New energy microgrid optimization scheduling method considering flexible load

Abstract

The invention relates to the technical field of micro-grid optimal scheduling, in particular to a new energy micro-grid optimal scheduling method considering flexible load, which comprises the following steps: constructing a flexible load response model, based on the maximum and minimum values of the main online shopping capacity and the maximum peak-valley difference rate allowed by the new energy system, a self-adaptive adjustment strategy of the flexible load adjustment cost is constructed; based on a self-adaptive adjustment strategy of the flexible load adjustment cost, constructing an optimal scheduling model taking the maximum annual comprehensive profit of the micro-grid system as a target; and optimizing and solving the optimal scheduling model. According to the method, the flexible load response model is constructed, the factors such as economy, technicality and safety are comprehensively considered, the adaptive adjustment strategy is formulated, and the efficient optimization algorithm is adopted to solve the optimization scheduling model, so that the annual comprehensive profit of the micro-grid system is maximized, the utilization rate and the absorption capacity of new energy are effectively improved, and the energy utilization rate of the micro-grid system is improved. Therefore, the operation efficiency and reliability of the new energy microgrid are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization scheduling, and in particular to a new energy microgrid optimization scheduling method considering flexible loads. Background Art

[0002] As the penetration rate of renewable energy continues to increase, the uncertainty of its output limits the absorption capacity of new energy. The application of microgrids can more accurately improve the absorption level of new energy and promote the transformation and upgrading of the energy structure. In this process, flexible loads, as an important part of the demand side, can effectively improve the load power curve of the power grid and reduce the system peak load and operating costs. Therefore, for the economic dispatch problem of new energy microgrids, it is imperative to consider the trend of flexible load characteristics.

[0003] Since microgrid optimization scheduling usually involves nonlinear multi-objective optimization problems, it is more suitable to be solved by artificial intelligence algorithms. Compared with mathematical programming, neural networks, and multi-objective optimization methods, intelligent optimization algorithms have obvious advantages, including:

[0004] (1) Wide range of applications: It can handle a variety of complex, multi-objective, nonlinear and uncertain problems without the need for precise mathematical models;

[0005] (2) Global search: It avoids the risk of mathematical programming methods falling into local optimal solutions and can better find the global optimal solution;

[0006] (3) Flexibility and scalability: The intelligent optimization algorithm is relatively easy to adjust and improve, and can be customized and optimized according to the problem requirements;

[0007] (4) Real-time performance: In some scenarios, through parallel computing or hybrid methods, intelligent optimization algorithms can improve computing speed while ensuring accuracy, meeting the requirements of real-time scheduling.

[0008] However, in the existing microgrid optimization scheduling methods, the flexible load regulation characteristics are not taken into account, and they often face challenges such as low resource utilization efficiency and solution algorithms. Therefore, how to improve the utilization rate of new energy and quickly formulate an optimization scheduling plan to ensure the safe, stable and economical operation of the power grid has become a difficult problem that needs to be solved urgently. Summary of the invention

[0009] To this end, the technical problem to be solved by the present invention is to overcome the problem that the microgrid optimization scheduling in the prior art does not take into account the flexible load regulation characteristics, resulting in the stability and economy of the microgrid operation to be improved.

[0010] In order to solve the above technical problems, the present invention provides a new energy microgrid optimization scheduling method considering flexible loads, comprising:

[0011] Build a flexible load response model, and build an adaptive adjustment strategy for the flexible load adjustment cost based on the maximum and minimum values ​​of the main grid power purchase and the maximum peak-to-valley difference rate allowed by the new energy system;

[0012] Based on the adaptive regulation strategy of flexible load regulation cost, an optimal dispatching model is constructed with the goal of maximizing the annual comprehensive profit of the microgrid system;

[0013] The optimization scheduling model is solved by searching for an optimal solution.

[0014] Preferably, the flexible load response model includes a curtailable load response model and a transferable load response model.

[0015] Preferably, the curtailable load response model is expressed as:

[0016]

[0017] Among them, P k,t is the load power after load reduction at time t; P base,t is the base load power at time t; P cut,t is the load power that can be reduced at time t, is a binary variable indicating whether load shedding is performed at time t; is the maximum allowable load reduction power; P X,t is the load demand at time t, is the maximum allowable load requirement; and The start time and end time of the reduction respectively.

[0018] Preferably, the transferable load response model is expressed as:

[0019]

[0020] Among them, P Z,t is the load power after load transfer at time t; P shift,t is the transferable load power at time t; is a binary variable indicating whether load transfer is performed at time t; P min is the minimum load power; P max is the maximum load power; is the load transfer start time; is the load transfer end time; E shift is the total amount of transferable load.

[0021] Preferably, an adaptive adjustment strategy for the flexible load adjustment cost is constructed based on the maximum and minimum values ​​of the main grid-purchased electricity and the maximum peak-to-valley difference rate allowed by the new energy system. The formula is expressed as follows:

[0022]

[0023] Among them, C L,C is the flexible load adjustment cost; P shift,t is the transferable load power at time t; b is a binary variable indicating whether load transfer is performed at time t; shift,t is the time-of-use electricity price; P cut,t is the load power that can be reduced at time t, b is a binary variable indicating whether load shedding is performed at time t; cut,t To reduce the unit price of load loss; E grid (t) is the amount of electricity purchased from the main grid during time period t; is the minimum amount of electricity required to be purchased in time period t; is the maximum amount of electricity required in time period t; F G is the peak-to-valley difference rate; It is the maximum peak-to-valley rate allowed by the new energy system.

[0024] Preferably, the optimization scheduling model includes an objective function and constraints;

[0025] The objective function of the optimization dispatch model includes the cost-effectiveness of new energy, the difference between electricity purchase and electricity sales costs, operation and maintenance costs, user response costs, and flexible load regulation costs;

[0026] The constraints of the optimization scheduling model include power balance constraints, power constraints for electric energy exchange between microgrids, new energy capacity constraints, energy storage battery charge constraints and node voltage constraints.

[0027] Preferably, a particle swarm optimization algorithm is used to optimize and solve the optimization scheduling model, including:

[0028] Set the particle swarm size, randomly initialize the speed and position of particles in the particle swarm in the search space; use the chaotic mapping to update the initialized particle swarm to obtain the target initial particle swarm

[0029] The objective function of the optimization scheduling model is used as the fitness function, the fitness value of the target initial particle swarm is calculated, and the initial local optimal particles and the global optimal particles are obtained;

[0030] Update the position and velocity of particles in the current particle swarm based on the global optimal particle;

[0031] Get the local optimal particle and the global optimal particle of the current iteration;

[0032] Determine whether the current iteration has reached the maximum iteration condition. If the current iteration has not reached the maximum iteration condition, return to update the position and velocity of the particles in the particle swarm; if the current iteration has reached the maximum iteration condition, exit the loop and output the global optimal particle as the optimal solution.

[0033] Preferably, the Bernoulli chaotic map is used to update the initialized particle swarm to obtain the target initial particle swarm, and the formula includes:

[0034]

[0035] x i+1 = l b +(u b -l b )·z i+1

[0036] Among them, x i and x i+1 are the initial positions of the i-th and i+1-th particles respectively; z i and z i+1 are the chaotic sequences corresponding to the i-th and i+1-th particles respectively; h is the chaos parameter; u b and l b are the upper and lower limits of the boundary respectively.

[0037] Preferably, the chaotic parameters are optimized using Lyapunov exponents.

[0038] Preferably, after the positions and velocities of particles in the current particle swarm are updated according to the global optimal particle, the positions of the updated particles are updated again using the spiral search formula in the whale optimization algorithm.

[0039] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0040] The present invention discloses a new energy microgrid optimization scheduling method considering flexible loads. First, a flexible load response model is constructed by utilizing the reducibility and transferability of flexible loads to reduce or redistribute the transferred power demand, thereby optimizing the overall power utilization efficiency, reducing energy consumption, and improving the utilization rate of new energy, effectively reducing costs and improving the absorption capacity of new energy, while alleviating the transmission pressure of the main grid interconnection line. An adaptive adjustment strategy is designed to preferentially shift the peak period of flexible load transfer to the peak period of new energy output, so as to reduce costs and improve the absorption of new energy, maximize the absorption of new energy, reduce dependence on traditional energy, and further reduce operating costs. Then, based on the adaptive adjustment strategy, the flexible load adjustment cost is considered in the optimization scheduling model, the economic goal of optimization is clarified, and the optimal operation plan of the microgrid is formulated by considering various cost factors, so as to improve the return on investment of new energy projects while reducing the overall operating costs.

[0041] Furthermore, the present invention optimizes the particle swarm optimization algorithm, uses chaotic mapping to initialize the particle swarm, and uses Bernoulli chaotic mapping optimized by Lyapunov exponent, and then after updating the position and velocity of the particles in each iteration, uses the spiral search formula in the whale optimization algorithm to perform a second update on the position of the updated particles, thereby solving the problem that the particle swarm optimization algorithm is prone to fall into local optimality, and improving the convergence speed of the algorithm and the exploration ability in the search space.

[0042] Therefore, the present invention constructs a flexible load response model, comprehensively considers factors such as economy, technology and safety, formulates an adaptive adjustment strategy, and adopts an efficient optimization algorithm to solve the optimization scheduling model to maximize the annual comprehensive profit of the microgrid system, effectively improves the utilization rate and absorption capacity of new energy, and successfully responds to the challenges brought by load fluctuations in the optimization control of microgrids, thereby significantly improving the operating efficiency and reliability of new energy microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0044] Figure 1 It is a flow chart of a new energy microgrid optimization dispatching method considering flexible loads according to the present invention;

[0045] Figure 2 It is a flow chart for finding the optimal solution to the optimization scheduling model;

[0046] Figure 3 is a grid structure diagram in an embodiment of the present invention;

[0047] Figure 4 is a time-of-use electricity price diagram in an embodiment of the present invention;

[0048] Figure 5 It is a comparison chart of the results of the optimization solution algorithm of the present invention and the existing algorithm. DETAILED DESCRIPTION

[0049] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0050] Embodiment 1

[0051] Reference Figure 1 As shown, the present invention provides a new energy microgrid optimization scheduling method considering flexible loads, comprising the following steps:

[0052] S1. Construct a flexible load response model, and construct an adaptive adjustment strategy for the flexible load adjustment cost based on the maximum and minimum values ​​of the main grid-purchased electricity and the maximum peak-to-valley difference rate allowed by the new energy system;

[0053] S2. Based on the adaptive regulation strategy of flexible load regulation cost, an optimal dispatching model is constructed with the goal of maximizing the annual comprehensive profit of the microgrid system;

[0054] S3. Finding the best solution for the optimization scheduling model.

[0055] Specifically, the S1 includes:

[0056] S1-1. Construct a flexible load response model, including a curtailable load response model and a transferable load response model.

[0057] The curtailable load response model is formulated as:

[0058]

[0059] Among them, P k,t is the load power after load reduction at time t; P base,t is the base load power at time t; P cut,t is the load power that can be reduced at time t, is a binary variable indicating whether load shedding is performed at time t; is the maximum allowable load reduction power; P X,t is the load demand at time t, is the maximum allowable load requirement; and The start time and end time of the reduction respectively.

[0060] The transferable load response model is formulated as:

[0061]

[0062] Among them, P Z,t is the load power after load transfer at time t; P shift,t is the transferable load power at time t; is a binary variable indicating whether load transfer is performed at time t; P min is the minimum load power; P max is the maximum load power; is the load transfer start time; is the load transfer end time; E shift is the total amount of transferable load.

[0063] S1-2. Construct an adaptive adjustment strategy for the flexible load adjustment cost based on the maximum and minimum values ​​of the main grid-purchased electricity and the maximum peak-to-valley difference rate allowed by the new energy system.

[0064] The present invention is based on a flexible load response model, utilizes the reducibility and transferability of flexible loads, and constructs an adaptive adjustment strategy for the flexible load regulation cost according to the maximum and minimum values ​​of the main grid purchased electricity and the maximum peak-to-valley difference rate allowed by the new energy system, so that the load during peak hours can be autonomously transferred to a reasonable new energy output period, specifically: the flexible load is preferentially transferred to the period with higher new energy output; when the new energy output is less, the transferable load will be transferred to the low-price period.

[0065] The formula of the adaptive adjustment strategy of the flexible load adjustment cost is expressed as:

[0066]

[0067] Among them, C L,C is the flexible load adjustment cost; b shift,t b is the time-of-use electricity price. When renewable energy is used for power generation, the time-of-use electricity price is zero; b cut,t To reduce the load loss unit price, if it is not reduced, the loss unit price is zero; E grid (t) is the amount of electricity purchased from the main grid during time period t; is the minimum amount of electricity required to be purchased in time period t; is the maximum amount of electricity required in time period t; F G is the peak-to-valley difference rate; It is the maximum peak-to-valley rate allowed by the new energy system.

[0068] The formula of the peak-to-valley difference rate is expressed as:

[0069]

[0070] Among them, P min is the minimum load power; Pmax is the maximum load power.

[0071] Specifically, an optimization scheduling model with the goal of maximizing the annual comprehensive profit of the microgrid system is constructed in S2, and the optimization scheduling model includes an objective function and constraints.

[0072] The objective function of the optimization dispatch model includes the cost-effectiveness of new energy, the difference between electricity purchase and electricity sales costs, operation and maintenance costs, user response costs, and flexible load adjustment costs. The formula is expressed as follows:

[0073] F(·)=minC coat =C t +C b +C s +C x +C L,C

[0074] Among them, F(·) is the objective function of the optimization scheduling model, C t For the cost-effectiveness of new energy, C b The difference between the cost of purchasing electricity and the cost of selling electricity, C s is the operation and maintenance cost, C x is the user response cost, C L,t Adjust costs for flexible loads.

[0075] This example uses the Levelized Cost of Energy (LCOE) method to evaluate the cost-effectiveness of new energy, which represents the cost per unit of electricity produced in a one-year cycle. t The specific formula is:

[0076]

[0077] Among them, P R Installed capacity for new energy sources; is the unit price of new energy installed capacity; r is the discount rate; E t is the power generation on day t; C s For operation and maintenance costs.

[0078] The difference between the cost of purchasing electricity and the cost of selling electricity C b The formula is:

[0079]

[0080] in, and are the costs of purchasing and selling electricity on day t, respectively; and They are the amount of electricity flowing from the main grid to the microgrid and the amount of electricity flowing from the microgrid to the main grid on the tth day respectively.

[0081] Operation and maintenance cost C s The formula is:

[0082] C s =C e +C o

[0083]

[0084] C o =C om +C op

[0085] Among them, C e C is the equipment maintenance cost; o is the operation and management cost; y is the number of new energy equipment; C p,k is the component cost of the kth new energy equipment; C m,k is the periodic maintenance cost of the kth new energy equipment; C r,k is the failure repair cost of the kth new energy equipment; C om The cost of monitoring and scheduling system; C op Cost of system optimization and management.

[0086] User response cost C x The formula is:

[0087]

[0088] in, and are the subsidy costs for flexible load shifting and curtailment, respectively.

[0089] In the economic dispatch operation of new energy microgrids, the power flow constraints and power balance constraints of the power grid must be met, the power exchange constraints between microgrids must be limited, and the voltage values ​​of all nodes in the network must be limited to the allowable range. Therefore, the constraints of the optimization dispatch model include power balance constraints, power exchange constraints between microgrids, new energy capacity constraints, energy storage battery charge constraints, and node voltage constraints.

[0090] The formula of the power balance constraint is expressed as:

[0091]

[0092] in, is the renewable energy power generation power of microgrid n at time t; is the load-side energy storage operating power of microgrid n at time t; is the load power of microgrid n at time t; P I,n and PO,n are the amount of electricity flowing into and out of the microgrid n, respectively; is the amount of electricity flowing into the main grid from microgrid n; N is the total number of microgrids.

[0093] The formula for the power constraint of electric energy exchange between microgrids is expressed as:

[0094]

[0095] in, and are the maximum amount of electricity flowing into and out of the microgrid, respectively; and are the minimum and maximum amount of electricity flowing from the main grid to the microgrid, respectively; and are the minimum and maximum amount of electricity flowing from the microgrid to the main grid.

[0096] The formula for new energy capacity constraint is expressed as:

[0097]

[0098]

[0099] Among them, P Gk and Q Gk are respectively the active power and reactive power of the new energy generator k; and are the minimum and maximum active power outputs of the new energy generator k, respectively; and are the minimum and maximum reactive power outputs of the new energy generator k respectively.

[0100] The formula for energy storage battery charge constraint is expressed as:

[0101]

[0102] SOC min ≤SOC(t)≤SOC mat

[0103] Among them, SOC(t-1) is the remaining power of the energy storage battery at time t-1 (State Of Charge, SOC); SOC(t) is the remaining power of the energy storage battery at time t; P charge (t) and P discharge (t) are the charging power and discharging power at time t, respectively; Δt is the time interval; SOC min and SOC max are the minimum and maximum values ​​of the remaining power of the energy storage battery; E max is the rated capacity of the energy storage battery.

[0104] The node voltage constraint is expressed as:

[0105]

[0106] in, and are the minimum and maximum voltage amplitudes of node j respectively; V j is the voltage amplitude at node j.

[0107] Preferably, refer to Figure 2 As shown, in S3, a particle swarm optimization algorithm (PSO) is used to optimize and solve the reducible load power and transferable load power in the optimization scheduling model, including:

[0108] S3-1. Set the particle swarm size and randomly initialize the speed and position of the particles in the particle swarm in the search space.

[0109] S3-2. Use Bernoulli chaotic mapping to update the initialized particle swarm and obtain the target initial particle swarm.

[0110] In order to increase the ergodicity of the initial population, this embodiment uses Bernoulli chaotic mapping to generate a chaotic sequence and initialize the positions of particles in the particle swarm. The specific formula is:

[0111]

[0112] x i+1 = l b +(u b -l b )·z i+1

[0113] Among them, x i and x i+1 are the initial positions of the i-th and i+1-th particles respectively; z i and z i+1 are the chaotic sequences corresponding to the i-th and i+1-th particles respectively; h is the chaos parameter; u b and l b are the upper and lower limits of the boundary respectively.

[0114] Preferably, this embodiment also uses the Lyapunov exponent to calculate the chaos parameter λ h Optimize, the specific formula is:

[0115]

[0116] Where h is the number of iterations, and F(·) is the objective function of the optimization scheduling model.

[0117] S3-3. The objective function of the optimization scheduling model is used as the fitness function, the fitness value of the target initial particle swarm is calculated, and the initial local optimal particle and the global optimal particle are obtained.

[0118] S3-4. Update the position and velocity of particles in the current particle swarm based on the global optimal particle.

[0119] Since the traditional particle swarm optimization algorithm will cause particles to fluctuate back and forth near the optimal solution during the particle position update process and it is difficult to find the optimal solution, this embodiment introduces a dynamic nonlinear learning factor and a global optimization factor to solve this problem, thereby increasing the search range in the early stage of the algorithm and enhancing the local search ability of the algorithm in the later stage as the dynamic nonlinear learning factor decreases.

[0120] In each iteration, the fitness value of each particle in the current particle swarm is calculated, and the particle with the smallest fitness value in the current particle swarm is taken as the local optimal particle of the current iteration. The global optimal particle is the particle with the smallest fitness value in all iterations.

[0121] The formula for particle update is as follows:

[0122]

[0123] in, and are the velocities of particle i at the hth and h+1th iterations, respectively; and are the positions of i at the hth and h+1th iterations, respectively, and λ q is the global optimization factor; x pb,h is the position of the local optimal particle in the hth iteration; x gb,h is the position of the global optimal particle at the hth iteration; r1 and r2 are two uniformly distributed random numbers in the range [0,1]; p1 and p2 are acceleration constants; b t is the dynamic nonlinear learning factor; H is the maximum number of iterations; b tmin and b tmax The minimum and maximum learning factors set for the initial parameters respectively.

[0124] S3-5. Use the spiral search formula in the Whale Optimization Algorithm (WOA) to adjust the position of the particle updated in S3-4 Perform a secondary update.

[0125] This embodiment introduces the spiral search formula in the whale optimization algorithm to update the position of the particle in S3-4. Perform a secondary update, the specific formula is:

[0126]

[0127] in, is the position of particle i after the second update of the spiral search formula in the h+1th iteration; D is the distance between particle i and the global optimal particle; I is a constant that defines the spiral shape; Q is a random number that follows a uniform distribution; T s is the particle upper limit safety limit; R is a random number directly in the range of [0,1]; L is a random number in the range of [-1,1].

[0128] S3-6. Calculate the fitness value of each particle in the particle swarm after the second update, and obtain the local optimal particle and the global optimal particle of the current iteration.

[0129] S3-7, determine whether the current iteration reaches the maximum iteration condition. If the current iteration does not reach the maximum iteration condition, return to S3-4 to update the position and speed of the particles in the particle swarm; if the current iteration reaches the maximum iteration condition, jump out of the loop and output the global optimal particle as the optimal solution.

[0130] Embodiment 2

[0131] This embodiment takes the improved IEEE33 node as an example, and its grid structure is as follows: Figure 3 There are 33 nodes along the feeder line, and 3 microgrid areas are set up. Node 7, node 11, node 17, node 21, node 25, node 27, and node 33 are connected to 2MW of distributed photovoltaic and 0.5MW of distributed energy storage respectively. The time-of-use electricity price is set as follows Figure 4 As shown, it is divided into four periods: peak, high, flat and low.

[0132] This embodiment simulates three different scenarios through Matlab, analyzes the positive impact of flexible loads in the economic dispatch operation of microgrids, and verifies the feasibility of the method proposed in this invention. Assume that 15% of the loads in the new energy system participate in the response, of which 7% are curtailable loads and 8% are transferable loads, and the peak-to-valley difference rate safety value is set to no less than 0.593. In order to conduct comparative analysis, this embodiment conducts experiments with two schemes, the specific contents are as follows:

[0133] Solution 1: A scheduling solution that considers load reduction and load transfer, i.e., the method proposed in the present invention;

[0134] Option 2: Traditional method.

[0135] The comparison results of the costs of different solutions are shown in Table 1.

[0136] Table 1. Comparison of costs of different solutions

[0137] Cost of each item / 10,000 yuan Solution 1 Solution 2 Cost-effectiveness of new energy projects 1252 1982 The difference between electricity purchase and electricity sales costs 102 258 Operation and maintenance costs 1559 1985 User response cost 475 / Flexible load adjustment cost 215 \ Total Cost 3603 4225

[0138] As can be seen from Table 1, the total cost of Scheme 1 is the lowest. Compared with Scheme 2, the total cost of Scheme 1 is reduced by 17%. This result fully demonstrates the importance and effectiveness of considering flexible load characteristics in the economic dispatch of microgrids in the present invention.

[0139] In order to further illustrate the effectiveness of the method proposed in the present invention, this example compares the optimization solution algorithm (LPSO-WOA) proposed in the present invention with the improved particle swarm algorithm (LPSO) fused with Tent chaos, the traditional particle swarm algorithm (PAO) and the convergence situation. The results of the convergence trend in microgrid economic dispatch are as follows: Figure 5 shown.

[0140] from Figure 5 It can be seen that the optimization solution algorithm proposed in the present invention can reach convergence equilibrium after about 20 iterations, which significantly improves the solution efficiency. In contrast, due to the instability of the artificial intelligence algorithm, the convergence curve of PSO shows large fluctuations. The LPSO algorithm, as a part of the algorithm of the present invention, can reach convergence equilibrium faster and more stably than PSO. In summary, the method of the present invention can not only effectively solve the problem of microgrid economic dispatch and reduce costs, but also improve the utilization rate of new energy.

[0141] In summary, the present invention describes a new energy microgrid optimization scheduling method considering flexible loads. First, a flexible load response model is constructed using the reducibility and transferability of flexible loads to reduce or redistribute the transferred power demand, thereby optimizing the overall power utilization efficiency, reducing energy consumption, and improving the utilization rate of new energy, effectively reducing costs and improving the absorption capacity of new energy, while alleviating the transmission pressure of the main grid interconnection line. An adaptive adjustment strategy is designed to reduce costs and improve the absorption of new energy, maximize the absorption of new energy, reduce dependence on traditional energy, and further reduce operating costs. Then, based on the adaptive adjustment strategy, the flexible load adjustment cost is considered in the optimization scheduling model, the economic goal of optimization is clarified, and the optimal operation plan of the microgrid is formulated by considering various cost factors, thereby improving the return on investment of new energy projects while reducing overall operating costs.

[0142] Furthermore, the present invention optimizes the particle swarm optimization algorithm, uses chaotic mapping to initialize the particle swarm, and uses Bernoulli chaotic mapping optimized by Lyapunov exponent, and then after updating the position and velocity of the particles in each iteration, uses the spiral search formula in the whale optimization algorithm to perform a second update on the position of the updated particles, thereby solving the problem that the particle swarm optimization algorithm is prone to fall into local optimality, and improving the convergence speed of the algorithm and the exploration ability in the search space.

[0143] Therefore, the present invention constructs a flexible load response model, comprehensively considers factors such as economy, technology and safety, formulates an adaptive adjustment strategy, and adopts an efficient optimization algorithm to solve the optimization scheduling model to maximize the annual comprehensive profit of the microgrid system, effectively improves the utilization rate and absorption capacity of new energy, and successfully responds to the challenges brought by load fluctuations in the optimization control of microgrids, thereby significantly improving the operating efficiency and reliability of new energy microgrids.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A new energy microgrid optimization scheduling method considering flexible loads, characterized in that: include: Build a flexible load response model, and build an adaptive adjustment strategy for the flexible load adjustment cost based on the maximum and minimum values ​​of the main grid power purchase and the maximum peak-to-valley difference rate allowed by the new energy system; Based on the adaptive regulation strategy of flexible load regulation cost, an optimal dispatching model is constructed with the goal of maximizing the annual comprehensive profit of the microgrid system; The optimization scheduling model is solved by searching for an optimal solution.

2. According to claim 1, a new energy microgrid optimization scheduling method considering flexible loads is characterized in that: The flexible load response model includes a curtailable load response model and a transferable load response model.

3. A new energy microgrid optimization scheduling method considering flexible load according to claim 2, characterized in that: The curtailable load response model is formulated as: Among them, P k,t is the load power after load reduction at time t; P base,t is the base load power at time t; P cut,t is the load power that can be reduced at time t, is a binary variable indicating whether load shedding is performed at time t; is the maximum allowable load reduction power; P X,t is the load demand at time t, is the maximum allowable load requirement; and The start time and end time of the reduction respectively.

4. A new energy microgrid optimization scheduling method considering flexible loads according to claim 2, characterized in that: The transferable load response model is formulated as: Among them, P Z,t is the load power after load transfer at time t; P shift,t is the transferable load power at time t; is a binary variable indicating whether load transfer is performed at time t; P min is the minimum load power; P max is the maximum load power; is the load transfer start time; is the load transfer end time; E shift is the total amount of transferable load.

5. The method for optimizing and dispatching a new energy microgrid considering flexible load according to claim 2, characterized in that: Based on the maximum and minimum values ​​of the main grid power purchase and the maximum peak-to-valley difference rate allowed by the new energy system, an adaptive adjustment strategy for the flexible load adjustment cost is constructed. The formula is expressed as follows: Among them, C L,C is the flexible load adjustment cost; P shift,t is the transferable load power at time t; b is a binary variable indicating whether load transfer is performed at time t; shift,t is the time-of-use electricity price; P cut,t is the load power that can be reduced at time t, b is a binary variable indicating whether load shedding is performed at time t; cut,t To reduce the unit price of load loss; E grid (t) is the amount of electricity purchased from the main grid during time period t; is the minimum amount of electricity required to be purchased in time period t; is the maximum amount of electricity required in time period t; F G is the peak-to-valley difference rate; It is the maximum peak-to-valley rate allowed by the new energy system.

6. A new energy microgrid optimization scheduling method considering flexible loads according to claim 5, characterized in that: The optimization scheduling model includes an objective function and constraint conditions; The objective function of the optimization dispatch model includes the cost-effectiveness of new energy, the difference between electricity purchase and electricity sales costs, operation and maintenance costs, user response costs, and flexible load regulation costs; The constraints of the optimization scheduling model include power balance constraints, power constraints for electric energy exchange between microgrids, new energy capacity constraints, energy storage battery charge constraints and node voltage constraints.

7. The method for optimizing and dispatching a new energy microgrid considering flexible loads according to claim 1, characterized in that: The particle swarm optimization algorithm is used to find the optimal solution for the optimization scheduling model, including: Set the particle swarm size and randomly initialize the speed and position of particles in the swarm within the search space; The initialization particle swarm is updated by using chaotic mapping to obtain the target initial particle swarm. The objective function of the optimization scheduling model is used as the fitness function to calculate the fitness value of the target initial particle swarm and obtain the initial local optimal particle and the global optimal particle. Update the position and velocity of particles in the current particle swarm based on the global optimal particle; Get the local optimal particle and the global optimal particle of the current iteration; Determine whether the current iteration has reached the maximum iteration condition. If the current iteration has not reached the maximum iteration condition, return to update the position and velocity of the particles in the particle swarm; if the current iteration has reached the maximum iteration condition, jump out of the loop and output the global optimal particle as the optimal solution.

8. The method for optimizing and dispatching a new energy microgrid considering flexible loads according to claim 7 is characterized in that: The Bernoulli chaotic map is used to update the initialized particle swarm to obtain the target initial particle swarm. The formula includes: x i+1 =l b +(u b -l b )·z i+1 Among them, x i and x i+1 are the initial positions of the i-th and i+1-th particles respectively; z i and z i+1 are the chaotic sequences corresponding to the i-th and i+1-th particles respectively; h is the chaos parameter; u b and l b are the upper and lower limits of the boundary respectively.

9. A new energy microgrid optimization scheduling method considering flexible loads according to claim 8, characterized in that: Lyapunov exponents are used to optimize the chaotic parameters.

10. The method for optimizing and dispatching a new energy microgrid considering flexible loads according to claim 7, characterized in that: After updating the position and velocity of particles in the current particle swarm according to the global optimal particle, the spiral search formula in the whale optimization algorithm is used to perform a second update on the position of the updated particle.

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