Micro-grid scheduling optimization method and system, terminal and storage medium

Abstract

The invention discloses a micro-grid scheduling optimization method and system, a terminal and a storage medium. A user model of each user node in a micro-grid is established in advance; the user model comprises a plurality of energy models: a heating ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-power grid energy transaction model and an inter-user energy transaction model; determining a balance relation between energy loss and output according to the user model, and determining constraint conditions; establishing a user cost function of each user node according to the cost function of each energy model; and establishing an optimization problem according to the user cost function of each user node, obtaining an optimal solution according to the constraint condition and the optimization problem through an alternating direction multiplier algorithm, and determining an optimal scheduling scheme of the microgrid. All users are designed as peer-to-peer nodes, and a distributed scheduling algorithm is adopted, so that the autonomy of the system can be enhanced, the communication burden can be reduced, and meanwhile, the efficient privacy protection level can be improved.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a microgrid scheduling optimization method, system, terminal and storage medium. Background Art

[0002] Global energy demand is rapidly increasing. Traditional energy distribution methods are no longer able to meet this growing demand, and innovative solutions are urgently needed to address energy shortages. Microgrids, as a new energy system, integrate multiple distributed energy resources, enabling collaboration between users and energy exchange with the grid. This not only effectively reduces overall energy demand but also lowers costs for individual users.

[0003] However, traditional microgrid systems often rely on centralized scheduling algorithms. Although they can achieve unified coordination of the system, they have the following problems in practical applications:

[0004] 1. Centralized systems are highly dependent on the central control center. Once the central control system fails, it may have a serious impact on the operation of the entire energy network and may even cause the system to paralyze.

[0005] 2. Centralized scheduling requires frequent two-way communication between the central management system and all relevant equipment. This large amount of communication demand not only increases the operating burden of the system, but may also cause communication delays or congestion, affecting the reliability of the system.

[0006] 3. Centralized scheduling involves sensitive local data, such as energy production capacity, real-time consumption data, and operational strategies. Under the centralized scheduling model, this information needs to be transmitted to the central system for processing, increasing the risk of privacy leakage.

[0007] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a microgrid scheduling optimization method, system, terminal and storage medium in response to the above-mentioned defects of the prior art, aiming to solve the problems in the prior art of microgrids adopting centralized scheduling, which have the risk of single point failure, high communication overhead and privacy leakage risk.

[0009] The technical solutions adopted by the present invention to solve the problem are as follows:

[0010] In a first aspect, an embodiment of the present invention provides a method for optimizing scheduling of a microgrid, the method comprising:

[0011] Pre-establishing a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a HVAC system model, a load model, a clean energy model, an energy storage system model, a user-grid energy transaction model, and an inter-user energy transaction model;

[0012] Determine a balance between energy consumption and output according to the user model, and determine constraint conditions according to the balance;

[0013] Establishing a user cost function for each user node according to the cost function of each energy model;

[0014] Establishing an optimization problem according to the user cost function of each user node, and obtaining an optimal solution according to the constraint conditions and the optimization problem by using an alternating direction multiplier algorithm;

[0015] An optimal scheduling scheme for the microgrid is determined according to the optimal solution.

[0016] In one embodiment, the HVAC system model is used to reflect the energy consumption generated by adjusting the indoor temperature through the HVAC system;

[0017] The load model is used to reflect the flexible load consumption and inflexible load consumption of each user node within a day; wherein the flexible load is the load with adjustable service time, the inflexible load consumption is the load with unadjustable service time, and the inflexible load consumption is a constant;

[0018] The clean energy model is used for the clean energy generated by each user node in each time period;

[0019] The energy storage system model is used to reflect the change in energy storage between the previous time period and the next adjacent time period;

[0020] The user-grid energy transaction model is used to reflect the purchased energy obtained by each user node from the grid in each time period;

[0021] The inter-user energy transaction model is used to reflect the transaction energy of each user node in each time period.

[0022] In one embodiment, determining the balance between energy consumption and output according to the user model includes:

[0023] determining the energy loss in each time period according to the user in each time period, the system power of the HVAC system model in each time period, the flexible load consumption and the non-flexible load consumption of the load model, and the system charging power of the energy storage system model;

[0024] Determine the energy output for each time period based on the purchased energy of the user-grid energy trading model, the system discharge power of the energy storage system model, the traded energy of the inter-user energy trading model, and the clean energy of the clean energy model in each time period;

[0025] Based on the energy loss and energy output in each time period, the balance relationship between energy loss and energy output is determined.

[0026] In one embodiment, a user cost function for each user node is established based on the cost function of each energy model, including:

[0027] Obtaining an incompatibility cost function of the HVAC system model, wherein the incompatibility cost function is used to reflect the deviation of the indoor temperature from the ideal value;

[0028] Obtaining a cost function of the energy storage system model, where the cost function is used to reflect charging and discharging losses of the energy storage system;

[0029] Obtaining a first energy exchange cost function of the user-grid energy transaction model;

[0030] Obtaining a second energy exchange cost function of the inter-user energy transaction model;

[0031] A user cost function of each user node is established according to the discomfort cost function, the cost function, the first energy exchange cost function, and the second energy exchange cost function.

[0032] In one embodiment, an optimization problem is established based on the user cost function of each user node, including:

[0033] Determining a total cost function of the user model based on each of the user cost functions;

[0034] Minimizing the total cost function is posed as an optimization problem.

[0035] In one embodiment, obtaining an optimal solution according to the constraints and the optimization problem using an alternating direction multiplier algorithm includes:

[0036] constructing an extended Lagrangian function according to the optimization problem;

[0037] Establishing a user-side algorithm and an aggregator-side algorithm for each user node according to the alternating direction multiplier algorithm and the extended Lagrangian function;

[0038] Iteratively updating local variables according to each of the user-side algorithms, and iteratively updating auxiliary variables and dual variables based on the updated local variables according to the aggregator-side algorithm; the local variables are the transaction energy of the inter-user energy trading model of each user node in the current iteration;

[0039] An optimal solution to the optimization problem is generated based on the iteratively updated local variables, the auxiliary variables, and the dual variables.

[0040] In one embodiment, iteratively updating local variables according to each of the user-side algorithms, and iteratively updating auxiliary variables and dual variables based on the updated local variables according to the aggregator-side algorithm, includes:

[0041] Each user node updates local variables through the user-side algorithm;

[0042] Collect updated local variables of neighboring nodes; wherein the neighboring nodes of each user node are determined based on an information exchange matrix between users, wherein neighboring nodes and non-neighboring nodes in the information exchange matrix correspond to different weight values;

[0043] The auxiliary variables are updated through the aggregator-side algorithm and the collected updated local variables, and the dual variables are updated according to the updated auxiliary variables.

[0044] In a second aspect, an embodiment of the present invention further provides a microgrid scheduling optimization system, the system comprising:

[0045] A model building module is used to pre-establish a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a heating, ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-grid energy transaction model, and an inter-user energy transaction model;

[0046] a relationship analysis module, configured to determine a balance between energy consumption and output according to the user model, and determine constraint conditions according to the balance;

[0047] A cost analysis module, configured to establish a user cost function for each user node based on the cost functions of each energy model;

[0048] A problem-solving module, configured to establish an optimization problem based on the user cost function of each user node, and obtain an optimal solution based on the constraint conditions and the optimization problem using an alternating direction multiplier algorithm;

[0049] A solution determination module is used to determine the optimal scheduling solution of the microgrid based on the optimal solution.

[0050] In a third aspect, an embodiment of the present invention further provides a terminal comprising a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing a microgrid dispatch optimization method as described above; and the processor is used to execute the programs.

[0051] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a plurality of instructions stored thereon, wherein the instructions are suitable for being loaded and executed by a processor to implement the steps of any of the microgrid dispatch optimization methods described above.

[0052] Beneficial effects of the present invention: The embodiment of the present invention pre-establishes a user model for each user node in a microgrid; the user model includes several energy models: a HVAC system model, a load model, a clean energy model, an energy storage system model, a user-grid energy trading model, and an inter-user energy trading model; the balance relationship between energy loss and output is determined based on the user model, and constraints are determined; a user cost function is established for each user node based on the cost function of each energy model; an optimization problem is established based on the user cost function of each user node, and the optimal solution is obtained according to the constraints and the optimization problem through the alternating direction multiplier algorithm, and the optimal scheduling scheme of the microgrid is determined. The present invention designs all users as peer nodes and adopts a decentralized scheduling algorithm, which can enhance the autonomy of the system, reduce the communication burden, and improve the level of efficient privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 It is a flow chart of a microgrid scheduling optimization method provided by an embodiment of the present invention.

[0055] Figure 2 Schematic diagram of energy flow in a system provided by an embodiment of the present invention.

[0056] Figure 3 4 is a comparison chart of outdoor temperature and indoor temperature provided by an embodiment of the present invention.

[0057] Figure 4 This is an energy consumption diagram of the HVAC system provided by an embodiment of the present invention.

[0058] Figure 5 Schematic diagram of a module of a microgrid scheduling optimization system provided by an embodiment of the present invention.

[0059] Figure 6 This is a principle block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention discloses a microgrid scheduling optimization method, system, terminal, and storage medium. To make the objectives, technical solutions, and effects of the present invention more clear and explicit, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention.

[0061] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the description of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0062] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0063] To address the above-mentioned shortcomings of the prior art, the present invention provides a method for scheduling optimization in a microgrid. The method pre-establishes a user model for each user node in the microgrid; the user model includes several energy models, including: a HVAC system model, a load model, a clean energy model, an energy storage system model, a user-grid energy trading model, and an inter-user energy trading model; determines the balance between energy loss and output based on the user model, and determines constraints based on the balance; establishes a user cost function for each user node based on the cost function of each energy model; establishes an optimization problem based on the user cost function of each user node, obtains the optimal solution based on the constraints and the optimization problem using an alternating direction multiplier algorithm, and determines the optimal scheduling scheme for the microgrid based on the optimal solution. The present invention designs all users as peer nodes and adopts a decentralized scheduling algorithm, which can enhance system autonomy, reduce communication burden, and improve efficient privacy protection.

[0064] like Figure 1 As shown, the method specifically includes the following steps:

[0065] Step S100, pre-establishing a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a HVAC system model, a load model, a clean energy model, an energy storage system model, a user-grid energy trading model, and an inter-user energy trading model.

[0066] Specifically, it is necessary to first build a dedicated user model for each user node in the microgrid. This is the basis for the entire analysis because different user nodes have differences in energy use, generation, and transaction. The user model consists of multiple types of energy models, including but not limited to:

[0067] HVAC system model, which can describe the operation mode and power requirements of the HVAC system at the user end, and will affect the energy consumption of the entire user node;

[0068] The load model represents the collection of other electrical devices at the user end besides HVAC. This model can reflect the power consumption characteristics of these electrical devices. The power factor and usage frequency of different types of loads may vary.

[0069] Clean energy model: The user side may have clean energy generation equipment, such as solar panels and small wind turbines. This model can describe the power generation characteristics of these clean energy generation equipment;

[0070] Energy storage system model, which can describe the charging and discharging characteristics and capacity limitations of the energy storage system. The energy storage system stores energy when there is excess energy and releases energy when there is insufficient energy.

[0071] The user-grid energy trading model describes the energy trading between users and the grid, for example, users purchase electricity from the grid at a low price during off-peak hours.

[0072] The inter-user energy transaction model can describe the energy transaction situation between users, for example, a user with excess electricity sells electricity to another adjacent user.

[0073] The system of this embodiment integrates multiple energy models and designs users as peer nodes, so that each user needs to consider not only the cost of household energy consumption but also the energy storage cost of the battery. Figure 2 The diagram shows the entire system, with arrows indicating the flow of energy. Besides consuming their own energy, users can sell excess energy to other users or store it. In other words, in addition to purchasing energy from the grid, users can also meet their energy needs with renewable energy or through inter-user electricity trading, further optimizing cost allocation and resource utilization.

[0074] In one implementation, the HVAC system model is used to reflect the energy consumption generated by adjusting the indoor temperature through the HVAC system;

[0075] The load model is used to reflect the flexible load consumption and inflexible load consumption of each user node within a day; wherein the flexible load is the load with adjustable service time, the inflexible load consumption is the load with unadjustable service time, and the inflexible load consumption is a constant;

[0076] The clean energy model is used for the clean energy generated by each user node in each time period;

[0077] The energy storage system model is used to reflect the change in energy storage between the previous time period and the next adjacent time period;

[0078] The user-grid energy transaction model is used to reflect the purchased energy obtained by each user node from the grid in each time period;

[0079] The inter-user energy transaction model is used to reflect the transaction energy of each user node in each time period.

[0080] Specifically, we first establish a model suitable for microgrids. Assume that the user node set is N = {1, 2, 3, …, n}, and each user i∈N is composed of multiple energy models, including the HVAC system model, load model, clean energy model, energy storage system model, user-grid energy trading model, and inter-user energy trading model.

[0081] For the HVAC system model: Heating, ventilation, and air conditioning (HVAC) systems are a core component of modern homes and buildings, responsible for regulating indoor temperature, humidity, and air quality to enhance occupant comfort. Through intelligent control and optimization, HVAC systems can significantly reduce energy consumption while maintaining a comfortable environment.

[0082] In this model, time is divided into h time intervals, denoted as H = {1, 2, ..., h}, and each time interval t represents one hour. In each time interval t, the HVAC system adjusts the indoor temperature according to the preferences of user i. The indoor temperature and outdoor temperature of the i-th user node at time t are given by and express. Figure 3 Shows the comparison between outdoor temperature and indoor temperature. Figure 4 Shows the energy consumption of HVAC system, and Figure 3 By comparison, it is found that under high temperature conditions, the energy consumption of the entire system is relatively large. The dynamic change of indoor temperature can be expressed by the following equation:

[0083]

[0084] Among them, C i and R i represents the operating parameters of the HVAC system of the i-th user node, η i represents the operating mode of the HVAC system at the i-th user node at time t (positive value indicates cooling, negative value indicates heating), represents the power of the HVAC system of the i-th user node at time t, Indicates the maximum power of the HVAC system.

[0085] Regarding the load model: Loads can be divided into flexible loads and non-flexible loads. Non-flexible loads usually cannot adjust their service time (such as air conditioners, lighting, refrigerators, etc.), while flexible loads can adjust their service time without affecting user demand (such as electric vehicle battery charging, washing machines, etc.). For the i-th user node at time t, the non-flexible load is Non-flexible load is The non-flexible load is defined as a constant, and the total flexible load satisfies:

[0086]

[0087] in, represents the flexible load consumption of user i in one day; Indicates the maximum power of the flexible load;

[0088] For clean energy model: In microgrid, clean energy is an important source of power supply. Assuming that the clean energy model is constant, use Represents the clean energy generated by the i-th user node at time t.

[0089] Regarding energy storage systems: Energy storage systems play a vital role in the power grid. They can store excess energy and release it when needed, helping to balance the supply and demand of the power grid. The mathematical model of the energy storage system is as follows:

[0090]

[0091] in, represents the energy storage capacity of the i-th user node at time t, and They represent the charging and discharging power of the i-th user node at time t, and the maximum energy storage capacity of the device is The maximum charge and discharge power is and η ESI and η ESO represent the charge and discharge efficiency respectively.

[0092] For the user-grid energy transaction model: users purchase energy from the grid, the grid operates as a unified system, and implements peak and valley electricity price strategies. The peak price and valley price are π high and π low , the peak price is higher than the valley price, and the relationship is expressed by the following equation:

[0093] π high =(1+α)π low , formula (9);

[0094] Where α>0 represents the multiplier coefficient of peak-valley electricity price. Energy trading is expressed as represents the energy purchased by the i-th user from the grid during time period t.

[0095] Regarding the energy trading model between users: In a microgrid, users can conduct energy transactions in a peer-to-peer manner to achieve optimal energy distribution. The energy transaction price between users is the same as the price of the power grid, and the user transaction model is similar to that of the power grid, as follows:

[0096]

[0097] Among them, when , it means selling energy.

[0098] Step S200: Determine a balance relationship between energy consumption and output according to the user model, and determine constraint conditions according to the balance relationship.

[0099] Specifically, based on the constructed user model, the balance between energy loss and output can be determined. In other words, this embodiment needs to consider the interrelationships between the generation, transmission, use, and loss of various energy sources in the microgrid. Based on this balance, the constraints used to solve the subsequent optimization problem are determined. For example, the constraints may include energy generation capacity limitations, energy storage system capacity limitations, user power demand limitations, and so on.

[0100] In one implementation, determining the balance between energy consumption and output according to the user model includes:

[0101] determining the energy loss in each time period based on the system power of the HVAC system model, the flexible load consumption and the non-flexible load consumption of the load model, and the system charging power of the energy storage system model in each time period;

[0102] Determine the energy output for each time period based on the purchased energy of the user-grid energy trading model, the system discharge power of the energy storage system model, the traded energy of the inter-user energy trading model, and the clean energy of the clean energy model in each time period;

[0103] Based on the energy loss and energy output in each time period, the balance relationship between energy loss and energy output is determined.

[0104] Specifically, this embodiment defines the components corresponding to the system's energy loss and output, and then determines the balance relationship between the two. The energy loss and energy output of each time period are compared, and the difference between the two is analyzed. If the energy output is greater than the energy loss, the system has an energy surplus; if the energy output is less than the energy loss, the system has an energy shortage. Based on the comparison results of energy loss and energy output, the energy balance state of the system can be analyzed, and then the dynamic balance of energy loss and energy output can be achieved by adjusting the charging and discharging strategy of the energy storage system, optimizing load management, increasing the use of clean energy, and other measures, providing a scientific basis for the energy management and optimization of the system.

[0105] For example, the energy loss and output of the entire system are balanced, and the balance relationship is as follows:

[0106]

[0107] Step S300: Establish a user cost function for each user node based on the cost function of each energy model.

[0108] Specifically, the user model integrates multiple types of energy models and jointly establishes the user cost function of the user node through the cost functions of each energy model, such as energy purchase cost, energy generation cost, charging and discharging cost of the energy storage system, and possible transaction costs.

[0109] In one implementation, a user cost function for each user node is established based on the cost function of each energy model, including:

[0110] Obtaining an incompatibility cost function of the HVAC system model, wherein the incompatibility cost function is used to reflect the deviation of the indoor temperature from the ideal value;

[0111] Obtaining a cost function of the energy storage system model, where the cost function is used to reflect charging and discharging losses of the energy storage system;

[0112] Obtaining a first energy exchange cost function of the user-grid energy transaction model;

[0113] Obtaining a second energy exchange cost function of the inter-user energy transaction model;

[0114] A user cost function of each user node is established according to the discomfort cost function, the cost function, the first energy exchange cost function, and the second energy exchange cost function.

[0115] Specifically, for the HVAC system model: This embodiment specifically sets the discomfort cost function of the heating, ventilation, and air conditioning system (i.e., the cost of temperature deviation from the ideal value), as shown below:

[0116]

[0117] Where d is the discomfort coefficient, is the ideal indoor temperature of the i-th user node. When it deviates from this temperature, the user will feel uncomfortable.

[0118] For the energy storage system model: the energy storage system will lose power during charging and discharging, and the cost function of the energy storage system is defined as follows:

[0119]

[0120] Where w is the loss coefficient.

[0121] For the user-grid energy transaction model: the user's total energy exchange cost is:

[0122]

[0123] Among them, H1 and H2 represent peak hours and off-peak hours respectively. Indicates the maximum amount of electricity purchased per unit time.

[0124] For the inter-user energy transaction model: the energy transaction price between users is the same as the price of the power grid, and the energy exchange cost between users is:

[0125]

[0126] Among them, when , it means selling energy.

[0127] The cost of a single user (i.e., the user cost function) in the distributed algorithm set in this embodiment is:

[0128]

[0129] In one implementation, in addition to the discomfort cost function of the HVAC system model, the user cost function further includes: a discomfort cost function corresponding to the load model and / or a discomfort cost function corresponding to the energy storage system model.

[0130] Specifically, to better reflect real-life needs, this embodiment can also design more types of user discomfort costs. For example, the discomfort cost function of the load model can be used to quantify the disruption to a user's life caused by unreasonable scheduling, such as being required to cook during non-meal times. The discomfort cost function of the energy storage system model can also be used to quantify the disruption to a user's life caused by unreasonable scheduling, such as charging or using electricity during inconvenient time periods.

[0131] Step S400: Establish an optimization problem according to the user cost function of each user node, and obtain an optimal solution according to the constraint conditions and the optimization problem through an alternating direction multiplier algorithm.

[0132] Step S500: Determine an optimal scheduling scheme for the microgrid based on the optimal solution.

[0133] Specifically, an optimization problem is established based on the user cost function of each user node. The goal of this optimization problem may be to minimize the total cost of the entire microgrid or maximize energy utilization efficiency. The optimal solution to the optimization problem is then obtained using the Alternating Direction Multiplier (ADMM) algorithm, based on the previously determined constraints and the optimization problem. This optimal solution is then used to develop an optimal scheduling plan for the microgrid. This optimal scheduling plan involves the operating strategies of different energy models in each user node, such as when to start charging or discharging the energy storage system and how users adjust their load usage time to achieve efficient and economical operation of the entire microgrid.

[0134] In one implementation, establishing an optimization problem based on the user cost function of each user node includes:

[0135] Determining a total cost function of the user model based on each of the user cost functions;

[0136] Minimizing the total cost function is posed as an optimization problem.

[0137] Specifically, the optimization objective of this embodiment is set to minimize the total cost of the entire microgrid. After determining the user cost function of each user, the total cost function is composed of the user cost functions of each user node. The optimization problem can be determined by combining the total cost function and the optimization objective.

[0138] For example, the cost of a single user (i.e., the user cost function) in the distributed algorithm set in this embodiment is:

[0139]

[0140] This model can be formulated as an optimization problem:

[0141]

[0142] Subject: Formula (1)-Formula (18).

[0143] In one implementation, obtaining an optimal solution according to the constraints and the optimization problem using an alternating direction multiplier algorithm includes:

[0144] constructing an extended Lagrangian function according to the optimization problem;

[0145] Establishing a user-side algorithm and an aggregator-side algorithm for each user node according to the alternating direction multiplier algorithm and the extended Lagrangian function;

[0146] Iteratively updating local variables according to each of the user-side algorithms, and iteratively updating auxiliary variables and dual variables based on the updated local variables according to the aggregator-side algorithm; the local variables are the transaction energy of the inter-user energy trading model of each user node in the current iteration;

[0147] An optimal solution to the optimization problem is generated based on the iteratively updated local variables, the auxiliary variables, and the dual variables.

[0148] Specifically, the algorithm framework is first established. Based on the alternating direction multiplier algorithm (ADMM) and the extended Lagrangian function, the user-side algorithm and the aggregator-side algorithm are established for each user node. The user-side algorithm is used to update local variables. The local variables are the transaction energy of each user node in the inter-user energy trading model in the current iteration. The aggregator-side algorithm is used to update the auxiliary variables and dual variables based on the updated local variables. In each round of iteration, each user node updates the local variables according to the user-side algorithm under certain constraints. The aggregator-side algorithm calculates the updated values of the auxiliary variables and dual variables based on the updated local variables of all user nodes. The above iterative update process of local variables, auxiliary variables, and dual variables is repeated until the algorithm converges. After the iterative update is completed, the optimal solution to the optimization problem is obtained based on the final local variables, auxiliary variables, and dual variables.

[0149] For example, this embodiment uses the alternating direction method of multipliers (ADMM) to decompose the problem, introduces auxiliary variables and constructs the extended Lagrangian function as follows:

[0150]

[0151] Among them, λ t represents the dual variable; ρ represents the penalty coefficient.

[0152] In the ADMM framework, updates are divided into three steps: updating local variables, updating auxiliary variables, and updating dual variables. Local variable updates can be completed locally by the user, while auxiliary and dual variable updates require the aggregator to collect information about local variables for calculation.

[0153] User-side algorithm (EP):

[0154]

[0155] Subject: formula (1)-formula (14), formula (16)-formula (18);

[0156] in, Represents the kth iteration The value of It is an auxiliary variable introduced and initialized to 0.

[0157] Aggregator side:

[0158] After collecting local variables from the user, updates to auxiliary variables can be calculated as follows:

[0159]

[0160] Once the auxiliary variables are obtained, the dual variables are updated as follows:

[0161]

[0162] In one implementation, iteratively updating local variables according to each of the user-side algorithms, and iteratively updating auxiliary variables and dual variables based on the updated local variables according to the aggregator-side algorithm, includes:

[0163] Each user node updates local variables through the user-side algorithm;

[0164] Collect updated local variables of neighboring nodes; wherein the neighboring nodes of each user node are determined based on an information exchange matrix between users, wherein neighboring nodes and non-neighboring nodes in the information exchange matrix correspond to different weight values;

[0165] The auxiliary variables are updated through the aggregator-side algorithm and the collected updated local variables, and the dual variables are updated according to the updated auxiliary variables.

[0166] Specifically, this embodiment further limits local information exchange between neighboring users. Through local computation, the system can achieve decentralized energy scheduling while ensuring user comfort. In each iteration, user nodes collect updated local variables from their neighboring nodes. Each user node's neighboring nodes are based on the information exchange matrix between users. The weights in the information exchange matrix reflect the intensity of information exchange between user nodes. If two user nodes are neighbors, the weight is larger; otherwise, the weight is smaller. During the iteration process, each user node updates its local variables according to the user-side algorithm. It then collects the updated local variables from its neighboring nodes. The aggregator then updates the auxiliary and dual variables based on the collected local variables. Finally, the algorithm is checked for convergence; if not, the iteration continues. In practical application scenarios, this embodiment can employ a decentralized algorithm design and introduce an unsuitable cost function to optimize HVAC system operation, improve user comfort, and optimize energy efficiency. This effectively avoids the communication burden, privacy leakage, and single point of failure issues that can arise with traditional centralized scheduling.

[0167] For example, this embodiment further sets a decentralized distributed algorithm. In the aforementioned distributed algorithm, each user needs to collect all Modify this part. Introduce the matrix W to represent the information exchange between users. The weight of non-neighbor nodes should be designed to be 0. Each user updates the auxiliary variable and the dual variable locally. The update process of the i-th user is as follows (where ∈ is a very small number):

[0168]

[0169] The algorithm flow for the i-th user is as follows (ζ is the minimum value):

[0170] (1) Initialize the dual variables and auxiliary variables.

[0171] (2) Calculation based on the EP algorithm

[0172] (3) Collect adjacent nodes

[0173] (4) Update the dual variables and auxiliary variables according to formula (24) and formula (25).

[0174] (5) Calculate ||λ i,t (k+1)-λ i,t (k)||<ζ is true. If not, continue to iterate steps (2), (3), (4), and (5).

[0175] (6) The iteration terminates and all variables are output.

[0176] During the whole process, users only need to share with their neighbors This effectively protects user privacy.

[0177] In one implementation, the model can be further extended to include the following:

[0178] Clean energy resources, such as small wind turbines and solar panels, can provide more clean energy input to the system;

[0179] Demand response models, which allow users to adjust their energy needs based on real-time electricity prices;

[0180] Electric vehicle charging model, including electric vehicles as part of the flexible load.

[0181] In one implementation, to further enhance the functionality and performance of the system, microgrid dispatch optimization methods can be combined with blockchain technology. By automating energy transactions through smart contracts, human intervention can be reduced and the system’s credibility improved.

[0182] The advantages of the present invention are:

[0183] 1. Decentralized Algorithm Design: Most existing technologies use centralized algorithms, relying on a central node to collect all data and perform optimization calculations. While this approach can achieve global optimization, it carries the risk of privacy leakage, and reliance on a central node can increase system vulnerability. This invention, on the other hand, employs a decentralized algorithm design, where each user exchanges information only with neighboring nodes, achieving optimization through a distributed iterative approach. This approach protects user privacy.

[0184] 2. Introduction of Discomfort Costs: Existing optimization techniques typically only consider direct economic factors such as energy costs and transaction costs, while ignoring user preferences for energy comfort. This invention, however, introduces a discomfort cost function that accounts for user discomfort when indoor temperatures deviate from the ideal value. By optimizing HVAC system operation, this approach not only reduces energy costs but also improves user comfort.

[0185] Based on the above embodiments, the present invention also provides a microgrid scheduling optimization system, such as Figure 5 As shown, the system includes:

[0186] A model building module is used to pre-establish a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a heating, ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-grid energy transaction model, and an inter-user energy transaction model;

[0187] a relationship analysis module, configured to determine a balance between energy consumption and output according to the user model, and determine constraint conditions according to the balance;

[0188] A cost analysis module, configured to establish a user cost function for each user node based on the cost functions of each energy model;

[0189] A problem-solving module, configured to establish an optimization problem based on the user cost function of each user node, and obtain an optimal solution based on the constraint conditions and the optimization problem using an alternating direction multiplier algorithm;

[0190] A solution determination module is used to determine the optimal scheduling solution of the microgrid based on the optimal solution.

[0191] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 6 As shown. The terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the terminal is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a dispatch optimization method for a microgrid is implemented. The display screen of the terminal can be a liquid crystal display or an electronic ink display.

[0192] Those skilled in the art will understand that Figure 6The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0193] In one implementation, the terminal has one or more programs stored in its memory, and is configured to be executed by one or more processors. The one or more programs include instructions for performing a dispatch optimization method for a microgrid.

[0194] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described embodiments. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0195] In summary, the present invention discloses a scheduling optimization method, system, terminal and storage medium for a microgrid. The method pre-establishes a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a HVAC system model, a load model, a clean energy model, an energy storage system model, a user-grid energy trading model and an inter-user energy trading model; the balance relationship between energy loss and output is determined according to the user model, and the constraints are determined according to the balance relationship; the user cost function of each user node is established according to the cost function of each energy model; an optimization problem is established according to the user cost function of each user node, and the optimal solution is obtained according to the constraints and the optimization problem through the alternating direction multiplier algorithm; the optimal scheduling scheme of the microgrid is determined according to the optimal solution. The present invention designs all users as peer nodes and adopts a decentralized scheduling algorithm, which can enhance the autonomy of the system, reduce the communication burden, and improve the level of efficient privacy protection.

[0196] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A microgrid scheduling optimization method, characterized in that: The method comprises: Pre-establishing a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a HVAC system model, a load model, a clean energy model, an energy storage system model, a user-grid energy transaction model, and an inter-user energy transaction model; Determine a balance between energy consumption and output according to the user model, and determine constraint conditions according to the balance; Establishing a user cost function for each user node according to the cost function of each energy model; Establishing an optimization problem according to the user cost function of each user node, and obtaining an optimal solution according to the constraint conditions and the optimization problem by using an alternating direction multiplier algorithm; An optimal scheduling scheme for the microgrid is determined according to the optimal solution.

2. The microgrid scheduling optimization method according to claim 1, characterized in that: The HVAC system model is used to reflect the energy consumption generated by adjusting the indoor temperature through the HVAC system; The load model is used to reflect the flexible load consumption and inflexible load consumption of each user node within a day; wherein the flexible load is the load with adjustable service time, the inflexible load consumption is the load with unadjustable service time, and the inflexible load consumption is a constant; The clean energy model is used for the clean energy generated by each user node in each time period; The energy storage system model is used to reflect the change in energy storage between the previous time period and the next adjacent time period; The user-grid energy transaction model is used to reflect the purchased energy obtained by each user node from the grid in each time period; The inter-user energy transaction model is used to reflect the transaction energy of each user node in each time period.

3. The microgrid scheduling optimization method according to claim 1, characterized in that: Determining a balance between energy consumption and output based on the user model includes: determining the energy loss in each time period according to the user in each time period, the system power of the HVAC system model in each time period, the flexible load consumption and the non-flexible load consumption of the load model, and the system charging power of the energy storage system model; Determine the energy output for each time period based on the purchased energy of the user-grid energy trading model, the system discharge power of the energy storage system model, the traded energy of the inter-user energy trading model, and the clean energy of the clean energy model in each time period; Based on the energy loss and energy output in each time period, the balance relationship between energy loss and energy output is determined.

4. The microgrid scheduling optimization method according to claim 1, characterized in that: According to the cost functions of the energy models, a user cost function for each user node is established, including: Obtaining a discomfort cost function of the HVAC system model, wherein the discomfort cost function is used to reflect the deviation of the indoor temperature from the ideal value; Obtaining a cost function of the energy storage system model, where the cost function is used to reflect charging and discharging losses of the energy storage system; Obtaining a first energy exchange cost function of the user-grid energy transaction model; Obtaining a second energy exchange cost function of the inter-user energy transaction model; A user cost function of each user node is established according to the discomfort cost function, the cost function, the first energy exchange cost function, and the second energy exchange cost function.

5. The microgrid scheduling optimization method according to claim 1, characterized in that: An optimization problem is established based on the user cost function of each user node, including: Determining a total cost function of the user model based on each of the user cost functions; Minimizing the total cost function is posed as an optimization problem.

6. The microgrid scheduling optimization method according to claim 1, characterized in that: Obtaining an optimal solution according to the constraints and the optimization problem using an alternating direction multiplier algorithm includes: constructing an extended Lagrangian function according to the optimization problem; Establishing a user-side algorithm and an aggregator-side algorithm for each user node according to the alternating direction multiplier algorithm and the extended Lagrangian function; Iteratively updating local variables according to each of the user-side algorithms, and iteratively updating auxiliary variables and dual variables based on the updated local variables according to the aggregator-side algorithm; the local variables are the transaction energy of the inter-user energy trading model of each user node in the current iteration; An optimal solution to the optimization problem is generated based on the iteratively updated local variables, the auxiliary variables, and the dual variables.

7. The microgrid scheduling optimization method according to claim 6, characterized in that: Iteratively updating local variables according to each of the user-side algorithms, and iteratively updating auxiliary variables and dual variables based on the updated local variables according to the aggregator-side algorithm, including: Each user node updates local variables through the user-side algorithm; Collect updated local variables of neighboring nodes; wherein the neighboring nodes of each user node are determined based on an information exchange matrix between users, wherein neighboring nodes and non-neighboring nodes in the information exchange matrix correspond to different weight values; The auxiliary variables are updated through the aggregator-side algorithm and the collected updated local variables, and the dual variables are updated according to the updated auxiliary variables.

8. A microgrid dispatch optimization system, characterized in that: The system comprises: A model building module is used to pre-establish a user model for each user node in the microgrid; the user model includes several energy models; the energy models include: a heating, ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-grid energy transaction model, and an inter-user energy transaction model; a relationship analysis module, configured to determine a balance between energy consumption and output according to the user model, and determine constraint conditions according to the balance; A cost analysis module, configured to establish a user cost function for each user node based on the cost functions of each energy model; A problem-solving module, configured to establish an optimization problem based on the user cost function of each user node, and obtain an optimal solution based on the constraint conditions and the optimization problem using an alternating direction multiplier algorithm; A solution determination module is used to determine the optimal scheduling solution of the microgrid based on the optimal solution.

9. A terminal, characterized in that: The terminal includes a memory and one or more processors; the memory stores one or more programs; the program includes instructions for executing the microgrid scheduling optimization method as described in any one of claims 1 to 7; and the processor is used to execute the program.

10. A computer-readable storage medium having a plurality of instructions stored thereon, characterized in that: The instructions are suitable for being loaded and executed by a processor to implement the steps of the microgrid dispatch optimization method as described in any one of claims 1 to 7.

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