embodiment. The functions specifically implemented by the present system embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0094] A microgrid mobile energy storage configuration device:

[0095] At least one processor;

[0096] At least one memory for storing at least one program;

[0097] When the at least one program is executed by the at least one processor, the at least one processor implements a microgrid mobile energy storage configuration method as described above.

[0098] The content in the above method embodiments is applicable to the present device embodiment. The functions specifically implemented by the present device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0099] A storage medium stores instructions executable by a processor, and the instructions executable by the processor are used to implement a microgrid mobile energy storage configuration method as described above when executed by the processor.

[0100] The content in the above method embodiments is applicable to this storage medium embodiment. The functions specifically implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0101] The above is a specific description of the preferred embodiments of the present invention. However, the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for configuring a mobile energy storage in a microgrid, characterized in that, It includes the following steps: Construct a voltage deviation coefficient objective function, a comprehensive energy storage cost objective function, and a grid active power loss objective function; Based on the voltage deviation coefficient objective function, the comprehensive energy storage cost objective function, and the grid active power loss objective function, considering the active power balance constraint and the energy storage battery constraint, and combining with the Euclidean distance formula, with the minimization of the voltage deviation coefficient, the comprehensive energy storage cost, and the grid active power loss as the goal, construct an energy storage optimal configuration model; Solve the energy storage optimal configuration model based on the cross-over algorithm and output the energy storage configuration plan.

2. The method for configuring a mobile energy storage in a microgrid according to claim 1, wherein, The voltage deviation coefficient objective function is expressed as follows: Among them, n N is the total number of system nodes; T is the number of time instants; U i,t is the voltage of node i at time t;; U e is the expected voltage value of each node; U p is the maximum allowable voltage deviation; f1 is the system voltage deviation coefficient index.

3. The method for configuring a mobile energy storage in a microgrid according to claim 1, wherein, The comprehensive energy storage cost objective function is expressed as follows: f2 = C1 + C2 Ce = k1·Pess + k2·Eess Cd = km·Ce Among them, C1 is the daily investment cost of energy storage; C2 is the daily operation and maintenance cost; C e is the investment cost of the energy storage device; and C d is the equipment depreciation cost; P ess is the total rated power of the energy storage battery; E ess is the rated capacity of the energy storage battery; k1 is the power cost coefficient of the energy storage battery; k2 is the capacity cost coefficient of the energy storage battery; k m is the equipment depreciation cost coefficient for the capacity decay of the energy storage battery; η is the capital recovery factor; r is the discount rate; y is the battery service life; C y for the energy storage device per unit capacity Annual operation and maintenance cost; f2 is the comprehensive cost of the system accessing energy storage.

4. The method for configuring a mobile energy storage for a microgrid according to claim 1, wherein The grid active power loss objective function is expressed as follows: where L is the set of branches of the system; Y ij,t is the line admittance between node i and node j; θ ij is the line impedance angle; U i,t is the voltage of node i at time t; U j,t is the voltage of node j at time t.

5. The method for configuring a mobile energy storage in a microgrid according to claim 1, wherein The active power balance constraint and the energy storage battery constraint are respectively expressed as follows: The active power balance constraint of the microgrid is: Among them, P G,u (t) is the power generation power of the u-th traditional generator set at time t, and n G is the number of traditional generator sets; P PV,v (t) is the power generation power of the v-th photovoltaic generator set at time t, and n PV is the number of photovoltaic generator sets; P ess,w (t) is the charge and discharge power of the w-th energy storage device at time t, and n ess is the number of energy storage devices; P Load,x (t) is the load power of the x-th node at time t, and n N is the number of system nodes; P Loss,y (t) is the power loss of the y-th branch at time t, and n L is the number of system branches; The constraint of the energy storage battery is: Among them, P ess (t) is the charge and discharge power of the energy storage battery at time t; P ess min is the minimum value of the charge and discharge power of the energy storage battery; P ess max is the maximum value of the charge and discharge power of the energy storage battery; E ess (t) is the battery capacity of the energy storage battery at time t; η ch is the discharge efficiency of the battery; η dh is the charge efficiency of the battery; S SOC,min is the minimum value of the state of charge of the energy storage battery; S SOC,max is the maximum value of the state of charge of the energy storage battery.

6. The method for configuring a mobile energy storage in a microgrid according to claim 1, wherein, The energy storage optimal configuration model is expressed as follows: Among them, f1 is the system voltage deviation coefficient index, f2 is the comprehensive cost of the system accessing energy storage, and f3 is the system active power loss; f1 * , f2 * , f3 * are the values of the corresponding objective function in the most ideal state, respectively.

7. The method for configuring a mobile energy storage in a microgrid according to claim 6, wherein, The step of solving the energy storage optimal configuration model based on the cross-over algorithm and outputting the energy storage configuration plan specifically includes: Obtain grid data and generate capacity parameter constraints for energy storage configuration; Combine the energy storage optimal configuration model to initialize the particle population, and set the upper and lower limits of the two values representing the capacity in the particle according to the capacity parameter constraints; Obtain the fitness function corresponding to each particle through power flow calculation and save the particle with the optimal fitness; Perform cross-over operations on the particles to obtain a new particle population; Repeatedly iterate and calculate the fitness function of the population particles until the maximum number of iterations is reached, and output the energy storage configuration plan of the optimal solution and the charge and discharge conditions of the energy storage within 24 hours.

8. A mobile energy storage configuration system for a microgrid, characterized in that, It includes: An objective function construction module for constructing a voltage deviation coefficient objective function, a comprehensive energy storage cost objective function, and a grid active power loss objective function; An optimal configuration model construction module, based on the voltage deviation coefficient objective function, the comprehensive energy storage cost objective function, and the grid active power loss objective function, considering the active power balance constraint and the energy storage battery constraint, and combining with the Euclidean distance formula, with the minimization of the voltage deviation coefficient, the comprehensive energy storage cost, and the grid active power loss as the goal, construct an energy storage optimal configuration model; A model solution module for solving the energy storage optimal configuration model based on the cross-over algorithm and outputting the energy storage configuration plan.

9. A microgrid mobile energy storage configuration device, characterized in that, It includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor realizes a microgrid mobile energy storage configuration method as described in any one of claims 1-7.

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