Method for improving power supply energy and economic benefit of regional power grid by considering energy storage

Abstract

The invention provides a method and system for improving power supply energy and economic benefits of a regional power grid by considering energy storage. The method comprises the steps of constructing a model foundation, setting a model, solving the model and the like, designs and realizes a power grid system comprising a distributed generator, a wind turbine, an energy storage system and a load, and aims to enhance the performance of the energy storage system in a power grid and utilize wind energy. And meanwhile, the efficiency, power output, investment, operation and maintenance cost and the like of the distributed generator and the wind turbine are calculated and set in detail. According to the method, the fitness function and the constraint condition are formulated, and the model is solved through the dragonfly optimization algorithm, so that the overall performance optimization of the power grid system is realized, the stability and the flexibility are improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and more specifically relates to a method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account. Background Art

[0002] At present, with the increasing application of renewable energy, especially wind power generation, photovoltaic power generation, etc., it has brought challenges to the existing power grid system. Due to the instability or uncertainty of energy sources such as wind power and solar power, the stability and reliability of the power grid may be affected, and an effective regulation measure needs to be implemented at this time. In addition, with the large-scale popularization of electric vehicles, the demand for electric vehicle charging has also posed new challenges to the power grid system. Especially during the peak charging time of vehicles, if it is not properly designed and planned, it is likely to cause a serious impact on the power grid.

[0003] In addition, facing the popularization of large-scale distributed generation, how to effectively coordinate distributed generation with the power grid to achieve optimized scheduling and management is also a major issue. Not only should the real-time balance of the power grid be considered, but also energy storage equipment should be fully utilized to improve power generation efficiency and reduce power generation costs.

[0004] There are some solutions to these existing problems. For example, through the intelligent transformation of the power grid, the advanced system control technology and energy management system can be used to monitor and manage the power grid in real time; or through the construction of large-scale energy storage power stations, the energy storage devices such as batteries or supercapacitors can be used to relieve the pressure on the power grid and improve the stability of the power grid. However, existing solutions often have some problems, such as high technical complexity, high cost, low return on investment, and difficult management. Therefore, researching and developing a new and more cost-effective method for power grid regulation has become an important topic.

[0005] The present invention is to solve the above-mentioned problems by constructing a power grid system model including wind power generation, energy storage system and load, and solving the model using the dragonfly optimization algorithm, thereby improving the power supply energy and economic benefits of the regional power grid to meet the challenges faced by the current power system. Summary of the invention

[0006] The purpose of the present invention is to provide a method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account. The problem to be solved is how to improve the power supply energy and economic benefits of a regional power grid by constructing and optimizing a power grid system model with wind power generation, diesel power generation and energy storage systems as main elements, and introducing novel adjustment and solution methods, so as to make full use of energy storage equipment to improve power generation efficiency and reduce costs.

[0007] In order to achieve the above object, the present invention is implemented by adopting the following technical scheme: a method for improving the power supply energy and economic benefits of a regional power grid by taking into account energy storage, comprising:

[0008] Construct model foundation; establish model; solve model;

[0009] A grid system consisting of three distributed generators (DGs), a wind turbine, an energy storage system and a load is established, integrating various components into an integrated system with the goal of enhancing the performance of the energy storage system in a grid that utilizes wind energy to a certain extent.

[0010] Furthermore, the model basis includes:

[0011] Diesel generators are used as backup power sources in the grid; the function of diesel generator power output depends on two variables: the lower heating value (LHV) of the fuel and the fuel consumption rate (FCR), which is formally expressed as follows from a mathematical point of view:

[0012] P DG =FCR×LHV (1)

[0013] LHV refers to Lower Heating Value, which quantifies the amount of heat energy released per unit of fuel consumed, usually expressed in joules per liter (J / L). FCR, on the other hand, stands for Fuel Consumption Rate, which indicates how quickly a diesel generator utilizes fuel, usually expressed in liters per hour (L / h).

[0014] DG efficiency is expressed as the ratio of its beneficial power output to the energy input obtained from the fuel source and is calculated as follows:

[0015]

[0016] Among them, η DG Refers to the efficiency of DG.

[0017] Furthermore, the model basis also includes a wind turbine, and the electricity generated by the wind turbine is achieved in the following ways:

[0018]

[0019] Among them, ν, ν rated , ν cutin and ν couout They represent the local speed, rated wind speed, cut-in wind speed and cut-out wind speed of the wind turbine respectively. Then, the rated power can be achieved by:

[0020]

[0021] Among them, η w and η rare the efficiency of the wind turbine and the efficiency of the reducer, respectively, and C p , a and A W represent the power coefficient, air density and swept area respectively.

[0022] Investment and O&M costs are often separated into two separate categories of wind turbine expenditures. The total amount paid for a wind turbine, including initial cost, installation cost and any financing charges, is called the net annual cost of the investment (IWT(NAV)). Typically, this cost is calculated as a net present value and distributed over the life of the wind turbine.

[0023] The investment cost and operation and maintenance costs of a wind turbine over its useful life are combined to give the total net annual cost (TNAC) of a wind turbine, a single figure used to assess the economic viability of a wind turbine. TNACWT can be calculated using the following formula:

[0024] TNAC WT =I WT(NAV) +OM WT (5)

[0025] Where:

[0026] OM WT =A W ×C OM-WT (6)

[0027]

[0028] Among them, C WT and C OM-WT represent the unit price of wind turbines,

[0029] TNAC WT represents the total net annual cost of the wind turbine;

[0030] I WT(NAV) represents the net annual cost of investment in a wind turbine, including initial cost, installation cost and various financing charges;

[0031] OM WT represents the annual operation and maintenance costs of the wind turbine;

[0032] A W represents the area of ​​the wind turbine;

[0033] C OM-WT represents the operation and maintenance cost of wind turbines per unit area;

[0034] C WT represents the cost of wind turbines per unit area (including installation costs);

[0035] i represents the discount rate;

[0036] n represents the age of the wind turbine;

[0037] That is, the cost of the wind turbine plus the cost of installing the wind turbine, plus the annual operating and maintenance costs of the wind turbine.

[0038] Further, the model building includes:

[0039] Fitness function: The total cost will be determined by adding the costs of wind farms, generators, and energy storage systems, and the objective function of maximizing the scale is given by the following formula:

[0040]

[0041] Subject to the following constraints

[0042]

[0043] Where j = 1, 2, ..., M, P L (t) is the load power required at time t,

[0044] J represents the objective function, i.e., the total cost;

[0045] C w (t) represents the cost of the wind farm at time t. The required investment as well as the cost of operation and maintenance;

[0046] C g (t) represents the cost of the generator at time t. It includes the initial investment cost, operation and maintenance costs;

[0047] C b (t) represents the cost of the energy storage system at time t, including initial investment cost, operation and maintenance cost;

[0048] P g (j, ι) represents the power output of the jth generator at time t;

[0049] and They represent the minimum and maximum power that the j-th generator can output respectively;

[0050] P w (t) represents the power output of the wind turbine at time t;

[0051] P b (t) represents the power output of the energy storage system at time t;

[0052] represents the total power output of the diesel generator generator at time t;

[0053] P L (t) represents the load power that needs to be supplied at time t;

[0054] Furthermore, for battery energy storage, the charging power and discharging power of the battery adopt the negative power and positive power convention. Therefore, the battery acts as a load when charging and as a generator when discharging. The optimization time interval (Δt) is assumed to be one hour

[0055]

[0056] in, and represents the minimum and maximum power available in the battery energy storage at time t, P b (t) represents the power output of the energy storage system at time t;

[0057] The battery discharge and charge constraints are as follows:

[0058] P b,dch (t)∈[0,P b ×μ b ](12)

[0059] -P b ×(1-μ b )≤P b,dch (t)≤0 (13)

[0060] Binary variable μ b Displays the working status of the battery. It is equal to 1 during discharge and equal to 0 during charging. The battery energy should never exceed the above limit.

[0061]

[0062] Determine the charge and discharge power according to the battery energy

[0063]

[0064] Among them, P b Define battery power, Δt is a 1-hour time interval, E b represents the energy at time t, P b,dch Specifies the discharge power during t.

[0065] Furthermore, the model is solved using the Dragonfly Algorithm (DA).

[0066] Furthermore, the dragonfly optimization algorithm is described as being inspired by the swarm behavior of dragonflies in nature, where a swarm of dragonflies uses three behavior modes simultaneously: dispersion, aggregation, and search. Each individual affects the behavior of all or some of the individuals;

[0067] Mathematical formula for updating the dragonfly position:

[0068]

[0069] represents the position of the i-th dragonfly in the n-th iteration;

[0070] represents the optimal position of food and safety observed by the i-th dragonfly in the n-th iteration;

[0071] a and c are weight parameters;

[0072] The elimination phase will be executed after a certain number of iterations of ep. In this phase, some solutions to the problem will be eliminated, specifically, those with relatively low cost values. The eliminated solutions will be replaced by random solutions, which can increase the diversity of solutions and prevent the algorithm from falling into the local optimal solution.

[0073] Furthermore, at each elimination phase (ep iteration):

[0074] Calculate the fitness value (e.g. total cost) of each solution (x_i);

[0075] Sort all solutions from small to large according to fitness value;

[0076] Replace the first et solutions with new random solutions;

[0077] Assuming the value of parameter ep is 40, the elimination phase will be performed every 40 iterations. A parameter et is also introduced to determine the number of solutions to be eliminated in each iteration. For example, if a population has 50 individuals and the value of et is 30, then in each elimination phase, the 30 solutions with the lowest cost values ​​will be eliminated and replaced with random solutions.

[0078] After the entire solution process is completed, the solution with the smallest cost value in the population is the optimal solution, which represents the best operating strategy of the power grid system under given cost constraints.

[0079] Beneficial effects of the present invention:

[0080] 1. The present invention constructs a power grid system model covering multiple units such as wind turbines, diesel generators, and energy storage systems, conducts in-depth analysis of key performance parameters such as power output, efficiency, and cost of each part, and sets corresponding adaptability functions and constraints based on this. Such comprehensive and in-depth design and adjustment greatly improves the overall performance of the power grid system.

[0081] 2. By introducing the energy storage system, the instability of renewable energy such as wind power generation can be effectively balanced, ensuring the stable operation of the power grid. At the same time, the dragonfly optimization algorithm is used to solve the model, making the power grid more adaptable and flexible, and able to effectively respond to various complex and changing load demands.

[0082] 3. The present invention comprehensively considers the investment cost and operation and maintenance cost of various components. Through optimized design, the total cost of power grid operation is greatly reduced, thereby improving the economic benefits of the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 The figure is a flow chart of the method of the present invention.

[0084] Figure 2 This is a diagram of the power grid system architecture of the present invention.

[0085] Figure 3 This is a model block diagram of the present invention.

[0086] Figure 4 This is a flow chart of the dragonfly optimization algorithm of the present invention. DETAILED DESCRIPTION

[0087] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0088] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0089] Example: Figure 1 As shown, the methods of taking energy storage into account to improve the energy and economic benefits of regional power grid power supply include:

[0090] S1. Building the model foundation

[0091] The present invention establishes a power grid system including three distributed generators (DG), a wind turbine, an energy storage system and a load, integrating various components into an integrated system, with the goal of enhancing the performance of the energy storage system in the power grid, which utilizes wind energy to a certain extent. Figure 2 The architecture of the proposed power grid system is shown.

[0092] (1) Diesel generator

[0093] The diesel generators serve as a backup power source in the grid. The system provides power in the event of reduced wind power generation or increased demand.

[0094] When the battery storage capacity is exhausted. The generator helps to provide a steady supply of electricity and helps maintain the stability of the grid. The function that represents the power output of a diesel generator depends on two variables: the lower heating value (LHV) of the fuel and the fuel consumption rate (FCR). From a mathematical point of view, the diesel generator power output can be expressed as follows:

[0095] P DG =FCR×LHV (1)

[0096] LHV refers to Lower Heating Value, which quantifies the amount of heat energy released per unit of fuel consumed, usually expressed in joules per liter (J / L). FCR, on the other hand, stands for Fuel Consumption Rate, which describes how quickly a diesel generator utilizes fuel, usually expressed in liters per hour (L / h).

[0097] DG efficiency is expressed as the ratio of its beneficial power output to the energy input obtained from the fuel source. It is calculated as follows:

[0098]

[0099] Among them, η DG Refers to the efficiency of DG.

[0100] (2) Wind turbines

[0101] Wind turbines are an environmentally sustainable energy source that converts mechanical energy from the movement of wind into electrical energy. The system consists of rotor blades that rotate when exposed to the wind, which in turn drives the generator to generate electricity. Wind turbines play a vital role in the power grid as they efficiently capture and utilize renewable energy to power the entire system. The electricity generated by wind turbines is achieved through:

[0102]

[0103] Among them, ν, ν rated , ν cutin and ν cououtThey respectively describe the local speed, rated wind speed, cut-in wind speed and cut-out wind speed of the wind turbine; Pr represents the rated power of the wind turbine; P WTG Represents the electrical power generated by the wind turbine. The rated power can then be achieved by:

[0104]

[0105] Among them, η w and η r respectively describe the WT generator efficiency and reducer efficiency, while C p , a and A W represent the power coefficient, air density and swept area respectively.

[0106] Investment and O&M costs are often separated into two separate categories of wind turbine expenditures. The total amount paid for a wind turbine, including initial cost, installation cost and any financing charges, is called the net annual cost of the investment (IWT(NAV)). Typically, this cost is calculated as a net present value and distributed over the life of the wind turbine.

[0107] The “Annual Operation and Maintenance Cost” (OMWT) describes the ongoing expenses associated with operating and maintaining a wind turbine, including maintenance, monitoring and repairs. This cost is also amortized over the life of the wind turbine and varies depending on the size and type of turbine, location and other factors.

[0108] The investment cost and operating and maintenance costs of a wind turbine over its lifetime are combined to give the total net annual cost (TNAC) of a wind turbine. This figure, expressed as a single number, is used to assess the economic viability of a wind turbine. TNAC WT The total net annual cost of a wind turbine can be calculated using the following formula:

[0109] TNAC WT =I WT(NAV) +OM WT (5)

[0110] Where:

[0111] OM WT =A W ×C OM-WT (6)

[0112]

[0113] Among them, C WT and C OM-WTThey represent the unit price of a wind turbine, i.e. the cost of the wind turbine plus the cost of installing the wind turbine, and the annual operation and maintenance cost of the wind turbine, respectively.

[0114] (3) AC / AC and AC / DC converters

[0115] AC / AC and AC / DC converters are essential components of the power grid by assisting in converting electrical energy between different voltage levels and forms. AC / AC converters convert alternating current (AC) from wind turbines and diesel generators to the voltage and frequency required by the grid. AC / DC converters convert alternating current (AC) power from wind turbines and diesel generators to direct current (DC) to charge battery storage systems.

[0116] 1) Switch control

[0117] The flow of electrical energy to the grid is controlled by a switch control system. Based on energy demand and availability, it manages the switching between various power sources, including wind turbines, diesel generators and battery storage. Switch control ensures optimal use of the energy at hand and maintains grid stability.

[0118] S2. Model building

[0119] The parameters used in the present invention include system parameters and algorithm parameters. The following are the specific parameters we use:

[0120] 1. Cost coefficient, fuel consumption, power output limit, rated power and power curve are parameters used by both diesel generators and wind turbines. These parameters are integrated into the meta-heuristic algorithm model by establishing the objective function and constraints of the optimization problem. The objective function includes the operating cost and dispatch cost of the grid, which depend on the power output and fuel consumption of the diesel generators and wind turbines. The constraints ensure that the load demand, wind, generator limits and power balance are met, ensuring that the power supply and demand are met and the generators operate within technical and operational limitations.

[0121] 2. For AC / AC converters and AC / DC converters, the parameters used are rated power, efficiency and losses. These parameters are incorporated into the meta-heuristic algorithm model by defining the power flow equation and loss equation of the power grid system. The power flow equation describes how to convert and distribute power between the grid components, while the loss equation takes into account the losses that occur during the power conversion and distribution process.

[0122] 3. The switch control utilizes several parameters, namely the switch state, the switch cost, and the switch constraints. These parameters are integrated into the metaheuristic algorithm model by defining the operation mode variable and the switch cost term in the optimization problem. The operation mode variable is a binary variable that represents the connection of the grid to the main grid or its operation in island mode, as well as the charge or discharge state of the battery. On the other hand, the switch cost term represents the additional cost incurred during the transition between different operation modes. In addition, the switch constraints are logical constraints that ensure the consistency and feasibility of the operation mode variables.

[0123] 4. For the battery energy storage system (BESS), it depends on various parameters such as maintenance and capital costs, interest rates, lifespan, round-trip efficiency, state of charge, depth of discharge, and power output limits. These parameters are incorporated into the model using a metaheuristic algorithm by defining the objective function, constraints, and fitness function of the optimization problem. The objective function includes the maintenance and capital costs of the battery, which are affected by interest rates and battery life. Constraints include the battery's state of charge, depth of discharge, and power output limits, ensuring that the battery operates within its optimal range and does not exceed its capacity or degrade too quickly. Finally, the fitness function includes the depth of discharge, which reflects the operating cost and degradation of the battery.

[0124] (1) Fitness function:

[0125] Energy storage system is a key part of the power grid, responsible for storing excess energy generated by diesel generators and wind turbines. It consists of various energy storage devices with different time scales to store electrical energy. By storing energy during periods of high production or low demand, and providing energy during periods of high demand or low production, BESS helps balance the supply and demand of electricity in the power grid. It improves the stability, reliability and effectiveness of the power grid.

[0126] The total cost will be determined by adding the costs of the wind farm, generators and energy storage systems. The objective function for maximizing scale is given by the following formula:

[0127]

[0128] Subject to the following constraints.

[0129]

[0130]

[0131] Where j = 1, 2, ..., M, P L (t) is the load power required at time t,

[0132] J represents the objective function, i.e., the total cost;

[0133] C w(t) represents the cost of the wind farm at time t. The required investment as well as the cost of operation and maintenance;

[0134] C g (t) represents the cost of the generator at time t. It includes the initial investment cost, operation and maintenance costs;

[0135] C b (t) represents the cost of the energy storage system at time t, including initial investment cost, operation and maintenance cost;

[0136] P g (j, ι) represents the power output of the jth generator at time t;

[0137] and They represent the minimum and maximum power that the j-th generator can output respectively;

[0138] P w (t) represents the power output of the wind turbine at time t;

[0139] P b (t) represents the power output of the energy storage system at time t;

[0140] represents the total power output of the diesel generator generator at time t;

[0141] P L (t) represents the load power that needs to be supplied at time t.

[0142] In addition, for battery energy storage, the charging power and discharging power of the battery adopt the negative power and positive power convention. Therefore, the battery acts as a load when charging and as a generator when discharging. The optimization time interval (Δt) is assumed to be one hour.

[0143]

[0144] in, and represents the minimum and maximum power available in the battery energy storage at time t, P b (t) represents the power output of the energy storage system at time t.

[0145] In addition, the battery discharge and charge constraints are as follows:

[0146] P b,dch (t)∈[0,P b ×μ b ] (12)

[0147] -P b ×(1-μ b)≤P b,dch (t)≤0 (13)

[0148] Binary variable μ b Displays the working status of the battery. It is equal to 1 during discharge and equal to 0 during charging. In addition, the battery energy should never exceed the above limit,

[0149]

[0150] E b (t) represents the energy stored in the energy storage battery at time t; Respectively represent the minimum and maximum values ​​of the energy storage battery;

[0151] Determine the charge and discharge power according to the battery energy

[0152]

[0153] Among them, P b Define battery power, Δt is a 1-hour time interval, E b represents the energy at time t, P b,dch The discharge power during the specified period t, E b (t+1) represents the energy storage battery energy at time t+1; Δt represents the operation time interval; P b,dch Indicates discharge power; η b,dch Indicates the efficiency of the battery during discharge; P b,ch Represents charging power; η b,ch Indicates the efficiency of the battery when charging.

[0154] The proposed method for determining the optimal battery capacity requires the use of the Dragonfly Optimization Algorithm, which is an improved meta-heuristic algorithm. The main goal of the algorithm is to achieve the optimal balance of power generation and load demand every hour, thereby promoting efficient operational performance. The algorithm is used to allocate power from generators and / or energy storage systems in situations where wind power generation is insufficient to meet demand. The algorithm incorporates the depth of discharge (DOD) of the battery as it has a direct impact on operating expenses.

[0155] The algorithm seeks to minimize the total cost associated with energy consumption by taking into account the depth of discharge (DOD) of the battery. The system allocates the optimal power output by considering the price formula associated with the available energy and generators, as well as the load demand per hour. In addition, the algorithm promotes adaptability in the utilization of existing resources. If the cost of a battery energy storage system (BESS) exceeds that of a generator, the algorithm can choose to use the generator to charge the battery.

[0156] In this case, the depth of discharge (DOD) of the battery is of great significance as a determining factor. At the end of each hour, the algorithm calculates the state of charge (SOC) of the battery and determines the actions required for the next hour. Optimizing the overall performance of the grid may require adjusting the power source or modifying the rates.

[0157] In which charging and discharging takes place. The main objective of the proposed approach is to minimize the overall cost of the power grid by utilizing a power planning strategy. The algorithm aims to achieve the best balance between power generation, load demand, and overall operating cost within the power grid by carefully selecting and optimizing the battery capacity. In the next section, the algorithm is explained in full.

[0158] S3. Model solution

[0159] like Figure 4 As shown, the model is solved using the Dragonfly Algorithm (DA);

[0160] S301. Initialize the dragonfly population: Each dragonfly in this population represents a possible solution. Each dragonfly has multiple attributes, including position (representing the value of the solution), speed (affecting the next position update), and behavior (used to control the way the dragonfly interacts with other members of the population). When initializing the position of each dragonfly, it should be randomly distributed in the solution space.

[0161] S302, calculate fitness: the fitness function is used to evaluate the quality of each dragonfly (solution). In this power grid optimization problem, the fitness function can be defined as the total benefit of the power grid.

[0162] S303, Update speed and position: The dragonfly updates its speed and position by imitating the behaviors of other dragonflies in the same group. These behaviors include separation, aggregation, alignment, exploration and migration. Specifically expressed as a mathematical formula, the speed and position update process is as follows:

[0163] The dragonfly optimization algorithm is described as being inspired by the actions of dragonfly swarms in nature, where dragonfly swarms use three behavioral modes simultaneously: dispersion, aggregation, and search. Each individual affects the behavior of all or some individuals;

[0164] Mathematical formula for updating the dragonfly position:

[0165]

[0166] represents the position of the i-th dragonfly in the n-th iteration;

[0167] represents the optimal position of food and safety observed by the i-th dragonfly in the n-th iteration;

[0168] a and c are weight parameters;

[0169] The weight parameters a and c are key parameters related to the dragonfly behavior pattern. Parameter a controls the dispersion pattern, that is, the attraction of individual dragonflies in the direction of food and the power to prevent them from avoiding the predicted dangerous direction, and parameter c controls the aggregation pattern, that is, the ability of individual dragonflies to aggregate in the direction of food and safety.

[0170] The values ​​of weight parameters a and c are set between 0 and 1. These two parameters can be fixed or dynamically changed, and need to be selected according to the characteristics and constraints of the optimization problem. In some cases, in order to ensure the global search capability and convergence of the Dragonfly Optimization Algorithm, a strategy of dynamically adjusting weight parameters is adopted.

[0171] For example, set the initial value a0, c0, and set the end value a end , c end , and then in each iteration, according to the increase in the number of iterations n, the values ​​of parameters a and c are dynamically adjusted, for example, a linear attenuation strategy is adopted as follows:

[0172] a(n)=a0-(a0-a end )*n / N

[0173] c(n)=c0-(c0-c end )*n / N

[0174] Among them, N is the total number of iterations set. In this way, the globality of the search can be guaranteed, and the ability of local search can be gradually improved, thereby improving the performance of the dragonfly optimization algorithm.

[0175] S304, elimination phase, will be executed once after a certain number of iterations ep. In this phase, some solutions to the problem will be eliminated, specifically, those with relatively low cost values ​​will be eliminated. The eliminated solutions will be replaced by random solutions, which can increase the diversity of solutions and prevent the algorithm from falling into the local optimal solution;

[0176] In each elimination phase (ep iteration):

[0177] Calculate the fitness value (e.g. total cost) of each solution (x_i);

[0178] Sort all solutions from small to large according to fitness value;

[0179] Replace the first et solutions with new random solutions;

[0180] Assuming the value of parameter ep is 40, the elimination phase will be performed every 40 iterations. A parameter et is also introduced to determine the number of solutions to be eliminated in each iteration. For example, if a population has 50 individuals and the value of et is 30, then in each elimination phase, the 30 solutions with the lowest cost values ​​will be eliminated and replaced with random solutions.

[0181] S305, check the termination condition: if the termination condition is met (for example, the maximum number of iterations is reached, or the global optimal solution does not change in multiple consecutive iterations), output the current global optimal solution; otherwise, return to step 2 for the next round of iterations.

[0182] After the entire solution process is completed, the solution with the smallest cost value in the population is the optimal solution, which represents the best operating strategy of the power grid system under given cost constraints.

[0183] This solution process is carried out in a population, which is a series of possible solutions. The optimization algorithm tries to find an optimal solution by constantly searching and trying new solutions in the population. The optimal solution is the solution that minimizes (or maximizes) the objective function value. In this process, there will be a basis for measuring the quality of the solution, that is, its corresponding objective function value, which is the cost value in this case.

[0184] After all the solution processes are completed, the solution with the smallest cost value in the population is taken as the optimal solution we have found. This optimal solution is actually an operation strategy of the power grid system, which indicates which of all possible operation strategies can meet the power supply demand of the power grid while making the operation cost of the entire power grid system the lowest. Therefore, this optimal solution represents the best operation strategy of the power grid system under a given cost constraint.

[0185] Assuming the value of parameter ep is 40, the elimination phase will be executed once every 40 iterations. The parameters mentioned above are already incorporated into the traditional FDO model. Their values ​​are determined by calculating the ratio of the initial population size. Based on the given population size of 50 individuals and the parameter of et value of 30, it can be inferred that in each iteration of the elimination process, candidates with a cumulative count of 30 with a lower cost value will be eliminated. That is, in the elimination phase, the cost value of each candidate solution is calculated, and then the 30 candidates with the lowest cost value are selected for elimination, and then these eliminated candidates are replaced with new random candidates to increase the diversity of solutions.

[0186] The term "th" indicates the ratio of the highest or lowest achievable magnitude within the chosen field of study. The parameter Th can take a value of 80, even though the initial range of parameter exploration is limited to a maximum value of -1 to 1. This represents a unique occurrence of the case that has been considered. At each step of the elimination process, the parameters of the particles are thoroughly evaluated to determine if they exceed 60% of the absolute value of the solution space. Once the prerequisites are met, the exploration domain of the considered parameters is refined.

[0187] Application examples:

[0188] Assume a grid system that includes 3 distributed diesel generators (DG), a wind turbine, an energy storage system (such as a lithium-ion battery), and a series of loads. The design of this small grid is mainly to maximize the use of wind energy resources, while taking into account the instability of wind energy, diesel generators and energy storage systems are used as auxiliary.

[0189] The power output function of distributed diesel generators is based on the lower heating value (LHV) and fuel consumption rate (FCR) of the fuel. Assume that the LHV of the fuel is 42,500 kJ / kg and the FCR is 0.242 kg / kWh. The maximum power of each generator is 50 kW, and the useful power generated per hour is about 12 kW.

[0190] The power calculation of a wind turbine varies according to the wind speed. In this example, it is assumed that the average wind speed in the last hour is 7.0m / s and the rated power of the turbine is 100kW. It is assumed that the efficiency of the turbine is 0.35, the efficiency of the reducer is 0.05, the power coefficient is 0.375, and the air density is 1.225kg / m 3 , the swept area is 314m 2 . It can be calculated that the power generated by the wind turbine in one hour is 74.71kW.

[0191] Secondly, for battery energy storage, the maximum charging power of the battery is set to 100kW, and the maximum discharging power is set to -100kW. Taking into account the charging and discharging efficiency of the battery, the actual power output of the battery needs to be adjusted. Under this assumption, the maximum power of the battery in the charging state (battery as load) is 100kW, and the maximum power in the discharging state (battery as power generation source) is -90kW.

[0192] Then, by using the fitness function and constraints, combined with the dragonfly optimization algorithm, the optimal generator output, battery charge / discharge strategy and load allocation method are determined to minimize the total system cost. In this embodiment, the total cost is reduced by about 15%.

[0193] It can be seen that the solution of the present invention is to improve the performance of the power grid and enhance the stability and flexibility of power supply by integrating various elements in the power grid, building and optimizing the model, and then reasonably scheduling and managing according to actual needs.

[0194] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0195] It should be understood that the detailed description of the technical solutions of the present invention by means of the preferred embodiments is illustrative rather than restrictive. A person skilled in the art may modify the technical solutions described in the embodiments, or replace some of the technical features by equivalents, based on reading the specification of the present invention; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the power supply energy and economic benefits of a regional power grid by taking into account energy storage, characterized in that: The method includes: Construct model foundation; establish model; solve model; A grid system consisting of three distributed generators (DGs), a wind turbine, an energy storage system and a load is established, integrating various components into an integrated system with the goal of enhancing the performance of the energy storage system in a grid that utilizes wind energy to a certain extent.

2. The method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account according to claim 1, characterized in that: The model basis includes: Diesel generators are used as backup power sources in the grid; the function of diesel generator power output depends on two variables: the lower heating value (LHV) of the fuel and the fuel consumption rate (FCR); from a mathematical point of view, it is formally expressed as follows: P DG =FCR×LHV (1) LHV refers to lower heating value, which quantifies the heat energy released per unit of fuel consumed, usually in joules per liter (J / L); FCR stands for fuel consumption rate, which indicates the rate at which a diesel generator utilizes fuel, usually in liters per hour (L / h); DG efficiency is expressed as the ratio of its beneficial power output to the energy input obtained from the fuel source and is calculated as follows: Among them, η DG Refers to the efficiency of DG.

3. The method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account according to claim 1, characterized in that: The model described also includes wind turbines, which generate electricity through: Among them, ν, ν rated , ν cutin and ν couout They represent the local speed, rated wind speed, cut-in wind speed and cut-out wind speed of the wind turbine generator set respectively. The rated power can be achieved in the following ways: Among them, η w and η r are the wind turbine efficiency and reducer efficiency, generator efficiency and reducer efficiency, respectively, and C p , a and A W represent the power coefficient, air density and swept area respectively.

4. The method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account according to claim 3 is characterized in that: The investment cost and operation and maintenance costs of a wind turbine over its useful life are combined to give the total net annual cost of the wind turbine, expressed as a single number and used to assess the economic viability of a wind turbine. TNAC WT Use the following formula to calculate: TNAC WT =I WT(NAV) +OM WT (5) Where: IF WT =A W ×C OM-WT (6) Among them, C WT and C OM-WT represent the unit price of wind turbines, TNAC WT represents the total net annual cost of the wind turbine; I WT(NAV) represents the net annual cost of investment in a wind turbine, including initial cost, installation cost and various financing charges; OM WT represents the annual operation and maintenance costs of the wind turbine; A W represents the area of ​​the wind turbine; C OM-WT represents the operation and maintenance cost of wind turbines per unit area; C WT represents the cost of wind turbines per unit area (including installation costs); i represents the discount rate; n represents the age of the wind turbine; That is, the cost of the wind turbine plus the cost of installing the wind turbine, plus the annual operating and maintenance costs of the wind turbine.

5. The method of claim 1 for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account, characterized in that: The model building includes: Fitness function: The total cost will be determined by adding the costs of wind farms, generators, and energy storage systems, and the objective function of maximizing the scale is given by the following formula: Subject to the following constraints Where j = 1, 2, ..., M, P L (t) is the load power required at time t, J represents the objective function, i.e., the total cost; C w (t) represents the cost of the wind farm at time t. The required investment as well as the cost of operation and maintenance; C g (t) represents the cost of the generator at time t. It includes the initial investment cost, operation and maintenance costs; C b (t) represents the cost of the energy storage system at time t, including initial investment cost, operation and maintenance cost; P g (j, ι) represents the power output of the jth generator at time t; and They represent the minimum and maximum power that the j-th generator can output respectively; P w (t) represents the power output of the wind turbine at time t; P b (t) represents the power output of the energy storage system at time t; represents the total power output of the diesel generator generator at time t; P L (t) represents the load power that needs to be supplied at time t.

6. The method of claim 5 for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account, characterized in that: For battery energy storage, the charging power and discharging power of the battery adopt the negative power and positive power convention. The battery acts as a load when charging and as a generator when discharging. The optimization time interval (Δt) is assumed to be one hour. in, and represents the minimum and maximum power available in the battery energy storage at time t, P b (t) represents the power output of the energy storage system at time t; The battery discharge and charge constraints are as follows: P b,dch (t)∈[0,P b ×μ b ] (12) -P b ×(1-μ b )≤P b,dch (t)≤0 (13) Binary variable μ b Displays the working status of the battery. It is equal to 1 during discharge and equal to 0 during charging. The battery energy should never exceed the above limit. Determine the charge and discharge power according to the battery energy Among them, P b Define battery power, Δt is a 1-hour time interval, E b represents the energy at time t, P b,dch Specifies the discharge power during t.

7. The method of claim 1 for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account, characterized in that: The model is solved using the Dragonfly Algorithm (DA).

8. The method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account according to claim 7 is characterized in that: The dragonfly optimization algorithm uses three modes simultaneously: dispersion, aggregation, and search. Each individual affects the behavior of all or some individuals. Mathematical formula for updating the dragonfly position: X i n represents the position of the i-th dragonfly in the n-th iteration; represents the optimal position of food and safety observed by the i-th dragonfly in the n-th iteration; a and c are weight parameters, The elimination phase will be executed once after a certain number of iterations ep. In this phase, some solutions to the problem will be eliminated, that is, those solutions with relatively low cost values ​​will be eliminated. The eliminated solutions will be replaced by random solutions, which can increase the diversity of solutions and prevent the algorithm from falling into the local optimal solution.

9. The method for improving the power supply energy and economic benefits of a regional power grid by taking energy storage into account according to claim 8, characterized in that: In the elimination phase, in each elimination phase, each ep iteration: Calculate the fitness value of each solution x_i; Sort all solutions from small to large according to fitness value; Replace the first et solutions with new random solutions; If the value of the parameter ep is 40, the elimination phase will be performed every 40 iterations. A parameter et is also introduced to determine the number of solutions to be eliminated in each iteration. If a population has 50 individuals and the value of et is 30, then in each elimination phase, the 30 solutions with the lowest cost values ​​will be eliminated and replaced with random solutions; After the entire solution process is completed, the solution with the smallest cost value in the population is the optimal solution, which represents the best operation strategy of the power grid system under given cost constraints.

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