Optimized configuration of photovoltaic storage charging station based on electricity-carbon index evaluation method and system

Abstract

The application discloses a novel electric-carbon index evaluation method and system based on optimization configuration of light-storage charging stations, relates to the technical field of carbon emission, and comprises the following steps: establishing an electric vehicle queuing model of the light-storage charging station and providing demand response data; considering the demand response data, establishing a target function based on a novel electric-carbon index evaluation model; setting constraint conditions based on actual conditions, and constructing an electric vehicle light-storage charging station capacity optimization configuration model; based on a particle swarm optimization algorithm, solving the electric vehicle light-storage charging station capacity optimization configuration model, and evaluating the carbon emission of the light-storage charging station. The application fully considers the operation characteristics of the energy storage power station in the power system, introduces two core indexes of increment size and participation level, provides a comprehensive and accurate evaluation method for the carbon emission of the light-storage charging station in the power system, can improve the operation efficiency of the power system, and can provide strong support for the sustainable development of the power industry.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology, and more specifically to a novel method and system for evaluating carbon emission indicators based on the optimized configuration of photovoltaic-energy storage charging stations. Background Technology

[0002] With the increasing severity of global climate change and environmental problems, reducing carbon emissions and promoting the development of green energy have become an international consensus. In the power system, photovoltaic-storage charging stations, as an important component of new energy sources, play a crucial role in achieving low-carbon goals through optimized configuration and efficient operation. However, in the existing power system, the assessment and management of photovoltaic-storage charging stations mainly rely on the traditional tiered carbon emission mechanism, which has certain problems in assessing carbon emissions during demand response peak shaving.

[0003] Currently, carbon emission management in power systems typically employs a tiered carbon emission mechanism. This mechanism sets different tiers based on a company's carbon emissions, levying higher carbon emission fees on companies with higher emissions and providing rewards to companies with lower emissions. However, in demand response and peak shaving scenarios, this mechanism only considers the incremental carbon emissions, neglecting the company's participation in the peak shaving process. Specifically, even if two companies participate in the same level of peak shaving, their contributions to the power system can differ significantly due to their different peak shaving capabilities. For example, a company with low peak shaving capability but high participation and a company with high peak shaving capability but low participation, participating in the same level of peak shaving, will have the same carbon emission assessment results, which is clearly unfair. However, the traditional tiered carbon emission mechanism fails to adequately reflect this difference, leading to unfair and inaccurate assessment results.

[0004] Under the existing tiered carbon emission control mechanism, the following main problems exist: First, the participation of enterprises in demand response and peak shaving is not fully considered, leading to unfair assessment results; second, there is a lack of comprehensive consideration for the optimal configuration of photovoltaic-storage-charging stations, making it difficult for these stations to achieve optimal efficiency during operation. Therefore, how to propose a new assessment model to more accurately evaluate the carbon emissions of photovoltaic-storage-charging stations in the power system and provide a scientific basis for their optimal configuration is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a novel method and system for evaluating carbon emission indicators based on the optimized configuration of photovoltaic-energy storage charging stations, which solves the problems existing in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A novel method for evaluating carbon emissions based on optimized configuration of photovoltaic-storage charging stations includes the following steps:

[0008] Establish a queuing model for electric vehicles at photovoltaic-storage charging stations to provide demand response data;

[0009] Considering demand response data, establish an objective function based on a novel electricity carbon index assessment model;

[0010] Based on actual conditions, constraints are set to construct a capacity optimization configuration model for electric vehicle photovoltaic-storage charging stations.

[0011] The particle swarm optimization algorithm is used to solve the capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations, and to evaluate the carbon emissions of photovoltaic-storage charging stations.

[0012] Optionally, the photovoltaic-storage charging station includes: photovoltaic cells, DC / AC modules, power distribution network, AC / DC modules, charging station, first DC / DC module, second DC / DC module, charging pile, and energy storage module; wherein, the photovoltaic cells, DC / AC modules, charging station, first DC / DC module, and charging pile are connected in sequence, and the power distribution network, AC / DC module, charging station, second DC / DC module, and energy storage module are connected in sequence.

[0013] Optionally, a queuing model for electric vehicles at photovoltaic-storage charging stations is established, which includes the following steps:

[0014] Based on the principles of queuing theory, for the M / G / S queuing system, initial values ​​are set for the starting SOC and starting charging time of electric vehicles that follow exponential and normal distributions, respectively.

[0015] By using Monte Carlo sampling, the starting SOC and starting charging time of multiple electric vehicles are randomly selected to generate an electric vehicle charging sequence.

[0016] Based on the electric vehicle charging sequence, the queuing process of electric vehicles at the photovoltaic-storage charging station is simulated, and an electric vehicle charging decision matrix D with dimension M×N is established.

[0017]

[0018] In the formula, D is a 0-1 matrix, and the matrix element d ij This indicates whether the i-th vehicle is charging during the j-th unit charging period.

[0019] Optionally, an objective function based on a novel carbon index assessment model is established, specifically as follows:

[0020] With the objective of minimizing the total investment cost of the photovoltaic-storage charging station, and considering its participation in grid load demand response, the total cost of the photovoltaic-storage charging station is factored into the unit time, resulting in the objective function:

[0021] f = min(F) S +DIC+DCC)

[0022] In the formula: F s The fixed investment cost is represented by DIC, the DR equipment investment cost is represented by DCC, and the DR compensation cost is represented by DCC.

[0023] The formula for calculating the investment cost of DR equipment is as follows:

[0024]

[0025] The formula for calculating DR compensation costs is as follows:

[0026]

[0027] In the formula: This represents the cost per unit capacity of DR equipment; k Di Let P be the DR percentage of node i, and let P be a constant between 0 and 1. Di *k Di Indicates the maximum responsive load; k c This represents the participation coefficient. This represents the compensation cost for providing a unit of energy load response, where T is the time during which demand response occurs throughout the year. N represents the load response. k Indicates the number of times the user participated in demand response; P Di This represents the maximum load of node i participating in the demand response.

[0028] Optional, fixed investment cost F s The calculation formula is:

[0029] F s =F s1 +F s2 +F gc +F gd

[0030] In the formula: F s1 Indicates the investment cost within the station, F s2 Indicates the investment cost of charging piles, F gc Indicates the cost of photovoltaic energy storage, F gd Indicates the cost of purchasing electricity;

[0031] Among them, the on-site investment cost F of the charging station s1 The calculation formula is:

[0032]

[0033] In the formula: C td C represents the cost of non-charging parking spaces. yuC represents operating costs. zm r0 represents the cost of electricity used in the office; r0 represents the discount rate; n cs Indicates the specific operating years of the charging station; S e α represents the total office capacity, β represents the power distribution cost per unit capacity, β represents the transformer load rate within the charging station, and z represents the investment cost ratio coefficient within the station.

[0034] Charging pile investment cost F s2 The calculation formula is:

[0035]

[0036] C gm =c k ×P k

[0037] C wa =k×C gm

[0038] In the formula: C pd C represents the power distribution cost of a single charging facility. gm C represents the purchase cost of a single charging facility. wa C represents the maintenance and installation cost of a single charging facility. cw This represents the land cost of a single charging parking space; c k P represents the purchase cost of charging facilities per unit power, k represents the ratio of maintenance and installation costs to purchase costs, and s represents the number of charging stations. k Indicates the rated charging power, and η represents the charging efficiency. Indicates the power factor angle;

[0039] Photovoltaic energy storage cost F gc Including photovoltaic installation costs and energy storage installation costs, the calculation formula is expressed as follows:

[0040] F gc =F pv +F sto

[0041] F pv =z pv C pv P pv +C mpv

[0042]

[0043] C mpv =k gc ×C pv ×P pv

[0044] Fsto =z sto [r p P sto +(r e -r r E sto ]+C msto

[0045]

[0046] C msto =k gc ×r e ·E sto

[0047] In the formula: F pv F represents the photovoltaic investment cost of the charging station. sto Indicates the energy storage investment cost of the charging station; C pv P represents the initial investment cost per unit capacity of photovoltaic power. pv C represents the photovoltaic configuration capacity. mpv Indicates the cost of photovoltaic maintenance, r gc n represents the discount rate for photovoltaics and energy storage. pv k represents the entire lifecycle of a photovoltaic system. gc This represents the annual maintenance cost coefficient for the photovoltaic-storage system; n sto Indicates the entire lifecycle of the energy storage system, r p r represents the unit power cost of energy storage. e r represents the unit cost of energy storage. r P represents the unit recycling cost. sto E represents the rated power of energy storage. sto Indicates the energy storage configuration capacity, C msto Indicates energy storage maintenance costs; z pv z represents the proportion coefficient of photovoltaic investment costs. sto This represents the cost ratio of energy storage configuration;

[0048] Electricity purchase cost F gd The cost of purchasing electricity from the grid to meet the charging needs of electric vehicles is calculated using the following formula:

[0049]

[0050] In the formula: n represents the number of peak, flat, and valley periods in a day, T i P represents the time span of period i. i C represents the amount of electricity purchased during time period i. gdi P represents the electricity price during time period i. i s k represents the electricity sold to the grid during time period i. gd This represents the ratio of the electricity price sold to the electricity price purchased.

[0051] Optional constraints, established based on actual circumstances, specifically include:

[0052] Power distribution capacity constraints, considering the limitations on the power distribution capacity of charging stations:

[0053]

[0054] The number of charging stations is limited by the available land area.

[0055] s≤s max

[0056] Equipment utilization is constrained by the economic constraints of charging infrastructure configuration; the limiting conditions for equipment utilization are as follows:

[0057] ρ≥ρ min

[0058] Energy storage charge and discharge constraints: the limitations on the charge and discharge power of energy storage devices, i.e., the charge and discharge rate.

[0059] -P sto ≤P d,csto ≤P sto

[0060] Demand response constraint: The amount of demand response a user participates in at any one time must not exceed the maximum load demand response amount.

[0061]

[0062] In the formula: S N S represents the power distribution capacity of the charging station. Nmax S represents the upper limit of the power distribution capacity of the charging station, and s represents the number of charging piles. e P represents the total office capacity, β represents the transformer load rate within the charging station, and P represents the total office capacity. k Indicates the rated charging power, and η represents the charging efficiency. Indicates the power factor angle; s max ρ represents the maximum number of charging piles; ρ represents the device utilization rate. min Indicates the lower limit of equipment utilization; P sto P represents the rated power of energy storage. d,csto Indicates the energy storage charge / discharge rate; k represents the load response. Di *P Di This indicates the maximum load that can be responded to.

[0063] Optionally, the capacity optimization configuration model for electric vehicle photovoltaic-storage charging stations is solved based on the particle swarm optimization algorithm, specifically as follows:

[0064] Using the particle swarm optimization algorithm, suppose the optimal value during a particle's journey is P. best And by filtering all individuals, the optimal value G for the population is obtained. best Each particle in the particle swarm is determined according to P best and G best The search is adjusted, and the optimal fitness value G is reached during the adjustment process. best That is, the optimal solution;

[0065] During the k-th search, particle i passes through reference P best and G best It updates its own parameters;

[0066]

[0067] In the formula: c1 and c2 represent acceleration factors, which are constants; rand() represents a random number between [0,1]; w represents the inertia factor; v id (t) represents the velocity of particle i at time t, p id (t) represents the value of the number of charger particles i during their travel at time t, x id (t) represents the position of the charger particle i at time t, p gd (t) represents the value of the photovoltaic particle during its travel at time t, x gd (t) represents the position of the photovoltaic particle at time t;

[0068] The ranges of position and velocity in the d-th dimension are respectively [-x d，max ,x d，max ] and [-v d，max ,v d，max In the iteration, if a certain dimension x id or v id If the value exceeds the boundary, the boundary value is used.

[0069] Based on the particle swarm optimization algorithm, we solve for the number of charger particles, photovoltaic capacity particles, and energy storage device capacity particles that meet the constraints.

[0070] A novel carbon emission index evaluation system based on optimized configuration of photovoltaic-energy storage charging stations includes:

[0071] The queuing process simulation module is used to establish a queuing model for electric vehicles at photovoltaic and energy storage charging stations and provide demand response data.

[0072] The objective function acquisition module considers demand response data and establishes an objective function based on a novel electric carbon index evaluation model.

[0073] The constraint setting module is used to set constraints based on actual conditions and build an optimized capacity configuration model for electric vehicle photovoltaic-storage charging stations.

[0074] The solution module is used to solve the capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations using the particle swarm optimization algorithm, and to evaluate the carbon emissions of photovoltaic-storage charging stations.

[0075] As can be seen from the above technical solution, compared with the prior art, the present invention provides a novel method and system for evaluating carbon dioxide emissions based on the optimized configuration of photovoltaic-energy storage charging stations, which has the following beneficial effects:

[0076] (1) Greater fairness: This invention introduces participation as a core indicator, which not only considers the incremental carbon emissions, but also fully considers the actual participation and contribution of enterprises in demand response peak shaving. Therefore, even if two enterprises participate in peak shaving to the same extent, due to their different peak shaving capabilities, the model can accurately assess their different contributions to the power system, thereby ensuring the fairness of the assessment results;

[0077] (2) Higher assessment accuracy: This invention, through a comprehensive assessment of two core indicators—incremental size and participation—can more comprehensively and accurately reflect the carbon emissions of photovoltaic-storage charging stations in the power system. This multi-dimensional assessment method is more accurate in reflecting the actual operating status and environmental benefits of photovoltaic-storage charging stations than the traditional single carbon emission indicator;

[0078] (3) Promoting Optimal Configuration: The model designed in this invention is not only used for evaluation, but also guides the optimal configuration of photovoltaic-storage charging stations. Through the model evaluation results, enterprises can understand their performance in demand response peak shaving, and make adjustments to resource allocation and operation management based on the evaluation results, thereby improving the operating efficiency of photovoltaic-storage charging stations and reducing carbon emissions;

[0079] (4) Promoting Low-Carbon Transformation: Because this invention can accurately assess the carbon emissions of photovoltaic-storage charging stations and provide a scientific basis for their optimized configuration, this model helps to promote the development of the power industry towards a lower-carbon and more environmentally friendly direction. By widely applying this model, the popularization and optimization of photovoltaic-storage charging stations can be promoted, further reducing the overall carbon emissions of the power system. Attached Figure Description

[0080] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0081] Figure 1A flowchart of a novel carbon emission index evaluation method based on optimized configuration of photovoltaic-storage charging stations provided by the present invention;

[0082] Figure 2 This is a schematic diagram of the structure of the photovoltaic energy storage charging station provided by the present invention;

[0083] Figure 3 The flowchart for solving the optimization model provided by this invention is shown. Detailed Implementation

[0084] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] This invention discloses a novel method for evaluating carbon dioxide emissions based on optimized configuration of photovoltaic-storage charging stations, such as... Figure 1 As shown, it includes the following steps:

[0086] Establish a queuing model for electric vehicles at photovoltaic-storage charging stations to provide demand response data;

[0087] Considering demand response data, establish an objective function based on a novel electricity carbon index assessment model;

[0088] Based on actual conditions, constraints are set to construct a capacity optimization configuration model for electric vehicle photovoltaic-storage charging stations.

[0089] The particle swarm optimization algorithm is used to solve the capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations, and to evaluate the carbon emissions of photovoltaic-storage charging stations.

[0090] To address the issues raised in the background section, this embodiment proposes an innovative and practical novel carbon emission assessment model based on the optimized configuration of photovoltaic-storage-charging stations. This model fully considers the participation of enterprises in demand response peak shaving and the impact of optimized configuration of photovoltaic-storage-charging stations on carbon emissions. By introducing two indicator models—increment size and participation level—it can more comprehensively assess the carbon emissions of photovoltaic-storage-charging stations in the power system and provide a scientific basis for their optimized configuration. This is of great significance for promoting green energy development and achieving low-carbon goals.

[0091] Next, for Figure 1 The novel carbon emission index evaluation method based on the optimized configuration of photovoltaic-storage charging stations is described in detail.

[0092] 1. Establish a queuing model for electric vehicles at photovoltaic-storage charging stations.

[0093] This embodiment fully considers the operational characteristics of energy storage power stations in power systems. By introducing two core indicators—incremental size and participation level—it provides a comprehensive and accurate assessment method for the carbon emissions of photovoltaic-storage charging stations in power systems. The incremental size indicator not only considers the electricity directly generated by the photovoltaic-storage charging station but also incorporates the carbon emissions indirectly reduced by replacing high-carbon-emission power sources, thus more comprehensively reflecting the actual contribution of photovoltaic-storage charging stations to the power system. The participation level indicator comprehensively considers multiple factors such as the response capability, response speed, and response effect of the photovoltaic-storage charging station under different time periods and different power demands, ensuring a true reflection of the actual performance of the photovoltaic-storage charging station in demand response.

[0094] Introducing two core indicators—the magnitude of the incremental change and the level of participation—to comprehensively assess the carbon emissions of photovoltaic-storage charging stations in the power system requires establishing an electric vehicle queuing model for photovoltaic-storage charging stations to provide crucial demand response data for this assessment process, thereby enabling a more accurate assessment of the carbon emissions of photovoltaic-storage charging stations.

[0095] like Figure 2 As shown, the photovoltaic-storage charging station includes: photovoltaic cells, DC / AC modules, power distribution network, AC / DC modules, charging station, first DC / DC module, second DC / DC module, charging pile, and energy storage module; wherein, the photovoltaic cells, DC / AC modules, charging station, first DC / DC module, and charging pile are connected in sequence, and the power distribution network, AC / DC module, charging station, second DC / DC module, and energy storage module are connected in sequence.

[0096] Furthermore, a queuing model for electric vehicles at photovoltaic-storage charging stations is established, which specifically includes the following steps:

[0097] Based on the principles of queuing theory, for the M / G / S queuing system, initial values ​​are set for the State of Charge (SOC) and the start of charging time of electric vehicles that follow exponential and normal distributions, respectively.

[0098] By using Monte Carlo sampling, the starting SOC and starting charging time of multiple electric vehicles are randomly selected to generate an electric vehicle charging sequence.

[0099] Based on the electric vehicle charging sequence, the queuing process of electric vehicles at the photovoltaic-storage charging station is simulated, and an electric vehicle charging decision matrix D with dimension M×N is established.

[0100]

[0101] In the formula, D is a 0-1 matrix, and the matrix element d ij This indicates whether the i-th vehicle is charging during the j-th unit charging period.

[0102] 2. Establish the objective function based on the novel carbon index evaluation model for electricity.

[0103] In power systems, peak shaving is a crucial component, aiming to ensure a balance between electricity supply and demand, especially during peak hours. Demand response is mainly divided into price-based demand response (PBDR) and incentive-based demand response (IBDR), both playing a vital role in power system peak shaving. PBDR is a dynamic pricing program where users compare their electricity consumption habits with the electricity prices offered by suppliers and decide whether to participate. IBDR does not involve a pricing mechanism but instead provides rewards to users participating in grid load response programs to incentivize their participation.

[0104] With the objective of minimizing the total investment cost of the photovoltaic-storage charging station, and considering its participation in grid load demand response, the total cost of the photovoltaic-storage charging station is factored into the unit time, resulting in the objective function:

[0105] f = min(F) S +DIC+DCC)

[0106] In the formula: F s The fixed investment cost is represented by DIC, the DR equipment investment cost, and DCC, the demand response (DR) compensation cost.

[0107] Fixed investment cost F s The calculation formula is:

[0108] F s =F s1 +F s2 +F gc +F gd

[0109] In the formula: F s1 Indicates the investment cost within the station, F s2 Indicates the investment cost of charging piles, F gc Indicates the cost of photovoltaic energy storage, F gd This indicates the cost of purchasing electricity.

[0110] (1) On-site investment cost F of the charging station s1 The calculation formula is:

[0111]

[0112] In the formula: C td C represents the cost of non-charging parking spaces. yu C represents operating costs. zmr0 represents the cost of electricity used in the office; r0 represents the discount rate; n cs Indicates the specific operating years of the charging station; S e α represents the total office capacity, β represents the power distribution cost per unit capacity, β represents the transformer load rate within the charging station, and z represents the investment cost ratio coefficient within the station.

[0113] Charging pile investment cost F s2 The calculation formula is:

[0114]

[0115] C gm =c k ×P k

[0116] C wa =k×C gm

[0117] In the formula: C pd C represents the power distribution cost of a single charging facility. gm C represents the purchase cost of a single charging facility. wa C represents the maintenance and installation cost of a single charging facility. cw This represents the land cost of a single charging parking space; c k P represents the purchase cost of charging facilities per unit power, k represents the ratio of maintenance and installation costs to purchase costs, and s represents the number of charging stations. k Indicates the rated charging power, and η represents the charging efficiency. It represents the power factor angle.

[0118] (2) Photovoltaic energy storage cost F gc Including photovoltaic installation costs and energy storage installation costs, the calculation formula is expressed as follows:

[0119] F gc =F pv +F sto

[0120] F pv =z pv C pv P pv +C mpv

[0121]

[0122] C mpv =k gc ×C pv ×P pv

[0123] F sto =z sto[r p P sto +(r e -r r E sto ]+C msto

[0124]

[0125] C msto =k gc ×r e ·E sto

[0126] In the formula: F pv F represents the photovoltaic investment cost of the charging station. sto Indicates the energy storage investment cost of the charging station; C pv P represents the initial investment cost per unit capacity of photovoltaic power. pv C represents the photovoltaic configuration capacity. mpv Indicates the cost of photovoltaic maintenance, r gc n represents the discount rate for photovoltaics and energy storage. pv k represents the entire lifecycle of a photovoltaic system. gc This represents the annual maintenance cost coefficient for the photovoltaic-storage system; n sto Indicates the entire lifecycle of the energy storage system, r p r represents the unit power cost of energy storage. e r represents the unit cost of energy storage. r P represents the unit recycling cost. sto E represents the rated power of energy storage. sto Indicates the energy storage configuration capacity, C msto Indicates energy storage maintenance costs; z pv z represents the proportion coefficient of photovoltaic investment costs. sto This represents the cost ratio of energy storage configuration.

[0127] (3) Electricity purchase cost F gd The cost of purchasing electricity from the grid to meet the charging needs of electric vehicles is calculated using the following formula:

[0128]

[0129] In the formula: n represents the number of peak, flat, and valley periods in a day, T i P represents the time span of period i. i C represents the amount of electricity purchased during time period i. gdi P represents the electricity price during time period i. i s k represents the electricity sold to the grid during time period i. gd This represents the ratio of the electricity price sold to the electricity price purchased.

[0130] (4) Participation in load demand response costs

[0131] In recent years, demand response (DR) has rapidly developed as a flexible resource for power systems. Numerous DR projects have been implemented worldwide, including pilot projects and potential surveys of DR in China. DR has applications in both power system operation and planning. The optimal allocation model proposed in this implementation considers the cost of participating load demand response and introduces two core indicators: increment size and participation level. The role of demand response in this model is to more accurately and comprehensively optimize power system dispatch and resource allocation, achieving a comprehensive assessment of the carbon emissions of photovoltaic-storage-charging stations within the power system.

[0132] Demand-side response mechanisms can generally be divided into price-based demand response and incentive-based demand response. Compared with price-based demand response, incentive-based demand response has higher dispatchability and faster response speed. In this embodiment, the response type selected is direct load control in incentive-based demand response.

[0133] The formula for calculating the investment cost of DR equipment is as follows:

[0134]

[0135] The formula for calculating DR compensation costs is as follows:

[0136]

[0137] In the formula: This represents the cost per unit capacity of DR equipment; k Di Let P be the DR percentage of node i, and let P be a constant between 0 and 1. Di *k Di Indicates the maximum responsive load; k c This represents the participation coefficient. This represents the compensation cost for providing a unit of energy load response, where T is the time during which demand response occurs throughout the year. N represents the load response. k Indicates the number of times the user participated in demand response; P Di This represents the maximum load of node i participating in the demand response.

[0138] Analysis of mean square error (RMSD) is used to evaluate absolute error. The RMSD of a variable is a commonly used indicator in probability theory to reflect the dispersion of a random variable. RMSD is an estimation function T for an unobservable parameter θ. The RMSD of the load response is defined as:

[0139]

[0140] The mean squared error (MSE) measures the magnitude of fluctuation in a sample; a larger MSE indicates greater fluctuation in the sample data. An absolute error assessment of the model's load demand response is performed to ensure the scientific validity and accuracy of the new electricity carbon index evaluation model.

[0141] 3. Establish constraints based on actual circumstances.

[0142] Power distribution capacity constraints: A charging station is a charging location with a fixed floor area and power distribution capacity. Therefore, the power distribution capacity limitations of the charging station should be considered when configuring it.

[0143]

[0144] The number of charging stations is constrained by the available land area; there is an upper limit to the number of charging stations that can be installed. The limitations are as follows:

[0145] s≤s max

[0146] The device utilization rate constraint reflects the economic constraints of charging facility configuration, requiring the setting of a lower limit for device utilization. The limiting conditions are as follows:

[0147] ρ≥ρ min

[0148] Energy storage charging and discharging constraints: The charging and discharging power of energy storage devices, i.e., the charging and discharging rate, has an upper limit. A negative power value indicates energy discharging, and a positive power value indicates energy charging. The charging and discharging state of an energy storage device is unique within any given time period. Its limitations are:

[0149] -P sto ≤P d,csto ≤P sto

[0150] Demand response constraint: The amount of demand response a user participates in at any one time must not exceed the maximum load demand response amount.

[0151]

[0152] In the formula: S N S represents the power distribution capacity of the charging station. Nmax S represents the upper limit of the power distribution capacity of the charging station, and s represents the number of charging piles. e P represents the total office capacity, β represents the transformer load rate within the charging station, and P represents the total office capacity. k Indicates the rated charging power, η represents the charging power. Indicates the power factor angle; s max ρ represents the maximum number of charging piles; ρ represents the device utilization rate. min Indicates the lower limit of equipment utilization; P sto P represents the rated power of energy storage. d,cstoIndicates the energy storage charge / discharge rate; k represents the load response. Di *P Di This indicates the maximum load that can be responded to.

[0153] The constraint is to implement demand response constraints. The DR ratio determines the maximum load, and the DR ratio can be set differently for different energy storage power stations.

[0154] In summary, the optimal configuration model for the capacity of electric vehicle photovoltaic-storage charging stations can be obtained as follows:

[0155]

[0156] 4. Solve the capacity optimization configuration model for electric vehicle photovoltaic-storage charging stations.

[0157] Using the particle swarm optimization algorithm, suppose the optimal value during a particle's journey is P. best And by filtering all individuals, the optimal value G for the population is obtained. best Each particle in the particle swarm is determined according to P best and G best The search is adjusted, and the optimal fitness value G is reached during the adjustment process. best That is, the optimal solution;

[0158] During the k-th search, particle i passes through reference P best and G best It updates its own parameters;

[0159]

[0160] In the formula: c1 and c2 represent acceleration factors, which are constants; rand() represents a random number between [0,1]; w represents the inertia factor; v id (t) represents the velocity of particle i at time t, p id (t) represents the value of the number of charger particles i during their travel at time t, x id (t) represents the position of the charger particle i at time t, p gd (t) represents the value of the photovoltaic particle during its travel at time t, x gd (t) represents the position of the photovoltaic particle at time t;

[0161] The ranges of position and velocity in the d-th (1≤d≤M) dimension are respectively [-x d，max ,x d，max ] and [-v d，max ,v d，max (The range of change can be made symmetrical through translation). During iteration, if a certain dimension x... id or vid If the value exceeds the boundary, the boundary value is used.

[0162] Based on the particle swarm optimization algorithm, the number of charger particles, photovoltaic capacity particles, and energy storage device capacity particles that meet the constraints are solved. The process is as follows: Figure 3 As shown.

[0163] and Figure 1 Corresponding to the method described above, this embodiment of the invention also provides a novel carbon emission index evaluation system based on optimized configuration of photovoltaic-storage charging stations, used for evaluating... Figure 1 The specific implementation of the method, as provided in this embodiment of the invention, is a novel energy carbon index evaluation system based on optimized configuration of photovoltaic-energy storage charging stations. This system can be applied to computer terminals or various mobile devices, and specifically includes:

[0164] The queuing process simulation module is used to establish a queuing model for electric vehicles at photovoltaic and energy storage charging stations and provide demand response data.

[0165] The objective function acquisition module considers demand response data and establishes an objective function based on a novel electric carbon index evaluation model.

[0166] The constraint setting module is used to set constraints based on actual conditions and build an optimized capacity configuration model for electric vehicle photovoltaic-storage charging stations.

[0167] The solution module is used to solve the capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations using the particle swarm optimization algorithm, and to evaluate the carbon emissions of photovoltaic-storage charging stations.

[0168] In summary, this embodiment introduces a participation index, significantly improving the fairness of the assessment by considering the actual participation and contribution of enterprises in demand response peak shaving. It overcomes the limitation of traditional tiered carbon emission mechanisms that only focus on incremental carbon emissions, enabling the assessment results to more accurately reflect the efforts and contributions of enterprises in the peak shaving process. By combining the two core indicators of incremental size and participation, a multi-dimensional assessment system is formed. This design makes the assessment results more comprehensive and accurate, reflecting the actual operating status and environmental benefits of photovoltaic-storage charging stations in the power system. The model assessment results provide guidance for enterprises to optimize their configurations. Enterprises can understand their performance in demand response peak shaving based on the assessment results and adjust their resource allocation and operation management strategies accordingly, thereby improving the operating efficiency of photovoltaic-storage charging stations and reducing carbon emissions. The data analysis and processing methods behind the model are crucial to its advantages. Through scientific methods of processing and analyzing large amounts of data, the carbon emission situation of photovoltaic-storage charging stations can be accurately assessed, providing strong support for the low-carbon transformation of the power industry. The integrated application of these technologies gives the new carbon index assessment model based on the optimized configuration of photovoltaic-storage-charging stations significant advantages in terms of fairness, assessment accuracy, promoting optimized configuration, and driving low-carbon transformation.

[0169] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0170] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the carbon emission index based on the optimized configuration of photovoltaic-storage charging stations, characterized in that, Includes the following steps: Establish a queuing model for electric vehicles at photovoltaic-storage charging stations to provide demand response data; Considering demand response data, establish an objective function for an evaluation model based on the carbon index of electricity; Based on actual conditions, constraints are set to construct a capacity optimization configuration model for electric vehicle photovoltaic-storage charging stations. The capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations is solved based on the particle swarm optimization algorithm to evaluate the carbon emissions of photovoltaic-storage charging stations. The establishment of a queuing model for electric vehicles at photovoltaic-storage charging stations includes the following steps: Based on the principles of queuing theory, for the M / G / S queuing system, initial values ​​are set for the starting SOC and starting charging time of electric vehicles that follow exponential and normal distributions, respectively. By using Monte Carlo sampling, the starting SOC and starting charging time of multiple electric vehicles are randomly selected to generate an electric vehicle charging sequence. Based on the electric vehicle charging sequence, the queuing process of electric vehicles at the photovoltaic-storage charging station is simulated, and an electric vehicle charging decision matrix D with dimension M×N is established. In the formula, D is a 0-1 matrix, and the matrix element d ij This indicates whether the i-th vehicle is charging during the j-th unit charging period; The objective function for establishing the carbon index assessment model is as follows: With the objective of minimizing the total investment cost of the photovoltaic-storage charging station, and considering its participation in grid load demand response, the total cost of the photovoltaic-storage charging station is factored into the unit time, resulting in the objective function: f=min(F S +DIC+DCC) In the formula: F s The fixed investment cost is represented by DIC, the DR equipment investment cost is represented by DCC, and the DR compensation cost is represented by DCC. The formula for calculating the investment cost of DR equipment is as follows: The formula for calculating DR compensation costs is as follows: In the formula: This represents the cost per unit capacity of DR equipment; k Di Let P be the DR percentage of node i, and let P be a constant between 0 and 1. Di *k Di Indicates the maximum responsive load; k c This represents the participation coefficient. This represents the compensation cost for providing a unit of energy load response, where T is the time during which demand response occurs throughout the year. N represents the load response. k Indicates the number of times the user participated in demand response; P Di This represents the maximum load of node i participating in the demand response.

2. The method for evaluating the carbon emission index based on the optimized configuration of photovoltaic-storage charging stations according to claim 1, characterized in that, The photovoltaic-storage charging station includes: photovoltaic cells, DC / AC modules, power distribution network, AC / DC modules, charging station, first DC / DC module, second DC / DC module, charging pile, and energy storage module; wherein, the photovoltaic cells, DC / AC modules, charging station, first DC / DC module, and charging pile are connected in sequence, and the power distribution network, AC / DC module, charging station, second DC / DC module, and energy storage module are connected in sequence.

3. The method for evaluating the carbon emission index based on the optimized configuration of photovoltaic-storage charging stations according to claim 1, characterized in that, Fixed investment cost F s The calculation formula is: F s =F s1 +F s2 +F gc +F gd In the formula: F s1 Indicates the investment cost within the station, F s2 Indicates the investment cost of charging piles, F gc Indicates the cost of photovoltaic energy storage, F gd Indicates the cost of purchasing electricity; Among them, the on-site investment cost F of the charging station s1 The calculation formula is: In the formula: C td C represents the cost of non-charging parking spaces. yu C represents operating costs. zm r0 represents the cost of electricity used in the office; r0 represents the discount rate; n cs Indicates the specific operating years of the charging station; S e α represents the total office capacity, β represents the power distribution cost per unit capacity, β represents the transformer load rate within the charging station, and z represents the investment cost ratio coefficient within the station. Charging pile investment cost F s2 The calculation formula is: C gm =c k ×P k C wa =k×C gm In the formula: C pd C represents the power distribution cost of a single charging facility. gm C represents the purchase cost of a single charging facility. wa C represents the maintenance and installation cost of a single charging facility. cw This represents the land cost of a single charging parking space; c k P represents the purchase cost of charging facilities per unit power, k represents the ratio of maintenance and installation costs to purchase costs, and s represents the number of charging stations. k Indicates the rated charging power, and η represents the charging efficiency. Indicates the power factor angle; Photovoltaic energy storage cost F gc Including photovoltaic installation costs and energy storage installation costs, the calculation formula is expressed as follows: F gc =F pv +F sto F pv =z pv C pv P pv +C mpv C mpv =k gc ×C pv ×P pv F sto =z sto [r p P sto +(r e -r r )E sto ]+C msto C msto =k gc ×r e ·E sto In the formula: F pv F represents the photovoltaic investment cost of the charging station. sto Indicates the energy storage investment cost of the charging station; C pv P represents the initial investment cost per unit capacity of photovoltaic power. pv C represents the photovoltaic configuration capacity. mpv Indicates the cost of photovoltaic maintenance, r gc n represents the discount rate for photovoltaics and energy storage. pv k represents the entire lifecycle of a photovoltaic system. gc This represents the annual maintenance cost coefficient for the photovoltaic-storage system; n sto Indicates the entire lifecycle of the energy storage system, r p r represents the unit power cost of energy storage. e r represents the unit cost of energy storage. r P represents the unit recycling cost. sto E represents the rated power of energy storage. sto Indicates the energy storage configuration capacity, C msto Indicates energy storage maintenance costs; z pv z represents the proportion coefficient of photovoltaic investment costs. sto This represents the cost ratio of energy storage configuration; Electricity purchase cost F gd The cost of purchasing electricity from the grid to meet the charging needs of electric vehicles is calculated using the following formula: In the formula: n represents the number of peak, flat, and valley periods in a day, T i P represents the time span of period i. i C represents the amount of electricity purchased during time period i. gdi P represents the electricity price during time period i. i s k represents the electricity sold to the grid during time period i. gd This represents the ratio of the electricity price sold to the electricity price purchased.

4. The method for evaluating the carbon emission index based on the optimized configuration of photovoltaic-storage charging stations according to claim 1, characterized in that, The constraints established based on actual circumstances specifically include: Power distribution capacity constraints, considering the limitations on the power distribution capacity of charging stations: The number of charging stations is limited by the available land area. s≤s max Equipment utilization is constrained by the economic constraints of charging infrastructure configuration; the limiting conditions for equipment utilization are as follows: ρ≥ρ min Energy storage charge and discharge constraints: the limitations on the charge and discharge power of energy storage devices, i.e., the charge and discharge rate. -P sto ≤P d,csto ≤P sto Demand response constraint: The amount of demand response a user participates in at any one time must not exceed the maximum load demand response amount. In the formula: S N S represents the power distribution capacity of the charging station. Nmax S represents the upper limit of the power distribution capacity of the charging station, and s represents the number of charging piles. e P represents the total office capacity, β represents the transformer load rate within the charging station, and P represents the total office capacity. k Indicates the rated charging power, and η represents the charging efficiency. Indicates the power factor angle; s max ρ represents the maximum number of charging piles; ρ represents the device utilization rate. min Indicates the lower limit of equipment utilization; P sto P represents the rated power of energy storage. d,csto Indicates the energy storage charge / discharge rate; k represents the load response. Di *P Di This indicates the maximum load that can be responded to.

5. The method for evaluating the carbon emission index based on the optimized configuration of photovoltaic-storage charging stations according to claim 1, characterized in that, The particle swarm optimization algorithm is used to solve the capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations. Specifically: Using the particle swarm optimization algorithm, suppose the optimal value during a particle's journey is P. best And by filtering all individuals, the optimal value G for the population is obtained. best Each particle in the particle swarm is determined according to P best and G best The search is adjusted, and the optimal fitness value G is reached during the adjustment process. best That is, the optimal solution; During the k-th search, particle i passes through reference P best and G best It updates its own parameters; In the formula: c1 and c2 represent acceleration factors, which are constants; rand() represents a random number between [0,1]; w represents the inertia factor; v id (t) represents the velocity of particle i at time t, p id (t) represents the value of the number of charger particles i during their travel at time t, x id (t) represents the position of the charger particle i at time t, p gd (t) represents the value of the photovoltaic particle during its travel at time t, x gd (t) represents the position of the photovoltaic particle at time t; The ranges of position and velocity in the d-th dimension are respectively [-x d，max ,x d，max ] and [-v d，max ,v d，max In the iteration, if a certain dimension x id or v id If the value exceeds the boundary, the boundary value is used. Based on the particle swarm optimization algorithm, we solve for the number of charger particles, photovoltaic capacity particles, and energy storage device capacity particles that meet the constraints.

6. A carbon emission index evaluation system based on optimized configuration of photovoltaic-energy storage charging stations, implementing the method of any one of claims 1-5, characterized in that, include: The queuing process simulation module is used to establish a queuing model for electric vehicles at photovoltaic and energy storage charging stations and provide demand response data. The objective function acquisition module considers demand response data and establishes an objective function based on the electricity carbon index evaluation model. The constraint setting module is used to set constraints based on actual conditions and build an optimized capacity configuration model for electric vehicle photovoltaic-storage charging stations. The solution module is used to solve the capacity optimization configuration model of electric vehicle photovoltaic-storage charging stations using the particle swarm optimization algorithm, and to evaluate the carbon emissions of photovoltaic-storage charging stations.

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