Micro-grid interactive response method giving consideration to grid operation cost and user utility

Abstract

The invention relates to the technical field of micro-grids, and particularly provides a micro-grid interactive response method giving consideration to grid operation cost and user utility, and the method comprises the steps: building a first optimization model according to grid operation state data by taking the minimization of the grid operation cost as a first objective function; according to the user utility data, taking user utility maximization as a second objective function, and taking comfort utility, adjustment willingness utility and charging completion utility as decision variables, constructing a second optimization model; coupling the first objective function and the second objective function to form a double-layer optimization model of the micro-grid; and solving the double-layer optimization model by adopting a multi-target coordination control algorithm to obtain a response scheme of the micro-grid. According to the method, the operation efficiency of the power grid is optimized, the operation cost is reduced, the individual requirements of users are also considered, and the stability, the self-adaptive capability and the flexibility of the micro-grid system are enhanced.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of microgrids, in particular to a microgrid interactive response method, device, medium and computer program product considering both grid operation cost and user utility. BACKGROUND

[0002] With the rapid development of distributed energy and renewable energy, microgrids, as an important part of smart power systems, have shown significant advantages in improving energy utilization efficiency, reducing carbon emissions, and enhancing the flexibility and reliability of power systems. However, in the practice of microgrids and their interactive scheduling, as the penetration rate of renewable energy such as photovoltaic and wind power increases, the scheduling complexity and uncertainty of the system are also increasingly prominent. On the one hand, with the expansion of the scale of microgrids and the diversification of user loads, the interaction between microgrids, load regulation and energy storage distribution need to be adjusted flexibly according to real-time operation state and user demand, while existing methods usually rely on fixed strategies or empirical rules, making it difficult to achieve real-time optimization and dynamic response. On the other hand, microgrid scheduling not only needs to consider economic efficiency, but also needs to consider multiple objectives such as renewable energy consumption, user comfort and charging completion time, making the scheduling problem from traditional single-objective optimization to complex multi-objective coordination optimization.

[0003] In existing research, microgrid scheduling optimization mostly uses enumeration method or traditional heuristic algorithm (such as genetic algorithm, particle swarm optimization, etc.) to solve possible load combination and energy storage distribution strategy. Although these methods can provide feasible solutions to a certain extent, due to large amount of calculation, slow convergence speed and easy to fall into local optimum, it is difficult to adapt to large-scale, multi-objective and high real-time microgrid interactive scheduling requirements. In addition, the existing method has limited response ability to user individual will, and usually cannot include user comfort, price sensitivity and load priority preferences into the optimization model, resulting in that the scheduling scheme may cause user experience decline or insufficient response problem in actual execution. SUMMARY

[0004] The purpose of the present application is to overcome the deficiencies of the prior art in dealing with multi-objective, multi-constrained microgrid interactive scheduling and user individual preference coordination, and to provide a microgrid interactive response method, device, medium and computer program product that can consider both grid operation optimization target and user individual will.

[0005] To achieve the above purpose, the application is implemented by using the following technical solutions:

[0006] In a first aspect, the application provides a microgrid interactive response method considering both grid operation cost and user utility, comprising:

[0007] According to the grid operation state data, a first optimization model is constructed with the minimization of grid operation cost as a first objective function;

[0008] According to the user utility data, a second optimization model is constructed with the maximization of user utility as a second objective function; wherein the decision variables of the second optimization model include: comfort utility, adjustment willingness utility and charging completion utility;

[0009] The first objective function and the second objective function are coupled to form a double-layer optimization model of the microgrid;

[0010] A multi-objective coordinated control algorithm is used to solve the double-layer optimization model of the microgrid to obtain a response scheme of the microgrid.

[0011] In some embodiments of the present application, according to the grid operation state data, a first optimization model is constructed with the minimization of grid operation cost as a first objective function, including:

[0012] Grid operation state data is collected; wherein the grid operation state data includes operation state data of the microgrid and operation state data of the upper grid of the microgrid;

[0013] First decision variables are set; wherein the first decision variables include: power generation, power purchase, power sale, energy storage charging power and energy storage discharging power;

[0014] Based on the grid operation state data and the first decision variables, a first objective function and a first constraint condition are obtained to construct a first optimization model; wherein the first constraint condition includes: power balance constraint, energy storage energy dynamic constraint and device operation boundary constraint.

[0015] In some embodiments of the present application, the first objective function is as follows:

[0016] ;

[0017] Wherein, Indicates the set of related units in the microgrid and the upper grid of the microgrid; Indicates the unit Generation cost function at t time; Indicates the unit Generation power at t time; Indicates the power purchase price at t time, Indicates the power sale price at t time; Indicates the power purchase at t time, Indicates the power sale at t time; Indicates the set of energy storage devices in the microgrid; denotes the degradation cost coefficient of the energy storage unit s; denotes the charging power of the energy storage unit s at time t; denotes the discharging power of the energy storage unit s at time t; denotes the penalty coefficient of energy curtailment, denotes the renewable power curtailed at time t, denotes the penalty coefficient of load imbalance, denotes the amount of load balancing relaxation at time t.

[0018] In some embodiments of the present application, the power balance constraint is as follows:

[0019] ;

[0020] wherein, denotes the available renewable power at time t, denotes the baseline load power at time t, denotes the amount of load change due to user-side response;

[0021] The energy storage energy dynamic constraint is as follows:

[0022] ;

[0023] wherein, denotes the state of charge of the energy storage unit s at time t+1; denotes the state of charge of the energy storage unit s at time t; denotes the charging efficiency of the energy storage unit s; denotes the discharging efficiency of the energy storage unit s, denotes the dispatch time step;

[0024] The device operation boundary constraint is as follows:

[0025] ;

[0026] ;

[0027] wherein, denotes the lower limit of the energy capacity of the energy storage unit s; denotes the upper limit of the energy capacity of the energy storage unit s; denotes the maximum charging power of the energy storage unit s; denotes the maximum discharging power of the energy storage unit s; denotes the maximum output of the unit ; denotes the maximum power purchase limit; denotes the maximum power selling limit.

[0028] In some embodiments of the present application, a second optimization model is constructed according to user utility data, with user utility maximization as a second objective function, including:

[0029] The user utility data is collected.

[0030] A second decision variable is set; wherein the second decision variable includes: comfort utility, adjustment willingness utility and charging completion utility.

[0031] Based on the user utility data and the second decision variable, a second objective function and a second constraint condition are obtained, thereby constructing a second optimization model; wherein the second constraint condition includes load boundary constraint and adjustment response constraint.

[0032] In some embodiments of the present application, the second objective function is as follows:

[0033]

[0034] Wherein, represents an optimization time period; represents the comfort utility of the user at time t; represents the adjustment willingness utility of the user at time t; represents the charging completion utility of the user at time t; represents the electricity cost of the user at time t.

[0035] In some embodiments of the present application, the comfort utility of the user at time t is calculated as follows:

[0036]

[0037] Wherein, represents the expected electricity power of the user at time t; represents the actual electricity power of the user at time t; represents a penalty factor of comfort deviation;

[0038] The calculation formula of the adjustment willingness utility of the user at time t is as follows:

[0039]

[0040] Wherein, represents the load adjustment power of the user at time t, represents the utility gain brought by unit adjustment amount;

[0041] The calculation formula of the charging completion utility of the user at time t is as follows:

[0042] ​​​​​​ ;

[0043] wherein, represents a target charging demand set by the user; represents a deadline of the actual charging amount of the user's charging device at the time t; represents a penalty coefficient of the unmet charging demand;

[0044] the user's electricity cost at the time t is calculated according to the following formula:

[0045] ;

[0046] wherein, represents the electricity price at the time t, represents the actual electricity power of the user.

[0047] In some embodiments of the present application, the load boundary constraint is as follows:

[0048] ;

[0049] wherein, represents the minimum power acceptable by the user; represents the maximum power acceptable by the user;

[0050] The adjustment response constraint is as follows:

[0051] ;

[0052] wherein, represents the maximum adjustable capacity on the user side.

[0053] In some embodiments of the present application, the double-layer optimization model of the micro-grid is as follows:

[0054] .

[0055] In some embodiments of the present application, the multi-objective coordination control algorithm is a multi-objective particle swarm optimization algorithm;

[0056] The multi-objective coordination control algorithm is used to solve the double-layer optimization model of the micro-grid to obtain the response scheme of the micro-grid, including:

[0057] According to the double-layer optimization model of the micro-grid, a first weight is set for the first objective function and a second weight is set for the second objective function;

[0058] constructing a total objective function based on the first objective function, the first weight, the second objective function and the second weight, for multi-objective particle swarm search, each particle corresponding to a combination of a set of decision variables of the first objective function and a set of decision variables of the second objective function;

[0059] the particle swarm updates the position and the velocity through multiple iterations, and adjusts the first weight and the second weight at each iteration;

[0060] when the convergence condition is met or the maximum number of iterations is reached, the iteration stops, and the combination of decision variables corresponding to the optimal fitness particle is output as the response scheme of the microgrid.

[0061] In some embodiments of the present application, the total objective function is as follows:

[0062] ;

[0063] wherein, represents the first weight; represents the second weight; ;

[0064] adjusting the first weight and the second weight at each iteration, comprising:

[0065] ;

[0066] wherein, represents the first weight after the k+1th iteration; represents the first weight after the kth iteration; represents the second weight after the k+1th iteration; represents the value of the first objective function at the kth iteration; represents the reference value of the first objective function.

[0067] In a second aspect, the present application also provides an electronic device, comprising a processor and a memory storing a program, the program comprising instructions which, when executed by the processor, cause the processor to perform the microgrid interaction response method described above.

[0068] In a third aspect, the present application also provides a non-transitory machine-readable medium storing computer instructions for causing the computer to perform the microgrid interaction response method described above.

[0069] In a fourth aspect, the present application also provides a computer program product comprising computer programs / instructions, characterized in that the computer programs / instructions are executed by a processor to implement the microgrid interaction response method described above.

[0070] The micro-grid interactive response method provided by the application considers the power grid operation cost and user utility, can establish a closed-loop optimization mechanism between the optimal target of power grid operation and user comfort and load regulation willingness through accurate collection and modeling of the operation state data of the micro-grid and the upper power grid and user individual preference parameters, and realizes dynamic balance of decision-making-response-feedback. Through the double-layer coupling model and the dynamic weight adjustment mechanism, the system can flexibly coordinate the power grid operation cost and user willingness, so that the micro-grid can adaptively adjust the load power, energy storage charging and discharging and charging strategy in the case of load fluctuation and renewable energy output change. At the same time, through the closed-loop optimization control, real-time monitoring and feedback correction are realized, and the feasibility and safety of the micro-grid response are improved. The method of the application not only optimizes the power grid operation efficiency and reduces the operation cost, but also considers the individual demand of users, enhances the stability, adaptive ability and flexibility of the system, so as to realize efficient, low-carbon and intelligent double-layer micro-grid operation and management. BRIEF DESCRIPTION OF DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the application, and for those skilled in the art, other embodiments can also be obtained from these drawings without creative labor.

[0072] Figure 1 is a flowchart of a micro-grid interactive response method considering power grid operation cost and user utility provided by an embodiment of the application;

[0073] Figure 2 is a structural schematic diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0074] The embodiments of the application will be described in detail with reference to the drawings. Although some embodiments of the application are shown in the drawings, it should be understood that the application can be implemented in various forms, and should not be interpreted as being limited to the embodiments described herein, on the contrary, these embodiments are provided to make the application more thorough and complete. It should be understood that the drawings and embodiments of the application are only for exemplary purposes, and are not used to limit the protection scope of the application.

[0075] As shown in Figure 1 , the embodiment of the application provides a micro-grid interactive response method considering power grid operation cost and user utility. Figure 1For the flowchart of the micro-grid interaction response method, the flowchart only shows the logical sequence of the method of the embodiment, and in other possible embodiments of the application, the steps shown or described can be completed in an order different from Figure 1 the order shown, without conflict.

[0076] Referring to Figure 1 , the method of the embodiment of the application specifically includes the following steps:

[0077] Step S101: According to the grid operation state data, a first optimization model is constructed with the minimization of grid operation cost as a first objective function.

[0078] Step S102: According to the user utility data, a second optimization model is constructed with the maximization of user utility as a second objective function.

[0079] In some embodiments, step S101 specifically includes the following steps S1011 to S1013.

[0080] Step S1011: Collecting grid operation state data.

[0081] Step S101 and step S102 have no sequence, and the collection of grid operation state data and the collection of user utility data are generally performed synchronously. The grid operation state data and the user utility data cover the voltage, current, load curve, frequency of the micro-grid and the upper grid, and the user comfort, price sensitivity, load priority and other information, thereby providing a data basis for the double-layer optimization modeling.

[0082] The grid operation state data includes the operation state data of the micro-grid and the operation state data of the upper grid of the micro-grid. The numerical information of the key operation indicators of the micro-grid and the upper grid can be obtained in real time through sensors or phasor measurement units installed at nodes, lines and power sources of the micro-grid, for example:

[0083] The voltage can be expressed in vector form ; wherein, is the voltage amplitude data of the i th node at time t, and N is the total number of nodes of the micro-grid and the upper grid of the micro-grid;

[0084] The load power curve can be expressed as ; wherein, is the load power of the i th node at time t.

[0085] The parameter information related to the user load adjustment willingness, comfort and charging demand can be obtained in real time through intelligent terminals, energy management systems or home / enterprise gateways installed at the user side, including:

[0086] User comfort index, reflecting the user's satisfaction with the environment or power service state, such as indoor temperature, humidity, lighting intensity or air conditioning running state, etc.; price sensitivity, indicating the user's response to price changes, reflecting the user's willingness to adjust the load during the price peak period or fluctuation; load priority, indicating the priority of different users or different load devices in the adjustment process; charging completion time requirement, indicating the user's expectation of the adjustable load or electric vehicle charging completion time.

[0087] After collecting the grid operation state data and user utility data, in order to ensure the comparability and uniformity of different dimension data in the double-layer optimization model of microgrid, the original data needs to be preprocessed and standardized, including: (1) Data cleaning and filtering: noise filtering is performed on the collected voltage data and load power data to remove abnormal values caused by sensor errors, communication delays, etc.; missing value completion and abnormal value correction are performed on user side parameters (user comfort index, price sensitivity, load priority, charging completion time requirement) to ensure data integrity. (2) Data normalization processing: in order to avoid weight deviation caused by different dimension data in optimization modeling, the grid operation data and part of the user individual preference parameters are normalized in interval.

[0088] The normalized grid operation state data and user individual parameters are integrated to form a unified input matrix.

[0089] Step S1012: setting a first decision variable.

[0090] The first decision variable includes: power generation, power purchase, power sale, energy storage charging power and energy storage discharging power.

[0091] Step S1013: obtaining a first target function and a first constraint condition based on the grid operation state data and the first decision variable, thereby constructing a first optimization model.

[0092] The overall goal of the first optimization model is to minimize the overall operation cost of the grid, considering factors such as generation cost, power purchase and sale cost, energy storage degradation cost, renewable energy loss and load imbalance penalty, thereby providing constraint conditions and price signals for user side optimization.

[0093] The first target function is as follows:

[0094] ;

[0095] Wherein, represents the set of related units in the microgrid and the upper grid of the microgrid; represents the unit The generation cost function at time t; generator set generation power at time t; purchasing electricity price at time t, selling electricity price at time t; purchased electricity power at time t, sold electricity power at time t; set of energy storage devices in microgrid; degradation cost coefficient of energy storage unit s; charging power of energy storage unit s at time t; discharging power of energy storage unit s at time t; abandonment penalty coefficient, abandoned renewable power at time t, load imbalance penalty coefficient, balance slack at time t.

[0096] The first constraint conditions include: power balance constraint, energy storage energy dynamic constraint and device operation boundary constraint.

[0097] Specifically, the power balance constraint is as follows:

[0098] ;

[0099] wherein, available renewable energy power (predicted value) at time t, baseline load power at time t, load change amount generated by user side response.

[0100] The energy storage energy dynamic constraint is as follows:

[0101] ;

[0102] wherein, state of charge of energy storage unit s at time t+1; state of charge of energy storage unit s at time t; charging efficiency of energy storage unit s; discharging efficiency of energy storage unit s, dispatching time step, in hours.

[0103] The device operation boundary constraint is as follows:

[0104] ;

[0105] ;

[0106] wherein, denotes a lower limit of the energy capacity of the energy storage unit s; denotes an upper limit of the energy capacity of the energy storage unit s; denotes a maximum charging power of the energy storage unit s; denotes a maximum discharging power of the energy storage unit s; denotes a maximum output of the unit ; denotes a maximum power purchase limit; denotes a maximum power sale limit.

[0107] Similar to step S101, in some embodiments, step S102 specifically includes steps S1021 to S1023.

[0108] Step S1021: Collect user utility data.

[0109] Step S1022: Set a second decision variable.

[0110] The second decision variable includes a comfort utility, a regulation willingness utility, and a charging completion utility.

[0111] Step S1023: Obtain a second objective function and a second constraint condition based on the user utility data and the second decision variable, so as to construct a second optimization model.

[0112] The optimization objective of the second optimization model is to maximize the utility of the user individual while taking into account factors such as comfort, load regulation preference, and charging completion time. On the premise of guaranteeing basic demand, the user is guided to participate in microgrid dispatching, and the flexibility and low carbon nature of overall operation are improved.

[0113] The second objective function is as follows:

[0114] ;

[0115] wherein, denotes an optimization scheduling time period; denotes a comfort utility of the user at time t; denotes a regulation willingness utility of the user at time t; denotes a charging completion utility of the user at time t; denotes a power consumption cost of the user at time t.

[0116] In some embodiments, the comfort utility of the user at time t is calculated according to the following formula:

[0117] ;

[0118] wherein, denotes an expected power consumption of the user at time t;​ represents the actual power consumption of the user at time t; represents the penalty factor of comfort deviation;

[0119] user's adjustment willingness utility at time t The calculation formula is as follows:

[0120] ;

[0121] wherein, represents the load adjustment power of the user at time t, represents the utility gain brought by unit adjustment amount;

[0122] user's charging completion utility at time t The calculation formula is as follows:

[0123] ;

[0124] wherein, represents the target charging demand set by the user; represents the actual charging amount of the user's charging equipment at the deadline ; represents the penalty coefficient of unmet charging demand;

[0125] user's power consumption cost at time t The calculation formula is as follows:

[0126] ;

[0127] wherein, represents the electricity price at time t, represents the actual power consumption of the user.

[0128] The second constraint condition includes load boundary constraint and adjustment response constraint.

[0129] Specifically, the load boundary constraint is as follows:

[0130] ;

[0131] wherein, represents the minimum power acceptable by the user; represents the maximum power acceptable by the user.

[0132] The adjustment response constraint is as follows:

[0133] ;

[0134] wherein, represents the maximum adjustable capacity on the user side.

[0135] Step S103: coupling the first objective function and the second objective function to form a bi-level optimization model of the micro-grid.

[0136] After the first optimization model with the objective of minimizing the grid operation cost and the second optimization model with the objective of maximizing the user utility are completed, the two models need to be coupled to form a complete bi-level optimization framework. The core idea of the coupling model is that the upper layer aims to minimize the system operation cost, and transmits the scheduling intention to the lower layer through the real-time electricity price signal The lower layer user optimizes the power consumption behavior according to the comfort level, electricity price sensitivity and load adjustment preference, and feeds back the actual load response result to the upper layer, which affects the generation and purchase decision, so as to realize dynamic balance.

[0137] The bi-level optimization model of the micro-grid is constructed as follows:

[0138] .

[0139] The key points of the bi-level coupling include:

[0140] Price signal coupling: the upper layer affects the load adjustment and charging strategy of the lower layer user by issuing the electricity price or incentive signal;

[0141] Load response coupling: the actual power and the adjusted power of the lower layer user are fed back to the upper layer after being summarized, which changes the power balance constraint and cost calculation of the grid;

[0142] Charging coupling: the charging energy of the electric vehicle or the energy storage of the lower layer affects the total load and energy storage scheduling, and further affects the generation and purchase strategy of the upper layer.

[0143] Step S104: a multi-objective coordinated control algorithm is used to solve the bi-level optimization model of the micro-grid, and a response scheme of the micro-grid is obtained.

[0144] In some embodiments of the application, the multi-objective coordinated control algorithm uses a multi-objective particle swarm optimization (MOPSO) algorithm, which realizes the multi-objective optimization of minimizing the cost of the upper layer and maximizing the utility of the lower layer user through the group cooperative search mechanism while ensuring the global optimization ability.

[0145] In some embodiments of the application, step S104 specifically includes the following steps S1041 to S1044.

[0146] Step S1041: According to the double-layer optimization model of the microgrid, a first weight is set for the first objective function, and a second weight is set for the second objective function.

[0147] Step S1042: Based on the first objective function, the first weight, the second objective function and the second weight, a total objective function is constructed for multi-objective particle swarm search, each particle corresponding to a combination of a set of decision variables of the first objective function and a set of decision variables of the second objective function.

[0148] The total objective function is as follows:

[0149] ;

[0150] wherein, represents the first weight; represents the second weight; .

[0151] Set the number of particles, upper and lower limit values of inertia weight and other algorithm parameters, and use chaotic mapping to initialize particle position to improve population diversity and avoid the algorithm from falling into local optimum too early.

[0152] Step S1043: The particle swarm updates the position and speed through multiple iterations, and adjusts the first weight and the second weight at each iteration.

[0153] The particle position update formula is as follows:

[0154] ;

[0155] ;

[0156] wherein, represents the position of the i-th particle at the k-th iteration; represents the speed of the i-th particle at the k-th iteration; represents the historical optimal position of the i-th particle; represents the global optimal position; represents a nonlinear inertia weight.

[0157] The first weight and the second weight are adjusted at each iteration, including:

[0158] ;

[0159] wherein, represents the first weight after the k+1-th iteration; represents the first weight after the k-th iteration; represents the second weight after the k+1-th iteration; represents the value of the first objective function at the k-th iteration; a reference value of the first objective function.

[0160] The weight coefficient is dynamically adjusted according to the change of the current upper-layer cost and lower-layer utility, so that the first objective function and the second objective function are balanced during the iteration process.

[0161] Step S1044: When the convergence condition is met or the maximum number of iterations is reached, the iteration is stopped, and the decision variable combination corresponding to the optimal fitness particle is output as the response scheme of the micro-grid.

[0162] During the iteration process, the trade-off solution of different particles on the upper-layer cost and lower-layer utility can be recorded to form a Pareto solution set covering different operation preferences, thereby providing diversified strategy selection for micro-grid dispatching

[0163] According to the optimization result, a micro-grid response scheme is generated, including a load adjustment instruction, a storage energy charging and discharging strategy, and an electric vehicle charging plan. The control instructions are issued to each micro-grid to realize the execution operation, and the running state and user response effect are monitored in real time to perform feedback correction, thereby realizing the closed-loop control and optimized execution of the micro-grid response.

[0164] The embodiment of the application further provides a non-transient machine readable medium storing a computer program, wherein the computer program is used for enabling the computer to execute the micro-grid interactive response method of the embodiment of the application which takes into account the grid operation cost and user utility when the computer program is executed by the processor of the computer.

[0165] The embodiment of the application further provides a computer program product comprising a computer program, wherein the computer program is used for enabling the computer to execute the micro-grid interactive response method of the embodiment of the application which takes into account the grid operation cost and user utility when the computer program is executed by the processor of the computer.

[0166] The embodiment of the application further provides an electronic device comprising at least one processor and a memory connected with the at least one processor in communication. The memory stores a computer program which can be executed by the at least one processor, and the computer program is used for enabling the electronic device to execute the micro-grid interactive response method of the embodiment of the application which takes into account the grid operation cost and user utility when the computer program is executed by the at least one processor.

[0167] Reference Figure 2, a block diagram of an electronic device that can be a server or a client as an embodiment of the present invention will now be described, which is an example of a hardware device that can be applied to aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components, their connections, and their functions, as described herein, are meant to be examples only, and are not intended to limit the implementations of the present invention described and / or claimed in this document.

[0168] As shown in Figure 2 , the electronic device includes a computing unit 101 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. Various programs and data required for operation of the electronic device can also be stored in the RAM 103. The computing unit 101, the ROM 102, and the RAM 103 are connected to each other through a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0169] Various components in the electronic device are connected to the I / O interface 105, including an input unit 106, an output unit 107, the storage unit 108, and a communication unit 109. The input unit 106 can be any type of device that can input information to the electronic device, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 107 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 108 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 109 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0170] The computing unit 101 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, or the like. The computing unit 101 performs various methods and processes described above. For example, in some embodiments, the method embodiments of the present creation can be implemented as a computer program tangibly embodied in a machine-readable medium, such as the storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 102 and / or the communication unit 109. In some embodiments, the computing unit 101 can be configured to perform the above-described methods by any other suitable means, such as by means of firmware.

[0171] Computer programs used to implement methods of the present embodiments can be written in any combination of one or more programming languages. These computer programs can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, enables the functions / operations specified in the flow charts and / or block diagrams to be implemented. The computer program can be executed in whole on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0172] In the context of the present embodiments, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared signals, or any suitable combination thereof. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0173] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0174] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0175] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0176] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0177] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A microgrid interactive response method that balances grid operating costs and user utility, characterized in that, include: Based on power grid operation status data, a first optimization model is constructed with minimizing power grid operation cost as the first objective function. Based on user utility data, a second optimization model is constructed with maximizing user utility as the second objective function; wherein, the decision variables of the second optimization model include: comfort utility, adjustment willingness utility, and charging completion utility; The first objective function and the second objective function are coupled to form a two-layer optimization model for the microgrid; A multi-objective coordinated control algorithm is used to solve the two-level optimization model of the microgrid and obtain the response scheme of the microgrid.

2. The microgrid interactive response method according to claim 1, characterized in that, Based on power grid operation status data, and with minimizing power grid operation costs as the primary objective function, a first optimization model is constructed, including: Collect power grid operation status data; wherein, the power grid operation status data includes microgrid operation status data and the operation status data of the upper-level power grid of the microgrid; Set a first decision variable; wherein the first decision variable includes: power generation, power purchase, power sales, energy storage charging power, and energy storage discharging power; Based on the power grid operation status data and the first decision variable, a first objective function and a first constraint condition are obtained, thereby constructing a first optimization model; wherein, the first constraint condition includes: power balance constraint, energy storage dynamic constraint, and equipment operation boundary constraint.

3. The microgrid interactive response method according to claim 2, characterized in that, The first objective function is as follows: ； in, This represents the collection of generating units within a microgrid and related generating units in the upper-level power grid of the microgrid. Indicates the unit The power generation cost function at time t; Indicates the unit Power generation at time t; This represents the electricity purchase price at time t. This represents the electricity price at time t; This represents the power purchased at time t. This represents the electricity sold at time t; This represents the collection of energy storage devices within a microgrid; This represents the degradation cost coefficient of energy storage unit s; This represents the charging power of energy storage unit s at time t; This represents the discharge power of energy storage unit s at time t; This represents the energy curtailment penalty coefficient. This represents the renewable power discarded at time t. This represents the penalty factor for load imbalance. This represents the equilibrium slack at time t.

4. The microgrid interactive response method according to claim 3, characterized in that, The power balance constraints are as follows: ； in, This represents the available power of renewable energy at time t. This represents the baseline load power at time t. This indicates the amount of load change caused by the user-side response; The dynamic constraints on the energy storage capacity are as follows: ； in, This represents the state of charge of energy storage unit s at time t+1; This represents the state of charge of energy storage unit s at time t; This indicates the charging efficiency of energy storage unit s; This represents the discharge efficiency of energy storage unit s. Indicates the scheduling time step; The equipment operating boundary constraints are as follows: ； ； in, This indicates the lower limit of the energy capacity of energy storage unit s; This indicates the upper limit of the energy capacity of energy storage unit s; This indicates the maximum charging power of energy storage unit s; This represents the maximum discharge power of energy storage unit s; Indicates the unit Maximum output; This indicates the maximum power purchase limit; This indicates the maximum power sales limit.

5. The microgrid interactive response method according to claim 3, characterized in that, Based on user utility data, and with maximizing user utility as the second objective function, a second optimization model is constructed, including: Collect user utility data; A second decision variable is set; wherein, the second decision variable includes: comfort utility, adjustment willingness utility, and charging completion utility; Based on the user utility data and the second decision variable, a second objective function and a second constraint are obtained, thereby constructing a second optimization model; wherein, the second constraint includes load boundary constraints and adjustment response constraints.

6. The microgrid interactive response method according to claim 5, characterized in that, The second objective function is as follows: ； in, Indicates the optimization time period; This represents the user's comfort utility at time t; This represents the user's willingness to adjust at time t; This represents the user's utility from completing charging at time t; This represents the electricity cost for the user at time t.

7. The microgrid interactive response method according to claim 6, characterized in that, User comfort utility at time t The calculation formula is as follows: ； in, This represents the user's expected power consumption at time t; This represents the user's actual power consumption at time t; A penalty factor representing comfort deviation; User's willingness to adjust at time t The calculation formula is as follows: ； in, This represents the user's load regulation power at time t. This represents the utility gain resulting from a unit adjustment. The utility of the user's charging completion at time t The calculation formula is as follows: ； in, This indicates the user's target charging needs; Indicates the deadline The actual charging amount of the user's charging device at that time; This represents the penalty coefficient for not meeting charging requirements; Electricity cost for the user at time t The calculation formula is as follows: ； in, This represents the electricity price at time t. This indicates the user's actual power consumption.

8. The microgrid interactive response method according to claim 7, characterized in that, The load boundary constraints are as follows: ； in, This indicates the minimum power that the user can accept; This indicates the maximum power that the user can accept; The adjustment response constraints are as follows: ； in, This indicates the maximum adjustable capacity on the user side.

9. The microgrid interactive response method according to claim 7, characterized in that, The two-level optimization model for microgrids is as follows: 。 10. The microgrid interactive response method according to claim 7, characterized in that, The multi-objective coordinated control algorithm is a multi-objective particle swarm optimization algorithm; A multi-objective coordinated control algorithm is used to solve the two-level optimization model of the microgrid to obtain the microgrid's response scheme, including: Based on the two-layer optimization model of the microgrid, a first weight is set for the first objective function, and a second weight is set for the second objective function; Based on the first objective function, the first weight, the second objective function, and the second weight, a total objective function is constructed for multi-objective particle swarm search. Each particle corresponds to a combination of decision variables of the first objective function and decision variables of the second objective function. The particle swarm updates its position and velocity through multiple iterations, and adjusts the first and second weights in each iteration; When the convergence condition is met or the maximum number of iterations is reached, the iteration stops, and the combination of decision variables corresponding to the particle with the best fitness is output as the response scheme of the microgrid.

11. The microgrid interactive response method according to claim 10, characterized in that, The overall objective function is as follows: ； in, Indicates the first weight; Indicates the second weight; ; Adjust the first and second weights in each iteration, including: ； in, This represents the first weight after the (k+1)th iteration; This represents the first weight after the k-th iteration; This represents the second weight after the (k+1)th iteration; This represents the value of the first objective function at the k-th iteration; This represents the baseline value of the first objective function.

12. An electronic device, comprising: A processor and a memory storing a program, characterized in that the program includes instructions that, when executed by the processor, cause the processor to perform the microgrid interactive response method according to any one of claims 1 to 11.

13. A non-transitory machine-readable medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the microgrid interactive response method according to any one of claims 1 to 11.

14. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the microgrid interactive response method according to any one of claims 1 to 11.

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