Electricity-gas-heat integrated energy microgrid optimal configuration system considering extreme events

Abstract

The invention discloses an electricity-gas-heat integrated energy microgrid optimal configuration system considering an extreme event, which belongs to the technical field of integrated energy system optimization and comprises the steps of constructing a system model, setting an objective function, calculating, setting constraint conditions, setting extreme safety constraints, and establishing a planning model and a solving model. The system model comprises a gas turbine, a gas-fired boiler, photovoltaic equipment, wind power equipment, an electric heating pump and battery energy storage and heat energy storage equipment, and is used for meeting the requirements of electric energy, heat energy and gas loads in the system and ensuring the balance of the electric quantity and heat of the system. By constructing an optimization model of a comprehensive energy system, the uncertainty of renewable energy output is processed by utilizing a static robust optimization technology, and the uncertainty of power grid or gas grid faults caused by extreme natural disasters is combined, so that the operation safety and stability of the system in the disaster period are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system optimization, and in particular relates to an electric-gas-heat integrated energy microgrid optimization configuration system taking extreme events into consideration. Background Art

[0002] At present, with the intensification of global climate change, the frequent occurrence of extreme weather and natural disasters has brought huge challenges to the safe operation of energy systems. Traditional energy systems usually rely on a single form of energy, and their risk resistance in the face of extreme events is weak, and they are prone to problems such as system interruption and insufficient energy supply. At the same time, the rapid development of renewable energy (such as wind power and photovoltaics) and the randomness and uncertainty of their output have also made modern energy systems face more uncertain factors in operation and planning.

[0003] The integrated energy system can improve energy utilization efficiency and system stability through the coupling and complementation of multiple energy forms such as electricity, heat, and gas. Especially in the electricity-heat-gas multi-energy complementary system, the combined use of multiple equipment such as gas turbines, gas boilers, and energy storage equipment can not only improve the flexibility of the system, but also provide backup energy under extreme conditions through energy storage technology. However, most of the current integrated energy system planning methods only consider the optimal configuration of equipment under daily operating conditions, and fail to effectively deal with the uncertainties caused by system failures and disasters under extreme events. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide an electric-gas-heat integrated energy microgrid optimization configuration system taking extreme events into consideration, thereby solving the problems in the above-mentioned background technology.

[0005] The objective of the present invention is achieved in this way: an electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events includes constructing a system model, setting an objective function, calculating, setting constraints, setting extreme safety constraints, establishing a planning model and solving a model; the system model includes a gas turbine, a gas boiler, a photovoltaic device, a wind power device, an electric heat pump, a battery energy storage device and a thermal energy storage device, which are used to meet the needs of electrical energy, thermal energy and gas loads within the system, and ensure the balance of system power and heat.

[0006] Furthermore, the objective function includes:

[0007] The total cost minimization function includes equipment investment cost, operation and maintenance cost, and transaction costs with the power grid and gas grid. The formula is:

[0008] C IES =C IN +C TR +C OP

[0009] In the formula, C IES is the total cost of the integrated energy system, C IN is the annual value of the investment cost of the comprehensive energy system, C TR The cost of purchasing electricity and natural gas for the integrated energy system, C OP It is the operation and maintenance cost of all equipment in the system.

[0010] Beneficial effects of the present invention:

[0011] 1. Improve the reliability and stability of the system: Use static robust models to deal with the uncertainty of renewable energy such as wind power and photovoltaics, reduce their impact on system operation, and ensure that the system can still operate safely and stably under extreme natural disasters.

[0012] 2. Enhance the system's ability to resist risks: Combined with the uncertainty set of fault time, considering the power grid or gas network failures caused by extreme events, the system's ability to respond to extreme events is enhanced, and its risk resistance is improved.

[0013] 3. Optimize energy utilization efficiency: The present invention balances the power, heat and gas demands within the system through multi-energy complementation and reasonable configuration of energy storage equipment, improves the comprehensive utilization efficiency of the system energy and reduces energy waste.

[0014] 4. Reduce system operating costs: By optimizing equipment configuration and operation strategies, the total system cost is minimized, equipment investment, operation and maintenance, and energy transaction costs are reduced, and the economic benefits of the system are improved.

[0015] 5. Improve system resilience: This method takes extreme events into consideration and ensures that the system can respond quickly and make effective adjustments when a failure occurs, thereby improving the flexibility and resilience of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the electric-heat-gas integrated energy system of the present invention;

[0017] Figure 2 It is a flow chart of the solution algorithm of the present invention;

[0018] Figure 3 It is a price chart of electricity and gas purchases for the integrated energy system of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described in detail below in conjunction with the accompanying drawings, just to more clearly illustrate and explain the present invention.

[0020] like Figure 1-3As shown, this embodiment discloses an electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events, which includes building a system model, setting an objective function, calculating, setting constraints, setting extreme safety constraints, establishing a planning model and solving a model; the system model includes a gas turbine, a gas boiler, a photovoltaic device, a wind power device, an electric heat pump, a battery energy storage device and a thermal energy storage device, which are used to meet the needs of electrical energy, thermal energy and gas loads within the system, and ensure the balance of system power and heat.

[0021] The total cost minimization objective function of the system is established, which specifically includes equipment investment cost, operation and maintenance cost, and transaction costs with the power grid and gas grid. The formula is as follows:

[0022] C IES =C IN +C TR +C OP

[0023] In the formula, C IES is the total cost of the integrated energy system, C IN is the annual value of the investment cost of the comprehensive energy system, C TR The cost of purchasing electricity and natural gas for the integrated energy system, C OP The operation and maintenance costs of all equipment in the system;

[0024] The static robust optimization model is used to deal with the output uncertainty of renewable energy sources such as wind power and photovoltaic power, while considering the time uncertainty of power grid or gas grid failure under extreme events. The constraints are:

[0025]

[0026] In the formula, They represent the connection state variables of the power grid or gas grid, respectively, and the connection state variables of the integrated energy system with the power grid or gas grid, respectively. 0 means disconnected from the external network, and 1 means connected to the external network;

[0027] Construct a charging and discharging optimization model for energy storage equipment, taking into account the energy storage charging and discharging power constraints and power state constraints. The specific formula is as follows:

[0028]

[0029] in, Indicates the operating power of the energy conversion equipment during the fault period. is the capacity of the energy storage device, Soc s,t The power state of the energy storage device;

[0030] During the fault period, it is allowed to reduce the electrical load and thermal load according to the system needs, and reduce the load factor L el and Lhl Respectively represent the proportion of electrical load and thermal load that can be reduced, ranging from 0 to 1, 0 means no load reduction is allowed, 1 means complete load reduction is allowed;

[0031] The column and constraint generation method is used to solve the above robust optimization model. Under extreme events, the main problem (MP) and subproblems (SP) of the problem are solved alternately to optimize the system configuration and ensure that the system can still meet the load demand during extreme events.

[0032] Using the linearization method to process the transaction model with the power grid and gas grid, the annual transaction cost is as follows:

[0033]

[0034] Where N s is the typical day obtained after scene reduction, the number of days in each scene is expressed as Ds, and satisfies ∑ s D s =365, Respectively represent the price of electricity purchased and sold from the external power grid, is the purchase price of natural gas, They represent the amount of electricity or gas purchased from the power grid or gas grid at time t on a typical day s, To add auxiliary variables, the reason for the above linear transformation is that under normal circumstances, the purchase price of electricity Higher than electricity price Satisfaction Therefore, when When is positive, When the objective function is optimal, when When it is a negative value, When the objective function is optimal,

[0035] Defining Energy Storage Variables Indicates the operating variables of the energy storage device under normal operation. Represents the operating variable during the fault period, which satisfies:

[0036]

[0037] In the formula, M represents a large number, The system status indicator variable is 1 when there is no fault and 0 when a fault occurs. The above constraints mean that during the period when an extreme event occurs, if the power grid or gas grid fails, the interaction power between the integrated energy system and the external power grid and gas grid during the corresponding period is 0.

[0038] In extreme events, energy conversion equipment such as gas turbines and electric heat pumps can adjust their output to ensure system energy balance. The constraint formula is as follows:

[0039]

[0040] In the formula, It represents the operating power of the energy conversion equipment during the fault period, that is,

[0041] Use the load factor L el and L hl To adjust the system load under extreme events, reduce the load factor to between 0 and 1, and ensure the safety of the system under extreme conditions.

[0042] By processing the uncertain variables in the system using the column generation method, the operating status of the electricity-heat-gas integrated energy system is gradually optimized, ensuring that it can maintain load demand under extreme events.

[0043] Establishing an integrated electricity-heat-gas energy system model:

[0044] First, a complete electric-heat-gas integrated energy system model is constructed. The system includes various types of energy equipment, including wind turbines (WT), photovoltaic power generation systems (PV), electric heat pumps (HP), gas turbines (GT), gas boilers (GB), battery energy storage systems (BS) and thermal energy storage systems (HS). These devices achieve coordinated complementarity between electric energy, thermal energy and natural gas through energy conversion and storage. The system structure is as follows: Figure 1 As shown in the figure, the gas turbine is an electric-heat-gas coupling device that can generate electricity and heat at the same time; the gas boiler is a heat-gas coupling device; photovoltaic and wind power are renewable energy devices; and the electric heat pump is an electric-heat coupling device. This multi-energy complementary system structure can improve energy utilization efficiency and enhance system flexibility and reliability.

[0045] Determine the planning objective function:

[0046] The core goal of the present invention is to minimize the total system cost while ensuring the reliability and robustness of the system. The objective function includes three main parts: investment cost, transaction cost and operation and maintenance cost. By optimizing the costs of these three aspects, a balance between the economy and reliability of the system can be achieved. The objective function is expressed as:

[0047] C IES =C IN +C TR +C OP

[0048] In the formula, C IES is the total cost of the integrated energy system, CIN is the annual value of the investment cost of the comprehensive energy system, C TR The cost of purchasing electricity and natural gas for the integrated energy system, C OP It is the operation and maintenance cost of all equipment in the system.

[0049] Calculate the annual value of investment cost:

[0050] The calculation of the equal annual value of investment cost takes into account the initial investment of all equipment in the system. By using the equal annual value method, the investment of equipment with different life cycles can be unified on an annual scale for comparison. The calculation formula is as follows:

[0051]

[0052] Cap χ,min ≤Cap χ ≤Cap χ,max

[0053] In the formula, C IN is the annual value of the investment cost of all equipment in the system, including wind power, photovoltaic, heat pump, gas turbine, gas boiler, electric storage and heat storage device; χ represents various equipment, r d is the depreciation rate of each equipment, taking 0.08; χ is the operating life of the equipment χ, c χ Cap represents the unit capacity construction cost of the equipment. χ Indicates the installed capacity of the equipment, Cap χ,min and Cap χ,max Respectively represent the upper and lower limits of the equipment planning capacity. The purpose of the above equal annual value form is to convert the equipment investment cost into annual cost, which is convenient for accumulation with transaction and operation and maintenance costs, thereby facilitating the calculation of the objective function.

[0054] Calculate the annual electricity and gas purchase costs:

[0055] The system conducts energy transactions with external power grids and natural gas networks. The calculation of this part of the cost needs to take into account the time-of-use electricity price and natural gas price. In order to more accurately reflect the long-term operating costs, the typical day method is used to represent the annual operation. The calculation formula is as follows:

[0056]

[0057] In the formula, Respectively represent the price of electricity purchased and sold from the external power grid, is the purchase price of natural gas, denote the amount of electricity or gas purchased from the power grid or gas grid at time t on a typical day s. In addition, let (·) + represents the projection operator on non-negative orthogonal, that is, (x) +:=max(x,0). The above model can be linearized by introducing auxiliary variables and further converted into:

[0058]

[0059] In the formula, To add auxiliary variables, the reason for the above linear transformation is that under normal circumstances, the purchase price of electricity Higher than electricity price Satisfaction Therefore, when When is positive, When the objective function is optimal, when When it is a negative value, When the objective function is optimal, Therefore, the linearized problem is equivalent to the original problem.

[0060] Calculate the annual operation and maintenance cost of the equipment:

[0061] The calculation of operation and maintenance costs takes into account the output of all equipment in typical operating scenarios. This includes the maintenance costs of renewable energy equipment, energy conversion equipment, and energy storage equipment. The calculation formula is as follows:

[0062]

[0063] In the formula, the equipment operation and maintenance cost includes the annual maintenance cost of renewable energy equipment, namely wind power and photovoltaic, energy conversion equipment gas turbine, heat pump and gas boiler, and energy storage equipment including battery energy storage and thermal energy storage; γ represents equipment other than energy storage devices, namely PV, WT, GT, HP, GB, θ represents energy storage devices, namely BS and HS; Indicates the output of non-energy storage devices, including They refer to the output of wind power, photovoltaic power, gas turbine and heat pump at time t in each typical scenario s respectively; and Represents the charging power of battery energy storage and thermal energy storage respectively and discharge power They respectively represent the unit operation and maintenance cost of the equipment.

[0064] Set up normal state operation constraints:

[0065] In order to ensure the reliability and economy of the system under normal operation, a series of constraints are set:

[0066] Energy conversion device constraints

[0067]

[0068] In the formula, is the output of the energy conversion device, Plan capacity for energy conversion devices to ensure that the output of various energy conversion equipment does not exceed its rated capacity.

[0069] Renewable Energy Equipment Constraints

[0070] Considering the volatility and uncertainty of wind power and photovoltaic power, a robust optimization method based on duality theory is adopted to describe their output characteristics.

[0071]

[0072] In the formula, Indicates the actual output of renewable energy, including wind power output and photovoltaic output Cap γ represents the planned capacity of renewable energy, Forecast per unit value for renewable energy output, Indicates the upper and lower values ​​of the predicted value; represents the dual variable, is a parameter for controlling the robustness of the model.

[0073] Energy storage equipment constraints

[0074] It includes the timing coupling relationship between charging and discharging power limitation, energy storage capacity limitation and charging and discharging status.

[0075]

[0076] In the formula, Cap θ Indicates the installed capacity of energy storage equipment, Indicates the remaining energy storage capacity. Indicates the maximum charge / discharge coefficient of energy storage, Indicates the minimum / maximum energy storage state coefficient, Refers to the charging and discharging efficiency coefficient of the energy storage device.

[0077] Energy balance constraints

[0078] Ensure that the system's electricity and heat supply and demand are balanced at every moment.

[0079]

[0080] In the formula, λ gt / η gt , hp and η gb Represent the energy conversion efficiency of gas turbine, electric heat pump and gas boiler respectively.

[0081] Set extreme event safety constraints:

[0082] In order to cope with possible extreme events (such as system failures caused by natural disasters), the present invention introduces a series of safety constraints:

[0083] Define the set of failure time uncertainties

[0084] Through binary variables To represent the fault state of the system at different times, define They represent the connection state variables between the integrated energy system and the power grid or gas grid, 0 means disconnected from the external network, and 1 means connected to the external network. Therefore, the uncertainty of system failure time can be characterized by 0-1 variables. The specific model is as follows:

[0085]

[0086] In the formula, T represents the time of day, which is 24 hours, and the parameter R I The above model can indicate that there may be continuous R I hours before the failure occurs, where the first item indicates the failure time is R I hours, and the second item indicates that the failure time is continuous.

[0087] Define the fault status indicator variable:

[0088] Used to indicate whether the system is in normal operation.

[0089]

[0090] In the formula, when there is no fault in the power grid and gas grid, the system status indicator variable is 1. When a fault occurs in the power grid or gas grid at any time, the indicator variable is 0, and its subsequent time is also 0.

[0091] Constraints on transaction variables during failure period:

[0092] Limit the amount of transactions the system can make with external grid and gas networks during a fault condition.

[0093]

[0094] In the formula, M represents a large number.

[0095] Energy storage equipment operation constraints during fault periods:

[0096] Ensure the normal operation of energy storage equipment during failure.

[0097]

[0098] In the formula, the energy storage device variable Indicates the operating variables under normal operation of the energy storage device. Indicates the running variables during the fault period

[0099] Energy conversion equipment constraints during fault periods:

[0100] Limit the output of energy conversion equipment under fault conditions.

[0101]

[0102] In the formula, It represents the operating power of the energy conversion equipment during the fault period, that is,

[0103] Energy balance constraints during fault period:

[0104] Ensure that the system can still meet a certain proportion of load demand under fault conditions.

[0105]

[0106] Where, L el and L hl They represent the proportion of electrical load and thermal load that can be reduced respectively, ranging from 0 to 1, where 0 means no load reduction is allowed and 1 means complete load reduction is allowed.

[0107] The general form of establishing a planning model is:

[0108] Integrating all the above constraints, we get a complete mathematical programming model:

[0109]

[0110] stFy≤w

[0111]

[0112] In the formula, y represents the decision variables, including the capacity and operation strategy of various equipment, z r Represents adjustment decisions under extreme events.

[0113] Solve the planning model using the column and constraint generation method:

[0114] Column-and-Constraint Generation (C&CG) is an effective method for solving large-scale robust optimization problems. For our robust planning model of an integrated power-heat-gas energy system, this method can gradually generate key constraints, thereby obtaining a high-quality solution within a reasonable computational time. The following are the detailed solution steps:

[0115] Set the initial value of the fault scenario W, the initial value of the number of iterations N = 1, the sub-problem objective function J = +∞, and the convergence accuracy d = 10 -3 .

[0116] In the determined set of failure scenarios Next, solve the main problem MP and obtain the comprehensive energy system planning and normal state operation plan y*.

[0117] According to the determined plan and the normal state running variable y*, solve the dual problem SP-I of the subproblem and obtain the fault scenario And the objective function value J.

[0118] Determine whether J is less than v. If so, end the iteration; if not, update N=N+1, return to step 2.

[0119] In the above-mentioned solving steps, step 2) and step 4) are linear programming and mixed integer linear programming problems respectively, which can be solved directly by commercial solvers such as Gurobi and CPLEX.

[0120] Power system model:

[0121] Model power system components such as generators, energy storage, and loads.

[0122] For generator set i, its output P at time t is i,t Should meet:

[0123] P i,min ≤P i,t ≤P i,max

[0124] Where P i,min and P i,max are the minimum and maximum output of generator set i respectively.

[0125] For energy storage device j, its charging and discharging work P j,t and energy storage E j,t Should meet:

[0126] -P j,max ≤P j,t ≤P j,max

[0127] -P j,max ≤P j,t ≤P j,max

[0128]

[0129] Among them, P j,max is the maximum charge and discharge power, Ej,min and E j,max are the minimum and maximum energy storage, η c and η d are the charging and discharging efficiencies respectively, and Δt is the time interval.

[0130] Thermal system model:

[0131] Model combined heat and power plants, gas boilers, and thermal storage.

[0132] For the combined heat and power unit k, its heat-to-electricity ratio ρ k,t At time t, it should satisfy:

[0133] ρ k,min ≤ρ k,t ≤ρ k,max

[0134] Among them, ρ k,min and ρ k,max are the minimum and maximum heat-to-electricity ratios of the cogeneration unit t, respectively.

[0135] For a gas boiler l, its heat output H l,t Should meet:

[0136] H l,min ≤H l,t ≤H l,max

[0137] H l,t =η l F l,t

[0138] Among them, H l,min and H l,max are the minimum and maximum heat output, η l is the boiler efficiency, F l,t is the gas input.

[0139] Natural Gas System Model:

[0140] Model components such as natural gas pipeline networks, compressors, and more.

[0141] For a natural gas pipeline m, its flow rate F m,t At time t, it should satisfy:

[0142] F m,min ≤F m,t ≤F m,max

[0143]

[0144] Among them, F m,min and F m,maxare the minimum and maximum flow rates respectively, Cm is the pipeline constant, p in,m,t and p out,m,t are the pressures at the inlet and outlet of the pipeline respectively.

[0145] Coupling device model:

[0146] Model energy conversion equipment such as gas turbines and electric heating plants.

[0147] For gas turbine n, its power output P e,n,t and heat output P h,n,t Should meet:

[0148] P e,n,t =η e,n F n,t

[0149] P h,n,t =η h,n F n,t

[0150] Among them, η e,n and η h,n are the electrical efficiency and thermal efficiency of the gas turbine, respectively, n,t is the gas input.

[0151] Step 2: Construct the objective function

[0152] The objective function consists of three main parts: investment cost, transaction fee and operation and maintenance cost. By optimizing the costs of these three aspects, a balance between the economy and reliability of the system can be achieved. The objective function is expressed as:

[0153] C IES =C IN +C TR +C OP

[0154] In the formula, C IES is the total cost of the integrated energy system, C IN is the annual value of the investment cost of the comprehensive energy system, C TR The cost of purchasing electricity and natural gas for the integrated energy system, C OP It is the operation and maintenance cost of all equipment in the system.

[0155] Calculate the annual value of investment cost:

[0156] The calculation of the equal annual value of investment cost takes into account the initial investment of all equipment in the system. By using the equal annual value method, the investment of equipment with different life cycles can be unified on an annual scale for comparison. The calculation formula is as follows:

[0157]

[0158] Cap χ,min ≤Cap χ ≤Cap χ,max

[0159] In the formula, χ represents various devices, r d is the depreciation rate of each equipment, taking 0.08; χ is the operating life of the equipment χ, c χ Cap represents the unit capacity construction cost of the equipment. χ Indicates the installed capacity of the equipment, Cap χ,min and Cap χ,max Respectively represent the upper and lower limits of the equipment planning capacity. The purpose of the above equal annual value form is to convert the equipment investment cost into annual cost, which is convenient for accumulation with transaction and operation and maintenance costs, thereby facilitating the calculation of the objective function.

[0160] Calculate the annual electricity and gas purchase costs:

[0161] The system conducts energy transactions with external power grids and natural gas networks. The calculation of this part of the cost needs to take into account the time-of-use electricity price and natural gas price. In order to more accurately reflect the long-term operating costs, the typical day method is used to represent the annual operation. The calculation formula is as follows:

[0162]

[0163] In the formula, Respectively represent the price of electricity purchased and sold from the external power grid, is the purchase price of natural gas, denote the amount of electricity or gas purchased from the power grid or gas grid at time t on a typical day s. In addition, let (·) + represents the projection operator on non-negative orthogonal, that is, (x) + :=max(x,0). The above model can be linearized by introducing auxiliary variables and further converted into:

[0164]

[0165] In the formula, To add auxiliary variables, the reason for the above linear transformation is that under normal circumstances, the purchase price of electricity Higher than electricity price Satisfaction Therefore, when When is positive, When the objective function is optimal, when When it is a negative value, When the objective function is optimal, Therefore, the linearized problem is equivalent to the original problem.

[0166] Calculate the annual operation and maintenance cost of the equipment:

[0167] The calculation of operation and maintenance costs takes into account the output of all equipment in typical operating scenarios. This includes the maintenance costs of renewable energy equipment, energy conversion equipment, and energy storage equipment. The calculation formula is as follows:

[0168]

[0169] In the formula, γ represents the equipment other than the energy storage device, i.e., PV, WT, GT, HP, GB, and θ represents the energy storage device, i.e., BS and HS; Indicates the output of non-energy storage devices, including They refer to the output of wind power, photovoltaic power, gas turbine and heat pump at time t in each typical scenario s respectively; and Represents the charging power of battery energy storage and thermal energy storage respectively and discharge power They respectively represent the unit operation and maintenance cost of the equipment.

[0170] Set up normal state operation constraints:

[0171] In order to ensure the reliability and economy of the system under normal operation, a series of constraints are set:

[0172] Energy conversion device constraints

[0173]

[0174] In the formula, is the output of the energy conversion device, Plan capacity for energy conversion devices to ensure that the output of various energy conversion equipment does not exceed its rated capacity.

[0175] Renewable Energy Equipment Constraints

[0176] Considering the volatility and uncertainty of wind power and photovoltaic power, a robust optimization method based on duality theory is adopted to describe their output characteristics.

[0177]

[0178] In the formula, Indicates the actual output of renewable energy, including wind power output and photovoltaic output Cap γ represents the planned capacity of renewable energy, Forecast per unit value for renewable energy output, Indicates the upper and lower values ​​of the predicted value; represents the dual variable, is a parameter for controlling the robustness of the model.

[0179] Energy storage equipment constraints

[0180] It includes the timing coupling relationship between charging and discharging power limitation, energy storage capacity limitation and charging and discharging status.

[0181]

[0182] In the formula, Cap θ Indicates the installed capacity of energy storage equipment, Indicates the remaining energy storage capacity. Indicates the maximum charge / discharge coefficient of energy storage, Indicates the minimum / maximum energy storage state coefficient, Refers to the charging and discharging efficiency coefficient of the energy storage device.

[0183] Energy balance constraints

[0184] Ensure that the system's electricity and heat supply and demand are balanced at every moment.

[0185]

[0186] In the formula, λ gt / η gt , hp and η gb Represent the energy conversion efficiency of gas turbine, electric heat pump and gas boiler respectively.

[0187] Set extreme event safety constraints:

[0188] In order to cope with possible extreme events (such as system failures caused by natural disasters), the present invention introduces a series of safety constraints:

[0189] Define the set of failure time uncertainties

[0190] Through binary variables To represent the fault state of the system at different times, define They represent the connection state variables between the integrated energy system and the power grid or gas grid, 0 means disconnected from the external network, and 1 means connected to the external network. Therefore, the uncertainty of system failure time can be characterized by 0-1 variables. The specific model is as follows:

[0191]

[0192] In the formula, T represents the time of day, which is 24 hours, and the parameter R I The above model can indicate that there may be continuous RI hours before the failure occurs, where the first item indicates the failure time is R I hours, and the second item indicates that the failure time is continuous.

[0193] Define the fault status indicator variable:

[0194] Used to indicate whether the system is in normal operation.

[0195]

[0196] In the formula, when there is no fault in the power grid and gas grid, the system status indicator variable is 1. When a fault occurs in the power grid or gas grid at any time, the indicator variable is 0, and its subsequent time is also 0.

[0197] Constraints on transaction variables during failure period:

[0198] Limit the amount of transactions the system can make with external grid and gas networks during a fault condition.

[0199]

[0200] In the formula, M represents a large number.

[0201] Energy storage equipment operation constraints during fault periods:

[0202] Ensure the normal operation of energy storage equipment during failure.

[0203]

[0204] In the formula, the energy storage device variable Indicates the operating variables under normal operation of the energy storage device. Indicates the running variables during the fault period

[0205] Energy conversion equipment constraints during fault periods:

[0206] Limit the output of energy conversion equipment under fault conditions.

[0207]

[0208] In the formula, It represents the operating power of the energy conversion equipment during the fault period, that is,

[0209] Energy balance constraints during fault period:

[0210] Ensure that the system can still meet a certain proportion of load demand under fault conditions.

[0211]

[0212] Where, L el and L hl They represent the proportion of electrical load and thermal load that can be reduced respectively, ranging from 0 to 1, where 0 means no load reduction is allowed and 1 means complete load reduction is allowed.

[0213] Step 3: Generate extreme event scenarios

[0214] 3.1 Modeling of meteorological factors:

[0215] According to the spatiotemporal distribution characteristics of extreme meteorological events, meteorological factors such as wind speed and irradiance are modeled.

[0216] 3.2 Insert short-time scale extreme meteorological events:

[0217] Insert multiple extreme meteorological events on short time scales (one day to multiple days) into the annual time series.

[0218] 3.3 Generate a complete annual meteorological scenario:

[0219] For wind speed, the Gaussian process regression (GPR) model is combined with the Copula function, and for irradiance, a data-knowledge joint driven method is adopted.

[0220] The mean function and covariance function of the GPR model are as follows:

[0221] m(x)=h(x)Tβ

[0222] k(x,x')=σf2e -1 / 2(x-x') TM(x-x')

[0223] Where h(x) is the basis function vector, β is the regression coefficient vector, σf2 is the signal variance, and M is the diagonal matrix of feature correlation length.

[0224] 3.4 Mapping to new energy output scenarios:

[0225] The wind speed-wind power output curve is used to map wind speed to wind power output, and the BP neural network is used to map irradiance to photovoltaic output.

[0226] 3.5 Generate system operation scenario:

[0227] Based on the renewable energy output scenario, the unit combination problem is solved and the power grid operation scenario is obtained.

[0228] Step 4: Build a robust optimization model

[0229] The system model and scenario in steps 1 to 3 are integrated into a two-stage robust optimization model. The model expression is as follows:

[0230]

[0231] stFy≤w

[0232]

[0233]

[0234] Among them, c is the vector form of various cost parameters, and y represents the variables involved in the normal operation state, including the planned capacity Cap χ , Energy conversion device output Renewable energy output and dual variables Energy storage equipment output And energy storage operation status Interaction power with external power grid / gas grid Formula (33) represents the normal operating state constraint condition, that is, it includes the constraint condition.

[0235] Step 5: Solve the planning model:

[0236] Column-and-Constraint Generation (C&CG) is an effective method for solving large-scale robust optimization problems. For our robust programming model of an integrated power-heat-gas energy system, this method can gradually generate key constraints, resulting in a high-quality solution within a reasonable computational time.

[0237] The following are the detailed solution steps:

[0238] 5.1 Initialization:

[0239] Set up an initial set of failure scenarios

[0240] Set the number of iterations N = 1

[0241] Set the initial value of the sub-problem objective function J = +∞

[0242] Define convergence accuracy δ = 10 -3

[0243] Initialize the decision variables y of the main problem, including the capacity and operation strategy of each device

[0244] 5.2 Solve the Master Problem (MP):

[0245] In the current fault scenario collection Next, construct and solve the following main problem:

[0246]

[0247] stFy≤w

[0248]

[0249] In the formula, The fault occurrence period obtained by solving the subproblem SP is a parameter in the main problem MP and changes with the increase of the number N of C&CG iterations.

[0250] 5.3 Construct and solve sub-problems (SP):

[0251] Using the y* obtained from the main problem, construct the following subproblems:

[0252] (SP)

[0253] stΞ e ={(e1,e2,z r ):e1≥0,e2≥0

[0254] Ky * +Rz r +Q(u+e1-e2)≤g:ν

[0255] Hz r ≤h:ε}

[0256] Where J represents the objective function of the subproblem SP, e1 and e2 represent auxiliary variables, and 1 is a calculation vector whose elements are all 1, that is, [1,1,…,1] T , whose vector dimension is consistent with the uncertain variable u, is used to calculate the objective function value after adding the slack variable. When the objective function value of the subproblem SP approaches 0, the SP problem is equivalent before and after relaxation. e Represents the set of lower-level problem constraints of the subproblem, where y* is the parameter obtained by solving the main problem, and ν and ε are the dual variables of the lower-level fault period constraints.

[0257] This subproblem is transformed into a mixed integer linear programming problem (MILP) through duality theory:

[0258] (SP-I)

[0259] stv Τ R+ε Τ H=0

[0260] -1≤Q T ν≤1

[0261] ν≥0,ε≥0,u∈Γ

[0262] Among them, the nonlinear term ν T Qu can be linearized using the big M method, namely:

[0263] ν s,t Q s,t -M(1-u s,t )≤δ s,t ≤ν s,t Q s,t +M(1-u s,t )

[0264] Mu s,t ≤δ s,t ≤Mu s,t

[0265] In the formula, δ s,t Indicates the addition of a new auxiliary variable.

[0266] 5.4 Convergence judgment:

[0267] Calculate the objective function value of the subproblem. If J≤δ, the algorithm converges and outputs the current optimal solution y*. Otherwise, execute step 5).

[0268] 5.5 Update Iteration:

[0269] Add the new failure scenario to the set: W = W∪{u*}

[0270] Update iteration number: N = N + 1

[0271] Return to step 2) and continue iterating

[0272] 5.6 Output results:

[0273] When the algorithm converges, the optimal equipment capacity configuration plan y* and the corresponding operation strategy are output.

[0274] Write relevant programs in Matlab2022b to design the regional integrated energy system structure as follows Figure 1 As shown. The historical data of photovoltaic, wind power and load are from the American Renewable Energy Laboratory. The typical scenario generation method is used to obtain three typical days, with a peak electric load of 450kW and a peak thermal load of 200kWh. The internal equipment investment and operation and maintenance costs of the comprehensive energy system are set to 20 years, with a depreciation rate of 0.08 and an energy conversion coefficient λ gt / η gt , hp and η gb They are set to 1.5 / 0.3, 1.2 and 0.9 respectively. The charging and discharging efficiency of the energy storage device are set to 0.94 and 1.06 respectively, and the robust model parameters of renewable energy It is set to 1 during the period of 01:00-03:00 for wind power and 11:00-16:00 for photovoltaic power, and its prediction deviation is ±10%. and natural gas prices like Figure 3 As shown, the reverse electricity price is 0.05$ / kWh.

[0275] Table 1 Equipment parameters of integrated energy system

[0276]

[0277]

[0278] In order to verify the effectiveness of the proposed robust planning model for the integrated energy system considering extreme events, four case studies are set for comparison. Case 1 does not consider the safety constraints of extreme events; Case 2 considers the disconnection of the pipeline caused by the external gas grid failure, while the grid is not disconnected, that is, the fault duration R is set. I :=[R e R g ] China power grid fault time R e =0; Case 3 considers that the external power grid failure causes the power line to be disconnected, while the gas grid is still working normally, that is, R is set g 0; Case 4 indicates that a serious incident caused the integrated energy system to be disconnected from the external gas grid and the power grid at the same time. For the above cases 2 to 4, the fault duration event is set to 4 hours, and the load factor L is reduced. el and L hl Set to 0, that is, load shedding is not allowed. The planning results of the four schemes are as follows:

[0279] Table 2 Planning results of different planning schemes

[0280] equipment Solution 1 Solution 2 Option 3 Option 4 Wind power 240 240 240 240 Photovoltaic 600 600 600 600 Electric heat pump 200 200 156 147 Gas boiler 87 87 107 140 gas turbine 0 0 25 0 Electric energy storage 243 243 1450 1777 Thermal energy storage 600 600 600 600

[0281] Unit: kWh

[0282] Table 3 Planning costs of different planning schemes (k$)

[0283]

[0284]

[0285] As can be seen from Tables 2 and 3, Scheme 1, which does not consider safety constraints, has the best economic performance because it does not need to consider energy balance under fault conditions; Scheme 2 considers the situation of disconnection from the external gas grid, but the planning scheme is the same as Scheme 1 because Scheme 1 plans electric heat pumps and thermal energy storage devices. Even if the gas grid fails and stops supplying gas for 4 hours, the electric heat pumps and thermal energy storage devices can maintain the balance of thermal load during the fault time. Compared with Scheme 2, Scheme 3 indicates that the input power is zero due to the external grid failure. In this case, the planned capacity of the electric heat pump decreases relative to Scheme 2, while the planned capacity of the gas boiler increases, and the planned capacity of the electric energy storage increases significantly. The reason is that in the case of possible failure of external power supply, in order to maintain the balance of the internal heat load in the region during the fault period, the heat load is met by the gas grid through the gas boiler, and its equipment capacity increases. In order to maintain the balance of the electric load, it is necessary to increase the power supply during the fault period or reduce the consumption. Therefore, the capacity of the electric energy storage device increases, while the electric heating equipment, as a demand, has a reduced planned capacity. Option 4 further considers the situation where the external gas grid and the power grid fail at the same time, resulting in energy supply failure. In this case, in order to simultaneously meet the balance of electrical load and thermal load during the fault period, the planned capacity of the energy storage equipment needs to be further increased.

[0286] It is worth pointing out that in the above-mentioned planning scheme, the capacity of renewable energy equipment including wind power and photovoltaic power reaches the upper limit of the planned capacity, and the planned capacity of gas turbines is relatively low. The reason is that renewable energy equipment does not require fuel costs, while the fuel cost and energy conversion coefficient of gas turbines are relatively low. Therefore, from an economic perspective, investors in integrated energy systems tend to plan equipment with lower investment costs and higher energy conversion efficiency, such as electric heat pumps and gas boilers. At the same time, from the perspective of energy storage equipment, even in scheme one that does not consider fault scenarios, a certain capacity of electric energy storage and thermal energy storage equipment is still configured. The reason is that due to the low price of reverse transmission to the upper power grid, the surplus output of renewable energy equipment in the system can be absorbed by the energy storage equipment, and used for heating or providing electricity to internal electrical loads during periods of high electricity purchase prices, thereby reducing the total cost of the system.

Claims

1. An electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events, characterized in that: It includes building a system model, setting objective functions, calculations, setting constraints, setting extreme safety constraints, establishing a planning model and solving a model; the system model includes gas turbines, gas boilers, photovoltaic equipment, wind power equipment, electric heat pumps, battery energy storage and thermal energy storage equipment, which are used to meet the needs of electrical energy, thermal energy and gas loads within the system, and ensure the balance of system power and heat.

2. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 1 is characterized in that: The objective function includes: The total cost minimization function includes equipment investment cost, operation and maintenance cost, and transaction costs with the power grid and gas grid. The formula is: C IES =C IN +C TR +C OP In the formula, C IES is the total cost of the integrated energy system, C IN is the annual value of the investment cost of the comprehensive energy system, C TR The cost of purchasing electricity and natural gas for the integrated energy system, C OP It is the operation and maintenance cost of all equipment in the system.

3. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 1 is characterized in that: The calculation includes calculating the annual value of investment cost, calculating the annual electricity and gas purchase costs, and calculating the annual operation and maintenance costs of the equipment; The calculation of the equal annual value of investment cost includes the calculation of the equal annual value of investment cost, taking into account the initial investment of all equipment in the system, and unifying the equipment investment of different life cycles on an annual scale for comparison by means of equal annual value. The calculation formula is: Cap χ,min ≤Cap χ ≤Cap χ,max In the formula, C IN is the annual value of the investment cost of all equipment in the system, including wind power, photovoltaic, heat pump, gas turbine, gas boiler, electric storage and heat storage device; χ represents various equipment, r d For each equipment depreciation rate, take 0.08; y χ is the operating life of the equipment χ, c χ Cap represents the unit capacity construction cost of the equipment. χ Indicates the installed capacity of the equipment, Cap χ,min and Cap χ,max Respectively represent the upper and lower limits of the equipment planning capacity; the purpose of the above equal annual value form is to convert the equipment investment cost into annual cost, which is convenient for accumulation with transaction and operation and maintenance costs, thereby facilitating the calculation of the objective function; The calculation of annual electricity and gas purchase costs includes energy transactions between the system and external power grids and natural gas networks. The calculation of this part of the cost takes into account the time-of-use electricity price and natural gas price, and uses the typical day method to represent the annual operation. The calculation formula is: In the formula, Respectively represent the purchase and sale prices of electricity from the external power grid, λ t g is the purchase price of natural gas, denote the amount of electricity or gas purchased from the power grid or gas grid at time t on a typical day s, respectively. Let (·) + represents the projection operator on non-negative orthogonal, that is, (x) + :=max(x,0), the above model can be linearized by introducing auxiliary variables and further converted into, In the formula, To add auxiliary variables, the reason for the above linear transformation is that under normal circumstances, the purchase price of electricity Higher than the electricity price Satisfaction when When is positive, When the objective function is optimal, when When it is a negative value, When the objective function is optimal, The annual operation and maintenance cost of the computing equipment includes the calculation of the operation and maintenance cost, taking into account the output of all equipment in a typical operation scenario, including the maintenance cost of renewable energy equipment, energy conversion equipment and energy storage equipment. The calculation formula is, In the formula, the equipment operation and maintenance cost includes the annual maintenance cost of renewable energy equipment, namely wind power and photovoltaic, energy conversion equipment gas turbine, heat pump and gas boiler, and the energy storage equipment includes the annual maintenance cost of battery energy storage and thermal energy storage; γ represents equipment other than energy storage devices, namely PV, WT, GT, HP, GB, and θ represents energy storage devices, namely BS and HS; Indicates the output of non-energy storage devices, including They refer to the output of wind power, photovoltaic power, gas turbine and heat pump at time t in each typical scenario s respectively; and Represents the charging power of battery energy storage and thermal energy storage respectively and discharge power τ γr / τ Sc / τ Sd They respectively represent the unit operation and maintenance cost of the equipment.

4. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 1 is characterized in that: The constraints include energy conversion equipment constraints, renewable energy equipment constraints, energy storage equipment constraints and energy balance constraints; The energy conversion device constraints are, In the formula, is the output of the energy conversion device, The planned capacity of energy conversion devices is used to ensure that the output of various energy conversion equipment does not exceed its rated capacity; The constraints of renewable energy equipment are as follows: Considering the volatility and uncertainty of wind power and photovoltaic power, a robust optimization method based on duality theory is used to describe their output characteristics. In the formula, Indicates the actual output of renewable energy, including wind power output and photovoltaic output Cap γ represents the planned capacity of renewable energy, Forecast per unit value for renewable energy output, Indicates the upper and lower values ​​of the predicted value; represents the dual variable, is the model robustness control parameter; The energy storage device constraints include the charging and discharging power limit, energy storage capacity limit, and the timing coupling relationship between the charging and discharging states. In the formula, Cap θ Indicates the installed capacity of energy storage equipment, Indicates the remaining energy storage capacity. Indicates the maximum charge / discharge coefficient of energy storage, Indicates the minimum / maximum energy storage state coefficient, Refers to the charging and discharging efficiency coefficient of the energy storage device; The energy balance constraint is to ensure that the supply and demand of electrical energy and thermal energy in the system are balanced at every moment. In the formula, λ gt , hp and η gb Represent the energy conversion efficiency of gas turbine, electric heat pump and gas boiler respectively.

5. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 1 is characterized in that: The extreme safety constraints include defining a set of failure time uncertainties, defining a failure state indicator variable, a failure period transaction variable constraint, a failure period energy storage device operation constraint, a failure period energy conversion device constraint, and a failure period energy balance constraint; The failure time uncertainty set is defined by binary variables To represent the fault state of the system at different times, define They represent the connection state variables between the integrated energy system and the power grid or gas grid, 0 means disconnection from the external network, and 1 means connection with the external network. The uncertainty of system failure time can be characterized by 0-1 variables. The specific model is: In the formula, T represents the time of day, which is 24 hours, and the parameter R I represents the fault duration. The above model indicates that there may be continuous R I hours before the failure occurs, where the first item indicates the failure time is R I hours, the second item indicates that the failure time is continuous; The fault status indicator variable is defined to indicate whether the system is in a normal operating state. In the formula, when there is no fault in the power grid and gas grid, the system status indicator variable is 1. When a fault occurs in the power grid or gas grid at any time, the indicator variable is 0, and its subsequent time is also 0; The transaction variable constraint during the fault period limits the transaction volume between the system and the external power grid and natural gas network under the fault state. In the formula, M represents a large number; The energy storage device operation constraints during the fault period ensure the normal operation of the energy storage device during the fault period. In the formula, the energy storage device variable Indicates the operating variables under normal operation of the energy storage device. Indicates the operating variables during the fault period; The energy conversion device constraint during the fault period is to limit the output of the energy conversion device under the fault state. In the formula, It represents the operating power of the energy conversion equipment during the fault period, that is, The energy balance constraint during the fault period ensures that the system can still meet a certain proportion of load demand in the fault state. Where, L el and L hl They represent the proportion of electrical load and thermal load that can be reduced respectively, ranging from 0 to 1, where 0 means no load reduction is allowed and 1 means complete load reduction is allowed.

6. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 1 is characterized in that: The mathematical programming model of the planning model is: In the formula, y represents the decision variables, including the capacity and operation strategy of various equipment, z r Represents adjustment decisions under extreme events.

7. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 1 is characterized in that: The solution model uses a column and constraint generation method that includes step-by-step generation of key constraints to obtain a high-quality solution within a reasonable computational time.

8. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 7 is characterized in that: The constraint generation method comprises the following steps: First, set the initial value of the fault scenario W, the initial value of the number of iterations N = 1, the sub-problem objective function J = +∞, and the convergence accuracy d = 10 -3 , Second, in the determined set of failure scenarios Next, solve the main problem MP and obtain the comprehensive energy system planning and normal state operation plan y*. Third, according to the determined plan and the normal state running variable y*, solve the dual problem SP-I of the subproblem and obtain the fault scenario and the objective function value J, Fourth, determine whether J is less than v. If so, end the iteration; if not, update N=N+1, return to step 2.

9. The electric-gas-heat integrated energy microgrid optimization configuration system considering extreme events according to claim 8 is characterized in that: In the above-mentioned solving steps, the second and fourth are linear programming and mixed integer linear programming problems, respectively, which are solved by using Gurobi or CPLEX commercial solvers.

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