Generator standby and demand response collaborative two-stage robust scheduling method for coping with source load uncertainty

Abstract

The invention discloses a generator standby and demand response collaborative two-stage robust scheduling method for dealing with source load uncertainty. The method comprises the following steps: collecting system information and constructing a box type source load uncertainty set; on the basis, a two-stage robust optimization model with the purpose of minimizing the traditional generation power is constructed, in the first stage, generator starting and stopping, output planning, standby capacity, energy storage, inter-network power and demand response calling are optimized in a collaborative mode, and in the second stage, standby calling, wind and light abandoning and load adjusting are optimized; and carrying out iterative solution by adopting a column and constraint generation algorithm, optimizing a pre-decision through a main problem, identifying a worst scene through a sub-problem, and generating a robust optimal scheduling scheme. According to the method, through collaborative optimization of generator standby and demand response, source-load dual uncertainty is effectively dealt with, and the renewable energy consumption level and the overall operation economy are remarkably improved while the operation reliability of the system is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of microgrid optimization scheduling technology, and in particular relates to a two-stage robust scheduling method that coordinates generator standby and demand response to address source-load uncertainty. Background Technology

[0002] As the global energy structure accelerates its transformation towards low-carbon and clean energy, the penetration rate of renewable energy sources, such as wind and solar power, in the power system has significantly increased. However, the strong intermittency of wind and solar power generation and the dynamic fluctuations in load demand pose severe scheduling challenges to microgrid systems. As a hub connecting distributed energy resources and end users, the optimal scheduling of microgrids must simultaneously consider the triple objectives of economic efficiency, reliability, and environmental benefits. In this context, demand response (DR) mechanisms, by guiding users to flexibly adjust their electricity consumption behavior, have become a key means of mitigating supply and demand fluctuations; while reserve collaborative optimization, by integrating the interactive capacity of generator units, energy storage devices, and distribution networks, provides the system with risk hedging capabilities. At the same time, the introduction of carbon trading mechanisms further promotes the low-carbon evolution of energy systems, but the coupling of multiple constraints also significantly increases the complexity of scheduling models. How to achieve economical and efficient operation of microgrids under the dual uncertainty of source and load has become a core issue that urgently needs to be addressed in the field of smart grids.

[0003] Existing research has explored microgrid optimization scheduling paths from multiple dimensions. Regarding flexible resource coordination, some results have verified the role of reserve capacity sharing in improving system flexibility by balancing demand response and the economic and environmental goals of carbon trading through two-stage robust optimization. For handling source-load uncertainty, two mainstream methods have been developed: stochastic optimization based on probability distributions (such as data-driven cascade hydro-solar-storage joint scheduling) and robust optimization based on sets (such as box-type uncertainty sets characterizing wind and solar fluctuations). The former relies on high-quality historical data to build probabilistic models, while the latter ensures the worst-case adaptability of the solution by setting uncertainty set boundaries. To compensate for the limitations of single methods, hybrid strategies are gradually emerging: debulking robust optimization integrates stochastic scenarios and uncertainty set boundaries, and scenario generation techniques such as Latin hypercube sampling (LHS) improve computational feasibility by efficiently extracting representative scenarios.

[0004] At the model solution level, the core challenge of robust optimization lies in the conservatism of the equilibrium solution and computational efficiency. Traditional Benders decomposition faces convergence bottlenecks in high-dimensional problems, while the Column and Constraint Generation (C&CG) algorithm significantly improves solution efficiency through a master-subproblem iterative mechanism. Some studies further introduce dynamic programming into two-stage robust models and handle data-driven uncertainties through interval robust optimization. It is worth noting that current research still has significant limitations: most models do not simultaneously integrate demand response, reserve coordination, and source-load uncertainty handling under the unified technical objective of minimizing traditional power generation, making it difficult to fully leverage the complementary potential of flexible resources in reducing dependence on traditional power generation; response strategies to intraday real-time fluctuations are simplistic, lacking a rolling optimization correction mechanism aimed at minimizing adjustments to traditional power generation; and the model construction often neglects refined modeling of key technical constraints such as the feasible region of reserve capacity, energy storage cycle life loss, and distribution network interactive power safety limits, affecting the practical operability of the solutions. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a two-stage robust scheduling method that coordinates generator standby and demand response to address source-load uncertainty, thereby resolving the issues present in the prior art.

[0006] To achieve the above objectives, this invention provides a two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty, comprising:

[0007] S1. Collect system information in the power system, including generator operating parameters, historical load data, wind and solar power output prediction error distribution, demand response resource information, and network topology information; based on the wind and solar power output prediction error distribution and the historical load data, construct a box-type source-load uncertainty set, and quantify the feasible region of generator reserve capacity and the boundary of demand response potential.

[0008] S2. Based on the system information, the uncertain set of box-type source loads, the feasible region of generator reserve capacity and the boundary of demand response potential, a two-stage robust optimization model is constructed with the goal of minimizing the traditional power generation of the system, which is used to make collaborative optimization decisions on generator reserve and demand response resources.

[0009] S3. The column and constraint generation algorithm is used to iteratively solve the two-stage robust optimization model to generate a robust optimal scheduling scheme. The column and constraint generation algorithm optimizes the first-stage decision through the main problem and identifies the worst uncertainty scenario through sub-problems to evaluate and correct the decision.

[0010] Optionally, the first-stage decision content of the two-stage robust optimization model includes generator start-up and shutdown status, basic output plan, reserve capacity, energy storage charging and discharging plan, power exchange with the distribution network, and demand response dispatch scheme.

[0011] Optionally, the constraints for the first stage of decision-making include generator output constraints, gas turbine ramping constraints, gas turbine start-up and shutdown constraints, gas turbine reserve capacity constraints, price-based demand response constraints, power purchase and sale constraints with the distribution network, energy storage operation constraints, and power balance constraints.

[0012] Optionally, the price-based demand response constraint is:

[0013] ;

[0014] in, The maximum value of the load using PDR. For user-involved load adjustments, and They are respectively The change in electricity price at any given time and its maximum value. This is the time-of-use electricity price change matrix. The number of hours in a day is 24 hours. This is the elasticity matrix of electricity volume and price.

[0015] Optionally, the energy storage operation constraints are:

[0016] ;

[0017] in, It is a 0-1 integer variable, representing the energy storage charging and discharging state; These are the maximum and minimum charging power values ​​for energy storage. These represent the maximum and minimum values ​​of the energy storage discharge power; These represent the initial capacity of the energy storage at the start of the scheduling process, as well as the maximum and minimum remaining capacity allowed during the scheduling process. This is the change over time, with a value of 1. This refers to the energy storage charging and discharging efficiency.

[0018] Optionally, the second-stage decision-making content of the two-stage robust optimization model includes reserve capacity call-up, wind and solar curtailment, and controllable load adjustment.

[0019] Optionally, the constraints that the second-stage decision-making needs to meet include power balance constraints, generator adjustment reserve capacity constraints, load shedding capacity constraints, wind power output constraints, photovoltaic power output constraints, and load fluctuation constraints.

[0020] Optionally, the load shedding capacity constraint is:

[0021] ;

[0022] in, The percentage of load that can be sheared. This represents the actual power output of the day's load. This refers to the daily load shedding.

[0023] Optionally, the iterative solution process using the column and constraint generation algorithm includes:

[0024] Solve the simplified moderator problem to obtain the initial feasible solution for the first stage of decision-making; set the lower bound of the objective function to negative infinity and the upper bound to positive infinity; set the convergence threshold and initialize the iteration counter to 0;

[0025] Solve the current principal problem that includes the cutting plane constraints generated by the historical iterations, obtain the first-stage decision solution and the corresponding objective function value, and update the global lower bound accordingly;

[0026] Fix the decision solution of the first stage, solve the sub-problems within the uncertain set of the box source load, and search for the worst uncertainty scenario that maximizes the total additional power generation required in the second stage.

[0027] Based on the solution results of the subproblems, construct Benders cut constraints and add them to the constraint set of the main problem;

[0028] Update the global upper bound using the results of the subproblems; determine whether the absolute value of the difference between the global upper bound and the global lower bound is less than or equal to the convergence threshold. If it is satisfied, terminate the iteration and output the current first-stage decision solution as the final robust optimal scheduling scheme; otherwise, update the iteration counter and resolve the current main problem for the next iteration.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] This invention proposes a two-stage robust scheduling method that coordinates generator reserve and demand response to address source-load uncertainty. Through a deep collaborative mechanism between demand response and generator reserve, it employs box-type uncertainty set modeling for uncertain parameters, significantly improving system operating efficiency and robustness while overcoming computational efficiency bottlenecks. At the technical performance level, with the unified goal of minimizing total traditional power generation, it collaboratively optimizes all dimensions of physical operation elements, including power generation planning, reserve reservation, demand response deployment, energy storage scheduling, and power interaction with the distribution network. Wind and solar curtailment and load shedding are used as technical adjustment means to ensure real-time power balance. Simulation verification shows that the collaborative scheduling mechanism of demand response and generator reserve achieves complementary operation between flexible load adjustment and generator-side reserve resources in reducing reliance on traditional power generation. Attached Figure Description

[0031] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0032] Figure 1 This is a flowchart of the column and constraint generation algorithm according to an embodiment of the present invention;

[0033] Figure 2 This is the multi-source coordinated operation state of the microgrid under scenario 1 of this embodiment of the invention;

[0034] Figure 3 This is the multi-source coordinated operation state of the microgrid under scenario 2 of this embodiment of the invention;

[0035] Figure 4 This is the multi-source coordinated operation state of the microgrid under scenario 3 of this embodiment of the invention;

[0036] Figure 5 This is the multi-source coordinated operation state of the microgrid under scenario 4 of this embodiment of the invention. Detailed Implementation

[0037] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0039] Example 1

[0040] This embodiment provides a two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty, including:

[0041] S1. Data Acquisition and Uncertainty Modeling Stage. This stage involves collecting generator operating parameters (ramp rate, output limits), historical load data, power generation prediction error distribution for new energy sources (wind / solar), demand response resources (reducible / transferable load, compensation costs), and network topology information from the power system. Based on historical prediction errors, a box-shaped source-load uncertainty set is constructed to quantify the feasible region of generator reserve capacity and the boundary of demand response potential.

[0042] S2. Two-stage robust optimization model construction stage. Based on the system information obtained in step S1, a two-stage robust optimization model is constructed with the minimization of the system's traditional power generation as the core objective. This model aims to systematically solve the technical challenges of power balance and operational stability under a high proportion of renewable energy access by coordinating the pre-scheduling decision in the first stage with the real-time scheduling decision in the second stage.

[0043] The first phase focuses on building robust day-ahead generation and load plans. By optimizing generator start-up and shutdown, base output, demand response dispatch, energy storage scheduling, and power exchange with the distribution network, the core objective is to minimize the planned total power generation of traditional generator units while meeting all physical safety constraints, so as to reserve sufficient reserve regulation capacity and increase the space for renewable energy consumption.

[0044] The second phase addresses the real-time deviation scenario between renewable energy and load fluctuations. Its core objective, based on the decisions made in the first phase, is to maintain real-time power balance and safe operation of the system with minimal additional power generation. This is achieved by optimizing reserve capacity deployment, wind and solar curtailment, and controllable load adjustment strategies to ensure power supply reliability and maximize the utilization of clean energy. Through phased optimization of these technical objectives, the model fundamentally enhances the system's resilience and energy efficiency in the face of source-load uncertainties.

[0045] Regarding constraint settings, the first phase mainly considers the following constraints: 1) Generator output constraints to ensure generators operate within safe capacity and prevent equipment overload damage; 2) Generator ramping constraints to limit the rate of change in output between adjacent time periods and avoid exceeding mechanical stress limits; 3) Generator start-up and shutdown constraints to ensure minimum continuous operation / downtime and reduce losses from frequent start-up and shutdown of units; 4) Generator reserve capacity constraints to reserve adjustment capacity to cope with uncertainties and maintain system dynamic stability; 5) Demand response constraints to prevent excessive load reduction from affecting users' normal electricity consumption; 6) Power purchase and sale constraints with the distribution network to prevent tie line power exceeding limits from causing disconnection accidents; 7) Energy storage operation constraints to ensure energy storage output is within the equipment's safe range; 8) Power balance constraints to maintain the instantaneous balance of system exchange and ensure system stability.

[0046] The second phase mainly considers the following constraints: 1) Power balance constraints, maintaining the instantaneous balance of system exchange and ensuring system stability; 2) Generator adjustment reserve capacity constraints, limiting the real-time output adjustment range of the unit to not exceed its reserved reserve capacity, and preventing equipment overload; 3) Load shedding capacity constraints, limiting the maximum load shedding amount, and preventing excessive load shedding from causing social losses; 4) Wind power and photovoltaic output constraints and load constraints, defining the feasible domain of wind and solar power output and load fluctuations, and describing the worst-case scenario.

[0047] S3. Model Solving and Robust Decision Generation Stage. The Column and Constraint Generation Algorithm (C&CG) is used to iteratively solve the scheduling model. The specific implementation process is as follows:

[0048] Step 1: Generate initial feasible solutions and set iteration parameters. First, solve the simplified moderator problem based on a deterministic scenario (such as the predicted mean values ​​of wind, solar, and load) to obtain the first-stage decision. Initialize the lower bound of the objective function LB = -∞ and the upper bound UB = +∞, and set the convergence threshold e and the iteration counter k = 0.

[0049] Step 2: Solve the main problem. Solve the current main problem (including the cutting plane constraints generated in previous iterations) to obtain the first-stage decision. and target value (Lower bound of traditional power generation). Update global lower bound. This solution represents the current optimal "defensive" pre-scheduling scheme.

[0050] Step 3: Solve the subproblems. Fix the first-stage decision. Solve the maximization-minimization subproblem: search within the uncertain source-load set U for the worst-case scenario that maximizes the total additional power generation required in the second stage. The subproblem outputs the total additional power generation required for the second stage in this scenario. and real-time scheduling strategies .

[0051] Step 4: Generate the cutting plane constraint. Construct the Benders Cut based on the sub-problem results. This cutting plane represents the constraint that, if the first-stage decision... Deviation from the current solution The worst-case scenario The total additional generating capacity required to be mobilized is at least [amount missing]. Add the cutting plane to the main problem and eliminate inefficient solutions.

[0052] Step 5: Update the upper bound and convergence criteria. Update the global upper bound. ,like Then output the current solution. If the solution is the robust optimal solution, then let k = k + 1 and return to Step 2 for the next iteration.

[0053] The two-stage robust optimization model established in step S2, with the objective of minimizing traditional power generation, is characterized as follows:

[0054] ;

[0055] ;

[0056] ;

[0057] In the formula, and These are two-stage optimization objectives. This is an uncertain set, including uncertainties in wind power output, photovoltaic power output, and load fluctuations. For the microgrid system to dispatch traditional power generation capacity, To provide power to the generator, For user-involved load adjustments, For energy storage charging and discharging power, For the power purchased and sold in the distribution network, For the upward and downward reserve capacity of the gas turbine; It is a 0-1 integer variable representing the start-up and shutdown status of the gas turbine; All variables are integers between 0 and 1, representing the on and off states of the gas turbine, respectively. In order to adjust the traditional power generation capacity within the day, This is the penalty coefficient for backup generator call-up. This is the wind curtailment penalty coefficient. This is the penalty coefficient for discarded light. This is the load shedding penalty factor. To adjust the output of the gas turbine upwards and downwards. For the actual output of wind power during the day, This represents the actual wind power consumption within the day. To contribute to the actual power of photovoltaics within the day, This represents the actual photovoltaic power consumption within the day. This refers to the daily load shedding.

[0058] The various constraints described in step S2 are characterized as follows:

[0059] Phase 1 constraints:

[0060] 1) Generator output constraints;

[0061] ;

[0062] In the formula, These represent the maximum and minimum output of the generator.

[0063] 2) Gas turbine ramping constraints;

[0064] ;

[0065] In the formula, This represents the gas turbine's uphill and downhill ramp rate per hour.

[0066] 3) Gas turbine start-stop constraints;

[0067] ;

[0068] 4) Gas turbine reserve capacity constraints;

[0069] ;

[0070] In the formula, This represents the maximum upward and downward reserve capacity of the gas turbine.

[0071] 5) Price-based demand response constraints;

[0072] ;

[0073] In the formula The maximum value of the load using PDR. and They are respectively The change in electricity price at any given time and its maximum value. This is the time-of-use electricity price change matrix. The normalized matrix information for electricity consumption and pricing is already included in the elasticity matrix, which is based on a 24-hour daily timeframe. middle.

[0074] 6) Constraints on power purchase and sale with the distribution network;

[0075] ;

[0076] In the formula This is a 0-1 integer variable representing the electricity purchase and sale status of the microgrid; This represents the maximum value of electricity purchase and sale for the microgrid.

[0077] 7) Constraints on energy storage operation;

[0078] ;

[0079] In the formula It is a 0-1 integer variable, representing the energy storage charging and discharging state; These are the maximum and minimum charging power values ​​for energy storage. These represent the maximum and minimum values ​​of the energy storage discharge power; These represent the initial capacity of the energy storage at the start of the scheduling process, as well as the maximum and minimum remaining capacity allowed during the scheduling process. This is the change over time, with a value of 1. This refers to the energy storage charging and discharging efficiency.

[0080] 8) Power balance constraints;

[0081]

[0082] In the formula These are the predicted outputs for wind power, solar power, and load, respectively.

[0083] Second-stage constraints:

[0084] 1) Power balance constraints;

[0085] ;

[0086] 2) Adjusting generator standby capacity constraints;

[0087] ;

[0088] 3) Load shedding capacity constraints;

[0089] ;

[0090] In the formula, The percentage of load that can be sheared. This represents the actual power output of the daily load.

[0091] 4) Output constraints and load constraints of wind power and photovoltaic power

[0092] .

[0093] The model solving stage described in step S3 uses the Column and Constraint Generation Algorithm (C&CG), based on Matlab, and calls CPLEX for solving. The specific process is as follows:

[0094] Step 1: Generate initial feasible solutions and set iteration parameters. First, solve the simplified moderator problem based on a deterministic scenario (such as the predicted mean values ​​of wind, solar, and load) to obtain the first-stage decision. Initialize the lower bound of the objective function LB = -∞ and the upper bound UB = +∞, and set the convergence threshold e and the iteration counter k = 0.

[0095] Step 2: Solve the main problem. Solve the current main problem (including the cutting plane constraints generated in previous iterations) to obtain the first-stage decision. and target value (Lower bound of traditional power generation). Update global lower bound. This solution represents the current optimal "defensive" pre-scheduling scheme.

[0096] Step 3: Solve the subproblems. Fix the first-stage decision. Solve the maximization-minimization subproblem: search within the uncertain source load set U for the worst-case scenario that maximizes the total additional power generation from the second-stage call. The subproblem outputs the total additional power generation required for the second stage in this scenario. and real-time scheduling strategies .

[0097] Step 4: Generate the cutting plane constraint. Construct the Benders Cut based on the sub-problem results. This cutting plane represents the constraint that, if the first-stage decision... Deviation from the current solution The worst-case scenario The total additional generating capacity required to be mobilized is at least [amount missing]. Add the cutting plane to the main problem and remove inefficient solutions. Step 5: Update the upper bound and convergence criteria. Update the global upper bound. ,like Then output the current solution. If the solution is the robust optimal solution, then let k = k + 1 and return to Step 2 for the next iteration.

[0098] The solution process of the Column and Constraint Generation (C&CG) algorithm is as follows: Figure 1 As shown. First, input the predicted data of microgrid wind power, photovoltaic power, and load, and set the number of iterations l=1. Set the upper bound of the objective function UB=∞ and the lower bound LB=-∞, and set the convergence criterion e. Second, solve the main problem to obtain the main problem objective function F(l) and control variable x(l), and update the lower bound LB(l+1)=max{F(l),LB(l)}. Then, solve the subproblems based on the results of the main problem to obtain the subproblem objective function f(l) and the worst operating scenarios u(l) and z(l), and update the upper bound UB(l+1)=min{f(l),UB(l)}. Finally, perform a convergence judgment: |UB(l+1)-LB(l+1)|≤e. If the convergence requirement is met, output the decision variable; otherwise, add new variables and constraints to the main problem.

[0099] The core innovations of this invention are as follows: First, a collaborative optimization mechanism that integrates generator reserve and demand response resources into a two-stage framework. Through collaborative modeling of reserved capacity and real-time dispatch power, the optimal technical coordination of flexibility resources is achieved while minimizing the traditional power generation target. Second, joint uncertainty modeling that establishes a multi-dimensional robust uncertainty set containing sources (wind and solar forecast deviations) and loads (load deviations), breaking through the limitation of traditional models that ignore demand response uncertainty. Third, robust equivalent transformation and efficient solution that employs a column sum and constraint generation (C&CG) algorithm. The main problem optimizes the first-stage pre-scheduling decision, while the sub-problems uncover the worst-case uncertainty scenario that maximizes the increase in traditional power generation. Iterative approximation ensures the optimality and robustness of the scheduling scheme under uncertain environments.

[0100] Four typical scenarios were simulated using historical data on wind, solar, and load in a certain region. By comparing the total power generation, renewable energy absorption rate, and reliability indicators under each scenario, the effectiveness of the proposed method in improving the system's energy utilization efficiency was verified.

[0101] Figure 2 Scenario 1: A scheme that does not consider demand response and generator backup in microgrid scheduling; Figure 3 Scenario 2: A scheme that only considers demand response participation and does not consider generator backup participation in microgrid scheduling; Figure 4 For scenario three: a scheme that only considers generator standby participation and does not consider demand response participation in microgrid scheduling; Figure 5 For scenario four: a scheme that simultaneously considers demand response and generator backup in microgrid scheduling.

[0102] The above four scenarios all aim to minimize the total traditional power generation of the microgrid's day-ahead and intraday dispatch system. The optimization results, obtained through Matlab optimization and dispatch analysis, are shown in Table 1. The optimization results for each scenario are as follows: Figure 2-5 As shown in Table 1, Scenario 1 has the highest total adjustment power and traditional unit power generation under the worst operating conditions because demand response and generator backup are not included. Compared to Scenario 1, Scenario 2, after introducing demand response, has a 2.15% decrease in total adjustment power and a 3.28% decrease in traditional unit power generation under the worst operating conditions, indicating that demand response reduces power generation pressure through load regulation. Comparing Scenario 1 and Scenario 3, due to the introduction of generator backup in Scenario 3, the total adjustment power under the worst operating conditions decreases by 0.8% and the traditional unit power generation decreases by 7.13%, indicating that while relying solely on generator backup improves the reliability of microgrid operation, the overall economic efficiency is poor. Compared to the previous three scenarios, Scenario 4, after combining demand response and generator backup for collaborative optimization, has the lowest total adjustment power and traditional unit power generation under the worst operating conditions, with a 25.01% reduction in traditional unit power generation, confirming that collaborative optimization can significantly reduce the traditional unit power generation. Furthermore, the worst-case load shedding was highest in Scenario 1 (without demand response and generator backup) and lowest in Scenario 4 (combining demand response and generator backup for collaborative optimization), with a reduction of 5.80%. This indicates that collaborative optimization enhanced robustness and reduced load losses under extreme events. Compared to not employing a complementary operation mechanism, under the worst-case condition, the system's power demand on traditional generators was significantly reduced, while the load shedding decreased by 5.8%, and the wind and solar curtailment rate decreased by 16.1%, effectively improving the level of renewable energy absorption and power supply reliability. Details are shown in Table 1.

[0103] Table 1

[0104] Scene Traditional generator unit power generation / kW standby capacity / kW Demand Response / kw Severe operating condition load shedding / (kW•h) Adjust power / kW under worst operating conditions 1 5621.4 0 0 36.2 97887.9 2 5437.2 0 1345.7 34.8 95782.1 3 5220.8 672 0 35.5 97103.4 4 4215.4 504 672 34.1 94412.0

[0105] analyze Figure 5 It can be seen that, after considering the coordinated optimization of demand response and generator reserves, the microgrid system experiences the lowest electricity price, the largest wind power output, and relatively smaller load demand during the period from 0:00 to 6:00. Wind power consumption is mainly promoted through energy storage charging and the addition of transferable loads, while system power balance is achieved through generator output and reserve capacity adjustments, and microgrid power purchases. During the peak electricity price period from 9:00 to 11:00, although load demand is high, the system sells excess electricity back to the grid to generate revenue after meeting load demand, as generators are operational, wind and solar power output is adequate, and energy storage is discharging. During the period from 12:00 to 17:00... With ample output from solar and wind power, and through load transfer and the addition of generator output and reserve capacity, there is no need for microgrid power purchase and sale. The absorption of wind and solar power is achieved through energy storage charging. Between 19:00 and 23:00, due to the reduction in solar power output, wind power output is insufficient to meet load demand. The shortfall in load demand is mainly supplemented by generator output and energy storage discharge. Between 20:00 and 21:00, excess electricity is sold to the grid to generate some revenue. Between 23:00 and 6:00 the next day, due to the lowest electricity price, electricity is mainly purchased for storage so that it can be discharged during peak electricity price periods when load demand is high, thereby reducing the power generation capacity of traditional generating units.

[0106] This invention relates to a two-stage robust scheduling method that coordinates generator reserve and demand response to address source-load uncertainty. Taking into account source-load uncertainty, multi-scenario comparative analysis through data simulation reveals that the coordinated scheduling mechanism of demand response and generator reserve, driven by the core objective of minimizing total power generation, achieves complementary operation of flexible load regulation and generator-side reserve resources. This significantly reduces reliance on traditional power generation while substantially lowering load shedding under worst-case conditions, ensuring system safety and stability. Optimized utilization of generator reserve capacity effectively enhances the system's ability to adapt to extreme conditions and its operational resilience, thereby improving the level of clean energy consumption and power supply reliability at the technical level.

[0107] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A robust two-stage scheduling method for coordinating generator standby and demand response to address source-load uncertainty, characterized in that, Includes the following steps: S1. Collect system information in the power system, including generator operating parameters, historical load data, wind and solar power output prediction error distribution, demand response resource information, and network topology information; based on the wind and solar power output prediction error distribution and the historical load data, construct a box-type source-load uncertainty set, and quantify the feasible region of generator reserve capacity and the boundary of demand response potential. S2. Based on the system information, the uncertain set of box-type source loads, the feasible region of generator reserve capacity and the boundary of demand response potential, a two-stage robust optimization model is constructed with the goal of minimizing the traditional power generation of the system, which is used to make collaborative optimization decisions on generator reserve and demand response resources. S3. The column and constraint generation algorithm is used to iteratively solve the two-stage robust optimization model to generate a robust optimal scheduling scheme. The column and constraint generation algorithm optimizes the first-stage decision through the main problem and identifies the worst uncertainty scenario through sub-problems to evaluate and correct the decision.

2. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 1, characterized in that, The first-stage decision-making content of the two-stage robust optimization model includes generator start-up and shutdown status, basic output plan, reserve capacity, energy storage charging and discharging plan, power exchange with the distribution network, and demand response dispatch scheme.

3. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 2, characterized in that, The constraints for the first stage of decision-making include generator output constraints, gas turbine ramping constraints, gas turbine start-up and shutdown constraints, gas turbine reserve capacity constraints, price-based demand response constraints, power purchase and sale constraints with the distribution network, energy storage operation constraints, and power balance constraints.

4. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 1, characterized in that, The price-based demand response constraint is: ； in, The maximum value of the load using PDR. For user-involved load adjustments, and They are respectively The change in electricity price at any given time and its maximum value. This is the time-of-use electricity price change matrix. The number of hours in a day is 24 hours. This is the elasticity matrix of electricity volume and price.

5. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 3, characterized in that, The energy storage operation constraints are as follows: ； in, It is a 0-1 integer variable, representing the energy storage charging and discharging state; These are the maximum and minimum charging power values ​​for energy storage. These represent the maximum and minimum values ​​of the energy storage discharge power; These represent the initial capacity of the energy storage at the start of the scheduling process, as well as the maximum and minimum remaining capacity allowed during the scheduling process. This is the change over time, with a value of 1. This refers to the energy storage charging and discharging efficiency.

6. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 1, characterized in that, The second-stage decision-making process of the two-stage robust optimization model includes reserve capacity allocation, wind and solar curtailment, and controllable load adjustment.

7. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 6, characterized in that, The constraints that the decision-making process in the second stage must meet include power balance constraints, generator reserve capacity adjustment constraints, load shedding capacity constraints, wind power output constraints, photovoltaic power output constraints, and load fluctuation constraints.

8. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 7, characterized in that, The load shedding capacity constraint is: ； in, The percentage of load that can be sheared. This represents the actual power output of the day's load. This refers to the daily load shedding.

9. The two-stage robust scheduling method for coordinating generator standby and demand response to address source-load uncertainty as described in claim 1, characterized in that, The iterative solution process using the column and constraint generation algorithm includes: Solve the simplified moderator problem to obtain the initial feasible solution for the first stage of decision-making; set the lower bound of the objective function to negative infinity and the upper bound to positive infinity; set the convergence threshold and initialize the iteration counter to 0; Solve the current principal problem that includes the cutting plane constraints generated by the historical iterations, obtain the first-stage decision solution and the corresponding objective function value, and update the global lower bound accordingly; Fix the decision solution of the first stage, solve the sub-problems within the uncertain set of the box source load, and search for the worst uncertainty scenario that maximizes the total additional power generation required in the second stage. Based on the solution results of the subproblems, construct Benders cut constraints and add them to the constraint set of the main problem; Update the global upper bound using the results of the subproblems; determine whether the absolute value of the difference between the global upper bound and the global lower bound is less than or equal to the convergence threshold. If it is satisfied, terminate the iteration and output the current first-stage decision solution as the final robust optimal scheduling scheme; otherwise, update the iteration counter and resolve the current main problem for the next iteration.

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