Island microgrid energy scheduling method and system based on chaotic particle swarm algorithm

Abstract

The invention discloses an island micro-grid energy scheduling method and system based on a chaotic particle swarm algorithm, and relates to the technical field of island micro-grid energy scheduling, and the method comprises the steps: initializing a chaotic particle swarm parameter, generating an initial selling price of island energy, and determining the actual load of each demand according to the initial selling price; determining the actual cost of each demand according to the actual load, and determining the net profit at the moment according to the actual cost; judging whether the net profit at the moment is superior to the profit of the current selling price, if so, updating the net profit, and otherwise, returning to the initialization stage; and after the net profit is updated, judging whether an iteration upper limit is reached, if so, outputting an optimal parameter, otherwise, returning to the initialization stage to continue iteration, finally obtaining the optimal parameter, and performing energy scheduling according to the optimal parameter. Through an elaborately designed initialization process, a detailed load model and a flexible objective function, the method can effectively process the cooperative scheduling problem of three loads of electricity, heat and cold.

Description

Technical Field

[0001] The present invention relates to the technical field of isolated island microgrid energy dispatching, and in particular to an isolated island microgrid energy dispatching method and system based on a chaotic particle swarm algorithm. Background Art

[0002] With the introduction of the concept of new power system, clean, efficient and economical energy supply mode has become the focus of the energy field. The isolated island microgrid is a comprehensive energy supply system of a park. The energy production, transmission, storage and distribution of the microgrid system in the same isolated island area need to be realized in a step-by-step manner and reasonably allocated. According to the nature of the internal energy of the isolated island microgrid, the internal energy structure of the isolated island microgrid can be divided into: electric loads such as energy storage batteries, electricity users in the isolated island, charging vehicles, thermal loads such as hot water loads, and cold loads such as user refrigeration loads. When the power dispatch is realized between the isolated island microgrids, the power efficiency and overall economy of the entire isolated island microgrid area will be greatly improved. However, there are multiple energy production units such as thermal power, photovoltaic power, wind power, etc. in the isolated island, multiple energy consumption units such as factories in the isolated island, charging stations in the isolated island, residents in the isolated island, etc., and there are multiple energy interest entities. How to develop the isolated island microgrid into an efficiency entity and realize the reasonable dispatch of the internal energy of the isolated island microgrid will be conducive to the stable and continuous operation of the isolated island microgrid. Therefore, this patent proposes a multi-microgrid energy scheduling method based on chaotic particle swarm algorithm to achieve the rational production, transmission and scheduling of energy within the multi-microgrid. Summary of the invention

[0003] In view of the problems existing in the existing island power grid energy dispatching, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is how to develop the isolated island microgrid into an efficient entity and realize the reasonable scheduling of the internal energy of the isolated island microgrid, which will be conducive to the stable and continuous operation of the isolated island microgrid.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides an isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm, which comprises:

[0007] Initializing the parameters of the chaotic particle swarm, generating the initial selling price of the island energy, and determining the actual load of each demand according to the initial selling price;

[0008] Determine the actual cost of each demand according to the actual load, and determine the net profit at this moment according to the actual cost;

[0009] Determine whether the net profit at the moment is better than the profit of the current selling price, if so, update the net profit, otherwise return to the initialization stage;

[0010] After updating the net profit, it is determined whether the iteration limit is reached. If so, the optimal parameters are output. Otherwise, it returns to the initialization stage to continue iterating, and finally the optimal parameters are obtained, and energy scheduling is performed according to the optimal parameters.

[0011] As a preferred solution of the isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm of the present invention, wherein: the actual load includes a first load, a second load and a third load;

[0012] The first load is solved according to the first load model, as shown in the following formula:

[0013] t e -t s ≥l

[0014] f≥0

[0015] f∈N

[0016]

[0017] Where i is the i-th element of vector f, P is the power of the j-th element at time t, and E j (t) is the electric load of the jth element at time t, E(t) is the electric load of all users at time t, n is the number of load users on the entire island, l is the required load duration, [t s →t e ] is the transferable time, [t s →t s +l] is from t s Working time to start working, t e The end time of work.

[0018] As a preferred solution of the isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm of the present invention, the second load is solved according to the second load model, as shown in the following formula:

[0019]

[0020] H jmin (t)≤H j (t)≤H jmax (t)

[0021]

[0022] Among them, c h is the specific heat capacity of the hot substance ρ h is the density of the hot substance, V h is the volume of hot material added at time t, T hp is the initial temperature of the hot substance, H j(t) is the thermal power of the hot substance at time t, H jmin (t) and H jmax (t) is the minimum and maximum thermal power of the hot material at time t, Δt1 is the heating time, [T hl ,T hh ] are the upper and lower limits of the second load respectively.

[0023] As a preferred solution of the isolated island microgrid energy dispatching method based on the chaotic particle swarm algorithm of the present invention, the third load is solved according to the third load model, as shown in the following formula:

[0024]

[0025] L jmin (t)≤L j (t)≤L jmax (t)

[0026]

[0027] Among them, c l is the specific heat capacity of the cold substance ρ l is the density of the cold substance, V l is the volume of cold material added at time t, T lp is the initial temperature of the cold substance, L j (t) is the heat power of the cold material at time t, L jmin (t) and L jmax (t) is the minimum and maximum cooling power of the cold material at time t, Δt2 is the cooling time, [T ll ,T lh ] are the upper and lower limits of the third load respectively.

[0028] As a preferred solution of the isolated island microgrid energy dispatching method based on the chaotic particle swarm algorithm of the present invention, wherein: the net profit at this moment is determined according to the actual cost at this moment, that is, the net profit at this moment can be obtained by subtracting the total cost at this moment from the total profit at this moment;

[0029] The total profit at this moment is:

[0030]

[0031] Where, C(t) is the total profit at time t, C1(t), C2(t), C3(t) are the selling prices of the first load, the second load, and the third load at time t, λ, β are the proportions of the first load converted to the second load and the third load, ε1, ε2 are the efficiencies of the first load converted to the second load and the third load;

[0032] The total cost at this moment is:

[0033]

[0034] Among them, M(t) is the total cost at time t, M1(t), M2(t), and M3(t) are the unit costs of the first load, the second load, and the third load at time t.

[0035] As a preferred solution of the isolated island microgrid energy dispatching method based on the chaotic particle swarm algorithm of the present invention, the net profit at this moment is as follows:

[0036] G(t)=C(t)-M(t)

[0037]

[0038] Among them, G(t) is the net profit at time t;

[0039] The total profit at this moment is determined as follows:

[0040]

[0041] Among them, G is the total net profit.

[0042] As a preferred solution of the isolated island microgrid energy dispatching method based on the chaotic particle swarm algorithm of the present invention, wherein: the determination of the net profit at this moment also includes constraints, and the constraints include a first load constraint, a second load constraint and a third load constraint;

[0043] The first load constraint is expressed as follows:

[0044]

[0045] Among them, EP(t) is the first load of island production at time t;

[0046] The second load constraint is expressed as follows:

[0047] HP(t)≥(1-λ)H(t)≥0,(λ∈[0,1])

[0048] Where HP(t) is the heat load produced by the island at time t;

[0049] The third load constraint is expressed as follows:

[0050] CP(t)≥(1-β)L(t)≥0,(β∈[0,1])

[0051] Among them, CP(t) is the cooling load produced by the island at time t.

[0052] In a second aspect, an embodiment of the present invention provides an isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm, which includes:

[0053] An initialization module, used to initialize the parameters of the chaotic particle swarm, generate an initial selling price of the island energy, and determine the actual load of each demand according to the initial selling price;

[0054] A profit calculation module, used to determine the actual cost of each demand according to the actual load, and determine the net profit at this moment according to the actual cost;

[0055] The judgment module is used to judge whether the net profit at the moment is better than the profit of the current selling price. If so, the net profit is updated, otherwise it returns to the initialization stage; after the net profit is updated, it is judged whether the iteration upper limit is reached. If so, the optimal parameters are output, otherwise it returns to the initialization stage to continue iterating, and finally the optimal parameters are obtained, and energy scheduling is performed according to the optimal parameters.

[0056] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned isolated island microgrid energy scheduling method based on chaotic particle swarm algorithm.

[0057] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned isolated island microgrid energy scheduling method based on chaotic particle swarm algorithm is implemented.

[0058] The beneficial effect of the present invention is to provide a comprehensive and efficient solution for optimizing complex multi-energy microgrid systems. Through a carefully designed initialization process, a detailed load model and a flexible objective function, the method can effectively handle the coordinated scheduling of three loads: electricity, heat and cold. It takes into account the efficiency loss of energy conversion, the diverse user needs and the system operation constraints, thereby maximizing energy utilization and optimizing economic benefits. The iterative optimization mechanism of the method ensures the possibility of continuous improvement, while the set iteration upper limit ensures the computational efficiency. The optimal electricity price parameters finally output provide reliable decision-making support for microgrid operators, and help to achieve smooth and efficient operation of isolated microgrids. This method not only improves the energy utilization efficiency, but also enhances the economy and reliability of the microgrid system, providing strong support for the development of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0060] Figure 1 This is a flow chart of the energy scheduling method for isolated island microgrid based on chaotic particle swarm algorithm. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0065] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0066] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] Example 1

[0068] Reference Figure 1 , which is the first embodiment of the present invention, and provides an isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm, comprising:

[0069] Initializing the parameters of the chaotic particle swarm and generating the initial energy selling price of the island energy are the key starting points of the optimization process. The algorithm sets the initial chaotic particle swarm size to 15, the maximum number of iterations to 132, and the chaotic coefficient to 2.8. These parameters provide the basis for the algorithm to search the space and convergence conditions. On this basis, the algorithm will generate a set of initial energy selling price plans, including the prices of electric load, heat load, and cooling load. These initial prices are the starting point of the optimization process and will affect the subsequent load demand calculation, cost estimation, and profit calculation.

[0070] The initial price generated by the chaotic mapping has good randomness and diversity, which helps the algorithm to explore in the complex multi-dimensional solution space and find the optimal price combination that can maximize net profit. This initialization process takes into account the complexity of the isolated microgrid system, including multiple energy forms, multiple production and consumption units, and multiple stakeholders, laying the foundation for subsequent iterative optimization. The goal is to ultimately output five optimal electricity price parameters to achieve reasonable scheduling of energy within the isolated microgrid and maximize economic benefits.

[0071] Furthermore, the actual load of each demand is determined based on the initial price.

[0072] For example, in a feasible embodiment, the electrical loads of various properties are solved as follows:

[0073] Specifically, the electrical loads of various properties are solved according to the first load model, the second load model and the third load model.

[0074] More specifically, the models for solving the electric loads of different properties are the electric load demand model, the heat load demand model, and the cooling load demand model. The electric load demand model is used to calculate the electric energy demand; the heat load demand model is used to calculate the heat energy required for heating; and the cooling load demand model is used to calculate the cooling energy required for cooling. By combining these three demands, the scheduling strategy is optimized, such as converting part of the heat and cooling energy through electric energy, so as to improve the overall energy efficiency and economic benefits.

[0075] Preferably, the calculation of the electric load demand takes into account the power user demand of the entire island, introduces parameters such as the number of users, load duration, and transferable time, and uses vectors to represent the power demand in different time periods. The core of the model is a time-power matrix, in which each element represents the power demand of a specific user at a specific moment. The specific formula is as follows:

[0076] t e -t s ≥l

[0077] f≥0

[0078] f∈N

[0079]

[0080] Where i is the i-th element of vector f, and P is the power of the j-th element at time t. j (t) is the electric load of the jth element at time t. E(t) is the electric load of all users at time t, n is the number of load users on the entire island, l is the required load duration, [t s →t e ] is the transferable time, [t s →t s +l] is from t s Working time to start working, t e The end time of work.

[0081] For users on the entire island, the heat load is not a fixed value, but has an acceptable range. As long as it does not exceed the upper and lower limits of this range, the heat load usually has an acceptable temperature range, rather than a fixed value. Then:

[0082]

[0083] H jmin (t)≤H j (t)≤H jmax (t)

[0084]

[0085] Among them, c h is the specific heat capacity of the hot substance ρ h is the density of the hot substance, V h is the volume of hot material added at time t, T hp is the initial temperature of the hot substance, H j (t) is the thermal power of the hot substance at time t. jmin (t) and H jmax(t) is the minimum and maximum thermal power of the hot material at time t, Δt1 is the heating time, [T hl ,T hh ] are the upper and lower limits of the heat load respectively.

[0086] Similar to the heating load, the cooling load is also within an acceptable range and also includes physical parameters and time factors, but the focus is on the cooling process. This model allows the system to optimize the use of cooling energy while meeting user comfort requirements, and there are:

[0087]

[0088] L jmin (t)≤L j (t)≤L jmax (t)

[0089]

[0090] Among them, c l is the specific heat capacity of the cold substance ρ l is the density of the cold substance, V l is the volume of cold material added at time t, T lp is the initial temperature of the cold substance, L j (t) is the heat power of the cold material at time t, L jmin (t) and L jmax (t) is the minimum and maximum cooling power of the cold material at time t, Δt2 is the cooling time, [T ll ,T lh ] are the upper and lower limits of the third load respectively.

[0091] In the energy dispatching system of isolated microgrids, electric loads have unique flexibility and can be converted into heat loads and cold loads through specific equipment, which provides more dispatching possibilities for the system. However, this conversion has certain efficiency losses, which need to be considered in the objective function. In contrast, heat loads and cold loads cannot be reversely converted into electric loads, and this characteristic limits the direction of energy flow. Based on these characteristics, the objective function is constructed as a mathematical model that comprehensively considers multiple factors. It includes the selling price of three types of loads: electricity, heat, and cold, the actual demand for each type of load, the conversion ratio of electric loads to heat loads and cold loads and their efficiency, and the production cost of each type of load. Through this objective function, the system aims to maximize the overall net profit while ensuring the balance of energy supply and demand and the rationality of the conversion process. This modeling method not only reflects the physical characteristics and economic factors of the energy system, but also provides a clear optimization direction for the optimization algorithm, which helps to achieve efficient utilization of energy and maximize economic benefits in isolated microgrids. The specific formula is as follows:

[0092]

[0093] Where C(t) is the total profit at time t, C1(t), C2(t), C3(t) are the selling prices of the electric load, heat load, and cooling load at time t, λ, β are the proportion of electric load converted to heat load and cooling load, ε1, ε2 are the efficiency of converting electric load to heat load and cooling load.

[0094]

[0095] Among them, M(t) is the total cost at time t, M1(t), M2(t), and M3(t) are the unit costs of electrical load, thermal load, and cooling load at time t.

[0096] G(t)=C(t)-M(t)

[0097]

[0098] Among them, G(t) is the net profit at time t.

[0099] Furthermore, calculate the total profit:

[0100]

[0101] Among them, G is the total net profit.

[0102] Furthermore, the constraints of the objective function are constructed. Specifically, the constraints include electric load constraints, heat load constraints, and cooling load constraints. Specifically, the electric load constraints are shown in the following formula:

[0103]

[0104] Among them, EP(t) is the electric load produced by the island at time t.

[0105] Specifically, the heat load constraint is as follows:

[0106] HP(t)≥(1-λ)H(t)≥0,(λ∈[0,1])

[0107] Among them, HP(t) is the heat load produced by the island at time t.

[0108] The cooling load constraint is as follows:

[0109] CP(t)≥(1-β)L(t)≥0,(β∈[0,1])

[0110] Among them, CP(t) is the cooling load produced by the island at time t.

[0111] Furthermore, the net profit obtained from the current iteration is compared with the best profit recorded previously. If the new profit is higher, the system will update the best net profit record of the isolated microgrid and save the corresponding parameter configuration. This step ensures that the algorithm can continue to evolve to a better solution. Next, the algorithm will check whether the preset iteration limit has been reached. If this limit is reached, it means that the algorithm has completed the predetermined search process, and the optimal parameters found will be output, including the best electricity price configuration. These two judgment steps together constitute the core loop and termination conditions of the algorithm, which not only ensures the possibility of continuous optimization, but also avoids the efficiency loss caused by over-calculation. In this way, the algorithm can find a near-optimal energy scheduling solution within limited computing resources, providing decision support for the efficient operation of the isolated microgrid.

[0112] Furthermore, this embodiment also provides an isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm, comprising:

[0113] An initialization module, used to initialize the parameters of the chaotic particle swarm, generate an initial selling price of the island energy, and determine the actual load of each demand according to the initial selling price;

[0114] A profit calculation module, used to determine the actual cost of each demand according to the actual load, and determine the net profit at this moment according to the actual cost;

[0115] The judgment module is used to judge whether the net profit at the moment is better than the profit of the current selling price. If so, the net profit is updated, otherwise it returns to the initialization stage; after the net profit is updated, it is judged whether the iteration upper limit is reached. If so, the optimal parameters are output, otherwise it returns to the initialization stage to continue iterating, and finally the optimal parameters are obtained, and energy scheduling is performed according to the optimal parameters.

[0116] This embodiment also provides a computer device, which is suitable for the case of an isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm as proposed in the above embodiment.

[0117] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0118] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for energy scheduling of an isolated island microgrid based on a chaotic particle swarm algorithm as proposed in the above embodiment is implemented.

[0119] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0120] Example 2

[0121] This is the second embodiment of the present invention. This embodiment provides an isolated island microgrid energy scheduling method based on a chaotic particle swarm algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0122] In order to verify the effectiveness of the isolated island microgrid energy scheduling method based on the chaotic particle swarm algorithm, this experiment selected an isolated island microgrid system in a coastal industrial park as the research object. The microgrid system includes multiple energy forms such as photovoltaic power generation, wind power generation, and gas turbine power generation, as well as multiple types of energy users such as industrial users, commercial users, and residential users. Before the experiment began, the system was firstly parameterized and initialized in detail.

[0123] During the initialization process, the parameters of the chaotic particle swarm algorithm were set as follows: the particle swarm size was 15, the maximum number of iterations was 132, and the chaos coefficient was 2.8. The selection of these parameters was based on multiple tests and optimizations in the early stage to ensure that the algorithm could quickly converge to a high-quality solution in this specific microgrid environment. Then, a set of initial energy selling price schemes, including the prices of electric load, heating load, and cooling load, were generated using the Logistic mapping function. This initialization method based on chaos theory significantly improved the diversity of the initial solutions and effectively avoided the risk of the algorithm falling into a local optimum.

[0124] Subsequently, based on the initial price plan, demand models for electric load, heat load and cooling load were constructed respectively. The electric load demand model takes into account the electricity consumption characteristics of 500 users in the park, including factors such as load duration and transferable time, and constructs a 24-hour time-power matrix. The heat load demand model sets a comfortable temperature range of 20℃ to 25℃, taking into account physical parameters such as specific heat capacity and density of thermal materials. The cooling load demand model sets a comfortable temperature range of 22℃ to 26℃, also taking into account relevant physical parameters.

[0125] After the model is built, iterative optimization begins. In each iteration, the algorithm first calculates the actual demand for each type of load based on the current price plan, and then calculates the operating costs and benefits of the system based on the demand. In particular, considering the characteristics that electrical loads can be converted into heat loads and cold loads, the electric heat conversion efficiency is set to 85% and the electric cooling conversion efficiency is set to 80% (i.e., COP is 0.8). These conversion efficiencies are incorporated into the objective function to more accurately reflect the actual operating conditions of the system.

[0126] The construction of the objective function comprehensively considers revenue, cost and various constraints. The revenue part includes the sales revenue of electricity, heat and cold energy; the cost part includes power generation cost, maintenance cost, etc.; the constraints include the balance constraints of various loads, the output constraints of power generation equipment, etc. The optimal price plan and scheduling strategy are found by maximizing net profit.

[0127] After each iteration, the algorithm compares the current net profit with the best historical record. If a better result is found, the best record is updated and the corresponding parameter configuration is saved. This process continues until the preset upper limit of 132 iterations is reached or there is no improvement for 30 consecutive iterations.

[0128] Finally, the algorithm outputs the optimal price parameters and scheduling scheme. In order to verify the effectiveness of this method, it is compared with the traditional linear programming method and the simple particle swarm algorithm. The experimental results are shown in the following table:

[0129] Table 1 Experimental data table

[0130]

[0131] Through the analysis of the table data, it can be clearly seen that the method of the present invention shows significant advantages in multiple key indicators.

[0132] First, in terms of economic benefits, the average daily net profit achieved by the method of the present invention reached 61,200 yuan, which is 17.7% higher than the traditional linear programming method and 10.3% higher than the simple particle swarm algorithm. This result fully demonstrates the excellent ability of this method in optimizing energy prices and scheduling strategies, and can better balance the supply and demand relationship and maximize the economic benefits of the system.

[0133] Secondly, in terms of load balancing performance, the method of the present invention controls the peak-to-valley difference to 720kW, which is 40% lower than the traditional method and 24.2% lower than the simple particle swarm algorithm. This significant improvement in indicators means that the system runs more smoothly, greatly reducing the dependence on peak power generation equipment, which is conducive to reducing the overall investment and operating costs of the system.

[0134] In terms of energy utilization, the method of the present invention has reached a high level of 87.6%, which is 9.1 percentage points higher than the traditional method and 5.3 percentage points higher than the simple particle swarm algorithm. This result shows that the method can more effectively coordinate the production and consumption of various types of energy, reduce energy waste, and improve the overall efficiency of the system.

[0135] In terms of algorithm performance, although the computation time of the method of the present invention (135 seconds) is slightly longer than that of the simple particle swarm algorithm (120 seconds), it is significantly shorter than that of the traditional linear programming method (180 seconds). More importantly, the method only needs 76 iterations to reach convergence, which is 22.4% less than the 98 iterations of the simple particle swarm algorithm. This shows that the method has a faster convergence speed and stronger global search ability, and can find high-quality solutions in a shorter time.

[0136] Finally, in terms of user satisfaction, the proposed method received a high score of 4.5 points (out of 5 points), far exceeding the 3.2 points of the traditional method and the 3.8 points of the simple particle swarm algorithm. This indicator reflects the user's comprehensive evaluation of energy supply stability, price rationality and comfort. The high score shows that the proposed method not only optimizes the economic benefits of the system, but also meets the diverse needs of users.

[0137] The superiority of the method of the present invention mainly comes from the following innovations: first, the chaos initialization strategy increases the diversity of initial solutions and effectively avoids the algorithm from falling into local optimality; second, the multi-objective optimization model comprehensively considers multiple aspects such as economic benefits, load balance and user satisfaction, making the optimization results more comprehensive and practical; third, the energy conversion efficiency and various constraints are considered, making the model closer to the operating characteristics of the actual system; finally, the dynamically adjusted iterative strategy improves the convergence speed of the algorithm and the quality of the solution.

[0138] In summary, the isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm proposed in this invention shows obvious advantages in economic benefits, system stability, energy utilization and user satisfaction. This method can not only effectively improve the operational efficiency and economic benefits of isolated island microgrids, but also provide valuable solutions for similar complex energy system optimization problems. In the future, it can be further considered to combine this method with machine learning and other technologies to cope with more complex and dynamic energy dispatching scenarios.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm, characterized by: include, Initializing the parameters of the chaotic particle swarm, generating the initial selling price of the island energy, and determining the actual load of each demand according to the initial selling price; Determine the actual cost of each demand according to the actual load, and determine the net profit at this moment according to the actual cost; Determine whether the net profit at the moment is better than the profit of the current selling price, if so, update the net profit, otherwise return to the initialization stage; After updating the net profit, it is determined whether the iteration limit is reached. If so, the optimal parameters are output. Otherwise, it returns to the initialization stage to continue iterating, and finally the optimal parameters are obtained, and energy scheduling is performed according to the optimal parameters.

2. The isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm as claimed in claim 1, characterized in that: The actual load includes a first load, a second load and a third load; The first load is solved according to the first load model, as shown in the following formula: t e -t s ≥l f≥0 f∈N Where i is the i-th element of vector f, P is the power of the j-th element at time t, and E j (t) is the electric load of the jth element at time t, E(t) is the electric load of all users at time t, n is the number of load users on the entire island, l is the required load duration, [t s →t e ] is the transferable time, [t s →t s +l] is from t s Working time to start working, t e The end time of work.

3. The isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm as claimed in claim 2, characterized in that: The second load is solved according to the second load model, as shown in the following formula: H jmin (t)≤H j (t)≤H jmax (t) Among them, c h is the specific heat capacity of the hot substance ρ h is the density of the hot substance, V h is the volume of hot material added at time t, T hp is the initial temperature of the hot substance, H j (t) is the thermal power of the hot substance at time t, H jmin (t) and H jmax (t) is the minimum and maximum thermal power of the hot material at time t, Δt1 is the heating time, [T hl ,T hh ] are the upper and lower limits of the second load respectively.

4. The isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm as claimed in claim 3, characterized in that: The third load is solved according to the third load model, as shown in the following formula: L jmin (t)≤L j (t)≤L jmax (t) Among them, c l is the specific heat capacity of the cold substance ρ l is the density of the cold substance, V l is the volume of cold material added at time t, T lp is the initial temperature of the cold substance, L j (t) is the heat power of the cold material at time t, L jmin (t) and L jmax (t) is the minimum and maximum cooling power of the cold material at time t, Δt2 is the cooling time, [T ll ,T lh ] are the upper and lower limits of the third load respectively.

5. The isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm as claimed in claim 4, characterized in that: The net profit at this moment is determined based on the actual cost at this moment, that is, the net profit at this moment can be obtained by subtracting the total cost at this moment from the total profit at this moment; The total profit at this moment is: Where, C(t) is the total profit at time t, C1(t), C2(t), C3(t) are the selling prices of the first load, the second load, and the third load at time t, λ, β are the proportions of the first load converted to the second load and the third load, ε1, ε2 are the efficiencies of the first load converted to the second load and the third load; The total cost at this moment is: Among them, M(t) is the total cost at time t, M1(t), M2(t), and M3(t) are the unit costs of the first load, the second load, and the third load at time t.

6. The isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm as claimed in claim 5, characterized in that: The net profit at this moment is as follows: G(t)=C(t)-M(t) Among them, G(t) is the net profit at time t; The total profit at this moment is determined as follows: Among them, G is the total net profit.

7. The isolated island microgrid energy dispatching method based on chaotic particle swarm algorithm as claimed in claim 6, characterized in that: The determination of the net profit at this moment also includes constraints, and the constraints include a first load constraint, a second load constraint and a third load constraint; The first load constraint is expressed as follows: Among them, EP(t) is the first load of island production at time t; The second load constraint is expressed as follows: HP(t)≥(1-λ)H(t)≥0,(λ∈[0,1]) Where HP(t) is the heat load produced by the island at time t; The third load constraint is expressed as follows: CP(t)≥(1-β)L(t)≥0,(β∈[0,1]) Among them, CP(t) is the cooling load produced by the island at time t.

8. A method for dispatching energy for an isolated island microgrid based on a chaotic particle swarm algorithm, based on the method for dispatching energy for an isolated island microgrid based on a chaotic particle swarm algorithm according to any one of claims 1 to 7, characterized in that: include, An initialization module is used to initialize the parameters of the chaotic particle swarm, generate an initial selling price of the island energy, and determine the actual load of each demand according to the initial selling price; A profit calculation module, used to determine the actual cost of each demand according to the actual load, and determine the net profit at this moment according to the actual cost; The judgment module is used to judge whether the net profit at the moment is better than the profit of the current selling price. If so, the net profit is updated, otherwise it returns to the initialization stage; after the net profit is updated, it is judged whether the iteration upper limit is reached. If so, the optimal parameters are output, otherwise it returns to the initialization stage to continue iterating, and finally the optimal parameters are obtained, and energy scheduling is performed according to the optimal parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the isolated island microgrid energy scheduling method based on chaotic particle swarm algorithm described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the isolated island microgrid energy scheduling method based on chaotic particle swarm algorithm described in any one of claims 1 to 7 are implemented.

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