Multi-time scale modeling and complementary analysis method of direct current equivalent energy storage in wind-light-water storage system

Abstract

The invention discloses a multi-time-scale modeling and complementary analysis method of direct current equivalent energy storage in a wind-light-water storage system, which comprises the following steps: constructing a wind-light-water-storage integrated base energy model for a wind power station, a photovoltaic power station and a cascade hydropower station in a region according to a wind-light-water-storage integrated region architecture; according to the wind-light-water-storage integrated base energy model, constructing a wind-light-water complementary characteristic evaluation index; taking a wind-light-water-storage integrated area as a sending end, and introducing a direct current equivalent energy storage model and constraint conditions to a power transmission line between the sending end and a receiving end; obtaining day-ahead dispatching output data of each unit at a sending end, and on the basis, performing rolling correction and adjustment on a day-ahead plan according to the obtained latest ultra-short-term prediction information by intra-day dispatching; the real-time scheduling is based on minute-level actual operation data so as to correct the deviation between the intra-day scheduling plan and the real-time working condition. According to the method, the operation flexibility, the transmission stability and the new energy consumption capability of the delivery system are remarkably improved, and key technical support is provided for constructing a novel power system.

Description

Technical Field

[0001] This invention discloses a multi-timescale modeling and complementary analysis method for DC equivalent energy storage in wind, solar, hydro, and energy storage systems, which relates to the field of grid-connected stability control of new energy sources. Background Technology

[0002] To address the challenges of large-scale development and consumption of clean energy under dual-carbon goals, the power system is shifting towards an integrated development model that complements multiple energy sources, including wind, solar, hydro, and energy storage. This model, through the synergy of various energy sources, is an important way to improve the consumption of new energy and system stability. However, there are complex spatiotemporal matching challenges between the strong volatility and randomness of wind and solar resources and the regulation capabilities of hydropower and energy storage.

[0003] To address the complex matching challenge between wind and solar volatility and regulation resources in multi-energy complementary systems ("wind-solar-hydro-storage"), existing methods fall short in characterizing their multi-dimensional dynamic synergy. Traditional analyses often rely on static indicators and single time scales, failing to quantify the dynamic matching and nesting relationships between short-term wind and solar fluctuations, diurnal variations, and the regulation capabilities of hydropower and energy storage across different time scales. At the modeling level, high-voltage direct current (HVDC) transmission channels are typically modeled as rigid transmission links with unadjustable power. This approach essentially simplifies them into passive, constant-power transmission pipes, ignoring their actual ability to flexibly adjust transmission power within their rated capacity by receiving scheduling commands. Furthermore, existing research provides only a general overview of the complementary synergy mechanism, resulting in a lack of precise theoretical support for capacity configuration and time-series scheduling. Therefore, a new analytical method is urgently needed to precisely characterize the dynamic coupling across multiple time scales and quantify the synergy mechanism. Summary of the Invention

[0004] This invention provides a multi-timescale modeling and complementary analysis method for DC equivalent energy storage in wind-solar-hydro-storage systems. On the one hand, it constructs an integrated wind-solar-hydro-storage base energy model and evaluation indicators for wind-solar-hydro complementary characteristics, and introduces a DC equivalent energy storage model. On the other hand, it applies multi-timescale coupling analysis based on the integrated wind-solar-hydro-storage base energy model and the DC equivalent energy storage model.

[0005] The technical solution of this invention is:

[0006] According to a first aspect of the present invention, a method for multi-timescale modeling and complementary analysis of DC equivalent energy storage in wind-solar-hydro-storage systems is provided, comprising the following steps:

[0007] Step 1: Based on the integrated wind-solar-hydro-storage regional architecture, construct an integrated wind-solar-hydro-storage base energy model for wind power stations, photovoltaic power stations, and cascade hydropower stations within the region; based on the integrated wind-solar-hydro-storage base energy model, construct evaluation indicators for the complementary characteristics of wind, solar, and hydropower; wherein, the integrated wind-solar-hydro-storage base energy model includes a wind power station power generation model, a photovoltaic power generation model, a cascade hydropower station power generation model, and constraints on the cascade hydropower station power generation model;

[0008] Step 2: Taking the integrated wind-solar-hydro-storage area as the sending end, introduce a DC equivalent energy storage model and constraints into the transmission line between the sending end and the receiving end;

[0009] Step 3: Based on the modeling in steps S1 and S2, the day-ahead scheduling output data of each unit at the sending end is obtained. On this basis, the day-ahead scheduling is rolled out and adjusted according to the latest ultra-short-term forecast information. The real-time scheduling is based on minute-level actual operating data to correct the deviation between the day-ahead scheduling plan and the real-time operating conditions.

[0010] Furthermore, the evaluation index for the wind-solar-hydro complementarity characteristics is expressed as follows:

[0011] ;

[0012] ;

[0013] In the formula, The coefficient representing the complementary difference between wind, solar, and water resources. for The output power of the fan at all times; yes The output power of the photovoltaic power station at any given time; for The first in the cascade hydropower station The first hydropower station The unit's output power; This refers to the number of hydropower stations in a cascade hydropower station system. This represents the average power of the wind-solar-hydro hybrid system within the sampled interval. Indicates the first The number of hydroelectric generator units owned by each hydroelectric power station; It is the end time of the scheduling cycle.

[0014] Furthermore, the DC equivalent energy storage model is expressed as follows:

[0015] ;

[0016] In the formula: This represents the equivalent energy storage capacity for the period from t1 to t2. This represents the maximum power transmitted by the DC transmission line. This represents the real-time power transmitted by the DC transmission line at time t.

[0017] Furthermore, the constraints of the DC equivalent energy storage model include: upper and lower power limits, daily planned quantity constraints, on-demand control of external transmission constraints, minimum adjustment time period constraints, and adjustment number constraints.

[0018] According to a second aspect of the present invention, a multi-timescale modeling and complementary analysis system for DC equivalent energy storage in wind, solar, hydro, and storage systems is provided, comprising modules of any of the methods described above.

[0019] According to a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described in any one of the preceding descriptions.

[0020] The beneficial effects of this invention are:

[0021] This invention systematically improves the operational efficiency of integrated wind, solar, hydro, and storage bases through the synergistic implementation of the following technical solutions: First, it employs a method of constructing models encompassing wind power, photovoltaic, and cascade hydropower stations, along with their complementary characteristic evaluation indicators. This achieves precise quantification and matching of wind and solar power volatility with hydropower regulation capabilities, thereby solving the problems of inaccurate multi-energy complementary potential assessment and insufficient basis for coordinated operation in traditional planning. This lays a quantitative foundation for the system's optimal capacity configuration and long-term operation strategy. Second, it adopts a method of introducing a DC equivalent energy storage model to model inter-regional power transmission channels. This transforms fixed power transmission sections into those with flexible power modulation capabilities, thereby solving the problems of system regulation resource shortages and limited transmission channel utilization and flexibility in scenarios with high proportions of renewable energy transmission, and improving the cross-temporal and spatial absorption capacity of renewable energy. Finally, a scheduling mechanism based on rolling optimization across multiple time scales of "day-to-day-real-time" was adopted, which enabled the power generation plan to closely follow ultra-short-term forecasts and real-time operating conditions for dynamic correction and rapid response. This solved the risk of the scheduling plan being out of sync with actual operation due to the strong volatility of new energy sources and forecast deviations, and ultimately achieved the comprehensive operational goal of ensuring grid safety and stability and maximizing the absorption rate of new energy sources. Attached Figure Description

[0022] Figure 1 This is a flowchart of the present invention.

[0023] Figure 2 This is a topology diagram of the complementary system of the "wind-solar-water-storage" integrated base.

[0024] Figure 3This is a framework diagram of the wind-solar hybrid characteristic analysis method.

[0025] Figure 4 This is a schematic diagram of the transmission principle of DC equivalent energy storage effect.

[0026] Figure 5 This is a diagram illustrating the principle of multi-timescale scheduling. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0028] Example 1: As Figures 1-5 As shown, a multi-timescale modeling and complementary analysis method for DC equivalent energy storage in wind-solar-hydro-storage systems is proposed.

[0029] Step 1: Based on the integrated wind-solar-hydro-storage regional architecture, construct an integrated wind-solar-hydro-storage base energy model for wind power stations, photovoltaic power stations, and cascade hydropower stations within the region; based on the integrated wind-solar-hydro-storage base energy model, construct evaluation indicators for the complementary characteristics of wind, solar, and hydropower; wherein, the integrated wind-solar-hydro-storage base energy model includes a wind power station power generation model, a photovoltaic power generation model, a cascade hydropower station power generation model, and constraints on the cascade hydropower station power generation model.

[0030] Furthermore, step 1 specifically includes: proposing a system modeling and analysis device for realizing the multi-energy complementary characteristics analysis of the integrated "wind-solar-water-storage" base. Figure 2 The system is a complementary topology that integrates wind, solar, hydro, and energy storage. The base consists of wind power generation, photovoltaic power generation, cascade hydropower stations, DC equivalent energy storage, and other equipment.

[0031] The system analysis structure comprises a multi-energy coupling modeling stage and a multi-dimensional quantitative analysis stage. The multi-energy coupling modeling stage, based on historical power output from wind, solar, hydro, and storage sources, reservoir regulation characteristics, and load data, constructs a fundamental model characterizing the output characteristics of various power sources. Through a multi-timescale coupled calculation module, it establishes and solves multi-dimensional complementary characteristic models, including those for seasonality, diurnal variation, power generation, and capacity. The multi-dimensional quantitative analysis stage, based on the model calculation results, quantifies the regulation demand of hydropower / energy storage, the mitigation effect of wind and solar power fluctuations, and the overall complementary potential of the system, forming a complete characteristic analysis result that provides direct input for subsequent optimized configuration.

[0032] 1.1 Wind Power Station Generation Model

[0033] Wind power generation is a clean energy technology that uses wind energy to drive wind turbines, converting mechanical energy into electrical energy. In multi-energy complementary systems, the output pattern of wind power can naturally complement that of photovoltaic and hydropower on intraday and seasonal scales, making it a key component for achieving smooth power flow and optimized operation of the system. The power output of wind turbine generators is mainly affected by factors such as turbine model and wind speed. Under the premise of a specific rated power unit, the relationship between the output power of the wind turbine and the hourly wind speed can be expressed as:

[0034] ;

[0035] In the formula, Let t be the output power of the wind turbine (MW); This indicates the rated power (MW) of the wind turbine. , and These represent the fan's starting wind speed, rated wind speed, and stopping wind speed (m / s), respectively. This represents the actual wind speed (m / s) at time t.

[0036] 1.2 Photovoltaic Power Station Power Generation Model

[0037] A photovoltaic (PV) power plant is a facility that directly converts solar energy into electrical energy. Its output is synchronized with solar irradiance, exhibiting regular diurnal fluctuations and seasonal variations, but it is intermittent and uncertain. The electricity generated by a PV power plant can be expressed through the relationship between instantaneous solar radiation density, PV panel temperature, and standard experimental conditions at rated capacity.

[0038] ;

[0039] In the formula, It is the output power (MW) of the photovoltaic power station at time t; It is the installed capacity (MW) of the photovoltaic power station; It is a power derating factor that takes into account factors such as inverter efficiency, and is usually 0.9; It is the actual light intensity (W / m²). It is the light intensity (W / m²) under standard test conditions. It is the power temperature coefficient (% / ℃); The actual temperature (°C) of the photovoltaic panel at time t. This is the photovoltaic panel temperature under standard test light intensity (25℃). The actual temperature of the photovoltaic panel is affected by parameters such as ambient temperature and irradiance, and can be calculated using the following formula:

[0040] T c e l l , t = T a , t + ( T c e l l , S T C − T a , S T C ) ( G t G S T C ) [ 1 − η e , S T C ( 1 − α p T c e l l , S T C ) τ β ] 1 + ( T c e l l , S T C − T a , S T C ) ( G t G S T C ) ( α p η e , S T C τ β ) ;

[0041] In the formula, Let t be the ambient temperature (°C). Photovoltaic efficiency under standard test conditions; Solar transmittance; Solar energy absorption rate; The default value is 0.9; This refers to the ambient temperature under standard test conditions (STC). The total solar irradiance received by the photovoltaic panel at time t (unit: W / m²) 2 ); Solar irradiance under standard test conditions (STC); The power temperature coefficient of a photovoltaic cell.

[0042] 1.3 Cascade Hydropower Station Power Generation Model

[0043] This invention proposes a cascade hydropower station, which refers to multiple hydropower stations built in a stepped manner from upstream to downstream on the same river. Its core advantage lies in achieving efficient utilization and centralized regulation of hydropower resources through "cascade development and step-by-step utilization." The upstream reservoirs of a cascade hydropower station typically have large storage capacities, enabling long-term energy storage and release to compensate for seasonal and long-term fluctuations in wind and solar power output; the downstream cascades can respond quickly, providing short-term flexible regulation capabilities to smooth out short-term fluctuations in wind and solar power output. This combination of long-term and short-term, multi-level coordinated regulation characteristics is the core foundation for supporting the stable grid connection of large-scale new energy sources and achieving multi-energy complementary and coordinated operation.

[0044] The water balance between cascade hydroelectric power stations can be expressed as:

[0045] ;

[0046] In the formula, It is the water storage capacity (m³) of the i-th hydropower station at time t. It is the inflow rate (m³ / s) of the i-th hydropower station at time t. It is the water discharge flow rate (m³ / s) of the i-th hydropower station at time t. It is the water flow rate (m³ / s) for power generation of the i-th hydropower station at time t. It is the overflow of the i-th hydropower station at time t (m³ / s). It is the time interval between the preceding and following moments, usually taken as 1 hour; This represents the number of hydroelectric generator units owned by the i-th hydroelectric power station.

[0047] The water levels of each hydropower station in a cascade hydropower station system are subject to the following constraints:

[0048] ;

[0049] In the formula, , and These represent the water level, minimum water level, and maximum water level (m) of the i-th hydropower station at time t. The initial and final water levels of the hydropower station follow the following constraints:

[0050] ;

[0051] In the formula, and t0 and t0 represent the water levels (m) of the i-th reservoir at the beginning and end of the scheduling cycle, respectively; t0 represents the start time of the scheduling cycle; and T represents the end time (h) of the scheduling cycle. and These are the initial and final water level setpoints (m), respectively.

[0052] Traffic flows are subject to the following constraints:

[0053] ;

[0054] In the formula, , and These represent the water discharge rate at time t and its lower and upper limits (m³ / s).

[0055] The power generation flow rate is subject to the following constraints:

[0056] ;

[0057] In the formula, and These are the lower and upper limits (m³ / s) of the power generation flow of the g-th generating unit of the i-th hydropower station. It is the operating state variable at time t; if the unit is running, then ,otherwise .

[0058] The power output is subject to the following constraints:

[0059] ;

[0060] In the formula, It is the output power of the g-th generating unit of the i-th hydropower station at time t. and, These are the lower and upper limits (MW) of the output power of the g-th generating unit of the i-th hydropower station.

[0061] The following constraints apply to the power output ramp-up (rate of increase or decrease) of each hydropower station in a cascade hydropower station system:

[0062] ;

[0063] In the formula, It is the output power (MW) of the g-th generating unit of the i-th hydropower station at time t; It is the maximum rate of change (MW) of the unit's output power within the scheduling cycle.

[0064] The water level-storage function can be expressed as:

[0065] ;

[0066] In the formula, It is the water level and water storage capacity curve function of the i-th hydropower station.

[0067] The power output of a hydroelectric power station can be expressed as:

[0068] ;

[0069] In the formula, It is the output power function of the g-th generating unit of the i-th hydropower station; The head (m) of the g-th generating unit of the i-th hydropower station at time t; It is the discharge flow of the g-th unit of the i-th hydropower station at time t; here, the power function is expressed as the product of the head, the discharge flow, and the integration coefficient K.

[0070] 1.4 Evaluation Indicators for Wind-Solar-Hydropower Complementarity

[0071] Due to the differences and complementarities in resource distribution and power output characteristics between wind, solar, and hydropower, it is necessary to comprehensively leverage the roles of intra-regional multi-energy complementarity and inter-regional power exchange to effectively improve the overall stability and reliability of the power system. The analytical framework for the complementary characteristics of wind, solar, and hydropower is as follows: Figure 3 As shown, within a region, the power output characteristics of wind, solar, and hydropower can effectively complement each other, mitigating local fluctuations; between regions, resource sharing can be achieved through grid interconnection, enhancing system regulation capabilities. To comprehensively evaluate the complementary benefits of the power system at the spatial level, the coefficient of variation (CV) can be used to characterize the overall stability of the system's power output. Its calculation formula is as follows:

[0072] ;

[0073] ;

[0074] In the formula, is the complementarity difference coefficient between wind, solar and hydropower sources; m is the number of hydropower stations in the cascade hydropower station; Let be the average power of the wind-solar-hydro hybrid system within the sampled interval. From the above formula, it can be seen that the smaller the CV (coefficient of performance), the more stable the combined power output of wind, solar, and hydropower, and the better the source-side complementary characteristics of the wind-solar-hydro system. To ensure stable output of the generated wind-solar-hydro scenario, i.e., good complementary characteristics, it is necessary to... Less than the reference values ​​given by the independent systems for wind, solar, and water. , and ,Right now ≤ , ≤ and ≤ The reference values ​​for wind, light, and water are calculated using the following formulas:

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] In the formula, For the first The wind power output value at time t on day t (output value is the output power); For the first The photovoltaic output value at time t on day t; For the first Heavenly Hydropower output value of a hydropower station at time t; For the first Average power output of Tianfeng wind power; For the first Average power output of Tiansheng Photovoltaic; For the first The average output power of all operating hydropower stations is given by T, where T is the total duration of each day and N is the total number of days. From the above calculation formula, it can be seen that the average output power of wind, solar, and hydropower stations differs depending on the number of days and duration, and therefore their complementary difference coefficients will also differ.

[0082] Step 2: Taking the integrated wind-solar-hydro-storage area as the sending end, introduce a DC equivalent energy storage model and constraints into the transmission line between the sending and receiving ends. By using a DC equivalent energy storage model with fast response, accurately characterize its dynamic characteristics and regulation potential of power throughput, and provide an accurate modeling basis for smoothing short-term fluctuations in wind and solar power and participating in intraday energy transfer.

[0083] Furthermore, step 2 specifically includes:

[0084] The core of this invention, employing the DC equivalent energy storage effect transmission principle, lies in transforming the UHVDC transmission channel from a traditional rigid power transmission carrier into a virtual energy storage unit with regulatory capabilities—the DC equivalent energy storage. The structure of the UHVDC transmission system realizing DC transmission and equivalent energy storage mainly consists of UHVDC transmission lines, sending-end and receiving-end converter stations, and other auxiliary control hardware. The UHVDC transmission line, in essence, is a long-distance, low-loss DC transmission line, serving as the physical carrier for power transmission. This invention, through a dual-channel configuration of the transmission lines, enhances system control flexibility and overcomes the bottleneck of single-channel transmission. The sending-end converter station rectifies AC power into DC power, flexibly adjusting its output power in response to control commands, providing power conversion support for equivalent energy storage. The receiving-end converter station receives DC power and inverts it into AC power to adapt to the receiving-end load, while simultaneously feeding back load fluctuation signals, working in conjunction with the sending end to ensure the stable implementation of the DC equivalent energy storage effect. Other auxiliary control hardware provides real-time data support for channel power adjustment by collecting data on sending-end renewable energy output, receiving-end load demand, and the transmission power of the DC channel.

[0085] The principle of transmitting DC equivalent energy storage effect is as follows: Figure 4 As shown. Traditional DC power transmission methods typically transmit power at a constant power level. However, when utilizing the DC equivalent energy storage effect for power transmission, the transmitted power can be flexibly adjusted according to real-time changes in load demand. When load demand is low, the sending-end system can "store" a portion of the power transmitted at a constant power level and release the "stored" power during peak load periods, thereby realizing a power transmission mechanism based on the DC equivalent energy storage effect.

[0086] The DC equivalent energy storage model is as follows:

[0087] ;

[0088] In the formula: This represents the equivalent energy storage capacity during the time period t1-t2. This represents the maximum power transmitted by the DC transmission line (i.e., the upper limit of the transmitted power). This represents the real-time power transmitted by the DC transmission line at time t.

[0089] The constraints of the DC equivalent energy storage model include:

[0090] The upper and lower limits of power are constrained as follows:

[0091] ;

[0092] In the formula, This represents the minimum power transmitted through the DC channel.

[0093] Daily planned quantity constraint is:

[0094] ;

[0095] In the formula, Q is the daily planned value of power transmission through the DC channel.

[0096] The constraint for adjusting outbound delivery on demand is:

[0097] ;

[0098] In the formula, This is the state variable for the power transmitted from the DC channel. If... =0, which indicates that the DC channel's external transmission status has not changed; if =1 indicates a change in the DC channel's external transmission status.

[0099] The minimum adjustment time period constraint is:

[0100] ;

[0101] In the formula, H is the minimum adjustment time period, that is, within the H period, an adjustment state is maintained.

[0102] The number of adjustments is constrained as follows:

[0103] ;

[0104] In the formula, The maximum number of times the DC channel can be adjusted per day.

[0105] The two DC lines have the same related constraints, which will not be repeated here.

[0106] The core function of DC equivalent energy storage lies in flexibly adjusting the power of ultra-high-voltage direct current (UHVDC) transmission channels, transforming them from traditional fixed transmission modes into virtual energy storage units with dynamic response capabilities. This effectively mitigates the random fluctuations in renewable energy output within integrated wind-solar-hydro-storage bases. By adjusting the transmitted power during off-peak hours and increasing it during peak hours based on real-time changes in the receiving-end grid load demand, this mechanism not only expands the equivalent storage capacity of the integrated base and reduces reliance on physical energy storage facilities such as pumped storage, but also enhances renewable energy absorption capacity and transmission flexibility through regional energy complementarity and coordination. Ultimately, this achieves multiple objectives: reducing total system operating costs, alleviating grid peak-shaving pressure, and optimizing resource utilization efficiency.

[0107] Step 3: Based on the modeling in steps S1 and S2, the day-ahead scheduling output data of each unit at the sending end is obtained. On this basis, the day-ahead scheduling is rolled out and adjusted according to the latest ultra-short-term forecast information. The real-time scheduling is based on minute-level actual operating data to correct the deviation between the day-ahead scheduling plan and the real-time operating conditions.

[0108] Furthermore, step 3 specifically includes:

[0109] Based on the modeling of steps S1 and S2, the day-ahead dispatch output data of each unit in the system is obtained. On this basis, intraday dispatching, according to the latest ultra-short-term forecast information, makes rolling corrections and adjustments to the day-ahead plan to cope with forecast errors and unforeseen circumstances. Finally, real-time dispatching, based on minute-level actual operating data, balances generation and load in real time to correct deviations between the intraday dispatching plan and real-time operating conditions, reducing load shedding and ensuring system stability. The entire process, through the coordination of day-ahead planning, intraday adjustments, and real-time control, effectively addresses the uncertainties of new energy sources and ensures the safe and economical operation of the system. A multi-timescale operation framework diagram is shown below. Figure 5 As shown.

[0110] Because the time scale changes, the expression for the power output ramp-up of the hydropower station units will change:

[0111] ;

[0112] In the formula, It is the output power (MW) of the g-th generating unit of the i-th hydropower station at time t; It is the maximum rate of change (MW) of the unit's output power within the scheduling cycle. This represents the number of parts per hour after refining the time scale.

[0113] In the day-ahead dispatch plan, the start-up and shutdown status of each generating unit is optimized and determined based on load forecasting and renewable energy output forecasting to form a day-ahead power generation plan. This plan serves as a basic constraint and is passed to the intraday dispatch phase. During intraday dispatch, the system performs rolling optimization of unit output based on updated ultra-short-term forecast data, precisely determining the power generation and output power of generating units in each time period to address forecast errors and real-time fluctuations. Finally, in the real-time dispatch phase, unit power is adjusted and corrected in a closed-loop manner at the minute level to achieve instantaneous balance between power generation and load. This progressively refined, closed-loop feedback multi-timescale coordinated optimization process improves the system's operational economy, safety, and renewable energy absorption capacity. In other words, the above-mentioned day-ahead planning, intraday rolling optimization, and real-time correction aim to generate power transmission targets at different time scales, from day-ahead to minute-level.

[0114] To address the challenges of strong power fluctuations and cross-temporal balance encountered when large-scale renewable energy is transmitted via DC channels, this invention deeply integrates the DC equivalent energy storage effect into a multi-timescale scheduling framework. At the day-ahead and intraday timescales, the scheduling plan, while considering forecasts and market information, fully assesses and utilizes the adjustable potential of the DC channel, optimizing its charging and discharging schedule as "virtual energy storage" to improve channel utilization and economic efficiency. At the real-time and second-level scales, the DC equivalent energy storage effect is directly invoked to rapidly absorb or release power, compensating for minute- to second-level fluctuations in renewable energy in real time and responding to system frequency regulation needs. This method, through the organic synergy of "multi-timescale planning and scheduling" and "real-time equivalent energy storage invocation," achieves graded and layered mitigation of renewable energy volatility, significantly reduces reliance on supporting physical energy storage, and significantly improves the operational flexibility, transmission stability, and renewable energy absorption capacity of the transmission system, providing key technical support for building a new type of power system.

[0115] By applying the above technical solutions, it can be seen that, based on the construction of the integrated "wind-solar-hydro-storage" base model using S1, S2, and S3, and within the framework of multiple time scales, simulation calculations are performed on the base to quantitatively evaluate the complementary benefits and operational characteristics of the system at multiple time scales. Furthermore, the spatiotemporal complementary mechanism of the coordinated operation between wind and solar power, hydropower, and DC equivalent energy storage is analyzed and revealed, providing a quantitative basis for the planning, design, and optimized operation of the base.

[0116] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A multi-timescale modeling and complementary analysis method for DC equivalent energy storage in wind-solar-hydro-storage systems, characterized in that, Includes the following steps: Step 1: Based on the integrated wind-solar-hydro-storage regional architecture, construct an integrated wind-solar-hydro-storage base energy model for wind power stations, photovoltaic power stations, and cascade hydropower stations within the region; based on the integrated wind-solar-hydro-storage base energy model, construct evaluation indicators for the complementary characteristics of wind, solar, and hydropower; wherein, the integrated wind-solar-hydro-storage base energy model includes a wind power station power generation model, a photovoltaic power generation model, a cascade hydropower station power generation model, and constraints on the cascade hydropower station power generation model; Step 2: Taking the integrated wind-solar-hydro-storage area as the sending end, introduce a DC equivalent energy storage model and constraints into the transmission line between the sending end and the receiving end; Step 3: Based on the modeling in steps S1 and S2, the day-ahead scheduling output data of each unit at the sending end is obtained. On this basis, the day-ahead scheduling is rolled out and adjusted according to the latest ultra-short-term forecast information obtained. Real-time scheduling is based on minute-level actual operating data to correct deviations between the daily scheduling plan and real-time operating conditions.

2. The method for multi-timescale modeling and complementary analysis of DC equivalent energy storage in wind-solar-hydro-storage systems according to claim 1, characterized in that, The evaluation index for the wind-solar-hydro complementarity characteristics is expressed as follows: ； ； In the formula, The coefficient representing the complementary difference between wind, solar, and water resources. for The output power of the fan at all times; yes The output power of the photovoltaic power station at any given time; for The first in the cascade hydropower station The first hydropower station The unit's output power; This refers to the number of hydropower stations in a cascade hydropower station system. This represents the average power of the wind-solar-hydro hybrid system within the sampled interval. Indicates the first The number of hydroelectric generator units owned by each hydroelectric power station; It is the end time of the scheduling cycle.

3. The method for multi-timescale modeling and complementary analysis of DC equivalent energy storage in wind-solar-hydro-storage systems according to claim 1, characterized in that, The DC equivalent energy storage model is expressed as follows: ； In the formula: This represents the equivalent energy storage capacity for the period from t1 to t2. This represents the maximum power transmitted by the DC transmission line. This represents the real-time power transmitted by the DC transmission line at time t.

4. The method for multi-timescale modeling and complementary analysis of DC equivalent energy storage in wind-solar-hydro-storage systems according to claim 1, characterized in that, The constraints of the DC equivalent energy storage model include: upper and lower power limits, daily planned output, on-demand power transmission, minimum adjustment time period, and number of adjustments.

5. A multi-timescale modeling and complementary analysis system for DC equivalent energy storage in wind-solar-hydro-storage systems, characterized in that, The module includes the method described in any one of claims 1-4.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-4.

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