An igdt-based scheduling optimization method for an electric-gas integrated energy system

Abstract

The application discloses an IGDT-based power-gas integrated energy system scheduling optimization method, which comprises the following steps: firstly, establishing a scheduling model of the power-gas integrated energy system under certainty; then, establishing an uncertainty model of wind power output; finally, establishing a scheduling optimization model of the power-gas integrated energy system; assuming that the predicted value of wind power is equal to the actual value, the scheduling reference value of the power-gas integrated energy system operation scheduling is obtained; then, the scheduling strategy of the power-gas integrated energy system is obtained in combination with a preset deviation factor.

Description

Technical Field

[0001] This invention belongs to the field of integrated electrical energy technology, and more specifically, relates to a scheduling optimization method for integrated electrical-gas energy systems based on IGDT. Background Technology

[0002] With global warming, especially in summer, my country's electricity consumption has surged, and traditional power generation is no longer sufficient to meet demand. This necessitates a "combined energy" system that integrates renewable and traditional energy sources, with the electric-gas coupling system being a popular choice. However, renewable energy generation is characterized by randomness, fluctuations, and intermittency, hindering its large-scale grid integration. This results in low wind energy absorption rates and wind curtailment, impacting the safe and economical operation of the entire electric-gas integrated energy system. Therefore, fully considering the uncertainty of wind power output is crucial when scheduling integrated energy systems with high wind penetration.

[0003] When considering the uncertainties of integrated energy systems, we can approach the issue from two perspectives: the load side and the generation side. On the load side, the orderly charging of electric vehicles participating in the overall integrated energy system scheduling is an effective way to increase the system's scheduling flexibility. On the generation side, we typically start by considering the uncertainty of wind power output. Currently, there are three main methods for handling uncertainty: fuzzy optimization, stochastic programming, and robust optimization. However, the key to fuzzy optimization lies in selecting a membership function to describe the uncertainties and their potential consequences, which is highly subjective. Stochastic programming is a probability-based analytical method that relies on a probabilistic model of the uncertainties. Robust optimization makes decisions based on the worst-case perturbation conditions given the range of uncertainties, which often leads to conservative results and poor economic efficiency.

[0004] In practical scheduling work, decision-makers often have expected cost budgets, but the traditional methods mentioned above cannot take these budgets into account. A new method for handling uncertainty, Information Gap Decision Theory (IGDT), can, based on a given expected planning objective and with limited information on uncertainties, determine the set of uncertainties that allow for the maximum acceptable fluctuation range of uncertain parameters during the optimization process. This allows for the quantification of the uncertainty in wind power output and the flexible solution of decision-making scheduling schemes. Therefore, it is necessary to invent an IGDT-based optimization scheduling method for integrated electric-gas energy systems that considers electric vehicle charging. This method jointly schedules the power system and the natural gas system, establishing an integrated electric-gas energy system scheduling model that considers wind power and electric vehicle charging. This increases the utilization rate of renewable energy, reduces the economic scheduling cost of the integrated electric-gas energy system, and also considers the randomness and volatility of wind power output, achieving coordinated operation of the entire integrated energy system in terms of economy and safety. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an IGDT-based scheduling optimization method for integrated electric and gas energy systems, which obtains the optimal scheduling scheme for integrated electric and gas energy systems under the consideration of fluctuations in uncertain variables.

[0006] To achieve the above-mentioned objectives, this invention provides a scheduling optimization method for an integrated electric-gas energy system based on IGDT, characterized by comprising the following steps:

[0007] (1) Construct a scheduling model for a deterministic integrated electricity-gas energy system;

[0008] (1.1) Constructing a power network model;

[0009]

[0010] in, Let P represent the output power of the i-th generator at time t. i G-max P i G-min These represent the upper and lower limits of the output of the i-th generator, respectively. P represents the maximum and minimum values ​​of the ramp constraint for the i-th generator; t W and Let represent the actual output power and predicted output power of the wind power at time t, respectively. Let P be the electrical energy consumed by the j-th electro-gas conversion device P2G at time t. t EV Let L be the load demand of the electric vehicle at time t. t Let N be the load demand of residential users at time t; E Represents a set of generators. NP2G This represents a collection of P2G devices;

[0011] (1.2) Constructing a natural gas network;

[0012]

[0013] in, These represent the upper and lower limits of power output from natural gas sources. No. The natural gas output of a gas source at time t; F k-n c represents the natural gas flow rate from node k to node n in the pipeline; k-n p represents the transmission parameters from node k to node n in the pipeline. k p n Let sgn(p) represent the air pressure intensities at nodes k and n, respectively;k ,p n ) is a directional parameter, representing the flow direction of natural gas in the pipeline. When sgn(p k ,p n ) = 1 indicates that natural gas flows from node k to node n, sgn(p k ,p n If ) = -1, it means that natural gas flows from node n to node k; Q represents the amount of natural gas transmitted by the j-th P2G to the natural gas network at time t; t F represents the natural gas node load value at time t; l N represents the transmission loss of the l-th branch in the natural gas network; G N represents the total number of natural gas sources. L Indicates the total number of natural gas pipelines;

[0014] (1.3) Constructing an electric vehicle charging station model;

[0015]

[0016] Among them, P t EV Let P represent the power of the electric vehicle connected to the grid at time t. t EV When P is positive, it indicates that the electric vehicle is charging; when P is positive, it indicates that the electric vehicle is charging. t EV When P is negative, it indicates that the electric vehicle is discharging after being connected to the grid; d P c These refer to the charging and discharging power of a single electric vehicle, respectively. The number of electric vehicles charging at time t; Q t Storage capacity of electric vehicle charging stations at time t; These represent the upper and lower limits of battery storage capacity for a single electric vehicle, respectively, N. EV Let η be the total number of electric vehicles; η is the charging and discharging efficiency of electric vehicles.

[0017] (1.4) Establish a linear model for the electro-gas conversion;

[0018]

[0019] Where ψ is the conversion efficiency of the P2G device, and H g This refers to the calorific value of natural gas.

[0020] (1.5) Establish the objective function;

[0021]

[0022] Among them, a i b ic i The energy consumption and power generation characteristics of the i-th generator are represented by these parameters. For the first The cost coefficient for the output of a gas source; λ represents the unit wind curtailment penalty coefficient; C t The price for charging and discharging an electric vehicle at time t, where T represents the total number of time moments;

[0023] (2) Establishing an uncertain set model of wind power output based on IGDT;

[0024]

[0025] Where α represents the uncertain radius of wind power output;

[0026] (3) Establish a scheduling optimization model for an integrated electric-gas energy system based on IGDT;

[0027] (3.1) Establish a robust model;

[0028] max{α:maxF(X,d)≥(1+δ)F o}

[0029] st

[0030]

[0031] Where F(X,d) represents the objective function for scheduling the integrated electric-gas energy system under deterministic conditions, X is the decision variable, and d represents the uncertainty parameter of the integrated electric-gas energy system; G(X,d) represents the constraints of the integrated electric-gas energy system scheduling model, F o The baseline value for scheduling is δ; δ represents the deviation factor.

[0032] (3.2) Establish an opportunity model;

[0033] min{α:minF(X,d)≤(1-δ)F o}

[0034] st

[0035]

[0036] (4) Real-time scheduling optimization of the integrated electric-gas energy system;

[0037] Real-time parameters of the integrated electric-gas energy system are collected and substituted into the scheduling model of the integrated electric-gas energy system under deterministic conditions established in step (1) to calculate the objective function F, and then the scheduling reference value F is obtained. oThen, the scheduling optimization model of the integrated electric-gas energy system established in step (3) is calculated by combining the preset deviation factor δ; among them, in the calculation of the robust scheduling scheme, the smaller the uncertainty radius α of wind power output, the smaller the scheduling cost, and when the wind power output is in Within this range, the scheduling cost is lower than the decision expectation value (1+δ)F. o This yields the maximum uncertainty radius under the dispatcher's expected cost, leading to an optimal dispatch scheme for the integrated electrical energy system under the dispatcher's pessimistic outlook on wind power output. In the computerized scheduling scheme, the smaller the uncertainty radius α of wind power output, the higher the dispatch cost, and when wind power output is within a certain range... Within this range, the scheduling cost is lower than the decision expectation value (1-δ)F. o At this point, the minimum uncertainty radius under the dispatcher's expected cost is obtained, and then the optimal dispatch scheme of the integrated electrical energy system under the dispatcher's positive attitude towards wind power output is derived; if neither the robust dispatch scheme nor the opportunistic dispatch scheme meets the dispatcher's requirements, the deviation factor parameter δ is changed and step (4) is repeated.

[0038] The objective of this invention is achieved as follows:

[0039] This invention presents an IGDT-based scheduling optimization method for integrated electric and gas energy systems. First, a deterministic scheduling model of the integrated electric and gas energy system is established. Then, an uncertain model of wind power output is established. Finally, a scheduling optimization model of the integrated electric and gas energy system is established. Assuming that the predicted and actual wind power values ​​are equal, the scheduling benchmark value for the operation and scheduling of the integrated electric and gas energy system is calculated. Then, combined with a preset deviation factor, the scheduling strategy of the integrated electric and gas energy system is obtained.

[0040] Meanwhile, the IGDT-based electric-gas integrated energy system scheduling optimization method of this invention also has the following beneficial effects:

[0041] (1) This invention proposes an integrated electric-gas energy system model that takes into account electric vehicle charging stations, enabling multiple energy sources to meet multiple loads. At the same time, this invention aims to minimize the cost of integrated electric-gas energy dispatch, taking into account the cost of wind curtailment, and achieves an optimized dispatch strategy with the lowest cost.

[0042] (2) This invention takes into account the demand response of electric vehicles, realizes the orderly charging of electric vehicles, and reduces the probability of power grid dispatch risk.

[0043] (3) This invention addresses the uncertainty of wind power output through Information Gap Decision Theory (IGDT). It also proposes robust and opportunistic models to reduce the sensitivity of decision-making schemes to uncertain parameters. Even when uncertain parameters change within a given range, the feasibility of the scheduling scheme remains guaranteed. This provides different scheduling strategies for decision-makers with varying risk appetites. Attached Figure Description

[0044] Figure 1 This is a flowchart of the power-gas integrated energy system scheduling optimization method based on IGDT of the present invention;

[0045] Figure 2 This is a schematic diagram of the IEEE-39 node power system;

[0046] Figure 3 It is a power output diagram for predicted electricity, gas load and wind energy. Detailed Implementation

[0047] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0048] Example

[0049] This embodiment uses, as follows: Figure 2 The following verification is performed using an electro-gas coupled system structure consisting of the IEEE-39-node power system and the Belgian 20-node natural gas system. The 39-node power system comprises 10 generating units with a total installed capacity of 6967 MW and a total power load of 5941.5 MW. The equipment parameters of the coal-fired units are shown in Table 1. In the 20-node natural gas network, there are 6 gas sources and 9 gas loads, with a total load of 2.4608 Mm. 3 The equipment parameters of the gas turbine unit are shown in Table 2. Figure 2 In the system, three wind turbines are connected at power system nodes 1, 14, and 16; electric vehicle charging nodes are located at power system nodes 3 and 35; and two P2G (Power to Gas) devices are connected at power system nodes 30, 33, and 37. The electrical load, gas load, and predicted wind power output demand are as follows: Figure 3 As shown. The total scheduling cycle is 24 hours, with a scheduling interval of 1 hour.

[0050] Table 1 shows the generator equipment parameters;

[0051]

[0052]

[0053] Table 2 shows the parameters of the natural gas source equipment;

[0054]

[0055] Below we combine Figure 2 This invention provides a detailed description of an optimized scheduling method for an integrated electric-gas energy system based on IGDT (Integrated Energy Data Center) and electric vehicle charging, as follows: Figure 1 As shown, it includes the following steps:

[0056] S1. Construct a scheduling model for a deterministic integrated electricity-gas energy system;

[0057] The scheduling model of the integrated electric-gas energy system mainly includes: power network model, natural gas network model, electric vehicle charging station model, linear model of electricity-to-gas conversion, and objective function.

[0058] S1.1 Constructing a power network model;

[0059]

[0060] in, Let P represent the output power of the i-th generator at time t. i G-max P i G-min These represent the upper and lower limits of the output of the i-th generator, respectively. P represents the maximum and minimum values ​​of the ramp constraint for the i-th generator; t W and Let represent the actual output power and predicted output power of the wind power at time t, respectively. Let P be the electrical energy consumed by the j-th electro-gas conversion device P2G at time t. t EV Let L be the load demand of the electric vehicle at time t. t Let N be the load demand of residential users at time t; E Represents a set of generators. NP2G This represents a collection of P2G devices;

[0061] S1.2, Construct a natural gas network model;

[0062]

[0063] in, These represent the upper and lower limits of power output from natural gas sources. No. The natural gas output of a gas source at time t; F k-n c represents the natural gas flow rate from node k to node n in the pipeline; k-np represents the transmission parameters from node k to node n in the pipeline. k p n Let sgn(p) represent the air pressure intensities at nodes k and n, respectively; k ,p n ) is a directional parameter, representing the flow direction of natural gas in the pipeline. When sgn(p k ,p n ) = 1 indicates that natural gas flows from node k to node n, sgn(p k ,p n If ) = -1, it means that natural gas flows from node n to node k; Q represents the amount of natural gas transmitted by the j-th P2G to the natural gas network at time t; t F represents the natural gas node load value at time t; l N represents the transmission loss of the l-th branch in the natural gas network; G N represents the total number of natural gas sources. L Indicates the total number of natural gas pipelines;

[0064] S1.3, Construct an electric vehicle charging station model;

[0065]

[0066] Among them, P t EV Let P represent the power of the electric vehicle connected to the grid at time t. t EV When P is positive, it indicates that the electric vehicle is charging; when P is positive, it indicates that the electric vehicle is charging. t EV When P is negative, it indicates that the electric vehicle is discharging after being connected to the grid; d P c These refer to the charging and discharging power of a single electric vehicle, respectively. The number of electric vehicles charging at time t; Q t Storage capacity of electric vehicle charging stations at time t; These represent the upper and lower limits of battery storage capacity for a single electric vehicle, respectively, N. EV Let η be the total number of electric vehicles; η is the charging and discharging efficiency of electric vehicles; the current mainstream electric vehicle charging power is 200kW. In this example, there are 100 electric vehicles, and the amount of electricity charged by each electric vehicle is fixed at 10% each time. The battery is considered fully charged when it reaches 90% of its storage capacity. The charging and discharging efficiency of electric vehicles is set to 0.8.

[0067] S1.4 Establish a linear model for the electro-gas conversion;

[0068]

[0069] Where ψ is the conversion efficiency of the P2G device, which is set to 0.3 here; H g The calorific value of natural gas is 9.8.

[0070] S1.5. Establish the objective function;

[0071]

[0072] in, For the operating cost of the generator, a i b i c i The energy consumption and power generation characteristics of the i-th generator are represented by these parameters. Cost of power output from natural gas sources For the first Cost coefficient of gas source output; The penalty for wind curtailment is λ, which represents the unit wind curtailment penalty coefficient, set here to $20 / MW; C t P t EV Total cost of charging and discharging at electric vehicle charging stations, C t The price for charging and discharging an electric vehicle at time t, where T represents the total number of time moments;

[0073] This invention aims to minimize the scheduling cost of an integrated electric-gas energy system. It takes into account the cost of wind curtailment and achieves a cost-effective optimized scheduling strategy, enabling multiple energy sources to meet multiple loads. Furthermore, it considers the demand response of electric vehicles, enabling orderly charging of electric vehicles and reducing the probability of distribution network scheduling risks.

[0074] S2. Establish an uncertain set model of wind power output based on IGDT;

[0075]

[0076] in, This represents the actual wind power output model, where α represents the uncertain radius of wind power output.

[0077] S3. Establish a scheduling optimization model for an integrated electric-gas energy system based on IGDT;

[0078] S3.1 Establish a robust model;

[0079] Robust models are used to find the maximum value of the uncertainty radius within an acceptable range for uncertainties in an integrated electric-gas energy system. The larger the uncertainty radius, the greater the acceptable range of fluctuations in the system's uncertainties, and the stronger the model's robustness. The specific model is as follows:

[0080] max{α:maxF(X,d)≥(1+δ)F o}

[0081] st

[0082]

[0083] Where F(X,d) represents the objective function for scheduling the integrated electric-gas energy system under deterministic conditions, X is the decision variable, and d represents the uncertainty parameter of the integrated electric-gas energy system; G(X,d) represents the constraints of the integrated electric-gas energy system scheduling model, F o δ represents the scheduling baseline value; δ represents the deviation factor. The larger the value, the stronger the system's risk avoidance ability and the more robust the scheduling scheme.

[0084] S3.2 Establish an opportunity model;

[0085] The chance model seeks the best possible optimization objective value. In this model, the smaller the radius of uncertainty, the easier it is to achieve the desired optimization objective value. However, the solution is more sensitive to fluctuations in uncertain variables. The specific model is as follows:

[0086] min{α:minF(X,d)≤(1-δ)F o}

[0087] st

[0088]

[0089] In this embodiment, the larger the δ deviation factor, the lower the scheduling cost that the decision-maker seeks, and the higher the risk level of the scheduling scheme.

[0090] S4, Real-time scheduling optimization of integrated electric-gas energy system;

[0091] Real-time parameters of the integrated electric-gas energy system are collected and substituted into the deterministic scheduling model of the integrated electric-gas energy system established in step S1 to calculate the objective function F. Assuming that the predicted and actual wind power values ​​are equal, the scheduling baseline value F is then obtained. o ;

[0092] Here, δ varies between 0.02 and 0.08, and δ is preset to 0.5. The scheduling optimization model of the integrated electric-gas energy system established in step S3 is calculated using the preset deviation factor δ.

[0093] In calculating robust scheduling schemes, the larger the uncertainty radius α of wind energy output, the greater the scheduling cost, and the more significant the wind energy output becomes. Within this range, the scheduling cost is lower than the decision expectation value (1+δ)F. oAt this point, the maximum uncertainty radius under the dispatcher's expected cost is obtained, and then the optimal dispatch scheme of the integrated electrical energy system under the dispatcher's pessimistic attitude towards wind power output is derived. Compared with the baseline dispatch value, the robust dispatch scheme has a higher cost value, but also higher security.

[0094] In the computer-based scheduling scheme, the smaller the uncertainty radius α of wind energy output, the lower the scheduling cost. Furthermore, when wind energy output is within... Within this range, the scheduling cost is lower than the decision expectation value (1-δ)F. o At this point, the minimum uncertainty radius under the dispatcher's expected cost is obtained, and then the optimal dispatch scheme of the integrated electrical energy system under the dispatcher's positive attitude towards wind power output is derived. Compared with the baseline dispatch value, the cost value of the opportunistic dispatch scheme is lower, but the security is also lower.

[0095] If neither the robust scheduling scheme nor the opportunistic scheduling scheme meets the scheduler's requirements, then change the deviation factor parameter δ and repeat step S4.

[0096] The S4 step effectively addresses the uncertainty in wind power output, ensuring the feasibility of the scheduling scheme even when uncertain parameters change within a given range. It provides different scheduling strategies for decision-makers with varying risk appetites.

[0097] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A scheduling optimization method for an integrated electric-gas energy system based on IGDT, characterized in that, Includes the following steps: (1) Construct a scheduling model for a deterministic integrated electricity-gas energy system; (1.1) Constructing a power network model; ； in, Indicates the first The output power of the generator at time t. , They represent the first The upper and lower limits of the generator's output; , For the first The maximum and minimum values ​​of the ramp constraint for the generator; and They represent in The actual and predicted output power of wind power at any given time. For the first The P2G electro-gas converter in The electrical energy consumed constantly for The load demand of electric vehicles at all times. for The load demand of residential users at all times; Represents a set of generators. This represents a collection of P2G devices; (1.2) Constructing a natural gas network; ； in, , These represent the upper and lower limits of power output from natural gas sources. No. One gas source Natural gas output at any given time; Represents nodes in the pipeline To node Natural gas flow rate; Nodes in the pipeline To node Transmission parameters; , Representing nodes respectively With nodes The air pressure intensity; Here, is a directional parameter, characterizing the flow direction of natural gas in the pipeline. Indicates that natural gas is generated by nodes Flow to Node , This indicates that natural gas originates from the node. Flow to Node ; Indicates the first A P2G in The amount of natural gas transmitted to the natural gas network in real time; express Natural gas node load value at any given time; This represents the transmission loss of the l-th branch in the natural gas network; Indicates the total quantity of natural gas sources. Indicates the total number of natural gas pipelines; (1.3) Construct an electric vehicle charging station model; ； in, express The power of electric vehicles connected to the grid at any time, when When it is positive, it indicates that the electric vehicle is charging. A negative value indicates that the electric vehicle is discharging after being connected to the grid. , These refer to the charging and discharging power of a single electric vehicle, respectively. for The number of electric vehicles that are constantly being charged; Electric vehicle charging stations Storage capacity at any given moment; , These represent the upper and lower limits of battery storage capacity for a single electric vehicle. The set of the total number of electric vehicles; Improve the charging and discharging efficiency of electric vehicles; (1.4) Establish a linear model for the electro-gas conversion; ； in, For the conversion efficiency of P2G devices, This refers to the calorific value of natural gas. (1.5) Establish the objective function; ； in, , , The energy consumption and power generation characteristics of the i-th generator are represented by these parameters. For the first Cost coefficient of gas source output; This represents the unit wind curtailment penalty coefficient; The price of charging and discharging an electric vehicle at time t. Indicates the total number of moments; (2) Establish an uncertain set model of wind power output based on IGDT; ； in, Indicates the uncertain radius of wind power output; (3) Establish a scheduling optimization model for an integrated electric-gas energy system based on IGDT; (3.1) Establish a robust model; ； in, This represents the objective function for scheduling a deterministic integrated electricity-gas energy system. As decision variables, Represents the uncertainty parameters of an integrated electric-gas energy system; The constraints represent the scheduling conditions of the integrated electricity-gas energy system. This serves as the scheduling baseline value. Indicates the deviation factor; (3.2) Establish an opportunity model; ； (4) Real-time scheduling optimization of the integrated electric-gas energy system; Collect real-time parameters of the integrated electric-gas energy system and substitute them into the scheduling model of the integrated electric-gas energy system under deterministic conditions established in step (1) to calculate the objective function. Then, the scheduling baseline value is obtained. Then, combined with the preset deviation factor Calculate the scheduling optimization model of the integrated electric-gas energy system established in step (3); among which, in calculating the robust scheduling scheme, when the uncertainty radius of wind power output is... The smaller the wind power output, the lower the dispatch cost, and when the wind power output is... Within this range, the scheduling cost is lower than the expected decision value. This yields the maximum uncertainty radius under the dispatcher's expected cost, leading to an optimal dispatch scheme for the integrated electrical energy system under the dispatcher's pessimistic outlook on wind power output. In the computerized scheduling scheme, when the uncertainty radius of wind power output... The smaller the wind power output, the higher the dispatch cost, and when the wind power output is... Within this range, the scheduling cost is lower than the expected decision value. This yields the minimum uncertainty radius under the dispatcher's expected cost, leading to an optimal dispatch scheme for the integrated electrical energy system under the dispatcher's positive attitude towards wind power output. If neither the robust dispatch scheme nor the opportunistic dispatch scheme meets the dispatcher's requirements, the deviation factor parameter is changed. Repeat step (4).

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