AC / DC hybrid power distribution network energy storage optimization configuration method considering carbon transaction mechanism

Abstract

The invention relates to an AC / DC hybrid power distribution network energy storage optimization configuration method considering a carbon transaction mechanism. The method comprises the following steps: constructing a carbon transaction mechanism mathematical model oriented to an AC / DC hybrid power distribution network; combining the carbon transaction mechanism mathematical model to establish an AC / DC hybrid power distribution network energy storage double-layer planning model; and solving the AC / DC hybrid power distribution network energy storage bilevel programming model by using an improved multi-target particle swarm optimization algorithm fused with chaos initialization. According to the method, a reasonable energy storage configuration scheme can be obtained, so that the vulnerability of the system and the carbon emission generated by operation are reduced, and the power balance maintaining capability of the system is improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage optimization configuration technology for AC / DC hybrid distribution networks, specifically relating to an energy storage optimization configuration method for AC / DC hybrid distribution networks that takes into account carbon trading mechanisms. Background Technology

[0002] With the high proportion of wind and solar energy integrated into the distribution network, traditional AC distribution networks face the problem of increased energy loss. DC grids, on the other hand, offer advantages such as flexible integration methods and low power transmission losses, enabling hybrid AC / DC distribution networks to more efficiently accommodate DC loads. They are expected to see widespread application in the near future. Energy storage, with its flexible charging and discharging regulation and power supply capabilities, can effectively alleviate the timing mismatch between renewable energy output and load. Considering these characteristics, researching the optimization of energy storage configuration in hybrid AC / DC distribution networks is of great significance for improving their economical and reliable operation.

[0003] The uncertainty and volatility of renewable energy output from sources such as wind and solar power pose significant challenges to real-time power balancing and energy storage configuration. With the establishment of China's carbon trading market, carbon trading costs have become a crucial factor to consider in energy storage configuration. Currently, most research on carbon trading focuses on integrated energy systems and microgrids, with few studies examining AC / DC hybrid distribution networks.

[0004] The overall vulnerability of power grids encompasses voltage vulnerability and load balance. Voltage and load are key reference indicators for the operation and planning of distribution networks, and their matching reflects the balance between supply and demand. Effective voltage regulation and load balancing can improve the stability, reliability, and economy of the power system. However, most studies focus on configuring energy storage from the perspectives of economy and low carbon emissions, with few studies considering the impact of the energy storage's access location on the overall vulnerability of the distribution network. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized configuration method for energy storage in AC / DC hybrid distribution networks that takes into account carbon trading mechanisms. This method is beneficial for obtaining reasonable energy storage configuration schemes to reduce system vulnerability and carbon emissions generated during operation, and to improve the system's ability to maintain power balance.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for optimizing the configuration of energy storage in an AC / DC hybrid distribution network that takes into account the carbon trading mechanism, comprising the following steps:

[0007] Construct a mathematical model for a carbon trading mechanism oriented towards AC / DC hybrid distribution networks;

[0008] A two-layer planning model for energy storage in AC / DC hybrid distribution networks is established by combining the mathematical model of the carbon trading mechanism.

[0009] An improved multi-objective particle swarm optimization algorithm with fused chaotic initialization is applied to solve the bi-level programming model of AC / DC hybrid distribution network energy storage.

[0010] Furthermore, the construction of the mathematical model for the carbon trading mechanism oriented towards AC / DC hybrid distribution networks specifically includes the following steps:

[0011] Step S11: Calculate the carbon trading allowances for the operation of the AC / DC hybrid distribution network:

[0012] Carbon emission allowances are determined based on the total load of the AC / DC hybrid distribution network system.

[0013]

[0014] In the formula, E np For system carbon emission quotas; μ load Carbon emission allowance per unit load; This refers to the system load power.

[0015] Step S12: Construct a mathematical model for the carbon trading mechanism:

[0016] The actual carbon emission share of AC / DC hybrid distribution network systems participating in carbon trading during operation and carbon trading costs for:

[0017]

[0018] In the formula, E act The actual carbon emissions of the system; γ th γ is the carbon emission factor per unit electrical power of a gas turbine. sub Carbon emission factor of the power purchased from the higher-level power grid unit; For the electrical power of the gas turbine; α represents the carbon trading cost; α represents the basic carbon trading price.

[0019] Furthermore, the establishment of the AC / DC hybrid distribution network energy storage two-layer planning model specifically includes the following steps:

[0020] Step S21: Construct a planning layer model with energy storage installation location and capacity as decision variables. Based on satisfying energy storage planning and state of charge constraints, and with investment cost, operation and maintenance cost, and energy storage operation cost as optimization objectives, pass topology parameters including energy storage device installation location and capacity to the operation layer model:

[0021] F1=min(f inv +f re +f cd (4)

[0022]

[0023] In the formula, N Ess f is the number of nodes connected to the energy storage device. inv For investment costs; E ins,i and P ins,i Let f be the installed capacity and installed power of the i-th energy storage device, respectively; α and β are the costs per unit installed capacity and per unit installed power, respectively; f is the cost of f. re For operation and maintenance costs; η loss y is the energy storage operation loss rate; N is the service life; y is the maintenance cost per unit capacity; f cd For energy storage operating costs; and These represent charging and discharging power, respectively; λ ch and λ dis These are the costs per unit of charging and discharging power, respectively.

[0024] The constraints of the planning layer model are:

[0025]

[0026] In the formula, and Let SOC be the minimum and maximum state of charge of the i-th energy storage unit. i,t Let be the state of charge of the i-th energy storage unit at time t; and Let be the minimum and maximum installed power of the i-th energy storage unit, respectively; Let be the output power of the i-th energy storage unit at time t; and Let be the minimum and maximum installed capacities of the i-th energy storage unit, respectively; Let be the actual capacity of the i-th energy storage unit at time t;

[0027] Step S22: Construct an operation layer model with the system scheduling scheme as the decision variable. Based on the energy storage configuration solved by the planning layer model, the operation layer model takes the overall system operating cost, network loss, and overall grid vulnerability index as optimization objectives, and the power flow balance of the AC / DC subgrid, the operating status of the converter station, and the operating status of the energy storage as constraints. It feeds back the operating parameters, including the output of various power sources and energy storage, to the planning layer.

[0028] F2=min(f1+f2+f3) (7)

[0029] In the formula, f1 represents the overall system operating cost; f2 represents network loss; and f3 represents the overall system vulnerability index.

[0030] The overall operating cost of the system includes carbon trading costs, electricity purchase costs, and power generation costs;

[0031]

[0032] In the formula, f s For the cost of purchasing and generating electricity; For carbon trading costs; N c Let P(i) be the number of scenes; P(i) be the probability of the i-th scene. and λ sub These are the power purchased from the upper-level power grid and the cost per unit of purchased power, respectively. and λ th These are the gas turbine's power generation capacity and the cost per unit of power generation; and λ pv These are photovoltaic power generation capacity and cost per unit of power generation;

[0033] To reduce network losses, the energy storage system in the distribution network is equivalent to a power source or load. By responding to load demand, the feeder current is reduced, thereby reducing network losses.

[0034]

[0035] In the formula, ω(i) is the set of parent nodes of node i; and These are the current and resistance of the AC branch line hi, respectively. and The current and resistance of the DC branch hi;

[0036] The comprehensive vulnerability index of the power grid is constructed from the perspective of system safe operation, combining voltage fluctuations and load distribution. The power grid's ability to withstand risks is measured by analyzing the degree of voltage deviation from the standard value of each node and the degree of balance of load distribution, thereby improving power quality.

[0037]

[0038] In the formula, AV t and BV t This indicates the voltage vulnerability and load balance at time t.

[0039] The formula for calculating voltage vulnerability is:

[0040]

[0041] In the formula, v i,t and u i,t Let i represent the voltage vulnerability and voltage value of node i at time t. The rated voltage of node i; Δu max This represents the maximum voltage offset; V i,t The normalized voltage vulnerability; and These represent the maximum and minimum node voltage vulnerability values ​​before normalization, respectively; N s This refers to the number of nodes in an AC / DC hybrid distribution network system.

[0042] The formula for calculating load balance is:

[0043]

[0044]

[0045] In the formula, h i,t and P i,t These represent the load fluctuation deviation and power of node i at time t, respectively. H represents the average power of node i over time T; i,t This is the normalized load deviation; and G represents the maximum and minimum values ​​of the nodal load fluctuation deviation before normalization, respectively; i,t This represents the ratio of the load deviation of node i at time t to the total deviation of all nodes in the system.

[0046] The constraints of the operational layer model include power flow balance constraints of the AC / DC subnetwork, converter station constraints, and energy storage operation constraints:

[0047] The power flow balance constraints of the AC / DC subnet are:

[0048]

[0049] In the formula, ω(i) is the set of parent nodes of node i; Let i be the set of end nodes of the branch with node i as the first end node; and These are the active power and reactive power of AC regional branch ij at time t, respectively. and These are the active power and reactive power of AC regional branch hi at time t, respectively. Let be the current in the AC branch hi at time t; and These are the resistance and reactance of the AC branch Hi, respectively; and These represent the injected active power and reactive power of node i in the AC region at time t, respectively. and Let be the active power of DC branch ij and hi at time t, respectively; Let be the current in the DC branch hi at time t; The resistance of the DC branch hi; Let be the injected power at node i in the DC region at time t;

[0050] The converter station constraints are:

[0051]

[0052] In the formula, and These represent the active power injected into the AC and DC networks of the converter station, respectively; μ is the power loss coefficient of the converter station. Power loss at the converter station;

[0053] Energy storage operation constraints are:

[0054]

[0055] In the formula, and Let μ represent the discharge power and charging power of the i-th energy storage unit at time t, respectively; ess It is a 0-1 function, representing the charging and discharging state of the energy storage system; and This represents the maximum charge and discharge value of the energy storage system. These represent the minimum and maximum capacity of the energy storage system, respectively; η ch η dis These are the charging and discharging efficiencies, respectively. Let represent the capacity of the i-th energy storage unit at time t.

[0056] Furthermore, the application of the improved multi-objective particle swarm optimization algorithm with chaotic initialization to solve the AC / DC hybrid distribution network energy storage two-level planning model specifically includes the following steps:

[0057] Step S31: Input the AC / DC hybrid distribution network system parameters, use the historical output data and meteorological data of the photovoltaic power station to characterize the photovoltaic output characteristics using the scenario generation method, and use the scenario reduction technology to obtain the scenario set;

[0058] Step S32: Edit the sequence of decision variables in the planning layer, initialize the chaotic position and velocity of particles, and pass parameters to the running layer;

[0059] Step S33: Based on the output of the planning layer and the objective function and constraints of the operation layer, obtain the optimized system operation strategy and return it to the planning layer;

[0060] Step S34: Based on the operation strategy fed back from the operation layer, calculate the energy storage planning cost of the planning layer, and obtain the population fitness and the installation location and capacity of the energy storage;

[0061] Step S35: Repeat the above steps to update the population; determine whether the convergence condition has been met. If yes, proceed to the next step; otherwise, go to step S34.

[0062] Step S36: Output the solution set of the Pareto front, namely the energy storage configuration scheme of the planning layer and the scheduling results of the operation layer.

[0063] Compared with existing technologies, the present invention has the following beneficial effects: The present invention provides an optimized configuration method for energy storage in AC / DC hybrid distribution networks that takes into account carbon trading mechanisms. To address the problems of power quality degradation and system energy imbalance caused by source-load fluctuations and mismatches, a two-layer planning method for energy storage is proposed, which takes into account economic efficiency, low carbon emissions, and the overall vulnerability of the power grid. The planning layer takes the energy storage planning cost as the optimization objective, while the operation layer takes the overall system operation cost, network loss, and overall power grid vulnerability index as optimization objectives. An improved particle swarm optimization algorithm with chaotic initialization is used to solve the configuration model, thereby obtaining a reasonable energy storage configuration scheme to reduce system vulnerability and carbon emissions generated during operation, and improve the system's ability to maintain power balance. Attached Figure Description

[0064] Figure 1 This is a structural diagram of the energy storage two-layer planning model according to an embodiment of the present invention;

[0065] Figure 2 This is a structural diagram of an AC / DC hybrid power distribution network system in an embodiment of the present invention;

[0066] Figure 3 This is a comparison chart of simulation results for electricity purchase, power generation costs, and carbon emissions in various scenarios according to embodiments of the present invention;

[0067] Figure 4 This is a diagram illustrating the system operation scheme under scenario 4 in this embodiment of the invention.

[0068] Figure 5 This is the energy storage operation scheme and SOC state diagram under scenario 4 in this embodiment of the invention. Detailed Implementation

[0069] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0070] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0071] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0072] This embodiment provides a method for optimizing the configuration of energy storage in an AC / DC hybrid distribution network that takes into account carbon trading mechanisms, including the following steps:

[0073] Step S1: Construct a mathematical model for a carbon trading mechanism oriented towards AC / DC hybrid distribution networks;

[0074] Step S2: Combine the mathematical model of the carbon trading mechanism to establish a two-layer planning model for AC / DC hybrid distribution network energy storage that takes into account economic efficiency, low carbon emissions, and the overall vulnerability of the power grid.

[0075] Step S3: Apply the improved multi-objective particle swarm optimization algorithm with fused chaotic initialization to solve the AC / DC hybrid distribution network energy storage two-level planning model.

[0076] In this embodiment, the following steps are also included:

[0077] Step S4: Collect system parameters and operating data, construct an improved IEEE 33-node AC / DC hybrid distribution network system, and conduct simulation verification.

[0078] In this embodiment, the construction of a mathematical model for a carbon trading mechanism oriented towards AC / DC hybrid distribution networks specifically includes the following steps:

[0079] Step S11: Calculate the carbon trading allowances for the operation of the AC / DC hybrid distribution network:

[0080] AC / DC hybrid distribution network systems generate carbon emissions during operation, and carbon regulatory authorities assign certain emission allowances to each entity within the system. Carbon emissions originate from the system's purchase of electricity from the upper-level grid and the operation of micro gas turbines. Photovoltaics, as a renewable energy source, do not emit carbon during system operation. Therefore, carbon emission allowances are determined based on the total load of the AC / DC hybrid distribution network system.

[0081]

[0082] In the formula, E np For system carbon emission quotas; μ load Carbon emission allowance per unit load; This represents the system load power.

[0083] Step S12: Construct a mathematical model for the carbon trading mechanism:

[0084] The actual carbon emission share of AC / DC hybrid distribution network systems participating in carbon trading during operation and carbon trading costs for:

[0085]

[0086] In the formula, Eact The actual carbon emissions of the system; γ th γ is the carbon emission factor per unit electrical power of a gas turbine. sub Carbon emission factor of the power purchased from the higher-level power grid unit; For the electrical power of the gas turbine; α represents the carbon trading cost; α represents the basic carbon trading price.

[0087] In this embodiment, the establishment of the AC / DC hybrid distribution network energy storage two-layer planning model specifically includes the following steps:

[0088] Step S21: Construct a planning layer model with energy storage installation location and capacity as decision variables. Based on satisfying energy storage planning and state of charge constraints, and with investment cost, operation and maintenance cost, and energy storage operation cost as optimization objectives, pass topology parameters including energy storage device installation location and capacity to the operation layer model:

[0089] F1=min(f inv +f re +f cd (4)

[0090]

[0091] In the formula, N Ess f is the number of nodes connected to the energy storage device. inv For investment costs; E ins,i and P ins,i Let f be the installed capacity and installed power of the i-th energy storage device, respectively; α and β are the costs per unit installed capacity and per unit installed power, respectively; f is the cost of f. re For operation and maintenance costs; η loss y is the energy storage operation loss rate; N is the service life; y is the maintenance cost per unit capacity; f cd For energy storage operating costs; and These represent charging and discharging power, respectively; λ ch and λ dis These are the costs per unit of charging and discharging power, respectively.

[0092] The constraints of the planning layer model are:

[0093]

[0094] In the formula, and Let SOC be the minimum and maximum state of charge of the i-th energy storage unit. i,t Let be the state of charge of the i-th energy storage unit at time t; and Let be the minimum and maximum installed power of the i-th energy storage unit, respectively; Let be the output power of the i-th energy storage unit at time t; and Let be the minimum and maximum installed capacities of the i-th energy storage unit, respectively; Let be the actual capacity of the i-th energy storage unit at time t.

[0095] Step S22: Construct an operation layer model with the system scheduling scheme as the decision variable. Based on the energy storage configuration solved by the planning layer model, the operation layer model takes the overall system operating cost, network loss, and overall grid vulnerability index as optimization objectives, and the power flow balance of the AC / DC subgrid, the operating status of the converter station, and the operating status of the energy storage as constraints. It feeds back the operating parameters, including the output of various power sources and energy storage, to the planning layer.

[0096] F2=min(f1+f2+f3) (7)

[0097] In the formula, f1 represents the overall system operating cost; f2 represents network loss; and f3 represents the overall system vulnerability index.

[0098] 1) Overall system operating cost. The overall system operating cost includes carbon trading costs, electricity purchase costs, and generation costs. Taking into account factors such as carbon trading costs, electricity purchase costs, and generation costs can help determine the optimal energy storage system configuration scheme and achieve multiple goals such as reducing energy costs, reducing carbon emissions, and improving energy utilization efficiency.

[0099]

[0100] In the formula, f s For the cost of purchasing and generating electricity; For carbon trading costs; N c Let P(i) be the number of scenes; P(i) be the probability of the i-th scene. and λ sub These are the power purchased from the upper-level power grid and the cost per unit of purchased power, respectively. and λ th These are the gas turbine's power generation capacity and the cost per unit of power generation; and λ pv These are photovoltaic power generation capacity and unit power generation cost, respectively.

[0101] 2) Network losses. Energy storage systems (ESS) in distribution networks can be considered equivalent to power sources or loads. By responding to load demands, they reduce feeder current, thereby lowering network loss costs.

[0102]

[0103] In the formula, ω(i) is the set of parent nodes of node i; and These are the current and resistance of the AC branch line hi, respectively. and This represents the current and resistance of the DC branch hi.

[0104] 3) Comprehensive Power Grid Vulnerability Index. From the perspective of system safety operation, a comprehensive power grid vulnerability index is constructed by combining voltage fluctuations and load distribution. The power grid's resilience is measured by analyzing the degree of voltage deviation from the standard value at each node and the degree of load distribution balance, thereby improving power quality.

[0105]

[0106] In the formula, AV t and BV t This represents the voltage vulnerability and load balance at time t.

[0107] The formula for calculating voltage vulnerability is:

[0108]

[0109] In the formula, v i,t and u i,t Let i represent the voltage vulnerability and voltage value of node i at time t. The rated voltage of node i; Δu max The maximum voltage offset is set to 0.07; V i,t The normalized voltage vulnerability; and These represent the maximum and minimum node voltage vulnerability values ​​before normalization, respectively; N s This refers to the number of nodes in a hybrid AC / DC distribution network system.

[0110] The formula for calculating load balance is:

[0111]

[0112] In the formula, h i,t and P i,t These represent the load fluctuation deviation and power of node i at time t, respectively. H represents the average power of node i over a given time range T. i,t This is the normalized load deviation; and G represents the maximum and minimum values ​​of the nodal load fluctuation deviation before normalization, respectively; i,t This represents the ratio of the load deviation of node i at time t to the total deviation of all nodes in the system.

[0113] The constraints of the operation layer model include power flow balance constraints of the AC / DC subgrid, converter station constraints, and energy storage operation constraints.

[0114] (1) The power flow balance constraints of the AC / DC subnet are:

[0115]

[0116] In the formula, ω(i) is the set of parent nodes of node i; Let i be the set of end nodes of the branch with node i as the first end node; and These are the active power and reactive power of AC regional branch ij at time t, respectively. and These are the active power and reactive power of AC regional branch hi at time t, respectively. Let be the current in the AC branch hi at time t; and These are the resistance and reactance of the AC branch Hi, respectively; and These represent the injected active power and reactive power of node i in the AC region at time t, respectively. and Let be the active power of DC branch ij and hi at time t, respectively; Let be the current in the DC branch hi at time t; The resistance of the DC branch hi; Let be the injected power at node i in the DC region at time t.

[0117] The first-stage objective function of the DC subgrid includes the operating cost of the energy storage system, the cost of purchasing and selling electricity from the AC subgrid, the cost of photovoltaic power generation in the DC region, and the cost of curtailment penalties. The second-stage objective function of the DC subgrid includes the actual cost of photovoltaic power generation in the DC region and the cost of adjusting for curtailment penalties.

[0118] (2) The converter station constraints are:

[0119]

[0120] In the formula, and These represent the active power injected into the AC and DC networks of the converter station, respectively; μ is the power loss coefficient of the converter station. This is to reduce power loss at the converter station.

[0121] (3) Energy storage operation constraints are:

[0122]

[0123] In the formula, and Let μ represent the discharge power and charging power of the i-th energy storage unit at time t, respectively; ess It is a 0-1 function, representing the charging and discharging state of the energy storage system; and This represents the maximum charge and discharge value of the energy storage system. These represent the minimum and maximum capacity of the energy storage system, respectively; η ch η dis These are the charging and discharging efficiencies, respectively. Let represent the capacity of the i-th energy storage unit at time t.

[0124] In this embodiment, the application of the improved multi-objective particle swarm optimization algorithm with fused chaotic initialization to solve the AC / DC hybrid distribution network energy storage two-level planning model specifically includes the following steps:

[0125] Step S31: Input the parameters of the AC / DC hybrid distribution network system. Using the historical output data and meteorological data of a photovoltaic power station, the photovoltaic output characteristics are characterized by an improved scenario generation method that combines meteorological influence factors and extreme learning machine. Scenario reduction technology is then used to obtain a scenario set with higher probability.

[0126] Step S32: Edit the sequence of decision variables in the planning layer, initialize the chaotic position and velocity of particles, and pass parameters to the running layer;

[0127] Step S33: Based on the output of the planning layer and the objective function and constraints of the operation layer, obtain the optimized system operation strategy and return it to the planning layer;

[0128] Step S34: Based on the operation strategy fed back from the operation layer, calculate the energy storage planning cost of the planning layer, and obtain the population fitness and the installation location and capacity of the energy storage;

[0129] Step S35: Repeat the above steps to update the population; determine whether the convergence condition has been met, i.e., the number of iterations reaches 20 or the absolute value of the error between two adjacent iterations is less than the given threshold of 0.5%; if satisfied, proceed to the next step; otherwise, go to step S34.

[0130] Step S36: Output the solution set of the Pareto front, namely the energy storage configuration scheme of the planning layer and the scheduling results of the operation layer.

[0131] In this embodiment, the process of collecting system parameters and operational data, and constructing an improved IEEE 33-node AC / DC hybrid distribution network system for simulation verification specifically includes the following steps:

[0132] Step S41: Collect historical photovoltaic power output data and corresponding meteorological data; collect data on existing generating units in the power grid, including the annual load, unit capacity and power limit of the power grid; collect AC / DC hybrid distribution network structure data, including line resistance and reactance, node voltage, etc.; collect data on time-of-use electricity pricing and carbon trading, etc.

[0133] Step S42: Based on the collected network structure data, construct the IEEE 33-node AC / DC hybrid distribution network system;

[0134] Step S43: Apply the collected data and evaluation metrics to verify the effectiveness of the proposed improved scenario generation method.

[0135] Step S44: Apply the proposed energy storage configuration method to the constructed distribution network system for simulation verification.

[0136] Next, this embodiment will be further explained with reference to specific parameters.

[0137] According to step S1, the carbon trading quota and actual carbon emissions of the AC / DC hybrid distribution network are calculated by formula (1) and formula (2). The actual carbon emissions participating in carbon trading are determined by formula (3). A carbon trading cost model for AC / DC hybrid distribution networks is established, where the carbon trading parameters are set as follows: the basic carbon trading price is 100 yuan / tCO2.

[0138] Based on step S2, establish a two-layer planning model for energy storage in AC / DC hybrid distribution networks that takes into account economic efficiency, low carbon emissions, and the overall vulnerability of the power grid. The model configuration is as follows: Figure 1 As shown:

[0139] 1) The planning layer model takes the energy storage installation location and capacity as decision variables. On the basis of satisfying the energy storage planning and state of charge constraints, it takes investment cost, operation and maintenance cost and energy storage operation cost as optimization objectives. It transmits topological parameters such as energy storage device installation location and capacity to the operation layer model. The mathematical model of the planning layer is shown in Equation (4-6).

[0140] 2) Based on the energy storage configuration obtained from the planning layer model, the operation layer model takes the system scheduling scheme as the decision variable, the comprehensive system operating cost, network loss and comprehensive grid vulnerability index as the optimization objectives, and the power flow balance of AC / DC subgrid, converter station operation status and energy storage operation status as constraints. It feeds back the operation parameters of various power sources and energy storage output to the planning layer. The mathematical model of the operation layer is shown in Equation (7-19).

[0141] According to step S3, the improved particle swarm optimization algorithm with fused chaotic initialization is used to solve the energy storage two-layer configuration model to obtain the optimal configuration scheme and operation strategy.

[0142] According to step S4, based on the collected network data, an IEEE 33-node topology for an AC / DC hybrid distribution network system is constructed to verify the proposed AC / DC hybrid distribution network optimization operation strategy. The distribution network system topology is as follows: Figure 2 As shown.

[0143] (1) Effectiveness analysis of energy storage configuration methods

[0144] To verify the effectiveness of the proposed energy storage configuration method, four scenarios were set up for simulation analysis:

[0145] Scenario 1: Without energy storage, calculate the economic efficiency and overall grid vulnerability based on the original system parameters;

[0146] Scenario 2: Configure energy storage and adopt a single-layer multi-objective optimization model. The planning results are aimed at economic efficiency and the overall vulnerability of the power grid.

[0147] Scenario 3: Configure energy storage, adopt a two-layer coupling model, and plan the results with the goal of economic efficiency and overall grid vulnerability.

[0148] Scenario 4: Configure energy storage, adopt a two-layer coupling model, and plan the results with the goals of economy, low carbon emissions and comprehensive grid vulnerability.

[0149] The energy storage planning schemes and configuration results for the above scenarios are shown in Table 1.

[0150] Table 1. Energy storage configuration schemes and results under different scenarios.

[0151]

[0152] Based on the energy storage configuration schemes and results, Scenario 1 and Scenario 2 show significant differences in system operating costs, network losses, and overall grid vulnerability indicators. The differences in network losses and overall grid vulnerability indicators between the two scenarios are due to two factors: firstly, energy storage devices can optimize load distribution and reduce distribution network feeder current, thereby reducing network losses; secondly, energy storage acts as a voltage regulator, reducing voltage fluctuations at distribution network nodes and lowering system voltage deviations. By improving the balance of load distribution and reducing voltage vulnerability, overall grid vulnerability is optimized. The significant difference in system operating costs between the two scenarios is because energy storage devices charge when there is a power surplus in the distribution network and discharge when the load is high, reducing peak-to-valley differences, mitigating gas turbine start-up and shutdown losses, and increasing photovoltaic absorption, thus lowering system operating costs. Comparing Scenario 2 and Scenario 3, the energy storage planning cost, system operating cost, network loss, and grid vulnerability index configured by the two-layer optimization model are RMB 0.5911 million, RMB 6.5537 million, 0.5812 MW, and 0.4265, respectively, representing reductions of 7.68%, 2.79%, 9.92%, and 6.88% compared to the single-layer optimization model. The two-layer optimization model, by considering the dynamic operation of energy storage devices, can more effectively coordinate source-load contradictions, improving the safety and economy of system operation. Comparing the results of Scenario 3 and Scenario 4, the differences in energy storage planning cost, network loss, and grid vulnerability are relatively small. However, the system operating cost of Scenario 4 is higher than that of Scenario 3. This is because Scenario 4 introduces a carbon trading mechanism, which includes carbon trading costs in the operating cost. In Scenario 4, to reduce the penalties caused by carbon emissions, the system will increase the consumption of renewable energy during dispatching, reducing the purchase of electricity from the upper-level grid and gas turbine power generation. Therefore, under the effect of the carbon trading mechanism, the operating cost of Scenario 4 is higher than that of Scenario 2 and Scenario 3, but carbon emissions are significantly reduced. Electricity purchase, generation costs, and carbon emissions under four scenarios, as follows: Figure 3 As shown.

[0153] Depend on Figure 3 It is evident that configuring energy storage in the distribution network reduces its dependence on the upstream power grid, significantly lowering carbon emissions from system operation. Compared to Scenario 2, Scenario 4 reduces carbon emissions by 15.1 tons, or 20.9%; compared to Scenario 3, Scenario 4 reduces carbon emissions by 12.3 tons, or 17.8%. This demonstrates that energy storage planning methods incorporating carbon trading mechanisms can effectively reduce carbon emissions.

[0154] Based on the configuration results of Scenario 4, the energy storage devices are located at nodes 4 and 21, with an installed capacity of 160 kWh for each. Figure 4 The system operation plan under this scenario is as follows: Figure 5 This refers to the operation plan and SOC status of the energy storage device.

[0155] Depend on Figure 4 It can be seen that the system operation results under scenario 4 meet the load requirements, confirming the rationality of the scenario setting. According to Figure 5 In terms of energy storage operation, both energy storage devices can charge during off-peak hours and generate electricity during peak hours, achieving spatial and temporal transfer of electrical energy through reasonable charging and discharging. The SOC of the energy storage devices is between 0.4 and 0.75, which not only extends the lifespan of the energy storage but also helps the energy storage supply power to the system the next day, improving the stability of the distribution network system.

[0156] In summary, the energy storage two-layer low-carbon planning method proposed in this invention effectively reduces the carbon emissions of the system operation. Under the carbon trading mechanism, to reduce the penalties caused by carbon emissions, the scheduling process will increase the consumption of renewable energy, reduce the purchase of electricity from the upper-level grid and the generation of gas turbines, thereby reducing the cost of electricity purchase and generation and reducing carbon emissions.

[0157] This invention provides an optimized configuration method for energy storage in AC / DC hybrid distribution networks, taking into account carbon trading mechanisms. It proposes a two-layer low-carbon planning method for energy storage, effectively reducing carbon emissions during system operation. Under the carbon trading mechanism, to mitigate penalties for carbon emissions, the scheduling process increases the consumption of renewable energy, reduces power purchases from the upstream grid and gas turbine power generation, thereby lowering power purchase and generation costs and reducing carbon emissions. This invention constructs a two-layer planning model that considers the comprehensive vulnerability of the power grid, taking into account the dynamic operation strategies of energy storage devices, more effectively coordinating source-load contradictions, and improving the safety and economy of system operation.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimal configuration of energy storage in an AC / DC hybrid power distribution network considering carbon trading mechanism, characterized in that, The method comprises the following steps: A mathematical model of a carbon trading mechanism for an AC-DC hybrid power distribution network is constructed; A double-layer planning model of energy storage for the AC-DC hybrid power distribution network is established in combination with the mathematical model of the carbon trading mechanism; The double-layer planning model of energy storage for the AC-DC hybrid power distribution network is solved by using an improved multi-objective particle swarm optimization algorithm fusing chaos initialization. 2.The method of claim 1, wherein The method of constructing the mathematical model of the carbon trading mechanism for the AC-DC hybrid power distribution network specifically comprises the following steps: Step S11: calculating carbon trading quotas for operation of the AC-DC hybrid power distribution network: The carbon emission quotas are determined according to the total load of the AC-DC hybrid power distribution network system: In the formula, E np is the system carbon emission quota; μ load is the carbon emission quota per unit load; is the system load power; Step S12: constructing the mathematical model of the carbon trading mechanism: The carbon emission share of an AC-DC hybrid power distribution network system actually participating in carbon trading during operation and carbon trading cost f CO2 is: In the formula, E act is the actual carbon emissions of the system; γ th is the carbon emission factor of the unit electric power of the gas turbine; γ sub is the carbon emission factor of the unit purchased electric power to the upper grid; is the electric power of the gas turbine; is the carbon trading cost; and α is the basic price of carbon trading. 3.The method of claim 1, wherein The method of establishing the double-layer planning model of energy storage for the AC-DC hybrid power distribution network specifically comprises the following steps: Step S21: constructing a planning layer model taking the installation location and capacity of energy storage as decision variables, and passing topological parameters including the installation location and capacity of energy storage to an operation layer model as optimization objectives on the basis of satisfying energy storage planning and state of charge constraints: F1 = min(f inv +f re +f cd ) (4) In the formula, N Ess is the number of access nodes of the energy storage device; f inv is the investment cost; E ins,i and P ins,i are the installed capacity and installed power of the ithenergy storage device, respectively; α and β are the costs of unit installed capacity and unit installed power, respectively; f re is the operation and maintenance cost; η loss is the energy storage operation loss rate; N is the service life; y is the unit capacity maintenance cost; f cd is the energy storage operation cost; and are the charging and discharging power, respectively; λ ch and λ dis are the costs of unit charging and discharging power, respectively; The constraint conditions of the planning layer model are: wherein, and are the minimum and maximum state of charge of the i-th energy storage; SOC i,t is the state of charge of the i-th energy storage at time t; and are the minimum and maximum installed power of the i-th energy storage; is the output power of the i-th energy storage at time t; and are the minimum and maximum installed capacity of the i-th energy storage; is the actual capacity of the i-th energy storage at time t. Step S22: constructing the operation layer model taking a system dispatching scheme as a decision variable, and feeding back operation parameters including outputs of various power sources and energy storage to the planning layer on the basis of the energy storage configuration solved by the planning layer model, the operation layer model taking system comprehensive operation cost, network loss and a system comprehensive vulnerability index as optimization objectives, and taking power flow balance of AC-DC subnetworks, operation states of converter stations and operation states of energy storage as constraint conditions: F2 = min (f1 + f2 + f3) (7) In the formula, f1 represents the system comprehensive operation cost, f2 represents the network loss, and f3 represents the system comprehensive vulnerability index; The system comprehensive operation cost includes carbon trading cost, power purchase cost and power generation cost; where f s is the cost of electricity purchase and generation; is the cost of carbon trading; N c is the number of scenarios; P(i) is the probability of the ith scenario; and λ sub are the power purchased from the upper grid and the cost per unit of the power purchased from the upper grid, respectively; and λ th are the power generated by the gas turbine and the cost per unit of the power generated by the gas turbine, respectively; and λ pv are the power generated by the photovoltaic and the cost per unit of the power generated by the photovoltaic, respectively. For the network loss, the energy storage system in the power distribution network is equivalent to a power source or a load, the load demand is responded to reduce feeder current, and thus the network loss is reduced; In the formula, ω(i) is the parent node set of i node; and are the current and resistance of the AC area branch hi, respectively; and are the current and resistance of the DC area branch hi, respectively; For the system comprehensive vulnerability index, the system safety operation angle is considered, and the system comprehensive vulnerability index is constructed in combination with voltage fluctuation and load distribution; the risk resistance ability of the power grid is measured by analyzing the degree of voltage offset standard value of each node and the balance degree of load distribution, and the power quality is improved; where AV t and BV t represent the voltage vulnerability and load balance degree at time t, respectively. The calculation formula of the voltage vulnerability is: where v i,t and u i,t are the voltage vulnerability and voltage value of node i at time t; is the rated voltage of node i; Δu max is the maximum voltage deviation; V i,t is the normalized voltage vulnerability; and respectively represent the maximum and minimum values of the node voltage vulnerability before normalization; N s is the number of nodes in the AC / DC hybrid distribution network system; The calculation formula of the load balance degree is: where h i,t and P i,t are the load fluctuation deviation and power of node i at time t, respectively; is the average power of node i in T time; H i,t is the normalized load deviation; and are the maximum and minimum values of the load fluctuation deviation of node i before normalization, respectively; G i,t is the ratio of the load deviation of node i at time t to the total deviation of the system nodes. The constraint conditions of the operation layer model include power flow balance constraints of AC-DC subnetworks, converter station constraints and energy storage operation constraints: The power flow balance constraints of the AC-DC subnetworks are: ω(i) is a parent node set of i node; is a branch end node set of i node as a head node; and are active power and reactive power of AC area branch ij at t moment respectively; and are active power and reactive power of AC area branch hi at t moment respectively; is current of AC area branch hi at t moment; and are resistance and reactance of AC area branch hi respectively; and are injected active power and reactive power of AC area i node at t moment respectively; and are active power of DC area branch ij and hi at t moment respectively; is current of DC area branch hi at t moment; is resistance of DC area branch hi; is injected power of DC area i node at t moment; The converter station constraints are: wherein and respectively the active power injected by the converter station into the AC network and into the DC network; μ is the converter station power loss coefficient; is the converter station loss power; The energy storage operation constraints are: wherein, and respectively represent the discharging power and charging power of the i-th energy storage at time t; μ ess is a 0-1 function, representing the charging and discharging state of the energy storage system; and are the maximum values of charging and discharging of the energy storage system; are respectively the minimum and maximum values of the capacity of the energy storage system; η ch , η dis are respectively the charging and discharging efficiencies; represents the capacity of the i-th energy storage at time t.

4. The method of claim 1, wherein, The method of applying the improved multi-objective particle swarm optimization algorithm fusing chaos initialization to solve the double-layer planning model of energy storage for the AC-DC hybrid power distribution network specifically comprises the following steps: Step S31: inputting system parameters of the AC-DC hybrid power distribution network, using scene generation method to depict photovoltaic output characteristics by using historical output data and meteorological data of photovoltaic power stations, and adopting scene reduction technology to obtain a scene set; Step S32: performing sequence editing on decision variables of the planning layer, and chaotically initializing positions and speeds of particles to pass parameters to the operation layer; Step S33: According to the output of the planning layer and the objective function and constraint condition of the operation layer, the optimized system operation strategy is obtained and returned to the planning layer; Step S34: According to the operation strategy fed back by the operation layer, the energy storage planning cost of the planning layer is calculated, and the population fitness and the installation location and capacity of the energy storage are obtained; Step S35: Repeat the above steps to update the population; judge whether the convergence condition is reached, yes to the next step, otherwise to step S34; Step S36: Output the solution set of the Pareto front, that is, the energy storage configuration scheme of the planning layer and the scheduling result of the operation layer.

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