Distributed renewable energy grid-connected system scheduling method

Abstract

The invention relates to the technical field of integrated energy system optimization scheduling, and discloses a distributed renewable energy grid-connected system scheduling method, which comprises the following steps: a day-ahead scheduling stage: a micro-grid operator sends an initialized sales electricity price to a load aggregator; according to the initialized sales electricity price, the load aggregator offers a transfer electricity load one day before, responds to the micro-grid operator, and transmits the responded load to the micro-grid operator; the micro-grid operator readjusts the scheduling arrangement according to the load response result; repeating the above steps until the micro-grid operator and the load aggregator realize game equilibrium; in the intra-day scheduling stage, a micro-grid operator updates a source load output prediction value once in each intra-day interval period, a scheduling plan of a next scheduling period is subjected to rolling optimization each time, and the deviation of a day-ahead scheduling plan is corrected; informing a load aggregator of an interruptible load scheme one scheduling period ahead of time, and repeating until an actual scheduling plan is finally determined; according to the invention, the energy consumption cost of the user is reduced while the microgrid income is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization scheduling technology, specifically a scheduling method for a distributed renewable energy grid-connected system. Background Technology

[0002] Renewable energy power generation mainly includes two methods: centralized grid connection and distributed grid connection. Among them, distributed generation has become an important development direction for future energy systems due to its advantages such as short construction period and easy decentralized access. With the transformation of information technology and market management models, the development of distributed renewable energy in my country has also presented diversified new concepts and new energy supply and consumption models, such as microgrids, aggregators, virtual power plants, and integrated energy services. These have brought new challenges to the safe and economical operation of distributed renewable energy grid-connected systems. Therefore, energy management of distributed renewable energy grid-connected systems, especially the optimized scheduling methods of multi-energy complementary systems such as wind, solar, and energy storage, urgently need in-depth research.

[0003] To address the uncertainty of day-ahead and intraday source-load power, current conventional methods assume a normal or t-location-scale distribution for the prediction error, or use heuristic moment matching to obtain target moments such as expectation and variance of historical scenarios before further obtaining representative scenarios. However, these methods struggle to characterize high-dimensional nonlinear mapping relationships and cannot deeply explore the distribution characteristics of source-load power. Therefore, some scholars have leveraged the powerful data feature extraction and mining capabilities of artificial intelligence methods, introducing generative adversarial networks (GANs) or long short-term memory neural networks into power optimization modeling. Furthermore, to fully explore the positive role of flexible resources such as adjustable loads in the economic dispatch of distributed energy systems, a common practice for adjustable flexible loads is to introduce price-based or incentive-based demand response mechanisms. Some scholars consider real-time pricing in the optimal dispatch of distributed energy, which can more accurately reflect real-time supply and demand relationships and offers greater economic benefits compared to time-of-use pricing. However, current research on multi-timescale dispatch of microgrids insufficiently considers the interaction of interests between microgrids and load aggregators, and lacks research on the coordinated optimal dispatch of flexible loads and multi-element energy storage. Summary of the Invention

[0004] The purpose of this invention is to provide a method for scheduling distributed renewable energy grid-connected systems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a method for scheduling a distributed renewable energy grid-connected system, comprising: During the day-ahead scheduling phase, a day-ahead response plan for each microgrid device and load is formulated to provide a reference for intraday optimized scheduling. A scheduling task is executed once every day-ahead interval. Step 1: Initialize the parameters for the microgrid operator and load aggregator; Step 2: Microgrid operators, with system revenue as the goal, guide transferable loads to participate in demand-side response by optimizing scheduling methods; Step 3: With the goal of reducing energy consumption costs, the load aggregator shifts the electricity load one day in advance, responding to the microgrid operator's incentive mechanism, and then passes the responded load to the microgrid operator; Step 4: Microgrid operators readjust scheduling arrangements based on load response results; Step 5: Repeat steps 2 to 4 until the microgrid operator and the load aggregator reach a game equilibrium; During the intraday scheduling phase, based on the previous day's scheduling results, microgrid operators take the previous day's resource scheduling status as a reference and aim to minimize the microgrid operating cost within the cycle. Step 6: The microgrid operator updates the source load output forecast once every day at intervals, and optimizes the scheduling plan for the next scheduling cycle each time, readjusting the power arrangement of energy storage, gas turbines and grid, and correcting the deviation of the day-ahead scheduling plan; Step 7: The microgrid operator arranges for the supercapacitor to charge and discharge, and notifies the load aggregator one scheduling cycle in advance that the load interruption plan can be implemented to meet the power balance within the scheduling period. However, only the scheduling plan for the first intraday interval period within the scheduling cycle is executed, and so on, until the actual scheduling plan is finally determined.

[0006] Furthermore, during the day-ahead scheduling phase, as the leader of the game, the microgrid operator aims to maximize daily revenue, and the objective function can be expressed as Formula 1: , formula 1; In the formula, Revenue from selling electricity to users; For electricity purchase costs; For gas turbine operation and maintenance costs; For the operation and maintenance costs of energy storage batteries; For carbon trading costs; The costs of curtailing wind and solar power; The objective function constraints for microgrid operators include: power balance constraints, upper and lower output limits constraints, gas turbine ramp-up and minimum operating time constraints, power interaction constraints between the microgrid and the external power grid, battery charging and discharging constraints, and electricity price constraints.

[0007] Furthermore, the optimization objective of the load aggregator is to maximize daily electricity consumption efficiency, and the objective function can be expressed as Formula 2: , formula 2; In the formula, Let be the user's utility function, representing the satisfaction of the load aggregator in participating in the demand-side response; it is often represented by a quadratic form. This represents the load power predicted at the previous day; , They are respectively in the time period Load power transferred in and out, The day-ahead interval period, of which , These are the electricity preference coefficients of load aggregators, which can affect the amount of electricity demanded by users; The objective function constraints for load aggregators as followers include demand-side response load constraints.

[0008] Furthermore, during the intraday scheduling phase, the objective function for minimizing the operating cost for microgrid operators within the scheduling cycle can be expressed as Formula 3: , formula 3; In the formula, The scheduling period; Compensation costs to be paid for intraday interruptible load calls; , They are respectively Day-ahead forecasts for wind and solar power during the specified time period; , These are the day-ahead on-grid values ​​for wind power and solar power, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. The compensation price for interruptible loads; This refers to the load reduction amount for a given period. The operating and maintenance costs of supercapacitors; , These are the maintenance and depreciation cost coefficients for supercapacitors, respectively. , For capacitors The charging and discharging power during a given period; The penalty cost for adjusting the scheduling plan due to intraday power forecast changes; , and These are the penalty costs for changes in energy storage charging and discharging power, changes in grid electricity sales and purchases, and changes in gas turbine output, respectively. For the day before Time-of-use gas turbine contribution; , The batteries were respectively at the date The charging and discharging power during a given period; , For storage batteries Charge and discharge power over time period; For gas turbine exist Output power during the time period; , These represent the power sold and purchased by the microgrid from the external grid, respectively. , Micronets were recently Electricity purchased and sold between the time period and the external network; The constraints include: power balance constraints, interrupted load constraints, gas turbine ramping constraints, intraday wind and solar grid-connected power constraints, intraday power adjustment constraints at corresponding times before and after the day, supercapacitor charging and discharging constraints, and intraday microgrid power source status constraints.

[0009] Furthermore, the power purchased and sold from the microgrid to the external grid during the day is set as a flexible parameter, expressed as Formula 4: , formula 4; In the formula, , The units Maximum and minimum output values; This is the output status indicator for the day-ahead gas turbine; , These are the battery's current charge / discharge status indicators; , These represent the maximum charging and discharging power of the battery.

[0010] Furthermore, the daily output penalty should meet the following requirements: .

[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention can protect the privacy of participants and effectively increase the revenue of microgrids while reducing the energy costs for users.

[0012] 2. This invention can effectively mitigate power fluctuations caused by day-ahead forecast errors, reduce microgrid operating costs, and improve the absorption rate of new energy sources. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the framework of the present invention; Figure 3a This is a simulation diagram of the actual and predicted day-ahead wind power, photovoltaic and load data of the microgrid of the present invention; Figure 3b This is a simulation diagram of the actual and predicted intraday wind power, photovoltaic and load data of the microgrid of the present invention; Figure 4 This is a simulation diagram of the optimization iteration results of the microgrid operator and load aggregator in this invention; Figure 5 This is a simulation diagram of the pricing results for load aggregators and microgrid operators according to the present invention; Figure 6 This is a simulation diagram of the electrical load curves before and after the user-side demand response of the present invention; Figure 7 This is a simulation diagram of the current-day optimized scheduling results of the present invention; Figure 8 This is a simulation diagram of the interruptible load recall scenario of the present invention; Figure 9 This is a simulation diagram of the charging and discharging power of the supercapacitor in scenario 1 of this invention; Figure 10 This is a simulation diagram of the intraday optimized scheduling results for scenario 1 of this invention; Figure 11a This is a simulation diagram comparing the day-ahead and intraday battery charging and discharging power scheduling results for scenario 1 of this invention. Figure 11b This is a simulation comparison of the day-ahead and intraday interactive power scheduling results between the microgrid and the external grid in Scenario 1 of this invention; Figure 11c This is a simulation diagram comparing the day-ahead and intraday power output scheduling results of the micro gas turbine in scenario 1 of this invention; Figure 12 This is a simulation diagram of the supercapacitor charging and discharging power in scenario 2 of the present invention; Figure 13a This is a simulation diagram comparing the day-ahead and intraday battery charging and discharging power scheduling results for scenario 2 of this invention. Figure 13b This is a simulation comparison of the day-ahead and intraday interactive power scheduling results between the microgrid and the external grid in Scenario 2 of this invention; Figure 13c This is a simulation diagram comparing the day-ahead and intraday power output scheduling results of the micro gas turbine in scenario 2 of this invention; Figure 14 This is a simulation diagram of the intraday optimized scheduling results for scenario 2 of the present invention; Figure 15 This is a simulation diagram of the interruptible load recall scenario in scenario 3 of this invention; Figure 16a This is a simulation diagram comparing the daytime and intraday battery charging and discharging power scheduling results for scenario 3 of this invention. Figure 16b This is a simulation comparison of the day-ahead and intraday interactive power scheduling results between the microgrid and the external grid in scenario 3 of this invention. Figure 16c This is a simulation diagram comparing the day-ahead and intraday power output scheduling results of the micro gas turbine in scenario 3 of this invention; Figure 17 This is a simulation result of the intraday optimized scheduling in scenario 2 of the present invention. Detailed Implementation

[0015] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0016] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the actual number, shape and size of the layers in the actual implementation. In the actual implementation, the form, number and proportion of each layer can be arbitrarily changed, and the layer layout may also be more complex.

[0017] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of the invention; however, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details.

[0018] Please see Figure 1 A method for scheduling a distributed renewable energy grid-connected system includes formulating a day-ahead response plan for each microgrid device and load during the day-ahead scheduling phase to provide a reference for intraday optimized scheduling, and executing a scheduling task once per day-ahead interval. Step 1: Initialize the parameters for the microgrid operator and load aggregator; Step 2: Microgrid operators, with system revenue as the goal, guide transferable loads to participate in demand-side response by optimizing scheduling methods; Step 3: With the goal of reducing energy consumption costs, the load aggregator shifts the electricity load one day in advance, responding to the microgrid operator's incentive mechanism, and then passes the responded load to the microgrid operator; Step 4: Microgrid operators readjust scheduling arrangements based on load response results; Step 5: Repeat steps 2 to 4 until the microgrid operator and the load aggregator reach a game equilibrium; During the intraday scheduling phase, based on the previous day's scheduling results, microgrid operators take the previous day's resource scheduling status as a reference and aim to minimize the microgrid operating cost within the cycle. Step 6: The microgrid operator updates the source load output forecast once every day at intervals, and optimizes the scheduling plan for the next scheduling cycle each time, readjusting the power arrangement of energy storage, gas turbines and grid, and correcting the deviation of the day-ahead scheduling plan; Step 7: The microgrid operator arranges for the supercapacitor to charge and discharge, and notifies the load aggregator one scheduling cycle in advance that the load interruption plan can be implemented to meet the power balance within the scheduling period. However, only the scheduling plan for the first intraday interval period within the scheduling cycle is executed, and so on, until the actual scheduling plan is finally determined.

[0019] Based on the source-load prediction results, this invention takes into account the characteristic that the source-load power prediction error decreases with the decrease of time scale, and introduces the multi-time scale optimization method into the research on the optimization scheduling problem of distributed renewable energy grid-connected systems. First, it proposes a day-ahead optimization strategy based on master-slave game theory, and establishes day-ahead and intraday optimization scheduling models based on source-load predicted power to promote the interaction between microgrids and users, while improving the economy of users and microgrid operators.

[0020] A microgrid system integrates power supply systems such as wind power, solar power, and gas turbines with users into a unified whole. For unified and optimized scheduling, the power supply system is equated to a microgrid operator, and the distributed users within the microgrid are equated to load aggregators. The microgrid operator is responsible for setting reasonable electricity prices and flexible load adjustment compensation prices, allocating power output from various generation units and energy storage devices, and engaging in electricity trading with load aggregators to obtain certain revenue. Load aggregators respond to the electricity consumption and compensation prices published by the microgrid operator, manage users in a unified manner, and optimize the load distribution of users.

[0021] In a microgrid system, load aggregators and microgrid operators are independent entities, each pursuing their own interests through specific strategies. The microgrid operator first formulates a 24-hour electricity sales strategy, while the load aggregator adjusts its electricity purchase plan for each time period based on its pricing scheme. The interaction variables between them are the electricity price sold to users and the users' electricity purchase plans. When the microgrid operator's electricity price changes, the load aggregator's electricity purchase plan will also be dynamically adjusted to minimize electricity purchase costs and dissatisfaction. Conversely, the microgrid operator will also re-examine and adjust its own electricity sales pricing strategy based on the load aggregator's electricity purchase plan to maximize electricity sales profits. Clearly, there is a game-theoretic relationship between the two, and their strategies are formulated in a sequential manner; therefore, their relationship can be described using a master-slave game model. By utilizing multi-timescale scheduling schemes and leveraging the characteristic that wind power, photovoltaic, and load power prediction errors decrease over time, more accurate scheduling plans can be formulated, thereby reducing system operating costs. In summary, this invention establishes a multi-timescale optimized scheduling framework based on a master-slave game model. Figure 2 .

[0022] Based on the proposed source-load forecasting method, the predicted source-load power values ​​for the day-ahead and intraday are obtained. Combined with the flexible loads of different resource categories on the load side, as shown in Table 1-1, market mechanisms such as Incentive Demand Response (IDR) are used to fully explore the dispatchable potential of flexible loads and establish optimized scheduling models for the day-ahead and intraday.

[0023] Table 11 IDR Resource Categories (1) Day-ahead scheduling strategy During the day-ahead scheduling phase, a scheduling task is executed every 24 hours. Microgrid operators, aiming for system revenue, optimize scheduling strategies to guide transferable loads to participate in demand-side response. Load aggregators, aiming to reduce energy consumption costs, transfer electricity loads one day in advance, responding to the microgrid operator's incentive mechanism and transferring the responded loads back to the microgrid operator. The microgrid operator then readjusts its scheduling arrangements based on the load response results. This process is repeated until a game equilibrium is reached. A 24-hour response plan for all microgrid equipment and loads is developed during the day-ahead phase to provide a reference for intraday optimized scheduling.

[0024] (2) Intraday scheduling strategy During the intraday phase, based on the day-ahead dispatch results, the microgrid operator updates the source and load output forecasts every 15 minutes. After each rolling optimization, the dispatch plan is updated 4 hours later, and the power allocation of energy storage, gas turbines, and the grid is readjusted to correct deviations in the day-ahead dispatch plan. Simultaneously, supercapacitors are scheduled to charge and discharge, and load aggregators are notified 4 hours in advance of interruptible load schemes to ensure power balance within the dispatch period. However, only the dispatch plan for the first 15 minutes of the dispatch cycle is executed, and so on, until the final actual dispatch plan is determined.

[0025] During the day-ahead scheduling phase, as the leader of the game, the microgrid operator aims to maximize daily revenue, and the objective function can be expressed as Formula 1: , formula 1; In the formula, Revenue from selling electricity to users; For electricity purchase costs; For gas turbine operation and maintenance costs; For the operation and maintenance costs of energy storage batteries; For carbon trading costs; The costs of curtailing wind and solar power; Revenue from selling electricity to users, , Formula 1-2; In the formula, The electricity price sold by microgrid operators; This represents the load power predicted at the previous day; , These represent the load power transferred in and out during the time period, respectively.

[0026] The cost of curtailment penalties for wind and solar power is omitted here, considering that the lifespan of wind and solar power plants can reach more than 20 years.

[0027] , Formula 1-3; In the formula, , These are the day-ahead forecasts for wind power and solar power, respectively, for the given time period. , These are the day-ahead on-grid values ​​for wind power and solar power, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. This refers to the day-ahead interval period during which the system operates.

[0028] Cost of purchasing and selling electricity from external power grids: , Formula 1-4; In the formula, , These represent the power sold or purchased by the microgrid from the external power grid during the specified time period. , These are the sales and purchase prices of electricity from the microgrid to the external power grid, respectively.

[0029] Gas turbine operation and maintenance costs: , Formula 1-5; In the formula, For the operation and maintenance costs of gas turbines; for Operating costs of time-limited gas turbines; For the maintenance costs of gas turbines; The selling price per cubic meter of natural gas; It has a low calorific value; For gas turbine Output power during the time period; For gas turbine efficiency; This represents the maintenance cost coefficient for the gas turbine.

[0030] Battery operation and maintenance costs: , Formula 1-6; In the formula, , These are the maintenance and depreciation cost coefficients, respectively. , This refers to the charging and discharging power of the battery during different time periods.

[0031] Carbon trading costs: , Formula 1-7; In the formula, For carbon trading prices; Carbon emissions from microgrid systems; For permitted, uncompensated carbon emission allowances; and These represent the carbon emission intensity per unit power of the external power grid and the gas turbine, respectively. Here, it is assumed that the external power grid is supplied by thermal power units. Emissions quota per unit power.

[0032] The objective function constraints for microgrid operators include: Power balance constraints: , Formula 1-8; In the formula, This represents the number of gas turbines in the microgrid.

[0033] Output upper and lower limit constraints: , Formula 1-9; In the formula, , The units The minimum and maximum output values; For the unit exist Power on / off status flags for different time periods; Gas turbine unit ramp-up and minimum operating time constraints: , formula 1-10; In the formula, , These are the uphill and downhill ramp rates of the gas turbine, respectively. For gas turbine Run to The continuous running time of the time period; For gas turbine Minimum allowed runtime; Microgrid-external grid interaction power constraints: , Formula 1-11; In the formula, , These represent the power sold and purchased by the microgrid from the external grid, respectively. Maximum power limits for tie lines between the external power grid and the microgrid; , These are the purchase and sale status flag bits between the external power grid and the microgrid; Battery charge and discharge constraints: , Formula 1-12; In the formula, , These are the maximum charging and discharging power of the battery, respectively. For the storage battery State of charge over a period of time; , These are the charging and discharging efficiencies of the battery, respectively. This refers to the capacity of the battery. , These are the maximum and minimum constraints for the state of charge of energy storage, respectively. , The batteries are respectively The charging and discharging status flag for a given period of time.

[0034] Electricity price constraints: , Formula 1-13; In the formula, , They are respectively external power grids Electricity purchase and sale prices during specific time periods.

[0035] The optimization objective of the load aggregator is to maximize daily electricity consumption efficiency, and the objective function can be expressed as Equation 2: , formula 2; In the formula, Let be the user's utility function, representing the satisfaction of the load aggregator in participating in the demand-side response; it is often represented by a quadratic form. This represents the load power predicted at the previous day; , They are respectively in the time period Load power transferred in and out, The day-ahead interval period, of which , These are the electricity preference coefficients of load aggregators, which can affect the amount of electricity demanded by users; The objective function constraints for load aggregators as followers include demand-side response load constraints: , Formula 2-2; In the formula, This is the load transfer ratio coefficient; , These are the transfer-in and transfer-out status flags for transferable loads during the time period.

[0036] To correct for day-ahead forecast errors, intraday optimization is performed using rolling optimization scheduling with 15-minute time intervals and a 4-hour scheduling cycle. The optimization objective is to minimize the microgrid's operating cost within the cycle, referencing the day-ahead scheduling status of each resource.

[0037] During the intraday scheduling phase, the objective function for minimizing the operating cost for microgrid operators within the scheduling cycle can be expressed as Formula 3: , formula 3; In the formula, The scheduling period; Compensation costs to be paid for intraday interruptible load calls; , They are respectively Day-ahead forecasts for wind and solar power during the specified time period; , These are the day-ahead on-grid values ​​for wind power and solar power, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. The compensation price for interruptible loads; This refers to the load reduction amount for a given period. The operating and maintenance costs of supercapacitors; , These are the maintenance and depreciation cost coefficients for supercapacitors, respectively. , For capacitors The charging and discharging power during a given period; The penalty cost for adjusting the scheduling plan due to intraday power forecast changes; , and The penalty costs are respectively for changes in energy storage charging and discharging power, changes in grid electricity sales and purchases, and changes in gas turbine output. To reduce the impact of the microgrid on the external grid, the daily output penalty should meet the following requirements: ; For the day before Time-of-use gas turbine contribution; , The batteries were respectively at the date The charging and discharging power during a given period; , For storage batteries Charge and discharge power over time period; For gas turbine exist Output power during the time period; , These represent the power sold and purchased by the microgrid from the external grid, respectively. , Micronets were recently Electricity purchased and sold between the time period and the external network; The constraints include: Power balance constraints: , Formula 3-2; In the formula, This is the intraday load forecast; , These are the actual daily grid connection values ​​for wind power and solar power, respectively. Interruption load constraints: , Formula 3-3; In the formula, This is the proportional coefficient for interrupted load; Gas turbine ramping constraints: , Formula 3-4; Intraday power constraints for wind and solar grid connection: , Formula 3-5; In the formula, , These are the daily power forecasts for wind power and solar power, respectively. Power adjustment constraints for the corresponding time of day before the current day: , Formula 3-6; In the formula, This represents the maximum allowable power variation at the corresponding time point within the day before the external power grid and the microgrid interconnection line; , This represents the maximum allowable variation in the charging and discharging power of the battery at the corresponding moment within the day before the given date. This represents the maximum permissible variation in the power generation capacity of the gas turbine at the corresponding moment within the day; Supercapacitor charge and discharge constraints: , Formula 3-7.

[0038] To ensure intraday supply and demand balance, the power purchased and sold by the microgrid to the external grid is set as a flexible parameter, unaffected by the day-ahead dispatch status, as expressed in Formula 4: , formula 4; In the formula, , The units The minimum and maximum output values; This is the output status indicator for the day-ahead gas turbine; , These are the battery's current charge / discharge status indicators; , These represent the maximum charging and discharging power of the battery.

[0039] The transaction process between microgrid operators and load aggregators conforms to a master-slave game model, using... Indicates microgrid operator, This represents the load aggregator. The game... This can be represented as Formula 5:

[0040] , formula 5; In the formula, As a leader To be a follower; for Electricity pricing strategy for the day for A strategy for invoking flexible loads within a day; , Within one day and The benefits.

[0041] When there is a set of strategies , and When both sides have reached their optimal payoffs, and neither can change their strategies to obtain a greater payoff, the game is said to have reached its stalemate. Equilibrium, that is, satisfying formula 5-2: , Formula 5-2; According to the master-slave game theory, if there exists a unique Equilibrium, the master-slave game model needs to satisfy three conditions simultaneously: (1) and The strategy space is a non-empty compact convex set; (2) Regarding the published pricing strategy, There exists a unique optimal response strategy for each; (3) Once the response behavior is determined, There exists a unique optimal solution.

[0042] To demonstrate that the master-slave game model proposed in this chapter has a unique existence... Equilibrium, as proven below: (1) As can be seen from the game theory model, The strategies must satisfy formula (1-13) and formula (2-2), so each participant's strategy set is non-empty, bounded and continuous.

[0043] (2) For followers The optimization model, for The first-order partial derivatives of the objective function are shown in equations (5-3) and (5-4), respectively: , Formula 5-3; , Formula 5-4; Setting the first-order partial derivative to 0, we get: , Formula 5-5; , Formula 5-6; The second partial derivative of Equation 2 is: , Formula 5-7; because Therefore, the objective function The second-order partial derivatives are all less than 0. and This is the maximum point of Formula 2. Combining the interval constraints on the variable values, the optimal solution can be expressed as:

[0044] , Formula 5-8; , Formula 5-9; Therefore when Once the strategy is determined, The optimization model has a unique optimal solution.

[0045] (3) Fixed strategy, objective function For electricity price A linear function that satisfies the definition of a convex function.

[0046] In conclusion, the game theory model proposed in this chapter has a unique existence. balanced.

[0047] In a competitive electricity market environment, information among participants is not transparent, making it difficult to obtain privacy information such as equipment parameters and satisfaction levels. Therefore, individual optimization for each participant is necessary. This paper employs ISSA combined with a CPLEX solver to solve the proposed model. The ISSA algorithm is used to initialize and update the electricity sales price for microgrid operators. Then, the lower-level CPLEX solution results are embedded into the iterative process of intelligent solving. This means that load aggregators only need to receive price signals from operators and provide feedback on optimal decisions, thus protecting the privacy and information security of all participants.

[0048] The specific solution process is as follows: (1) Initialize the parameters of the microgrid operator and the load aggregator, set the algorithm population size, number of iterations and other parameters, and randomly initialize the sales price of the microgrid operator. And transmit the parameter values ​​to the load aggregator; (2) Load aggregator Optimize the load transfer amount using the CPLEX solver with the goal of maximizing it. , ,storage The results will be optimized and the final purchased electricity volume will be returned to the microgrid operator. (3) Based on the optimized electricity purchase results from the load aggregator, the CPLEX solver is used to... Given the objective function, solve for the output power of each device; (4) Based on the fitness value of each particle The ISSA algorithm is used to update the search population and fitness values, and the optimal fitness value of each generation is calculated. (5) Repeat steps (2) to (4). If the maximum number of iterations is reached, end the loop and output the output power of each device and the user's demand response. (6) Use forecasting methods to predict intraday time periods The magnitude of the source load power is determined based on the intraday optimization target. To determine the power of equipment such as batteries, supercapacitors, and gas turbines, as well as the load power of interruptible loads within a day.

[0049] (7) Perform rolling optimization in 15-minute intervals, update the scheduling arrangements for the next time period, until the entire scheduling plan for the day is completed.

[0050] This invention uses microgrid data from a certain region as an example for simulation. Figure 3a and Figure 3bThe data presents the actual and predicted day-ahead and intraday source-load data for the microgrid, respectively. The capacity for dispatchable loads and interruptible charges shall not exceed 5% and 10% of the total load, respectively. The electricity purchase price from the external grid for microgrid operators is shown in Table 1-2.

[0051] Table 12 Electricity Purchase Price from the Grid by Microgrid Operators The electricity price sold to the external grid is 0.36 yuan / kW·h, and the maximum allowable transmission power of the interconnection line between the microgrid and the external grid is 80kW. The penalty factor for wind and solar power curtailment is 0.5 yuan / kW·h. The daily incentive subsidy price is 0.14 yuan / kW·h. The parameters of the batteries and supercapacitors are shown in Table 1-3.

[0052] Table 13 Energy Storage Equipment Parameters There are 3 gas turbines, and the unit parameters are shown in Table 1-4: Table 14 Gas Turbine Parameters The optimization and iteration results of microgrid operators and load aggregators are as follows: Figure 4 As shown in the figure, the results converged after 150 iterations, demonstrating the good convergence performance of the ISSA-CPLEX solution method proposed in this chapter. During the iteration process, the microgrid operator's revenue generally increased, while the load aggregator's electricity consumption gradually decreased, reflecting the game-theoretic relationship between the two. When the Stackelberg equilibrium is reached, their strategies remain unchanged; neither party can unilaterally alter their strategy to gain more revenue. The final optimization results for the microgrid operator and the load aggregator are 5339 yuan and 5842 yuan, respectively.

[0053] The pricing outcomes for load aggregators and microgrid operators are as follows: Figure 5 The optimized electricity sales price range for microgrid operators to loads falls within the range of the grid's time-of-use electricity price and the grid connection price. The optimized hourly electricity price for microgrid operators is quite similar to the grid's time-of-use price, with peak distribution between 10:00 and 15:00. This is to ensure the profitability of microgrid operators during peak load periods and to reduce load generation during peak hours to some extent. The electricity load curves before and after user-side demand response are shown below. Figure 6As shown, under the incentive effect of electricity prices, the load, while ensuring that the total electricity consumption remains unchanged, responds to the incentive policy of electricity prices by increasing electricity consumption during periods of lower electricity prices and decreasing electricity demand during periods of higher electricity prices in order to reduce electricity costs. Therefore, the load curve after participating in demand response exhibits the characteristics of "peak shaving and valley filling" compared with the load before optimization. Before optimization, the maximum peak point and minimum valley point of the load occurred at 12:00 and 5:00, respectively, with magnitudes of 517kW and 197kW, and a maximum peak-valley difference of 320kW. After optimization, the distribution time of the maximum peak point and minimum valley point of the load is the same as that of the load before optimization, with magnitudes of 465.3kW and 216.7kW, respectively, and a maximum peak-valley difference of 248.6kW. The maximum peak-valley difference after optimization is 71.4kW lower than that before optimization. It can be concluded that the adoption of real-time incentive electricity price measures can greatly reduce the load fluctuation and play a role in smoothing the load curve.

[0054] Figure 7 To optimize dispatching results, during the period from 1:00 to 06:00, due to the high output of wind power and low user electricity consumption, batteries are charged first. When the charging reaches its limit, excess wind power is sold to the external grid through the interconnection line to generate revenue. At 07:00, user electricity consumption exceeds wind power generation. Since grid electricity sales are low during this period, microgrid operators purchase electricity from the external grid and sell it to users, thus earning the price difference. From 08:00 onwards, wind and solar power supply occur simultaneously. Due to high user electricity consumption, micro gas turbines need to be activated to provide power during this period. Between 19:00 and 21:00, wind and solar power output is relatively low, and the three micro gas turbines generate electricity at full capacity. The shortfall in load demand is supplemented by the discharge of batteries. During this period, the external grid electricity price is relatively high, which reduces the cost for microgrid operators to purchase electricity from the external grid. Since the state of charge of the batteries must be consistent with the state of charge at the beginning of the dispatch cycle at the end of the dispatch cycle, the batteries no longer discharge between 22:00 and 24:00. During this period, when the power supply of the microgrid operator cannot meet the load demand, it purchases electricity from the external grid to supply the load.

[0055] Intraday optimized scheduling is a correction of power deviation. Figures 8 to 1 Figure 1 shows the optimization results during the intraday dispatch phase. Intraday power correction can utilize interrupted loads and supercapacitors; when neither can meet the requirements, further regulation can be achieved by utilizing power exchanged with the external power grid. From Figure 8 It is known that load interruptions mainly occur during peak load periods of 10:00-12:00 and 20:00. Reducing load can alleviate the regulatory pressure on power supply equipment caused by excessive load during the daytime optimization scheduling phase. Figure 9It can be seen that in order to reduce the adjustment amount of battery charging and discharging power and external grid connection power during the day, the scheduling difference caused by the forecast deviation between the day and the previous day is mainly regulated by supercapacitors during the day scheduling phase. Figure 10 As shown in the figure, during periods of low electricity prices, batteries and supercapacitors are mainly used for charging. During other periods, renewable wind and solar energy, as well as gas turbines, are primarily used to power the load. When wind and solar output decreases, batteries provide power to the external grid. Figure 11 shows that at 6:00 AM, due to limitations in the operating status of batteries and gas turbines (i.e., to maintain consistency with the day-ahead dispatch status), reliance on the external grid for power supply to the load occurs, resulting in fluctuations in tie-line power. At other times, the operation remains largely consistent with the day-ahead dispatch phase.

[0056] To verify the effectiveness of the strategies proposed in this chapter, three scenarios were set up for comparative analysis, as detailed below: Scenario 1: This chapter proposes a strategy that introduces interruptible loads and supercapacitors to participate in the optimal scheduling of the microgrid system during the intraday scheduling phase. Scenario 2: During the intraday scheduling phase, only supercapacitors are considered for participation in the optimized scheduling of the microgrid system, without considering interruptible loads for intraday scheduling; Scenario 3: During the intraday scheduling phase, only interruptible loads are considered for participation in the optimized scheduling of the microgrid system, without considering the participation of supercapacitors in intraday scheduling.

[0057] Tables 1-5 and 1-6 show the wind curtailment rate, solar curtailment rate, and economic indicators for the three scenarios, respectively.

[0058] Table 15 Wind and solar curtailment rates under different scenarios Table 16 Operating Costs for Different Scenarios Figures 8 to 1 1 represents the optimized scheduling result for scenario 1. Figures 12 to 14 This is the optimized scheduling result for scenario 2; the optimized scheduling result for scenario 3 is shown below. Figures 15 to 17 .

[0059] As shown in Table 15, the curtailment rates and curtailment values ​​of scenarios 1 and 2 are quite similar, both being better than those of scenario 3. However, scenario 3 exhibits a significantly higher wind curtailment rate. Figure 17It can be seen that during the period from 1:00 to 6:00, wind power generation is sufficient, and there is still a surplus after meeting the electricity demand of the load. Since the charging and discharging state of the battery is set to be consistent with the charging and discharging state of the battery the day before in the intraday optimization model, and an adjustment penalty is set for the adjustment amount of the charging and discharging power of the battery, a large tie line power adjustment penalty is also set in order to reduce the impact of the microgrid on the external grid. Therefore, the battery will not have a large adjustment amount during this period. In Scenario 3, due to the lack of flexible charging devices during the period of abundant wind power, wind curtailment can only be chosen. However, in Scenario 1 and Scenario 2, due to the introduction of supercapacitors, charging operations can be carried out during this period to store some of the surplus wind power and improve the wind power consumption rate.

[0060] As shown in Table 16, Scenario 1 is more economical than Scenario 2 and Scenario 3, with total scheduling costs reduced by RMB 0.65 million and RMB 1.15 million, respectively. The cost difference mainly comes from the penalty cost of intraday equipment output adjustment. As shown in Figures 11(a), 13(a), and 16(a), the intraday battery scheduling adjustment compared to the previous day is relatively small in all three scenarios. As shown in Figures 11(b), 13(b), and 16(b), Scenario 1 has the smallest intraday interactive power adjustment compared to the previous day, resulting in the lowest intraday adjustment cost, while Scenario 3 has the largest intraday interactive power adjustment compared to the previous day, thus corresponding to the largest intraday adjustment penalty cost. As shown in Figures 11(c), 13(c), and 16(c), the intraday gas turbine unit adjustment amounts in Scenario 1 and Scenario 2 are relatively close, smaller than the adjustment amount in Scenario 3. Because the gas turbine adjustment penalty factor is set to a smaller value, the penalty cost of the gas turbine accounts for a smaller proportion of the overall penalty cost. Therefore, the intraday adjustment penalty cost mainly comes from the penalty for changes in interactive power. This leads to the conclusion that, in intraday scheduling, the scheduling strategy corresponding to Scenario 1 has better economic efficiency and renewable energy absorption capacity.

[0061] This invention uses different wind power prediction results for comparative analysis, and the optimization results of the parameters are shown in Table 17.

[0062] Table 07 Economic Costs of Different Forecasting Methods As shown in the table, the wind power prediction method proposed in this paper has higher prediction accuracy than the other two methods and is closer to the actual wind power value. Therefore, it requires the lowest penalty cost. The total cost of the proposed method is reduced by RMB 438.91 and RMB 873.87 compared with the CEEMD-BP and EEMD-BP methods, respectively. This shows that improving the prediction accuracy of source load power can effectively optimize the economic dispatch operation of microgrid systems.

[0063] Based on Stackelberg game theory, this invention establishes a master-slave game interaction model between MGO and users. Compared with existing technologies, this invention has the following effective effects: (1) This invention can protect the privacy of participants, effectively increase microgrid revenue by 2.69%, and reduce user energy costs by 28%. (2) This invention can effectively smooth out power fluctuations caused by day-ahead forecast errors, reduce microgrid operating costs, and improve the absorption rate of new energy sources.

[0064] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0065] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for scheduling a distributed renewable energy grid-connected system, characterized in that: include: During the day-ahead scheduling phase, a day-ahead response plan for each microgrid device and load is formulated to provide a reference for intraday optimized scheduling. A scheduling task is executed once every day-ahead interval. Step 1: Initialize the parameters for the microgrid operator and load aggregator; Step 2: Microgrid operators, with system revenue as the goal, guide transferable loads to participate in demand-side response by optimizing scheduling methods; Step 3: With the goal of reducing energy consumption costs, the load aggregator shifts the electricity load one day in advance, responding to the microgrid operator's incentive mechanism, and then passes the responded load to the microgrid operator; Step 4; Microgrid operators readjust scheduling arrangements based on load response results; Step 5: Repeat steps 2 to 4 until the microgrid operator and the load aggregator reach a game equilibrium; During the intraday scheduling phase, based on the previous day's scheduling results, microgrid operators take the previous day's resource scheduling status as a reference and aim to minimize the microgrid operating cost within the cycle. Step 6: The microgrid operator updates the source load output forecast once every day at intervals, and optimizes the scheduling plan for the next scheduling cycle each time, readjusting the power arrangement of energy storage, gas turbines and grid, and correcting the deviation of the day-ahead scheduling plan; Step 7: The microgrid operator arranges for the supercapacitor to charge and discharge, and notifies the load aggregator one scheduling cycle in advance that the load interruption plan can be implemented to meet the power balance within the scheduling period. However, only the scheduling plan for the first intraday interval period within the scheduling cycle is executed, and so on, until the actual scheduling plan is finally determined.

2. The method for scheduling a distributed renewable energy grid-connected system according to claim 1, characterized in that, During the day-ahead scheduling phase, as the leader of the game, the microgrid operator aims to maximize daily revenue, and the objective function can be expressed as Formula 1: , Formula 1; In the formula, Revenue from selling electricity to users; For electricity purchase costs; For gas turbine operation and maintenance costs; For the operation and maintenance costs of energy storage batteries; For carbon trading costs; The costs of curtailing wind and solar power; The objective function constraints for microgrid operators include: power balance constraints, upper and lower output limits constraints, gas turbine ramp-up and minimum operating time constraints, power interaction constraints between the microgrid and the external power grid, battery charging and discharging constraints, and electricity price constraints.

3. The method for scheduling a distributed renewable energy grid-connected system according to claim 2, characterized in that, The optimization objective of the load aggregator is to maximize daily electricity consumption efficiency, and the objective function can be expressed as Equation 2: , Formula 2; In the formula, Let be the user's utility function, representing the satisfaction of the load aggregator in participating in the demand-side response; it is often represented by a quadratic form. This represents the load power predicted at the previous day; , They are respectively in the time period Load power transferred in and out, The day-ahead interval period, of which , These are the electricity preference coefficients of load aggregators, which can affect the amount of electricity demanded by users; The objective function constraints for load aggregators as followers include demand-side response load constraints.

4. The method for scheduling a distributed renewable energy grid-connected system according to claim 3, characterized in that, During the intraday scheduling phase, the objective function for minimizing the operating cost for microgrid operators within the scheduling cycle can be expressed as Formula 3: , Formula 3; In the formula, The scheduling period; Compensation costs to be paid for intraday interruptible load calls; , They are respectively Day-ahead forecasts for wind and solar power during the specified time period; , These are the day-ahead on-grid values ​​for wind power and solar power, respectively. , These are the unit penalties for wind and solar power curtailment, respectively. The compensation price for interruptible loads; This refers to the load reduction amount for a given period. The operating and maintenance costs of supercapacitors; , These are the maintenance and depreciation cost coefficients for supercapacitors, respectively. , For capacitors The charging and discharging power during a given period; The penalty cost for adjusting the scheduling plan due to intraday power forecast changes; , and These are the penalty costs for changes in energy storage charging and discharging power, changes in grid electricity sales and purchases, and changes in gas turbine output, respectively. For the day before Time-of-use gas turbine contribution; , The batteries were respectively at the date The charging and discharging power during a given period; , For storage batteries Charge and discharge power over time period; For gas turbine exist Output power during the time period; , These represent the power sold and purchased by the microgrid from the external grid, respectively. , Micronets were recently Electricity purchased and sold between the time period and the external network; The constraints include: power balance constraints, interrupted load constraints, gas turbine ramping constraints, intraday wind and solar grid-connected power constraints, intraday power adjustment constraints at corresponding times before and after the day, supercapacitor charging and discharging constraints, and intraday microgrid power source status constraints.

5. The method for scheduling a distributed renewable energy grid-connected system according to claim 4, characterized in that, The power purchased and sold from the microgrid to the external grid during the day is set as a flexible parameter, expressed as Formula 4: , Formula 4; In the formula, , The units Maximum and minimum output values; This is the output status indicator for the day-ahead gas turbine; , These are the battery's current charge / discharge status indicators; , These represent the maximum charging and discharging power of the battery.

6. The method for scheduling a distributed renewable energy grid-connected system according to claim 4, characterized in that, The daily output penalty should be set to meet the following requirements: .

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