Electricity-hydrogen cooperative control method based on distributed model predictive control

Abstract

The invention relates to an electricity-hydrogen cooperative control method based on distributed model predictive control, which solves the control problem of a distributed renewable power supply and a hydrogen energy unit in a light-hydrogen micro-grid, integrates a traditional hierarchical control structure into a single-layer structure, can reduce the communication burden, and improves the communication efficiency. And a large-scale optimization problem is divided into a plurality of small-scale optimization problems, so that the calculation time is saved. The method comprises the following steps: firstly, establishing a converter-based subsystem model according to the characteristics of a distributed power supply, and meanwhile, considering the influence of partial shading conditions on photovoltaic output characteristics; secondly, searching all local maximum power points of the photovoltaic array under the condition of partial shading and comparing the values of the local maximum power points so as to determine the global maximum available power; maximum light energy capture, power balance, DC bus voltage stabilization and hydrogen flow management are used as global performance indexes; and finally, designing a distributed model predictive control algorithm based on global performance indexes, and solving to obtain a Nash optimal solution of each subsystem. Through the communication unit, each sub-controller obtains global information and continuously moves to a Pareto optimal solution through iteration, so that the global performance under a distributed structure is improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid control, and in particular to an electric-hydrogen coordinated control method based on distributed model predictive control. Background Technology

[0002] With electricity demand continuously growing, coal, oil, and natural gas are being overused for power generation to meet this demand, leading to carbon emissions and massive depletion of fossil resources. The transition to a net-zero emissions global society is one of humanity's most pressing priorities, requiring a profound transformation in transportation, consumption, and production methods. Driven by the accelerated global deployment of renewable energy infrastructure, renewable energy has become the preferred approach in various strategic considerations. However, the inherent volatility and intermittency of renewable energy sources such as solar power place stringent demands on storage infrastructure. Hydrogen, with its superior energy density and environmentally friendly characteristics, has become an important energy storage option. Some studies emphasize that hydrogen can bridge the gap between renewable energy generation and demand, thus playing a significant role in energy decarbonization. Scholars have confirmed that battery-hydrogen hybrid storage configurations outperform pure battery ultra-large-capacity storage configurations in ensuring reliable off-grid power supply.

[0003] The primary control objective of photovoltaic-hydrogen microgrids is to maintain a balance between energy (i.e., electricity / hydrogen) supply and demand under fluctuating solar irradiance and dynamic load conditions. Existing control schemes mainly employ a hierarchical architecture, with the upper layer managing energy dispatch and coordinating power flow between subsystems, and the lower layer performing reference signal tracking. However, potential constraint conflicts sometimes arise between the hierarchical control layers, making the upper-level optimization problem infeasible or the lower layer unable to track setpoints that violate constraints. In recent decades, Model Predictive Control (MPC), leveraging its ability to handle system constraints in multivariable control environments, has become a mature method for microgrid regulation. For large-scale power systems, centralized MPC can achieve satisfactory global optimal solutions. However, the enormous computational burden makes it difficult to guarantee its real-time performance. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides an electric-hydrogen coordinated control method based on distributed model predictive control, in order to further improve control performance.

[0005] This invention provides an electric-hydrogen coordinated control method based on distributed model predictive control, comprising:

[0006] Step 1: Collect environmental data such as temperature and solar irradiance, establish a mathematical model of the solar-hydrogen microgrid under partial shading conditions, and form a coupling relationship between subsystems through converter connection;

[0007] Step 2: Use the global maximum power point tracking algorithm to search for the maximum available power of the photovoltaic array;

[0008] Step 3: Maximum light energy capture, power balance, DC bus voltage stability, and hydrogen flow management are used as global performance indicators;

[0009] Step 4: Design a distributed model predictive control algorithm based on global performance indicators, and solve for the Nash optimal solution of each subsystem;

[0010] Step 5: Through the communication unit, each sub-controller obtains global information, namely the predictions of other subsystems regarding future states at the previous sampling time. A new iterative solution is then performed based on the updated information until the set convergence conditions are met. Ultimately, a control sequence close to the Pareto optimal solution is generated.

[0011] Furthermore, the mathematical model of the light-hydrogen microgrid under partially shading conditions is achieved through mechanistic modeling, including:

[0012] Step 1, establish series and parallel connections with numbers n respectively. s and n p The expression for the output characteristics of a photovoltaic array:

[0013]

[0014] Due to the influence of irradiance, the above voltage and current parameters change, which can be expressed as:

[0015]

[0016] Where, ΔT=TT b , These are temperature deviation and irradiance deviation, respectively.

[0017] Step 2: Apply Kirchhoff's voltage / current laws to establish the mathematical model of the Boost converter:

[0018]

[0019] Step 3, use the state-space averaging method to process the switching signal S pv Converted to its duty cycle signal u pv :

[0020]

[0021] Step 4, the mathematical model of the photovoltaic unit based on the converter can be expressed as:

[0022]

[0023] Subject to 0≤x pv,1 <V string ,u pvThe constraint is ∈[0,1]. Where, x... pv =[V pv ,I L V dc ] T y pv =P pv ,

[0024] Step 4: Establish mathematical models for the fuel cell, supercapacitor, electrolyzer, and hydrogen storage tank units in sequence according to the above method.

[0025] Furthermore, the global maximum power point tracking method is achieved by successively comparing the net power difference between two adjacent local maximum power points, including:

[0026] Step 1: Measure the solar irradiance received by n photovoltaic cells connected in series and arrange them in descending order;

[0027] Step 2: Calculate the maximum power under the first irradiance as the first local maximum power;

[0028] Step 3: The short-circuit current under the second irradiance is used as the segment point current between the first and second cells. By comparing the power difference between the segment point and these two adjacent local maximum power points, a higher local maximum power can be indirectly obtained.

[0029] Step 4, similar to step 3, involves successively comparing to find the global maximum power point.

[0030] 1. Furthermore, the distributed model predictive control algorithm designed based on global performance indicators is implemented by sharing information between subsystems through a communication network, including:

[0031] Step 1, Design global performance metrics:

[0032] J = w1J1 + w2J2 + w3J3 + w4J4

[0033] in, This indicates the power balance between photovoltaic (PV) units, fuel cell (FC) units, supercapacitor (SC) units, electrolyzer (AE) units, and the load (L). This indicates that the DC bus voltage has stabilized to the set value. Indicates maximum light energy capture. This indicates the hydrogen charge status management of the hydrogen storage tank;

[0034] Step 2: Each sub-controller collects predicted information about the states of other subsystems, solves the above optimization problem, and obtains its own Nash optimal solution.

[0035] Step 3, determine the convergence condition If the conditions are met, it means that the solution obtained is close to the Pareto optimal solution; otherwise, return to step 2.

[0036] Step 4: Output the obtained optimal solution to the subsystem.

[0037] Compared with existing technologies, the advantages of this invention lie in integrating the energy management layer and power regulation layer of the traditional hierarchical control structure into a single layer, simplifying the control structure, avoiding potential constraint violations between upper and lower layers, and reducing communication burden. Addressing the geographical dispersion and fluctuating environmental characteristics of distributed power sources, a distributed model predictive control algorithm is employed. A global performance index is designed, and information is exchanged between subsystems through a communication network. Iterative solutions are used to make the Nash optimal solution of each subsystem approach the global Pareto optimal solution. This method achieves near-centralized model predictive control performance while significantly reducing computational burden. Attached Figure Description

[0038] Figure 1 This is the maximum power point tracking algorithm in an embodiment of the present invention.

[0039] Figure 2 This invention provides an embodiment of an electric-hydrogen coordinated control method based on distributed model predictive control. Detailed Implementation

[0041] Preferred embodiments of the invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the invention and are not intended to limit the scope of protection of the invention.

[0042] See Figure 1 The diagram shown is a flowchart of the maximum power point tracking algorithm according to an embodiment of the present invention. The steps of this implementation method include:

[0043] Step 1: Collect temperature and irradiance data of the photovoltaic array from the sensor data center at a sampling frequency of 100 microseconds, and sort the collected irradiance data in descending order.

[0044] Step 2, and initialize the first local maximum power, i.e. the maximum power of a single photovoltaic cell under the highest measured irradiance, as the global maximum power.

[0045] Step 3: Using the short-circuit current of the photovoltaic cell under the next irradiance as the segmentation point of the photovoltaic power-voltage characteristic curves under two irradiances, calculate the power difference between the segmentation point power and the two adjacent local maximum power, i.e., P. dec and P inc If P dec <P incThis indicates that the next local maximum power is higher than the current local maximum power. Therefore, the former is then compared with the already searched global maximum power P. gmpp The size relationship between them, if higher than P gmpp Then update P gmpp If P dec >P inc This indicates that P does not need to be updated. gmpp .

[0046] Step 4, iterative termination judgment step, if the search count j <n s This indicates that the search is not yet complete; return to step 3. Continue until the entire photovoltaic array is searched and the true P is found. gmpp .

[0047] See Figure 2 As shown, it is a block diagram of the electric-hydrogen coordinated control based on distributed model predictive control according to an embodiment of the present invention. The steps of this implementation method include:

[0048] Step 1: Determine environmental condition parameters and load requirements, and construct the global performance index J;

[0049] Step 2: When each sub-controller uses the global performance index to solve the optimization problem, it only makes predictions and performs rolling optimization based on its own state and control variables. Predictions of other states involved in the global performance index are obtained from other sub-controllers via the communication network. Each sub-controller then obtains its own Nash optimal solution.

[0050] Step 3: The Nash optimal solutions of each subsystem obtained in Step 2 are shared through the communication network, and the solution is iteratively solved based on the previous solution to find a solution that is closer to Pareto optimal.

[0051] Step 4: Output the obtained optimal solution to the subsystem.

[0052] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for coordinated control of electricity and hydrogen based on distributed model predictive control, characterized in that, The prediction method includes the following steps: Step 1: Collect environmental data such as temperature and solar irradiance, establish a mathematical model of the solar-hydrogen microgrid under partial shading conditions, and form a coupling relationship between subsystems through converter connection; Step 2: Use the global maximum power point tracking algorithm to search for the maximum available power of the photovoltaic array; Step 3: Maximum light energy capture, power balance, DC bus voltage stability, and hydrogen flow management are used as global performance indicators. Step 4: Design a distributed model predictive control algorithm based on global performance indicators, and solve for the Nash optimal solution of each subsystem; Step 5: Through the communication unit, each sub-controller obtains global information, namely the predictions of other subsystems regarding future states at the previous sampling time. A new iterative solution is then performed based on the updated information until the set convergence conditions are met. Ultimately, a control sequence close to the Pareto optimal solution is generated.

2. The mathematical model of the photo-hydrogen microgrid under partially shading conditions as described in claim 1 is achieved through mechanistic modeling, including: Step 1, establish series and parallel connections with numbers n respectively. s and n p The expression for the output characteristics of a photovoltaic array: The voltage and current parameters mentioned above change due to the influence of irradiance, which can be expressed as: Where, ΔT=TT b , P pv =I pv ·V pv =f1(V pv )·V pv ; Step 2: Apply Kirchhoff's voltage / current laws to establish the mathematical model of the Boost converter: Step 3, use the state-space averaging method to process the switching signal S pv Converted to its duty cycle signal u pv : Step 4, the mathematical model of the photovoltaic unit based on the converter can be expressed as: Subject to 0≤x pv,1 <V string ,u pv The constraint is ∈[0,1]. Where, x... pv =[V pv ,I L V dc ] T y pv =P pv , Step 4: Establish mathematical models for the fuel cell, supercapacitor, electrolyzer, and hydrogen storage tank units in sequence according to the above method.

3. The global maximum power point tracking method according to claim 1 is achieved by successively comparing the net power difference between two adjacent local maximum power points, including: Step 1: Measure the solar irradiance received by n photovoltaic cells connected in series and arrange them in descending order; Step 2: Calculate the maximum power under the first irradiance as the first local maximum power; Step 3: The short-circuit current under the second irradiance is used as the segment point current between the first and second cells. By comparing the power difference between the segment point and these two adjacent local maximum power points, a higher local maximum power can be indirectly obtained. Step 4, similar to step 3, involves successively comparing to find the global maximum power point.

4. The distributed model predictive control algorithm based on global performance indicators as described in claim 1 is implemented by sharing information between subsystems through a communication network, including: Step 1, Design global performance metrics: J = w1J1 + w2J2 + w3J3 + w4J4 in, This indicates the power balance between photovoltaic (PV) units, fuel cell (FC) units, supercapacitor (SC) units, electrolyzer (AE) units, and the load (L). This indicates that the DC bus voltage has stabilized to the set value. Indicates maximum light energy capture. This indicates the hydrogen charge status management of the hydrogen storage tank; Step 2: Each sub-controller collects predicted information about the states of other subsystems, solves the above optimization problem, and obtains its own Nash optimal solution. Step 3, determine the convergence condition If the conditions are met, it means that the solution obtained is close to the Pareto optimal solution; otherwise, return to step 2. Step 4: Output the obtained optimal solution to the subsystem.

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