A dispatching method for multi-gas-electricity integrated energy system considering multiple uncertainties

Abstract

The present invention relates to the field of integrated energy scheduling technology, and proposes a scheduling method for a multi-gas-electric integrated energy system that takes into account multiple uncertainties, including establishing an energy storage device model for a single gas-electric integrated energy system and a demand response model for the user end; constructing an optimization objective function based on the multi-gas-electric integrated energy system scheduling model, and giving constraints; based on an asymmetric Nash bargaining model, using an improved particle swarm alternating multiplier method to solve the optimal solution of the optimization objective function. The multiple subsystems in the integrated energy system of the present invention play a role in resource complementarity in time and space, thereby improving the overall efficiency of the system; using an asymmetric Nash bargaining method to ensure that each participant benefits fairly and enhance individual willingness to participate; specifically considering the specific contribution of each participant to the cooperation, so that the allocation mechanism is more reasonable and effective; considering the multiple uncertainties of renewable energy and load, a solution is provided for the optimal scheduling of a multi-gas-electric integrated energy system under an uncertain environment.

Description

[Technical field]

[0001] The present invention relates to the technical field of integrated energy dispatching, and in particular to a multi-gas-electricity integrated energy system dispatching method taking into account multiple uncertainties. [Background technology]

[0002] The integrated energy system is one of the key energy technologies of the future. It can effectively solve the problem of large-scale distributed power supply to the power grid and reduce the adverse effects on the operation of the power grid. However, the traditional independent energy-saving system has limited regulation capabilities and cannot further consume renewable energy. The multi-integrated energy system can realize the complementary utilization of energy and has significant advantages in increasing the power consumption of renewable energy, reducing the system operation cost, reducing the power interaction with the main grid, and improving the system backup capacity.

[0003] In the gas-electricity integrated energy system, energy storage devices and flexible loads can participate in energy transactions in the electricity market, but the multi-gas-electricity integrated energy system participating in electricity market transactions will face many problems such as competition, transaction settlement, etc. How to build a scientific and reasonable low-carbon economic multi-energy trading mechanism has become a hot topic in the multi-gas-electricity integrated energy market.

[0004] Chinese patent CN109523096A proposes an optimization scheduling method for a gas-electricity combined system with interruptible loads for backup auxiliary services, which solves the technical problem of insufficient flexibility of the gas-electricity combined system caused by relying solely on the backup of conventional generator sets. However, this method does not take into account the intermittent and volatile nature of wind and solar energy, and the randomness of their power generation brings uncertainty to power system planning.

[0005] Chinese patent CN110808615A proposes a method for optimizing the scheduling of gas-fired virtual power plants that takes uncertainty into account. This method takes into account the intermittent and volatile nature of wind and solar energy, and converts surplus wind and solar energy into natural gas for storage through energy substitution, thereby reducing the amount of wind and solar energy abandoned and improving the efficiency of clean energy utilization. The methanation process of power-to-gas uses carbon dioxide as raw material, reducing the carbon dioxide emissions of virtual power plants.

[0006] However, this method does not effectively complement the resources of multiple subsystems in time and space, and the overall benefit improvement is not obvious; it does not consider the fair benefits of each participant, which affects the willingness of each individual to participate. [Summary of the invention]

[0007] The purpose of the present invention is to solve the problems in the prior art and provide a multi-gas-electricity integrated energy system scheduling method that takes into account multiple uncertainties, which can effectively complement the resources of multiple subsystems in time and space to improve the overall efficiency; it can consider the fair benefits of each participant and enhance the willingness of each individual to participate.

[0008] To achieve the above object, the present invention proposes a multi-gas-electricity integrated energy system scheduling method considering multiple uncertainties, comprising:

[0009] Establish a single gas-electricity integrated energy system energy storage device model and a user-side demand response model;

[0010] According to the multi-gas-electricity integrated energy system dispatch model, the optimization objective function is constructed and the constraints are given;

[0011] Based on the asymmetric Nash bargaining model, the optimal solution of the optimization objective function is solved using the alternating multiplier method of improved particle swarm.

[0012] Preferably, the single gas-electricity integrated energy system comprises a photovoltaic power generation device, a carbon capture device, a gas generator set, a power-to-gas device, an energy storage battery and a gas storage tank;

[0013] The general model of energy storage capacity when the energy storage device is connected to the system and participates in coordinated optimization operation is expressed as:

[0014]

[0015] Among them, E t represents the storage capacity of the energy storage device during the period t; P char,t Represents the energy storage and charging power of the energy storage device at time t; P dis,t Represents the energy storage and release power of the energy storage device in time period t; η loss , η char and η dis They are the energy storage loss rate, energy charging efficiency and energy discharging efficiency of the energy storage device respectively;

[0016] The upper and lower limits of the energy storage device charging and discharging power meet the following constraints:

[0017] E min ≤E t ≤E max (2)

[0018] 0≤P char,t ≤α char,t P char,max (3)

[0019] 0≤P dis,t ≤α dis,t P dis,max (4)

[0020] α char,t +α dis,t ≤1 (5)

[0021] E1=E T+1 (6)

[0022] Among them, E min and E max Respectively represent the lower and upper limits of the energy storage capacity of the energy storage device; α char,t and α dis,t Respectively represent the charging and discharging states, which are 0 and 1 variables. 1 indicates that the device is in the charging or discharging state, and 0 indicates that the device stops charging or discharging. char,max and P dis,max Indicates the maximum charging and discharging power of the energy storage device.

[0023] Preferably, the demand response model expression of the user end is:

[0024] L e t =L e,o t +ΔL e t (7)

[0025] L g t =L g,o t +ΔL g t (8)

[0026] Among them, L e t and L g t Respectively represent the electricity and gas loads after participating in demand response; L e,o t and L g,o t Respectively represent

[0027]

[0028] The percentage of decrease / increase in the response change of electricity and gas loads should meet the following constraints:

[0029] -ΔL e max ≤ΔL e t ≤ΔL e max (9)

[0030] -ΔL g max ≤ΔL g t ≤ΔL g max (10)

[0031] Among them, ΔL e max and ΔLg max Respectively represent the maximum allowable changes in electrical and gas loads.

[0032] Preferably, the multi-gas-electricity integrated energy system scheduling model is composed of multiple single gas-electricity integrated energy systems of the same type, each single gas-electricity integrated energy system includes a photovoltaic power generation device, a carbon capture device, a gas unit, an electricity-to-gas device, an energy storage battery and a gas storage tank; the optimization goal of the multi-gas-electricity integrated energy system scheduling model is to minimize the operating cost of the multi-gas-electricity integrated energy system while minimizing CO2 emissions; the constraints are divided into constraints within a single gas-electricity integrated energy system and constraints between multiple gas-electricity integrated energy systems.

[0033] Preferably, the operating costs of the multi-gas-electricity integrated energy system include the cost of purchasing electricity from the grid, the cost of purchasing gas, the cost of generating electricity from coal-fired units, the cost of operation and maintenance, and the cost of CO2 emissions;

[0034] The operating cost expression of the multi-gas-electricity integrated energy system is as follows:

[0035] C O =C B +C G +C Y +C C (11)

[0036]

[0037]

[0038]

[0039]

[0040] In the formula, C O is the total operating cost of the multi-gas-electricity integrated energy system; C B , C G , C Y and C C are energy transaction cost, power generation cost, maintenance cost and CO2 emission cost respectively; T represents the total operation cycle; C e,t and C g,t are the prices of electricity and gas purchased in period t respectively; and are the electricity and gas purchased in period t respectively; P i,t is the output power of the coal-fired unit at time t; C G , YW G They represent the fuel cost and operation and maintenance cost of the i-th coal-fired unit at time t; I represents the total number of coal-fired units; LHV gasis the lower calorific value of natural gas; η i,t is the conversion efficiency of the i-th coal-fired unit at time t; K YW,i is the maintenance factor of the coal-fired unit; ω e ,ω g are the CO2 consumed by electricity purchased from the power grid and the natural gas grid; μ i is the CO2 emission coefficient of the coal-fired unit; C g is the unit CO2 gas emission cost; is the mass of CO2 captured.

[0041] Preferably, the constraints include unit output constraints, energy purchase constraints, power-to-gas constraints, carbon capture constraints within a single gas-electricity integrated energy system, and electric power interaction constraints and gas power interaction constraints between multiple gas-electricity integrated energy systems;

[0042] Internal constraints of a single gas-electricity integrated energy system:

[0043] 1) Power balance constraints:

[0044]

[0045] in, It represents the power consumption of the power-to-gas equipment during the period t; and are the charging and discharging power of the battery in time period t respectively;

[0046] 2) Gas energy balance constraints:

[0047]

[0048] Among them, L g,t Indicates the original predicted value of gas load in period t, ΔL g,t It represents the change of gas load demand response in period t; and are respectively the charging and deflating power of the gas tank in period t; η P2G is the efficiency of the power-to-gas process;

[0049] 3) Unit power constraints:

[0050] P i,min ≤P i,t ≤P i,max (18)

[0051] Among them, P i,max and P i,min are the upper and lower limits of the unit output in the i-th integrated energy system;

[0052] 4) P2G output constraints:

[0053] P P2G,min ≤P P2G ≤P P2G,max (19)

[0054] Among them, P P2G,min and P P2G,max It is the upper and lower limits of P2G output;

[0055] 5) Carbon capture device constraints:

[0056]

[0057] η min ≤η i,t ≤η max (twenty one)

[0058] Among them, P CC,i,t is the energy consumption of the carbon capture device of coal-fired unit i during period t; is the energy consumption of carbon capture equipment operation, η i,t is the corresponding carbon capture equipment power; η max and η min are the maximum and minimum values ​​of carbon capture efficiency;

[0059] Constraints between multiple gas-electricity integrated energy systems:

[0060] 1) Electric power interaction constraints:

[0061]

[0062] in, is the electric power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system in period t; and are the lower limit and upper limit of the electric power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system, respectively;

[0063] 2) Gas power interaction constraints:

[0064]

[0065] in, is the gas power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system in period t; and are the lower limit and upper limit of the gas power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system, respectively.

[0066] As a preferred method, based on the asymmetric Nash bargaining model, the optimization objective function is split into two convex sub-problems, which are defined as follows:

[0067]

[0068]

[0069] Among them, C i and are the cooperative operation cost and non-cooperative operation cost of the i-th gas-electricity integrated energy system, respectively; To solve the optimal value of the operation of the i-th gas-electricity integrated energy system; G i The cost delivered by the i-th gas-electricity integrated energy system after negotiation with other subsystems.

[0070] Preferably, the optimization objective function of the two convex subproblems of the asymmetric Nash bargaining model, and the steps of solving the optimal solution using the improved particle swarm algorithm are:

[0071] Step 1: Set parameters: Set the relevant parameters of the improved particle swarm algorithm and initialize the parameters of the multi-gas-electricity integrated energy system;

[0072] Step 2: Upper-level optimization: Considering multiple uncertain factors in the scheduling process, establish the uncertainty set model of the system, encode and form the initial uncertainty set population of the particle swarm algorithm, that is, randomly generate N groups of uncertain scenario sets, and pass the uncertain scenario sets to the lower-level sub-problems;

[0073] Step 3: Optimization of the lower-level algorithm: Using the alternating particle swarm algorithm, the optimization objective functions of the two convex subproblems of the asymmetric Nash bargaining model are iteratively calculated under the coupling variables of electrical interaction power and bargaining cost, respectively, to solve the optimal solution of the i-th gas-electricity integrated energy system under the convergence state;

[0074] Step 4: Return to the upper optimization layer: replace the worst scenario of the current state with the minimum optimal solution obtained in step 3, randomly generate a new uncertainty set again, and repeat step 4;

[0075] Step 5: Final optimization: If the final calculation obtains the optimal solution under the convergence state of the entire system, save the optimization strategy results in the worst scenario and the lower-level problem and end the program; otherwise, return to step 4.

[0076] The beneficial effects of the present invention are as follows: through energy sharing, the present invention enables multiple subsystems in the integrated energy system to complement each other in time and space, thereby improving the overall efficiency of the system; an asymmetric Nash bargaining method is adopted to ensure that each participant can obtain benefits fairly and enhance the willingness of each individual to participate; at the same time, the specific contribution of each participant to the cooperation is specifically considered to make the allocation mechanism more reasonable and effective; considering the multiple uncertainties of renewable energy and load, an uncertainty set model is established, providing a solution for the optimal scheduling of multi-gas-electric integrated energy systems under uncertain environments.

[0077] The features and advantages of the present invention will be described in detail through embodiments in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0078] Figure 1 This is a flowchart of a method for dispatching a multi-gas-electricity integrated energy system considering multiple uncertainties according to the present invention;

[0079] Figure 2 The renewable energy output and load forecast values ​​of three gas-electricity integrated energy systems according to an embodiment of the present invention;

[0080] Figure 3a Schematic diagram of renewable energy photovoltaics under the worst scenario of gas-electricity integrated energy system 1;

[0081] Figure 3b It is a schematic diagram of the electric load under the worst scenario of the gas-electricity integrated energy system 1;

[0082] Figure 3c Schematic diagram of renewable energy photovoltaics under the worst scenario of gas-electricity integrated energy system 2;

[0083] Figure 3d This is a schematic diagram of the electric load in the worst scenario of the gas-electricity integrated energy system 2;

[0084] Figure 3e Schematic diagram of renewable energy photovoltaics under the worst scenario of gas-electricity integrated energy system 3;

[0085] Figure 3f Schematic diagram of the electric load under the worst scenario 3 of the gas-electricity integrated energy system. [Specific implementation method]

[0086] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0087] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0088] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0089] Embodiment 1:

[0090] Reference Figure 1 ~3 is an embodiment of the present invention, which provides a scheduling method for a multi-gas-electricity integrated energy system taking into account multiple uncertainties. The method is based on a single gas-electricity integrated energy system, establishes its energy storage device model and demand response model, and uses the single gas-electricity integrated energy system as a subsystem to form a multi-gas-electricity integrated energy system, constructs a low-carbon economic optimization objective function for the multi-gas-electricity integrated energy system and gives constraints at the same time, considers the multiple uncertainties of renewable energy and electricity load in the multi-gas-electricity integrated energy system, and uses the alternating multiplier method of the improved particle swarm algorithm to solve the optimal scheduling of the asymmetric Nash bargaining model of the multi-gas-electricity integrated energy system under these uncertainties.

[0091] Reference Figure 1 The present invention provides a multi-gas-electricity integrated energy system scheduling method considering multiple uncertainties, which is mainly implemented through the following steps:

[0092] S1: Establish the energy storage device model of a single gas-electricity integrated energy system and the demand response model at the user end;

[0093] Furthermore, the single gas-electricity integrated energy system includes photovoltaic power generation equipment, carbon capture equipment, gas-fired units, power-to-gas equipment, energy storage batteries and gas storage tanks.

[0094] The general model of energy storage capacity when the energy storage device is connected to the system and participates in coordinated optimization operation is expressed as:

[0095]

[0096] Among them, E t represents the storage capacity of the energy storage device during the period t; P char,t Represents the energy storage and charging power of the energy storage device at time t; P dis,t Represents the energy storage and release power of the energy storage device in time period t; η loss , η char and η dis They are the energy storage loss rate, charging efficiency and discharging efficiency of the energy storage device respectively.

[0097] The upper and lower limits of the energy storage device charging and discharging power meet the following constraints:

[0098] E min ≤Et ≤E max (2)

[0099] 0≤P char,t ≤α char,t P char,max (3)

[0100] 0≤P dis,t ≤α dis,t P dis,ma x (4)

[0101] α char,t +α dis,t ≤1 (5)

[0102] E1=E T+1 (6)

[0103] Among them, E min and E max Respectively represent the lower and upper limits of the energy storage capacity of the energy storage device; α char,t and α dis,t Respectively represent the charging and discharging states, which are 0 and 1 variables. 1 indicates that the device is in the charging or discharging state, and 0 indicates that the device stops charging or discharging. char,max and P dis,max Indicates the maximum charging and discharging power of the energy storage device.

[0104] The demand response model expression at the user end is:

[0105] L e t =L e,o t +ΔL e t (7)

[0106] L g t =L g,o t +ΔL g t (8)

[0107] Among them, L e t and L g t Respectively represent the electricity and gas loads after participating in demand response; L e,o t and L g,o t Respectively represent

[0108]

[0109] The percentage of decrease / increase in the response change of electricity and gas loads should meet the following constraints:

[0110] -ΔL e max ≤ΔL e t ≤ΔL e max (9)

[0111] -ΔL g max ≤ΔL g t ≤ΔL g max (10)

[0112] Where, ΔL e max and ΔL g max Respectively represent the maximum allowable changes in electrical and gas loads.

[0113] S2: Based on the multi-gas-electricity integrated energy system dispatch model, the optimization objective function is constructed and the constraints are given;

[0114] Furthermore, the multi-gas-electricity integrated energy system scheduling model is composed of multiple single gas-electricity integrated energy systems of the same type, each of which includes a photovoltaic power generation device, a carbon capture device, a gas unit, a power-to-gas device, an energy storage battery, and a gas storage tank. The optimization goal of the multi-gas-electricity integrated energy system scheduling model is to minimize the operating cost of the multi-gas-electricity integrated energy system while minimizing CO2 emissions. The constraints are divided into constraints within a single gas-electricity integrated energy system and constraints between multiple gas-electricity integrated energy systems.

[0115] Furthermore, the operating costs of the multi-gas-electricity integrated energy system include the cost of purchasing electricity from the grid, the cost of purchasing gas, the cost of power generation from coal-fired units, the cost of operation and maintenance, and the cost of CO2 emissions.

[0116] The operating cost expression of the multi-gas-electricity integrated energy system is as follows:

[0117] C O =C B +C G +C Y +C C (11)

[0118]

[0119]

[0120]

[0121]

[0122] In the formula, C O is the total operating cost of the multi-gas-electricity integrated energy system; C B , C G , C Y and C C are energy transaction cost, power generation cost, maintenance cost and CO2 emission cost respectively; T represents the total operation cycle; C e,t and C g,t are the prices of electricity and gas purchased in period t respectively; and are the electricity and gas purchased in period t respectively; P i,t is the output power of the coal-fired unit at time t; C G , YW G They represent the fuel cost and operation and maintenance cost of the i-th coal-fired unit at time t; I represents the total number of coal-fired units; LHV gas is the lower calorific value of natural gas; η i,t is the conversion efficiency of the i-th coal-fired unit at time t; K YW,i is the maintenance factor of the coal-fired unit; ω e ,ω g are the CO2 consumed by electricity purchased from the power grid and the natural gas grid; μ i is the CO2 emission coefficient of the coal-fired unit; C g is the unit CO2 gas emission cost; is the mass of CO2 captured.

[0123] The constraints include unit output constraints, energy purchase constraints, power-to-gas constraints, carbon capture constraints within a single gas-electricity integrated energy system, and electric power interaction constraints and gas power interaction constraints between multiple gas-electricity integrated energy systems.

[0124] Internal constraints of a single gas-electricity integrated energy system:

[0125] 1) Power balance constraints:

[0126]

[0127] in, It represents the power consumption of the power-to-gas equipment during the period t; and are the charging and discharging power of the battery during time period t respectively.

[0128] 2) Gas energy balance constraints:

[0129]

[0130] Among them, Lg,t Indicates the original predicted value of gas load in period t, ΔL g,t It represents the change of gas load demand response in period t; and are respectively the charging and deflating power of the gas tank in period t; η P2G is the efficiency of the power-to-gas process.

[0131] 3) Unit power constraints:

[0132] P i,min ≤P i,t ≤P i,max (18)

[0133] Among them, P i,max and P i,min are the upper and lower limits of the unit output in the i-th integrated energy system.

[0134] 4) P2G output constraints:

[0135] P P2G,min ≤P P2G ≤P P2G,max (19)

[0136] Among them, P P2G,min and P P2G,max The upper and lower limits of P2G output.

[0137] 5) Carbon capture device constraints:

[0138]

[0139] η min ≤η i,t ≤η max (twenty one)

[0140] Among them, P CC,i,t is the energy consumption of the carbon capture device of coal-fired unit i during period t; is the energy consumption of carbon capture equipment operation, η i,t is the corresponding carbon capture equipment power; η max and η min are the maximum and minimum values ​​of carbon capture efficiency.

[0141] Constraints between multiple gas-electricity integrated energy systems:

[0142] 1) Electric power interaction constraints:

[0143]

[0144] in, is the electric power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system in period t; and are the lower limit and upper limit of the electric power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system respectively.

[0145] 2) Gas power interaction constraints:

[0146]

[0147] in, is the gas power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system in period t; and are the lower limit and upper limit of the gas power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system, respectively.

[0148] S3: Based on the asymmetric Nash bargaining model, the alternating multiplier method of the improved particle swarm is used to solve the optimal solution of the optimization objective function under multiple uncertain factors such as renewable energy output and load forecasting;

[0149] Furthermore, based on the asymmetric Nash bargaining model, the optimization objective function is split into two convex sub-problems, defined as follows:

[0150]

[0151]

[0152] Among them, C i and are the cooperative operation cost and non-cooperative operation cost of the i-th gas-electricity integrated energy system, respectively; To solve the optimal value of the operation of the i-th gas-electricity integrated energy system; G i The cost delivered by the i-th gas-electricity integrated energy system after negotiation with other subsystems.

[0153] Furthermore, the specific steps of solving the optimal solution of the optimization objective function under multiple uncertain factors such as renewable energy output and load forecasting by using the alternating multiplier method of the improved particle swarm are as follows:

[0154] Step 1: Set parameters. Set the relevant parameters of the improved particle swarm algorithm and initialize the parameters of the three gas-electricity integrated energy systems;

[0155] The specific parameters of the gas-electric integrated energy system are as follows: the upper and lower limits of P2G output are 800kW and 0 respectively, and the efficiency is 60%; the upper and lower limits of carbon capture output are 100kW and 0 respectively, the carbon dioxide capture rate is 90%, and the emission intensity is 0.2; the electricity and gas loads can be adjusted within a certain range, in which the total amount of electricity and gas loads before and after adjustment remains unchanged, and the maximum allowable adjustment ratios of electricity and gas loads in each period of a day are 5% and 3% respectively; the rated capacity and upper limit of the charging and discharging power of the energy storage battery are 300kWh and 60kW respectively, the maximum and minimum values ​​of the state of charge are 0.9 and 0.1 respectively, the initial state of charge is 0.2, and the charging and discharging efficiency coefficients are both 0.95; the rated capacity and upper limit of the storage and discharge power of the gas storage tank are 60kWh and 12kW respectively, the maximum and minimum values ​​of the state of charge are 0.9 and 0.1 respectively, the initial state of charge is 0.5, and the charging and discharging efficiency coefficients are both 0.95. Figure 2 It is the forecast value of renewable energy output and load in the three gas-electricity integrated energy systems without considering multiple uncertain factors. Figure 2 The middle line is based on the left endpoint, and from top to bottom it is: gas load 1, gas load 3, electric load 1, electric load 3, electric load 2, gas load 2, PV 2, PV 1, PV 3. Among them, PV 2, PV 1, and PV 3 overlap at the leftmost end. At 10 hours, PV 2 is slightly higher than PV 3, and PV 3 is slightly higher than PV 1.

[0156] Step 2: Upper-level optimization. Considering multiple uncertain factors in the scheduling process, establish the uncertainty set model of the system, encode and form the initial uncertainty set population of the particle swarm algorithm, that is, randomly generate N groups of uncertain scenario sets, and pass the uncertain scenario sets to the lower-level sub-problems;

[0157] The multiple uncertainties include renewable energy output and load forecasting. The uncertainty set model of renewable energy output is established as follows:

[0158]

[0159]

[0160]

[0161]

[0162] Among them, U and Z represent the uncertain parameter set and the uncertain deviation set respectively; represents the renewable energy output and load forecast value during period t in a day, Indicates the maximum deviation of renewable energy output, that is, the accuracy of renewable energy output and load; tIt indicates the degree of deviation of the actual renewable energy output and load from the predicted renewable energy output and load; Γ represents the uncertainty budget, which can be understood as the degree of influence of uncertainty on decision-making. If Γ = 0, there is no deviation, and the corresponding robust optimization model is equivalent to the deterministic model.

[0163] Step 3: Optimization of the lower-level algorithm. Using the alternating multiplier method of the improved particle swarm algorithm, the optimization objective functions of the two convex subproblems of the asymmetric Nash bargaining model are iteratively calculated under the coupling variables of electrical interaction power and bargaining cost, respectively, to solve the optimal solution of the i-th gas-electricity integrated energy system under the convergence state;

[0164] Among them, when each coupling variable is the interaction of electricity and gas power, the optimization objective function of the i-th gas-electricity integrated energy system is:

[0165]

[0166] Iterate the calculation until the interaction between the electricity and gas power of each gas-electricity integrated energy system reaches convergence. The calculation results are expressed as:

[0167]

[0168] Among them, when each coupling variable is the bargaining cost, the optimization objective function of the i-th gas-electricity integrated energy system is:

[0169]

[0170] Iterate the calculation until the bargaining cost interaction of each subsystem reaches convergence. The calculation result is expressed as:

[0171]

[0172] Step 4: Return to the upper optimization layer. Replace the worst scenario of the current state with the smallest optimal solution obtained in step 3, randomly generate a new uncertainty set again, and repeat step 4;

[0173] Step 5: Final optimization. If the final calculation obtains the optimal solution of the entire system under convergence, save the optimization strategy results of the worst scenario and the lower-level problem and end the program; otherwise, return to step 4.

[0174] Embodiment 2:

[0175] Referring to Figure 3, an embodiment of the present invention provides a multi-gas-electricity integrated energy system scheduling method considering multiple uncertainties. In order to verify its beneficial effects, it is compared with a scenario of cooperative operation of three gas-electricity integrated energy zones without considering multiple uncertainties such as renewable energy output and load forecasting, and is simulated and verified in Matlab software, and the simulation results are given.

[0176] Scenario 1 is the situation when three gas-electricity integrated energy zones are operated in cooperation under the condition of considering multiple uncertainties; Scenario 2 is the situation when three gas-electricity integrated energy zones are operated in cooperation without considering multiple uncertainties; Through simulation calculation, it can be concluded that the total cost of the system operation in scenario 1 is 23438.66 yuan, of which the total cost of each gas-electricity integrated energy system is 7938.73 yuan for the first one, 8096.70 yuan for the second one, and 7403.22 yuan for the third one. The total cost of the system operation in scenario 2 is 22422.13 yuan, of which the total cost of each gas-electricity integrated energy system is 7576.17 yuan for the first one, 7779.87 yuan for the second one, and 7066.09 yuan for the third one. The various costs of scenario 1 are higher than those of scenario 2 because the uncertainty of renewable energy and load is taken into account, which causes the increase of system dispatching cost caused by natural extreme scenarios.

[0177] The asymmetric Nash bargaining method is used to redistribute and adjust the costs of each gas-electricity integrated energy system. The specific bargaining process of scenario 1 and scenario 2 is shown in Table 1 and Table 2:

[0178]

[0179]

[0180] Table 2: Bargaining process of various gas-electricity integrated energy systems in scenario 2

[0181]

[0182] It can be seen from Table 1 and Table 2 that after the bargaining transfer process, the cost of each subsystem has been reduced compared with independent operation, which increases the stability of the gas-electricity integrated energy system alliance cooperation. In addition, the contribution of each subsystem is specifically considered in the process of allocating costs to each subsystem. For scenario 2, the cooperative surplus of subsystem 1, subsystem 2, and subsystem 3 is 91.6 yuan, 207.69 yuan, and 116.4 yuan, respectively, which shows that in the process of energy sharing, subsystem 2 contributes the most, followed by subsystem 3, and finally subsystem 1. For scenario 1, the cooperative surplus of subsystem 1, subsystem 2, and subsystem 3 is 812.8 yuan, 21.34 yuan, and 1665.04 yuan, respectively, which shows that in the process of energy sharing, subsystem 3 contributes the most, followed by subsystem 1, and finally subsystem 2.

[0183] Figure 3 shows the worst source-load robust scenario set of the three gas-electricity integrated energy subsystems in scenario 1. Figure 3a It is a schematic diagram of renewable energy photovoltaics under the worst scenario of gas-electricity integrated energy system 1; Figure 3b It is a schematic diagram of the electric load under the worst scenario of the gas-electricity integrated energy system 1; Figure 3cIt is a schematic diagram of renewable energy photovoltaics under the worst scenario of gas-electricity integrated energy system 2; Figure 3d It is a schematic diagram of the electric load under the worst scenario of the gas-electricity integrated energy system 2; Figure 3e It is a schematic diagram of renewable energy photovoltaics under the worst scenario of gas-electricity integrated energy system 3; Figure 3f It is a schematic diagram of the electric load under the worst scenario of the gas-electric integrated energy system 3. In Figure 3, the upper dotted line is the upper limit, the lower dotted line is the lower limit, and the middle line is the worst scenario data. It can be seen from the figure that under the worst robust scenario set, the three gas-electric integrated energy subsystems have the highest level of resistance to uncertain risks, and in this case, the degree of damage to the economic performance of the system is the highest. This provides a solution for how dispatchers can share energy and reasonably coordinate the resources in each energy subsystem under the worst circumstances.

[0184] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-gas-electricity integrated energy system scheduling method considering multiple uncertainties, characterized in that: include: Establish a single gas-electricity integrated energy system energy storage device model and a user-side demand response model; According to the multi-gas-electricity integrated energy system dispatch model, the optimization objective function is constructed and the constraints are given; Based on the asymmetric Nash bargaining model, the optimal solution of the optimization objective function is solved by using the alternating multiplier method of improved particle swarm. The single gas-electricity integrated energy system includes a photovoltaic power generation device, a carbon capture device, a gas generator set, a power-to-gas device, an energy storage battery and a gas storage tank; The general model of energy storage capacity when the energy storage device is connected to the system and participates in coordinated optimization operation is expressed as: Among them, E t represents the storage capacity of the energy storage device during the period t; P char,t Represents the energy storage and charging power of the energy storage device at time t; P dis,t Represents the energy storage and release power of the energy storage device in time period t; η loss , η char and η dis They are the energy storage loss rate, energy charging efficiency and energy discharging efficiency of the energy storage device respectively; The upper and lower limits of the energy storage device charging and discharging power meet the following constraints: AND min ≤E t ≤E max (2) 0≤P char,t ≤α char,t P char,max (3) 0≤P dis,t ≤α dis,t P dis,max (4) α char,t +α dis,t ≤1 (5) E1=E T+1 (6) Among them, E min and E max Respectively represent the lower and upper limits of the energy storage capacity of the energy storage device; α char,t and α dis,t Respectively represent the charging and discharging states, which are 0 and 1 variables. 1 indicates that the device is in the charging or discharging state, and 0 indicates that the device stops charging or discharging. char,max and P dis,max Indicates the maximum charging and discharging power of the energy storage device; The demand response model expression of the user end is: L e t =L e,o t +ΔL e t (7) L g t =L g,o t +ΔL g t (8) Among them, L e t and L g t Respectively represent the electricity and gas loads after participating in demand response; L e,o t and L g,o t They represent the initial forecast values ​​of electricity and gas loads before participating in demand response; ΔL e t , ΔL g t Respectively represent the response changes of electricity and gas loads, satisfying the requirement that the total amount remains unchanged before and after the response changes The percentage of decrease / increase in the response change of electricity and gas loads should meet the following constraints: -ΔL e max ≤ΔL e t ≤ΔL e max (9) -ΔL g max ≤ΔL g t ≤ΔL g max (10) Where, ΔL e max and ΔL g max Respectively represent the maximum value of the allowable variation of the electric and gas loads; The multi-gas-electricity integrated energy system scheduling model is composed of multiple single gas-electricity integrated energy systems of the same type, each of which includes a photovoltaic power generation device, a carbon capture device, a gas unit, an electric-to-gas device, an energy storage battery and a gas storage tank; the optimization goal of the multi-gas-electricity integrated energy system scheduling model is to minimize the operating cost of the multi-gas-electricity integrated energy system while minimizing CO2 emissions; the constraints are divided into constraints within a single gas-electricity integrated energy system and constraints between multiple gas-electricity integrated energy systems; The operating costs of the multi-gas-electricity integrated energy system include the cost of purchasing electricity from the grid, the cost of purchasing gas, the cost of generating electricity from coal-fired units, the cost of operation and maintenance, and the cost of CO2 emissions; The operating cost expression of the multi-gas-electricity integrated energy system is as follows: C O =C B +C G +C Y +C C (11) In the formula, C O is the total operating cost of the multi-gas-electricity integrated energy system; C B , C G , C Y and C C are energy transaction cost, power generation cost, maintenance cost and CO2 emission cost respectively; T represents the total operation cycle; C e,t and C g,t are the prices of electricity and gas purchased in period t respectively; and are the electricity and gas purchased in period t respectively; P i,t is the output power of the coal-fired unit at time t; C G , YW G They represent the fuel cost and operation and maintenance cost of the i-th coal-fired unit at time t; I represents the total number of coal-fired units; LHV gas is the lower calorific value of natural gas; η i,t is the conversion efficiency of the i-th coal-fired unit at time t; K YW,i is the maintenance factor of the coal-fired unit; ω e ,ω g are the CO2 consumed by electricity purchased from the power grid and the natural gas grid; μ i is the CO2 emission coefficient of the coal-fired unit; C g is the unit CO2 gas emission cost; is the mass of CO2 captured; The constraints include unit output constraints, energy purchase constraints, power-to-gas constraints, carbon capture constraints within a single gas-electricity integrated energy system, and electric power interaction constraints and gas power interaction constraints between multiple gas-electricity integrated energy systems; Internal constraints of a single gas-electricity integrated energy system: 1) Power balance constraints: in, It represents the power consumption of the power-to-gas equipment during the period t; and are the charging and discharging power of the battery in time period t respectively; 2) Gas energy balance constraints: Among them, L g,t Indicates the original predicted value of gas load in period t, ΔL g,t It represents the change of gas load demand response in period t; and are respectively the charging and deflating power of the gas tank in period t; η P2G is the efficiency of the power-to-gas process; 3) Unit power constraints: P i,min ≤P i,t ≤P i,max (18) Among them, P i,max and P i,min are the upper and lower limits of the unit output in the i-th integrated energy system; 4) P2G output constraints: P P2G,min ≤P P2G ≤P P2G,max (19) Among them, P P2G,min and P P2G,max It is the upper and lower limits of P2G output; 5) Carbon capture device constraints: or min ≤η i,t ≤η max (21) Among them, P CC,i,t is the energy consumption of the carbon capture device of coal-fired unit i during period t; is the energy consumption of carbon capture equipment operation, η i,t is the corresponding carbon capture equipment power; η max and η min are the maximum and minimum values ​​of carbon capture efficiency; Constraints between multiple gas-electricity integrated energy systems: 1) Electric power interaction constraints: in, is the electric power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system in period t; and are the lower limit and upper limit of the electric power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system, respectively; 2) Gas power interaction constraints: in, is the gas power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system in period t; and are the lower and upper limits of the gas power interaction between the i-th gas-electricity integrated energy system and the j-th gas-electricity integrated energy system, respectively; Based on the asymmetric Nash bargaining model, the optimization objective function is split into two convex sub-problems, which are defined as follows: Among them, C i and are the cooperative operation cost and non-cooperative operation cost of the i-th gas-electricity integrated energy system, respectively; To solve the optimal value of the operation of the i-th gas-electricity integrated energy system; G i The cost delivered after negotiation between the i-th gas-electricity integrated energy system and the remaining subsystems; The optimization objective function of the two convex subproblems of the asymmetric Nash bargaining model and the steps of solving the optimal solution using the improved particle swarm algorithm are as follows: Step 1: Set parameters: Set the relevant parameters of the improved particle swarm algorithm and initialize the parameters of the multi-gas-electricity integrated energy system; Step 2: Upper-level optimization: Considering multiple uncertain factors in the scheduling process, establish the uncertainty set model of the system, encode and form the initial uncertainty set population of the particle swarm algorithm, that is, randomly generate N groups of uncertain scenario sets, and pass the uncertain scenario sets to the lower-level sub-problems; Step 3: Optimization of the lower-level algorithm: Using the alternating particle swarm algorithm, the optimization objective functions of the two convex subproblems of the asymmetric Nash bargaining model are iteratively calculated under the coupling variables of electrical interaction power and bargaining cost, respectively, to solve the optimal solution of the i-th gas-electricity integrated energy system under the convergence state; Step 4: Return to the upper optimization layer: replace the worst scenario of the current state with the smallest optimal solution obtained in step 3, randomly generate a new uncertainty set again, and repeat step 4; Step 5: Final optimization: If the final calculation obtains the optimal solution under the convergence state of the entire system, save the optimization strategy results in the worst scenario and the lower-level problem and end the program; otherwise, return to step 4.

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