A method and system for coordinated optimization scheduling of transmission and distribution based on integrated electrical and thermal energy systems

Abstract

This invention discloses a transmission and distribution coordinated optimization scheduling method and system based on an integrated electrical and thermal energy system. The method includes: constructing a transmission and distribution coordinated optimization scheduling model based on electrical and thermal, with the optimization objective of minimizing the operating cost of transmission network units and the integrated electrical and thermal operating cost in the active distribution network; decoupling the model to obtain an active distribution network model and a transmission network model; using the transmission and distribution network exchange power as a coupling variable, assigning the exchange power obtained after parallel optimization of the active distribution network model as a coupling variable to the transmission network model; performing unit combination decision optimization on the transmission network model after the value is assigned, and assigning the obtained exchange power to the active distribution network model; continuing until the optimization objective and the coupling constraint of transmission and distribution network exchange power are satisfied to obtain the optimal unit combination operation scheduling scheme. Based on the multi-energy coupling characteristics of electrical and thermal energy and the physical interconnection characteristics of transmission and distribution, a coordinated optimization framework is constructed, proposing a scheduling approach of unified analysis and distributed optimization across the entire network.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system coordination and optimization technology, and in particular to a method and system for coordinated optimization scheduling of transmission and distribution based on an integrated electrical and thermal energy system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In recent years, research on integrated energy systems (IES) has yielded several advancements. At the transmission grid (TG) level, some studies have proposed a two-layer optimization model for electric-gas IES that considers the conversion of electricity to gas to absorb wind power, aiming to achieve peak shaving and valley filling by improving wind power absorption capacity. Other studies have proposed a distributed bar-based coordinated optimization scheduling model for electric-gas IES to address higher-order uncertainties in wind power, studying the flexibility and economy of scheduling decisions under various scenarios and verifying that the coordinated operation of the electric-gas coupled system can improve wind power absorption capacity. At the active distribution network (ADN) level, some studies have proposed a regional electric-heat IES optimization scheduling model based on hierarchical autonomy, exploring the impact of heating network flexibility on IES economics and verifying that coordinated complementarity between electric and heat can improve the economic efficiency of system operation. Still other studies have proposed a day-ahead scheduling model based on electricity, heat, and natural gas IES to address uncertainties in wind power and market prices.

[0004] However, the above studies only focus on the optimal scheduling of a single level of the transmission or distribution network, which makes it difficult to fully leverage the complementary advantages of the dispatchable resources of each level of the power grid.

[0005] Therefore, to achieve optimal allocation of network resources and improve the absorption of new energy, it is necessary to break down barriers between different levels of the power grid and adopt a transmission-distribution coordinated dispatching mode. In this mode, some literature proposes a distributed coordination framework to solve the economic dispatching problem of the transmission and distribution sub-networks, verifying the superiority of transmission-distribution coordinated dispatching in dealing with renewable energy fluctuations; others propose a hierarchical distributed coordinated optimization dispatching model considering wind power uncertainty, verifying that transmission-distribution coordination can coordinate network resources and improve the ability to cope with wind power uncertainty; still others propose a robust reserve dispatching model for a transmission-distribution coupled system, fully utilizing the power generation resources of both sides and tapping the potential of the active distribution network to cope with uncertainty.

[0006] However, the above studies rely solely on the physical interconnection and information exchange of boundary coupling variables to achieve coordinated optimization scheduling of the transmission and distribution power grids, but do not consider the deep coupling of multiple energy systems such as electricity, gas, and heat, making it difficult to fully tap the flexible complementary potential of multiple energy sources in power grids at all levels.

[0007] In summary, the traditional operating model of separating electricity, gas, and heat energy systems and isolating transmission and distribution networks makes it difficult to achieve globally optimal scheduling strategies by tapping into the resources of the entire network, thus affecting the wind power absorption capacity. In the field of IES research, studies only focus on the optimal scheduling of a single level of the transmission or distribution network, making it difficult to fully leverage the complementary advantages of dispatchable resources at each level of the grid. In the field of transmission and distribution coordinated optimization research, most studies rely only on the physical interconnection and information exchange of boundary coupling variables to achieve coordinated optimal scheduling of the transmission and distribution grids, but do not consider the deep coupling of multiple energy systems (electricity, gas, and heat), making it difficult to fully explore the flexible complementary potential of multiple energy sources in each level of the grid. Summary of the Invention

[0008] To address the aforementioned issues, this invention proposes a transmission and distribution coordinated optimization scheduling method and system based on an integrated electrical and thermal energy system. It adopts a transmission and distribution coordinated scheduling mode, constructs a coordinated optimization framework based on the multi-energy coupling characteristics of electrical and thermal systems and the physical interconnection features of transmission and distribution, jointly optimizes the active distribution network and the transmission network, and proposes a scheduling approach of unified analysis and distributed optimization across the entire network.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system, comprising:

[0011] With the goal of minimizing the operating costs of transmission network units and the comprehensive operating costs of electrical and thermal systems in the active distribution network, and with constraints on the transmission network, the active distribution network, the power grid, natural gas grid, heat grid, electrical and thermal coupling, and the power exchange coupling constraint of the transmission and distribution network as the conditions, a transmission and distribution coordinated optimization scheduling model based on electrical and thermal systems is constructed.

[0012] By decoupling the transmission and distribution coordinated optimization scheduling model, we obtain an active distribution network model that includes power grid constraints, natural gas grid constraints, heating network constraints, and electrical-thermal coupling constraints, and a transmission network model that includes transmission network constraints.

[0013] Using the power exchange of the transmission and distribution network as a coupling variable, distributed optimization is performed on the active distribution network model and the transmission network model. The power exchange obtained after the parallel optimization of the active distribution network model is assigned as a coupling variable to the transmission network model. After the transmission network model is assigned a value, unit combination decision optimization is performed, and the obtained power exchange is assigned as a coupling variable to the active distribution network model.

[0014] After iterative optimization, the optimal unit combination operation and scheduling scheme is obtained until the optimization objective and the power coupling constraint of the transmission and distribution network are met.

[0015] Secondly, the present invention provides a transmission and distribution coordinated optimization scheduling system based on an integrated electrical and thermal energy system, comprising:

[0016] The model building module is configured to minimize the operating cost of transmission network units and the comprehensive electrical and thermal operating cost in the active distribution network as the optimization objective, and to construct a transmission and distribution coordinated optimization scheduling model based on electrical and thermal constraints, as well as power grid constraints, natural gas grid constraints, heat grid constraints, electrical and thermal coupling constraints in the active distribution network, and power exchange coupling constraints in the transmission and distribution network.

[0017] The model decoupling module is configured to decouple the transmission and distribution coordinated optimization scheduling model to obtain an active distribution network model including power grid constraints, natural gas grid constraints, heating network constraints and electrical-thermal coupling constraints, and a transmission network model including transmission network constraints;

[0018] The iterative optimization module is configured to perform distributed optimization on the active distribution network model and the transmission network model with the power exchange of the transmission and distribution network as the coupling variable. The power exchange obtained after the parallel optimization of the active distribution network model is assigned as the coupling variable to the transmission network model. After the power exchange network model is assigned the value, the unit combination decision optimization is performed, and the obtained power exchange is assigned as the coupling variable to the active distribution network model.

[0019] The scheduling module is configured to iteratively optimize the system until the optimization objective and the power coupling constraints of the transmission and distribution network are met, so as to obtain the optimal unit combination operation scheduling scheme.

[0020] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0021] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] Traditional operating modes, characterized by the separation of electricity, gas, and heat energy systems and the fragmentation of transmission and distribution networks, struggle to achieve globally optimal scheduling strategies, thus impacting wind power absorption capacity. To address the shortcomings of traditional operating modes, this invention proposes a transmission and distribution coordinated optimization scheduling method and system based on an integrated electricity, gas, and heat energy system. Employing a coordinated scheduling mode, it constructs a coordinated optimization framework based on the multi-energy coupling characteristics of electricity, gas, and heat and the physical interconnection features of transmission and distribution. This framework jointly optimizes the active distribution network and the transmission network, proposing a scheduling approach that combines unified analysis across the entire network with distributed optimization.

[0024] This invention proposes a transmission and distribution coordinated optimization scheduling method and system based on an integrated electrical and thermal energy system. The method performs layer-by-layer modeling of the transmission and distribution coordinated optimization scheduling of the three coupled electrical, thermal, and electrical networks. The power exchanged by the transmission and distribution network tie lines is used as the coupling variable, which is equivalent to a virtual energy station. The objective cascade analysis method is used to achieve decoupling and distributed parallel optimization of the active distribution network and transmission network considering electrical and thermal factors.

[0025] This invention proposes a transmission and distribution coordinated optimization scheduling method and system based on an integrated electrical and thermal energy system. For natural gas networks, an incremental piecewise linearization method is used to handle the non-convexity of the natural gas network, transforming the model into a mixed integer linear programming problem.

[0026] This invention optimizes the allocation of resources across the entire network and improves the absorption of new energy sources. It breaks down barriers between different levels of the power grid, fully leverages the interconnection and mutual assistance of multiple energy resources in each system, achieves deep coupling of electrical and thermal energy and energy conversion horizontally to reduce energy costs, and achieves transmission and distribution coordination vertically to enhance the ability to cope with the uncertainties of wind power.

[0027] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0028] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0029] Figure 1 This is a schematic diagram of the transportation and distribution coordinated scheduling framework considering IES provided in Embodiment 1 of the present invention;

[0030] Figure 2 This is a schematic diagram of the electrical-gas-heat interconnection of an active distribution network provided in Embodiment 1 of the present invention;

[0031] Figure 3 This is a schematic diagram of incremental piecewise linearization provided in Embodiment 1 of the present invention;

[0032] Figure 4 This is a schematic diagram of the power transmission and distribution network decoupling mechanism provided in Embodiment 1 of the present invention;

[0033] Figure 5 This is a schematic diagram of the ATC-based transmission and distribution coordinated unit combination algorithm provided in Embodiment 1 of the present invention;

[0034] Figure 6 This is a structural diagram of the T6D2 system provided in Embodiment 1 of the present invention;

[0035] Figure 7This is a schematic diagram of the active distribution network gas heat load and wind power prediction curves provided in Embodiment 1 of the present invention;

[0036] Figure 8 This is a schematic diagram of the predicted electrical load of the power transmission and distribution network provided in Embodiment 1 of the present invention;

[0037] Figure 9 This is a schematic diagram of the algorithm convergence curve provided in Embodiment 1 of the present invention;

[0038] Figures 10(a)-10(d) This is a schematic diagram of the unit combination decision results for four scenarios provided in Embodiment 1 of the present invention;

[0039] Figure 11 This is a schematic diagram of the optimization results for scenario 4 provided in Embodiment 1 of the present invention;

[0040] Figure 12 This is a schematic diagram of wind power consumption under different penetration rates provided in Embodiment 1 of the present invention;

[0041] Figure 13 This is a schematic diagram of the number of generator units started in each time period provided in Embodiment 1 of the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0044] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0045] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0046] Example 1

[0047] Integrated energy systems (IES) provide strong support for improving energy efficiency and reducing emissions. The widespread adoption of active distribution networks has led to a continuous increase in the proportion of multi-energy flow loads and energy conversion equipment (such as combined heat and power units and gas turbines) in end-user energy consumption, thereby enhancing the interaction between the transmission grid (TG) and the active distribution network (ADN). Therefore, how to tap into ADN resources and achieve coordinated dispatch with the TG, thereby improving overall energy efficiency and the capacity to absorb renewable energy, has become a critical issue that urgently needs to be addressed.

[0048] Traditional operating models that separate electricity, gas, and heat energy systems and fragmented transmission and distribution networks make it difficult to leverage the resources of the entire network to achieve globally optimal scheduling strategies, thus impacting the absorption capacity of wind power. In the field of Energy Systems Engineering (IES), research focuses only on the optimal scheduling of a single level of the transmission or distribution network, failing to fully utilize the complementary advantages of dispatchable resources at each level. In the field of transmission-distribution coordinated optimization, most studies rely solely on the physical interconnection and information exchange of boundary coupling variables, achieving coordinated optimal scheduling of the transmission and distribution networks, but neglecting the deep coupling of multiple energy systems (electricity, gas, and heat), thus failing to fully explore the flexible complementary potential of various energy sources within each level of the grid.

[0049] Therefore, it is necessary to jointly optimize the active distribution network and transmission network, considering the multi-energy coupling of electricity, heat, and gas, to fully leverage the interconnection and mutual assistance of multi-energy flow resources at each system level. This involves achieving deep coupling and energy conversion of electricity, gas, and heat horizontally to reduce energy costs, and achieving transmission and distribution coordination vertically to enhance the system's ability to cope with wind power uncertainties. Solution methods for transmission and distribution coordination optimization models are mainly divided into centralized and distributed approaches. Since the generation, load, and network information of each independent system are separated, centralized decision-making methods struggle to collect large amounts of private information, thus affecting the optimality of scheduling results. Therefore, distributed optimization methods have emerged that only require the exchange of boundary information between transmission and distribution levels. Mature techniques such as Dantzig-Wolfe decomposition, Benders decomposition, and Lagrange dual relaxation have laid the theoretical foundation for distributed parallel optimization.

[0050] Building upon this, some literature employs the alternating direction multiplier method (ADMM) to achieve coordinated operation of the distribution network and microgrid in a two-level robust optimization. However, ADMM cannot guarantee convergence in non-convex problems, and therefore cannot be directly applied to models containing mixed integer variables of 0-1 unit combination states. The traditional Lagrange relaxation method (TLR) is the most representative method for relaxing coupling constraints and iteratively updating Lagrange multipliers. Some literature, based on TLR, introduces a quadratic penalty term into the objective function, proposing the augmented Lagrange relaxation (ALR) algorithm to accelerate convergence. Other literature further utilizes the auxiliary problem principle (APP) to make the objective function of ALR separable, but its convergence speed needs improvement. Some literature employs an improved parallel subspace algorithm to achieve coordinated operation of the transmission and distribution networks at both levels, optimizing the traditional power imbalance problem at the boundary of transmission and distribution split scheduling. Some literature proposes a generalized master-slave split theory, heterogeneously decomposing the generalized transmission and distribution coordination model into a power generation and transmission optimization sub-problem and a distribution optimization sub-problem, which are solved in a distributed manner by the power generation and transmission control centers and the distribution control centers, respectively. Based on this, other literature uses the analytical target cascading (ATC) algorithm from large-scale system decomposition theory to decouple the coupling relationships of boundary nodes, decomposing the transmission and distribution coordination unit combination problem into a transmission network sub-problem and several distribution network sub-problems, thereby achieving distributed parallel solving of each sub-problem. The effectiveness of this distributed algorithm in solving large-scale 0-1 mixed-integer linear programming problems is verified.

[0051] To address the aforementioned problems, this embodiment proposes a transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system, including:

[0052] With the goal of minimizing the operating costs of transmission network units and the comprehensive operating costs of electrical and thermal systems in the active distribution network, and with constraints on the transmission network, the active distribution network, the power grid, natural gas grid, heat grid, electrical and thermal coupling, and the power exchange coupling constraint of the transmission and distribution network as the conditions, a transmission and distribution coordinated optimization scheduling model based on electrical and thermal systems is constructed.

[0053] By decoupling the transmission and distribution coordinated optimization scheduling model, we obtain an active distribution network model that includes power grid constraints, natural gas grid constraints, heating network constraints, and electrical-thermal coupling constraints, and a transmission network model that includes transmission network constraints.

[0054] Using the power exchange of the transmission and distribution network as a coupling variable, distributed optimization is performed on the active distribution network model and the transmission network model. The power exchange obtained after the parallel optimization of the active distribution network model is assigned as a coupling variable to the transmission network model. After the transmission network model is assigned a value, unit combination decision optimization is performed, and the obtained power exchange is assigned as a coupling variable to the active distribution network model.

[0055] After iterative optimization, the optimal unit combination operation and scheduling scheme is obtained until the optimization objective and the power coupling constraint of the transmission and distribution network are met.

[0056] The method of this embodiment will be described in detail below.

[0057] Electricity-Gas-Heat Interconnection Systems (IES) serve as a crucial energy support for power systems. Through power transmission and distribution between the transmission and distribution networks, they enable the consumption of multiple energy sources and the integration of renewable energy across different levels. Consideration is given to transmission and distribution interconnection systems for electricity-gas-heat IES, such as... Figure 1 As shown, this includes energy interaction links with the upstream transmission network and energy conversion links within the active distribution network;

[0058] Among them, (1) the energy interaction link with the upper-level transmission network. Considering that the hierarchical structure of the transmission-distribution network is consistent with the basic idea of ​​ATC, the ATC method can be applied to the transmission-distribution joint optimization scheduling model proposed in this embodiment. The transmission-distribution collaborative optimization idea based on ATC and considering IES is: taking the minimum cost of the entire system as the optimization objective, the optimization objective is distributed downwards in TG→ADN, and the response is fed back upwards in ADN→TG. The transmission and distribution network performs parallel optimization according to the allocated target value, and only the tie-line exchange power of the transmission and distribution network is used as the coupling variable to realize the iterative optimization of the two-level transmission and distribution network until the convergence condition is met.

[0059] (2) Energy conversion links within the active distribution network. Electricity, gas, and heat are converted into each other through CHP units and gas turbines, and the control quantities of the transmission and distribution layers are linked for decision-making, realizing unified analysis and optimization of the entire system. This scheduling strategy of "multi-energy complementarity of electricity, gas, and heat + mutual assistance of transmission and distribution across the entire network" not only provides a new way for renewable energy consumption, but also has great significance for energy conservation and environmental protection.

[0060] In this embodiment, the transmission and distribution coordination mathematical model considering the electricity-gas-heat IES is constructed with the minimum sum of the combined cost of the transmission network units and the operating cost of the active distribution network (including the heating network and the natural gas network) as the optimization objective function. The cost of renewable energy is ignored, and the fuel cost of energy coupling equipment such as CHP units and gas turbines is included in the gas supply cost, as shown in equation (1):

[0061]

[0062] In the formula: f0 represents the transmission network operating cost; K represents the active distribution network set; For the k-th active distribution network (ADN) k Costs, including power grid costs Natural gas network costs and heating network costs

[0063] In this embodiment, the power transmission network operation cost function is:

[0064]

[0065] Where: T is the set of time periods divided within the research period; G is the set of thermal power units in the power transmission network; C g P is the output power cost characteristic function of unit g; g,t The output of unit g during time period t; u g,t The start-up and shutdown status of unit g during time period t; The startup cost of unit g during time period t; These refer to the upward and downward adjustment of the secondary frequency regulation for unit g during time period t, respectively, for standby purposes. These are the cost coefficients for increasing and decreasing reserve of unit g during secondary frequency regulation in time period t, respectively.

[0066] In this embodiment, the operating cost of the active distribution network includes: ADN k Grid operating costs, ADN k Natural gas network operating costs and ADN k Heating network operating costs;

[0067] Among them, (1) ADN k The power grid operating cost is:

[0068]

[0069] In the formula: ele represents the set of nodes in the power network; C k,w This represents the cost coefficient for wind curtailment. P k,w,t Forecast and actual values ​​of wind power; For ADN k Energy storage system e (denoted as ESS) e The charging and discharging cost coefficient during time period t; C k,dg P is the output power cost characteristic function of the unit dg; k,dg,t For ADN k The output of the medium-sized generator unit dg during time period t; ADN k The power loss of node b in time period t and its cost coefficient; For ADNk ESS e The charging and discharging power during time period t; ADN k ESS e Provides cost factors for increasing and decreasing reserve while charging; ADN k ESS e Provides cost factors for increasing and decreasing reserve during discharge; ΔP ADN k ESS e Upward and downward reserve provided during charging; ADN k ESS e Upward and downward reserve are provided during the discharge state.

[0070] (2) ADN k The operating cost of the natural gas network is:

[0071]

[0072] In the formula: S represents the set of natural gas sources in the natural gas network; ADN k The gas source output and cost coefficient of the natural gas system; ADN k The natural gas system gas loss load and its penalty cost coefficient.

[0073] (3) ADN k The operating cost of the heating network is:

[0074]

[0075] In the formula: M is the set of nodes in the heating network; For ADN k The heat loss load of the heating network system and its penalty cost coefficient.

[0076] In this embodiment, the constraints include transmission network constraints, active distribution network constraints, and transmission-distribution and electricity-gas-heat coupling constraints;

[0077] Among them, (1) Transmission network constraints: active power balance constraints (6); upper and lower limits of thermal power unit output power constraints (7); unit ramping rate constraints (8); unit minimum start-up and shutdown time constraints (9); node power balance and line power flow constraints, phase angle constraints (10); grid frequency and primary and secondary frequency regulation constraints (11); power transmission constraints of transmission and distribution network tie lines (12); specifically including:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] The power balance constraint considering uncertainties is:

[0086]

[0087] When ΔD d,t , The uncertainty intervals are respectively When, equation (13) can be equivalent to the form of equation (14).

[0088]

[0089] In the formula: D is the load set; For the power transmission network TG and ADN k Inter-line switching power; P d,t Let d be the predicted value of TG load in time period t; These refer to the upward and downward frequency adjustment for standby of unit g during time period t; These represent the upper and lower limits of the output of coal-fired unit g during time period t; These represent the unit's upward and downward ramp rates, respectively. These represent the initial start-up and shutdown times of unit g, respectively. These are the minimum start-up and shutdown times for unit g, respectively; f ij,t , These represent the transmission power and upper limit of line ij, respectively; B ij For line ij susceptance; θ i,t θ refb,t Let Δf be the phase angle of node i and the relaxed node in time interval t, respectively; max This represents the maximum permissible frequency deviation. These represent the boundaries of the decision value deviating from the rated frequency during time period t; R g Δ is the frequency regulation coefficient of unit g; g This is the upper limit of the secondary frequency regulation power of the AGC unit. TG and ADN respectively kThe upper and lower limits of the transmission power of the inter-line connection.

[0090] In this embodiment, a typical electrical-thermal interconnection system structure is as follows: Figure 2 As shown; active distribution network constraints include power grid constraints, natural gas grid constraints, heating grid constraints, and electric-gas-heat coupling constraints;

[0091] Among them, (1) power grid constraints include: power balance constraints under deterministic and uncertain scenarios (15) and (16); conventional unit output and ramp rate constraints (17); tie line transmission power constraints (18); node power balance constraints (19); energy storage system charging and discharging constraints (20); and energy storage system energy storage and regulation constraints (21); specifically:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] In the formula: E is the energy storage set; .∈b represents the set of objects connected to the grid node b; For ADN k Power exchange with the TG communication line; The uncertainty range is D k,d,t For ADN k The predicted value of medium load d in time period t; ADN k Electrical output of CHP unit γ and gas turbine u during time period t; ADN k Upper and lower limits of the output of conventional generating units (dg); ADN k The upward and downward ramp rates of the medium-sized generator unit; ADN k The uncertainty range of the relative deviation between the load and wind power prediction values ​​is estimated using an inaccurate Dirichlet model. These represent the charging and discharging states of energy storage system e during time period t; These represent the charging power of the energy storage system e and its permissible upper limit; These represent the discharge power and its permissible upper limit for the energy storage system e; E k,e,t The energy stored in the energy storage system e during time period t; These represent the charging and discharging efficiencies of the energy storage system e, respectively; E k,e,0 E k,e,T ADN k Electricity values ​​at the beginning and end of the dispatch cycle; ADN k The upper and lower limits of the allowable energy storage capacity of the energy storage system; ADN k The upper and lower limits of the allowable transmission power of the TG connection line; Δt is taken as 1h.

[0100] (2) By Figure 2 As shown, the natural gas network mainly includes gas sources, pipelines, pressurization stations, and gas load users. The constraints of the natural gas network include: node flow balance constraints (22); pipeline flow constraints (23); gas source output constraints (24); node pressure constraints (25); and pressurization station constraints (26); specifically:

[0101]

[0102]

[0103]

[0104]

[0105] p k,n,t ≤β k,com p k,m,t (26)

[0106] In the formula: These are the upper and lower limits of the gas source output, respectively. ADN k Gas load and unmet gas flow rate at natural gas network node m during the mid-period t; G k,mn For ADN k The average flow rate of the natural gas pipeline mn in China; ADN k The upper and lower limits of flow rate in the middle pipe mn; ADN k Natural gas consumption of gas turbine u and CHP unit γ; ADN k The upper and lower limits of pressure at node m in the natural gas system; β k,ξ For ADN kThe pressurization coefficient ξ of the medium-pressure station; p k,m,t p k,n,t ADN k The pressure at the first and last nodes m and n of the pressurization station during the mid-term t period.

[0107] (3) By Figure 2 As shown, due to the short secondary pipeline, the heating network model only models the primary pipeline. The heating network constraints include: heat power balance constraints (27); supply water temperature constraints at the CHP unit outlet and the heat exchange station inlet (28); and heating network temperature constraints (29); specifically:

[0108]

[0109]

[0110]

[0111] In the formula: For ADN k Thermal output of the CHP unit γ; For ADN k During the mid-period, the heat power at node o of the heating network was not met; For ADN k The required heat power of heat exchange station h during the intermediate time period t; c is the specific heat capacity of water; ADN k During the mid-t period, the hot water flow rate, return water temperature, supply water temperature and their upper and lower limits at the γ outlet of the CHP unit; ADN respectively k The value in Ω represents the hot water flow rate, supply temperature, return temperature, and their upper and lower limits at the inlet of the heat exchange station; pipe- Ω pipe+ Let m be the set of pipelines with node o as the endpoint and node o as the starting point, respectively; k,hl,t For ADN k The water flow rate of the hot water pipe hl during time period t; ADN k The inlet and outlet temperatures of the water supply pipeline hl during time period t; These are the inlet and outlet temperatures of the return water pipe hl, respectively. ADN k The mixing temperature at node o on the supply and return water pipes during the mid-term period t is equal to the inlet temperature of all pipes flowing out of that node.

[0112] (4) By Figure 2As shown, the multi-energy coupling elements are the CHP unit and the gas turbine. The energy coupling constraints include: the CHP unit's electro-gas-thermal coupling constraint (30) and the gas turbine's electro-gas coupling constraint (31).

[0113]

[0114]

[0115] In the formula: For the conversion efficiency of the CHP unit γ; H GV It has a high calorific value (39 MJ / m³) for natural gas. 3 ); It is the conversion efficiency of the gas turbine u.

[0116] In this embodiment, the tie-line switching power is selected as the transmission and distribution coupling variable, and the TG and ADN are used. k The switching power of the interconnecting lines is equivalent to the virtual load of TG and ADN, respectively. k The virtual generator. Furthermore, in uncertain scenarios, wind power volatility [ΔPDG] n ,dn wt,ΔPDG n The load uncertainties [ΔDdn dt,ΔDup dt] and [ΔPT→D,dn k,t,ΔPT→D,up k,t] and [ΔPD→T,dn k,t,ΔPD→T,up k,t] will cause fluctuations in the switching power of the transmission and distribution tie line, respectively. Therefore, the transmission and distribution coupling constraints can be obtained as follows:

[0117]

[0118] In the formula: σ represents the virtual load of the transmission network. k,t Indicates Active Distribution Network (ADN) k A virtual generator.

[0119] In summary, equations (1)-(32) constitute the unit combination optimization decision model considering the transmission and distribution coordination of electric-gas-thermal coupling in this embodiment.

[0120] In this embodiment, in the natural gas network model, equation (23) is the Weymouth equation, whose non-convexity poses a challenge to solving the optimal scheduling problem. To reduce the difficulty of solving the problem, this embodiment uses incremental piecewise linearization to linearize the square of the pipeline flow on the left side of equation (23) and introduces a new variable p. s By replacing the pressure square term on the right side of equation (23), the non-convex problem is transformed into a mixed integer linear programming problem.

[0121] The nonlinear function of natural gas pipeline flow rate is of the form f(x) = x2 , Figure 3 This is a schematic diagram of incremental piecewise linearization. The piecewise steps are as follows:

[0122] (1) Based on the degree of nonlinearity, the number of segments NP-1 should be selected appropriately;

[0123] (2) Solve for each piecewise discrete point x1, x2, ..., x within the domain of the independent variable x. NP ;

[0124] (3) Solve for the function value f(x) corresponding to each segment point x;

[0125] (4) Introduce auxiliary variables ψ and φ, and linearize x and f(x) according to the following steps.

[0126]

[0127]

[0128]

[0129]

[0130] In the formula: For the first The position on each segmented interval is represented by a 0-1 variable; is a binary variable; where equation (35) means that the segment interval must be filled continuously without interruption when segmenting.

[0131] Using the above method, equation (23) can be linearized into equations (37)-(41):

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] Therefore, equations (1)-(22), (24)-(32) and (37)-(41) constitute a combined model of transmission and distribution units that takes into account the electric-gas-heat IES, which can be solved directly by mixed integer linear programming.

[0138] In this embodiment, the proposed model is simplified in matrix form:

[0139]

[0140]

[0141]

[0142]

[0143] In equation (42): α and β represent the decision variables of the transmission and distribution networks, respectively; Representing the transmission network and ADN respectively k The power coupling variable of the interconnection line corresponds to and its upper and lower limits; σ k Indicates ADN k Coupled with the power of the interconnection lines between the transmission networks, corresponding to and its upper and lower limits, among which,

[0144] Equation (43) represents the transmission network constraints, where, The set of equality constraints representing the power transmission network. The set of inequality constraints representing the power transmission network;

[0145] Equation (44) is ADN k Constraints, among which, Indicates ADN k Equality constraints, Indicates ADN k Inequality constraints;

[0146] Equation (45) Indicates TG and ADN k The switching power coupling constraint.

[0147] Due to coupling constraints This weakens the autonomous capability of the power transmission and distribution network, making it impossible to directly achieve distributed solutions. Therefore, a Lagrange penalty function π is introduced into equation (42) for relaxation:

[0148]

[0149]

[0150]

[0151] In the formula: ⊙ represents the Hadamard product; λ k μ k These are the multiplier vectors of the first and second terms of π, respectively.

[0152] In this embodiment, according to Figure 4The decoupling mechanism shown achieves decoupling of the transmission-distribution network optimization model. Therefore, in distributed optimization, for equation (47), in the transmission network, Set as After optimization And pass it to ADN k As a known quantity for lower-level optimization decision-making; for equation (48), σ is used in the active distribution network. k Set as After optimization This information is then transmitted to the transmission network as known data for higher-level optimization decisions. Thus, distributed optimization is achieved by iteratively updating the Lagrange multipliers to bring the objective closer to its optimum.

[0153] The transmission network model after transmission-distribution decoupling consists of equation (49) and transmission network constraints; while the active distribution network model after decoupling consists of equation (50) and active distribution network constraints.

[0154]

[0155]

[0156] In the formula:

[0157] In this embodiment, the decoupling of the transmission-distribution network optimization model based on ATC includes: the ATC algorithm iterates sequentially according to the inner and outer loop convergence criteria (51)-(52) and the multiplier update rule (53) until convergence;

[0158] The inner loop convergence criterion is the tie-line switching power. The difference between two adjacent iterations is sufficiently small; the outer loop convergence criterion is the power exchanged on the tie lines. The difference in w iterations is small enough; at the same time, the difference in the total operating cost of the transmission and distribution network between two adjacent iterations is also small enough.

[0159]

[0160]

[0161]

[0162] To accelerate convergence, δ is usually taken as 2 ≤ δ ≤ 3; in this model, δ = 2.5 is taken; the initial value λ is taken as... k,t =1,μ k,t =1.

[0163] In this embodiment, the flow of the ATC-based transmission and distribution coordinated unit combination optimization algorithm considering the electrical-gas-thermal IES is as follows: Figure 5 As shown, the steps are as follows:

[0164] 1) Parameter initialization; set initial values ​​for unit parameters, penalty function multipliers, virtual loads, virtual generators, etc., and set the iteration number w = 0 and v = 0.

[0165] 2) Parallel optimization of the distribution network layer; the inner loop iteration begins, setting w = w + 1, and the virtual generator is obtained through active parallel optimization of the distribution network. Assign it to the upper-level transmission network as a known quantity for transmission network optimization.

[0166] 3) Transmission network layer optimization decision-making; Based on the known information obtained from 2), the transmission network performs unit combination optimization decision-making to obtain virtual loads. Assign it to the lower-level distribution network as a known quantity for parallel optimization of the distribution network layer.

[0167] 4) Determine the convergence condition of the inner loop; determine whether the convergence condition (51) is met. and If both conditions are met, proceed to step 5); otherwise, return to step 2 and continue executing the inner loop.

[0168] 5) Determine the convergence condition of the outer loop; if both the exchange power and cost meet the convergence condition (52), the optimization ends and the optimal value is output; otherwise, proceed to step 6) and update the multipliers.

[0169] 6) Update the penalty function multiplier; set v = v + 1, w = 0, update the multiplier according to (53), and then go to step 2) to restart the inner loop.

[0170] Taking a transmission network with 6 nodes and 2 distribution networks (T6D2) and a transmission network with 118 nodes and 10 distribution networks (T118D10) as examples, the effectiveness of the transmission and distribution coordinated unit combination model considering integrated energy is verified. The GAMS software is used to call the CPLEX solver for the solution. The computer configuration is Win10 system, AMD R7-5800H processor, 16 GB memory, and the simulation time scale is 1 day, divided into 24 time periods.

[0171] T6D2 system such as Figure 6As shown, the system consists of one TG and two ADNs. The TG comprises three coal-fired units, seven power lines, and one electrical load. The two ADNs are connected to nodes 3 and 4 of the TG, respectively. Each ADN contains a 6-6-8 node electric-gas-heat IES. The power grid includes one 6MW diesel generator, one 2MW wind turbine, and one 500kW energy storage system. The natural gas grid includes two gas sources, two pressurization stations, two gas loads, and seven natural gas pipelines. The heating grid includes four heat exchange stations and six hot water pipelines. The conversion between the three energy sources is achieved through CHP units and gas turbines. The off-load cost factor is set at $100 / MWh, the allowable frequency regulation range during normal operation is 50±0.1Hz, the per-unit regulation power of each coal-fired unit is 20, and the per-unit frequency regulation effect of the load is 2.89%. The predicted values ​​for ADN gas heat load and wind power are as follows: Figure 7 As shown, the predicted power load of the transmission and distribution networks is as follows: Figure 8 As shown.

[0172] To verify the effectiveness of ATC in collaborative distributed solution for transportation and distribution, it was compared with centralized, ADMM, and TLR methods respectively; the comparison results are shown in Table 1, and the convergence curves are shown in... Figure 9 As shown.

[0173] Table 1 Optimization results of different algorithms

[0174]

[0175] As shown in Table 1, in terms of economics, compared to the ATC algorithm which relies solely on boundary information for optimization, the centralized algorithm integrates and optimizes all information from the ADN (electricity-gas-heat) and TG (transmission and distribution network), thus achieving the best cost performance. While ATC does not demonstrate higher economic efficiency or faster convergence speed compared to the centralized method, it only requires exchanging a small amount of information between the main transmission and distribution network entities to achieve transmission and distribution coordination. The centralized method, on the other hand, generates a large amount of information exchange, cannot guarantee the privacy of market participants' information, and its cost is only 0.13% lower than ATC. Furthermore, the TLR, ADMM, and ATC algorithms have 12, 6, and 5 iterations respectively, indicating that the ATC algorithm has better convergence performance.

[0176] In summary, the ATC algorithm, based on the TLR algorithm, introduces a second-order penalty term into the cost function, thus shortening the optimization time. Table 1 shows that the TLR and ADMM algorithms are inferior to the ATC algorithm in terms of convergence speed and cost-effectiveness. Therefore, considering all factors, the ATC method can leverage the synergistic effect of power generation resources in the transmission and distribution network to achieve overall optimization for all stakeholders.

[0177] Multi-scenario optimization result analysis: For the transmission and distribution interconnection system, in order to analyze the impact of IES on the optimization results, this embodiment constructs the following four different scenarios. Scenario 1: Transmission and distribution coordinated optimization without considering IES; Scenario 2: Transmission and distribution coordinated optimization only considering gas network; Scenario 3: Transmission and distribution coordinated optimization only considering heating network; Scenario 4: Transmission and distribution coordinated optimization considering IES.

[0178] In Scenario 1, since the ADN does not include IES, the natural gas network constraint, heating network constraint, and corresponding energy coupling constraint are removed; Scenario 2 and Scenario 3 only consider the relevant constraints of the gas network and heating network, respectively; Scenario 4 is the model of this embodiment. The unit combination decision results under different scenarios are as follows: Figures 10(a)-10(d) As shown, in Scenario 1, units G1 and G3 remain operational throughout the entire scheduling period due to their low marginal costs. Unit G2 is shut down from 3:00-4:00 and 22:00-24:00 because its startup and operating costs are high, and it only provides power during periods of high load demand. Compared to Scenario 1, Scenario 2 uses the natural gas grid as a resource for ADN energy coupling, with gas turbines converting some natural gas into electricity to support the grid's power demand. However, since the gas production cost of natural gas source equipment is much higher than the power generation cost of coal-fired units, system power balance cannot rely on gas turbine scheduling for extended periods. Therefore, based on the results of Scenario 1, unit G2, which has the worst economic efficiency, only experiences two more hours of downtime (5:00-6:00), and still needs to continuously supply power as a power generation resource during peak load periods from 7:00-21:00. In Scenario 3, because the CHP unit participates in the energy conversion of the electricity-heat system, the pressure on the coal-fired units to handle peak shaving is reduced, causing unit G2, which has the highest marginal cost, to shut down between 7:00 and 9:00, thus saving system operating costs. In Scenario 4, because the gas turbine and CHP unit share the peak shaving task, unit G2 is continuously shut down between 3:00 and 24:00. Therefore, considering the coordinated optimization of transmission and distribution within the IES significantly reduces system operating costs.

[0179] To analyze the impact of IES on transport and distribution coordination effects Figure 11 Given the diesel generator set, gas turbine, CHP unit, gas source output, and electrical load curves in the Active Distribution Network (ADN1) under Scenario 4, from... Figure 11As can be seen from the model in this embodiment, due to the anti-peak-shaving characteristics of wind power, when the load demand is low, the actual output of wind power is close to the predicted value, and the remaining load demand is mainly borne by the more economical diesel generator sets. When the load demand increases, such as during the periods of 9:00-13:00 and 18:00-21:00, the diesel generator sets increase their output, while the CHP generator sets maintain full capacity. The gas turbines and wind power work together to regulate the peak load, on the one hand to compensate for the power shortage, and on the other hand to provide more dispatchable space for wind power consumption. During the periods of 6:00-11:00 and 14:00-16:00, the load demand increases, and due to the limitations of wind power output and diesel generator set output, the power shortage cannot be avoided. However, during the periods of 1:00-5:00, 12:00-14:00, and 17:00-24:00, the sum of the output of each unit already meets the power load demand of the active distribution network in real time, so there is no need to receive power relief from the upper transmission network. This fully demonstrates that considering the transmission and distribution coordination of IES can improve the distributed autonomy capability of the active distribution network.

[0180] Furthermore, during the periods of 1:00-8:00 and 23:00-24:00, the gas supply of the gas turbine cannot escape the limitation of the gas source capacity, resulting in insufficient gas turbine output and reduced peak-shaving capacity. This is because the gas network storage is not considered in the model of this paper, and the gas load satisfaction depends only on the gas source output, which affects the flexibility and economy of scheduling.

[0181] To analyze the ability of the power-gas-heat IES transmission and distribution coordination to cope with wind power uncertainties, IDM was used to estimate the range of wind power uncertainties. The prediction error range was set to [0.1, 0.5] with a step size of 0.1. The optimization results for each scenario are shown in Table 2.

[0182] Table 2 Optimization results for addressing wind power uncertainties in various scenarios

[0183]

[0184] Table 2 shows that when the wind power prediction error is 0.1, comparing the transmission network operating costs of scenarios 1-4, it can be found that compared to scenario 1, the costs of scenarios 2-4 decreased by 7.66%, 9.13%, and 11.67%, respectively. This indicates that considering the Electric-Gas-Heat (IES) can reduce the operating costs of the transmission network and improve the economic efficiency of system operation. When the wind power prediction error is 0.4, the exchange power of scenario 1 is 139.275MW, and the exchange power of scenario 4 is 40.414MW, a reduction of 70.98% compared to scenario 1. This shows that the transmission and distribution system centered on the IES and using multi-energy coupling as a means can, to some extent, compensate for the economic losses caused by power exchange when operating in a coordinated manner. It can be seen that under the four scenarios, with the increase of wind power uncertainty error, the exchange power between TG and ADN and the cost of TG show a non-uniform increasing trend. This indicates that while transmission and distribution coordination improves the system's ability to cope with wind power uncertainty, it sacrifices the economic efficiency of system operation to some extent. However, when the wind power prediction error increases to 0.5, scenario 1 becomes unsolvable due to the limited transmission capacity of the tie lines, demonstrating that relying solely on tie line power exchange cannot cope with the strong uncertainties of wind power. Optimal solutions still exist in scenarios 2–4, further illustrating that considering the electricity-gas-heat IES expands the scheduling solution space, reduces energy exchange between transmission and distribution networks, and improves the system's distributed autonomy.

[0185] To verify the advantages of the electro-gas-thermal IES in addressing wind power integration, assuming a wind power penetration rate range of [15%, 50%] with a step size of 5%, scenario 4 was selected as the baseline scenario. Figure 12 The wind power absorption capacity of four scenarios under different penetration rates was compared. Figure 12 It is evident that in Scenario 1, the wind power absorption capacity begins to gradually decline when the penetration rate reaches 20%, while in Scenario 4, a significant downward trend only begins when the penetration rate reaches 25%. When the penetration rate reaches 50%, the wind power absorption index corresponding to Scenario 1 is 65%; while the wind power absorption indices corresponding to Scenario 2 to 4 are 73%, 76%, and 84%, respectively. The wind power absorption capacity of Scenario 4 is approximately 19% higher than that of Scenario 1. This demonstrates that considering the flexible coupling of multiple energy sources (electricity, gas, and heat) is beneficial for improving the system's wind power absorption capacity.

[0186] To further verify the applicability of the proposed model and method in large-scale systems, the T118D10 system is analyzed as an example. The TG (Transmission Network Grid) contains 54 generators, 91 load nodes, and 186 lines. Technical data such as the capacity of the coal-fired units, ramp rates, and lines in the TG are referenced from existing literature. Ten ADNs (Automatic Generation Networks) are connected to transmission network nodes 3, 10, 27, 32, 44, 59, 76, 78, 84, and 101, with each ADN consisting of 6-6-8 node electrical-gas-thermal IES (Environmental, Electrical, and Thermal Systems).

[0187] Table 3 shows the transmission and distribution coordination optimization results considering the Electricity-Gas-Heat (ECH) IES under the four scenarios. In scenario 4, the transmission network operating cost is US$1,256,964.83, and the unit start-up and shutdown cost is US$4,219, which are 28.51% and 14.27% lower than those in scenario 1, respectively. This is because the ECH IES distributes the electricity demand through energy conversion, thereby reducing the start-up and shutdown costs of transmission network units and the generation cost, and improving the economic efficiency of transmission network operation.

[0188] Table 3 Optimization Results of the T118D10 Node System

[0189]

[0190] Regarding the cost of wind curtailment, in Scenario 1, conventional units cannot provide sufficient backup to cope with wind power fluctuations and can only meet power balance through wind curtailment, with a curtailment cost of $9457.68, significantly higher than the curtailment costs in Scenarios 2-4. In Scenario 2, since the gas source output cost is much higher than the wind curtailment cost, power balance is met at the expense of wind power absorption, so the curtailment cost in Scenario 2 is higher than in Scenarios 3-4. However, the conversion effect of the gas turbine absorbs some wind power, so the curtailment cost in Scenario 2 is lower than in Scenario 1. The curtailment cost in Scenario 4 is $6748.16, which is 28.65% lower than in Scenario 1. It can be seen that in ADN, through the organic coordination of electric-gas-thermal multi-energy coupling equipment, the remaining wind power is converted into natural gas energy and thermal energy respectively, which can smooth the power fluctuations of wind power, enable effective interaction between multiple energy flows, promote wind power absorption, and greatly improve the economic efficiency of system operation. The interconnection power in scenarios 2-4 is less than that in scenario 1, indicating that the coordinated participation of the gas network and the heating network improves the energy utilization rate of the active distribution network. By achieving supply and demand balance through multi-energy coupling, the pressure on the transmission network to relieve the active distribution network is reduced, thus strengthening the distributed autonomy capability of the active distribution network.

[0191] Analysis of unit combination effect, the number of units started in each time period within the scheduling cycle, such as... Figure 13 As shown, the changes in the start-up and shutdown status of the units follow the trend of load changes. At the same time, considering the optimization effect of the unit combination in the transmission and distribution system after the coupling of electricity, gas and heat, the number of units started has decreased significantly, especially during the period from 18:00 to 24:00. This indicates that the multi-energy coupling transmission and distribution coordinated optimization not only alleviates the pressure on the coal-fired units in the transmission network, but also avoids frequent start-up and shutdown of the units. This allows wind power to squeeze the output space of coal-fired units, improves the system economy, and reduces carbon emissions, thus achieving good environmental benefits and meeting the dual-carbon energy goal.

[0192] To demonstrate the applicability of the ATC algorithm in large-scale systems, four algorithms were compared in the T118D10 system. The iterative solution results are shown in Table 4. It can be seen that, in terms of iteration count and solution efficiency, the ATC algorithm has 16 iterations and an iteration time of 24.076 seconds, showing a significant advantage over the TLR and ADMM algorithms. Regarding operating costs, the ATC algorithm yielded a transmission network cost of $1,256,964.828, which is 1.2% and 0.63% lower than the TLR and ADMM algorithms, respectively. In terms of distribution network cost and total cost, ATC is significantly superior to the other two distributed algorithms. Although the centralized algorithm has the lowest cost, in a market environment, it is difficult to accurately obtain the private information of various stakeholders, thus affecting its actual operating cost. Furthermore, it is evident that the total operating cost obtained by ATC is only 0.07% higher than the centralized optimization result. Therefore, from the perspective of overall optimization, the ATC algorithm can still maintain good computational efficiency and convergence in large-scale transmission and distribution coordinated systems, demonstrating good engineering applicability.

[0193] Table 4 Comparison of Algorithm Optimization Results

[0194]

[0195]

[0196] Example 2

[0197] This embodiment provides a transmission and distribution coordinated optimization scheduling system based on an integrated electrical and thermal energy system, including:

[0198] The model building module is configured to minimize the operating cost of transmission network units and the comprehensive electrical and thermal operating cost in the active distribution network as the optimization objective, and to construct a transmission and distribution coordinated optimization scheduling model based on electrical and thermal constraints, as well as power grid constraints, natural gas grid constraints, heat grid constraints, electrical and thermal coupling constraints in the active distribution network, and power exchange coupling constraints in the transmission and distribution network.

[0199] The model decoupling module is configured to decouple the transmission and distribution coordinated optimization scheduling model to obtain an active distribution network model including power grid constraints, natural gas grid constraints, heating network constraints and electrical-thermal coupling constraints, and a transmission network model including transmission network constraints;

[0200] The iterative optimization module is configured to perform distributed optimization on the active distribution network model and the transmission network model with the power exchange of the transmission and distribution network as the coupling variable. The power exchange obtained after the parallel optimization of the active distribution network model is assigned as the coupling variable to the transmission network model. After the power exchange network model is assigned the value, the unit combination decision optimization is performed, and the obtained power exchange is assigned as the coupling variable to the active distribution network model.

[0201] The scheduling module is configured to iteratively optimize the system until the optimization objective and the power coupling constraints of the transmission and distribution network are met, so as to obtain the optimal unit combination operation scheduling scheme.

[0202] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0203] In further embodiments, the following is also provided:

[0204] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0205] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0206] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0207] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0208] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0209] Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in connection with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0210] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system, characterized in that, include: With the goal of minimizing the operating costs of transmission network units and the comprehensive operating costs of electrical and thermal systems in the active distribution network, and with constraints on the transmission network, the active distribution network, the power grid, natural gas grid, heat grid, electrical and thermal coupling, and the power exchange coupling constraint of the transmission and distribution network as the conditions, a transmission and distribution coordinated optimization scheduling model based on electrical and thermal systems is constructed. By decoupling the transmission and distribution coordinated optimization scheduling model, we obtain an active distribution network model that includes power grid constraints, natural gas grid constraints, heating network constraints, and electrical-thermal coupling constraints, and a transmission network model that includes transmission network constraints. Using the power exchange of the transmission and distribution network as a coupling variable, distributed optimization is performed on the active distribution network model and the transmission network model. The power exchange obtained after the parallel optimization of the active distribution network model is assigned as a coupling variable to the transmission network model. After the transmission network model is assigned a value, unit combination decision optimization is performed, and the obtained power exchange is assigned as a coupling variable to the active distribution network model. The power coupling constraint of the power transmission and distribution network is: In the formula: This represents the virtual load of the power transmission network; Indicates Active Distribution Network (ADN) k A virtual generator; For ADN k Power exchange with the TG communication line; The uncertainty range is ; For the power transmission network TG and ADN k Inter-line switching power; The uncertainty range is ; The electrical-thermal coupling constraints include: CHP unit electrical-gas-thermal coupling constraints and gas turbine electrical-gas coupling constraints; The electrical-thermal coupling constraints of CHP units are: The electro-pneumatic coupling constraint of the gas turbine is: In the formula: , ADN k CHP Units γ ,gas turbine u During the period t The electrical output; For ADN k CHP Units γ Heat output; For CHP units γ Conversion efficiency; It has a high calorific value; It is a gas turbine u Conversion efficiency; , ADN k medium gas turbine u and CHP unit γ Natural gas consumption; After iterative optimization, the optimal unit combination operation and scheduling scheme is obtained until the optimization objective and the power coupling constraint of the transmission and distribution network are met.

2. The transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system as described in claim 1, characterized in that, The operating cost of the power transmission network units is: ， In the formula: T The set of time periods divided within the research period; G A collection of thermal power units for power transmission networks; C g For the unit g The output power cost characteristic function; For the unit g During the period t contribution; For the unit g During the period t The start / stop status; For the unit g During the period t Startup costs; , The units g During the period t The up and down modulation of the secondary frequency is on standby; , The units g During the period t Cost coefficients for increasing and decreasing reserve in secondary frequency regulation; Alternatively, the comprehensive operating cost of electrical and thermal systems in an active distribution network includes the operating costs of the power grid, the natural gas grid, and the heating grid. The operating cost of the power grid is as follows: ， In the formula: ele Represents a set of nodes in a power network; C k,w This represents the cost coefficient for wind curtailment. , Forecast and actual values ​​of wind power; , For energy storage systems e During the period t The charging and discharging cost coefficient; For the unit dg The output power cost characteristic function; For the unit dg During the period t contribution; , They are nodes b During the period t The power loss and its cost coefficient; , Energy system e During the period t The charging and discharging power; , energy system e Provides cost factors for increasing and decreasing reserve while charging; , energy system e Provide cost factors for increasing and decreasing reserve during discharge; , energy system e Upward and downward reserve provided during charging; , energy system e Upward and downward reserve provided during discharge; Alternatively, the operating cost of the natural gas network is: ， In the formula: S It is a collection of natural gas sources for the natural gas network; , These refer to the gas source output and cost coefficient of the natural gas system; , These are the natural gas system gas loss load and its penalty cost coefficient; Alternatively, the operating cost of the heating network is: ， In the formula: M A set of nodes in the heating network; , This represents the heat loss load of the heating network system and its penalty cost coefficient.

3. The transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system as described in claim 1, characterized in that, The power grid constraints include: active power balance constraints, upper and lower limits of thermal power unit output power constraints, unit ramping rate constraints, unit minimum start-up and shutdown time constraints, node power balance and line power flow constraints, phase angle constraints, power grid frequency and primary and secondary frequency regulation constraints, and power transmission constraints of power exchange lines in the power transmission and distribution network. Alternatively, the power grid constraints include: power balance constraints under deterministic and uncertain scenarios, conventional unit output and ramp rate constraints, tie line transmission power constraints, node power balance constraints, energy storage system charging and discharging constraints, and energy storage system energy storage and regulation constraints. Alternatively, the natural gas network constraints include: node flow balance constraints, pipeline flow constraints, gas source output constraints, node pressure constraints, and booster station constraints; Alternatively, the heat network constraints may include: heat power balance constraints, water supply temperature constraints at the outlet of the CHP unit and at the inlet of the heat exchange station, and heat network temperature constraints.

4. The transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system as described in claim 1, characterized in that, The constructed electrical-thermal transmission and distribution coordinated optimization scheduling model is linearized using an incremental piecewise method to convert it into a mixed-integer linear programming model.

5. The transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system as described in claim 1, characterized in that, The decoupled transmission network model includes: The decoupled active distribution network model includes: in, T To study the set of time periods divided within the research cycle, K For active distribution network aggregation, Indicates the first k The virtual load of a power transmission network at time t; Indicates the first k A virtual generator in an active distribution network at time t; It represents the Hadamardi (or Hadama) stack.

6. The transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system as described in claim 1, characterized in that, After decoupling the transmission and distribution coordinated optimization scheduling model, the power exchanged by the tie line between the transmission network and the active distribution network is equivalent to the virtual load of the transmission network and the virtual generator of the active distribution network, respectively. The unit parameters, penalty function multipliers, virtual loads and virtual generators are initialized, and the number of inner loop iterations and outer loop iterations are set. In the inner loop iteration, the virtual generators obtained from the parallel optimization of the active distribution network are assigned to the transmission network as known quantities for the optimization of the transmission network. The transmission network performs unit combination decision optimization based on the known quantities and assigns the resulting virtual loads to the active distribution network as known quantities for the parallel optimization of the active distribution network. After the power coupling constraint of the power transmission and distribution network satisfies the inner loop convergence condition, it is determined whether the optimization objective and the power coupling constraint of the power transmission and distribution network satisfy the outer loop convergence condition. If not, the penalty function multiplier is updated, and the inner loop iteration continues until the inner loop convergence condition and the outer loop convergence condition are satisfied, thus obtaining the optimal unit combination operation scheduling scheme.

7. The transmission and distribution coordinated optimization scheduling method based on an integrated electrical and thermal energy system as described in claim 6, characterized in that, The inner loop convergence condition is that the difference in exchange power between two adjacent iterations is sufficiently small. The outer loop convergence condition is that the difference in exchange power during the inner loop iteration is small enough, and the difference in the total operating cost of the transmission and distribution network between two adjacent iterations is also small enough.

8. A transmission and distribution coordinated optimization scheduling system based on an integrated electrical and thermal energy system, characterized in that, include: The model building module is configured to minimize the operating cost of transmission network units and the comprehensive electrical and thermal operating cost in the active distribution network as the optimization objective, and to construct a transmission and distribution coordinated optimization scheduling model based on electrical and thermal constraints, as well as power grid constraints, natural gas grid constraints, heat grid constraints, electrical and thermal coupling constraints in the active distribution network, and power exchange coupling constraints in the transmission and distribution network. The model decoupling module is configured to decouple the transmission and distribution coordinated optimization scheduling model to obtain an active distribution network model including power grid constraints, natural gas grid constraints, heating network constraints and electrical-thermal coupling constraints, and a transmission network model including transmission network constraints; The iterative optimization module is configured to perform distributed optimization on the active distribution network model and the transmission network model with the power exchange of the transmission and distribution network as the coupling variable. The power exchange obtained after the parallel optimization of the active distribution network model is assigned as the coupling variable to the transmission network model. After the power exchange network model is assigned the value, the unit combination decision optimization is performed, and the obtained power exchange is assigned as the coupling variable to the active distribution network model. The power coupling constraint of the power transmission and distribution network is: In the formula: This represents the virtual load of the power transmission network; Indicates Active Distribution Network (ADN) k A virtual generator; For ADN k Power exchange with the TG communication line; The uncertainty range is ; For the power transmission network TG and ADN k Inter-line switching power; The uncertainty range is ; The electrical-thermal coupling constraints include: CHP unit electrical-gas-thermal coupling constraints and gas turbine electrical-gas coupling constraints; The electrical-thermal coupling constraints of CHP units are: The electro-pneumatic coupling constraint of the gas turbine is: In the formula: , ADN k CHP Units γ ,gas turbine u During the period t The electrical output; For CHP units γ Conversion efficiency; It has a high calorific value; It is a gas turbine u Conversion efficiency; , ADN k medium gas turbine u and CHP unit γ Natural gas consumption; The scheduling module is configured to iteratively optimize the system until the optimization objective and the power coupling constraints of the transmission and distribution network are met, so as to obtain the optimal unit combination operation scheduling scheme.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-7.

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