Optical storage building intraday optimization method, device and equipment considering carbon emission sensitivity

Abstract

The invention provides a light storage building intraday optimization method, device and equipment considering carbon emission sensitivity, and relates to the technical field of energy scheduling. The method comprises the following steps: establishing a comprehensive model comprising photovoltaic power generation, light storage building load, an energy storage device and carbon emission; on the basis of the comprehensive model, performing intra-day optimization solution by taking the minimization of the operation cost and the minimization of the total carbon emission as double targets, and taking a solution result as a reference solution; calculating a global carbon emission reduction elastic coefficient, a node carbon emission reduction cooperation index and a local carbon emission marginal substitution rate according to the reference solution; and according to the global carbon emission reduction elastic coefficient, the node carbon emission reduction cooperation index and the local carbon emission marginal substitution rate, performing intra-day optimization solution by taking the operation cost minimization and the total carbon emission minimization as double targets again, and obtaining an intra-day optimization scheme of the optical storage building. According to the method, the final solution with higher robustness and interpretability can be obtained, so that low-carbon, efficient, economical and safe cooperative operation of the power distribution network can be realized.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to a method, apparatus and equipment for intraday optimization of photovoltaic-storage buildings that takes into account carbon emission sensitivity. Background Technology

[0002] With the advancement of global energy structure transformation, the construction of low-carbon and clean energy systems has become an important development direction for the power industry. As a representative of clean and renewable energy, photovoltaic power generation is widely used in buildings, industrial parks, and power distribution networks due to its advantages such as cleanliness, renewability, and high technological maturity.

[0003] However, photovoltaic (PV) power generation is characterized by significant intermittency and fluctuations. Its output is greatly affected by factors such as sunlight intensity and weather conditions, making it difficult to perfectly match with electricity load. This results in low PV energy utilization and increased pressure on distribution network operation and dispatch. To alleviate the contradiction between PV output and electricity demand, integrated PV-energy storage technology has gradually gained attention. By configuring energy storage devices in PV systems, excess power can be stored, and released during peak load periods or when PV output is insufficient, achieving peak shaving and valley filling, and time-shifting of electricity, effectively improving the capacity for renewable energy absorption and power supply reliability. Simultaneously, introducing adjustable time-shifted loads on the building side allows for flexible adjustments to electricity consumption plans within a certain time frame, further optimizing the energy structure and improving interaction with the grid.

[0004] However, existing optimization methods for photovoltaic-storage buildings primarily focus on economic efficiency, neglecting the significant impact of carbon emission constraints on system dispatch. In traditional dispatch models, power purchase and sale strategies in distribution networks are mainly based on electricity price signals or operating costs, lacking dynamic assessment and response to differences in carbon emissions from power sources, making it difficult to balance economic efficiency with decarbonization goals. Furthermore, the carbon emission intensity in the power system exhibits significant uncertainty, especially with a high proportion of renewable energy integration. Errors in renewable energy output prediction directly transmit to the carbon emission calculation process, increasing the complexity and uncertainty of dispatch optimization. In recent years, carbon emission flow analysis methods have been increasingly applied to power system carbon emission assessment. By establishing the carbon emission flow relationship between power sources and load nodes, the contribution of different power sources and transmission paths to the node's carbon emission intensity can be accurately quantified. This method provides a technical foundation for introducing carbon emission constraints and carbon sensitivity analysis into dispatch optimization. However, most current research remains at the static calculation level of carbon emissions, lacking deep integration with economic dispatch models, and failing to form a coordinated optimization mechanism that balances economic benefits and carbon reduction goals. On the other hand, while multi-objective optimization techniques have been extensively studied in energy system dispatching, in the context of photovoltaic-storage buildings, due to the diverse equipment types, complex operational constraints, and the need to simultaneously consider factors such as power flow safety, voltage stability, and equipment lifespan, traditional multi-objective optimization methods suffer from shortcomings in terms of solution uniformity, convergence, and computational efficiency. This bottleneck is particularly pronounced in scenarios requiring real-time or frequent intraday optimization. Furthermore, the lack of quantitative sensitivity analysis indicators to guide dispatching decisions regarding the trade-off between carbon emissions and operating costs hinders the widespread application of low-carbon operation strategies in practical engineering projects.

[0005] Therefore, there is an urgent need for an intraday optimization method for photovoltaic-storage buildings that can comprehensively consider operational economics and carbon emission impacts, introduce carbon emission uncertainty descriptions, and have carbon emission sensitivity analysis capabilities, so as to achieve low-carbon, efficient, economical, and safe coordinated operation of the distribution network. Summary of the Invention

[0006] This invention provides a method, apparatus, and equipment for intraday optimization of photovoltaic-storage buildings that takes into account carbon emission sensitivity, in order to solve the problem of achieving low-carbon, efficient, economical, and safe coordinated operation of power distribution networks.

[0007] In a first aspect, embodiments of the present invention provide a method for intraday optimization of photovoltaic-storage buildings that considers carbon emission sensitivity, comprising: Establish a comprehensive model that includes photovoltaic power generation, photovoltaic-storage building load, energy storage devices, and carbon emissions; Based on the comprehensive model, intraday optimization is performed with the dual objectives of minimizing operating costs and minimizing total carbon emissions, and the solution is used as a reference solution. Based on the reference solution, calculate the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate; Based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate, the intraday optimization solution is re-solved with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain the intraday optimization scheme for photovoltaic-storage buildings.

[0008] Secondly, embodiments of the present invention provide a photovoltaic-storage building daytime optimization device that considers carbon emission sensitivity, comprising: The modeling module is used to create a comprehensive model that includes photovoltaic power generation, photovoltaic-storage building load, energy storage devices, and carbon emissions. The first dual-objective optimization solution module is used to perform intraday optimization based on the comprehensive model, with the dual objectives of minimizing operating costs and minimizing total carbon emissions, and to use the solution results as a reference solution. The carbon emission index assessment module is used to calculate the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate based on the reference solution. The second dual-objective optimization solution module is used to perform intraday optimization solution with the dual objectives of minimizing operating costs and minimizing total carbon emissions based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index and the local carbon emission marginal substitution rate, so as to obtain the intraday optimization scheme of the photovoltaic-storage building.

[0009] 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.

[0010] In this embodiment of the invention, a comprehensive model including photovoltaic power generation, photovoltaic-storage building load, energy storage device, and carbon emissions is first established. Then, based on the comprehensive model, intraday optimization is performed with the dual objectives of minimizing operating costs and minimizing total carbon emissions. The solution is used as a reference solution. Then, based on the reference solution, the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate are calculated. Finally, based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate, intraday optimization is performed again with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain the intraday optimization scheme for photovoltaic-storage buildings. Thus, by introducing the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate to the reference solution for secondary correction and optimization, a more robust and interpretable final solution is obtained, which helps to achieve low-carbon, efficient, economical, and safe coordinated operation of the distribution network. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the implementation of the intraday optimization method for photovoltaic-storage buildings that considers carbon emission sensitivity, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the photovoltaic-storage building daytime optimization device considering carbon emission sensitivity provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0012] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0013] In the field of power distribution network and building energy system operation optimization, the combination of photovoltaic power generation and energy storage technology has become an important way to improve the utilization rate of renewable energy and enhance power supply reliability. Integrated photovoltaic-energy storage systems, by configuring energy storage devices on the photovoltaic power generation side, can achieve time-based energy transfer. That is, when photovoltaic power generation exceeds load demand, surplus energy is stored and released during periods of insufficient power generation or peak demand, thus alleviating the imbalance between power supply and demand and reducing the pressure on the power grid for peak regulation. This model has broad application prospects in buildings, industrial parks, microgrids, and other scenarios.

[0014] In existing technologies, the optimization of photovoltaic (PV) and energy storage (ESS) systems primarily focuses on economic objectives, typically using the minimization of operating costs as the optimization criterion. Typical methods include PV power generation models considering factors such as irradiance, temperature, and module efficiency, while energy storage models address charge / discharge efficiency, capacity limitations, and state-of-charge constraints. Adjustable load models simulate the building-side power consumption adjustment capabilities. These models, combined with optimization methods such as linear programming and mixed-integer linear programming, can generate optimal scheduling schemes for a given period, given predicted PV output, load forecast data, and electricity price information. However, research on carbon emission constraints in the optimal scheduling of PV-ESS buildings is relatively limited. Some studies have proposed introducing carbon emission costs into economic scheduling, estimating carbon emissions from different power sources and electricity purchasing behaviors using carbon emission factors, converting these emissions into economic costs, and incorporating them into the optimization objective along with operating costs. However, these methods are mostly based on fixed carbon emission factors, neglecting the dynamic changes in carbon emission intensity over time and space. Especially in grids with a high proportion of renewable energy integration, carbon emission intensity is significantly affected by fluctuations in renewable energy output, and its uncertainty has a considerable impact on the optimization results. In recent years, carbon emission flow analysis methods have been increasingly applied to power systems. By establishing the carbon emission flow relationship between power sources and load nodes, carbon emission intensity can be quantified down to the node level. This method can be combined with power flow analysis to achieve the allocation and tracking of carbon emissions in power transmission paths. However, existing research mostly treats carbon emission flow analysis as a standalone assessment tool, without deep integration with intraday optimal scheduling, and lacks an index system based on carbon emission sensitivity to guide the dynamic trade-off between carbon reduction and economic efficiency. Regarding multi-objective optimization methods, existing techniques include weighted sum methods, constraint methods, genetic algorithms, particle swarm optimization, and normal boundary cross methods. Among these, the weighted sum method has a simple structure but uneven solution distribution; intelligent optimization methods such as genetic algorithms and particle swarm optimization can handle complex nonlinear problems, but they are computationally intensive and have slow convergence speeds, making it difficult to meet the real-time requirements of frequent intraday optimization; the NBI method may converge to local optima in some problems, affecting the globality of the solution.

[0015] In summary, existing technologies have formed a certain technical system for the operation optimization of photovoltaic and energy storage buildings, including joint scheduling models for photovoltaics and energy storage, adjustable load modeling methods, multi-objective optimization algorithms, and some carbon emission assessment methods. However, the following shortcomings still exist: Most methods focus on economic optimization, incorporating carbon emission factors only as additional costs, lacking accurate modeling and uncertainty description of dynamic carbon emission intensity. While carbon emission flow analysis can accurately track emission distribution, its integration in scheduling optimization is insufficient, failing to form a closed-loop optimization mechanism that coordinates low carbon emissions with economic efficiency. Multi-objective optimization methods still fall short in generating uniform Pareto solutions and improving computational efficiency, making them difficult to directly apply to intraday optimization scenarios requiring high-frequency scheduling. Carbon emission sensitivity analysis methods have not yet been systematically embedded into the operational optimization framework of photovoltaic-storage buildings, and cannot guide the dynamic adjustment of operational strategies in real time.

[0016] Therefore, it is still necessary to propose a photovoltaic-storage building intraday optimization technology that can comprehensively consider economic efficiency and carbon emissions, introduce the uncertainty of carbon emission intensity, combine sensitivity analysis indicators, and adopt an efficient uniform solution generation method to make up for the shortcomings of existing technologies and meet the needs of low-carbon economic operation.

[0017] See Figure 1 The document illustrates a flowchart of the intraday optimization method for photovoltaic-storage buildings that considers carbon emission sensitivity, as provided in an embodiment of the present invention, and details are as follows: Step 101: Establish a comprehensive model that includes photovoltaic power generation, photovoltaic-storage building load, energy storage devices, and carbon emissions.

[0018] Among them, the photovoltaic power generation model: The output of photovoltaic (PV) power generation is significantly affected by various environmental factors, including sunlight intensity, operating mode, and environmental conditions. Given the physical parameters of the PV array components, these factors collectively determine the output characteristics of the PV power generation equipment. These output characteristics can be expressed as: ; In the formula: This represents the actual light intensity radiated onto the surface of the photovoltaic array at time t. Under standard test conditions (usually) The ratio of the light intensity to the standard light intensity reflects the strength of the current lighting conditions relative to the standard lighting conditions. Indicates the rated power of photovoltaic power generation equipment. This represents the output power of the photovoltaic power generation equipment at time t; This is the derating factor for photovoltaic power generation equipment, used to indicate the degree of reduction in actual output relative to rated output; This indicates the operating temperature of photovoltaic power generation equipment under standard conditions. This represents the operating temperature of the photovoltaic power generation equipment at time t, in units of... .

[0019] Photovoltaic-storage building load model: The load side of photovoltaic-storage buildings contains numerous time-shiftable loads that can interact amicably with the power grid. These loads possess a certain degree of flexibility, allowing for adjustment or transfer within a specific timeframe to meet system operational requirements. Their model can be represented as follows: ; ; In the formula: This indicates that the time-shiftable load has adjustable power at time t; Indicates in Total electricity demand of time-shiftable loads within a given time period; This represents the lower limit of the adjustable power of the time-shiftable load at time t. This represents the upper limit of the adjustable power of the time-shiftable load at time t.

[0020] The control cost of a time-shiftable load at time t can be expressed as: ; In the formula: This represents the control cost of the time-shiftable load at time t; This represents the compensation coefficient for time-shiftable loads, in units of... ; This indicates the original power consumption plan for loads that can be shifted over time.

[0021] The above four formulas contain absolute values, which are nonlinear functions that have undergone linearization by introducing two non-negative auxiliary variables. , Consider this as a linear programming problem: ; And the following constraints must be met: ; Energy storage model: Energy storage devices can absorb excess electrical energy when there is overcapacity and release electrical energy when the power supply cannot meet the load demand, thus playing a role in "peak shaving and valley filling." Ignoring the self-discharge effect of the energy storage device, the stored energy during the charging and discharging process can be expressed as: ; In the formula: This represents the amount of electricity stored by the energy storage device at time t. This represents the amount of electricity stored by the energy storage device at the initial moment, in units of... ; This represents the charging power of the energy storage device at time t. These represent the discharge power of the energy storage device at time t; This represents the charging efficiency coefficient of energy storage devices. This represents the discharge efficiency coefficient of the energy storage device; For time intervals.

[0022] To avoid overcharging and over-discharging during the operation of energy storage devices, the following constraints must be met: (1) Charge / discharge state constraints: To prevent energy storage devices from charging and discharging simultaneously, charging / discharging state logic constraints must be met. At the same time, to extend the lifespan of energy storage devices and improve their economic efficiency, the number of charging / discharging state transitions also needs to be limited.

[0023] ; In the formula: This indicates the charging state of the energy storage device at time t. This represents the discharge state of the energy storage device at time t. Both variables are 0-1 variables. (The last two characters, "t", appear to be 0-1 and are likely part of a larger sentence.) =1, otherwise The value is 1.

[0024] ; In the formula: This indicates the upper limit of the number of charge / discharge state transitions.

[0025] The charge / discharge state constraints contain absolute values, which are linearized by introducing auxiliary variables, as shown in the following equation: ; The original constraints can then be transformed into: ; (2) Charging and discharging power constraints: ; In the formula: The upper limit of charging power for energy storage devices. This represents the upper limit of the discharge power of the energy storage device.

[0026] (3) Charge state constraints: To prevent energy storage devices from being overcharged or over-discharged, state of charge constraints must be met: ; In the formula: This refers to the upper limit of the amount of electricity that an energy storage device can store. This represents the lower limit of the amount of electricity that an energy storage device can store.

[0027] To ensure the stability and efficiency of energy storage devices during the cyclic control process, it is generally desirable for the remaining capacity of the energy storage device to be equal at the beginning and end of the control process. ; In the formula: This indicates the control period, which is 24 hours in day-ahead control.

[0028] The charging and discharging cost of energy storage devices at time t It can be represented as: ; In the formula: This indicates the cost coefficient of energy storage equipment.

[0029] Carbon emission analysis and modeling: Due to the intermittent and fluctuating nature of renewable energy generation, balancing the supply and demand of the power system becomes more difficult, further exacerbating the uncertainty of carbon emission intensity. Therefore, considering the uncertainty of carbon emission intensity is particularly important, and it is necessary to conduct relevant optimization operation research to ensure system stability. To facilitate the quantitative analysis of the distribution characteristics and mechanisms of energy networks in the power system, this paper uses the carbon emission flow method to analyze the carbon emissions of the power system.

[0030] (1) Carbon emission intensity model Carbon emission intensity is an indicator that measures the amount of carbon emissions transferred per unit of electricity exchanged. It reflects the carbon emission efficiency of a power system during power generation. The calculation of carbon emission intensity is typically based on factors such as the power generation structure of the power system, the carbon emission factors of various power sources, and the amount of electricity generated. The specific calculation method is as follows: ; In the formula: This represents the carbon emission intensity of a photovoltaic-storage building's grid-connected node, in units of... ; This represents the carbon flow rate from the branch to this node, in units of... ; This represents the active power flowing to this node (mainly considering that carbon emissions in the power system are primarily related to active power flow). This represents the grid loss rate of electricity supplied to the grid from the generation point to the grid connection point in fossil fuel energy. This indicates the grid loss rate of clean energy supplied to the grid from the generation point to the grid connection point; This indicates the mass of fossil fuels consumed to supply one unit of electricity to the power grid. It represents the amount of carbon emissions produced per unit mass of fossil fuel consumption.

[0031] (2) Uncertainty analysis of carbon emissions Due to the randomness and volatility of renewable energy sources such as photovoltaics, the composition of electricity sources in photovoltaic-storage buildings exhibits a certain degree of uncertainty. The carbon emission intensity calculation formula reveals that the predicted photovoltaic power... The volatility and randomness of carbon emissions make carbon emission intensity an uncertain parameter, meaning that carbon emission intensity inherits the uncertainty characteristics of the output of new energy sources such as photovoltaics.

[0032] Among various methods for representing uncertain sets, the polyhedral uncertain set has become the preferred method due to its unique linear structure and ease of uncertainty control. Its linear properties make model solving more efficient and better able to handle real-world uncertainties. Specifically, it can be expressed as: ; In the formula: Represents an uncertain set of carbon emission intensity; This represents the predicted carbon emission intensity of the power system at time t; This represents the deviation between the predicted and actual values ​​of electricity carbon emission intensity at time t; An uncertain adjustment parameter representing the intensity of carbon emissions from electricity.

[0033] (3) Carbon emission cost model This embodiment employs an initial carbon emission allowance allocation method based on power generation capacity. This method determines the initial carbon emission allowance for each power generation unit through a specific mathematical expression. The expression for the initial carbon emission allowance is as follows: ; In the formula: This represents the initial carbon emission allowance for the distribution network at time t; This represents the emission allocation per unit of electricity, in units of... ; This represents the carbon emission allocation per unit heat output of a micro gas turbine. This represents the electrical power output of the micro gas turbine at time t.

[0034] The carbon emissions of a power distribution network can be calculated as follows: ; In the formula: This represents the carbon emissions of the power distribution network at time t; =0.6101 represents the carbon emission coefficient of a micro gas turbine.

[0035] In summary, the carbon trading cost of the distribution network at time t is: ; In the formula: Let be the carbon trading cost of the distribution network at time t; The carbon trading price is taken here. .

[0036] Step 102: Based on the integrated model, perform intraday optimization with the dual objectives of minimizing operating costs and minimizing total carbon emissions, and use the solution as a reference solution.

[0037] In this embodiment, when performing intraday optimization of the photovoltaic-storage building, the optimization objective is to minimize the operating cost of the distribution network and carbon emissions. The output of the micro gas turbine, the energy storage charging and discharging power, and the power purchase and sale of the distribution network from the current time to 24:00 are calculated. For example, this can be performed once every 15 minutes.

[0038] The objectives of power distribution network operation include operating costs and total carbon emissions. Operating costs include fuel costs. Operation and maintenance costs Electricity transaction costs with the power grid Defined as: ; ; ; ; Carbon emissions of micro gas turbines It can be represented as: ; In the formula: Indicates the first The carbon emission coefficient of a micro gas turbine; express Time of the first The output electrical power of a micro gas turbine.

[0039] Electricity of the main power grid : .

[0040] In the formula: This represents the carbon emission coefficient of electricity purchased from the main grid. This indicates the amount of electricity purchased on the main network.

[0041] The total carbon emissions of the system can be expressed as: ; ; In the formula: This indicates the price of natural gas, in units of... ; Indicates the first The efficiency of a miniature gas turbine; This indicates the rated power of the diesel generator; Indicates the first The operating and maintenance costs of a photovoltaic power generation unit; Indicates the first Taiwanese photovoltaic units Always put in the effort; Indicates the first The operating and maintenance costs of the energy storage equipment; and They represent the first Taiwan energy storage equipment Constant charging and discharging power; This indicates the price of electricity purchased from the main grid; This indicates the control cycle, which is 2 hours. Daily optimization is performed every 15 minutes.

[0042] Constraints: (1) Equipment constraints The constraints of the equipment include: ; ; ; ; ; ; In the formula: This indicates the predicted photovoltaic output; This indicates the upper limit of the output of a micro gas turbine; This indicates the ramp output limit of the micro gas turbine; This indicates the maximum output capacity of the diesel generator; Indicates the climbing power limit of the diesel generator; Indicates energy storage devices The charging and discharging state is variable; and Indicates energy storage devices Charge and discharge efficiency; and These represent energy storage devices. exist Energy stored at time 1 and the initial time; and Indicates energy storage devices Upper and lower limits of stored energy.

[0043] Of the constraints on the aforementioned equipment, the second defines the power limit of photovoltaic power generation. The third defines the power constraints and ramping constraints of the micro gas turbine. The fourth defines the charge / discharge state limits of the battery. The fifth defines the charge / discharge power constraints of the energy storage device. The sixth defines the energy equation and energy level limits of the energy storage device.

[0044] (2) Current constraints Current constraints include: ; ; ; Furthermore, the power balance constraint at each bus is defined as follows: ; In the formula: and Representing branches exist Active power and reactive power at any given time; and Indicates load exist Active power and reactive power at any given time; Indicates the magnitude of the reference voltage; Represents a node The voltage amplitude; and Represents a node Upper and lower limits of voltage amplitude; Indicates a branch Upper limit of active power; and This represents the resistance and reactance of branch b.

[0045] In the above power flow constraints, the second term defines the branch power flow model based on the linearized model. The third term defines the bus voltage magnitude and branch power flow constraints.

[0046] Based on the above model, an optimization process for coordinated control of operating costs and carbon emissions of photovoltaic-storage buildings in power distribution networks is proposed. In the decision-making stage, the normalized normal constraint (NNC) method is used to solve the bi-objective optimization model, obtaining a uniformly distributed Pareto front, thus comprehensively revealing the relationship between operating costs and carbon emissions of the power distribution network. Then, the TOPSIS method is used to select the final operating point from the Pareto front as the reference solution. In the sensitivity analysis stage, three indicators are proposed to calculate the sensitivity to global and local carbon emission reduction.

[0047] Among them, genetic algorithms, particle swarm optimization, and weighted sum methods can be used to handle multi-objective optimization problems. Constraint methods, normal boundary crossing methods, and normalized normal constraint methods are among the methods used. Genetic algorithms and particle swarm optimization (PSO) are not only time-consuming to solve large-scale optimization problems, but they also cannot guarantee the convergence of Pareto optimal solutions. Weighted sum methods cannot guarantee Pareto optimal solutions in some non-convex cases, and the generated Pareto front is generally not uniform. Normal boundary crossing methods are prone to getting trapped in local optima. Compared to other multi-objective optimization methods, the NNC method can generate a more uniformly distributed Pareto solution set.

[0048] When only a single goal is considered At that time, assuming This is the optimal solution. The steps for obtaining the Pareto front using the NNC method are as follows: Step 1: Calculate the extreme points of the Pareto front. For the bi-objective optimization problem, solve each single-objective optimization problem separately to obtain the two extreme points of the Pareto front. and .

[0049] Step 2: Objective function normalization: Assume Define the Pareto optimum for the standardized objective function. , and The optimal point of Pareto is as follows: and The distance. Then , Therefore, the normalized objective function can be expressed as: ; Step 3: Determine the Utopia Line The direction.

[0050] Step 4: Calculate the normalized increment ,in It is the number of solutions on the Utopia Line.

[0051] Step 5: Generate uniformly distributed points on the ideal line. For ,set up By setting It can generate points that are evenly distributed along an ideal line.

[0052] Step 6: Generate normalized Pareto points: For each uniformly distributed point on the ideal line, the normalized Pareto points can be obtained by solving the following mixed integer linear programming (MILP) problem: ; Step 7: Calculate the original objective function: For each normalized Pareto point, calculate the original objective function value based on the inverse transform. .

[0053] After obtaining the Pareto front, a comprehensive evaluation method is needed to select a reference solution from the Pareto optimal set, such as the entropy method, the weighted average method, and the TOPSIS method. The TOPSIS method has a rigorous logical structure and a simple calculation process, where all points are ranked according to their proximity to the optimal solution. The point with the highest TOPSIS score is considered optimal because it has the smallest relative distance to the optimal solution. Furthermore, the TOPSIS method does not require pre-determining any subjective parameters, making it convenient to use. Therefore, the TOPSIS method is chosen to select the final reference solution.

[0054] Step 103: Based on the reference solution, calculate the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate.

[0055] In one embodiment, calculating the global carbon reduction elasticity coefficient based on the reference solution includes: Obtain the reference solution and the corresponding operating costs and total carbon emissions.

[0056] By applying a perturbation to the reference solution, we can obtain the changes in operating costs and total carbon emissions after applying the perturbation.

[0057] Calculate the ratio of the change in operating costs to the total operating costs to obtain the relative rate of change in operating costs, and calculate the ratio of the change in total carbon emissions to the total carbon emissions to obtain the relative rate of change in carbon emissions.

[0058] The ratio of the relative change rate of operating costs to the relative change rate of carbon emissions is calculated to obtain the global carbon emission reduction elasticity coefficient.

[0059] In one embodiment, calculating the node carbon reduction synergy index based on the reference solution includes: Based on the reference solution, the local carbon emission reduction of each node in the photovoltaic-storage building is obtained, and local carbon emission reduction constraints are applied to each node in the photovoltaic-storage building to obtain the change in the total carbon emissions corresponding to each node after the application of local carbon emission reduction constraints.

[0060] Calculate the ratio of the change in total carbon emissions at each node to the carbon reduction at the corresponding node to obtain the node carbon reduction synergy index for each node.

[0061] In one embodiment, calculating the local carbon emission marginal substitution rate based on the reference solution includes: Based on the reference solution, while keeping the operating cost constraint constant, local emission reduction is simulated for each node in the photovoltaic-storage building to obtain the local carbon emission reduction of each node, and the changes in carbon emission reduction of other nodes are observed.

[0062] Calculate the ratio of the change in carbon emission reduction at other nodes to the local carbon emission reduction at each node to obtain the local marginal substitution rate of carbon emissions at each node.

[0063] Building upon the aforementioned intraday optimization method, this invention introduces three indicators: the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate, systematically improving the optimization process. Through the organic combination of these three types of indicators, this invention achieves multi-level integrated control encompassing global adaptive constraints, local allocation optimization, and global consistency verification, ensuring superior performance in terms of economy, emission reduction intensity, and global coordination.

[0064] Among them, the Global Carbon Reduction Elasticity (GCRE) is: GCRE is an indicator reflecting the sensitivity of the relative change in operating costs to carbon emissions. It examines the ratio of the relative rate of change in costs to the relative rate of change in emissions, revealing the economic elasticity of the system for further emissions reductions—that is, what percentage of additional costs are required to further reduce carbon emissions by a certain proportion. A low GCRE value indicates that the system can achieve additional emissions reductions at a lower cost, possessing strong potential for low-cost carbon reduction. A high GCRE value indicates that further carbon reductions will lead to a relatively rapid increase in operating costs, suggesting that optimization models should avoid overly tightening carbon targets.

[0065] The introduction of this indicator frees the measurement of carbon emission reduction benefits from the limitations of system scale and absolute values, enabling direct comparison of emission reduction costs under different scenarios or systems. GCRE not only reveals the marginal economic rationality of continuing to reduce carbon emissions, but also provides a scientific basis for dynamically adjusting global carbon constraints. When GCRE is in a low range, it indicates that further carbon reduction still has strong cost-effectiveness; when its value rises rapidly, it suggests that excessive emission reduction may bring economic burdens, thus providing a reasonable boundary for system operation.

[0066] Calculation method: Assuming at a certain operating point, the operating cost is C and the total carbon emissions are E, when carbon emissions decrease by ΔE while costs increase by ΔC, GCRE is defined as follows: ; This indicates the relative rate of change in operating costs.

[0067] This indicates the relative rate of change in carbon emissions.

[0068] Numerically, GCRE corresponds to the "relative slope" of a point on the Pareto curve.

[0069] Local Carbon Synergy Index (LCSI): LCSI is used to measure the synergistic amplification effect of local carbon emissions reduction of a certain node or device on global carbon emissions reduction. Its core is to compare the proportional relationship between local emissions reduction and the change in global emissions, so as to reveal the role of this node in global emissions reduction. LCSI focuses on the spatial coupling relationship of emissions reduction effects, that is, when a device reduces emissions by 1 unit, the amount of carbon emissions that the system as a whole can reduce. If LCSI > 1, it means that local emissions reduction not only reduces the emissions of this device, but also drives the emissions reduction of other links in the system, with a synergistic effect, and should be considered first. If 0 < LCSI < 1, it means that local emissions reduction is partially offset, and global emissions reduction is less than the local emissions reduction amount, and the effect is limited. If LCSI < 0, it indicates that local emissions reduction leads to an increase in emissions of other parts of the system, showing an abnormal phenomenon of "local emissions reduction but global increase".

[0070] The introduction of LCSI enables the system to identify which nodes' emissions reduction behaviors can bring a "multiplier effect", that is, local emissions reduction drives a greater decline in the overall emissions of the system; it can also reveal which nodes' emissions reduction is easily offset by other links or even causes an emissions rebound. Through this indicator, optimal scheduling can preferentially select nodes with synergistic effects to implement carbon reduction, thereby significantly improving the cost-effectiveness of global emissions reduction.

[0071] Calculation method: Let the emissions reduction of the i-th node / device be , and the change in the total emissions of the system be , then LCSI is defined as: ; This ratio directly reflects the amplification or attenuation effect of local emissions reduction on global emissions reduction.

[0072] Marginal Carbon Substitution Ratio (MCSR): MCSR is an indicator used to examine whether local emission reduction measures are effective on a global scale. It more intuitively describes the proportion of local emission reduction offset by emissions from other nodes, representing the amount by which a 1-unit reduction in local emissions is replaced by an increase in emissions elsewhere. The local carbon emission marginal substitution rate is used to measure the true contribution of local emission reduction on a global scale, that is, how much of the locally reduced emissions will be offset by emissions from other devices or nodes in the system. It emphasizes the substitution and transfer effects rather than just the sensitivity relationship. If MCSR = 0, it means that local emission reduction is completely converted into global net emission reduction; if 0 < MCSR < 1, it means that local emission reduction is partially offset and the global emission reduction amount is less than the local emission reduction amount; if MCSR ≥ 1, it means that local emission reduction is completely offset or even leads to an increase in global emissions, and at this time the emission reduction measure is ineffective at the system level.

[0073] Calculation method: ; Here, the system operating cost constraint is kept unchanged, that is, under the condition of cost conservation, the emission transfer effect is measured.

[0074] In this embodiment, the three indicators respectively correspond to the continuous links of global diagnosis (GCRE) - local optimization (LCSI) - global verification (MCSR): GCRE is positioned at the global level to reveal the marginal economic rationality of further carbon reduction.

[0075] LCSI is positioned at the node level to help identify high-value local emission reduction locations.

[0076] MCSR is positioned at the system verification level to prevent conflicts between local and global goals.

[0077] The combination of the three forms a complete carbon emission sensitivity analysis and optimization decision-making system, which not only improves the scientificity and interpretability of the optimization method, but also ensures the economy, effectiveness and robustness of the operation results.

[0078] Step 104: According to the global carbon emission reduction elasticity coefficient, the node carbon emission reduction coordination index and the local carbon emission marginal substitution rate, perform intraday optimization and solution with the minimization of operating cost and the total carbon emission as the dual objectives again to obtain the intraday optimization plan for the photovoltaic-storage building.

[0079] In one embodiment, step 104 includes: Adjust the global carbon emission target according to the global carbon emission reduction elasticity coefficient, identify and rank the priority emission reduction nodes according to the node carbon emission reduction coordination index, and determine the risk nodes that may cause an increase in global carbon emissions according to the local carbon emission marginal substitution rate.

[0080] Based on the adjusted global carbon emission target, priority emission reduction nodes, and risk nodes, the intraday optimization solution is re-applied with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain the intraday optimization scheme for photovoltaic-storage buildings.

[0081] In one embodiment, adjusting the global carbon emission target based on the global carbon emission reduction elasticity coefficient includes: Determine whether the global carbon emission reduction elasticity coefficient is located in the first interval, the second interval, or the maximum value of the first interval is less than or equal to the minimum value of the second interval, and the maximum value of the second interval is less than or equal to the minimum value of the third interval.

[0082] If the global carbon emission reduction elasticity coefficient is in the first range, then the total carbon emission ceiling in the global carbon emission target will be lowered.

[0083] If the global carbon emission reduction elasticity coefficient is in the second range, then the global carbon emission target will remain unchanged.

[0084] If the global carbon emission reduction elasticity coefficient is in the third range, the upper limit of total carbon emissions in the global carbon emission target will be increased.

[0085] In one embodiment, identifying and ranking priority emission reduction nodes based on the node carbon emission reduction synergy index includes: The node carbon reduction synergy index of each node is sorted.

[0086] According to the ranking results, nodes with a carbon emission reduction synergy index greater than the first threshold are designated as first priority emission reduction nodes, nodes with a carbon emission reduction synergy index greater than the second threshold and less than or equal to the first threshold are designated as second priority emission reduction nodes, and nodes with a carbon emission reduction synergy index less than or equal to the second threshold are designated as non-priority emission reduction nodes. The second threshold is less than the first threshold.

[0087] In one embodiment, risk nodes that may trigger a reversal in global carbon emissions are identified based on the local marginal substitution rate of carbon emissions, including: The local carbon emission marginal substitution rate of each node is compared with the third threshold and the fourth threshold, respectively, where the fourth threshold is less than the third threshold.

[0088] If the local carbon emission marginal substitution rate of a node is greater than or equal to the third threshold, then the node is identified as a risk node that may trigger a reversal in global carbon emissions.

[0089] If the local carbon emission marginal substitution rate of a node is less than the third threshold but greater than the fourth threshold, then the node is identified as a restricted risk node, and an upper limit for the emission reduction of the node is set.

[0090] If the local carbon emission marginal substitution rate of a node is less than or equal to the fourth threshold, then the node is identified as a non-risk node.

[0091] In this embodiment, after the reference solution is calculated and the GCRE value is obtained, the computer enters the dynamic adjustment phase of the global target constraint. When the GCRE value is low (e.g., close to the first interval of 0-1), it means that the relative economic cost of unit carbon emission reduction is small, and the system still has the potential for low-cost emission reduction. At this time, the computer automatically tightens the global carbon emission target, specifically by lowering the allowable total carbon emission limit in the optimization model, so that the system approaches a lower carbon operating state. When the GCRE value is at a moderate level (e.g., in the second interval of 1-2), it indicates that the cost of carbon emission reduction has begun to rise but is still within a reasonable range. At this time, the computer maintains the existing global carbon constraint unchanged, keeping the operation in the current equilibrium state, and avoiding excessive pursuit of carbon reduction that leads to a rapid increase in costs. When the GCRE value is too high (e.g., greater than 2 or even higher in the third interval), it indicates that if emission reduction continues, it will lead to a significant increase in operating costs, and carbon reduction lacks economic rationality. At this time, the computer relaxes the global carbon emission target, that is, moderately increases the carbon emission limit, to avoid excessive carbon reduction leading to an unacceptable economic burden. Through this segmented logic based on GCRE, the system's global carbon emission target is no longer rigid and fixed, but can be adaptively adjusted according to the marginal emission reduction cost, forming a dynamic balance between economic efficiency and low carbon emissions.

[0092] After calculating the LCSI values ​​for each node, the computer enters the local task allocation phase. For nodes with an LCSI value greater than 1 (i.e., the first threshold is 1): this indicates that local emission reductions can have a greater overall emission reduction effect than the node itself, exhibiting a synergistic effect. The computer will prioritize allocating more emission reduction tasks to these nodes (i.e., first-priority emission reduction nodes), allowing them to undertake a larger proportion of emission reductions, thereby maximizing the overall benefit. For nodes with an LCSI value between 0 and 1 (i.e., the second threshold is 0): this indicates that while local emission reductions have a positive effect, there is some offsetting effect. These nodes (i.e., second-priority emission reduction nodes) will be allocated to a lower priority level, undertaking limited emission reduction tasks to supplement the overall emission reduction. For nodes with an LCSI value less than or close to 0: this indicates that local emission reductions are not only ineffective but may even lead to a reversal in global emissions. These nodes (i.e., non-priority emission reduction nodes) will be given extremely low weight in the allocation process or directly removed from the emission reduction task list. Through this LCSI-based sorting and allocation, computers can perform differentiated scheduling of emission reduction tasks, achieving "priority to efficient nodes and de-weighting of redundant nodes," thereby improving the overall cost-effectiveness of emission reduction.

[0093] After the MCSR calculation is completed, the computer enters the global consistency verification phase to detect whether there is a risk of emission rebound in local measures. When the MCSR is close to 0 (i.e., the fourth threshold is 0): it indicates that local emission reduction is completely converted into global emission reduction and there is no offset. The measures for such nodes (i.e., non-risk nodes) can be retained and executed freely. When 0 < MCSR < 1 (i.e., the third threshold is 1): it indicates that part of the local emission reduction is offset and the global benefit is discounted. While retaining the measures for such nodes (and limited risk nodes), the computer sets an upper limit on the emission reduction amplitude, that is, over-allocation of tasks is not allowed to avoid too low global benefits. When MCSR ≥ 1: it indicates that the local emission reduction is completely offset and even causes an increase in global emissions. The measures for such nodes are marked as "risk nodes" by the system, and the computer will automatically screen out or strictly limit these measures and not include them in the final optimal solution. Through this verification mechanism, the computer can ensure that local emission reduction measures do not undermine the global emission reduction goal and avoid unreasonable results such as "apparent emission reduction but an increase in the total system emissions".

[0094] The core idea of this method is to adopt a closed-loop optimization mechanism of "two-stage + three-index secondary analysis", that is, first generate a candidate solution set through conventional two-objective optimization, and then introduce three newly proposed indexes (GCRE, LCSI, MCSR) based on the reference solution to perform secondary correction and optimization on the reference solution, so as to obtain a more robust and interpretable final solution. The whole process can be divided into the following stages: The first stage: Two-objective optimization and reference solution selection In the initial stage of optimization, using building load prediction, photovoltaic power output prediction, electricity price curve, and carbon emission factor as inputs, a two-objective mixed integer linear programming model for minimizing operating cost and carbon emission is established. To ensure the comprehensiveness of the solution, the normalized method of normal constraint (NNC) method is used to generate a uniformly distributed Pareto front solution set, so as to fully characterize the trade-off relationship between operating cost and carbon emission. Subsequently, the TOPSIS method is used to select the reference point closest to the ideal solution on the Pareto front as the optimization result of the first stage. This reference solution provides a preliminary balance between economy and low carbon, but does not consider the dynamic diagnosis at the global and local levels.

[0095] The second stage: Global diagnosis and local analysis based on three indexes Based on the reference solution, three indexes, namely the global carbon emission reduction elasticity coefficient (GCRE), the node carbon emission reduction coordination index (LCSI), and the local carbon emission marginal substitution rate (MCSR), are introduced.

[0096] First, the relative sensitivity of operating costs to changes in carbon emissions is measured using GCRE (Global Carbon Reduction Efficiency), allowing for a global assessment of the marginal cost of further carbon reduction. A low GCRE indicates that further carbon reduction remains economically viable, and the optimization model will automatically tighten carbon emission constraints. Conversely, a high GCRE indicates that continued carbon reduction will lead to a sharp increase in costs, in which case carbon constraints should be appropriately relaxed to maintain economic viability.

[0097] Secondly, the emission reduction synergy of different devices or nodes is assessed using the LCSI (Limited Capacity Index). If the LCSI of a node is greater than 1, it indicates that the emission reduction of that node can amplify the overall emission reduction effect and should be prioritized for emission reduction. If the LCSI is close to zero or negative, it indicates that the emission reduction contribution of that node is limited or even causes a reverse effect on emissions. Based on this, the optimization process will allocate carbon reduction tasks in a differentiated manner, ensuring that resources are prioritized for nodes with high cost-effectiveness and significant effects.

[0098] Finally, the MCSR (Macro-Minute Reduction Scale) is used to examine the true contribution of local emission reductions to global emissions. Keeping operating costs constant, if the MCSR is close to zero, it indicates that local emission reductions are completely converted into net system emission reductions; if the MCSR is significantly greater than zero, it suggests that local emission reductions are offset by emissions from other nodes, and may even lead to an increase in global emissions. In this case, the optimization model will limit the emission reduction magnitude of such nodes or reduce their weights to avoid strategy failure.

[0099] Phase 3: Secondary Correction and Final Solution Generation Guided by the results of global diagnosis and local analysis, the system corrects and reconstructs the reference solution: on the one hand, it dynamically adjusts the global carbon emission target based on GCRE to couple it with the marginal emission reduction cost; on the other hand, it optimizes the allocation based on LCSI results, prioritizing low-cost and efficient carbon reduction tasks; simultaneously, it uses MCSR for global verification to ensure that the optimization results will not lead to a rebound in global emissions due to local imbalances. Under the combined effect of these three indicators, the reconstructed optimization model is solved again to generate a final operating scheme that takes into account economy, global coordination, and local effectiveness.

[0100] Phase 4: Rolling Execution and Dynamic Updates After the final operational plan is issued and implemented, the system enters the next scheduling cycle as time progresses. At this point, the rolling optimization window advances, acquiring new load forecasts, photovoltaic forecasts, and carbon emission data, and the entire process described above is repeated. Through continuous iteration and updates, the optimization method can dynamically adapt to changes in the external environment, maintaining a scientific balance between cost control and carbon emission reduction throughout the entire daily operation.

[0101] This process can be summarized as follows: first, a reference solution is obtained using Pareto+TOPSIS; then, global rationality is diagnosed using GCRE; local nodes are optimized using LCSI; and global consistency is verified using MCSR, ultimately forming the final solution after two rounds of revision. This hierarchical and progressive logic not only continues the original multi-objective optimization framework but also improves the scientific nature and robustness of the decision-making through new indicators.

[0102] For example, the computer execution process is as follows: First, the computer receives external input data, including building load forecasts, photovoltaic power generation forecasts, electricity price curves, carbon emission factors, energy storage device parameters, and grid operation constraints. Then, the input data is formatted and normalized to establish the database required for intraday scheduling and to initialize the optimization model parameters.

[0103] Subsequently, the computer constructs a dual-objective mixed-integer linear programming model with the goals of minimizing operating costs and total carbon emissions. In the model, the computer loads constraints such as equipment operation, power balance, and power flow security, and calls its built-in optimization solver to perform the solution. Using the normalized normal constraint method, the computer generates a uniformly distributed Pareto front solution set, thereby characterizing the trade-off between operating costs and carbon emissions.

[0104] Next, the computer evaluates the Pareto front solution set, calculates the proximity of each candidate solution to the ideal solution using the TOPSIS method, and automatically selects the solution with the highest score as the reference running scheme (see section [section name]). This reference solution is the preliminary result generated by the computer in the first stage.

[0105] Building upon this foundation, the computer enters a secondary analysis phase. First, by perturbing the carbon emission constraints of the reference solution, the computer calculates the relative rate of change between operating costs and carbon emissions, thereby obtaining the Global Carbon Reduction Elasticity (GCRE) coefficient. Based on the GCRE magnitude, the computer dynamically adjusts the tightening or loosening of the global carbon constraints. Second, the computer applies local emission reduction constraints to each node or device, records changes in the total system emissions, calculates the Node Carbon Reduction Coordination Index (LCSI), and identifies and ranks nodes with priority for emission reduction based on its value. Finally, while maintaining constant operating cost constraints, the computer simulates local emission reductions and observes emission changes at other nodes, calculating the Local Carbon Emission Marginal Substitution Rate (MCSR) to verify whether local emission reductions will lead to a rebound in global emissions.

[0106] After calculating the three types of indicators, the computer synthesizes the results of GCRE, LCSI, and MCSR to perform a secondary correction on the reference solution. On one hand, the GCRE value determines the tightness of the global carbon emission target; on the other hand, the LCSI allocates emission reduction tasks to each node. Simultaneously, the MCSR is used to screen out or restrict local measures that might cause a reversal in global emissions (such as risk nodes or restricting risk nodes). Based on this, the computer reconstructs the corrected optimization model and calls the optimization solver again to generate the final running solution.

[0107] Finally, the computer outputs the optimization results, including the output plans of each unit, the charging and discharging strategies of energy storage devices, and the power purchased and sold with the main grid. These results are then issued as dispatch instructions for execution. As time progresses, the computer advances according to the rolling forecast window, automatically reading the latest forecast data and repeating the entire process to achieve dynamic optimization and rolling correction of daily operations.

[0108] Compared to existing intraday optimization methods, the innovation of this invention lies in introducing and integrating three indicators: the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate, and applying them throughout the entire intraday optimization solution process. Existing methods often suffer from problems such as rigid global objectives, coarse local allocation, and inconsistencies between local and global approaches. In contrast, this invention achieves a systematic breakthrough in three aspects: First, it introduces a global carbon emission reduction elasticity coefficient at the global level, so that carbon emission constraints are no longer fixed but can be adaptively adjusted according to the current marginal emission reduction cost. When the global carbon emission reduction elasticity coefficient is low, the optimization model will actively tighten carbon constraints to capture low-cost emission reduction opportunities; when the global carbon emission reduction elasticity coefficient is high, the model will automatically relax constraints to avoid excessive economic burden. Through this mechanism, the system achieves real-time coupling between global constraints and the marginal cost of emission reduction, thereby obtaining a more scientific dynamic balance between economic efficiency and emission reduction intensity. Second, it introduces a node carbon emission reduction synergy index at the local allocation level, and allocates global carbon reduction tasks differently according to the emission reduction cost-effectiveness of different equipment. Unlike traditional methods that typically employ average allocation or static weighting, this invention prioritizes resources with high carbon reduction synergy indices (such as energy storage and portable loads) to undertake more carbon reduction tasks, while reducing constraints on equipment with low carbon reduction synergy indices. This achieves better carbon reduction results with the same system operating costs. This mechanism not only improves the precision of carbon reduction task allocation but also effectively enhances the overall efficiency of system carbon reduction. Furthermore, by introducing a marginal carbon emission substitution rate at the global consistency level, a feedback verification mechanism from local behavior to global emissions is established. This indicator can identify and suppress unreasonable schemes that may lead to "local emission reductions triggering a global rebound." This invention ensures that the optimization results maintain a downward trend in carbon emissions on a global scale by limiting the carbon reduction magnitude of overly sensitive equipment or setting penalty mechanisms. Compared with traditional methods, this design effectively avoids the problem of conflict between local and global objectives, improving the robustness and reliability of the optimization results.

[0109] 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.

[0110] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0111] Figure 2 A schematic diagram of the intraday optimization device for photovoltaic-storage buildings considering carbon emission sensitivity provided in an embodiment of the present invention is shown. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown, and are described in detail below: like Figure 2As shown, the photovoltaic-storage building-intraday optimization device considering carbon emission sensitivity includes: Modeling module 21 is used to build a comprehensive model that includes photovoltaic power generation, photovoltaic-storage building load, energy storage devices, and carbon emissions.

[0112] The first dual-objective optimization solution module 22 is used to perform intraday optimization based on the comprehensive model, with the dual objectives of minimizing operating costs and minimizing total carbon emissions, and the solution results are used as reference solutions.

[0113] The carbon emission index assessment module 23 is used to calculate the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate based on the reference solution.

[0114] The second dual-objective optimization solution module 24 is used to perform intraday optimization solution with the dual objectives of minimizing operating costs and minimizing total carbon emissions based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index and the local carbon emission marginal substitution rate, so as to obtain the intraday optimization scheme of the photovoltaic-storage building.

[0115] In one possible implementation, the carbon emission indicator assessment module 23 is specifically used for: Obtain the reference solution and the corresponding operating costs and total carbon emissions.

[0116] By applying a perturbation to the reference solution, we can obtain the changes in operating costs and total carbon emissions after applying the perturbation.

[0117] Calculate the ratio of the change in operating costs to the total operating costs to obtain the relative rate of change in operating costs, and calculate the ratio of the change in total carbon emissions to the total carbon emissions to obtain the relative rate of change in carbon emissions.

[0118] The ratio of the relative change rate of operating costs to the relative change rate of carbon emissions is calculated to obtain the global carbon emission reduction elasticity coefficient.

[0119] In one possible implementation, the carbon emission indicator assessment module 23 is specifically used for: Based on the reference solution, the local carbon emission reduction of each node in the photovoltaic-storage building is obtained, and local carbon emission reduction constraints are applied to each node in the photovoltaic-storage building to obtain the change in the total carbon emissions corresponding to each node after the application of local carbon emission reduction constraints.

[0120] Calculate the ratio of the change in total carbon emissions at each node to the carbon reduction at the corresponding node to obtain the node carbon reduction synergy index for each node.

[0121] In one possible implementation, the carbon emission indicator assessment module 23 is specifically used for: Based on the reference solution, while keeping the operating cost constraint constant, local emission reduction is simulated for each node in the photovoltaic-storage building to obtain the local carbon emission reduction of each node, and the changes in carbon emission reduction of other nodes are observed.

[0122] Calculate the ratio of the change in carbon emission reduction at other nodes to the local carbon emission reduction at each node to obtain the local marginal substitution rate of carbon emissions at each node.

[0123] In one possible implementation, the second bi-objective optimization solution module 24 is specifically used for: The global carbon emission reduction target is adjusted based on the global carbon emission reduction elasticity coefficient, priority emission reduction nodes are identified and ranked based on the node carbon emission reduction synergy index, and risk nodes that may trigger a reversal in global carbon emissions are determined based on the local carbon emission marginal substitution rate.

[0124] Based on the adjusted global carbon emission target, priority emission reduction nodes, and risk nodes, the intraday optimization solution is re-applied with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain the intraday optimization scheme for photovoltaic-storage buildings.

[0125] In one possible implementation, the second bi-objective optimization solution module 24 is specifically used for: Determine whether the global carbon emission reduction elasticity coefficient is located in the first interval, the second interval, or the maximum value of the first interval is less than or equal to the minimum value of the second interval, and the maximum value of the second interval is less than or equal to the minimum value of the third interval.

[0126] If the global carbon emission reduction elasticity coefficient is in the first range, then the total carbon emission ceiling in the global carbon emission target will be lowered.

[0127] If the global carbon emission reduction elasticity coefficient is in the second range, then the global carbon emission target will remain unchanged.

[0128] If the global carbon emission reduction elasticity coefficient is in the third range, the upper limit of total carbon emissions in the global carbon emission target will be increased.

[0129] In one possible implementation, the second bi-objective optimization solution module 24 is specifically used for: The node carbon reduction synergy index of each node is sorted.

[0130] According to the ranking results, nodes with a carbon emission reduction synergy index greater than the first threshold are designated as first priority emission reduction nodes, nodes with a carbon emission reduction synergy index greater than the second threshold and less than or equal to the first threshold are designated as second priority emission reduction nodes, and nodes with a carbon emission reduction synergy index less than or equal to the second threshold are designated as non-priority emission reduction nodes. The second threshold is less than the first threshold.

[0131] In one possible implementation, the second bi-objective optimization solution module 24 is specifically used for: The local carbon emission marginal substitution rate of each node is compared with the third threshold and the fourth threshold, respectively, where the fourth threshold is less than the third threshold.

[0132] If the local carbon emission marginal substitution rate of a node is greater than or equal to the third threshold, then the node is identified as a risk node that may trigger a reversal in global carbon emissions.

[0133] If the local carbon emission marginal substitution rate of a node is less than the third threshold but greater than the fourth threshold, then the node is identified as a restricted risk node, and an upper limit for the emission reduction of the node is set.

[0134] If the local carbon emission marginal substitution rate of a node is less than or equal to the fourth threshold, then the node is identified as a non-risk node.

[0135] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0136] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 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 32 in electronic device 3.

[0137] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0138] 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.

[0139] 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.

[0140] 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 method for intraday optimization of photovoltaic-storage buildings considering carbon emission sensitivity, characterized in that, include: Establish a comprehensive model that includes photovoltaic power generation, photovoltaic-storage building load, energy storage devices, and carbon emissions; Based on the comprehensive model, intraday optimization is performed with the dual objectives of minimizing operating costs and minimizing total carbon emissions, and the solution is used as a reference solution. Based on the reference solution, calculate the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate; Based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate, the intraday optimization solution is re-solved with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain the intraday optimization scheme for photovoltaic-storage buildings.

2. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 1, characterized in that, Based on the aforementioned reference solution, the calculation of the global carbon emission reduction elasticity coefficient includes: Obtain the operating cost and total carbon emissions corresponding to the reference solution; A perturbation is applied to the reference solution to obtain the changes in operating costs and total carbon emissions after the perturbation is applied to the reference solution; Calculate the ratio of the change in operating costs to the total operating costs to obtain the relative rate of change in operating costs, and calculate the ratio of the change in total carbon emissions to the total carbon emissions to obtain the relative rate of change in carbon emissions. The ratio of the relative change rate of operating costs to the relative change rate of carbon emissions is calculated to obtain the global carbon emission reduction elasticity coefficient.

3. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 1, characterized in that, Based on the reference solution, the calculation of the node carbon emission reduction synergy index includes: Based on the reference solution, the local carbon emission reduction of each node in the photovoltaic-storage building is obtained, and local carbon emission reduction constraints are applied to each node in the photovoltaic-storage building to obtain the change in the total carbon emissions corresponding to each node after the application of local carbon emission reduction constraints. Calculate the ratio of the change in total carbon emissions at each node to the carbon reduction at the corresponding node to obtain the node carbon reduction synergy index for each node.

4. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 1, characterized in that, Based on the aforementioned reference solution, calculating the local marginal carbon emission substitution rate includes: Based on the reference solution, while keeping the operating cost constraint unchanged, local emission reduction is simulated for each node in the photovoltaic-storage building to obtain the local carbon emission reduction of each node, and the change in carbon emission reduction of other nodes is observed. Calculate the ratio of the change in carbon emission reduction at other nodes to the local carbon emission reduction at each node to obtain the local marginal substitution rate of carbon emissions at each node.

5. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 1, characterized in that, Based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate, an intraday optimization solution is re-applied with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain an intraday optimization scheme for photovoltaic-storage buildings, including: The global carbon emission reduction target is adjusted according to the global carbon emission reduction elasticity coefficient, priority emission reduction nodes are identified and ranked according to the node carbon emission reduction coordination index, and risk nodes that may cause a reversal in global carbon emissions are determined according to the local carbon emission marginal substitution rate. Based on the adjusted global carbon emission target, the priority emission reduction nodes, and the risk nodes, the intraday optimization solution is re-solved with the dual objectives of minimizing operating costs and minimizing total carbon emissions to obtain the intraday optimization scheme for photovoltaic-storage buildings.

6. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 5, characterized in that, Adjusting the global carbon emission target based on the global carbon emission reduction elasticity coefficient includes: Determine whether the global carbon emission reduction elasticity coefficient is located in the first interval, the second interval, or whether the maximum value of the first interval is less than or equal to the minimum value of the second interval, and the maximum value of the second interval is less than or equal to the minimum value of the third interval; If the global carbon emission reduction elasticity coefficient is located in the first interval, the upper limit of total carbon emissions in the global carbon emission target will be reduced. If the global carbon emission reduction elasticity coefficient is located in the second range, then the global carbon emission target is kept unchanged. If the global carbon emission reduction elasticity coefficient is located in the third interval, the upper limit of total carbon emissions in the global carbon emission target will be increased.

7. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 5, characterized in that, Based on the node carbon reduction synergy index, priority emission reduction nodes are identified and ranked, including: The node carbon reduction synergy index of each node is sorted. According to the sorting results, nodes with a carbon emission reduction synergy index greater than the first threshold are designated as first priority emission reduction nodes, nodes with a carbon emission reduction synergy index greater than the second threshold and less than or equal to the first threshold are designated as second priority emission reduction nodes, and nodes with a carbon emission reduction synergy index less than or equal to the second threshold are designated as non-priority emission reduction nodes, wherein the second threshold is less than the first threshold.

8. The intraday optimization method for photovoltaic-storage buildings considering carbon emission sensitivity according to claim 5, characterized in that, Based on the aforementioned local carbon emission marginal substitution rate, risk nodes that may trigger a reversal in global carbon emissions are identified, including: The local carbon emission marginal substitution rate of each node is compared with a third threshold and a fourth threshold, respectively, wherein the fourth threshold is less than the third threshold; If the local carbon emission marginal substitution rate of a certain node is greater than or equal to the third threshold, then the node is identified as a risk node that may trigger a reversal in global carbon emissions. If the local carbon emission marginal substitution rate of a certain node is less than the third threshold and greater than the fourth threshold, then the node is identified as a restricted risk node, and an upper limit for the emission reduction of the node is set. If the local carbon emission marginal substitution rate of a node is less than or equal to the fourth threshold, then the node is determined to be a non-risk node.

9. A photovoltaic-storage building daytime optimization device considering carbon emission sensitivity, characterized in that, include: The modeling module is used to create a comprehensive model that includes photovoltaic power generation, photovoltaic-storage building load, energy storage devices, and carbon emissions. The first dual-objective optimization solution module is used to perform intraday optimization based on the comprehensive model, with the dual objectives of minimizing operating costs and minimizing total carbon emissions, and to use the solution results as a reference solution. The carbon emission index assessment module is used to calculate the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index, and the local carbon emission marginal substitution rate based on the reference solution. The second dual-objective optimization solution module is used to perform intraday optimization solution with the dual objectives of minimizing operating costs and minimizing total carbon emissions based on the global carbon emission reduction elasticity coefficient, the node carbon emission reduction synergy index and the local carbon emission marginal substitution rate, so as to obtain the intraday optimization scheme of the photovoltaic-storage building.

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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