Wind-solar-water multi-energy complementary virtual power plant optimization scheduling method, system and medium

Abstract

The invention belongs to the technical field of virtual power plants, and relates to a wind-solar-water multi-energy complementary virtual power plant optimization scheduling method and system and a medium. The hydropower energy storage scheduling model under the long-term scale is beneficial to formulating a global scheduling strategy of hydropower resources, and through long-term energy storage scheduling, hydropower can balance medium and long term supply and demand more efficiently, and real-time dependence is reduced. The wind and light energy storage dynamic response model under the short-term scale enables wind and light to actively participate in peak regulation, the hydroelectric pressure is shared, and the system flexibility is improved. The differentiated peak regulation cost allocation model is beneficial to excitation of active peak regulation of wind and light, optimization of resource allocation and reduction of peak regulation burden of water and electricity. On the basis of scheduling, response and cost allocation, the output plan of hydropower and wind and light energy storage is dynamically coordinated, and the virtual power plant scheduling strategy is generated, so that wind, light and water coordinated scheduling is perfected, and the effect of multi-energy complementation is improved while the dependence on hydropower scheduling is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants and relates to a virtual power plant optimization scheduling method, system and medium for wind, solar and water multi-energy complementarity. Background Art

[0002] As an innovative energy management model, virtual power plants are becoming a key force driving energy transformation and the development of smart grids. This new energy management model leverages advanced information and communications technologies, the Internet of Things, and intelligent control systems to integrate dispersed, small-scale power resources into a unified and coordinated "virtual power plant." This model efficiently integrates and optimizes the configuration of previously dispersed, relatively small-scale power resources, such as household solar panels, small wind turbines, energy storage batteries, and various distributed energy devices, to create a powerful and highly coordinated "virtual power plant" system.

[0003] Unlike traditional physical power plants, virtual power plants do not have physical power generation facilities or large-scale power generation equipment. Instead, they aggregate multiple distributed energy resources into a single controllable unit through digital means and participate in power markets or grid dispatching like traditional power plants.

[0004] At present, there are application practices of virtual power plants based on the multi-energy complementarity of wind, solar and hydropower in this field. However, the scheduling of such virtual power plants is mainly based on fixed strategy scheduling schemes, and traditional virtual power plants are heavily dependent on hydropower, resulting in a high wind and solar power curtailment rate, and the effect of multi-energy complementarity is not ideal. Summary of the Invention

[0005] The purpose of the present invention is to provide a virtual power plant optimization scheduling method, system and medium with wind, solar and hydropower multi-energy complementarity, so as to solve the technical problems of traditional virtual power plants being heavily dependent on hydropower and having inflexible scheduling schemes.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for optimizing the scheduling of a virtual power plant with wind, solar, and hydropower multi-energy complementarity, comprising the following steps: Obtain hydropower energy system resource scheduling planning strategy data based on hydropower energy system operation planning data and long-term hydropower energy storage scheduling model; Based on the real-time operating status data of the wind and solar energy power system and the dynamic response model of wind, solar and energy storage at a short-term scale, the real-time dispatching control instruction data of the wind and solar energy power system is obtained; According to the actual power generation of wind and solar power, the actual grid-connected power of hydropower, the total peak-shaving cost and the total power generation, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar power are obtained in combination with the differentiated peak-shaving cost allocation model; According to the dynamic constraints of hydropower peak-shaving initiative, combined with the resource scheduling planning strategy data of the hydropower energy system, the real-time scheduling and control instruction data of the wind and solar energy power system, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar, the output plans of hydropower and wind, solar and energy storage are dynamically coordinated to generate a virtual power plant scheduling strategy.

[0007] Furthermore, the hydropower energy system operation planning data includes: historical hydrological data, electricity price forecasts, long-term load forecasts and reservoir capacity constraints; The water and power system resource scheduling planning strategy data includes: reservoir water release plan, medium and long-term power generation plan and energy storage capacity reservation strategy; The real-time operating status data of the wind and solar energy power system includes: real-time wind and solar power generation data, grid load fluctuations and energy storage SOC status; The real-time dispatching control instruction data of the wind and solar energy power system includes: energy storage charging and discharging instructions, wind and solar output adjustment and short-term power allocation plan.

[0008] Furthermore, the method further includes the following steps: Define the coupling interface between the hydropower energy storage scheduling model at the long-term scale and the wind-solar energy storage dynamic response model at the short-term scale, and couple the hydropower energy storage scheduling model at the long-term scale with the wind-solar energy storage dynamic response model at the short-term scale.

[0009] Furthermore, the coupling interface between the hydropower energy storage scheduling model and the wind and solar energy storage dynamic response model is defined as follows: The hydropower energy storage scheduling model transfers the peak-shaving capacity boundary to the wind and solar energy storage dynamic response model. The specific formula is as follows:

[0010] in, is the hydropower peak regulation capacity boundary, To provide the greatest technical support for hydropower stations, Baseline output for hydropower; When the prediction error of the wind, solar and energy storage dynamic response model exceeds the preset threshold, the hydropower scheduling baseline correction instruction is triggered.

[0011] Furthermore, the time scale of the hydropower energy storage scheduling model under the long-term scale is monthly, and the constraints include: Water balance equation:

[0012] Output upper and lower limits:

[0013] in, For the period Reservoir water storage capacity, For the period Reservoir inflow, Time The discharge flow of hydropower station for power generation, For the period Reservoir discharge; For the period The actual output of the hydropower station, The minimum output of the hydropower station technology, Provide the greatest technical contribution to hydropower stations; The time scale of the wind-solar-energy storage dynamic response model under the short-term scale is hours. Based on the ultra-short-term wind-solar power forecast results, the output fluctuation range is generated, and the energy storage charge state is corrected in real time. The energy storage charge state correction equation for correcting the energy storage charge state is as follows:

[0014] in, For the period The state of charge of the energy storage, For energy storage charging efficiency, is the energy storage discharge efficiency, For the period Charging power of energy storage, For the period Discharge power of energy storage.

[0015] Furthermore, the method further includes the following steps: Based on the quantitative indicators of peak load regulation contribution, a differentiated peak load regulation cost allocation model for hydropower, wind power and photovoltaic power is constructed; The calculation method of the peak load contribution quantitative index is as follows:

[0016] in, For the The output difference of the power supply during peak and valley periods, For the The peak load contribution weight of the power source; The differentiated peak-shaving cost allocation model includes a differentiated peak-shaving cost allocation model for hydropower and a differentiated peak-shaving cost allocation model for wind power and photovoltaic power; The differentiated peak-shaving cost allocation model for hydropower is as follows:

[0017] The differentiated peak-shaving cost sharing model for wind power and photovoltaic power is as follows:

[0018] in, The peak-shaving costs shared by hydropower, Peak-shaving costs shared by wind and solar power; is the total peak-shaving cost; The actual amount of hydropower on the grid, is the actual power generation of wind and solar power, is the total power generation, is the weight of hydropower peak regulation contribution, is the peak load contribution weight of wind and solar power, and the sum of the contribution weights of all power sources is 1.

[0019] Furthermore, the dynamic constraint condition for the hydropower peak regulation initiative is:

[0020] in, For real-time peak load compensation price, is the net income after hydropower participates in peak regulation, is the benchmark power generation income of hydropower that does not participate in peak regulation, The additional output provided by hydropower for peak load regulation, is the opportunity cost, including the loss of water abandonment and the penalty of power generation plan deviation.

[0021] Furthermore, the method further includes the following steps: A rolling horizon control framework is used, combined with a mixed integer quadratic programming algorithm, to regularly update the power output plan. A scenario tree is generated based on the wind and solar forecast error distribution for robust optimization. The objective function of the sliding horizon control framework is:

[0022] Among them, the weight and is the target weight coefficient, which is dynamically adjusted according to the carbon price signal; The actual output of wind, solar and energy storage in period t For the period Predicted values of wind, solar and energy storage.

[0023] In a second aspect, the present invention provides a virtual power plant optimization scheduling system for wind, solar, and hydropower multi-energy complementarity, comprising: The hydropower scheduling planning strategy data acquisition module is used to obtain the hydropower energy system resource scheduling planning strategy data based on the hydropower energy system operation planning data and the long-term hydropower energy storage scheduling model; Wind and solar dispatch control instruction data acquisition module, used to obtain real-time dispatch control instruction data of wind and solar energy power system based on real-time operation status data of wind and solar energy power system and dynamic response model of wind and solar energy storage under short-term scale; The peak-shaving cost acquisition module is used to obtain the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar power based on the actual power generation of wind and solar power, the actual grid-connected power of hydropower, the total peak-shaving cost and the total power generation in combination with the differentiated peak-shaving cost allocation model; The scheduling strategy generation module is used to dynamically coordinate the output plans of hydropower and wind, solar and energy storage according to the dynamic constraints of hydropower peak-shaving initiative, combined with the hydropower energy system resource scheduling planning strategy data, the wind and solar energy power system real-time scheduling control instruction data, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar, to generate a virtual power plant scheduling strategy.

[0024] In a third aspect, the present invention provides a storage medium storing computer program instructions. When the computer program instructions are loaded and executed by a processor, the processor executes a method for optimizing the scheduling of a virtual power plant with wind, solar and hydropower multi-energy complementarity.

[0025] Compared with the prior art, the present invention has the following beneficial effects: The hydropower energy storage scheduling model of the present invention at a long-term scale is conducive to the formulation of a global scheduling strategy for hydropower resources. Through long-term energy storage scheduling, hydropower can more efficiently balance medium- and long-term supply and demand and reduce real-time dependence. The dynamic response model of wind, solar and energy storage at a short-term scale enables wind and solar to actively participate in peak regulation, share hydropower pressure, and enhance system flexibility. The differentiated peak-shaving cost-sharing model is conducive to incentivizing wind and solar to actively regulate peak power, optimize resource allocation, and reduce the peak-shaving burden of hydropower. Based on scheduling, response and cost sharing, the present invention dynamically coordinates the output plans of hydropower and wind, solar and energy storage, and generates a virtual power plant scheduling strategy, thereby improving the coordinated scheduling of wind, solar and hydropower, while reducing dependence on hydropower scheduling and enhancing the effect of multi-energy complementarity.

[0026] Based on the quantitative indicators of peak-shaving contribution, the present invention constructs a differentiated peak-shaving cost sharing model for hydropower, wind power and photovoltaic power, and combines it with real-time peak-shaving compensation price and opportunity cost calculation to ensure that the net income of hydropower participating in peak-shaving is not lower than the preset threshold. Through economic incentives, the initiative of hydropower peak-shaving is enhanced, while the wind and solar power curtailment rate is reduced, achieving a balance of interests among multiple parties.

[0027] The system of the present invention includes a hydropower scheduling planning strategy data acquisition module, a wind and solar scheduling control instruction data acquisition module, a peak-shaving cost acquisition module and a scheduling strategy generation module; the hydropower scheduling planning strategy data acquisition module is used to obtain hydropower energy system resource scheduling planning strategy data based on the hydropower energy system operation planning data combined with the hydropower energy storage scheduling model under the long-term scale; the wind and solar scheduling control instruction data acquisition module is used to obtain wind and solar energy power system real-time scheduling control instruction data based on the wind and solar energy power system real-time operation status data combined with the wind and solar energy storage dynamic response model under the short-term scale; the peak-shaving cost acquisition module is used to obtain the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar based on the actual wind and solar power generation, the actual hydropower grid-connected power, the total peak-shaving cost and the total power generation combined with the differentiated peak-shaving cost allocation model; the scheduling strategy generation module is used to dynamically coordinate the output plans of hydropower and wind and solar energy storage based on the dynamic constraint conditions of hydropower peak-shaving initiative combined with the hydropower energy system resource scheduling planning strategy data, the wind and solar energy power system real-time scheduling control instruction data, the hydropower shared peak-shaving cost and the wind and solar shared peak-shaving cost to generate a virtual power plant scheduling strategy. The various modules work together to improve the coordinated scheduling of wind, solar and water, reducing dependence on hydropower scheduling while enhancing the effect of multi-energy complementarity.

[0028] The electronic equipment of the present invention can also solve the technical problems of traditional virtual power plants' heavy dependence on hydropower and inflexible scheduling schemes, improve the scheduling of wind, solar and water coordination, reduce dependence on hydropower scheduling, and enhance the effect of multi-energy complementarity. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 A system module connection diagram of an embodiment of the present invention; Figure 3 This is a flow chart of a method according to another embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," and the like in the description of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0032] The present invention is described in further detail below with reference to the accompanying drawings: See also Figure 1 The present invention discloses a virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity, comprising the following steps: S1, based on the hydropower energy system operation planning data combined with the hydropower energy storage scheduling model at a long-term scale, obtains the hydropower energy system resource scheduling planning strategy data, which is conducive to formulating the global scheduling strategy of hydropower resources. Through long-term energy storage scheduling, hydropower can more efficiently balance medium- and long-term supply and demand and reduce real-time dependence.

[0033] In this embodiment, the hydropower energy system operation planning data includes: historical hydrological data, electricity price forecasts, long-term load forecasts, and reservoir capacity constraints; The water and power system resource scheduling planning strategy data includes: reservoir water release plan, medium and long-term power generation plan and energy storage capacity reservation strategy; S2 uses real-time operational status data from wind and solar power systems combined with a short-term dynamic response model for wind, solar, and energy storage to obtain real-time dispatch control command data for the wind and solar power systems. Traditional solutions, due to the uncertainty of wind and solar power, struggle with flexible dispatch, leading to over-reliance on hydropower for emergency response. This short-term dynamic response model for wind, solar, and energy storage enables wind and solar power to proactively participate in peak load regulation, sharing hydropower pressure and improving system flexibility.

[0034] The real-time operating status data of the wind and solar energy power system includes: real-time wind and solar power generation data, grid load fluctuations and energy storage SOC status; The real-time dispatching control instruction data of the wind and solar energy power system includes: energy storage charging and discharging instructions, wind and solar output adjustment and short-term power allocation plan.

[0035] In this embodiment, the following steps are also included: Define the coupling interface between the hydropower energy storage scheduling model at the long-term scale and the wind-solar energy storage dynamic response model at the short-term scale, and couple the hydropower energy storage scheduling model at the long-term scale with the wind-solar energy storage dynamic response model at the short-term scale.

[0036] Preferably, the coupling interface between the hydropower energy storage scheduling model and the wind-solar energy storage dynamic response model is defined as follows: The hydropower energy storage scheduling model transfers the peak-shaving capacity boundary to the wind and solar energy storage dynamic response model. The specific formula is as follows:

[0037] in, is the hydropower peak regulation capacity boundary, To provide the greatest technical support for hydropower stations, Baseline output for hydropower; When the prediction error of the wind, solar and energy storage dynamic response model exceeds the preset threshold, the hydropower scheduling baseline correction instruction is triggered.

[0038] In this embodiment, the time scale of the hydropower energy storage scheduling model under the long-term scale is monthly, and the constraints include: Water balance equation:

[0039] Output upper and lower limits:

[0040] in, For the period Reservoir water storage capacity, For the period Reservoir inflow, Time The discharge flow of hydropower station for power generation, For the period Reservoir discharge; For the period The actual output of the hydropower station, The minimum output of the hydropower station technology, Provide the greatest technical contribution to hydropower stations; The time scale of the wind-solar-energy storage dynamic response model under the short-term scale is hours. Based on the ultra-short-term wind-solar power forecast results, the output fluctuation range is generated, and the energy storage charge state is corrected in real time. The energy storage charge state correction equation for correcting the energy storage charge state is as follows:

[0041] in, For the period The state of charge of the energy storage, For energy storage charging efficiency, is the energy storage discharge efficiency, For the period Charging power of energy storage, For the period Discharge power of energy storage.

[0042] S3: Based on the actual wind and solar power generation, the actual hydropower grid-connected power generation, the total peak-shaving cost, and the total power generation, a differentiated peak-shaving cost allocation model is used to determine the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar power. In traditional solutions, the majority of peak-shaving costs are borne by hydropower, resulting in poor economic efficiency and high dependency. This differentiated peak-shaving cost allocation model helps incentivize proactive peak-shaving by wind and solar power, optimizes resource allocation, and reduces the peak-shaving burden on hydropower.

[0043] In this embodiment, the following steps are also included: Based on the quantitative indicators of peak load regulation contribution, a differentiated peak load regulation cost allocation model for hydropower, wind power and photovoltaic power is constructed; The calculation method of the peak load contribution quantitative index is as follows:

[0044] in, For the The output difference of the power supply during peak and valley periods, For the The peak load contribution weight of the power source; The differentiated peak-shaving cost allocation model includes a differentiated peak-shaving cost allocation model for hydropower and a differentiated peak-shaving cost allocation model for wind power and photovoltaic power; The differentiated peak-shaving cost allocation model for hydropower is as follows:

[0045] The differentiated peak-shaving cost sharing model for wind power and photovoltaic power is as follows:

[0046] in, The peak-shaving costs shared by hydropower, Peak-shaving costs shared by wind and solar power; is the total peak-shaving cost; The actual amount of hydropower on the grid, is the actual power generation of wind and solar power, is the total power generation, is the weight of hydropower peak regulation contribution, is the peak load contribution weight of wind and solar power, and the sum of the contribution weights of all power sources is 1.

[0047] S4 dynamically coordinates the output plans of hydropower, wind, solar, and energy storage based on the dynamic constraints of hydropower peak-shaving initiative, combined with hydropower energy system resource scheduling planning strategy data, wind and solar power system real-time scheduling control command data, and the peak-shaving costs shared by hydropower and wind and solar. This generates a virtual power plant scheduling strategy. This improves the coordinated scheduling of wind, solar, and hydropower, reducing reliance on hydropower scheduling while enhancing the effectiveness of multi-energy complementarity.

[0048] In this embodiment, the dynamic constraint condition for the hydropower peak load regulation initiative is:

[0049] in, For real-time peak load compensation price, is the net income after hydropower participates in peak regulation, is the benchmark power generation income of hydropower that does not participate in peak regulation, The additional output provided by hydropower for peak load regulation, is the opportunity cost, including the loss of water abandonment and the penalty of power generation plan deviation.

[0050] In this embodiment, the following steps are also included: A rolling horizon control framework is used, combined with a mixed integer quadratic programming algorithm, to regularly update the power output plan. A scenario tree is generated based on the wind and solar forecast error distribution for robust optimization. The objective function of the sliding horizon control framework is:

[0051] Among them, the weight and is the target weight coefficient, which is dynamically adjusted according to the carbon price signal; The actual output of wind, solar and energy storage in period t For the period Predicted values of wind, solar and energy storage.

[0052] This embodiment also includes a peak-shaving cost allocation fairness verification step, which is as follows: By comparing the calculation results of the Shapley value method with the actual allocation ratio, the weight coefficient in the contribution quantification formula is dynamically adjusted.

[0053] Based on the above method, the present invention also discloses a virtual power plant optimization scheduling system with wind, solar and water multi-energy complementarity, see Figure 2 ,include: The hydropower scheduling planning strategy data acquisition module is used to obtain the hydropower energy system resource scheduling planning strategy data based on the hydropower energy system operation planning data and the long-term hydropower energy storage scheduling model; Wind and solar dispatch control instruction data acquisition module, used to obtain real-time dispatch control instruction data of wind and solar energy power system based on real-time operation status data of wind and solar energy power system and dynamic response model of wind and solar energy storage under short-term scale; The peak-shaving cost acquisition module is used to obtain the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar power based on the actual power generation of wind and solar power, the actual grid-connected power of hydropower, the total peak-shaving cost and the total power generation in combination with the differentiated peak-shaving cost allocation model; The scheduling strategy generation module is used to dynamically coordinate the output plans of hydropower and wind, solar and energy storage according to the dynamic constraints of hydropower peak-shaving initiative, combined with the hydropower energy system resource scheduling planning strategy data, the wind and solar energy power system real-time scheduling control instruction data, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar, to generate a virtual power plant scheduling strategy.

[0054] The system of the present invention can solve the technical problems of traditional virtual power plants' heavy dependence on hydropower and inflexible scheduling schemes, improve the scheduling of wind, solar and water coordination, reduce dependence on hydropower scheduling, and enhance the effect of multi-energy complementarity.

[0055] Example 2: See also Figure 3 This embodiment discloses a method for optimizing the scheduling of a virtual power plant with wind, solar, and hydropower multi-energy complementarity, comprising the following steps: Step S1: Construct a hydropower energy storage scheduling model at a long-term scale and a wind-solar energy storage dynamic response model at a short-term scale, and define a coupling interface between the hydropower energy storage scheduling model and the wind-solar energy storage dynamic response model; Step S2: Based on the quantitative indicators of peak-shaving contribution, a differentiated peak-shaving cost allocation model for hydropower, wind power, and photovoltaic power is constructed, where hydropower is allocated according to the proportion of grid-connected power, and wind power and photovoltaic power are allocated according to the proportion of power generation; Step S3: Setting dynamic constraints on the initiative of hydropower peak regulation, combining real-time peak regulation compensation prices and opportunity cost calculations, so that the net benefits of hydropower participating in peak regulation are not less than a preset threshold; Step S4: Based on the output results of steps S1-S3, dynamically coordinate the output plans of hydropower and wind, solar and energy storage to generate a virtual power plant scheduling strategy.

[0056] In one embodiment, in step S1, the time scale of the hydropower energy storage scheduling model is monthly, and the constraints include: Water balance equation:

[0057] Output upper and lower limits:

[0058] in, For the period Reservoir water storage capacity, For the period Reservoir inflow, Time The discharge flow of hydropower station for power generation, For the period Reservoir discharge; For the period The actual output of the hydropower station, The minimum output of the hydropower station technology, Provide the greatest technical contribution to hydropower stations; The time scale of the wind-solar-energy storage dynamic response model is hourly. It generates the output fluctuation range based on the ultra-short-term wind-solar power forecast results and corrects the energy storage charge state in real time, that is, the energy storage charge state correction equation:

[0059] in, For the period The state of charge of the energy storage, For energy storage charging efficiency, is the energy storage discharge efficiency, For the period Charging power of energy storage, For the period Discharge power of energy storage.

[0060] In one embodiment, in step S1, the coupling interface includes: The long-term hydropower dispatch model transfers the peak-shaving capacity boundary to the short-term model:

[0061] in, is the hydropower peak regulation capacity boundary, To provide the greatest technical support for hydropower stations, Baseline output for hydropower; When the short-term wind and solar power forecast error exceeds the preset threshold, the hydropower scheduling baseline correction instruction is triggered.

[0062] In one embodiment, in step S2, the peak shaving contribution quantification index is calculated as follows:

[0063] in, For the The output difference of the power supply during peak and valley periods, For the The peak load contribution weight of this type of power supply.

[0064] In one embodiment, in step S2, the formula of the peak-shaving cost sharing model is as follows: Hydropower:

[0065] Scenery:

[0066] in, The peak-shaving costs shared by hydropower, Peak-shaving costs shared by wind and solar power; is the total peak-shaving cost; The actual amount of hydropower on the grid, is the actual power generation of wind and solar power, is the total power generation, is the weight of hydropower peak regulation contribution, is the peak load contribution weight of wind and solar power, and the sum of the contribution weights of all power sources is 1.

[0067] In one embodiment, in step S3, the dynamic constraint condition for the hydropower peak regulation initiative is:

[0068] in, For real-time peak load compensation price, is the net income after hydropower participates in peak regulation, is the benchmark power generation income of hydropower that does not participate in peak regulation, The additional output provided by hydropower for peak load regulation, is the opportunity cost, including the loss of water abandonment and the penalty of power generation plan deviation.

[0069] In one embodiment, in step S4, a rolling horizon control framework is used in combination with a mixed integer quadratic programming algorithm to update the output plan once every time T, and a scenario tree is generated based on the wind and solar prediction error distribution for robust optimization.

[0070] In one embodiment, the objective function of the rolling horizon control framework is:

[0071] Among them, the weight and is the target weight coefficient, which is dynamically adjusted according to the carbon price signal; The actual output of wind, solar and energy storage in period t For the period Predicted values of wind, solar and energy storage.

[0072] In one embodiment, the step of verifying the fairness of the peak-shaving cost sharing is further included: by comparing the calculation result of the Shapley value method with the actual sharing ratio, the weight coefficient in the contribution quantification formula is dynamically adjusted.

[0073] To achieve the above objectives, the present invention also provides a virtual power plant optimization scheduling system with wind, solar and hydropower multi-energy complementarity, including: Dispatch and response module: Builds a long-term hydropower energy storage dispatch model and a short-term wind and solar energy storage dynamic response model, and defines the coupling interface between the two models. Cost sharing module: Based on the quantitative indicators of peak-shaving contribution, a differentiated peak-shaving cost sharing model is constructed for hydropower, wind power, and photovoltaic power. Hydropower is shared according to the proportion of grid-connected power, while wind power and photovoltaic power are shared according to the proportion of power generation. Dynamic Constraint Module: Sets dynamic constraints on the initiative of hydropower peak-shaving, combines real-time peak-shaving compensation prices with opportunity cost calculations, and ensures that the net benefits of hydropower participating in peak-shaving are not lower than the preset threshold; Dispatching strategy module: Based on the above output results, it dynamically coordinates the output plans of hydropower, wind power, solar power and energy storage, and generates a virtual power plant dispatching strategy.

[0074] Compared with the prior art, the present invention has the following beneficial effects: According to the present invention, based on scheduling, response and cost sharing, the output plans of hydropower and wind, solar and energy storage are dynamically coordinated to generate a virtual power plant scheduling strategy, thereby improving the scheduling of wind, solar and hydropower collaboration, reducing dependence on hydropower scheduling, and enhancing the effect of multi-energy complementarity.

[0075] According to the present invention, based on the quantitative indicators of peak-shaving contribution, a differentiated peak-shaving cost sharing model for hydropower, wind power and photovoltaics is constructed, and combined with the real-time peak-shaving compensation price and opportunity cost calculation, the net income of hydropower participating in peak-shaving is not lower than the preset threshold. Through economic incentives, the initiative of hydropower peak-shaving is enhanced, while the wind and solar power curtailment rate is reduced, thereby achieving a balance of interests among multiple parties.

[0076] Example 3: like Figure 3 As shown, this embodiment provides a virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity. The implementation principle of this method is as follows: a long-term hydropower model generates a benchmark output. (refers to the power generation level of a hydropower station as a basic operating reference in the power system, usually used for power dispatching, stability analysis and economic evaluation) and peak capacity boundary (The hydropower peak-shaving capacity boundary refers to the limit of the adjustable range of the hydropower station's power generation capacity when participating in the peak-shaving operation of the power system, which is usually determined by technical, physical and environmental factors.) Peak and hydro are used as constraints, combined with ultra-short-term forecasts to generate output fluctuation ranges, and the peak-shaving cost allocation module calculates the contribution of each power source. i, and allocate peak-shaving costs. The dynamic optimization engine refreshes the output plan every time T to ensure hydropower revenue constraints and grid security. The value of T can be, for example, 10 minutes, 15 minutes, 30 minutes, etc.

[0077] The specific steps are as follows: Step S1: Construct a long-term hydropower energy storage scheduling model and a short-term wind and solar energy storage dynamic response model, and define a coupling interface between the two models; S11. Input reservoir inflow forecast data (historical hydrological data + weather forecast) and use stochastic dynamic programming (SDP) to solve the monthly power generation plan. The objective function is:

[0078] is the time-of-use electricity price (yuan / MWh), is the penalty coefficient for abandoned water (yuan / m³), For the period The non-power generation water flow that is released actively or passively due to the water level exceeding the safety capacity or scheduling demand in the reservoir is For hydropower stations during the period The actual output power is determined by the turbine efficiency, water head height and water discharge flow.

[0079] The time scale of the hydropower storage scheduling model is monthly, and the constraints include: Water balance equation:

[0080] Output upper and lower limits:

[0081] in, For the period Reservoir water storage capacity (unit: m³), For the period Reservoir inflow (unit: m³ / s), Time Hydropower station discharge flow (unit: m³ / s), For the period Reservoir discharge flow (unit: m³ / s); For the period The actual output of the hydropower station (unit: MW), The minimum output of the hydropower station technology, Provide the greatest technical contribution to hydropower stations; Output: Baseline output plan , peak-shaving capacity boundary .

[0082] S12. The time scale of the wind, solar, and energy storage dynamic response model is hourly. Based on the ultra-short-term wind and solar power forecast results, the output fluctuation range is generated and the energy storage charge state is corrected in real time. That is, the energy storage charge state correction equation is:

[0083] in, For the period The state of charge of the energy storage (unit: %, range 0~100%), is the energy storage charging efficiency (unit: %), such as 90%), is the energy storage discharge efficiency (unit: %, such as 95%), For the period Charging power of energy storage (unit: MW), For the period Discharge power of energy storage (unit: MW).

[0084] Peak load capacity constraint: The combined peak load capacity of wind, solar and energy storage shall not exceed ; In extreme scenarios (forecast error > 15%), a hydropower baseline correction request is triggered.

[0085] The coupling interface between the two models includes the following: S11.1. Transferring peak-shaving capacity boundaries from the long-term hydropower dispatch model to the short-term model:

[0086] in, is the hydropower peak-shaving capacity boundary (characterizing the flexible peak-shaving capability of hydropower outside the long-term dispatch baseline, serving as the constraint boundary of the short-term wind-solar-storage energy model), is the maximum technical output of the hydropower station (determined by turbine design parameters and water resource conditions (such as reservoir water level), and is a hard constraint of the long-term scheduling model), Hydropower benchmark output (generated based on reservoir water inflow forecasts, load demand and electricity price policies, reflecting the conventional power generation plan of hydropower as a baseload power source); S11.2. When the short-term wind and solar power forecast error exceeds the preset threshold, the hydropower dispatch baseline correction instruction is triggered.

[0087] Step S2: Based on the quantitative indicators of peak load contribution, a differentiated peak load cost allocation model for hydropower, wind power and photovoltaic power is constructed. In this step, hydropower is allocated based on the proportion of grid-connected electricity, while wind power and photovoltaic power are allocated based on the proportion of power generation. The calculation method for the quantitative index of peak load contribution is as follows:

[0088] in, For the The output difference of the power source in peak and valley periods (unit: MW), For the The peak load contribution weight of this type of power supply.

[0089] The calculation method is as follows:

[0090] in, For the The average output of the power source during peak hours (unit: MW), For the The average output of this type of power source during off-peak hours (unit: MW).

[0091] The formula for the peak load cost allocation model is as follows: Hydropower:

[0092] Scenery:

[0093] in, The peak-shaving costs shared by hydropower, Peak-shaving cost shared by wind and solar power (unit: yuan); is the total peak-shaving cost (unit: yuan), The actual amount of hydropower connected to the grid (unit: MWh), is the actual power generation of wind and solar power (unit: MWh), is the total electricity generation (unit: MWh), is the weight of hydropower's peak-shaving contribution (indicating the proportion of hydropower in the system's total peak-shaving capacity, and determining its proportion of shared peak-shaving costs), is the peak-shaving contribution weight of wind and solar power (indicating the proportion of the combined peak-shaving capacity of wind and solar power, and determining the proportion of the overall peak-shaving cost shared by wind and solar power), and the sum of the contribution weights of all power sources is 1. Table 1 is an example table of relevant parameters in an embodiment.

[0094] Table 1, Example:

[0095] Apportionment calculation: Total peak-shaving cost = 10000 =10,000 yuan; Total power generation = 1000 =1000MWh; Water and electricity cost sharing: =0.6×10000×(800 / 1000)=4800 =0.6×10000×(800 / 1000)=4800 yuan; Wind and solar shared costs: = (0.08+0.32)×10000 ×(200 / 1000) = 800 yuan.

[0096] Step S3: Set the dynamic constraint conditions for hydropower peak load regulation initiative, combined with the real-time peak load regulation compensation price and opportunity cost calculation The purpose of this step is to ensure that the net benefit of hydropower participating in peak load regulation is not less than the preset threshold, so as to improve the initiative of hydropower dispatching. The dynamic constraint conditions for the initiative of hydropower peak load regulation are:

[0097] in, is the real-time peak load compensation price (unit: yuan / MWh), is the net income after hydropower participates in peak regulation (unit: yuan), is the benchmark power generation income of hydropower that does not participate in peak regulation (unit: yuan), The additional output provided by hydropower for peak load regulation (unit: MW), is the opportunity cost (unit: yuan), including the loss of water abandonment and the penalty of power generation plan deviation; in, The calculation method is:

[0098] in, is the actual hydropower output (unit: MW), It is the benchmark output of hydropower (unit: MW).

[0099] Example: =Benchmark income 300 + Peak load compensation 0.2 × 150 − Abandoned water loss 50 = 280 yuan ≥ =250 yuan.

[0100] Step S4: Based on the output results of steps S1 to S3, dynamically coordinate the output plans of hydropower and wind, solar and energy storage to generate a virtual power plant scheduling strategy.

[0101] In this step, a rolling horizon control framework is used in combination with a mixed integer quadratic programming algorithm (using the CPLEX solver to perform mixed integer quadratic programming). The output plan is updated every time T, and a scenario tree is generated based on the wind and solar forecast error distribution for robust optimization. The value of T can be, for example, 10 minutes, 15 minutes, 30 minutes, etc.

[0102] The objective function of the sliding horizon control framework is:

[0103] Among them, the weight and is the target weight coefficient, which is dynamically adjusted according to the carbon price signal. The adjustment rules are as follows: carbon price increases by 10%: (Low carbon weight) increased from 0.4 to 0.6; peak shaving costs exceeded budget: (Economic weight) increased from 0.5 to 0.7; For the period Actual output of wind, solar and energy storage (unit: MW) For the period The predicted value of wind, solar and energy storage (unit: MW). This value can also be predicted using existing prediction models.

[0104] In the further installation of the preferred room, it also includes the step of verifying the fairness of the peak-shaving cost sharing: by comparing the calculation results of the Shapley value method with the actual sharing ratio, the weight coefficient in the contribution quantification formula is dynamically adjusted to make the contribution quantification model more in line with the actual marginal peak-shaving contribution.

[0105] Through the above method, based on scheduling, response and cost sharing modeling, the output plans of hydropower and wind, solar and energy storage are dynamically coordinated to generate a virtual power plant scheduling strategy, thereby improving the coordinated scheduling of wind, solar and hydropower, reducing dependence on hydropower scheduling, and enhancing the effect of multi-energy complementarity.

[0106] Based on the method of this embodiment, this embodiment also provides a virtual power plant optimization scheduling system for wind, solar, and hydropower multi-energy complementarity, including: Dispatch and response module: Builds a long-term hydropower energy storage dispatch model and a short-term wind and solar energy storage dynamic response model, and defines the coupling interface between the two models. Cost sharing module: Based on the quantitative indicators of peak-shaving contribution, a differentiated peak-shaving cost sharing model is constructed for hydropower, wind power, and photovoltaic power. Hydropower is shared according to the proportion of grid-connected power, while wind power and photovoltaic power are shared according to the proportion of power generation. Dynamic Constraint Module: Sets dynamic constraints on the initiative of hydropower peak-shaving, combines real-time peak-shaving compensation prices with opportunity cost calculations, and ensures that the net benefits of hydropower participating in peak-shaving are not lower than the preset threshold; Dispatching strategy module: Based on the above output results, it dynamically coordinates the output plans of hydropower, wind power, solar power and energy storage, and generates a virtual power plant dispatching strategy.

[0107] It should be noted that the above-mentioned functional modules correspond one-to-one with the steps of the wind, solar, and water multi-energy complementarity virtual power plant optimization scheduling method provided in Example 1. The specific functions implemented are the same as the wind, solar, and water multi-energy complementarity virtual power plant optimization scheduling method provided in Example 1, and the beneficial effects achieved are also the same as the beneficial effects achieved by the wind, solar, and water multi-energy complementarity virtual power plant optimization scheduling method provided in Example 1.

[0108] An electronic device comprises: a processor; a memory for storing computer program instructions; and steps for implementing a method for optimizing the scheduling of a virtual power plant with wind, solar and hydropower multi-energy complementarity when executing the computer program.

[0109] A storage medium stores computer program instructions. When the computer program instructions are loaded and run by a processor, the processor executes a virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity.

[0110] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. 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.

[0112] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1The steps for the function specified in one or more boxes.

[0114] The above content is only for explaining the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A virtual power plant optimization scheduling method for wind, solar and hydropower multi-energy complementarity, characterized by: The following steps are involved: Obtain hydropower energy system resource scheduling planning strategy data based on hydropower energy system operation planning data and long-term hydropower energy storage scheduling model; Based on the real-time operating status data of the wind and solar energy power system and the dynamic response model of wind, solar and energy storage at a short-term scale, the real-time dispatching control instruction data of the wind and solar energy power system is obtained; According to the actual power generation of wind and solar power, the actual grid-connected power of hydropower, the total peak-shaving cost and the total power generation, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar power are obtained in combination with the differentiated peak-shaving cost allocation model; According to the dynamic constraints of hydropower peak-shaving initiative, combined with the resource scheduling planning strategy data of the hydropower energy system, the real-time scheduling and control instruction data of the wind and solar energy power system, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar, the output plans of hydropower and wind, solar and energy storage are dynamically coordinated to generate a virtual power plant scheduling strategy.

2. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 1 is characterized in that: The hydropower energy system operation planning data includes: historical hydrological data, electricity price forecasts, long-term load forecasts and reservoir capacity constraints; The water and power system resource scheduling planning strategy data includes: reservoir water release plan, medium and long-term power generation plan and energy storage capacity reservation strategy; The real-time operating status data of the wind and solar energy power system includes: real-time wind and solar power generation data, grid load fluctuations and energy storage SOC status; The real-time dispatching control instruction data of the wind and solar energy power system includes: energy storage charging and discharging instructions, wind and solar output adjustment and short-term power allocation plan.

3. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 1 is characterized in that: The following steps are also included: Define the coupling interface between the hydropower energy storage scheduling model at the long-term scale and the wind-solar energy storage dynamic response model at the short-term scale, and couple the hydropower energy storage scheduling model at the long-term scale with the wind-solar energy storage dynamic response model at the short-term scale.

4. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 3 is characterized in that: The coupling interface between the hydropower energy storage scheduling model and the wind and solar energy storage dynamic response model is defined as follows: The hydropower energy storage scheduling model transfers the peak-shaving capacity boundary to the wind and solar energy storage dynamic response model. The specific formula is as follows: in, is the hydropower peak regulation capacity boundary, To provide the greatest technical support for hydropower stations, Baseline output for hydropower; When the prediction error of the wind, solar and energy storage dynamic response model exceeds the preset threshold, the hydropower scheduling baseline correction instruction is triggered.

5. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 1 is characterized in that: The time scale of the hydropower energy storage scheduling model under the long-term scale is monthly, and the constraints include: Water balance equation: Output upper and lower limits: in, For the period Reservoir water storage capacity, For the period Reservoir inflow, Time The discharge flow of hydropower station for power generation, For the period Reservoir discharge; For the period The actual output of the hydropower station, The minimum output of the hydropower station technology, Provide the greatest technical contribution to hydropower stations; The time scale of the wind-solar-energy storage dynamic response model under the short-term scale is hours. Based on the ultra-short-term wind-solar power forecast results, the output fluctuation range is generated, and the energy storage charge state is corrected in real time. The energy storage charge state correction equation for correcting the energy storage charge state is as follows: in, For the period The state of charge of the energy storage, For energy storage charging efficiency, is the energy storage discharge efficiency, For the period Charging power of energy storage, For the period Discharge power of energy storage.

6. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 1 is characterized in that: The following steps are also included: Based on the quantitative indicators of peak load regulation contribution, a differentiated peak load regulation cost allocation model for hydropower, wind power and photovoltaic power is constructed; The calculation method of the peak load contribution quantitative index is as follows: in, For the The output difference of the power supply during peak and valley periods, For the The peak load contribution weight of the power source; The differentiated peak-shaving cost allocation model includes a differentiated peak-shaving cost allocation model for hydropower and a differentiated peak-shaving cost allocation model for wind power and photovoltaic power; The differentiated peak-shaving cost allocation model for hydropower is as follows: The differentiated peak-shaving cost sharing model for wind power and photovoltaic power is as follows: in, The peak-shaving costs shared by hydropower, Peak-shaving costs shared by wind and solar power; is the total peak-shaving cost; The actual amount of hydropower on the grid, is the actual power generation of wind and solar power, is the total power generation, is the weight of hydropower peak load regulation contribution, is the peak load contribution weight of wind and solar power, and the sum of the contribution weights of all power sources is 1.

7. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 1 is characterized in that: The dynamic constraint conditions for the hydropower peak regulation initiative are: in, For real-time peak load compensation price, is the net income after hydropower participates in peak regulation, is the benchmark power generation income of hydropower that does not participate in peak regulation, The additional output provided by hydropower for peak load regulation, is the opportunity cost, including the loss of water abandonment and the penalty of power generation plan deviation.

8. The virtual power plant optimization scheduling method for wind, solar and water multi-energy complementarity according to claim 1 is characterized in that: The following steps are also included: A rolling horizon control framework is used, combined with a mixed integer quadratic programming algorithm, to regularly update the power output plan. A scenario tree is generated based on the wind and solar forecast error distribution for robust optimization. The objective function of the sliding horizon control framework is: Among them, the weight and is the target weight coefficient, which is dynamically adjusted according to the carbon price signal; The actual output of wind, solar and energy storage in period t For the period Predicted values of wind, solar and energy storage.

9. The virtual power plant optimization scheduling system with wind, solar and hydropower complementary energy is characterized by: include: The hydropower scheduling planning strategy data acquisition module is used to obtain the hydropower energy system resource scheduling planning strategy data based on the hydropower energy system operation planning data and the long-term hydropower energy storage scheduling model; Wind and solar dispatch control instruction data acquisition module, used to obtain real-time dispatch control instruction data of wind and solar energy power system based on real-time operation status data of wind and solar energy power system and dynamic response model of wind and solar energy storage under short-term scale; The peak-shaving cost acquisition module is used to obtain the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar power based on the actual power generation of wind and solar power, the actual grid-connected power of hydropower, the total peak-shaving cost and the total power generation in combination with the differentiated peak-shaving cost allocation model; The scheduling strategy generation module is used to dynamically coordinate the output plans of hydropower and wind, solar and energy storage according to the dynamic constraints of hydropower peak-shaving initiative, combined with the hydropower energy system resource scheduling planning strategy data, the wind and solar energy power system real-time scheduling control instruction data, the peak-shaving cost shared by hydropower and the peak-shaving cost shared by wind and solar, to generate a virtual power plant scheduling strategy.

10. An electronic device comprising: Processor; memory, electronic device used to store computer program instructions; characterized in that it is used to implement the steps of the wind, solar and water multi-energy complementary virtual power plant optimization scheduling method as described in any one of claims 1-8 when executing the computer program.

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