Source grid load storage participation power grid peak regulation optimization method based on time sequence scene

Abstract

The invention belongs to the technical field of power system dispatching automation, and particularly relates to a source network load storage participation power grid peak regulation optimization method based on a time sequence scene. Comprising the steps of performing data integration based on short-term plan data and ultra-short-term plan data, and generating a time sequence scene set for day-ahead and intra-day peak regulation optimization of a power grid; generating a candidate measure set of day-ahead and intra-day power grid peak regulation optimization according to adjustable characteristics and time sequence characteristics of source grid load storage multi-class adjustment resources in the system; based on the time sequence scene set, a day-ahead peak regulation optimization scheduling model with the lowest power grid operation cost as the target is constructed, and day-ahead power grid peak regulation optimization adjustment is carried out by adjusting multiple types of adjustment resources of source grid load storage; and based on the time sequence scene set, constructing an intra-day peak regulation optimization scheduling model with the lowest power grid operation cost as a target, and performing intra-day power grid peak regulation optimization adjustment by adjusting multiple types of adjustment resources of source grid load storage. According to the method, the effect of cooperative control of various resources such as source network load storage by considering the time sequence characteristics is fully exerted.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching automation, and in particular relates to a method for optimizing power grid peak regulation involving source, grid, load and storage based on a time sequence scenario. Background Art

[0002] With the development of clean and renewable energy generation technology, the proportion of new energy such as wind power and photovoltaics in my country's power system has continued to increase. Due to the access of large-scale wind and solar resources, the complexity and uncertainty of the operation mode of the power grid have increased significantly. On the load side, there is inevitably a certain prediction error in the load. Therefore, the strong uncertainty on both the source and load sides is an issue that must be considered in the regulation of the new power system.

[0003] At present, in typical application scenarios such as peak shaving and valley filling in power grids and new energy consumption, the role of various resources such as sources, grids, loads and storage has not been fully utilized. There is a lack of coordinated control capabilities for various resources such as sources, grids, loads and storage, and it cannot be applied to the formulation of peak-shaving plans for the power grid a day before and within the day.

[0004] Therefore, a method is needed to accurately and quickly simplify and cluster uncertain models, and involve multiple types of regulating resources such as source, grid, load and storage in the process of power grid peak regulation optimization. With the goal of minimizing power grid operation costs, the day-ahead and intra-day peak regulation optimization is performed by adjusting multiple types of regulating resources such as source, grid, load and storage, providing strong support for day-ahead and intra-day power grid dispatching. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned prior art, the present invention provides a method for optimizing power grid peak load regulation based on time series scenarios. The purpose is to generate a time series scenario set for power grid day-ahead and intraday peak load regulation optimization based on short-term planning data and ultra-short-term planning data, and to optimize day-ahead and intraday peak load regulation by adjusting multiple types of regulating resources of source, grid, load and storage, so as to achieve the purpose of the invention of providing strong support for day-ahead and intraday power grid dispatching.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0007] A method for optimizing power grid peak load regulation based on a time sequence scenario with source, grid, load and storage participating, comprising:

[0008] Integrate short-term and ultra-short-term planning data to generate a time series scenario set for day-ahead and intra-day peak load optimization of the power grid;

[0009] According to the adjustable characteristics and timing characteristics of multiple types of regulation resources in the system, such as source, grid, load and storage, a set of candidate measures for optimizing the peak load regulation of the power grid before and during the day is generated;

[0010] Based on the time series scenario set, a day-ahead peak-shaving optimization dispatching model is constructed with the goal of minimizing the grid operation cost. The day-ahead grid peak-shaving optimization adjustment is performed by adjusting multiple types of regulation resources such as source, grid, load and storage.

[0011] Based on the time series scenario set, a daily peak-shaving optimization scheduling model is constructed with the goal of minimizing the grid operation cost. The daily grid peak-shaving optimization adjustment is carried out by adjusting multiple types of regulation resources such as source, grid, load and storage.

[0012] Furthermore, the generation process of the time series scenario set for the day-ahead and day-intraday power grid peak load optimization includes:

[0013] Obtain short-term and ultra-short-term planning data and their probability distribution information, and determine the planned periods with insufficient upper and lower reserves as the periods requiring peak load regulation based on the power supply balance;

[0014] For the planned period that requires peak load regulation, Latin hypercube sampling is used to sample the random variables of power generation, load and energy storage provided by the scheduling plan data in sequence, and the initial time series scenario set and its probability for the optimization of day-ahead and intraday peak load regulation of the power grid are obtained by combination;

[0015] Check whether the adjacent running scenarios in the initial timing scenario set meet the timing constraints in chronological order, and remove the running scenarios with later timing in the adjacent running scenarios that do not meet the timing constraints;

[0016] The remaining initial time series scenarios are simplified and clustered to obtain the time series scenario set and its probability for the day-ahead and day-intraday peak load optimization of the power grid.

[0017] Furthermore, the timing constraints include: unit ramp rate constraints and energy storage charging and discharging duration constraints.

[0018] Furthermore, the generation process of the candidate measure set for power grid peak load optimization includes:

[0019] The start and stop of thermal power units, the start and stop of pumped storage units, and the adjustment of maintenance plans are selected as the candidate set of measures for day-ahead peak regulation. The output adjustment of hydropower units, the output adjustment of thermal power units, the charging and discharging of energy storage, and the activation and deactivation of flexible loads are selected as the candidate set of measures for intraday peak regulation.

[0020] For the various peak load regulation candidate measures set in the previous day, firstly sort them according to the priority of pumped storage start-up and stop, thermal power unit start-up and stop, and maintenance plan adjustment, and then sort them according to the adjustment cost of the candidate measures from small to large in the second level;

[0021] For each set of candidate peak-shaving measures within the day, they are first sorted according to the priority of energy storage charging and discharging, hydropower unit output adjustment, thermal power unit output adjustment and flexible load adjustment, and then sorted at the second level according to the adjustment cost of the candidate measures from small to large.

[0022] Furthermore, the day-ahead peak load optimization scheduling model based on the time series scenario includes:

[0023] The objective function of the day-ahead peak load optimization scheduling model is as follows:

[0024]

[0025] Where T is the total number of short-term planning periods, N t is the number of scenarios in planning period t, ρ n is the probability of scene n, N G is the number of thermal power units, is the power generation cost of thermal power unit i, is the active power of thermal power unit i in scenario n during planning period t, N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, K is the cost of wind and solar power abandonment, ΔP pw,t,n is the wind power abandoned in scenario n during planning period t, ΔP pv,t,n is the abandoned optical power in scenario n during the planned period t;

[0026] The constraints of the day-ahead peak load optimization scheduling model are as follows:

[0027]

[0028] In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

[0029] Furthermore, the intraday peak load optimization scheduling model based on the time series scenario includes:

[0030] The objective function of the intraday peak load optimization scheduling model is as follows:

[0031]

[0032] Where T is the total number of short-term planning periods, N t is the number of scenarios in planning period t, ρ n is the probability of scenario n; N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, N ess is the number of energy storage devices, is the energy storage cost of energy storage device i, is the active power of energy storage device i in scenario n during planning period t, N ld is the flexible load number, is the cost of flexible load i, is the active power of flexible load i in scenario n during planning period t, P e,t is the electricity price at time t, ΔP load,t,n is the load adjustment active power in scenario n at time t, K is the wind and solar curtailment cost, ΔP pw,t,n is the wind power abandoned in scenario n at time t, ΔP pv,t,n K is the abandoned optical power in scene n at time t; ess is the storage adjustment cost, ΔP ess,t,n is the energy storage adjustment in scenario n during planning period t;

[0033] The constraints of the intraday peak load optimization scheduling model are as follows:

[0034]

[0035] In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

[0036] A source-grid-load-storage participating power grid peak load optimization device based on a time sequence scenario, comprising:

[0037] The first calculation module is used to integrate data based on short-term planning data and ultra-short-term planning data to generate a time series scenario set for day-ahead and intra-day peak load optimization of the power grid;

[0038] The second calculation module is used to generate a set of candidate measures for optimizing the peak load regulation of the power grid in the day and within the day according to the adjustable characteristics and timing characteristics of multiple types of regulation resources in the system, including source, grid, load and storage;

[0039] The third calculation module is used to build a day-ahead optimization dispatching model based on a time series scenario set with the goal of minimizing the grid operation cost, and optimize the day-ahead grid peak regulation by adjusting multiple types of regulation resources such as source, grid, load and storage;

[0040] The fourth calculation module is used to build a daily optimization scheduling model based on a set of time series scenarios with the goal of minimizing the grid operation cost, and to optimize the grid peak regulation during the day by adjusting multiple types of regulation resources such as source, grid, load and storage.

[0041] Furthermore, the device is used to implement any one of the steps of a method for optimizing power grid peak load regulation based on a time sequence scenario involving source, grid, load and storage.

[0042] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, any one of the steps of a method for optimizing power grid peak regulation involving source, grid, load and storage based on a time sequence scenario is implemented.

[0043] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, any one of the steps of a method for optimizing power grid peak load regulation involving source, grid, load and storage based on a time sequence scenario is implemented.

[0044] The present invention has the following beneficial effects and advantages:

[0045] Based on the short-term plan data and the ultra-short-term plan data, data integration is performed to generate a set of time series scenarios for optimizing the day-ahead and intra-day peak-shaving of the power grid; based on the adjustable characteristics and time series characteristics of the multiple types of regulating resources including source, grid, load and storage in the system, a set of candidate measures for optimizing the day-ahead and intra-day peak-shaving of the power grid is generated; based on the time series scenarios, a day-ahead optimization dispatching model is constructed with the goal of minimizing the operation cost of the power grid, and the day-ahead peak-shaving is optimized and adjusted by adjusting the multiple types of regulating resources including source, grid, load and storage; based on the time series scenarios, an intra-day optimization dispatching model is constructed with the goal of minimizing the operation cost of the power grid, and the intra-day peak-shaving is optimized and adjusted by adjusting the multiple types of regulating resources including source, grid, load and storage, providing strong support for the day-ahead and intra-day power grid dispatching.

[0046] At the same time, the present invention gives full play to the role of coordinated control of various resources such as source, grid, load and storage considering timing characteristics, filling the technical gap in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0048] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0049] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0051] Refer to the following Figure 1 The technical solutions of some embodiments of the present invention are described.

[0052] Example 1

[0053] The present invention provides an embodiment, which is a method for optimizing power grid peak load regulation by involving source, grid, load and storage based on a time sequence scenario. Figure 1 As shown, Figure 1 It is a flow chart of the method of the present invention.

[0054] like Figure 1 As shown, a method for optimizing power grid peak load regulation based on a time sequence scenario in which source, grid, load and storage participate in power grid peak load regulation in an embodiment of the present application includes:

[0055] Step S1. Integrate the data based on the short-term plan data and the ultra-short-term plan data to generate a time series scenario set for the peak load optimization of the power grid before and during the day;

[0056] Step S2. Generate a set of candidate measures for optimizing peak load regulation of the power grid in the day ahead and within the day based on the adjustable characteristics and timing characteristics of multiple types of regulation resources in the system, including source, grid, load and storage;

[0057] Step S3. Based on the time series scenario set, a day-ahead peak-shaving optimization scheduling model is constructed with the goal of minimizing the grid operation cost, and the day-ahead grid peak-shaving optimization adjustment is performed by adjusting multiple types of regulation resources such as source, grid, load and storage;

[0058] Step S4. Based on the time series scenario set, a daily peak-shaving optimization scheduling model is constructed with the goal of minimizing the grid operation cost, and the daily grid peak-shaving optimization adjustment is performed by adjusting multiple types of regulation resources such as source, grid, load and storage.

[0059] In some embodiments, in step S2, the process of generating the candidate measure set for the day-ahead and day-intraday power grid peak load optimization includes the following steps:

[0060] Step S201. Obtain short-term and ultra-short-term planning data and their probability distribution information, and determine the planned period with insufficient upper and lower reserves as the period requiring peak load regulation according to the power supply balance;

[0061] Step S202. For the planned period that requires peak load regulation, Latin hypercube sampling is used to sample random variables such as power generation, load and energy storage provided by the scheduling plan data in sequence in time sequence, and the initial time sequence scenario set and its probability for the day-ahead and intra-day peak load regulation optimization of the power grid are obtained by combination;

[0062] Step S203. Check whether the adjacent running scenarios in the initial timing scenario set meet the timing constraints in chronological order, and remove the running scenarios with later timing in the adjacent running scenarios that do not meet the timing constraints;

[0063] Step S204. Simplify and cluster the remaining initial time series scenarios to obtain a set of time series scenarios and their probabilities for optimizing the peak load regulation of the power grid before and during the day;

[0064] Furthermore, in some embodiments, in step S203, the timing constraints include: unit ramp rate constraints and energy storage charging and discharging duration constraints.

[0065] In some embodiments, in step S3, the generation process of the candidate measure set for power grid peak load optimization includes the following steps:

[0066] Step S301. Select thermal power unit start-up and shutdown, pumped storage start-up and shutdown, and maintenance plan adjustment as the candidate set of day-ahead peak-shaving measures, and select hydropower unit output adjustment, thermal power unit output adjustment, energy storage charging and discharging, and flexible load startup and shutdown as the candidate set of day-ahead peak-shaving measures;

[0067] Step S302. For the set of candidate measures for peak load regulation on the day before, firstly sort them according to the priority of pumped storage start-up and stop, thermal power unit start-up and stop, and maintenance plan adjustment, and then sort them according to the adjustment cost of the candidate measures from small to large in a second order;

[0068] Step S303. For each set of candidate peak-shaving measures within the day, firstly sort them according to the priority of energy storage charging and discharging, hydropower unit output adjustment, thermal power unit output adjustment and flexible load adjustment, and then sort them according to the adjustment cost of the candidate measures from small to large.

[0069] In some embodiments, in step S3, the day-ahead peak load optimization scheduling model based on the time series scenario includes:

[0070] The objective function of the day-ahead peak load optimization scheduling model is as follows:

[0071]

[0072] Where T is the total number of short-term planning periods, N t is the number of scenarios in planning period t, ρ n is the probability of scene n, N G is the number of thermal power units, is the power generation cost of thermal power unit i, is the active power of thermal power unit i in scenario n during planning period t, N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, K is the cost of wind and solar power abandonment, ΔP pw,t,n is the wind power abandoned in scenario n during planning period t, ΔP pv,t,n is the abandoned optical power in scenario n during the planned period t.

[0073] The constraints of the day-ahead peak load optimization scheduling model are as follows:

[0074]

[0075] In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

[0076] In some embodiments, in step S4, the intraday peak load optimization scheduling model based on the time series scenario includes:

[0077] The objective function of the intraday peak load optimization scheduling model is as follows:

[0078]

[0079] Where T is the total number of short-term planning periods, N t is the number of scenarios in planning period t, ρ n is the probability of scenario n; N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, N ess is the number of energy storage devices, is the energy storage cost of energy storage device i, is the active power of energy storage device i in scenario n during planning period t, N ld is the flexible load number, is the cost of flexible load i, is the active power of flexible load i in scenario n during planning period t, P e,t is the electricity price at time t, ΔP load,t,n is the load adjustment active power in scenario n at time t, K is the wind and solar curtailment cost, ΔP pw,t,nis the wind power abandoned in scenario n at time t, ΔP pv,t,n K is the abandoned optical power in scene n at time t; ess is the storage adjustment cost, ΔP ess,t,n is the energy storage adjustment in scenario n during planning period t.

[0080] The constraints of the intraday peak load optimization scheduling model are as follows:

[0081]

[0082] In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

[0083] Example 2

[0084] The present invention further provides an embodiment, which is a source-grid-load-storage participating power grid peak load optimization device based on a time sequence scenario, comprising:

[0085] The first calculation module is used to integrate data based on short-term planning data and ultra-short-term planning data to generate a time series scenario set for day-ahead and intra-day peak load optimization of the power grid;

[0086] The second calculation module is used to generate a set of candidate measures for optimizing the peak load regulation of the power grid in the day and within the day according to the adjustable characteristics and timing characteristics of multiple types of regulation resources in the system, including source, grid, load and storage;

[0087] The third calculation module is used to build a day-ahead optimization dispatching model based on a time series scenario set with the goal of minimizing the grid operation cost, and optimize the day-ahead grid peak regulation by adjusting multiple types of regulation resources such as source, grid, load and storage;

[0088] The fourth calculation module is used to build an intraday optimization dispatching model based on a time series scenario set with the goal of minimizing the grid operation cost, and to optimize the grid peak load regulation within the day by adjusting multiple types of regulation resources such as source, grid, load and storage;

[0089] In some embodiments, data integration is performed based on short-term planning data and ultra-short-term planning data to generate a time series scenario set for day-ahead and intra-day peak load optimization of the power grid;

[0090] Furthermore, in some embodiments, the process of generating a time sequence scenario set for power grid peak load optimization is as follows:

[0091] Obtain short-term and ultra-short-term planning data and their probability distribution information, and determine the planned periods with insufficient upper and lower reserves as the periods requiring peak load regulation based on the power supply balance;

[0092] For the planned period that requires peak load regulation, Latin hypercube sampling is used to sample the random variables such as power generation, load and energy storage provided by the scheduling plan data in sequence, and the initial time series scenario set and its probability for the day-ahead and intra-day peak load regulation optimization of the power grid are obtained by combination;

[0093] Check whether the adjacent running scenarios in the initial timing scenario set meet the timing constraints in chronological order, and remove the running scenarios with later timing in the adjacent running scenarios that do not meet the timing constraints;

[0094] The remaining initial time series scenarios are simplified and clustered to obtain the time series scenario set and its probability for the day-ahead and day-intraday peak load optimization of the power grid.

[0095] Furthermore, in some embodiments, the timing constraints include unit ramp rate constraints and energy storage charging and discharging duration constraints.

[0096] In some embodiments, data integration is performed based on short-term planning data and ultra-short-term planning data to generate a set of candidate measures for day-ahead and intra-day peak load optimization of the power grid.

[0097] Furthermore, in some embodiments, the process of generating a candidate measure set for power grid peak load optimization is as follows:

[0098] The start and stop of thermal power units, the start and stop of pumped storage units, and the adjustment of maintenance plans are selected as the candidate set of measures for day-ahead peak regulation. The output adjustment of hydropower units, the output adjustment of thermal power units, the charging and discharging of energy storage, and the activation and deactivation of flexible loads are selected as the candidate set of measures for intraday peak regulation.

[0099] For the set of candidate measures for day-ahead peak load regulation, firstly sort them according to the priority of pumped storage start-up and stop, thermal power unit start-up and stop, and maintenance plan adjustment, and then sort them according to the adjustment cost of candidate measures from small to large in the second level;

[0100] For each set of candidate peak-shaving measures within the day, they are first sorted according to the priority of energy storage charging and discharging, hydropower unit output adjustment, thermal power unit output adjustment and flexible load adjustment, and then sorted at the second level according to the adjustment cost of the candidate measures from small to large.

[0101] In some embodiments, a day-ahead optimization scheduling model is constructed based on a set of time-series scenarios with the goal of minimizing grid operation costs, and day-ahead grid peak regulation is optimized by adjusting multiple types of regulation resources such as source, grid, load and storage.

[0102] Furthermore, in some embodiments, the day-ahead peak load optimization scheduling model based on the time series scenario is as follows:

[0103] The objective function of the day-ahead peak load optimization scheduling model is as follows:

[0104]

[0105] Where T is the total number of short-term planning periods, N t is the number of scenarios in planning period t, ρ n is the probability of scene n, N G is the number of thermal power units, is the power generation cost of thermal power unit i, is the active power of thermal power unit i in scenario n during planning period t, N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, K is the cost of wind and solar power abandonment, ΔP pw,t,n is the wind power abandoned in scenario n during planning period t, ΔP pv,t,n is the abandoned optical power in scenario n during the planned period t.

[0106] The constraints of the day-ahead peak load optimization scheduling model are as follows:

[0107]

[0108] In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system,P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

[0109] In some embodiments, an intraday optimization dispatching model with the goal of minimizing the grid operation cost is constructed based on a time series scenario set, and the intraday grid peak regulation is optimized and adjusted by adjusting multiple types of regulation resources such as source, grid, load and storage;

[0110] Furthermore, in some embodiments, the intraday peak load optimization scheduling model based on the time series scenario is as follows:

[0111] The intraday peak load optimization scheduling model based on the time series scenario is as follows:

[0112] The objective function of the intraday peak load optimization scheduling model is as follows:

[0113]

[0114] Where T is the total number of short-term planning periods, N t is the number of scenarios in planning period t, ρ n is the probability of scenario n; N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, N ess is the number of energy storage devices, is the energy storage cost of energy storage device i, is the active power of energy storage device i in scenario n during planning period t, N ld is the flexible load number, is the cost of flexible load i, is the active power of flexible load i in scenario n during planning period t, P e,t is the electricity price at time t, ΔP load,t,n is the load adjustment active power in scenario n at time t, K is the wind and solar curtailment cost, ΔP pw,t,n is the wind power abandoned in scenario n at time t, ΔPpv,t,n K is the abandoned optical power in scene n at time t; ess is the storage adjustment cost, ΔP ess,t,n is the energy storage adjustment in scenario n during planning period t.

[0115] The constraints of the intraday peak load optimization scheduling model are as follows:

[0116]

[0117] In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

[0118] Example 3

[0119] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of the method for optimizing power grid peak load regulation based on a time sequence scenario involving source, grid, load and storage are implemented as described in Example 1.

[0120] Example 4

[0121] Based on the same inventive concept, an embodiment of the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for optimizing power grid peak load regulation based on a time sequence scenario involving source, grid, load and storage are implemented as described in Example 1.

[0122] In the description of the present invention, "several" means more than one, "many" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0123] In the description of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0124] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing power grid peak load regulation based on a time sequence scenario involving source, grid, load and storage, characterized by: include: Integrate short-term and ultra-short-term planning data to generate a time series scenario set for day-ahead and intra-day peak load optimization of the power grid; According to the adjustable characteristics and timing characteristics of multiple types of regulation resources in the system, such as source, grid, load and storage, a set of candidate measures for optimizing the peak load regulation of the power grid before and during the day is generated; Based on the time series scenario set, a day-ahead peak-shaving optimization dispatching model is constructed with the goal of minimizing the grid operation cost. The day-ahead grid peak-shaving optimization adjustment is performed by adjusting multiple types of regulation resources such as source, grid, load and storage. Based on the time series scenario set, a daily peak-shaving optimization scheduling model is constructed with the goal of minimizing the grid operation cost. The daily grid peak-shaving optimization adjustment is carried out by adjusting multiple types of regulation resources such as source, grid, load and storage.

2. According to claim 1, a method for optimizing power grid peak load regulation based on time sequence scenarios involving source, grid, load and storage, characterized in that: The generation process of the time series scenario set for the day-ahead and day-intraday power grid peak load optimization includes: Obtain short-term and ultra-short-term planning data and their probability distribution information, and determine the planned periods with insufficient upper and lower reserves as the periods requiring peak load regulation based on the power supply balance; For the planned period that requires peak load regulation, Latin hypercube sampling is used to sample the random variables of power generation, load and energy storage provided by the scheduling plan data in sequence, and the initial time series scenario set and its probability for the optimization of day-ahead and intraday peak load regulation of the power grid are obtained by combination; Check whether the adjacent running scenarios in the initial timing scenario set meet the timing constraints in chronological order, and remove the running scenarios with later timing in the adjacent running scenarios that do not meet the timing constraints; The remaining initial time series scenarios are simplified and clustered to obtain the time series scenario set and its probability for the day-ahead and day-intraday peak load optimization of the power grid.

3. According to claim 2, a method for optimizing power grid peak load regulation based on time sequence scenarios involving source, grid, load and storage is characterized by: The timing constraints include: unit ramp rate constraints and energy storage charging and discharging duration constraints.

4. According to the method of optimizing power grid peak load regulation based on source-grid-load-storage participation in time sequence scenarios according to claim 1, it is characterized by: The generation process of the candidate measure set for power grid peak load optimization includes: The start and stop of thermal power units, the start and stop of pumped storage units, and the adjustment of maintenance plans are selected as the candidate set of measures for day-ahead peak regulation. The output adjustment of hydropower units, the output adjustment of thermal power units, the charging and discharging of energy storage, and the activation and deactivation of flexible loads are selected as the candidate set of measures for intraday peak regulation. For the various peak load regulation candidate measures set in the previous day, firstly sort them according to the priority of pumped storage start-up and stop, thermal power unit start-up and stop, and maintenance plan adjustment, and then sort them according to the adjustment cost of the candidate measures from small to large. For each set of candidate peak-shaving measures within the day, they are first sorted according to the priority of energy storage charging and discharging, hydropower unit output adjustment, thermal power unit output adjustment and flexible load adjustment, and then sorted at the second level according to the adjustment cost of the candidate measures from small to large.

5. According to the method of optimizing power grid peak load regulation based on source-grid-load-storage participation in time sequence scenarios according to claim 1, it is characterized by: The day-ahead peak load optimization scheduling model based on the time series scenario includes: The objective function of the day-ahead peak load optimization scheduling model is as follows: Where T is the total number of short-term planning periods, N t is the number of scenarios in the planning period t, ρ n is the probability of scene n, N G is the number of thermal power units, is the power generation cost of thermal power unit i, is the active power of thermal power unit i in scenario n during planning period t, N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, K is the cost of wind and solar power abandonment, ΔP pw,t,n is the wind power abandoned in scenario n during planning period t, ΔP pv,t,n is the abandoned optical power in scenario n during the planned period t; The constraints of the day-ahead peak load optimization scheduling model are as follows: In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

6. According to a method for optimizing power grid peak load regulation based on source-grid-load-storage participation in a time sequence scenario according to claim 1, the characteristics are: The intraday peak load optimization scheduling model based on time series scenarios includes: The objective function of the intraday peak load optimization scheduling model is as follows: Where T is the total number of short-term planning periods, N t is the number of scenarios in the planning period t, ρ n is the probability of scenario n; N pw is the number of wind turbines, is the power generation cost of wind turbine i, is the active power of wind turbine i in scenario n during planning period t, N pv is the number of photovoltaic units, is the power generation cost of photovoltaic unit i, is the active power of PV unit i in scenario n during planning period t, N ess is the number of energy storage devices, is the energy storage cost of energy storage device i, is the active power of energy storage device i in scenario n during planning period t, N ld is the flexible load number, is the cost of flexible load i, is the active power of flexible load i in scenario n during planning period t, P e,t is the electricity price at time t, ΔP load,t,n is the load adjustment active power in scenario n at time t, K is the wind and solar curtailment cost, ΔP pw,t,n is the wind power abandoned in scenario n at time t, ΔP pv,t,n K is the abandoned optical power in scene n at time t; ess is the storage adjustment cost, ΔP ess,t,n is the energy storage adjustment in scenario n during planning period t; The constraints of the intraday peak load optimization scheduling model are as follows: In the formula, is the active power of load j in scenario n during planning period t, is the transmission power of DC segment k in scenario n during planning period t, k loss is the system network loss rate, is the upper limit of active power of unit i, is the lower limit of active power of unit i, is the spare space threshold on the system, P lmt is the system spare space threshold, S Gi is the rated power of unit i, is the upward and downward climbing power of unit i, Δt is the interval between time t and t+1, is the maximum and minimum capacity of wind turbine i, is the maximum and minimum energy of photovoltaic unit i; is the stored energy of energy storage device i in scenario n during planning period t, is the maximum and minimum storage energy of energy storage device i.

7. A source-grid-load-storage participating power grid peak load optimization device based on a time sequence scenario, characterized by: include: The first calculation module is used to integrate data based on short-term planning data and ultra-short-term planning data to generate a time series scenario set for day-ahead and intra-day peak load optimization of the power grid; The second calculation module is used to generate a set of candidate measures for optimizing the peak load regulation of the power grid in the day and within the day according to the adjustable characteristics and timing characteristics of multiple types of regulation resources in the system, including source, grid, load and storage; The third calculation module is used to build a day-ahead optimization dispatching model based on a time series scenario set with the goal of minimizing the grid operation cost, and optimize the day-ahead grid peak regulation by adjusting multiple types of regulation resources such as source, grid, load and storage; The fourth calculation module is used to build a daily optimization scheduling model based on a set of time series scenarios with the goal of minimizing the grid operation cost, and to optimize the grid peak regulation during the day by adjusting multiple types of regulation resources such as source, grid, load and storage.

8. According to claim 1, a source-grid-load-storage participating in power grid peak load optimization device based on time sequence scenario is characterized by: The device is used to implement the steps of a method for optimizing power grid peak load regulation based on a time sequence scenario involving source, grid, load and storage as described in any one of claims 1 to 8.

9. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the steps of any one of claims 1-8 are implemented in a method for optimizing power grid peak load regulation based on a time sequence scenario involving source, grid, load and storage.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of claims 1 to 8 of a method for optimizing power grid peak load regulation involving source, grid, load and storage based on a time sequence scenario.

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