Multi-microgrid optimal scheduling method and system considering uncertainty of new energy power generation

Abstract

The invention relates to the technical field of power grid dispatching, in particular to a multi-micro-grid optimal dispatching method and system considering new energy power generation uncertainty, and the method comprises the steps: building an upper layer model which is a revenue maximization objective function of a micro-grid operator, and a lower layer model which is a revenue maximization objective function of the micro-grid operator; the lower-layer model is a cost minimization objective function of the microgrid cluster; solving the upper layer model to obtain the purchase and sale electricity price by taking the income maximization of the micro-grid operator as a target, and transmitting the purchase and sale electricity price and a carbon emission factor dynamically released by the micro-grid operator to a lower layer model; updating the cost minimization objective function based on the carbon emission factor, solving the lower-layer model by taking the cost minimization of the microgrid cluster as an objective to obtain the purchased and sold electric quantity, and transmitting the purchased and sold electric quantity to the upper-layer model; performing iterative solution until the upper-layer model and the lower-layer model obtain optimal solutions, and performing optimal scheduling on the micro-grid cluster based on the optimal solutions; the overall operation efficiency of the power system can be improved.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, specifically to a multi-microgrid optimal dispatching method and system that considers the uncertainties of new energy power generation. Background Technology

[0002] Against the backdrop of the global energy transition, new energy sources are developing rapidly, but their large-scale integration into the power grid brings numerous challenges. New energy power generation is significantly constrained by natural conditions, exhibiting marked intermittency and volatility. For example, wind power depends on the magnitude and stability of wind, while solar power depends on the intensity and duration of sunlight. This makes it difficult to accurately predict and effectively control the power output of new energy generation. This uncertainty places enormous pressure on the stable operation of the power grid, easily leading to problems such as voltage fluctuations, frequency deviations, and power imbalances, seriously threatening the safe and reliable power supply of the grid.

[0003] Microgrids, as an emerging form of power system, organically integrate distributed energy resources, energy storage devices, and controllable loads, demonstrating great potential in alleviating the challenges of integrating new energy sources into the grid. They enable autonomous management and optimized dispatch of internal energy resources, mitigating the impact of new energy uncertainties on the main power grid to some extent. With the continuous development of the electricity market, multiple microgrids often belong to different stakeholders, each aiming to maximize their own interests during operation. This makes traditional optimization dispatch strategies applicable to a single stakeholder insufficient, as complex competition and cooperation exist among microgrids in areas such as power trading and resource allocation, requiring a more effective coordination mechanism to optimize overall efficiency.

[0004] With the introduction of the "dual carbon" goal, carbon emissions have become a global focus, bringing new challenges and opportunities to the operation and development of power systems. In the power industry, carbon emissions mainly originate from traditional energy generation processes. While the proportion of renewable energy in microgrids is gradually increasing, some traditional energy generation equipment may still exist, and its carbon emissions cannot be ignored. In the optimal dispatch of multiple microgrids, carbon emissions are crucial. A reasonable dispatch strategy can reduce carbon emissions while meeting electricity demand, contributing to the achievement of the "dual carbon" goal. However, previous studies have largely neglected carbon emission factors. When formulating optimal dispatch schemes for microgrids, they have only focused on the balance of electricity supply and demand and economic benefits, without fully considering the environmental impact of carbon emissions and the strict constraints that future policies will place on carbon emissions. This makes existing dispatch strategies unable to adapt to the new requirements of low-carbon development, making it difficult to balance economic benefits with environmental protection responsibilities.

[0005] Current research utilizes game theory to analyze the equilibrium relationships of interests among and within microgrids. However, most existing studies are based on fixed electricity trading prices, neglecting the dynamic pricing process involving the interaction between microgrids and their distributors (DSOs). In practice, the purchase and sale prices set by the DSO directly influence the microgrid's generation plans and electricity trading strategies; conversely, factors such as the microgrid's generation output, electricity demand, and energy storage status also significantly impact the DSO's pricing decisions, resulting in a close interplay of interests between the two parties.

[0006] Master-slave game models provide an effective approach to studying such problems, but solving these models faces numerous challenges. They are characterized by complexity, nonlinearity, and nonconvexity. Traditional numerical optimization methods based on Karush-Kuhn-Tucker (KKT) conditions not only require the lower-level model to be a convex program, but also necessitate that upper-level decisions acquire all parameter information from the lower level, raising sensitive privacy concerns in practical applications. Intelligent optimization algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), and simulated annealing (SA), offer greater flexibility and practicality, enabling rapid solutions to optimization problems while effectively protecting the privacy of the lower-level components.

[0007] In summary, in order to better address the challenges posed by the uncertainty of new energy sources to the operation of multiple microgrids, achieve a balance of interests between microgrids and DSOs, and improve the overall operating efficiency of the power system, it is urgent to propose an innovative multi-microgrid optimization scheduling method that takes into account the uncertainty of new energy sources. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for optimizing the scheduling of multiple microgrids that takes into account the uncertainties of new energy power generation, in order to improve the overall operating efficiency of the power system.

[0009] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for optimal scheduling of multiple microgrids that considers the uncertainty of new energy power generation, the method comprising the following steps: An upper-level model and a lower-level model are established. The upper-level model is the objective function for maximizing the revenue of the microgrid operator, and the lower-level model is the objective function for minimizing the cost of the microgrid cluster. The revenue maximization objective function is based on the electricity purchase and sale price set by the microgrid operator for the microgrid, and the cost minimization objective function is based on the electricity purchase and sale volume and carbon emission factor of the microgrid cluster. The electricity purchase and sale price includes the purchase price and the sale price; the electricity purchase and sale volume includes the purchased electricity volume and the sold electricity volume. With the goal of maximizing the revenue of microgrid operators, the starfish optimization algorithm is used to solve the upper-level model to obtain the purchase and sale price of electricity, and the purchase and sale price of electricity and the carbon emission factor dynamically released by the microgrid operator are passed to the lower-level model. The cost minimization objective function is updated based on the carbon emission factor. The lower-level model is solved with the goal of minimizing the cost of the microgrid cluster to obtain the purchased and sold electricity volume, which is then passed to the upper-level model. The upper-level model and the lower-level model are iteratively solved until both the upper-level model and the lower-level model obtain the optimal solution. Based on the optimal solution, the microgrid cluster is optimized and scheduled.

[0010] Optionally, the upper-level model is established in the following way: Obtain the feed-in tariff and grid tariff from microgrid operators, as well as the purchase price and sales price of electricity set by microgrid operators for microgrids; the feed-in tariff is the price at which microgrid operators sell electricity to grid companies, and the grid tariff is the price at which grid companies sell electricity to microgrid operators; Based on the on-grid electricity price, grid electricity price, purchase price, and sales price, a revenue maximization objective function for microgrid operators is established as the upper-level model.

[0011] Optionally, the profit maximization objective function of the microgrid operator is: ; In the formula, This indicates the revenue of microgrid operators. This refers to the price at which microgrid operators sell electricity to the grid company, i.e., the feed-in tariff. The price at which the power grid company sells electricity to microgrid operators, i.e., the grid electricity price; and These represent the electricity sold by the microgrid operator to the power grid company and the electricity purchased from the power grid company, respectively. and This refers to the electricity purchase price and sales price set by the microgrid operator for the microgrid cluster; This represents the amount of electricity purchased by the i-th microgrid from the microgrid operator at time t; Let be the amount of electricity sold by the i-th microgrid to the microgrid operator at time t; The electricity purchase price and the electricity sales price satisfy the following formula: .

[0012] Optionally, the lower-level model is established in the following manner: Identify shared energy storage devices, new energy devices, gas turbines, and loads in a microgrid cluster to establish power balance constraints for the microgrid cluster; Under power balance constraints, a cost minimization objective function for microgrid clusters is established by combining the cost of microgrids and total carbon emissions, serving as the lower-level model.

[0013] Optionally, determining the shared energy storage devices, new energy devices, gas turbines, and loads in the microgrid cluster to establish power balance constraints for the microgrid cluster includes: Establish a shared energy storage device model for a microgrid cluster and determine the charging and discharging power constraints of the shared energy storage device model; Multiple new energy power generation scenarios are generated using scenario analysis, and the output constraints of each new energy power generation scenario are determined. Set power generation constraints, start-up and stop constraints, and ramp-up constraints for the gas turbine; Identify the interruptible loads, transferable loads, and stationary loads within the load; determine the maximum interruptible power limit for interruptible loads and the transfer time range limit for transferable loads; Power balance constraints for microgrid clusters are established based on the charging and discharging power constraints of shared energy storage equipment models, the output constraints of various new energy power generation scenarios, the power generation constraints of gas turbines, start-up and stop constraints and ramping constraints, the maximum interruptible power constraints of interruptible loads, and the transfer time range constraints of transferable loads.

[0014] Optionally, the objective function for minimizing the cost of the microgrid cluster by combining the cost of the microgrid and the total carbon emissions includes: The total carbon emissions of the microgrid cluster are determined based on the carbon emissions of the gas turbines in each microgrid and the carbon emissions borne by the electricity purchased by each microgrid. The objective function for minimizing the cost of a microgrid cluster is established based on the operating cost of gas turbines, the cost of purchasing and selling electricity, the cost of carbon emissions, the cost of interruptible loads, the cost of transferable loads, and the cost of using shared energy storage devices.

[0015] Optionally, the objective function for minimizing the cost of the microgrid cluster is: ; ; ; ; ; In the formula, This indicates the cost of a microgrid cluster. This indicates the operating cost of the gas turbine. , , This is the cost coefficient. To reduce the cost of using shared energy storage devices, The cost factor for charging shared energy storage devices. This represents the cost factor for discharging energy using shared energy storage devices. This represents the cost of load demand response. This represents the interruptible load cost factor. This represents the cost factor for transferable loads. Indicates the carbon emission cost of microgrids. This indicates the price per unit of carbon emissions.

[0016] Secondly, embodiments of the present invention provide a multi-microgrid optimized dispatching system that considers the uncertainty of new energy power generation, the system comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method as described in any of the preceding statements.

[0017] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform the method as described in any of the preceding claims.

[0018] The beneficial effects of this invention are as follows: This invention provides a method and system for optimal scheduling of multiple microgrids considering the uncertainties of new energy power generation. The system comprises a two-layer scheduling framework consisting of a power grid company, microgrid operators, and a microgrid cluster, including an upper-layer model and a lower-layer model. The upper-layer model is solved using the starfish optimization algorithm, with the goal of maximizing the revenue of the microgrid operators and the decision variables being the purchase price and the sales price of electricity. The lower-layer model aims to minimize the cost of the microgrid cluster, with the purchase and sales volume as the decision variables. It is solved using a specialized solver, considering various constraints. Optimal coordinated scheduling of the microgrid cluster is achieved through iterative solutions at both layers, thereby improving the overall operating efficiency of the power system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the multi-microgrid optimal scheduling method considering the uncertainties of new energy power generation in an embodiment of the present invention; Figure 2This is a diagram of the two-layer scheduling framework of the upper-layer model and the lower-layer model in this embodiment of the invention; Figure 3 This is a diagram showing the optimization results of internal scheduling of a microgrid in one embodiment of the present invention; Figure 4 This is a diagram showing the optimization results of internal scheduling of a microgrid in another embodiment of the present invention; Figure 5 Here is a diagram showing the microgrid internal scheduling optimization results in another embodiment of the present invention: Figure 6 This is a schematic diagram of the structure of a multi-microgrid optimized dispatching system that considers the uncertainty of new energy power generation in an embodiment of the present invention. Detailed Implementation

[0021] The following will clearly and completely describe the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0022] The multi-microgrid dispatch frameworks in related technologies are mostly single-layer or simple structures, which make it difficult to coordinate the complex interests and operational constraints between microgrid operators and multiple microgrids. They cannot fully leverage the advantages of two-layer frameworks in resource allocation and system coordination, thus limiting the improvement of overall system performance.

[0023] In addition, the related technologies also have the following problems:

[0024] ① Existing multi-microgrid dispatching technologies do not fully consider the uncertainties of new energy sources, making it difficult for dispatching schemes to adapt to the fluctuating characteristics of new energy power generation, failing to achieve optimal resource allocation, and affecting the stability and reliability of microgrid clusters.

[0025] ② Most current microgrid optimization and dispatch technologies do not take carbon emissions into account, lack effective control and optimization of carbon emissions, do not meet the requirements of low-carbon environmental protection and sustainable development, and are not conducive to coping with increasingly stringent environmental policies and carbon emission restrictions.

[0026] ③ Microgrids are equipped with separate energy storage systems, which are more expensive and have lower energy storage utilization rates.

[0027] ④ For the optimization and scheduling of complex systems such as multi-microgrids, traditional algorithms suffer from low computational efficiency and slow solution speed when dealing with large-scale, high-dimensional problems. They are unable to meet the real-time requirements in actual operation and cannot quickly obtain the optimal or near-optimal scheduling scheme.

[0028] See Figure 1This invention provides a method for optimal scheduling of multiple microgrids considering the uncertainties of new energy power generation. The method includes the following steps: S100, establish an upper-level model and a lower-level model. The upper-level model is the profit maximization objective function of the microgrid operator, and the lower-level model is the cost minimization objective function of the microgrid cluster. The profit maximization objective function is established based on the electricity purchase and sale price set by the microgrid operator for the microgrid, and the cost minimization objective function is established based on the electricity purchase and sale volume and carbon emission factor of the microgrid cluster. The electricity purchase and sale price includes the purchase price and the sale price; the electricity purchase and sale volume includes the purchased electricity volume and the sold electricity volume. Specifically, a two-layer dispatch framework model for multiple microgrids is first established. This model comprises a power grid company, microgrid operators, and a microgrid cluster. To maximize their own interests, the power generation and consumption within a microgrid are not necessarily in equilibrium within a given time period. When a microgrid exhibits a power surplus, it can be defined as a "power-rich microgrid"; conversely, when a power shortage occurs, it is called a "power-deficient microgrid." Based on this characteristic, this invention proposes... Figure 2 The diagram illustrates the trading mechanism between microgrid operators and microgrids: Microgrid operators are responsible for setting electricity purchase and sales prices. Excess microgrids sell their surplus electricity to the microgrid operator at the sales price, while shortage microgrids purchase the required surplus electricity from the microgrid operator at the purchase price. The microgrid operator, based on the electricity exchange between microgrids and the grid connection price, conducts corresponding transactions in the electricity market, profiting from the price difference. The trading relationship is as follows: Figure 2 As shown.

[0029] S200 aims to maximize the revenue of microgrid operators. It uses the starfish optimization algorithm to solve the upper-level model to obtain the purchase and sale price of electricity, and then transmits the purchase and sale price of electricity and the carbon emission factor dynamically released by the microgrid operator to the lower-level model. Specifically, the Starfish Optimization Algorithm (SOA) is used to solve the upper-level optimization problem. SOA is a novel swarm intelligence optimization algorithm based on simulating the foraging behavior of starfish in the marine environment. Its core principle is to treat each starfish as a potential solution, with the starfish's position in the search space corresponding to the parameter values ​​of the solution. By simulating the starfish's complex foraging strategies, its position is continuously updated, thereby exploring the globally optimal solution.

[0030] In the upper-level model, the optimization objective is to maximize the revenue of the microgrid operator, and the decision variable is the electricity purchase price set by the microgrid operator for the microgrid. and electricity sales price During the algorithm's execution, the starfish population is initialized, with each starfish position vector corresponding to a different combination of electricity purchase and sales prices. The fitness value corresponding to each starfish position, i.e., the microgrid operator's revenue, is calculated based on the microgrid operator's revenue maximization formula (1).

[0031] Subsequently, the starfish population is sorted according to its fitness value to select the current optimal solution. In each iteration, the location is updated by simulating three main foraging behaviors of starfish: exploration, development, and regeneration. The behavior of moving closer to food prompts starfish to move towards the current optimal solution; random movement gives the algorithm the ability to escape local optima, enhancing global search performance; local search focuses on a refined search in the vicinity of the current starfish location to find a better solution. After multiple iterations, until the preset termination conditions are met, such as reaching the maximum number of iterations or the fitness value converges, the optimal solution obtained at this point is the electricity purchase price and sales price set by the upper-level microgrid operator for the microgrid.

[0032] S300, based on the carbon emission factor, update the cost minimization objective function, solve the lower-level model with the goal of minimizing the cost of the microgrid cluster, obtain the purchased and sold electricity, and pass the purchased and sold electricity to the upper-level model; Specifically, after the upper-level model successfully solves for the electricity purchase price and the electricity sales price, it passes them to the lower-level model, along with a dynamic carbon emission factor. This carbon emission factor is dynamically released by the microgrid operator to the microgrid cluster. The lower-level model aims to minimize microgrid costs, based on the electricity purchased between the microgrid and the microgrid operator. and the sale of electricity As decision variables, this model needs to simultaneously consider various constraints within the microgrid, such as the uncertainty of new energy generation, the capacity and charging / discharging power limitations of energy storage devices, the power generation and start-up / shutdown constraints of gas turbines, the characteristic constraints of various loads, and power balance constraints. Under the premise of satisfying the above constraints, a professional solver is used for direct solution. Based on efficient optimization algorithms, the solver handles complex nonlinear optimization problems involving numerous constraints and variables. Through continuous iterative calculations, it ultimately determines the electricity purchase and sale volume that minimizes the microgrid cost, as well as the load arrangement and energy storage usage strategies within the microgrid, achieving optimal operation of the microgrid under given electricity purchase and sale prices and carbon emission factors.

[0033] S400 iteratively solves the upper-level model and the lower-level model until both the upper-level model and the lower-level model obtain the optimal solution, and optimizes the scheduling of the microgrid cluster based on the optimal solution.

[0034] It should be noted that the optimal solution of the upper-level model includes the optimal electricity purchase price and the optimal electricity sales price, while the optimal solution of the lower-level model includes the optimal electricity purchase volume and the optimal electricity sales volume.

[0035] For iterative solutions, after the lower-level model completes the solution and obtains the electricity purchase and sale information, it feeds this information back to the upper-level model. The upper-level model then recalculates the fitness value and solves the problem again based on the latest electricity purchase and sale information. Through this interaction and iterative solution process between the upper and lower levels, the upper level continuously adjusts the electricity purchase and sale prices, while the lower level adjusts the electricity purchase and sale volume and internal operating strategies based on the new prices. This gradually optimizes the interests of both levels, ultimately maximizing their global benefits. This allows the entire microgrid cluster to achieve optimal coordinated scheduling and operation, considering the uncertainties of renewable energy generation and carbon emissions. The resulting microgrid internal scheduling optimization results are as follows: Figure 3 , Figure 4 and Figure 5 As shown.

[0036] The iterative solution process between the upper and lower layer models includes passing the electricity purchase price and electricity sales price obtained from the upper layer model to the lower layer model, and solving the lower layer model to minimize the microgrid cost. Through information interaction and iterative solution process between the upper and lower layer models, the electricity purchase price, electricity sales price and the internal operation strategy of the microgrid are adjusted to achieve optimal coordinated scheduling and operation of the entire microgrid cluster.

[0037] This invention discloses a multi-microgrid optimal scheduling method that considers the uncertainty of new energy power generation and incorporates carbon emission issues into the optimization process to obtain the optimal scheduling solution. This invention provides a two-layer optimal scheduling framework and method for multi-microgrids, and uses the starfish optimization algorithm for optimization. Compared with some swarm intelligence optimization algorithms such as Whale Optimization (WOA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Biogeographical Optimization (BBO), this method can accelerate convergence, achieve higher accuracy, and easily escape local optima. It can obtain the optimal solution under multi-microgrid operating conditions.

[0038] In some embodiments, the upper-level model is established in the following manner: S111, obtain the feed-in tariff and grid tariff of the microgrid operator, as well as the purchase price and sales price of electricity set by the microgrid operator for the microgrid; the feed-in tariff is the price at which the microgrid operator sells electricity to the grid company, and the grid tariff is the price at which the grid company sells electricity to the microgrid operator; S112, Based on the on-grid electricity price, grid electricity price, purchase price and sales price, establish the profit maximization objective function of the microgrid operator as the upper-level model.

[0039] Specifically, an upper-level model is established for microgrid operators. These operators set purchase and sales prices for the electricity purchased by their microgrids and transact with the grid company to generate revenue. By establishing this upper-level model, the revenue calculation method and the constraints on purchase and sales prices are clarified.

[0040] In some embodiments, the profit maximization objective function of the microgrid operator is: (1); In the formula, This indicates the revenue of microgrid operators. This refers to the price at which microgrid operators sell electricity to the grid company, i.e., the feed-in tariff. The price at which the power grid company sells electricity to microgrid operators, i.e., the grid electricity price; and These represent the electricity sold by the microgrid operator to the power grid company and the electricity purchased from the power grid company, respectively. and This refers to the electricity purchase price and sales price set by the microgrid operator for the microgrid cluster; This represents the amount of electricity purchased by the i-th microgrid from the microgrid operator at time t; Let be the amount of electricity sold by the i-th microgrid to the microgrid operator at time t.

[0041] It should be noted that the on-grid electricity price and grid electricity price To keep it a fixed value, by optimizing the electricity purchase price and electricity sales price This maximizes profits; To encourage transactions between microgrid clusters and microgrid operators, the electricity purchase price and electricity sales price set by the microgrid operators satisfy the following formula: (2); In equation (2), if the purchase price of electricity set by the microgrid operator is no greater than the grid electricity price and the sales price of electricity is no less than the grid-connected electricity price, then the microgrid will choose to trade with the microgrid operator in order to maximize its own interests.

[0042] In some embodiments, the lower-level model is established in the following manner: S121, identify the shared energy storage devices, new energy devices, gas turbines and loads in the microgrid cluster in order to establish the power balance constraints of the microgrid cluster; S122, under the power balance constraint, establishes the cost minimization objective function of the microgrid cluster by combining the cost of the microgrid and the total carbon emissions, as the lower-level model.

[0043] In some embodiments, determining the shared energy storage devices, new energy devices, gas turbines, and loads in the microgrid cluster to establish power balance constraints for the microgrid cluster includes: Establish a shared energy storage device model for a microgrid cluster and determine the charging and discharging power constraints of the shared energy storage device model; Multiple new energy power generation scenarios are generated using scenario analysis, and the output constraints of each new energy power generation scenario are determined. Set power generation constraints, start-up and stop constraints, and ramp-up constraints for the gas turbine; Identify the interruptible loads, transferable loads, and stationary loads within the load; determine the maximum interruptible power limit for interruptible loads and the transfer time range limit for transferable loads; Power balance constraints for microgrid clusters are established based on the charging and discharging power constraints of shared energy storage equipment models, the output constraints of various new energy power generation scenarios, the power generation constraints of gas turbines, start-up and stop constraints and ramping constraints, the maximum interruptible power constraints of interruptible loads, and the transfer time range constraints of transferable loads.

[0044] Specifically, the first step is to establish a shared energy storage device model. For microgrid clusters, individually configured energy storage devices have low utilization rates, leading to resource idleness and waste, and increasing operating costs. However, by constructing a shared energy storage device model, energy storage devices are centrally configured and serve multiple microgrids, enabling optimized sharing of energy storage resources. On the one hand, this effectively increases the overall charging and discharging frequency and duration of energy storage devices, greatly improving utilization; on the other hand, it reduces the energy storage configuration cost of a single microgrid, alleviating the economic burden. Simultaneously, shared energy storage devices can enhance the microgrid cluster's ability to cope with the intermittency and volatility of distributed power sources, improving the overall stability and reliability of the cluster, and promoting the efficient consumption and application of renewable energy in the microgrid cluster.

[0045] In some embodiments, establishing a shared energy storage device model for a microgrid cluster and determining the charging and discharging power constraints of the shared energy storage device model includes: Establish a shared energy storage device model, where the capacity of the shared energy storage device is represented as: (3); In the formula, and Let represent the charging power and discharging power of the i-th microgrid using the shared energy storage device at time t, respectively. , These represent the charging efficiency and discharging efficiency of the shared energy storage device, respectively.

[0046] To ensure the continuous operation of shared energy storage devices, it is assumed that the total charging and discharging power of the energy storage devices during the working cycle is 0, i.e.: (4); Furthermore, when using shared energy storage devices in a microgrid, the capacity limit must not be exceeded, denoted as: (5); In the formula, This indicates the minimum capacity of the shared energy storage device. This indicates the maximum capacity of the shared energy storage device.

[0047] Considering the maximum charging and discharging power limitations when using shared energy storage devices in a microgrid, the following charging and discharging power constraints apply: (6); (7); In the formula, and These represent the maximum charging power and maximum discharging power of the shared energy storage device, respectively.

[0048] In some embodiments, the objective function for minimizing the cost of a microgrid cluster, which combines the cost of the microgrid with the total carbon emissions, includes: The total carbon emissions of the microgrid cluster are determined based on the carbon emissions of the gas turbines in each microgrid and the carbon emissions borne by the electricity purchased by each microgrid. The objective function for minimizing the cost of a microgrid cluster is established based on the operating cost of gas turbines, the cost of purchasing and selling electricity, the cost of carbon emissions, the cost of interruptible loads, the cost of transferable loads, and the cost of using shared energy storage devices.

[0049] Specifically, for microgrid clusters, microgrid clusters are equipped with new energy equipment, gas turbines and various loads (including interruptible loads, transferable loads and stationary loads).

[0050] The output of new energy equipment is subject to uncertainty and instability. This invention considers the uncertainty of new energy power generation and uses scenario analysis to address it. Through extensive historical data and probabilistic statistical models, multiple possible new energy power generation scenarios are generated, each with a corresponding probability of occurrence. Assuming W new energy power generation scenarios are generated, the output of the new energy equipment at time s in the s-th scenario is... Its probability of occurrence is And it meets the output constraints of new energy power generation scenarios. .

[0051] As a crucial power generation device in microgrids, gas turbines must meet certain power generation constraints. The power generation capacity of gas turbines... Minimum power generation required and maximum power generation Between, that is: (8); Meanwhile, gas turbines are subject to start-up and stop constraints during operation, and a minimum start-up time must be met during startup. The minimum stopping time must be met when stopping. Assume the state of the gas turbine at time t is ( Indicates that the program is running. (Indicates stopping), then: (9); Meanwhile, the gas turbine is equipped with ramp constraints, as detailed below: (10); In the formula, and These represent the downhill and uphill ramp rates of the gas turbine, respectively.

[0052] Interruptible loads in microgrids and transferable load It offers a degree of flexibility during the scheduling process. Interruptible loads can be interrupted for specific periods, provided that basic user needs are met; their maximum interruptible power is [amount missing]. ,Right now: (11); Transferable loads can be transferred within a certain time range. Let's assume the transfer time range for transferable loads is... Then we have: (12); Fixed load The demand at any given moment is relatively fixed, serving as a fundamental component of the microgrid load demand.

[0053] Therefore, there is a power balance constraint for the entire microgrid cluster, as shown in the formula: (13); In the formula, Let s be the predicted output value of new energy in the s-th new energy power generation scenario. Formula (13) is used to describe the power balance constraint inside the microgrid.

[0054] Considering carbon emissions, the main sources of carbon emissions in the entire microgrid cluster are gas turbine power generation and electricity purchased from the grid company. The carbon emissions from gas turbine power generation can be expressed as: (14); In the formula, Let represent the carbon emissions of the gas turbine in the i-th microgrid. The carbon emission coefficient of the gas turbine. Let be the output of the gas turbine in the i-th microgrid at the i-th time.

[0055] Meanwhile, microgrids also bear carbon emission responsibility when purchasing electricity from microgrid operators. In this case, the microgrid operator publishes a dynamic carbon emission factor, which is calculated based on the purchased electricity volume. The formula for the carbon emission amount borne by the purchased electricity volume is as follows: (15); In the formula, This represents the carbon emissions borne by the i-th microgrid when purchasing electricity from the microgrid operator. Let be the dynamic carbon emission factor. Then the formula for the total carbon emissions of microgrid i is: (16); Microgrids aim to minimize costs, which include gas turbine operating costs, electricity purchase and sale costs, carbon emission costs, load interruption and shifting costs, and the cost of using shared energy storage devices.

[0056] In some embodiments, the cost minimization objective function of the microgrid cluster is: (17); (18); (19); (20); (twenty one); In the formula, This indicates the cost of a microgrid cluster. This indicates the operating cost of the gas turbine. , , This is the cost coefficient. To reduce the cost of using shared energy storage devices, The cost factor for charging shared energy storage devices. This represents the cost factor for discharging energy using shared energy storage devices. This represents the cost of load demand response. This represents the interruptible load cost factor. This represents the cost factor for transferable loads. Indicates the carbon emission cost of microgrids. This indicates the price per unit of carbon emissions.

[0057] Compared with related technologies, the present invention has the following technical improvements and effects: The constructed multi-microgrid two-layer dispatch framework utilizes time-of-use pricing and dynamic carbon emission factors to build an efficient trading model, closely connecting various stakeholders to achieve system collaborative optimization and efficient operation. It overcomes the shortcomings of traditional single-layer or simple structural frameworks in coordinating interest relationships and operational constraints, fully leverages the advantages of two-layer architecture in resource allocation and system coordination, and improves the overall system performance.

[0058] Considering the significant uncertainties inherent in renewable energy power generation, this solution employs scenario analysis to comprehensively and meticulously analyze renewable energy output by generating multiple scenarios with varying probabilities of occurrence. Based on extensive historical data and probabilistic statistical models, a series of scenarios reflecting different renewable energy power generation states are generated, with each scenario assigned a corresponding probability of occurrence. This accurately characterizes the uncertainties of renewable energy power generation, providing a reliable basis for subsequent system optimization and scheduling, and effectively improving the reliability and stability of system operation.

[0059] The framework utilizes shared energy storage devices to provide strong support for microgrid optimization. By centrally configuring shared energy storage devices to serve multiple microgrids, the utilization efficiency of energy storage resources is effectively improved. On the one hand, the shared energy storage device model greatly increases the overall charging and discharging frequency and duration of energy storage devices, avoiding the low utilization rate and resource waste that occur when individual microgrids are configured with energy storage devices, thus reducing operating costs. On the other hand, shared energy storage devices enhance the ability of microgrid clusters to cope with the intermittency and volatility of distributed power sources. This allows microgrids to maintain power supply and demand balance and ensure stable operation when facing uncertainties in renewable energy generation output through the regulating role of the energy storage system.

[0060] The starfish optimization algorithm is used to solve the upper-level problem, which fully leverages the algorithm's unique advantages in handling complex optimization problems to achieve efficient and accurate solutions for the upper-level model. Furthermore, the globally optimal solution is obtained through iterative solving.

[0061] and Figure 1 The corresponding method is referenced. Figure 6 This invention provides a multi-microgrid optimized dispatching system that considers the uncertainty of new energy power generation, comprising: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0062] It is evident that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0063] Furthermore, embodiments of the present invention also disclose a computer program product or computer program stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, causing the computer device to perform the described method. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0064] It will be understood by those skilled in the art that all or some of the methods and systems disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0065] The above is a detailed description of the preferred embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions are included within the scope defined by the claims of this disclosure.

Claims

1. A method for optimal scheduling of multiple microgrids considering the uncertainties of new energy power generation, characterized in that, The method includes the following steps: An upper-level model and a lower-level model are established. The upper-level model is the objective function for maximizing the revenue of the microgrid operator, and the lower-level model is the objective function for minimizing the cost of the microgrid cluster. The revenue maximization objective function is based on the electricity purchase and sale price set by the microgrid operator for the microgrid, and the cost minimization objective function is based on the electricity purchase and sale volume and carbon emission factor of the microgrid cluster. The electricity purchase and sale price includes the purchase price and the sale price; the electricity purchase and sale volume includes the purchased electricity volume and the sold electricity volume. With the goal of maximizing the revenue of microgrid operators, the starfish optimization algorithm is used to solve the upper-level model to obtain the purchase and sale price of electricity, and the purchase and sale price of electricity and the carbon emission factor dynamically released by the microgrid operator are passed to the lower-level model. The cost minimization objective function is updated based on the carbon emission factor. The lower-level model is solved with the goal of minimizing the cost of the microgrid cluster to obtain the purchased and sold electricity volume, which is then passed to the upper-level model. The upper-level model and the lower-level model are iteratively solved until both the upper-level model and the lower-level model obtain the optimal solution. Based on the optimal solution, the microgrid cluster is optimized and scheduled.

2. The method according to claim 1, characterized in that, The upper-level model is established in the following way: Obtain the feed-in tariff and grid tariff from microgrid operators, as well as the purchase price and sales price of electricity set by microgrid operators for microgrids; the feed-in tariff is the price at which microgrid operators sell electricity to grid companies, and the grid tariff is the price at which grid companies sell electricity to microgrid operators; Based on the on-grid electricity price, grid electricity price, purchase price, and sales price, a revenue maximization objective function for microgrid operators is established as the upper-level model.

3. The method according to claim 2, characterized in that, The profit maximization objective function of the microgrid operator is: ； In the formula, This indicates the revenue of microgrid operators. This refers to the price at which microgrid operators sell electricity to the grid company, i.e., the feed-in tariff. The price at which the power grid company sells electricity to microgrid operators, i.e., the grid electricity price; and These represent the electricity sold by the microgrid operator to the power grid company and the electricity purchased from the power grid company, respectively. and This refers to the electricity purchase price and sales price set by the microgrid operator for the microgrid cluster; This represents the amount of electricity purchased by the i-th microgrid from the microgrid operator at time t; Let be the amount of electricity sold by the i-th microgrid to the microgrid operator at time t; The electricity purchase price and the electricity sales price satisfy the following formula: .

4. The method according to claim 3, characterized in that, The lower-level model is established in the following way: Identify shared energy storage devices, new energy devices, gas turbines, and loads in a microgrid cluster to establish power balance constraints for the microgrid cluster; Under power balance constraints, a cost minimization objective function for microgrid clusters is established by combining the cost of microgrids and total carbon emissions, serving as the lower-level model.

5. The method according to claim 4, characterized in that, The process of determining shared energy storage devices, new energy devices, gas turbines, and loads in a microgrid cluster to establish power balance constraints for the microgrid cluster includes: Establish a shared energy storage device model for a microgrid cluster and determine the charging and discharging power constraints of the shared energy storage device model; Multiple new energy power generation scenarios are generated using scenario analysis, and the output constraints of each new energy power generation scenario are determined. Set power generation constraints, start-up and stop constraints, and ramp-up constraints for the gas turbine; Identify the interruptible loads, transferable loads, and stationary loads within the load; determine the maximum interruptible power limit for interruptible loads and the transfer time range limit for transferable loads; Power balance constraints for microgrid clusters are established based on the charging and discharging power constraints of shared energy storage equipment models, the output constraints of various new energy power generation scenarios, the power generation constraints of gas turbines, start-up and stop constraints and ramping constraints, the maximum interruptible power constraints of interruptible loads, and the transfer time range constraints of transferable loads.

6. The method according to claim 4, characterized in that, The objective function for minimizing the cost of a microgrid cluster, which combines the cost of the microgrid with the total carbon emissions, includes: The total carbon emissions of the microgrid cluster are determined based on the carbon emissions of the gas turbines in each microgrid and the carbon emissions borne by the electricity purchased by each microgrid. The objective function for minimizing the cost of a microgrid cluster is established based on the operating cost of gas turbines, the cost of purchasing and selling electricity, the cost of carbon emissions, the cost of interruptible loads, the cost of transferable loads, and the cost of using shared energy storage devices.

7. The method according to claim 6, characterized in that, The objective function for minimizing the cost of the microgrid cluster is: ； ； ； ； ； In the formula, This indicates the cost of a microgrid cluster. This indicates the operating cost of the gas turbine. , , This is the cost coefficient. To reduce the cost of using shared energy storage devices, The cost factor for charging shared energy storage devices. This represents the cost factor for discharging energy using shared energy storage devices. This represents the cost of load demand response. This represents the interruptible load cost factor. This represents the cost factor for transferable loads. Indicates the carbon emission cost of microgrids. This indicates the price per unit of carbon emissions.

8. A multi-microgrid optimized dispatching system considering the uncertainties of new energy power generation, characterized in that, The system includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to perform the method as described in any one of claims 1 to 7.

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