Energy management optimization method and device for wind, light, hydrogen and fuel cogeneration system

Abstract

The invention relates to an energy management optimization method and device for a wind-light-hydrogen-fuel co-production system, and the method comprises the steps: carrying out the modeling of a component main body of the wind-light-hydrogen-fuel co-production system, and obtaining an operation model of each component main body; screening a preset number of typical scenes based on a scene reduction method of probability distance, and obtaining scene characteristic curves of the corresponding typical scenes at different time scales; establishing a distribution robust optimization model, an intra-day rolling optimization model and an intra-day real-time optimization model based on the operation model of each component main body and the screened typical scene; and solving and obtaining an optimization strategy based on the distribution robust optimization model, the intra-day rolling optimization model and the intra-day real-time optimization model. According to the method, seamless connection from global planning to real-time adjustment is realized based on three-layer model cooperation of distributed robust optimization, intra-day rolling optimization and intra-day real-time optimization, so that the economy of long-term operation of the system is ensured, and the stability of coping with uncertainty fluctuation is improved.

Description

Technical Field

[0001] This application relates to the field of energy management technology, specifically to an energy management optimization method and apparatus for a wind-solar-hydrogen-fuel cogeneration system. Background Technology

[0002] As the global energy structure shifts towards cleaner and lower-carbon energy, the penetration rate of renewable energy sources such as wind and solar power continues to increase. However, wind and solar power generation is characterized by significant intermittency, volatility, and uncertainty, and large-scale grid connection poses a severe challenge to the safe and stable operation of the power system and efficient energy management. To improve the absorption capacity of renewable energy, optimize energy allocation, and reduce carbon emissions, the wind-solar-hydrogen combined cycle power system has emerged as a new type of multi-energy complementary energy system.

[0003] The wind-solar-hydrogen combined cycle system integrates wind and solar power generation, hydrogen production, storage and application, as well as traditional electrical energy storage and diverse load demands, forming a complex system with coupled electricity, hydrogen and gas energy flows. This system can effectively mitigate fluctuations in wind and solar power output through the flexible conversion and storage of hydrogen, improving the flexibility and reliability of energy supply, while reducing dependence on traditional fossil fuels, thus aligning with low-carbon development goals.

[0004] However, the efficient operation of a wind-solar-hydrogen combined heat and power (CHP) system highly depends on a scientifically sound energy management strategy. On the one hand, the system includes various heterogeneous devices such as hydrogen-blended gas turbines, electrolyzers, and energy storage units. The operating characteristics of each device differ greatly, and there are complex constraints (such as power limits, ramp-up constraints, and energy storage capacity constraints), requiring precise modeling to reflect the actual operating conditions. On the other hand, wind and solar power output and electricity load demand are highly uncertain, and traditional deterministic optimization methods are difficult to cope with such fluctuations, which can easily lead to optimization strategies deviating from reality and affecting the system's economy and stability.

[0005] Existing energy management methods are mostly designed for single-energy systems or simple multi-energy systems, and have limitations in handling the uncertainties across multiple time scales, the collaborative optimization of multiple devices, and complex constraints of combined wind, solar, hydrogen, and gas power systems. For example, some methods use only a single optimization model, making it difficult to balance long-term planning and real-time adjustments; scenario processing technologies do not accurately characterize uncertainties when selecting typical scenarios, resulting in insufficient robustness of optimization results; at the same time, the integration and utilization of flexible resources such as demand response are not fully utilized, failing to fully tap the economic operating potential of the system.

[0006] Therefore, how to construct an energy management optimization model that considers the characteristics of multiple devices, uncertainties in multiple scenarios, and multiple time scales, and achieve the safe, economical, and efficient operation of wind-solar-hydrogen-gas combined production systems, has become a key technical problem that urgently needs to be solved. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this application provides an energy management optimization method and apparatus for a wind-solar-hydrogen cogeneration system, specifically adopting the following technical solution:

[0008] An energy management optimization method for a wind-solar-hydrogen cogeneration system, the method comprising the following steps:

[0009] The main components of the wind-solar-hydrogen-gas combined production system are modeled separately to obtain the operation model of each component; wherein the main components include at least a hydrogen-blended gas turbine, an electrolyzer, an energy storage unit, and an electrical load;

[0010] The probabilistic distance-based scenario reduction method filters a preset number of typical scenarios and obtains the scenario characteristic curves of the corresponding typical scenarios at different time scales; wherein the scenario characteristic curves include wind and solar power output curves, load forecast curves, and demand response curves.

[0011] Based on the operating models of each component and the selected typical scenarios, we establish a sub-blue bar optimization model, an intraday rolling optimization model, and an intraday real-time optimization model.

[0012] The optimization strategy is obtained by solving the bibliometric optimization model, the intraday rolling optimization model, and the intraday real-time optimization model.

[0013] Optionally: The modeling process for the operating model of the hydrogen-blended gas turbine includes:

[0014] The amount of natural gas and hydrogen input to the hydrogen-blended gas turbine at time t is obtained, as well as the energy conversion rate of the hydrogen-blended gas turbine is obtained;

[0015] The gas input power of the hydrogen-blended gas turbine at time t is calculated based on the amount of natural gas and hydrogen input at time t; wherein the gas input power of the hydrogen-blended gas turbine satisfies the upper and lower limits of the input power, the natural gas ratio constraint, and the ramp power constraint of the hydrogen-blended gas turbine.

[0016] The output power of the hydrogen-infused gas turbine at time t is calculated based on the gas input power and energy conversion rate of the hydrogen-infused gas turbine at time t.

[0017] Optionally: The modeling process for the operating model of the electrolyzer includes:

[0018] Obtain the amount of electricity input to the electrolytic cell at time t and the energy conversion rate of the electrolytic cell; wherein the amount of electricity input to the electrolytic cell at time t must meet the upper and lower limits of the input power and the ramp power constraint of the electrolytic cell;

[0019] The hydrogen energy output power of the electrolyzer at the corresponding time is calculated based on the amount of electricity input to the electrolyzer at time t and the energy conversion rate of the electrolyzer.

[0020] Optionally: The modeling process for the operation model of the energy storage unit includes:

[0021] The charging power, discharging power, stored energy, and charging / discharging efficiency of the energy storage unit at time t are obtained; the energy storage unit includes an electrical energy storage unit and a hydrogen energy storage unit; wherein the charging power satisfies the charging power constraint, and the discharging power satisfies the discharging power constraint.

[0022] The charging and discharging power of the energy storage unit at time t is calculated based on the charging power, discharging power, and charging and discharging efficiency of the energy storage unit at time t.

[0023] The energy storage capacity of the energy storage unit at time t+1 is calculated based on the charging and discharging power and stored energy of the energy storage unit at time t; wherein the stored energy of the energy storage unit must meet the energy storage capacity constraint and the periodic energy balance constraint.

[0024] Optionally: The modeling process for the operating model of the electrical load includes:

[0025] Obtain the fixed load power of the electrical load at time t and the adjustable load power considering demand response;

[0026] The electrical load power at the corresponding time is calculated based on the fixed electrical load power and the adjustable load power of the electrical load at time t.

[0027] Optionally: The step of the scene reduction method based on probability distance to filter a preset number of typical scenes includes:

[0028] Calculate the geometric distance between any scene in the historical sample scenes and the remaining scenes respectively;

[0029] The probabilistic distance between the current scene and the remaining scenes is calculated based on the geometric distance and occurrence probability of the two scenes respectively.

[0030] The sum of the probabilistic distances between the current scene and the remaining scenes is calculated based on the probabilistic distance between the current scene and the remaining scenes.

[0031] Based on minimizing the sum of probabilistic distances, corresponding scenarios are selected as scenarios to be reduced.

[0032] Filter out the scenario with the smallest probability distance from the scenario to be reduced, and merge and update the occurrence probabilities of the scenario to be reduced and the filtered scenario;

[0033] Determine whether the number of remaining scenarios meets the preset number. If it does, use the remaining scenarios as typical scenarios. If it does not meet the preset number, determine the next scenario to be reduced based on the remaining scenarios.

[0034] Optional: The sub-Blu-ray bar optimization model is used to provide a full-day clearing plan for the wind-solar-hydrogen cogeneration system. The sub-Blu-ray bar optimization model adopts a two-stage, three-layer optimization strategy. In the first stage, the main components and adjustable load of the wind-solar-hydrogen cogeneration system are used as decision variables to achieve the optimal comprehensive demand response cost. In the second stage, the maximum probability distribution that minimizes the energy purchase cost and the cost of wind and solar curtailment is found among the probability distributions of selected typical scenarios.

[0035] Optionally: The intraday rolling optimization model is used to smooth out rapid fluctuations in wind power and load over long time scales based on the output results of the sub-bulb optimization model, and the intraday rolling optimization model adopts virtual droop control; the intraday real-time optimization model is used to smooth out rapid fluctuations in wind power and load over short time scales based on the output results of the intraday rolling optimization model, and the intraday real-time optimization model adopts a combination of virtual inertial control and virtual droop control.

[0036] Optionally: The step of obtaining the optimization strategy based on the sub-bar optimization model, the intraday rolling optimization model, and the intraday real-time optimization model includes:

[0037] Based on the selected typical scenarios, obtain the scenario characteristic curves of the corresponding scenarios at different time scales;

[0038] The daily clearing plan for the current scenario is obtained by solving the Brussels bar optimization model.

[0039] Based on the input of the full-day clearing plan, the intraday rolling optimization model is used to solve the energy management plan for the current scenario on a long intraday timescale.

[0040] The energy management plan is input into the intraday real-time optimization model based on the intraday long-term energy management plan, and then the energy management plan for the current scenario on the intraday short-term time scale is obtained.

[0041] Furthermore, this application also discloses an energy management and optimization device for a wind-solar-hydrogen cogeneration system, the device comprising:

[0042] The main model building module is used to model the main components of the wind-solar-hydrogen-gas combined production system and obtain the operation model of each component; wherein the main components include at least a hydrogen-blended gas turbine, an electrolyzer, an energy storage unit, and an electrical load;

[0043] The typical scenario screening module is used to screen a preset number of typical scenarios based on the scenario reduction method of probability distance, and to obtain the scenario characteristic curves of the corresponding typical scenarios at different time scales; wherein the scenario characteristic curves include wind and solar power output curves, load forecast curves and demand response curves.

[0044] The optimization model building module is used to establish a sub-optimal optimization model, an intraday rolling optimization model, and an intraday real-time optimization model based on the operating models of each component and the selected typical scenarios.

[0045] The optimization model solving module is used to obtain optimization strategies based on the Blob optimization model, intraday rolling optimization model, and intraday real-time optimization model.

[0046] Beneficial effects

[0047] The technical solution of this application achieves the following beneficial effects:

[0048] This application's energy management optimization method, through targeted modeling of the main components of a wind-solar-hydrogen-fuel cogeneration system, fully considers the operating characteristics and constraints of different equipment, making the model more closely resemble actual operating scenarios. Furthermore, the scenario reduction method based on probabilistic distance selects typical scenarios, retaining key scenario characteristics. By reducing redundant scenarios, it reduces the input dimension of subsequent optimization models while maintaining optimization accuracy, thus improving solution efficiency. In addition, the scenario reduction method addresses the stochasticity of wind and solar power output and load, covering major uncertainties and improving system robustness. Moreover, this method, based on the collaborative model of three layers—partial-scale optimization, intraday rolling optimization, and intraday real-time optimization—achieves seamless integration from global planning to real-time adjustments, ensuring both the long-term economic efficiency of the system and enhancing its stability in the face of uncertain fluctuations. Attached Figure Description

[0049] Figure 1 This is a flowchart of an energy management optimization method for a wind-solar-hydrogen-gas cogeneration system, as described in this application.

[0050] Figure 2 This is a structural diagram of the wind-solar-hydrogen-gas cogeneration system in the embodiments of this application.

[0051] Figure 3 This is a framework diagram of the optimization ideas of the Blue Bar Optimization Model, the Intraday Rolling Optimization Model, and the Intraday Real-Time Optimization Model in the embodiments of this application.

[0052] Figure 4 This is a diagram showing the results of wind and solar curtailment obtained using energy management optimization methods in the embodiments of this application.

[0053] Figure 5 This is a diagram showing the adjustable load response results obtained using the energy management optimization method in the embodiments of this application.

[0054] Figure 6 This is a diagram of the output of a hydrogen-blended gas turbine in a wind-solar-hydrogen-gas cogeneration system according to an embodiment of this application.

[0055] Figure 7This is a diagram showing the energy change of the electric energy storage unit in the wind-solar-hydrogen-gas cogeneration system according to an embodiment of this application.

[0056] Figure 8 This is a frequency fluctuation diagram of the wind-solar-hydrogen-gas cogeneration system according to an embodiment of this application.

[0057] Figure 9 This is a diagram showing the energy change of the hydrogen storage unit in the wind-solar-hydrogen-gas cogeneration system according to an embodiment of this application.

[0058] Figure 10 This is a structural diagram of the energy management optimization device for a wind-solar-hydrogen-gas cogeneration system in an embodiment of this application.

[0059] Figure 11 This is a structural diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0060] The present application will now be further described with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application and should not be construed as limiting the scope of protection of the present application. It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present application.

[0061] Specifically, such as Figure 1 As shown in the figure, this embodiment discloses an energy management optimization method for a wind-solar-hydrogen-fuel cogeneration system, the method comprising the following steps:

[0062] First, a model is created for the combined wind, solar, hydrogen, and fuel cell system:

[0063] This embodiment models the main components of a wind-solar-hydrogen-gas cogeneration system to obtain the operational model of each component; such as Figure 2 As shown, the main components include a wind and solar power generation unit, a hydrogen-blended gas turbine, an electrolyzer, an energy storage unit, and an electrical load, and the energy storage unit includes at least an electrical energy storage unit and a hydrogen energy storage unit.

[0064] The electricity generated by the wind and solar power generation unit is partly fed directly to the grid load to meet electricity demand; another part is directly stored in the energy storage unit. When the power output from the wind and solar power generation unit to the grid decreases, the energy storage unit can reverse and output power back to the grid; the remaining power is used as hydrogen production power input into the electrolyzer.

[0065] The electrolyzer uses electricity generated from hydrogen production to electrolyze water into hydrogen gas. The resulting hydrogen gas is fed into a hydrogen-blended gas turbine, which is then combined with natural gas from the pipeline network. After mixing, the hydrogen-blended gas turbine generates electricity, which is then supplied to the grid load, forming an "electricity-hydrogen-electricity" energy conversion cycle. When the hydrogen-blended gas turbine is not operating or its power is reduced, hydrogen can be stored in a hydrogen storage unit. When the hydrogen-blended gas turbine is operating or its power is increased, the stored hydrogen is fed into the turbine, achieving multi-energy complementarity and flexible energy allocation, improving energy utilization efficiency and system stability. Simultaneously, it can be supplemented with natural gas to ensure a continuous energy supply.

[0066] Specifically, the modeling process for the operation model of the hydrogen-blended gas turbine in this embodiment includes:

[0067] First, obtain the amount of natural gas input to the hydrogen-blended gas turbine at time t. and hydrogen quantity And obtain the energy conversion rate η of the hydrogen-blended gas turbine. GT ;

[0068] Subsequently, based on the amount of natural gas input to the hydrogen-blended gas turbine at time t... and hydrogen quantity Calculate the gas input power of the hydrogen-infused gas turbine at the corresponding time. The gas input power of the hydrogen-blended gas turbine mentioned above The upper and lower limits of the input power, the natural gas ratio, and the ramp power constraint of the hydrogen-blended gas turbine are satisfied.

[0069] Based on the gas input power of the hydrogen-infused gas turbine at time t and energy conversion rate η GT The output power of the hydrogen-infused gas turbine at the corresponding time point was calculated.

[0070] The operating model of the aforementioned hydrogen-blended gas turbine can be represented as follows:

[0071]

[0072] In the above formula η is the output power of the hydrogen-infused gas turbine at time t; GT The energy conversion efficiency of hydrogen-doped gas turbines; The input power of the natural gas-hydrogen mixture for the hydrogen-blended gas turbine at time t; These are the upper and lower limits of the input power for a hydrogen-blended gas turbine, respectively. These are the upper and lower limits of the ramp power of hydrogen-blended gas turbines, respectively. These represent the amounts of hydrogen and natural gas in the natural gas-hydrogen mixture input to the hydrogen-blended gas turbine at time t, respectively. This represents the minimum proportion of natural gas in the gas mixture input to the hydrogen-blended gas turbine. The operating model of this hydrogen-blended gas turbine incorporates natural gas proportion constraints and ramp-up power constraints, accurately reflecting the power output characteristics under a "natural gas + hydrogen" mixed input.

[0073] Furthermore, the modeling process for the operation model of the electrolytic cell in this embodiment includes:

[0074] First, obtain the amount of electricity input to the electrolytic cell at time t. and the energy conversion rate η of the electrolytic cell pem The amount of electricity input to the electrolytic cell at time t; The upper and lower limits of the input power and the ramp power constraint of the electrolytic cell must be met.

[0075] Based on the amount of electricity input to the electrolyzer at time t and the energy conversion rate η of the electrolytic cell pem The hydrogen energy output power of the electrolyzer at the corresponding time was calculated.

[0076] The operating model of the aforementioned electrolytic cell can be represented as follows:

[0077]

[0078] In the above formula η is the hydrogen energy output of the electrolyzer at time t; pem The energy conversion efficiency of the electrolytic cell; Let t be the electrical energy input into the electrolytic cell at time t; These are the upper and lower limits of the input power of the electrolytic cell, respectively; These represent the upper and lower limits of the ramp-up of the electrolyzer. The above model focuses on the relationship between input power constraints and energy conversion, and can accurately quantify the conversion process from electrical energy to hydrogen energy.

[0079] Furthermore, the energy storage unit described in this embodiment includes two types: an electrical energy storage unit and a hydrogen energy storage unit. Therefore, both are modeled separately.

[0080] The modeling process for the operation model of the hydrogen energy storage unit includes:

[0081] First, obtain the charging power of the hydrogen energy storage unit at time t. Energy release power Energy storage Charging efficiency and energy release efficiency The charging power mentioned above The discharge power satisfies the charging power constraint. Satisfy discharge power constraints;

[0082] Based on the charging power of the hydrogen energy storage unit at time t Energy release power Charging efficiency and energy release efficiency The charging and discharging power of the hydrogen energy storage unit at the corresponding time point was calculated.

[0083] Based on the charging and discharging power of the energy storage unit at time t and energy storage The energy stored in the energy storage unit at time t+1 is calculated; wherein the energy stored in the energy storage unit must satisfy the energy storage capacity constraint and the periodic energy balance constraint.

[0084] The operating model of the aforementioned hydrogen energy storage unit can be expressed as follows:

[0085]

[0086] In the above formula These represent the charging and discharging power of hydrogen storage at time t, respectively. This represents the charge / discharge state of hydrogen storage at time t. Indicates charging. Indicates the release of energy; This represents the maximum charge / discharge power for hydrogen energy storage. These are the charging and discharging efficiencies of hydrogen energy storage, respectively. The self-loss rate of hydrogen energy storage; Let be the charge / discharge power of the hydrogen storage at time t; Let be the energy stored in hydrogen at time t; These represent the upper and lower limits of hydrogen energy storage capacity, respectively. This means that the hydrogen storage capacity state should be equal at the beginning and end of an optimization cycle.

[0087] The modeling process for the operation model of the aforementioned energy storage unit includes:

[0088] First, obtain the charging power of the energy storage unit at time t. Energy release power Energy storage Charging efficiency η ch,e and energy release efficiency η dis,e The charging power mentioned above The discharge power satisfies the charging power constraint. Satisfy discharge power constraints;

[0089] Based on the charging power of the hydrogen energy storage unit at time t Energy release power Charging efficiency ηch,e and energy release efficiency η dis,e The charging and discharging power of the hydrogen energy storage unit at the corresponding time point was calculated.

[0090] Based on the charging and discharging power of the energy storage unit at time t and energy storage The energy stored in the energy storage unit at time t+1 is calculated; wherein the energy stored in the energy storage unit must satisfy the energy storage capacity constraint and the periodic energy balance constraint.

[0091] The operating model of the above-mentioned energy storage unit can be expressed as:

[0092]

[0093] In the above formula These represent the charging and discharging power of the stored energy at time t, respectively. This represents the charging and discharging status of the stored energy at time t. Indicates charging. Indicates the release of energy; η is the maximum charge / discharge power of the electrical energy storage. ch,e η dis,e These represent the charge / discharge efficiency of electrical energy storage; χ ES,e The self-loss rate of electrical energy storage; Let t be the charging and discharging power of the stored energy; Let t be the energy stored in electricity. These represent the upper and lower limits of the capacity of electrical energy storage, respectively. This means that the energy storage capacity state should be equal at the beginning and end of an optimization cycle.

[0094] The energy storage unit models described above in this embodiment can take into account charging and discharging efficiency, capacity constraints, and periodic energy balance, thus fully characterizing the dynamic charging and discharging characteristics of the energy storage system.

[0095] Furthermore, the modeling process for the operating model of the electrical load in this embodiment includes:

[0096] First, obtain the fixed electrical load power of the electrical load at time t. And adjustable load power when considering demand response

[0097] Finally, based on the fixed electrical load power at time t... and adjustable load power Calculate the electrical load power of the electrical load at the corresponding time.

[0098] The operating model of the aforementioned electrical load can be expressed as:

[0099]

[0100] In the above formula The power of the fixed electrical load at time t; The adjustable load power at time t considering demand response; Let be the electrical load power at time t. This model, by distinguishing between fixed loads and adjustable loads, more realistically reflects the load's flexible adjustment capability.

[0101] In addition, the wind-solar-hydrogen cogeneration system in this embodiment also needs to meet power balance constraints:

[0102]

[0103] In the above formula Let t be the power purchased at time t; Photovoltaic output at time t; Let t be the wind power output at time t; Let t be the system transmission loss power at time t.

[0104] Then, typical scenarios were selected:

[0105] This embodiment uses a probabilistic distance-based scenario reduction method to select a preset number of typical scenarios and obtains the scenario characteristic curves of the corresponding typical scenarios at different time scales; wherein the scenario characteristic curves include wind and solar power output curves, load forecast curves, and demand response curves.

[0106] Specifically, the steps of the above-mentioned probabilistic distance-based scene reduction method for selecting a preset number of typical scenes include:

[0107] Calculate the geometric distance d(s) between any scene in the historical sample scene S and the remaining scenes. (n) ,s (m) ).

[0108] Based on the geometric distance d(s) between the two scenes (n) ,s (m) And the probability of occurrence p (n) Calculate the probabilistic distance D between the current scene and the remaining scenes respectively. (n) .

[0109] Based on the probabilistic distance D between the current scene and the remaining scenes (n) Calculate the sum of the probabilistic distances between the current scene and the remaining scenes.

[0110] Based on minimizing the sum of probabilistic distances, corresponding scenarios are selected as scenarios to be reduced. Right now

[0111] The scenario with the smallest probability distance from the scenario to be reduced is selected, and the occurrence probabilities of the scenario to be reduced and the selected scenario are merged and updated; that is...

[0112] Determine whether the number of remaining scenarios meets the preset number. If it does, use the remaining scenarios as typical scenarios. If it does not meet the preset number, determine the next scenario to be reduced based on the remaining scenarios.

[0113] The aforementioned scenario reduction method preserves key scenario characteristics. By combining scenario occurrence probability and geometric distance, it ensures that the selected typical scenarios represent the core fluctuation characteristics of wind and solar power output, load forecasting, and demand response. It also reduces computational load by reducing redundant scenarios, thereby reducing the input dimension of subsequent optimization models and improving solution efficiency while ensuring optimization accuracy. At the same time, this method adapts to uncertainty. For the randomness of wind and solar power output and load, typical scenarios can cover the main uncertainties, providing a basis for robust optimization.

[0114] It should be noted that in this embodiment, the selected time scales for scene characteristic curves at different time scales are 24h, 4h, and 15min.

[0115] Then, an optimization model is established:

[0116] This embodiment establishes a sub-optimal model, an intraday rolling optimization model, and an intraday real-time optimization model based on the operating models of each component and the selected typical scenarios.

[0117] The sub-Bruker bar optimization model is used to provide a 24-hour clearing plan for the wind-solar-hydrogen-fuel cogeneration system. The sub-Bruker bar optimization model adopts a two-stage, three-layer optimization strategy of min-max-min. The first stage, the min problem, uses the main components of the wind-solar-hydrogen-fuel cogeneration system and the adjustable load as decision variables to achieve the optimal comprehensive demand response cost. The second stage, the max-min problem, uses the probability distribution of selected typical scenarios to find the maximum probability distribution that minimizes the energy purchase cost and the cost of wind and solar curtailment.

[0118] Furthermore, the objective function of the sub-bar optimization model in this embodiment is:

[0119]

[0120] In the above formula, x represents the first-stage variable; y represents the second-stage variable; p k Let ξ be the probability of the k-th scene occurring; k y represents the k-th typical scene obtained using the probabilistic distance-based scene reduction method; k Let r(x,ξ) be the second-stage variable in the k-th scenario; k is the number of discrete scenarios; k) represents the objective function value of the inner-layer minimum problem; U(x,ξ) k Given a set of (x, ξ) k ) Optimize variable y k The feasible region; h(x), 0 are constraints that only relate to the variables in the first stage; g(x,ξ) k ,y k ), 0 represents the second-stage constraint; T represents the prediction time domain of the sub-Bruker optimization; λ LOAD,e To compensate for the unit cost of adjustable load; Let t be the power purchased at time t; Let t be the gas purchase power. The electricity purchase price at time t is based on time-of-use pricing. The gas price at time t; λ represents the maximum wind power output at time t; WT Penalty costs for units that curtail wind power; λ represents the maximum photovoltaic output at time t; PV The penalty cost per unit of abandoned light.

[0121] Furthermore, the intraday rolling optimization model in this embodiment is used to smooth out rapid fluctuations in wind power and load over a 4-hour timescale based on the output results of the sub-bulb optimization model. The intraday rolling optimization model adopts virtual droop control, which is used to control frequency recovery and long-term stability and control the charging and discharging of energy storage units when applied to the intraday timescale. The corresponding objective function includes frequency change as a penalty and power guidance as a control for the charging and discharging of energy storage.

[0122] The objective function of the intraday rolling optimization model can be expressed as:

[0123]

[0124] In the above formula, ΔC OP.L ΔC represents the total power adjustment cost of each energy coupling unit over a long timescale; BUY.L For purchasing power adjustment costs over a long timescale; C WT.L The cost of wind curtailment over a long timescale; C PV.L The cost of wastage over a long timescale; Δf is the frequency deviation; α is the frequency deviation penalty coefficient; K DR This is the virtual droop control coefficient for energy storage; This is the amount of energy storage power guided by the system. δ represents the power change of each energy coupling unit. m Penalize the unit cost for power changes in each energy coupling unit; δ represents the change in energy purchase capacity over a long time scale. nThe penalty unit cost is changed for the energy purchase power of each component; PEM represents electrolyzer, GT represents hydrogen-blended gas turbine; ES represents energy storage unit, including electric energy storage and hydrogen energy storage; E represents energy coupling unit associated with electricity, G represents energy coupling unit associated with natural gas; T1 represents the forecast time domain of intraday rolling optimization; The wind power output at time t over a long timescale; The maximum wind power output at time t over a long time scale; The photovoltaic output at time t over a long timescale; K represents the maximum photovoltaic output at time t over a long time scale. G K represents the frequency regulation coefficient of a hydrogen-blended gas turbine. L ΔP is the frequency regulation coefficient of the load; L This represents the change in system power over a long time scale.

[0125] The intraday real-time optimization model is used to smooth out rapid fluctuations in wind power and load over a short timescale of 15 minutes, based on the output of the intraday rolling optimization model. While pursuing economy and cleanliness, it also considers frequency stability to avoid drastic frequency fluctuations. The intraday real-time optimization model combines virtual inertial control and virtual droop control. Virtual inertial control simulates the inertial response of a traditional synchronous generator, reducing the rate of frequency change and maximum frequency deviation in the initial stage of disturbances, playing a role in the dynamic process with an action time on the order of seconds. Virtual droop control, applied to short timescales, simulates the droop characteristics of a traditional synchronous generator, improving the steady-state characteristics of the frequency, with an action time on the order of seconds to minutes. The hydrogen-blended gas turbine has a relatively fast response capability, enabling rapid power output, with an action time on the order of minutes.

[0126] The objective function of the intraday real-time optimization model can be expressed as:

[0127]

[0128] In the above formula, ΔC OP,S To optimize the total cost of power adjustment for each energy coupling unit in real time; ΔC BUY,S To adjust costs for real-time energy purchases; The penalty unit cost is increased by changing the energy storage capacity; λ GT The unit cost is penalized for changing the power output of hydrogen-blended gas turbines; This refers to the change in power output of the hydrogen-infused gas turbine. T1 represents the change in energy storage capacity; T2 represents the intraday real-time optimized forecast time domain. For real-time changes in energy purchase capacity; C WT,S For real-time wind curtailment penalty costs; C PV,S To incur real-time penalties for light waste; The real-time wind power output at time t; The real-time maximum wind power output at time t; Real-time photovoltaic output at time t; ΔP represents the maximum real-time photovoltaic output at time t. S K represents the real-time power change. IR K represents the virtual inertia coefficient for electrical energy storage. GT This represents the frequency regulation coefficient of a hydrogen-blended gas turbine.

[0129] Finally, the optimization model is solved:

[0130] This embodiment obtains the optimization strategy based on the split-bulk optimization model, the intraday rolling optimization model, and the intraday real-time optimization model. For example... Figure 3 As shown, the specific steps include:

[0131] First, based on the selected typical scenarios, obtain the scenario characteristic curves of the corresponding scenarios at different time scales;

[0132] Subsequently, the daily clearing plan for the current scenario is obtained by solving the DRO (Divided-Brow Bar Optimization) model. The DRO model can balance the system's economy and robustness, solve the cost-risk trade-off problem in multiple scenarios, and obtain the basic scheduling strategy.

[0133] Then, based on the full-day clearing plan input, the intraday rolling optimization model is solved to obtain the energy management plan for the current scenario on a long intraday timescale.

[0134] The energy management plan is input into the intraday real-time optimization model based on the intraday long-term energy management plan, and then the energy management plan for the current scenario on the intraday short-term time scale is obtained.

[0135] This embodiment relies on real-time forecast updates (wind and solar power, load) and refines the data layer by layer through "day-ahead → intraday long → intraday short". With finer time granularity (5-15 minutes), the scheduling scheme converges from macroscopic feasibility to microscopic precision, making up for the forecast error at the day-ahead level and achieving both global optimization and local precision. This satisfies the long-term economic operation of the power grid while ensuring real-time power balance and equipment safety.

[0136] Furthermore, this embodiment constructs Figure 2 The structure of the wind-solar-hydrogen-fuel cogeneration system shown is used for scheme verification.

[0137] Specifically, this embodiment uses the MATLAB platform to build a wind-solar-hydrogen-gas combined production system. Then, it identifies 10 typical scenarios from a large number of historical sample scenarios and obtains the probability of occurrence for each scenario as an initial probability distribution. For example, based on the selected typical scenarios, this embodiment reveals that wind power output is difficult to predict, solar power output is high during the day and low at night, with the highest output at noon, and load demand peaks in the morning and evening. These typical scenarios provide data support for subsequent multi-timescale calculations.

[0138] Then, using the MATLAB platform, objective functions for the bibliometric optimization model, the intraday rolling optimization model, and the intraday real-time optimization model were established, and the Gurobi solver was called for fast solving to obtain the solution results of this embodiment. The intraday real-time scheduling was calculated every 15 minutes, for a total of 96 calculations over 24 hours. Specific calculation results are shown in Table 1 and... Figure 4-9 As shown.

[0139] Table 1

[0140]

[0141] Combining Table 1 and Figure 4-9 The results show that when the output of new energy sources exceeds the load demand within 24 hours, both electric energy storage and hydrogen storage can store energy according to the power guide term. When the output of new energy sources is less than the load demand, electric energy storage, hydrogen storage, hydrogen-blended gas turbines, and load demand response can all act quickly to smooth out fluctuations. Furthermore, the curtailment of wind and solar power, regulation costs, and system frequency fluctuations are all relatively small. This indicates that the method in this embodiment maintains system stability while ensuring green and economical operation.

[0142] In addition, such as Figure 10 As shown, this application also discloses an energy management optimization device for a wind-solar-hydrogen-fuel cogeneration system, the device comprising:

[0143] The main model building module is used to model the main components of the wind-solar-hydrogen-gas combined production system and obtain the operation model of each component; wherein the main components include at least a hydrogen-blended gas turbine, an electrolyzer, an energy storage unit, and an electrical load;

[0144] The typical scenario screening module is used to screen a preset number of typical scenarios based on the scenario reduction method of probability distance, and to obtain the scenario characteristic curves of the corresponding typical scenarios at different time scales; wherein the scenario characteristic curves include wind and solar power output curves, load forecast curves and demand response curves.

[0145] The optimization model building module is used to establish a sub-optimal optimization model, an intraday rolling optimization model, and an intraday real-time optimization model based on the operating models of each component and the selected typical scenarios.

[0146] The optimization model solving module is used to obtain optimization strategies based on the Blob optimization model, intraday rolling optimization model, and intraday real-time optimization model.

[0147] The apparatus provided in this application embodiment can achieve... Figure 1 To avoid repetition, the various processes implemented in the method embodiments will not be described again here.

[0148] like Figure 11 As shown in the illustration, this application also provides an electronic device, including a processor and a memory, and a program or instructions stored in the memory and executable on the processor, which, when executed by the processor, implement as follows: Figure 1 The various processes of the method embodiments shown are all capable of achieving the same technical effect, and will not be described again here to avoid repetition.

[0149] This application embodiment also provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the above-described functionality. Figure 1 The various processes described in the embodiments of the method described herein can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0150] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the above-described... Figure 1 The various processes described in the embodiments of the method described herein can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0151] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0152] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another device, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0154] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0155] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0156] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0157] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a device (which may be a terminal or platform, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0158] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An energy management optimization method for a wind-solar-hydrogen-fuel cogeneration system, characterized in that, The method includes the following steps: The main components of the wind-solar-hydrogen-gas combined production system are modeled separately to obtain the operation model of each component; wherein the main components include at least a hydrogen-blended gas turbine, an electrolyzer, an energy storage unit, and an electrical load; The probabilistic distance-based scenario reduction method filters a preset number of typical scenarios and obtains the scenario characteristic curves of the corresponding typical scenarios at different time scales; wherein the scenario characteristic curves include wind and solar power output curves, load forecast curves, and demand response curves. Based on the operating models of each component and the selected typical scenarios, we establish a sub-blue bar optimization model, an intraday rolling optimization model, and an intraday real-time optimization model. The optimization strategy is obtained by solving the bibliometric optimization model, the intraday rolling optimization model, and the intraday real-time optimization model.

2. The energy management optimization method according to claim 1, characterized in that, The modeling process for the operating model of the hydrogen-blended gas turbine includes: The amount of natural gas and hydrogen input to the hydrogen-blended gas turbine at time t is obtained, as well as the energy conversion rate of the hydrogen-blended gas turbine is obtained; The gas input power of the hydrogen-blended gas turbine at time t is calculated based on the amount of natural gas and hydrogen input at time t; wherein the gas input power of the hydrogen-blended gas turbine satisfies the upper and lower limits of the input power, the natural gas ratio constraint, and the ramp power constraint of the hydrogen-blended gas turbine. The output power of the hydrogen-infused gas turbine at time t is calculated based on the gas input power and energy conversion rate of the hydrogen-infused gas turbine at time t.

3. The energy management optimization method according to claim 1, characterized in that, The modeling process for the operation model of the electrolyzer includes: Obtain the amount of electricity input to the electrolytic cell at time t and the energy conversion rate of the electrolytic cell; wherein the amount of electricity input to the electrolytic cell at time t must meet the upper and lower limits of the input power and the ramp power constraint of the electrolytic cell; The hydrogen energy output power of the electrolyzer at the corresponding time is calculated based on the amount of electricity input to the electrolyzer at time t and the energy conversion rate of the electrolyzer.

4. The energy management optimization method according to claim 1, characterized in that, The modeling process for the operation model of the energy storage unit includes: The charging power, discharging power, stored energy, and charging / discharging efficiency of the energy storage unit at time t are obtained; the energy storage unit includes an electrical energy storage unit and a hydrogen energy storage unit; wherein the charging power satisfies the charging power constraint, and the discharging power satisfies the discharging power constraint. The charging and discharging power of the energy storage unit at time t is calculated based on the charging power, discharging power, and charging and discharging efficiency of the energy storage unit at time t. The energy storage capacity of the energy storage unit at time t+1 is calculated based on the charging and discharging power and stored energy of the energy storage unit at time t; wherein the stored energy of the energy storage unit must meet the energy storage capacity constraint and the periodic energy balance constraint.

5. The energy management optimization method according to claim 1, characterized in that, The modeling process for the operating model of the electrical load includes: Obtain the fixed load power of the electrical load at time t and the adjustable load power considering demand response; The electrical load power at the corresponding time is calculated based on the fixed electrical load power and the adjustable load power of the electrical load at time t.

6. The energy management optimization method according to claim 1, characterized in that, The steps of the scene reduction method based on probability distance to filter a preset number of typical scenes include: Calculate the geometric distance between any scene in the historical sample scenes and the remaining scenes respectively; The probabilistic distance between the current scene and the remaining scenes is calculated based on the geometric distance and occurrence probability of the two scenes respectively. The sum of the probabilistic distances between the current scene and the remaining scenes is calculated based on the probabilistic distance between the current scene and the remaining scenes. Based on minimizing the sum of probabilistic distances, corresponding scenarios are selected as scenarios to be reduced. Filter out the scenario with the smallest probability distance from the scenario to be reduced, and merge and update the occurrence probabilities of the scenario to be reduced and the filtered scenario; Determine whether the number of remaining scenarios meets the preset number. If it does, use the remaining scenarios as typical scenarios. If it does not meet the preset number, determine the next scenario to be reduced based on the remaining scenarios.

7. The energy management optimization method according to claim 1, characterized in that, The sub-Blu-ray bar optimization model is used to provide a full-day clearing plan for the wind-solar-hydrogen-fuel cogeneration system. The sub-Blu-ray bar optimization model adopts a two-stage, three-layer optimization strategy. In the first stage, the main components and adjustable load of the wind-solar-hydrogen-fuel cogeneration system are used as decision variables to achieve the optimal comprehensive demand response cost. In the second stage, the maximum probability distribution that minimizes the energy purchase cost and the cost of wind and solar curtailment is found among the probability distributions of selected typical scenarios.

8. The energy management optimization method according to claim 7, characterized in that, The intraday rolling optimization model is used to smooth out rapid fluctuations in wind power and load over long time scales based on the output results of the sub-blob bar optimization model. The intraday rolling optimization model adopts virtual droop control. The intraday real-time optimization model is used to smooth out rapid fluctuations in wind power and load over short timescales based on the output of the intraday rolling optimization model. The intraday real-time optimization model adopts a combination of virtual inertial control and virtual droop control.

9. The energy management optimization method according to claim 1, characterized in that, The steps for obtaining the optimization strategy based on the split-bar optimization model, intraday rolling optimization model, and intraday real-time optimization model include: Based on the selected typical scenarios, obtain the scenario characteristic curves of the corresponding scenarios at different time scales; The daily clearing plan for the current scenario is obtained by solving the Brussels bar optimization model. Based on the input of the full-day clearing plan, the intraday rolling optimization model is used to solve the energy management plan for the current scenario on a long intraday timescale. The energy management plan is input into the intraday real-time optimization model based on the intraday long-term energy management plan, and then the energy management plan for the current scenario on the intraday short-term time scale is obtained.

10. An energy management optimization device for a wind-solar-hydrogen-fuel cogeneration system, characterized in that, The device includes: The main model building module is used to model the main components of the wind-solar-hydrogen-gas combined production system and obtain the operation model of each component; wherein the main components include at least a hydrogen-blended gas turbine, an electrolyzer, an energy storage unit, and an electrical load; The typical scenario screening module is used to screen a preset number of typical scenarios based on the scenario reduction method of probability distance, and to obtain the scenario characteristic curves of the corresponding typical scenarios at different time scales; wherein the scenario characteristic curves include wind and solar power output curves, load forecast curves and demand response curves. The optimization model building module is used to build sub-optimal models, intraday rolling optimization models, and intraday real-time optimization models based on the operating models of each component and the selected typical scenarios. The optimization model solving module is used to obtain optimization strategies based on the Blob optimization model, intraday rolling optimization model, and intraday real-time optimization model.

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