Method and system for joint operation of multiple virtual power plants sharing hydrogen storage

Abstract

The application discloses a method and system for joint operation of multiple virtual power plants sharing hydrogen storage energy, establishes an upper optimization model with the maximum operation net profit of a hydrogen storage energy operator as an optimization target and a lower optimization model with the minimum joint operation cost of each virtual power plant as an optimization target, obtains actual values, fluctuation ranges and predicted values of light and wind power generation of each virtual power plant, and establishes wind and light uncertainty constraint conditions; the upper optimization model is solved according to state information to obtain an optimal solution of the operation net profit of the hydrogen storage energy operator, and the lower optimization model is solved to obtain a basic value of the joint operation cost of each virtual power plant under the wind and light uncertainty constraint conditions; the maximum sum of the fluctuation ranges of the light and wind power generation is taken as a risk target function, a risk-avoiding deviation parameter is introduced to construct a first constraint, and a benefit-pursuing deviation parameter is introduced to construct a second constraint; and the optimal solution of the lower optimization model meeting the first constraint and the second constraint is used to realize the joint operation optimization of the multiple virtual power plants.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization and scheduling, and in particular to a method and system for joint operation of multiple virtual power plants considering shared hydrogen energy storage. Background Art

[0002] Under the dual carbon background, the installed capacity of renewable energy units such as photovoltaic and wind power has been increasing year by year. However, since photovoltaic and wind power are greatly affected by environmental factors and their output is obviously intermittent and volatile, their large-scale access to the power grid will have a certain impact on the stability of the power system, power quality and operation planning.

[0003] Existing technologies use information and communication technologies to organically integrate distributed resources such as power sources, loads, and storage within the power system industry chain, forming an integrated digital energy management system for power generation, grid operation, load management, and storage, significantly facilitating the integration of renewable energy. While energy storage technology can effectively address the uncertainty associated with renewable energy processing, its high installation costs hinder its widespread adoption. Shared energy storage, on the other hand, allows all users to share the cost of storage and enjoy shared services. Its innovations in business models and pricing mechanisms can be effectively applied in real time and drive the rapid development of energy storage technology. However, when integrating renewable energy, virtual power plants are currently significantly impacted by the intermittent and volatile nature of renewable energy sources such as wind power and photovoltaics. These uncertainties present significant challenges in their scheduling, including grid stability issues, power balancing difficulties, and reduced power quality. While energy storage technology can mitigate the uncertainty associated with renewable energy to a certain extent, its high cost remains a major obstacle to its widespread adoption. In scenarios where multiple virtual power plants operate in concert, effectively allocating and reducing storage costs is a pressing issue. Finally, in the scenario of joint operation of multiple virtual power plants, how to optimize the operation strategy of each virtual power plant under uncertain conditions, and how to effectively utilize shared energy storage resources to improve overall operating efficiency and economic benefits are still difficulties in current research. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method and system for the joint operation of multiple virtual power plants taking into account shared hydrogen energy storage. The method and system determine the optimal value of the operating net profit of the shared hydrogen energy storage operator under the optimal robustness of the uncertainty of wind power and photovoltaic output, increase the photovoltaic power generation output and wind power generation output under the pursuit of efficiency, and reduce the photovoltaic power generation output and wind power generation output under the risk avoidance. This not only improves the robustness of the system in the face of uncertainty in wind power and photovoltaic output, but also optimizes the resource allocation and scheduling strategies of multiple virtual power plants while reducing energy storage costs, thereby enhancing the overall economic benefits and stability of the system.

[0005] The present invention adopts the following technical solutions.

[0006] The present invention proposes a method for joint operation of multiple virtual power plants considering shared hydrogen energy storage, comprising:

[0007] Obtain status information of distributed resources within each virtual power plant system;

[0008] Based on a two-layer model structure, an optimal scheduling model for the joint operation of multiple virtual power plants is established, including an upper-layer optimization model with the optimization goal of maximizing the operating net profit of the shared hydrogen energy storage operator and a lower-layer optimization model with the optimization goal of minimizing the joint operating costs of each virtual power plant.

[0009] Based on the state information, the upper-level optimization model is iteratively solved to obtain the optimal solution for the shared hydrogen energy storage operator's operating net profit. The actual value, fluctuation range, and predicted value of the photovoltaic and wind power generation of each virtual power plant are obtained to establish wind and solar uncertainty constraints. Based on the optimal solution for the shared hydrogen energy storage operator's operating net profit, the lower-level optimization model is iteratively solved under the wind and solar uncertainty constraints to obtain the basic value of the joint operating cost of each virtual power plant.

[0010] The maximum sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant is taken as the risk objective function. After introducing the risk aversion deviation parameter, the first constraint of the optimization objective of the lower-level optimization model is constructed with the basic value of the joint operation cost of each virtual power plant. After introducing the benefit pursuit deviation parameter, the second constraint of the optimization objective of the lower-level optimization model is constructed with the basic value of the joint operation cost of each virtual power plant. According to the optimal solution of the operating net profit of the shared hydrogen energy storage operator, the optimal solution of the lower-level optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan of multiple virtual power plants.

[0011] Preferably, the upper-level optimization model takes maximizing the net profit of the shared hydrogen energy storage operator as the optimization goal, satisfying the following relationship:

[0012]

[0013] Where, F up The net profit of the shared hydrogen energy storage operator; B HS,ele The economic benefits of shared hydrogen energy storage operators in providing services to virtual power plants; rent is the leasing cost coefficient of shared hydrogen energy storage; C HS,inv is the equivalent annual cost of the initial investment in shared hydrogen energy storage; C HS,ope The equivalent annual cost of shared hydrogen storage operation and maintenance expenses.

[0014] Preferably, the joint constraints satisfied by the optimization objective of the upper-level optimization model include:

[0015] The maximum operating power of the shared hydrogen storage energy electrolyzer and fuel cell is less than the rental value, and the rental scale has an upper limit, satisfying the following relationship:

[0016]

[0017] wherein P EL (t) and P FC (t) are the electrolysis power and combustion power of the shared hydrogen storage energy at time t, respectively; and are the maximum allowed electrolyzer power and fuel cell power of the shared hydrogen storage energy, respectively; and are the upper limits of the maximum allowed electrolyzer power and fuel cell power of the shared hydrogen storage energy, respectively; B EL (t) and B FC (t) are the corresponding Boolean variables of the electrolyzer and fuel cell of the shared hydrogen storage energy, respectively, B EL (t) taking 1 and B FC (t) taking 0 indicate that the shared hydrogen storage energy is in the electrolysis state at time t, B EL (t) taking 0 and B FC (t) taking 1 indicate that the shared hydrogen storage energy is in the combustion state at time t;

[0018] The charge and discharge power of the shared hydrogen storage energy and each virtual power plant satisfy the power balance constraint at time t, satisfying the following relationship:

[0019]

[0020] wherein, and are the power sold and purchased by the kth virtual power plant to the shared hydrogen storage energy at time t; t = 1, 2, …, T, and T is a scheduling period;

[0021] The hydrogen tank hydrogen flow balance constraint satisfies the following relationship:

[0022]

[0023] wherein v EL (t) and v FC (t) are the hydrogen production amount of the electrolyzer and the hydrogen consumption amount of the fuel cell of the shared hydrogen storage energy at time t, respectively; η EL and η FC are the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell, respectively; and are the hydrogen storage amount or hydrogen output amount of the hydrogen tank at time t; n H is the hydrogen heat value constant;

[0024] The hydrogen tank storage state constraint satisfies the following relationship:

[0025]

[0026] Where SOH(t) is the gas content of the hydrogen storage tank during period t; is the rated hydrogen storage mass of the hydrogen storage tank; SOH max and SOH min are the maximum and minimum gas storage states allowed by the hydrogen storage tank respectively; Δt is the scheduling decision time interval.

[0027] Preferably, the lower-level optimization model takes the minimum joint operation cost of each virtual power plant as the optimization goal, satisfying the following relationship:

[0028]

[0029] Where, F down is the joint operating cost of each virtual power plant; is the cost of electricity purchase and sale from the external power grid to the kth virtual power plant during the dispatch period; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch period; is the cost of wind and solar power curtailment of the kth virtual power plant during the dispatch period.

[0030] Preferably, the optimal solution of the upper-layer optimization model includes: the optimal economic benefit of the shared hydrogen energy storage operator in providing services to each virtual power plant, the optimal annualized cost of the initial investment in shared hydrogen energy storage, and the optimal annualized cost of the operation and maintenance costs of shared hydrogen energy storage;

[0031] The optimal economic benefit of the shared hydrogen energy storage operator in providing services to each virtual power plant is the sum of the electricity purchase and sales fees paid by the kth virtual power plant to the shared hydrogen energy storage during the scheduling period in the lower-level optimization model.

[0032] Preferably, the joint constraints satisfied by the optimization objectives of the lower-level optimization model include:

[0033] The power balance constraints among shared hydrogen energy storage, multiple virtual power plants, and the power grid satisfy the following relationship:

[0034]

[0035] Where, and are the power purchased and sold from the external grid by the kth virtual power plant in period t, and are the electricity sales power and electricity purchase power of the kth virtual power plant for shared hydrogen energy storage in period t, is the load demand within the kth virtual power plant during period t; and They represent the photovoltaic and wind power grid-connected power within the kth virtual power plant in period t respectively;

[0036] The amount of electricity purchased and sold by each virtual power plant to the grid is less than the maximum limit, satisfying the following relationship:

[0037]

[0038] Where, is the maximum limit;

[0039] The wind and solar renewable energy consumption constraints satisfy the following relationship:

[0040]

[0041] Where α is the minimum requirement for the virtual power plant to absorb renewable energy; and They represent the maximum photovoltaic power generation and the maximum wind power generation of the kth virtual power plant in period t, and are the power rejection of photovoltaic and wind power within the kth virtual power plant in period t.

[0042] Preferably, the wind and solar uncertainty constraint condition satisfies the following relationship:

[0043]

[0044] Where, is the wind and solar uncertainty constraint, are the fluctuation amplitudes of photovoltaic power generation and wind power generation of the kth virtual power plant, are the predicted values ​​of photovoltaic power generation and wind power generation of the kth virtual power plant in period t, respectively; are the actual values ​​of photovoltaic power generation and wind power generation of the kth virtual power plant in period t, respectively.

[0045] Preferably, the risk objective function satisfies the following relationship:

[0046]

[0047] Where δ is the sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant, t = 1, 2, …, T, and T is a scheduling period.

[0048] Preferably, the first constraint of the optimization objective of the lower-level optimization model satisfies the following relationship:

[0049]

[0050] Where, F downis the joint operating cost of each virtual power plant, is the basic value of the joint operation cost of each virtual power plant, γ ra is the risk aversion bias parameter.

[0051] Preferably, the second constraint of the optimization objective of the lower-level optimization model satisfies the following relationship:

[0052]

[0053] Where, F down is the joint operating cost of each virtual power plant, is the basic value of the joint operation cost of each virtual power plant, γ os Pursue deviation parameters for efficiency.

[0054] The present invention also provides a multi-virtual power plant joint operation system considering shared hydrogen energy storage, comprising:

[0055] The acquisition module is used to obtain the status information of distributed resources within each virtual power plant system;

[0056] An optimization scheduling model establishment module is used to establish an optimization scheduling model for the joint operation of multiple virtual power plants based on a two-layer model structure. It includes: an upper-layer optimization model with the optimization goal of maximizing the operating net profit of the shared hydrogen energy storage operator; and a lower-layer optimization model with the optimization goal of minimizing the joint operating cost of each virtual power plant.

[0057] The model iterative solution module is used to iteratively solve the upper-level optimization model based on the state information to obtain the optimal solution for the shared hydrogen energy storage operator's operating net profit; obtain the actual value, fluctuation range, and predicted value of the photovoltaic power generation and wind power generation of each virtual power plant, and establish the wind and solar uncertainty constraint conditions; based on the optimal solution for the shared hydrogen energy storage operator's operating net profit, under the wind and solar uncertainty constraint conditions, iteratively solve the lower-level optimization model to obtain the basic value of the joint operating cost of each virtual power plant;

[0058] The joint operation optimization module is used to take the maximum sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant as the risk objective function, introduce the risk aversion deviation parameter, and use the basic value of the joint operation cost of each virtual power plant to construct the first constraint of the optimization objective of the lower-level optimization model. After introducing the benefit pursuit deviation parameter, the basic value of the joint operation cost of each virtual power plant is used to construct the second constraint of the optimization objective of the lower-level optimization model. According to the optimal solution of the operating net profit of the shared hydrogen energy storage operator, the optimal solution of the lower-level optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan of multiple virtual power plants.

[0059] The present invention also provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; and the processor is used to operate according to the instructions to execute steps of the method.

[0060] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when the program is executed by a processor.

[0061] The beneficial effects of the present invention are that, compared with the prior art, at least the present invention includes: a method for the joint operation of multiple virtual power plants taking into account shared hydrogen energy storage, including determining the specific entities of the multiple virtual power plants participating in the joint operation and the types and scales of their internal distributed resources. Then, status information is established for the internal distributed resources of each virtual power plant for joint operation optimization decision-making. Secondly, the leasing scale of shared hydrogen energy storage and the operating mode of each virtual power plant are solved based on a two-layer optimization scheduling model. Then, the fee settlement is completed based on the energy interaction between each virtual power plant and the shared hydrogen energy storage and the power network. Finally, a scheduling cycle is completed, and the leased shared hydrogen energy storage resources are released. The present invention can optimize the shared hydrogen energy storage leasing capacity and charging and discharging strategies, improve the absorption capacity and operating efficiency of renewable energy of multiple virtual power plants, and comprehensively consider the comprehensive interests of multiple parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a flow chart of the method for joint operation of multiple virtual power plants considering shared hydrogen energy storage proposed in the present invention. DETAILED DESCRIPTION

[0063] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] The present invention proposes a method for joint operation of multiple virtual power plants taking into account shared hydrogen energy storage. The distributed resources within each virtual power plant system include: photovoltaic power generation, wind power generation, and shared hydrogen energy storage; Figure 1 Shown, including:

[0065] Step 1: Obtain the status information of distributed resources within each virtual power plant system, including: photovoltaic power generation forecast value, wind power generation forecast value, shared hydrogen energy storage charging and discharging status and capacity, and electricity price.

[0066] Specifically, before the joint operation of multiple virtual power plants, the subject of each virtual power plant participating in the joint operation needs to be determined first, including the types and construction scale of distributed resources inside each virtual power plant system. In the embodiment of the present application, the distributed resources include but are not limited to photovoltaic power generation, wind power generation, and shared hydrogen energy storage. Among them, photovoltaic power generation, as an important renewable energy resource in virtual power plants, converts solar energy into electric energy. A virtual power plant can contain multiple distributed photovoltaic power plants, and the installed capacity of each power plant is 10 MW. Wind power generation converts wind energy into electric energy through wind turbines, and a virtual power plant can have several distributed wind power plants, and the installed capacity of each wind power plant is 20 MW.

[0067] Step 2, based on the double-layer model structure, an optimal scheduling model for the joint operation of multiple virtual power plants is established, including an upper-layer optimization model with the maximum net profit of the shared hydrogen energy storage operator as the optimization target, and a lower-layer optimization model with the minimum joint operation cost of each virtual power plant as the optimization target.

[0068] Specifically, the upper-layer optimization model takes the maximum net profit of the shared hydrogen energy storage operator as the optimization target, and satisfies the following relationship:

[0069]

[0070] In the formula, F up is the net profit of the shared hydrogen energy storage operator; B HS,ele is the economic benefit of the shared hydrogen energy storage operator in the process of providing services for each virtual power plant; λ rent is the rental cost coefficient of shared hydrogen energy storage; C HS,inv is the equivalent annual cost of the initial investment cost of shared hydrogen energy storage; C HS,ope is the equivalent annual cost of the operation and maintenance cost of shared hydrogen energy storage.

[0071] In the embodiment, F up is the net profit of the shared hydrogen energy storage operator in the whole life cycle.

[0072] Among them, the economic benefit of the shared hydrogen energy storage operator in the process of providing services for each virtual power plant satisfies the following relationship:

[0073]

[0074] In the formula, p HS,sell (t) and p HS,buy (t) are the selling price and buying price of each virtual power plant for shared hydrogen energy storage at time t, respectively; and are the selling power and buying power of the kth virtual power plant for shared hydrogen energy storage at time t, respectively; C serviceThe service fee that each virtual power plant participating in the joint operation needs to pay to the shared hydrogen energy storage operator, k = 1, 2, …, K, K is the number of virtual power plants; t = 1, 2, …, T, T is a scheduling cycle; Δt is the scheduling decision time interval.

[0075] The equivalent annual cost of the initial investment in shared hydrogen energy storage satisfies the following relationship:

[0076]

[0077] Where, and are the rated power of the electrolyzer, the rated power of the fuel cell and the rated capacity of the hydrogen storage tank for the leased shared hydrogen energy storage; c EL 、c FC and c STO are the unit cost coefficients of electrolyzer, fuel cell and hydrogen storage tank respectively; β is the annual interest rate; Y HS The service life of the shared hydrogen energy storage system.

[0078] The equivalent annual cost of shared hydrogen energy storage operation and maintenance expenses satisfies the following relationship:

[0079]

[0080] Where, is the operation and maintenance cost coefficient of shared hydrogen energy storage.

[0081] Specifically, the optimization objective of the upper-level optimization model satisfies the following joint constraints:

[0082] 1) The maximum operating power of the shared hydrogen energy storage electrolyzer and fuel cell is less than the lease value, and there is an upper limit on the lease scale, satisfying the following relationship:

[0083]

[0084] Where, P EL (t) and P FC (t) are the electrolysis power and combustion power of shared hydrogen energy storage in period t, respectively; and are the maximum permitted leased electrolyzer power and fuel cell power of shared hydrogen energy storage, respectively; and B are the maximum power limits of the electrolyzer and fuel cell for the shared hydrogen energy storage that can be leased; EL (t) and B FC (t) are Boolean variables corresponding to the electrolyzer and fuel cell for shared hydrogen energy storage, B EL (t) takes 1 and B FC(t) takes 0 to indicate that the shared hydrogen energy storage is in electrolysis state during period t, and B EL (t) takes 0 and B FC (t) takes 1 to indicate that the shared hydrogen energy storage is combustion during period t.

[0085] 2) The charging and discharging power of the shared hydrogen energy storage and the power balance constraints of each virtual power plant in period t satisfy the following relationship:

[0086]

[0087] 3) The hydrogen flow balance constraint in and out of the hydrogen storage tank satisfies the following relationship:

[0088]

[0089] Where, v EL (t) and v FC (t) are the hydrogen production of the shared hydrogen energy storage electrolyzer and the hydrogen consumption of the fuel cell during period t; η EL and η FC are the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell respectively; and are the hydrogen storage capacity or hydrogen output of the hydrogen storage tank during period t; n H is the calorific value constant of hydrogen.

[0090] 4) The gas storage state constraint of the hydrogen storage tank satisfies the following relationship:

[0091]

[0092] Where SOH(t) is the gas content of the hydrogen storage tank during period t; is the rated hydrogen storage mass of the hydrogen storage tank; SOH max and SOH min They are the maximum and minimum gas storage states allowed by the hydrogen storage tank.

[0093] Specifically, the lower-level optimization model takes the minimum joint operation cost of each virtual power plant as the optimization goal, satisfying the following relationship:

[0094]

[0095] Where, F down is the joint operating cost of each virtual power plant; is the cost of electricity purchase and sale from the external power grid to the kth virtual power plant during the dispatch period; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch period; is the cost of wind and solar power curtailment of the kth virtual power plant during the dispatch period.

[0096] Under the operating mechanism of shared hydrogen energy storage, each virtual power plant needs to pay service fees to the shared energy storage operator; at the same time, constraints on wind and solar power curtailment are introduced to increase the absorption rate of new energy.

[0097] The cost of electricity purchase and sale from the external power grid to the kth virtual power plant during the dispatch period satisfies the following relationship:

[0098]

[0099] Where p grid,buy (t) and p grid,sell (t) are the unit price of electricity purchased and sold by the virtual power plant from the external power grid during period t; and They are respectively the purchased power and sold power of the kth virtual power plant from the external power grid in period t.

[0100] The electricity purchase and sales fees paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch period satisfy the following relationship:

[0101]

[0102] The economic benefit of the shared hydrogen energy storage operator in providing services to each virtual power plant is the sum of the electricity purchase and sales fees paid by the kth virtual power plant to the shared hydrogen energy storage during the scheduling cycle.

[0103] The cost of wind and solar curtailment of the kth virtual power plant during the dispatch period satisfies the following relationship:

[0104]

[0105] Where λ ab is the penalty coefficient for curtailing wind and solar power; p ab is the average grid-connected price of wind and photovoltaic power; and are the power rejection of photovoltaic and wind power within the kth virtual power plant in period t.

[0106] Specifically, the optimization objective of the lower-level optimization model satisfies the following joint constraints:

[0107] 1) The power balance constraint among shared hydrogen energy storage, multiple virtual power plants, and the power grid satisfies the following relationship:

[0108]

[0109] Where, is the load demand within the kth virtual power plant during period t; and are the photovoltaic and wind power grid-connected powers within the kth virtual power plant in period t.

[0110] 2) To achieve autonomous coordination among multiple virtual power plants as much as possible, the amount of electricity purchased and sold by each virtual power plant to the grid is less than the maximum limit, satisfying the following relationship:

[0111]

[0112] Where, is the maximum limit.

[0113] 3) The wind and solar renewable energy consumption constraints must satisfy the following relationship:

[0114]

[0115] Where α is the minimum requirement for the virtual power plant to absorb renewable energy; and They represent the maximum photovoltaic power generation and the maximum wind power generation of the kth virtual power plant in period t respectively.

[0116] Step 3: Based on the state information, the upper-level optimization model is iteratively solved to obtain the optimal solution for the operating net profit of the shared hydrogen energy storage operator; the actual value, fluctuation range, and predicted value of the photovoltaic power generation and wind power generation of each virtual power plant are obtained, and the wind and solar uncertainty constraints are established; based on the optimal solution of the upper-level optimization model, under the wind and solar uncertainty constraints, the optimization objective of the lower-level optimization model is solved to obtain the basic value of the joint operating cost of each virtual power plant;

[0117] The optimal solution of the upper-level optimization model includes: the optimal economic benefit of the shared hydrogen energy storage operator in providing services to each virtual power plant, the optimal equivalent annual cost of the initial investment cost of shared hydrogen energy storage, and the optimal equivalent annual cost of the operation and maintenance cost of shared hydrogen energy storage.

[0118] After the optimal solution of the shared hydrogen energy storage operator's operating net profit is obtained by iteratively solving the upper-level optimization model according to the status information, since the upper-level optimization model of the present invention takes the maximization of the shared hydrogen energy storage operator's operating net profit over the entire life cycle as the optimization goal, the lower-level optimization model uses the optimal solution of the upper-level optimization model as a coupling variable, that is, the equivalence relationship between the economic benefits of the shared hydrogen energy storage operator corresponding to the optimal solution in providing services to each virtual power plant and the sum of the electricity purchase and sales fees paid by the kth virtual power plant to the shared hydrogen energy storage during the scheduling period is used to realize the coupling of the upper-level optimization model and the lower-level optimization model.

[0119] Specifically, the wind and solar uncertainty constraints satisfy the following relationship:

[0120]

[0121] Where, are the fluctuation amplitudes of photovoltaic power generation and wind power generation of the kth virtual power plant, are the predicted values ​​of photovoltaic power generation and wind power generation of the kth virtual power plant in period t, respectively; are the actual values ​​of photovoltaic power generation and wind power generation of the kth virtual power plant in period t, respectively.

[0122] According to the optimal solution of the upper-level optimization model, under the uncertainty constraints of wind and solar power, the optimization objectives of the lower-level optimization model are solved to obtain the basic value of the joint operating cost of each virtual power plant. This has high robustness and provides a stable and feasible space that can adapt to the uncertain characteristics of wind and solar power output for determining the optimal operating plan under the subsequent balance game of risk avoidance and risk pursuit.

[0123] Step 4: Take the maximum sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant as the risk objective function, introduce the risk aversion deviation parameter, and use the basic value of the joint operation cost of each virtual power plant to construct the first constraint of the optimization objective of the lower-level optimization model. After introducing the benefit pursuit deviation parameter, use the basic value of the joint operation cost of each virtual power plant to construct the second constraint of the optimization objective of the lower-level optimization model. According to the optimal solution of the upper-level optimization model, the optimal solution of the lower-level optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan of multiple virtual power plants.

[0124] The optimal solution of the lower-level optimization model includes: the optimal value of the electricity purchase and sales fee of each virtual power plant from the external power grid during the dispatching cycle, the optimal value of the electricity purchase and sales fee paid by the virtual power plant to the shared hydrogen energy storage during the dispatching cycle, and the optimal value of the wind and solar power curtailment cost of the virtual power plant during the dispatching cycle.

[0125] The risk objective function satisfies the following relationship:

[0126]

[0127] Where δ is the sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant.

[0128] The first constraint of the optimization objective of the lower-level optimization model satisfies the following relationship:

[0129]

[0130] Where, F down is the joint operating cost of each virtual power plant, is the basic value of the joint operation cost of each virtual power plant, γ ra is the risk aversion bias parameter.

[0131] When the optimization objective of the lower-level optimization model satisfies the first constraint, it means that when the operating net profit of the shared hydrogen energy storage operator is optimal, the lower limits of photovoltaic power generation output and wind power generation output are reduced in order to avoid risks, thereby achieving the optimal solution of the lower-level optimization model that does not lead to excessive wind and solar power abandonment rates due to risk aversion, and at the same time satisfies the first constraint and wind and solar power uncertainty constraints. It is a multi-virtual power plant joint operation plan that ensures the utilization rate of new energy under the uncertainty of wind and solar power output.

[0132] The second constraint of the optimization objective of the lower-level optimization model satisfies the following relationship:

[0133]

[0134] Where, F down is the joint operating cost of each virtual power plant, is the basic value of the joint operation cost of each virtual power plant, γ os Pursue deviation parameters for efficiency.

[0135] When the optimization objective of the lower-level optimization model satisfies the second constraint, it means that when the operating net profit of the shared hydrogen energy storage operator is optimal, the upper limit of photovoltaic power generation output and wind power generation output is increased in pursuit of efficiency. At this time, the proportion of new energy increases significantly, resulting in a decrease in system stability and reliability. The optimal solution of the lower-level optimization model that satisfies the second constraint and the wind and solar uncertainty constraint conditions is a multi-virtual power plant joint operation scheme that considers the uncertainty of wind and solar output to improve system stability.

[0136] The optimization objectives of the lower-level optimization model proposed in the present invention simultaneously satisfy the first constraint and the second constraint, which not only addresses the uncertainty of wind and solar power output, but also balances the needs of increasing the utilization rate of new energy and improving system stability, and realizes a comprehensive optimization of opportunistic multi-virtual power plant joint operation based on benefit pursuit and robust multi-virtual power plant joint operation based on risk avoidance.

[0137] In the embodiment, γ ra +γ os =1, the risk aversion bias parameter and the benefit pursuit bias parameter are both set to 0.5. By adjusting the specific values ​​of the risk aversion bias parameter and the benefit pursuit bias parameter, it is possible to select a personalized operation plan based on the risk tolerance and operation strategy of each virtual power plant. For virtual power plants with weak risk tolerance, increasing the risk aversion bias parameter and reducing the benefit pursuit bias parameter will lower the lower limit of reducing photovoltaic power generation output and wind power generation output, and increase the upper limit of reducing photovoltaic power generation output and wind power generation output; for virtual power plants pursuing operational benefits, reducing the risk aversion bias parameter and increasing the benefit pursuit bias parameter will increase the lower limit of reducing photovoltaic power generation output and wind power generation output, and lower the upper limit of reducing photovoltaic power generation output and wind power generation output.

[0138] The present invention promotes the two-tier scheduling optimization of collaborative autonomy of multiple virtual power plants by adopting shared hydrogen energy storage, comprehensively considers the cooperative game between shared hydrogen energy storage operators and multiple virtual power plant entities, and completes the fee settlement according to the energy interaction between each virtual power plant and the shared hydrogen energy storage and the power network. In the process of energy interaction between each virtual power plant and the shared hydrogen energy storage and the power network, the energy interaction model between each virtual power plant and the shared hydrogen energy storage system and the power network is used to calculate the relevant technical parameters, including the power purchase and sales power of each virtual power plant, the charging and discharging power of the shared hydrogen energy storage, and the hydrogen storage capacity of the hydrogen storage tank. Specifically, based on the energy interaction situation, the power purchase and sales power of each virtual power plant, the leasing scale and charging and discharging strategy of the hydrogen energy storage system, and the real-time storage capacity of the hydrogen storage tank are obtained. When a scheduling cycle is completed, the leased shared hydrogen energy storage resources are released.

[0139] The present invention also provides a multi-virtual power plant joint operation system considering shared hydrogen energy storage, comprising:

[0140] The acquisition module is used to obtain the status information of distributed resources within each virtual power plant system, including: photovoltaic power generation forecast value, wind power generation forecast value, shared hydrogen energy storage charge and discharge status and capacity, and electricity price;

[0141] An optimization scheduling model establishment module is used to establish an optimization scheduling model for the joint operation of multiple virtual power plants based on a two-layer model structure. It includes: an upper-layer optimization model with the optimization goal of maximizing the operating net profit of the shared hydrogen energy storage operator; and a lower-layer optimization model with the optimization goal of minimizing the joint operating cost of each virtual power plant.

[0142] The model iterative solution module is used to iteratively solve the upper-level optimization model based on the state information to obtain the optimal solution for the operating net profit of the shared hydrogen energy storage operator; obtain the actual value, fluctuation range and predicted value of the photovoltaic power generation and wind power generation of each virtual power plant, and establish the wind and solar uncertainty constraint conditions; based on the optimal solution of the upper-level optimization model, under the wind and solar uncertainty constraint conditions, solve the optimization objectives of the lower-level optimization model to obtain the basic value of the joint operating cost of each virtual power plant;

[0143] The joint operation optimization module is used to take the maximum sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant as the risk objective function, introduce the risk aversion deviation parameter, and use the basic value of the joint operation cost of each virtual power plant to construct the first constraint of the optimization objective of the lower-level optimization model; introduce the benefit pursuit deviation parameter, and use the basic value of the joint operation cost of each virtual power plant to construct the second constraint of the optimization objective of the lower-level optimization model; according to the optimal solution of the upper-level optimization model, the optimal solution of the lower-level optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan of multiple virtual power plants.

[0144] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0145] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0146] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0147] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for joint operation of multiple virtual power plants considering shared hydrogen energy storage, characterized in that: include: Obtain status information of distributed resources within each virtual power plant system; Based on the two-layer model structure, an optimized scheduling model for the joint operation of multiple virtual power plants is established, including: The upper-level optimization model, which takes the maximization of the operating net profit of the shared hydrogen energy storage operator as the optimization goal, satisfies the following relationship: Where, F up The net profit of the shared hydrogen energy storage operator; B HS,ele The economic benefits of shared hydrogen energy storage operators in providing services to virtual power plants; rent is the leasing cost coefficient of shared hydrogen energy storage; C HS,inv is the equivalent annual cost of the initial investment in shared hydrogen energy storage; C HS,ope The equivalent annual cost of shared hydrogen storage operation and maintenance expenses; The lower-level optimization model, which takes the minimum joint operation cost of each virtual power plant as the optimization objective, satisfies the following relationship: Where, F down is the joint operating cost of each virtual power plant; is the cost of electricity purchase and sale from the external power grid to the kth virtual power plant during the dispatch period; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch period; is the wind and solar curtailment cost of the kth virtual power plant during the dispatch period, and K is the number of virtual power plants; The upper optimization model is iteratively solved based on the status information to obtain the optimal solution for the operating net profit of the shared hydrogen energy storage operator. The optimal solution of the upper optimization model includes: the optimal value of the economic benefits of the shared hydrogen energy storage operator in providing services to each virtual power plant, the optimal value of the equal annual cost of the initial investment cost of shared hydrogen energy storage, and the optimal value of the equal annual cost of the operation and maintenance cost of shared hydrogen energy storage. The optimal value of the economic benefits of the shared hydrogen energy storage operator in providing services to each virtual power plant is the sum of the purchase and sale fees paid by the kth virtual power plant to the shared hydrogen energy storage in the scheduling cycle in the lower optimization model; the actual value, fluctuation range and predicted value of the photovoltaic power generation and wind power generation of each virtual power plant are obtained, and the wind and solar uncertainty constraint conditions are established; according to the optimal solution of the operating net profit of the shared hydrogen energy storage operator, under the wind and solar uncertainty constraint conditions, the lower optimization model is iteratively solved to obtain the basic value of the joint operation cost of each virtual power plant; The maximum sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant is taken as the risk objective function. After introducing the risk aversion deviation parameter, the first constraint of the optimization objective of the lower-level optimization model is constructed with the basic value of the joint operation cost of each virtual power plant. After introducing the benefit pursuit deviation parameter, the second constraint of the optimization objective of the lower-level optimization model is constructed with the basic value of the joint operation cost of each virtual power plant. According to the optimal solution of the operating net profit of the shared hydrogen energy storage operator, the optimal solution of the lower-level optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan of multiple virtual power plants.

2. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 1 is characterized in that: The joint constraints satisfied by the optimization objectives of the upper-level optimization model include: The maximum operating power of the shared hydrogen energy storage electrolyzer and fuel cell is less than the lease value, and there is an upper limit on the lease scale, satisfying the following relationship: Where, P EL (t) and P FC (t) are the electrolysis power and combustion power of shared hydrogen energy storage in period t, respectively; and are the maximum permitted leased electrolyzer power and fuel cell power of shared hydrogen energy storage, respectively; and B are the maximum power limits of the electrolyzer and fuel cell for the shared hydrogen energy storage that can be leased; EL (t) and B FC (t) are Boolean variables corresponding to the electrolyzer and fuel cell for shared hydrogen energy storage, B EL (t) takes 1 and B FC (t) takes 0 to indicate that the shared hydrogen energy storage is in electrolysis state during period t, and B EL (t) takes 0 and B FC (t) takes 1 to indicate that the shared hydrogen energy storage is combustion during period t; The charging and discharging power of the shared hydrogen energy storage and the power balance constraints of each virtual power plant in period t satisfy the following relationship: Where, and are the electricity sales power and electricity purchase power of the kth virtual power plant for shared hydrogen energy storage in period t, respectively; t = 1, 2, …, T, where T is a scheduling period; The hydrogen flow balance constraint in and out of the hydrogen storage tank satisfies the following relationship: Where, v EL (t) and v FC (t) are the hydrogen production of the shared hydrogen energy storage electrolyzer and the hydrogen consumption of the fuel cell during period t; η EL and η FC are the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell respectively; and are the hydrogen storage capacity or hydrogen output of the hydrogen storage tank during period t; n H is the calorific value constant of hydrogen; The gas storage state constraint of the hydrogen storage tank satisfies the following relationship: Where SOH(t) is the gas content of the hydrogen storage tank during period t; is the rated hydrogen storage mass of the hydrogen storage tank; SOH max and SOH min are the maximum and minimum gas storage states allowed by the hydrogen storage tank respectively; Δt is the scheduling decision time interval.

3. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 1, characterized in that: The joint constraints satisfied by the optimization objectives of the lower-level optimization model include: The power balance constraints among shared hydrogen energy storage, multiple virtual power plants, and the power grid satisfy the following relationship: Where, and are the power purchased and sold from the external grid by the kth virtual power plant in period t, and are the electricity sales power and electricity purchase power of the kth virtual power plant for shared hydrogen energy storage in period t, is the load demand within the kth virtual power plant during period t; and They represent the photovoltaic and wind power grid-connected power within the kth virtual power plant in period t respectively; The amount of electricity purchased and sold by each virtual power plant to the grid is less than the maximum limit, satisfying the following relationship: Where, is the maximum limit; The wind and solar renewable energy consumption constraints satisfy the following relationship: Where α is the minimum requirement for the virtual power plant to absorb renewable energy; and They represent the maximum photovoltaic power generation and the maximum wind power generation of the kth virtual power plant in period t, and are the power rejection of photovoltaic and wind power within the kth virtual power plant in period t.

4. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 1, characterized in that: The wind and solar uncertainty constraints satisfy the following relationship: Where, is the wind and solar uncertainty constraint, are the fluctuation amplitudes of photovoltaic power generation and wind power generation of the kth virtual power plant, are the predicted values ​​of photovoltaic power generation and wind power generation of the kth virtual power plant in period t, respectively; are the actual values ​​of photovoltaic power generation and wind power generation of the kth virtual power plant in period t, respectively.

5. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 4 is characterized in that: The risk objective function satisfies the following relationship: Where δ is the sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant, t = 1, 2, …, T, and T is a scheduling period.

6. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 5, characterized in that: The first constraint of the optimization objective of the lower-level optimization model satisfies the following relationship: Where, F down is the joint operating cost of each virtual power plant, is the basic value of the joint operation cost of each virtual power plant, γ ra is the risk aversion bias parameter.

7. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 5, characterized in that: The second constraint of the optimization objective of the lower-level optimization model satisfies the following relationship: Where, F down is the joint operating cost of each virtual power plant, is the basic value of the joint operation cost of each virtual power plant, γ os Pursue deviation parameters for efficiency.

8. A multi-virtual power plant joint operation system considering shared hydrogen energy storage, used to implement the multi-virtual power plant joint operation method considering shared hydrogen energy storage according to any one of claims 1 to 7, characterized in that: include: The acquisition module is used to obtain the status information of distributed resources within each virtual power plant system; The optimization scheduling model establishment module is used to establish an optimization scheduling model for the joint operation of multiple virtual power plants based on a two-layer model structure, including: The upper-level optimization model, which takes the maximization of the operating net profit of the shared hydrogen energy storage operator as the optimization goal, satisfies the following relationship: Where, F up The net profit of the shared hydrogen energy storage operator; B HS,ele The economic benefits of shared hydrogen energy storage operators in providing services to virtual power plants; rent is the leasing cost coefficient of shared hydrogen energy storage; C HS,inv is the equivalent annual cost of the initial investment in shared hydrogen energy storage; C HS,ope The equivalent annual cost of shared hydrogen storage operation and maintenance expenses; The lower-level optimization model, which takes the minimum joint operation cost of each virtual power plant as the optimization objective, satisfies the following relationship: Where, F down is the joint operating cost of each virtual power plant; is the cost of electricity purchase and sale from the external power grid to the kth virtual power plant during the dispatch period; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch period; is the wind and solar curtailment cost of the kth virtual power plant during the dispatch period, and K is the number of virtual power plants; The model iterative solution module is used to iteratively solve the upper optimization model according to the state information to obtain the optimal solution of the operating net profit of the shared hydrogen energy storage operator. The optimal solution of the upper optimization model includes: the optimal value of the economic benefits of the shared hydrogen energy storage operator in providing services to each virtual power plant, the optimal value of the equal annual cost of the initial investment cost of shared hydrogen energy storage, and the optimal value of the equal annual cost of the operation and maintenance cost of shared hydrogen energy storage. The optimal value of the economic benefits of the shared hydrogen energy storage operator in providing services to each virtual power plant is the sum of the purchase and sale fees paid by the kth virtual power plant to the shared hydrogen energy storage in the scheduling cycle in the lower optimization model; the actual value, fluctuation range and predicted value of the photovoltaic power generation and wind power generation of each virtual power plant are obtained, and the wind and solar uncertainty constraint conditions are established; according to the optimal solution of the operating net profit of the shared hydrogen energy storage operator, under the wind and solar uncertainty constraint conditions, the lower optimization model is iteratively solved to obtain the basic value of the joint operation cost of each virtual power plant; The joint operation optimization module is used to take the maximum sum of the fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant as the risk objective function, introduce the risk aversion deviation parameter, and use the basic value of the joint operation cost of each virtual power plant to construct the first constraint of the optimization objective of the lower-level optimization model. After introducing the benefit pursuit deviation parameter, the basic value of the joint operation cost of each virtual power plant is used to construct the second constraint of the optimization objective of the lower-level optimization model. According to the optimal solution of the operating net profit of the shared hydrogen energy storage operator, the optimal solution of the lower-level optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan of multiple virtual power plants.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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