Multi-virtual power plant combined operation method and system considering shared hydrogen energy storage

Abstract

The invention discloses a multi-virtual power plant combined operation method and system considering shared hydrogen energy storage. An upper-layer optimization model taking the maximum operation net profit of a shared hydrogen energy storage operator as an optimization target and a lower-layer optimization model taking the minimum combined operation cost of each virtual power plant as an optimization target are established; actual values, fluctuation amplitudes and predicted values of the light and wind power generating capacity of each virtual power plant are obtained, and wind and light uncertainty constraint conditions are established; solving an upper-layer optimization model according to the state information to obtain an optimal solution of the operation net profit of the shared hydrogen energy storage operator, and solving a lower-layer optimization model to obtain a combined operation cost basic value of each virtual power plant under a wind-solar uncertainty constraint condition; with the maximum fluctuation amplitude sum of photovoltaic and wind power generation capacity as a risk objective function, introducing a risk avoidance deviation parameter to construct a first constraint, and introducing a benefit pursuit deviation parameter to construct a second constraint; and the optimal solution of the lower-layer optimization model meeting the first constraint and the second constraint realizes the optimal joint operation of the multiple virtual power plants.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization and dispatching, and in particular to a method and system for joint operation of multiple virtual power plants taking into account 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] In the existing technology, virtual power plants use information and communication technology to organically combine distributed resources such as source, load, and storage in the power system industry chain, forming a digital energy management system that integrates source, grid, load, and storage, which greatly promotes the consumption of renewable energy. Although energy storage technology can effectively assist in solving the problem of uncertainty in renewable energy processing, its high assembly cost hinders its large-scale and widespread application. Shared energy storage can enable all users to share the energy storage cost and enjoy energy storage services together. Its innovation in business model and pricing mechanism 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 greatly affected by the intermittent and volatile nature of renewable energy such as wind power and photovoltaics. These uncertainties cause virtual power plants to face huge challenges in the scheduling process, such as grid stability issues, power balancing difficulties, and reduced power quality. Although energy storage technology can alleviate the uncertainty of renewable energy to a certain extent, the high cost of energy storage is still the main obstacle to its large-scale promotion. Especially in the scenario of joint operation of multiple virtual power plants, how to effectively share and reduce energy storage costs has become an urgent problem to be solved. 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 solve 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, which determines 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, increases the photovoltaic power generation output and wind power generation output under the pursuit of efficiency, and reduces the photovoltaic power generation output and wind power generation output under 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 the energy storage cost, thereby enhancing the overall economic benefits and stability of the system.

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

[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 the two-layer model structure, an optimization dispatching model for the joint operation of multiple virtual power plants is established, including: an upper optimization model with the maximum net profit of the shared hydrogen energy storage operator as the optimization target, and a lower optimization model with the minimum joint operation cost of each virtual power plant as the optimization target;

[0009] The upper optimization model is iteratively solved according to the state information 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; according to the optimal solution for the operating net profit of the shared hydrogen energy storage operator, under the wind and solar uncertainty constraints, the lower optimization model is iteratively solved to obtain the basic value of the joint operation cost of each virtual power plant;

[0010] The maximum sum of fluctuations 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 basic value of the joint operation cost of each virtual power plant is used to construct the first constraint of the optimization target of the lower 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 target of the lower optimization model. According to the optimal solution of the net operating profit of the shared hydrogen energy storage operator, the optimal solution of the lower 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 optimization model takes the maximum net profit of the shared hydrogen energy storage operator as the optimization goal, satisfying the following relationship:

[0012]

[0013] In the formula, F up B is the net operating profit of the shared hydrogen energy storage operator; HS,ele The economic benefits of shared hydrogen 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 optimization model include:

[0015] 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:

[0016]

[0017] 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 The maximum allowed leased shared hydrogen energy storage electrolyzer power and fuel cell power, respectively; and The maximum power limit of the electrolyzer and fuel cell for the shared hydrogen energy storage that can be leased is respectively; B 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 the 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;

[0018] The charging and discharging power of the shared hydrogen energy storage and the power balance constraints of each virtual power plant during period t satisfy the following relationship:

[0019]

[0020] In the formula, and They 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, T is a scheduling cycle;

[0021] The balance constraint of hydrogen flow in and out of the hydrogen storage tank satisfies the following relationship:

[0022]

[0023] In the formula, 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 They are the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell; 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;

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

[0025]

[0026] Where SOH(t) is the gas content of the hydrogen storage tank during period t; SOH is the rated hydrogen storage mass of the hydrogen storage tank; 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 optimization model takes the minimum joint operation cost of each virtual power plant as the optimization goal, satisfying the following relationship:

[0028]

[0029] In the formula, 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 cycle; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch cycle; is the wind and solar power abandonment cost of the kth virtual power plant during the dispatch period.

[0030] Preferably, 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 equivalent annual value cost of the initial investment cost of the shared hydrogen energy storage, and the optimal value of the equivalent annual value cost of the shared hydrogen energy storage operation and maintenance cost;

[0031] The optimal value of 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 period in the lower-level optimization model.

[0032] Preferably, the joint constraints satisfied by the optimization objective of the lower 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] In the formula, and are the power purchased and sold by the kth virtual power plant from the external power grid 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 inside the kth virtual power plant in period t respectively;

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

[0037]

[0038] In the formula, is the maximum limit;

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

[0040]

[0041] In the formula, 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 They are respectively 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] In the formula, is the uncertainty constraint of wind and solar power, are the fluctuation ranges 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 cycle.

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

[0049]

[0050] In the formula, 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 optimization model satisfies the following relationship:

[0052]

[0053] In the formula, 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] A collection module is used to obtain status information of distributed resources within each virtual power plant system;

[0056] 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: an upper optimization model with the maximum operating net profit of the shared hydrogen energy storage operator as the optimization target, and a lower optimization model with the minimum joint operation cost of each virtual power plant as the optimization target;

[0057] The model iteration 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; 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; 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, iteratively solve the lower optimization model to obtain the basic value of the joint operation 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 target of the lower optimization model, and introduce the benefit pursuit deviation parameter to construct the second constraint of the optimization target of the lower optimization model 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 optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan for multiple virtual power plants.

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

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

[0061] The beneficial effects of the present invention are that, compared with the prior art, at least the present invention discloses a method for joint operation of multiple virtual power plants taking into account shared hydrogen energy storage, including determining the specific subjects 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 operation mode of each virtual power plant are solved based on the 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 in multiple virtual power plants, and comprehensively consider the comprehensive interests of multiple parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It 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] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope 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 As 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 multiple virtual power plants are jointly operated, it is first necessary to determine the entities of each virtual power plant participating in the joint operation, including the types and construction scale of distributed resources within each virtual power plant system. In an embodiment of the present invention, 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 a virtual power plant, converts solar energy into electrical energy. A virtual power plant may include multiple distributed photovoltaic power plants, each with an installed capacity of 10MW. Wind power generation converts wind energy into electrical energy through wind turbines. A virtual power plant may have several distributed wind farms, each with an installed capacity of 20MW.

[0067] Step 2: Based on the two-layer model structure, an optimization scheduling model for the joint operation of multiple virtual power plants is established, including: an upper-layer optimization model with the maximization of the net operating profit of the shared hydrogen energy storage operator as the optimization goal, and a lower-layer optimization model with the minimization of the joint operation cost of each virtual power plant as the optimization goal.

[0068] Specifically, the upper-level optimization model takes the maximum net profit of the shared hydrogen energy storage operator as the optimization goal, satisfying the following relationship:

[0069]

[0070] In the formula, F up B is the net operating profit of the shared hydrogen energy storage operator; HS,ele The economic benefits of shared hydrogen 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.

[0071] In the embodiment, F up It is the net operating profit of the shared hydrogen energy storage operator over the entire life cycle.

[0072] Among them, the economic benefits of the shared hydrogen energy storage operator in providing services to each virtual power plant satisfy the following relationship:

[0073]

[0074] In the formula, p HS,sell (t) and p HS,buy (t) are the electricity selling price and electricity purchasing price of each virtual power plant for shared hydrogen energy storage during 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; 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] In the formula, and are the rated power of the electrolyzer, the rated power of the fuel cell and the rated capacity of the hydrogen storage tank of 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] In the formula, 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 The maximum allowed leased shared hydrogen energy storage electrolyzer power and fuel cell power, respectively; and The maximum power limit of the electrolyzer and fuel cell for the shared hydrogen energy storage that can be leased is respectively; B 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 the electrolysis state during period t, and B EL (t) takes 0 and B FC (t) takes the value of 1, which means that the shared hydrogen energy storage is burned 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 during period t satisfy the following relationship:

[0086]

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

[0088]

[0089] In the formula, 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 They are the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell; 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; SOH is the rated hydrogen storage mass of the hydrogen storage tank; max and SOH min They are respectively 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 and satisfies the following relationship:

[0094]

[0095] In the formula, 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 cycle; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch cycle; is the wind and solar power abandonment cost 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 abandonment 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] In the formula, 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 power purchased and sold by the kth virtual power plant from the external power grid in period t.

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

[0104]

[0105] In the formula, λ ab is the penalty coefficient for wind and solar power abandonment; p ab is the average grid-connected price of wind and photovoltaic power; and They are respectively 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] In the formula, is the load demand within the kth virtual power plant during period t; and They are respectively the photovoltaic and wind power grid-connected power within the kth virtual power plant in period t.

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

[0111]

[0112] In the formula, is the maximum limit.

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

[0114]

[0115] In the formula, 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, iteratively solve the upper optimization model according to 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; according to the optimal solution of the upper optimization model, under the wind and solar uncertainty constraint conditions, solve the optimization target of the lower optimization model to obtain the basic value of the joint operation cost of each virtual power plant;

[0117] The optimal solutions of the upper-level optimization model include: 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 equivalent annual cost of the initial investment cost of shared hydrogen energy storage, and the optimal value of the equivalent annual cost of the shared hydrogen energy storage operation and maintenance cost.

[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 achieve 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] In the formula, are the fluctuation ranges 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 objective of the lower-level optimization model is solved to obtain the basic value of the joint operating cost of each virtual power plant. It 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 balanced game of risk avoidance and risk pursuit.

[0123] Step 4, taking the maximum sum of fluctuation amplitudes of photovoltaic power generation and wind power generation under the joint operation of each virtual power plant as the risk objective function, introducing the risk aversion deviation parameter and using 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 optimization model, introducing the benefit pursuit deviation parameter and using 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 optimization model; according to the optimal solution of the upper optimization model, the optimal solution of the lower optimization model that simultaneously satisfies the first constraint and the second constraint is used as the joint operation plan for 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 abandonment 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 optimization model satisfies the following relationship:

[0129]

[0130] In the formula, 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 target 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, so that the wind and solar power abandonment rate will not be too high due to risk aversion. At the same time, the optimal solution of the lower-level optimization model that meets the first constraint and the wind and solar power uncertainty constraint conditions is achieved. It is a multi-virtual power plant joint operation plan that takes into account the uncertainty of wind and solar power output to ensure the utilization rate of new energy.

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

[0133]

[0134] In the formula, 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 target of the lower-level optimization model satisfies the second constraint, it means that when the net operating 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. At the same time, the optimal solution of the lower-level optimization model that meets 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 objective of the lower-level optimization model proposed in the present invention satisfies the first constraint and the second constraint at the same time, which not only copes with 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 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 according to the risk tolerance and operation strategy of each virtual power plant. For virtual power plants with weak risk tolerance, the risk aversion bias parameter is increased while the benefit pursuit bias parameter is reduced, so that the lower limit of reducing photovoltaic power generation output and wind power generation output is reduced, and the upper limit of reducing photovoltaic power generation output and wind power generation output is increased; for virtual power plants pursuing operational benefits, the risk aversion bias parameter is reduced while the benefit pursuit bias parameter is increased, so that the lower limit of reducing photovoltaic power generation output and wind power generation output is increased, and the upper limit of reducing photovoltaic power generation output and wind power generation output is reduced.

[0138] The present invention promotes the two-layer 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 relevant technical parameters are calculated through the energy interaction model between each virtual power plant and the shared hydrogen energy storage system and the power network, 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 the distributed resources within each virtual power plant system, including: the predicted value of photovoltaic power generation, the predicted value of wind power generation, the charging and discharging status and capacity of shared hydrogen energy storage, and the electricity price;

[0141] 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: an upper optimization model with the maximum operating net profit of the shared hydrogen energy storage operator as the optimization target, and a lower optimization model with the minimum joint operation cost of each virtual power plant as the optimization target;

[0142] The model iteration 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; 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; according to the optimal solution of the upper optimization model, under the wind and solar uncertainty constraint conditions, solve the optimization target of the lower optimization model to obtain the basic value of the joint operation 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 construct the first constraint of the optimization target of the lower optimization model 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 target of the lower 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 upper optimization model, the optimal solution of the lower optimization model that simultaneously satisfies 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, a method and / or a 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 may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may 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 of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) 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 disk 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 of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted 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 a 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, optical fiber transmissions, wireless transmissions, 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 for storage in the computer-readable storage medium in each computing / processing device.

[0147] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related 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++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely 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 completely 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., using 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 customized by utilizing the state information of the computer-readable program instructions, and 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 relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within 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 optimization dispatching model for the joint operation of multiple virtual power plants is established, including: an upper optimization model with the maximum net profit of the shared hydrogen energy storage operator as the optimization target, and a lower optimization model with the minimum joint operation cost of each virtual power plant as the optimization target; The upper optimization model is iteratively solved according to the state information 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; according to the optimal solution for the operating net profit of the shared hydrogen energy storage operator, under the wind and solar uncertainty constraints, 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 fluctuations 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 basic value of the joint operation cost of each virtual power plant is used to construct the first constraint of the optimization target of the lower 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 target of the lower optimization model. According to the optimal solution of the net operating profit of the shared hydrogen energy storage operator, the optimal solution of the lower 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 upper optimization model takes the maximum net profit of the shared hydrogen energy storage operator as the optimization goal, satisfying the following relationship: In the formula, F up B is the net operating profit of the shared hydrogen energy storage operator; HS,ele The economic benefits of shared hydrogen 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.

3. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 2 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 The maximum allowed leased shared hydrogen energy storage electrolyzer power and fuel cell power, respectively; and The maximum power limit of the electrolyzer and fuel cell for the shared hydrogen energy storage that can be leased is respectively; B 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 the 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 during period t satisfy the following relationship: In the formula, and They 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, T is a scheduling cycle; The balance constraint of hydrogen flow in and out of the hydrogen storage tank satisfies the following relationship: In the formula, 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 They are the hydrogen production efficiency of the electrolyzer and the power generation efficiency of the fuel cell; 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; SOH is the rated hydrogen storage mass of the hydrogen storage tank; 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.

4. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 2 is characterized in that: The lower optimization model takes the minimum joint operation cost of each virtual power plant as the optimization goal and satisfies the following relationship: In the formula, 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 cycle; The electricity purchase and sales fee paid by the kth virtual power plant to the shared hydrogen energy storage during the dispatch cycle; is the wind and solar power abandonment cost of the kth virtual power plant during the dispatch period.

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 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 equivalent annual cost of the initial investment cost of the shared hydrogen energy storage, and the optimal value of the equivalent annual cost of the operation and maintenance cost of the shared hydrogen energy storage; The optimal value of 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 period in the lower-level optimization model.

6. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 4 is 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: In the formula, and are the power purchased and sold by the kth virtual power plant from the external power grid 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 inside the kth virtual power plant in period t respectively; The amount of electricity purchased and sold by each virtual power plant to the power grid is less than the maximum limit, satisfying the following relationship: In the formula, is the maximum limit; The wind and solar renewable energy consumption constraints satisfy the following relationship: In the formula, 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 They are respectively the power rejection of photovoltaic and wind power within the kth virtual power plant in period t.

7. 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: In the formula, is the uncertainty constraint of wind and solar power, are the fluctuation ranges 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.

8. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 7 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 cycle.

9. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 8, characterized in that: The first constraint of the optimization objective of the lower optimization model satisfies the following relationship: In the formula, 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.

10. The method for joint operation of multiple virtual power plants considering shared hydrogen energy storage according to claim 8, characterized in that: The second constraint of the optimization objective of the lower optimization model satisfies the following relationship: In the formula, 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.

11. A multi-virtual power plant joint operation system considering shared hydrogen energy storage, characterized in that: include: A collection module is used to obtain 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: an upper optimization model with the maximum operating net profit of the shared hydrogen energy storage operator as the optimization target, and a lower optimization model with the minimum joint operation cost of each virtual power plant as the optimization target; The model iteration 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; 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; 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, iteratively solve the lower optimization model 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 target of the lower optimization model, and introduce the benefit pursuit deviation parameter to construct the second constraint of the optimization target of the lower optimization model 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 optimization model that simultaneously meets the first constraint and the second constraint is used as the joint operation plan for multiple virtual power plants.

12. 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 10.

13. 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 10 are implemented.

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