Carbon emission oriented microgrid optimization scheduling method, device and equipment are considered

Abstract

The application provides a kind of micro-grid optimization scheduling method, device and equipment considering carbon emission oriented, it is related to electric power technical field.The method comprises: obtaining the electric load data of demand side user;According to the preset carbon quota of electric load data and demand side user cluster, determine the carbon emission of demand side user cluster participating in carbon market, and according to the carbon emission of participating in carbon market, determine the carbon reward and punishment value of demand side user cluster;Carbon potential of each node in micro-grid is calculated, carbon unit price is determined according to carbon reward and punishment value and carbon potential, and carbon responsibility factor of demand side user is determined according to carbon unit price;According to carbon unit price, micro-grid decision model is established with micro-grid benefit as target, demand side decision model is established with electricity purchasing carbon benefit and satisfaction of demand side as target, and decision model is solved with carbon responsibility factor as constraint, and micro-grid optimization scheduling scheme is obtained.The application can solve the problem of insufficient carbon emission driving and lack of collaborative regulation of existing strategy.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a microgrid optimization scheduling method, apparatus and equipment that takes carbon emission orientation into account. Background Technology

[0002] In recent years, with the accelerated pace of global carbon emission reduction, the clean transformation of the power system has become a core path to address climate change. However, resources such as small-scale wind power, solar power, and flexible loads are characterized by their small scale and scattered distribution, making it difficult for them to directly participate in system regulation and market transactions. As a result, their enormous carbon emission reduction capacity has not been effectively released for a long time.

[0003] Microgrids, as autonomous systems aggregating distributed renewable energy generation units such as photovoltaics and wind turbines, energy storage systems, thermal power generation, and controllable distributed power sources such as dispatchable gas turbines and fuel cells, as well as demand-side users, can achieve flexible interaction with the main grid. Microgrids not only achieve the physical aggregation and coordinated operation of distributed resources, but also, through internal dynamic control commands and intelligent scheduling strategies, accurately transmit system-level low-carbon operation signals to each distributed energy unit, energy storage system, and flexible load, thereby guiding them to adjust their own operating status and actively participate in the system's low-carbon balance, forming a "source-source-output" balance. Dutch The overall synergy of "storage" and coordinated carbon reduction.

[0004] Existing microgrid dispatch strategies generally suffer from insufficient carbon emission drivers and a lack of coordinated regulation, failing to fully tap the demand side's ability to participate in carbon emission control. Summary of the Invention

[0005] This invention provides a microgrid optimization scheduling method, apparatus, and equipment that takes carbon emission orientation into account, in order to solve the problems of insufficient carbon emission driving force and lack of coordinated regulation in existing strategies.

[0006] In a first aspect, embodiments of the present invention provide a microgrid optimization scheduling method considering carbon emission orientation, comprising: Obtain electricity load data from demand-side users; Based on the electricity load data and the preset carbon quota of the demand-side user cluster, the carbon emissions of the demand-side user cluster participating in the carbon market are determined, and the carbon reward / penalty value of the demand-side user cluster is determined based on the carbon emissions of the carbon market participation. Calculate the carbon potential of each node in the microgrid, determine the carbon unit price based on the carbon reward / penalty value and the carbon potential, and determine the carbon responsibility factor of demand-side users based on the carbon unit price. Based on the carbon unit price, a microgrid decision-making model is established with the benefit of the microgrid as the objective, and a demand-side decision-making model is established with the carbon benefit and satisfaction of the demand side as the objective. The decision-making model is then solved with the carbon responsibility factor as a constraint to obtain an optimal microgrid scheduling scheme.

[0007] In one possible implementation, determining the carbon emissions of the demand-side user cluster participating in the carbon market based on the electricity load data and the pre-set carbon quota of the demand-side user cluster includes: The overall carbon emissions of the demand-side user cluster are determined based on the aforementioned electricity load data. The carbon emissions of the demand-side user cluster participating in the carbon market are determined based on the difference between the total carbon emissions and the preset carbon quota.

[0008] In one possible implementation, determining the carbon reward / penalty value for the demand-side user cluster based on the carbon emissions of the participants in the carbon market includes: When the carbon emissions from participating in the carbon market are less than zero, the carbon reward / penalty value is determined to be a reward value, and the magnitude of the reward value is negatively correlated with the carbon emissions from participating in the carbon market. When the carbon emissions from participating in the carbon market are greater than zero, the carbon reward / penalty value is determined to be a penalty value, and the magnitude of the penalty value is positively correlated with the carbon emissions from participating in the carbon market.

[0009] In one possible implementation, determining the carbon unit price based on the carbon reward / penalty value and the carbon potential includes: according to Determine the carbon unit price; in, For users The price per unit of carbon at time t, Carbon reward / penalty value, For preset coefficients, Preset carbon quotas for demand-side user clusters For the overall carbon emissions of the demand-side user cluster, Let J be the carbon potential at time t.

[0010] In one possible implementation, determining the carbon responsibility factor for demand-side users based on the carbon unit price includes: according to Determine the carbon responsibility factor; in, For user i's carbon responsibility factor, For users The electrical load consumed at time t, Preset carbon quotas for demand-side user clusters For users The price per unit of carbon at time t, For users The electrical load purchased at time t, where I is the user set, T is the time period, and k is a preset coefficient.

[0011] In one possible implementation, the establishment of a microgrid decision-making model with the benefit of the microgrid as the objective, and the establishment of a demand-side decision-making model with the carbon benefits and satisfaction of the demand side as the objectives, include: Establish a microgrid decision model for ; Establish a demand-side decision-making model for ; in, The target value of the microgrid decision model. F user This is the target value for the demand-side decision-making model. For users The electrical load consumed at time t, For the various costs of microgrids, For users The electrical load purchased at time t, Here, I represents the user satisfaction index, and T represents the time period.

[0012] In one possible implementation, the step of solving the decision model with the carbon responsibility factor as a constraint includes: Establish constraints that include carbon responsibility factors, and solve the microgrid decision model and the demand-side decision model using a game theory algorithm based on the constraints. The carbon responsibility factor constraint is ;in, For user i's carbon responsibility factor, , These are the minimum and maximum limits for the carbon responsibility factor.

[0013] In one possible implementation, the constraints also include: microgrid power balance constraints, output constraints of individual power generation devices, and demand-side load constraints.

[0014] Secondly, embodiments of the present invention provide a microgrid optimized scheduling device that takes carbon emission orientation into account, comprising: The acquisition module is used to acquire the electrical load data of users on the demand side; The determination module is used to determine the carbon emissions of the demand-side user cluster participating in the carbon market based on the electricity load data and the preset carbon quota of the demand-side user cluster, and to determine the carbon reward / penalty value of the demand-side user cluster based on the carbon emissions of the carbon market participation. The calculation module is used to calculate the carbon potential of each node in the microgrid, determine the carbon unit price based on the carbon reward / penalty value and the carbon potential, and determine the carbon responsibility factor of the demand-side users based on the carbon unit price. The decision module is used to establish a microgrid decision model with the benefit of the microgrid as the objective, based on the carbon unit price, to establish a demand-side decision model with the carbon benefit and satisfaction of the demand side as the objective, and to solve the decision model with the carbon responsibility factor as the constraint to obtain the microgrid optimal scheduling scheme.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0016] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: In this embodiment of the invention, firstly, based on the electricity load data and the preset carbon quota of the demand-side user cluster, the carbon emissions of the demand-side user cluster participating in the carbon market are determined, and the carbon reward and penalty value of the demand-side user cluster is determined based on the carbon emissions from participating in the carbon market. This step establishes a microgrid carbon reward and penalty calculation model that considers carbon emission guidance, forming a carbon emission benchmark for the demand-side cluster. Next, the carbon potential of each node in the microgrid is calculated, and the carbon unit price is determined based on the carbon reward and penalty value and the carbon potential. The carbon responsibility factor for demand-side users is then determined based on the carbon unit price. This step introduces the carbon potential signal as a guiding medium to establish a carbon emission responsibility guidance strategy for demand-side users within the cluster, forming a carbon unit price for demand-side electricity consumption behavior to guide rational energy use by the demand side. Finally, based on the carbon unit price, a microgrid decision-making model is established with the microgrid's interests as the objective, and a demand-side decision-making model is established with the demand side's carbon purchase benefits and satisfaction as the objectives. The decision-making model is then solved with the carbon responsibility factor as a constraint to obtain an optimized microgrid scheduling scheme. This step, by establishing a microgrid-demand-side carbon emission interaction model and introducing a carbon emission guidance scheme, minimizes carbon emissions within the system and ensures demand-side satisfaction. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the microgrid optimization scheduling method considering carbon emission orientation provided in this embodiment of the invention. Figure 2 This is a flowchart illustrating the implementation of the hybrid intelligent optimization algorithm provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the test results provided in the embodiments of the present invention. Figure 1 ; Figure 4 This is a schematic diagram of the test results provided in the embodiments of the present invention. Figure 2 ; Figure 5 This is a schematic diagram of the test results provided in the embodiments of the present invention. Figure 3 ; Figure 6 This is a schematic diagram of the test results provided in the embodiments of the present invention. Figure 4 ; Figure 7 This is a schematic diagram of the structure of the microgrid optimization scheduling device considering carbon emission orientation provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] To address the issues of insufficient carbon emission-driven mechanisms and inadequate coordinated control in microgrid dispatch strategies, this invention proposes a carbon emission-oriented microgrid optimization dispatch strategy. This strategy establishes a real-time model of the distribution of "carbon potential" and the path of "carbon flow" within the system, explores the demand-side participation in carbon emission control, and forms a carbon emission responsibility transmission mechanism linking both power generation and consumption sides. The strategy first establishes a real-time carbon emission perception and responsibility tracing model for the microgrid. Its core lies in calculating the carbon potential and carbon emission flow of each node within the system, clearly tracing and transferring the carbon emission responsibility traditionally attributed to the power generation side to the electricity demand side in the form of dynamic carbon emission signals. This mechanism enables loads to perceive the carbon emission intensity corresponding to their own electricity consumption behavior, thus providing a quantitative basis for guiding them to proactively adjust their electricity consumption patterns. Using this carbon emission signal as the core driver, a coordinated optimization dispatch framework connecting "source-grid-load" is constructed, generating control commands that match low-carbon goals, ultimately achieving proactive reduction of carbon emission levels in microgrid operation and refined management throughout the entire process.

[0020] See Figure 1 The document illustrates a flowchart of the implementation of a microgrid optimization scheduling method considering carbon emission orientation provided by an embodiment of the present invention, which is described in detail below: Step S101: Obtain the electrical load data of demand-side users.

[0021] This step is the foundational data collection and classification stage for microgrid carbon trading and energy dispatch. The core is to first acquire raw demand-side load data, and then, combining the characteristics of distributed resources and the requirements of carbon trading applications, complete the aggregation of distributed resources and the refined classification of demand-side loads. This provides data support for subsequent carbon emission accounting, source-grid-load coordinated dispatch, and carbon trading revenue optimization. Based on the characteristics of distributed resources and their application in carbon trading, the microgrid aggregates various energy sources such as wind power and photovoltaic power, and classifies the demand side into loads that can be reduced or transferred.

[0022] Step S102: Based on the electricity load data and the preset carbon quota of the demand-side user cluster, determine the carbon emissions of the demand-side user cluster participating in the carbon market, and determine the carbon reward / penalty value of the demand-side user cluster based on the carbon emissions from participating in the carbon market.

[0023] In one possible implementation, the step includes: Step S1021: Determine the overall carbon emissions of the demand-side user cluster based on the electricity load data.

[0024] The total carbon emissions of a power cluster are not a static value, but rather change dynamically with the structure of the electricity sources supplying it. At any given time, if the proportion of high-carbon-emission power sources such as thermal power generation in the microgrid or main grid is high, the carbon potential of the system supplying electricity to the cluster increases, and the carbon emissions borne by users within the cluster per unit of electricity consumed increase accordingly. Conversely, if the proportion of renewable energy generation such as photovoltaic and wind power increases, the system's carbon potential decreases, and the overall carbon emissions of the cluster decrease accordingly. This is specifically represented by the following formula:

[0025] in, The total number of users in the community; For users The electrical load during time period t; This represents the total number of power source types (e.g., thermal power, wind power, photovoltaic power, etc.). The percentage of electricity from the k-th type during time period t; For the first l Carbon emission intensity of electricity sources is relatively high for thermal power and close to zero for renewable energy generation.

[0026] Step S1022: Based on the difference between the total carbon emissions and the preset carbon allowance, determine the carbon emissions of the demand-side user cluster participating in the carbon market.

[0027] in, Carbon emissions for participating in the carbon market; For the overall carbon emissions of demand-side clusters; Preset carbon quotas for demand-side clusters of microgrids.

[0028] Step S1023, determine the carbon reward / penalty value: when the carbon emissions from participating in the carbon market are less than zero, the carbon reward / penalty value is determined as a reward value, and the magnitude of the reward value is negatively correlated with the carbon emissions from participating in the carbon market; when the carbon emissions from participating in the carbon market are greater than zero, the carbon reward / penalty value is determined as a penalty value, and the magnitude of the penalty value is positively correlated with the carbon emissions from participating in the carbon market.

[0029] For example, a tiered carbon trading mechanism is adopted between the microgrid and the demand-side cluster, setting periodic carbon emission quotas as the benchmark for their carbon emission responsibilities. When the cluster's actual carbon responsibility is lower than its quota, the resulting quota surplus can be represented as a callable low-carbon capacity resource in system optimization, and given higher scheduling priority or a better adjustment coefficient through optimization algorithms, thereby achieving an equivalent transformation of resource value technically. When the cluster's actual carbon responsibility exceeds its quota, the resulting quota gap will trigger a tiered, progressively increasing responsibility adjustment coefficient. This coefficient increases non-linearly as the gap widens, significantly amplifying the weight of excess carbon emissions in the system optimization objective, thereby driving the scheduling strategy to prioritize its suppression and adjustment. This is specifically represented by the following formula:

[0030] in, This serves as the base coefficient for carbon trading. Preset limit; This is the carbon reward coefficient; The increase in the base coefficient for carbon trading; This represents the carbon reward and penalty situation for the demand-side cluster. A positive value indicates that carbon emissions are currently in excess, while a negative value indicates that the carbon emission allowance has not been fully utilized.

[0031] Step S103: Calculate the carbon potential of each node in the microgrid, determine the carbon unit price based on the carbon reward / penalty value and carbon potential, and determine the carbon responsibility factor of demand-side users based on the carbon unit price.

[0032] First, carbon emissions from the power generation phase are transferred to the demand side through carbon emission flows, guiding electricity consumption behavior on the demand side and generating carbon potential at various nodes, thereby affecting the carbon emissions corresponding to the current power consumption. This is specifically represented by the following formula:

[0033] In the formula: Let J be the carbon potential at time t; Let be the active power flowing from node j to node a at time t; For the set of units connected to node a; Let be the carbon potential of unit i at time t; The active power output of unit i at time t; This represents the set of paths flowing from node j to node a.

[0034] Then, using the zero-carbon responsibility baseline of the demand-side cluster under ideal conditions of pure clean energy power supply as a reference, the unit carbon responsibility adjustment coefficient is determined by calculating the deviation between the cluster's actual carbon responsibility and this baseline; then, based on this coefficient and combined with the actual electricity load ratio of each user, the overall carbon responsibility adjustment amount of the alliance is allocated to each user, thereby generating a differentiated carbon responsibility component for each user.

[0035] The specific representation is shown in the following formula: according to Determine the carbon responsibility factor; in, For user i's carbon responsibility factor, For users The electrical load consumed at time t, Preset carbon quotas for demand-side user clusters For users The price per unit of carbon at time t, For users The electrical load purchased at time t, where I is the user set, T is the time period, and k is a preset coefficient.

[0036] Step S104: Based on the carbon unit price, establish a microgrid decision model with the benefit of the microgrid as the objective, establish a demand-side decision model with the carbon benefit and satisfaction of the demand side as the objective, and solve the decision model with the carbon responsibility factor as the constraint to obtain the microgrid optimal scheduling scheme.

[0037] Based on the system's carbon flow dynamics and node carbon potential, and taking into account carbon emission guiding factors, this model constructs a microgrid decision-making model and a demand-side decision-making model, respectively, clarifying the roles of both parties in the transmission and coordination of carbon emission responsibility, and providing a clear mathematical model foundation for subsequent collaborative optimization solutions.

[0038] The specific representation is shown in the following formula:

[0039]

[0040] in, The target value of the microgrid decision model. F user This is the target value for the demand-side decision-making model. For users The electrical load consumed at time t, For the various costs of microgrids, For users The electrical load purchased at time t, Here, I represents the user satisfaction index, and T represents the time period.

[0041]

[0042]

[0043] in, , , , These are the microgrid operation and maintenance coefficient, energy purchase coefficient, environmental coefficient, and demand response coefficient, respectively. , These represent the user's preference coefficients for electricity consumption, which reflect the user's energy demand preferences and influence the magnitude of demand.

[0044]

[0045]

[0046]

[0047]

[0048] in, , , These are the operation and maintenance cost coefficients for wind turbines, photovoltaic equipment, and gas turbines, respectively. , , These refer to the output power of equipment such as wind turbines, photovoltaic equipment, and gas turbines; Electricity prices in the electricity market; Let t be the overall energy consumption cost of the microgrid; The emission coefficient of Class K pollutants generated from electricity purchase; This is the penalty coefficient factor; , These are the incentive coefficients for users participating in load reduction and load transfer in the microgrid, respectively. , These refer to the electrical loads that can be reduced or transferred in a microgrid.

[0049] To ensure the practical feasibility of the optimization model, a complete set of physical operation constraints needs to be constructed. Inequality constraints can be introduced from aspects such as power balance, power generation output, carbon responsibility factor, and demand-side load, as follows: (1) Microgrid power balance constraints Microgrids integrate resources such as distributed photovoltaic, distributed wind power, and gas turbines, and sell electrical energy to users. Their internal electrical energy is subject to power balance constraints. The expression is:

[0050] (2) Output constraints of each power generation device Each power generation device has maximum and minimum output limits to ensure safe and stable operation. The specific expression is:

[0051] (3) Dynamic carbon responsibility factor constraint In the iterative solution process of the hybrid intelligent optimization algorithm, a dynamic carbon responsibility factor is introduced as a key control parameter to achieve refined guidance of demand-side cluster carbon emission behavior. The dynamic carbon responsibility factor has a maximum and a minimum value, and its expression is:

[0052] (4) Demand-side load constraints

[0053] in, Let t be the photovoltaic power generation of user i within time t; Let t be the wind power generation capacity of user i within time t; The initial electrical load for user i; The electrical load transferred to user i.

[0054] In this embodiment of the invention, firstly, based on the electricity load data and the preset carbon quota of the demand-side user cluster, the carbon emissions of the demand-side user cluster participating in the carbon market are determined, and the carbon reward and penalty value of the demand-side user cluster is determined based on the carbon emissions from participating in the carbon market. This step establishes a microgrid carbon reward and penalty calculation model that considers carbon emission guidance, forming a carbon emission benchmark for the demand-side cluster. Next, the carbon potential of each node in the microgrid is calculated, and the carbon unit price is determined based on the carbon reward and penalty value and the carbon potential. The carbon responsibility factor for demand-side users is then determined based on the carbon unit price. This step introduces the carbon potential signal as a guiding medium to establish a carbon emission responsibility guidance strategy for demand-side users within the cluster, forming a carbon unit price for demand-side electricity consumption behavior to guide rational energy use by the demand side. Finally, based on the carbon unit price, a microgrid decision-making model is established with the microgrid's interests as the objective, and a demand-side decision-making model is established with the demand side's carbon purchase benefits and satisfaction as the objectives. The decision-making model is then solved with the carbon responsibility factor as a constraint to obtain an optimized microgrid scheduling scheme. This step, by establishing a microgrid-demand-side carbon emission interaction model and introducing a carbon emission guidance scheme, minimizes carbon emissions within the system and ensures demand-side satisfaction.

[0055] like Figure 2 As shown, to efficiently solve the aforementioned complex nonlinear, multi-constraint collaborative optimization model, a hybrid intelligent optimization algorithm combining global optimization and local exact search is introduced to collaboratively optimize the source-load response behavior. The specific process of the optimization algorithm is as follows: (1) Initialization Input system prediction data, equipment parameters and carbon emission-oriented initial parameters, and randomly generate an initial microgrid population consisting of multiple potential scheduling schemes.

[0056] (2) Local precise optimization on the demand side The system carbon emission guidance signal implicit in the current microgrid population is sent to each demand-side user or cluster. Each demand-side entity uses this signal as a key input and executes local exact optimization algorithms such as quadratic programming in parallel and independently to solve its own optimal power consumption adjustment strategy under the current system state.

[0057] (3) System-level effect evaluation By aggregating the optimized response results from all demand sides and combining them with the scheduling schemes in the microgrid population, the overall power balance and carbon emission indicators of the system are recalculated, thereby evaluating the comprehensive adaptability of each microgrid scheduling scheme under the current demand-side response (with carbon reduction effect as the core metric).

[0058] (4) Global search and population evolution Based on fitness, a genetic algorithm is used to perform selection, crossover, and mutation operations on the microgrid population to eliminate inferior scheduling schemes and generate a new generation of scheduling schemes with higher potential performance.

[0059] (5) Convergence judgment and output Steps two through four are repeated to form a closed-loop collaborative optimization process of "microgrid strategy proposal—precise demand-side response—system evaluation feedback—population iterative evolution". When the algorithm determines that the system state tends to be stable, that is, when the microgrid scheduling strategy and demand-side response behavior reach a collaborative equilibrium solution, the iteration terminates and outputs the final global optimized scheduling scheme and the corresponding demand-side response strategy.

[0060] In one possible implementation, the optimal solution satisfying all constraints is calculated based on the above steps, generating the final sequence of scheduling instructions. These instructions are then issued to each demand-side user for specific load control, thus completing a full optimization scheduling cycle. This embodiment establishes a closed-loop feedback and rolling execution mechanism to achieve adaptive and continuous optimization of the scheduling. During the execution of scheduling instructions, the system continuously collects key state quantities through a real-time monitoring unit, including the actual output of renewable energy, the actual power of various loads, and the actual carbon potential of nodes. Through algorithmic control, these measured data are compared in real time with predicted values ​​and optimized setpoints to generate state deviations. This deviation information serves as feedback input, triggering the next round of rolling optimization: at a preset time trigger point, the algorithm re-executes the optimization process based on the latest measured system state and updated predicted information, dynamically refreshing and correcting the scheduling plan for subsequent periods. This mechanism ensures that the scheduling strategy can respond to system uncertainties in real time, thereby achieving adaptive, closed-loop, and continuous optimization operation of the microgrid driven by carbon emissions.

[0061] The following simulation example verifies this strategy.

[0062] This study uses a microgrid in a specific region for basic data reference and simulation research. The microgrid established in the example integrates wind turbines, photovoltaic (PV) turbines, thermal power plants, load shedding, and load transfer. The dispatch cycle is 24 hours, divided into hourly segments. The simulation diagrams for wind power, PV, and load forecasting are shown below. Figure 3 As shown.

[0063] Under the current microgrid carbon emission dispatch framework, to further guide source-load-storage coordinated carbon reduction, this embodiment superimposes a dynamic responsibility adjustment coefficient reflecting the system's carbon emission stress level onto the system's real-time carbon potential signal, thereby strengthening the guidance of electricity consumption behavior during high-carbon emission periods in the optimization model. For example... Figure 4 As shown in the diagram. Specifically, during peak periods when wind and solar power output is insufficient and the overall carbon potential of the system is high, load demand must rely more heavily on high-carbon-emission energy sources. In this case, by dynamically increasing the carbon responsibility adjustment coefficient of relevant demand-side clusters during this period through algorithms, their electricity consumption behavior during this period carries a higher carbon emission weight in the optimization objective function. This guides the demand side to prioritize adjusting its power consumption or time periods, thereby achieving stronger constraints and guidance on the total carbon emissions of the system at the technical level.

[0064] exist Figure 5 The diagram illustrates a comparison of demand-side load under the carbon emission-oriented strategy of this invention. During the periods of 0:00-7:00 and 23:00-24:00, the overall carbon potential of the system is typically at a low level. At this time, the microgrid energy management system issues a low-carbon-potential guidance signal to the demand side. To optimize their carbon responsibility, the demand side tends to adjust some time-shiftable loads to operate during these periods, resulting in an increase in the load curve during these times. During regular periods such as 8:00-10:00 and 14:00-16:00, the system carbon potential is at a moderate level. Users, while meeting basic energy needs and comfort, make minor adjustments to their electricity consumption behavior based on the received dynamic carbon potential information, resulting in relatively gentle load changes. During peak load periods such as 11:00-13:00 and 17:00-22:00, the system often needs to call upon high-carbon-emission power sources due to insufficient renewable energy output, leading to a significant increase in real-time carbon potential. At this time, the microgrid system will issue a high carbon emission responsibility warning signal, which may trigger a tiered responsibility adjustment coefficient for excess carbon emission clusters. To avoid high carbon responsibility, the demand side will respond actively by reducing unnecessary loads or further transferring adjustable loads, resulting in a decrease in load during this period.

[0065] from Figure 6Analysis of the operational strategies of each device reveals that, under the carbon emission-oriented optimization framework of this invention, the microgrid's scheduling decisions are guided by the overall carbon potential optimization of the system. Its operational logic is as follows: to reduce the system's operating carbon potential, the VPP prioritizes the use of zero-carbon or low-carbon energy sources such as photovoltaic and wind power generation while meeting user load demands. High-carbon-emission thermal power generation units, energy storage devices, and electricity purchased from the external grid serve as supplementary means of power balancing and low-carbon regulation.

[0066] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0067] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0068] Figure 7 A schematic diagram of a microgrid optimization scheduling device considering carbon emission orientation provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown.

[0069] like Figure 7 As shown, the microgrid optimization dispatching device 7, which considers carbon emission orientation, includes: Acquisition module 71 is used to acquire electrical load data of demand-side users; The determination module 72 is used to determine the carbon emissions of the demand-side user cluster participating in the carbon market based on the electricity load data and the preset carbon quota of the demand-side user cluster, and to determine the carbon reward and penalty value of the demand-side user cluster based on the carbon emissions of participating in the carbon market. The calculation module 73 is used to calculate the carbon potential of each node in the microgrid, determine the carbon unit price based on the carbon reward and penalty value and the carbon potential, and determine the carbon responsibility factor of the demand-side users based on the carbon unit price. Decision module 74 is used to establish a microgrid decision model based on carbon unit price with the benefit of microgrid as the objective, establish a demand-side decision model with the carbon benefit and satisfaction of demand-side electricity purchase as the objective, and solve the decision model with carbon responsibility factor as constraint to obtain the microgrid optimal scheduling scheme.

[0070] In one possible implementation, the determining module 72 is used for: Determine the overall carbon emissions of the demand-side user cluster based on electricity load data; The carbon emissions of demand-side user clusters participating in the carbon market are determined based on the difference between total carbon emissions and preset carbon allowances.

[0071] In one possible implementation, the determining module 72 is used for: When carbon emissions from participating in the carbon market are less than zero, a carbon reward / penalty value is determined as the reward value, and the size of the reward value is negatively correlated with the carbon emissions from participating in the carbon market. When carbon emissions from participating in the carbon market are greater than zero, a carbon reward / penalty value is determined as a penalty value, and the magnitude of the penalty value is positively correlated with the carbon emissions from participating in the carbon market.

[0072] In one possible implementation, the computation module 73 is used for: according to Determine the carbon unit price; in, For users The price per unit of carbon at time t, Carbon reward / penalty value, For preset coefficients, Preset carbon quotas for demand-side user clusters For the overall carbon emissions of the demand-side user cluster, Let t be the carbon potential at node J at time t.

[0073] In one possible implementation, the computation module 73 is used for: according to Determine the carbon responsibility factor; in, For user i's carbon responsibility factor, For users The electrical load consumed at time t, Preset carbon quotas for demand-side user clusters For users The price per unit of carbon at time t, For users The electrical load purchased at time t, where I is the user set, T is the time period, and k is a preset coefficient.

[0074] In one possible implementation, the decision module 74 is used for: Establish a microgrid decision model for ; Establish a demand-side decision-making model for ; in, The target value of the microgrid decision model is... F user This is the target value for the demand-side decision-making model. For users The electrical load consumed at time t, For the various costs of microgrids, For users The electrical load purchased at time t Here, I represents the user satisfaction index, and T represents the time period.

[0075] In one possible implementation, the decision module 74 is used for: Establish constraints that include carbon responsibility factors, and solve the microgrid decision model and demand-side decision model using a game theory algorithm based on these constraints. Carbon responsibility factor constraint ;in, For user i's carbon responsibility factor, , These are the minimum and maximum limits for the carbon responsibility factor.

[0076] In one possible implementation, the constraints also include: microgrid power balance constraints, output constraints of each power generation device, and demand-side load constraints.

[0077] In this embodiment of the invention, firstly, based on the electricity load data and the preset carbon quota of the demand-side user cluster, the carbon emissions of the demand-side user cluster participating in the carbon market are determined, and the carbon reward and penalty value of the demand-side user cluster is determined based on the carbon emissions from participating in the carbon market. This step establishes a microgrid carbon reward and penalty calculation model that considers carbon emission guidance, forming a carbon emission benchmark for the demand-side cluster. Next, the carbon potential of each node in the microgrid is calculated, and the carbon unit price is determined based on the carbon reward and penalty value and the carbon potential. The carbon responsibility factor for demand-side users is then determined based on the carbon unit price. This step introduces the carbon potential signal as a guiding medium to establish a carbon emission responsibility guidance strategy for demand-side users within the cluster, forming a carbon unit price for demand-side electricity consumption behavior to guide rational energy use by the demand side. Finally, based on the carbon unit price, a microgrid decision-making model is established with the microgrid's interests as the objective, and a demand-side decision-making model is established with the demand side's carbon purchase benefits and satisfaction as the objectives. The decision-making model is then solved with the carbon responsibility factor as a constraint to obtain an optimized microgrid scheduling scheme. This step, by establishing a microgrid-demand-side carbon emission interaction model and introducing a carbon emission guidance scheme, minimizes carbon emissions within the system and ensures demand-side satisfaction.

[0078] Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 8 As shown, the electronic device 8 of this embodiment includes a processor 80 and a memory 81. The memory 81 stores a computer program 82. When the processor 80 executes the computer program 82, it implements the steps in the various method embodiments described above. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module in the various device embodiments described above.

[0079] For example, computer program 82 may be divided into one or more modules / units, which are stored in memory 81 and executed by processor 80 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 82 in electronic device 8.

[0080] Electronic device 8 may include, but is not limited to, processor 80 and memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of electronic device 8 and does not constitute a limitation on electronic device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 8 may also include input / output devices, network access devices, buses, etc.

[0081] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0082] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0083] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A microgrid optimal scheduling method considering carbon emission orientation, characterized in that, include: Obtain electricity load data from demand-side users; Based on the electricity load data and the preset carbon quota of the demand-side user cluster, the carbon emissions of the demand-side user cluster participating in the carbon market are determined, and the carbon reward / penalty value of the demand-side user cluster is determined based on the carbon emissions of the carbon market participation. Calculate the carbon potential of each node in the microgrid, determine the carbon unit price based on the carbon reward / penalty value and the carbon potential, and determine the carbon responsibility factor of demand-side users based on the carbon unit price. Based on the carbon unit price, a microgrid decision-making model is established with the benefit of the microgrid as the objective, and a demand-side decision-making model is established with the carbon benefit and satisfaction of the demand side as the objective. The decision-making model is then solved with the carbon responsibility factor as a constraint to obtain an optimal microgrid scheduling scheme.

2. The microgrid optimization scheduling method considering carbon emission orientation according to claim 1, characterized in that, The step of determining the carbon emissions of the demand-side user cluster participating in the carbon market based on the electricity load data and the preset carbon quota of the demand-side user cluster includes: The overall carbon emissions of the demand-side user cluster are determined based on the aforementioned electricity load data. The carbon emissions of the demand-side user cluster participating in the carbon market are determined based on the difference between the total carbon emissions and the preset carbon quota.

3. The microgrid optimization scheduling method considering carbon emission orientation according to claim 1, characterized in that, The step of determining the carbon reward / penalty value for the demand-side user cluster based on the carbon emissions of those participating in the carbon market includes: When the carbon emissions from participating in the carbon market are less than zero, the carbon reward / penalty value is determined to be a reward value, and the magnitude of the reward value is negatively correlated with the carbon emissions from participating in the carbon market. When the carbon emissions from participating in the carbon market are greater than zero, the carbon reward / penalty value is determined to be a penalty value, and the magnitude of the penalty value is positively correlated with the carbon emissions from participating in the carbon market.

4. The microgrid optimization scheduling method considering carbon emission orientation according to claim 1, characterized in that, The step of determining the carbon unit price based on the carbon reward / penalty value and the carbon potential includes: according to Determine the carbon unit price; in, For users The price per unit of carbon at time t, Carbon reward / penalty value, For preset coefficients, Preset carbon quotas for demand-side user clusters For the overall carbon emissions of the demand-side user cluster, Let J be the carbon potential at time t.

5. The microgrid optimization scheduling method considering carbon emission orientation according to claim 1, characterized in that, The process of determining the carbon responsibility factor for demand-side users based on the carbon unit price includes: according to Determine the carbon responsibility factor; in, For user i's carbon responsibility factor, For users The electrical load consumed at time t, Preset carbon quotas for demand-side user clusters For users The price per unit of carbon at time t, For users The electrical load purchased at time t, where I is the user set, T is the time period, and k is a preset coefficient.

6. The microgrid optimal scheduling method considering carbon emission orientation according to any one of claims 1 to 5, characterized in that, The aforementioned microgrid decision-making model, which aims to achieve microgrid benefits, and the demand-side decision-making model, which aims to achieve demand-side carbon emissions from electricity purchases and customer satisfaction, include: Establish a microgrid decision model for ; Establish a demand-side decision-making model for ; in, The target value of the microgrid decision model. F user This is the target value for the demand-side decision-making model. For users The electrical load consumed at time t, For the various costs of microgrids, For users The electrical load purchased at time t, Here, I represents the user satisfaction index, and T represents the time period.

7. The microgrid optimization scheduling method considering carbon emission orientation according to claim 6, characterized in that, The decision-making model, which uses the carbon responsibility factor as a constraint, includes: Establish constraints that include carbon responsibility factors, and solve the microgrid decision model and the demand-side decision model using a game theory algorithm based on the constraints. The carbon responsibility factor constraint is ;in, For user i's carbon responsibility factor, , These are the minimum and maximum limits for the carbon responsibility factor.

8. The microgrid optimal scheduling method considering carbon emission orientation according to claim 7, characterized in that, The constraints also include: microgrid power balance constraints, output constraints of each power generation device, and demand-side load constraints.

9. A microgrid optimization dispatching device considering carbon emission orientation, characterized in that, include: The acquisition module is used to acquire the electrical load data of users on the demand side; The determination module is used to determine the carbon emissions of the demand-side user cluster participating in the carbon market based on the electricity load data and the preset carbon quota of the demand-side user cluster, and to determine the carbon reward / penalty value of the demand-side user cluster based on the carbon emissions of the carbon market participation. The calculation module is used to calculate the carbon potential of each node in the microgrid, determine the carbon unit price based on the carbon reward / penalty value and the carbon potential, and determine the carbon responsibility factor of the demand-side users based on the carbon unit price. The decision module is used to establish a microgrid decision model with the benefit of the microgrid as the objective, based on the carbon unit price, to establish a demand-side decision model with the carbon benefit and satisfaction of the demand side as the objective, and to solve the decision model with the carbon responsibility factor as the constraint to obtain the microgrid optimal scheduling scheme.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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