Power distribution system source-load collaborative regulation method and system based on low-carbon demand response

Abstract

The present application belongs to the field of power distribution automation, and particularly relates to a power distribution system source-load collaborative regulation method and system based on low-carbon demand response. The method takes the minimum total cost of the power distribution system as the optimization target, establishes a two-stage robust optimization model of the power distribution system source side considering the uncertainty of photovoltaic output, the optimization target of the first stage is the minimum cost of the generator in different operating states, and the optimization target of the second stage is the minimum operating cost of the power distribution system in the worst scenario; the dispatching and power flow calculation results of the power distribution system are solved, and the minimum total carbon emission cost of the power distribution system is taken as the optimization target to guide the low-carbon demand response of the load side, obtain the adjusted load data of the power distribution system, and further optimize the total cost of the power distribution system to the lowest total cost of the power distribution system. The method can realize the robust operation of the economic low-carbon oriented power distribution system automation, improve the accuracy of carbon emission from the perspective of carbon potential representing power consumption, expand the application scenarios of carbon flow theory, and has fast solving speed.

Description

Technical Field

[0001] This invention belongs to the field of power distribution automation, specifically relating to a source-load coordinated control method and system for power distribution systems based on low-carbon demand response. Background Technology

[0002] Driven by the global energy structure transformation and the "dual carbon" goal, the modern power system is undergoing a revolutionary restructuring. [1] As a crucial component of the new power system, the distribution system bears the pressure of absorbing a high proportion of new energy sources and transitioning to a low-carbon model. It faces multiple challenges, including insufficient new energy absorption capacity, declining operational economics, and a lack of incentives for carbon emission reduction. [2] With the large-scale integration of diverse and flexible resources on both the power source and load sides, the power distribution system is gradually transforming from the traditional "power source follows load" to "power source interacts with load," urgently requiring the exploration of operating modes that can balance economy and low carbon emissions. [3] .

[0003] In the context of new power system construction, source-load coordination in distribution systems can mobilize resources within the system to fully interact, facilitating collaborative optimization decision-making among various stakeholders and serving as an effective means to achieve economical operation of the distribution system. (Literature) [4-5] This demonstrates that by constructing a source-load collaborative optimization model, the spatiotemporal matching optimization of distributed resources and flexible loads can be achieved, resulting in optimal economic performance for the entire system. (Reference) [6] This paper proposes a source-load collaborative operation strategy for distribution systems considering load aggregators. The objective function is to minimize the total cost of source-load collaborative operation, achieving overall system economic optimization. (References) [7] A regional integrated energy system optimization scheduling method based on source-load coordinated peak shaving is proposed. Natural gas heat pumps are introduced on the source side to give full play to the complementary characteristics of electricity and gas energy. On the load side, a demand response model based on electricity price is established to realize peak shaving and valley filling of electricity load. The simulation results show that the proposed method can improve the economic operation of the system.

[0004] Demand response (DR) is an important means of improving the economic efficiency of power system source-load coordinated operation. It emphasizes the two-way interaction between the supply and demand sides of the power grid, where the demand side adjusts its load demand according to electricity market prices and grid requirements to obtain certain benefits. [8] While traditional demand response can regulate load through incentive signals such as electricity prices, it fails to adequately tap the low-carbon potential of demand-side resources, making it difficult to meet the current urgent need for carbon reduction. [9] To address the lack of carbon reduction incentives in traditional demand response mechanisms, researchers both domestically and internationally have proposed numerous research findings. (Literature)

[10] The concept of low carbon demand response (LCDR) is proposed. LCDR is a novel power system regulation mechanism aimed at reducing carbon emissions. It primarily uses carbon market prices as incentive signals to guide users to proactively change their electricity consumption behavior, thereby reducing system carbon emissions and promoting the integration of renewable energy sources. (Literature)

[11] Based on low-carbon demand response, a source-load coordinated low-carbon optimization method for an electric-electric interconnected system is proposed, which realizes the low-carbon and economical operation of the system.

[0005] Combining carbon flow theory and robust optimization can provide new ideas for the low-carbon and economical operation of power systems.

[12] .literature

[13] A comprehensive energy optimization model considering extended carbon emission flows is established, and a two-stage robust optimization is introduced to handle the uncertainties in wind and solar power output, achieving the effect of low-carbon and economical system operation. Currently, some studies have focused on the positive role of source-load synergy combined with low-carbon demand response in the economical and low-carbon operation of power systems. [Literature]

[14] A source-load coordinated optimization model was constructed, integrating upper-level economic dispatch and lower-level low-carbon demand response. Simultaneously, the impact of energy storage on carbon flow and carbon potential was dynamically quantified, fully exploring the carbon reduction potential of diverse loads. (References)

[15] Based on master-slave game theory, a low-carbon optimization method for integrated energy systems is proposed, exploring the relationship between nodal carbon potential and carbon market price, and achieving a win-win situation for both system low-carbon development and economic benefits.

[0006] 1. Existing source-load coordinated optimization methods still need to improve the robustness of power distribution systems that are oriented towards economy and low carbon emissions to address the issue of reduced economic efficiency and low carbon emissions caused by power imbalances due to fluctuations in renewable energy output.

[0007] 2. Existing low-carbon optimization methods based on carbon flow theory have not adequately studied source-load synergy and have given little consideration to the impact of zero-carbon and carbon-carrying electrical energy generated by distributed power sources on carbon flow tracking in the system.

[0008] 3. Existing source-load coordinated regulation based on carbon potential typically requires iteratively transferring node carbon potential and load power between the source-side scheduling model and the load-side demand response model. As the adjustable load scale increases, repeated solutions prolong the regulation time, posing a risk of exceeding the scheduling timescale. Therefore, a rapid regulation method is needed that can directly determine the node load adjustment rate using existing optimization results.

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[0019]

[11] Liu Kezhen, Li Linyun, Lin Zheng, Zhao Qingli, Yang Fubao, Wang Xuantao. Low-carbon economic dispatch of source-load interaction in electric-electric interconnected systems considering carbon emission flows [J]. Electric Power Automation Equipment, 2024, 44(05): 1-10.

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[12] Wang Chong, Ge Yulin, Ju Ping, et al. Low-carbon economic operation of power distribution system based on dynamic allocation and scheduling priority of carbon emissions [J]. Power System Technology, 2023, 47(11): 4467-4476.

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[13] Zhang Xiaoyan, Wang Luyu, Huang Lei, Wang Shenghan, Wang Chutong, Guo Chuangxin. Integrated energy optimization scheduling in industrial parks considering extended carbon emission flows and carbon trading negotiation models [J]. Automation of Electric Power Systems, 2023, 47(09): 34-46.

[0022]

[14] Zhang Yumin, Sun Pengkai, Meng Xiangjian, Ji Xingquan, Yang Ming, Li Xiaoyu. Low-carbon economic dispatch of integrated energy system based on carbon potential-energy price dual response [J]. Automation of Electric Power Systems, 2024, 48(09): 21-33.

[0023]

[15] Yu Yang, Wu Qian, Huo Yuhang, Gao Shixuan, Xia Yuxing, Cai Xinlei. Low-carbon economic dispatch of integrated energy system based on master-slave game considering adaptive tiered carbon potential-carbon price and supply and demand dual response [J]. Power System Technology, 2024, 48(07): 2702-2714. Summary of the Invention

[0024] The purpose of this invention is to overcome at least one deficiency of the prior art and to provide a source-load coordinated control method and system for power distribution systems based on low-carbon demand response.

[0025] The technical solution adopted in this invention is: A first aspect of the present invention provides: a source-load coordinated control method for a power distribution system based on low-carbon demand response, comprising: Based on power distribution system operation data, and with the goal of minimizing the total operating cost of the power distribution system, a two-stage robust optimization model for the power distribution system source side is established, taking into account the uncertainty of photovoltaic output. The optimization objectives for the first and second stages are to minimize the generator operating cost and the power distribution system operating cost under the worst scenario, respectively. The robust optimization model is solved to output the power distribution system scheduling results and power flow calculation results; Based on the power distribution system scheduling results and power flow calculation results, and with the goal of minimizing the total carbon emission cost of the power distribution system, the load side is regulated to implement low-carbon demand response, specifically including: (1) Calculate the uncorrected node carbon potential NCI of the distribution system; (2) Update the carbon carrying capacity of the energy storage based on the power flow calculation results and the energy storage charge and discharge status, and calculate the dynamic carbon emission intensity of the energy storage; (3) Based on the calculation results of generator carbon emission intensity, energy storage dynamic carbon emission intensity and line power flow, generate the corrected node carbon potential of the distribution system; (4) After the first source-load co-optimization is completed on the scheduling day, the modified node carbon potential of the same node at each time period is used. and load adjustment rate Construct node-to-node carbon potential-load adjustment rate curves (NCI-LARC). , , For load adjustment, , and These represent the increases and decreases in load, respectively. To determine the baseline load power before participating in low-carbon demand response, in each formula, the subscript n is the index corresponding to the node, and t is the index corresponding to the time. In subsequent regulation cycles, the current corrected node carbon potential of node n is input into the NCI-LARC corresponding to node n to directly obtain the load adjustment rate of node n. (5) Generate load adjustment instructions based on the load adjustment rate, adjustable load boundary and power distribution system operation constraints and send them to the adjustable load terminal for execution; (6) Collect feedback operation data and check the node carbon potential, power flow constraint and load regulation deviation accordingly. If the preset conditions are not met, the load regulation command is rolled back and the NCI-LARC is updated.

[0026] In some instances, the objective function for the source-side two-stage robust optimization of a distribution system considering the uncertainty of photovoltaic output is shown in equation (1): Equation (1), where: and These are the objective functions for the first and second stages, respectively. It is an uncertain set of two-stage robust optimization; The first-stage optimization objective is to minimize the total cost of the generator under different operating states, and the objective function is shown in equation (2): Equation (2) In the formula: The number of generators , , and These represent the generator's operating status—running, starting, stopping, and maintenance—obtained by the sensor through operating status sampling. , , and t represents the corresponding cost coefficient; i is the index corresponding to the generator, and t is the index corresponding to the time.

[0027] In some instances, the optimization objective of the second stage is to minimize the total operating cost of the power distribution system under the worst-case scenario, and the objective function is shown in equation (3): Equation (3) In the formula: Generator output, The actual photovoltaic power absorbed by the power distribution system. For energy storage charging power, For energy storage discharge power, Here, k represents the available photovoltaic output, and m represents the index corresponding to photovoltaic power and m represents the index corresponding to energy storage power.

[0028] In some instances, the carbon emissions transferred to the load side of the distribution system are measured using Nodal Carbon Intensity (NCI), and the load side implements low-carbon demand response based on the NCI. NCI characterizes the carbon emissions generated per unit of electricity consumed by the load side from an electricity consumption perspective, and is mathematically represented as: Equation (4) In the formula: and These are the uncorrected NCI values ​​for nodes n and j during time period t, respectively; the positive active power flow direction of line l is from node j to node n; Provide power to the power source; Power Source Carbon Intensity (PSCI) is a measure of the amount of carbon emissions carried per unit of electrical energy in a power injection system. The set of all lines that inject active power flow into node n; Let n be the set of all power sources connected to node n.

[0029] In some instances, an improved nodal carbon potential calculation method is used to dynamically correct the nodal carbon potential in equation (4) that originally did not consider the carbon emissions of the photovoltaic-storage unit. The dynamic carbon emission intensity of energy storage is defined as follows: Equation (5) In the formula: The dynamic carbon emission intensity of energy storage represents the amount of carbon emissions carried by a unit of electrical energy injected into the power distribution system due to energy storage during discharge. The unit is the same as that of NCI. and These are the carbon dioxide carried by energy storage and the amount of electricity stored, respectively. and These represent the initial carbon carrying capacity and the initial energy storage capacity, respectively; m is the node number corresponding to the photovoltaic-storage unit. The dynamic carbon emission intensity PEDCI of photovoltaic energy storage is defined as shown in equation (6): Equation (6), where: It is PEDCI, with the same units as NCI; The modified node carbon potential of the power distribution system after the proposed improved node carbon potential calculation method is shown in equation (7). and The corrected NCI values ​​for nodes n and j in time period t are respectively. For generator carbon emission intensity; and These represent the set of all generators and the set of photovoltaic-storage units connected to node n, respectively. Equation (7).

[0030] In some instances, the low-carbon demand response, based on power flow calculations and power information at each node, outputs adjusted load power at each node and issues load adjustment commands. It also receives feedback from the load control terminal regarding the actual executed power and execution status. When the feedback deviation exceeds a preset threshold, the load adjustment commands are corrected. The objective is to minimize the total carbon emission cost of the power distribution system, mathematically represented as follows: Equation (8) Equation (9) In the formula: The objective function for low-carbon demand response on the load side is... The carbon emission cost coefficient for the power distribution system. Let n be the load adjustment amount for node n during time period t. and This represents the load increase and decrease of node n during time period t; The upper and lower limits of the load-side low-carbon demand response adjustment are shown in equations (10) and (11): Equation (10) Equation (11).

[0031] In some instances, the power distribution system operation data includes at least the photovoltaic predicted power, actual photovoltaic power absorbed, generator operating status, generator output, energy storage state of charge, energy storage charging and discharging power, load power, power interaction between the power distribution system and the upstream grid, line power flow, and adjustable load boundaries.

[0032] A second aspect of the present invention provides: a power distribution system source-load coordinated control system based on a low-carbon demand response controller, used to execute the power distribution system source-load coordinated control method described in the first aspect of the present invention, comprising a sensor module, a data processing module, a memory, and a communication module, wherein: The sensor module is used to collect power distribution system operation data and feedback operation data after the adjustable load terminal executes load adjustment commands; The data processing module calculates and outputs load adjustment commands executed by the adjustable load terminal based on the data collected by the sensor module, and constructs the node carbon potential-load adjustment rate curve NCI-LARC for each node; The memory is used to store the node carbon potential-load adjustment rate curve NCI-LARC of each node; The communication module is used to transmit the data collected by the sensor module and the load adjustment commands output by the data processing module.

[0033] In some instances, the operational data includes at least the photovoltaic predicted power, actual photovoltaic power absorbed, generator operating status, generator output, energy storage state of charge, energy storage charging and discharging power, load power, power interaction between the distribution system and the upstream grid, line power flow, and adjustable load boundaries.

[0034] In some instances, the data processing module includes an edge computing terminal, which calls the node carbon potential-load regulation rate curve in the memory, calculates and generates load regulation instructions, and realizes rapid source-load coordination optimization of the power distribution system based on the node carbon potential-load regulation rate curve.

[0035] The beneficial effects of this invention are: 1) Some embodiments of this invention utilize the initial source-load co-optimization results to construct and store node-by-node carbon potential-load adjustment rate curves. In subsequent regulation cycles, the node load adjustment rate is directly determined based on the current corrected node carbon potential, avoiding repeated solutions to the low-carbon demand response optimization model, shortening the source-load co-optimization time, and reducing the computational pressure caused by the expansion of adjustable load scale. The node carbon potential-load adjustment rate curves characterize the direct correspondence between load-side adjustment intentions and node carbon potentials, avoiding excessive iteration of the low-carbon demand response controller and improving the overall solution speed of source-load co-optimization.

[0036] 2) Some examples of the present invention are based on a source-load coordinated control method and system for power distribution systems with low-carbon demand response. From the perspectives of improving the robustness of the power distribution system against fluctuations in new energy output and carbon emission reduction, the system reduces various costs and total costs, thereby achieving robust operation of the power distribution system with an economic and low-carbon orientation.

[0037] 3) The low-carbon demand response proposed in some examples of this invention introduces the dynamic carbon emission intensity of power sources on the basis of existing carbon flow theory, considers the impact of distributed power sources on system carbon flow tracking, improves the accuracy of carbon potential characterization of carbon emissions from the perspective of electricity consumption, and expands the application scenarios of carbon flow theory. Attached Figure Description

[0038] Figure 1 This is a framework diagram of the source-load coordinated control method and system of the power distribution system based on low-carbon demand response, which is the framework diagram of the present invention.

[0039] Figure 2 It is the NCI-LARC of a certain node in the power distribution system. Detailed Implementation

[0040] A source-load coordinated control method for power distribution systems based on low-carbon demand response includes: The power distribution system operation data is acquired through sensor modules. The operation data includes at least the photovoltaic predicted power, the actual photovoltaic power absorbed, the generator operating status, the generator output, the energy storage state of charge, the energy storage charging and discharging power, the load power, the interaction power between the power distribution system and the upper-level power grid, the line power flow, and the adjustable load boundary. The above raw data is processed by the data processing module. With the goal of minimizing the total operating cost of the power distribution system, a robust optimization model for the source side of the power distribution system considering the uncertainty of photovoltaic output is established. The optimization goal of the first stage is to minimize the operating cost of the generator, and the optimization goal of the second stage is to minimize the operating cost of the power distribution system under the worst scenario. Solve the robust optimization model of the power distribution system source side, output the power distribution system scheduling results and power flow calculation results, and store them in the data storage; Based on the power distribution system scheduling results and power flow calculation results, and with the goal of minimizing the total carbon emission cost of the power distribution system, a low-carbon demand response controller is constructed to regulate the load side for low-carbon demand response. Specifically, this includes: (1) Calculate the uncorrected node carbon potential NCI of the distribution system; (2) Update the carbon carrying capacity of the energy storage based on the power flow calculation results and the energy storage charge and discharge status, and calculate the dynamic carbon emission intensity of the energy storage; (3) Based on the calculation results of generator carbon emission intensity, energy storage dynamic carbon emission intensity and line power flow, generate the corrected node carbon potential of the distribution system; (4) After the first source-load co-optimization is completed on the scheduling day, the modified node carbon potential of the same node at each time period is used. and load adjustment rate Construct node-to-node carbon potential-load adjustment rate curves (NCI-LARC). , , For load adjustment, , and These represent the increases and decreases in load, respectively. To determine the baseline load power before participating in low-carbon demand response, in each formula, the subscript n is the index corresponding to the node, and t is the index corresponding to the time. In subsequent regulation cycles, the current corrected node carbon potential of node n is input into the NCI-LARC corresponding to node n to directly obtain the load adjustment rate of node n. (5) Generate load adjustment instructions based on the load adjustment rate, adjustable load boundary and power distribution system operation constraints and send them to the adjustable load terminal for execution; (6) Collect feedback operation data and check the node carbon potential, power flow constraint and load regulation deviation accordingly. If the preset conditions are not met, the load regulation command is rolled back and the NCI-LARC is updated.

[0041] In some instances, two-stage robust optimization is used to solve the robust optimization model of the power distribution system source side. The objective function of the two-stage robust optimization of the power distribution system source side considering the uncertainty of photovoltaic output is shown in Equation (1): Equation (1), where: and These are the objective functions for the first and second stages, respectively. It is an uncertain set of two-stage robust optimization; The first-stage optimization objective is to minimize the total cost of the generator under different operating states, and the objective function is shown in equation (2): Equation (2) In the formula: The number of generators , , and These represent the generator's operating status—running, starting, stopping, and maintenance—obtained by the sensor through operating status sampling. , , and t represents the corresponding cost coefficient; i is the index corresponding to the generator, and t is the index corresponding to the time.

[0042] In some instances, the optimization objective of the second stage is to minimize the total operating cost of the power distribution system under the worst-case scenario, and the objective function is shown in equation (3): Equation (3) In the formula: Generator output, The actual photovoltaic power absorbed by the power distribution system. For energy storage charging power, For energy storage discharge power, Here, k represents the available photovoltaic output, and m represents the index corresponding to photovoltaic power and m represents the index corresponding to energy storage power.

[0043] In some instances, a low-carbon demand response controller is constructed to measure the carbon emissions of the distribution system transferred to the load side through the node carbon potential (NCI). The load side then performs low-carbon demand response based on the distribution system NCI. The NCI characterizes the carbon emissions generated by the load side for each unit of electricity consumed from the perspective of electricity consumption, and is mathematically represented as: Equation (4) In the formula: and These are the uncorrected NCI values ​​for nodes n and j during time period t, respectively; the positive active power flow direction of line l is from node j to node n; Provide power to the power source; Power Source Carbon Intensity (PSCI) is a measure of the amount of carbon emissions carried per unit of electrical energy in a power injection system. The set of all lines that inject active power flow into node n; Let n be the set of all power sources connected to node n.

[0044] In some instances, an improved nodal carbon potential calculation method is used to dynamically correct the nodal carbon potential in equation (4) that originally did not consider the carbon emissions of the photovoltaic-storage unit. The dynamic carbon emission intensity of energy storage is defined as follows: Equation (5) In the formula: The dynamic carbon emission intensity of energy storage represents the amount of carbon emissions carried by a unit of electrical energy injected into the power distribution system due to energy storage during discharge. The unit is the same as that of NCI. and These are the carbon dioxide carried by energy storage and the amount of electricity stored, respectively. and These represent the initial carbon carrying capacity and the initial energy storage capacity, respectively; m is the node number corresponding to the photovoltaic-storage unit. The dynamic carbon emission intensity PEDCI of photovoltaic energy storage is defined as shown in equation (6): Equation (6), where: It is PEDCI, with the same units as NCI; Therefore, the node carbon potential of the distribution system after being corrected by the proposed improved node carbon potential calculation method is shown in Equation (7); and The corrected NCI values ​​for nodes n and j in time period t are respectively. For generator carbon emission intensity; and These represent the set of all generators and the set of photovoltaic-storage units connected to node n, respectively. Equation (7).

[0045] In some instances, the objective function of the low-carbon demand response optimization model is shown in Equation (8). The low-carbon demand response controller receives the power flow calculation results and power information of each node in the power distribution system, outputs the adjusted load power of each node, and issues load adjustment commands to the load control terminal via the IEC 61850 communication protocol. It also receives the actual execution power and execution status feedback from the load control terminal and corrects the load adjustment commands when the feedback deviation exceeds a preset threshold. The objective is to minimize the total carbon emission cost of the power distribution system. Equation (8) Equation (9) In the formula: The objective function for low-carbon demand response on the load side is... The carbon emission cost coefficient for the power distribution system. Let n be the load adjustment amount for node n during time period t. and This represents the load increase and decrease of node n during time period t; The upper and lower limits of the load-side low-carbon demand response adjustment are shown in equations (10) and (11): Equation (10) Equation (11).

[0046] The following are some of the terms and abbreviations used in this invention: 1. Demand Response (DR) 2. Low Carbon Demand Response (LCDR) 3. Nodal Carbon Intensity (NCI) 4. Power Source Carbon Intensity (PSCI) 5. Power Source Dynamic Carbon Intensity (PSDCI) 6. Photovoltaic Energy Storage Dynamic Carbon Intensity (PEDCI) 7. Nodal carbon intensity-load adjustment rate curve (NCI-LARC).

[0047] The technical solution of the present invention will be further illustrated below with examples.

[0048] 1. A source-load coordinated control framework for power distribution systems based on low-carbon demand response controllers

[0049] This invention assigns the responsibility for carbon emissions from the power distribution system to the load side, and the total cost of the power distribution system equals the sum of the total operating cost and the total carbon emission cost. To accurately measure the carbon emissions transferred to the load side and guide the low-carbon demand response controller in implementing low-carbon demand response, this invention proposes a source-load coordinated optimization method for the power distribution system oriented towards low-carbon demand response, the framework of which is shown in the figure below. Figure 1 As shown, it specifically includes: With the goal of minimizing the total operating cost of the power distribution system, a robust optimization model for the source side of the power distribution system considering the uncertainty of photovoltaic output is established. The first-stage optimization objective is to minimize the cost of gas generators (or other types of carbon emission generators, such as thermal power) under different operating states, and the second-stage optimization objective is to minimize the operating cost of the power distribution system under the worst-case scenario. Solve the robust optimization model of the power distribution system source side and output the power distribution system scheduling results and power flow calculation results; Based on the power distribution system scheduling results and power flow calculation results, and with the goal of minimizing the total carbon emission cost of the power distribution system, the low-carbon demand response controller is guided to perform low-carbon demand response, specifically including: Calculate the uncorrected carbon potential at the nodes of the power distribution system; (1) Calculate the uncorrected carbon potential of the distribution system nodes; (2) Update the carbon carrying capacity of the energy storage based on the power flow calculation results and the energy storage charge and discharge status, and calculate the dynamic carbon emission intensity of the energy storage; (3) Based on the calculation results of generator carbon emission intensity, energy storage dynamic carbon emission intensity and line power flow, generate the corrected node carbon potential of the distribution system; (4) Input the corrected node carbon potential of the power distribution system into the node carbon potential-load regulation rate curve to obtain the node load regulation rate of the node; (5) Generate load adjustment instructions based on the load adjustment rate, adjustable load boundary and power distribution system operation constraints; (6) The load adjustment command is sent to the adjustable load terminal for execution via the power distribution automation communication interface; (7) After the adjustable load terminal is executed, the feedback operation data is collected, and the node carbon potential, power flow constraint and load adjustment deviation are checked according to the feedback operation data. If the preset conditions are not met, the load adjustment command is rolled to correct.

[0050] 2. Robust Optimization Model for the Source Side of the Distribution System Considering the Uncertainty of Photovoltaic Output

[0051] 2.1 Two-stage robust optimization objective function for the source side of the power distribution system The objective function for the two-stage robust optimization of the power distribution system source side considering the uncertainty of photovoltaic output is shown in equation (1): Equation (1) In the formula: and These are the objective functions for the first and second stages, respectively. It is a two-stage robust optimization adjustable polyhedron uncertainty set.

[0052] 2.2 First-stage objective function The first stage objective is to minimize the total cost of the gas generator under the four states, and the objective function is shown in equation (2): Equation (2) In the formula: The number of gas generators. , , and These represent the generator's operating status—running, starting, stopping, and maintenance—obtained by the sensor through operating status sampling. , , and Here, represents the corresponding cost coefficient. `i` is the index corresponding to the generator, and `t` is the index corresponding to the time.

[0053] 2.3 Second-stage objective function The goal of the second stage is to minimize the total operating cost of the power distribution system under the worst-case scenario, and the objective function is shown in equation (3): Equation (3) In the formula: Gas generator output, The actual photovoltaic power absorbed by the power distribution system. and These represent the energy storage charging and discharging power, This indicates the interaction power between the power distribution system and the upstream power grid. Here, k represents the available photovoltaic output, and m represents the index corresponding to photovoltaic power and m represents the index corresponding to energy storage power.

[0054] 3. Low-carbon demand response controller model

[0055] 3.1 Improved Nodal Carbon Potential Calculation Method for Power Distribution Systems Based on Dynamic Carbon Emission Intensity of Power Sources This invention uses nodal carbon intensity (NCI) to measure the carbon emissions transferred to the load side of the power distribution system, and the load side performs low-carbon demand response based on the NCI of the power distribution system. NCI characterizes the carbon emissions generated by the load side for consuming a unit of electrical energy from the perspective of electricity consumption, with units of gCO2 / (kW·h).

[0056] Equation (4) In the formula: and These are the uncorrected NCI values ​​for nodes n and j during time period t, respectively; the positive active power flow direction of line l is from node j to node n; Provide power to the power source; Power Source Carbon Intensity (PSCI) is a measure of the amount of carbon emissions carried per unit of electrical energy in a power injection system. The set of all lines that inject active power flow into node n; Let n be the set of all power sources connected to node n.

[0057] Photovoltaics are zero-carbon power sources, but energy storage stores carbon-based energy generated by carbon-based power sources (such as gas generators and coal-fired generators). Therefore, the photovoltaic-energy storage unit formed by photovoltaic and energy storage configurations is not a zero-carbon power source. Therefore, this invention introduces the concept of power source dynamic carbon intensity (PSDCI) and proposes an improved method for calculating nodal carbon potential, dynamically correcting the nodal carbon potential in equation (4) that originally did not consider the carbon emissions of the photovoltaic-energy storage unit. First, the dynamic carbon intensity of energy storage is defined as follows: Equation (5) In the formula: The dynamic carbon emission intensity of energy storage represents the amount of carbon emissions carried by a unit of electrical energy injected into the power distribution system due to energy storage during discharge. The unit is the same as that of NCI. and These are the carbon dioxide carried by energy storage and the amount of electricity stored, respectively. and These represent the initial carbon carrying capacity and the initial energy storage capacity, respectively; m is the node number corresponding to the photovoltaic-storage unit.

[0058] The photovoltaic energy storage dynamic carbon intensity (PEDCI) is defined as shown in equation (6): Equation (6) In the formula: It is PEDCI, and the unit is the same as NCI.

[0059] Therefore, the nodal carbon potential of the distribution system after correction by the proposed improved nodal carbon potential calculation method is shown in equation (7). and The corrected NCI values ​​for nodes n and j in time period t are respectively. Carbon emission intensity of gas generators; and These represent the set of all gas generators and the set of photovoltaic storage units connected to node n, respectively.

[0060] Equation (7).

[0061] 3.2 Low-carbon demand response optimization model for the load side of the power distribution system The low-carbon demand response controller receives the power flow calculation results and power information of each node in the power distribution system, outputs the adjusted load power of each node, and sends load adjustment commands to the load control terminal via the IEC 61850 communication protocol. It also receives feedback from the load control terminal regarding the actual executed power and execution status, and corrects the load adjustment commands when the feedback deviation exceeds a preset threshold. The objective function of the low-carbon demand response controller is shown in equation (8), with the objective of minimizing the total carbon emission cost of the power distribution system. Equation (8) Equation (9) In the formula: The objective function for low-carbon demand response on the load side is... The carbon emission cost coefficient for the power distribution system. Let n be the load adjustment amount for node n during time period t. and Let n be the load increase and decrease amount during time period t.

[0062] The upper and lower limits of the load-side low-carbon demand response adjustment are shown in equations (10) and (11): Equation (10) Equation (11).

[0063] 3.3 A Fast Optimization Method for Source-Load Coordination in Distribution Systems Based on Nodal Carbon Potential-Load Regulation Rate Curves To address the issue of excessively long solution time in multiple iterations of low-carbon demand response controllers, this invention proposes a rapid source-load coordination optimization method for power distribution systems based on nodal carbon intensity-load adjustment rate curves (NCI-LARC). Rearranging equations 9 to 11 yields: Equation (12) Equation (13) The load adjustment rate is calculated by the low-carbon demand response controller after the first source-load co-optimization on the scheduling day. and node carbon potential Using basic data, the horizontal axis represents the node's carbon potential over all time periods, and the vertical axis represents the node's load regulation rate over all time periods, to construct the NCI-LARC. The NCI-LARC of a specific node in the distribution system is selected for display, such as... Figure 2 As shown, the LCDR willingness of this node exhibits distinct phased characteristics as carbon potential fluctuates: when NCI is in the low range, the load side shows a strong tendency to increase electricity consumption, and the adjustment rate remains at a positive extreme value; as NCI gradually increases, user behavior changes, the tendency to consume electricity weakens while the demand to reduce load increases; once the carbon intensity exceeds the threshold of 357.62 gCO2 / (kW·h), the load side maintains the maximum negative adjustment willingness, responding to the low-carbon guidance signal by reducing electricity consumption during high-carbon periods.

[0064] After obtaining the NCI-LARC of all nodes in the power distribution system, the low-carbon demand response is directly performed according to the correspondence between the node carbon potential and load regulation rate of each node's NCI-LARC, without solving the proposed low-carbon demand response optimization model through a solver. This avoids excessive iteration of the source-load model and improves the overall solution speed.

[0065] The above is a further detailed description of the present invention and should not be considered as a limitation on the specific implementation of the present invention. For those skilled in the art, simple deductions or substitutions without departing from the concept of the present invention are all within the protection scope of the present invention.

Claims

1. A source-load coordinated control method for a power distribution system based on low-carbon demand response, characterized in that, include: Based on power distribution system operation data, and with the goal of minimizing the total operating cost of the power distribution system, a two-stage robust optimization model for the power distribution system source side is established, taking into account the uncertainty of photovoltaic output. The optimization objectives for the first and second stages are to minimize the generator operating cost and the power distribution system operating cost under the worst scenario, respectively. The robust optimization model is solved to output the power distribution system scheduling results and power flow calculation results; Based on the power distribution system scheduling results and power flow calculation results, and with the goal of minimizing the total carbon emission cost of the power distribution system, the load side is regulated to implement low-carbon demand response, specifically including: (1) Calculate the uncorrected node carbon potential NCI of the distribution system; (2) Update the carbon carrying capacity of the energy storage based on the power flow calculation results and the energy storage charge and discharge status, and calculate the dynamic carbon emission intensity of the energy storage; (3) Based on the calculation results of generator carbon emission intensity, energy storage dynamic carbon emission intensity and line power flow, generate the corrected node carbon potential of the distribution system; (4) After the first source-load co-optimization is completed on the scheduling day, the modified node carbon potential of the same node at each time period is used. and load adjustment rate Construct node-to-node carbon potential-load adjustment rate curves (NCI-LARC). , , For load adjustment, , and These represent the increases and decreases in load, respectively. To determine the baseline load power before participating in low-carbon demand response, in each formula, the subscript n is the index corresponding to the node, and t is the index corresponding to the time. In subsequent regulation cycles, the current corrected node carbon potential of node n is input into the NCI-LARC corresponding to node n to directly obtain the load adjustment rate of node n. (5) Generate load adjustment instructions based on the load adjustment rate, adjustable load boundary and power distribution system operation constraints and send them to the adjustable load terminal for execution; (6) Collect feedback operation data and check the node carbon potential, power flow constraint and load regulation deviation accordingly. If the preset conditions are not met, the load regulation command is rolled back and the NCI-LARC is updated.

2. The power distribution system source-load coordinated control method according to claim 1, characterized in that, The objective function for the two-stage robust optimization of the power distribution system source side considering the uncertainty of photovoltaic output is shown in equation (1): Equation (1), In the formula: and These are the objective functions for the first and second stages, respectively. It is an uncertain set of two-stage robust optimization; The first-stage optimization objective is to minimize the total cost of the generator under different operating states, and the objective function is shown in equation (2): Equation (2) In the formula: The number of generators , , and These represent the generator's operating status—running, starting, stopping, and maintenance—obtained by the sensor through operating status sampling. , , and t represents the corresponding cost coefficient; i is the index corresponding to the generator, and t is the index corresponding to the time.

3. The power distribution system source-load coordinated control method according to claim 2, characterized in that, The optimization objective of the second stage is to minimize the total operating cost of the power distribution system under the worst-case scenario, and the objective function is shown in equation (3): Equation (3) In the formula: Generator output, The actual photovoltaic power absorbed by the power distribution system. For energy storage charging power, For energy storage discharge power, Here, k represents the available photovoltaic output, and m represents the index corresponding to photovoltaic power and m represents the index corresponding to energy storage power.

4. The power distribution system source-load coordinated control method according to claim 3, characterized in that, The carbon emissions transferred to the load side of the power distribution system are measured through Nodal Carbon Intensity (NCI). The load side then implements low-carbon demand response based on the NCI. NCI characterizes the carbon emissions generated per unit of electricity consumed by the load side from an electricity consumption perspective, and is mathematically represented as: Equation (4) In the formula: and These are the uncorrected NCI values ​​for nodes n and j during time period t, respectively. Let be the positive active power flow of line l during time period t, and let the positive active power flow direction of line l be from node j to node n. Provide power to the power source; Power Source Carbon Intensity (PSCI) is a measure of the amount of carbon emissions carried per unit of electrical energy in a power injection system. The set of all lines that inject active power flow into node n; Let n be the set of all power sources connected to node n.

5. The power distribution system source-load coordinated control method according to claim 4, characterized in that, An improved method for calculating nodal carbon potential is used to dynamically correct the nodal carbon potential in equation (4) that originally did not consider the carbon emissions of the photovoltaic-storage unit. The dynamic carbon emission intensity of energy storage is defined as follows: Equation (5) In the formula: The dynamic carbon emission intensity of energy storage represents the amount of carbon emissions carried by a unit of electrical energy injected into the power distribution system due to energy storage during discharge. The unit is the same as that of NCI. and These are the carbon dioxide carried by energy storage and the amount of electricity stored, respectively. and These represent the initial carbon carrying capacity and the initial energy storage capacity, respectively; m is the node number corresponding to the photovoltaic-storage unit. The dynamic carbon emission intensity PEDCI of photovoltaic energy storage is defined as shown in equation (6): Equation (6), In the formula: It is PEDCI, with the same units as NCI; The modified node carbon potential of the power distribution system after the proposed improved node carbon potential calculation method is shown in equation (7). and The corrected NCI values ​​for nodes n and j in time period t are respectively. For generator carbon emission intensity; and These represent the set of all generators and the set of photovoltaic-storage units connected to node n, respectively. Equation (7).

6. The power distribution system source-load coordinated control method according to claim 3, characterized in that, The low-carbon demand response, based on the power flow calculation results of the distribution system and the power information of each node, outputs adjusted load power for each node and issues load adjustment commands. It receives feedback from the load control terminal on the actual executed power and execution status, and corrects the load adjustment commands when the feedback deviation exceeds a preset threshold. The goal is to minimize the total carbon emission cost of the distribution system, mathematically represented as follows: Equation (8) Equation (9) In the formula: The objective function for low-carbon demand response on the load side is... The carbon emission cost coefficient for the power distribution system. Let n be the load adjustment amount for node n during time period t. and This represents the load increase and decrease of node n during time period t; The upper and lower limits of the load-side low-carbon demand response adjustment are shown in equations (10) and (11): Equation (10) Equation (11).

7. The source-load coordinated control method for a power distribution system according to claim 1, characterized in that, The power distribution system operation data includes at least the photovoltaic predicted power, actual photovoltaic power absorbed, generator operating status, generator output, energy storage state of charge, energy storage charging and discharging power, load power, power interaction between the power distribution system and the upstream power grid, line power flow, and adjustable load boundary.

8. A power distribution system source-load coordinated control system based on a low-carbon demand response controller, used to execute the power distribution system source-load coordinated control method according to any one of claims 1 to 7, comprising a sensor module, a data processing module, a memory, and a communication module, characterized in that, The sensor module is used to collect power distribution system operation data and feedback operation data after the adjustable load terminal executes load adjustment commands; The data processing module calculates and outputs load adjustment commands executed by the adjustable load terminal based on the data collected by the sensor module, and constructs the node carbon potential-load adjustment rate curve NCI-LARC for each node; The memory is used to store the node carbon potential-load adjustment rate curve NCI-LARC of each node; The communication module is used to transmit the data collected by the sensor module and the load adjustment commands output by the data processing module.

9. The power distribution system source-load coordinated control system according to claim 8, characterized in that, The operational data includes at least the photovoltaic predicted power, actual photovoltaic power absorbed, generator operating status, generator output, energy storage state of charge, energy storage charging and discharging power, load power, power interaction between the distribution system and the upstream power grid, line power flow, and adjustable load boundary.

10. The power distribution system source-load coordinated control system according to claim 8, characterized in that, The data processing module includes an edge computing terminal, which calls the node carbon potential-load adjustment rate curve in the memory, calculates and generates load adjustment instructions, and realizes rapid optimization of the power distribution system source-load coordination based on the node carbon potential-load adjustment rate curve.

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