Demand Response-Based Active Day-ahead Low-Carbon Dispatch Method and Device for Distribution Networks

Abstract

This invention relates to a demand-response-based proactive distribution network day-ahead low-carbon dispatching method and apparatus. The method includes acquiring operating parameter data of various stakeholders, constructing a proactive distribution network day-ahead low-carbon dispatching model with minimum system cost as the primary objective, minimum system carbon emissions as the first sub-objective, and minimum user load offset as the second sub-objective, iteratively optimizing the model until convergence, outputting multiple feasible results, and using trapezoidal fuzzy membership functions to transform the values ​​of each objective function to obtain a fuzzy comprehensive decision model, outputting the optimal dispatching plan for the proactive distribution network. This invention comprehensively considers energy costs, carbon emission costs, and user energy experience on the proactive distribution network side, proposing a demand-response-based proactive distribution network day-ahead low-carbon dispatching system with bidirectional interaction between source, grid, load, and storage, overcoming the shortcomings of current dispatching methods that only consider user cost, and is more conducive to achieving dual carbon objectives.

Description

Technical Field

[0001] This invention belongs to the field of power dispatching technology, specifically relating to a day-ahead low-carbon dispatching method and device for an active distribution network based on demand response. Background Technology

[0002] Driven by the "dual-carbon" strategic goal, my country is gradually shifting towards a new power system model dominated by new energy sources, moving towards low-carbon and clean energy. In this context, the distribution network will gradually transform from a passive power system with unidirectional energy flow to an active distribution network system with responsive capabilities, incorporating distributed generation resources, energy storage, and demand response users. Currently, distributed generation resources, user-side energy storage, and demand response are largely dispatched separately and in a decentralized manner, without systematic consideration of the coordinated dispatch of various resources within the same distribution network, making it impossible to achieve coordinated optimization of various resources on the distribution network side. The main problems in coordinating and managing these resources are as follows: First, most user-side distributed generation resources are intermittent power sources such as distributed wind and solar power; accurately predicting the uncertain output of wind and solar power is a technical challenge. Second, user-side energy storage and demand response are essentially economic behaviors that respond to wholesale electricity market prices; simultaneously minimizing carbon emissions and energy costs in the distribution network system is a technical challenge.

[0003] Therefore, there is an urgent need for a demand-response-based proactive day-ahead low-carbon dispatching method for distribution networks to reduce carbon emissions and electricity costs while ensuring the safe operation of the distribution network system. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a demand-response-based active distribution network day-ahead low-carbon dispatching method and apparatus to solve the problem that the prior art cannot reduce the carbon emissions of the distribution network system and reduce the electricity cost of the system while ensuring the safe operation of the distribution network system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a demand-response-based active distribution network day-ahead low-carbon dispatching method, comprising:

[0006] Obtain the operating parameter data of each entity; the operating parameter data of each entity includes: power user classification information, distributed generator set information, and energy storage unit capacity information;

[0007] With the primary objective of minimizing system cost, the first sub-objective of minimizing system carbon emissions, and the second sub-objective of minimizing user load offset, an active day-ahead low-carbon dispatch model for distribution networks is constructed.

[0008] The day-ahead low-carbon dispatch model of the active distribution network is iteratively optimized until the model converges, and several feasible results are output.

[0009] A fuzzy comprehensive decision model is obtained by transforming the values ​​of each objective function using a trapezoidal fuzzy membership function.

[0010] Based on the fuzzy comprehensive decision model, the optimal scheduling plan for the active distribution network is output; the optimal scheduling plan for the active distribution network includes the output plan of distributed wind turbines, the operation plan of energy storage units, and the electricity consumption plan of users.

[0011] Furthermore, electricity users are categorized into demand-response users and non-demand-response users based on their responsiveness. Since user electricity consumption often presents different scenarios, it is necessary to input the typical load values ​​for non-adjustable and adjustable users during different time periods t under different scenarios s. and and the probability ρ of different scenarios s occurring s The model for the variation of adjustable user load from time period t to t' is as follows:

[0012]

[0013] Distributed generator sets are divided into two categories: conventional distributed generator sets and distributed wind turbine generator sets.

[0014] The information on conventional distributed generator sets includes cost information and performance information, wherein the cost information includes operating cost C. YX Start-up cost C YX and downtime costs C TJ ;

[0015] The operating cost is

[0016]

[0017] Where, δ i , χ i θ i All are cost parameters for gas turbine unit i; P RQ (i,t) represents the output of gas turbine unit i at time t; the startup cost of unit i. Downtime costs It is usually a fixed value;

[0018] The performance information includes the unit's maximum output P. i max Minimum output P i min Minimum continuous downtime Minimum continuous boot time Maximum climbing rate and maximum rate of descent

[0019] The information of the distributed wind turbine generator set is as follows:

[0020]

[0021] Among them, P N,FD V represents the maximum installed capacity of the unit, where v is the wind speed. Ci V R V Co These are the minimum generating wind speed, the minimum full-output wind speed, and the wind speed at which the turbine is cut off;

[0022] Energy storage unit capacity information is

[0023]

[0024] in, These represent the state of charge, discharge power, and charging power of the energy storage at time t, respectively.

[0025] Furthermore, the system costs include: the cost of distributed conventional generator sets, the costs incurred in trading with the main grid and adjacent distribution networks, the operating costs of energy storage units, and the costs incurred in demand response; the main objective function of the active distribution network day-ahead low-carbon dispatch model is...

[0026]

[0027] Where, μ QD (i,t) and μ TJ (i,t) represents the 0 and 1 variables of the operating state of the distributed conventional generator set (QD=1, TJ=0); P t WM P t WG , These represent the power output sold, the power output purchased, the power price sold, and the power price purchased at time t, respectively. Let represent the discharge and charging power of the energy storage system at time t, respectively. K represents the charging / generating price of the energy storage system at time t, respectively. d It is the demand response price, Δ d D represents the fluctuation ratio of the adjustable load. d,t Let z be the total power consumption of the d-th load at time t. d,t The response variables are 0 and 1 for the adjustable load.

[0028] Furthermore, under the condition of minimizing system cost, the first sub-objective function with minimizing system carbon emissions as the primary sub-objective is:

[0029]

[0030] In the formula, αi β i ε i For the system's carbon emission factors, The carbon emission factor for purchased electricity.

[0031] Furthermore, under the conditions of minimizing system cost and system emissions, the second sub-objective function, with minimizing user load offset as the second sub-objective, is as follows:

[0032]

[0033]

[0034]

[0035] Where, ρ s Let D be the probability of the s-th scene. E (s,t) represents the load distribution in the s-th scenario. This represents the optimal load distribution for the system.

[0036] Furthermore, the constraints corresponding to the multi-objective function of the active distribution network day-ahead low-carbon dispatch model include:

[0037] Network constraints include: active power balance constraints and reactive power balance constraints;

[0038] Active power balance constraint is

[0039] Reactive power balance constraint is

[0040] Among them, P gi and Q gi P represents the active and reactive power generated by generator set at node i. di and Q di It represents the active and reactive power requirements of user i at node, V. i and δ i It is the voltage value and voltage phase angle at node i, Y ij and θ ij These are the elements of the admittance matrix and the phase angle, respectively.

[0041] Distributed generator constraints include: upper and lower output limits, ramping constraints, and minimum start / stop constraints;

[0042] Output upper and lower limit constraints are

[0043] Climbing constraint is

[0044]

[0045] Minimum start / stop constraints are

[0046]

[0047] Among them, I i (t) represents the startup state of the i-th unit at time t; T i on T i off These represent the time the machine was powered on and the time it was powered off, respectively.

[0048] Energy storage constraints include: maximum generation constraints, minimum generation constraints, and energy storage constraints;

[0049] Transmission power constraints

[0050]

[0051]

[0052] in, These are the upper and lower limits of the transmission capacity of the tie line, respectively.

[0053] Load balance constraints

[0054]

[0055] Furthermore, the trapezoidal fuzzy membership function is used to transform the values ​​of each objective function in the following way:

[0056]

[0057] in, and These are the maximum and minimum values ​​of the i-th objective function, respectively;

[0058] The fuzzy comprehensive decision model is obtained as follows

[0059]

[0060] Where, α i For fuzzy decision weights with different objective functions.

[0061] This application provides a demand-response-based active distribution network day-ahead low-carbon dispatching device, comprising:

[0062] The acquisition module is used to acquire the operating parameter data of each entity; the operating parameter data of each entity includes: power user classification information, distributed generator set information, and energy storage unit capacity information;

[0063] The module is used to construct an active distribution network day-ahead low-carbon dispatch model with the primary objective of minimizing system cost, the first sub-objective of minimizing system carbon emissions, and the second sub-objective of minimizing user load offset.

[0064] The convergence module is used to iteratively optimize the day-ahead low-carbon dispatch model of the active distribution network until the model converges and outputs multiple feasible results.

[0065] The transformation module is used to transform the values ​​of each objective function using trapezoidal fuzzy membership functions to obtain a fuzzy comprehensive decision model;

[0066] The output module is used to output the optimal scheduling plan for the active distribution network based on the fuzzy comprehensive decision model; the optimal scheduling plan for the active distribution network includes the output plan of distributed wind turbine units, the operation plan of energy storage units, and the electricity consumption plan of users.

[0067] The beneficial effects that can be achieved by adopting the above technical solution in this invention include:

[0068] This invention provides a day-ahead low-carbon dispatching method and apparatus for an active distribution network based on demand response. This application comprehensively considers the overall energy consumption characteristics of the active distribution network and proposes a carbon emission assessment model for the active distribution network, which can accurately assess the carbon emissions of the active distribution network. Considering the optimal load distribution of adjustable users in different scenarios, an optimal load transfer model that considers the actual electricity consumption experience of users is proposed, which can reduce user costs while enhancing the user's electricity consumption experience. This application also comprehensively considers the energy cost, carbon emission cost, and user energy consumption experience of the active distribution network, proposing a day-ahead low-carbon dispatching method for an active distribution network based on demand response and bidirectional interaction between source, grid, load, and storage. This overcomes the shortcomings of current dispatching methods that only consider user cost and is more conducive to achieving dual carbon objectives. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram illustrating the steps of the demand response-based proactive distribution network low-carbon dispatching method of the present invention.

[0071] Figure 2 This is a flowchart illustrating the day-ahead low-carbon dispatching method for active distribution networks based on demand response according to the present invention.

[0072] Figure 3This is a schematic diagram of the day-ahead low-carbon dispatching device for the active distribution network based on demand response according to the present invention. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0074] Existing technologies primarily focus on controlling different resources individually. Regarding demand-side response, researchers either propose response strategies for individual entities based on wholesale market prices or consider users' demand response capabilities, proposing distributed real-time pricing models based on message exchange between smart meters and energy suppliers, or conducting quantitative analyses of the demand response characteristics of different types of users. In terms of distributed renewable energy dispatching, some scholars have constructed frameworks for the economic emission dispatching problem of microgrids, while others have studied day-ahead coordinated dispatching mechanisms for energy storage and distributed renewable units. However, current joint dispatching mechanisms mainly consider the joint dispatching problem of isolated microgrids, without addressing joint dispatching methods for grid-connected microgrids. In summary, existing technologies have explored optimal microgrid planning under renewable energy and uncertainty conditions. However, the impact of customer clusters on optimal dispatching and demand-side response control has not been considered in these studies.

[0075] The following describes a specific demand-response-based proactive distribution network day-ahead low-carbon dispatching method provided in an embodiment of this application, with reference to the accompanying drawings.

[0076] First, it should be noted that in the active distribution network system of this invention, the distribution network operator can regulate distributed wind power, gas turbines, energy storage systems, adjacent microgrids, and loads through a central controller. The distribution network operator optimizes the operation of various resources within the distribution network based on the day-ahead optimization method proposed in this invention.

[0077] like Figure 1 As shown in the embodiments of this application, the day-ahead low-carbon dispatching method for active distribution networks based on demand response includes:

[0078] S101, Obtain the operating parameter data of each entity; the operating parameter data of each entity includes: power user classification information, distributed generator set information, and energy storage unit capacity information;

[0079] (I) Regarding electricity user classification information

[0080] Electricity users can be categorized into demand-response users and non-demand-response users based on their responsiveness. User electricity consumption often presents different scenarios; therefore, it is necessary to input the typical load values ​​for non-adjustable and adjustable users during different time periods t under different scenarios s. and and the probability ρ of different scenarios s occurring s .

[0081] The variation pattern of adjustable user load from time period t to t' needs to be modeled in the following way.

[0082]

[0083] (ii) Information on distributed generator sets

[0084] Distributed generator sets are divided into two categories: conventional distributed generator sets and distributed wind turbine generator sets.

[0085] The information required for conventional distributed generator sets includes cost and performance information. Cost information includes operating cost C. YX Startup cost C QD Downtime cost C TJ .

[0086] Operating costs are typically quadratic functions. For unit i, its power generation cost satisfies the following formula:

[0087]

[0088] Where, δ i , χ i θ i P represents the cost parameter of gas turbine unit i; RQ (i,t) represents the output of gas turbine unit i at time t; the startup cost of unit i. Downtime costs It is usually a fixed value.

[0089] The performance information required for gas turbine units includes the unit's maximum output P. i max Minimum output P i min Minimum continuous downtime Minimum continuous boot time Maximum climbing rate Maximum rate of descent

[0090] Among them, information related to distributed wind turbine generators, including the power generation capacity P of distributed wind turbine generators. FD(v) Affected by wind speed, the specific formula is as follows:

[0091]

[0092] In the formula, P N,FD V represents the maximum installed capacity of the unit, where v is the wind speed. Ci V R V Co These are the minimum generating wind speed, the minimum full-output wind speed, and the wind speed at which the unit is cut off.

[0093] (III) Regarding the capacity information of energy storage units

[0094] When dispatching energy storage, the dispatching agency needs to ensure that the state of charge (SBC) of the energy storage is maintained within a certain range. The SBC of the energy storage can be calculated using the following formula:

[0095]

[0096] In the formula, These represent the state of charge, discharge power, and charging power of the energy storage at time t, respectively.

[0097] S102, Based on the aforementioned operating parameter data, with the primary objective of minimizing system cost, the primary sub-objective of minimizing system carbon emissions, and the secondary sub-objective of minimizing user load offset, construct an active distribution network day-ahead low-carbon dispatch model.

[0098] It should be noted that the day-ahead low-carbon dispatch model for the active distribution network provided in this application includes one main optimization objective and two sub-optimization objectives, for a total of three optimization objectives. The optimization objectives and constraints can be modeled as follows:

[0099] The primary goal is to minimize the electricity costs of the distribution network system. The costs of the distribution network system include four components: the cost of distributed conventional generator sets, the costs incurred in trading with the main grid and neighboring distribution networks, the operating costs of energy storage units, and the costs incurred in demand response.

[0100]

[0101] In the formula, μ QD (i,t) and μ TJ (i,t) represents the 0 and 1 variables of the operating state of the distributed conventional generator set (QD=1, TJ=0); P t WM P t WG , These represent the power output sold, the power output purchased, the power price sold, and the power price purchased at time t, respectively. Let represent the discharge and charging power of the energy storage system at time t, respectively. K represents the charging / generating price of the energy storage system at time t, respectively. d It is the demand response price, Δ d D represents the fluctuation ratio of the adjustable load. d,t Let z be the total power consumption of the d-th load at time t. d,t The response variables are 0 and 1 for the adjustable load.

[0102] Solve the main objective function, perform initial optimization, and obtain the case with the lowest system cost.

[0103] Then, under the condition of lowest system cost, the first sub-objective function with the lowest system carbon emissions as the first sub-objective also needs to meet the requirement of lowest carbon emissions under the condition of lowest electricity cost.

[0104]

[0105] In the formula, α i β i ε i For the system's carbon emission factors, The carbon emission factor for purchased electricity.

[0106] Then, under the conditions of minimizing system cost and system emissions, the second sub-objective function with minimizing user load offset as the second sub-objective is:

[0107]

[0108] In the formula, ρ s Let D be the probability of the s-th scene. E (s,t) represents the load distribution in the s-th scenario. This represents the optimal load distribution for the system.

[0109]

[0110] The optimal load distribution is calculated as follows:

[0111]

[0112] The constraints corresponding to the multi-objective function of the day-ahead low-carbon dispatch model for active distribution networks provided in this application include:

[0113] 1) Network constraints: Distribution network systems require active power balance and reactive power balance constraints.

[0114]

[0115]

[0116] In the formula, P gi and Qgi P represents the active and reactive power generated by generator set at node i. di and Q di It represents the active and reactive power requirements of user i at node, V. i and δ i It is the voltage value and voltage phase angle at node i, Y ij and θ ij These are the elements of the admittance matrix and the phase angle. The following equation guarantees that the voltage remains within the safe threshold range.

[0117] V i min ≤V i ≤V i max (12)

[0118] 2) Constraints of distributed generator sets: The operation of distributed conventional generator sets needs to meet the following constraints: upper and lower limits of output, ramping constraints, and minimum start-stop constraints.

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] In the formula, I i (t) represents the startup state of the i-th generating unit at time t. i on T i off This refers to the time the machine was powered on and the time it was powered off.

[0125] 3) Energy storage constraints: Energy storage units must meet maximum and minimum power generation constraints, as well as energy storage constraints, during operation. Specifically:

[0126]

[0127]

[0128] v e (t)+u e (t)≤1 (20)

[0129]

[0130] In the formula, v e (t) and u e(t) represents the 0 and 1 variables of the energy storage charging and discharging state.

[0131] D min d,t z d,t ≤D d,t ≤D max d,t z d,t (twenty two)

[0132] For each adjustable load that needs power supply within a specific time period, the following constraints can be used:

[0133]

[0134] 4) Transmission power constraints: The connection between the distribution network and the main grid and other adjacent distribution networks needs to meet certain transmission constraints.

[0135]

[0136]

[0137] In the formula, These are the upper and lower limits of the transmission capacity of the tie line, respectively.

[0138] 5) Load balance constraints,

[0139]

[0140] S103, iteratively optimize the day-ahead low-carbon dispatch model of the active distribution network until the model converges, and output multiple feasible results;

[0141] In this application, the main objective function is first solved to obtain the case with the minimum system cost. Then, the second and third sub-objective functions are solved. Since this application involves multiple objective functions, the solution obtained by using the column and equation method contains multiple feasible results.

[0142] S104, the trapezoidal fuzzy membership function is used to transform the values ​​of each objective function to obtain the fuzzy comprehensive decision model;

[0143] Because the solution obtained by the column and equation method contains multiple feasible results, in order to obtain the non-dominated Pareto optimal solution, trapezoidal fuzzy membership functions are used to transform the values ​​of each objective function, and then the optimal scheduling method is selected.

[0144]

[0145] In the formula, f i Let be the per-unit value of the i-th objective function. and These are the maximum and minimum values ​​of the i-th objective function, respectively.

[0146] The transformed values ​​are used for decision-making using the following model:

[0147]

[0148] In the formula, α i For fuzzy decision weights with different objective functions.

[0149] S105, Based on the fuzzy comprehensive decision model, output the optimal scheduling plan for the active distribution network; the optimal scheduling plan for the active distribution network includes the output plan of distributed wind turbine units, the operation plan of energy storage units, and the electricity consumption plan of users.

[0150] Finally, based on the fuzzy comprehensive decision-making results, the optimal day-ahead scheduling plan is output, including the output plan of distributed wind turbines, the operation plan of energy storage units, and the electricity consumption plan of users.

[0151] The working principle of the demand-response-based active distribution network day-ahead low-carbon dispatch method is as follows: Figure 2 As shown, this application first obtains the operating parameter data of each entity; the operating parameter data of each entity includes: power user classification information, distributed generator unit information, and energy storage unit capacity information; based on the operating parameter data, with the minimum system cost as the main objective, the minimum system carbon emissions as the first sub-objective, and the minimum user load offset as the second sub-objective, an active distribution network day-ahead low-carbon dispatch model is constructed; the active distribution network day-ahead low-carbon dispatch model is iteratively optimized until the model converges, and multiple feasible results are output; trapezoidal fuzzy membership functions are used to transform the values ​​of each objective function to obtain a fuzzy comprehensive decision model; based on the fuzzy comprehensive decision model, the optimal dispatch plan for the active distribution network is output; the optimal dispatch plan for the active distribution network includes the output plan of distributed wind turbine units, the operation plan of energy storage units, and the electricity consumption plan of users.

[0152] like Figure 3 As shown in the figure, this application provides a demand-response-based active distribution network day-ahead low-carbon dispatching device, including:

[0153] The acquisition module 201 is used to acquire the operating parameter data of each entity; the operating parameter data of each entity includes: power user classification information, distributed generator set information and energy storage unit capacity information;

[0154] Module 202 is used to construct an active distribution network day-ahead low-carbon dispatch model with the primary objective of minimizing system cost, the first sub-objective of minimizing system carbon emissions, and the second sub-objective of minimizing user load offset.

[0155] The convergence module 203 is used to iteratively optimize the day-ahead low-carbon dispatch model of the active distribution network until the model converges and outputs multiple feasible results.

[0156] The conversion module 204 is used to convert the values ​​of each objective function using trapezoidal fuzzy membership functions to obtain a fuzzy comprehensive decision model;

[0157] The output module 205 is used to output the optimal scheduling plan for the active distribution network based on the fuzzy comprehensive decision model; the optimal scheduling plan for the active distribution network includes the output plan of distributed wind turbine units, the operation plan of energy storage units, and the electricity consumption plan of users.

[0158] The working principle of the demand-response-based active distribution network day-ahead low-carbon dispatching device provided in this application embodiment is as follows: The acquisition module 201 acquires the operating parameter data of each entity; the operating parameter data of each entity includes: electricity user classification information, distributed generator unit information, and energy storage unit capacity information; the construction module 202 constructs an active distribution network day-ahead low-carbon dispatching model with the primary objective of minimizing system cost, the first sub-objective of minimizing system carbon emissions, and the second sub-objective of minimizing user load offset; the convergence module 203 iteratively optimizes the active distribution network day-ahead low-carbon dispatching model until the model converges, outputting multiple feasible results; the transformation module 204 uses a trapezoidal fuzzy membership function to transform the values ​​of each objective function to obtain a fuzzy comprehensive decision model; the output module 205 outputs the optimal dispatching plan for the active distribution network based on the fuzzy comprehensive decision model; the optimal dispatching plan for the active distribution network includes the output plan of distributed wind turbine units, the operation plan of energy storage units, and the electricity consumption plan of users.

[0159] In summary, this invention provides a demand-response-based proactive distribution network day-ahead low-carbon dispatching method and apparatus, which has the following beneficial effects.

[0160] 1. Taking into account the comprehensive energy consumption characteristics of the active distribution network, a carbon emission assessment model for the active distribution network is proposed, which can accurately assess the carbon emission situation of the active distribution network.

[0161] 2. Considering the optimal load distribution of adjustable users in different scenarios, an optimal load transfer model that takes into account the actual power consumption experience of users is proposed, which can enhance the power consumption experience of users while reducing user costs.

[0162] 3. Taking into account the energy cost, carbon emission cost, and user energy experience of the active distribution network, a day-ahead low-carbon dispatching system based on demand response and bidirectional interaction between source, grid, load, and storage is proposed. This system makes up for the shortcomings of the current dispatching system that only considers user cost and is more conducive to the achievement of dual carbon objectives.

[0163] It is understood that the method embodiments provided above correspond to the device embodiments described above, and the specific details can be referred to each other, which will not be repeated here.

[0164] 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 implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0165] 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 1 A device that provides the functions specified in one or more boxes.

[0166] 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 operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction methods implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] 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.

[0168] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A demand-response-based proactive distribution network day-ahead low-carbon dispatching method, characterized in that, include: Obtain the operating parameter data of each entity; The operating parameter data of each entity includes: electricity user classification information, distributed generator set information, and energy storage unit capacity information; Based on the aforementioned operating parameter data, an active distribution network day-ahead low-carbon dispatch model is constructed with the primary objective of minimizing system cost, the primary sub-objective of minimizing system carbon emissions, and the secondary sub-objective of minimizing user load offset. The day-ahead low-carbon dispatch model of the active distribution network is iteratively optimized until the model converges, and several feasible results are output. A fuzzy comprehensive decision model is obtained by transforming the values ​​of each objective function using a trapezoidal fuzzy membership function. Based on the fuzzy comprehensive decision-making model, an optimal scheduling plan for the active distribution network is output; the optimal scheduling plan for the active distribution network includes the output plan of distributed wind turbines, the operation plan of energy storage units, and the electricity consumption plan of users. Electricity users are categorized into those capable of demand response and those unable to, based on their responsiveness. Since user electricity consumption often occurs in different scenarios, it is necessary to input user data for each scenario. s Different time periods t Typical load values ​​for non-adjustable and adjustable users and and different scenarios s probability of occurrence From time period t to The adjustable user load variation model is as follows: ； Distributed generator sets are divided into two categories: conventional distributed generator sets and distributed wind turbine generator sets. The information on conventional distributed generator sets includes cost information and performance information, wherein the cost information includes operating costs. Start-up costs and downtime costs ; The operating cost is in, , , All are gas turbine units i Cost parameters; For gas turbine units i exist Constant output; unit i Startup costs Downtime costs It is usually a fixed value; The performance information includes the unit's maximum output. , minimum output Minimum continuous downtime Minimum continuous power-on time Maximum climbing rate and maximum rate of descent ; The information of the distributed wind turbine generator set is as follows: in, This is the maximum installed capacity of the unit. For wind speed, , , These are the minimum generating wind speed, the minimum full-output wind speed, and the wind speed at which the turbine is cut off; Energy storage unit capacity information is in, , , Energy storage t State of charge, discharge power, and charging power at any given time. The discharge power coefficient, This refers to the charging power coefficient. System costs include: the cost of distributed conventional generator sets, the costs incurred in trading with the main grid and adjacent distribution networks, the operating costs of energy storage units, and the costs incurred in demand response; the main objective function of the active distribution network day-ahead low-carbon dispatch model is... in, and 0 and 1 variables representing the operating status of distributed conventional generator sets; , , , These represent the power output sold, the power output purchased, the power price sold, and the power price purchased at time t, respectively. , Let represent the discharge and charging power of the energy storage system at time t, respectively. , These represent the charging / generating prices of the energy storage system at time t, respectively. It is the demand response price. The fluctuation ratio of adjustable load, Let d be the total power consumption of the d-th load at time t. The response variables are 0 and 1 for the adjustable load; Under the condition of minimizing system cost, the first sub-objective function with minimizing system carbon emissions as the primary sub-objective is: In the formula, , , For the system's carbon emission factors, The carbon emission factor for purchased electricity For time period; Under the conditions of minimizing system cost and system emissions, the second sub-objective function, with minimizing user load offset as the second sub-objective, is: in, For the first The probability of each scenario. In the first Load distribution in each scenario This represents the optimal load distribution for the system. For time period.

2. The method according to claim 1, characterized in that, The constraints corresponding to the multi-objective function of the active distribution network day-ahead low-carbon dispatch model include: Network constraints include: active power balance constraints and reactive power balance constraints; Active power balance constraint is Reactive power balance constraint is in, and It is a node i The active and reactive power generated by the generator set and It is a node i The user's active and reactive power requirements and It is a node i Voltage value and voltage phase angle, and These are the elements of the admittance matrix and the phase angle, respectively. Distributed generator constraints include: upper and lower output limits, ramping constraints, and minimum start / stop constraints; Output upper and lower limit constraints are Climbing constraint is Minimum start / stop constraints are in, For the first i Taiwanese crew t The startup status at any given moment; , These represent the time the machine was powered on and the time it was powered off, respectively. For the maximum climbing speed, The maximum rate of descent; Energy storage constraints include: maximum generation constraints, minimum generation constraints, and energy storage constraints; Transmission power constraints in, , These are the upper and lower limits of the transmission capacity of the tie line, respectively. and These represent the power transmitted and received by the system via the tie line at time t, respectively. Load balance constraints 。 3. The method according to claim 2, characterized in that, The trapezoidal fuzzy membership function is used to transform the values ​​of each objective function in the following way. in, Let be the per-unit value of the i-th objective function. and The first i The minimum and maximum values ​​of the objective function; The fuzzy comprehensive decision model is obtained as follows in, These are the fuzzy decision weights for different objective functions.

4. A demand-response-based active distribution network day-ahead low-carbon dispatching device, characterized in that, include: The acquisition module is used to acquire the operating parameter data of each entity; The operating parameter data of each entity includes: electricity user classification information, distributed generator set information, and energy storage unit capacity information; The module is used to construct an active distribution network day-ahead low-carbon dispatch model with the primary objective of minimizing system cost, the first sub-objective of minimizing system carbon emissions, and the second sub-objective of minimizing user load offset. The convergence module is used to iteratively optimize the day-ahead low-carbon dispatch model of the active distribution network until the model converges and outputs multiple feasible results. The transformation module is used to transform the values ​​of each objective function using trapezoidal fuzzy membership functions to obtain a fuzzy comprehensive decision model; The output module is used to output the optimal scheduling plan for the active distribution network based on the fuzzy comprehensive decision model; the optimal scheduling plan for the active distribution network includes the output plan of distributed wind turbine units, the operation plan of energy storage units, and the electricity consumption plan of users; Electricity users are categorized into those capable of demand response and those unable to, based on their responsiveness. Since user electricity consumption often occurs in different scenarios, it is necessary to input user data for each scenario. s Different time periods t Typical load values ​​for non-adjustable and adjustable users and and different scenarios s probability of occurrence From time period t to The adjustable user load variation model is as follows: ； Distributed generator sets are divided into two categories: conventional distributed generator sets and distributed wind turbine generator sets. The information on conventional distributed generator sets includes cost information and performance information, wherein the cost information includes operating costs. Start-up costs and downtime costs ; The operating cost is in, , , All are gas turbine units i Cost parameters; For gas turbine units i exist Constant output; unit i Startup costs Downtime costs It is usually a fixed value; The performance information includes the unit's maximum output. , minimum output Minimum continuous downtime Minimum continuous power-on time Maximum climbing rate and maximum rate of descent ; The information of the distributed wind turbine generator set is as follows: in, This is the maximum installed capacity of the unit. For wind speed, , , These are the minimum generating wind speed, the minimum full-output wind speed, and the wind speed at which the turbine is cut off; Energy storage unit capacity information is in, , , Energy storage t State of charge, discharge power, and charging power at any given time. The discharge power coefficient, This refers to the charging power coefficient. System costs include: the cost of distributed conventional generator sets, the costs incurred in trading with the main grid and adjacent distribution networks, the operating costs of energy storage units, and the costs incurred in demand response; the main objective function of the active distribution network day-ahead low-carbon dispatch model is... in, and 0 and 1 variables representing the operating status of distributed conventional generator sets; , , , These represent the power output sold, the power output purchased, the power price sold, and the power price purchased at time t, respectively. , Let represent the discharge and charging power of the energy storage system at time t, respectively. , These represent the charging / generating prices of the energy storage system at time t, respectively. It is the demand response price. The fluctuation ratio of adjustable load, Let d be the total power consumption of the d-th load at time t. The response variables are 0 and 1 for the adjustable load; Under the condition of minimizing system cost, the first sub-objective function with minimizing system carbon emissions as the primary sub-objective is: In the formula, , , For the system's carbon emission factors, The carbon emission factor for purchased electricity For time period; Under the conditions of minimizing system cost and system emissions, the second sub-objective function, with minimizing user load offset as the second sub-objective, is: in, For the first The probability of each scenario. In the first Load distribution in each scenario This represents the optimal load distribution for the system. For time period.

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