An Energy Management and Optimal Scheduling Method for a Renewable Energy System

Abstract

The present invention discloses an energy management and optimal scheduling method for a renewable energy system, belonging to the technical field of energy management, which includes collecting real-time data of the system and judging whether each module in the renewable energy system can operate normally; designing multi-objective optimization functions under three different scenarios, respectively considering the cases of good wind and light conditions (high-speed operation), general wind and light conditions (low-speed operation), and the worst wind and light conditions (minimum operation); monitoring the power of each module, determining which scenario the system is in according to the relationship between the input total power and the load power, so as to select different optimal scheduling objectives; balancing the energy conversion under different scenarios through the charge and discharge of the energy storage module composed of supercapacitors and lithium batteries. The present invention can realize the steady-state operation of the hydrogen production system in the renewable energy system, the optimal scheduling considering multi-scenario conditions and multi-objectives on the basis of ensuring the long-term stable operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an energy management and optimal scheduling method for a renewable energy system. Background Art

[0002] In the context of the current global energy structure transformation and climate change, the efficient utilization and management of renewable energy have increasingly become a key area of scientific and technological development. Especially in the utilization of renewable energy such as wind energy and solar energy, due to their natural intermittency and instability, how to effectively manage and optimize the storage and conversion of these energies has become a major challenge for improving energy utilization efficiency and economy.

[0003] Hydrogen production, as an efficient energy storage and carrier method, converts electrical energy into chemical energy through electrolysis of water, providing a feasible solution to address the discontinuity of renewable energy supply. However, how to optimize the operation strategy of the hydrogen production system under different environmental conditions and operating requirements, maximize the economic benefits and energy conversion efficiency of the system, and ensure the stability and reliability of the system under various operating conditions, remains a technical problem. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an energy management and optimal scheduling method for a renewable energy system. Considering the optimal scheduling of a renewable energy hydrogen production system under three wind and light conditions, three objective functions are proposed, respectively considering the maximum economic benefits and maximum energy conversion efficiency under high-speed and normal-speed operation conditions of the system; under the most basic operation conditions, the maximum cost control considering fault situations is considered; by adopting a multi-objective improved firefly algorithm (MOMFA), the optimal scheduling and energy management of the renewable energy hydrogen production system under different wind and light conditions are realized.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is:

[0006] An energy management and optimal scheduling method for a renewable energy system, comprising the following steps:

[0007] S1, collect data of each module of the renewable energy hydrogen production system, detect whether each module can operate normally, stop running if there is a fault, and proceed to step S2 if there is no fault;

[0008] The renewable energy system includes a photovoltaic module, a wind power module, a power grid module, a lithium battery and a supercapacitor module, an electrolyzer module and a compressed hydrogen storage module.

[0009] S2, by setting multi-objective functions and constraints;

[0010] S3, obtain the optimal energy scheduling and collaborative optimization solutions under different conditions through the multi-objective improved firefly algorithm.

[0011] S4. Monitor the power of each module, determine which scenario the system is in according to the relationship between the total input power and the load power, and thus select different optimal scheduling objectives. Balance the energy conversion in different scenarios through the charging and discharging of the energy storage module composed of supercapacitors and lithium batteries.

[0012] A further improvement of the technical solution of the present invention lies in: in S2, determining the operation constraint conditions and multi-objective functions includes the following:

[0013] S21. Taking a 24-hour level as the time scale, generating a scheduling instruction every 1 day. Considering economic benefits, it is specifically divided into four parts: the first part is the income from the production and sale of hydrogen in the hydrogen refueling station; the second part is the operation and maintenance costs of renewable energy power generation equipment, which mainly considers the operation and maintenance costs of photovoltaic panels and wind turbines. The third part is the operation and maintenance costs of the electrolyzer and considers the degradation of the electrolyzer according to the time scale. The fourth part is the cost of purchasing hydrogen externally and using grid power supply; the economic management objective needs to maximize the sum of the four parts, that is:

[0014] F1(x) = F1(C system )

[0015]

[0016] Among them, C system represents the economic benefit objective function, C pv (t) represents the operation and maintenance cost of the photovoltaic panel considering the degradation situation, C wt (t) represents the operation and maintenance cost of the wind turbine considering the degradation situation, C bat (t) represents the operation and maintenance coefficient of the lithium battery and supercapacitor module considering the degradation situation under unit power, C ele (t) represents the operation and maintenance coefficient of the electrolyzer module considering the degradation situation under unit power, C off represents the cost of purchasing hydrogen externally, C fee represents the electricity price, C feeH represents the selling price of hydrogen, P grid represents the grid power, C H is the price per kilogram of hydrogen, m off (t) represents the mass of hydrogen transported externally, m pro (t) represents the mass of hydrogen produced by the hydrogen refueling station;

[0017] S22. Generate a scheduling instruction every 1 day with a 24-hour time scale, considering energy efficiency, which is specifically divided into four parts; the first part is the supply efficiency of renewable energy power generation equipment, which mainly considers the supply efficiency of photovoltaic panels and wind turbines; the second part is the power efficiency provided by the power grid and lithium batteries; the third part is the hydrogen energy efficiency generated by electrolyzers; the fourth part is the energy loss generated to maintain the system operation or when the system is in an abnormal operation and maintenance state; the energy efficiency target needs to maximize the ratio of output efficiency to consumption efficiency, that is:

[0018] F2(x) = F2(η system )

[0019]

[0020] Among them, η system represents the energy efficiency objective function, P pv1 (t) represents the power of the photovoltaic panel supply system, P wt1 (t) represents the power of the wind turbine supply system, P pv2 (t) and P wt2 (t) represent the remaining power sold, P bat (t), P grid (t) respectively represent the power provided by the lithium battery and supercapacitor module, and the power grid, P loss (t) represents the power loss generated to maintain the system operation or when the system is in an abnormal operation and maintenance state, P com (t) represents the compressor working power, represents the mass of hydrogen generated, e H represents the hydrogen energy power density;

[0021] S23. Generate a scheduling instruction every 1 hour with a minute-level time scale, considering the minimum operation and maintenance cost of energy supply equipment under the lowest operating state of the system, which is specifically divided into two parts; the first part is the operation and maintenance cost of the energy supply equipment, and the second part is the repair cost generated when a certain energy supply equipment fails, and the cost generated by the remaining energy supply equipment and the power grid sharing the energy demand;

[0022] The maximum cost control target needs to minimize the cost of the system operating at the lowest level, that is:

[0023] F3(x) = F3(C operation )

[0024]

[0025] Among them,

[0026] C operation represents the system operation cost objective function; Cpv (t) represents the operation and maintenance cost of the photovoltaic panel considering the degradation situation; R pv (j1) represents the repair cost when the photovoltaic panel fails; j1 represents the repair time of the photovoltaic panel; P pv (t + j2 + j3) represents the power supplied by the photovoltaic panel to the system when the wind turbine, lithium battery or supercapacitor module may fail; C wt (t) represents the operation and maintenance cost of the wind turbine considering the degradation situation; R wt (j2) represents the repair cost when the wind turbine fails; j2 represents the repair time of the wind turbine; P wt (t + j3 + j1) represents the power supplied by the photovoltaic panel to the system when the photovoltaic panel, lithium battery or supercapacitor module may fail; C bat (t) represents the operation and maintenance coefficient of the lithium battery and supercapacitor module considering the degradation situation; R bat (j3) represents the repair cost when the lithium battery and supercapacitor module fail; j3 represents the repair time of the lithium battery and supercapacitor module; P bat (t + j2 + j1) represents the power supplied by the photovoltaic panel to the system when the wind turbine and photovoltaic panel may fail; P grid (t + j1 + j2 + j3) represents the power supplied by the photovoltaic panel to the system when the photovoltaic panel, wind turbine and photovoltaic panel may fail;

[0027] S24, it is determined that the constraints that the energy management needs to meet include;

[0028] 1) The lithium battery constraints mainly include charge and discharge power constraints and SOC constraints:

[0029] Pb atm i n ≤Pb at (t)≤Pb atmax (t)

[0030] Pb atmax (t)≤Pb atmax

[0031] SOC(t + 1) = SOC(t) + Pb at (t)ΔT / Eb at

[0032] SOC min ≤SOC(t)≤SOC max

[0033] Among them, P batmin 、P batmax (t)、P batmax 、SOC min 、SOC max, ΔT respectively represent the minimum operating power of the lithium battery, the maximum operating power of the lithium battery, the maximum operating power of the lithium battery considering degradation, the minimum value of the lithium battery SOC, the maximum value of the lithium battery SOC, and the time interval of the scheduling period;

[0034] 2) The constraint conditions of the electrolyzer mainly include the operating power constraint and the hydrogen production constraint. The input power range of the electrolyzer is from minimum operation to full power operation. Since a lower current density will cause the hydrogen concentration in oxygen to be too low, resulting in an explosion risk, usually the minimum power of the electrolyzer is 10% of its rated power:

[0035] P elemin ≤P ele (t)≤P elemax (t)

[0036] P elemax (t)≤P elemax

[0037]

[0038] Among them, P elemin 、P elemax 、HHV、η ele respectively represent the minimum operating power of the electrolyzer, the maximum operating power of the electrolyzer, the higher heating value of hydrogen, and the electrolyzer efficiency. P elemax (t) represents the maximum operating power considering the degradation of the electrolyzer;

[0039] 3) The operation of the compressor mainly considers its power consumption, and its value is directly related to the hydrogen production of the electrolyzer:

[0040] P com (t)=λ com m h2 (t)

[0041] Among them, λ com represents the energy consumption coefficient of the compressor;

[0042] 4) The LOH constraint of the hydrogen storage tank:

[0043] LOH(t + 1)=LOH(t)+(mh2(t + 1)+m o ff(t + 1)-ml oa d(t + 1))ΔT / m max

[0044] LOH min ≤LOH(t)≤LOH max

[0045] Among them, LOH min 、LOH max 、mstore and m load and m max respectively represent the minimum value of the LOH of the hydrogen storage tank, the maximum value of the LOH of the hydrogen storage tank, the mass of hydrogen inside the hydrogen storage tank, the hydrogen demand of the hydrogen load, and the maximum mass of hydrogen that the hydrogen storage tank is allowed to store;

[0046] 5) The grid power needs to meet certain constraints:

[0047] P gridmin ≤ P grid (t) ≤ P gridmax

[0048] where P gridmin and P gridmax respectively represent the minimum value of the grid power and the maximum value of the grid power;

[0049] 6) The electrical energy of the system needs to be maintained in balance:

[0050] P pv1 (t) + P pv2 (t) = P pv (t)

[0051] P wt1 (t) + P wt2 (t) = P wt (t)

[0052] P pv (t) + P wt (t) = P e le(t) + P co m(t) + P g rid(t) + Pbat(t) + Ploss(t)

[0053] 7) The average failure frequency of each device needs to meet certain constraints:

[0054] f setmin < fi(t) < f setmax

[0055] where f setmin represents the minimum failure frequency set when the device leaves the factory, and f setmax represents the maximum failure frequency set when the device leaves the factory.

[0056] A further improvement of the technical solution of the present invention lies in: In S3, the specific steps are as follows:

[0057] S31, define a multi-objective problem:

[0058]

[0059] where x idenotes the vector of design or control variables; F m (x) represents the objective function to be optimized. The optimization problem is subject to a set of inequality and equality constraints, denoted by G j (x) and H k (x) respectively; the design variables or control variables are bounded by lower and upper bounds, denoted by L and U respectively; after step S31 is completed, step S32 is entered for algorithm initialization;

[0060] S32, initialize the population. Set the firefly population to N, and each firefly position x i is randomly generated in the decision variable space, and these positions represent different solutions;

[0061] S33, for each firefly i, calculate the solution of the objective function, and the brightness I i is related to the objective function value and reflects the performance of a certain solution on the objective function. Usually, in multi-objective optimization, the brightness is obtained through non-dominated sorting.

[0062] S34, position update. In this process, each firefly moves or adjusts its position according to the performance of other fireflies around it to find a better solution:

[0063]

[0064] where, is the position of the current firefly at time t, is the position of the firefly with higher brightness at time t, β0 is the base value of attraction, which controls the willingness of the firefly to move towards the individual with higher brightness, γ is the light absorption coefficient, which affects the attenuation rate of brightness with distance, is the Euclidean distance between fireflies, α is the step size parameter, which controls the amplitude of random movement, and rand is a random number uniformly distributed in [0,1], which is used to introduce randomness and increase the diversity of search solutions.

[0065] A further improvement of the technical solution of the present invention lies in: in S33, the specific steps include:

[0066] 1) Each solution is assigned a level. Level 1 is all non-dominated solutions in the current population, level 2 is the non-dominated solutions among the remaining solutions after deleting level 1, and so on. The non-dominated sorting level of each firefly for two objectives is rank i ;

[0067] 2) The crowding distance is an index to measure the distribution density of solutions in its level. The crowding distance distance i is used to maintain the diversity of solutions and prevent the algorithm from only aggregating in a certain area;

[0068] 3) Define the brightness I iA function related to the sorting level. Usually, the brightness can be set to

[0069] A further improvement of the technical solution of the present invention lies in: in S34, it specifically includes:

[0070] 1) Iteration and convergence: Repeatedly perform the brightness evaluation and position update process until the termination condition is met, that is, the maximum number of iterations is reached or the improvement of the solution is lower than a certain threshold;

[0071] 2) Obtaining non-dominated solutions and Pareto optimality: In multi-objective optimization, non-dominated sorting is usually used to evaluate the quality of solutions; a solution is considered non-dominated if it is not simultaneously surpassed by any other solution in all objectives; the Pareto optimal solution set is composed of these non-dominated solutions, representing the best solution for trade-offs between objective functions; according to the weights considered by the decision maker, the optimal solution is selected from the Pareto optimal solution set.

[0072] A further improvement of the technical solution of the present invention lies in: in step 4, it specifically includes: performing power tracking on the photovoltaic module and the wind power module, and judging whether P 光+风 is greater than the load power P 负 ; if P 光+风 > P 负 , then start the electrolyzer module to produce hydrogen and simultaneously charge the lithium battery and the supercapacitor module, and solve the optimal energy scheduling scheme through an algorithm, with the goal of maximum economic benefit and maximum energy efficiency, until it is judged that the compressed hydrogen storage module, the lithium battery and the supercapacitor module are full, at which time the power supply is stopped and the remaining power is sold; if P 光+风 < P 负 , then first judge whether P 光+风+电容+电池 is greater than the load power P 负 , if P 光+风+电容+电池 < P 负 , first judge whether the SOC of the lithium battery is greater than 20%. If the SOC of the lithium battery < 20%, then judge that the current energy supply and energy storage are both seriously insufficient, and the energy is only used to maintain the most basic operation of the system, with the goal of the maximum cost control optimization scheme considering the fault situation; if the SOC of the lithium battery is greater than 20%, then enable the supercapacitor and the lithium battery to supply power together, and then judge whether the energy can supply the electrolyzer and the load to operate. If P 光+风+电容+电池 > P 负+电解槽 , then solve the optimal energy scheduling scheme under general wind and light conditions through an algorithm, with the goal of maximizing system reliability and maximum energy efficiency, until the hydrogen storage tank is full and the power supply to the electrolyzer is stopped; if P 光+风+电容+电池 < P 负+电解槽 , then judge that the energy supply is seriously insufficient, and the energy is only used to maintain the most basic operation of the system, with the goal of the maximum cost control optimization scheme considering the fault situation.

[0073] Due to the above technical solution, the technical progress achieved by the present invention is as follows:

[0074] 1. The present invention designs an energy management and optimal scheduling method applicable to renewable energy hydrogen production systems, which can achieve the maximum economic benefits and maximum energy efficiency operation of the system on the basis of ensuring the long-term stable operation of the system, and realizes the reasonable scheduling of the steady-state operation and efficient operation of the renewable energy hydrogen production system containing hydrogen energy storage.

[0075] 2. The energy management method of the integrated energy system proposed by the present invention considers both economic benefits and energy efficiency goals compared with the traditional energy management method, and can allocate the weights of the two according to the wishes of the decision maker to achieve more reasonable operation, including the energy management of electrolyzers, fuel cells, and hydrogen storage tanks, and has wider applicability.

[0076] 3. The present invention adopts a multi-objective collaborative optimization scheduling method, uses a multi-objective modified firefly algorithm (MOMFA), innovatively combines the optimization algorithm with multiple optimization scheduling objective functions, triggers with the minimum value during the charging process; uses the dynamic programming algorithm during the discharging process to solve the optimal battery pack configuration, maximize the discharging capacity of the battery pack, make full use of redundant batteries, balance quickly and with few switch actions in the case of inconsistent battery packs, and maximize the discharging amount of the battery pack. Description of the Drawings

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts;

[0078] Figure 1 is a schematic structural diagram of the renewable energy system in the embodiment of the present invention;

[0079] Figure 2 is a schematic diagram of the energy management method of the renewable energy system in the embodiment of the present invention;

[0080] Figure 3 is a schematic diagram of the process of the optimization scheduling method in the embodiment of the present invention. Detailed Embodiments

[0081] It should be noted that the terms "including" and "having" and any variations thereof in the description, claims and above-mentioned drawings of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0082] The present invention will be further described in detail below with reference to the drawings and embodiments:

[0083] As Figure 1 shown, the renewable energy hydrogen production system includes a photovoltaic module, a wind power module, a power grid module, a lithium battery and a supercapacitor module, an electrolyzer module and a compressed hydrogen storage module. The electric energy generated by the photovoltaic module and the wind power module powers the load. When there is surplus in the total power generation of the two, a part of the electric energy is first converted into hydrogen energy through the electrolyzer and stored in the hydrogen storage tank, and the other part is stored in the lithium battery and the supercapacitor module. If there is still surplus, it is sold; the power grid module and the lithium battery and the supercapacitor module are connected to supply power when the photovoltaic module and the wind power module cannot meet the load power supply, and can decide whether to start the electrolyzer module to convert electric energy into hydrogen energy storage after economic judgment; the lithium battery and the supercapacitor module can be used as an alternative power supply method to supply power to the load.

[0084] Build an energy management strategy and an optimal scheduling model for the wind-solar power generation hydrogen production energy storage system;

[0085] As Figure 2 shown, an energy management and optimal scheduling method for a renewable energy system provided by the present invention specifically includes the following steps:

[0086] S1. Collect data of each module of the renewable energy hydrogen production system, detect whether each module can operate normally, stop running if there is a fault, and proceed to step S2 if there is no fault;

[0087] The renewable energy system includes a photovoltaic module, a wind power module, a power grid module, a lithium battery and a supercapacitor module, an electrolyzer module and a compressed hydrogen storage module,

[0088] S2. By setting multi-objective functions and constraints;

[0089] The steps to determine the operating constraint conditions and multi-objective functions are as follows:

[0090] S21. Generate a scheduling instruction every 1 day on a 24-hour time scale, mainly considering economic benefits, which is specifically divided into four parts. The first part is the revenue from the production and sale of hydrogen by the hydrogen refueling station; the second part is the operation and maintenance costs of renewable energy power generation equipment, mainly considering the operation and maintenance costs of photovoltaic panels and wind turbines. The third part is the operation and maintenance costs of the electrolyzer and considers the degradation of the electrolyzer according to the time scale. The fourth part is the cost of purchasing hydrogen externally and using grid energy. The economic management objective is to maximize the sum of the above four items after addition and subtraction, that is:

[0091] F1(x) = F1(C sys t em )

[0092]

[0093] Among them, C system represents the economic benefit objective function, C pv (t) represents the operation and maintenance costs of photovoltaic panels considering degradation, C wt (t) represents the operation and maintenance costs of wind turbines considering degradation, C bat (t) represents the operation and maintenance coefficient of lithium battery and supercapacitor modules considering degradation under unit power, C ele (t) represents the operation and maintenance coefficient of electrolyzer modules considering degradation under unit power, C off represents the cost of purchasing hydrogen externally, C fee represents the electricity price, C feeH represents the selling price of hydrogen, P grid represents the grid power, C H is the price per kilogram of hydrogen, m off (t) represents the mass of hydrogen transported externally, m pro (t) represents the mass of hydrogen produced by the hydrogen refueling station;

[0094] S22. Generate a scheduling instruction every 1 day on a 24-hour time scale, mainly considering energy efficiency, which is specifically divided into four parts. The first part is the supply efficiency of renewable energy power generation equipment, mainly considering the supply efficiency of photovoltaic panels and wind turbines; the second part is the power efficiency provided by the grid and lithium batteries; the third part is the hydrogen energy efficiency generated by the electrolyzer; the fourth part is the energy loss generated to maintain the system operation or the system in an abnormal operation and maintenance state. The energy efficiency objective is to maximize the ratio of the output efficiency to the consumption efficiency, that is:

[0095] F2(x) = F2(η system )

[0096]

[0097] Among them, η system represents the energy efficiency objective function, and P pv1 (t) represents the power of the photovoltaic panel supply system, and P wt1 (t) represents the power of the wind turbine supply system, and P pv2 (t) and P wt2 (t) represent the surplus power sold, and P bat (t), P grid (t) respectively represent the power provided by the lithium battery and supercapacitor module, and the power grid, and P loss (t) represents the power loss generated when maintaining the system operation or when the system is in normal operation and maintenance, and P com (t) represents the working power of the compressor, represents the mass of hydrogen produced, eH represents the hydrogen energy power density;

[0098] S23 generates scheduling instructions every 1 hour on a minute - level time scale, mainly considering the minimum operation and maintenance cost of the energy supply equipment under the lowest operating state of the system, which is specifically divided into two parts. The first part is the operation and maintenance cost of the energy supply equipment, and the second part is the repair cost generated when a certain energy supply equipment fails, and the cost generated by the remaining energy supply equipment and the power grid sharing the energy demand.

[0099] The maximum - degree cost - control objective needs to minimize the cost of the system operating at the lowest level, that is:

[0100] F3(x) = F3(C operation )

[0101]

[0102] Among them, C operation represents the system operation cost objective function; C pv (t) represents the operation and maintenance cost of the photovoltaic panel considering degradation; R pv (j1) represents the repair cost when the photovoltaic panel fails; j1 represents the repair time of the photovoltaic panel; P pv (t + j2 + j3) represents the power supplied by the photovoltaic panel to the system when the wind turbine, lithium battery or supercapacitor module may fail; C wt (t) represents the operation and maintenance cost of the wind turbine considering degradation; R wt (j2) represents the repair cost when the wind turbine fails; j2 represents the repair time of the wind turbine; P wt (t + j3 + j1) represents the power supplied by the photovoltaic panel to the system when the photovoltaic panel, lithium battery or supercapacitor module may fail; C bat(t) represents the operation and maintenance coefficient of the lithium - battery and super - capacitor module considering the degradation situation; R bat (j3) represents the repair cost when the lithium - battery and super - capacitor module fails; j3 represents the repair time of the lithium - battery and super - capacitor module; P bat (t + j2 + j1) represents the power supplied by the photovoltaic panel to the system when the wind turbine and photovoltaic panel may fail; P grid (t + j1 + j2 + j3) represents the power supplied by the photovoltaic panel to the system when the photovoltaic panel, wind turbine, and photovoltaic panel may fail. S24, the constraints that the energy management needs to meet include the following parts:

[0103] 1) The lithium - battery constraints mainly include charge - discharge power constraints and SOC constraints:

[0104] Pb atm i n ≤Pb at (t)≤Pb atmax (t)

[0105] Pb atmax (t)≤Pb atmax

[0106] SOC(t + 1)=SOC(t)+Pb at (t)ΔT / Eb at

[0107] SOC min ≤SOC(t)≤SOC max

[0108] Among them, P batmin 、P batmax (t)、P batmax 、SOC min 、SOC max 、ΔT respectively represent the minimum operating power of the lithium - battery, the maximum operating power of the lithium - battery, the maximum operating power of the lithium - battery considering degradation, the minimum value of the lithium - battery SOC, the maximum value of the lithium - battery SOC, and the time interval of the scheduling period;

[0109] 2) The constraint conditions of the electrolyzer mainly include operating power constraints and hydrogen production constraints. The input power range of the electrolyzer is from minimum operation to full - power operation. Since a lower current density will cause the hydrogen concentration in oxygen to be too low, resulting in an explosion risk, usually the minimum power of the electrolyzer is 10% of its rated power:

[0110] P elemin ≤P ele (t)≤P elemax (t)

[0111] P elemaxP(t) ≤ P elemax

[0112]

[0113] wherein, P elemin 、P elemax 、HHV, η ele respectively represent the minimum operating power of the electrolyzer, the maximum operating power of the electrolyzer, the higher heating value of hydrogen, and the electrolyzer efficiency. P elemax (t) represents the maximum operating power considering the degradation of the electrolyzer.

[0114] 3) The operation of the compressor mainly considers its power consumption, and its value is directly related to the hydrogen production of the electrolyzer:

[0115] P com (t) = λ com mh2(t)

[0116] wherein, λ com represents the energy consumption coefficient of the compressor.

[0117] 4) LOH constraint of the hydrogen storage tank:

[0118] LOH(t + 1) = LOH(t) + (m h2 (t + 1) + m off (t + 1) - m load (t + 1))ΔT / m max

[0119] LOH min ≤ LOH(t) ≤ LOH max

[0120] wherein, LOH min 、LOH max 、m store 、m load 、m max respectively represent the minimum value of LOH of the hydrogen storage tank, the maximum value of LOH of the hydrogen storage tank, the mass of hydrogen inside the hydrogen storage tank, the hydrogen demand of the hydrogen load, and the maximum mass of hydrogen allowed to be stored in the hydrogen storage tank.

[0121] 5) The grid power needs to meet certain constraints:

[0122] P gridmin ≤ P grid (t) ≤ P gridmax

[0123] wherein, P gridmin 、P gridmax respectively represent the minimum value of the grid power and the maximum value of the grid power.

[0124] 6) The electrical energy of the system needs to maintain balance:

[0125] P pv1 (t)+P pv2 (t)=P pv (t)

[0126] P wt1 (t)+P wt2 (t)=P wt (t)

[0127] P pv (t)+P wt (t)=P ele (t)+P com (t)+P grid (t)+Pb at (t)+Pl oss (t)

[0128] 7) The average failure frequency of each device satisfies certain constraints:

[0129] f setmin <fi(t)<f setmax

[0130] where f setmin and f setmax respectively represent the minimum and maximum failure frequencies set when the device leaves the factory.

[0131] S3. Obtain the optimal energy scheduling and collaborative optimization solutions under different conditions through the multi-objective improved firefly algorithm;

[0132] As Figure 3 shown, the specific steps are as follows:

[0133] S31. Define the multi-objective problem:

[0134]

[0135] where x i represents the design or control variable vector, F m represents the objective function to be optimized, and the optimization problem is subject to a set of inequality and equality constraints, represented by G j (x) and H k (x) respectively. The design variables or control variables are bounded by lower and upper bounds, represented by L and U respectively. After completing step a, enter step b for algorithm initialization;

[0136] S32. Initialize the population, set the firefly population to N, and each firefly position x i is randomly generated in the decision variable space, and these positions represent different solutions;

[0137] S33. For each firefly \(i\), calculate the solution of the objective function. The brightness \(I\) i is related to the objective function value and reflects the performance of a certain solution on the objective function. Usually in multi-objective optimization, the brightness is obtained through non-dominated sorting. The specific steps include:

[0138] 1) Each solution is assigned a level. Level 1 is all non-dominated solutions in the current population, level 2 is the non-dominated solutions among the remaining solutions after removing level 1, and so on. The non-dominated sorting level of each firefly for two objectives is \(rank\) i ;

[0139] 2) The crowding distance is an index to measure the distribution density of solutions in its level. Use the crowding distance \(distance\) i to maintain the diversity of solutions and prevent the algorithm from only aggregating in a certain area;

[0140] 3) Define the brightness \(I\) i as a function related to the sorting level. Usually, the brightness can be set as

[0141] S34. Position update. In this process, each firefly (solution) moves or adjusts its position (a point in the solution space) according to the performance of other fireflies around it to find a better solution:

[0142]

[0143] where, represents the position of the current firefly at time \(t\), represents the position of the firefly with higher brightness (better solution) at time \(t\), \(\beta_0\) represents the base value of attraction, controlling the willingness of the firefly to move towards the individual with higher brightness, \(\gamma\) represents the light absorption coefficient, affecting the attenuation speed of brightness (attraction) with distance, is the Euclidean distance between fireflies (solutions), \(\alpha\) represents the step size parameter, controlling the amplitude of random movement, and \(rand\) is a random number uniformly distributed in \([0, 1]\), used to introduce randomness and increase the diversity of searching for solutions. Specifically, it includes:

[0144] 1) Iteration and convergence: Repeat the brightness evaluation and position update process until the termination condition is met (reaching the maximum number of iterations or the improvement of the solution is lower than a certain threshold);

[0145] 2) Obtaining non-dominated solutions and Pareto optimality: In multi-objective optimization, non-dominated sorting is usually adopted to evaluate the quality of solutions. A solution is considered non-dominated if it is not simultaneously surpassed by any other solution in all objectives. The Pareto optimal solution set consists of these non-dominated solutions, which represent the best solutions for trade-offs among objective functions. According to the weights considered by the decision maker, the optimal solution is selected from the Pareto optimal solution set.

[0146] S4. Monitor the power of each module, determine which scenario the system is in according to the relationship between the total input power and the load power, and thus select different optimal scheduling objectives. Balance the energy conversion in different scenarios through the charging and discharging of the energy storage module composed of the supercapacitor and the lithium battery.

[0147] Specifically, perform power tracking on the photovoltaic module and the wind power module, and judge whether P 光+风 is greater than the load power P 负 ; if P 光+风 > P 负 , start the electrolyzer module to produce hydrogen and simultaneously charge the lithium battery and the supercapacitor module, and solve the optimal energy scheduling scheme through an algorithm. The objectives are maximum economic benefit and maximum energy efficiency until it is judged that the compressed hydrogen storage module, the lithium battery and the supercapacitor module are full. At this time, stop power supply and sell the remaining power; if P 光+风 < P 负 , first judge whether P 光+风+电容+电池 is greater than the load power P 负 ; if P 光+风+电容+电池 < P 负 , first judge whether the SOC of the lithium battery is greater than 20%. If the SOC of the lithium battery < 20%, it is judged that the current energy supply and energy storage are both seriously insufficient, and the energy is only used to maintain the most basic operation of the system. The objective is the maximum cost control optimization scheme considering the fault situation; if the SOC of the lithium battery is greater than 20%, enable the lithium battery and the supercapacitor module to supply power together, and then judge whether the energy can supply the electrolyzer module and the load to operate. If P 光+风+电容+电池 > P 负+电解槽 , solve the optimal energy scheduling scheme under general wind and light conditions through an algorithm. The objectives are to maximize the system reliability and maximum energy efficiency until the compressed hydrogen storage module is full, and stop power supply to the electrolyzer module; if P 光+风+电容+电池 < P 负+电解槽 , it is judged that the energy supply is seriously insufficient, and the energy is only used to maintain the most basic operation of the system. The objective is the maximum cost control optimization scheme considering the fault situation.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An energy management and optimal scheduling method for a renewable energy system, characterized in that: It includes the following steps: S1. Collect data of each module of the renewable energy hydrogen production system, detect whether each module can operate normally. If there is a fault, stop the operation. If there is no fault, proceed to step S2; The renewable energy system includes a photovoltaic module, a wind power module, a power grid module, a lithium battery and a supercapacitor module, an electrolyzer module and a compressed hydrogen storage module. S2. Set multi-objective functions and constraints; S3. Obtain the optimal solutions for energy scheduling and collaborative optimization under different conditions through the multi-objective improved firefly algorithm; The specific steps are as follows: S31. Define the multi-objective problem: where, x i represents the design or control variable vector; F m (x) represents the objective function to be optimized, and the optimization problem is subject to a set of inequality and equality constraints, represented by G j (x) and H k (x) respectively; the design variables or control variables are bounded by lower and upper bounds, represented by L and U respectively; after step S31 is completed, step S32 is entered for algorithm initialization; S32. Initialize the population. Set the firefly population size as N, and the position of each firefly \(x\) is randomly generated in the decision variable space. These positions represent different solutions; i ​ S33. For each firefly i, calculate the solution of the objective function, the brightness I i is related to the objective function value and reflects the performance of a certain solution on the objective function. In multi-objective optimization, the brightness is obtained through non-dominated sorting; S34. Update the position. In this process, each firefly moves or adjusts its position according to the performance of other fireflies around it to find a better solution: Among them, is the position of the current firefly at time t + 1; is the position of the current firefly at time t; is the position of the firefly with higher brightness at time t; β0 is the base value of attraction, controlling the willingness of the firefly to move towards the individual with higher brightness; γ is the light absorption coefficient, affecting the attenuation rate of brightness with distance, is the Euclidean distance between fireflies; α is the step size parameter, controlling the amplitude of random movement; rand is a random number uniformly distributed in [0,1], used to introduce randomness and increase the diversity of search solutions; S4. Monitor the power of each module, determine which scenario the system is in according to the relationship between the input total power and the load power, and thus select different optimal scheduling objectives. The energy conversion under different scenarios is balanced by the charge and discharge of the energy storage module composed of the supercapacitor and the lithium battery.

2. The energy management and optimal scheduling method of a renewable energy system according to claim 1, characterized in that: In S33, the specific steps include: 1) Each solution is assigned a level. Level 1 consists of all non-dominated solutions in the current population. Level 2 consists of non-dominated solutions among the remaining solutions after removing those in level 1, and so on. The non-dominated sorting level of each firefly for the two objectives is rank i ; 2) The crowding distance is an index for measuring the distribution density of solutions in their hierarchy, and the crowding distance is used i to maintain the diversity of solutions and prevent the algorithm from clustering only in a certain area; 3) Define the brightness I i as a function related to the sorting level, and the brightness is set to 3. The energy management and optimal scheduling method for a renewable energy system according to claim 1, characterized in that: In S34, it specifically includes: 1) Iteration and convergence: Repeat the process of brightness evaluation and position update until the termination condition is met, that is, the maximum number of iterations is reached or the improvement of the solution is lower than a certain threshold; 2) Obtain non-dominated solutions and Pareto optimality: In multi-objective optimization, non-dominated sorting is used to evaluate the quality of solutions; a solution is considered non-dominated if it is not simultaneously surpassed by any other solution in all objectives; the Pareto optimal solution set is composed of these non-dominated solutions, representing the best solution for the trade-off between objective functions; select the optimal solution from the Pareto optimal solution set according to the weights considered by the decision maker.

Images