Optimization control method and system for isolated island operation of multi-energy microgrid

Abstract

The invention belongs to the technical field of power grid optimization control, and provides an optimization control method and system for island operation of a multi-energy micro-grid, and the method comprises the steps: obtaining the operation parameters of the multi-energy micro-grid; on the basis of the acquired operation parameters, an optimization control model is constructed by taking the minimum total operation cost during island operation of the multi-energy micro-grid as an objective function; and taking the total operation cost as iteration fitness, and combining a layered and partitioned optimization search algorithm to carry out iteration solution of the constructed optimization control model, so as to obtain a multi-energy operation mode in the micro-grid and complete optimization control of island operation of the multi-energy micro-grid. According to the layered and partitioned optimization search algorithm, a population interaction mode is determined on the basis of a bird mating algorithm, partitioned optimization search is carried out according to the population interaction mode, and layered optimization search is carried out in combination with the inertia weight of the population interaction mode.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid optimization control, and in particular relates to an optimization control method and system for island operation of a multi-energy microgrid. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As an autonomous system, the multi-energy microgrid system integrates multiple energy sources such as photovoltaic, wind power, and thermal energy with different characteristics, and realizes coordinated planning, optimized operation, collaborative management, interactive response, and mutual complementation among multiple energy systems. As a small and independent system compatible with the environment, it plays a great role in improving the utilization rate of new energy. However, the current optimization control method for the island operation of multi-energy microgrids has the following problems: (1) Due to the uncertainty of renewable energy output in remote areas, the abandonment rate is high.

[0004] (2) Microgrids have the characteristics of complex states, multiple controlled modes and flexible operation. Therefore, it is difficult to characterize the model. It is impossible to ensure that the central control platform can respond to the system quickly and allow the controlled system to receive feedback information in a timely manner.

[0005] (3) The algorithm during the optimization control period needs to meet the parallel optimization scheduling calculation of each sub-region, which results in too many decision variables and cannot converge well. It is difficult for the calculation results to meet the accuracy requirements at the same time. Summary of the invention

[0006] To solve the above problems, the present invention proposes an optimization control method and system for the isolated operation of a multi-energy microgrid. Combined with the energy interaction characteristics of each power generation unit, user electricity consumption, gas turbine, etc. in the isolated operation of a multi-energy microgrid, an optimization scheduling model is established, and the minimization of operating cost is used as the objective function. The hierarchical partition optimization search method (HP-OSM) is used to solve the objective function, thereby improving the new energy consumption rate in the multi-energy microgrid system and accurately controlling it, improving the solution speed and ensuring the superiority of the solution; it solves the current problems of power supply and new energy consumption for residents in remote areas under the coordinated control of multi-energy systems.

[0007] According to some embodiments, a first solution of the present invention provides an optimization control method for island operation of a multi-energy microgrid, which adopts the following technical solution: An optimization control method for island operation of a multi-energy microgrid, comprising: Obtain the operating parameters of the multi-energy microgrid; Based on the obtained operating parameters, an optimization control model is constructed with the minimum total operating cost when the multi-energy microgrid is operating in an isolated island as the objective function; Taking the total operating cost as the iterative fitness, the constructed optimization control model is iteratively solved in combination with the hierarchical partition optimization search algorithm to obtain the operation mode of multiple energy sources in the microgrid and complete the optimization control of the island operation of the multi-energy microgrid; wherein, the hierarchical partition optimization search algorithm determines the population interaction pattern based on the bird mating algorithm, performs partition optimization search according to the population interaction pattern, and performs hierarchical optimization search in combination with the inertia weight of the population interaction pattern.

[0008] As a further technical limitation, in the process of solving the constructed optimization control model with the total operating cost as the iterative fitness and combining the hierarchical partition optimization search algorithm, the population fitness is calculated based on the bird mating algorithm, and the population interaction mode is determined according to the obtained fitness; the inertia weights of the determined population interaction modes are calculated separately based on the adaptive update method, and the population is partitioned to obtain evaluation indicators under different population interaction modes, and the optimization control model is solved according to the obtained evaluation indicators.

[0009] Furthermore, the evaluation indicators under the different population interaction modes are: (1) Parthenogenesis uses only fitness to evaluate the genetic quality of chicks; (2) using comparative indexes for evaluation and screening in monogamy; (3) Both polyandry and polygyny adopt the sum of reproductive mechanisms that comprehensively consider fitness and comparison index; (4) In the hybridization mode, only the comparative index is used to screen the offspring.

[0010] As a further technical limitation, the objective function of the optimization control model is: ; ; ; ; ; ; ; ; in, is the total operating cost of the microgrid; For power generation unit i Operation and maintenance costs, For power generation unit i Unit operation and maintenance costs; For each power generation unit i No. t Output during time period; is the fuel cost of MT, LHV is the lower calorific value of the fuel gas, is the unit price of MT fuel gas, The efficiency of MT; For each unit i The construction cost, For power generation unit i The useful life of For power generation unit i The unit capacity investment price, r is the annual deposit interest rate, For power generation unit i Capacity factor; , Penalty costs for wind and solar curtailment, , Penalty costs for wind and solar curtailment units, , for t The amount of wind and solar power abandoned during the period; The economic compensation cost for users participating in the incentive-based demand response. is the unit compensation cost of interruptible load, For interruptible loads t Load interruption power during the period, The economic subsidy cost for users participating in price-based demand response is is the unit compensation cost of the translatable load, For the translatable load, transfer to the t Power during the period.

[0011] Furthermore, the constraints of the optimization control model include at least source-load power balance constraints, power generation unit constraints, energy storage constraints and demand response constraints.

[0012] As a further technical limitation, the multiple energy sources in the microgrid include at least photovoltaics, wind power, gas turbines and energy storage; the total operating cost of the multi-energy microgrid when operating in an isolated island includes the operation and maintenance cost, fuel cost, investment depreciation cost, wind abandonment penalty cost, solar abandonment penalty cost and demand-side response cost of the multiple energy sources in the microgrid.

[0013] According to some embodiments, the second solution of the present invention provides an optimization control system for island operation of a multi-energy microgrid, which adopts the following technical solutions: An optimization control system for island operation of a multi-energy microgrid, comprising: An acquisition module, configured to acquire operating parameters of the multi-energy microgrid; A construction module is configured to construct an optimization control model based on the acquired operation parameters and taking the minimization of the total operation cost when the multi-energy microgrid is islanded as the objective function; The optimization module is configured to use the total operating cost as the iterative fitness, and to perform iterative solution of the constructed optimization control model in combination with the hierarchical partition optimization search algorithm, so as to obtain the operation mode of multiple energy sources in the microgrid and complete the optimization control of the island operation of the multi-energy microgrid; wherein the hierarchical partition optimization search algorithm determines the population interaction pattern based on the bird mating algorithm, performs partition optimization search according to the population interaction pattern, and performs hierarchical optimization search in combination with the inertia weight of the population interaction pattern.

[0014] According to some embodiments, a third solution of the present invention provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of an optimization control method for island operation of a multi-energy microgrid as described in the first solution of the present invention.

[0015] According to some embodiments, a fourth solution of the present invention provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the optimization control method for island operation of a multi-energy microgrid as described in the first scheme of the present invention are implemented.

[0016] According to some embodiments, a fifth solution of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code executes the steps in the optimization control method for island operation of a multi-energy microgrid as described in the first embodiment of the present invention.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention combines the energy interaction characteristics of various power generation units, user electricity consumption, gas turbines, etc. in the multi-energy microgrid island operation system, establishes a system optimization scheduling model, and aims to minimize the system operation cost, thereby improving the new energy consumption rate in the multi-energy microgrid system and accurately controlling it.

[0018] The present invention proposes the HP-OSM algorithm to solve the problem of high model search dimension and complex constraints, which effectively improves the solution speed and ensures the superiority of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0020] Figure 1 This is a flow chart of an optimization control method for island operation of a multi-energy microgrid in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of a multi-energy microgrid operating in an isolated island in Embodiment 1 of the present invention; Figure 3 This is a structural block diagram of the optimization control system for the island operation of a multi-energy microgrid in the second embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0023] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0024] In the present invention, terms such as "upper", "lower", "left", "right", "front", "back", "vertical", "horizontal", "side", "bottom", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationships of the various parts or elements of the present invention, and do not specifically refer to any part or element in the present invention and should not be understood as limitations on the present invention.

[0025] In the present invention, terms such as "fixed connection", "connected", "connection", etc. should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. For relevant scientific research or technical personnel in this field, the specific meanings of the above terms in the present invention can be determined according to specific circumstances, and they cannot be understood as limitations on the present invention.

[0026] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0027] Embodiment 1 Embodiment 1 of the present invention introduces an optimization control method for island operation of a multi-energy microgrid.

[0028] like Figure 1 An optimization control method for island operation of a multi-energy microgrid is shown, comprising the following steps: Step S01: obtaining basic parameters involved in power models of various power generation units, including parameters of new energy units, gas turbine units, and energy storage device parameters, and collecting new energy output data within the day; Step S02: Taking the minimization of the microgrid operation cost as the control target, an objective function of the microgrid economic dispatch model is established; Step S03: Initialize the decision variables and corresponding parameters of the multi-energy microgrid system and solve them using the HP-OSM algorithm; Step S04: Execute the control target according to the solution result to complete the optimization control of the multi-energy microgrid island operation.

[0029] As one or more implementation methods, the structure of the multi-energy microgrid in island operation in this embodiment is as follows: Figure 2 As shown, the photovoltaic light intensity and measured temperature data in the input area, the rated wind speed of the wind turbine, the cut-in and cut-out wind speed and its characteristic parameters, the capacity of the battery of the energy storage system, the unit compensation cost of the price-based demand response, and the unit compensation cost of the incentive-based demand response; the output in each scheduling cycle is fitted according to the special environmental parameters of the microgrid new energy input.

[0030] Build a new energy system model in a multi-energy microgrid, and build mathematical models of different power generation units based on the topological structure of the power generation system of different energy sources and considering the characteristics of the external environment, including the following steps: First, the photovoltaic (PV) power model is built. The output power of photovoltaic cells is also related to the actual ambient temperature, and its mathematical model can be expressed by the following formula:

[0031] in, is the output power of the photovoltaic cell; Indicates the maximum power of a photovoltaic unit under standard conditions; is the light irradiance intensity; is the light irradiance under standard conditions; is the temperature coefficient; is the measured temperature during the operation of the photovoltaic cell; is the temperature under standard conditions.

[0032] The power model of the wind turbine (WT) is modeled. Through the recorded data of many actual wind speed measurements, the conclusion shows that the wind speed approximately obeys the Weibull distribution, and its probability density function can be described as:

[0033] in, is the actual wind speed at the moment; is the shape parameter, generally ranging from 1.8 to 2.8; is a scale parameter, which is regarded as the average wind speed in a certain period of time.

[0034] The relationship between fan speed and output power:

[0035] in, , are the actual output power and rated power of the fan respectively; , , They are rated wind speed, cut-in and cut-out wind speeds respectively; , , They are characteristic parameters respectively.

[0036] The power model of the gas turbine (Micro-turbines, MT) is modeled, and the mathematical expression of the total efficiency of the micro gas turbine is shown as follows:

[0037] The calculation expression of the power generation cost of the micro gas turbine is as follows:

[0038] in, is the unit price of fuel gas, is the lower calorific value of the fuel gas. for t The output of the time period MT, for t The power generation efficiency of MT in the period.

[0039] To model the battery, the energy storage system (ESS) generally uses the state of charge to represent the current remaining power, which is defined as the ratio of the current remaining power to the energy storage capacity, expressed as follows:

[0040] in, for t The state of charge value of the time storage system, is the capacity of the energy storage system; for t The remaining power of the energy storage system in a time period is related to the current operating state of the energy storage system and is determined by the overpower of the energy storage charging and discharging state in two adjacent time periods, as shown below:

[0041] in, , The charging and discharging efficiency of the energy storage system; , The charging and discharging power of the energy storage system; , It is a 0-1 variable, indicating the current working status of the energy storage system.

[0042] In addition to the state of charge, the number of cycles of energy storage system is also an important indicator of its operating performance. Its value has a great impact on the power generation cost of the energy storage system. The operating conditions of the energy storage system directly affect its number of cycles, especially the discharge depth of the energy storage ( ) can be expressed by the following formula:

[0043] in, is the number of cycles of energy storage use. It can be found that the number of cycles of energy storage use is a variable that is closely related to the depth of discharge. A, B, C, D, E is the fitting parameter.

[0044] As one or more implementation methods, the objective function of the optimal control of the island operation of the multi-energy microgrid is established, mainly considering the operation and maintenance cost, fuel cost, investment depreciation cost, wind abandonment, solar abandonment penalty cost and demand-side response cost subsidy of each power generation unit, and the objective function of the microgrid economic dispatch model is established as follows: ; ; ; ; ; ; ; ; in, is the total operating cost of the microgrid; For power generation unit i Operation and maintenance costs, For power generation uniti Unit operation and maintenance costs; For each power generation unit i No. t Output during time period; is the fuel cost of MT, LHV is the lower calorific value of the fuel gas, is the unit price of MT fuel gas, The efficiency of MT; For each unit i The construction cost, For power generation unit i The useful life of For power generation unit i The unit capacity investment price, r is the annual deposit interest rate, For power generation unit i Capacity factor; , Penalty costs for wind and solar curtailment, , Penalty costs for wind and solar curtailment units, , for t The amount of wind and solar power abandoned during the period; The economic compensation cost for users participating in the incentive-based demand response. is the unit compensation cost of interruptible load, For interruptible loads t Load interruption power during the period, The economic subsidy cost for users participating in price-based demand response is is the unit compensation cost of the translatable load, For the translatable load, transfer to the t Power during the period.

[0045] As one or more implementation methods, the optimization control model of the energy microgrid island operation needs to meet the source-load power balance constraint, the power generation unit constraint, the energy storage constraint, and the demand response constraint. The constraints are as follows: (1) Source-load power balance constraints

[0046] in, Indicates energy storage t Output during the period, if >0 means the energy storage is in the discharge process. <0 means the energy storage is in the charging process; for t Time period load forecast power, and The translatable loads are tThe total load power transferred in and out during a period.

[0047] (2) Power generation unit constraints

[0048] in, , For power generation unit i No. t Output threshold for a period of time. , For power generation unit i The maximum and minimum climbing rates.

[0049] (3) Battery-related constraints During the operation of energy storage, its state of charge can be calculated using the following formula:

[0050] Energy storage can only be charged or discharged during a specific period of time, so the following constraints must be met:

[0051] Energy storage should also meet the following constraints during operation:

[0052] in, for t The state of charge of the time storage, , It is a 0-1 variable, representing the working state of energy storage; , The maximum and minimum values ​​of the state of charge; , for t Charging and discharging power during the time period, , are the maximum and minimum values ​​of charging power, , are the maximum and minimum values ​​of discharge power.

[0053] (4) Demand response constraints For a certain period of time, the total load shift constraint should be met:

[0054] in, , They are t The maximum total load power that can be transferred in and out during a period of time.

[0055] The interruptible load should also meet the following constraints:

[0056] in, Interruptible load t The maximum number of interruptions during a time period.

[0057] As one or more implementations, a hierarchical partitioned optimization search method (HP-OSM) is used to solve an optimal control strategy applicable to a multi-energy microgrid system, including: The decision variables in the optimization control model of the multi-energy microgrid are set, including energy storage scheduling strategy, wind power and photovoltaic consumption strategy and gas turbine operation strategy. The HP-OSM algorithm is improved on the search framework of Bird Mating Optimizer (BMO), where the gene of each bird represents an individual, and its encoding method is as follows:

[0058] in, is an individual consisting of a set of decision variables; t For scheduling time.

[0059] After initializing parameters such as population size, ratio of male and female birds, and number of iterations, the initial feasible solutions are divided into female birds (upper layer) and male birds (lower layer) according to their fitness (i.e., the total cost of optimization control): female birds are birds with better genes and are divided into two groups in the population, namely, parthenogenetics and polyandry; male birds are divided into three groups, namely, monogamy, polygamy, and hybridization.

[0060] The above five groups of interaction patterns are used as different partitions to perform selection, crossover and mutation operations according to the formula to obtain the genes of the chicks: among them, the genes of the chicks produced under parthenogenesis and polyandry are based on the genes of the female bird, produced by slight mutations or combining with multiple male bird genes; while the genes of the chicks produced under the monogamous system, polygamous system and hybrid system are based on the genes of the male bird, produced by combining with the genes of a single or multiple female birds respectively.

[0061] The five interaction modes serve as five search areas, all with personalized reproduction characteristics. The calculation formula for the resulting chick genes is as follows: (1) When the interaction mode is parthenogenetic, the chicks' genes undergo the following changes:

[0062] (2) When the interaction mode is polyandry, the chicks' genes undergo the following changes:

[0063] (3) When the interaction mode is monogamous, the genes of the chicks undergo the following changes:

[0064] (4) When the interaction mode is polygamous, the chicks' genes undergo the following changes:

[0065] (5) When the interaction mode is hybridization, the chick genes generate chaotic sequences; in, For chick genes; For male bird genes; is the inertia weight; The genes of the female bird selected by the male bird; For one Matrix; Number of females selected for males; For female bird genes; The genes of the male bird selected by the female bird; l The number of males selected for the females.

[0066] Among them, due to the inertia weight involved in the chick gene calculation formula of the five interaction modes This determines the tendency of the chicks to continue the coding of the previous generation and affects their search range. Therefore, according to the different levels of the five interaction modes, the adaptive change rules suitable for different search areas are set as follows: (1) Inertia weights of parthenogenetic and monogamous models Both are adopted in the early stage of iteration To expand the search radius, the later To conduct fine mining of local areas. and The calculation formula is as follows:

[0067] in, , are the maximum and minimum values ​​of the inertia weight initially set; , The codes corresponding to the birds with the best and worst evaluation indicators in the current search area; The code for the bird with the average evaluation index; is the code of the current bird. When , it means that the current bird is better than the average level of the population, so it will decrease To perform local search; when When the number of birds is less than the average level of the population, it means that the current number of birds is lower than the average level of the population and needs to be increased. to perform a global search.

[0068]

[0069] Among them, when When , it means that the code of the bird in the current search area is quite different from the optimal solution code, so it will increase To expand the search scope, , it means that the current bird's code is close to the optimal decoding code, so it will reduce To carry out local refined excavation.

[0070] (2) Both the polyandry and hybrid modes use adaptive methods with a larger search radius In order to jump out of the local optimum, the influence of the male birds in the lower layer on the chicks is weakened.

[0071] (3) In polygamous systems, since chicks are greatly influenced by the female birds above them, they always adopt Conduct a narrow and detailed search.

[0072] According to the genes of the chicks produced by these five interaction modes, different reproduction mechanisms are used to conduct partition search for new feasible solutions, and the evaluation indicators under different interaction modes are calculated: fitness is used to evaluate the quality of the chicks' genes in parthenogenesis; the comparison index is used for evaluation and screening in monogamy; both polyandry and polygamy adopt a reproduction mechanism that comprehensively considers fitness and the comparison index, and the evaluation index is the sum of the two, among which fitness accounts for 70% and 30% respectively; in the hybrid mode, only the comparison index is used to screen offspring.

[0073] If the chick's gene is higher than the parent's gene according to the evaluation index, it will be replaced, otherwise it will remain unchanged.

[0074] Among them, the evaluation index of the genetic quality of chicks in each interaction mode includes not only fitness but also a comparative index that takes into account both fitness and coding differences. The expression of the comparative index is as follows:

[0075] in, For individuals j The encoding difference with the individual with the best fitness, For individuals j The relative average fitness of . It is calculated as follows:

[0076]

[0077] in,

[0078] in, is the sequence weight; and Individual j and the current best fitness individualk decision variables; is the position weight; is the current optimal fitness; For individuals j Adaptability; is the average fitness.

[0079] If the number of iterations is met, the optimal individual gene is output as the result. If it is not met, the evaluation index of the individuals in the population is calculated, and the levels are divided according to the evaluation index, and the process is repeated until the number of iterations is met.

[0080] The reproduction mechanism corresponding to each interaction mode attaches different importance to fitness and comparison index. The evaluation index is calculated in each region as follows: (1) Since female birds are usually at a higher level of fitness, they should continue to improve their upward exploration ability. Parthenogenetics only use fitness to evaluate the genetic quality of chicks. The fitness F is calculated as follows: the greater the fitness, the better the genes; that is, ; Where C is the initial setting constant, Initially set to a positive number.

[0081] (2) In monogamous systems, since male and female birds have similar influences on their chicks, a comparative index is used for evaluation. The larger the comparative index, the better the genes. (3) Both polyandry and polygyny adopt a reproduction mechanism that comprehensively considers fitness and comparison index, but the former pays more attention to fitness because in this mode, the chicks produced are more influenced by the male birds in the lower layer; correspondingly, polygyny emphasizes the comparison index to offset the influence of female birds over-guiding their offspring in a single direction (i.e., the direction of the minimum value of the objective function). The evaluation index is the sum of the two, among which fitness accounts for 70% and 30% respectively, and the comparison index accounts for 30% and 70% respectively. The larger the evaluation index value, the better the gene; (4) In the hybridization model, since the fitness of the parents is generally low, only the comparison index is used as the evaluation criterion. The larger the comparison index, the better the gene, so as to promote a broad search for potential better solutions.

[0082] The genes of the individuals in the population are evaluated according to the above method. If it is calculated that the genes of the chicks are better than those of their parents, the chicks will replace their parents and integrate into the population. At this point, one iteration is completed.

[0083] When the number of iterations reaches the preset total number of iterations, the individual code with the best gene is selected as the result output. The corresponding code output is the power output of the power generation unit, the power of the energy storage unit, and the load transfer power, which are transmitted to each unit as instructions to achieve the purpose of minimizing cost control.

[0084] This embodiment combines the energy interaction characteristics of various power generation units, user electricity consumption, gas turbines, etc. in the multi-energy microgrid island operation system, establishes a system optimization scheduling model, and aims to minimize the system operation cost, thereby improving the new energy consumption rate in the multi-energy microgrid system and accurately controlling it.

[0085] The HP-OSM algorithm is proposed to solve the problem of optimization scheduling models with a large number of heterogeneous decision variables and constraints and time coupling. The feasible solutions and search patterns are personalized and layered, and the partition screening and iteration are performed to solve the problems of high model search dimension and complex constraints, which effectively improves the solution speed and ensures the superiority of the solution.

[0086] Embodiment 2 Embodiment 2 of the present invention introduces an optimization control system for island operation of a multi-energy microgrid.

[0087] like Figure 3 An optimization control system for island operation of a multi-energy microgrid is shown, comprising: An acquisition module, configured to acquire operating parameters of the multi-energy microgrid; A construction module is configured to construct an optimization control model based on the acquired operation parameters and taking the minimization of the total operation cost when the multi-energy microgrid is islanded as the objective function; The optimization module is configured to use the total operating cost as the iterative fitness, and to perform iterative solution of the constructed optimization control model in combination with the hierarchical partition optimization search algorithm, so as to obtain the operation mode of multiple energy sources in the microgrid and complete the optimization control of the island operation of the multi-energy microgrid; wherein the hierarchical partition optimization search algorithm determines the population interaction pattern based on the bird mating algorithm, performs partition optimization search according to the population interaction pattern, and performs hierarchical optimization search in combination with the inertia weight of the population interaction pattern.

[0088] The detailed steps are the same as those of the optimization control method for island operation of a multi-energy microgrid provided in Example 1, and will not be repeated here.

[0089] Embodiment 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0090] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of an optimization control method for island operation of a multi-energy microgrid as described in Embodiment 1 of the present invention.

[0091] The detailed steps are the same as those of the optimization control method for island operation of a multi-energy microgrid provided in Example 1, and will not be repeated here.

[0092] Embodiment 4 A fourth embodiment of the present invention provides an electronic device.

[0093] An electronic device comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the steps in the optimization control method for island operation of a multi-energy microgrid as described in the first embodiment of the present invention are implemented.

[0094] The detailed steps are the same as those of the optimization control method for island operation of a multi-energy microgrid provided in Example 1, and will not be repeated here.

[0095] Embodiment 5 Embodiment 5 of the present invention provides a computer program product.

[0096] A computer program product includes software codes, wherein the program in the software codes executes the steps in the optimization control method for island operation of a multi-energy microgrid as described in the first embodiment of the present invention.

[0097] The detailed steps are the same as those of the optimization control method for island operation of a multi-energy microgrid provided in Example 1, and will not be repeated here.

[0098] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. An optimization control method for island operation of a multi-energy microgrid, characterized in that: include: Obtain the operating parameters of the multi-energy microgrid; Based on the obtained operating parameters, an optimization control model is constructed with the minimum total operating cost when the multi-energy microgrid is operating in an isolated island as the objective function; Taking the total operating cost as the iterative fitness, the constructed optimization control model is iteratively solved in combination with the hierarchical partition optimization search algorithm to obtain the operation mode of multiple energy sources in the microgrid and complete the optimization control of the island operation of the multi-energy microgrid; wherein, the hierarchical partition optimization search algorithm determines the population interaction pattern based on the bird mating algorithm, performs partition optimization search according to the population interaction pattern, and performs hierarchical optimization search in combination with the inertia weight of the population interaction pattern.

2. The optimization control method for island operation of a multi-energy microgrid as described in claim 1, characterized in that: In the process of solving the constructed optimization control model with the total operating cost as the iterative fitness and combining the hierarchical partition optimization search algorithm, the population fitness is calculated based on the bird mating algorithm, and the population interaction mode is determined according to the obtained fitness; the inertia weights of the determined population interaction mode are calculated separately based on the adaptive update method, and the population is partitioned to obtain the evaluation indicators under different population interaction modes, and the optimization control model is solved according to the obtained evaluation indicators.

3. The optimization control method for island operation of a multi-energy microgrid as described in claim 2, characterized in that: The evaluation indicators under the different population interaction modes are: (1) Parthenogenesis uses only fitness to evaluate the genetic quality of chicks; (2) using comparative indexes for evaluation and screening in monogamy; (3) Both polyandry and polygyny adopt the sum of reproductive mechanisms that comprehensively consider fitness and comparison index; (4) In the hybridization mode, only the comparative index is used to screen the offspring.

4. The optimization control method for island operation of a multi-energy microgrid as claimed in claim 1, characterized in that: The objective function of the optimization control model is: ； ； ； ； ； ； ； ； in, is the total operating cost of the microgrid; For power generation unit i Operation and maintenance costs, For power generation unit i Unit operation and maintenance costs; For each power generation unit i No. t Output during time period; is the fuel cost of MT, LHV is the lower calorific value of the fuel gas, is the unit price of MT fuel gas, The efficiency of MT; For each unit i The construction cost, For power generation unit i The useful life of For power generation unit i The unit capacity investment price, r is the annual deposit interest rate, For power generation unit i Capacity factor; , Penalty costs for wind and solar curtailment, , Penalty costs for wind and solar curtailment units, , for t The amount of wind and solar power abandoned during the period; The economic compensation cost for users participating in the incentive-based demand response. is the unit compensation cost of interruptible load, For interruptible loads t Load interruption power during the period, The economic subsidy cost for users participating in price-based demand response is is the unit compensation cost of the translatable load, For the translatable load, transfer to the t Power during the period.

5. The optimization control method for island operation of a multi-energy microgrid as claimed in claim 4, characterized in that: The constraints of the optimization control model include at least source-load power balance constraints, power generation unit constraints, energy storage constraints and demand response constraints.

6. The optimization control method for island operation of a multi-energy microgrid as claimed in claim 1, characterized in that: The multiple energy sources in the microgrid include at least photovoltaics, wind power, gas turbines and energy storage; the total operating cost of the multi-energy microgrid when operating in an isolated island includes the operation and maintenance cost of the multiple energy sources in the microgrid, fuel cost, investment depreciation cost, wind abandonment penalty cost, solar abandonment penalty cost and demand-side response cost.

7. An optimization control system for island operation of a multi-energy microgrid, characterized in that: include: An acquisition module, configured to acquire operating parameters of the multi-energy microgrid; A construction module is configured to construct an optimization control model based on the acquired operation parameters and taking the minimization of the total operation cost when the multi-energy microgrid is islanded as the objective function; The optimization module is configured to use the total operating cost as the iterative fitness, and to perform iterative solution of the constructed optimization control model in combination with the hierarchical partition optimization search algorithm, so as to obtain the operation mode of multiple energy sources in the microgrid and complete the optimization control of the island operation of the multi-energy microgrid; wherein the hierarchical partition optimization search algorithm determines the population interaction pattern based on the bird mating algorithm, performs partition optimization search according to the population interaction pattern, and performs hierarchical optimization search in combination with the inertia weight of the population interaction pattern.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of an optimization control method for island operation of a multi-energy microgrid as described in any one of claims 1 to 6 are implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the optimization control method for island operation of a multi-energy microgrid are implemented as described in any one of claims 1-6.

10. A computer program product comprising software code, characterized in that The program in the software code executes the steps of an optimization control method for island operation of a multi-energy microgrid as described in any one of claims 1-6.

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