Micro-grid optimization scheduling method, device, equipment and storage medium

Abstract

The invention discloses a micro-grid optimization scheduling method, device and equipment and a storage medium, and relates to the technical field of micro-grid optimization scheduling, and the micro-grid optimization scheduling method comprises the steps: taking the user satisfaction and the load change rate as a first objective function, and constructing an upper layer scheduling model; solving the upper-layer scheduling model to obtain a solving result; constructing a lower-layer scheduling model by taking the operation cost and the pollution discharge cost of the micro-grid as a second objective function according to the solving result; and starting from the win-win target of the user side and the power generation side, the economical efficiency and the environmental protection property of micro-grid operation are improved in a balanced manner. Due to the fact that the micro-grid optimization scheduling method is a micro-grid optimization scheduling technical scheme, compared with an existing demand response strategy and a micro-grid scheduling mode which only consider time-of-use electricity price or fixed incentive price, the micro-grid optimization scheduling method can balance economical efficiency and environmental friendliness of micro-grid operation under the condition that the electricity utilization satisfaction degree of users is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of microgrid optimal scheduling, and in particular, to a microgrid optimal scheduling method, device, equipment, and storage medium. Background Art

[0002] With the rapid development of new energy technologies and the wide application of distributed power sources in the power system, the microgrid (MG) has attracted increasing attention as a flexible and efficient form of energy management. However, the economic dispatch and operation optimization of MG face many challenges, including the dynamic regulation of loads, the design of user response mechanisms, and the improvement of the efficiency and quality of optimization algorithms. To address these issues, the present invention proposes a two-layer model for MG optimal scheduling, aiming to balance the economy and environmental friendliness of MG under the condition of ensuring user power consumption comfort. Summary of the Invention

[0003] The main purpose of the present application is to provide a microgrid optimal scheduling method, device, equipment, and storage medium, aiming to solve the technical problem of how to balance the economy and environmental friendliness of microgrid operation under the condition of ensuring user power consumption satisfaction.

[0004] To achieve the above object, the present application proposes a microgrid optimal scheduling method, and the microgrid optimal scheduling method includes:

[0005] Construct an upper-layer scheduling model with user satisfaction and load change rate as the first objective function;

[0006] Solve the upper-layer scheduling model to obtain a solution result;

[0007] According to the solution result, construct a lower-layer scheduling model with the microgrid operation cost and pollution discharge cost as the second objective function;

[0008] Solve the lower-layer scheduling model to obtain the microgrid optimal scheduling result.

[0009] Optionally, the constructing an upper-layer scheduling model with user satisfaction and load change rate as the first objective function includes:

[0010] Considering maximizing user satisfaction and maximizing load change rate, construct an upper-layer scheduling model with the weighted sum of the user satisfaction and load change rate being the smallest as the first objective function and load transfer constraints and load curtailment constraints as the first constraint conditions.

[0011] Optionally, the solution result includes: a load scheduling result and an optimal incentive coefficient, and the solving the upper-layer scheduling model to obtain a solution result includes:

[0012] Taking the incentive coefficient in the demand response model as a variable, use the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the upper-layer scheduling model, obtain the solution result, and obtain the load scheduling result and the optimal incentive coefficient.

[0013] Optionally, constructing the lower-layer scheduling model with the operating cost and pollution discharge cost of the microgrid as the second objective function according to the solution result includes:

[0014] Considering minimizing the operating cost of the microgrid and minimizing the pollution discharge cost of the microgrid, taking the weighted sum minimum of the operating cost and pollution discharge cost of the microgrid as the second objective function, and taking the solution result, clean energy, distributed power sources, energy storage, and the output power of the power grid as the second constraint condition, construct the lower-layer scheduling model.

[0015] Optionally, solving the lower-layer scheduling model to obtain the optimal scheduling result of the microgrid includes:

[0016] Taking the solution result, the clean energy, the distributed power sources, the energy storage, and the output power of the power grid as variables, use the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the lower-layer scheduling model, and obtain the optimal scheduling result of the microgrid.

[0017] Optionally, the inertia weight update rule of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is as follows:

[0018]

[0019] where w represents the inertia weight, k represents the number of iterations, F represents the fitness, are respectively the fitness, the worst fitness, the best fitness of the i-th particle in the k-th iteration, and the fitness corresponding to the global optimal position of the particle at the k-th iteration, p worst and p best are the worst solution and the best solution under the current iteration, w min and w max are the minimum value and the maximum value of the inertia weight.

[0020] Optionally, the particle position update rule of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is as follows:

[0021]

[0022]

[0023] where v represents the particle optimization speed, k represents the number of iterations, x represents the particle position, F represents the fitness, and respectively represent the velocity of the d-th dimension of the i-th particle at the k-th and (k + 1)-th iterations, and respectively represent the position of the d-th dimension of the i-th particle at the k-th and (k + 1)-th iterations, and are respectively the personal best position and the global best position of the d-th dimension of the i-th particle at the k-th iteration, is the intermediate value of the position update of the d-th dimension of the i-th particle at the k-th iteration, c1 and c2 are acceleration constants, r1, r2, r3 are random numbers in the interval [0, 1], ub d 、lb d are the minimum and maximum values of the position of the d-th particle, represents the fitness of the intermediate value of the position update of the i-th particle at the (k + 1)-th iteration, Fi represents the fitness of the i-th particle at the k-th iteration, and respectively represent the position of the i-th particle at the (k + 1)-th iteration.

[0024] In addition, to achieve the above object, the present application also proposes a microgrid optimal scheduling device, and the microgrid optimal scheduling device includes:

[0025] A first construction module, configured to construct an upper-layer scheduling model with the user satisfaction and the load change rate as the first objective function;

[0026] A first solving module, configured to solve the upper-layer scheduling model to obtain a solution result;

[0027] A second construction module, configured to construct a lower-layer scheduling model with the microgrid operation cost and the pollution discharge cost as the second objective function according to the solution result;

[0028] A second solving module, configured to solve the lower-layer scheduling model to obtain a microgrid optimal scheduling result.

[0029] In addition, to achieve the above object, the present application also proposes a microgrid optimal scheduling device, and the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the microgrid optimal scheduling method as described above.

[0030] In addition, to achieve the above object, the present application also proposes a storage medium, the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the microgrid optimal scheduling method as described above are implemented.

[0031] One or more technical solutions proposed in this application have at least the following technical effects: By taking the incentive coefficient in the demand response model as a variable, using an adaptive multi-objective particle swarm algorithm combined with the osprey algorithm to solve the upper-layer scheduling model, obtaining a solution result, obtaining a load scheduling result and an optimal incentive coefficient, defining the input variables of the demand response model, and proposing a suitable improved algorithm to solve the model, it is possible to achieve the targeted formulation of the incentive plan, while improving the solution speed and solution accuracy of the algorithm, and improving the effect of optimal scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 Schematic diagram of the microgrid structure provided for the microgrid optimal scheduling method of this application;

[0035] Figure 2 Schematic flow chart provided for Embodiment 1 of the microgrid optimal scheduling method of this application;

[0036] Figure 3 Schematic flow chart provided for Embodiment 2 of the microgrid optimal scheduling method of this application;

[0037] Figure 4 Schematic flow chart provided for Embodiment 2 of the microgrid optimal scheduling method of this application;

[0038] Figure 5 Schematic brief flow chart of the adaptive multi-objective particle swarm algorithm combined with the osprey algorithm provided for Embodiments 2 and 3 of this application;

[0039] Figure 6 Schematic diagram of the module structure of the microgrid optimal scheduling device for the embodiments of this application;

[0040] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the microgrid optimal scheduling method for the embodiments of this application.

[0041] The implementation, functional features, and advantages of the purpose of this application will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not used to limit the present application.

[0043] To better understand the technical solutions of the present application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0044] Figure 1 It is a schematic diagram of the microgrid structure provided for the first embodiment of the microgrid optimal scheduling method of the present application.

[0045] The structure of the microgrid involved in the embodiments of the present application is as Figure 1 shown, mainly including a distributed generation system and a microgrid scheduling device. Among them, the distributed generation system includes a wind turbine WT, a photovoltaic panel PV, a micro gas turbine MT, a diesel generator DE, an electrical energy storage system ESS, and a fuel cell FC connected to the busbar to which the user load is connected. The microgrid scheduling device is connected between the distributed generation system and the power grid and is used to control the output of each component of the microgrid.

[0046] The main solution of the embodiments of the present application is: taking the user satisfaction and the load change rate as the first objective function, constructing an upper-layer scheduling model; solving the upper-layer scheduling model; according to the solution result, taking the microgrid operation cost and the pollution discharge cost as the second objective function, constructing a lower-layer scheduling model; solving the lower-layer scheduling model to obtain the microgrid optimal scheduling result.

[0047] The execution subject of this embodiment can be an intelligent control device with data processing, network communication, and program running functions, such as a microgrid central controller, a distributed energy management system, an intelligent electric meter, etc., or a power grid scheduling terminal, an energy management device, a microgrid optimal scheduling device, etc. that can implement the above functions. Hereinafter, the microgrid optimal scheduling device will be taken as an example to illustrate this embodiment and the following embodiments.

[0048] The embodiments of the present application provide a microgrid optimal scheduling method, referring to Figure 2 . Figure 2 It is a schematic flow chart provided for the first embodiment of the microgrid optimal scheduling method of the present application.

[0049] In this embodiment, the microgrid optimal scheduling method includes the following steps:

[0050] Step S10, taking the user satisfaction and the load change rate as the first objective function, constructing an upper-layer scheduling model;

[0051] It should be noted that the functions of the upper-layer scheduling model include implementing a demand response strategy that minimizes user satisfaction and minimizes the load change rate, that is, guiding users' electricity consumption behaviors by changing electricity prices or incentive prices, so as to optimize the load curve. The input parameters of the model include the original load curve, time-of-use electricity price, and price elasticity matrix. The control variable is the incentive coefficient I(t) for each time period, and the output is the post-scheduling load curve reflecting the compliance scheduling result and the optimal incentive coefficient at this time. In the upper-layer model, the calculation method of the load after demand response is as follows:

[0052]

[0053] L(t) = (1 - λ)L0(t) + L IBDR (t)

[0054] where, L IBDR (t) is the load participating in the demand response; L0(t) and L(t) are the user loads of the user at the t-th time period before and after participating in the demand response plan; ρ(t) and ρ0(t) are the electricity prices at the t-th period before and after the implementation of the time-of-use electricity price, I(t) represents the incentive coefficient paid to the users for the demand response plan at the t-th time period, I ∈ [0, 1]. λ is the degree of user participation in the demand response. The self-elasticity and cross-elasticity are represented by E(t, t) and E(t, h) respectively.

[0055] The user satisfaction index defined in this embodiment includes the relative change in the user's load demand. When the relative change is small, the user satisfaction is high; when the relative change is large, the user satisfaction is low. The user satisfaction F11 can be calculated by the following formula:

[0056]

[0057] In specific applications, the user satisfaction can also be evaluated from the aspect of the change rate of electricity charges before and after the response. When the user satisfaction reflects the change rate of electricity charges, F11’ is calculated by the following formula:

[0058]

[0059] In the formula, C0(t) and C(t) are the electricity charge prices that the user needs to pay for their own electricity consumption demands at the t-th time period before and after the response respectively, and CI(t) is the reward price obtained by the user at the t-th time period when participating in the demand response strategy. The calculation method of CI(t) is generally the product of the load change amount at the t-th time period, the incentive coefficient, and the time-of-use electricity price.

[0060] Further, in order to comprehensively evaluate the adjustments brought by the demand response strategy to users' electricity consumption behaviors and evaluate the potential and enthusiasm of users to continue participating in demand response, the electricity consumption habits and electricity bill changes of users can also be comprehensively considered, and the weighted sum of F11 and F11' is used as a parameter for evaluating user satisfaction. When calculating, the specific weights of F11 and F11' can be flexibly adjusted according to the focus of actual applications, and this embodiment does not limit them.

[0061] The load change rate index of this embodiment includes the relative change degree of the peak-valley difference of the load curve in the microgrid system. Among them, the peak-valley difference refers to the difference between the highest value and the lowest value of the load. At this time, a large load change rate indicates a large change degree of the peak-valley difference. The load change rate F12 is calculated by the following formula:

[0062]

[0063] In addition, the load change rate index can also include parameters such as the change amount of load smoothness and the change amount of the peak-valley difference of the net load after the access of wind and light, which can reflect the change of the fluctuation degree of the load curve on the microgrid access side before and after demand response.

[0064] Step S20, solve the upper-layer scheduling model to obtain a solution result;

[0065] It should be noted that any computer intelligent algorithm can be selected to solve the objective function corresponding to the upper-layer scheduling model, obtain the incentive coefficients that maximize user satisfaction and load change rate and the corresponding scheduled load curve, and transmit the incentive coefficients and the corresponding scheduled load curve to the lower-layer scheduling model.

[0066] Step S30, construct a lower-layer scheduling model with the operating cost and pollution discharge cost of the microgrid as the second objective function according to the solution result;

[0067] It should be noted that the purposes of the lower-layer scheduling include obtaining the output plan arrangements of each power generation device in the microgrid under the condition of minimizing the operating cost and pollution discharge cost of the microgrid. The input parameters of the lower-layer scheduling model include the solution result, incentive coefficients, scheduled load curve, time-of-use electricity price, price elasticity matrix, unit parameters of distributed generation devices, pollutant emission coefficients of distributed generation devices and other necessary parameters for microgrid scheduling research. When the scheduling requirements are more diversified, the second objective function can further include more indicators. For example, when considering the clean energy consumption rate, the second objective function can also include parameters such as the cost of abandoning wind and light.

[0068] Step S40, solve the lower-layer scheduling model to obtain the optimal scheduling result of the microgrid.

[0069] It should be noted that any computer intelligent algorithm can be selected to solve the objective function corresponding to the lower-layer scheduling model, and the optimal scheduling result of the microgrid is the output plan arrangement of each generating unit when the weighted sum of the minimum operating cost and pollution discharge cost of the microgrid is the smallest.

[0070] The second objective function corresponding to the lower-layer scheduling model is expressed as:

[0071]

[0072] Among them, ɑ2 and β2 are the weights of the operating cost and pollution discharge cost of the microgrid when calculating the second objective function respectively.

[0073] In this embodiment, by taking the user satisfaction and load change rate as the first objective function, an upper-layer scheduling model is constructed; the upper-layer scheduling model is solved; according to the solution result, with the operating cost and pollution discharge cost of the microgrid as the second objective function, a lower-layer scheduling model is constructed; the lower-layer scheduling model is solved to obtain the optimal scheduling result of the microgrid. The economic and environmental optimization scheduling of the microgrid system is carried out from the user side and the power generation side, considering the user needs and line loads, and taking into account the economy and environmental protection of the microgrid operation.

[0074] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as the above-mentioned embodiment one can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , Figure 3 is the flow schematic diagram provided for the second embodiment of the microgrid optimal scheduling method of the present application. The step S10 further includes the following steps:

[0075] Step S101: Considering maximizing user satisfaction and maximizing load change rate, with the weighted sum of the user satisfaction and load change rate being the smallest as the first objective function, and the load transfer constraint and load curtailment constraint as the first constraint condition, an upper-layer scheduling model is constructed.

[0076] It should be noted that the first objective function corresponding to the upper-layer scheduling model is:

[0077]

[0078] Among them, ɑ1 and β1 are the weights of user satisfaction and load change rate when calculating the first objective function respectively. The load transfer constraint and load curtailment constraint represent the demand response strategy expressed by the upper-layer model to guide the load transferred by users to other time periods and the load curtailed in each time period cannot exceed a certain limit.

[0079] In addition, other constraints can also be considered, such as the total load after scheduling cannot exceed the total load before scheduling, and the peak-valley difference after scheduling cannot be greater than the peak-valley difference before scheduling.

[0080] Step S20 further includes the following steps:

[0081] In step S201, taking the incentive coefficient in the demand response model as a variable, use the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the upper-layer scheduling model, obtain the solution result, the load scheduling result and the optimal incentive coefficient.

[0082] The inertia weight update rule of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is as follows:

[0083]

[0084] Where w represents the inertia weight, k represents the number of iterations, F represents the fitness, are the fitness of the i-th particle, the worst fitness, the best fitness in the k-th iteration respectively, and the fitness corresponding to the global optimal position of the particle at the k-th iteration, p worst and p best are the worst solution and the best solution under the current iteration, w min and w max are the minimum value and the maximum value of the inertia weight.

[0085] The particle position update rule of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is as follows:

[0086]

[0087]

[0088] Where v represents the particle optimization speed, k represents the number of iterations, x represents the particle position, F represents the fitness, and represent the speed of the d-th dimension of the i-th particle under the k-th and k + 1-th iterations respectively, and represent the position of the d-th dimension of the i-th particle under the k-th and k + 1-th iterations respectively, and are the individual optimal position and the group optimal position of the d-th dimension of the i-th particle under the k-th iteration respectively, is the position update intermediate value of the d-th dimension of the i-th particle under the k-th iteration, c1 and c2 are acceleration constants, r1, r2, r3 are random numbers in the interval [0, 1], ub d 、lb d are the minimum value and the maximum value of the d-th particle position, represents the fitness of the position update intermediate value of the i-th particle at the k + 1-th iteration, Fi represents the fitness of the i-th particle under the k-th iteration, and respectively represent the position of the i-th particle in the (k + 1)-th iteration.

[0089] It should be noted that the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is based on the particle swarm optimization algorithm and introduces the idea of the osprey algorithm. It can not only introduce one of the exploration and exploitation stages of the osprey algorithm as a mutation strategy into the update strategy of the traditional multi-objective particle swarm optimization algorithm, but also directly combine the update strategies of the two, and use the update result of the osprey algorithm as the basis for the update of the particle swarm optimization algorithm for secondary update. This embodiment does not limit this, and in specific applications, the combined algorithm can achieve better solution results.

[0090] In this embodiment, by taking the incentive coefficient in the demand response model as a variable, using the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the upper-layer scheduling model, the solution result is obtained, the load scheduling result and the optimal incentive coefficient are obtained, the input variables of the demand response model are defined, and a suitable improved algorithm is proposed to solve the model, which can realize the targeted formulation of the incentive plan, and at the same time improve the solution speed and solution accuracy of the algorithm, and improve the effect of optimal scheduling.

[0091] Based on the above embodiments of the present application, in the third embodiment of the present application, the same or similar content as the above embodiments can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 4 , Figure 4 is a schematic flow chart provided by the third embodiment of the microgrid optimal scheduling method of the present application. The step S30 further includes the following steps:

[0092] Step S301, considering minimizing the microgrid operation cost and minimizing the microgrid sewage discharge cost, taking the weighted sum of the microgrid operation cost and the microgrid sewage discharge cost as the second objective function, and taking the solution result, clean energy, distributed power supply, energy storage, and the output power of the power grid as the second constraint condition, to construct a lower-layer scheduling model.

[0093] It should be noted that the microgrid operation cost F21 defined in this embodiment may include the operation and maintenance costs of all distributed generation devices, the electricity trading cost, and the demand response cost.

[0094]

[0095] Among them, C om is the operation cost of the n-th unit in the microgrid; C grid is the electricity trading cost with the power grid; C DR is the demand response cost generated by the demand response plan corresponding to the upper-layer model; is the operation and maintenance cost and output power of the nth unit in the microgrid at the tth time period; μ n is the operation and maintenance coefficient of the nth unit in the microgrid are the electricity purchase and sale prices and electricity purchase and sale volumes at time period t respectively; I t is the incentive coefficient at the tth time period; is the load participating in the interruptible demand response.

[0096] The microgrid sewage discharge cost F22 defined in this embodiment may include the sewage treatment costs of all distributed generation devices, including the treatment costs of carbon dioxide, carbon monoxide, sulfur dioxide and nitrogen oxides.

[0097]

[0098] Among them, is the emission coefficient of the tth type of pollutant emitted by the nth device.

[0099] In practical applications, the second constraint condition includes the equality constraints and inequality constraints of the microgrid and the power system, such as the upper and lower power limits and ramping constraints of each component in the microgrid, the interaction constraints between the microgrid and the power grid, the capacity constraints of energy storage, etc. All constraints that need to be considered when ensuring the stable operation of the power system.

[0100] The step S40 further includes the following steps:

[0101] Step S401, taking the solution result, the output power of the clean energy, the distributed power source, the energy storage and the power grid as variables, and using the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the lower-layer scheduling model to obtain the microgrid optimal scheduling result.

[0102] For the optimization process of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm, see Figure 5 .

[0103] This embodiment defines the second objective function and the second constraint condition, as well as the input variables and output results of the microgrid economic-environmental protection scheduling. Solving with the improved algorithm can realize the targeted formulation of the incentive plan, improve the solving speed and accuracy at the same time, provide a scheduling basis for the lower-layer model, and improve the overall optimal scheduling effect.

[0104] It should be noted that the above examples are only for understanding this application and do not constitute a limitation to the microgrid optimal scheduling method of this application. Based on this technical concept, more forms of simple transformation are within the protection scope of this application.

[0105] This application also provides a microgrid optimal scheduling device. Please refer to Figure 6 The microgrid optimal scheduling device includes:

[0106] The first construction module 10 is used to construct an upper-layer scheduling model with the user satisfaction and the load change rate as the first objective function;

[0107] The first solving module 20 is used to solve the upper-layer scheduling model to obtain a solving result;

[0108] The second construction module 30 is used to construct a lower-layer scheduling model with the microgrid operation cost and the pollution discharge cost as the second objective function according to the solving result;

[0109] The second solving module 40 is used to solve the lower-layer scheduling model to obtain a microgrid optimal scheduling result.

[0110] The microgrid optimal scheduling device provided by this application adopts the microgrid optimal scheduling method in the above embodiment, and can solve the technical problem of how to balance the economy and environmental friendliness of microgrid operation under the condition of ensuring user power consumption satisfaction in microgrid optimal scheduling. Compared with the prior art, the beneficial effects of the microgrid optimal scheduling device provided by this application are the same as those of the microgrid optimal scheduling method provided by the above embodiment, and other technical features in the microgrid optimal scheduling device are the same as the features disclosed in the method of the above embodiment, which will not be elaborated here.

[0111] This application provides a microgrid optimal scheduling device, and the microgrid optimal scheduling device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the microgrid optimal scheduling method in the first embodiment above.

[0112] Refer to the following Figure 7 , which shows a schematic structural diagram of a microgrid optimal scheduling device suitable for implementing the embodiments of this application. The microgrid optimal scheduling device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions: tablet computers), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The microgrid optimal scheduling device shown is only an example, and should not bring any limitation to the functions and usage scopes of the embodiments of this application.

[0113] Such as Figure 7As shown, the microgrid optimal scheduling device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the microgrid optimal scheduling device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the microgrid optimal scheduling device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a microgrid optimal scheduling device with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems can be alternatively implemented or had.

[0114] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program device, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0115] The microgrid optimal scheduling device provided by the present application adopts the microgrid optimal scheduling method in the above-mentioned embodiments, and can solve the technical problem of how to balance the economy and environmental friendliness of microgrid operation under the condition of ensuring user satisfaction with power consumption in microgrid optimal scheduling. Compared with the prior art, the beneficial effects of the microgrid optimal scheduling device provided by the present application are the same as those of the microgrid optimal scheduling method provided by the above-mentioned embodiments, and other technical features in the microgrid optimal scheduling device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0116] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0117] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0118] This application provides a computer-readable storage medium with computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the microgrid optimal scheduling method in the above embodiments.

[0119] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0120] The above computer-readable storage medium can be included in the microgrid optimal scheduling device; it can also exist separately without being assembled into the microgrid optimal scheduling device.

[0121] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0122] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0123] The modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0124] The readable storage medium provided in this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned microgrid optimal scheduling method, and can solve the technical problem of how to balance the economy and environmental friendliness of microgrid operation under the condition of ensuring user power consumption satisfaction. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the microgrid optimal scheduling method provided in the above embodiments, and will not be elaborated here.

[0125] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the microgrid optimal scheduling method as described above.

[0126] The computer program product provided by the present application can solve the technical problem of microgrid optimal scheduling. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the microgrid optimal scheduling method provided by the above embodiment, and will not be elaborated here.

[0127] The above are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the specification and drawings of the present application under the technical concept of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A microgrid optimal scheduling method, characterized in that, The microgrid optimal scheduling method includes the following steps: Taking the user satisfaction and the load change rate as the first objective function, an upper-layer scheduling model is constructed; The upper-layer scheduling model is solved to obtain a solution result; According to the solution result, taking the microgrid operation cost and the pollution discharge cost as the second objective function, a lower-layer scheduling model is constructed; The lower-layer scheduling model is solved to obtain the microgrid optimal scheduling result.

2. The microgrid optimal scheduling method according to claim 1, wherein The constructing of the upper-layer scheduling model by taking the user satisfaction and the load change rate as the first objective function includes: Considering maximizing the user satisfaction and maximizing the load change rate, taking the weighted sum minimum of the user satisfaction and the load change rate as the first objective function, and taking the load transfer constraint and the load curtailment constraint as the first constraint conditions, an upper-layer scheduling model is constructed.

3. The microgrid optimal scheduling method according to claim 2, wherein, The solution result includes: the load scheduling result and the optimal incentive coefficient. The solving of the upper-layer scheduling model to obtain the solution result includes: Taking the incentive coefficient in the demand response model as a variable, using the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the upper-layer scheduling model to obtain the solution result, and obtaining the load scheduling result and the optimal incentive coefficient.

4. The microgrid optimal scheduling method according to claim 1, characterized in that The constructing of the lower-layer scheduling model by taking the microgrid operation cost and the pollution discharge cost as the second objective function according to the solution result includes: Considering minimizing the microgrid operation cost and minimizing the microgrid pollution discharge cost, taking the weighted sum minimum of the microgrid operation cost and the microgrid pollution discharge cost as the second objective function, and taking the solution result, the clean energy, the distributed power source, the energy storage, and the output power of the power grid as the second constraint conditions, a lower-layer scheduling model is constructed.

5. The microgrid optimal scheduling method according to claim 4, wherein The solving of the lower-layer scheduling model to obtain the microgrid optimal scheduling result includes: Taking the solution result, the clean energy, the distributed power source, the energy storage, and the output power of the power grid as variables, using the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm to solve the lower-layer scheduling model to obtain the microgrid optimal scheduling result.

6. The microgrid optimal scheduling method according to claim 3 or 5, characterized in that The inertia weight update rule of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is as follows: Among them, w represents the inertia weight, k represents the number of iterations, and F represents the fitness. They are the fitness of the i-th particle, the worst fitness, the best fitness in the k-th iteration, and the fitness corresponding to the global optimal position of the particle at the k-th iteration, respectively. p worst and p best are the worst solution and the best solution under the current iteration, respectively. w min and w max are the minimum value and the maximum value of the inertia weight, respectively.

7. The microgrid optimal scheduling method according to claim 6, wherein The particle position update rule of the adaptive multi-objective particle swarm optimization algorithm combined with the osprey algorithm is as follows: Among them, v represents the particle optimization speed, k represents the number of iterations, x represents the particle position, and F represents the fitness. and represent the velocity of the d-th dimension of the i-th particle under the k-th and (k + 1)-th iterations respectively. and represent the position of the d-th dimension of the i-th particle under the k-th and (k + 1)-th iterations respectively. and are respectively the individual optimal position and the global optimal position of the d-th dimension of the i-th particle under the k-th iteration. is the intermediate value of the position update of the d-th dimension of the i-th particle under the k-th iteration. c1 and c2 are acceleration constants, and r1, r2, r3 are random numbers in the interval [0, 1]. ub d , lb d are the minimum and maximum values of the d-th particle position, and F i k_P represents the fitness of the intermediate value of the position update of the i-th particle at the (k + 1)-th iteration. Fi represents the fitness of the i-th particle under the k-th iteration. and represent the positions of the i-th particle under the (k + 1)-th iteration respectively.

8. A microgrid optimal scheduling device, characterized in that, The microgrid optimal scheduling device includes: A first construction module, configured to construct an upper-layer scheduling model by taking the user satisfaction and the load change rate as the first objective function; A first solving module, configured to solve the upper-layer scheduling model to obtain a solution result; A second construction module, configured to construct a lower-layer scheduling model by taking the microgrid operation cost and the pollution discharge cost as the second objective function according to the solution result; A second solving module, configured to solve the lower-layer scheduling model to obtain the microgrid optimal scheduling result.

9. A microgrid optimal scheduling device, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the microgrid optimal scheduling method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the microgrid optimal scheduling method according to any one of claims 1 to 7 are implemented.

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