Island multi-integrated energy system operation optimization method based on electricity and hydrogen energy storage sharing

Abstract

The invention discloses an island multi-integrated energy system operation optimization method based on electricity-hydrogen energy storage sharing, and the method comprises the following steps: constructing an island electricity-hydrogen integrated energy system which comprises a plurality of integrated energy systems and a shared energy storage station; establishing an energy cooperation sharing model and a benefit distribution model of the island electricity-hydrogen comprehensive energy system; and solving the energy cooperation sharing model and the benefit distribution model by adopting an optimization algorithm to obtain an optimal scheduling strategy and a benefit distribution scheme of the shared energy storage station and each integrated energy system. According to the method, the improved adaptive ADMM algorithm is adopted to carry out distributed solution, so that the calculation efficiency is effectively improved, and the privacy of each participant is protected. According to the method, joint optimization of electric energy storage and hydrogen energy storage is comprehensively considered, and the cross-space-time energy scheduling capability of the system is improved. The strategy can improve the operation economy of the island energy system and improve the energy utilization efficiency, optimizes the energy transaction mode, and provides a new frame support for the intelligent scheduling of the ocean island energy system.

Description

An Optimization Method for the Operation of Isolated Multi-Energy Systems Based on Electricity-Hydrogen Energy Storage Sharing Technical Field

[0001] This invention belongs to the field of energy optimization technology, and in particular relates to an operation optimization method for isolated multi-energy systems based on shared electricity and hydrogen storage. Background Technology

[0002] With the acceleration of global energy transition and the increasing demand for sustainable development, integrated energy systems (IES) for remote islands with integrated electro-hydrogen coupling devices are gradually becoming an important way to solve the imbalance between energy supply and demand on isolated islands and improve energy efficiency.

[0003] By combining clean energy technologies such as electricity, hydrogen, wind, and photovoltaics with energy storage systems, IES can achieve efficient synergy and optimized management of multiple energy sources within a localized area. In the energy systems of offshore islands, using wind power to produce hydrogen and combining it with shared energy storage stations (SESS) can not only improve the absorption rate of wind power and reduce wind curtailment, but also provide flexible energy supply solutions through hydrogen storage and dispatch, further enhancing the system's economy and flexibility.

[0004] However, the uncertainty and volatility of wind power output lead to significant supply-demand imbalances and economic conflicts between electricity and hydrogen production. Currently, although various energy system optimization and dispatch schemes exist, most focus on optimizing single energy systems, lacking sufficient research on energy cooperation and benefit distribution among stakeholders in multi-energy systems. Furthermore, existing dispatch schemes fail to effectively consider energy sharing and cooperative game mechanisms, resulting in an unequal distribution of benefits among participants, insufficient willingness to cooperate, and consequently affecting the overall optimization effect of the system.

[0005] Therefore, how to solve the energy supply and demand mismatch in isolated systems and improve the stability and economy of the system through multi-energy collaborative optimization scheduling strategies has become an urgent technical problem to be solved. Summary of the Invention

[0006] The problem this invention aims to solve is to provide an operational optimization method for isolated multi-energy systems based on shared electricity and hydrogen storage. This method is based on a cooperative game theory strategy for energy cooperation and benefit distribution, providing theoretical support and technical guidance for this problem. It aims to maximize the balance of interests among the various energy entities within the system and promote the sustainable development of energy systems in remote islands.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an optimization method for the operation of an isolated multi-integrated energy system based on shared electric-hydrogen energy storage, comprising the following steps: S1: constructing an isolated electric-hydrogen integrated energy system containing multiple integrated energy systems and a shared energy storage station; S2: establishing an energy cooperation and sharing model and a benefit distribution model for the isolated electric-hydrogen integrated energy system; S3: solving the energy cooperation and sharing model and the benefit distribution model using an optimization algorithm to obtain the optimal scheduling strategy and benefit distribution scheme for the shared energy storage station and each of the integrated energy systems.

[0008] Furthermore, in S1, the integrated energy system includes, but is not limited to, a commercial tourism integrated energy system, an industrial integrated energy system, and an electric hydrogen energy refueling station.

[0009] Furthermore, in S1, the integrated energy system includes a seawater desalination unit and a wind and solar hydrogen production unit. The seawater desalination unit is used to address the lack of freshwater resources on the island, and the wind and solar hydrogen production unit is used to generate electricity from renewable energy sources on the island.

[0010] Furthermore, the shared energy storage station includes electrical energy storage equipment and hydrogen energy storage equipment, which stores surplus electricity or hydrogen when wind and solar power output is high, and releases the stored energy when electricity or hydrogen demand increases, in order to support the stable operation of the system. The shared energy storage station responds to changes in the island's energy system and coordinates the energy interaction between multiple energy entities.

[0011] Furthermore, S2 includes the following steps: S21: With the goal of minimizing the total operating cost of the shared energy storage station and all the integrated energy systems, construct the energy cooperation and sharing model to determine the optimal energy interaction plan; the total operating cost includes the independent operating cost of each integrated energy system, the operating cost of the shared energy storage station, and the energy interaction cost between them; S22: Based on the optimal operating cost obtained from the energy cooperation and sharing model, construct the benefit distribution model based on asymmetric Nash bargaining, and distribute the residual benefits generated by energy cooperation based on the energy contribution of each participant as their bargaining power.

[0012] Furthermore, S22 includes the following steps: S221: Calculate the total output energy and total input energy of each of the integrated energy systems and the shared energy storage station in energy cooperation; S222: Construct a nonlinear energy sharing mapping function based on the natural constant e to quantify the contribution of each participant; S223: Construct a benefit allocation optimization problem with the objective of maximizing the weighted product of the savings in operating costs after cooperation relative to the operating costs when not cooperating, where the weights are the contributions of each participant.

[0013] Furthermore, in S3, the adaptive alternating direction multiplier method is used to solve the energy cooperation and sharing model and / or the benefit distribution model in a distributed manner; the adaptive alternating direction multiplier method adaptively adjusts the penalty parameter in the iteration process according to the norm ratio of the original residual and the dual residual.

[0014] Furthermore, the present invention also provides an apparatus for performing the above-described data processing method.

[0015] Furthermore, the present invention also provides an apparatus including a memory, a processor, and an algorithm stored in the memory and executable on the processor, wherein the processor implements the above-described data processing method when executing the computer program.

[0016] Furthermore, the present invention also provides a computer-readable storage medium storing a computer algorithm, which, when executed by a processor, performs the above-described data processing.

[0017] The advantages and positive effects of this invention are as follows: 1. To address the issues of energy utilization efficiency and reduced operating costs of isolated island microgrid communities, this invention constructs an integrated energy system model for isolated islands, incorporating energy sharing among multiple types of IESs and SESSs. Based on this model, a two-stage scheduling optimization of the isolated island energy system is achieved: energy cooperation stage optimization and cooperation benefit stage optimization. Specifically, to ensure the enthusiasm of IES operators and SESS operators in participating in energy cooperation, a benefit allocation strategy based on asymmetric Nash bargaining is proposed. This strategy uses their respective energy interaction inputs / outputs as contribution indicators to rationally allocate the costs and benefits generated during energy cooperation between IES individuals and SESSs.

[0018] 2. This invention is based on convex optimization theory and uses an adaptive parameter-improved ADMM algorithm to solve the established two-stage optimization model, effectively improving computational efficiency. Traditional ADMM algorithm simulations often encounter the problem of iterative convergence being sensitive to the penalty parameter. However, the AADMM algorithm can coordinate the convergence of the original residual and the dual residual through adaptive updates of the penalty parameter, thereby reducing convergence time.

[0019] 3. This invention comprehensively considers the joint optimization of electrical energy storage and hydrogen energy storage to improve the system's cross-temporal and spatial energy dispatch capability. The proposed strategy can improve the operational economy and energy utilization efficiency of isolated energy systems, optimize energy trading models, and provide a new framework for the intelligent dispatch of energy systems in remote ocean islands. Attached Figure Description

[0020] Figure 1 is a framework diagram of an isolated community energy system according to an embodiment of the present invention.

[0021] Figure 2 is a solution framework diagram of the isolated community cooperation model according to an embodiment of the present invention.

[0022] Figure 3 is a comparison chart of the operating costs of various entities in an isolated community under different scenarios according to an embodiment of the present invention.

[0023] Figure 4 is a comparison chart of the revenue distribution of different negotiation methods in the embodiments of the present invention.

[0024] Figure 5 is a graph showing the sensitivity analysis of the base number of the contribution factor in an embodiment of the present invention.

[0025] Figure 6 is a comparison chart of the convergence of the algorithms in the embodiments of the present invention.

[0026] Figure 7 shows the renewable energy output data and various energy load demands of each park in a specific embodiment of the present invention.

[0027] Figure 8 shows the electricity purchase and sale prices and the purchased hydrogen prices of various operators and the upper-level distribution network within the community according to a specific embodiment of the present invention. Detailed Implementation

[0028] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The embodiments of the present invention will be further described below with reference to the accompanying drawings: An operation optimization method for an isolated multi-integrated energy system based on shared electric-hydrogen energy storage includes the following steps: S1: Constructing an isolated electric-hydrogen integrated energy system comprising multiple integrated energy systems and a shared energy storage station. Energy utilization efficiency is improved and system operating costs are reduced through collaborative optimization.

[0030] S2: Establish an energy cooperation and sharing model and a benefit distribution model for the isolated hydroelectric integrated energy system. The energy cooperation and sharing model is based on energy storage during periods of energy redundancy and energy supply during peak loads or periods of energy shortage, thereby balancing energy supply and demand within the isolated system. The benefit distribution model uses the energy interaction input / output of each integrated energy system as a contribution indicator to rationally allocate the costs and benefits generated during energy cooperation.

[0031] S3: Optimization algorithms are employed to solve the energy cooperation and sharing model and the benefit distribution model, yielding the optimal scheduling strategy and benefit distribution scheme for shared energy storage stations and various integrated energy systems. The Adaptive Alternating Direction Multiplier Method (AADMM) is used for distributed solution, mitigating the risk of privacy leaks among participating entities and reducing the computational difficulty caused by high-dimensional variables and complex constraints, thereby improving solution efficiency. The AADMM algorithm can dynamically adjust the penalty parameters based on real-time energy sharing data to accelerate convergence and improve optimization efficiency.

[0032] Specifically, the energy entities provided in this embodiment can be divided into the following categories according to the different functions of the park: Commercial Tourism Integrated Energy System (CTIES): CTIES mainly serves the electricity and water load needs of the resort commercial center and tourism facilities on the island.

[0033] Industrial Integrated Energy System (IIES): Primarily targeting industrial users such as hydrogen-consuming industries and seawater desalination plants on the island, it is responsible for meeting the electricity, hydrogen, and water loads within the industrial park.

[0034] Electric Hydrogen Energy Supply Station (EHESS): An important component of the island's energy system, it primarily provides energy replenishment services for electric vehicles and hydrogen-filled trailers, meeting the station's electric hydrogen energy needs.

[0035] If the island If there is no cooperation with SESS, then The optimization objective is to minimize its own operating cost. Among them, CTIES does not have a hydrogen load, so its cost formula does not include the cost of hydrogen production or purchase; EHESS only supplies the demand for electricity and hydrogen, so its cost formula does not include the cost related to seawater desalination; while IIES provides electricity, hydrogen and water loads, and the total includes the production costs of various heterogeneous energy sources. The operating costs (taking IIES as an example) are as follows: in: In the formula, represent Total operating costs; The incentive cost for load transfer is mainly related to the unit incentive cost. and the actual load Expected load related; For the cost of seawater desalination, The unit energy cost of RO equipment For time period t Total energy consumption for seawater desalination; To reduce the cost of hydrogen production via electrolyzers, The unit cost of hydrogen production The amount of hydrogen produced during time period t; for The interaction costs with the upstream distribution network and the cost of purchasing hydrogen involve the purchase and sale of electricity. , and purchased hydrogen and the purchase and sale price of electricity , HeGou Hydrogen Price .

[0036] also, Internally, it should meet energy supply and demand balance constraints (including electricity balance, hydrogen supply balance, and freshwater supply balance), load transfer constraints, and external energy purchase constraints. In the formula, The hydrogen production power consumption of the PEM equipment during time period t; The renewable energy power generation during time period t; The amount of hydrogen produced by PEM during time period t; The hydrogen load demand during period t; The water load demand for period t; The water consumption for PEM hydrogen production during time period t; The RO equipment's water production rate during time period t; , These are the upper and lower limits of power after load transfer.

[0037] The island IES constructed in this invention mainly involves two key devices: a small-scale seawater desalination device and a wind and solar hydrogen production device.

[0038] The energy consumption models for these two devices are constructed below to guide [further development]. Conduct energy management.

[0039] Small-scale seawater desalination units are widely used to solve the problem of freshwater scarcity on islands. Currently, the mainstream seawater desalination units mainly adopt reverse osmosis (RO) membrane desalination technology.

[0040] The energy consumption model for the permeable water produced by a single RO unit of reverse osmosis seawater desalination is as follows: In formula (12), This refers to the amount of freshwater permeation. RO high-pressure pump power; SEC is specific energy consumption; The osmotic pressure coefficient; R represents the initial concentration of seawater; R is the recovery rate, calculated from the freshwater permeation. and seawater inflow Decide.

[0041] A typical seawater desalination plant consists of r RO units connected in parallel. Therefore, the total energy consumption and freshwater production of the RO system are: In equation (13), For time period t Total energy consumption for seawater desalination; The RO equipment's water production rate during time period t; , These are the upper and lower limits of energy consumption for RO equipment; for Minimum water load for internal users during time period t.

[0042] Hydrogen production via electrolysis is a common practice on renewable energy islands, typically utilizing wind turbines or photovoltaic (PV) equipment. However, wind and solar power output exhibit fluctuations. Proton exchange membrane (PEM) electrolyzers, with their rapid response characteristics, can address these issues. The PEM model is as follows: In formula (14), Hydrogen production capacity; Water required for hydrogen production; Power consumption; The higher heating value of hydrogen is taken as 39.4 kWh / kg; For hydrogen production efficiency; This refers to the water consumption coefficient for hydrogen production; furthermore, and These are the upper and lower limits of the operating power of the electrolytic cell.

[0043] This embodiment establishes a shared energy storage station based on the principles of shared electricity and shared hydrogen. Assuming the original SESS does not interact with any IES (Environmental Energy Storage System), the SESS's operating cost optimization problem requires determining its charging and discharging power and hydrogen charging / discharging amounts over different time periods. Furthermore, it is also necessary to determine the electricity absorbed or fed back from the upstream distribution network and the amount of purchased hydrogen. The cost minimization problem for the independent operation of the SESS can be defined as: where: In the formula, The total cost of SESS; mainly composed of the cost of energy storage. and the cost of hydrogen storage tanks composition. , These are the unit loss costs for electrical energy storage and hydrogen storage tanks, respectively. , These represent the charging and discharging power of the energy storage during time period t; , These represent the purchased power and sold power of the energy storage system during time period t, respectively. The amount of hydrogen purchased during time period t; , These represent the amount of hydrogen charged and released during hydrogen storage period t.

[0044] Energy storage needs to operate within a certain range based on its State of Charge (SOC) value, and its charging and discharging power should have a certain upper limit per unit time period. The mathematical model for energy storage is as follows: In equation (18), This refers to the state index value of electrical energy storage; and These are the initial and final electrical energy state values, respectively. , These are the upper and lower limits of the electrical energy storage state; Configure capacity for electrical energy storage; , For energy storage charging and discharging efficiency; , These are the upper limits of charge and discharge power, respectively. , These represent the electricity purchased and sold by SESS to the upstream distribution network.

[0045] Similarly, the amount of hydrogen added or released into the hydrogen storage tank also needs to meet certain constraints: In equation (19), This refers to the real-time hydrogen storage capacity of SESS. , These are the upper and lower limits for hydrogen storage in hydrogen storage tanks; , These represent the upper limits for the amount of hydrogen charged or released.

[0046] To ensure the feasibility of energy cooperation, the energy exchange power and payment / benefit between IES and SESS must be consistent. represent The amount of electricity used in interaction with SESS, if ,express During time period t, its redundant power is transferred to SESS; conversely, during time period t, its redundant power is transferred to SESS. , then it means Obtain power from SESS. Similarly, represent The amount of hydrogen interacting with SESS during time period t, if ,express The remaining hydrogen is transferred to SESS; conversely, , is Hydrogen supply from SESS will support load demand. Regarding interaction costs, [the following is unclear and likely incomplete: "let"] express Energy sharing costs paid / received express Costs need to be paid to SESS; express Revenue is generated from SESS operators. Furthermore, [the text abruptly ends here]. and These represent the amounts of electricity and hydrogen that SESS is willing to receive from each IES, respectively. or These represent SESS from time t. Receive electricity or hydrogen; if or Then SESS will transfer either electricity or hydrogen to [the appropriate location] during time period t. Similarly, let This represents the energy sharing costs collected / paid by SESS, if... SESS from To gain benefits, if SESS then... Payment Costs. For both parties involved in the collaboration, since the energy storage sharing partnership is established within an isolated community, it is essential to satisfy the energy sharing balance and payment / revenue balance between IES and SESS. This is described by the following formula: The proposed energy cooperation problem P1 for island IES and SESS aims to minimize the cooperative operating cost. This goal includes all energy costs of all IES and SESS, as well as their payment / revenue costs, as detailed below: In energy cooperation, the energy balance constraints of IES and SESS are shown in equations (24)-(28) above. Wherein, if For CTIES, let the equation (24) be... The value is 0, and the IES model does not contain equation (25); if For EHESS, then in equation (24) The value is 0, and there is no equation (26). Furthermore, unlike the independent operation scenarios of individual operators, the purchased hydrogen for the cooperative community is solely the responsibility of SESS. Equation (22) makes... Therefore, payment costs and benefits This does not affect the cost of energy cooperation. This also indicates that in the proposed energy cooperation model, individual interests and collective benefits are not contradictory. Therefore, it can be considered... Payment / revenue Payments / revenues with SESS The solution to this problem, which is a benefit distribution problem in energy cooperation, will be discussed in the next section. The energy cooperation problem will be broken down into a two-stage optimization problem.

[0047] To ensure commensurate benefits for all participating parties and enhance their participation, a benefit distribution model based on asymmetric Nash bargaining is constructed. Finally, the AADMM algorithm is used to solve the model and perform simulation verification.

[0048] Specifically, constructing a benefit-sharing model based on asymmetric Nash bargaining includes the following steps: if there is no energy cooperation within the isolated energy system, IESs and SESS each optimize their operating costs, i.e., interactive power. , , , and interaction costs , These are all zero values ​​or zero vectors. The optimal solution to problem P0 is usually denoted as... Under non-cooperative circumstances The operating costs of SESS are denoted as follows: and Furthermore, it can be assumed that the optimal solution to the energy cooperation problem P1, in which both IES and SEES participate, is... ,and The operating costs of SESS and SESS are respectively and Theoretically, energy cooperation can lead to a reduction in the operating costs of isolated systems, i.e. This is also a fundamental condition for the establishment of cooperative game theory. The following analysis is based on the distribution of benefits under this condition.

[0049] It is noted that the isolated hydrogen energy system composed of IESs and SESS has benefited from minimizing collaborative energy costs. This stage requires a fair allocation model to determine the expenditure / benefit costs of IESs and SESS. Naturally, the Nash bargaining method, a widely used cost allocation or benefit-sharing method in collaborative scenarios, can be used to address this issue. However, the standard Nash bargaining method simply maximizes the product of the benefits of all participants, which often results in all participants receiving the same benefit. This allocation method may affect the cooperative enthusiasm of some participants who contribute significantly. Generally speaking, since different IESs and SESSs contribute differently to the cooperation and are of varying importance, directly using the standard allocation model is unreasonable. Participants with greater contributions should receive more benefits during the bargaining stage. Based on this, we propose a benefit allocation model based on asymmetric bargaining: using the energy contribution of IES and SESS entities as bargaining power to further share the benefits of energy cooperation. It is certain that this is relatively reasonable and sustainable for the participating IES and SESS entities.

[0050] Within the isolated community Energy sharing with SESS can reduce dependence on energy purchases from upstream energy operators. In essence, all participants in the community, whether energy inputs or outputs, can be considered contributors. A key difference is that entities providing energy within the cooperative community are generally considered to have a higher contribution than those receiving the same amount of energy. Therefore, this section uses a nonlinear energy-sharing mapping method to quantify the contributions of cooperative entities, thereby rationally allocating the benefits generated by the cooperation. First, we define energy cooperation in isolated communities... Total output energy Total input energy and the total input energy of SESS and total output energy The specific formula is as follows: Secondly, a nonlinear energy-sharing mapping method is constructed using the natural constant e as the base. The individual contribution of SESS can be quantified as follows: In the formula, the definition is... , This represents the maximum energy output and input of each IES and SESS in the isolated community. Furthermore, as shown in equation (33), the contribution... It is non-negative, meaning that the contribution of each entity is guaranteed to be at least zero.

[0051] Thus, entities participating in energy cooperation within an isolated community can negotiate with each other based on their respective contributions to share the benefits of cost reductions generated during the energy cooperation phase. Similarly, the problem of benefit distribution P2 among cooperative participants with quantifiable contributions can be defined as: (36) The relevant constraints are as follows: Equations (37) and (38) ensure that Both SESS and [other entities] can benefit from energy cooperation and sharing. The decision variables for the above questions include [various variables]. Expenditure / Revenue Expenses / Revenues of SESS However, the inseparable objective function in problem P2 makes it difficult to solve using a distributed approach. Therefore, by taking the logarithm of the objective function, the original problem is equivalently transformed into a similar problem P1 with a decomposable two-part structure: Specifically, the solution steps for the islanded multi-energy system community cooperation model are as follows: For the energy cooperation problem P1, we can define... for The set of decision variable vectors, and the definition for An independent set of constraints. Similarly, we can define... Let SESS be the set of decision variable vectors and the set of independent constraints of SESS. The constraint set {(20)-(21)} is used as... Coupling constraints between and SESS. To avoid By mitigating privacy data leaks from SESS and reducing the computational and communication overhead of centralized optimization, the energy cooperation problem P1 in isolated systems can be solved using a distributed optimization approach.

[0052] Problem P1 can be solved using the ADMM algorithm. The augmented Lagrangian function for problem P1 is: In the formula, Defined as a penalty parameter; Defined as Lagrange multipliers. Problem P1 is solved in a distributed manner, where... SESS needs to update its decision variables locally according to the ADMM program. Assuming k is the current iteration number, the following steps need to be performed in each iteration: Subproblem (41) is solved in parallel by IESs, where each Requires current interaction information from SESS , And cooperative multiplier parameters from IEMS , Sub-problem (42) is to be resolved by the SESS operator, whereby SESS needs to come from... Current interaction information And information from IEMS as well. Finally, subproblems (43) and (44) require interaction information from both IESs and SESS, and are handled by IEMS.

[0053] In solving the benefit distribution problem, it's noted that problem P2 is a concave function and has a two-part structure consisting of a separable objective function and coupled constraints. Therefore, P2 can also be solved in a distributed manner using the ADMM framework. The equivalent augmented Lagrange formula for problem P2 is as follows: In the formula, It is a given penalty parameter. It is a coupling constraint The dual multiplier vectors, and let .

[0054] Based on this reconstruction, as long as the feasible domain of the decision variables is not empty, problem P2 has a unique optimal solution. The specific reason is explained as follows: Since the Hessian matrix of the objective function of P2 can be easily proven to be positive definite, the above problem, together with the linear constraint, is a strictly convex optimization problem. Problem P2 considers feasible solutions if and only if inequalities (37) and (38) hold: make The solution set of expression (45) Obviously It is finite. Therefore, any solution very close to the boundary of the set {(37), (38)} will result in a negative infinite logarithmic objective value, which is less than... Therefore, the optimal solution is at point The surrounding area, rather than near the open boundary. Therefore, the above problem has a unique optimal solution. .

[0055] The steps for solving problem P2 using the ADMM algorithm are as follows: The improved ADMM algorithm framework demonstrates the convergence of the ADMM algorithm, which has been validated in numerous previous studies. Problems P1 and P2 constructed in this invention can both be solved using the ADMM algorithm framework. However, traditional ADMM algorithm simulations often encounter the problem of iterative convergence being sensitive to the penalty parameter (e.g., in P1). and Although ADMM can converge to any fixed value of the penalty parameter, choosing a larger or smaller penalty parameter may result in requiring more iterations to reach convergence. The following example, using problem P2, illustrates how to adjust the penalty parameter during simulation. The value of is used to reduce the convergence time. We introduce Adaptive update scheme: In the formula, k is the number of iterations, and the original residual is... Dual residual ,also, It is a given constant. Since choosing a small value for the fixed penalty parameter will lead to slow convergence of the multipliers, while choosing a large value for the penalty parameter will lead to slow convergence of the decision variables, the main idea of ​​this scheme is to adaptively adjust the penalty parameter to coordinate the convergence of the original residual and the dual residual.

[0056] Taking problem P1 as an example, the adaptive parameter improvement algorithm (AADMM) of the ADMM algorithm is given, and its specific process is as follows: The following specific embodiment illustrates the invention: Taking an island in Guangdong as the simulation object, it is assumed that there are three different energy entities on the island: CTIES, IIES, and EHESS. A SESS is also provided. The renewable energy output data and various energy load demands of each park are shown in Figure 7. The scheduling cycle is 1 day. Furthermore, the maximum transfer load of each park is set at 20%, meaning the actual load fluctuation can be between 80% and 120%. The electricity purchase and sale prices between operators and the upstream distribution network, as well as the purchased hydrogen price, are shown in Figure 8. The SESS, referencing a power station and hydrogen refueling station in Foshan, has a total energy storage capacity of 2000 kWh, a maximum chargeable / dischargeable power of 500 kW, a charge / discharge efficiency of 0.95, and a rated SOC range of [10%, 90%]; the rated hydrogen storage capacity is 200 kg, the maximum chargeable / dischargeable hydrogen amount is 50 kg, and the rated hydrogen storage range is [10 kg, 200 kg]. The equipment parameters and cost coefficients within the IES park are shown in Table 2, and the equipment parameters of the SESS are shown in Table 3.

[0057] Table 2 Parameters of the Isolated Integrated Energy Subsystem Table 3 Parameter Settings for Shared Energy Storage Stations Taking the isolated island energy system framework diagram shown in Figure 1 as an example, consider an isolated island energy system containing n IES parks and one SESS. The scheduling cycle T contains t time slots, each lasting one hour. The IES can be divided into the following categories based on their park functions: Commercial Tourism Integrated Energy System (CTIES): CTIES primarily serves the electricity and water load needs of the island's resort commercial centers and tourism facilities. Industrial Integrated Energy System (IIES): Primarily serves industrial users such as hydrogen-consuming industries and desalination plants on the island, responsible for meeting the electricity, hydrogen, and water loads within the park. Electric Hydrogen Energy Supply Station (EHESS): An important component of the island energy system, primarily providing energy supply services for electric vehicles and hydrogen-filled trailers, meeting the station's electric hydrogen energy needs. A common feature is that each IES is equipped with clean energy power generation equipment, fully utilizing the island's abundant wind and solar resources for "self-consumption of electricity and grid connection of surplus electricity."

[0058] SESS is a diversified energy storage and replenishment station integrating electrical and hydrogen energy storage. Through resource aggregation and coordinated scheduling, it provides flexible and efficient energy sharing services to multiple users. Assuming a small distribution network exists on the island, and both IES and SESS are connected to it, they can both absorb electricity from the distribution network and supply excess electricity back to it. Furthermore, considering the underdeveloped hydrogen market on the island, IES and SESS can only purchase hydrogen from upstream hydrogen suppliers and cannot profit from hydrogen sales. Distributed and intelligent controllers are embedded in the information interaction devices of each energy subsystem and managed by IEMS, enabling IES and SESS operators to control their energy supply and demand, perform local two-way communication, and execute optimization algorithms.

[0059] The solution framework for the isolated community cooperation model, as shown in Figure 2, is as follows: The solution process for the isolated multi-energy system community cooperation model consists of two main stages. First, in the non-cooperative scenario, each IES and SESS independently optimizes its operating results, using minimizing its own operating cost as the benchmark (Problem P0). Then, in the cooperative scenario, the model is divided into two stages: the first stage is the energy cooperation stage (Problem P1), where all IES and SESS cooperate by sharing energy resources (such as electricity and hydrogen) to minimize the overall energy cost and coordinate energy allocation among energy entities; the second stage is the benefit distribution stage (Problem P2), which uses an asymmetric Nash bargaining model to distribute benefits reasonably based on the benefits generated by energy cooperation, ensuring that each party receives corresponding returns according to its contribution, and that the cooperation has incentive compatibility. The entire solution process uses a combination of CPLEX and AADMM algorithms to transform the energy cooperation problem into an optimization problem. The hierarchical solution method effectively reduces computation time, ensuring the real-time nature and accuracy of the optimization results, thus ensuring the optimality and sustainability of cooperation and benefit distribution.

[0060] Figure 3 illustrates the economic analysis of energy cooperation in isolated communities during the energy cooperation phase. Optimization solutions are performed for scenarios 1, 2, and 3 of the energy cooperation phase to verify the effectiveness of the multi-stakeholder cooperation strategy for isolated communities proposed in this chapter in improving the economic efficiency of isolated energy system operation. Positive costs are considered expenditures, and negative costs are considered revenues.

[0061] ( Note: Scene 1: SESS only engages in electricity trading or hydrogen purchase transactions with the upstream distribution network in the retail market, and does not conduct energy interaction within the isolated community; Scenario 2: Within the isolated community Energy cooperation can be carried out between them, and electricity can be purchased and sold from the upstream distribution network or hydrogen can be purchased from hydrogen energy operators, but SESS will not participate in the cooperation; Scenario 3: Consider Community energy cooperation with SESS involves internal energy sharing and trading between IES and SESS, using standard Nash bargaining methods for pricing. Additionally, both parties can engage in electricity purchase and sale transactions with the upstream distribution network, and SESS can purchase hydrogen from hydrogen operators. When considering energy interaction and cooperation among individual IES entities within the isolated community, the economic benefits are increased by 6344.69 yuan compared to a system where each entity operates independently; further considering the addition of shared energy storage stations... The economic benefits of community energy cooperation with SESS increased by RMB 13,322.28 compared to the independent operation scenario (Scenario 1). Analysis shows that energy cooperation enables active internal energy interaction between IES individuals and SESS operators within the isolated community, effectively promoting local energy utilization efficiency and reducing the cost of purchasing energy from the upstream energy market. Specifically, compared to Scenario 1, the operating costs of CTIES operators, IIES operators, and EHESS operators in Scenario 3 decreased by 38.78%, 26.0%, and 44.54%, respectively. Furthermore, compared to Scenario 1 and 2, the revenue of SESS operators increased by RMB 3,420.57 in Scenario 3, indicating that SESS operators participating in energy cooperation can flexibly choose trading partners that can increase their revenue at different times, rather than just engaging in single transactions with upstream energy operators.

[0062] Figure 4 compares the revenue distribution of different negotiation methods in the revenue distribution stage: it compares the contribution factor, interaction cost, and revenue increase of the traditional negotiation method and the asymmetric negotiation method based on contribution indicators. In Scenario 3 (standard negotiation mode), the energy contribution of each cooperating entity is considered consistent, and the difference in profit distribution between the entities is relatively small compared to Scenario 4 (asymmetric negotiation mode), achieving equal distribution. However, it does not consider the differences in contribution among entities, which may lead to a decrease in the participation enthusiasm of entities with larger contributions. In Scenario 4, because EHESS and IIES have higher contribution factors, meaning they output more energy in energy interactions than other entities, their interaction costs increase by RMB 178.1 and RMB 198.05 respectively compared to Scenario 3. CTIES and SESS, due to their lower contribution factors, need to reduce their share of the revenue from cooperation. SESS's contribution factor is mainly related to its charging and discharging power and the amount of hydrogen charged and discharged; in Scenario 4, SESS's interaction cost is reduced by 6.37% compared to Scenario 3. Therefore, the proposed benefit-sharing model is more reasonable for energy cooperation entities in isolated communities.

[0063] ( Note: Scenario 3: Consider Scenario 4: Building upon the isolated community energy cooperation model in Scenario 3, an asymmetric Nash bargaining method based on contribution index is used to allocate the energy cooperation benefits between IES and SESS. As shown in Figure 5, the contribution factor base sensitivity analysis diagram is provided. In the cooperation benefit allocation model, we use a nonlinear contribution mapping method based on an exponential function to quantify the contribution of each entity. Theoretically, an exponential function with a larger base tends to allocate more contribution benefits to entities that output energy in energy interaction, while entities that receive energy in energy interaction receive less contribution benefits. To quantify the impact of the base value, this paper uses different bases (including 2, e, and 4) for simulation comparison. In isolated communities, the contribution factors of each entity increase with the base number. However, different base numbers lead to significant differences in the interaction costs of the entities. For example, in SESS interaction costs, the interaction cost benefit when the base number is 2 is 24.83% lower than when the base number is e, while the interaction cost benefit when the base number is 4 is 28.12% higher than when the base number is e. Furthermore, entities with larger contribution factors have greater cooperation benefits, and their interaction costs are lower than other entities regardless of the base number, consistent with theoretical analysis. Therefore, in actual cooperation, IEMS needs to reasonably consider the demands of all parties and adjust the base number to quantify the energy contribution of cooperative entities in isolated communities.

[0064] Figure 6 shows a comparison of algorithm convergence: this part of the simulation uses the improved AADMM algorithm for optimization and compares it with the standard ADMM algorithm to verify the superiority of the improved method in terms of convergence speed and computational efficiency. The simulation parameters of the two algorithms are consistent, among which... Penalty parameters , , At the convergence threshold In both the energy cooperation phase and the profit distribution phase, the AADMM algorithm demonstrates faster convergence speeds for both the original and dual residuals compared to the standard approach. Furthermore, the AADMM algorithm achieves convergence times of 16 and 8 iterations in each phase, respectively, representing reductions of 64 and 23 iterations compared to the standard algorithm. This is because AADMM dynamically adjusts the penalty parameter based on changes in the dual residual during iteration, resulting in more coordinated convergence speeds for the original and dual residuals and thus improving overall convergence efficiency. In contrast, the convergence speed of the standard ADMM algorithm is influenced by a fixed penalty parameter, and improper selection can lead to slow convergence. This verifies the effectiveness and convergence of the AADMM algorithm.

[0065] The advantages and positive effects of this invention are as follows: 1. To address the issues of energy utilization efficiency and reduced operating costs of isolated island microgrid communities, this invention constructs an integrated energy system model for isolated islands, incorporating energy sharing among multiple types of IESs and SESSs. Based on this model, a two-stage scheduling optimization of the isolated island energy system is achieved: energy cooperation stage optimization and cooperation benefit stage optimization. Specifically, to ensure the enthusiasm of IES operators and SESS operators in participating in energy cooperation, a benefit allocation strategy based on asymmetric Nash bargaining is proposed. This strategy uses their respective energy interaction inputs / outputs as contribution indicators to rationally allocate the costs and benefits generated during energy cooperation between IES individuals and SESSs.

[0066] 2. This invention is based on convex optimization theory and uses an adaptive parameter-improved ADMM algorithm to solve the established two-stage optimization model, effectively improving computational efficiency. Traditional ADMM algorithm simulations often encounter the problem of iterative convergence being sensitive to the penalty parameter. However, the AADMM algorithm can coordinate the convergence of the original residual and the dual residual through adaptive updates of the penalty parameter, thereby reducing convergence time.

[0067] 3. This invention comprehensively considers the joint optimization of electrical energy storage and hydrogen energy storage to improve the system's cross-temporal and spatial energy dispatch capability. The proposed strategy can improve the operational economy and energy utilization efficiency of isolated energy systems, optimize energy trading models, and provide a new framework for the intelligent dispatch of energy systems in remote ocean islands.

[0068] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An optimization method for the operation of isolated multi-energy systems based on shared electricity, hydrogen, and energy storage, characterized by: The process includes the following steps: S1: Constructing an isolated electric-hydrogen integrated energy system comprising multiple integrated energy systems and a shared energy storage station; S2: Establishing an energy cooperation and sharing model and a benefit distribution model for the isolated electric-hydrogen integrated energy system; S3: Solving the energy cooperation and sharing model and the benefit distribution model using an optimization algorithm to obtain the optimal scheduling strategy and benefit distribution scheme for the shared energy storage station and each of the integrated energy systems.

2. The method for optimizing the operation of an isolated multi-energy system based on shared electricity and hydrogen storage as described in claim 1, characterized in that: In S1, the integrated energy system includes, but is not limited to, a commercial tourism integrated energy system, an industrial integrated energy system, and an electric hydrogen energy refueling station.

3. The method for optimizing the operation of an isolated multi-energy system based on shared hydrogen-electric energy storage as described in claim 1 or 2, characterized in that: In S1, the integrated energy system includes a seawater desalination unit and a wind and solar hydrogen production unit. The seawater desalination unit is used to address the lack of freshwater resources on the island, and the wind and solar hydrogen production unit is used to generate electricity from renewable energy sources on the island.

4. The method for optimizing the operation of an isolated multi-energy system based on shared hydrogen-electric energy storage as described in claim 1 or 2, characterized in that: The shared energy storage station includes electrical energy storage equipment and hydrogen energy storage equipment. It stores surplus electricity or hydrogen when wind and solar power output is high, and releases the stored energy when the demand for electricity or hydrogen increases to support the stable operation of the system. The shared energy storage station responds to changes in the island's energy system and coordinates the energy interaction between multiple energy entities.

5. The method for optimizing the operation of an isolated multi-energy system based on shared hydrogen-electric energy storage as described in claim 1 or 2, characterized in that: S2 includes the following steps: S21: With the goal of minimizing the total operating cost of the shared energy storage station and all the integrated energy systems, construct the energy cooperation and sharing model to determine the optimal energy interaction plan; the total operating cost includes the independent operating cost of each integrated energy system, the operating cost of the shared energy storage station, and the energy interaction cost between them; S22: Based on the optimal operating cost obtained from the energy cooperation and sharing model, construct the benefit distribution model based on asymmetric Nash bargaining, and distribute the residual benefits generated by energy cooperation based on the energy contribution of each participant as their bargaining power.

6. The method for optimizing the operation of an isolated multi-energy system based on shared electricity and hydrogen storage as described in claim 5, characterized in that: S22 includes the following steps: S221: Calculate the total output energy and total input energy of each integrated energy system and the shared energy storage station in energy cooperation; S222: Construct a nonlinear energy sharing mapping function based on the natural constant e to quantify the contribution of each participant; S223: Construct a benefit allocation optimization problem with the objective of maximizing the weighted product of the savings in operating costs after cooperation relative to the operating costs when not cooperating, where the weights are the contributions of each participant.

7. The method for optimizing the operation of an isolated multi-energy system based on shared hydrogen-electric energy storage as described in claim 1 or 2, characterized in that: In S3, the adaptive alternating direction multiplier method is used to solve the energy cooperation and sharing model and / or the benefit distribution model in a distributed manner; the adaptive alternating direction multiplier method adaptively adjusts the penalty parameter in the iteration process according to the norm ratio of the original residual and the dual residual.

8. An apparatus, characterized in that: Run the data processing method as described in any one of claims 1 to 7.

9. An apparatus comprising a memory, a processor, and an algorithm stored in the memory and executable on the processor, characterized in that: When the processor executes a computer program, it implements the data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer algorithm, characterized in that, When the computer algorithm is executed by the processor, it performs the data processing as described in any one of claims 1 to 7.

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