Power distribution network and cloud energy storage optimization scheduling method and system considering life loss

Abstract

The invention provides a power distribution network and cloud energy storage optimization scheduling method and system considering life loss, and relates to the technical field of power distribution network scheduling, and the method comprises the steps: constructing a cloud energy storage life loss cost model; a cloud energy storage service transaction mechanism, diversified unit collaboration, an economic power purchase strategy and a new energy consumption dynamic reward and punishment mechanism are considered, and a node voltage safety boundary, cloud energy storage charging and discharging state linkage limitation and capacity upper and lower limit multi-dimensional safety checking mechanism are strengthened in power balance constraint. Establishing a multi-target collaborative scheduling model fused with the cloud energy storage dynamic life cost in a scheduling optimization layer; and solving the multi-target collaborative scheduling model by adopting a hybrid optimization algorithm, introducing genetic algorithm crossover and mutation behaviors, optimizing learning factors and weight coefficients, and solving an optimal value of a target function of the multi-target collaborative scheduling model, namely the minimum value of the operation cost of the power distribution network. And meanwhile, an optimal scheduling plan of each unit in the power distribution network and a charging and discharging plan of the cloud energy storage system are given.

Description

Technical Field

[0001] This disclosure relates to the field of distribution network dispatching technology, specifically to a method and system for optimizing the dispatching of distribution networks and cloud energy storage that takes into account lifespan loss. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] In recent years, energy storage technology has been vigorously developed to address the uncertainty of renewable energy output, such as wind and solar power. With the increasing proportion of new energy sources connected to the distribution network and the advancement of energy storage technology, cloud energy storage may become a new approach to user-side energy storage management in the future. The distribution network user side has a large number of decentralized energy storage resources with high idle rates and complex management dimensions. Rational utilization of these resources can not only promote the absorption of new energy and reduce the operating costs of the distribution network, but also improve the utilization rate of idle decentralized energy storage resources. Therefore, researching how to facilitate information interaction between the distribution network and cloud energy storage systems can help ensure the stable operation of the distribution network under fluctuations in wind and solar power output by fully utilizing the flexible control capabilities of cloud energy storage systems.

[0004] Currently, most optimization and scheduling methods in distribution networks neglect or simplify the cost of battery life at energy storage stations, and do not consider the impact of battery charge / discharge depth and number of cycles on battery cycle life. Existing research methods typically only optimize the objective function within the distribution network to maximize economic benefits, rarely considering the interaction structure between the distribution network and cloud energy storage systems, thus failing to achieve rational resource allocation and maximize economic benefits. Furthermore, while previous studies have investigated the relationship between battery cycle life cost and discharge depth, they have not incorporated battery life attrition models for distribution network system optimization, making it difficult to reflect real-world operating conditions. Specifically, some existing solutions still suffer from the following problems: (1) In existing cloud energy storage system planning, battery life loss cost is often simplified to a fixed value, which fails to accurately reflect the actual operating characteristics. Especially when the system operating temperature is stable, there is a significant nonlinear correlation between battery cycle life and depth of discharge. The traditional fixed cost model cannot adapt to the real impact of different charge and discharge depth conditions on life decay. This leads to the distortion of battery life loss cost calculation when optimizing charge and discharge plans, making it difficult to accurately assess the economics of the entire energy storage life cycle; (2) In the existing power grid dispatch, the flexible control capability of distributed energy storage has not been fully explored, resulting in high operating costs, insufficient new energy consumption, and weak support capability for emergency conditions. Traditional models usually do not consider the economic indicators of energy storage life loss cost when balancing economic objectives, and do not effectively integrate multi-dimensional cost factors such as dynamic service cost of cloud energy storage, start-up and shutdown loss of gas turbine units, and renewable energy curtailment penalty; at the same time, they lack coordinated constraint management of energy storage charging and discharging boundaries, system power balance, and voltage stability, making it difficult to achieve the unity of cost optimization and reliable operation. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure proposes an optimized scheduling method and system for distribution networks and cloud energy storage that considers lifetime loss. By constructing a dynamic loss cost model driven by charge and discharge depth, it characterizes the nonlinear correlation between battery cycle life and discharge depth, as well as the impact of depth state, achieving accurate quantification of single-cycle loss cost. It also establishes a multi-objective collaborative model that integrates dynamic lifetime cost of energy storage, strengthens multi-dimensional safety verification mechanisms such as node voltage safety boundaries, cloud energy storage charge and discharge state linkage restrictions, and capacity upper and lower limits in power balance constraints, and adopts an adaptive adjustment hybrid optimization algorithm to improve global search efficiency.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions: Optimal scheduling methods for distribution networks and cloud energy storage that consider lifetime loss include: Based on the power consumption characteristics of cloud energy storage systems and taking into account the impact of charge and discharge depth, a cloud energy storage lifespan loss cost model is constructed. Based on the cloud energy storage lifetime loss cost model, this paper considers the cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and dynamic reward and punishment mechanism for new energy consumption, and strengthens the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the economic efficiency of the distribution network, a multi-objective collaborative scheduling model integrating the dynamic lifetime cost of cloud energy storage is established at the scheduling optimization layer. A hybrid optimization algorithm is used to solve the multi-objective collaborative scheduling model. The crossover and mutation behavior of the genetic algorithm is introduced to optimize the learning factor and weight coefficients. The optimal value of the objective function of the multi-objective collaborative scheduling model is found, which is the minimum value of the distribution network operation cost. At the same time, the optimal scheduling plan of each unit in the distribution network and the charging and discharging plan of the cloud energy storage system are given.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions: Distribution network and cloud energy storage optimization scheduling systems that consider lifetime loss include: The damage cost model building module is used to build a cloud energy storage lifespan loss cost model based on the power consumption characteristics of the cloud energy storage system and taking into account the impact of charge and discharge depth. The collaborative scheduling model construction module is used to build a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage based on the cloud energy storage lifetime loss cost model, considering cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and dynamic reward and punishment mechanism for new energy consumption, and strengthens the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the economic efficiency of the distribution network, a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage is established at the scheduling optimization layer. The optimization and solution module is used to solve the multi-objective collaborative scheduling model using a hybrid optimization algorithm. It introduces the crossover and mutation behavior of the genetic algorithm to optimize the learning factor and weight coefficients, and solves the optimal value of the objective function of the multi-objective collaborative scheduling model, which is the minimum operating cost of the distribution network. At the same time, it provides the optimal scheduling plan for each unit in the distribution network and the charging and discharging plan for the cloud energy storage system.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for optimizing the scheduling of power distribution networks and cloud energy storage, taking into account lifetime loss.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned method for optimizing the scheduling of power distribution networks and cloud energy storage, taking into account lifetime loss.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the optimized scheduling method for power distribution networks and cloud energy storage that takes into account lifetime loss.

[0011] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosed method for optimizing the scheduling of distribution networks and cloud energy storage, considering lifespan degradation, overcomes the limitations of traditional models that fix lifespan degradation costs by constructing a nonlinear quantitative correlation between discharge depth and battery cycle life. It introduces a social discount rate and annual value cost conversion mechanism to dynamically allocate the initial investment in energy storage across the entire operating cycle, establishing a single-cycle lifespan degradation cost model linked to charge / discharge depth. Based on this, a multi-dimensional collaborative scheduling model incorporating gas turbines, cloud energy storage, and renewable energy is constructed. By jointly optimizing the energy storage lifespan degradation cost, cloud energy storage service cost, turbine generation cost, electricity purchase cost, and curtailment penalty cost considering discharge depth, the total system operating cost is minimized. Simultaneously, it fully considers operating conditions such as cloud energy storage charge / discharge power constraints, capacity constraints, distribution network power balance, and voltage safety, effectively addressing the economic bias caused by neglecting dynamic lifespan degradation in traditional scheduling models. This significantly improves the economy and safety of distribution network operation in scenarios with high proportions of renewable energy access.

[0012] This disclosure presents a distribution network and cloud energy storage optimization scheduling method considering lifetime loss. It employs a novel hybrid optimization algorithm that combines the crossover and mutation mechanism of genetic algorithms with the fast convergence characteristics of particle swarm optimization, significantly improving the algorithm's global optimization capability and convergence efficiency. It adopts a dynamic adaptive parameter adjustment strategy to balance global and local search capabilities, effectively avoiding getting trapped in local optima and premature convergence problems. At the same time, it reduces the computational complexity of the algorithm and solves the scheduling plan of each unit in the distribution network and the charging and discharging plan of the cloud energy storage system. Attached Figure Description

[0013] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0014] Figure 1 This is a flowchart of a distribution network and cloud energy storage optimization scheduling method considering lifetime loss according to an embodiment of the present disclosure; Figure 2 This is a schematic diagram of the iterative optimization process of the hybrid optimization algorithm in an embodiment of this disclosure; Figure 3 This describes the relationship between energy storage cycle life and depth of discharge in embodiments of this disclosure. Detailed Implementation

[0015] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0016] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0017] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0018] Example 1 One embodiment of this disclosure provides a method for optimizing the scheduling of distribution networks and cloud energy storage considering lifetime loss. The method includes the following steps: Step 1: Based on the power consumption characteristics of cloud energy storage systems and taking into account the impact of charge and discharge depth, construct a cloud energy storage lifespan loss cost model; Step 2: Based on the cloud energy storage lifetime loss cost model, consider the cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and new energy consumption dynamic reward and punishment mechanism, and strengthen the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the distribution network economy, establish a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage at the scheduling optimization layer. Step 3: A hybrid optimization algorithm is used to solve the multi-objective collaborative scheduling model. The crossover and mutation behavior of the genetic algorithm is introduced to optimize the learning factor and weight coefficients. The optimal value of the objective function of the multi-objective collaborative scheduling model is found, which is the minimum operating cost of the distribution network. At the same time, the optimal scheduling plan of each unit in the distribution network and the charging and discharging plan of the cloud energy storage system are given.

[0019] As one embodiment, the distribution network and cloud energy storage optimization scheduling method considering lifespan loss disclosed herein firstly breaks through the traditional fixed lifespan cost assumption at the lifespan assessment layer, constructing a dynamic loss cost model driven by charge / discharge depth. This model accurately quantifies the single-cycle loss cost by precisely characterizing the nonlinear correlation between battery cycle life and discharge depth, as well as the influence mechanism of depth state. Secondly, at the scheduling optimization layer, a multi-objective collaborative model integrating dynamic lifespan cost of energy storage is established. This model comprehensively considers the cloud energy storage service trading mechanism, diversified unit collaboration, economic electricity purchase strategy, and dynamic reward and punishment mechanism for new energy consumption, i.e., the corresponding cloud energy storage system usage cost, The algorithm addresses four key aspects: the cost of gas turbine power generation, the cost of purchasing electricity from the upstream grid, and the penalty for renewable energy curtailment. It strengthens multi-dimensional safety verification mechanisms within the power balance constraints, including node voltage safety boundaries, linked restrictions on cloud energy storage charging and discharging states, and upper and lower capacity limits. These correspond to the charging and discharging power constraints and capacity constraints of the cloud energy storage system. Finally, considering the highly nonlinear and multi-constraint characteristics of the scheduling model, a hybrid optimization algorithm incorporating genetic mutation mechanisms and dynamic parameter adaptive adjustment is designed. This multi-strategy fusion mechanism effectively avoids premature convergence and improves global search efficiency, significantly enhancing the solution accuracy and computational performance of large-scale complex scheduling problems. The specific implementation process is as follows: Step 1: Based on the power consumption characteristics of cloud energy storage systems and taking into account the impact of charge and discharge depth, construct a cloud energy storage lifespan loss cost model; Specifically, a cloud energy storage system is constructed by acquiring distributed energy storage resources on the user side. The cloud energy storage system is a virtual energy storage system. In the scheduling process, information such as the capacity, charging / discharging power, and charging / discharging demand of the user-side energy storage is first acquired and its power consumption characteristics are analyzed. Secondly, the available charging / discharging power for each time period is analyzed based on historical operating data and reported to the distribution network dispatch center.

[0020] In traditional cloud energy storage systems, the battery lifespan cost of distributed energy storage resources is a fixed value. However, in actual operation, the energy storage lifespan degradation is affected by various factors such as the depth of charge / discharge and operating temperature, with the depth of discharge being the most critical factor affecting battery lifespan and cost. When the battery management system controls the operating temperature of the energy storage station within a relatively stable range using temperature control devices such as air cooling and liquid cooling, the battery cycle life and depth of discharge exhibit a power function relationship, specifically as follows: Figure 3 As shown.

[0021] Furthermore, this disclosure employs the rainflow counting method to calculate the cycle life of the battery at different depths of charge and discharge. Its mathematical expression is: (1) in, This refers to the number of battery cycles required to reach the end of its lifespan at 100% charge. This refers to the number of cycles the battery completes at the end of its lifespan at a depth of discharge D.α is a constant obtained by fitting the rainflow counting method.

[0022] This disclosure, considering the impact of charge and discharge depth on the charge and discharge plan of cloud energy storage system, introduces a nonlinear relationship between battery cycle life and discharge depth to model the cost of battery single cycle life loss.

[0023] First, calculate the number of cycles when the battery reaches the end of its lifespan, based on equation (1) when the energy storage power station is operating at a depth of discharge D. N D Based on this, the battery life of the energy storage power station under discharge depth D is determined by the following formula: (2) (3) in, Battery lifespan; This refers to the number of battery cycles per year in the baseline year of the calculation. To calculate the base year Daily energy storage power station charging and discharging operation status, 0-1 state variables; For the battery at the depth of discharge D, the first The number of daily cycles.

[0024] Because energy storage power stations have a limited operational lifespan, their lifespan depreciation cost cannot be directly calculated using the initial investment cost of the batteries. Instead, it is necessary to use the social discount rate for correction to calculate the annual return on investment, i.e., the annual value of the battery investment cost. The calculation formula is as follows: (4) in, This represents the annual value of battery investment costs. This refers to the initial investment cost; The social discount rate.

[0025] To characterize the impact of different depths of discharge on battery life loss costs, a correlation between battery depth of discharge and cost per battery cycle was further constructed. The nonlinear function model is as follows: (5) Equation (5) is linearized piecewise as follows: (6) in, Let D be the depth of discharge and s be the upper limit of the number of battery cycles.

[0026] Step 2: Based on the cloud energy storage lifetime loss cost model, consider the cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and new energy consumption dynamic reward and punishment mechanism, and strengthen the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the distribution network economy, establish a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage at the scheduling optimization layer. Specifically, since distributed energy storage has significant advantages such as flexible control, wide distribution and dual characteristics of power supply and load, this disclosure makes full use of these advantages to establish an optimized scheduling model of distribution network and cloud energy storage system to improve the operating efficiency and economy of distribution network.

[0027] The multi-objective cooperative scheduling model focuses on constructing a comprehensive objective function to optimize the economics of the distribution network, including: (7) in, For the operating costs of the distribution network, Costs related to battery depth of discharge and lifespan loss per battery cycle. For the cost of using cloud energy storage systems, For the cost of generating electricity from gas turbine units, The cost of purchasing electricity from the upstream power grid for the distribution network. The cost of curtailing renewable energy.

[0028] Furthermore, the specific costs of each component in the overall objective function are as follows: (1) Cost of using cloud energy storage system When the power distribution network needs to charge / discharge the cloud energy storage system, it needs to pay the corresponding service fee to the cloud energy storage operator. The cloud energy storage operator only needs to pay the net charging fee to the power distribution network for the net charging amount of the cloud energy storage system. By flexibly scheduling the charging and discharging of cloud energy storage users, the cloud energy storage operator alleviates the pressure on the power distribution network in terms of peak shaving and valley filling, load fluctuations, and unstable output of new energy sources, while reducing the operating cost of the power distribution network.

[0029] (8) in, For time indexing, For the total scheduling period, For cloud energy storage systems in the power distribution network, for Time-based cloud energy storage system The discharge power, For cloud energy storage system The unit price for charging / discharging services, For cloud energy storage system Net charge amount, For cloud energy storage system Net charging unit price.

[0030] (2) Power generation cost of gas turbine units (9) Wherein, G represents the set of gas turbine units in the power distribution network. (·) represents the heat rate curve. Let g be the output power of the gas turbine unit during time period t. , These represent the fuel consumption of the gas turbine unit during time period t, specifically the fuel consumption during startup and shutdown. Let t be the fuel price per unit during time period t.

[0031] (3) Electricity purchase cost (10) Where Z represents the set of substations in the distribution network. The power purchased by the distribution network from the upper-level power grid through substation z during time period t. Let t be the electricity purchase price corresponding to time period t.

[0032] (4) Penalties for curtailment of renewable energy (11) Among them, S, These are collections of photovoltaic generator sets and wind turbine generator sets in the power distribution network. , They are respectively Periodic photovoltaic power generation units Wind turbine generator sets The predicted output value, , They are respectively Periodic photovoltaic power generation units Wind turbine generator sets The actual output value, , These are the unit prices for the curtailment penalties for photovoltaic generator sets and wind turbine generator sets, respectively.

[0033] Furthermore, the constraints of the objective function of the multi-objective collaborative scheduling model integrating the dynamic lifetime cost of cloud energy storage include the charging and discharging power constraints of the cloud energy storage system, the capacity constraints of the cloud energy storage system, the power balance constraints of the distribution network, and the node voltage constraints. Specifically: (1) Charging and discharging power constraints of cloud energy storage system Cloud energy storage operators need to estimate the charging demand of the cloud energy storage users they manage and the acceptable discharge / charging capacity limits for each time period in order to determine the upper and lower limits of the charging and discharging power of the cloud energy storage system for each time period, as well as the upper and lower limits of the net charging power for the day.

[0034] (12) (13) (14) (15) in, , These represent the upper limits of the discharge power and charging power of the cloud energy storage system e during time period t, respectively. , These represent the lower limits of the discharge power and charging power of the cloud energy storage system e during time period t, respectively. The charging power of cloud energy storage system e during time period t. , These are the upper and lower limits of the net charging capacity of the cloud energy storage system.

[0035] (2) Capacity constraints of cloud energy storage system (16) (17) in, Let t be the capacity of cloud energy storage system e. , These represent the upper and lower limits of the capacity of the cloud energy storage system.

[0036] (3) Power balance constraints of distribution network The output of each unit in the distribution network, the charging and discharging power of the cloud energy storage system, the load and the power loss of the network must be balanced in each scheduling period. The active power balance constraint is Equation (18), and the reactive power balance constraint is Equation (19).

[0037] (18) (19) in, The set of nodes where the load is located; for Total network loss power of all lines in the distribution network during the time period; , They are respectively Time-of-use load The active and reactive power requirements.

[0038] (4) Node voltage constraints Node voltage constraints aim to ensure the stable operation of the distribution network, especially the safety of node voltages. The voltage values ​​at nodes must be maintained between the minimum and maximum operating voltages.

[0039] (20) in, This represents the node voltage during time period t. and These represent the minimum and maximum values ​​of the node voltage, respectively.

[0040] Step 3: Use a hybrid optimization algorithm to solve the multi-objective collaborative scheduling model. Introduce crossover and mutation behavior of genetic algorithm to optimize the learning factor and weight coefficient, and solve for the optimal value of the objective function of the multi-objective collaborative scheduling model, which is the minimum value of the distribution network operation cost. At the same time, give the optimal scheduling plan of each unit in the distribution network and the charging and discharging plan of the cloud energy storage system.

[0041] Specifically, compared with genetic algorithms, particle swarm optimization (PSO) has the advantage of fast convergence speed, but it is prone to getting trapped in local optima, leading to premature convergence. Genetic algorithms, on the other hand, have strong optimization capabilities, but they have a certain disadvantage in convergence speed. For the constructed multi-constraint objective function model and to output the optimal scheduling scheme, this disclosure considers introducing the crossover and mutation behaviors of genetic algorithms into particle swarm optimization to compensate for the shortcomings of both algorithms.

[0042] The algorithm essentially incorporates the idea of ​​genetic algorithm into particle swarm algorithm. First, a group of particles is initialized based on particle swarm algorithm. Each particle has an initial position and velocity. The velocity and position of the particles are updated according to the following formula (21) to initially seek the optimal model.

[0043] (twenty one) in, The weighting coefficient determines the particle's optimization ability; and These are individual learning factors and social learning factors, respectively. and Each number is a random number in the range of 0 to 1. For the m-th particle, the d-th dimension is... k -1 generation speed; and These are the individual extreme values ​​and the total extreme values, respectively. For the m-th particle d Vidi k -1 generation position. To prevent particles from exceeding the boundary, the particle population obtained from the iteration is substituted into formulas (12)-(20) for restriction, and the particle values ​​exceeding the boundary are replaced with boundary values, while also ensuring the optimization effect of subsequent hybridization and mutation.

[0044] Furthermore, the particle population is substituted into formula (7) to calculate the fitness function value of the objective function by replacing the particle values ​​exceeding the boundary value with the velocity and position of the particles that do not exceed the boundary value. To avoid getting trapped in local optima during the solution process, optimization is performed on both the learning factor and the weight coefficient. Regarding the learning factor, a dynamic inertia factor, which is better than a fixed value, is used to obtain the optimization result. The dynamic inertia factor can change linearly during the search for the optimal solution, or it can be dynamically changed according to a certain metric function. Regarding the weight coefficient, a linear decreasing weight method is used to optimize the setting. ω Then the first k During the next iteration ω The values ​​and c1 and c2 are respectively: (twenty two) in, and These are the maximum and minimum values ​​of ω, respectively; This represents the maximum number of iterations. a and b As a constant, the above equation controls... , Change size fit ω Make changes to reduce the possibility of getting trapped in local optima. When the problem space is large, initially allow... ω Take the larger value. Take the larger value. Taking a smaller value gives the algorithm a stronger global search capability in the initial stage, enabling it to explore previously unreachable areas. As the number of iterations increases, it allows... ω Gradually decrease, Take the smaller value. Taking a larger value gives the algorithm a stronger ability to find local solutions in the later stages, allowing the particles to perform a fine search in the vicinity of the current solution, which helps to improve the convergence speed of the algorithm.

[0045] To further address the problem of particle swarm optimization (PSO) easily getting trapped in local optima, a small number of individuals are mutated. Let's assume that the number of individuals to be mutated in the current particle population is... The resulting new individual is: (twenty three) in, For the first particle population q One portion, ω Here, c is the weighting coefficient, c is a constant, and rand is a random number between 0 and 1. This represents the upper boundary of the population. By optimizing the mutation of the particle population in each generation through mutation operations, mutated particles that are far away from most particles in the population are obtained. These mutated particles then search for other optimal solutions, achieving information transmission and effectively avoiding getting trapped in local optima.

[0046] After the above calculations and hybridization optimization of the particle swarm, the particle swarm is constrained again using formulas (12)-(20) to prevent hybridization mutations from causing particles to exceed the boundary. Then, the optimal position of each particle in the latest particle swarm and the global optimal position are determined and iterated to finally obtain the optimal configuration of the energy storage capacity. The computational difficulty of the GA-PSO (hybrid optimization algorithm) algorithm disclosed in this paper is reduced compared with the traditional particle swarm algorithm, allowing the particle swarm to seek the optimal solution in a low-dimensional space and improving the computational accuracy. The number of particles is 200, and the number of iterations k is 300. and By taking values ​​of 0.9 and 0.4 respectively, the optimal value of the objective function is obtained through continuous iteration, which is the minimum operating cost of the distribution network. At the same time, the scheduling plan of each unit in the distribution network and the charging and discharging plan of the cloud energy storage system are given.

[0047] Example 2 One embodiment of this disclosure provides a power distribution network and cloud energy storage optimized scheduling system that considers lifetime loss, including: The damage cost model building module is used to build a cloud energy storage lifespan loss cost model based on the power consumption characteristics of the cloud energy storage system and taking into account the impact of charge and discharge depth. The collaborative scheduling model construction module is used to build a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage based on the cloud energy storage lifetime loss cost model, considering cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and dynamic reward and punishment mechanism for new energy consumption, and strengthens the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the economic efficiency of the distribution network, a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage is established at the scheduling optimization layer. The optimization and solution module is used to solve the multi-objective collaborative scheduling model using a hybrid optimization algorithm. It introduces the crossover and mutation behavior of the genetic algorithm to optimize the learning factor and weight coefficients, and solves the optimal value of the objective function of the multi-objective collaborative scheduling model, which is the minimum operating cost of the distribution network. At the same time, it provides the optimal scheduling plan for each unit in the distribution network and the charging and discharging plan for the cloud energy storage system.

[0048] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for optimizing the scheduling of power distribution networks and cloud energy storage, taking into account lifetime loss.

[0049] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When these computer instructions are executed by a processor, they implement the aforementioned method for optimizing the scheduling of power distribution networks and cloud energy storage, taking into account lifetime loss.

[0050] Example 5 One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the aforementioned method for optimizing the scheduling of power distribution networks and cloud energy storage that takes into account lifetime loss.

[0051] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for optimizing the scheduling of distribution networks and cloud energy storage considering lifetime losses, characterized in that, include: Based on the power consumption characteristics of cloud energy storage systems and taking into account the impact of charge and discharge depth, a cloud energy storage lifespan loss cost model is constructed. Based on the cloud energy storage lifetime loss cost model, this paper considers the cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and dynamic reward and punishment mechanism for new energy consumption, and strengthens the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the economic efficiency of the distribution network, a multi-objective collaborative scheduling model integrating the dynamic lifetime cost of cloud energy storage is established at the scheduling optimization layer. A hybrid optimization algorithm is used to solve the multi-objective collaborative scheduling model. The crossover and mutation behavior of the genetic algorithm is introduced to optimize the learning factor and weight coefficients. The optimal value of the objective function of the multi-objective collaborative scheduling model is found, which is the minimum value of the distribution network operation cost. At the same time, the optimal scheduling plan of each unit in the distribution network and the charging and discharging plan of the cloud energy storage system are given.

2. The method for optimized scheduling of distribution networks and cloud energy storage considering lifetime loss as described in claim 1, characterized in that, A cloud energy storage system is constructed based on user-side distributed energy storage resources. The cloud energy storage system acquires information on the capacity, charging and discharging power, and charging and discharging demand of user-side energy storage, analyzes its power consumption characteristics, and analyzes the available charging and discharging power for each time period based on historical operating data. Considering the influence of the depth of charge and discharge on the energy storage life decay, a nonlinear relationship between battery cycle life and depth of discharge is introduced to model the battery single cycle life loss cost, thus obtaining the cloud energy storage life loss cost model.

3. The method for optimized scheduling of distribution networks and cloud energy storage considering lifetime loss as described in claim 1, characterized in that, The objective function of the multi-objective collaborative scheduling model that integrates cloud energy storage dynamic lifetime cost is to minimize the distribution network operating cost. The distribution network operating cost includes the cost of battery discharge depth and single battery cycle life loss, the cost of using cloud energy storage system, the cost of gas turbine power generation, the cost of the distribution network purchasing electricity from the upper-level grid, and the cost of renewable energy curtailment penalty.

4. The method for optimized scheduling of distribution networks and cloud energy storage considering lifetime loss as described in claim 1, characterized in that, The constraints of the objective function of the multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage include the charging and discharging power constraints of the cloud energy storage system, the capacity constraints of the cloud energy storage system, the power balance constraints of the distribution network, and the node voltage constraints.

5. The method for optimized scheduling of distribution networks and cloud energy storage considering lifetime loss as described in claim 1, characterized in that, A hybrid optimization algorithm is used to solve the multi-objective cooperative scheduling model. The hybrid optimization algorithm introduces the crossover and mutation behavior of the genetic algorithm into the particle swarm optimization algorithm. A group of particles is initialized based on the particle swarm optimization algorithm. Each particle has an initial position and velocity. The velocity and position of the particles are updated to initially seek the optimal model. In order to prevent particles from exceeding the boundary, the particle population obtained by iteration is restricted and the values ​​of particles that exceed the boundary are replaced with boundary values. At the same time, the optimization effect of subsequent crossover and mutation is guaranteed.

6. The method for optimized scheduling of distribution networks and cloud energy storage considering lifetime loss as described in claim 5, characterized in that, The fitness function of the objective function is calculated by replacing the values ​​of particles that exceed the boundary value with the values ​​of particles that do not exceed the boundary value, and the velocity and position of particles that do not exceed the boundary value. The learning factor adopts a dynamic inertia factor that is better than a fixed value to obtain the optimization result. The dynamic inertia factor changes linearly during the search for the optimal solution, or changes dynamically according to a certain metric function. The weight coefficient is optimized by using a linear decreasing weight method. The mutation operation is used to optimize the mutation of the particle population in each generation to obtain mutated particles that are far away from most particles in the particle population, and let the mutated particles find other optimal solutions.

7. A power distribution network and cloud energy storage optimized dispatch system considering lifetime loss, characterized in that, include: The damage cost model building module is used to build a cloud energy storage lifespan loss cost model based on the power consumption characteristics of the cloud energy storage system and taking into account the impact of charge and discharge depth. The collaborative scheduling model construction module is used to build a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage based on the cloud energy storage lifetime loss cost model, considering cloud energy storage service trading mechanism, diversified unit collaboration, economic power purchase strategy and dynamic reward and punishment mechanism for new energy consumption, and strengthens the node voltage safety boundary, cloud energy storage charging and discharging status linkage limit and capacity upper and lower limit multi-dimensional safety verification mechanism in the power balance constraint. With the goal of minimizing the economic efficiency of the distribution network, a multi-objective collaborative scheduling model that integrates the dynamic lifetime cost of cloud energy storage is established at the scheduling optimization layer. The optimization and solution module is used to solve the multi-objective collaborative scheduling model using a hybrid optimization algorithm. It introduces the crossover and mutation behavior of the genetic algorithm to optimize the learning factor and weight coefficients, and solves the optimal value of the objective function of the multi-objective collaborative scheduling model, which is the minimum operating cost of the distribution network. At the same time, it provides the optimal scheduling plan for each unit in the distribution network and the charging and discharging plan for the cloud energy storage system.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power distribution network and cloud energy storage optimization scheduling method considering lifetime loss as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the power distribution network and cloud energy storage optimization scheduling method considering lifetime loss as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the power distribution network and cloud energy storage optimization scheduling method considering lifetime loss as described in any one of claims 1-6.

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