Large-scale distributed energy storage collaborative optimization method and system based on multi-objective particle swarm optimization

Abstract

The invention discloses a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm. The method comprises the steps of obtaining charge and discharge parameters of a power distribution network containing distributed energy storage batteries, and establishing a first objective function based on the charge and discharge parameters of the power distribution network containing the distributed energy storage batteries, the objective function comprises a power distribution network voltage deviation value and the current-day operation cost; establishing constraint conditions based on the first objective function, wherein the constraint conditions comprise storage battery SOC and power constraint and node power balance constraint; and constructing an improved particle swarm algorithm containing an inertia weight factor and a learning factor, and solving the first objective function according to the improved particle swarm algorithm. The operation efficiency of the power distribution network is improved, the operation cost is reduced, and meanwhile the voltage stability of the power distribution network and reasonable charging and discharging of the energy storage battery are ensured. Through the optimization algorithm, the charging and discharging states of the energy storage battery can be effectively balanced, the service life of the battery is prolonged, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy storage collaborative optimization, and in particular to a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm. Background Art

[0002] With the continuous advancement of smart grid and distributed energy technology, the development trend of photovoltaic, wind power and other distributed units connected to the distribution network has already formed. However, due to the randomness of their access points, discontinuity and volatility of output, they may have a negative impact on the stability of the power grid, leading to voltage fluctuations, system flow changes and line congestion, and exacerbating the phenomenon of wind and solar power abandonment.

[0003] At the same time, the rapid economic development has brought about a continuous increase in electricity demand, but the widening peak-to-valley difference has also brought considerable pressure to the regulation of the power grid. In addition to responding through load demand response and other means, the characteristics of distributed energy storage systems such as bidirectional power output, rapid response, and flexible installation locations can provide possible solutions to these problems. At present, as an emerging technology and resource, distributed energy storage still faces problems such as low efficiency, high idle rate, and single functional scenario during use. Therefore, it is necessary to coordinate control and optimize the operation of distributed energy storage to improve the reliability and power supply quality of the power system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a large-scale distributed energy storage collaborative optimization method based on a multi-objective particle swarm algorithm, comprising:

[0009] Acquire charging and discharging parameters of a distribution network including distributed energy storage batteries, and establish a first objective function based on the charging and discharging parameters of the distribution network including distributed energy storage batteries, wherein the objective function includes a voltage deviation value of the distribution network and an operating cost for the day;

[0010] Establishing constraint conditions based on the first objective function, wherein the constraint conditions include battery SOC and power constraints and node power balance constraints;

[0011] An improved particle swarm algorithm including an inertia weight factor and a learning factor is constructed, and the first objective function is solved according to the improved particle swarm algorithm.

[0012] As a preferred solution of the large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm described in the present invention, wherein: the acquisition of charging and discharging parameters of the distribution network containing distributed energy storage batteries includes:

[0013] The charging and discharging parameters of the distribution network containing the distributed energy storage battery include:

[0014]

[0015] Among them, SOC(t) is the remaining capacity of the battery at time t; P bess (t) is the charge and discharge power of the battery at time t. When it is positive, it indicates charging, and when it is negative, it indicates discharging; η + , η - are charge and discharge efficiency respectively;

[0016] Maximum continuous charging power P BS.c.MAX and maximum continuous discharge power P BS.d.MAX With energy storage rated power P n The relationship is as follows:

[0017] P BS.c.MAX =N c.max ·P n

[0018] P BS.dMAX =N d.max ·P n

[0019] Among them, N c.max N is the maximum charging multiple allowed for energy storage; d.max The maximum discharge multiple allowed for energy storage.

[0020] As a preferred solution of the large-scale distributed energy storage collaborative optimization method based on the multi-objective particle swarm algorithm described in the present invention, wherein: the first objective function based on the charging and discharging parameters of the distribution network containing distributed energy storage batteries is established, including:

[0021] The operating costs for the day include the cost of purchasing electricity from the main power grid, the cost of generating electricity from the distributed renewable energy generation system, the cost of configuring and operating the energy storage system, and the cost of network loss, which are specifically expressed as follows:

[0022] C sum=C base +C bess +C p +C loss

[0023] Among them, C sum is the total operating cost, C base is the cost of purchasing electricity from the main grid, C bess Assign operating costs to the energy storage system, C p is the power generation cost of distributed renewable energy generation system, C loss The network loss cost.

[0024] As a preferred solution of the large-scale distributed energy storage collaborative optimization method based on the multi-objective particle swarm algorithm described in the present invention, wherein: the first objective function based on the charging and discharging parameters of the distribution network containing the distributed energy storage battery is also included:

[0025] The distribution network voltage deviation value includes a voltage deviation value obtained by calculating the absolute value of the difference between the voltage per unit value and the reference value of each node at each hour and taking it as another objective function. The calculation formula is as follows:

[0026]

[0027] Among them, U d is the voltage deviation value, T represents the number of hours, the value is 24, N is the number of nodes, and U is the per-unit value of the node voltage.

[0028] As a preferred solution of the large-scale distributed energy storage collaborative optimization method based on the multi-objective particle swarm algorithm described in the present invention, wherein: the constraint conditions based on the first objective function are established including:

[0029] The battery SOC and power constraints are expressed as:

[0030] SOC min ≤SOC(t)≤SOC max

[0031]

[0032] Among them, SOC min and SOC max are the upper and lower limits of the remaining power of the energy storage battery, P BS.c.max and P BS.d.max are the maximum power of charge and discharge respectively; P BS.c (t) and P BS.d (t) are the absolute values ​​of the charging and discharging powers at time t, respectively.

[0033] As a preferred solution of the large-scale distributed energy storage collaborative optimization method based on the multi-objective particle swarm algorithm described in the present invention, wherein: the constraint conditions based on the first objective function are also established:

[0034] The node power balance constraint is expressed as:

[0035] P load =P BESS +PV+P a

[0036] Among them, P load is the grid load; P BESS To contribute to the energy storage system, P BESS >0, the energy storage battery discharges, P BESS When <0, the energy storage battery is charged; PV is the output power of the photovoltaic power generation system; P a To optimize the grid load.

[0037] As a preferred solution of the large-scale distributed energy storage collaborative optimization method based on the multi-objective particle swarm algorithm described in the present invention, wherein: the improved particle swarm algorithm containing the inertia weight factor and the learning factor is constructed, and the first objective function is solved according to the improved particle swarm algorithm, including:

[0038] The modified inertia weight factor and learning factor are introduced to improve the improved particle swarm algorithm. The particle speed after improvement is as follows:

[0039] v i =ω×v i +c1×rand(1)×(pbest i -x i )+c2×rand(1)×(REP[h]-x i )

[0040]

[0041] Among them, IT is the current iteration number; MI is the total iteration number; inertia weight factor Learning Factor i = 1, 2, ..., N, N is the total number of particles in this group; v i is the speed of the particle; rand(1) represents a random number between (0,1); x i is the current position of the particle; c1 and c2 are learning factors; v i The maximum value is V max , V maxis greater than 0; ω is called the inertia factor, and its value is non-negative. In each iteration, the particle is updated by tracking two "extreme values" (pbest, gbest). REP[h] is an external storage library defined instead of gbest, which is used to store non-dominated particles found during the search process. During the iteration, a particle REP[h] is selected as gbest. The selection method of REP[h] is to first divide the external storage library into several grids, regard the number of particles in each grid as the distribution density of the grid, and the probability of each grid being selected is the inverse of its distribution density. Finally, the grid is selected by roulette, and a random number is generated to select the particle in the grid, that is, REP[h].

[0042] In a second aspect, the present invention provides a large-scale distributed energy storage collaborative optimization system based on a multi-objective particle swarm algorithm, comprising:

[0043] An objective function establishment module, used to obtain the charging and discharging parameters of the distribution network containing the distributed energy storage battery, and establish a first objective function based on the charging and discharging parameters of the distribution network containing the distributed energy storage battery, wherein the objective function includes a voltage deviation value of the distribution network and an operating cost of the day;

[0044] A constraint establishment module, used to establish constraint conditions based on the first objective function, wherein the constraint conditions include battery SOC and power constraints and node power balance constraints;

[0045] A solution module is used to construct an improved particle swarm algorithm containing an inertia weight factor and a learning factor, and solve the first objective function according to the improved particle swarm algorithm.

[0046] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0048] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm, obtains the charging and discharging parameters of a distribution network containing distributed energy storage batteries, establishes a first objective function based on the charging and discharging parameters of a distribution network containing distributed energy storage batteries, and the objective function includes the voltage deviation value of the distribution network and the operating cost of the day; establishes constraints based on the first objective function, and the constraints include the battery SOC and power constraints and the node power balance constraints; constructs an improved particle swarm algorithm containing an inertia weight factor and a learning factor, and solves the first objective function according to the improved particle swarm algorithm. The operating efficiency of the distribution network is improved, the operating cost is reduced, and the voltage stability of the distribution network and the reasonable charging and discharging of the energy storage battery are ensured. Through the optimization algorithm, the charging and discharging state of the energy storage battery can be effectively balanced, the battery life can be extended, and the maintenance cost can be reduced. In addition, the invention can also cope with the intermittent and uncertain nature of renewable energy, improve the distribution network's ability to accept renewable energy, and thus promote the widespread application of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0050] Figure 1 A method flow chart of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a typical photovoltaic daily load curve of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of typical daily load curves of various scenarios of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of the distribution of Pareto solutions of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0054] Figure 5A schematic diagram of node voltage changes in an industrial mixed zone scenario of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0055] Figure 6 A schematic diagram of voltage changes at old cell nodes of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0056] Figure 7 A schematic diagram of voltage changes at commercial hybrid zone nodes of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0057] Figure 8 An internal structural diagram of a computer device of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] Example 1

[0060] Reference Figure 1-Figure 8 , which is the first embodiment of the present invention, and provides a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm, including:

[0061] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0062] Improved Particle Swarm Optimization (IPSO) is used to solve the problem of collaborative optimization when large-scale distributed energy storage systems are connected to the grid. The algorithm improves the optimization performance by introducing a variable inertia weight factor and a learning factor. The following is an explanation of some key points of the improved particle swarm algorithm: Variable inertia weight factor: The inertia weight factor determines the balance between the current position of the particle and the previous position, which affects the global search ability and local search ability of the algorithm. By changing the value of the inertia weight factor, a better balance can be achieved between the global search phase and the local search phase of the algorithm. In the improved algorithm, the inertia weight factor may be adjusted linearly or nonlinearly with the number of iterations to promote the exploration and development capabilities of the algorithm. Learning factor: The learning factor affects the speed at which particles approach the global optimal solution and the individual optimal solution. The improved algorithm usually uses two different learning factors, one for accelerating the movement to the global optimal solution and the other for accelerating the movement to the individual optimal solution. These factors can be fixed or dynamically adjusted according to the iteration process.

[0063] There are some problems in the related technologies, such as slow convergence speed of optimization algorithms, easy to fall into local optimal solutions, and insufficient processing capabilities for large-scale problems.

[0064] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to explain in detail how to implement the large-scale distributed energy storage collaborative optimization method based on the multi-objective particle swarm algorithm;

[0065] Figure 1 A method flow chart of a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm is shown, including:

[0066] S101, obtaining charging and discharging parameters of a distribution network including distributed energy storage batteries, and establishing a first objective function based on the charging and discharging parameters of the distribution network including distributed energy storage batteries, wherein the objective function includes a voltage deviation value of the distribution network and an operating cost of the day;

[0067] In the embodiment of the present application, the core performance of the distributed energy storage battery lies in its charging and discharging capabilities, and its mathematical model needs to be centered on the three points of charging and discharging power and state of charge (SOC). SOC is the amount of electricity in the battery, and its value changes accordingly during the charging and discharging process. Obtaining the charging and discharging parameters of the distribution network containing distributed energy storage batteries includes:

[0068] The charging and discharging parameters of the distribution network containing distributed energy storage batteries include:

[0069]

[0070] Among them, SOC(t) is the remaining capacity of the battery at time t; P bess (t) is the charge and discharge power of the battery at time t. When it is positive, it indicates charging, and when it is negative, it indicates discharging; η + , η - They are charge and discharge efficiency respectively; the power of a normally used battery needs to be maintained within a certain range, generally 0.1 to 0.9 of the rated capacity.

[0071] Maximum continuous charging power P BS.c.MAX and maximum continuous discharge power P BS.d.MAX With energy storage rated power P n The relationship is as follows:

[0072] P BS.c.MAX =N c.max ·P n

[0073] P BS.dMAX =N d.max ·P n

[0074] Among them, N c.max N is the maximum charge multiple allowed for energy storage, usually 0.6 to 1; d.max The maximum discharge multiple allowed for energy storage, usually between 1 and 1.4.

[0075] It should be noted that the benefit of selecting the above-mentioned charging and discharging parameters of the distribution network containing distributed energy storage batteries is that it can more accurately simulate and control the actual operation of the energy storage batteries in the distribution network, thereby optimizing the voltage deviation value and operating cost of the distribution network.

[0076] In an embodiment of the present application, establishing a first objective function based on charging and discharging parameters of a distribution network including distributed energy storage batteries includes:

[0077] The operating costs for the day include the cost of purchasing electricity from the main power grid, the cost of generating electricity from the distributed renewable energy generation system, the cost of configuring and operating the energy storage system, and the cost of network loss, as shown below:

[0078] C sum =C base +C bess +C p +C loss

[0079] Among them, C sum is the total operating cost, C base is the cost of purchasing electricity from the main grid, C bess Assign operating costs to the energy storage system, C pis the power generation cost of distributed renewable energy generation system, C loss The network loss cost.

[0080] Furthermore, the cost of purchasing electricity from the main grid needs to consider the type of electricity used in the scenario, which is divided into residential electricity and industrial and commercial electricity. The three levels of peak-flat-valley electricity prices should also be considered. The cost of purchasing electricity from the main grid is:

[0081]

[0082] Among them, L s is the total load power in the main power grid at each moment; S ci is the charge and discharge value of the energy storage system at each moment, which is a negative value when charging and a positive value when discharging; C e (i) is the unit electricity price at the corresponding time; T is the number of hours per day, which is 24.

[0083] It should be noted that for the power generation cost of distributed renewable energy power generation systems, since the acquisition of renewable energy itself does not require any fees, and the charging and discharging of the energy storage system does not require any fees, this part actually reflects the cost required to configure the equipment, which is the cost per unit of electricity obtained by comprehensively considering the equipment configuration cost, installation and maintenance, and equipment service life.

[0084] Furthermore, the unit electricity price of photovoltaic power generation is recorded as m yuan, and the unit electricity cost of energy storage system charging and discharging is n yuan. Therefore, the distributed renewable energy power generation cost and energy storage system operation cost are as follows:

[0085]

[0086] Where abs(x(i)) is the absolute value of the charge and discharge of the energy storage system per unit time, and p(i) is the output value of the photovoltaic power generation system per unit time.

[0087] Furthermore, the distribution network loss costs:

[0088]

[0089] P j is the active power flowing through the branch; U N is the rated voltage of the line; η j is the load power factor flowing through the line.

[0090] In the embodiment of the present application, establishing a first objective function based on charging and discharging parameters of a distribution network including distributed energy storage batteries also includes:

[0091] The voltage deviation value of the distribution network includes the voltage deviation value obtained by calculating the absolute value of the difference between the voltage per unit value and the reference value of each node at each hour and taking it as another objective function. The calculation formula is as follows:

[0092]

[0093] Among them, U d is the voltage deviation value, T represents the number of hours, the value is 24, N is the number of nodes, and U is the per-unit value of the node voltage.

[0094] It should be noted that the advantage of the above-mentioned first objective function is that it can take into account both economy and power quality, and achieve the goals of minimizing cost and voltage deviation by optimizing the charging and discharging parameters of the distribution network.

[0095] S102, establishing constraints based on the first objective function, where the constraints include battery SOC and power constraints and node power balance constraints;

[0096] In the embodiment of the present application, establishing the constraint condition based on the first objective function includes:

[0097] The battery SOC and power constraints are expressed as:

[0098] SOC min ≤SOC(t)≤SOC max

[0099]

[0100] Among them, SOC min and SOC max are the upper and lower limits of the remaining power of the energy storage battery, P BS.c.max and P BS.d.max are the maximum power of charge and discharge respectively; P BS.c (t) and P BS.d (t) are the absolute values ​​of the charging and discharging powers at time t, respectively.

[0101] In the embodiment of the present application, establishing the constraint condition based on the first objective function further includes:

[0102] The node power balance constraint is expressed as:

[0103] P load =P BESS +PV+P a

[0104] Among them, P load is the grid load; P BESS To contribute to the energy storage system, P BESS >0, the energy storage battery discharges, PBESS When <0, the energy storage battery is charged; PV is the output power of the photovoltaic power generation system; P a To optimize the grid load.

[0105] It should be noted that in order to ensure the stable operation of the distribution network and meet the power demand of users, the setting of constraints is crucial. The battery SOC and power constraints ensure that the energy storage battery operates within a safe range to avoid excessive charging and discharging that shortens or damages the battery life. At the same time, the node power balance constraint ensures the balance between the grid load and the output power of the power generation system to prevent the situation of insufficient supply or oversupply.

[0106] It should be noted that the benefit of selecting the above constraints is that it can ensure that the distribution network can remain stable under various operating conditions while meeting the power needs of users. By reasonably setting the battery SOC and power constraints, it is possible to effectively avoid overcharging or over-discharging of the energy storage battery during operation, thereby extending the battery life and ensuring its performance. In addition, the setting of node power balance constraints can ensure that the grid load and the output power of the power generation system are balanced in different time periods, avoiding instability in grid operation caused by mismatch between supply and demand.

[0107] In practical applications, other factors that affect the operation of the distribution network need to be considered, such as weather conditions, equipment failures, emergencies, etc. Therefore, when establishing constraints, corresponding safety margins and emergency response mechanisms need to be introduced to deal with various possible situations.

[0108] S103, constructing an improved particle swarm algorithm including an inertia weight factor and a learning factor, and solving the first objective function according to the improved particle swarm algorithm.

[0109] In an embodiment of the present application, an improved particle swarm algorithm including an inertia weight factor and a learning factor is constructed, and solving the first objective function according to the improved particle swarm algorithm includes:

[0110] Involving the particle swarm algorithm for multiple objectives, the problem to be optimized is initialized as a group of random particles (random solutions), and then the optimal solution is found through iteration. In each iteration, the particles are updated by tracking two "extreme values" (pbest, gbest). After finding these two optimal values, the particles usually update their speed and position through the following two formulas.

[0111] v i =ω×v i +c1×rand(1)×(pbest i -x i )+c2×rand(1)×(gbest i -x i )

[0112] x i =x i +v i

[0113] Where i = 1, 2, ..., N, N is the total number of particles in this group; v i is the speed of the particle; rand(1) represents a random number between (0,1); x i is the current position of the particle; c1 and c2 are learning factors; v i The maximum value is V max (greater than 0); ω is called the inertia factor, and its value is non-negative.

[0114] The modified inertia weight factor and learning factor are introduced to improve the improved particle swarm algorithm. The particle speed after improvement is as follows:

[0115] v i =ω×v i +c1×rand(1)×(pbest i -x i )+c2×rand(1)×(REP[h]-x i )

[0116]

[0117]

[0118] Among them, IT is the current iteration number; MI is the total iteration number; inertia weight factor Learning Factor i = 1, 2, ..., N, N is the total number of particles in this group; v i is the speed of the particle; rand(1) represents a random number between (0,1); x i is the current position of the particle; c1 and c2 are learning factors; v i The maximum value is V max , V max is greater than 0; ω is called the inertia factor, and its value is non-negative. In each iteration, the particle is updated by tracking two "extreme values" (pbest, gbest). REP[h] is an external storage library defined instead of gbest, which is used to store non-dominated particles found during the search process. During the iteration, a particle REP[h] is selected as gbest. The selection method of REP[h] is to first divide the external storage library into several grids, regard the number of particles in each grid as the distribution density of the grid, and the probability of each grid being selected is the inverse of its distribution density. Finally, the grid is selected by roulette, and a random number is generated to select the particle in the grid, that is, REP[h].

[0119] In the embodiment of the present application, the problem of solving the optimal solution of multiple objectives mainly lies in the selection of pbest and REP[h].

[0120] (1) For a multi-objective optimization problem, if all the indicators of solution a are better than those of solution b, solution a is said to dominate solution b. If the indicators have their own advantages and disadvantages, they are said to be non-dominated.

[0121] (2) The goal of the multi-objective improved particle swarm algorithm is to find a set of solutions that are non-dominated with each other, that is, the Pareto optimal solution set.

[0122] (3) Due to the existence of multiple objective functions, it is difficult to intuitively judge the quality of a particle and its pbest. If all indicators of particle i are better than its pbest, that is, particle i dominates pbest, then the current particle i is the latest pbest. Otherwise, in the non-dominated case, a particle is randomly selected as the latest pbest.

[0123] (4) In the improved algorithm, REP[h] replaces the external storage defined by gbest, which is used to store the non-dominated particles found during the search process. In the iteration process, a particle REP[h] is selected as gbest. The selection method of REP[h] is to first divide the external storage into several grids, regard the number of particles in each grid as the distribution density of the grid, and the probability of each grid being selected is the inverse of its distribution density. Finally, the grid is selected by roulette, and a random number is generated to select the particle in the grid, that is, REP[h].

[0124] (5) During iteration, first select the optimal particles as a whole and individually using the above rules, then find the Pareto solution set through iteration, and finally select the most suitable solution from the Pareto solution set according to the requirements.

[0125] In summary, the present invention proposes a large-scale distributed energy storage collaborative optimization method based on a multi-objective particle swarm algorithm, obtains the charging and discharging parameters of the distribution network containing distributed energy storage batteries, establishes a first objective function based on the charging and discharging parameters of the distribution network containing distributed energy storage batteries, and the objective function includes the voltage deviation value of the distribution network and the operating cost of the day; establishes constraints based on the first objective function, and the constraints include battery SOC and power constraints and node power balance constraints; constructs an improved particle swarm algorithm containing inertia weight factors and learning factors, and solves the first objective function according to the improved particle swarm algorithm. The operating efficiency of the distribution network is improved, the operating cost is reduced, and the voltage stability of the distribution network and the reasonable charging and discharging of the energy storage battery are ensured. Through the optimization algorithm, the charging and discharging state of the energy storage battery can be effectively balanced, the battery life can be extended, and the maintenance cost can be reduced. In addition, the present invention can also cope with the intermittent and uncertain nature of renewable energy, improve the distribution network's ability to accept renewable energy, and thus promote the widespread application of green energy.

[0126] This embodiment also provides a large-scale distributed energy storage collaborative optimization system based on a multi-objective particle swarm algorithm, including:

[0127] An objective function establishment module is used to obtain the charging and discharging parameters of the distribution network containing the distributed energy storage battery, and establish a first objective function based on the charging and discharging parameters of the distribution network containing the distributed energy storage battery, wherein the objective function includes the voltage deviation value of the distribution network and the operating cost of the day;

[0128] A constraint establishment module, used to establish constraint conditions based on the first objective function, the constraint conditions including battery SOC and power constraints and node power balance constraints;

[0129] The solution module is used to construct an improved particle swarm algorithm containing an inertia weight factor and a learning factor, and solve the first objective function according to the improved particle swarm algorithm.

[0130] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0131] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a large-scale distributed energy storage collaborative optimization method based on a multi-objective particle swarm algorithm is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0132] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0133] Obtaining charging and discharging parameters of a distribution network including distributed energy storage batteries, and establishing a first objective function based on the charging and discharging parameters of the distribution network including distributed energy storage batteries, wherein the objective function includes a voltage deviation value of the distribution network and an operating cost of the day;

[0134] Establishing constraint conditions based on the first objective function, the constraint conditions including battery SOC and power constraints and node power balance constraints;

[0135] An improved particle swarm algorithm containing inertia weight factor and learning factor is constructed, and the first objective function is solved according to the improved particle swarm algorithm.

[0136] Example 2

[0137] Reference Figure 2-Figure 7 , which is an embodiment of the present invention, provides a large-scale distributed energy storage collaborative optimization method and system based on a multi-objective particle swarm algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0138] Taking the IEEE33 node as an example, combined with the actual photovoltaic output curve and load curve, three groups of photovoltaic and energy storage integrated equipment and five groups of distributed energy storage equipment are set. The dimensions of the particle search space, that is, the number of variables, are 72 and 97, respectively, representing the output of three energy storage batteries in 24 hours, the output of eight energy storage batteries in 24 hours, and the access locations of five of the energy storage systems. It should be noted that the access location must be an integer, so it should be rounded. The number of iterations and the number of populations are 150. The learning factors are set to 0.1 and 0.2, respectively. The repository size of the Pareto solution set is set to 100.

[0139] From the Pareto optimal solution set, it can be found that as the number of planned energy storage increases, the non-dominated solutions in the storage are significantly reduced, and non-feasible solutions, that is, solutions that do not meet the constraints, appear. This also shows that with the increase of spatial dimensions, that is, variables, the complexity of exploring the optimal solution will increase significantly. Observing different load scenarios, after connecting to distributed energy storage, the overall node voltage deviation value of the system has decreased significantly. Considering the cost and loss, compared with the traditional particle swarm algorithm, the cost dropped from 5251.82 yuan to 4229.745 yuan, the loss dropped from 4353.8kw·h to 4338.9kw·h, and the voltage deviation dropped by 0.1%.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0141] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0142] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0143] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0145] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0146] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A large-scale distributed energy storage collaborative optimization method based on a multi-objective particle swarm algorithm, characterized in that: include: Acquire charging and discharging parameters of a distribution network including distributed energy storage batteries, and establish a first objective function based on the charging and discharging parameters of the distribution network including distributed energy storage batteries, wherein the objective function includes a voltage deviation value of the distribution network and an operating cost for the day; Establishing constraint conditions based on the first objective function, wherein the constraint conditions include battery SOC and power constraints and node power balance constraints; An improved particle swarm algorithm including an inertia weight factor and a learning factor is constructed, and the first objective function is solved according to the improved particle swarm algorithm.

2. The large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm according to claim 1 is characterized in that: The acquisition of charging and discharging parameters of the distribution network containing distributed energy storage batteries includes: The charging and discharging parameters of the distribution network containing the distributed energy storage battery include: Among them, SOC(t) is the remaining capacity of the battery at time t; P bess (t) is the charge and discharge power of the battery at time t. When it is positive, it indicates charging, and when it is negative, it indicates discharging; η + , η - are charge and discharge efficiency respectively; Maximum continuous charging power P BS.c.MAX and maximum continuous discharge power P BS.d.MAX With energy storage rated power P n The relationship is as follows: P BS.c.MAX =N c.max ·P n P BS.dMAX =N d.max ·P n Among them, N c.max N is the maximum charging multiple allowed for energy storage; d.max The maximum discharge multiple allowed for energy storage.

3. The large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm according to claim 2 is characterized in that: The first objective function of establishing charging and discharging parameters of the distribution network containing distributed energy storage batteries includes: The operating costs for the day include the cost of purchasing electricity from the main power grid, the cost of generating electricity from the distributed renewable energy generation system, the cost of configuring and operating the energy storage system, and the cost of network loss, which are specifically expressed as follows: C sum =C base +C bess +C p +C loss Among them, C sum is the total operating cost, C base is the cost of purchasing electricity from the main grid, C bess Assign operating costs to the energy storage system, C p is the power generation cost of distributed renewable energy generation system, C loss The network loss cost.

4. The large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm according to claim 3 is characterized in that: The first objective function of establishing charging and discharging parameters of the distribution network including the distributed energy storage battery also includes: The distribution network voltage deviation value includes a voltage deviation value obtained by calculating the absolute value of the difference between the voltage per unit value and the reference value of each node at each hour and taking it as another objective function. The calculation formula is as follows: Among them, U d is the voltage deviation value, T represents the number of hours, the value is 24, N is the number of nodes, and U is the per-unit value of the node voltage.

5. The large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm according to claim 4 is characterized in that: The establishing of the constraint condition based on the first objective function comprises: The battery SOC and power constraints are expressed as: SOC min ≤SOC(t)≤SOC max Among them, SOC min and SOC max are the upper and lower limits of the remaining power of the energy storage battery, P BS.c.max and P BS.d.max are the maximum power of charge and discharge respectively; P BS.c (t) and P BS.d (t) are the absolute values ​​of the charging and discharging powers at time t, respectively.

6. The large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm according to claim 5 is characterized in that: The establishing of the constraint condition based on the first objective function further includes: The node power balance constraint is expressed as: P load =P BESS +PV+P a Among them, P load is the grid load; P BESS To provide energy storage system, P BESS >0, the energy storage battery discharges, P BESS When <0, the energy storage battery is charged; PV is the output power of the photovoltaic power generation system; P a To optimize the grid load.

7. The large-scale distributed energy storage collaborative optimization method based on multi-objective particle swarm algorithm according to claim 6 is characterized in that: The step of constructing an improved particle swarm algorithm including an inertia weight factor and a learning factor, and solving the first objective function according to the improved particle swarm algorithm comprises: The modified inertia weight factor and learning factor are introduced to improve the improved particle swarm algorithm. The particle speed after improvement is as follows: v i =ω×v i +c1×rand(1)×(pbest i -x i )+c2×rand(1)×(REP[h]-x i ) Among them, IT is the current iteration number; MI is the total iteration number; inertia weight factor Learning Factor N is the total number of particles in the group; v i is the speed of the particle; rand(1) represents a random number between (0,1); x i is the current position of the particle; c1 and c2 are learning factors; v i The maximum value is V max , V max is greater than 0; ω is called the inertia factor, and its value is non-negative. In each iteration, the particle is updated by tracking two "extreme values" (pbest, gbest). REP[h] is an external storage library defined instead of gbest, which is used to store non-dominated particles found during the search process. During the iteration, a particle REP[h] is selected as gbest. The selection method of REP[h] is to first divide the external storage library into several grids, regard the number of particles in each grid as the distribution density of the grid, and the probability of each grid being selected is the inverse of its distribution density. Finally, the grid is selected by roulette, and a random number is generated to select the particle in the grid, that is, REP[h].

8. A large-scale distributed energy storage collaborative optimization system based on a multi-objective particle swarm algorithm, characterized in that: include: An objective function establishment module, used to obtain the charging and discharging parameters of the distribution network containing the distributed energy storage battery, and establish a first objective function based on the charging and discharging parameters of the distribution network containing the distributed energy storage battery, wherein the objective function includes a voltage deviation value of the distribution network and an operating cost of the day; A constraint establishment module, used to establish constraint conditions based on the first objective function, wherein the constraint conditions include battery SOC and power constraints and node power balance constraints; A solution module is used to construct an improved particle swarm algorithm containing an inertia weight factor and a learning factor, and solve the first objective function according to the improved particle swarm algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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