Space-time coordinated scheduling method and system for integrated energy system considering cross-region interaction

Abstract

The application discloses a kind of comprehensive energy system space-time coordination scheduling method and system considering cross-zone interaction, comprising: to the comprehensive energy system of existing cross-zone interaction, with minimizing the running cost of whole scheduling cycle as target, the wind power output value of day-ahead scheduling stage is predicted, and the typical random scene of day-ahead scheduling stage is obtained according to the generation and reduction of random scene;By predicting the real-time fluctuation value of wind power in real-time scheduling stage, determine the current output plan, with minimizing the running deviation as target, with typical random scene as reference, correct the current output plan, to obtain optimal operation strategy;To the region of cross-zone interaction in comprehensive energy system, by setting constraint condition, decouple to regional interaction line, and by introducing alternating direction multiplier method for distributed iterative calculation, to obtain the optimal scheduling strategy between regions. Coordinate the economy and accuracy of different time scales, while using distributed algorithm to complete the interaction optimization between regions.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system scheduling technology, and in particular to a spatiotemporal coordinated scheduling method and system for integrated energy systems that considers cross-regional interaction. 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] Compared to single energy supply systems, integrated energy systems (IES) can meet the demands of multiple loads, including electricity, heat, and natural gas. However, the complex energy coupling relationships and the natural physical differences among multiple energy networks pose challenges to the refined operation of IESs. With the introduction of intermittent renewable energy sources and the establishment of cross-regional energy network interactions, the operation of IESs faces greater uncertainty; moreover, the protection of information privacy between sub-regions further exacerbates the difficulty of unified dispatch.

[0004] In terms of time, with the large-scale introduction of intermittent wind power, optimized scheduling based on the day-ahead long time scale is no longer applicable to the real-time power balance of the actual system, and may even produce incorrect solutions when the predicted input deviates. In addition, in real-time short-time scale scheduling, operational accuracy is emphasized, while the time requirements for unit start-up, shutdown and ramp-up are ignored.

[0005] In a spatial dimension, inter-regional energy complementarity and sharing drive the establishment of a cross-regional interactive global energy internet. Sub-regional energy systems belonging to different stakeholders are connected through transmission lines (natural gas pipelines), operating independently with incomplete information sharing. Due to the increasing scale and limitations on information privacy protection between sub-regions, centralized dispatching would impose a significant computational burden and be difficult to apply.

[0006] Although some studies have investigated the distributed scheduling problem of cross-regional IES, they have mainly focused on coordination frameworks and computational speed, while there has been less research on scheduling accuracy and multi-timescale coordination of cross-regional IES. Summary of the Invention

[0007] To address the aforementioned issues, this invention proposes a spatiotemporal coordinated scheduling method and system for integrated energy systems that considers cross-regional interaction. This method coordinates the economy and accuracy across different time scales and employs a distributed algorithm to optimize inter-regional interactions.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a spatiotemporal coordinated scheduling method for an integrated energy system that considers cross-regional interaction, comprising:

[0010] For integrated energy systems with cross-regional interaction, with the goal of minimizing the operating cost of the entire dispatch cycle, the wind power output value during the day-ahead dispatch phase is predicted, and typical random scenarios during the day-ahead dispatch phase are obtained based on the generation and reduction of random scenarios.

[0011] The current power output plan is determined by predicting the real-time fluctuation value of wind power during the real-time scheduling phase. With the goal of minimizing operational deviation, and with typical random scenarios as a reference, the current power output plan is corrected to obtain the optimal operation strategy.

[0012] For regions with cross-regional interaction in an integrated energy system, constraints are set to decouple the regional interaction lines, and distributed iterative calculations are performed by introducing the alternating direction multiplier method to obtain the optimal scheduling strategy between regions.

[0013] As an alternative implementation, a comprehensive energy system model including a natural gas transmission network and a power network is constructed. The natural gas transmission network is constructed based on the pipeline pressure level, pipeline coefficient, natural gas compressibility coefficient, and environmental parameters, and satisfies node gas flow balance constraints, maximum compressibility ratio constraints, and natural gas pressure constraints. The power network includes unit constraints and network constraints. Unit constraints include upper and lower limits of output power constraints, ramping constraints, and heat-to-power ratio constraints. Network constraints include power balance constraints, phase angle constraints, and line transmission capacity constraints.

[0014] As an alternative implementation, the constraints of the cross-regional interaction area include consistent constraints on the voltage, gas pressure and phase angle of the two regions of the cross-regional interaction, as well as consistent constraints on the gas flow and electrical power of the pipelines and lines on both sides.

[0015] As an alternative implementation method, the branch decomposition method is used when decoupling regional interactive lines, and the given accuracy requirements of the original residual and dual residual are met to ensure the consistency of the boundary decoupling variables during iterative updates.

[0016] As an alternative implementation method, the full scheduling cycle operating cost includes the coal consumption cost of coal-fired units in the two regions interacting across regions, the cost of purchased natural gas, and the cost of wind curtailment penalty.

[0017] As an alternative implementation method, the objective function for the real-time scheduling phase is:

[0018]

[0019] in, This indicates the calculation of the control quantity value at time t+Δt at time t; Q k χ represents the coefficient matrix of the control variables; χ is the probability value of a typical scenario.

[0020] As an alternative implementation, in the real-time scheduling phase, optimal condition decomposition is introduced to decompose the serial optimization on a continuous time scale into parallel optimization computation on multiple single time segments.

[0021] Secondly, the present invention provides a spatiotemporal coordinated scheduling system for an integrated energy system that considers cross-regional interaction, comprising:

[0022] The day-ahead dispatching prediction module is configured to predict the wind power output during the day-ahead dispatching phase for integrated energy systems with cross-regional interactions, with the goal of minimizing the operating cost of the entire dispatching cycle, and to obtain typical random scenarios for the day-ahead dispatching phase based on the generation and reduction of random scenarios.

[0023] The real-time scheduling phase prediction module is configured to determine the current output plan by predicting the real-time fluctuation value of wind power during the real-time scheduling phase, with the goal of minimizing the operation deviation and using typical random scenarios as a reference, to correct the current output plan and obtain the optimal operation strategy.

[0024] The spatial dimension optimization module is configured to decouple the regional interaction lines in the integrated energy system by setting constraints, and to obtain the optimal scheduling strategy between regions by introducing the alternating direction multiplier method for distributed iterative calculation.

[0025] 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.

[0026] 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.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] This invention proposes a spatiotemporal coordination scheduling method and system for integrated energy systems that considers cross-regional interaction, coordinating the economy and accuracy at different time scales, and using a distributed algorithm to complete the interaction optimization between regions.

[0029] This invention proposes a spatiotemporal coordination scheduling method and system for integrated energy systems considering cross-regional interaction. Based on the alternating direction multiplier method, it completes the distributed solution of the integrated energy system with cross-regional interaction. Local optimization solutions are achieved only through real-time transmission of information (including power, gas flow rate, phase angle, and gas pressure) at the connecting sections, eliminating the need for complete information from the opposite side. This effectively solves the information barrier problem in cross-regional interaction.

[0030] This invention proposes a spatiotemporal coordinated scheduling method and system for integrated energy systems that considers cross-regional interaction. Based on stochastic model predictive control, a multi-timescale collaborative optimization strategy is established, which takes into account both the economy of long-cycle scheduling and the accuracy of short-cycle scheduling. Rolling optimization and closed-loop feedback of model predictive control are introduced, so that the optimization results can accurately track the changes in system uncertainties.

[0031] This invention proposes a spatiotemporal coordination scheduling method and system for integrated energy systems that considers cross-regional interaction. Based on optimal condition decomposition, the time scale is decomposed, and the serial calculation of the entire continuous period is decomposed into parallel solution based on multiple time period sub-problems, thereby improving computational efficiency.

[0032] 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

[0033] 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.

[0034] Figure 1 This is a schematic diagram of the distributed decoupling framework of the cross-regional interactive integrated energy system provided in Embodiment 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of a multi-timescale coordination framework based on stochastic model predictive control provided in Embodiment 1 of the present invention;

[0036] Figure 3 This is a schematic diagram of the cross-regional interactive testing system provided in Embodiment 1 of the present invention;

[0037] Figure 4 This is a simulation diagram of the wind power absorption capacity in area A under cases 1 and 3 provided in Embodiment 1 of the present invention;

[0038] Figure 5 This is a simulation diagram of the output of the electro-gas converter and gas source in region A under cases 1 and 3, provided in Embodiment 1 of the present invention.

[0039] Figure 6 This is a simulation diagram of the electric power and gas flow rate of cross-regional interaction provided in Embodiment 1 of the present invention under Case 3;

[0040] Figure 7 This is a power diagram of the thermal power unit output under conditions 2, 3 and 4 provided in Embodiment 1 of the present invention;

[0041] Figure 8This is a power diagram of the gas turbine output under cases 2, 3 and 4 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] To address the economic efficiency and reliability challenges of integrated energy systems facing large-scale uncertain wind power introduction, this paper proposes a spatiotemporal coordinated scheduling method for integrated energy systems that considers cross-regional interaction, decomposing the scheduling problem of integrated energy systems from a spatiotemporal perspective. In the temporal dimension, the scheduling process is extended to a two-stage optimization problem based on stochastic model predictive control. Interactive energy systems (IES) on both sides implement slow-response unit ramping, controllable unit output, and boundary transmission schemes during the day-ahead scheduling phase, and implement rolling adjustments and closed-loop feedback during the real-time scheduling phase to further refine the operation plan based on updated predicted wind power scenarios. In the spatial dimension, to address information barriers between independent regions, a distributed optimization framework based on the alternating direction multiplier method is established, achieving rapid iterative calculations solely through the state transmission information of the interactive lines. Furthermore, cross-regional energy optimization cooperation stabilizes wind power uncertainty fluctuations and improves operational economics.

[0048] Therefore, the spatiotemporal coordination scheduling method for integrated energy systems considering cross-regional interaction proposed in this embodiment specifically includes:

[0049] For integrated energy systems with cross-regional interaction, with the goal of minimizing the operating cost of the entire dispatch cycle, the wind power output value during the day-ahead dispatch phase is predicted, and typical random scenarios during the day-ahead dispatch phase are obtained based on the generation and reduction of random scenarios.

[0050] The current power output plan is determined by predicting the real-time fluctuation value of wind power during the real-time scheduling phase. With the goal of minimizing operational deviation, and with typical random scenarios as a reference, the current power output plan is corrected to obtain the optimal operation strategy.

[0051] For regions with cross-regional interaction in an integrated energy system, constraints are set to decouple the regional interaction lines, and distributed iterative calculations are performed by introducing the alternating direction multiplier method to obtain the optimal scheduling strategy between regions.

[0052] In this embodiment, a comprehensive energy system model is first constructed. The natural gas transmission network shares certain similarities with the power grid, both originating from gas sources (power sources), undergoing voltage regulation via compressors (transformers), and being transmitted to load centers via pipelines (lines). During operation, both must satisfy the power flow equations of the pipelines (lines), the generalized Kirchhoff's laws for the nodes (buses), and the network's own physical transmission limit constraints.

[0053] In this embodiment, natural gas is extracted from a gas well and injected into a pipeline. It is then pressurized by a compressor and transported over long distances within the pipeline. Simultaneously, due to the compressibility of natural gas, some of it is dynamically stored within the pipeline. At each node, the pipeline natural gas interacts with multi-energy coupling equipment and loads. The steady-state flow rate of natural gas in the pipeline is related to the pipeline pressure level, pipeline coefficient, the compressibility coefficient of natural gas, and environmental parameters. Without considering the elevation difference of the pipeline, the steady-state pressure-flow relationship of natural gas transmission in the pipeline can be described using the Weymouth equation, the expression of which is as follows:

[0054]

[0055]

[0056]

[0057]

[0058] in, This is the average gas flow rate of the pipeline, and its value is related to the flow rate at both ends of the pipeline; s l,t ω is the direction function of the flow rate; l.t This is the pipeline coefficient, whose value is related to parameters such as pipeline length, diameter, temperature, and compressibility; p sl,t ,p el,tThese are the air pressure values ​​at the beginning and end of the pipeline, respectively; T0 and p0 are the temperature and pressure under standard conditions, and D... l and L l T represents the diameter and length of the pipe. la,t and Z la,t For the temperature and compressibility coefficient of the pipeline, f sl,t and f el,t These represent the flow rates at the beginning and end of the pipeline, respectively, where G is the atmospheric pressure under standard conditions, and ε is the flow rate at the beginning and end of the pipeline. l φ is the coefficient of friction of the pipeline. gas This refers to the collection of all pipelines in the natural gas pipeline network.

[0059] For any node in a natural gas network, the gas flow balance must be satisfied. In an integrated energy system, the interaction of natural gas includes not only traditional gas wells and gas loads, but also various types of energy coupling units and energy storage units, such as gas turbines, electric-to-gas converters, and gas boilers. This embodiment, based on an energy collection station model, gives the node gas flow balance equations for a natural gas pipeline network under multi-energy coupling.

[0060]

[0061] The left side of the equation represents the gas injection flow rate at the node, which is the output of the gas well, upstream pipeline, and energy collection station in sequence; the right side of the equation represents the gas output flow rate at the node, which is the gas load, downstream pipeline, compressor consumption, and input of the energy collection station in sequence.

[0062] The compressor is a key component for long-distance natural gas transmission. It adjusts the network pressure level to ensure the natural gas pressure at the load end meets user needs. In this embodiment, to comprehensively consider the operating costs of the natural gas network, the compressor's power consumption is converted into a gas consumption equation under standard conditions.

[0063]

[0064]

[0065] in, This refers to the power consumption of the compressor. To improve the compressor's operating efficiency; This refers to the airflow rate through the compressor; This refers to the compressor inlet pressure. λ is the compressor outlet pressure; λ is the empirical coefficient of the equation. The gas compressibility coefficient; The temperature of the natural gas; This is the compressor gas consumption coefficient.

[0066] At the same time, the compressor's operation needs to meet the constraint of its maximum compression ratio:

[0067]

[0068] in, This represents the maximum compression ratio.

[0069] Power grid operation constraints include unit constraints and network constraints. Units include thermal power units and combined heat and power (CHP) units. Unit constraints include upper and lower limits for output power and ramping constraints. For CHP units, to ensure economic efficiency, the heat-to-power ratio constraint must also be met, as detailed below:

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] in, and These are the lower and upper limits of the output power of thermal power units; and The lower and upper limits of the output power of the combined heat and power unit; and These are the lower and upper limits of the heat-to-power ratio for combined heat and power (CHP) units; and These represent the maximum downhill and uphill speeds of the thermal power unit. and These represent the maximum downhill and uphill speeds of the combined heat and power (CHP) unit.

[0076] Network constraints include power balance constraints, phase angle constraints, and line transmission capacity constraints. First, any node must satisfy the power conservation constraint, as shown in equation (14). Second, the line must satisfy the DC power flow constraint and the upper limit constraint of transmission power, as shown in equation (15).

[0077]

[0078]

[0079] Among them, P ij,t Let t be the line power at time t; θ represents the maximum electrical power transmitted by the line. ij,t The phase angle difference between the beginning and end of the line; x ij,t For line reactance; Loads connected to the power grid.

[0080] In this embodiment, energy storage units are also included, mainly comprising two types of energy storage: electrical storage and thermal storage, both of which have similar mathematical models. Taking the energy storage unit as an example, it needs to meet constraints on state of charge, charge capacity, charging and discharging power limits, and charging and discharging operating state during operation.

[0081] Specifically, the state of charge of the energy storage unit in each time period is related to the state of charge of the previous time period and the charging and discharging power of the current time period, as shown in equations (16)-(17).

[0082]

[0083]

[0084] in, and For thermal storage and energy storage units, the state of charge is μ. TES and μ EES This is the self-loss coefficient for thermal storage and energy storage units; and This is the energy storage and discharge efficiency coefficient of the energy storage device; and This is the efficiency coefficient for heat storage and heat release of the thermal storage device; and The energy storage and discharge power of the energy storage unit; and This refers to the heat storage and heat release power of the thermal storage unit.

[0085] Equations (18)-(19) are the charge capacity constraints of the energy storage unit; in addition, in order to leave enough margin for the next scheduling cycle, this embodiment adds the full cycle constraint of energy storage, that is, at the end of the entire scheduling cycle, the charge state of the energy storage unit needs to be restored to the initial value, as shown in Equations (20)-(21).

[0086]

[0087]

[0088]

[0089]

[0090] in, and These are the lower and upper limits of the thermal storage capacity of the thermal storage unit; and These are the lower and upper limits of the energy storage capacity of the energy storage unit; and The thermal storage status during the first and last periods of the thermal storage unit's scheduling cycle; for The state of charge of the energy storage unit during the first and last periods of the scheduling cycle.

[0091] The operating state constraints of energy storage devices mainly refer to the fact that the same unit can only be in one state of charging or discharging. In this embodiment, binary functions are introduced to describe the operating state of energy storage and the charging and discharging power limits of energy storage, as shown in equations (22)-(27).

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] in, and A binary function describing the operating status of a thermal storage unit; and This represents the maximum thermal power of the thermal storage unit for storing and releasing heat. and The maximum electrical power of the energy storage and discharging unit; and A binary function to describe the operating status of the energy storage unit.

[0099] In this embodiment, the optimal scheduling of inter-regional interactions within an integrated energy system is decomposed spatially. These inter-regional integrated energy systems are connected via power transmission lines and natural gas pipelines. Unlike centralized scheduling, there is no unified upper-level coordination and dispatch center between the interacting regional systems; each region belongs to an independent stakeholder. Due to privacy restrictions, the interacting parties sign contracts regarding transaction volume and transmission methods, but no longer share information such as local network architecture, unit equipment operation, load characteristics, and scheduling schemes. In this context, the interacting parties can only obtain real-time transmission status information at the connection lines and pipelines. Therefore, the optimal scheduling of inter-regional integrated energy systems cannot be achieved using a centralized control model; a distributed optimal scheduling strategy relying on boundary coupling information is required.

[0100] In view of the interactive characteristics of the cross-regional integrated energy system, this embodiment adopts boundary decoupling calculation based on branch decomposition method to decouple the regional interactive lines; at the same time, constraints are set for the two regions of cross-regional interaction when they are independently optimized and scheduled. The constraints include the consistent constraints of voltage, gas pressure and phase angle of virtual nodes on both sides, and the consistent constraints of gas flow and electric power of pipelines and lines on both sides, as shown in Equation (28) and Equation (29), respectively.

[0101]

[0102] in, The flow rate of the pipeline coupled to the boundary of region A; For the flow rate of the pipeline coupled at the boundary of region B; The air pressure value of the virtual coupling node in region A; The pressure value of the virtual coupling node in region B.

[0103]

[0104] in, The electrical power of the line coupled to the boundary of region A; The electrical power of the line coupled to the boundary of region B; The voltage amplitude of the virtual coupling node in region A; The voltage amplitude of the virtual coupling node in region B; The phase angle value of the virtual coupling node in region A; The phase angle value of the virtual coupling node in region B.

[0105] In this embodiment, the regional interactive lines are decoupled based on the branch decomposition method, and the Alternating Direction Multiplier Method (ADMM) is introduced for distributed iterative computation. ADMM is derived from the dual ascent method and the Lagrange multiplier method, combining the good decomposability and convergence of both algorithms. Its general form is as follows:

[0106] For constrained convex optimization problems:

[0107]

[0108] Where x and z are different independent control variables, and A, B, and c are the coefficient matrices of the equality constraints.

[0109] Construct the Lagrange function of the above equation as follows:

[0110]

[0111] Where ρ is the penalty coefficient.

[0112] Under these conditions, the iterative solution process for the problem is as follows:

[0113]

[0114]

[0115] λ k+1 =λ k +ρ(Ax k+1 +Bz k+1 -c) (34)

[0116] The above solution process can be simplified as follows:

[0117]

[0118]

[0119] Therefore, the iterative solution process of ADMM can be simplified to the following form:

[0120]

[0121]

[0122] λ k+1 =λ k +ρ(Ax k+1 +Bz k+1 -c) (39)

[0123] like Figure 1 The diagram shows a distributed decoupled architecture for an integrated energy system with cross-regional interactions. Energy transactions between regions are conducted via natural gas pipelines and power transmission lines. Based on the branch decomposition method, each subsystem satisfies the constraints of equations (40)-(41). According to the above ADMM solution process, the Lagrange equation for this decoupled architecture is as follows:

[0124]

[0125]

[0126] Among them, F A and F B These are the cost functions for regions A and B, respectively. and These are the boundary coupling variable matrices for regions A and B, respectively; ρ s,cp,t For Lagrange multipliers; To augment the penalty term coefficient; and These are the control values ​​for regions A and B, respectively. The power of the line coupled to the boundary of region A; Let A be the phase angle of the busbar coupling at the boundary of region A. The flow rate of the natural gas pipeline coupled to the boundary of region A; The node pressure is the coupling at the boundary of region A.

[0127] Pick Substituting into the above equation, we can further simplify it to:

[0128]

[0129] The calculation iteration process is as follows:

[0130]

[0131]

[0132]

[0133] In distributed solution, to ensure that the boundary decoupling variables remain consistent during iterative updates, the ADMM calculation process must also meet the given accuracy requirements of the original residual and the dual residual, which is calculated as follows:

[0134]

[0135]

[0136] In this embodiment, for integrated energy systems with cross-regional interactions, a time-scale decomposition is performed considering the uncertainties of wind power, such as... Figure 2 The diagram shows a multi-timescale coordination framework based on stochastic model predictive control, which includes a long-cycle day-ahead scheduling phase and a short-cycle real-time scheduling phase. In the day-ahead scheduling phase, wind power output values ​​are obtained based on a long-cycle wind power prediction model, and typical stochastic scenarios with probabilities are obtained through stochastic scenario generation and scenario reduction. The objective function of the day-ahead scheduling phase is to minimize the operating cost of the entire scheduling cycle. For integrated energy systems with cross-regional interactions, the objective function is as shown in equations (48)-(49).

[0137]

[0138]

[0139] Where χ is the probability value of a typical scenario; C k ξ is the cost coefficient; ξ is the system's control variable; PC s,k,t For the active power output of coal-fired power units; PG s,k,t For the active power output of the gas turbine; For wind power absorption; θ s,i,t The phase angle of the node; The charging power for the energy storage unit; fS is the discharge power of the energy storage unit.s,k,t fP is the gas flow rate of purchased natural gas. s,k,t The flow rate of natural gas consumed by the power-to-gas converter; fl s,mn,t p represents the flow rate of the natural gas pipeline. s,n,t This refers to the gas pressure value at the natural gas network node. The gas charging flow rate for the gas storage unit; This refers to the venting flow rate of the gas storage unit.

[0140] In equation (48), the day-ahead operating costs include, in order, the coal consumption cost, the cost of purchased natural gas, and the cost of wind curtailment penalty for coal-fired units in region A, and the coal consumption cost, the cost of purchased natural gas, and the cost of wind curtailment penalty for coal-fired units in region B.

[0141] In this embodiment, the role of the day-ahead scheduling phase is to ensure the economic efficiency of the entire scheduling cycle, and then to enter the real-time scheduling phase. The accuracy of the control commands in the real-time scheduling phase is the key to ensuring the stable and reliable operation of the system. In the real-time scheduling phase, the real-time fluctuation value of wind power is obtained based on the short-cycle prediction model, and the typical wind power scenario with probability is obtained through the random scenario method to determine the current output plan, with the goal of minimizing the operating deviation. The typical random scenario obtained in the day-ahead scheduling phase is used as a reference input to correct the output plan in the real-time scheduling phase. Therefore, the goal of the real-time scheduling phase is as shown in equation (50).

[0142]

[0143] in, This indicates the calculation of the control quantity value at time t+Δt at time t; Q k Let ψ be the coefficient matrix of the control quantity. s For the set of all typical probability scenarios, ψ Δt For the set of all-period step sizes; ψ A ψ represents the set of all controllable units in region A; B This refers to the set of all controllable units in region B.

[0144] In order to improve the control accuracy in the real-time scheduling stage, this embodiment adds a closed-loop feedback loop. The measurement value after each rolling cycle is used as the initial value for the next rolling calculation, and the real state of the system can be updated in real time, as shown in equations (51)-(52).

[0145]

[0146]

[0147] in, This is the initial value of the control quantity for the real-time rolling cycle. This refers to the actual value of the control quantity obtained after the control quantity is issued.

[0148] The temporal and spatial complexity of the inter-regional interactive integrated energy system places a significant computational burden on the optimization process. During continuous optimization across the entire lifecycle, the sequential optimization of the first 24 time periods will greatly increase the computational load and time. To improve the model's computational efficiency and ensure the effectiveness of the scheduling strategy in the face of real-world systems, this embodiment introduces Optimality Condition Decomposition (OCD), which decomposes the sequential optimization across continuous time scales into parallel optimization computations across multiple single time segments.

[0149] First, the general solution form for OCD is given:

[0150]

[0151] Where f is the objective function, h1 is the equality constraint of the optimization problem, x is the control variable of the optimization problem, and c is the continuity constraint between the control variables.

[0152] The Lagrange relaxation method is used to handle the above equality constraints, and the Lagrange function is constructed as shown in equation (54):

[0153]

[0154] Where λ is a Lagrange multiplier.

[0155] The original problem can be decomposed into two independent subproblems by fixing the control variables, as shown in equations (55) and (56).

[0156]

[0157]

[0158] Update the Lagrange multipliers and use the subgradient technique:

[0159]

[0160]

[0161] Therefore, the original problem is ultimately decomposed into two independent subproblems to be solved:

[0162]

[0163]

[0164] According to the general solution process of the optimal condition decomposition method, the key to problem decomposition lies in handling the coupling constraints between control variables. In a cross-regional interactive integrated energy system, the coupling constraints between sub-problems at different time points include the ramp-up constraints of thermal power units and the state-of-charge constraints of energy storage units, as follows:

[0165]

[0166]

[0167]

[0168] Both the ramp constraint and the state of charge constraint are state constraints of the control variables between two consecutive time segments. Referring to the decomposition method of OCD, the above three equality constraints are processed. Taking day-ahead scheduling as an example, the full-cycle serial optimization calculation can be decomposed into parallel iterative solutions of 24 time segment sub-problems.

[0169] First, the objective function can be simplified to the following form:

[0170]

[0171] For each subproblem, its Lagrangian function is:

[0172]

[0173] After decomposition, for any subproblem, its ramp-up and energy storage state constraints are replaced as follows:

[0174]

[0175]

[0176]

[0177] To verify the effectiveness of the optimized scheduling method in this embodiment, a cross-regional interactive integrated energy system composed of IEEE24-GAS20 and IEEE30-GAS48 was constructed, such as... Figure 3 As shown, IEEE24-GAS20 in area A is configured as an energy system with a high proportion of wind power. Meanwhile, the energy systems on both sides are connected to natural gas pipelines through multiple power transmission lines. Each sub-area contains multiple types of energy conversion and storage units in the integrated energy system.

[0178] The long-term timescale is set as day-ahead optimization scheduling, with an optimization step size of 1 hour and an optimization cycle of 24 hours. The short-term timescale is real-time scheduling, with an optimization step size of 5 minutes and an optimization cycle of 1 hour, and rolling optimization is performed until the entire 24-hour scheduling is completed. To better demonstrate the effectiveness and superiority of the scheduling strategy in this embodiment, four types of comparative test scenarios are set as follows:

[0179] Scenario 1: Areas A and B only perform day-ahead independent scheduling.

[0180] Scenario 2: Regions A and B implement day-ahead and real-time coordinated independent scheduling.

[0181] Scenario 3: Regions A and B perform day-ahead distributed interactive scheduling.

[0182] Scenario 4: Regions A and B perform day-ahead and real-time coordinated distributed interactive scheduling.

[0183] like Figure 4-8 As shown, region A has a high proportion of wind power. Constrained by factors such as source-load balance, thermal power unit ramp-up, and transmission line capacity, significant wind curtailment occurs when wind power fluctuates rapidly. By considering the interaction and cooperation between inter-regional integrated energy systems, region A can transmit surplus wind power to region B. Through inter-regional transmission and the coordination of multi-energy coupled units within the system, wind curtailment is essentially eliminated, effectively improving the system's operational flexibility and economy.

[0184] like Figure 6 As shown, comparing the wind power consumption situation reveals that during periods of rapid wind power growth and curtailment, the exchange power between the two regions increases significantly, validating the effectiveness of the distributed coordination proposed in this paper. Simultaneously, in an integrated energy system with interconnected electricity and gas, the scheduling pressure of wind power will be transmitted to the natural gas network. For example... Figure 5 As shown, during periods when wind power is difficult to absorb, the power-to-gas (EPG) turbines generate significant output to consume excess wind power by converting it into natural gas, correspondingly reducing the output of the natural gas source. When inter-regional cooperation is established, the system, based on scheduling costs, will selectively transmit excess wind power to the opposite region via inter-regional transmission lines to reduce system energy loss. At this time, the EPG turbines serve only as backups at a lower output level.

[0185] Furthermore, cooperation between inter-regional systems involves not only electricity but also gas. From Figure 6 It can be seen that, unlike electricity interaction, natural gas transmission remains at a relatively stable level. During periods of significant increase in gas load, the volume of inter-regional transactions will decrease due to the scheduling pressure on gas sources. Through inter-regional cooperation in electricity and natural gas, the scheduling pressure in a single region can be effectively alleviated through multi-energy complementarity and regional complementarity.

[0186] Through day-ahead and real-time coordination, the system's optimization scheme effectively tracks real-time random fluctuations in wind power while ensuring economic efficiency, as fully verified in Chapter 3. After considering inter-regional interaction, the scheduling pressure on sub-regions is relieved in each scheduling cycle. For example... Figure 7 As shown, the output of thermal power units in region A during day-ahead scheduling deviates significantly from actual demand. Intraday correction of thermal power output can effectively coordinate with rapidly fluctuating wind power output. It is worth noting that in scenario 4, by considering inter-regional interaction, thermal power output is significantly improved. Effective inter-regional transfer of wind power provides flexibility for thermal power scheduling, avoiding coal consumption losses caused by thermal power units operating at low output levels. Meanwhile, gas turbines, due to gas cost constraints, primarily serve as system backups, maintaining a relatively stable output level. Figure 8 As shown.

[0187] Example 2

[0188] This embodiment provides a spatiotemporal coordinated scheduling system for an integrated energy system that considers cross-regional interaction, including:

[0189] The day-ahead dispatching prediction module is configured to predict the wind power output during the day-ahead dispatching phase for integrated energy systems with cross-regional interactions, with the goal of minimizing the operating cost of the entire dispatching cycle, and to obtain typical random scenarios for the day-ahead dispatching phase based on the generation and reduction of random scenarios.

[0190] The real-time scheduling phase prediction module is configured to determine the current output plan by predicting the real-time fluctuation value of wind power during the real-time scheduling phase, with the goal of minimizing the operation deviation and using typical random scenarios as a reference, to correct the current output plan and obtain the optimal operation strategy.

[0191] The spatial dimension optimization module is configured to decouple the regional interaction lines in the integrated energy system by setting constraints, and to obtain the optimal scheduling strategy between regions by introducing the alternating direction multiplier method for distributed iterative calculation.

[0192] 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.

[0193] In further embodiments, the following is also provided:

[0194] 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.

[0195] 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.

[0196] 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.

[0197] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0198] 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.

[0199] 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.

[0200] 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 method for spatiotemporal coordinated scheduling of integrated energy systems considering cross-zone interactions, characterized in that, Comprise: For the integrated energy system with cross-region interaction, predict the wind power output value in the day-ahead scheduling stage to minimize the total scheduling period operation cost, and obtain the typical random scenario in the day-ahead scheduling stage according to the generation and reduction of the random scenario; Determine the current output plan by predicting the real-time fluctuation value of wind power in the real-time scheduling stage, correct the current output plan to obtain the optimal operation strategy by taking the typical random scenario as a reference, and minimizing the operation deviation as an objective; For the region with cross-region interaction in the integrated energy system, decouple the regional interaction line by setting constraint conditions, and perform distributed iterative calculation by introducing the alternating direction multiplier method to obtain the optimal scheduling strategy between regions; Wherein, the objective function of the day-ahead scheduling stage is: wherein, is a probability value of a typical scenario; is a cost coefficient; is a control variable of the system; is an active power of the coal-fired unit; is an active power of the gas turbine; is an accommodation amount of wind power; is a node phase angle; is a charging power of the electric energy storage unit; is a discharging power of the electric energy storage unit; is a gas flow of purchased natural gas; is a flow of consumed natural gas of the electric-to-gas unit; is a natural gas pipeline flow; is a natural gas network node pressure value; is a charging flow of the gas storage unit; is a discharging flow of the gas storage unit; Wherein, the objective function of the real-time scheduling stage is: wherein, is a control variable of the system, represents a control value at time instant; time instant; is a coefficient matrix of the control value; is a probability value of a typical scenario; is a set of all typical probability scenarios, is a set of full cycle steps; is a set of all controllable units in area A; is a set of all controllable units in area B. 2.The method of claim 1, wherein, An integrated energy system model including a natural gas transmission network and a power network is constructed; wherein the natural gas transmission network is constructed according to the pressure level of the pipeline, the pipeline coefficient, the compression coefficient of natural gas and the environmental parameters, and meets the node gas flow balance constraint, the maximum compression ratio constraint and the natural gas pressure constraint; the power network includes unit constraints and network constraints; the unit constraints include upper and lower limit constraints of output power, climbing constraints and heat-to-power ratio constraints; the network constraints include power balance constraints, phase angle constraints and line transmission capacity constraints. 3.The method of claim 1, wherein, The constraint conditions of the region with cross-region interaction include the consistency constraints of voltage, gas pressure and phase angle of the two sides of the region with cross-region interaction, and the consistency constraints of gas flow and electric power of the pipelines and lines on both sides. 4.The method of claim 1, wherein, When decoupling the regional interaction line, the branch decomposition method is adopted, and the accuracy requirements of the given original residual and dual residual are met to ensure the consistency of the boundary decoupling variables in iterative updating. 5.The method of claim 1, wherein, The total scheduling period operation cost includes the coal consumption cost of the coal-fired units in the two sides of the region with cross-region interaction, the cost of purchased natural gas and the wind curtailment penalty cost. 6.The method of claim 1, wherein, In the real-time scheduling stage, the optimal condition decomposition is introduced to decompose the serial optimization in the continuous time scale into parallel optimization calculation of multiple single time sections.

7. A space-time coordinated scheduling system for integrated energy systems considering cross-zone interactions, characterized in that, Comprise: The day-ahead scheduling stage prediction module is configured to predict the wind power output value in the day-ahead scheduling stage to minimize the total scheduling period operation cost for the integrated energy system with cross-region interaction, and obtain the typical random scenario in the day-ahead scheduling stage according to the generation and reduction of the random scenario; The real-time scheduling stage prediction module is configured to determine the current output plan by predicting the real-time fluctuation value of wind power in the real-time scheduling stage, correct the current output plan to obtain the optimal operation strategy by taking the typical random scenario as a reference, and minimizing the operation deviation as an objective; The spatial dimension optimization module is configured to decouple the regional interaction line by setting constraint conditions for the region with cross-region interaction in the integrated energy system, and perform distributed iterative calculation by introducing the alternating direction multiplier method to obtain the optimal scheduling strategy between regions; Wherein, the objective function of the day-ahead scheduling stage is: wherein, is a probability value of a typical scenario; is a cost coefficient; is a control variable of the system; is an active power output of a coal-fired unit; is an active power output of a gas turbine; is an accommodation amount of wind power; is a node phase angle; is a charging power of an electric energy storage unit; is a discharging power of an electric energy storage unit; is a gas flow rate of purchased natural gas; is a flow rate of consumed natural gas of an electric-to-gas unit; is a natural gas pipeline flow rate; is a natural gas network node gas pressure value; is a charging flow rate of a gas storage unit; is a discharging flow rate of a gas storage unit; Wherein, the objective function of the real-time scheduling stage is: wherein, is a control variable of the system, represents a control value at time instant; time instant; is a coefficient matrix of the control value; is a probability value of a typical scenario; is a set of all typical probability scenarios, is a set of full cycle steps; is a set of all controllable units in area A; is a set of all controllable units in area B.

8. An electronic device, comprising: A memory and a processor, and computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, complete the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Computer program product for storing computer instructions which, when executed by a processor, perform the method of any one of claims 1-6.

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