Multi-microgrid system double-layer optimization method and system considering energy storage and new energy

Abstract

The invention provides a multi-microgrid system double-layer optimization method and system considering energy storage and new energy. The invention provides an innovative double-layer optimization scheduling method which is specially designed for a multi-microgrid system so as to realize efficient integration of energy storage equipment and renewable energy power generation. By adopting an improved sparrow search algorithm, the method aims to solve the problem of instability and high economic cost in a traditional scheduling strategy. In the upper layer, the optimization model focuses on the stability and reliability of the whole power distribution system, and the energy utilization efficiency and the system stability are ensured by globally coordinating the energy distribution of each micro-grid. In the lower layer, the optimization model in each micro-grid is dedicated to fine management of local energy resources and energy storage equipment so as to realize cost benefit maximization.

Description

Technical Field

[0001] The present application relates to a two-layer optimization method and system for a multi-microgrid system considering energy storage and new energy, and belongs to the technical field of power system energy management. Background Art

[0002] With the widespread access to renewable energy, multi-microgrid systems play an increasingly important role in supporting distributed generation and improving energy efficiency. Optimizing scheduling strategies is essential to ensure the economy and reliability of the system. However, existing multi-microgrid system scheduling methods often focus on a single energy source or lack effective integration of energy storage systems, which limits their ability to cope with the intermittency and uncertainty of renewable energy, resulting in low scheduling efficiency and imprecise cost control in practical applications.

[0003] Existing technical solutions include the use of heuristic algorithms, such as particle swarm optimization (PSO) and genetic algorithms, to deal with scheduling problems in multi-microgrid systems. Although these methods have made some progress in dealing with optimization problems, they usually face challenges such as slow convergence, easy to fall into local optimal solutions, and sensitivity to parameter settings. In addition, the existing economic cost model is not comprehensive enough and fails to fully consider the cost-effectiveness of energy storage systems and the complex constraints encountered in actual grid operations, such as the safety of grid operation, regulatory requirements, and changes in market demand. Summary of the invention

[0004] In view of the above problems, the present invention proposes a two-layer optimization method and system for a multi-microgrid system considering energy storage and new energy, so as to solve the challenges faced by existing optimization algorithms such as slow convergence speed, easy to fall into local optimal solutions, sensitivity to parameter settings, and the problem that the existing economic cost model construction is not comprehensive enough.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a two-layer optimization method for a multi-microgrid system considering energy storage and new energy, which includes:

[0007] Constructing a multi-microgrid system model, and defining performance parameters and cost parameters for each energy generation unit and energy storage unit in the multi-microgrid system model;

[0008] Define the objective function and constraint conditions of the two-layer optimization model of the multi-microgrid system model; wherein the two-layer optimization model includes a distribution network layer optimization scheduling model and a microgrid layer optimization model, the goal of the distribution network layer optimization scheduling model is to minimize the fluctuation of power and voltage, and the goal of the microgrid layer optimization model is to minimize the overall operating cost of the microgrid;

[0009] The initial sparrow algorithm is combined with the golden sine strategy and the adaptive t-distribution strategy to establish an improved sparrow search algorithm model.

[0010] The improved sparrow search algorithm model is used to solve the double-layer optimization model of the multi-microgrid system model, and an optimization strategy is generated according to the solution result.

[0011] Based on the above method, optionally, the objective function of the distribution network layer optimization scheduling model is:

[0012] minF=w1f1+w2f2+w3f3

[0013] Among them, f1, f2 and f3 represent the quantitative indicators of voltage stability, frequency stability and power supply reliability respectively, and w1-w3 are weight coefficients;

[0014] Voltage stability f1 is a measure of the ability of the power system to maintain the user voltage within an acceptable range without voltage collapse, which is measured by considering the voltage deviation of all microgrids and distribution network nodes:

[0015]

[0016] Where T is the time period in the dispatching cycle, N is the set of all nodes in the microgrid and distribution network, and V n (t) is the voltage amplitude of node n in time period t, V ref is the reference voltage amplitude;

[0017] Frequency stability refers to the ability of the entire system to maintain a power balance between generation and load, avoiding frequency deviation beyond the allowable range, and is measured by frequency deviation:

[0018]

[0019] Among them, f sys (t) is the system frequency in time period t, f ref is the reference frequency;

[0020] Power supply reliability refers to the ability of the system to continuously provide electricity to users, which is measured by the amount of electricity lost during power outages:

[0021]

[0022] Among them, P n (t) is the load demand of node n in period t, U n (t) is a binary variable, indicating whether node n has a power outage during period t, 1 indicates a power outage, and 0 indicates no power outage.

[0023] Based on the above method, optionally, the constraint conditions of the distribution network layer optimization scheduling model include:

[0024] a) Power balance constraint: Ensure that at any given point in time, the total supply of the distribution network matches the total load demand:

[0025]

[0026] Among them, P load (t) is the total system load power during period t, P loss (t) is the power loss, P MGi,out (t) is the output power of the i-th microgrid in period t, P DN,in / out (t) is the interaction power between the distribution network and the microgrid;

[0027] b) Voltage stability constraint: Limit the voltage of each node in the system to within the allowable range to ensure voltage stability:

[0028]

[0029] Where Vj(t) is the voltage of node j at time period t, V min and V max are the lower and upper limits of voltage;

[0030] c) Frequency stability constraint: Ensure that the system frequency is within the allowable deviation range:

[0031]

[0032] Where f(t) is the system frequency during period t, f min and f max are the lower and upper limits of the frequency;

[0033] d) Interaction power constraints between microgrid and distribution network: Limit the interaction power between microgrid and distribution network to the physically and technically feasible range:

[0034]

[0035] Among them, P DN,min and P DN,max are the lower and upper limits of the interaction power.

[0036] Based on the above method, optionally, the objective function of the microgrid layer optimization scheduling model is:

[0037] minC=w1C1+w2C2

[0038] Among them, C1 is the power generation cost, C2 is the pollution control cost, w1 and w2 are weight coefficients;

[0039] The power generation cost includes fuel cost, maintenance cost, load reduction compensation cost, energy transaction cost with the main power grid and depreciation cost of each power generation unit:

[0040]

[0041] Where T is the dispatch period, a is the microgrid consideration coefficient; C f (t) represents the fuel cost of each distributed energy consumption at time t; C OM (t) represents the maintenance cost of each unit at time t; C L (t) is the power outage compensation fee during load shedding; C grid represents the energy transaction cost between the microgrid and the main grid; C DEP (t) is the depreciation cost of each generator set at time t;

[0042]

[0043] C L =C bu ×Load(t)

[0044] C grid =CP(t)×CGP(t)-CS(t)×CSP(t)

[0045] Where N is the number of generators connected to the microgrid; i is the type of distributed energy; C fuel is the price of natural gas; LHV means the lower calorific value of natural gas; P i (t) represents the output active power of distributed energy i at time t; η i (t) represents the fuel combustion efficiency of distributed energy source i at time t, which is the percentage of generated electrical energy relative to the thermal energy input of the fuel; C INS,i represents the installation cost of distributed energy i; f cf,i is the capacity factor; P ri is the rated power of distributed energy i; f cr is the capital recovery coefficient, d is the annual interest rate, l is the service life of the system; C L represents the power outage compensation cost during load shedding; C bu represents the compensation loss cost generated by the microgrid during the load reduction process; Load(t) represents the load reduction in time period t; CP(t) and CS(t) represent the purchase and sale prices between the microgrid and the main grid during time period t; CGP(t) and CSP(t) represent the amount of electricity purchased from the microgrid and sold to the main grid during time period t, respectively;

[0046] The pollution control cost is:

[0047]

[0048] Where b = 0; M is the number of types of pollutants emitted; K is the type of gas emissions from the microgrid; C k is the unit processing fee; ik represents the penalty cost of generating k pollutants per unit of electricity generated by the i-generation unit; r gridk represents the penalty cost of pollutant k generated by the main grid.

[0049] Based on the above method, optionally, the constraints of the microgrid layer optimization scheduling model include:

[0050] g) Power balance constraints:

[0051]

[0052] in, represents the system load power during period t; It represents the power loss of the system during the power transmission process at time t; is the active power provided by each distributed generator set in time period t; It represents the active power exchanged between the microgrid and the main grid during period t;

[0053] h) Distributed generation output power constraints:

[0054]

[0055] Among them, P i min and P i max are the minimum and maximum output power limits of distributed generator set i at time t, respectively;

[0056] i) Exchange constraints between microgrid and distribution network:

[0057]

[0058] in, and They represent the minimum and maximum values ​​of the power exchanged between the microgrid and the distribution network respectively;

[0059] j) Spinning reserve capacity constraints:

[0060]

[0061] Where L% is the demand factor of the microgrid system load for the reserve capacity; u s % is the demand coefficient of the rotating reserve capacity of wind power and solar power generation units; wp (t) is the total nominal capacity of wind turbines and solar power generators in the system at time t;

[0062] k) Greenhouse gas and pollutant emission limits:

[0063]

[0064] In the formula, and They represent the emission limits of CO2, SO2 and NO2 gases during the operation of the microgrid respectively;

[0065] l) Energy storage unit power constraints

[0066]

[0067] In the formula, and They represent the maximum power limit and minimum power limit of the energy storage device for charging and discharging respectively; δ is the scheduling time period.

[0068] Based on the above method, optionally, the initial sparrow algorithm is combined with the golden sine strategy and the adaptive t distribution strategy to establish an improved sparrow search algorithm model, including:

[0069] Initialize the population and randomly generate multiple sparrow individuals to represent potential solutions; determine the discoverer and follower through fitness evaluation; update the positions of the discoverer and follower. The discoverer adjusts its position based on the current worst individual position and random number, while the follower adjusts its position based on the distance from the discoverer and the number of iterations; at the same time, the scout position is updated based on the current best and worst individual positions and individual fitness;

[0070] The Golden-S strategy is introduced to improve the position of the SSA discoverer; the t-distribution mutation operator is used to perturb the position of the sparrow; and an improved sparrow search algorithm model is obtained.

[0071] Based on the above method, optionally, the two-layer optimization model of the multi-microgrid system model is solved by using the improved sparrow search algorithm model, including:

[0072] Select simulation platform and prepare data for multi-microgrid system model;

[0073] Set algorithm parameters, including safety value ST, finder probability PD and follower probability SD;

[0074] Collect cost data of each microgrid and initialize the basic voltage and frequency of the distribution network to ensure that the algorithm runs in the correct system state;

[0075] The improved sparrow search algorithm model is applied to optimize the scheduling strategy of multi-microgrid system.

[0076] Based on the above method, optionally, generating an optimization strategy according to the solution result includes:

[0077] Draw a fitness curve to monitor the convergence behavior of the algorithm during the iteration process to ensure that the algorithm can effectively approach the optimal solution;

[0078] Analyze the output of each power generation unit and verify whether the scheduling strategy reasonably utilizes all available energy, including renewable energy and traditional power generation resources, as well as the charging and discharging strategy of energy storage equipment;

[0079] Calculate and compare the average operating costs under different dispatch strategies, including energy costs, operation and maintenance costs, and transaction costs with the upper-level power grid, to evaluate the economic feasibility of each strategy;

[0080] Evaluate the impact of dispatch strategies on the stability of the distribution network, including the fluctuation range of voltage and frequency, line load conditions, and the ability to respond to potential faults, to ensure that the selected strategy can maintain stable operation of the power grid under various conditions.

[0081] In a second aspect, the embodiment of the present application further provides a two-layer optimization system for a multi-microgrid system considering energy storage and new energy, which includes:

[0082] A system model building module, used to build a multi-microgrid system model and define performance parameters and cost parameters for each energy generation unit and energy storage unit in the multi-microgrid system model;

[0083] An optimization model building module, used to define the objective function and constraint conditions of the two-layer optimization model of the multi-microgrid system model; wherein the two-layer optimization model includes a distribution network layer optimization scheduling model and a microgrid layer optimization model, the goal of the distribution network layer optimization scheduling model is to minimize the fluctuation of power and voltage, and the goal of the microgrid layer optimization model is to minimize the overall operating cost of the microgrid;

[0084] The algorithm model improvement module combines the initial sparrow algorithm with the golden sine strategy and the adaptive t-distribution strategy to establish an improved sparrow search algorithm model;

[0085] A solution module is used to solve the double-layer optimization model of the multi-microgrid system model using the improved sparrow search algorithm model, and generate an optimization strategy according to the solution result.

[0086] The present application provides a two-layer optimization method and system for a multi-microgrid system considering energy storage and new energy, and proposes a two-layer optimization scheduling strategy for a multi-microgrid system. This strategy uses an improved sparrow search algorithm to comprehensively consider the integration of energy storage equipment (energy storage units) and new energy (energy generation units) to maximize cost-effectiveness and system stability. At the upper layer (distribution network layer), the model focuses on the reliability of the entire distribution system and coordinates the energy distribution of each microgrid; at the lower layer (microgrid layer), the model refines the energy management and energy storage equipment scheduling of each microgrid. By simulating the foraging behavior of sparrows, the algorithm enhances the global search and local refinement capabilities, ensures the economy and real-time feasibility of the scheduling plan, promotes the efficient use of green energy, and meets the goal of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. In addition, these drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application for those skilled in the art by referring to specific embodiments.

[0088] Figure 1 A schematic flow chart of a two-layer optimization method for a multi-microgrid system considering energy storage and new energy provided by an embodiment of the present application;

[0089] Figure 2 A schematic diagram of the operation flow of an improved sparrow search algorithm model provided in one embodiment of the present application;

[0090] Figure 3 A schematic structural diagram of a two-layer optimization system for a multi-microgrid system taking into account energy storage and new energy, provided as an embodiment of the present application. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0092] Existing optimization scheduling methods for multi-microgrid systems usually face challenges such as slow convergence, easy to fall into local optimal solutions, and sensitivity to parameter settings. In addition, the existing economic cost model is not comprehensive enough and fails to fully consider the cost-effectiveness of energy storage systems and the complex constraints encountered in actual grid operations, such as the safety of grid operation, regulatory requirements, and changes in market demand. Specifically, there are the following shortcomings:

[0093] 1. In multi-microgrid systems, energy storage systems (ESS) play an important role in balancing supply and demand and enhancing system stability, especially when dealing with the intermittency and instability of renewable energy such as solar energy. However, existing studies often fail to fully consider the role of ESS in the system, which limits the system's ability to cope with the volatility of renewable energy in actual operation.

[0094] 2. Although optimization algorithms such as particle swarm optimization (PSO) and genetic algorithms can effectively solve scheduling problems in some cases, they may face challenges such as slow convergence, easy to fall into local optimal solutions or high computational complexity in practical applications. These limitations may affect the performance and applicability of the algorithms in real-time microgrid scheduling.

[0095] 3. In the study of multi-microgrid systems, the cost model of energy storage and photovoltaic systems is not adequately considered, and the economic dynamics in microgrid operation are not fully reflected. This may lead to the economic efficiency of the optimization strategy in practical applications not meeting expectations, and ignore the key role of cost-benefit analysis in microgrid scheduling decisions.

[0096] In view of the above problems, the present invention proposes an innovative two-layer optimization scheduling method, which is specially designed for multi-microgrid systems to achieve efficient integration of energy storage equipment and renewable energy power generation. By adopting an improved sparrow search algorithm, the method aims to solve the instability problem and high economic cost in traditional scheduling strategies. At the upper level, the optimization model focuses on the stability and reliability of the entire distribution system, and aims to ensure energy utilization efficiency and system stability by globally coordinating the energy distribution of each microgrid. At the lower level, the optimization model within each microgrid is committed to the refined management of local energy resources and energy storage equipment to maximize cost-effectiveness. The following is a non-restrictive description of the specific implementation scheme through several examples or embodiments.

[0097] like Figure 2 As shown, the embodiment of the present application provides a two-layer optimization method for a multi-microgrid system considering energy storage and new energy, including the following steps:

[0098] Step S101: construct a multi-microgrid system model, and define performance parameters and cost parameters for each energy generation unit and energy storage unit in the multi-microgrid system model.

[0099] Specifically, first, a comprehensive microgrid model is established, which takes into account a variety of energy generation and storage technologies such as wave power generation, photovoltaic systems, micro gas turbines, fuel cells, and energy storage devices. Then, performance parameters are defined for each energy generation unit and energy storage unit in the model. These parameters reflect their operating characteristics under different operating conditions, including but not limited to power generation efficiency, response time, charge and discharge rate, etc. At the same time, cost parameters are set for each unit, including initial investment cost, operation and maintenance costs, fuel cost, and expected service life, etc., to facilitate subsequent economic analysis.

[0100] In addition, multiple microgrid models are connected to the active distribution network to ensure that the model can simulate the power exchange between the microgrid and the distribution network, as well as the power interaction between microgrids. In the current model, the purchase of heat energy from the outside is not considered for the time being, but the focus is on the generation, storage and distribution of electric energy.

[0101] Step S102: define the objective function and constraints of the two-layer optimization model of the multi-microgrid system model; wherein the two-layer optimization model includes a distribution network layer optimization scheduling model and a microgrid layer optimization model, the goal of the distribution network layer optimization scheduling model is to minimize power and voltage fluctuations, and the goal of the microgrid layer optimization model is to minimize the overall operating cost of the microgrid.

[0102] Specifically, in the two-layer optimization framework of the multi-microgrid system, the optimization scheduling models of the upper layer (distribution network layer) and the lower layer (microgrid layer) each have clear objective functions and constraints to ensure the efficient and stable operation of the entire system, and the objective functions and constraints of the distribution network layer optimization scheduling model and the microgrid layer optimization scheduling model are defined respectively.

[0103] In some embodiments, the objective function of the distribution network layer optimization scheduling model is:

[0104] minF=w1f1+w2f2+w3f3 (1)

[0105] Among them, f1, f2 and f3 represent the quantitative indicators of voltage stability, frequency stability and power supply reliability respectively, and w1-w3 are weight coefficients;

[0106] Voltage stability f1 is a measure of the ability of the power system to maintain the user voltage within an acceptable range without voltage collapse, which is measured by considering the voltage deviation of all microgrids and distribution network nodes:

[0107]

[0108] Where T is the time period in the dispatching cycle, N is the set of all nodes in the microgrid and distribution network, and V n (t) is the voltage amplitude of node n in time period t, Vref is the reference voltage amplitude;

[0109] Frequency stability refers to the ability of the entire system to maintain a power balance between generation and load, avoiding frequency deviation beyond the allowable range, and is measured by frequency deviation:

[0110]

[0111] Among them, f sys (t) is the system frequency in time period t, f ref is the reference frequency;

[0112] Power supply reliability refers to the ability of the system to continuously provide electricity to users, which is measured by the amount of electricity lost during power outages:

[0113]

[0114] Among them, P n (t) is the load demand of node n in period t, U n (t) is a binary variable, indicating whether node n has a power outage during period t, 1 indicates a power outage, and 0 indicates no power outage.

[0115] Furthermore, the constraints of the distribution network layer optimization scheduling model include:

[0116] a) Power balance constraint: Ensure that at any given point in time, the total power supply of the distribution network (including power purchased from the upper grid and the power generation output of each microgrid) matches the total load demand:

[0117]

[0118] Among them, P load (t) is the total system load power during period t, P loss (t) is the power loss, P MGi,out (t) is the output power of the i-th microgrid in period t, P DN,in / out (t) is the interaction power between the distribution network and the microgrid (positive value indicates inflow from the distribution network, negative value indicates outflow);

[0119] b) Voltage stability constraint: Limit the voltage of each node in the system to within the allowable range to ensure voltage stability:

[0120]

[0121] Where Vj(t) is the voltage of node j at time period t, V min and V max are the lower and upper limits of voltage;

[0122] c) Frequency stability constraint: Ensure that the system frequency is within the allowable deviation range:

[0123]

[0124] Where f(t) is the system frequency during period t, f min and f max are the lower and upper limits of the frequency;

[0125] d) Interaction power constraints between microgrid and distribution network: Limit the interaction power between microgrid and distribution network to the physically and technically feasible range:

[0126]

[0127] Among them, P DN,min and P DN,max are the lower and upper limits of the interaction power.

[0128] In addition, in some embodiments, the objective function of the microgrid layer optimization scheduling model is:

[0129] minC=w1C1+w2C2 (8)

[0130] Among them, C1 is the power generation cost, C2 is the pollution control cost, w1 and w2 are weight coefficients;

[0131] The power generation cost includes fuel cost, maintenance cost, load reduction compensation cost, energy transaction cost with the main power grid and depreciation cost of each power generation unit:

[0132]

[0133] Where T is the dispatch period, a is the microgrid consideration coefficient; C f (t) represents the fuel cost of each distributed energy consumption at time t; C OM (t) represents the maintenance cost of each unit at time t; C L (t) is the power outage compensation fee during load shedding; C grid represents the energy transaction cost between the microgrid and the main grid; C DEP (t) is the depreciation cost of each generator set at time t;

[0134]

[0135] C L =C bu ×Load(t)(12)

[0136]

[0137] Where N is the number of generators connected to the microgrid; i is the type of distributed energy; C fuel is the price of natural gas; LHV means the lower calorific value of natural gas; P i (t) represents the output active power of distributed energy i at time t; η i (t) represents the fuel combustion efficiency of distributed energy source i at time t, which is the percentage of generated electrical energy relative to the thermal energy input of the fuel; C INS,i represents the installation cost of distributed energy i; f cf,i is the capacity factor; P ri is the rated power of distributed energy i; f cr is the capital recovery coefficient, d is the annual interest rate, l is the service life of the system; C L represents the power outage compensation cost during load shedding; C bu represents the compensation loss cost generated by the microgrid during the load reduction process; Load(t) represents the load reduction in time period t; CP(t) and CS(t) represent the purchase and sale prices between the microgrid and the main grid during time period t; CGP(t) and CSP(t) represent the amount of electricity purchased from the microgrid and sold to the main grid during time period t, respectively;

[0138] The pollution control cost is:

[0139]

[0140] Where b = 0; M is the number of types of pollutants emitted; K is the type of gas emissions from the microgrid; C k is the unit processing fee; ik represents the penalty cost of generating k pollutants per unit of electricity generated by the i-generation unit; r gridk represents the penalty cost of pollutant k generated by the main grid

[0141] Furthermore, the constraints of the microgrid-level optimization scheduling model include:

[0142] a) Power balance constraints:

[0143]

[0144] in, represents the system load power during period t; It represents the power loss of the system during the power transmission process at time t; is the active power provided by each distributed generator set in time period t; It represents the active power exchanged between the microgrid and the main grid during period t;

[0145] b) Distributed generation output power constraints:

[0146] Pi min ≤P Gi ≤P i max (16)

[0147] Among them, P i min and P i max are the minimum and maximum output power limits of distributed generator set i at time t, respectively;

[0148] c) Exchange constraints between microgrid and distribution network:

[0149]

[0150] in, and They represent the minimum and maximum values ​​of the power exchanged between the microgrid and the distribution network respectively;

[0151] d) Spinning reserve capacity constraints:

[0152]

[0153] Where L% is the demand factor of the microgrid system load for the reserve capacity; u s % is the demand coefficient of the rotating reserve capacity of wind power and solar power generation units; wp (t) is the total nominal capacity of wind turbines and solar power generators in the system at time t;

[0154] e) Greenhouse gas and pollutant emission limits:

[0155]

[0156] In the formula, and They represent the emission limits of CO2, SO2 and NO2 gases during the operation of the microgrid respectively;

[0157] f) Energy storage unit power constraints

[0158]

[0159] In the formula, and They represent the maximum power limit and minimum power limit of the energy storage device for charging and discharging respectively; δ is the scheduling time period.

[0160] Step S103: combining the initial sparrow algorithm with the golden sine strategy and the adaptive t distribution strategy to establish an improved sparrow search algorithm model.

[0161] Specifically, the purpose of this step is to establish an improved sparrow search algorithm for the scheduling strategy of double-layer optimization of multi-microgrid systems.

[0162] In some embodiments, step S103 includes an initial sparrow algorithm and an improved sparrow algorithm, which are as follows:

[0163] (1) Initial sparrow algorithm: Initialize the population and randomly generate multiple sparrow individuals to represent potential solutions; determine the discoverer and follower through fitness evaluation; update the positions of the discoverer and follower. The discoverer adjusts its position based on the current worst individual position and random number, while the follower adjusts its position based on the distance from the discoverer and the number of iterations; at the same time, the scout position is updated based on the current best and worst individual positions and individual fitness;

[0164] These include:

[0165] 1) Update the finder location:

[0166]

[0167] Where: j = 1, 2, 3, ..., d, iter max represents the maximum number of iterations of the algorithm; t represents the current number of iterations; a is a random number in the interval (0,1]; R2 is the alarm value in the interval [0,1]; ST is the safety value in the interval [0.5,1]; Q is a random number that follows a normal distribution; L is an l×d matrix in which all elements are equal to 1.

[0168] 2) Update follower position

[0169]

[0170] Where: X worst Indicates the position of the individual with the worst fitness value in t iterations; Represents the position of the finder with the best fitness value in the t+1 iteration; A is a 1×d matrix whose elements are randomly assigned to 1 or -1.

[0171] 3) Update the scout's location

[0172]

[0173] in: Indicates the position of the individual with the best fitness value in the current iteration; β is the step size control coefficient, a random number with a mean of 0 and a variance of 1; K belongs to [-1, 1]; f i is the fitness value of the current individual; f g and f w Represent the fitness values ​​of the best and worst individuals respectively; this constant is very small.

[0174] (2) Improved Sparrow Algorithm: The initial Sparrow Algorithm is combined with the Golden-S strategy and the adaptive t-distribution strategy to improve the global search capability and local search accuracy of the algorithm.

[0175] These include: introducing the Golden-S strategy to improve the SSA discoverer position; using the t-distribution mutation operator to perturb the sparrow's position; and obtaining an improved sparrow search algorithm model. The details are as follows:

[0176] 1) Introducing the Golden-S strategy to improve the SSA discoverer position

[0177]

[0178] Among them: R1 belongs to [0,2π] and is a random number representing the individual's moving distance, and R2 belongs to [0,π] and is a random number representing the moving direction.

[0179] 2) Use the t-distribution mutation operator to perturb the position of the sparrow

[0180] In order to improve the algorithm's early global search capability and later local development capability, the t-distribution mutation operator is used to perturb the position of the sparrow. The degree of freedom parameter of the t-distribution is set to the number of iterations iter, as follows:

[0181]

[0182] in: represents the position of the sparrow after the disturbance, Indicates the position of sparrow i at the tth iteration

[0183] In order to reduce the calculation time, the dynamic selection probability p is used to adjust the use of the adaptive t distribution mutation factor, as shown in the following formula:

[0184]

[0185] maxiter represents the maximum number of iterations; iter is the current number of iterations, ω1 is the upper limit of the dynamic selection probability; ω2 controls the rate of change of the dynamic selection probability.

[0186] The specific execution process of the improved sparrow search algorithm is as follows Figure 2 .

[0187] Step S104: using the improved sparrow search algorithm model to solve the double-layer optimization model of the multi-microgrid system model, and generating an optimization strategy according to the solution results.

[0188] Specifically, after obtaining the improved sparrow search algorithm model, it is applied to solve the double-layer optimization model of the multi-microgrid system model. Then, the corresponding optimization strategy can be generated according to the solution result.

[0189] In some embodiments, the specific steps of solving the double-layer optimization model of the multi-microgrid system model using the improved sparrow search algorithm model include:

[0190] 1) Select the simulation platform and prepare the data for the multi-microgrid system model; for example, the Python 3.7 platform and the wave energy and photovoltaic data of a specific region, collect the number of microgrids and their respective characteristic data, which will be used as part of the algorithm input;

[0191] 2) Set algorithm parameters, including safety value ST, finder probability PD and follower probability SD;

[0192] 3) Collect cost data of each microgrid, including power generation cost, energy storage cost, maintenance cost, etc. At the same time, initialize the basic voltage and frequency of the distribution network to ensure that the algorithm runs in the correct system state;

[0193] 4) Apply the improved sparrow search algorithm model to optimize the dispatch strategy of the multi-microgrid system. The algorithm will consider the interaction between the distribution network and each microgrid, as well as the energy management and energy storage device dispatch within the microgrid. Through iterative search, the algorithm will find the optimal dispatch strategy that meets all constraints to achieve system power balance and cost minimization.

[0194] In some embodiments, the specific steps of generating an optimization strategy according to the solution result include:

[0195] 1) Draw a fitness curve to monitor the convergence behavior of the algorithm during the iteration process to ensure that the algorithm can effectively approach the optimal solution;

[0196] 2) Analyze the output of each power generation unit and verify whether the scheduling strategy reasonably utilizes all available energy, including renewable energy and traditional power generation resources, as well as the charging and discharging strategy of energy storage equipment;

[0197] 3) Calculate and compare the average operating costs under different dispatch strategies, including energy costs, operation and maintenance costs, and transaction costs with the upper-level power grid, to evaluate the economic feasibility of each strategy;

[0198] 4) Evaluate the impact of the dispatch strategy on the stability of the distribution network, including the fluctuation range of voltage and frequency, line load conditions, and the ability to respond to potential faults, to ensure that the selected strategy can maintain the stable operation of the power grid under various conditions.

[0199] Based on the above scheme, this application proposes a two-layer optimization scheduling strategy for multi-microgrid systems. This strategy comprehensively considers the integration of energy storage devices (energy storage units) and new energy (energy generation units) through an improved sparrow search algorithm to maximize cost-effectiveness and system stability. At the upper layer (distribution network layer), the model focuses on the reliability of the entire distribution system and coordinates the energy distribution of each microgrid; at the lower layer (microgrid layer), the model refines the energy management and energy storage device scheduling of each microgrid. By simulating the foraging behavior of sparrows, the algorithm enhances the global search and local refinement capabilities, ensures the economy and real-time feasibility of the scheduling scheme, promotes the efficient use of green energy, and meets the goal of sustainable development.

[0200] Specifically, one of the core innovations of the present invention is to construct a comprehensive microgrid scheduling model, which not only covers various energy forms such as wave energy, photovoltaics, micro gas turbines, fuel cells, etc., but also comprehensively considers the role of energy storage equipment, thereby achieving efficient management of renewable energy volatility and cost-effective optimization.

[0201] In addition, an improved sparrow search algorithm was developed to solve the optimal scheduling problem of microgrids. The algorithm significantly improves the search efficiency and solution quality by introducing the golden ratio and adaptive t distribution strategy, especially when dealing with microgrid scheduling problems with complex constraints and multiple objectives.

[0202] The above scheme can not only balance the volatility of renewable energy and reduce operating costs, but also enhance the system's adaptability to changes in power supply and demand, while taking into account environmental benefits, reducing pollution emissions, and promoting the efficient use of green energy. Simulation verification shows that the present invention has achieved remarkable results in improving the search and convergence performance of the algorithm, ensuring the feasibility of real-time application, and showing obvious advantages in economic efficiency and operational performance, providing a multi-microgrid system with an efficient, economical, stable and environmentally friendly energy management solution.

[0203] In addition, the embodiment of the present application provides a two-layer optimization system for a multi-microgrid system taking into account energy storage and new energy. Figure 3 As shown, the system includes:

[0204] A system model building module 31 is used to build a multi-microgrid system model and define performance parameters and cost parameters for each energy generation unit and energy storage unit in the multi-microgrid system model;

[0205] An optimization model building module 32 is used to define the objective function and constraint conditions of the two-layer optimization model of the multi-microgrid system model; wherein the two-layer optimization model includes a distribution network layer optimization scheduling model and a microgrid layer optimization model, the goal of the distribution network layer optimization scheduling model is to minimize the fluctuation of power and voltage, and the goal of the microgrid layer optimization model is to minimize the overall operating cost of the microgrid;

[0206] The algorithm model improvement module 33 combines the initial sparrow algorithm with the golden sine strategy and the adaptive t distribution strategy to establish an improved sparrow search algorithm model;

[0207] The solution module 34 is used to solve the double-layer optimization model of the multi-microgrid system model by using the improved sparrow search algorithm model, and generate an optimization strategy according to the solution result.

[0208] Among them, regarding the specific implementation methods of each module of the above-mentioned multi-microgrid system two-layer optimization system considering energy storage and new energy, reference can be made to the corresponding contents in the aforementioned method embodiments, which will not be repeated here.

[0209] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0210] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0211] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0212] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0213] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0214] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0215] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0216] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A two-layer optimization method for a multi-microgrid system considering energy storage and new energy, characterized in that: include: Constructing a multi-microgrid system model, and defining performance parameters and cost parameters for each energy generation unit and energy storage unit in the multi-microgrid system model; Define the objective function and constraint conditions of the two-layer optimization model of the multi-microgrid system model; wherein the two-layer optimization model includes a distribution network layer optimization scheduling model and a microgrid layer optimization model, the goal of the distribution network layer optimization scheduling model is to minimize the fluctuation of power and voltage, and the goal of the microgrid layer optimization model is to minimize the overall operating cost of the microgrid; The initial sparrow algorithm is combined with the golden sine strategy and the adaptive t-distribution strategy to establish an improved sparrow search algorithm model. The improved sparrow search algorithm model is used to solve the double-layer optimization model of the multi-microgrid system model, and an optimization strategy is generated according to the solution result.

2. The method according to claim 1, characterized in that The objective function of the distribution network layer optimization scheduling model is: minF=w1f1+w2f2+w3f3 Among them, f1, f2 and f3 represent the quantitative indicators of voltage stability, frequency stability and power supply reliability respectively, and w1-w3 are weight coefficients; Voltage stability f1 is a measure of the ability of the power system to maintain the user voltage within an acceptable range without voltage collapse, which is measured by considering the voltage deviation of all microgrids and distribution network nodes: Where T is the time period in the dispatching cycle, N is the set of all nodes in the microgrid and distribution network, and V n (t) is the voltage amplitude of node n in time period t, V ref is the reference voltage amplitude; Frequency stability refers to the ability of the entire system to maintain a power balance between generation and load, avoiding frequency deviation beyond the allowable range, and is measured by frequency deviation: Among them, f sys (t) is the system frequency in time period t, f ref is the reference frequency; Power supply reliability refers to the ability of the system to continuously provide electricity to users, which is measured by the amount of electricity lost during power outages: Among them, P n (t) is the load demand of node n in period t, U n (t) is a binary variable, indicating whether node n has a power outage during period t, 1 indicates a power outage, and 0 indicates no power outage.

3. The method according to claim 2, characterized in that The constraints of the distribution network layer optimization scheduling model include: a) Power balance constraint: Ensure that at any given point in time, the total supply of the distribution network matches the total load demand: Among them, P load (t) is the total system load power during period t, P loss (t) is the power loss, P MGi,out (t) is the output power of the i-th microgrid in period t, P DN,in / out (t) is the interaction power between the distribution network and the microgrid; b) Voltage stability constraint: Limit the voltage of each node in the system to within the allowable range to ensure voltage stability: Where Vj(t) is the voltage of node j at time period t, V min and V max are the lower and upper limits of voltage; c) Frequency stability constraint: Ensure that the system frequency is within the allowable deviation range: Where f(t) is the system frequency during period t, f min and f max are the lower and upper limits of the frequency; d) Interaction power constraints between microgrid and distribution network: Limit the interaction power between microgrid and distribution network to the physically and technically feasible range: Among them, P DN,min and P DN,max are the lower and upper limits of the interaction power.

4. The method according to claim 3, characterized in that: The objective function of the microgrid layer optimization scheduling model is: minC=w1C1+w2C2 Among them, C1 is the power generation cost, C2 is the pollution control cost, w1 and w2 are weight coefficients; The power generation cost includes fuel cost, maintenance cost, load reduction compensation cost, energy transaction cost with the main power grid and depreciation cost of each power generation unit: Where T is the dispatch period, a is the microgrid consideration coefficient; C f (t) represents the fuel cost of each distributed energy consumption at time t; C OM (t) represents the maintenance cost of each unit at time t; C L (t) is the power outage compensation fee during load shedding; Cgrid represents the energy transaction cost between the microgrid and the main grid; C DEP (t) is the depreciation cost of each generator set at time t; C L =C bu ×Load(t) C grid =CP(t)×CGP(t)-CS(t)×CSP(t) Where N is the number of generators connected to the microgrid; i is the type of distributed energy; C fuel is the price of natural gas; LHV means the lower calorific value of natural gas; P i (t) represents the output active power of distributed energy i at time t; η i (t) represents the fuel combustion efficiency of distributed energy source i at time t, which is the percentage of generated electrical energy relative to the thermal energy input of the fuel; C INS,i represents the installation cost of distributed energy i; f cf,i is the capacity factor; P ri is the rated power of distributed energy i; f cr is the capital recovery coefficient, d is the annual interest rate, l is the service life of the system; C L represents the power outage compensation cost during load shedding; C bu represents the compensation loss cost generated by the microgrid during the load reduction process; Load(t) represents the load reduction in time period t; CP(t) and CS(t) represent the purchase and sale prices between the microgrid and the main grid during time period t; CGP(t) and CSP(t) represent the amount of electricity purchased from the microgrid and sold to the main grid during time period t, respectively; The pollution control cost is: Where b = 0; M is the number of types of pollutants emitted; K is the type of gas emissions from the microgrid; C k is the unit processing fee; ik represents the penalty cost of generating k pollutants per unit of electricity generated by the i-generation unit; r gridk represents the penalty cost of pollutant k generated by the main grid.

5. The method according to claim 4, characterized in that The constraints of the microgrid-level optimization scheduling model include: a) Power balance constraints: in, represents the system load power during period t; It represents the power loss of the system during the power transmission process at time t; is the active power provided by each distributed generator set in time period t; It represents the active power exchanged between the microgrid and the main grid during period t; b) Distributed generation output power constraints: in, and are the minimum and maximum output power limits of distributed generator set i at time t, respectively; c) Exchange constraints between microgrid and distribution network: in, and They represent the minimum and maximum values ​​of the power exchanged between the microgrid and the distribution network respectively; d) Spinning reserve capacity constraints: Where L% is the demand factor of the microgrid system load for the reserve capacity; u s % is the demand coefficient of the rotating reserve capacity of wind power and solar power generation units; wp (t) is the total nominal capacity of wind turbines and solar power generators in the system at time t; e) Greenhouse gas and pollutant emission limits: In the formula, and They represent the emission limits of CO2, SO2 and NO2 gases during the operation of the microgrid respectively; f) Energy storage unit power constraints In the formula, and They represent the maximum power limit and minimum power limit of the energy storage device for charging and discharging respectively; δ is the scheduling time period.

6. The method according to claim 1, characterized in that The initial sparrow algorithm is combined with the golden sine strategy and the adaptive t distribution strategy to establish an improved sparrow search algorithm model, including: Initialize the population and randomly generate multiple sparrow individuals to represent potential solutions; determine the discoverer and follower through fitness evaluation; update the positions of the discoverer and follower. The discoverer adjusts its position based on the current worst individual position and random number, while the follower adjusts its position based on the distance from the discoverer and the number of iterations; at the same time, the scout position is updated based on the current best and worst individual positions and individual fitness; The Golden-S strategy is introduced to improve the position of the SSA discoverer; the t-distribution mutation operator is used to perturb the position of the sparrow; and an improved sparrow search algorithm model is obtained.

7. The method according to claim 1, characterized in that The double-layer optimization model of solving the multi-microgrid system model by using the improved sparrow search algorithm model includes: Select simulation platform and prepare data for multi-microgrid system model; Set algorithm parameters, including safety value ST, finder probability PD and follower probability SD; Collect cost data of each microgrid and initialize the basic voltage and frequency of the distribution network to ensure that the algorithm runs in the correct system state; The improved sparrow search algorithm model is applied to optimize the scheduling strategy of multi-microgrid system.

8. The method according to claim 7, characterized in that The generating of the optimization strategy according to the solution result comprises: Draw a fitness curve to monitor the convergence behavior of the algorithm during the iteration process to ensure that the algorithm can effectively approach the optimal solution; Analyze the output of each power generation unit and verify whether the scheduling strategy reasonably utilizes all available energy, including renewable energy and traditional power generation resources, as well as the charging and discharging strategy of energy storage equipment; Calculate and compare the average operating costs under different dispatch strategies, including energy costs, operation and maintenance costs, and transaction costs with the upper-level power grid, to evaluate the economic feasibility of each strategy; Evaluate the impact of dispatch strategies on the stability of the distribution network, including the fluctuation range of voltage and frequency, line load conditions, and the ability to respond to potential faults, to ensure that the selected strategy can maintain stable operation of the power grid under various conditions.

9. A two-layer optimization system for multi-microgrid systems considering energy storage and new energy, characterized in that: include: A system model building module, used to build a multi-microgrid system model and define performance parameters and cost parameters for each energy generation unit and energy storage unit in the multi-microgrid system model; An optimization model building module, used to define the objective function and constraint conditions of the two-layer optimization model of the multi-microgrid system model; wherein the two-layer optimization model includes a distribution network layer optimization scheduling model and a microgrid layer optimization model, the goal of the distribution network layer optimization scheduling model is to minimize the fluctuation of power and voltage, and the goal of the microgrid layer optimization model is to minimize the overall operating cost of the microgrid; The algorithm model improvement module combines the initial sparrow algorithm with the golden sine strategy and the adaptive t-distribution strategy to establish an improved sparrow search algorithm model; A solution module is used to solve the double-layer optimization model of the multi-microgrid system model using the improved sparrow search algorithm model, and generate an optimization strategy according to the solution result.

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