An electricity-hydrogen combined storage planning method and system

Abstract

The present application relates to the technical field of electric hydrogen storage system, and particularly relates to an electric hydrogen storage planning method and system. The method comprises the following steps: constructing a battery energy storage model about battery state of charge and a hydrogen energy storage model about electrolytic cell operation power, hydrogen remaining amount of hydrogen storage tank and fuel cell operation power; calculating LCOE and SSR in a given time, and establishing a target optimization model of the electric hydrogen storage system with LCOE and SSR as the objective function; and determining the optimal capacity configuration of the electric hydrogen storage system by using a multi-objective particle swarm optimization algorithm. The above method optimizes the capacity configuration and operation of the electric hydrogen storage system by using the multi-objective particle swarm optimization algorithm under the consideration of the degradation and replacement costs of system equipment, introduces an electrochemical model to improve the model fidelity, realizes the reasonable configuration of the capacity of the electric hydrogen storage system, and improves the optimization effect.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a planning method and system for combined electricity and hydrogen storage. Background Technology

[0002] With the continuous development and improvement of energy storage technology, the combined use of different types of energy storage systems has gradually become a research hotspot. Hybrid battery energy storage systems and battery-hydrogen hybrid energy storage systems, for example, are gaining increasing attention due to their ability to effectively address the intermittency and volatility of renewable energy output. To better leverage the advantages of battery-hydrogen energy storage systems in future new energy power generation and improve the system's energy storage economics, it is necessary to plan the configuration of batteries and hydrogen energy storage within the system and optimize its operation based on seasonal changes and electricity price fluctuations during subsequent operation.

[0003] Currently, the construction cost of existing electro-hydrogen energy storage systems is high, especially in long-term, large-scale applications. The cost of building such systems is extremely high, and standalone hydrogen energy storage and battery storage are not economically viable for long-term energy storage. Furthermore, energy storage planning suffers from complex models and high solution difficulty, while energy storage device modeling is simple, resulting in low model fidelity and minimal consideration of the impact of equipment degradation and replacement costs on energy storage revenue. In capacity configuration optimization, traditional strategy gradient methods are easily affected by step size, leading to long optimization times, limited updates to new strategies, and susceptibility to planning outdated strategies. Moreover, current capacity configuration optimization strategies are weakly integrated with intelligent algorithms, making it difficult to address the strong nonlinearity of electro-hydrogen energy storage systems. Summary of the Invention

[0004] To address at least one deficiency in the existing electro-hydrogen combined storage system, an embodiment of the present invention provides an electro-hydrogen combined storage planning method, comprising the following steps:

[0005] Construct a battery energy storage model based on the battery's state of charge;

[0006] Based on the hydrogen energy storage system, the operating power P of the electrolyzer is constructed respectively. EL (t), fuel cell operating power P FC Hydrogen energy storage model for hydrogen storage tank (t) and the remaining hydrogen amount LOH(t);

[0007] Based on the system's residual value C within a given time period SAL,tot System capital expenditure C CAPEX,tot Operating expenses C OM,tot and reset expenditure C REP,tot Calculate the total net present value cost C of the system. NPC,tor ;

[0008] Based on the system's total net present value cost C NPC,tot Electricity purchase cost Cele Total power generation E tot,j Electricity purchase E ele Calculate LCOE for a given time period; calculate SSR based on grid power and load power;

[0009] A target optimization model for the combined electric-hydrogen energy storage system is established by combining battery energy storage model and hydrogen energy storage model with minimizing LCOE and maximizing SSR as objective functions. The Pareto solution set of the target optimization model is calculated using the multi-objective particle swarm optimization algorithm, and the optimal capacity configuration of the combined electric-hydrogen energy storage system is determined based on the Pareto solution set.

[0010] In one embodiment, the battery energy storage model regarding the battery's state of charge is constructed as follows:

[0011] P BT,ch (t)=max(P pv (t)-P load (t),0)

[0012] P BT,dc (t)=max(P load (t)-P pv (t),0)

[0013]

[0014] In the formula, P BT,ch (t), P BT,dc (t), P pv (t), P load (t) represents the battery system charging power, battery system discharging power, photovoltaic system power generation, and system load power at time t, respectively; σ BT η is the self-discharge coefficient of the battery, Δt is the simulation time step of 1 hour; BT,ch η BT,dc η BT,conv Cap BT These are battery charging efficiency, battery discharging efficiency, battery inverter efficiency, and battery rated capacity, respectively.

[0015] In one embodiment, a hydrogen energy storage model is constructed based on the hydrogen energy storage system, with respect to the electrolyzer operating power, the remaining hydrogen in the hydrogen storage tank, and the fuel cell operating power, respectively:

[0016] P EL (t)=min(max(P pv (t)-P load (t),0),P EL,ref )

[0017] P FC (t)=min(max(P load(t)-P pv (t),0),P FC,ref )

[0018]

[0019] In the formula, P EL (t), P FC (t) represents the operating power of the electrolyzer and fuel cell at time t, respectively; P EL,ref P FC,ref These are the rated power of the electrolyzer and the fuel cell, respectively; η EL η FC The efficiencies of the electrolyzer and fuel cell, respectively; Cap H2 This refers to the capacity of the hydrogen storage tank.

[0020] In one embodiment, constructing a battery energy storage model regarding the battery's state of charge further includes modeling the power efficiency of the electrolyzer and fuel cell, wherein the power efficiency model is as follows:

[0021]

[0022] η ele =η f ·η V

[0023]

[0024] In the formula, V th The thermal equilibrium potential is taken as V under standard conditions. th =1.48V; η V For voltage efficiency, η ele η represents the efficiency of the electrolytic cell. f denoted as Faraday efficiency; i represents the current density of the electrolyzer; and f1 and f2 are the fitting parameters for the efficiency of the electrolyzer.

[0025] In one embodiment, based on the system's residual value C over a given time period SAL,tot System capital expenditure C CAPEX,tot Operating expenses C OM,tot and reset expenditure C REP,tot Calculate the total net present value cost C of the system. NPC,tot The formula is:

[0026] C NPC,tot =C CAPEX,tot +C OM,tot +C REP,tot -C SAL,tot

[0027] Among them, the system's residual value C SAL,tot The formula is:

[0028]

[0029] In the formula, L rem L represents the remaining lifespan of the system equipment, and L represents the total lifespan of the system equipment.

[0030] Operating expenses C OM,tot The formula is:

[0031] C OM,tot =C OM_var,tot +C OM_fix,tot

[0032]

[0033] In the formula, C OM_var,tot C OM_fix,tot These are the system's variable operating costs and the system's fixed operating costs, respectively. OM_var,PV C OM_var,BT C OM_var,EL C OM_var,FC C OM_var,HT These are the variable operating costs of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively; C OM_fix,PV C OM_fix,BT C OM_fix,EL C OM_fix,FC C OM_fix,HT These are the fixed operating costs of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively; L PR Let j represent the duration of the project lifecycle, j be the time period, and d be the effective discount rate.

[0034] Reset expenditure C REP,tot The formula is:

[0035]

[0036] In the formula, C REP,BT,j C REP,EL,j C REP,EL,j These represent the replacement costs of the battery, the electrolyzer, and the fuel cell, respectively, within the j-th time period.

[0037] In one embodiment, based on the total net present value cost C of the system NPC,tot Electricity purchase cost C ele Total power generation E tot,j Electricity purchase E ele The formula for calculating LCOE over a given time period is:

[0038]

[0039] In the formula, the total power generation E tot,jE represents the total power generation provided by the system in the j-th time period, expressed in kWh. ele Let represent the electricity purchased by the system from the grid during the j-th time period, expressed in kWh.

[0040] In one embodiment, the formula for calculating the SSR index based on grid power and load power is as follows:

[0041]

[0042] In the formula, N represents the number of hours in a year, which is taken as 8760. Let t be the power of the power grid in hour t. Let t be the load power in hour t.

[0043] In one embodiment, the method further includes the steps of: setting a system operation strategy and optimizing the operation strategy based on a near-end strategy algorithm to obtain an optimal operation strategy; the system operation strategy is to store the surplus power generated by the generator set in a battery energy storage system or a hydrogen energy storage system, and when the power generation of the generator set is insufficient, the power grid supplies power to the load.

[0044] In one embodiment, optimizing the running strategy based on the near-end policy algorithm to obtain the optimal running strategy includes the following steps:

[0045] Construct a Markov decision process model, which is expressed as follows:

[0046] S t =(P laod (t),P pv (t),maxCH BT (t),maxDCH BT (t),maxCH HT (t),

[0047] maxDCH HT (t),SOC BT (t),LOH HT (t))

[0048] In the formula, P laod (t), P pv (t), maxCH BT (t), maxDCH BT (t), maxCH HT (t), maxDCH HT (t), SOC BT (t), LOH HT(t) represents the load power at time t, the power of the photovoltaic power generation system, the maximum discharge power of the battery, the maximum charging power of the battery, the maximum input power of the electrolyzer, the maximum output power of the fuel cell, the state of charge of the battery, and the remaining amount of hydrogen in the hydrogen storage tank.

[0049] Define decision model a at time t. t and reward function r t (s t ,a t Define the decision model a at time t. t for:

[0050]

[0051] In the formula, P BT (t), P HT (t) represents the power of the battery energy storage system and the hydrogen energy storage system at time t, respectively;

[0052] Define reward function r t (s t ,a t )for:

[0053] r t (s t ,a t ) = (energy load (t)·price gird (t))

[0054] In the formula, energy load (t), price gird (t) represents the electricity required by the load at time t and the current electricity price on the grid, respectively;

[0055] Based on the aforementioned Markov decision process model and decision model a t and reward function r t (s t ,a t The optimal running strategy is determined using the near-end strategy algorithm.

[0056] Another embodiment of the present invention provides an electro-hydrogen combined storage system, including an electro-hydrogen combined storage planning method as described in any of the above embodiments.

[0057] Based on the above, compared with the prior art, the electro-hydrogen combined storage planning method provided by the embodiments of the present invention utilizes a multi-objective particle swarm optimization algorithm to optimize the capacity configuration and operation of the electro-hydrogen combined storage system while fully considering the costs of system equipment degradation and replacement. Furthermore, it introduces an electrochemical model to improve model fidelity, thereby effectively solving the strong nonlinearity problem of the electro-hydrogen combined storage system, achieving a reasonable configuration of the capacity of the electro-hydrogen combined storage system, and improving the optimization effect.

[0058] Other features and beneficial effects of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other beneficial effects of the invention can be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Unless otherwise specified, the positional relationships shown in the drawings in the following description are based on the direction in which the components are drawn in the figure.

[0060] Figure 1 A flowchart of a planning method for combined heat and power (CHP) energy storage is provided for one embodiment of the present invention;

[0061] Figure 2 A flowchart of a planning method for combined heat and power (CHP) energy storage is provided for another embodiment of the present invention;

[0062] Figure 3 This is a schematic diagram of a hybrid energy storage microgrid system.

[0063] Figure 4 Pareto frontiers for different storage methods in battery energy storage systems and combined battery-hydrogen energy storage systems under current cost and seasonal load scenarios;

[0064] Figure 5 This is the system operation result of the PPO algorithm optimization of the battery energy storage system under seasonal load in one embodiment;

[0065] Figure 6 The system operation results of the PPO algorithm optimization for the combined electric and hydrogen storage system under seasonal load in another embodiment are shown. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The technical features designed in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0067] In the description of this invention, it should be noted that all terms used in this invention (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and should not be construed as limiting the invention; it should be further understood that the terms used in this invention should be understood to have the same meaning as those in the context of this specification and in the relevant field, and should not be understood in an idealized or overly formal sense, except as expressly defined in this invention.

[0068] Hydrogen-electric energy storage systems store electrical energy using both battery and hydrogen storage systems. Currently, existing systems suffer from complex and difficult-to-solve models in battery and hydrogen storage configuration planning, while the storage devices themselves are relatively simple to model, and operational optimization relies on traditional policy gradient methods. Therefore, existing hydrogen-electric energy storage systems exhibit at least the following problems:

[0069] 1. Due to current technological limitations, the construction cost of hydrogen energy storage systems is high, especially in long-term and large-scale applications. The construction cost of battery energy storage systems is extremely high, and existing solutions that provide standalone hydrogen energy storage and battery energy storage are not economically viable in the field of long-term energy storage.

[0070] 2. Regarding the capacity configuration and cost of energy storage systems, the models used do not involve electrochemical dynamic models, resulting in low model fidelity. The operating costs and equipment replacement costs of energy storage systems account for a large proportion of the total cost and cannot be ignored. Existing studies rarely consider the costs of equipment degradation and replacement.

[0071] 3. In terms of operational optimization, most studies involve traditional algorithms that cannot solve the strong nonlinearity problem of combined heat and power (CHP) storage systems.

[0072] 4. Most studies only focus on the system planning or operation optimization of energy storage systems, but the cost of energy storage systems is greatly affected by system planning and operation optimization.

[0073] To address at least one of the aforementioned problems, this invention provides a planning method and system for combined electric and hydrogen storage. The upper layer of the planning method uses a multi-objective particle swarm optimization (MOPSO) algorithm to generate a large number of economically feasible system capacity configuration schemes, while the lower layer uses a proximal policy optimization (PPO) algorithm to optimize different system configurations to the lowest cost.

[0074] To increase operating speed, capacity configuration optimization and operating strategy optimization are conducted in two separate phases to avoid mutual interference between them. In the capacity configuration optimization phase, a fixed operating strategy can be adopted, prioritizing lithium batteries in both charging and discharging, to quickly determine the feasible optimal capacity configuration for the system. In the operating strategy optimization phase, more complex operating strategies can be used to accurately determine the optimal solution for the system. Separating the solutions for capacity configuration and operating strategy improves the efficiency of the problem-solving process. In the capacity configuration optimization phase, a simple operating strategy is used to quickly determine the optimal capacity configuration for the system. Using the known optimal capacity configuration from the operating strategy optimization phase, the optimal operating strategy for the system can be accurately determined, thus avoiding the challenge of using complex operating strategies when determining capacity configuration.

[0075] For multi-objective optimization problems, the MOPSO algorithm is applied to find Pareto optimal solutions, such as maximizing energy self-sufficiency rate (SSR) and minimizing levelized cost of energy (LCOE). To accelerate the upper-level solution process of MOPSO, a fixed optimization strategy is adopted, prioritizing lithium batteries for both charging and discharging. For 8760 hours per year, the fitness function calculates the system's operating cost while considering equipment lifespan degradation, following the input of the multi-objective particle swarm optimization strategy flowchart. The annual operating cost and electricity purchased from the grid are then used to calculate the LCOE and SSR for the preset lifespan.

[0076] Regarding the system operation optimization, this invention develops a microgrid simulation environment for energy storage systems based on Open-AI-gym, and applies the near-end strategy optimization (PPO) algorithm to optimize the minimum operating cost of the system under different scenarios.

[0077] The following is a detailed description of the planning method for combined electric and hydrogen storage provided in the embodiments of the present invention, in conjunction with specific schemes.

[0078] Please see Figure 1 An embodiment of the present invention provides a method for planning combined heat and power (CHP) energy storage, the method comprising at least the following steps:

[0079] Construct a battery energy storage model based on the battery's state of charge;

[0080] Based on the hydrogen energy storage system, hydrogen energy storage models were constructed for the operating power of the electrolyzer, the remaining hydrogen in the hydrogen storage tank, and the operating power of the fuel cell.

[0081] Based on the system's residual value C within a given time period SAL,tot System capital expenditure C CAPEX,tot Operating expenses C OM,tot and reset expenditure C REP,totCalculate the total net present value cost C of the system. NPC,tot ;

[0082] Based on the system's total net present value cost C NPC,tot Electricity purchase cost C ele Total power generation E tot,j Electricity purchase E ele Calculate LCOE for a given time period; calculate SSR based on grid power and load power;

[0083] A target optimization model for the combined electric-hydrogen energy storage system is established by combining battery energy storage model and hydrogen energy storage model with minimizing LCOE and maximizing SSR as objective functions. The Pareto solution set of the target optimization model is calculated using the multi-objective particle swarm optimization algorithm, and the optimal capacity configuration of the combined electric-hydrogen energy storage system is determined based on the Pareto solution set.

[0084] In this specific implementation, a lithium battery is preferred as the energy storage component. The battery energy storage model regarding the battery's state of charge is constructed as follows:

[0085] P BT,ch (t)=max(P pv (t)-P load (t),0)

[0086] P BT,dc (t)=max(P load (t)-P pv (t),0)

[0087]

[0088] In the formula, P BT,ch (t), P BT,dc (t), P pv (t), P load (t) represents the battery system charging power, battery system discharging power, photovoltaic system power generation, and system load power at time t, respectively; σ BT η is the self-discharge coefficient of the battery, Δt is the simulation time step of 1 hour; BT,ch η BT,dc η BT,conv Cap BT These are battery charging efficiency, battery discharging efficiency, battery inverter efficiency, and battery rated capacity, respectively.

[0089] Since the hydrogen energy storage system consists of an independent alkaline electrolyzer, a hydrogen storage tank, and a fuel cell, the alkaline electrolyzer, hydrogen storage tank, and fuel cell are modeled separately to obtain the hydrogen energy storage model as follows:

[0090] P EL (t)=min(max(P pv(t)-P load (t),0),P EL,ref )

[0091] P FC (t)=min(max(P load (t)-P pv (t),0),P FC,ref )

[0092]

[0093] In the formula, P EL (t), P FC (t) represents the operating power of the electrolyzer and fuel cell at time t, respectively; P EL,ref P FC,ref These are the rated power of the electrolyzer and the fuel cell, respectively; η EL η FC The efficiencies of the electrolyzer and fuel cell, respectively; Cap H2 This refers to the capacity of the hydrogen storage tank.

[0094] To adapt to fluctuations in new energy sources, the electrolyzer and fuel cell need to be dynamically adjusted. Therefore, the hydrogen energy storage model also includes modeling the power efficiency of the electrolyzer and fuel cell. The power efficiency model is as follows:

[0095]

[0096] η ele =η f ·η V

[0097]

[0098] In the formula, V th The thermal equilibrium potential is taken as V under standard conditions. th =1.48V; η V For voltage efficiency, η ele η represents the efficiency of the electrolytic cell. f denoted as Faraday efficiency; i represents the current density of the electrolyzer; and f1 and f2 are the fitting parameters for the efficiency of the electrolyzer.

[0099] For the fuel cell section, the SSR index refers to the proportion of renewable energy in the system's final energy consumption, and its calculation formula is as follows:

[0100]

[0101] In the formula, N represents the number of hours in a year, which is taken as 8760; Let t be the power of the power grid in hour t. Let t be the load power in hour t.

[0102] In a preferred embodiment, based on the system's residual value C over a given time period SAL,tot System capital expenditure C CAPEX,tot Operating expenses C OM,tot and reset expenditure C REP,tot Calculate the total net present value cost C of the system. NPC,tot The formula can be:

[0103] C NPC,tot =C CAPEX,tot +C OM,tot +C REP,tot -C SAL,tot

[0104] Among them, system capital expenditure C CAPEX,tot This represents the initial construction cost of the combined electric and hydrogen storage system, specifically calculated based on the actual cost of purchasing the combined electric and hydrogen storage equipment.

[0105] Operating expenses C OM,tot The formula is:

[0106] C OM,tot =C OM_var,tot +C OM_fix,tot

[0107]

[0108] C OM_fix,i =C CAPEX,i ·r O&M,i

[0109] In the formula, C OM_var,tot C OM_fix,tot These are the system's variable operating costs and the system's fixed operating costs, respectively. OM_var,PV C OM_var,BT C OM_var,EL C OM_var,FC C OM_var,HT These are the variable operating costs of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively; C OM_fix,PV C OM_fix,BT C OM_fix,EL C OM_fix,FC C OM_fix,HT These are the fixed operating costs of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively; where C OM_fix,PV C OM_fix,BT C OM_fix,EL C OM_fix,fC C OM_fix,HT It can be uniformly denoted as C OM_fix,i Then C in the formula OM_fix,i Let i represent the fixed operating costs of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively. CCAPEX,i Let i represent the initial construction cost of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively; r O&M,i Let i represent the operating cost coefficient for photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively; L PR Let j represent the duration of the project lifecycle, j be time, and d be the effective discount rate; where d can be obtained using the formula:

[0110]

[0111] In the formula, d n Here, is the nominal discount rate, and ir is the inflation rate.

[0112] System residual value C SAL,tot The formula is:

[0113]

[0114] C SAL,tot =C SAL ·r rep,i

[0115] In the formula, L rem Let L be the remaining lifespan of the system equipment, and r be the total lifespan of the system equipment. rep,i Replacement cost coefficient;

[0116] Reset expenditure C REP,tot The formula is:

[0117]

[0118] In the formula, C REP,BT,j C REP,EL,j C REP,EL,j These represent the replacement costs of the battery, the electrolyzer, and the fuel cell, respectively, within the j-th time period.

[0119] Since LCOE can be used to evaluate the techno-economic feasibility of renewable energy systems with various energy storage configurations under grid integration, it is based on the total net present value cost C of the system. NPC,tot Electricity purchase cost C ele Total power generation E tot,j Electricity purchase E ele The formula for calculating LCOE over a given time period is:

[0120]

[0121] In the formula, the total power generation E tot,j E represents the total power generation provided by the system in the j-th time period, expressed in kWh. eleLet represent the electricity purchased by the system from the grid during the j-th time period, expressed in kWh.

[0122] like Figure 2 As shown, regarding the system operation optimization part, in a preferred embodiment, the combined electric and hydrogen energy storage planning method further includes the following steps: setting a system operation strategy and optimizing the operation strategy based on a near-end strategy algorithm to obtain the optimal operation strategy; the system operation strategy is to store the power generation surplus of the generator set in a battery energy storage system or a hydrogen energy storage system, and when the power generation of the generator set is insufficient, the power grid supplies power to the load.

[0123] In practical implementation, the system operation optimization part optimizes the operation strategy based on the near-end strategy algorithm to obtain the optimal operation strategy, including the following steps:

[0124] Construct a Markov decision process model, which is expressed as follows:

[0125] S t =(P laod (t),P pv (t),maxCH BT (t),maxDCH BT (t),maxCH HT (t),

[0126] maxDCH HT (t),SOC BT (t),LOH HT (t))

[0127] In the formula, S t ∈s;P laod (t), P pv (t), maxCH BT (t), maxDCH BT (t), maxCH HT (t), maxDCH HT (t), SOC BT (t), LOH HT (t) represents the load power, photovoltaic generator power, maximum discharge power of the battery, maximum charging power of the battery, maximum input power of the electrolyzer, maximum output power of the fuel cell, state of charge of the battery, and remaining hydrogen in the hydrogen storage tank at time t.

[0128] Define decision model a at time t. t and reward function r t (s t ,a t ); where, the decision model a is defined at time t. t for:

[0129]

[0130] In the formula, P BT (t), P HT (t) represents the power of the battery energy storage system and the hydrogen energy storage system at time t, respectively. Here, decision model a is set. t The control variable is the charging and discharging power of the energy storage system. By obtaining the charging and discharging power of the battery, the input power of the electrolyzer, and the output power of the fuel cell, the output power of the power grid can be calculated according to the energy balance equation.

[0131] The reward is set as the cost of purchasing system electricity from the grid at each time step; therefore, the reward function r is defined. t (s t ,a t )for:

[0132] r t (s t ,a t ) = (energy load (t)·price gird (t))

[0133] In the formula, energy load (t), price gird (t) represents the electricity required by the load at time t and the current electricity price on the grid, respectively.

[0134] Based on the aforementioned Markov decision process model and decision model a t and reward function r t (s t ,a t The optimal running strategy is determined using the near-end strategy algorithm.

[0135] like Figure 3 As shown, taking a hybrid energy storage microgrid system consisting of a photovoltaic power generation system, a battery energy storage system, a hydrogen energy storage system, a DC bus, and a load as an example, the photovoltaic power generation system, battery energy storage system, and hydrogen energy storage system are each connected to the load via a DC bus to supply power to the load. The battery energy storage system can use lithium batteries or lead-acid batteries, etc., while the hydrogen energy storage system can use alkaline electrolyzers or proton exchange membrane electrolyzers for hydrogen production. Hydrogen storage methods can include hydrogen storage tanks, high-pressure hydrogen storage, etc., and the specific design depends on actual needs and is not limited here.

[0136] Tables 1, 2, 3, and 4 below provide data on annual electricity price fluctuations and seasonal load fluctuations in a certain region, respectively, to facilitate a comparison of two energy storage methods: battery energy storage systems and combined battery and hydrogen energy storage systems.

[0137]

[0138]

[0139] Table 1

[0140]

[0141] Table 2

[0142]

[0143] Table 3

[0144]

[0145] Table 4

[0146] Under tiered pricing, considering only the scenario where SSR (Saturation Renewable Energy) is greater than 50%, and taking into account future demand for high-proportion renewable energy and seasonal load fluctuations, such as... Figure 4 As shown, based on the above cost and specification data and using the planning method for combined battery and hydrogen energy storage provided by this invention, the Pareto fronts of battery energy storage systems and combined battery and hydrogen energy storage systems under different scenarios can be obtained respectively. Figure 4 The results show that, considering electricity price fluctuations and seasonal load fluctuations, hybrid energy storage systems exhibit better economic feasibility compared to standalone battery energy storage systems when the SSR index exceeds 90%.

[0147] like Figure 5 , Figure 6 As shown, based on the aforementioned cost and specification data and using the electro-hydrogen combined storage planning method provided by this invention, the optimized operation results of the battery energy storage system and the electro-hydrogen combined storage system under seasonal loads can be obtained respectively. According to these results, after a certain number of optimization events, Figure 5 The optimized electricity price decreased by 8.2% compared to the price before optimization. Figure 6 The optimized electricity price decreased by 8.1% compared to the unoptimized price, indicating that the combined battery and hydrogen energy storage system is more suitable for seasonal energy storage scenarios. Furthermore, under the current tiered electricity pricing system, the average electricity cost of a grid system composed of a photovoltaic power generation system and a combined battery and hydrogen energy storage system is lower than the grid purchase cost, demonstrating cost-effectiveness for systems with seasonally fluctuating loads. In this system, the battery energy storage system can meet short-term energy storage needs, while the hydrogen energy storage system can meet long-term, large-scale energy storage needs.

[0148] The present invention also provides an electro-hydrogen combined storage system, including an electro-hydrogen combined storage planning method as described in any of the above embodiments.

[0149] This combined electro-hydrogen storage system utilizes a multi-objective particle swarm optimization algorithm to optimize capacity configuration and operation while fully considering the costs of system equipment degradation and replacement. Furthermore, an electrochemical model is introduced to improve model fidelity, effectively addressing the strong nonlinearity of the combined electro-hydrogen storage system and achieving rational capacity configuration, thus enhancing optimization results. Its specific functions, roles, and methods can be found in the above-described method embodiments and will not be repeated here.

[0150] In summary, the planning method and system for combined heat and power (CHP) energy storage provided by this invention have the following advantages:

[0151] I. Optimize the capacity configuration of the combined battery and hydrogen energy storage system to effectively improve the economic performance of long-term, large-scale energy storage applications and overcome the problem of high cost of single battery energy storage or hydrogen energy storage.

[0152] Second, in the process of capacity configuration optimization, the costs of equipment degradation and replacement are fully considered, and an electrochemical dynamic model is incorporated to effectively improve the model fidelity and the accuracy of the optimization results.

[0153] Third, during the operation optimization process, a near-end optimization strategy is adopted to effectively solve the strong nonlinearity problem of the combined heat and power (CHP) system equipment;

[0154] Fourth, considering the significant cost impact of both capacity configuration and operation optimization, an integrated optimization scheme for capacity configuration and operation is proposed.

[0155] Furthermore, those skilled in the art should understand that although many problems exist in the prior art, each embodiment or technical solution of the present invention can be improved in only one or a few aspects, without necessarily solving all the technical problems listed in the prior art or the background art simultaneously. Those skilled in the art should understand that any content not mentioned in a claim should not be construed as a limitation on that claim.

[0156] Although this paper frequently uses terms such as battery energy storage system, battery energy storage model, hydrogen energy storage system, hydrogen energy storage model, LCOE, and multi-objective particle swarm optimization algorithm, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of the invention; interpreting them as any additional limitation would contradict the spirit of the invention. The terms "first," "second," etc. (if present) in the specification, claims, and accompanying drawings of the embodiments of the invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A planning method for combined heat and power (CHP) energy storage, characterized in that, Includes the following steps: Construct a battery energy storage model based on the battery's state of charge; Constructing a battery energy storage model regarding the battery's state of charge also includes modeling the power efficiency of the electrolyzer and fuel cell. The power efficiency model is as follows: In the formula, The thermal equilibrium potential is taken as the standard condition. ; For voltage efficiency, For the efficiency of the electrolytic cell, For Faraday efficiency; The current density of the electrolytic cell. , These are the fitting parameters for the efficiency of the electrolytic cell; Based on the hydrogen energy storage system, the operating power of the electrolyzer was constructed. Fuel cell operating power Remaining hydrogen volume in the hydrogen storage tank Hydrogen energy storage model; Based on the system's residual value over a given time period System capital expenditure Operating expenses and reset expenditure Calculate the total net present value cost of the system. Its formula is: Among them, the system's residual value The formula is: In the formula, For the remaining lifespan of the system equipment, The total lifespan of the system equipment; Replacement cost coefficient; Operating expenses The formula is: ； ； In the formula, , These are the system's variable operating costs and the system's fixed operating costs, respectively. , , , , These are the variable operating costs for photovoltaics, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively. , , , , These are the fixed operating costs of photovoltaic cells, batteries, electrolyzers, fuel cells, and hydrogen storage tanks, respectively. The duration of the project lifecycle. For time, For the effective discount rate; Reset expenditure The formula is: In the formula, , , The first Replacement costs for batteries, electrolyzers, and fuel cells within a given time period; Based on the system's total net present value cost Electricity purchase cost Total power generation Electricity purchase Calculate within a given time period ; Calculation based on grid power and load power ; by Minimize and Maximizing the target function is used to establish the target optimization model of the combined battery energy storage model and the hydrogen energy storage model. The Pareto solution set of the target optimization model is calculated using the multi-objective particle swarm optimization algorithm, and the optimal capacity configuration of the combined battery energy storage system is determined based on the Pareto solution set. The system operation strategy is set and optimized based on the near-end strategy algorithm to obtain the optimal operation strategy; the system operation strategy is to store the power generation surplus of the generator set in the battery energy storage system or the hydrogen energy storage system, and when the power generation of the generator set is insufficient, the power grid supplies power to the load. The optimization of the running strategy based on the near-end policy algorithm to obtain the optimal running strategy includes the following steps: Construct a Markov decision process model, which is expressed as follows: In the formula, , , , , , , , They are time The load power, photovoltaic power generation system power, maximum discharge power of the battery, maximum charging power of the battery, maximum input power of the electrolyzer, maximum output power of the fuel cell, state of charge of the battery, and remaining hydrogen in the hydrogen storage tank. Define time separately Decision-making model at time and reward function Define time Decision-making model at time for: In the formula, , For battery energy storage systems and hydrogen energy storage systems, respectively, the time... The power; Define reward function for: In the formula, , They are time The electricity required to supply the load and the current electricity price on the grid; Based on the aforementioned Markov decision process model and decision model and reward function The optimal running strategy is determined using a proximate strategy algorithm.

2. The planning method for combined heat and power (CHP) energy storage according to claim 1, characterized in that, The battery energy storage model regarding the battery's state of charge is constructed as follows: In the formula, , , , They are time The charging power of the battery system, the discharging power of the battery system, the power generation of the photovoltaic system, and the system load power at the location; This represents the battery's self-discharge coefficient. To simulate a 1-hour time step; , , , These are battery charging efficiency, battery discharging efficiency, battery inverter efficiency, and battery rated capacity, respectively.

3. The planning method for combined heat and power (CHP) energy storage according to claim 1, characterized in that, Based on the hydrogen energy storage system, the following hydrogen energy storage models are constructed, considering the operating power of the electrolyzer, the remaining hydrogen in the hydrogen storage tank, and the operating power of the fuel cell: In the formula, , Electrolyzer and fuel cell, respectively, in time Operating power at the following levels; , These are the rated power of the electrolyzer and the fuel cell, respectively. , The efficiencies are those of the electrolyzer and the fuel cell, respectively. This refers to the capacity of the hydrogen storage tank.

4. The planning method for combined heat and power (CHP) energy storage according to claim 1, characterized in that: Based on the system's total net present value cost Electricity purchase cost Total power generation Electricity purchase Calculate within a given time period The formula is: In the formula, the total power generation This represents the total power generation provided by the system in the j-th time period, expressed in kWh. Let represent the electricity purchased by the system from the grid during the j-th time period, expressed in kWh.

5. The planning method for combined heat and power (CHP) energy storage according to claim 1, characterized in that: Calculation based on grid power and load power The formula for the exponent is: In the formula, The total number of hours throughout the year is 8760. Let t be the power of the power grid in hour t. Let t be the load power in hour t.

6. A combined heat and power (CHP) system, characterized in that: This includes the planning method for combined heat and power (CHP) energy storage as described in any one of claims 1-5.

Images