Random robust optimization method considering multiple uncertainties
By employing a three-stage stochastic robust optimization method combined with column and constraint generation algorithms, the problems of conservatism and high cost in traditional robust optimization methods are solved, achieving efficient and economical scheduling and resource optimization of integrated energy systems.
Patent Information
- Application Number
- CN202511999015.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-03-20
AI Technical Summary
Traditional two-stage robust optimization methods may lead to overly conservative and costly scheduling schemes in integrated energy systems, while also exhibiting high computational complexity and low efficiency, especially when dealing with multiple uncertain scenarios.
A three-stage stochastic robust optimization method is adopted to minimize the planning cost under the basic scenario, the power interaction cost under the worst electricity price scenario, and the operating cost of the integrated energy system. The model is solved by combining column and constraint generation algorithms to obtain the power output of each device.
It enhances the system's ability to mitigate uncertainty risks, balances economy and robustness, makes the model more consistent with engineering realities, and has high computational efficiency, effectively optimizing equipment operation and resource allocation.
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Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the field of comprehensive energy system optimization scheduling, and relates to a random robust optimization method considering multiple uncertainties. BACKGROUND
[0002] With the promotion of multi-energy complementation and energy cascade utilization of renewable energy and comprehensive energy systems (comprehensive energy systems), the efficiency, economy and low-carbon advantage of the comprehensive energy systems gradually appear. However, the comprehensive energy systems also face many challenges, such as renewable energy fluctuation, market price fluctuation and multiple uncertainties of multi-energy load demand change. In order to cope with these challenges, the robust optimization method has been widely used in the solution of such problems due to its characteristics of modeling without distributed uncertainty set. However, the traditional two-stage robust optimization method may lead to a conservative or infeasible scheduling scheme in actual operation. To solve this problem, a three-stage robust optimization model emerges as the times require, which can better simulate the gradual emergence of uncertainty by introducing a non-prospective decision-making process and provide a more flexible solution for actual scheduling operation. The traditional three-stage robust model may lead to a conservative and high-cost scheduling result due to the dependence on the worst-case scenario modeling. At the same time, the calculation complexity of such a model is high, and especially when multiple uncertainty scenarios need to be handled at the same time, the solving efficiency becomes a problem to be solved. SUMMARY
[0003] To solve the problems in the prior art, the application provides a random robust optimization method considering multiple uncertainties.
[0004] The technical scheme adopted by the application is as follows:
[0005] The application discloses a three-stage random robust optimization method considering multiple uncertainties, which is used for obtaining power output of each device in a comprehensive energy system, and comprises the following steps:
[0006] 1) A first-stage optimization model is established with the objective of minimizing the planning cost of the comprehensive energy system in a basic scenario;
[0007] 2) A second-stage optimization model is established with the objective of minimizing the power interaction cost of the comprehensive energy system and a regional power grid in a worst electricity price scenario;
[0008] 3) A third-stage optimization model is established with the objective of minimizing the operation cost of the comprehensive energy system;
[0009] 4) A three-stage random robust model is constructed based on the three optimization models in steps 1) to 3), and a column and constraint generation algorithm is used to solve the three-stage random robust model to obtain the power output of each device in the comprehensive energy system.
[0010] Compared with the prior art, the present application has the following beneficial effects:
[0011] The present application proposes a three-stage random robust optimization method considering multiple uncertainties. The system verification results show that in the operation optimization process of the integrated energy system, the multiple uncertainties of electricity price and source and load are considered, and the ability of the system to prevent uncertainty risks is improved. Compared with the traditional model, the proposed model can well balance the relationship between system economy and robustness, and the worst scenario is more in line with the engineering practice, which confirms the effectiveness and superiority of the proposed model. In addition, the double-layer column and constraint algorithm can efficiently solve the complex model established. BRIEF DESCRIPTION OF DRAWINGS
[0012] Figure 1 The flow chart of the column and constraint generation algorithm in the random robust optimization method considering multiple uncertainties of the present application;
[0013] Figure 2 The iteration process chart of the column and constraint generation algorithm in the random robust optimization method considering multiple uncertainties of the present application;
[0014] Figure 3 The power balance scheduling plan chart of the random robust optimization method considering multiple uncertainties of the present application;
[0015] Figure 4 The heat balance scheduling plan chart of the random robust optimization method considering multiple uncertainties of the present application;
[0016] Figure 5 The cold balance scheduling plan chart of the random robust optimization method considering multiple uncertainties of the present application;
[0017] Figure 6 The direct current channel scheduling plan chart of the random robust optimization method considering multiple uncertainties of the present application. DETAILED DESCRIPTION
[0018] The present application will be further described and explained with specific embodiments. The embodiments are only exemplary and do not circumscribe the scope of the present disclosure. The technical features of each embodiment in the present application can be combined accordingly without conflict.
[0019] This invention aims to find the economically optimal dispatch scheme for an integrated energy system under multiple uncertainties, including electricity prices and source load. The dispatch scheme includes the equipment capacity configuration of the IES (Integrated Energy System), the optimal operation plan for power supply equipment, the optimal operation plan for heating equipment, the optimal operation plan for cooling equipment, and the DC transmission plan. Therefore, this invention provides a three-stage stochastic robust optimization method considering multiple uncertainties. The method includes the following steps: Step 1: First-stage optimization: Establishing a first-stage optimization model with the objective of minimizing the planning cost of the integrated energy system under the basic scenario. The objective function and decision variables of the first-stage optimization model are as follows:
[0020]
[0021]
[0022] in, These are the decision variables for the first stage; The planning cost of an integrated energy system in the basic scenario; This refers to equipment within an integrated energy system; Indicates device The collection includes combined heat and power units, carbon capture equipment, electric-to-gas units, electric chillers, gas boilers, absorption chillers, energy storage batteries, and thermal storage tanks. This represents the mean vector of combined heat and power (CHP) equipment, carbon capture equipment, power-to-gas (HPC) units, electric chillers, gas boilers, or absorption chillers. This indicates the carbon capture capacity of combined heat and power (CHP) equipment, carbon capture equipment, power-to-gas conversion units, electric chillers, gas boilers, or absorption chillers. This indicates the power investment factor for energy storage batteries or thermal storage tanks; This represents the power of device k; The discount rate; This represents the lifespan of device k; This represents the maximum output power of the combined heat and power unit. This represents the maximum power output of the carbon capture equipment. This represents the maximum power of the electro-pneumatic unit; This represents the maximum cooling power of the electric chiller. This represents the maximum thermal power of the gas-fired boiler. This represents the maximum cooling power of the absorption chiller. The CO2 value captured by the energy storage battery; The CO2 value captured by the heat storage tank; This represents the maximum power of the energy storage battery. This is the maximum power of the heat storage tank; is a binary variable of the t-th moment of the transaction between the integrated energy system and the energy storage battery; is a binary variable of the t-th moment of the transaction between the integrated energy system and the heat storage tank; is a binary variable of the t-th moment of the transaction between the integrated energy system and the regional power grid.
[0023] Step two: second stage optimization: a second stage optimization model is established to minimize the power interaction cost between the integrated energy system and the regional power grid under the worst electricity price scenario.
[0024] The objective function and decision variables of the second stage optimization model are as follows:
[0025]
[0026]
[0027] wherein, is a set of electricity purchase price and electricity sale price of the integrated energy system from the regional power grid at the t-th moment under consideration of uncertainty, . is the electricity purchase price of the integrated energy system from the regional power grid at the t-th moment under consideration of uncertainty, is the electricity sale price of the integrated energy system to the regional power grid at the t-th moment under consideration of uncertainty, is a set of price uncertainty considering uncertainty; is a second stage decision variable; is the power interaction cost between the integrated energy system and the regional power grid under the worst electricity price scenario; is the electricity purchase power of the integrated energy system from the regional power grid at the t-th moment; is the electricity sale power of the integrated energy system to the regional power grid at the t-th moment.
[0028] The set of price uncertainty considering uncertainty composed of uncertain price variables is a set of price sale uncertainty including and a set of price purchase uncertainty .
[0029] The set of price purchase uncertainty is:
[0030]
[0031] wherein, is the basic data of the electricity purchase price of the integrated energy system from the regional power grid at the t-th moment; , is the positive and negative deviation binary variable of the electricity purchase price of the integrated energy system from the regional power grid at the t-th moment; , is the positive or negative deviation power of the electricity purchase price of the integrated energy system from the regional power grid at time t; is the electricity purchase price uncertainty budget.
[0032]
[0033] wherein, is the base data of the electricity sale price of the integrated energy system to the regional power grid at time t; , is the positive or negative deviation binary variable of the electricity sale price of the integrated energy system to the regional power grid at time t; , is the positive or negative deviation power of the electricity sale price of the integrated energy system to the regional power grid at time t; is the electricity sale price uncertainty budget.
[0034] Step three: third stage optimization: a third stage optimization model is established with the objective of minimizing the operation cost of the integrated energy system. The objective function and decision variables of the third stage optimization model are:
[0035]
[0036] wherein, is the probability of the occurrence of the s-th scenario; is the scenario probability uncertainty space; is the source and load output uncertainty space; is the set of uncertainty variables; is the third stage decision variable; is the total number of scenarios; is the gas purchase cost of natural gas; is the energy storage charging and discharging cost; is the DC channel power deviation penalty cost.
[0037] In this embodiment, wherein the operation cost of the renewable energy integrated energy system includes the gas purchase cost of natural gas , the energy storage charging and discharging cost , and the DC channel power deviation penalty cost , and the specific cost calculation formula is:
[0038]
[0039]
[0040]
[0041] wherein, is the gas price of natural gas, is the dispatching period; Let t be the volume of natural gas consumed by the combined heat and power unit. Let t be the volume of natural gas consumed by the gas-fired boiler. Let t be the volume of natural gas consumed in the electro-gas conversion. This is the energy storage depreciation factor. Let t be the charging power of the energy storage battery at time t; Let t be the discharge power of the energy storage battery at time t; Let t be the charging power of the heat storage tank at time t; Let t be the discharge power of the heat storage tank at time t; This is the DC power offset penalty coefficient. Let t be the DC channel transmission power at time t; Let be the net load at the receiving end of the DC channel at time t. The above scenario has a probability uncertainty space. for:
[0042]
[0043] in, Let be the probability of the s-th scene occurring. The number of scenes after clustering; Let be the probability of occurrence at the initial moment of the s-th scene; The allowable deviation for the 1-norm condition; for - Norm conditional allowable deviation; the calculation formula is:
[0044]
[0045] in, This represents the total number of scenarios, i.e., the sum of the number of scenarios before clustering. The confidence level is the 1-norm. for - Confidence level of the norm.
[0046] Uncertainty space of source load output for:
[0047]
[0048] in, For wind power uncertainties; For uncertainties in photovoltaics; For uncertainties in electrical load; For uncertainties in heat load; The variable is the uncertainty of the cooling load.
[0049] Step 4: Construct a three-stage stochastic robust model based on the three optimization models from Step 1 to Step 3, and then use the column and constraint generation algorithm to solve the three-stage stochastic robust model to obtain the power output of each device in the integrated energy system.
[0050] For step four, the following will further explain three points: the objective function used in the optimization solution process, the constraints, and the flexibility requirement constraints among the constraints:
[0051] 1) Objective function
[0052] Specifically, the first stage involves deciding on the integrated energy system's operation plan and unit investment scale before uncertainties occur. The second stage, based on the decisions made in the first stage, considers the uncertainty of regional grid trading prices and decides on the power exchange scheme between the integrated energy system and the regional grid. The third stage, based on the decisions from the first two stages, first identifies the worst-case source-load probability distribution of the integrated energy system, then identifies the worst-case output of the integrated energy system's source loads, and formulates the optimal unit operation strategy. These three stages are continuously optimized alternately until the decisions made in the first stage satisfy the requirements of all scenarios within the uncertainty sets of the second and third stages. The formula for the objective function of the three-stage stochastic robust model of the integrated energy system is as follows:
[0053]
[0054] 2) Constraints
[0055] The integrated energy system mainly consists of four parts: a renewable energy power generation (REPG) subsystem, a CHP-P2G-CCS subsystem, a DC transmission channel subsystem, and other auxiliary equipment. The REPG subsystem includes wind turbines and photovoltaic power. The CHP-P2G-CCS subsystem includes key equipment such as combined heat and power (CHP) units, carbon capture equipment, and power-to-gas conversion units. Auxiliary equipment includes gas-fired boilers (GB), absorption chillers (AC), electric chillers (EC), energy storage batteries (ESS), and thermal storage tanks (HSD). Furthermore, the integrated energy system also interacts with the regional power grid. The regional power grid contains a certain amount of wind, solar, and thermal power units to provide energy support for the integrated energy system's power transmission plan, but the net support power is zero during the calculation period to ensure the self-sufficiency rate of the integrated energy system for DC power transmission. The specific constraints during operation include: CHP unit constraints coupled with P2G-CCS, gas boiler constraints, energy storage equipment constraints, EC constraints, AC constraints, DC transmission channel constraints, carbon trading cost constraints, trading constraints with the regional power grid, and power balance constraints.
[0056] Next, the three-stage stochastic robust model is solved using a column and constraint generation algorithm, including:
[0057] Step 1: Initialize the outer CCG problem, including the upper bound of the outer CCG problem. and the lower realm ,make , and initialize the number of iterations. At the same time, determine the convergence termination condition threshold. =0.01.
[0058] Step 2: Solve the outer CCG principal problem to obtain the optimal solution. Then update the lower bound of the outer CCG problem, even if ,in, This serves as the lower bound for the updated outer CCG problem. These are intermediate variables that characterize the problems in the second and third stages of the iteration process; It is a matrix with constant coefficients.
[0059] Step 3: Solve the inner CCG problem. Initialize the inner CCG problem by initializing its upper bound. and the lower realm ,make , and initialize the number of iterations. At the same time, determine the convergence termination condition threshold. .
[0060] Step 4: Solve the inner CCG principal problem to obtain the optimal solution. Update the lower bound of the inner CCG problem so that ,in, This serves as the lower bound for the updated inner CCG problem. Electricity price matrix, These are intermediate variables that characterize the third-stage problem during the iteration process.
[0061] Step 5: Solve the inner CCG subproblem to obtain the optimal solution. Update the upper bound of the inner CCG problem so that ,in, This is the upper bound of the updated inner CCG problem. It is a matrix with constant coefficients. It is a matrix with constant coefficients. For the uncertainty of electricity prices,
[0062] Let's recheck whether the convergence condition is met at this point. If the convergence condition is met, the inner CCG problem is terminated (i.e., steps 3-5), and step 7 is continued; otherwise, step 6 is continued.
[0063] Step 6: Update the inner CCG master problem and update the iteration count of the inner CCG problem, so that the updated iteration count of the inner CCG problem is... for And return to step 4.3).
[0064] Step 7: Check if the convergence condition is met at this point. If the convergence condition is met, the solution process ends, and the power output of each device in the integrated energy system is obtained; otherwise, the outer CCG master problem is updated, and the iteration number of the outer CCG problem is also updated, so that the iteration number of the updated outer CCG problem is... for Then return to step 2.
[0065] This invention uses data from a real IES demonstration project in Inner Mongolia, China, to construct a calculation example for verification. The IES equipment capacity configuration results are shown in Table 1. Among them, the configuration capacity of electric energy storage and thermal storage tanks is the highest, both reaching 2000MWh. Among other equipment configurations, the CHP unit has the highest configuration capacity.
[0066] Table 1. Results of Integrated Energy System Equipment Capacity Configuration
[0067]
[0068] In the scheduling process, the iterative solution process is as follows: Figure 2 As shown, REIRES' scheduling plan is as follows: Figures 3-6 The figures show the optimal operating plans for power supply, heating, and cooling equipment, as well as the DC transmission plans for each scenario. This optimized scheduling method fully considers multi-energy complementarity, energy supply and demand matching, and system economy, ensuring the optimal operating state of different energy equipment at each time period, while rationally allocating electricity and heat resources to improve energy utilization efficiency. Furthermore, through optimized scheduling of DC channels, not only are cross-regional power transmission losses reduced, but the synergistic complementarity between different energy networks is also enhanced, achieving the goal of efficient and low-carbon energy scheduling. The application of this method can effectively improve the stability, economy, and renewable energy absorption capacity of integrated energy systems, providing theoretical support and practical basis for the development of future smart energy systems.
[0069] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. Those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.
Claims
1. A stochastic robust optimization method considering multiple uncertainties, characterized in that, The method is used to obtain the power output of each device in an integrated energy system, and the method includes the following steps: 1) To minimize the planning cost of the integrated energy system under the basic scenario, a first-stage optimization model is established; 2) To minimize the power interaction cost between the integrated energy system and the regional power grid under the worst-case electricity price scenario, a second-stage optimization model is established; 3) To minimize the operating cost of the integrated energy system, a third-stage optimization model is established; 4) Construct a three-stage stochastic robust model based on the three optimization models in steps 1)-3), and then use the column and constraint generation algorithm to solve the three-stage stochastic robust model to obtain the power output of each device in the integrated energy system.
2. The stochastic robust optimization method considering multiple uncertainties according to claim 1, characterized in that, In step 1), the objective function and decision variables of the first-stage optimization model are as follows: ; ； in, These are the decision variables for the first stage; The planning cost of an integrated energy system in the basic scenario; This refers to equipment within an integrated energy system; Indicates equipment The collection includes combined heat and power units, carbon capture equipment, electric-to-gas units, electric chillers, gas boilers, absorption chillers, energy storage batteries, and thermal storage tanks. This represents the mean vector of combined heat and power (CHP) equipment, carbon capture equipment, power-to-gas (HPC) units, electric chillers, gas boilers, or absorption chillers. This indicates the carbon capture capacity of combined heat and power (CHP) equipment, carbon capture equipment, power-to-gas conversion units, electric chillers, gas boilers, or absorption chillers. This indicates the power investment factor for energy storage batteries or thermal storage tanks; This represents the power of device k; The discount rate; This represents the lifespan of device k; This represents the maximum output power of the combined heat and power unit. This represents the maximum power output of the carbon capture equipment. This represents the maximum power of the electro-pneumatic unit; This represents the maximum cooling power of the electric chiller. This represents the maximum thermal power of the gas-fired boiler. This represents the maximum cooling power of the absorption chiller. The CO2 value captured by the energy storage battery; The CO2 value captured by the heat storage tank; This represents the maximum power of the energy storage battery. This is the maximum power of the heat storage tank; For the binary variable at time t of the integrated energy system and energy storage battery transaction; For the binary variable at time t of the transaction between the integrated energy system and the thermal storage tank; This is a binary variable at time t representing the transaction between the integrated energy system and the regional power grid.
3. The stochastic robust optimization method considering multiple uncertainties according to claim 1, characterized in that, In step 2), the objective function and decision variables of the second-stage optimization model are as follows: ； ； in, Let t be the set of electricity purchase prices from and sales prices to the regional grid for the integrated energy system at time t, taking uncertainty into account. ; The electricity purchase price of the integrated energy system from the regional grid at time t, taking into account uncertainties; Considering uncertainties, the electricity price sold by the integrated energy system to the regional power grid at time t; For the set of uncertainties in electricity prices, taking into account uncertainty; These are the decision variables for the second stage. The power interaction cost between the integrated energy system and the regional power grid under the worst-case electricity price scenario; The power purchased by the integrated energy system from the regional power grid at time t; Let t be the power output of the integrated energy system sold to the regional power grid at time t.
4. The stochastic robust optimization method considering multiple uncertainties according to claim 3, characterized in that, The set of uncertainties in electricity prices considering uncertainty Includes sets of uncertain electricity prices and selling prices And the set of uncertain electricity purchase prices The set of uncertain electricity purchase prices for: ； in, This provides the basic data on the electricity purchase price from the regional power grid by the integrated energy system at time t. , Let be the positive and negative binary variable representing the electricity purchase price of the integrated energy system from the regional power grid at time t; , Let t be the positive and negative offset power of the integrated energy system's electricity purchase price from the regional power grid at time t; Budgeting for uncertainties in electricity purchase prices; The set of uncertain electricity prices for: ； in, This provides the basic data for the electricity price sold by the integrated energy system to the regional power grid at time t. , Let be the positive and negative deviation binary variable of the electricity price sold by the integrated energy system to the regional power grid at time t; , Let t be the positive and negative offset power of the electricity price sold by the integrated energy system to the regional power grid at time t; Budgeting for uncertainties in electricity sales prices.
5. The stochastic robust optimization method considering multiple uncertainties according to claim 1, characterized in that, In step 3), the objective function and decision variables of the third-stage optimization model are as follows: ； in, Let be the probability of the s-th scene occurring; For the scenario probability uncertainty space; The uncertainty space of the source load output; It is a set of uncertain variables; These are the decision variables for the third stage; Total number of scenes; The cost of purchasing natural gas; Cost of energy storage charging and discharging; The cost of DC channel power offset penalty.
6. The stochastic robust optimization method considering multiple uncertainties according to claim 5, characterized in that, The cost of purchasing natural gas for: ； in, For the price of natural gas, The scheduling period; Let t be the volume of natural gas consumed by the combined heat and power unit. Let t be the volume of natural gas consumed by the gas-fired boiler. Let t be the volume of natural gas consumed in the electro-gas conversion. The above refers to the cost of energy storage charging and discharging. for: ； in, This is the energy storage depreciation factor; Let t be the charging power of the energy storage battery at time t; Let t be the discharge power of the energy storage battery at time t; Let t be the charging power of the heat storage tank at time t; Let t be the discharge power of the heat storage tank at time t; DC channel power offset penalty cost for: ； in, This is the DC power offset penalty coefficient; Let t be the DC channel transmission power at time t; Let t be the net load at the receiving end of the DC channel.
7. The stochastic robust optimization method considering multiple uncertainties according to claim 5, characterized in that, The scenario probability uncertainty space for: ； in, The number of scenes after clustering; Let be the probability of occurrence at the initial moment of the s-th scene; The allowable deviation for the 1-norm condition; for - Norm condition allowable deviation; ； in, Total number of scenes; The confidence level is the 1-norm. for - Confidence level of the norm; The uncertainty space of the source load output for: ； in, For wind power uncertainties; For uncertainties in photovoltaics; For uncertainties in electrical load; For uncertainties in heat load; The variable is the uncertainty of the cooling load.
8. The stochastic robust optimization method considering multiple uncertainties according to claim 1, characterized in that, In step 4), the objective function of the three-stage stochastic robust model is: ； in, These are the decision variables for the first stage; The planning cost of an integrated energy system in the basic scenario; Let t be the set of electricity purchase prices from the grid and electricity sales prices to the grid for the integrated energy system at time t, taking uncertainty into account. ; The electricity purchase price from the grid by the integrated energy system at time t, taking uncertainty into account. The electricity purchase price from the grid by the integrated energy system at time t, taking uncertainty into account. Considering uncertainties, the integrated energy system's electricity sales price to the grid at time t; These are the decision variables for the second stage. The power interaction cost between the integrated energy system and the power grid under the worst-case electricity price scenario; Let be the probability of the s-th scene occurring; For the scenario probability uncertainty space; The uncertainty space of the source load output; For a set of uncertain variables, These are the decision variables for the third stage; Total number of scenes; The cost of purchasing natural gas; Cost of energy storage charging and discharging; The cost of DC channel power offset penalty.
9. The stochastic robust optimization method considering multiple uncertainties according to claim 1, characterized in that, Step 4), which involves using a column and constraint generation algorithm to solve the three-stage stochastic robust model, includes: 4.1) Initialize the outer-layer CCG problem, including its upper bound. and the lower realm ,make , Reinitialize the number of iterations for the outer CCG problem. And determine the convergence termination condition threshold. ; 4.2) Solve the outer CCG principal problem to obtain the optimal solution. Update the lower bound of the outer CCG problem, i.e. ,in, This serves as the lower bound for the updated outer CCG problem. It is a matrix with constant coefficients. These are intermediate variables that characterize the second-stage problem and the third-stage problem during the iteration process; 4.3) Initialize the inner CCG problem, including its upper bound. and the lower realm ,Right now , Initialize the number of iterations for the inner CCG problem. And determine the convergence termination condition threshold. ; 4.4) Solve the inner CCG principal problem to obtain the optimal solution. Update the lower bound of the inner CCG problem, i.e. ,in, This serves as the lower bound for the updated inner CCG problem. Electricity price matrix, These are intermediate variables characterizing the third-stage problem during the iteration process; 4.5) Solve the inner CCG subproblem to obtain the optimal solution. Update the upper bound of the inner CCG problem so that ,in, This is the upper bound of the updated inner CCG problem. It is a matrix with constant coefficients. It is a matrix with constant coefficients. Uncertainty about electricity prices Check if the convergence condition is met. If the convergence condition is met, proceed to step 4.7; otherwise, proceed to step 4.
6. 4.6) Update the inner CCG master problem and update the iteration count of the inner CCG problem, so that the updated iteration count of the inner CCG problem is... for And return to step 4.3). 4.7) Check whether the convergence condition is met at this point. If the convergence condition is met, the solution process ends, and the power output of each device in the integrated energy system is obtained; otherwise, the outer CCG master problem is updated, and the iteration number of the outer CCG problem is also updated, so that the iteration number of the updated outer CCG problem is... for And return to step 4.2).