A multi-microgrid energy management method, device, equipment and medium

Abstract

The present application relates to the field of energy management, and discloses a kind of multi-microgrid energy management method, device, equipment and medium, for the microgrid unit consisting of micro gas turbine, energy storage, renewable energy and load, first obtain its last actual micro gas turbine power, current actual energy storage energy state and current forecast data;Subsequently, in combination with the boundary conditions such as tie line power of neighbor microgrid last cycle, nominal optimization is carried out, and the optimization result including micro gas turbine power, battery charging and discharging power and next cycle nominal energy storage state is obtained;Then, according to the difference between current nominal and actual energy storage energy state, the real-time correction amount of battery net power is calculated;Finally, by executing the synthesis of nominal instruction and real-time correction amount, accurate management of microgrid unit is realized. The safe, economic, autonomous and collaborative operation effect of multi-microgrid system in uncertain environment is improved.

Description

Technical Field

[0001] This invention relates to the field of energy management, and specifically to a method, apparatus, equipment and medium for energy management of multiple microgrids. Background Technology

[0002] As the global energy structure transitions towards cleaner and lower-carbon energy sources, the penetration rate of renewable energy sources (RES) in power distribution networks is continuously increasing. Meanwhile, multi-microgrid (MMG) systems, due to their advantages such as flexible dispatch capabilities, local autonomy, and enhanced power supply reliability, are gradually becoming an important development direction for future smart power distribution systems. An MMG system consists of multiple interconnected microgrids, each typically equipped with renewable energy units (such as photovoltaics and wind power), battery energy storage systems (BESS), and traditional distributed energy sources (such as micro gas turbines and gas turbines). Through energy sharing and coordinated control, the overall system's economy and security are improved.

[0003] However, due to the significant fluctuations in natural conditions (such as solar radiation and wind speed) affecting the power generation capacity of RES (Resistant Energy Source), its output power exhibits considerable uncertainty and unpredictability. Simultaneously, the power load is also influenced by multiple factors, including user behavior, time cycles, and environmental changes, exhibiting dynamic, stochastic, and time-varying characteristics. These uncertainties significantly exacerbate the operational complexity of the MMG (Multi-Level Marketing) system, especially in islanded operation mode. In this mode, the system must rely on local resources to complete multiple control tasks, including frequency, voltage, and energy balance, placing higher demands on the dynamic response capability and robustness of the control strategy.

[0004] Traditional energy management methods mostly adopt a centralized optimization scheduling model, the core idea of ​​which is for a single master controller to collect the operating data of each microgrid and calculate the global optimal solution. However, in actual deployment, this type of method faces problems such as large communication volume, high data latency, and the risk of single point of failure in the system, making it difficult to meet the real-time dynamic scheduling requirements of large-scale, multi-node MMG systems. Summary of the Invention

[0005] The technical problem to be solved by this invention is that in actual deployment, there are problems such as large communication volume, high data latency, and risk of single point of failure in the system, which make it difficult to adapt to the real-time dynamic scheduling requirements of large-scale, multi-node MMG systems. The purpose is to provide a multi-microgrid energy management method, device, equipment and medium, which solves the problems in the above-mentioned prior art.

[0006] This invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a multi-microgrid energy management method, applied to a multi-microgrid system structure composed of multiple microgrid units, each microgrid unit including a micro-turbine, an energy storage system, renewable energy, and loads, the method comprising:

[0008] For each microgrid unit, the following are obtained: the previous actual micro-gas turbine power value in the previous cycle, the current actual energy storage status of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle.

[0009] The optimization results are obtained by performing nominal optimization processing on the previous tie-line power value of the neighboring microgrid units in the previous cycle, the current actual energy storage state, the previous actual microturbine power value, the current predicted renewable energy power value, and the current predicted load value of the microgrid unit. The optimization results include the nominal control command of the microgrid unit in the current cycle and the next nominal energy storage state in the next cycle. The nominal control command includes the nominal power value of the microturbine, the nominal charging power value of the battery of the energy storage system, and the nominal discharging power value of the battery. The previous tie-line power value is obtained based on the nominal optimization processing performed on the neighboring microgrid units in the previous cycle.

[0010] Based on the current nominal energy state and the current actual energy state of the current period, the current correction amount for the battery net power is obtained;

[0011] The microgrid unit is managed by executing the current correction amount and the nominal control command in the current cycle.

[0012] Preferably, obtaining the previous actual micro-turbine power value of the micro-turbine in the previous cycle, the current actual energy storage state of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle includes:

[0013] The previous actual power value of the micro gas turbine in the previous cycle, the charging efficiency value and the discharging efficiency value of the energy storage system, and the previous actual charging power value and the previous actual discharging power value of the energy storage system in the previous cycle are obtained.

[0014] Based on the charging efficiency and discharging efficiency values ​​of the energy storage system, as well as the previous actual charging power and previous actual discharging power values ​​of the energy storage system in the previous cycle, the current actual energy storage state of the energy storage system in the current cycle is calculated.

[0015] The current predicted renewable energy power value for the current cycle is obtained based on meteorological forecast data and the characteristics of the renewable energy power generation unit.

[0016] The current predicted load value for the current cycle is obtained based on historical load data, behavior patterns, and the meteorological forecast data.

[0017] Preferably, the nominal optimization process based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the current actual energy storage state, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value to obtain the optimization result includes:

[0018] The objective function is constructed based on the operating cost of the micro gas turbine and the loss penalty caused by frequent battery charging and discharging.

[0019] Decision variables are constructed based on the micro-gas turbine power value, the battery charging power value and battery discharging power value of the energy storage system, and the tie-line power value between the microgrid unit and the neighboring microgrid unit;

[0020] A constraint set is constructed based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value;

[0021] The nominal optimization process is performed based on the objective function, the decision variables, and the constraint set to obtain the optimization result.

[0022] Preferably, the step of constructing a constraint set based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual microturbine power value, the current predicted renewable energy power value, and the current predicted load value includes:

[0023] Based on the current actual energy storage state of the energy storage system, the nominal charging power of the battery, the nominal discharging power of the battery, and the charging efficiency and discharging efficiency of the energy storage system, a state space constraint is constructed to determine the nominal energy storage state of the next cycle.

[0024] Based on the previous tie-line power value, the current predicted renewable energy power value, and the current predicted load value, a power balance constraint is constructed to ensure that the total power generation and total power consumption of the microgrid unit are equal at each moment.

[0025] Based on the previous actual micro gas turbine power value and the physical operating limits of the micro gas turbine and the energy storage system, equipment operating constraints are constructed to limit the variation range of the nominal power of the micro gas turbine, the nominal charging power of the battery, and the nominal discharging power of the battery.

[0026] Preferably, the step of constructing equipment operating constraints to limit the variation range of the nominal power of the micro-turbine, the nominal charging power of the battery, and the nominal discharging power of the battery, based on the previous actual micro-turbine power value and the physical operating limits of the micro-turbine and the energy storage system, includes:

[0027] Based on the previous actual micro gas turbine power value and the preset maximum power change of the micro gas turbine, a ramp constraint is constructed to limit the nominal power change of the micro gas turbine in the current cycle.

[0028] Based on the rated capacity and safe operating range of the energy storage system, an energy storage capacity constraint is constructed to limit the upper and lower limits of the nominal energy state.

[0029] Based on the maximum allowable power of the energy storage system, construct charging and discharging power constraints to limit the upper and lower limits of the nominal charging power and nominal discharging power of the battery;

[0030] A mutual exclusion constraint is constructed based on the nominal charging power and the nominal discharging power of the battery.

[0031] Preferably, obtaining the current correction amount for the battery net power based on the current nominal state of energy and the current actual state of energy in the current period includes:

[0032] Subtracting the current actual energy storage state from the current nominal energy storage state yields the state error value for the current period.

[0033] The state error value is multiplied by a preset feedback gain coefficient to obtain the current correction amount for the battery net power. The feedback gain coefficient is preset according to the load level of the microgrid unit and the rated capacity of the energy storage system, and is dynamically adjusted according to the severity of the disturbance.

[0034] Preferably, the optimization result further includes the current tie-line power value, and the method further includes:

[0035] In the current cycle, all microgrid units are locally optimized sequentially according to a predefined order;

[0036] For any microgrid cell currently being optimized, the tie-line power values ​​calculated and published by all neighboring microgrid cells in the previous cycle are used as the boundary conditions for nominal optimization in the current cycle.

[0037] After completing the nominal optimization process, each microgrid unit broadcasts the calculated current tie-line power value between itself and its neighboring microgrid units to all relevant neighboring microgrid units.

[0038] The next microgrid unit in the predefined sequence, after receiving the current tie-line power value broadcast by the upstream microgrid unit, performs nominal optimization processing in combination with the tie-line power values ​​of the other neighboring microgrid units in the previous cycle;

[0039] Repeat the above process until all microgrid units in the predefined sequence have completed the optimization calculation for the current cycle.

[0040] In a second aspect, the present invention provides a multi-microgrid energy management device, applicable to a multi-microgrid system structure composed of multiple microgrid units, each microgrid unit including a micro-turbine, an energy storage system, renewable energy, and loads, the device comprising:

[0041] The acquisition module is used to acquire, for each microgrid unit, the previous actual micro-gas turbine power value of the micro-gas turbine in the previous cycle, the current actual energy storage energy status of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle;

[0042] The nominal optimization module is used to perform nominal optimization processing based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the current actual energy storage energy state, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value, to obtain the optimization processing result. The optimization processing result includes the nominal control command of the microgrid unit in the current cycle and the next nominal energy storage energy state in the next cycle. The nominal control command includes the nominal power value of the micro gas turbine, the nominal charging power value of the battery of the energy storage system, and the nominal discharging power value of the battery. The previous tie-line power value is obtained based on the nominal optimization processing performed on the neighboring microgrid units in the previous cycle.

[0043] The correction module is used to obtain the current correction amount for the battery net power based on the current nominal energy state and the current actual energy state of the current cycle.

[0044] The execution module is used to execute the current correction amount and the nominal control command on the microgrid unit in the current cycle to realize the management of the microgrid unit.

[0045] Thirdly, the present invention provides an electronic device comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the computer program instructions, when executed by the processor, implement the method of the first aspect.

[0046] Fourthly, the present invention provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the method of the first aspect.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] 1. By constructing an objective function centered on the fuel cost of the micro-turbine and the penalty for battery wear, and solving the nominal optimization problem, the lowest-cost operating plan can be automatically generated. This guides the system to prioritize the use of renewable energy and intelligently schedules the micro-turbine and energy storage system to operate within their efficient operating range, avoiding equipment operation under adverse conditions. This significantly reduces the overall operating cost of the system, including fuel costs and equipment depreciation.

[0049] 2. By introducing a feedback correction mechanism based on state errors, the system is provided with proactive anti-interference capabilities. When unpredictable fluctuations occur in renewable energy output or load demand, the controller can adjust the net output of the energy storage system in real time, quickly smooth out power differences, and stabilize key state parameters such as energy storage energy within a safe range. This effectively prevents safety issues such as equipment overload and overcharging / over-discharging of energy storage caused by disturbances, ensuring the robust reliability of the system.

[0050] 3. A distributed coordination mechanism was established by requiring each microgrid unit to use its neighbors' tie-line power plans as boundary conditions during optimization. The direct technical effect is that each autonomous microgrid can proactively consider the operating status and plans of its neighbors when making decisions, thereby spontaneously forming power support and load balancing. This achieves optimized energy allocation at the interconnected system level, improving overall power supply reliability and asset utilization.

[0051] 4. The distributed architecture allows each microgrid unit to perform autonomous optimization relying only on local information and limited neighbor information. When the system needs to be expanded to add new microgrids or existing units are decommissioned, there is no need to change the core control logic of other units; only the network topology and communication links need to be updated. This greatly facilitates the smooth expansion and flexible reconfiguration of the system and reduces the complexity of operation and maintenance.

[0052] 5. The nominal optimization considers the ramp-up constraints of the micro gas turbine and the energy dynamics of the stored energy, ensuring that the generated planned commands conform to the dynamic response characteristics of the physical equipment. This avoids the controller issuing sudden commands that the equipment cannot execute (such as requiring a sudden jump in micro gas turbine power), ensuring a smooth transition of power changes and dynamic stability of the system, thereby improving equipment lifespan and operational quality. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0054] Figure 1 A flowchart illustrating the multi-microgrid energy management method provided by the present invention;

[0055] Figure 2 This is a schematic diagram of the sequential update scheme provided by the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of the multi-microgrid energy management device provided by the present invention;

[0057] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0059] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0060] It should be noted that all actions involving the acquisition of signals, information, or data in this invention are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0061] Example 1

[0062] Please see Figure 1 This invention provides a multi-microgrid energy management method, applied to a multi-microgrid system structure composed of multiple microgrid units. Each microgrid unit includes a micro-turbine, an energy storage system, renewable energy, and loads. The method includes:

[0063] S1. For each microgrid unit, obtain the previous actual micro-gas turbine power value in the previous cycle, the current actual energy storage status of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle.

[0064] This step, serving as the starting point for each control cycle, is motivated by the need to establish an accurate and consistent initial state and prediction benchmark for subsequent optimization decisions, avoiding decision-making biases caused by information asynchrony or lag. The current actual energy storage state refers to the remaining energy of the energy storage system accurately measured by the battery management system at the start of the cycle, for example, 200 kWh; the previous actual microturbine power value is the stable output of the microturbine at the end of the previous cycle, for example, 50 kW. This value serves as the benchmark for calculating the allowable range of microturbine power variation in the current cycle (e.g., ramp-up constraints). The current predicted renewable energy power value and the current predicted load value are derived from weather forecast models and load forecasting algorithms, respectively, for example, predicting 40 kW of photovoltaic power generation and 100 kW of load demand for the next period. This step can be implemented by deploying corresponding sensors and communication interfaces to collect local device status in real time and retrieve the latest prediction data sequence from the upstream prediction system. The technical effect of this step is to provide reliable input information for the entire control process, ensuring the real-time nature of decision-making.

[0065] Furthermore, obtaining the previous actual micro-turbine power value of the micro-turbine in the previous cycle, the current actual energy storage state of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle includes:

[0066] The previous actual power value of the micro gas turbine in the previous cycle, the charging efficiency value and the discharging efficiency value of the energy storage system, and the previous actual charging power value and the previous actual discharging power value of the energy storage system in the previous cycle are obtained.

[0067] Based on the charging efficiency and discharging efficiency values ​​of the energy storage system, as well as the previous actual charging power and previous actual discharging power values ​​of the energy storage system in the previous cycle, the current actual energy storage state of the energy storage system in the current cycle is calculated.

[0068] The current predicted renewable energy power value for the current cycle is obtained based on meteorological forecast data and the characteristics of the renewable energy power generation unit.

[0069] The current predicted load value for the current cycle is obtained based on historical load data, behavior patterns, and the meteorological forecast data.

[0070] The motivation behind this invention is to provide a precise, autonomous, and reproducible system state initialization method to ensure the accuracy of subsequent optimization decisions.

[0071] The implementation process is as follows: First, the previous actual micro-turbine power value (e.g., 50kW) is read from the system database. This historical data forms the basis for calculating the feasible domain of the micro-turbine power in the current cycle. Next, to obtain the current actual energy storage state, we do not simply read the instantaneous readings of the battery management system. Instead, we employ a dynamic update model based on physical laws: obtaining the previous actual charging power value and the previous actual discharging power value from historical records (e.g., 0kW charging and 5kW discharging in the previous cycle), and combining this with the device's inherent charging efficiency and discharging efficiency values ​​(e.g., both 0.95), the current energy is calculated using the formula: Current Energy = Energy of the previous cycle + (Charging efficiency × Charging power - Discharging power / Discharging efficiency) × Time interval. This yields a highly reliable initial state value that is consistent with the system model, avoiding state jumps and laying a stable foundation for subsequent prediction and control. Then, for the forecast data, the current predicted renewable energy power value is calculated by inputting meteorological forecast data (such as irradiance and wind speed) into a model of power generation unit characteristics (such as the photovoltaic panel power-irradiance curve); while the current predicted load value is comprehensively predicted by analyzing historical load data, identifying behavioral patterns (such as morning and evening peak hours), and considering meteorological forecast data (such as the impact of temperature on air conditioning load). The technical effect is to provide the optimizer with a standard scenario of the future operating environment, making optimization decisions forward-looking, and ensuring that all data sources are clear.

[0072] S2. Based on the previous tie-line power value of the neighboring microgrid unit in the previous cycle, the current actual energy storage state, the previous actual micro-turbine power value, the current predicted renewable energy power value, and the current predicted load value, nominal optimization processing is performed to obtain the optimization result. The optimization result includes the nominal control command of the microgrid unit in the current cycle and the next nominal energy storage state in the next cycle. The nominal control command includes the nominal power value of the micro-turbine, the nominal charging power value of the battery of the energy storage system, and the nominal discharging power value of the battery. The previous tie-line power value is obtained based on the nominal optimization processing performed on the neighboring microgrid unit in the previous cycle.

[0073] The motivation behind this invention is to address the problem of achieving both economic efficiency and coordinated operation of multiple microgrid systems in the context of uncertainties in renewable energy and load. Nominal optimization refers to the process of formulating an optimal operating plan for an idealized nominal system while ignoring unpredictable transient disturbances. The previous tie-line power value represents the power exchange plan between the neighboring microgrid unit and the current microgrid unit in the previous cycle. For example, if a neighbor plans to send 10kW of power to the current microgrid unit, this will be used as a necessary boundary condition for the current microgrid unit to optimize itself. This step first requires constructing an optimization problem containing an objective function and a set of constraints: the objective function minimizes the micro-turbine fuel cost and battery loss, while the constraint set includes system dynamic constraints starting from the current actual energy storage state, power balance constraints incorporating the neighbor tie-line power plan, and ramping constraints based on the previous actual micro-turbine power value. Subsequently, an optimization solver is used to calculate a set of optimal nominal control commands for future periods (e.g., micro-turbine generating 55kW, battery discharging 5kW) and the expected next nominal energy storage state. The technical effect of this step is to generate an optimal running trajectory that has the lowest cost under ideal conditions and is in line with the neighbor plan. At the same time, by using neighbor information as a fixed parameter, the complex global optimization problem is decomposed into local subproblems that can be solved in parallel, thus providing a foundation for distributed optimization.

[0074] Further, S2, based on the previous tie-line power value of the neighboring microgrid unit adjacent to the microgrid unit in the previous cycle, the current actual energy storage state, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value, nominal optimization processing is performed to obtain the optimization processing result, including:

[0075] S21. Construct an objective function based on the operating cost of the micro-engine and the loss penalty caused by frequent battery charging and discharging.

[0076] The operating cost of the micro-engine refers to the cost of its fuel consumption (such as natural gas), which is usually proportional to the square of the output power. The penalty for frequent charging and discharging of the battery is to quantify the aging cost caused by battery recycling; excessive use will shorten its lifespan. To achieve this, appropriate weights need to be assigned to these two types of costs, and a quadratic objective function needs to be constructed to minimize them.

[0077] Specifically, the objective function can be:

[0078]

[0079] in, The objective function value, The number of time periods. Let k be the output power value of the micro gas turbine during time period k. This is a weight matrix representing the operating costs of micro gas turbines. Let k be the battery charging power value during time period k. Weight matrix for battery charging losses, Let k be the battery discharge power over time period k. This is the weight matrix for battery discharge loss.

[0080] The technical effect of this step is to transform complex management objectives into a calculable mathematical indicator, guiding the optimizer to automatically find the optimal operating scheme with the lowest cost and least equipment wear.

[0081] S22. Construct decision variables based on the micro-gas turbine power value, the battery charging power value and battery discharging power value of the energy storage system, and the tie-line power value between the microgrid unit and the neighboring microgrid unit;

[0082] Among these variables, the micro-turbine power, battery charging power, and battery discharging power are directly controllable variables within the microgrid unit; while the tie-line power is a coupled variable requiring coordinated decision-making for energy interaction between the microgrid and its neighbors. This step involves defining the sequence of these power values ​​over a future prediction time domain (from the current time k to k+N-1) as an unknown vector to be solved. The technical effect of this step is to define the solution space for the optimization problem, and all subsequent constraints will be applied to these decision variables, ensuring that the optimization result is a complete, feasible, and automatically coordinated set of scheduling instructions.

[0083] S23. Construct a constraint set based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value;

[0084] The motivation behind this step is to ensure that the optimization result is physically achievable and that the equipment operates within a safe range. The previous tie-line power value, serving as a boundary condition from the neighbor, ensures the feasibility of distributed solution; the previous actual microturbine power value directly determines the power variation range of the microturbine in the current cycle. Implementing this step requires systematically integrating multiple constraints: First, based on the current predicted renewable energy power value and the current predicted load value, and substituting the previous tie-line power value from the neighbor, a power balance constraint is constructed. Second, a ramp constraint is constructed using the previous actual microturbine power value. Finally, dynamic constraints, energy boundary constraints, power boundary constraints, and charge / discharge mutual exclusion constraints for the energy storage system are also required. The technical effect of this step is to transform complex physical world rules and safety management requirements into mathematical boundaries that the optimization algorithm can recognize and process, thereby fundamentally guaranteeing the feasibility and safety of the optimization output result.

[0085] Furthermore, in step S23, a constraint set is constructed based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value, including:

[0086] S231. Based on the current actual energy storage state of the energy storage system, the nominal charging power of the battery, the nominal discharging power of the battery, and the charging efficiency and discharging efficiency of the energy storage system, construct a state space constraint for determining the nominal energy storage state of the next cycle.

[0087] The motivation behind this step is to introduce a causal relationship in the time dimension into the optimization problem, ensuring that the plan formulated by the optimizer conforms to the physical evolution of the energy storage system, thereby making future predictions based on the current state accurate and feasible. The current actual energy storage state is the starting point for constructing this constraint, i.e., the initial condition; the nominal charging power and nominal discharging power of the battery are control actions that the optimizer can determine; while charging efficiency and discharging efficiency are key equipment parameters that inevitably involve losses during energy conversion. This step is implemented by substituting the above parameters into a discretized energy conservation equation, the core logic of which is: Nominal energy storage state in the next cycle = Current actual energy storage state + (Charging efficiency × Charging power × Time) - (Discharging power / Discharging efficiency × Time). The technical effect of this step is to transform the dynamic characteristics of the battery into a hard mathematical constraint, forcing the optimizer to consider the impact of the current charge level and charging / discharging efficiency when planning the charging and discharging strategy, thereby avoiding unrealistic schemes such as planning high-power discharge when the charge level is low, ensuring the temporal consistency and physical feasibility of the optimization results.

[0088] State-space constraints can be: ;

[0089] in, For time period The energy storage state, Time period The energy storage state, For charging efficiency; This refers to the discharge efficiency.

[0090] S232. Based on the previous tie-line power value, the current predicted renewable energy power value, and the current predicted load value, construct a power balance constraint that ensures the total power generation and total power consumption of the microgrid unit are equal at each moment.

[0091] The motivation behind this step is to ensure that the optimization scheme adheres to the instantaneous balance law in the power system. This is the cornerstone for maintaining system frequency stability and ensuring power quality, and it also embodies the design principle of power mutual assistance between microgrids. The previous tie-line power value serves as a known boundary condition from the neighboring system, representing the power injection or demand from the external system onto this microgrid. The currently predicted renewable energy power value and the currently predicted load value together define the net power deficit or surplus of this microgrid unit in the current cycle. This step involves constructing an equation constraint, the standard form of which is: Micro-turbine power + Discharge power + Predicted renewable energy power - Charging power + Power from neighbors = Predicted load power (Note: the sign convention for power flow may differ, but the physical essence is the same). The technical effect of this step is to mathematically couple all generation units, energy storage systems, loads, and interconnections into a whole. This ensures that any adjustment to any variable by the optimizer (such as increasing micro-turbine output) must be linked to adjustments to other variables (such as reducing charging or changing tie-line power) to satisfy this equation, thereby ensuring the instantaneous power balance of the system under any operation and naturally integrating distributed coordination into local optimization.

[0092] The power balance constraint can be: ;

[0093] in, Let k be the actual active power generated by renewable energy sources during the time period. Let k be the sum of the active power consumed by all local loads during the time period k. The time period k is the algebraic sum of the tie-line power values ​​exchanged with all adjacent microgrid units via the tie-line.

[0094] S233. Based on the previous actual micro gas turbine power value and the physical operating limits of the micro gas turbine and the energy storage system, construct equipment operating constraints to limit the variation range of the nominal power of the micro gas turbine, the nominal charging power of the battery, and the nominal discharging power of the battery.

[0095] The motivation behind this step is to set safe operating boundaries for all controllable devices, preventing the optimizer from outputting operating instructions that exceed the physical capabilities of the devices or damage their lifespan in pursuit of economic efficiency. The previous actual micro-turbine power value serves as the benchmark defining the micro-turbine's ramp-up capability; without it, the smooth variation of its power cannot be constrained. Physical operating limits include the micro-turbine's maximum / minimum output, the energy storage system's maximum charging and discharging power, and the safe upper and lower limits of the stored energy. This step involves establishing a set of inequality constraints for each device: for the micro-turbine, a power variation range constraint needs to be constructed with its previous cycle's actual output as the center and the maximum ramp rate as the radius; for the energy storage system, upper and lower limit constraints on its power and energy need to be constructed, typically including mutually exclusive constraints to prevent simultaneous charging and discharging. The technical effect of this step is to define a safe decision space for the optimization problem, limiting the mathematical solution to within the practically permissible range of engineering, fundamentally eliminating the possibility of equipment overload, damage, or operation under dangerous conditions.

[0096] Furthermore, S233, based on the previous actual micro-turbine power value and the physical operating limits of the micro-turbine and the energy storage system, constructs equipment operating constraints to limit the variation range of the nominal power of the micro-turbine, the nominal charging power of the battery, and the nominal discharging power of the battery, including:

[0097] Based on the previous actual micro gas turbine power value and the preset maximum power change of the micro gas turbine, a ramp constraint is constructed to limit the nominal power change of the micro gas turbine in the current cycle.

[0098] Based on the rated capacity and safe operating range of the energy storage system, an energy storage capacity constraint is constructed to limit the upper and lower limits of the nominal energy state.

[0099] Based on the maximum allowable power of the energy storage system, construct charging and discharging power constraints to limit the upper and lower limits of the nominal charging power and nominal discharging power of the battery;

[0100] A mutual exclusion constraint is constructed based on the nominal charging power and the nominal discharging power of the battery.

[0101] The climbing constraint can be:

[0102] P g (k+1)=P g (k)+ΔP g (k),

[0103] P g,min ≤ΔP g ≤P g,max ;

[0104] Among them, P g (k) represents the output power of the micro-engine with time period k; ΔPg P represents the change in power of the micro-turbine over a time period k, i.e., the rate of increase in speed. g (k+1) represents the output power of the micro-turbine with a time period of k+1, P g,min The minimum ramp limit for the output power variation of the micro-turbine; P g,max This is the maximum ramp limit for the output power variation of the micro gas turbine.

[0105] Energy storage capacity constraints can be: ,

[0106] in, The minimum energy storage state, This is the state of maximum energy storage.

[0107] The charge / discharge power constraint can be: ;

[0108] in, The maximum threshold for battery charging power value. This is the maximum threshold value for battery discharge power.

[0109] Mutual exclusion constraints can be: .

[0110] S24. Perform nominal optimization processing based on the objective function, the decision variables, and the constraint set to obtain the optimization result.

[0111] The motivation behind this step is to comprehensively solve the aforementioned objective function and constraints to obtain an action plan. Implementing this step requires inputting the complete optimization problem (a constrained quadratic programming problem) constructed in steps S21 to S23 into a mathematical optimization solver for computation. The solver will automatically find the optimal values ​​of a set of decision variables using algorithms such as the interior-point method and the effective set method. These values ​​satisfy all constraints while minimizing the objective function. The technical effect of this step is to ultimately produce a quantifiable and optimal scheduling plan that guides the system's operation. It transforms human management intentions and the physical laws of the system into a clear, executable optimal trajectory, and is the core computational link for the rolling optimization of the entire model predictive control strategy.

[0112] S3. Based on the current nominal energy state and the current actual energy state of the current cycle, obtain the current correction amount for the battery net power;

[0113] The motivation behind this step is to address the inevitable discrepancy between the nominal plan and actual operation in S2, thereby enhancing the system's robustness against uncertainty. The current nominal energy storage state is the energy value the system should be at at the end of the current cycle, calculated based on predicted data in step S2, for example, 198 kWh. The current actual energy storage state, however, is the measured energy value at the end of the current cycle, which may be only 196 kWh due to disturbances. This step first subtracts the two to obtain the state error (i.e., -2 kWh), quantifying the impact of the disturbance on the system. Then, this error is multiplied by a preset, negative feedback gain (e.g., -0.5) to obtain the current correction to the battery's net power (i.e., +1 kW). This correction ensures that, since the actual energy is lower than planned, the system automatically compensates for this shortfall in the next cycle by increasing charging or decreasing discharging (equivalent to increasing net charging power by 1 kW). The technical effect of this step is to transform the abstract state error into a specific, executable power adjustment command, thereby improving the ability to actively resist interference and ensuring that the system does not deviate from safety constraints during actual operation.

[0114] Further, S3, based on the current nominal energy state and the current actual energy state of the current cycle, obtains the current correction amount for the battery net power, including:

[0115] S31. Subtract the current actual energy storage state from the current nominal energy storage state to obtain the state error value for the current period;

[0116] The motivation behind this step is to provide feedback signals for robust control, quantifying the deviation between the ideal system plan and the actual situation. The current nominal energy storage state is the ideal energy value that the energy storage system should be in at the end of the current cycle, calculated based on predicted data and the system model, referring to step S2. It represents the operating trajectory point expected by the optimizer. The current actual energy storage state, on the other hand, is the actual energy value the system is in at the end of the current cycle, obtained in real-time through sensor measurements. It includes the impact of all unforeseen disturbances (such as fluctuations in renewable energy output and sudden load changes). The state error value e(k) = current actual energy storage state x(k) - current nominal energy storage state x nom (k).

[0117] S32. Multiply the state error value by a preset feedback gain coefficient to obtain the current correction amount for the battery net power. The feedback gain coefficient is preset according to the load level of the microgrid unit and the rated capacity of the energy storage system, and is dynamically adjusted according to the severity of the disturbance.

[0118] The motivation behind this step is to transform the state error obtained in the previous step into a specific, executable power adjustment command, thereby designing a controller capable of automatically suppressing disturbances and returning the system state to its nominal trajectory. The feedback gain coefficient is the amplification factor, determining the system's response strength to errors; the battery net power is defined here as a single variable where discharge power is positive and charging power is negative, making the application of the correction amount more flexible. This step can be implemented as: Current correction amount ΔP(k) = Feedback gain coefficient K * State error value e(k). Here, K is preset to a negative value. When the actual state is lower than the nominal state (e(k) is negative), the correction amount is positive, meaning the system needs to increase net discharge power (or decrease net charging power) to improve energy storage, and vice versa. The technical effect of this step is that it can detect the deviation caused by uncertainty in real time and immediately generate a counterforce to offset the deviation. By pre-setting the gain according to the load level and energy storage capacity, the control strength is matched with the system scale. By dynamically adjusting the gain according to the severity of the disturbance (such as increasing the absolute value of K when the disturbance is large), the controller achieves adaptive optimization, avoids over-adjustment in stable conditions, and responds quickly in turbulent conditions, thereby significantly enhancing the stability and robustness of the system under various operating conditions.

[0119] For example, to systematically describe the disturbance impact of renewable energy and load forecasting errors in microgrids, this invention employs an interval modeling method: the error is uniformly incorporated into the disturbance vector δ(k), constructing a disturbance set W={δ(k)|||δ(k)||∞≤ε}, where ε is estimated using historical sampling data to determine the confidence interval (e.g., the 95% confidence region). This set can be statistically obtained through a sliding window to determine the maximum disturbance boundary, ensuring that the model is applicable to time-dependent, dynamically distributed systems. Furthermore, to address the model prediction problem under such disturbances, the controller adopts a dual-track strategy of nominal trajectory + error correction. The nominal trajectory is used to find the optimal path to the target, while the error trajectory is used for deviation feedback. The control input is decomposed into: u(k)=v(k)+K·e(k), where K is the feedback gain matrix and e(k) is the state deviation. By tightening the feasible set through Pontryagin difference operations, even in the worst case, the system state x(k) can be guaranteed to meet energy and security constraints, thereby enhancing the system's robustness.

[0120] This invention employs the discrete Lyapunov method to verify the stability of the controller. The error state e(k) is defined, and the Lyapunov function V(e) = eᵀP is constructed. eWhere P is a symmetric positive definite matrix, and if V(e(k+1))-V(e(k))<0, the system tends to be stable. By constructing an inequality matrix to solve for the condition Lyapunov(A+BK)<0, the feedback gain K that satisfies the condition can be numerically optimized. In actual deployment, to reduce the difficulty of parameter tuning, a gain preset mechanism is adopted: the initial value of K is set according to the microgrid load level and battery capacity, and corrected through online simulation. Especially when the disturbance is severe, the strategy of increasing the value of K is adopted to accelerate the error convergence speed, so as to avoid the battery SOC (State of Charge) exceeding the limit or energy imbalance. In addition, to avoid frequent switching, the update cycle of K is limited to once within dozens of rolling optimization cycles, taking into account both dynamic response and system stability.

[0121] To ensure system stability, A+BK must be a Hurwitz matrix (with eigenvalue magnitudes less than 1 in discrete systems).

[0122] To ensure that the constraints are satisfied under disturbance, constraint tightening is introduced:

[0123] ,

[0124] in, In the first Nominal energy storage state over a time period For the upper and lower limits of energy of energy storage systems, For all possible state errors, In the first The feasible domain of the original control input for each time period. For equipment operation constraints, For feedback gain, ⊖ represents the Pontryagin difference operation, used to tighten the constraint range.

[0125] S4. Execute the current correction amount and the nominal control command on the microgrid unit in the current cycle to realize the management of the microgrid unit.

[0126] The motivation behind this invention is to synthesize the optimization plan and robust correction into a final execution command, thereby achieving precise and safe management of microgrid units. Implementing this step may include: for micro-turbines, due to their large inertia and slow response, the nominal power value of the micro-turbine given by S2 is usually executed directly; while for energy storage systems with rapid response, the current correction amount (an adjustment value for net power) calculated by S3 needs to be intelligently allocated to the nominal charging power value and the nominal discharging power value of the battery, strictly adhering to charging and discharging mutual exclusion constraints. For example, if the original plan was to discharge 5kW, and the correction amount is +1kW, then the actual battery discharge may be controlled at 4kW. Finally, the synthesized control command is issued to the local controller for execution. The technical effect of this step is to ultimately close the perception-decision-execution control loop, thereby achieving dynamic and robust energy management in uncertain environments.

[0127] Furthermore, the optimization result also includes the current tie-line power value, and the method further includes:

[0128] In the current cycle, all microgrid units are locally optimized sequentially according to a predefined order;

[0129] For any microgrid cell currently being optimized, the tie-line power values ​​calculated and published by all neighboring microgrid cells in the previous cycle are used as the boundary conditions for nominal optimization in the current cycle.

[0130] After completing the nominal optimization process, each microgrid unit broadcasts the calculated current tie-line power value between itself and its neighboring microgrid units to all relevant neighboring microgrid units.

[0131] The next microgrid unit in the predefined sequence, after receiving the current tie-line power value broadcast by the upstream microgrid unit, performs nominal optimization processing in combination with the tie-line power values ​​of the other neighboring microgrid units in the previous cycle;

[0132] Repeat the above process until all microgrid units in the predefined sequence have completed the optimization calculation for the current cycle.

[0133] In this embodiment, the core of the invention lies in introducing a predefined optimization sequence. At the beginning of each rolling optimization cycle (e.g., time k), all microgrid units do not perform optimization calculations simultaneously, but rather sequentially according to a fixed order (e.g., MG1→MG2→……→MGN), as follows: Figure 2 As shown. Figure 2 in, u r(k−1) represents the decision variable for the r-th microgrid unit at time k−1, N1 represents the multi-microgrid unit system structure (i.e., MG1, ..., MGN), N1\1 represents the other microgrid units in N1 excluding the first microgrid unit, u1(k) represents the tie-line power plan for MG1 at time k, u2(k) represents the tie-line power plan for MG2 at time k, and u r (k) represents the tie-line power plan for the r-th microgrid unit at time k, and N1\N-1 represents the other microgrid units in N1 besides the (N-1)-th microgrid unit.

[0134] For the first microgrid unit in the sequence to be optimized (taking MG1 as an example), the boundary conditions upon which its optimization depends are the tie-line power plans calculated and published by all its neighboring microgrid units (MG2, ..., MGN) at the end of the previous cycle (k-1). MG1 is based on {u r (k-1)} r∈{N1} Solve its local nominal optimization problem. Once the solution is found, MG1 will immediately obtain its optimal tie-line power plan for the current period (k) and immediately broadcast this latest plan to all its neighboring microgrid units.

[0135] For the next microgrid unit in the sequence (taking MG2 as an example), it has already received the latest plan that MG1 just calculated in the current period (k). For other neighbors (such as MG3), since they have not yet been optimized, MG2 still uses their old plans from the previous period (k-1). After integrating this information (MG1's new plan + MG3 - MGN's old plan), MG2 performs its own optimization, calculates its own latest plan for period k, and broadcasts it.

[0136] This process is carried out sequentially according to a predetermined order. For microgrid units later in the sequence (taking MGN as an example), the advantage is that when it starts optimizing, it has already received the latest plans of MG1 to MGN-1 for the current period (k), thus enabling it to make decisions under boundary conditions that are more updated and closer to the global optimum.

[0137] Example 2

[0138] Please see Figure 3 This invention provides a multi-microgrid energy management device, applied to a multi-microgrid system structure composed of multiple microgrid units. Each microgrid unit includes a micro-turbine, an energy storage system, renewable energy, and loads. The device includes:

[0139] The acquisition module 301 is used to acquire, for each microgrid unit, the previous actual micro-gas turbine power value of the micro-gas turbine in the previous cycle, the current actual energy storage energy state of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle;

[0140] The nominal optimization module 302 is used to perform nominal optimization processing based on the previous tie-line power value of the neighboring microgrid unit in the previous cycle, the current actual energy storage energy state, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value of the adjacent microgrid unit, to obtain the optimization processing result. The optimization processing result includes the nominal control command of the microgrid unit in the current cycle and the next nominal energy storage energy state in the next cycle. The nominal control command includes the nominal power value of the micro gas turbine, the nominal charging power value of the battery of the energy storage system, and the nominal discharging power value of the battery. The previous tie-line power value is obtained based on the nominal optimization processing performed by the neighboring microgrid unit in the previous cycle.

[0141] The correction module 303 is used to obtain the current correction amount for the battery net power based on the current nominal energy state and the current actual energy state of the current cycle.

[0142] The execution module 304 is used to execute the current correction amount and the nominal control command on the microgrid unit in the current cycle to realize the management of the microgrid unit.

[0143] It should be noted that each module and unit in the multi-microgrid energy management device in this embodiment corresponds one-to-one with each step in the multi-microgrid energy management method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned multi-microgrid energy management method, and will not be repeated here.

[0144] Example 3

[0145] Please see Figure 4 This embodiment provides an electronic device, including at least one processor 401 and a memory 402. Optionally, the device further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0146] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0147] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0148] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0149] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0150] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0151] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0152] The present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0153] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0154] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0155] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0158] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0160] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-microgrid energy management method, characterized in that, The method, applicable to a multi-microgrid system architecture consisting of multiple microgrid units, each microgrid unit including a micro gas turbine, an energy storage system, renewable energy sources, and loads, comprises: For each microgrid unit, the following are obtained: the previous actual micro-gas turbine power value in the previous cycle, the current actual energy storage status of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle. The optimization results are obtained by performing nominal optimization processing on the previous tie-line power value of the neighboring microgrid units in the previous cycle, the current actual energy storage state, the previous actual microturbine power value, the current predicted renewable energy power value, and the current predicted load value of the microgrid unit. The optimization results include the nominal control command of the microgrid unit in the current cycle and the next nominal energy storage state in the next cycle. The nominal control command includes the nominal power value of the microturbine, the nominal charging power value of the battery of the energy storage system, and the nominal discharging power value of the battery. The previous tie-line power value is obtained based on the nominal optimization processing performed on the neighboring microgrid units in the previous cycle. Based on the current nominal energy state and the current actual energy state of the current period, the current correction amount for the battery net power is obtained; The microgrid unit is managed by executing the current correction amount and the nominal control command in the current cycle. The nominal optimization process, based on the previous tie-line power value of neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the current actual energy storage state, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value, yields the optimization result, including: The objective function is constructed based on the operating cost of the micro gas turbine and the loss penalty caused by frequent battery charging and discharging. Decision variables are constructed based on the micro-gas turbine power value, the battery charging power value and battery discharging power value of the energy storage system, and the tie-line power value between the microgrid unit and the neighboring microgrid unit; A constraint set is constructed based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value; The nominal optimization process is performed based on the objective function, the decision variables, and the constraint set to obtain the optimization result.

2. The multi-microgrid energy management method according to claim 1, characterized in that, The process of obtaining the previous actual micro-turbine power value in the previous cycle, the current actual energy storage status of the energy storage system in the current cycle, the current predicted renewable energy power value in the current cycle, and the current predicted load value in the current cycle includes: The previous actual power value of the micro gas turbine in the previous cycle, the charging efficiency value and the discharging efficiency value of the energy storage system, and the previous actual charging power value and the previous actual discharging power value of the energy storage system in the previous cycle are obtained. Based on the charging efficiency and discharging efficiency values ​​of the energy storage system, as well as the previous actual charging power and previous actual discharging power values ​​of the energy storage system in the previous cycle, the current actual energy storage state of the energy storage system in the current cycle is calculated. The current predicted renewable energy power value for the current cycle is obtained based on meteorological forecast data and the characteristics of the renewable energy power generation unit. The current predicted load value for the current cycle is obtained based on historical load data, behavior patterns, and the meteorological forecast data.

3. The multi-microgrid energy management method according to claim 1, characterized in that, The constraint set is constructed based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual microturbine power value, the current predicted renewable energy power value, and the current predicted load value, including: Based on the current actual energy storage state of the energy storage system, the nominal charging power of the battery, the nominal discharging power of the battery, and the charging efficiency and discharging efficiency of the energy storage system, a state space constraint is constructed to determine the nominal energy storage state of the next cycle. Based on the previous tie-line power value, the current predicted renewable energy power value, and the current predicted load value, a power balance constraint is constructed to ensure that the total power generation and total power consumption of the microgrid unit are equal at each moment. Based on the previous actual micro gas turbine power value and the physical operating limits of the micro gas turbine and the energy storage system, equipment operating constraints are constructed to limit the variation range of the nominal power of the micro gas turbine, the nominal charging power of the battery, and the nominal discharging power of the battery.

4. The multi-microgrid energy management method according to claim 3, characterized in that, The step of constructing equipment operating constraints to limit the variation range of the nominal power of the micro-turbine, the nominal charging power of the battery, and the nominal discharging power of the battery, based on the previous actual micro-turbine power value and the physical operating limits of the micro-turbine and the energy storage system, includes: Based on the previous actual micro gas turbine power value and the preset maximum power change of the micro gas turbine, a ramp constraint is constructed to limit the nominal power change of the micro gas turbine in the current cycle. Based on the rated capacity and safe operating range of the energy storage system, an energy storage capacity constraint is constructed to limit the upper and lower limits of the nominal energy state. Based on the maximum allowable power of the energy storage system, construct charging and discharging power constraints to limit the upper and lower limits of the nominal charging power and nominal discharging power of the battery; A mutual exclusion constraint is constructed based on the nominal charging power and the nominal discharging power of the battery.

5. The multi-microgrid energy management method according to claim 1, characterized in that, The step of obtaining the current correction amount for the battery net power based on the current nominal state of energy and the current actual state of energy in the current period includes: Subtracting the current actual energy storage state from the current nominal energy storage state yields the state error value for the current period. The state error value is multiplied by a preset feedback gain coefficient to obtain the current correction amount for the battery net power. The feedback gain coefficient is preset according to the load level of the microgrid unit and the rated capacity of the energy storage system, and is dynamically adjusted according to the severity of the disturbance.

6. The multi-microgrid energy management method according to claim 1, characterized in that, The optimization result also includes the current tie-line power value, and the method further includes: In the current cycle, all microgrid units are locally optimized sequentially according to a predefined order; For any microgrid cell currently being optimized, the tie-line power values ​​calculated and published by all neighboring microgrid cells in the previous cycle are used as the boundary conditions for nominal optimization in the current cycle. After completing the nominal optimization process, each microgrid unit broadcasts the calculated current tie-line power value between itself and its neighboring microgrid units to all relevant neighboring microgrid units. The next microgrid unit in the predefined sequence, after receiving the current tie-line power value broadcast by the upstream microgrid unit, performs nominal optimization processing in combination with the tie-line power values ​​of the other neighboring microgrid units in the previous cycle; Repeat the above process until all microgrid units in the predefined sequence have completed the optimization calculation for the current cycle.

7. A multi-microgrid energy management device, characterized in that, Applied to a multi-microgrid system architecture consisting of multiple microgrid units, each microgrid unit including a micro-turbine, an energy storage system, renewable energy, and loads, the device includes: The acquisition module is used to acquire, for each microgrid unit, the previous actual micro-gas turbine power value of the micro-gas turbine in the previous cycle, the current actual energy storage energy status of the energy storage system in the current cycle, the current predicted renewable energy power value of the renewable energy in the current cycle, and the current predicted load value of the load in the current cycle; The nominal optimization module is used to perform nominal optimization processing based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the current actual energy storage energy state, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value, to obtain the optimization processing result. The optimization processing result includes the nominal control command of the microgrid unit in the current cycle and the next nominal energy storage energy state in the next cycle. The nominal control command includes the nominal power value of the micro gas turbine, the nominal charging power value of the battery of the energy storage system, and the nominal discharging power value of the battery. The previous tie-line power value is obtained based on the nominal optimization processing performed on the neighboring microgrid units in the previous cycle. The correction module is used to obtain the current correction amount for the battery net power based on the current nominal energy state and the current actual energy state of the current cycle. An execution module is used to execute the current correction amount and the nominal control command on the microgrid unit in the current cycle to realize the management of the microgrid unit; The nominal optimization module is also used for: The objective function is constructed based on the operating cost of the micro gas turbine and the loss penalty caused by frequent battery charging and discharging. Decision variables are constructed based on the micro-gas turbine power value, the battery charging power value and battery discharging power value of the energy storage system, and the tie-line power value between the microgrid unit and the neighboring microgrid unit; A constraint set is constructed based on the previous tie-line power value of the neighboring microgrid units adjacent to the microgrid unit in the previous cycle, the previous actual micro gas turbine power value, the current predicted renewable energy power value, and the current predicted load value; The nominal optimization process is performed based on the objective function, the decision variables, and the constraint set to obtain the optimization result.

8. An electronic device, characterized in that, include: The system comprises at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the multi-microgrid energy management method as described in any one of claims 1-6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the multi-microgrid energy management method as described in any one of claims 1-6 is implemented.

Images