A short-term economic dispatch method and system for a wind-solar-hydro-thermal combined system

Abstract

The application relates to the technical field of power system operation and optimal dispatching, and provides a wind-solar-hydro-thermal-storage combined system short-term economic dispatching method and system. The method obtains predicted data and parameter data in a dispatching period, and constructs hourly dispatching variables of wind power, photovoltaic power, hydroelectric power, thermal power and energy storage. An objective function including new energy curtailment loss, hydroelectric power curtailment loss, pumped storage circulating loss and thermal power operation cost is established with the optimal operation economy as the target, corresponding operation constraints are set, and a to-be-solved dispatching model is formed. The nonlinear term of the thermal power operation cost is linearized, the water head of each power station is taken as an iterative parameter to linearize the hydroelectric power output formula and circularly solve, and hourly operation results of various power sources are obtained. The method coordinates the operation relationship of different types of power sources in the combined system through the dispatching model, improves the new energy consumption capacity of the wind-solar-hydro-thermal-storage combined system on the basis of meeting actual operation requirements, and improves the overall operation economy of the system.

Description

Technical Field

[0001] This application relates to the field of power system operation and optimal dispatch technology, specifically to a short-term economic dispatch method and system for a combined wind, solar, hydro, thermal and energy storage system. Background Technology

[0002] With a high proportion of renewable energy integrated into the power system, wind and solar power output is significantly affected by meteorological conditions, exhibiting strong volatility and uncertainty. To ensure the safe and stable operation of the power system, more power sources with regulation capabilities are needed to participate in coordinated operation. Hydropower is characterized by flexible start-up and shutdown and rapid regulation, thermal power can provide base output and peak-shaving support, while pumped storage can absorb electricity during off-peak hours and release electricity during peak hours, possessing peak-shaving and valley-filling capabilities and rapid response. Therefore, the joint and optimized scheduling of wind, solar, hydro, thermal, and pumped storage has become an important technical direction for improving the absorption of renewable energy and reducing system operating costs.

[0003] In actual dispatching, the operating characteristics of various power sources differ significantly. Hydropower unit output is constrained by factors such as inflow, reservoir capacity, and water balance; thermal power unit output is constrained by minimum technical output, ramp-up capability, and fuel costs; and pumped storage units must simultaneously consider operational limitations related to pumping, power generation, and reservoir capacity changes. The uncertainties of wind and solar power output further increase the difficulty of dispatching. If these constraints are not adequately considered, the dispatching results may fail to meet actual operational requirements, potentially leading to insufficient renewable energy absorption, increased peak-shaving pressure on thermal power, or inadequate utilization of pumped storage.

[0004] While existing wind, solar, hydro, thermal, and energy storage joint dispatch methods can achieve coordinated operation of multiple energy sources to a certain extent, the actual operational constraints on hydropower, thermal power, and pumped storage are relatively simple and fail to fully reflect the mutual influence of multiple types of power sources in actual operation. This makes it difficult for dispatch schemes to achieve a good balance between new energy absorption capacity and operational economy. Summary of the Invention

[0005] This invention aims to solve the problems in the prior art and provide a short-term economic dispatch method and system for a combined wind, solar, hydro, thermal and energy storage system that can improve the absorption capacity of new energy sources and reduce the operating cost of the system.

[0006] To achieve the above objectives, the first aspect of this application provides a short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system, comprising the following steps:

[0007] S1. Obtain the prediction data and parameter data of the wind-solar-hydro-thermal-storage integrated system during the scheduling cycle, process the prediction data to a unified time scale, and combine it with the parameter data to obtain the scheduling input data. S2. Construct scheduling decision variables for each time period within the scheduling cycle to obtain a time-series decision variable set; wherein, the scheduling decision variables include wind power scheduling variables, photovoltaic scheduling variables, hydropower scheduling variables, thermal power scheduling variables, and energy storage equipment scheduling variables; S3. Construct a joint economic dispatch model for wind, solar, hydro, thermal, and energy storage based on the dispatch input data and the time-series decision variable set. The joint economic dispatch model for wind, solar, hydro, thermal, and energy storage aims to optimize the economic efficiency of system operation. It establishes an objective function that includes the losses from curtailment of new energy power, water curtailment of hydropower, cycle losses of pumped storage, and operating costs of thermal power. It also establishes a set of operating constraints corresponding to the operating characteristics of wind power, photovoltaic, hydropower, thermal power, and energy storage equipment to obtain the dispatch model to be solved. S4. Linearize and iteratively solve the scheduling model to be solved, including linearizing the nonlinear terms in the thermal power operating cost and using the head parameters that affect the hydropower output calculation as iterative update parameters; solve the linearized scheduling model to be solved based on the current head parameters to obtain the single-iteration scheduling result; update the head parameters according to the single-iteration scheduling result, and continue to solve based on the updated head parameters until the preset convergence condition is met to obtain the short-term economic scheduling scheme; The short-term economic dispatch scheme includes the hourly operation results of the wind power, photovoltaic, hydropower, thermal power and energy storage equipment within the dispatch cycle.

[0008] A second aspect of this application provides a short-term economic dispatch system for a combined wind, solar, hydro, thermal, and energy storage system, comprising a processor and a memory. The memory stores a program or instructions that can run on the processor, and when the program or instructions are executed by the processor, they implement the steps of the method described above.

[0009] Compared with the prior art, this application has the following beneficial effects: By incorporating the forecast and parameter data, scheduling variables, economic objectives, and operational constraints of wind power, photovoltaic, hydropower, thermal power, and energy storage equipment into a short-term scheduling model, and by processing the nonlinear term of thermal power operating costs and the changes in hydropower head, the model can coordinate the operational relationships of different types of power sources in the combined system, reflect the mutual influence of multiple types of power sources in actual operation, and avoid the scheduling results deviating from actual operational needs due to oversimplification of constraints. This allows the wind-solar-hydro-thermal-storage combined system to improve the capacity for renewable energy absorption and enhance the overall economic efficiency of the system while adhering to actual operational needs. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the iterative solution process for the short-term economic dispatch of a combined wind, solar, hydro, thermal and energy storage system provided in one embodiment of this application; Figure 3 This is a base load forecast curve diagram in one embodiment of this application; Figure 4 This is a wind power available output prediction curve diagram in one embodiment of this application; Figure 5 This is a photovoltaic power output prediction curve in one embodiment of this application; Figure 6 This is a graph showing the overall scheduling results of a combined wind, solar, hydro, thermal, and energy storage system in one embodiment of this application. Figure 7 This is a graph showing the wind power output and wind curtailment situation in one embodiment of this application; Figure 8 This is a graph showing the photovoltaic output and curtailment situation in one embodiment of this application; Figure 9 This is a thermal power output curve diagram of one embodiment of this application; Figure 10 This is a diagram showing the power output distribution of each hydropower station in one embodiment of this application; Figure 11 This is a diagram illustrating the operation of a first pumped storage power station in one embodiment of this application. Figure 12 This is a diagram illustrating the operation of the second pumped storage power station in one embodiment of this application. Detailed Implementation

[0012] To facilitate understanding of this application, the following description will be more comprehensive and detailed in conjunction with the accompanying drawings and preferred embodiments, but the scope of protection of this application is not limited to the following specific embodiments.

[0013] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of this application.

[0014] Please see Figure 1An embodiment of the present invention provides a short-term economic dispatch method for a combined wind, solar, hydro, thermal and energy storage system, comprising the following steps: S1. Obtain the forecast data and parameter data of the wind-solar-hydro-thermal-storage integrated system during the scheduling cycle, process the forecast data to a unified time scale, and combine it with the parameter data to obtain the scheduling input data.

[0015] In this step, the forecast data for the corresponding scheduling period (future time period) can specifically come from a load forecasting system or a power dispatching system, while the parameter data can be obtained from a dispatching database. The scheduling period can be divided into multiple scheduling time periods according to a preset time interval, for example, divided into 24 scheduling time periods by hour. The forecast data represents the changes in system load, available output of new energy sources, etc., over time within the scheduling period. The parameter data represents the operating capacity and operating boundaries of equipment such as hydropower, thermal power, and pumped storage power stations. The forecast data is processed to a unified time scale to become time-series data corresponding to each scheduling time period.

[0016] S2. Construct scheduling decision variables, which include wind power scheduling variables, photovoltaic scheduling variables, hydropower scheduling variables, thermal power scheduling variables, and energy storage equipment scheduling variables, to obtain a time series decision variable set.

[0017] In this step, decision variables are established for wind power, photovoltaic, hydropower, thermal power, and energy storage equipment based on the operational demands of each time period within the scheduling cycle. The time-series decision variable set is used to represent the hourly operational status of the combined wind-solar-hydro-thermal-energy-storage system throughout the entire scheduling cycle. This variable set can simultaneously express the renewable energy consumption, hydropower generation and water curtailment, thermal power output, and the power generation, pumping, and energy storage status of pumped storage power stations.

[0018] S3. Construct a joint economic dispatch model for wind, solar, hydro, thermal, and energy storage based on the dispatch input data and time-series decision variable set. The joint economic dispatch model for wind, solar, hydro, thermal, and energy storage aims to optimize the economic operation of the system. It establishes an objective function that includes the losses from new energy curtailment, hydropower curtailment, pumped storage cycle losses, and thermal power operating costs. It also establishes a set of operating constraints corresponding to the operating characteristics of wind power, photovoltaic, hydropower, thermal power, and energy storage equipment to obtain the dispatch model to be solved.

[0019] S4. Linearize and iteratively solve the scheduling model to be solved, including linearizing the nonlinear terms in the thermal power operating cost and using the head parameters that affect the hydropower output calculation as iterative update parameters; solve the linearized scheduling model to be solved based on the current head parameters to obtain the single-iteration scheduling result; update the head parameters according to the single-iteration scheduling result, and continue to solve based on the updated head parameters until the preset convergence condition is met to obtain the short-term economic scheduling scheme.

[0020] In this step, a mixed integer linear programming (MILP) solver can be called to solve the model in a hierarchical iterative manner. The operating cost of thermal power plants usually includes nonlinear terms related to the output of thermal power plants. Directly solving these terms will increase the difficulty of solving the model. Therefore, the nonlinear terms are linearized.

[0021] The short-term economic dispatch plan includes the hourly operation results of wind power, photovoltaic power, hydropower, thermal power and energy storage equipment during the dispatch cycle.

[0022] In this embodiment, the prediction and parameter data, scheduling variables, economic objectives and operational constraints of the wind-solar-hydro-thermal-storage integrated system are unified into a short-term scheduling model, and the solution is completed through linearization and head iterative update. This results in a short-term economic scheduling scheme that takes into account the consumption of new energy sources, utilization of hydropower, regulation of pumped storage and the economic efficiency of thermal power operation, thereby improving the executability and economy of the short-term scheduling results of multi-energy systems.

[0023] In one embodiment, the prediction data in S1 includes a load prediction sequence, a runoff prediction sequence, a wind power availability prediction sequence, and a photovoltaic power availability prediction sequence; the parameter data includes the output coefficient, upper and lower limits of output, downstream tailwater level parameters, water level-storage capacity curve, and initial reservoir capacity of daily regulating hydropower stations; the output coefficient and upper and lower limits of output of non-daily regulating hydropower stations; the power generation, pumping power, pumping efficiency, power generation efficiency, energy storage capacity, and initial energy storage of pumped storage power stations; and the upper and lower limits of output, maximum gradeability, and power generation cost coefficient of thermal power.

[0024] Specifically, the load forecast sequence is used to determine the electricity demand that the system needs to meet in each time period; the runoff forecast sequence is used to determine the inflow to the power station in each time period; and the wind power availability forecast sequence and photovoltaic power availability forecast sequence are used to determine the maximum available output of wind power and photovoltaic power in each time period. The downstream tailwater level parameters, water level-storage capacity curve, and initial reservoir capacity of the daily regulating hydropower station are used to describe the changes in water volume and head of the hydropower station during the scheduling cycle. The power generation, pumping power, efficiency parameters, and energy storage capacity of the pumped storage power station are used to describe its charging and discharging capabilities and changes in energy state. The upper and lower limits of thermal power output, maximum ramp rate, and power generation cost coefficient are used to describe the operating range, regulation capacity, and operating cost of thermal power units. By setting the above data, a complete input foundation can be provided for subsequent model building, avoiding the inaccurate reflection of the actual operating conditions of the system due to missing input data.

[0025] In one embodiment, in S2, the wind power dispatch variables include wind power output and wind curtailment; the photovoltaic dispatch variables include photovoltaic output and solar curtailment; the hydropower dispatch variables include daily regulating hydropower station output, daily regulating hydropower station power generation flow, daily regulating hydropower station water curtailment flow, daily regulating hydropower station reservoir capacity, and non-daily regulating hydropower station output; the energy storage device dispatch variables include pumped storage power station power generation, pumped storage power station pumping power, pumped storage power station energy storage, pumped storage power station power generation state variables, and pumped storage power station pumping state variables; and the thermal power dispatch variables include thermal power output, thermal power unit start-up state variables, and thermal power piecewise linearized output increment.

[0026] By setting the above variables, the model can fully express the operating status of various power sources and energy storage devices during the scheduling cycle.

[0027] In one embodiment, the objective function in S3 takes the following form:

[0028] In the formula, For wind power curtailment losses, For losses due to solar power curtailment, For water wastage losses from hydropower projects, This is due to the losses in the pumped storage cycle. For the operating costs of thermal power plants, , , , These represent the penalty coefficients for cycle losses due to wind curtailment, solar curtailment, hydro curtailment, and pumped storage, respectively. The optimal system operating economy is specifically represented by the objective function value. Minimum, It represents the comprehensive operating cost after adjusting for losses from renewable energy curtailment, hydropower curtailment, pumped storage cycle losses, and thermal power operating costs during the dispatch cycle.

[0029] Specifically, the objective function comprehensively reflects the system's operational economy through multiple loss and cost terms. Wind power curtailment losses and photovoltaic power curtailment losses guide the model to reduce unused renewable energy. Hydropower curtailment losses guide the model to reduce ineffective water curtailment and improve hydropower utilization efficiency. Pumped storage cycle losses reflect the efficiency losses in the energy conversion process of pumped storage. Thermal power operating costs reflect the fuel costs or equivalent operating costs incurred during the power generation process of thermal power units. By weighted summing of these terms, the model can prioritize obtaining the optimal scheduling result for system operational economy while satisfying operational constraints.

[0030] In a further embodiment, the wind power curtailment loss is specifically as follows:

[0031] The specific losses from solar power curtailment are as follows:

[0032] The specific losses from hydropower wastewater are as follows:

[0033] The specific losses in pumped storage cycle are:

[0034] The specific operating costs of thermal power plants are as follows: ; In the formula, For the wind power during the time period of efforts, For the photovoltaic in the time period of efforts, For daily regulating hydroelectric power station The output coefficient, For the aforementioned daily regulating hydropower station During the period Hydropower head, For the aforementioned daily regulating hydropower station During the period The discharge flow rate, Pumped storage power station During the period Pumping power, For pumping efficiency.

[0035] Specifically, wind power curtailment loss and photovoltaic curtailment loss reflect the level of new energy consumption through wind power output and photovoltaic output, respectively. Hydropower curtailment loss is calculated based on the daily regulating hydropower station's output coefficient, generating head, and curtailment flow rate, and is used to measure the loss of hydropower utilization caused by curtailment. Pumped storage cycle loss is calculated based on pumping efficiency and pumping power, and is used to reflect the energy loss of pumped storage power stations during the pumping and storage process. Thermal power operating costs are expressed as a quadratic function of thermal power output, which reflects the impact of changes in thermal power unit output on operating costs. Through the above specific calculation methods, the objective function can unify new energy consumption, hydropower utilization, energy storage regulation, and thermal power costs into a single economic dispatch objective.

[0036] In one embodiment, the set of operating constraints includes power balance constraints, wind and solar power output constraints, hydropower output, water flow process, reservoir capacity and power generation head constraints, pumped storage power generation, pumping, mutually exclusive operating conditions and energy storage constraints, thermal power output and ramping constraints.

[0037] In a specific embodiment, the power balance constraint is as follows:

[0038] In the formula: for Wind power output (MW) during the period; for Total photovoltaic output during the period (MW); A collection of hydroelectric power stations; For hydroelectric power station exist Active power output (MW) over a given period; A collection of pumped storage power stations; For pumped storage power station exist Power generation (MW) during the time period; For pumped storage power station exist Pumping power (MW) during the time period; For thermal power plants Active power during a given time period; for Time-of-use power demand (MW); For time period sets, .

[0039] Wind power output constraints are:

[0040] In the formula: for Actual wind power output (MW) during the time period; The installed capacity (MW) of the wind power station; for Wind power output coefficient for different time periods.

[0041] The photovoltaic output constraint is:

[0042] In the formula: for Actual photovoltaic output (MW) during the period; The installed capacity of a photovoltaic power station (MW); for Photovoltaic output coefficient during the time period.

[0043] Hydropower stations are responsible for supplying base load power to the base station and must meet guaranteed output requirements to ensure the reliability of the power grid. Hydropower output constraints include: ①Daily output constraints of regulating hydropower stations:

[0044] In the formula: For hydroelectric power station Guaranteed output (MW); For hydroelectric power station exist Active power output (MW) over a given period; For hydroelectric power station Installed capacity (MW); This is a collection of daily regulating hydroelectric power stations.

[0045] ② Output constraints of non-daily regulating hydropower stations:

[0046] In the formula: It is a collection of non-daily regulating hydropower stations, including seasonal and annual regulating stations.

[0047] The output constraint of the hydropower unit is: Each generating unit in a hydropower station operates independently, and the output of a single unit is constrained by its operating state, as expressed in the following formula:

[0048] In the formula: For hydroelectric power station No. Taiwanese crew Active power output (MW) over a given period; For hydroelectric power station No. Rated capacity of the unit (MW); For hydroelectric power station No. Taiwanese crew The running state variables of the time period, 1 indicates running, 0 indicates stopped; For hydroelectric power station A collection of generator sets.

[0049] The total output of a hydroelectric power station is equal to the sum of the outputs of its individual generating units.

[0050] In the formula: For hydroelectric power station exist Total output (MW) over the period; For hydroelectric power station No. Taiwanese crew Power output per time period (MW); For hydroelectric power station A collection of generator sets.

[0051] The start-stop constraints for hydropower units are:

[0052] In the formula: For hydroelectric power station No. Taiwanese crew The start-up state variables of the time period, ; For hydroelectric power station No. Taiwanese crew The downtime status variable during the period, ; For hydroelectric power station No. Taiwanese crew The running state variables of the time period; For hydroelectric power station No. Taiwanese crew The running status variables of a time period.

[0053] The start / stop mutual exclusion constraint is:

[0054] In the formula: To start the state variables; This is a shutdown state variable. This constraint ensures that the unit cannot be started and shut down simultaneously during the same period.

[0055] The water balance constraint of the hydropower station is:

[0056] In the formula: For hydroelectric power station exist Storage capacity at the end of the period (10,000 m³); For hydroelectric power station exist Storage capacity (10,000 m³) at the end of the period, when hour, Initial storage capacity; For hydroelectric power station exist Inbound flow rate (m³ / s) for a given time period; For hydroelectric power station exist Power generation flow rate (m³ / s) during the time period; For hydroelectric power station exist Overflow flow rate for the time period (m³ / s); 0.36 is the unit conversion factor to convert m³ / s·h to 10,000 m³.

[0057] The reservoir capacity constraint of the hydropower station is:

[0058] In the formula: For hydroelectric power station The minimum storage capacity, i.e. dead storage capacity (10,000 m³). For hydroelectric power station exist Storage capacity at the end of the period (10,000 m³); For hydroelectric power station The maximum reservoir capacity, i.e. the reservoir capacity corresponding to the normal water level (in ten thousand m³).

[0059] The non-negativity constraint for the overflow of the hydropower station is:

[0060] In the formula: For hydroelectric power station exist Overflow flow rate (m³ / s) for a given period of time.

[0061] The hydroelectric head for power generation is calculated as follows:

[0062] In the formula: For hydroelectric power station exist Hydropower head (m) for a given time period; For hydroelectric power station exist The upstream water level (m) at the end of the period is obtained by interpolation of the reservoir capacity-water level curve; For hydroelectric power station The downstream tailwater level (m) is a fixed value.

[0063] The expression for determining the upstream water level using the reservoir capacity-water level relationship is:

[0064] In the formula: For hydroelectric power station exist Upstream water level (m) at the end of the time period; For hydroelectric power station The reservoir capacity-water level interpolation function; For hydroelectric power station exist Storage capacity at the end of the period (in ten thousand m³).

[0065] The power generation constraint of pumped storage power stations is:

[0066] In the formula: For pumped storage power station exist Power generation (MW) during the time period; For pumped storage power station Installed capacity (MW); For pumped storage power station exist Power generation state variables during a given period 1 indicates power generation, and 0 indicates non-power generation.

[0067] The pumping power constraint of the pumped storage power station is:

[0068] In the formula: For pumped storage power station exist Pumping power (MW) during the time period; For pumped storage power station Installed capacity (MW); For pumped storage power station exist Pumping state variables during the time period 1 indicates pumping status, and 0 indicates non-pumping status.

[0069] The mutual exclusion constraints for the operating conditions of pumped storage power stations are as follows:

[0070] In the formula: For pumped storage power station exist Power generation state variables for a given time period; For pumped storage power station exist Pumping state variables for a given time period. This constraint ensures that a pumped storage power station cannot be in both power generation and pumping states at the same time.

[0071] The dynamic constraints on the energy storage capacity of pumped storage power stations are:

[0072] In the formula: For pumped storage power station exist Energy storage at the end of the period (MWh); For pumped storage power station exist Energy storage at the end of the period (MWh), when hour, For initial energy storage; For pumped storage power station exist Power generation (MW) during the time period; For pumping efficiency; For pumped storage power station exist Pumping power (MW) during the time period; The time step is (h). In this embodiment, the power generation efficiency of the pumped storage power station is taken as 1.0, therefore no power generation efficiency correction term is set in the dynamic constraints of energy storage.

[0073] The energy storage range constraints for pumped storage power stations are:

[0074] In the formula: For pumped storage power station exist Energy storage at the end of the period (MWh); For pumped storage power station Maximum energy storage capacity (MWh).

[0075] The end-of-day energy storage recovery constraint for pumped storage power stations is:

[0076] In the formula: For pumped storage power station In the Energy stored at the end of the period (i.e., the end of the day) (MWh); For pumped storage power station The initial energy storage (MWh). This constraint ensures that the pumped storage power station returns to its initial energy storage state at the end of the daily operating cycle.

[0077] The output constraints of thermal power plants are:

[0078] In the formula: For thermal power units During the period contribution; and thermal power units Minimum and maximum output; For thermal power units During the period The boot state variable, when When the time indicates power-on, The time indicates that the machine is stopped; A collection of thermal power units; This is the set of scheduling time periods.

[0079] The ramp rate constraint for thermal power plants is:

[0080] In the formula: For thermal power units During the period Power output (MW); For thermal power units Maximum climb rate (MW) within a scheduling period.

[0081] In S4, the nonlinear terms in the operating cost of thermal power plants are linearized, specifically including: thermal power units During the period The quadratic operating cost function corresponding to the output is taken as the object to be linearized. The quadratic operating cost function is expressed as:

[0082] In the formula, For the thermal power unit During the period Operating costs For the thermal power unit During the period of efforts, , , For the thermal power unit The power generation cost coefficient; thermal power units The output range is divided into Given a series of continuous linear segments, each segment node satisfies:

[0083] In the formula, For the thermal power unit Minimum output, For the thermal power unit Maximum output For thermal power units The number of segments, For thermal power units The first segment node;

[0084] In the formula, For the thermal power unit During the period The power-on status variables; Set segmented value ranges for the output increment variable:

[0085] Based on the difference in operating cost and output at two adjacent segment nodes, determine the first... The unit incremental cost coefficient corresponding to the segment:

[0086] In the formula, For thermal power units The Segment unit incremental cost coefficient, For thermal power units At the segment node The corresponding operating costs.

[0087] Based on the output increment variable and the unit increment cost coefficient, the quadratic operating cost function is converted into piecewise linear cost terms to obtain the linearized thermal power operating cost terms. The linearized cost terms of each thermal power unit and each time period are summed to obtain the linearized thermal power operating cost.

[0088] Specifically, the operating cost of thermal power plants is originally a quadratic function, and directly introducing it into the scheduling model would increase the solution complexity. By dividing the output range of thermal power plants into multiple continuous segments and using the unit incremental cost coefficient of each segment to represent the cost change of the corresponding segment, the nonlinear operating cost of thermal power plants can be approximately converted into a linear expression. This makes the scheduling model suitable for solving using mixed-integer linear programming methods, while preserving the relationship of cost increase as thermal power output increases, thus improving the model's solution efficiency.

[0089] In one embodiment, in S4, the iterative update of the head parameter includes: Based on the initial reservoir capacity of each daily regulating hydropower station, the initial upstream water level is calculated by interpolation according to the reservoir capacity-water level characteristic curve, and the initial power generation head for each period is determined by combining the downstream tailwater level, so as to obtain the initial water head parameters. The initial water head parameters are used as the known water head parameters when solving the linearized scheduling model for the first time. In the In each iteration, the known head parameters are substituted into the power-flow relationship to solve the linearized scheduling model and obtain the scheduling result of a single iteration. Based on the reservoir capacity at the end of the time period in the single iteration scheduling result, the corresponding upstream water level is calculated by interpolation using the reservoir capacity-water level characteristic curve, and the downstream tailwater level is combined to obtain the calculated head for this iteration. The head calculated in this iteration is weighted with the known head parameters used in the previous iteration according to the relaxation factor to obtain the known head to be used in the next iteration. The update formula is as follows:

[0090] in:

[0091] The head calculated in this iteration satisfies:

[0092] In the formula, For daily regulating hydroelectric power station During the period The The known head parameters used in this iteration For the first The head obtained from the next iteration For the first The known head parameters used in this iteration The relaxation factor, For the first The next iteration of the Sino-Japanese regulating hydropower station During the period The corresponding upstream water level, For daily regulating hydroelectric power station During the period The corresponding downstream tailwater level.

[0093] Based on the iterative update of the head parameters, the updated head parameters are obtained and used for the next solution.

[0094] Specifically, since the power generation of a daily regulating hydropower station is related to its power generation flow and head, and the head is affected by reservoir capacity and water level changes, directly handling head changes and hydropower output relationships simultaneously during model solving would increase the difficulty of nonlinear solutions. This embodiment first uses the head parameters as known parameters in a single model solution, and then calculates the upstream water level and head based on the reservoir capacity results obtained from the solution. By weighting the newly calculated head with the previously known head using a relaxation factor, fluctuations in adjacent iterations can be reduced, making the head update process more stable. Thus, while maintaining the linear solution form of the model, the head parameters used in the hydropower output calculation can be gradually corrected.

[0095] In one embodiment, the preset convergence condition is that the maximum change in the known hydropower head between two adjacent iterations is less than or equal to a preset convergence threshold, expressed as:

[0096] In the formula, For the collection of daily regulating hydroelectric power stations, For the set of scheduling periods, This is the preset convergence threshold.

[0097] When the preset convergence condition is met, the calculation is terminated and the short-term economic scheduling scheme is output.

[0098] When the preset convergence condition is not met, the updated head parameters are used to solve the next mixed integer linear programming subproblem until the preset convergence condition is met or the preset maximum number of iterations is reached, thus obtaining the short-term economic scheduling scheme.

[0099] Specifically, by comparing the known hydropower heads used in two adjacent iterations, it can be determined whether the hydropower head parameters have stabilized. When the maximum change is less than or equal to the preset convergence threshold, it indicates that continuing the iteration has little impact on the hydropower head parameters and scheduling results, and the calculation can be terminated and the current scheduling result output. When the maximum change is still greater than the preset convergence threshold, it indicates that the hydropower head parameters still need to be corrected, and the next solution needs to be performed. If the number of iterations reaches the preset maximum number of iterations, the currently obtained scheduling scheme can also be output to avoid the calculation process from continuing indefinitely. Through the above convergence judgment method, both computational accuracy and solution efficiency can be taken into account, making the short-term economic scheduling scheme more feasible.

[0100] To further illustrate the method of the present invention, the following describes the short-term economic dispatch process of a combined wind, solar, hydro, thermal, and energy storage system with reference to a specific embodiment. The specific solution process is as follows: Figure 2 As shown.

[0101] This embodiment studies a combined wind-solar-hydro-thermal-pumped storage system comprised of power stations within a clean energy base. The system has a total installed capacity of approximately 31.837 million kW. The power source composition is as follows: Eight cascade hydropower stations with a total installed capacity of 2.327 million kW, distributed across two river basins. The first basin contains five power stations, four of which are daily regulating stations and one is a seasonal regulating station. The second basin contains three power stations, two of which are daily regulating stations and one is an annual regulating station. Wind power has an installed capacity of 560,000 kW, consisting of one wind power station. The total installed photovoltaic capacity is 21 million kW, including photovoltaic power station 1 (16.99 million kW) and photovoltaic power station 2 (4.01 million kW). The total installed pumped storage capacity is 4.95 million kW, including pumped storage power station 1 (2.25 million kW) and pumped storage power station 2 (2.7 million kW). There is one thermal power station with an installed capacity of 3 million kW.

[0102] Table 1 Summary of System Power Configurations in Examples

[0103] In this embodiment, the scheduling cycle is 24 hours and the time step is 1 hour (this invention does not limit the scheduling cycle or time step in actual application). The model input data includes prediction data and parameter data. The prediction data includes load prediction sequences, runoff prediction sequences, wind power availability prediction sequences, and photovoltaic availability prediction sequences; the parameter data includes hydropower station operating parameters, pumped storage power station operating parameters, and thermal power operating parameters.

[0104] The operating parameters of hydropower stations include the output coefficient, upper and lower limits of output, downstream tailwater level parameters, water level-storage capacity curve and initial reservoir capacity of daily regulating hydropower stations, as well as the output coefficient and upper and lower limits of output of non-daily regulating hydropower stations; the operating parameters of pumped storage power stations include power generation, pumping power, pumping efficiency, power generation efficiency, energy storage capacity and initial energy storage; the operating parameters of thermal power stations include the upper and lower limits of output of thermal power units, maximum gradeability and power generation cost coefficient.

[0105] The prediction data and parameter data used in this embodiment are shown below.

[0106] Table 2 Base Load and Wind / Solar Forecast Curves

[0107] Load and wind / solar forecast curves are as follows Figure 3 , 4 As shown in Figure 5.

[0108] Table 3 Key Parameters for Each Power Station

[0109] Table 4 Key Parameters of Pumped Storage Power Station

[0110] This model uses plant-level modeling for thermal power plants. The thermal power plant uses one equivalent unit with a minimum output of 900MW and a maximum output of 3000MW. The thermal power plant operating cost coefficients a, b, and c are 0.00035, 210, and 20000, respectively, and the maximum ramp rate is 600MW / h.

[0111] The initial states of each power station are as follows: The initial state parameters of the eight conventional hydropower stations are summarized by basin as follows. For daily regulating power stations, the initial reservoir capacity is calculated as dead reservoir capacity plus 0.8 times the regulating reservoir capacity, and the initial head is obtained by interpolation of the reservoir capacity-water level curve. For seasonal and annual regulating power stations, a simplified treatment with a fixed rated head is adopted, and the initial reservoir capacity is not calculated.

[0112] Table 5 Initial State of Hydropower Stations in the First Basin

[0113] Table 6 Initial State of Hydropower Stations in the Second Basin

[0114] The initial state parameters of the two pumped storage power stations are shown in the table below: Table 7 Initial State of Pumped Storage Power Station

[0115] The penalty factor for wind power curtailment loss is set at 260 yuan / MWh. Considering that solar power has a stronger regularity than wind power and its priority for grid integration is lower, the penalty factor for solar power curtailment loss is set at 240 yuan / MWh. The penalty factor for hydropower curtailment loss is set at 200 yuan / MWh, and the penalty factor for pumped storage cycle loss is set at 60 yuan / MWh. Both wind power and solar power curtailment represent real-time, irreversible losses in renewable energy grid integration, hence the use of higher penalty factors.

[0116] The above predicted and parameter data are used as input data for scheduling, and are input into the wind-solar-hydro-thermal-storage joint economic scheduling model for iterative solution. The maximum number of iterations is preset to 200, the relaxation factor α is set to 0.25, and the preset convergence threshold ε is set to 0.2m. Under the above parameter conditions, the linearized scheduling model is solved using a mixed-integer linear programming iterative solution method to obtain a short-term economic scheduling scheme. This short-term economic scheduling scheme includes the hourly operation results of wind power, photovoltaic, hydropower, thermal power, and energy storage equipment within the scheduling cycle, as well as the hourly generation flow, water discharge flow, and reservoir capacity changes of each regulating hydropower station.

[0117] The results show that, under the premise of satisfying system power balance constraints, hydropower reservoir capacity constraints, pumped storage power station operation constraints, and upper and lower limits and ramp-up constraints of thermal power unit output, the output of various power sources can be allocated according to load demand, available renewable energy output, and energy storage status. During periods of high available wind and solar power, the model prioritizes renewable energy output and reduces wind and solar curtailment by reducing some hydropower output and scheduling pumped storage power station pumping. During periods of high system net load, the model meets load demand by increasing thermal power unit output, increasing daily regulating hydropower station power generation flow, and scheduling pumped storage power station power generation.

[0118] Statistical analysis of the dispatch results yields the component values ​​of the objective function, including wind curtailment loss, solar curtailment loss, hydropower curtailment loss, pumped storage cycle loss, and thermal power operating costs, as detailed in Table 8. The load forecast curve is shown below. Figure 3 As shown in the figure, the overall scheduling result curve is as follows: Figure 6 As shown, wind power output and wind curtailment are as follows: Figure 7 As shown, the photovoltaic output and curtailment situation are as follows: Figure 8 As shown, the thermal power output curve is as follows: Figure 9 As shown, the power output distribution results of daily regulating hydropower stations and non-daily regulating hydropower stations are as follows: Figure 10 As shown, the operation process of each pumped storage power station is as follows: Figure 11 and Figure 12 As shown.

[0119] Table 8. Component values ​​of the objective function

[0120] The scheduling results show that the main form of renewable energy curtailment is solar power curtailment, which is concentrated during peak solar output periods in the daytime. From 9:00 AM to 4:00 PM, solar power output is high, and pumped-storage hydropower stations are primarily in pumping operation, while the output of hydropower and thermal power units decreases accordingly, with thermal power units generally operating near their minimum output. This indicates that the model provides space for solar power consumption by reducing some hydropower output and scheduling pumped-storage hydropower stations to pump water. However, due to the large concentrated solar power output during midday and limitations imposed by load levels, minimum output constraints of thermal power units, and the upper limit of pumping power at pumped-storage hydropower stations, some solar power curtailment still exists.

[0121] The overall dispatch results show that hydropower output decreases during peak solar PV periods and increases in the evening and at night after solar PV output weakens, demonstrating the peak-shaving role of hydropower. Pumped storage power stations mainly pump water during the daytime when renewable energy is abundant, and switch to power generation in the evening and at night when load is high and solar PV output decreases, thus playing a role in peak shaving and valley filling. Thermal power units basically maintain minimum output operation from 0h to 16h, and gradually increase output after 17h as solar PV output decreases, reaching a higher level during the evening peak period. This indicates that thermal power units mainly provide basic support during peak renewable energy periods, and play a compensatory power supply role when renewable energy output decreases and the system net load increases.

[0122] The above results demonstrate that this embodiment can jointly schedule wind power, photovoltaic, hydropower, thermal power and energy storage equipment under the condition of satisfying the set of operating constraints, and obtain a short-term economic scheduling scheme for the wind-solar-hydro-thermal-storage integrated system.

[0123] This application also provides a short-term economic dispatch system for a combined wind, solar, hydro, thermal and energy storage system, including a processor and a memory. The memory stores a program or instructions that can run on the processor. When the program or instructions are executed by the processor, they implement the various steps of the above method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0124] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the various processes of the above-described method embodiments and achieve the same technical effects. To avoid repetition, these will not be described again here.

[0125] The processor can be the processor in the aforementioned system. Readable storage media include computer-readable storage media, such as computer read-only memory, random access memory, magnetic disks, or optical disks.

[0126] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as a read-only memory). The device includes a number of instructions in a ROM (random access memory), RAM (magnetic disk), or optical disk to cause a terminal to execute the methods described in the various embodiments of this application.

[0128] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system, characterized in that, Including the following steps: S1. Obtain the prediction data and parameter data of the wind-solar-hydro-thermal-storage integrated system during the scheduling cycle, process the prediction data to a unified time scale, and combine it with the parameter data to obtain the scheduling input data. S2. Construct scheduling decision variables for each time period within the scheduling cycle to obtain a time-series decision variable set; wherein, the scheduling decision variables include wind power scheduling variables, photovoltaic scheduling variables, hydropower scheduling variables, thermal power scheduling variables, and energy storage equipment scheduling variables; S3. Construct a joint economic dispatch model for wind, solar, hydro, thermal, and energy storage based on the dispatch input data and the time-series decision variable set. The joint economic dispatch model for wind, solar, hydro, thermal, and energy storage aims to optimize the economic efficiency of system operation. It establishes an objective function that includes the losses from curtailment of new energy power, water curtailment of hydropower, cycle losses of pumped storage, and operating costs of thermal power. It also establishes a set of operating constraints corresponding to the operating characteristics of wind power, photovoltaic, hydropower, thermal power, and energy storage equipment to obtain the dispatch model to be solved. S4. Linearize and iteratively solve the scheduling model to be solved, including linearizing the nonlinear terms in the thermal power operating cost and using the head parameters that affect the hydropower output calculation as iterative update parameters; solve the linearized scheduling model to be solved based on the current head parameters to obtain a single iteration scheduling result; update the head parameters according to the single iteration scheduling result, and continue to solve based on the updated head parameters until the preset convergence condition is met to obtain a short-term economic scheduling scheme; The short-term economic dispatch scheme includes the hourly operation results of the wind power, photovoltaic, hydropower, thermal power and energy storage equipment within the dispatch cycle.

2. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 1, characterized in that, In S3, the objective function takes the following form: In the formula, For wind power curtailment losses, For losses due to solar power curtailment, For water wastage losses from hydropower projects, This is due to the losses in the pumped storage cycle. For the operating costs of thermal power plants, , , , These are the penalty coefficients for the cycle losses of wind curtailment, solar curtailment, hydro curtailment, and pumped storage, respectively.

3. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 2, characterized in that, The specific details of the wind power curtailment loss are as follows: The photovoltaic curtailment loss specifically refers to: The specific details of the water wastage loss from hydropower are as follows: The pumped storage cycle loss specifically refers to: The specific operating costs of the thermal power plant are as follows: ； In the formula, For the wind power during the time period of efforts, For the photovoltaic in the time period of efforts, For daily regulating hydroelectric power station The output coefficient, For the aforementioned daily regulating hydropower station During the period Hydropower head, For the collection of daily regulating hydroelectric power stations, For the aforementioned daily regulating hydropower station During the period The discharge flow rate, Pumped storage power station During the period Pumping power, For pumping efficiency, , , All are thermal power operating cost coefficients.

4. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 1, characterized in that, The set of operational constraints includes power balance constraints, wind and solar power output constraints, hydropower output, water flow process, reservoir capacity and power generation head constraints, pumped storage power generation, pumping, mutually exclusive operating conditions and energy storage constraints, thermal power output and ramping constraints.

5. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 1, characterized in that, In step S4, the nonlinear term in the thermal power plant operating cost is linearized, specifically including: thermal power units During the period The quadratic operating cost function corresponding to the output is taken as the object to be linearized, and the quadratic operating cost function is expressed as: In the formula, For the thermal power unit During the period Operating costs For the thermal power unit During the period of efforts, , , For the thermal power unit The power generation cost coefficient; thermal power units The output range is divided into Given a series of continuous linear segments, each segment node satisfies: In the formula, For the thermal power unit Minimum output, For the thermal power unit Maximum output For the thermal power unit The number of segments, For the thermal power unit The first segment node; Define thermal power units During the period The The variable of segment output increment is Then thermal power units During the period The output is expressed as: In the formula, For the thermal power unit During the period The power-on status variables; Set segmented value ranges for the output increment variable: Based on the difference in operating cost and output at two adjacent segment nodes, determine the first... The unit incremental cost coefficient corresponding to the segment: In the formula, For the thermal power unit The Segment unit incremental cost coefficient, For the thermal power unit At the segment node The corresponding operating costs; Based on the output increment variable and the unit increment cost coefficient, the quadratic operating cost function is converted into piecewise linear cost terms to obtain the linearized thermal power operating cost terms. The linearized cost terms of each thermal power unit and each time period are summed to obtain the linearized thermal power operating cost.

6. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 1, characterized in that, In step S4, the iterative update of the head parameter includes: Based on the initial reservoir capacity of each daily regulating hydropower station, the initial upstream water level is calculated by interpolation according to the reservoir capacity-water level characteristic curve, and the initial power generation head for each period is determined by combining the downstream tailwater level, so as to obtain the initial water head parameters. The initial water head parameters are used as the known water head parameters when solving the linearized scheduling model for the first time. In the In each iteration, the known head parameters are substituted into the power-flow relationship to solve the linearized scheduling model and obtain the single-iteration scheduling result. Based on the reservoir capacity at the end of the time period in the single iteration scheduling result, the corresponding upstream water level is calculated by interpolation using the reservoir capacity-water level characteristic curve, and the downstream tailwater level is combined to obtain the calculated head for this iteration. The known head calculated in this iteration is weighted with the known head parameters used in the previous iteration using a relaxation factor to obtain the known head to be used in the next iteration. The update formula is as follows: in, ; The head calculated in this iteration satisfies: In the formula, For daily regulating hydroelectric power station During the period The The known head parameters used in this iteration For the first The head obtained from the next iteration For the first The known head parameters used in this iteration The relaxation factor; Based on the iterative update of the head parameters, the updated head parameters are obtained, and the updated head parameters are used for the next solution.

7. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 6, characterized in that, The preset convergence condition is: the maximum change in the known hydroelectric head between two adjacent iterations is less than or equal to the preset convergence threshold, expressed as: In the formula, For the collection of daily regulating hydroelectric power stations, For the set of scheduling periods, The preset convergence threshold is used; When the preset convergence condition is met, the calculation is terminated and the short-term economic scheduling scheme is output. When the preset convergence condition is not met, the updated head parameters are used to solve the next mixed-integer linear programming subproblem until the preset convergence condition is met or the preset maximum number of iterations is reached. The feasible scheduling result obtained from the last iteration is then output as the short-term economic scheduling scheme.

8. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 1, characterized in that, The prediction data in S1 includes load prediction sequence, runoff prediction sequence, wind power available power prediction sequence, and photovoltaic power available power prediction sequence; The parameter data includes the output coefficient, upper and lower limits of output, downstream tailwater level parameters, water level-storage capacity curve and initial reservoir capacity of daily regulating hydropower stations; the output coefficient and upper and lower limits of output of non-daily regulating hydropower stations; the power generation, pumping power, pumping efficiency, power generation efficiency, energy storage capacity and initial energy storage of pumped storage power stations; and the upper and lower limits of output, maximum gradeability and power generation cost coefficient of thermal power plants.

9. The short-term economic dispatch method for a combined wind, solar, hydro, thermal, and energy storage system according to claim 1, characterized in that, In S2, the wind power dispatch variables include wind power output and wind curtailment; the photovoltaic dispatch variables include photovoltaic output and solar curtailment; the hydropower dispatch variables include daily regulating hydropower station output, daily regulating hydropower station power generation flow, daily regulating hydropower station water curtailment flow, daily regulating hydropower station reservoir capacity, and non-daily regulating hydropower station output; the energy storage equipment dispatch variables include pumped storage power station power generation, pumped storage power station pumping power, pumped storage power station energy storage, pumped storage power station power generation status variables, and pumped storage power station pumping status variables; and the thermal power dispatch variables include thermal power output, thermal power unit start-up status variables, and thermal power segmented linearized output increment.

10. A short-term economic dispatch system for a combined wind, solar, hydro, thermal, and energy storage system, characterized in that, It includes a processor and a memory, wherein the memory stores a program or instructions executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 9.

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