Multi-time-scale joint optimization scheduling method and system for electricity-gas interconnection system

Abstract

The present invention provides a multi-time-scale joint optimization scheduling method and system for an electric-gas interconnected system, which obtains operating parameter data of an electric power network and a natural gas network; based on the obtained operating parameter data, a typical scenario with probability is generated according to a long-term wind power forecast result of the day before, and the day-ahead optimization is performed with the minimum total operating cost as the goal, so as to obtain the combination strategy of the units under each typical scenario; based on the combination strategy of the units, intraday optimization is performed with the minimum expected sum of the total operating cost under each typical scenario as the goal, so as to obtain the optimal intraday unit scheduling strategy; based on the obtained optimal economic scheduling strategy, real-time optimization is performed with the minimum deviation between the real-time stage output and the intraday stage reference as the goal, so as to obtain the optimal real-time scheduling strategy of the units; the present invention applies the random model predictive control method to the renewable uncertainty problem in IEGS, so as to balance the economy and accuracy of the operation strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling of an electricity-gas interconnection system, and in particular to a multi-time scale joint optimization scheduling method and system for an electricity-gas interconnection system. Background Art

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

[0003] Compared with a single energy supply system, the integrated electricity and gas system (IEGS) has become a more flexible and efficient energy management system that can meet the needs of multiple loads such as electricity, heat, and natural gas. Preliminary research has been conducted in unified modeling, energy flow analysis, and coordinated optimization of IEGS. However, the complex energy coupling relationship and the natural physical differences of multiple energy networks have brought challenges to the refined operation of IEGS. In addition, with the introduction of intermittent renewable energy, the operation of IEGS faces higher uncertainty.

[0004] Especially with the massive introduction of intermittent wind power, the optimal dispatch based on the long day-ahead time scale is no longer suitable for the real-time power balance of the actual system, and may even produce wrong solutions when the predicted input deviates from the actual. In addition, some technicians attach great importance to the operation accuracy in the real-time short-time scale dispatch, and ignore the time requirements for the start-up and shutdown of the unit. Therefore, multi-time scale coordinated dispatch is proposed as an effective strategy to achieve a better trade-off between accuracy and economy at different time granularities. The key to this choice is that the unit adjustment rules must coordinate the different response speeds of the units, especially for IEGS with multiple types of coupling and storage units. Among them, gas turbines (GT) and power-to-gas units (P2G) are more flexible and faster-responding energy sources, smoothing the imported wind power and load in the real-time stage. During the peak load period, GT is promoted to supplement the shortage of thermal power units. In addition, P2G is used to convert excess wind power into natural gas and inject it into the natural gas pipeline network, providing a more economical and flexible way for wind power consumption. The cooperation between GT and P2G realizes the two-way interaction between the power and natural gas systems, and has obvious dispatching potential under uncertain fluctuations. For slow response sources, some researchers have demonstrated that the start-stop and ramp-up plans should be arranged in advance in the previous day to meet economic operation. The above research shows that solving the coordination problem of multiple time scales in IEGS is crucial to achieve economic and accurate operation.

[0005] However, the inventors found that in the above scheme, the optimal scheduling process is still open-loop control, which does not take into account the feedback correction of the actual system to the control process; nor does it take into account the coordination of day-ahead optimization, intra-day optimization and real-time optimization, so that the final operation scheduling plan cannot achieve effective cost control. Summary of the invention

[0006] In order to address the deficiencies in the prior art, the present invention provides a multi-time-scale joint optimization scheduling method and system for an electricity-gas interconnected system, which applies a stochastic model predictive control method to the renewable uncertainty problem in an electricity-gas interconnected system (IEGS), while balancing the economy and accuracy of the operation strategy; optimizes the start and stop scheme of the units in the day-ahead stage, and calculates the optimal achievable power curve in a rolling manner as a reference for the intra-day stage; in addition, a closed-loop deviation correction based on the latest state is realized in the real-time stage, thereby ensuring the reliability of the optimization strategy.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides a multi-time scale joint optimization scheduling method for an electricity-gas interconnection system.

[0009] A multi-time scale joint optimization scheduling method for an electricity-gas interconnection system includes the following processes:

[0010] Obtaining operational parameter data of power and natural gas networks;

[0011] Based on the acquired operating parameter data and the long-term wind power forecast results, typical scenarios with probabilities are generated, and the day-ahead optimization is performed with the minimum total operating cost as the goal to obtain the combination strategy of the units under each typical scenario;

[0012] According to the combined strategy of the units, the daily optimization is carried out with the goal of minimizing the expected sum of total operating costs under each typical scenario to obtain the optimal daily unit scheduling strategy;

[0013] According to the obtained optimal economic dispatch strategy, real-time optimization is performed with the goal of minimizing the deviation between the real-time stage output and the intraday stage reference to obtain the optimal unit real-time dispatch strategy.

[0014] A second aspect of the present invention provides a multi-time-scale joint optimization scheduling system for an electricity-gas interconnection system.

[0015] A multi-time-scale joint optimization dispatching system for an electricity-gas interconnection system, comprising:

[0016] The data acquisition module is configured to: acquire operation parameter data of the power network and the natural gas network;

[0017] The day-ahead optimization module is configured to: generate typical scenarios with probabilities based on the acquired operating parameter data and the day-ahead long-period wind power forecast results, perform day-ahead optimization with the goal of minimizing the total operating cost, and obtain the combination strategy of the units under each typical scenario;

[0018] The intraday optimization module is configured to: perform intraday optimization based on the combined strategy of the units, with the goal of minimizing the expected sum of total operating costs under each typical scenario, and obtain the optimal intraday unit scheduling strategy;

[0019] The real-time optimization module is configured to: perform real-time optimization based on the obtained optimal economic dispatch strategy with the goal of minimizing the deviation between the real-time stage output and the intraday stage reference, so as to obtain the optimal real-time dispatch strategy for the unit.

[0020] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the multi-time-scale joint optimization scheduling method for an electric-gas interconnection system as described in the first aspect of the present invention.

[0021] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the multi-time-scale joint optimization scheduling method for an electric-gas interconnection system as described in the first aspect of the present invention are implemented.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The multi-time-scale joint optimization scheduling method and system for the electric-gas interconnected system described in the present invention applies the random model predictive control method to the renewable uncertainty problem in the electric-gas interconnected system (IEGS), while balancing the economy and accuracy of the operation strategy, optimizing the unit start-up and shutdown plan in the day-ahead stage, and rolling calculating the optimal achievable power curve as a reference for the intra-day stage. In addition, closed-loop deviation correction based on the latest state is realized in the real-time stage to ensure the reliability of the optimization strategy.

[0024] 2. The multi-time-scale joint optimization scheduling method and system for the electricity-gas interconnection system described in the present invention expands the optimal scheduling into three sub-stage optimization problems of day-ahead, intraday and real-time to coordinate the economy and accuracy of operation; different optimization strategies are designed in each scheduling stage according to the operating characteristics of multi-energy coupling units and high-penetration intermittent wind power; in the day-ahead stage, the start-up and shutdown plans of slow-response units are arranged for the unit combination problem, while the day-ahead start-up and shutdown plans are maintained in the intraday stage to ensure economic optimization operation throughout the entire period; in the intraday stage, MPC-based rolling optimization is implemented to obtain an operation plan with the lowest operating cost target; in addition, the output plan is rolled out in the real-time stage based on the ultra-short-term forecast value, and a closed-loop feedback mechanism is introduced to achieve accurate power balance; in each stage, random decision-making is performed based on the random scenario method to effectively capture the uncertainty in IEGS.

[0025] 3. The multi-time-scale joint optimization scheduling method and system for the electricity-gas interconnection system described in the present invention establishes an IEGS model that takes into account two-way interaction, and explores the flexible scheduling potential under the deep coupling of multiple energy sources. Especially in the real-time stage, the multi-energy coupling conversion and storage equipment can simultaneously minimize the economic cost and improve the wind power absorption capacity.

[0026] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 This is a schematic diagram of the IEGS structure provided in Example 1 of the present invention.

[0029] Figure 2 A schematic diagram of a multi-time scale coordinated scheduling framework provided in Example 1 of the present invention.

[0030] Figure 3 Schematic diagram of the test example system 1 provided in Example 1 of the present invention.

[0031] Figure 4 This is a schematic diagram of the start and stop plan of the unit in the day-ahead phase provided in Example 1 of the present invention.

[0032] Figure 5 This is a schematic diagram of the output plan of a thermal power unit in three stages provided in Example 1 of the present invention.

[0033] Figure 6 A schematic diagram of the consumption of wind power in three stages provided in Example 1 of the present invention.

[0034] Figure 7 This is a schematic diagram of the operating status of the electric energy storage provided in Example 1 of the present invention during the day-ahead stage.

[0035] Figure 8 A schematic diagram of the output of a gas turbine in real time provided in Example 1 of the present invention.

[0036] Fig. 9 A schematic diagram of the output of a gas turbine in real time provided in Example 1 of the present invention.

[0037] Fig.10 This is a schematic diagram of the output of the power-to-gas unit provided in Example 1 of the present invention in the real-time stage.

[0038] Fig.11This is a schematic diagram of error and relative error analysis (ω=10) provided in Example 1 of the present invention.

[0039] Fig.12 This is a schematic diagram of error and relative error analysis (ω=50) provided in Example 1 of the present invention.

[0040] Fig.13 Schematic diagram of error and relative error analysis (ω=100) provided in Example 1 of the present invention.

[0041] Fig.14 Schematic diagram of error and relative error analysis (ω=150) provided in Example 1 of the present invention.

[0042] Fig.15 Schematic diagram of the test example system 2 provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0046] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0047] Embodiment 1:

[0048] Embodiment 1 of the present invention provides a multi-time scale joint optimization scheduling method for an interconnected electricity-gas system (IEGS) based on stochastic model predictive control (MPC). In the proposed scheduling method, the optimal scheduling is expanded into three sub-stage optimization problems: day-ahead, intra-day, and real-time, to coordinate the economy and accuracy of the operation.

[0049] According to the operating characteristics of multi-energy coupling units and high-penetration intermittent wind power, different optimization strategies are designed in each dispatching stage. In the day-ahead stage, the start-stop plan of the slow-response unit is arranged for the unit combination problem, while the day-ahead start-stop plan is maintained in the intraday stage to ensure economic optimization operation throughout the entire period. In the intraday stage, MPC-based rolling optimization is implemented to obtain the operation plan with the lowest operating cost target. In addition, the output plan is rolled adjusted in the real-time stage based on the ultra-short-term forecast value, and a closed-loop feedback mechanism is introduced to achieve accurate power balance. In each stage, random decision-making is performed based on the random scenario method to effectively capture the uncertainty in IEGS. In addition, the proposed nonlinear model is converted into a mixed integer second-order cone programming problem (MISOCP) using McCormick relaxation, which can be effectively solved by commercial solvers. The simulation results on the IEEE39-GAS20 and IEEE118-GAS40 test systems demonstrate the superiority of the method in terms of operation economy and wind power utilization, and also verify the effectiveness of the method in accurately tracking random fluctuations without obvious computational burden.

[0050] Specifically, it includes the following:

[0051] S1: Electrical interconnection system optimization model

[0052] This embodiment proposes an integrated electricity and natural gas system (IEGS) considering two-way interaction, such as Figure 1 As shown, it includes electricity and natural gas networks, thermal power units, wind power, gas sources, gas turbines (GT), power-to-gas (P2G), electric energy storage (EES) and natural gas storage (NGS). GT generates electricity quickly by burning natural gas to supplement the demand for electricity load. In addition, P2G converts electricity into natural gas through the process of electrolysis and methane synthesis. Thanks to the faster ramp rate, the cooperation of GT and P2G supports a more economical and efficient way to stabilize the fluctuations of wind power and load. The typical mathematical models of GT and P2G are given in (1)-(4). In the optimal scheduling, the output satisfies the conversion relationship between electricity and natural gas, and is also limited by the installed capacity.

[0053]

[0054]

[0055]

[0056]

[0057] The flow of natural gas under steady state can be modeled by Weymouth, as shown in equation (5). The flow direction in the pipeline is determined by the pressure at the outlet and inlet, as shown in equation (6), and the flow rate is related to the pipeline parameters, as shown in equation (7). In the natural gas system, the gas turbine consumes natural gas as load, and the power-to-gas converter produces natural gas as the gas source. Therefore, considering that the gas storage device participates in the optimal scheduling, the balance relationship of each node can be described as equation (8). At the same time, the gas source output is limited by the installed capacity, as shown in equation (9), and the pipeline transportation capacity considers the node pressure and pipeline capacity constraints, as shown in equations (10)-(11).

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] The energy stored in a natural gas storage (NGS) can be described as (12), which is related to the loss coefficient, the previous storage state, and the charge and discharge flow rate. The maximum and minimum states of the charge constraint can be described as (13). At the same time, in order to leave a margin for the next operation period, the NGS needs to restore the initial production capacity at the end of the operation period, as shown in equation (14). In addition, the NGS can only operate in one state, charging or discharging. Therefore, only state functions of 0 and 1 can be used to constrain the operating state, as shown in (15)-(17).

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] In order to meet the computing time requirements of the system in the short cycle stage, this part uses the second-order cone relaxation method to transform the non-convex nonlinear problem into a second-order cone programming problem, and then directly calls the commercial solver CPLEX for solving it.

[0073] The relaxation process is as follows:

[0074]

[0075]

[0076]

[0077] Binary variables Introduced to indicate the flow direction of the pipeline, π m,t It is introduced to represent the square of the pipeline pressure. When the flow direction of the pipeline is from node m to node n, When the flow direction of the pipeline is from node n to node m, Non-negative auxiliary variables are introduced to represent the left bilinear product term, as shown in formula (21).

[0078]

[0079] The McCormick method is used to relax the equation:

[0080]

[0081]

[0082]

[0083]

[0084] From this, the Weymouth equation can be further transformed into the general form of a second-order cone:

[0085]

[0086] In order to reduce the relaxation gap and ensure that the solution is equivalent to the original problem, a linear penalty term is added to the objective function to make the value of the inequality approach the equality sign.

[0087]

[0088] The power system model mainly includes node power balance constraints, generator capacity and ramp constraints, and line power constraints, as shown in (28)-(33). The left side of the power balance equation includes thermal power units, gas turbines, energy storage and discharge, upstream lines, and wind power access. The right side includes loads, power-to-gas, energy storage charging, downstream lines, loads, and wind curtailment. At the same time, similar to NGS, the EES model is proposed in (34)-(39), including charging state constraints, capacity constraints, and charging and discharging state constraints.

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] S2: Multi-timescale coordinated scheduling

[0102] Establish a multi-time scale collaborative optimization scheduling model framework to track wind power fluctuations and obtain the optimal economic operation plan for the entire system, such as Figure 2 shown.

[0103] In the day-ahead stage, typical scenarios with probabilities are generated based on the day-ahead long-term wind power forecast results and input into the day-ahead optimization scheduling. The time step is 1 hour and the scheduling cycle is 24 hours. The objective function is to minimize the total operating cost. Considering the operating characteristics of the unit, the start-stop and ramp-up plans of the slow-responding units are mainly determined in the day-ahead stage, and the optimization model considers the time constraints of the operation. By realizing the start-stop and ramp-up of the units in advance in the day-ahead stage, the global economic optimization of the scheduling can be achieved.

[0104] Due to the deviation caused by the long-term prediction in the day-ahead stage, it is difficult to meet the demand for power balance. In the intraday stage, short-term prediction and optimization operations are performed with ΔT as the time step. In order to meet the economic requirements, the start and stop and ramp plans of the units on the day before are maintained, and the output plans of the units are adjusted with the minimum operating cost as the goal. In the intraday stage, the random model predictive control method is introduced, with t+MΔT as the rolling optimization window, and each rolling only executes the first period plan, and the rolling of the next t+ΔT period is based on the updated prediction scenario. Repeat the above process until the full scheduling cycle optimization is completed.

[0105] Although the coordination and cooperation in the day-ahead and intraday stages reduces the impact of wind power fluctuations on the safe operation of the system, it is still an open-loop optimization and lacks precise control. In the real-time stage, ultra-short-term wind power forecasts are made with Δt (Δt<ΔT) as the time step. At the same time, the prediction model is updated based on real-time feedback information to obtain random scenarios for real-time tracking of wind power fluctuations. In order to balance the economy and the accuracy of scheduling, the deviation target is adopted in the real-time stage, and the unit output is further adjusted on the basis of the intraday scheduling output plan to meet the requirements of system power balance.

[0106] (1) Day-ahead stage

[0107] First, the day-ahead stage is a unit combination problem, and it is necessary to arrange the start-up and shutdown plan of the slow-response thermal power units. The objective function of the day-ahead stage is still the lowest cost, which includes the coal consumption cost, startup cost, shutdown cost of the thermal power units, as well as the cost of purchased natural gas, wind curtailment penalty cost, and load loss cost, as shown in formula (40).

[0108]

[0109]

[0110] In the day-ahead stage, the control variables include the unit operating status and output variables, as shown in equation (41). Similarly, the operation and start-up and shutdown costs are considered in the optimization objective. In equation (40), the first term is the operating cost of the thermal power unit, which can be calculated by equation (42). The others are the cost of purchasing natural gas, the penalty cost of wind curtailment and load reduction. It is worth noting that the output cost function of the thermal power unit is a quadratic programming problem. In order to improve the solution efficiency, (42) is relaxed to (43) and further transformed into a second-order cone programming problem, as shown in (44)-(45).

[0111]

[0112]

[0113]

[0114]

[0115] For each stage, the optimization model contains an objective function and constraints. The constraints include unit and transmission network constraints. First, the unit constraints include the operating status and output capacity of coupled units (GT, P2G), thermal power units, wind turbines, gas sources, electric energy storage and gas energy storage, as shown in (1)-(4). The transmission network constrains the operating characteristics of the natural gas and power networks. In the natural gas network, a steady-state gas flow model is proposed and further transformed into a second-order cone problem, as shown in (5)-(7), (18)-(26). In addition, the constraints include node flow balance, node pressure and pipeline flow constraints, as shown in (8)-(11). In the power network, the constraints include DC power flow, node power balance, line power and node phase angle constraints, as shown in (28)-(31).

[0116] In the day-ahead phase, the optimization model must not only satisfy the constraints proposed in Section 2 above, but also satisfy the start / stop state constraints in (46)-(48) and the start / stop time constraints in (49)-(50).

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] (2) Intraday stage

[0125] In the intraday stage, the start and stop plan of the previous day is maintained, and the objective function is the minimum expected sum of the total operating cost of the system optimization cycle under each scenario, as shown in formula (53):

[0126]

[0127]

[0128] Compared with the day-ahead stage, the intraday stage implements rolling optimization based on MPC, and the start-stop cost is no longer included in the optimization target, which has been determined in the day-ahead stage. At the same time, the control variables only include unit output variables, as shown in (54).

[0129] (3) Real-time stage

[0130] The goal of the optimization calculation is to minimize the deviation between the real-time stage output and the intraday stage reference, as shown in (55). It is used to represent the control variable, which is further adjusted in the real-time stage to follow the random fluctuations of wind power, as shown in (56). In the real-time stage, the output model of the control variable is proposed in (57) with the time step as the time step.

[0131]

[0132]

[0133]

[0134] With the current prediction accuracy, advanced MPC control cannot ensure that wind power output is consistent with the predicted value, resulting in a deviation between the advanced controllable distributed generation output and the actual active power output. Therefore, a feedback correction link is required to use the current actual active power value of the system as the initial value of a new round of rolling optimization scheduling to form a closed-loop control, as shown in formula (58).

[0135]

[0136] (4) Random scene method

[0137] Due to the frequent changes in wind speed, there is a large deviation in the wind power forecast value. As the optimization time increases, the deviation of the forecast value increases significantly, causing the optimization strategy to deviate from the actual demand. How to quickly obtain a reasonable forecast value becomes a key issue. Therefore, this section proposes a random scenario method based on the time series forecasting model (ARMA) to obtain a typical wind power scenario that is more in line with the random fluctuations of wind power at different time scales, and adjusts the model predictive control based on a single forecast value to a random model predictive control based on a typical scenario. First, a time series forecasting model is established based on historical measurement data, as shown in Equation (59).

[0138]

[0139] The wind power prediction error can be approximated to satisfy the normal distribution, and its probability density function is shown in formula (60):

[0140]

[0141] Based on the forecast data obtained by the time series forecasting model and the probability distribution of wind power forecast errors, the corresponding random scenarios can be generated by using the scenario method. Taking long-time scale scheduling as an example, we first generate a random sampling array on the probability distribution function of wind power forecast errors based on the Monte Carlo random simulation method, and then obtain a large number of wind power random output scenarios with equal probability, and then use the scenario reduction method to obtain a typical scenario with probability. Compared with the scenario method in Chapter 2, the forecast model can be adjusted according to the latest measured values ​​during the intraday and real-time stages based on the time series forecasting model, so as to obtain updated wind power forecast values, realize the continuous update of rolling optimization and real-time feedback of measured values.

[0142] S3: Case Analysis

[0143] In order to verify the effectiveness of the proposed multi-time scale collaborative optimization scheduling method, an IEEE39-GAS20 test system was established. Figure 3 As shown. The coupling unit includes GT and P2G, which realize the bidirectional energy conversion between the power and natural gas systems. The energy storage unit includes electric energy storage (EES) and natural gas storage (NGS).

[0144] Set the time interval to 1 hour to get a 24-hour optimized scheduling plan in the day-ahead stage. Set the time interval to 15 minutes, the control range to 16, and perform a rolling optimization every 4 hours until the entire 24-hour optimization is completed in the intraday stage. In the real-time stage, set the time interval to 5 minutes, the control range to 12, and perform a rolling optimization every 1 hour. The optimized operation plans for units at different stages are as follows: Figure 4-10 As shown. In the day-ahead stage, by arranging the start and stop of the units in advance and taking into account the long-term load changes, the entire circular economy solution is obtained. In the proposed test case, the optimal start and stop plan of ten thermal power units was obtained in the day-ahead stage, as shown in Figure 4 As shown in the figure, the units with lower cost coefficients are almost always in the on state. They maintain the highest operating efficiency, such as units 1, 2, 5, and 7. The units with higher cost coefficients are used as backup supplements when the load and wind power fluctuate greatly, such as units 9 and 10.

[0145] Compared with the long-term scheduling in the day ahead, the short-term scheduling in the day can accurately track the rapid fluctuation of wind power and meet the requirements of safe and reliable operation of the actual system. Therefore, in order to coordinate economy and accuracy, the early start and stop scheme is maintained in the day and real-time stages, and the output of thermal power units is further adjusted to balance the random fluctuations of the system. Figure 5 The output of thermal power units at different stages is compared, and the thermal power units can be adjusted quickly in real time to meet the energy supply balance of the system during the rapid fluctuation of wind power, such as time intervals 6-8 and 22-24. In the stable stage of wind power, the three-stage dispatch strategy remains at a similar level.

[0146] In real-time short-cycle scheduling, the random prediction scenario of wind power is more in line with actual fluctuations to guide system operation. Figure 6 In the figure, the difference between the predicted access and wind power consumption is the abandoned wind part, which is the shaded part in the figure. Comparing the three scheduling stages, the model proposed in this paper significantly reduces the abandoned wind volume after the correction calculation in the real-time stage, such as time interval 16-20. And the operation strategy can effectively track the changes in wind power, such as time interval 4-15. At the same time, the remaining wind power is converted into natural gas through the power-to-gas unit and directly injected into the natural gas pipeline, which effectively reduces the gas purchase cost, such as Figure 7 shown.

[0147] The power and natural gas systems achieve two-way energy interaction through the cooperation of gas turbines and power-to-gas units, providing a more economical and effective way to absorb renewable energy and balance peak and valley differences. At the same time, energy storage devices, as a flexibly configured resource, can realize the transfer of energy across time periods, further releasing scheduling capabilities. Figure 6 and Figure 8 As a result of the operation, when wind power increases rapidly and other units find it difficult to ramp up quickly, such as time intervals 3-4 and 19-21, the energy storage will store excess power. In addition, during the load increase phase, the energy storage will provide a reserve to meet the system source-load balance. In order to leave a margin for the next dispatch cycle, the energy storage charging state in this model is restored to the initial value after the entire cycle ends.

[0148] The operation status of the coupled conversion unit is limited by the electricity and gas network. Due to the high price of natural gas, the gas turbine is basically used as a reserve, with the operating cost as the target. At peak load, the gas turbine meets the system source load balance and operates at the minimum output level during the load valley. At the same time, compared Fig. 9 In the operating conditions of the gas turbines at different nodes, the gas turbine connected to the power system bus 4 has a higher load fluctuation and produces more output. The gas turbine with thermal power units on line 31 maintains a lower output level, which shows the advantage of gas turbines as fast backup.

[0149] On the other hand, power-to-gas devices provide an effective way to absorb wind energy, converting excess electricity into natural gas and injecting it directly into natural gas pipelines. The output of power-to-gas units varies with the random fluctuations of wind power, such as Fig.10 As shown. At the same time, compared with the units connected to different natural gas network nodes, the power-to-gas unit connected to node 11 has a higher output and directly supplies the gas load. Therefore, the output of the power-to-gas unit is not only limited by its own maximum operation, but also by the transmission capacity of the connected natural gas pipeline network. In addition, configuring mobile gas storage tanks to cooperate with the power-to-gas unit will be an effective way to absorb wind power.

[0150] In order to demonstrate the effectiveness of McCormick relaxation in nonlinear natural gas pipeline network problems, Figure 11-14 The error and relative error analysis of 19 pipelines in the test system considering different penalty coefficient values ​​are given in . Compared with directly solving MINLP, MISOCP based on McCormick relaxation can effectively avoid local optimal solutions and improve the solution efficiency through commercial solvers.

[0151] According to the above results, when ω = 10, the error range of the 19 pipelines is within 1×10 -6 ~2×10 -6 ; When ω=50, the error range of 19 pipelines is within 3×10 -7 ~7×10 -7 ; When ω=100, the error range of 19 pipelines is within 2.5×10 -8 ~5×10 -8 ; When the penalty coefficient increases, the error decreases rapidly. When ω>100, the order of magnitude of the error remains at 10 -8 At the same time, by comparing the change of relative error, when the penalty coefficient increases, the relative error can be reduced to 0.003%. For engineering applications, the above error and relative error can meet the requirements, so the McCormick relaxation introduced in this embodiment is effective.

[0152] Then, the scalability of the proposed model is verified on a large-scale test system, e.g. Fig.15 The large-scale test system model consists of an IEEE 118-bus power system and a 40-node natural gas system. The proposed optimization is calculated by calling the CPLEX commercial solver in MATLAB R2021a based on the Yalmip toolbox, and the PC environment is IntelXeon Gold 5118 CPU@2.30GHZ RAM64GB.

[0153] The solution time of the two test systems in the three stages is shown in Table 1. It can be seen that when the system scale is expanded, the solution time of the system can still meet the actual operation requirements. Among them, the unit combination problem in the day-ahead stage requires the most computing time. At the same time, compared with the IEEE39-GAS20 system, the number of units in the IEEE118-GAS40 system increased from 10 to 54, and the optimization calculation time increased by 5 times. However, for 24-hour scheduling one day in advance, the computing time burden is acceptable. For the intraday and real-time stages with high computing time requirements, the computing time of the two test systems is within 1 minute. As the system scale triples, the computing time increases by two to three times. Especially for the 1-hour scheduling in the real-time stage, rolling every five minutes can still be solved quickly.

[0154] Table 1: Computation time comparison

[0155]

[0156] Under the background of large-scale grid-connected IEGS of intermittent wind power, the multi-time scale collaborative optimization scheduling method based on MPC proposed in this embodiment also has the following advantages:

[0157] The multi-energy coupling resources in IEGS can be fully utilized. Considering the two-way interaction of electricity and natural gas networks, the peak-valley difference can be effectively smoothed and the flexibility of the system can be released. In addition, more wind power can be absorbed to reduce the system operation cost.

[0158] The economy and accuracy of IEGS operations can be coordinated. Compared with single-scale dispatch, long-term dispatch not only retains the economy of operation but also provides flexible space. Combined with small-scale dispatch, flexible resources (such as energy storage devices and energy coupling units) can be quickly adjusted according to updated forecast values, effectively improving the accuracy of system operation.

[0159] By introducing MPC based on random scenarios into the intraday and real-time stages, the model can effectively cope with wind power fluctuations and improve renewable energy consumption.

[0160] Embodiment 2:

[0161] Embodiment 2 of the present invention provides a multi-time scale joint optimization scheduling system for an electricity-gas interconnection system, including:

[0162] The data acquisition module is configured to: acquire operation parameter data of the power network and the natural gas network;

[0163] The day-ahead optimization module is configured to: generate typical scenarios with probabilities based on the acquired operating parameter data and the day-ahead long-period wind power forecast results, perform day-ahead optimization with the goal of minimizing the total operating cost, and obtain the combination strategy of the units under each typical scenario;

[0164] The intraday optimization module is configured to: perform intraday optimization based on the combined strategy of the units, with the goal of minimizing the expected sum of total operating costs under each typical scenario, and obtain the optimal intraday unit scheduling strategy;

[0165] The real-time optimization module is configured to: perform real-time optimization based on the obtained optimal economic dispatch strategy with the goal of minimizing the deviation between the real-time stage output and the intraday stage reference, so as to obtain the optimal real-time dispatch strategy for the unit.

[0166] The working method of the system is the same as the multi-time-scale joint optimization scheduling method for the electricity-gas interconnection system provided in Example 1, and will not be repeated here.

[0167] Embodiment 3:

[0168] Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the multi-time-scale joint optimization scheduling method for an electric-gas interconnection system as described in Embodiment 1 of the present invention.

[0169] Embodiment 4:

[0170] Embodiment 1 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the multi-time-scale joint optimization scheduling method for an electric-gas interconnection system as described in Embodiment 4 of the present invention are implemented.

[0171] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0172] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0173] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0175] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0176] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-time scale joint optimization scheduling method for an electricity-gas interconnection system, characterized by: The process includes: Obtaining operational parameter data of power and natural gas networks; Based on the acquired operating parameter data and the long-term wind power forecast results, typical scenarios with probabilities are generated, and the day-ahead optimization is performed with the minimum total operating cost as the goal to obtain the combination strategy of the units under each typical scenario; According to the combined strategy of the units, the daily optimization is carried out with the goal of minimizing the expected sum of total operating costs under each typical scenario to obtain the optimal daily unit scheduling strategy; According to the obtained optimal intra-day unit dispatching strategy, real-time optimization is performed with the goal of minimizing the deviation between the real-time stage output and the intra-day stage reference to obtain the optimal unit real-time dispatching strategy; In the day-ahead optimization, the start-stop and ramp-up plans of thermal power units with response speeds less than the preset value are determined with a time step of 1 hour and a dispatch cycle of 24 hours; The total operating cost optimized today includes: the coal consumption cost of thermal power units, startup cost, shutdown cost, external natural gas cost, wind curtailment penalty cost and load loss cost; In intraday optimization, ΔT Perform short-term prediction and optimization operations as time steps, maintain the start-up, shutdown and ramp-up plans of thermal power units optimized on the day before, and adjust the output plans of each thermal power unit with the minimum operating cost as the goal; In the intraday optimization stage, the random model predictive control method is introduced, with t+MΔT as the rolling optimization window. Each rolling only executes the first period plan, and the rolling of the next t+ΔT period is carried out according to the updated forecast scenario. The above process is repeated until the full-day optimization is completed; In real-time optimization, ultra-short-term wind power forecasting is performed with Δt as the time step, and the forecasting model is updated based on real-time feedback information to obtain a random scenario of real-time tracking of wind power fluctuations, where Δt < ΔT; In real-time optimization, the goal is to minimize the deviation between the real-time stage output and the intraday stage reference, and adjust the unit output based on the intraday dispatch output plan to meet the system power balance; In real-time optimization, the current actual active power value of the system is used as the initial value of a new round of rolling optimization scheduling for closed-loop control.

2. The multi-time scale joint optimization scheduling method for the electricity-gas interconnection system according to claim 1, characterized in that: Based on the random scenario method of the time series prediction model, typical wind power scenarios that conform to the random fluctuations of wind power are obtained at different time scales.

3. The multi-time scale joint optimization scheduling method for the electricity-gas interconnection system according to claim 2, characterized in that: Based on the Monte Carlo random simulation method, a random sampling array is generated on the probability distribution function of wind power prediction error to obtain multiple wind power random output scenarios with equal probability. The scenario reduction method is used to obtain typical scenarios with probability.

4. A multi-time scale joint optimization dispatching system for an electricity-gas interconnection system, characterized by: include: The data acquisition module is configured to: acquire operation parameter data of the power network and the natural gas network; The day-ahead optimization module is configured to: generate typical scenarios with probabilities based on the acquired operating parameter data and the day-ahead long-period wind power forecast results, perform day-ahead optimization with the goal of minimizing the total operating cost, and obtain the combination strategy of the units under each typical scenario; The intraday optimization module is configured to: perform intraday optimization based on the combined strategy of the units and take the minimum expected sum of total operating costs under each typical scenario as the goal to obtain the optimal intraday unit scheduling strategy; The real-time optimization module is configured to: Day unit The dispatching strategy is optimized in real time with the goal of minimizing the deviation between the real-time output and the intraday reference to obtain the optimal real-time dispatching strategy for the unit; In the day-ahead optimization, the start-stop and ramp-up plans of thermal power units with response speeds less than the preset value are determined with a time step of 1 hour and a dispatch cycle of 24 hours; The total operating cost optimized today includes: the coal consumption cost of thermal power units, startup cost, shutdown cost, external natural gas cost, wind curtailment penalty cost and load loss cost; In intraday optimization, ΔT Perform short-term prediction and optimization operations as time steps, maintain the start-up, shutdown and ramp-up plans of thermal power units optimized on the day before, and adjust the output plans of each thermal power unit with the minimum operating cost as the goal; In the intraday optimization stage, the random model predictive control method is introduced, with t+MΔT as the rolling optimization window. Each rolling only executes the first period plan, and the rolling of the next t+ΔT period is carried out according to the updated forecast scenario. The above process is repeated until the full-day optimization is completed; In real-time optimization, ultra-short-term wind power forecasting is performed with Δt as the time step, and the forecasting model is updated based on real-time feedback information to obtain a random scenario of real-time tracking of wind power fluctuations, where Δt < ΔT; In real-time optimization, the goal is to minimize the deviation between the real-time stage output and the intraday stage reference, and adjust the unit output based on the intraday dispatch output plan to meet the system power balance; In real-time optimization, the current actual active power value of the system is used as the initial value of a new round of rolling optimization scheduling for closed-loop control.

5. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the multi-time-scale joint optimization scheduling method for an electricity-gas interconnection system as described in any one of claims 1 to 3 are implemented.

6. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the multi-time-scale joint optimization scheduling method for the electric-gas interconnection system as described in any one of claims 1-3 are implemented.

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