DETAILED ACTION
This communication is a Non-Final Office Action rejection on the merits. Claim 1 is currently pending and have been addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without reciting significantly more.
Independent Claim 1
Step One - First, pursuant to step 1 in the January 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG”) on 84 Fed. Reg. 53, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - Claim 1 recites: A method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station, comprising the following steps: Step 1: setting a number N of charging piles, a maximum charge power P of the charging piles, a rated capacity κ.sub.e.sup.cap of hydrogen energy, a maximum charge-discharge power h.sup.cap, charge efficiency η.sup.c, discharge efficiency η.sup.dc, a time of use [T.sub.num,s.sup.i,T.sub.num,e.sup.i] of the charging piles and required charge energy E.sub.num.sup.i of a hydrogen-photovoltaic-storage-charging integrated energy station, wherein i indicates a serial number of each charging pile, T.sub.num,s.sup.i and T.sub.num,e.sup.i indicate a start time of num.sup.th use of an i.sup.th charging pile and an end time of the num.sup.th use of the i.sup.th charging pile, and E.sub.num.sup.i indicates the charge energy required for the num.sup.th use of the i.sup.th charging pile; Step 2: establishing an operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein operation scheduling model is expressed by formula (1): in formula (1), indicates an operating cost of the integrated energy station at a current time, wt indicates a mains electricity price at the current time, p' indicates a total charge power of the integrated energy station, r indicates a distributed photovoltaic output at the current time, h.sub.t,c and h.sub.t,dc respectively indicate a maximum permissible charge power and a maximum permissible discharge power of the hydrogen energy at the current time, and p.sub.t,i indicates a charge power of the i.sup.th charging pile at the current time; obtaining constrains of the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein the constraints are expressed by formula (2): in formula (2), b, indicates an energy level of the hydrogen energy at the current time; h.sub.t indicates an output power of the hydrogen energy at the current time; T.sub.t.sup.i indicates a remaining charge time of the i.sup.th charging pile at the current time; L.sub.t.sup.i indicates whether a vehicle is being charged by the i.sup.th charging pile at the current time, wherein when L.sub.t.sup.i is 1, it indicates that a vehicle is being charged by the i.sup.th charging pile at the current time, and if L.sub.t.sup.i is 0, it indicates that no vehicle is being charged by the i.sup.th charging pile at the current time; τ.sub.t+1.sup.i indicates a retention time for charging of an electric vehicle that arrives at a charging station and uses the i th charging pile at a next time; E.sub.t.sup.i indicates remaining charge energy of the i.sup.th charging pile at the current time; τ.sub.t+1.sup.i indicates charge energy required by the electric vehicle that arrives at the charging station and uses the i.sup.th charging pile at the next time; and Step 3: iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station by means of an improved algorithm to obtain an individual position with a maximum predation benefit in a current wolf population, and outputting the individual position as an optimal scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein a specific process of iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station comprises: Step 3.1: optimizing and improving an initial wolf population of the algorithm to obtain an ultimate initial wolf population, which specifically comprises the following steps: Step 3.1.1: setting a number g of coarse populations and a number k of excellent wolf individuals in the iterative solving process, and initializing an iteration r to satisfy r=1; Step 3.1.2: representing position information X.sub.j of a j.sup.th wolf according to a matrix formed by charge powers of the N charge piles at each time, wherein the position information X.sub.j of the j.sup.th wolf is expressed by formula (3): Step 3.1.3: randomly generating an initial wolf population formed by m wolves, randomly generating 24x N values according to a value range of p.sub.t,i in formula (2), and substituting the values into formula (3) to obtain position information of one wolf in the initial wolf population; repeating the step until position information of each wolf in the wolf population is generated; collecting the position information of all the wolves in the initial wolf population to form a position information set X of the initial wolf population, wherein the position information set X is expressed by formula (4): in formula (4), X.sub.1 is first position information of a first wolf in the initial wolf population, X.sub.2 is second position information of a second wolf in the initial wolf population, and X.sub.m is m.sup.th position information of a m.sup.th wolf in the initial wolf population; Step 3.1.4: substituting the first position information in the position information set X of the initial wolf population into the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station for calculation, and taking a calculation result as a predation benefit of the first wolf in the initial wolf population; repeating the process until the predation benefit of each wolf in the initial wolf population is obtained; sorting the predation benefits of all the wolves in the initial wolf population in a descending order, and plotting a first predation benefit curve; calculating similarities between the first predation benefit curve and five standard predation benefit curves, and selecting a curve type of the standard predation benefit curve with a maximum similarity as a curve type of the first predation benefit curve; calculating a noise level between the standard predation benefit curve with the maximum similarity and the first predation benefit curve; and Step 3.1.5: obtaining values of optimized parameters Z.sub.1, Z.sub.2, Z.sub.3 and Z.sub.4 of the initial wolf population according to the curve type and the noise level of the first predation benefit curve obtained in Step 3.1.4, and calculating a size s of the ultimate initial population according to formula (5): in formula (5), e is a natural base; Step 3.2: normalizing first three wolves in the ultimate initial wolf population as a α wolf, a β wolf and a δ wolf respectively, and calculating distances from each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population to the α wolf, the β wolf and the β wolf according to formula (6): in formula (6), j is a natural number which is greater than or equal to 4 and less than or equal to s; D.sub.α(j) is a distance from the j.sup.th wolf in the ultimate initial wolf population to the α wolf; D.sub.β(j) is a distance from the j.sup.th wolf in the ultimate initial wolf population to the β wolf; D.sub.δ(j) is a distance from the j.sup.th wolf in the ultimate initial wolf population to the δ wolf; X.sub.α(r), X.sub.β(r) and X.sub.δ(r) are respectively position information of the α wolf, the β wolf and the δ wolf; X.sub.j(r) is position information of the j.sup.th wolf; C.sub.α, C.sub.β and C.sub.δ are distance coefficients of the α wolf, the β wolf and the δ wolf respectively; U.sub.α,1, U.sub.β,1 and U.sub.δ,1 are random numbers which are randomly generated within [0,1] and distributed uniformly; Step 3.3: updating position information, in a next iteration, of each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population according to formulat (7): in formula (7), X.sub.j(r+1) is the position information of the j.sup.th wolf in the next iteration; X.sub.α(r+1), X.sub.β(r+1) and X.sub.δ(r+1) are respectively the position information of the α wolf, the β wolf and the δ wolf in the next iteration; A.sub.α, A.sub.β and A.sub.δ are respectively distance update coefficients of the α wolf, the β wolf and the δ wolf; U.sub.α,2, U.sub.β,2 and U.sub.δ,2 are respectively random numbers that are randomly generated within [0, 1] and distributed uniformly; r is a current iteration; R is a maximum iteration; after the position information, in the next iteration, of each wolf other than the α wolf, the β wolf and the δ wolf in the initial wolf population is calculated, increasing the current iteration r by 1, and determining whether the current iteration r is greater than or equal to the maximum iteration R; if so, outputting the position information, in the current iteration, of all the wolves in the ultimate initial wolf population; if not, substituting the position information, in the current iteration, of all the wolves in the ultimate initial wolf population into Step 3.2 and Step 3.3 for iterative calculation again until the current iteration r is greater than or equal to the maximum iteration R; and Step 3.4: sequentially substituting the position information of all the wolves in the ultimate initial wolf population output in Step 3.3 into the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station for calculation to obtain predation benefits of all the wolves in the ultimate initial wolf population; and the selecting the position information of the wolf with the maximum predation benefit in the ultimate initial wolf population as the optimal operation scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station. These claim elements are considered to be abstract ideas because they are directed to “mathematical concepts” which include “mathematical calculations.” In this case, using an algorithm for generating a scheduling scheme encompasses a mathematical calculation. If a claim limitation, under its broadest reasonable interpretation, covers mathematical calculations, then it falls within the “mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 - The judicial exception is not integrated into a practical application. Claim 1 includes additional elements: an improved grey wolf optimization algorithm.
The grey wolf optimization algorithm is merely used to obtain an individual position with a maximum predation benefit in a current wolf population, and outputting the individual position as an optimal scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein a specific process of iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station comprises: Step 3.1: optimizing and improving an initial wolf population of the grey wolf optimization algorithm to obtain an ultimate initial wolf population; Step 3.2: normalizing first three wolves in the ultimate initial wolf population as a α wolf, a β wolf and a δ wolf respectively, and calculating distances from each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population to the α wolf, the β wolf and the δ wolf; Step 3.3: updating position information, in a next iteration, of each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population; Step 3.4: sequentially substituting the position information of all the wolves in the ultimate initial wolf population output in Step 3.3 into the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station for calculation to obtain predation benefits of all the wolves in the ultimate initial wolf population; and the selecting the position information of the wolf with the maximum predation benefit in the ultimate initial wolf population as the optimal operation scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station (Paragraphs 0029-0039). Merely stating that the step is performed by an algorithm results in “apply it” on a computer (MPEP 2106.05f). This elements of “grey wolf optimization algorithm” is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer element. Accordingly, alone and in combination, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B - The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” the concept of solving an optimization problem. The specification shows that the grey wolf optimization algorithm is merely used to obtain an individual position with a maximum predation benefit in a current wolf population, and outputting the individual position as an optimal scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein a specific process of iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station comprises: Step 3.1: optimizing and improving an initial wolf population of the grey wolf optimization algorithm to obtain an ultimate initial wolf population; Step 3.2: normalizing first three wolves in the ultimate initial wolf population as a α wolf, a β wolf and a δ wolf respectively, and calculating distances from each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population to the α wolf, the β wolf and the δ wolf; Step 3.3: updating position information, in a next iteration, of each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population; Step 3.4: sequentially substituting the position information of all the wolves in the ultimate initial wolf population output in Step 3.3 into the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station for calculation to obtain predation benefits of all the wolves in the ultimate initial wolf population; and the selecting the position information of the wolf with the maximum predation benefit in the ultimate initial wolf population as the optimal operation scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station (Paragraphs 0029-0039). Further, the step of “iteratively solving” is considered a well-understood, routine, and conventional function since it's just “performing repetitive calculations” (MPEP 2106.05(d)). Thus, nothing in the claim adds significantly more to the abstract idea. The claim is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Liu (CN 115358806 A), in view of Ren (CN 114676534 A), in further view of Dong (CN 115411756 A).
Regarding claim 1, Liu discloses a method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station, comprising the following steps (Page 3, Contents of the Invention, The invention aims at the disadvantages of the above requirements and technology, claims a method based on renewable energy power generation of electric hydrogen comprehensive energy system cost optimization method, can calculate the minimum operation cost and maximum energy production through real-time information. In order to realize the purpose of the present invention, the adopted technical solution is as follows: An electric hydrogen comprehensive energy system cost optimization method based on renewable energy power generation, comprising the following steps: S1, according to the operation mode electro hydrogen the comprehensive energy system, combining the real-time price, load fluctuation and maintenance coefficient constructing system cost model, model comprises a fan system, a photovoltaic system, a hydrogen generating system and a hydrogen fuel vehicle):
Step 1: setting a number N of charging piles, a maximum charge power P of the charging piles, a rated capacity κ.sub.e.sup.cap of hydrogen energy, a maximum charge-discharge power h.sup.cap, …, a time of use [T.sub.num,s.sup.i,T.sub.num,e.sup.i] of the charging piles and required charge energy E.sub.num.sup.i of a hydrogen-photovoltaic-storage-charging integrated energy station, wherein i indicates a serial number of each charging pile, T.sub.num,s.sup.i and T.sub.num,e.sup.i indicate a start time of num.sup.th use of an i.sup.th charging pile and an end time of the num.sup.th use of the i.sup.th charging pile, and E.sub.num.sup.i indicates the charge energy required for the num.sup.th use of the i.sup.th charging pile (Page 3, Contents of the Invention, S1, according to the operation mode electro hydrogen the comprehensive energy system, combining the real-time price, load fluctuation and maintenance coefficient constructing system cost model, model comprises a fan system, a photovoltaic system, a hydrogen generating system and a hydrogen fuel vehicle; S2, mathematizing the system cost model, obtaining the constraint condition under dynamic hydrogen load, constructing the target function of electro hydrogen comprehensive energy source system, respectively is the minimum cost, maximum generated energy and maximum hydrogen production amount; Page 7, he hydrogen production system is provided with an energy storage battery (battery), charging in the electric price valley, discharging to the fuel cell in peak period of electricity price, the purpose is to further reduce the operation cost of the system; Examiner interprets “load fluctuation” as the “required charge energy”);
Step 2: establishing an operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein operation scheduling model is expressed by formula (1) (Page 3, Contents of the Invention, S1, according to the operation mode electro hydrogen the comprehensive energy system, combining the real-time price, load fluctuation and maintenance coefficient constructing system cost model, model comprises a fan system, a photovoltaic system, a hydrogen generating system and a hydrogen fuel vehicle; S2, mathematizing the system cost model, obtaining the constraint condition under dynamic hydrogen load, constructing the target function of electro hydrogen comprehensive energy source system, respectively is the minimum cost, maximum generated energy and maximum hydrogen production amount; Page 7, The comprehensive consideration of the electro hydrogen, the electric hydrogen comprehensive energy system optimization scheduling has guidance significance, gives the comprehensive analysis method of the hydrogen energy benefit. when calculating the different electric hydrogen comprehensive energy system benefit, it only needs to change the input parameter to obtain the minimum cost, maximizing the optimal solution output of electric energy and hydrogen energy):
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in formula (1), indicates an operating cost of the integrated energy station at a current time, wt indicates a mains electricity price at the current time, p' indicates a total charge power of the integrated energy station, r indicates a distributed photovoltaic output at the current time, h.sub.t,c and h.sub.t,dc respectively indicate a maximum permissible charge power and a maximum permissible discharge power of the hydrogen energy at the current time, and p.sub.t,i indicates a charge power of the i.sup.th charging pile at the current time; obtaining constrains of the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein the constraints are expressed by formula (2) (Page 3, Contents of the Invention, S1, according to the operation mode electro hydrogen the comprehensive energy system, combining the real-time price, load fluctuation and maintenance coefficient constructing system cost model, model comprises a fan system, a photovoltaic system, a hydrogen generating system and a hydrogen fuel vehicle; S2, mathematizing the system cost model, obtaining the constraint condition under dynamic hydrogen load, constructing the target function of electro hydrogen comprehensive energy source system, respectively is the minimum cost, maximum generated energy and maximum hydrogen production amount; Page 5, As the optimization scheme of the invention, in the step S2, system cost model mathematical, as follows: S2-1 is combined with the electro hydrogen operation mode of the comprehensive energy system, adding constraint condition, comprising electric energy balance constraint, hydrogen energy balance constraint, maximum minimum power constraint of the fan system and the photovoltaic system, hydrogen fuel vehicle mileage constraint; Page 7, Specific implementation examples, The hydrogen production system is provided with an energy storage battery (battery), charging in the electric price valley, discharging to the fuel cell in peak period of electricity price, the purpose is to further reduce the operation cost of the system):
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in formula (2), b, indicates an energy level of the hydrogen energy at the current time; h.sub.t indicates an output power of the hydrogen energy at the current time; T.sub.t.sup.i indicates a remaining charge time of the i.sup.th charging pile at the current time; L.sub.t.sup.i indicates whether a vehicle is being charged by the i.sup.th charging pile at the current time, wherein when L.sub.t.sup.i is 1, it indicates that a vehicle is being charged by the i.sup.th charging pile at the current time, and if L.sub.t.sup.i is 0, it indicates that no vehicle is being charged by the i.sup.th charging pile at the current time; τ.sub.t+1.sup.i indicates a retention time for charging of an electric vehicle that arrives at a charging station and uses the i th charging pile at a next time; E.sub.t.sup.i indicates remaining charge energy of the i.sup.th charging pile at the current time; τ.sub.t+1.sup.i indicates charge energy required by the electric vehicle that arrives at the charging station and uses the i.sup.th charging pile at the next time (Page 3, Contents of the Invention, S1, according to the operation mode electro hydrogen the comprehensive energy system, combining the real-time price, load fluctuation and maintenance coefficient constructing system cost model, model comprises a fan system, a photovoltaic system, a hydrogen generating system and a hydrogen fuel vehicle; Page 3, In Equation 2: Cpvm is photovoltaic system maintenance costs Cpvgf is the photovoltaic system net charge, Upv2h is photovoltaic system to the hydrogen system electricity selling income, Upv2g is photovoltaic system to the electric network electricity selling income; Page 5, S 1-3 to the electric price of the fixed fan system and the photovoltaic system of the previous day, determining the low price and high price of the fan system and the photovoltaic system, charging the energy storage battery electro hydrogen the comprehensive energy system at low price, discharging the fuel cell electro hydrogen the comprehensive energy system when high electricity price; using hydrogen fuel vehicle real-time dynamic load fluctuation to form a complete electric hydrogen comprehensive energy system; Page 10, Hydrogen fuel automobile cost model is: (14) In the formula 14: K is hydrogen fuel vehicle number, SOCFCVMAX is hydrogen fuel vehicle hydrogen storage state upper limit, SOCFCV0, k is the initial hydrogen storage state quantity of the k-th hydrogen fuel vehicle, VFCV is hydrogen fuel vehicle hydrogen storage volume, χ k is the driving mileage of the k-th hydrogen fuel vehicle, ω is the unit mileage hydrogen consumption; T1 is hydrogen fuel vehicle operation period; The income of hydrogen selling is: (15) In the formula 15: pFCV is hydrogen price; S1-3 to the electric price of the fixed fan system and the photovoltaic system of the previous day, determining the low price and high price of the fan system and the photovoltaic system, charging the energy storage battery electrohydrogen the comprehensive energy system at low price, discharging the fuel cell electrohydrogen the comprehensive energy system when high electricity price; using hydrogen fuel vehicle real-time dynamic load fluctuation to form a complete electric hydrogen comprehensive energy system);
and Step 3: iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station by means of an improved grey wolf optimization algorithm to obtain an individual position with a maximum predation benefit in a current wolf population, and outputting the individual position as an optimal scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station, wherein a specific process of iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station comprises (Page 3, S3, Aiming at electro hydrogen comprehensive energy system, setting minimum cost, maximum generated energy, maximum hydrogen production amount three target function, the higher the production cost is higher, in order to find the balance point of three target, using improved multi-target grey wolf algorithm to obtain the global optimal solution, so as to solve the multi-target problem):
Step 3.1: optimizing and improving an initial wolf population of the grey wolf optimization algorithm to obtain an ultimate initial wolf population, which specifically comprises the following steps (Page 3, S3, Aiming at electro hydrogen comprehensive energy system, setting minimum cost, maximum generated energy, maximum hydrogen production amount three target function, the higher the production cost is higher, in order to find the balance point of three target, using improved multi-target grey wolf algorithm to obtain the global optimal solution, so as to solve the multi-target problem):
Step 3.1.1: setting a number g of coarse populations and a number k of excellent wolf individuals in the iterative solving process, and initializing an iteration r to satisfy r=1 (Page 6, As the optimization scheme of the invention, in the step S3, using improved multi-target grey wolf algorithm to solve the multi-target problem, as follows: S3-1 the fan system and the photovoltaic system are generated at the same time within 24 hours, the fan system and the photovoltaic system device output of the same time section are divided into a group for initialization and update, the position Xi of the three-head gray wolf in the i-th time period is);
Step 3.1.2: representing position information X.sub.j of a j.sup.th wolf according to a matrix formed by charge powers of the N charge piles at each time, wherein the position information X.sub.j of the j.sup.th wolf is expressed by formula (3) (Page 6, As the optimization scheme of the invention, in the step S3, using improved multi-target grey wolf algorithm to solve the multi-target problem, as follows: S3-1 the fan system and the photovoltaic system are generated at the same time within 24 hours, the fan system and the photovoltaic system device output of the same time section are divided into a group for initialization and update, the position Xi of the three-head gray wolf in the i-th time period is: In the formula 25: i represents 1-24 hours, xa, i represents the position of the ith grey wolf i hour, xb, i represents the position of the b head grey wolf i hour, xc, i represents the position of the c-th grey wolf i hour):
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Step 3.1.3: randomly generating an initial wolf population formed by m wolves, randomly generating 24x N values according to a value range of p.sub.t,i in formula (2), and substituting the values into formula (3) to obtain position information of one wolf in the initial wolf population; repeating the step until position information of each wolf in the wolf population is generated; collecting the position information of all the wolves in the initial wolf population to form a position information set X of the initial wolf population, wherein the position information set X is expressed by formula (4) (Page 6, As the optimization scheme of the invention, in the step S3, using improved multi-target grey wolf algorithm to solve the multi-target problem, as follows: S3-1 the fan system and the photovoltaic system are generated at the same time within 24 hours, the fan system and the photovoltaic system device output of the same time section are divided into a group for initialization and update, the position Xi of the three-head gray wolf in the i-th time period is: In the formula 25: i represents 1-24 hours, xa, i represents the position of the ith grey wolf i hour, xb, i represents the position of the b head grey wolf i hour, xc, i represents the position of the c-th grey wolf i hour; S3-2 the electric hydrogen comprehensive energy system target function and constraint condition input improved multi-target grey wolf algorithm, setting the number of grey wolf, maximum iteration times, search range and external population Archive parameter, then grey wolf initialization, checking whether the satisfy condition, until generating a sufficient number of qualified individual):
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in formula (4), X.sub.1 is first position information of a first wolf in the initial wolf population, X.sub.2 is second position information of a second wolf in the initial wolf population, and X.sub.m is m.sup.th position information of a m.sup.th wolf in the initial wolf population (Page 6, As the optimization scheme of the invention, in the step S3, using improved multi-target grey wolf algorithm to solve the multi-target problem, as follows: S3-1 the fan system and the photovoltaic system are generated at the same time within 24 hours, the fan system and the photovoltaic system device output of the same time section are divided into a group for initialization and update, the position Xi of the three-head gray wolf in the i-th time period is: In the formula 25: i represents 1-24 hours, xa, i represents the position of the ith grey wolf i hour, xb, i represents the position of the b head grey wolf i hour, xc, i represents the position of the c-th grey wolf i hour; S3-2 the electric hydrogen comprehensive energy system target function and constraint condition input improved multi-target grey wolf algorithm, setting the number of grey wolf, maximum iteration times, search range and external population Archive parameter, then grey wolf initialization, checking whether the satisfy condition, until generating a sufficient number of qualified individual);
Step 3.1.4: substituting the first position information in the position information set X of the initial wolf population into the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station for calculation, and taking a calculation result as a predation benefit of the first wolf in the initial wolf population; repeating the process until the predation benefit of each wolf in the initial wolf population is obtained; sorting the predation benefits of all the wolves in the initial wolf population in a descending order, and plotting a first predation benefit curve; calculating similarities between the first predation benefit curve and five standard predation benefit curves, and selecting a curve type of the standard predation benefit curve with a maximum similarity as a curve type of the first predation benefit curve; calculating a noise level between the standard predation benefit curve with the maximum similarity and the first predation benefit curve (Page 6, As the optimization scheme of the invention, in the step S3, using improved multi-target grey wolf algorithm to solve the multi-target problem, as follows: S3-1 the fan system and the photovoltaic system are generated at the same time within 24 hours, the fan system and the photovoltaic system device output of the same time section are divided into a group for initialization and update, the position Xi of the three-head gray wolf in the i-th time period is: In the formula 25: i represents 1-24 hours, xa, i represents the position of the ith grey wolf i hour, xb, i represents the position of the b head grey wolf i hour, xc, i represents the position of the c-th grey wolf i hour; Page 12, S3-2 the electric hydrogen comprehensive energy system target function and constraint condition input improved multi-target grey wolf algorithm, setting the number of grey wolf, maximum iteration times Maxlter, search range and external population Archive parameter, then performing grey wolf initialization, checking whether the satisfy condition, until generating a sufficient number of qualified individuals. As shown in FIG. 4 is the gray wolf initial position of the update diagram, from FIG. 4 can be seen the position of the gray wolf according to the position of the middle prey (X*, Y*) to update, A is a vector coefficient, A determines whether the new position close to the target or far away from the target gray wolf, when | A | > = 1, is far away from the target, showing stronger global search capability, when | A | is less than 1, close to the target, showing stronger local search capability. the position updating formula is as follows: In the formula 26: wherein C and A are vector coefficients; D is the distance between the individual in the wolf group and the target prey; t is iteration times; X is grey wolf position; XP is the target hunt position, r1, r2 is the random number in the range of [0, 1], a is the control parameter, the value is in the range of [0, 2] and the iteration times of the algorithm is increased; Examiner interprets “new position close to the target” as the “benefit curve”);
and Step 3.1.5: obtaining values of optimized parameters Z.sub.1, Z.sub.2, Z.sub.3 and Z.sub.4 of the initial wolf population according to the curve type and the noise level of the first predation benefit curve obtained in Step 3.1.4, and calculating a size s of the ultimate initial population according to formula (5) (Page 12, As shown in FIG. 4 is the gray wolf initial position of the update diagram, from FIG. 4 can be seen the position of the gray wolf according to the position of the middle prey (X*, Y*) to update, A is a vector coefficient, A determines whether the new position close to the target or far away from the target gray wolf, when | A | > = 1, is far away from the target, showing stronger global search capability, when | A | is less than 1, close to the target, showing stronger local search capability. the position updating formula is as follows: In the formula 26: wherein C and A are vector coefficients; D is the distance between the individual in the wolf group and the target prey; t is iteration times; X is grey wolf position; XP is the target hunt position, r1, r2 is the random number in the range of [0, 1], a is the control parameter, the value is in the range of [0, 2] and the iteration times of the algorithm is increased):
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in formula (5), e is a natural base (S3-2 the electric hydrogen comprehensive energy system target function and constraint condition input improved multi-target grey wolf algorithm, setting the number of grey wolf, maximum iteration times, search range and external population Archive parameter, then grey wolf initialization, checking whether the satisfy condition, until generating a sufficient number of qualified individual);
Step 3.2: normalizing first three wolves in the ultimate initial wolf population as a α wolf, a β wolf and a δ wolf respectively, and calculating distances from each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population to the α wolf, the β wolf and the β wolf according to formula (6) (Pages 12-13, In the formula 26: wherein C and A are vector coefficients; D is the distance between the individual in the wolf group and the target prey; t is iteration times; X is grey wolf position; XP is the target hunt position, r1, r2 is the random number in the range of [0, 1], a is the control parameter, the value is in the range of [0, 2] and the iteration times of the algorithm is increased. S3-3 selecting a, b and c three-head gray wolf from Archive according to wheel disc wagering method, the rest grey wolf according to the position of a, b and c three-head gray wolf to update according to formula 27, checking whether the satisfy condition, until generating a sufficient number of qualified individual; As shown in FIG. 5 is a grey wolf food sketch map, namely grey wolf in searching the prey, updating the position of the schematic diagram. the position updating formula is as follows. In the formula 27: X α, β, δ is the current position of a, b, c three-head gray wolf, D α, β, δ respectively represents the distance of a, b, c three-head wolf and other individuals, X1, 2, 3 respectively define the step length and direction of the candidate wolf individual towards the a, b, c, C1, C2, C3 and a1, a2, a3 is the random vector, X is the position vector of a, b, c three-head gray wolf, X (t + 1) is the final position of the candidate wolf):
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in formula (6), j is a natural number which is greater than or equal to 4 and less than or equal to s; D.sub.α(j) is a distance from the j.sup.th wolf in the ultimate initial wolf population to the α wolf; D.sub.β(j) is a distance from the j.sup.th wolf in the ultimate initial wolf population to the β wolf; D.sub.δ(j) is a distance from the j.sup.th wolf in the ultimate initial wolf population to the δ wolf; X.sub.α(r), X.sub.β(r) and X.sub.δ(r) are respectively position information of the α wolf, the β wolf and the δ wolf; X.sub.j(r) is position information of the j.sup.th wolf; C.sub.α, C.sub.β and C.sub.δ are distance coefficients of the α wolf, the β wolf and the δ wolf respectively; U.sub.α,1, U.sub.β,1 and U.sub.δ,1 are random numbers which are randomly generated within [0,1] and distributed uniformly (Pages 12-13, In the formula 26: wherein C and A are vector coefficients; D is the distance between the individual in the wolf group and the target prey; t is iteration times; X is grey wolf position; XP is the target hunt position, r1, r2 is the random number in the range of [0, 1], a is the control parameter, the value is in the range of [0, 2] and the iteration times of the algorithm is increased. S3-3 selecting a, b and c three-head gray wolf from Archive according to wheel disc wagering method, the rest grey wolf according to the position of a, b and c three-head gray wolf to update according to formula 27, checking whether the satisfy condition, until generating a sufficient number of qualified individual; As shown in FIG. 5 is a grey wolf food sketch map, namely grey wolf in searching the prey, updating the position of the schematic diagram. the position updating formula is as follows. In the formula 27: X α, β, δ is the current position of a, b, c three-head gray wolf, D α, β, δ respectively represents the distance of a, b, c three-head wolf and other individuals, X1, 2, 3 respectively define the step length and direction of the candidate wolf individual towards the a, b, c, C1, C2, C3 and a1, a2, a3 is the random vector, X is the position vector of a, b, c three-head gray wolf, X (t + 1) is the final position of the candidate wolf);
Step 3.3: updating position information, in a next iteration, of each wolf other than the α wolf, the β wolf and the δ wolf in the ultimate initial wolf population according to formula (7) (Pages 12-13, S3-3 selecting a, b and c three-head gray wolf from Archive according to wheel disc wagering method, the rest grey wolf according to the position of a, b and c three-head gray wolf to update according to formula 27, checking whether the satisfy condition, until generating a sufficient number of qualified individual. As shown in FIG. 5 is a grey wolf food sketch map, namely grey wolf in searching the prey, updating the position of the schematic diagram. the position updating formula is as follows. In the formula 27: X α, β, δ is the current position of a, b, c three-head gray wolf, D α, β, δ respectively represents the distance of a, b, c three-head wolf and other individuals, X1, 2, 3 respectively define the step length and direction of the candidate wolf individual towards the a, b, c, C1, C2, C3 and a1, a2, a3 is the random vector, X is the position vector of a, b, c three-head gray wolf, X (t + 1) is the final position of the candidate wolf. As can be seen from FIG. 5, the position of the candidate solution is finally located in the random round position defined by a, b and c. In general, a, b and c need to first predict the approximate position of the prey (potential optimal solution), and then other candidate wolf randomly updates their position near the prey under the guidance of the current optimal three wolves. S3-4 according to the electric hydrogen comprehensive energy system target function in step S2, calculating the target function value of the grey wolf, determining non-dominated individual, updating Archive. S3-5 repeating step, S3-2 S3-3 until reaching the maximum iteration times, at this time, outputting the gray wolf position in the Archive, namely a group of Pareto solution in the electric hydrogen comprehensive energy system cost optimization):
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in formula (7), X.sub.j(r+1) is the position information of the j.sup.th wolf in the next iteration; X.sub.α(r+1), X.sub.β(r+1) and X.sub.δ(r+1) are respectively the position information of the α wolf, the β wolf and the δ wolf in the next iteration; A.sub.α, A.sub.β and A.sub.δ are respectively distance update coefficients of the α wolf, the β wolf and the δ wolf; U.sub.α,2, U.sub.β,2 and U.sub.δ,2 are respectively random numbers that are randomly generated within [0, 1] and distributed uniformly; r is a current iteration; R is a maximum iteration (Pages 12-13, In the formula 26: wherein C and A are vector coefficients; D is the distance between the individual in the wolf group and the target prey; t is iteration times; X is grey wolf position; XP is the target hunt position, r1, r2 is the random number in the range of [0, 1], a is the control parameter, the value is in the range of [0, 2] and the iteration times of the algorithm is increased. S3-3 selecting a, b and c three-head gray wolf from Archive according to wheel disc wagering method, the rest grey wolf according to the position of a, b and c three-head gray wolf to update according to formula 27, checking whether the satisfy condition, until generating a sufficient number of qualified individual. As shown in FIG. 5 is a grey wolf food sketch map, namely grey wolf in searching the prey, updating the position of the schematic diagram. the position updating formula is as follows. In the formula 27: X α, β, δ is the current position of a, b, c three-head gray wolf, D α, β, δ respectively represents the distance of a, b, c three-head wolf and other individuals, X1, 2, 3 respectively define the step length and direction of the candidate wolf individual towards the a, b, c, C1, C2, C3 and a1, a2, a3 is the random vector, X is the position vector of a, b, c three-head gray wolf, X (t + 1) is the final position of the candidate wolf. As can be seen from FIG. 5, the position of the candidate solution is finally located in the random round position defined by a, b and c. In general, a, b and c need to first predict the approximate position of the prey (potential optimal solution), and then other candidate wolf randomly updates their position near the prey under the guidance of the current optimal three wolves. S3-4 according to the electric hydrogen comprehensive energy system target function in step S2, calculating the target function value of the grey wolf, determining non-dominated individual, updating Archive. S3-5 repeating step, S3-2 S3-3 until reaching the maximum iteration times, at this time, outputting the gray wolf position in the Archive, namely a group of Pareto solution in the electric hydrogen comprehensive energy system cost optimization);
after the position information, in the next iteration, of each wolf other than the α wolf, the β wolf and the δ wolf in the initial wolf population is calculated, increasing the current iteration r by 1, and determining whether the current iteration r is greater than or equal to the maximum iteration R; if so, outputting the position information, in the current iteration, of all the wolves in the ultimate initial wolf population; if not, substituting the position information, in the current iteration, of all the wolves in the ultimate initial wolf population into Step 3.2 and Step 3.3 for iterative calculation again until the current iteration r is greater than or equal to the maximum iteration R (Pages 12-13, S3-3 selecting a, b and c three-head gray wolf from Archive according to wheel disc wagering method, the rest grey wolf according to the position of a, b and c three-head gray wolf to update according to formula 27, checking whether the satisfy condition, until generating a sufficient number of qualified individual. As shown in FIG. 5 is a grey wolf food sketch map, namely grey wolf in searching the prey, updating the position of the schematic diagram. the position updating formula is as follows. In the formula 27: X α, β, δ is the current position of a, b, c three-head gray wolf, D α, β, δ respectively represents the distance of a, b, c three-head wolf and other individuals, X1, 2, 3 respectively define the step length and direction of the candidate wolf individual towards the a, b, c, C1, C2, C3 and a1, a2, a3 is the random vector, X is the position vector of a, b, c three-head gray wolf, X (t + 1) is the final position of the candidate wolf. As can be seen from FIG. 5, the position of the candidate solution is finally located in the random round position defined by a, b and c. In general, a, b and c need to first predict the approximate position of the prey (potential optimal solution), and then other candidate wolf randomly updates their position near the prey under the guidance of the current optimal three wolves. S3-4 according to the electric hydrogen comprehensive energy system target function in step S2, calculating the target function value of the grey wolf, determining non-dominated individual, updating Archive. S3-5 repeating step, S3-2 S3-3 until reaching the maximum iteration times, at this time, outputting the gray wolf position in the Archive, namely a group of Pareto solution in the electric hydrogen comprehensive energy system cost optimization);
and Step 3.4: sequentially substituting the position information of all the wolves in the ultimate initial wolf population output in Step 3.3 into the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station for calculation to obtain predation benefits of all the wolves in the ultimate initial wolf population; and the selecting the position information of the wolf with the maximum predation benefit in the ultimate initial wolf population as the optimal operation scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station (Pages 12-13, S3-3 selecting a, b and c three-head gray wolf from Archive according to wheel disc wagering method, the rest grey wolf according to the position of a, b and c three-head gray wolf to update according to formula 27, checking whether the satisfy condition, until generating a sufficient number of qualified individual. As shown in FIG. 5 is a grey wolf food sketch map, namely grey wolf in searching the prey, updating the position of the schematic diagram. the position updating formula is as follows. In the formula 27: X α, β, δ is the current position of a, b, c three-head gray wolf, D α, β, δ respectively represents the distance of a, b, c three-head wolf and other individuals, X1, 2, 3 respectively define the step length and direction of the candidate wolf individual towards the a, b, c, C1, C2, C3 and a1, a2, a3 is the random vector, X is the position vector of a, b, c three-head gray wolf, X (t + 1) is the final position of the candidate wolf. As can be seen from FIG. 5, the position of the candidate solution is finally located in the random round position defined by a, b and c. In general, a, b and c need to first predict the approximate position of the prey (potential optimal solution), and then other candidate wolf randomly updates their position near the prey under the guidance of the current optimal three wolves. S3-4 according to the electric hydrogen comprehensive energy system target function in step S2, calculating the target function value of the grey wolf, determining non-dominated individual, updating Archive. S3-5 repeating step, S3-2 S3-3 until reaching the maximum iteration times, at this time, outputting the gray wolf position in the Archive, namely a group of Pareto solution in the electric hydrogen comprehensive energy system cost optimization; Page 14, step five, the electro-hydrogen comprehensive energy system target function and constraint condition input improved multi-target grey wolf algorithm, obtaining the optimal solution of maximum hydrogen production amount and maximum generated energy under the minimum cost).
Although Liu discloses a method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station using an improved gray wolf optimization algorithm (e.g., solving an optimization problem based on multiple parameters), Liu does not specifically disclose other parameters that may contribute to an optimal solution (e.g., charge efficiency and discharge efficiency)
However, Ren discloses Step 1: setting a number N of charging piles, a maximum charge power P of the charging piles, a rated capacity κ.sub.e.sup.cap of hydrogen energy, a maximum charge-discharge power h.sup.cap, charge efficiency η.sup.c, discharge efficiency η.sup.dc, a time of use [T.sub.num,s.sup.i,T.sub.num,e.sup.i] of the charging piles and required charge energy E.sub.num.sup.i of a hydrogen-photovoltaic-storage-charging integrated energy station … (Page 5, Hydrogen storage operation constraint shown as follows: in the formula, and is the estimated c of the scene s of the t-th scheduling period of the g-th micro-grid inside hydrogen energy storage of the charging power and discharge power. and is a two-dimensional variable representing the charging state and the discharging state of the hydrogen energy storage. τ HS is the capacity-power conversion coefficient of the hydrogen energy storage. ε is an infinity constant. is the electric quantity stored in the t-th scheduling period hydrogen energy storage. is the electric quantity stored in the t-1 scheduling period hydrogen energy storage. Capg, HS is the planning capacity of hydrogen energy storage. the alpha HS is the lower limit coefficient of the hydrogen energy storage electric quantity. is the initial electric quantity state of the first scene of hydrogen energy storage. is hydrogen storage the s-th scene, initial electric quantity state of the s-1 scene. is the final electric quantity state of the s-1 scene of hydrogen energy storage. is the last state of the last scene. The beta HS is the initial electric quantity coefficient of the hydrogen energy storage. A HS is the self-discharging rate of the battery energy storage. and it is the charging efficiency and discharging efficiency of the battery energy storage, The maximum number of times of the change of the charging and discharging state in one day can be inside for the hydrogen storage. Dp is an annual inside of days. ω s-1 is the probability of the s-1 scene).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station using an improved gray wolf optimization algorithm (e.g., solving an optimization problem based on multiple parameters) of the invention of Liu to further incorporate other scheduling parameters (e.g., charge efficiency and discharge efficiency) of the invention of Ren because doing so would allow the method to formulate a scheduling period of the charging power and discharge power based on charging efficiency and discharging efficiency of the battery energy storage (see Ren, Page 5). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Although the combination of Liu and Ren discloses a method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station using an improved gray wolf optimization algorithm (e.g., solving an optimization problem based on multiple parameters, wherein charging period is during the electricity price valley), the combination of Liu and Ren does not specifically disclose wherein the charging period includes the start time and end time of the charging pile).
However, Dong discloses wherein i indicates a serial number of each charging pile, T.sub.num,s.sup.i and T.sub.num,e.sup.i indicate a start time of num.sup.th use of an i.sup.th charging pile and an end time of the num.sup.th use of the i.sup.th charging pile, and E.sub.num.sup.i indicates the charge energy required for the num.sup.th use of the i.sup.th charging pile (Page 4, Based on the analysis, the invention claims a light storage charging station electric vehicle three-stage optimization method based on grey wolf algorithm. firstly, considering potential conformability of electric automobile charging and photovoltaic power generation, building optical storage charging station area system structure model, secondly, according to the American Safety Administration (NHTS) survey statistics of the whole-American private car, researching electric automobile user travel rule, combining temperature and traffic factors, The unordered charging load is predicted by the Monte Carlo method. then building an energy scheduling model of the optical storage charging station, combining with the optimal scheduling model with lowest charging station purchasing power and minimum power distribution network peak valley difference as optimization index, establishing an ordered charging model, calculating to obtain the starting charging time of the electric automobile through the grey wolf algorithm, so as to adjust the charging period of the electric automobile for orderly charging scheduling, finally realizing the economic operation of the optical storage charging station, at the same time, reducing the impact of large-scale electric automobile charging the distribution network).
It would have been obvious to one ordinary skill in the art before the effective filing date to modify the method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station using an improved gray wolf optimization algorithm (e.g., solving an optimization problem based on multiple parameters, wherein charging period is during the electricity price valley) of the invention of Liu to further specify the charging start time and end time of the charging pile of the invention of Dong because doing so would allow the method to adjust the charging period of the electric automobile for orderly charging scheduling (see Dong, Page 4). Further, the claimed invention is merely a combination of old elements, and in combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Fang (CN 117728435 A) – discloses optimization of the parameter setting in the charging mode by using the improved gray wolf algorithm comprises the following steps: the alpha wolf, beta wolf, delta wolf, omega wolf and alpha wolf are the group leaders according to the position; beta wolf and delta wolf are middle layers for assisting alpha wolf and managing omega wolf; omega wolf is the bottom layer, its function is detecting and preying; they respectively correspond to the optimal solution, sub-optimal solution, sub-optimal solution and searching population of the intelligent algorithm; the optimization process of the grey wolf algorithm is the process of the high priority wolf guiding the low priority wolf searching target, when reaching the maximum iteration times, alpha wolf is the optimal solution to be solved (see at least Pages 3-4).
Khalid et al. (US 2024/0119281 A1) – discloses renewable energy is one of the fastest growing energy technologies, and in particular, solar energy is preferred as it helps to generate power cost effectively and with zero carbon emissions. However, the inherent intermittent nature of solar power due to variations in the sunlight, e.g., caused by moving clouds, makes it a challenge to dispatch uninterrupted power into grid. The resultant fluctuating power can cause various problems in the grid such as frequency deviations, voltage hindrances, and excessive peak loads which ultimately would lead to electricity blackouts or power outages in the grids. Therefore, to encourage the delivery of large-scale solar power into the grid, solar photovoltaic (PV) power output needs to be smoothed out before it can be dispatched into the grid in a controlled manner. An energy storage system (ESS) can be integrated with the renewable energy (RE) resources for power supply regulation, management, and optimal operation. In particular, a battery energy storage system (BESS) can be integrated with the RE systems to produce promising results. The BESS can be integrated with the solar PV to mitigate the issue of the fluctuating solar power. Improving the lifespan of the BESS while lessening the operating expenses is a well-investigated area Studies have recommended innovative supervision procedures for improving the lifetime of the BESS while determining the battery charging/discharging power (see at least Paragraph 0006).
Nimma (Nimma, K.S., Al-Falahi, M.D., Nguyen, H.D., Jayasinghe, S.D.G., Mahmoud, T.S. and Negnevitsky, M., 2018. Grey wolf optimization-based optimum energy-management and battery-sizing method for grid-connected microgrids. Energies, 11(4), p.847) – discloses a GWO is used to solve the operation management issues in the microgrid by finding the optimal values of the parameters that help to minimize the operational cost of the generation sources in the microgrid and fulfil all the constraints (13)–(25) in each step of the GWO algorithm. Figure 2 shows the flowchart of the grey wolf algorithm performance for operation management in the microgrid (see at least Pages 7-8).
Krishna (Krishna, R. and S, H., 2024. Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm. IET Renewable Power Generation, 18(16), pp.3640-3658) – discloses to evaluate the generation cost for the generated random population using (1). The solutions are arranged in the ascending order of the generation cost. This is indicative of how far the prey is from the specific wolf (alpha [α], beta [β], delta [δ]) based on their fitness values. At the end of the step 3, the three best solutions (power matrix) are identified, and the corresponding fitness value (generation cost) is evaluated (see at least Page 3647).
Issam (Issam, B., Issam, A. and Hamza, B., 2017, October. Design of gray wolf optimizer for improving photovoltaic—Hydrogen hybrid system. In 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B) (pp. 1-5). IEEE) – discloses an optimal photovoltaic-hydrogen standalone power system using gray wolf optimizer technique. The system composed of photovoltaic generator and a hydrogen production system. Electrical power from solar conversion meets the user loads and the surplus used for water electrolysis to produce hydrogen.
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/MARJORIE PUJOLS-CRUZ/Examiner, Art Unit 3624