Electricity-heat-hydrogen multi-element energy storage coordinated integrated energy system planning optimization method

Abstract

The invention discloses an electricity-heat-hydrogen multi-element energy storage collaborative comprehensive energy system planning optimization method. The method comprises the following steps: respectively constructing an electricity energy storage model, a heat energy storage model and a hydrogen energy storage model; an electricity-heat-hydrogen multi-element energy storage cooperative operation strategy is analyzed and formulated; formulating an electricity-heat-hydrogen multi-element energy storage collaborative comprehensive energy system planning optimization strategy; and then, an electricity-heat-hydrogen multi-element energy storage collaborative comprehensive energy system planning optimization model is constructed. Hydrogen energy storage is introduced to participate in electricity-heat-hydrogen multi-element energy storage cooperative control, a multi-scene cooperative operation strategy is designed, the flexibility and economical efficiency of the system are improved by utilizing complementarity of the hydrogen energy storage, the electricity-heat-hydrogen multi-element energy storage and the multi-scene cooperative operation strategy, and the renewable energy consumption capacity is enhanced. And an optimization model containing load side response is established, the energy storage working state is dynamically adjusted, and the operation cost is reduced. Multi-energy flow interaction is analyzed, the system boundary is expanded, the coupling depth is enhanced, collaborative optimization and efficient energy configuration are achieved, and the engineering application value is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system planning, and specifically relates to an integrated energy system planning optimization method for coordinated electricity-heat-hydrogen multi-energy storage. Background Art

[0002] In recent years, renewable energy sources such as wind power and photovoltaics have developed rapidly. However, renewable energy has significant randomness, intermittency and volatility, and its output cannot be stable and continuous. This feature poses a huge challenge to the security and stability of the power grid. In order to avoid frequent power fluctuations and large-scale power outages, the power grid has set strict restrictions on the access ratio of renewable energy. This restriction not only affects the absorption level of renewable energy, but also restricts its large-scale application and the full release of economic benefits. Therefore, how to improve the absorption ratio of renewable energy and optimize the operating efficiency of the energy system while ensuring the safety of the power grid has become an important issue that needs to be solved. To address the above problems, current research mostly adopts the combination of energy storage technology and integrated energy systems, and improves system flexibility and resource utilization through multi-energy storage collaborative optimization.

[0003] There are obvious deficiencies in existing technologies: First, the energy storage coordination strategy is single, and most studies only focus on electric energy storage or thermal energy storage. There is a lack of comprehensive coordinated design of electric-thermal-hydrogen multi-energy storage, and the complementary advantages of multiple energy storage forms in cost and response speed are not fully utilized; second, the modeling accuracy is not high. The existing models do not describe the electric-thermal-hydrogen multi-energy flow coupling mechanism, such as energy flow under peak and valley electricity prices, and electric-thermal-hydrogen interaction, in detail, and fail to effectively reflect the priority ranking and economic differences of various energy storage equipment in different operating scenarios; third, the system boundary division is insufficient, hydrogen energy storage is not effectively incorporated into the comprehensive energy system, and there is a lack of in-depth analysis of the complex electric-thermal-hydrogen coupling process (such as electrolysis hydrogen production, waste heat recovery, etc.). Summary of the Invention

[0004] In order to solve at least one technical problem existing in the above-mentioned background technology, the present invention provides a comprehensive energy system planning optimization method with coordinated electricity-heat-hydrogen multi-energy storage.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing the planning of an integrated energy system with coordinated electricity, heat and hydrogen multi-energy storage, which includes the following key steps:

[0006] S110, based on the energy storage control unit, load forecasting unit, data acquisition unit and communication interface, respectively builds electric energy storage

[0007] Model, thermal energy storage model, hydrogen energy storage model, and real-time collection of operating status parameters of each energy storage device;

[0008] S120, according to the energy storage device operating state and external load prediction results, analyzing and formulating the electric-thermal-hydrogen multi-energy storage collaborative operation strategy through the central optimization control unit, and issuing the control instructions to the energy storage device through the communication interface in real time;

[0009] S130, based on the electric-thermal-hydrogen multi-energy storage collaborative operation strategy, formulating the comprehensive energy system planning optimization strategy considering the power market electricity price curve, load prediction data

[0010] and energy storage device operation constraints;

[0011] S140, constructing an electric-thermal-hydrogen multi-energy storage collaborative comprehensive energy system planning optimization model including electric energy storage, thermal energy storage, hydrogen energy storage and load side response model, the constraint conditions including power balance constraint, device operation constraint, historical load constraint, total investment cost constraint and user unit energy cost constraint;

[0012] S150, the optimization model is solved by the particle swarm algorithm with improved individual learning speed, and the optimal operation scheme of the electric-thermal-hydrogen multi-energy storage collaborative comprehensive energy system is obtained, and the control strategy is issued in real time.

[0013] Preferably, the data acquisition unit includes current sensor, voltage sensor, temperature sensor, hydrogen flow meter, for respectively collecting the operation data of electric energy storage, thermal energy storage and hydrogen energy storage device in real time, and uploading the collected data to the central optimization control unit through the communication interface:

[0014] The expression of the electric energy storage model is as follows:

[0015]

[0016] The expression of the thermal energy storage model is as follows:

[0017] H loss = U·A·(T t in -T t out )

[0018]

[0019] The expression of the hydrogen energy storage model is as follows:

[0020] O pH2 (t) = η p2h P p2h (t)

[0021] P fc (t) = η fc O cH2 (t)

[0022] Preferably, the electric-thermal-hydrogen multi-energy storage collaborative operation strategy includes: the operation strategy of the electric, thermal, and hydrogen three energy storage devices under the function of peak-valley arbitrage for stabilizing renewable energy output fluctuation, ensuring supply-demand balance, and peak-valley electricity price;

[0023] Wherein, the part of the energy storage system stabilizing power exceeding μ, the expression of the stabilizing strategy is:

[0024]

[0025] Specifically, ① when the power generation power increases and the power fluctuation is greater than μ, P EES (t) is negative, the energy storage battery is charged; ② when the power generation power decreases and the power fluctuation is greater than μ, P EES (t) is positive, the energy storage battery is discharged; ③ when the power fluctuation does not exceed μ, the energy storage battery does not work;

[0026] The strategy for ensuring supply-demand balance is: ① when the power generation power is greater than the electricity demand, the order of adjusting the output of the battery, the heat storage tank, and the power consumption device to absorb the excess power is set according to the principle of cost optimization; ② when the power generation power is less than the electricity demand, the cost-optimal output order is set by comparing the cost of battery discharge and grid power purchase; ③ when the power generation power is equal to the electricity demand, there is no operation;

[0027] The strategy for peak-valley arbitrage is: ① when the electricity price is in the low valley period, the energy storage system starts to store energy; ② when the electricity price is in the peak period, the energy storage system discharges to reduce grid power purchase; ③ when the electricity price is in the flat period, the difference between the low valley electricity price energy storage cost and the flat period electricity price discharge income is compared with the difference between the flat period electricity price energy storage cost and the peak electricity price discharge income, and the economic optimal charging and discharging strategy is selected.

[0028] Preferably, the comprehensive energy system planning optimization strategy based on electric-thermal-hydrogen multi-energy storage collaboration includes: the total electric load model of the power subsystem includes: the user electric load prediction value output by the load prediction unit; the heat pump output, the electric boiler output, and the hydrogen electrolyzer output measured by the data acquisition unit in real time; and the multi-energy load dynamic change model is constructed after the output power of each subsystem is converted by the conversion efficiency;

[0029] The expression of the total electric load model of the power subsystem is:

[0030]

[0031] Wherein, the output scheduling strategy of the thermal subsystem includes: the combined heat and power device prioritizes heat supply, and when insufficient, the heat pump, electric boiler, and heat storage tank are sequentially called for supplement; and the heat storage tank adjusts the heat output according to the optimized scheduling instruction.

[0032] The model of the thermal subsystem is:

[0033]

[0034]

[0035] Specifically, if the combined heat and power equipment can meet the user's heat load, other equipment does not need to work; if it cannot meet, there will be a remaining heat load; if the heat pump cannot meet the remaining heat load, the electric boiler will work to supplement; when the heat supply equipment cannot meet the heat load, the heat storage tank will work to release heat; the heat storage tank adjusts the output strategy according to the multi-element energy storage optimization strategy.

[0036] Preferably, the target function of the electric-thermal-hydrogen multi-element energy storage coordinated comprehensive energy system planning optimization model includes: minimizing the total life cycle cost of the system, considering the equipment purchase cost, operation and maintenance cost, and energy purchase cost; meeting the power balance constraint, equipment operation range constraint, historical maximum load constraint, total investment cost constraint and user affordable unit energy cost constraint; the power balance constraint is composed of real-time matching of the output of the electric power, heat and hydrogen energy storage equipment and the user load:

[0037] The target function expression is:

[0038]

[0039] The constraint conditions are as follows:

[0040] The power balance constraint expression is:

[0041]

[0042] The equipment operation constraint is:

[0043]

[0044] The historical maximum electric load and heat load constraint is:

[0045]

[0046] The total investment cost constraint expression is:

[0047]

[0048] The user affordable unit energy cost constraint expression is:

[0049]

[0050] Preferably, the solution method of the electric-thermal-hydro multi-energy storage collaborative comprehensive energy system planning optimization model is a particle swarm optimization algorithm with improved individual learning speed, and the improvement method comprises the following steps: dynamically adjusting the inertia weight parameter, so that the algorithm has strong global search ability in the early stage and accelerates the local convergence speed in the later stage; introducing a historical optimal fitness change rate constraint to prevent premature convergence; and designing a fitness adaptive convergence threshold to realize real-time adjustment of the iteration stop condition according to the optimization target.

[0051] The speed and position expression of the particle evolution in the iteration process are as follows:

[0052]

[0053] Preferably, the solution method of the electric-thermal-hydro multi-energy storage collaborative comprehensive energy system planning optimization model comprises the following steps:

[0054] ①, initialization: setting the performance parameters of the algorithm and initializing the algorithm;

[0055] ②, calculating the fitness value: calculating the fitness value of each particle according to the objective function of the planning optimization model, and recording the individual optimal value and the group optimal value;

[0056] ③, updating the particle position and speed: adjusting the speed and position of the particle according to the improved update formula, and calculating the fitness function value of each particle after updating, when the fitness value of the particle in this iteration is less than the historical fitness value, then the individual extreme value is updated; otherwise, it is not updated;

[0057] ④, iteration and updating of the particle group: comparing the individual extreme value with the global extreme value, if the fitness of the individual extreme value is less than the fitness of the global extreme value, then the global extreme value and its position are updated;

[0058] ⑤, particle crossover: if the particle meets the crossover condition, the selective crossover operation is performed on the particle; otherwise, the crossover operation is not performed;

[0059] ⑥, repeating steps ② to ⑤;

[0060] ⑦, iteration to convergence: judging whether to stop according to the adaptive convergence condition, and outputting the optimal solution.

[0061] Compared with the prior art, the beneficial effects of the present application are as follows:

[0062]

[0063]

[0064] BRIEF DESCRIPTION OF DRAWINGS

[0065] One or more embodiments are illustrated by way of example in the figures that are not intended to be limiting of the embodiments. Unless otherwise specifically noted, the drawings shown are not necessarily to scale.

[0066] Fig. 1 A flow chart of a comprehensive energy system planning optimization method based on electric-thermal-hydrogen multi-energy storage collaboration of embodiments of the present application;

[0067] Fig. 2 A typical electric-thermal-hydrogen multi-energy storage comprehensive energy system framework diagram of embodiments of the present application;

[0068] Fig. 3 ​​​A planning optimization strategy of an integrated energy system based on electro-thermal-hydrogen multi-energy storage coordination is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0069] The technical solutions of the present application are further described below in combination with the drawings and embodiments.

[0070] An electro-thermal-hydrogen multi-energy storage coordinated integrated energy system planning optimization method is provided in the present embodiment, as shown in the figure, mainly including the following steps: Figs. 1-3

[0071] S110, based on the energy storage control unit, the load prediction unit, the data acquisition unit and the communication interface, respectively constructing the electric energy storage model, the thermal energy storage model and the hydrogen energy storage model, and collecting the operating state parameters of each energy storage device in real time;

[0072] S120, according to the operating state of the energy storage device and the external load prediction result, analyzing and formulating the electro-thermal-hydrogen multi-energy storage coordinated operation strategy through the central optimization control unit, and issuing the control instructions to the energy storage device in real time through the communication interface;

[0073] S130, based on the electro-thermal-hydrogen multi-energy storage coordinated operation strategy, formulating the integrated energy system planning optimization strategy considering the power market electricity price curve, the load prediction data and the energy storage device operation constraint;

[0074] S140, constructing an electro-thermal-hydrogen multi-energy storage coordinated integrated energy system planning optimization model including the electric energy storage, the thermal energy storage, the hydrogen energy storage and the load side response model, and the constraint conditions including the power balance constraint, the device operation constraint, the historical load constraint, the total investment cost constraint and the user unit energy cost constraint;

[0075] S150, iteratively solving the optimization model by using the particle swarm algorithm with improved individual learning speed to obtain the optimal operation scheme of the electro-thermal-hydrogen multi-energy storage coordinated integrated energy system, and issuing the control strategy in real time.

[0076] Specifically, the electric energy storage model in S110 is:

[0077]

[0078] wherein E(t) is the electric quantity stored by the lead-acid battery at time t, kW; E(t-1) is the electric quantity stored by the lead-acid battery at time t-1, kW; δ e is the self-loss coefficient of the electric energy of the lead-acid battery; P EES (t) is the output power of the lead-acid battery (positive value for discharging and negative value for charging), and the subscript EES represents Electric Energy Storage, kW; and​ respectively, the charging and discharging efficiency of the lead-acid battery; ΔT is the time interval; and respectively, the charging and discharging state of the lead-acid battery, 1 indicates that it is in the charging and discharging state, and 0 indicates no state; SOC(t) is the state of charge of the battery at time t; SOC(0) is the initial state of charge of the battery; X EES is the storage capacity of the battery, kW·h.

[0079] The application breaks through the limitation of the prior art that only a single type of energy storage is optimized, and proposes a collaborative operation strategy design suitable for multiple scenes. In actual operation, when renewable energy such as wind power and photovoltaic power is generated, the system preferentially uses low-cost electricity to drive the electrolyzer to produce hydrogen, converting electrical energy into hydrogen energy storage; at the peak of electricity consumption, hydrogen energy storage generates electricity through fuel cells, heat storage releases heat, and electrical energy storage collaborates to meet load demand. Electrical energy storage, heat storage and hydrogen energy storage have natural complementary characteristics in response speed, cost structure and energy density. The application makes full use of these characteristics to improve the overall flexibility and economy of the system, effectively improves the consumption capacity of renewable energy such as wind power and photovoltaic power, reduces the phenomenon of wind power and photovoltaic power curtailment, and also responds to China's hydrogen energy industry development strategy, which has a positive role in promoting the engineering application of hydrogen energy storage technology in comprehensive energy systems.

[0080] Specifically, the heat storage model in S110 is:

[0081] H loss = U·A·(T t in -T t out ) (3)

[0082]

[0083] Wherein, H loss is the energy loss of the heat storage tank, kW; U is the thermal conductivity of the heat storage tank, W / (m 2 ·K); A is the heat exchange area of the heat storage tank, m 2 ; T t in and T t out are the temperatures of the medium at the inlet and outlet of the heat storage tank, respectively, K; H(t) is the energy stored in the heat storage tank at time t, kW; δ h is the heat energy self-loss coefficient of the heat storage tank; P TES (t) is the output power of the heat storage tank (positive value for heat release, negative value for heat storage), subscript TES represents Thermal Energy Storage (thermal energy storage), kW; and respectively, the heat storage and release efficiency of the heat storage tank; and respectively, the heat storage and release state of the heat storage tank, 1 represents the heat storage and release state, 0 represents no state; SOH(t) is the heat state of the heat storage tank at time t; X TES is the heat storage capacity of the heat storage tank, kW·h.

[0084] Specifically, the hydrogen energy storage model in S110 is:

[0085] O pH2 (t) = η p2h P p2h (t) (6)

[0086] P fc (t) = η fc O cH2 (t) (7)

[0087] Wherein, O pH2 (t) is the hydrogen production power of the electrolyzer at time t, kW; P p2h (t) is the power input to the electrolyzer at time t, kW; η p2h is the efficiency of electrolytic hydrogen production; P fc (t) is the output power of the fuel cell at time t, kW; O cH2 (t) is the hydrogen consumption power of the fuel cell at time t, kW; η fc is the power generation efficiency of the fuel cell.

[0088] Specifically, the multi-energy storage collaborative operation strategy of electricity-heat-hydrogen in S120 is the operation strategy of the three energy storage devices of electricity, heat and hydrogen under the three functions of suppressing renewable energy output fluctuation, ensuring supply and demand balance, and peak-valley arbitrage.

[0089] Specifically, the model for suppressing renewable energy output fluctuation in S120 is:

[0090]

[0091] Wherein, κ(t) represents the renewable energy power fluctuation rate, %; P REG (t) is the renewable energy power, subscript REG represents Renewable Energy Generation (Renewable Energy Generation), kW; P REG.IC represents the total installed capacity of renewable energy power generation, subscript REG_IC represents Renewable Energy Generation Installed Capacity (Renewable Energy Generation Installed Capacity), kW.

[0092] The energy storage system smoothes the part of power exceeding μ, and the expression of its smoothing strategy is:

[0093]

[0094] wherein μ is the power fluctuation threshold. The specific meaning of formula (9) is: ① when the power generation increases and the power fluctuation is greater than μ, P EES (t) is negative, the energy storage battery is charged; ② when the power generation decreases and the power fluctuation is greater than μ, P EES (t) is positive, the energy storage battery is discharged; ③ when the power fluctuation does not exceed μ, the energy storage battery does not work.

[0095] Specifically, the supply-demand balance in the S120 is mainly the supply-demand balance of the power subsystem, and the strategy is: ① when the power generation is greater than the power demand (i.e. power surplus), the order of battery, heat storage tank and power consumption device output adjustment to absorb the excess power is set according to the principle of cost optimization; ② when the power generation is less than the power demand (i.e. power shortage), the cost-optimal output order is set by comparing the cost of battery discharge and grid power purchase; ③ when the power generation is equal to the power demand, there is no operation.

[0096] Specifically, the peak-valley arbitrage in the S120 is: ① when the electricity price is in the low valley period, the energy storage system starts to store energy; ② when the electricity price is in the high peak period, the energy storage system discharges to reduce grid power purchase; ③ when the electricity price is in the flat period, the difference between the low valley electricity price energy storage cost and the flat period electricity price discharge benefit, and the difference between the flat period electricity price energy storage cost and the high peak electricity price discharge benefit are compared, and the most economical charging and discharging strategy is selected. For the power subsystem, when the difference between the low valley electricity price charging cost and the flat period electricity price discharge benefit is less than the difference between the flat period electricity price storage cost and the high peak electricity price discharge benefit, the discharge strategy is selected; otherwise, the charging strategy is selected. For the heat subsystem, when the difference between the low valley electricity price heat storage cost and the flat period electricity price discharge benefit is less than the difference between the flat period electricity price heat storage cost and the high peak electricity price discharge benefit, the heat discharge strategy is selected; otherwise, the heat storage strategy is selected. For the hydrogen energy storage subsystem, when the difference between the low valley electricity price hydrogen production cost and the flat period electricity price discharge benefit is less than the difference between the flat period electricity price hydrogen production cost and the high peak electricity price discharge benefit, the fuel cell discharge strategy is selected; otherwise, the hydrogen storage strategy is selected.

[0097] Specifically, the comprehensive energy system planning optimization strategy based on the coordination of electric-thermal-hydrogen multi-energy storage in the S130 is as shown in Fig. 3 .

[0098] Specifically, the total electric load model of the power subsystem in the S130 is:

[0099]

[0100] wherein L ele(t) is the total electrical load, kW; P is the user electrical load, kW; rb (t) is the heat pump output, kW; eb (t) is the electric boiler output, kW; p2h (t) is the hydrogen production electrolyzer output, kW; rb η eb η p2h η heat are the conversion efficiencies of the heat pump, the electric boiler and the hydrogen production electrolyzer, respectively, %.

[0101] Specifically, the thermal subsystem in S130: if the combined heat and power equipment can meet the user's heat load, other equipment does not need to output; if it cannot meet, there will be a residual heat load. If the heat pump cannot meet the residual heat load, the electric boiler output will be supplemented. When the heat supply equipment cannot meet the heat load, the heat storage tank will output heat; the heat storage tank will also adjust the output strategy according to the multi-element energy storage optimization strategy. The model is as follows:

[0102]

[0103] L rb (t) is the heat load, kW; P is the output of the combined heat and power equipment, kW; P is the maximum output of the combined heat and power equipment, kW; eb (t) is the heat pump output, kW; P is the maximum heat pump output, kW; p2h (t) is the electric boiler output, kW; P is the maximum electric boiler output, kW; k (t) is the electrolyzer output, kW; P is the maximum electrolyzer output, kW.

[0104] Specifically, the objective function of the electric-thermal-hydrogen multi-element energy storage coordinated comprehensive energy system planning optimization model in S140 is:

[0105]

[0106] F is the annual cost of the system, yuan; X k is the capacity of device k, kW·h; C p,k is the unit purchase cost of device k, yuan; F cr (k) is the discount factor; C m is the device maintenance cost, yuan; C e is the cost of purchasing energy from the outside world by the system, yuan; i is the discount rate; y k is the life of the device; c m,k is the operation and maintenance cost of device k, yuan; Wk (t) is the operating power of device k, kW; c gas is the natural gas price, yuan / m 3 ; F d (t) is the amount of natural gas consumed by the system at time t, m 3 ; c grid (t) is the power grid price at time t, yuan / kW·h; P grid (t) is the amount of electricity purchased by the system at time t, kW·h.

[0107] Specifically, the power-heat-hydrogen multi-energy storage collaborative comprehensive energy system planning optimization model in the S140 has the following constraints:

[0108] Power balance constraint:

[0109]

[0110] Wherein, L ele (t) is the user electric load in the system, kW; P pv (t) is the photovoltaic output power, kW; P wt (t) is the wind power output power, kW; P grid (t) is the power purchased from the power distribution network, kW; is the power supply power of the combined heat and power device at time t, kW; P EES (t) is the battery energy storage device output power, kW; P fc (t) is the hydrogen fuel cell output power, kW; L heat (t) is the user heat load in the system, kW; is the heat supply power of the combined heat and power device at time t, kW; P rb (t) is the heat supply power of the heat pump at time t, kW; P eb (t) is the heat supply power of the electric boiler at time t, kW; P TES (t) is the heat supply power of the heat storage tank at time t, kW;

[0111] Device operation constraint:

[0112]

[0113] Wherein, is the upper limit of photovoltaic output, kW; is the upper limit of wind power output, kW; and are the upper and lower limits of the power purchased from the power distribution network, kW; and are the upper and lower limits of the power supply of the combined heat and power unit, kW; and Upper and lower limits of heat supply power for cogeneration units, kW; and Upper and lower limits of battery energy storage output, kW; and Upper and lower limits of fuel cell output, kW; and Upper and lower limits of heat pump output, kW; and Upper and lower limits of electric boiler output, kW; and Upper and lower limits of thermal storage tank output, kW.

[0114] Historical maximum electrical and thermal load constraints:

[0115]

[0116] wherein, and are the historical maximum electrical and thermal loads, kW.

[0117] Total investment cost constraint:

[0118]

[0119] wherein, is the maximum total investment cost, yuan.

[0120] User-acceptable unit energy cost constraint:

[0121]

[0122] wherein, is the maximum user-acceptable unit energy cost, yuan.

[0123] Specifically, the method for solving the electrical-thermal-hydrogen multi-element energy storage coordinated comprehensive energy system planning optimization model in the S150 is a particle swarm algorithm with improved individual learning speed, the evolution speed and position of the particle in the iteration process can be expressed as:

[0124]

[0125] wherein, is the optimal particle evolution speed; ω j is the inertia weight of particle evolution speed; r1 and r2 are coefficients, taking values between (0, 1); c1 is the individual learning factor; c2 is the group learning factor; is the historical particle individual fitness extreme value; is the particle position.

[0126] Specifically, the process of solving the electric-thermal-hydro multi-element energy storage coordination comprehensive energy system planning optimization model in S150 is as follows:

[0127] ① Initialization: set the performance parameters of the algorithm, and initialize the algorithm;

[0128] ② Calculate the fitness value: according to the optimization target and the decision variable, calculate the fitness value of each particle to screen the optimal particle;

[0129] ③ Update the particle: update the speed and position of the particle, and calculate the fitness function value of the updated particle, when the fitness value of the current iteration particle is less than the historical fitness value, then update the individual extreme value; otherwise, do not update;

[0130] ④ Iterative update of the particle swarm: compare the individual extreme value of each particle with the global extreme value, if the fitness of the individual extreme value is less than the fitness of the global extreme value, then update the global extreme value and its position;

[0131] ⑤ Particle crossover: if the particle meets the crossover condition, perform selective crossover operation on the particle; otherwise, do not perform crossover operation;

[0132] ⑥ Repeat steps ② to ⑤;

[0133] ⑦ Stop: when the iteration stop criterion is reached, output the final result.

[0134] It should be noted that in this paper, relational terms such as first and second are used merely to distinguish one entity or action from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0135] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for planning and optimizing an integrated energy system with synergies of electricity-heat-hydrogen multi-energy storage, characterized in that, The method comprises the following key steps: S110, based on the energy storage control unit, the load prediction unit, the data acquisition unit and the communication interface, an electric energy storage model, a thermal energy storage model and a hydrogen energy storage model are respectively constructed, and the operating state parameters of each energy storage device are acquired in real time; S120, according to the operating state of the energy storage device and the external load prediction result, the central optimization control unit analyzes and formulates an electric-thermal-hydrogen multi-energy storage collaborative operation strategy, and the control instruction is transmitted to the energy storage device in real time through the communication interface; S130, based on the electric-thermal-hydrogen multi-energy storage collaborative operation strategy, a comprehensive energy system planning optimization strategy considering the power market electricity price curve, the load prediction data and the operating constraints of the energy storage device is formulated; S140, an electric-thermal-hydrogen multi-energy storage collaborative comprehensive energy system planning optimization model is constructed, and the constraint conditions include power balance constraint, device operation constraint, historical load constraint, total investment cost constraint and user unit energy cost constraint; S150, the optimization model is iteratively solved by using a particle swarm algorithm with improved individual learning speed, and an optimal operation scheme of the electric-thermal-hydrogen multi-energy storage collaborative comprehensive energy system is obtained, and the control strategy is transmitted in real time.

2. The method for planning and optimizing an integrated energy system with electricity, heat and hydrogen multi-energy storage synergy according to claim 1, characterized in that: The data acquisition unit comprises a current sensor, a voltage sensor, a temperature sensor and a hydrogen flowmeter, which are used to acquire the operating data of the electric energy storage device, the thermal energy storage device and the hydrogen energy storage device in real time respectively, and transmit the acquired data to the central optimization control unit through the communication interface; The expression of the electric energy storage model is as follows: The expression of the thermal energy storage model is as follows: H loss = U · A · (T t in -T t out ) The expression of the hydrogen energy storage model is as follows: O pH2 (t) = η p2h P p2h (t) P fc (t) = η fc O cH2 (t). 3.The electric-thermal-hydrogen multi-energy storage coordinated integrated energy system planning optimization method of claim 1, wherein, The electric-thermal-hydrogen multi-energy storage collaborative operation strategy comprises the operation strategies of the electric energy storage device, the thermal energy storage device and the hydrogen energy storage device in three functions, i.e., suppressing renewable energy output fluctuation, ensuring supply-demand balance and realizing peak-valley arbitrage; Wherein, the expression of the suppression strategy of the energy storage system for the part exceeding μ is as follows: Specifically, ① when the power generation power increases and the power fluctuation is greater than μ, P EES (t) is negative, the energy storage battery is charged; ② when the power generation power decreases and the power fluctuation is greater than μ, P EES (t) is positive, the energy storage battery is discharged; ③ when the power fluctuation does not exceed μ, the energy storage battery does not work; The strategy for ensuring supply-demand balance is as follows: ① when the power generation is greater than the power demand, the order of the output adjustment of the battery, the heat storage tank and the power consumption device is set according to the principle of cost optimization to absorb the excess power; ② when the power generation is less than the power demand, the cost-optimal output order is set by comparing the cost of battery discharge and grid power purchase; ③ when the power generation is equal to the power demand, no operation is performed; The strategy for peak-valley arbitrage is as follows: ① when the electricity price is in the low valley period, the energy storage system starts to store energy; ② when the electricity price is in the high peak period, the energy storage system discharges energy to reduce grid power purchase; ③ when the electricity price is in the flat period, the difference between the low valley electricity price energy storage cost and the flat period electricity price discharge income, and the difference between the flat period electricity price energy storage cost and the high peak electricity price discharge income are compared, and the most economical charging and discharging strategy is selected. 4.The electric-thermal-hydrogen multi-energy storage coordinated comprehensive energy system planning optimization method of claim 1, wherein, The total electric load model of the power subsystem of the electric-thermal-hydrogen multi-energy storage collaborative comprehensive energy system planning optimization strategy comprises: the user electric load prediction value output by the load prediction unit; the heat pump output, the electric boiler output and the hydrogen electrolyzer output measured by the data acquisition unit in real time; and the multi-energy load dynamic change model is constructed after the output power of each subsystem is converted by the conversion efficiency. The total electric load model expression of the power subsystem is: The output scheduling strategy of the thermal subsystem includes: the cogeneration equipment gives priority to heat supply, and when the heat is insufficient, the heat pump, electric boiler and heat storage tank are used in sequence to supplement it; the heat storage tank adjusts the storage and release heat output according to the optimized scheduling instructions; The model of the thermal subsystem is: The specific meaning is: if the cogeneration equipment can meet the user's thermal load, other equipment does not need to output; if it cannot be met, there will be residual thermal load; if the heat pump cannot meet the residual thermal load, the electric boiler will output to supplement; when the heating equipment cannot meet the thermal load, the heat storage tank will output heat; the heat storage tank will adjust its output strategy according to the multi-energy storage optimization strategy.

5. The method of claim 1, wherein, The objective function of the integrated energy system planning optimization model for the coordinated electric-thermal-hydrogen multi-energy storage system includes: minimizing the total cost of the system over its entire life cycle, comprehensively considering equipment acquisition costs, operation and maintenance costs, and energy acquisition costs; and satisfying power balance constraints, equipment operating range constraints, historical maximum load constraints, total investment cost constraints, and user-affordable unit energy cost constraints. The power balance constraint is composed of real-time matching of the output of electric, thermal, and hydrogen energy storage equipment with user loads: The objective function expression is: The constraints are as follows: The power balance constraint expression is: Equipment operation constraints: Historical maximum electrical load and thermal load constraints: The total investment cost constraint expression is: The unit energy cost constraint expression that users can bear is: 6.The electric-thermal-hydrogen multi-energy storage coordinated integrated energy system planning optimization method of claim 1, wherein, The proposed solution to the integrated energy system planning optimization model for the coordinated electricity-heat-hydrogen multi-element energy storage is a particle swarm algorithm that improves individual learning speed. The improvements include: dynamically adjusting the inertia weight parameter to give the algorithm stronger global search capabilities in the early stages and faster local convergence in the later stages; introducing a historical optimal fitness change rate constraint to prevent premature convergence; and designing an adaptive fitness convergence threshold to achieve real-time adjustment of the iteration stop condition based on the optimization goal. The evolution speed and position expressions of the particles during the iteration process are:

7. The method of claim 5, wherein the method further comprises: The method for solving the integrated energy system planning optimization model for electricity-heat-hydrogen multi-energy storage synergy includes the following steps: ①, Initialization: Set the performance parameters of the algorithm and initialize the algorithm; ② Calculate fitness value: Calculate the fitness value of each particle according to the objective function of the planning optimization model, and record the individual optimal value and the group optimal value; ③. Update particle position and speed: adjust the particle speed and position according to the improved update formula, and calculate the fitness function value of each particle after the update. When the fitness value of the particle in this iteration is less than the historical fitness value, the individual extreme value is updated; otherwise, it is not updated; ④ Iteratively update the particle swarm: compare the individual extreme value of each particle with the global extreme value. If the fitness of the individual extreme value is less than the fitness of the global extreme value, update the global extreme value and its position. ⑤. Particle crossover: If the particle meets the crossover condition, a selective crossover operation is performed on the particle; otherwise, no crossover operation is performed; ⑥. Repeat steps ② to ⑤; ⑦. Iterate until convergence: Determine whether to stop based on the adaptive convergence conditions and output the optimal solution.

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