Micro-grid energy management method, system and device and storage medium

Abstract

The invention relates to the technical field of energy management, in particular to a micro-grid energy management method, system and device and a storage medium, and the method comprises the steps: obtaining the operation parameters of each device of a micro-grid, and constructing a corresponding constraint model according to the operation parameters of each device; obtaining the carbon quota of the micro-grid, and constructing a carbon transaction mathematical model of the micro-grid in the calling period based on the carbon quota; constructing a target function model of the day-ahead scheduling of the micro-grid based on the constraint model of each device and the carbon transaction mathematical model; based on the objective function model and the constraint model, constructing an optimization model of the micro-grid; and solving the optimization model of the micro-grid to obtain a day-ahead scheduling result. And the low-carbon economic operation capability of the grid-connected micro-grid is realized.

Description

Technical Field

[0001] This disclosure relates to the field of energy management technology, and in particular to a microgrid energy management method, system, device and storage medium. Background Technology

[0002] With the continuous growth of new energy installed capacity and the in-depth implementation of the "carbon peak and carbon neutrality" goals, the low-carbon and intelligent transformation of the power system has become a development trend. Microgrids, due to their characteristics of local consumption of distributed energy, energy autonomy, and flexible dispatch, have become an important carrier for achieving clean energy consumption and carbon emission reduction goals. However, the randomness and volatility of renewable energy output such as photovoltaic and wind power, coupled with the time-varying nature of load-side demand, pose uncertainties to the economic operation and energy optimization dispatch of microgrids. Meanwhile, with the gradual implementation of carbon emission constraints and carbon trading mechanisms, how to achieve low-carbon and economical energy management while ensuring system security and energy supply balance has become a key issue that urgently needs research.

[0003] Existing research on microgrid energy management methods mostly employ deterministic optimization models, failing to adequately consider the uncertainties caused by renewable energy output and load forecasting errors, leading to deviations from expectations in actual operation. Furthermore, carbon emission modeling is often based on fixed carbon prices or linear carbon costs, neglecting the segmented marginal cost characteristics under tiered carbon trading mechanisms, and thus failing to reflect the dynamic impact of carbon emission levels on operating costs. For the demand response component, existing models typically simplify load adjustability and compensation costs, failing to fully utilize the potential for flexible regulation. Therefore, there is an urgent need to propose a method to improve the low-carbon economic operation capability and dispatch optimization accuracy of grid-connected microgrids. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides a microgrid energy management method, system, device, and storage medium. The method includes: Obtain the operating parameters of each device in the microgrid, and construct a constraint model for each device based on the operating parameters of each device; Obtain the carbon allowance of the microgrid, and construct a carbon trading mathematical model for the microgrid during the retrieval period based on the carbon allowance; The objective function model for the day-ahead scheduling of the microgrid is constructed based on each of the aforementioned constraint models and the carbon trading mathematical model. Based on the objective function model and various constraint models, an optimization model for the microgrid is constructed. The optimization model of the microgrid is solved to obtain the day-ahead scheduling results.

[0005] Further, the step of obtaining the carbon allowance of the microgrid and constructing a carbon trading mathematical model for the microgrid during the retrieval period based on the carbon allowance includes: Obtain the carbon quota of the microgrid during the scheduling cycle; The carbon quota is divided into at least one tiered range to construct a segmented reward mechanism; Based on the segmented reward mechanism, a mathematical model for carbon trading of the microgrid during the retrieval cycle is constructed.

[0006] Furthermore, the step of dividing the carbon quota into at least one tiered range and constructing a segmented reward mechanism includes: When the cumulative carbon emissions over a period of time are less than the carbon allowance, the microgrid generates revenue by selling back the carbon credits in segments. When the cumulative carbon emissions over a period exceed the carbon quota, the microgrid pays costs for the excess amount in stages.

[0007] Furthermore, the construction of the microgrid optimization model based on the objective function model and various constraint models includes: Based on the factor variables within the constraint model, construct a variable set; The set of variables is introduced into the objective function model to obtain the optimized model.

[0008] Furthermore, the construction of the variable set based on the factor variables within the constraint model includes: The set of variables includes the renewable power output error of the photovoltaic power generation model and the wind power generation model in the constraint model, as well as the load forecasting error of the demand response model; Weights are assigned to the renewable energy output errors of the photovoltaic power generation model and the wind power generation model, and the load forecasting error of the demand response model, respectively, to obtain a set of variables.

[0009] Furthermore, solving the optimization model of the microgrid to obtain the day-ahead scheduling results includes: Dual transformation is performed on the subproblems within the optimization model to obtain an optimization model for a single-layer problem; The optimization model of the single-layer problem is solved by a solver to obtain the day-ahead scheduling result.

[0010] This disclosure also provides a microgrid energy management system, including: The acquisition module is used to acquire the operating parameters of each device in the microgrid and construct a constraint model for each device based on the operating parameters of each device. The quota module is used to obtain the carbon quota of the microgrid and construct a carbon trading mathematical model of the microgrid within the retrieval period based on the carbon quota. The construction module is used to construct the objective function model for the day-ahead scheduling of the microgrid based on each of the constraint models and the carbon trading mathematical model; An optimization module is used to construct an optimization model for the microgrid based on the objective function model and various constraint models. The solver module is used to solve the optimization model of the microgrid and obtain the day-ahead scheduling results.

[0011] This disclosure also provides a computer device, characterized in that it includes a memory and a processor, the memory storing computer-readable instructions, and the processor executing the computer-readable instructions to implement the steps of a microgrid energy management method.

[0012] This disclosure also provides a computer-readable storage medium storing computer-readable instructions, the steps of a microgrid energy management method implemented when the computer-readable instructions are executed by a processor.

[0013] The technical solution provided in this disclosure has the following advantages compared with the prior art: This disclosure enables microgrids to ensure the feasibility of dispatching schemes and equipment compatibility by acquiring equipment operating parameters and establishing constraint models. By introducing carbon quotas and carbon trading mechanisms, microgrids can control carbon emissions while optimizing energy production, participate in the carbon market, and obtain economic benefits. By constructing an objective function model and integrating equipment constraints and carbon trading rules, the system can balance economic, environmental, and feasibility objectives, optimizing the overall operating efficiency of the microgrid. The construction of the optimization model provides a clear decision-making framework for the microgrid. Under this framework, all optimization objectives and constraints are considered uniformly, enabling the microgrid to obtain the optimal dispatching strategy and ensuring that its operating efficiency and carbon emission control reach the optimal state under various uncertainties. This achieves low-carbon economic operation capabilities and optimizes dispatching accuracy. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0015] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the microgrid energy management method described in an embodiment of this disclosure; Figure 2 As described in the embodiments of this disclosure Figure 1A schematic diagram illustrating the method for constructing a mathematical model for carbon trading in China; Figure 3 This is a schematic diagram of the microgrid energy management system described in an embodiment of this disclosure. Detailed Implementation

[0017] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0018] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0019] Figure 1 This is a schematic diagram of the microgrid energy management method described in the embodiments of this disclosure; as follows: Figure 1 As shown, a microgrid energy management method includes: Step S1: Obtain the operating parameters of each device in the microgrid, and construct a constraint model for each device based on the operating parameters of each device. Specifically, the operating parameters of various devices in the microgrid are obtained, including irradiance, temperature, and rated parameters of photovoltaic (PV) modules; wind speed, air density, and turbine power curves for wind power generation; ramping capability, minimum start-up and shutdown power, and ramping constraints for generator sets; state of charge, upper limit of charging and discharging power, and efficiency of energy storage systems; power limits and dispatchable range of grid interaction; and predicted values ​​and response capabilities of demand-side loads. Based on these operating parameters, device constraint models are constructed for PV power generation, wind power generation, generator sets, energy storage, grid interaction, demand response, and power balance constraints, ensuring that the output status of each device is subject to corresponding physical constraints and scheduling rules. By constructing constraint models for various devices based on real operating parameters, it is possible to effectively ensure that subsequent scheduling optimization results are consistent with the actual operating capabilities of the devices, avoiding the problem of unexecutable scheduling instructions due to ignoring device performance boundaries. Furthermore, since each model adopts a structured constraint approach for individual devices, the scalability of the optimization model is improved. The corresponding constraint models can be flexibly adjusted according to the expansion or update of microgrid devices, thereby improving the engineering adaptability of the scheduling strategy.

[0020] Step S2: Obtain the carbon allowance of the microgrid and construct a mathematical model for carbon trading of the microgrid during the retrieval period based on the carbon allowance. Specifically, the carbon allowances of the microgrid during the dispatch cycle are obtained, and a carbon trading mathematical model is constructed based on these allowances. First, a baseline carbon allowance is obtained according to the periodic carbon emission target or allowance allocation scheme. This allowance is further divided into multiple tiered intervals, and a corresponding reward or penalty price is set for each tiered interval. When actual carbon emissions are lower than the allowance, the remaining carbon emissions are calculated according to the tiered intervals; when emissions exceed the allowance, the excess emissions are calculated according to the tiered intervals, thus forming a segmented carbon trading behavior model. This carbon trading mathematical model uses a piecewise function structure to characterize differentiated economic signals for different carbon emission intervals. By obtaining carbon allowances and constructing a carbon trading mathematical model that conforms to the tiered mechanism, microgrid dispatch not only focuses on energy costs but also presents the implicit economic costs of carbon emissions, achieving a unified optimization of economics and environmental protection. Simultaneously, the tiered segmented reward mechanism makes microgrid operation strategies more flexible, incentivizing microgrids to actively participate in the regional carbon market, increasing revenue through the rational use of remaining allowances, or reducing potential excess costs by controlling emissions, thereby improving the overall efficiency of dispatch.

[0021] Step S3: Construct the objective function model for microgrid day-ahead scheduling based on each constraint model and the carbon trading mathematical model; Specifically, based on the constructed constraint models of each device and the carbon trading mathematical model, an objective function model for day-ahead dispatching of microgrids is built. The objective function typically considers factors such as economic costs, carbon trading revenue, equipment operating losses, and load response costs. By incorporating the weights of equipment operating costs, electricity purchase and sale costs, and carbon trading prices into a unified objective function, a quantitative description of output decisions for each time period within the day-ahead dispatching cycle is achieved. The objective function model undergoes piecewise linearization to make it suitable for subsequent solution. By integrating the constraints of each device and the carbon trading model into the objective function, a comprehensive trade-off of multiple objectives can be achieved globally, ensuring that the dispatching results are economical, low-carbon, and engineering feasible. This objective function model has a clear structure, facilitating parameter extension or adjustment in different microgrid scenarios and improving the generalization ability of the method.

[0022] Step S4: Based on the objective function model and various constraint models, construct the optimization model of the microgrid; Specifically, an optimization model for microgrids is constructed based on an objective function model and a constraint model. This optimization model uses the objective function as the decision index to be minimized or maximized, and integrates photovoltaic power generation models, wind power generation models, generator set models, energy storage models, grid interaction models, demand response models, and power balance constraints into a unified constraint system. This allows the optimization solver to obtain the optimal scheduling result under all physical and operational conditions. The model form is typically a mixed-integer linear programming or robust optimization model, and the inclusion of uncertainty scenario sets, distributed errors, or robust set constructions can be selected according to requirements. By constructing a unified optimization model, all equipment constraints and operational transaction rules are integrated into a solvable mathematical framework, making microgrid scheduling no longer dependent on manual experience but based on a systematic and global optimization process, significantly improving operational efficiency and scheduling quality. Furthermore, this model is adaptable to microgrids of different sizes, and flexible application across regions and equipment types can be achieved through parameter adjustments.

[0023] Step S5: Solve the optimization model of the microgrid to obtain the day-ahead scheduling results.

[0024] Specifically, a solver is used to solve the optimization model to obtain the day-ahead dispatch results of the microgrid. During the solution process, the piecewise carbon trading model can be linearized to ensure the overall optimization problem meets the requirements of a mixed-integer linear structure. After the solution process is completed, the output results include generator start-up and shutdown status, output power of various renewable resources, energy storage charging and discharging strategies, load response, and power interaction with the grid. The final dispatch results can indicate the optimal dispatch scheme for each time period and can be directly used as execution commands for the microgrid operation control system. By solving the optimization model, globally optimal or near-optimal day-ahead dispatch schemes can be obtained, which significantly reduces operating costs, improves carbon emission reduction, and enhances the safety of microgrid operation compared to traditional empirical dispatch methods. Furthermore, the solution results have advantages such as strong executability and clear structure, providing a reliable planning basis for the actual operation of the microgrid and contributing to the achievement of economical, safe, and stable operation.

[0025] Figure 2 As described in the embodiments of this disclosure Figure 1 A schematic diagram illustrating the methods for constructing mathematical models for carbon trading; as shown. Figure 2 As shown, in another embodiment of this disclosure, step S2, obtaining the carbon quota of the microgrid and constructing a carbon trading mathematical model of the microgrid within the retrieval period based on the carbon quota, includes: step S21, obtaining the carbon quota of the microgrid within the retrieval period; step S22, dividing the carbon quota into at least one tiered interval and constructing a segmented reward mechanism; step S23, constructing a carbon trading mathematical model of the microgrid within the retrieval period based on the segmented reward mechanism.

[0026] Specifically, the baseline carbon allowance for the microgrid within the target dispatch cycle is obtained. Then, the carbon allowance is mapped to the predicted emissions from generators and electricity purchases within the microgrid during this cycle. A piecewise pricing function (e.g., a piecewise linear function with excess / remaining allowance as independent variables) is designed as the core expression of the carbon trading mathematical model. This piecewise function is then transformed into an algebraic expression that can be embedded in the optimization model through piecewise variables (or linearized piecewise variables), so that it can be used as an objective function term or constraint term in daily dispatch optimization. By explicitly defining the source of carbon allowances and mathematically functionalizing the carbon trading mechanism, carbon costs or carbon benefits can be directly internalized into day-ahead dispatch decisions. This allows dispatch to consider both power economics and carbon objectives, thereby minimizing carbon costs or maximizing carbon benefits while ensuring operational feasibility. This is beneficial for microgrids to participate in the carbon market and obtain economic returns.

[0027] In another embodiment of this disclosure, step S22, dividing the carbon quota into at least one tiered interval and constructing a segmented reward mechanism, includes: When the cumulative carbon emissions over a period are less than the carbon allowance, the microgrid can generate revenue by selling back the carbon allowance in stages. When the cumulative carbon emissions over a period exceed the carbon allowance, the microgrid pays costs for the excess amount in increments over segments.

[0028] Specifically, based on authoritative allocation or operational strategies, the benchmark carbon allowance is mapped to multiple tiered threshold sequences. A pre-set repurchase price (for calculating remaining repurchase revenue) and / or purchase price (for calculating excess purchase costs) are provided for each tier. Intra-segment variables and their upper / lower bound constraints are introduced into the model to accurately calculate sales revenue or excess costs through segmented accumulation. The sales price sequence and purchase price sequence are set according to market rules, thereby realizing a carbon trading model that integrates segmented rewards or penalties. The tiered (segmented) incentive mechanism accurately reflects the economic signal of low-emission benefits, excess emission penalties, and escalating penalties, enhancing the self-driving capability of scheduling for carbon emission reduction. Simultaneously, segmented linearization facilitates efficient solution in commercial solvers, balancing simulation accuracy and engineering feasibility.

[0029] In another embodiment of this disclosure, step S4, constructing an optimization model for the microgrid based on the objective function model and the constraint model, includes: constructing a variable set based on the factor variables within the constraint model; and introducing the variable set into the objective function model to obtain the optimization model.

[0030] Specifically, the various constraint models (photovoltaic power generation model, wind power generation model, generator set model, energy storage model, grid interaction model, demand response model, and power balance constraints, etc.) and the cost / benefit terms defined in the carbon trading mathematical model are respectively expressed as algebraic constraints and cost function terms. Then, following the paradigm of objective function, constraint set, and optimization model, the objective function model (including generator operating costs, electricity purchase and sale costs, energy storage degradation losses, demand response costs, and tiered carbon trading costs, etc.) is combined with all equipment and market constraints to form a global optimization problem. In terms of modeling details, the objective function and constraints are linearized or piecewise approximated as necessary according to the nonlinear or piecewise characteristics of the problem to determine the problem type, and the variables, constraints, and objectives are uniformly encoded into mathematical expressions that can be recognized by the solver. Unifying economic objectives, equipment physical constraints, and carbon trading rules in the same optimization model allows for a global perspective to weigh the synergistic effects of power generation, energy storage, electricity purchase, and load response, thereby obtaining the optimal or near-optimal day-ahead dispatch scheme while taking into account the feasibility of constraints, improving the economic and environmental performance of system operation.

[0031] In another embodiment of this disclosure, a variable set is constructed based on the factor variables within the constraint model, including: The set of variables includes the renewable output error of the photovoltaic power generation model and the wind power generation model in the constraint model, and the load forecasting error of the demand response model; Weights were assigned to the renewable energy output errors of the photovoltaic power generation model and the wind power generation model, as well as the load forecasting error of the demand response model, to obtain the variable set.

[0032] Specifically, uncertainties affecting scheduling results are identified from the constraint model, including renewable energy output errors in photovoltaic and wind power models, and load forecasting errors in demand response models. Weight coefficients are assigned to these variables for each time period or resource type (to reflect the importance or correlation of each uncertainty), and a polyhedral or budget-constrained robust uncertainty set is constructed based on these weights. This variable set (uncertainty vector set) is used as the inner layer of the optimization model or a scenario generator to characterize the disturbance space. By accurately identifying and quantifying uncertainties affecting system operation and constructing a system-level variable set, the optimization model can explicitly consider prediction errors and correlations in decision-making, thereby improving the robustness and reliability of the scheduling scheme in real-world operating environments. Simultaneously, the conservatism can be controllably adjusted through weights and budget parameters, balancing economic efficiency and risk mitigation.

[0033] In another embodiment of this disclosure, step S5, solving the optimization model of the microgrid to obtain the day-ahead scheduling result, includes: performing dual transformation on the subproblems within the optimization model to obtain the optimization model of a single-layer problem; and solving the optimization model of the single-layer problem using a solver to obtain the day-ahead scheduling result.

[0034] Specifically, the maximization of uncertainty subproblems in the inner layer of the original robust optimization problem or bi-level optimization problem is transformed into dual constraints and dual variables by solving the duality (or utilizing strong duality and linear programming duality theory), thus converting the bi-level problem into a single-level optimization problem with additional constraints. When piecewise carbon trading or integer variables exist, the problem is further organized into a MILP format using piecewise linearization and integer variable handling techniques. Subsequently, a solver (e.g., Gurobi, CPLEX) is used to numerically solve the single-level problem, and feasibility verification and sensitivity analysis are performed after the solution to verify the robustness of the results. Dualization simplifies the complex bi-level structure into a single-level problem, improving the applicability and efficiency of the solver. Simultaneously, by incorporating uncertainty into the dualization constraints, day-ahead scheduling decisions with worst-case guarantees can be obtained within an acceptable computational time, enabling microgrids to maintain economical and safe operation even when facing prediction deviations.

[0035] In another embodiment of this disclosure, the constraint model includes a photovoltaic power generation model, a wind power generation model, a generator set model, an energy storage model, a grid interaction model, a demand response model, and power balance constraints.

[0036] Specifically, each sub-model is constructed based on the equipment operating parameters: the photovoltaic power generation model is derived from irradiance, module temperature, and module parameters; the wind power generation model is derived from wind speed curves; the generator set model is represented by upper and lower limits, ramp constraints, start-up or shutdown logic, and fuel or emission factors; the energy storage model includes upper and lower limits of charge and discharge power, dynamic equations, efficiency, and degradation costs; the grid interaction model reflects the upper and lower limits of power purchase and sale, smoothing constraints, and grid-side carbon factors; the demand response model uses baselines, price elasticity matrices, and time-series constraints for loads that can be reduced or transferred; and power balance constraints impose energy conservation constraints on all power generation, energy storage, power purchase and sale, and load in each time period (considering losses and predicted disturbances). The data for each sub-model is obtained from on-site meteorological data interfaces, market data interfaces, and historical databases, and is input after preprocessing. Clearly and completely list all physical and market constraints that constitute the microgrid dispatch problem, ensuring that the optimization model can fully reflect the system operating characteristics and external market rules, thereby providing strong executability and engineering significance for the day-ahead dispatch scheme obtained, and providing a clear modular foundation for subsequent expansion (adding power flow constraints, distribution network constraints or more complex carbon market rules).

[0037] In another embodiment of this disclosure, mathematical models and operational constraints are established for each major device in the grid-connected microgrid system (including photovoltaic power generation system, wind power generation system, diesel / gas turbine unit, energy storage system and grid connection interface) to provide a foundation for subsequent energy optimization scheduling.

[0038] In the photovoltaic power generation model, the output power of the photovoltaic module in time period t is expressed as:

[0039] in, Photovoltaic conversion efficiency; The area of ​​the photovoltaic array; Solar irradiance; For component temperature; Temperature power coefficient; This is a reference temperature.

[0040] Due to environmental influences, photovoltaic power output is subject to uncertainty; prediction errors must be considered. Its actual output power can be written as:

[0041] in, This is the predicted value of photovoltaic output power; This represents the maximum fluctuation range for photovoltaic power.

[0042] The output constraints of the photovoltaic inverter are:

[0043] In a wind power generation model, the three-segment relationship between the wind turbine's output power and wind speed is expressed as:

[0044] in, This refers to the actual wind speed; To cut in wind speed; Rated wind speed; To cut off the wind speed; This is the rated output of the fan.

[0045] Considering wind speed prediction errors The actual output power is:

[0046] in, This is the predicted output power of the wind turbine; This represents the maximum fluctuation range of the wind turbine.

[0047] Diesel / gas generator set model, the power output of gas or diesel generator set meets the following upper and lower limits of output and generator set start-stop constraints:

[0048]

[0049]

[0050] in, The actual output of the diesel / gas generator set during time period t; This represents the lower limit of the output power of the diesel / gas generator set. This is the upper limit of the output of the diesel / gas generator set; For climbing power limits; This is the limit for landslide power.

[0051] Energy storage system model:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] in, Power for charging energy storage systems; This refers to the discharge power of the energy storage system. This refers to the maximum charging power. This refers to the upper limit of the discharge power. and These are binary variables representing the charging and discharging states, respectively. The energy stored in the energy storage system during time period t; and These are the upper and lower limits of the stored energy; The charging and discharging efficiency of the energy storage system.

[0058] In the upstream grid interaction model, under grid-connected conditions, the power exchange between the microgrid and the upstream grid satisfies:

[0059]

[0060]

[0061] To limit the impact of power fluctuations on the upstream power grid, a smoothing constraint is defined:

[0062] in: This represents the power interacting with the upstream power grid during time period t. For the power purchased; This refers to the power output for electricity sales. and These are the upper limits for electricity purchase and sale; This represents the upper limit of the variation in power exchange between the microgrid and the upper-level grid in adjacent time periods.

[0063] In the demand response model, flexible loads within a microgrid can be divided into transferable loads and reduceable loads, each corresponding to different regulation mechanisms and cost functions.

[0064]

[0065]

[0066]

[0067] in, This is the price demand elasticity matrix, which reflects the elasticity of response of electricity price adjustment at time t to load change at time j; and These represent the load change and the initial load for time period t, respectively. and These represent the change in electricity price and the initial electricity price for time period j, respectively. and These represent the changes in load that can be reduced and load that can be transferred after demand response; and These represent the changes in reduceable load and transferable load over time period t, respectively. The electricity price for period t after the demand response; The initial electricity price for the period t preceding the demand response.

[0068] Power balance constraints:

[0069] Also consider load forecasting error The actual load during time period t can be expressed as:

[0070] in, The actual load power during time period t. For the predicted load power of a number of segments t, This represents the maximum fluctuation in load.

[0071] Establishing a mathematical model for a tiered carbon trading mechanism for microgrid systems includes the following steps: The carbon emissions from microgrid systems mainly come from gas / diesel generator sets and electricity purchased from the upstream grid, with the corresponding carbon emissions as follows:

[0072]

[0073]

[0074] in, , , These are the carbon emissions from gas / diesel generator sets, the carbon emissions from electricity purchased from the upstream power grid, and the total carbon emissions from microgrids; and These are the carbon emission factors for gas / diesel generator sets and the power grid, respectively.

[0075] To construct a segmented incentive mechanism, the carbon emission baseline allowance for the microgrid system during the dispatch cycle is set as follows: It is further divided into K stepped intervals:

[0076] Based on the above interval definition, the carbon trading price of the system within time period t can be expressed as:

[0077] in: For tiered carbon trading prices; The base price for carbon trading; This is to increase the price. The carbon trading price rises in stages as carbon emissions increase. When carbon emissions are less than the baseline carbon allowance, the remaining carbon allowances can be sold to generate revenue, forming a tiered reward and punishment mechanism.

[0078] Based on microgrid equipment models, flexible load dispatching models, and tiered carbon trading mechanisms, a low-carbon economic optimization objective function model for day-ahead microgrid dispatching is constructed, which includes the following steps: Taking into account the constraints of distributed power sources, energy storage systems, flexible load dispatching, and tiered carbon trading mechanisms, a low-carbon economic optimization objective function for day-ahead dispatching of microgrids is established. This objective function aims to minimize the total economic cost of microgrid operation, while also considering carbon emission costs, load response costs, and equipment operating economics, thereby achieving multi-source collaborative optimization dispatching of grid-connected microgrids.

[0079] After comprehensively considering various cost factors, the overall optimization objective of the system is:

[0080]

[0081] in, These represent the operating costs of the gas / diesel generator set, the energy storage system cost, the electricity purchase and sale cost from the upstream power grid, the demand response dispatch cost, and the tiered carbon trading cost, respectively. This refers to the fuel characteristic coefficient of the generator unit; These are the power loss coefficient and degradation penalty factor of the energy storage system, respectively. Time-of-use electricity pricing; This is a penalty factor for fluctuations in electricity purchases. This is the load response cost coefficient.

[0082] Constructing an uncertainty set based on the uncertain variables within a microgrid system involves the following steps: In the grid-connected operation of microgrids, the main uncertainties include: uncertainty in renewable energy output, as wind and solar power are affected by weather conditions, leading to deviations between predicted and actual values; and uncertainty in load demand, as load power fluctuates around predicted values ​​due to user behavior and the external environment. Let the vector of uncertainties in day-ahead dispatch optimization of the microgrid be:

[0083] For the microgrid system in this invention, in order to simultaneously consider the upper and lower limits of prediction error, the correlation between various uncertainties, and the overall uncertainty intensity constraint, the polyhedral uncertainty set is defined as follows:

[0084] in, These are the weighting coefficients that reflect the correlation between the uncertain variables; The upper bound of the overall uncertainty disturbance of the microgrid in time period t is used to control the conservatism of the polyhedral set.

[0085] Based on the cost objective function and constraint model of the microgrid, a robust optimization model of the microgrid system considering demand response and tiered carbon trading mechanism is constructed, and the day-ahead scheduling result is obtained by solving the uncertainty set. The specific steps include the following: Based on the mathematical model of the microgrid system and various constraints, a robust optimization model including demand response and tiered carbon trading mechanisms is established to minimize the worst-case cost of system operation under the influence of uncertain parameters, so as to achieve the optimal decision for day-ahead low-carbon economic dispatch.

[0086] First, define the decision variables as follows: .

[0087] The robust optimization model for microgrids is constructed as follows:

[0088]

[0089] Dualizing the inner minimization problem of the above model reduces the entire model to a single-layer maximization problem. The transformed model is as follows:

[0090]

[0091] The transformed single-layer optimization model described above can be solved directly using a solver.

[0092] Figure 3 This is a schematic diagram of the microgrid energy management system described in an embodiment of this disclosure; as follows: Figure 3 As shown, this disclosure also provides a microgrid energy management system, including: The acquisition module is used to acquire the operating parameters of each device in the microgrid and to construct a constraint model for each device based on the operating parameters of each device. The quota module is used to obtain the carbon quota of the microgrid and to build a mathematical model for carbon trading of the microgrid within the retrieval period based on the carbon quota. The module is used to construct the objective function model for microgrid day-ahead scheduling based on each constraint model and the carbon trading mathematical model; The optimization module is used to construct an optimization model for the microgrid based on the objective function model and various constraint models. The solver module is used to solve the optimization model of the microgrid and obtain the day-ahead scheduling results.

[0093] This disclosure also provides a computer device, characterized in that it includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of a microgrid energy management method.

[0094] This disclosure also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of a microgrid energy management method.

[0095] 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 one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0096] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A microgrid energy management method, characterized in that, include: Obtain the operating parameters of each device in the microgrid, and construct a constraint model for each device based on the operating parameters of each device; Obtain the carbon allowance of the microgrid, and construct a carbon trading mathematical model for the microgrid during the retrieval period based on the carbon allowance; The objective function model for the day-ahead scheduling of the microgrid is constructed based on each of the aforementioned constraint models and the carbon trading mathematical model. Based on the objective function model and various constraint models, an optimization model for the microgrid is constructed. The optimization model of the microgrid is solved to obtain the day-ahead scheduling results.

2. The microgrid energy management method according to claim 1, characterized in that, The step of obtaining the carbon allowance of the microgrid and constructing a carbon trading mathematical model for the microgrid during the retrieval period based on the carbon allowance includes: Obtain the carbon quota of the microgrid during the scheduling cycle; The carbon quota is divided into at least one tiered range to construct a segmented reward mechanism; Based on the segmented reward mechanism, a mathematical model for carbon trading of the microgrid during the retrieval cycle is constructed.

3. The microgrid energy management method according to claim 2, characterized in that, The step of dividing the carbon quota into at least one tiered range and constructing a segmented reward mechanism includes: When the cumulative carbon emissions over a period of time are less than the carbon allowance, the microgrid generates revenue by selling back the carbon credits in segments. When the cumulative carbon emissions over a period exceed the carbon quota, the microgrid pays costs for the excess amount in stages.

4. The microgrid energy management method according to claim 1, characterized in that, The construction of the microgrid optimization model based on the objective function model and various constraint models includes: Based on the factor variables within the constraint model, construct a variable set; The set of variables is introduced into the objective function model to obtain the optimized model.

5. The microgrid energy management method according to claim 3, characterized in that, The variable set constructed based on the factor variables within the constraint model includes: The set of variables includes the renewable power output error of the photovoltaic power generation model and the wind power generation model in the constraint model, as well as the load forecasting error of the demand response model; Weights are assigned to the renewable energy output errors of the photovoltaic power generation model and the wind power generation model, and the load forecasting error of the demand response model, respectively, to obtain a set of variables.

6. The microgrid energy management method according to claim 1, characterized in that, Solving the optimization model of the microgrid to obtain the day-ahead scheduling results includes: Dual transformation is performed on the subproblems within the optimization model to obtain an optimization model for a single-layer problem; The optimization model of the single-layer problem is solved by a solver to obtain the day-ahead scheduling result.

7. A microgrid energy management system, characterized in that, include: The acquisition module is used to acquire the operating parameters of each device in the microgrid and construct a constraint model for each device based on the operating parameters of each device. The quota module is used to obtain the carbon quota of the microgrid and construct a carbon trading mathematical model of the microgrid within the retrieval period based on the carbon quota. The construction module is used to construct the objective function model for the day-ahead scheduling of the microgrid based on each of the constraint models and the carbon trading mathematical model; An optimization module is used to construct an optimization model for the microgrid based on the objective function model and various constraint models. The solution module is used to solve the optimization model of the microgrid and obtain the day-ahead scheduling results.

8. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the microgrid energy management method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the microgrid energy management method as described in any one of claims 1 to 6.

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