Variable frequency drilling rig hybrid energy storage capacity configuration method based on adaptive grey wolf algorithm

Abstract

The present application relates to the technical field of alternating current variable frequency drilling machine energy storage, and particularly relates to a variable frequency drilling machine hybrid energy storage capacity configuration method based on an adaptive grey wolf algorithm. The method comprises: establishing a full life cycle cost model of the alternating current variable frequency drilling machine hybrid energy storage system as a target function; taking the energy storage unit capacity and state of charge, power balance, load power shortage rate and diesel generator power as constraint conditions; using an adaptive grey wolf algorithm improved by an adaptive adjustment strategy and a jump-out local optimal strategy to solve the target function with the minimum full life cycle cost of the alternating current variable frequency drilling machine hybrid energy storage system as an optimization target, so as to obtain the optimal capacity configuration of the alternating current variable frequency drilling machine hybrid energy storage system. The present application can obtain the optimal energy storage configuration, has a fast convergence speed in the configuration solving process, and can avoid falling into a local optimum.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage for AC variable frequency drilling rigs, and in particular to a hybrid energy storage capacity configuration method for variable frequency drilling rigs based on an adaptive grey wolf algorithm. Background Art

[0002] During the lowering of an AC variable frequency drill rig, the drill tool drives the drawworks motor in reverse, generating a large amount of feedback energy that is typically dissipated by the brake unit. Furthermore, during the lowering and lowering of the drill rig, there are necessary rest periods during which the rig is in a light load state, while the diesel generator set continues to operate, resulting in diesel waste.

[0003] By introducing a hybrid energy storage system consisting of supercapacitors and batteries to recover this energy, if the capacity of the hybrid energy storage system is configured too large, it will lead to unnecessary waste and increase overall operating costs. Conversely, if the capacity of the hybrid energy storage system is configured too small, it will not be able to meet the expected energy storage needs.

[0004] Commonly used methods in hybrid energy storage system capacity configuration include particle swarm optimization and traditional grey wolf algorithm. In particular, the traditional grey wolf algorithm is prone to falling into local optimality during operation and has a slow convergence speed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the existing technology and provide a hybrid energy storage capacity configuration method for variable frequency drilling rigs based on the adaptive gray wolf algorithm. The method can obtain the optimal energy storage configuration of the AC variable frequency drilling rig, and has a fast convergence speed in the configuration solution process and can avoid falling into local optimality.

[0006] In order to solve the above technical problems, the technical solution of the present invention is: a method for configuring hybrid energy storage capacity of a variable frequency drilling rig based on an adaptive gray wolf algorithm, comprising:

[0007] A full life cycle cost model for an AC variable frequency drilling rig hybrid energy storage system was established as the objective function, with energy storage unit capacity and state of charge, power balance, load power shortage rate, and diesel generator power as constraints.

[0008] An adaptive grey wolf algorithm improved by adaptive adjustment strategy and jumping out of local optimal strategy is adopted. With the minimization of the full life cycle cost of the AC variable frequency drilling rig hybrid energy storage system as the optimization goal, the objective function is solved and the optimal capacity configuration of the AC variable frequency drilling rig hybrid energy storage system is obtained.

[0009] Furthermore, the expression of the objective function is:

[0010] LCC

[0011] =C I +C O +C M +CD +F gen

[0012] =(1+f oc +f mc +f dc )f dc mP c +(1+f ob +f mb +f db )f db nP b +F gen

[0013] Where C I 、C O 、C M 、C D They are equipment purchase cost, operation cost, maintenance cost and disposal cost; F gen is the operating cost of the diesel generator set; f oc 、f mc 、f dc 、f dc 、P c They are the overcapacity operation coefficient, maintenance coefficient, treatment coefficient, depreciation coefficient and unit price; f ob 、f mb 、f db 、f db 、P b They are the operating coefficient, maintenance coefficient, processing coefficient, depreciation coefficient and unit price of the battery respectively; m and n are the excess capacity and the number of batteries respectively.

[0014] Diesel generator set operating cost F gen The expression is:

[0015] F gen =a i +b i P i (t)+c i (P i (t)) 2

[0016] Where, P i (t) is the power of the i-th diesel generator; i = 1, 2, 3, ..., n, n is the number of diesel generators; a i 、b i 、c i is the coefficient of the fuel cost function of the i-th diesel generator.

[0017] Furthermore, the capacity and state of charge constraints of the energy storage unit are:

[0018]

[0019] Where, E c is the capacity of the supercapacitor bank; E cmin is the minimum capacity of the overcapacity group; E cmax is the maximum capacity of the overcapacity group; E b is the battery pack capacity; E bmin is the minimum capacity of the battery pack; E bmax is the maximum capacity of the battery pack; S c S is the state of charge of the supercapacitor bank; cmin S is the lower limit of the charge state of the overcapacity group; cmax S is the upper limit of the state of charge of the overcapacity group; b S is the state of charge of the battery pack; bmin S is the lower limit of the battery pack charge state; bmax The upper limit of the battery pack's state of charge;

[0020] The power balance constraint is:

[0021] P gen +P bat +P sc =P load

[0022] Where, P gen is the working power of the diesel generator set; P bat is the battery pack power; P sc is the supercapacitor group power; P load is the load power;

[0023] The load power failure rate constraint is:

[0024] f(LPSP)≤f(LPSPmax)

[0025] Where, f(LPSP) is the load power failure rate, f(LPSPmax) is the maximum load power failure rate, E ps (t) is the load power shortage; E(t) is the load demand;

[0026] The power constraint of the diesel generator is:

[0027] 0≤P≤P

[0028] gen genmax

[0029] Where, P gen is the working power of the diesel generator set; P genmax It is the maximum operating power of the diesel generator set.

[0030] Furthermore, the steps of the adaptive grey wolf algorithm are:

[0031] (1) Initialize the gray wolf population; initialize a, A, and C; initialize the values ​​of α, β, and δ;

[0032] (2) Calculate the fitness value of each gray wolf individual and the average fitness value of the gray wolf group f avg ;

[0033] (3) Compare the fitness values ​​of the gray wolf individuals and select the optimal solutions α, β and δ under the current number of iterations;

[0034] (4) Update the current position of the gray wolf, update a, A and C;

[0035] (5) Improve the position update equation through adaptive adjustment strategy, and use the fitness values ​​of the three gray wolves, α, β, and δ, to mutate the position of the current gray wolf individual;

[0036] (6) Generate new individuals outside the current optimal solution area by jumping out of the local optimal strategy, compare the fitness values ​​and retain the optimal solution;

[0037] (7) Determine whether the maximum number of iterations has been reached. If so, the algorithm stops; otherwise, return to step (2).

[0038] Furthermore, the adaptive adjustment strategy is:

[0039] Compare the current individual gray wolf fitness value with the average fitness value of the gray wolf group;

[0040] If the current individual gray wolf fitness value does not exceed the average fitness value of the gray wolf group, continue to use the traditional strategy to update the current individual gray wolf position;

[0041] If the current individual gray wolf fitness value is greater than the average fitness value of the gray wolf group, the fitness values ​​of the three gray wolves α, β and δ are used to mutate the current individual gray wolf position.

[0042] Furthermore, the formula for the adaptive adjustment strategy is:

[0043]

[0044] Where, f α , f β , f δ are the fitness values ​​of α, β and δ respectively, The three gray wolf individuals α, β and δ are obtained by random vector changes.

[0045] Furthermore, the formula for jumping out of the local optimal strategy is:

[0046]

[0047] Where, To jump out of the local optimal individual position; is the individual position after the position update; is a random number between [-2,-1] and [1,2], This is the optimal location for the current gray wolf population.

[0048] After adopting the above technical solution, the present invention uses an adaptive adjustment strategy and a local optimal escape strategy to improve the traditional gray wolf algorithm to solve the capacity configuration of the hybrid energy storage system, which not only reduces the full life cycle cost of the hybrid energy storage system, but also accelerates the convergence speed of the gray wolf algorithm and improves the economy of the AC variable frequency drilling rig hybrid energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a hybrid energy storage capacity configuration method for a variable frequency drilling rig based on an adaptive grey wolf algorithm according to the present invention;

[0050] Figure 2 Flowchart of the adaptive grey wolf algorithm of the present invention;

[0051] Figure 3 This is the structural diagram of the AC variable frequency drilling rig hybrid energy storage system;

[0052] Figure 4 This is a comparison curve of the optimal solutions of the traditional grey wolf algorithm and the adaptive grey wolf algorithm. DETAILED DESCRIPTION

[0053] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0054] like Figure 1 、 Figure 2 and Figure 3 As shown, a hybrid energy storage capacity configuration method for a variable frequency drilling rig based on an adaptive grey wolf algorithm includes:

[0055] Step 1: Establish a full life cycle cost model for the AC variable frequency drilling rig hybrid energy storage system as the objective function;

[0056] The objective function is the full life cycle cost of the hybrid energy storage system, including the purchase cost of the energy storage device, operation and maintenance cost, and processing cost. The expression can be:

[0057] LCC

[0058] =C I +C O +C M +C D +F gen

[0059] =(1+f oc +f mc +f dc )f dc nP c +(1+f ob +f mb +f db )f db nP b +F gen

[0060] Where C I 、C O 、C M 、C D They are equipment purchase cost, operation cost, maintenance cost and disposal cost; F gen is the operating cost of the diesel generator set; f oc 、f mc 、f dc 、f dc 、P c They are the overcapacity operation coefficient, maintenance coefficient, treatment coefficient, depreciation coefficient and unit price; f ob 、f mb 、f db 、f db 、P b They are the operating coefficient, maintenance coefficient, processing coefficient, depreciation coefficient and unit price of the battery respectively; m and n are the excess capacity and the number of batteries respectively.

[0061] Diesel generator set operating cost F gen The expression is:

[0062] F gen =a i +b i P i (t)+c i (P i (t)) 2

[0063] Where, P i (t) is the power of the i-th diesel generator; i = 1, 2, 3, ..., n, n is the number of diesel generators; a i 、b i 、c i is the coefficient of the fuel cost function of the i-th diesel generator.

[0064] Step 2: Use the energy storage unit capacity and state of charge, power balance, load power shortage rate, and diesel generator power as constraints;

[0065] Among them, the energy storage unit capacity and state of charge constraints are:

[0066]

[0067] Where, E c is the capacity of the supercapacitor bank; E cmin is the minimum capacity of the overcapacity group; E cmax is the maximum capacity of the overcapacity group; E b is the battery pack capacity; E bmin is the minimum capacity of the battery pack; E bmax is the maximum capacity of the battery pack; S c S is the state of charge of the supercapacitor bank; cmin S is the lower limit of the charge state of the overcapacity group; cmax S is the upper limit of the state of charge of the overcapacity group; b S is the state of charge of the battery pack; bmin S is the lower limit of the battery pack charge state; bmax The upper limit of the battery pack's state of charge;

[0068] The power balance constraint is:

[0069] P gen +P bat +P sc =P load

[0070] Where, P gen is the working power of the diesel generator set; P bat is the battery pack power; P sc is the supercapacitor group power; P load is the load power;

[0071] The load power failure rate constraint is:

[0072] f(LPSP)≤f(LPSP max)

[0073] Where, f(LPSP) is the load power failure rate, f(LPSP max) is the maximum load power failure rate, E ps (t) is the load power shortage; E(t) is the load demand;

[0074] The power constraint of the diesel generator is:

[0075] 0≤P gen ≤P gen max

[0076] Where, P gen is the working power of the diesel generator set; P genmax It is the maximum operating power of the diesel generator set.

[0077] In step three, the adaptive grey wolf algorithm improved by the adaptive adjustment strategy and the jump-out-of-local-optimal strategy is adopted. The objective function is solved with the minimum life cycle cost of the AC variable frequency drilling rig hybrid energy storage system as the optimization goal, and the optimal capacity configuration of the AC variable frequency drilling rig hybrid energy storage system is obtained.

[0078] The steps of the adaptive grey wolf algorithm are:

[0079] (1) Initialize the gray wolf population; initialize a, A, and C; initialize the values ​​of α, β, and δ;

[0080] (2) Calculate the fitness value of each gray wolf individual and the average fitness value of the gray wolf group f avg ;

[0081] (3) Compare the fitness values ​​of the gray wolf individuals and select the optimal solutions α, β and δ under the current number of iterations;

[0082] (4) Update the current position of the gray wolf, update a, A and C;

[0083] (5) Improve the position update equation through adaptive adjustment strategy, and use the fitness values ​​of the three gray wolves, α, β, and δ, to mutate the position of the current gray wolf individual;

[0084] (6) Generate new individuals outside the current optimal solution area by jumping out of the local optimal strategy, compare the fitness values ​​and retain the optimal solution;

[0085] (7) Determine whether the maximum number of iterations has been reached. If so, the algorithm stops; otherwise, return to step (2).

[0086] Specifically, the adaptive adjustment strategy is:

[0087] Compare the current individual gray wolf fitness value with the average fitness value of the gray wolf group;

[0088] If the current individual gray wolf fitness value does not exceed the average fitness value of the gray wolf group, continue to use the traditional strategy to update the current individual gray wolf position;

[0089] If the current individual gray wolf fitness value is greater than the average fitness value of the gray wolf group, the fitness values ​​of the three gray wolves α, β and δ are used to mutate the current individual gray wolf position.

[0090] The formula for the adaptive adjustment strategy can be:

[0091]

[0092] Where, f α , f β , f δ are the fitness values ​​of α, β and δ respectively, The three gray wolf individuals α, β and δ are obtained by random vector changes.

[0093] Compared with the traditional gray wolf algorithm, which only performs average calculation on α, β and δ when updating the position, it cannot reflect their true position relationship, has a larger search range and slow convergence speed. The gray wolf algorithm with adaptive adjustment strategy can adjust the mutation strategy according to the current individual fitness value, move closer to a better search range, and further improve the convergence speed of the algorithm.

[0094] The formula for jumping out of the local optimal strategy can be:

[0095]

[0096] Where, To jump out of the local optimal individual position; is the individual position after the position update; r is a random number between [-2, -1] and [1, 2], is the optimal individual position of the current gray wolf population.

[0097] The strategy of escaping the local optimum uses the position vector difference between the optimal individual and the updated individual to mutate the current optimal individual and randomly generate new individuals that are not included in the current optimal solution area, thereby escaping the local optimum and increasing population diversity.

[0098] A series-parallel supercapacitor group consisting of m single supercapacitors can store the following energy:

[0099]

[0100] Where, E C The energy stored in the supercapacitor bank; U USC is the capacitance value of a single supercapacitor; E Cmax The upper limit of energy storage of supercapacitor bank; E Cmin The lower limit of energy storage of supercapacitor bank; C USC is the capacitance value of a single supercapacitor; U USC is the terminal voltage of the single supercapacitor; U USCmax 、U USCmin It is the upper and lower limits of the working voltage of a single supercapacitor.

[0101] The energy that can be stored in a battery pack consisting of n batteries is:

[0102] E B =nC bat U bat / 10 3

[0103] E Bmin=nC bat U bat (1-DOD) / 10 3

[0104] Where, E B is the rated storage capacity of the battery pack; E Bmin is the minimum remaining stored energy; C bat is the rated capacity of a single battery; U bat is the rated voltage of a single battery; DOD is the maximum depth of discharge.

[0105] Taking the ZJ70DB AC variable frequency drilling rig as an example, a full life cycle cost model of a hybrid energy storage system is constructed. The Grey Wolf Algorithm is used to solve the AC variable frequency drilling rig hybrid energy storage system model with the goal of minimizing the full life cycle cost, verifying the feasibility of the capacity optimization configuration method of the present invention.

[0106] Energy loss occurs at each level of transmission in the energy storage system, so the efficiency of each level must be considered. Considering the friction of the rock wall and the buoyancy of the mud during the drilling operation, correction factors are applied to the drill string. K1 is the static load correction factor when the drill string is lowered, and K2 is the static load correction factor when the drill string is raised. The basic parameters are shown in Table 1.

[0107] Table 1 shows the basic parameters of the drilling rig;

[0108] Drill pipe specifications 5.5×2.54cm Lowering static load correction factor <![CDATA[K1=0.65]]> Tripping cycle T=180s Lifting static load correction factor <![CDATA[K2=1.16]]> Weight of drill pipe per meter in air <![CDATA[ρ1=36kg·m -1 ]]> Length of each column (3 pieces) h=27m Empty hook lowering weight (including top drive) G=23t Variable frequency drive motor efficiency <![CDATA[η d =0.95]]> Total mechanical efficiency of winch <![CDATA[η j =0.7]]> Winch inverter efficiency <![CDATA[η BP =0.98]]> Converter device efficiency η=0.95 Gravity <![CDATA[g≈10m·s -2 ]]>

[0109] Table 2 shows the basic parameters of diesel generators:

[0110] Rated power 1230kW speed 1800rpm Emissions / Fuel Strategy Low fuel consumption and low emissions frequency 50Hz Voltage 380-13800V Power Factor 0.8 Fuel consumption 319.5L / H diesel fuel 0#

[0111] Table 3 shows the parameters of supercapacitor and battery:

[0112]

[0113]

[0114] Combined with the example data, the experiment was carried out in Matlab, and the experimental results are summarized in Table 4.

[0115] Table 4 shows the test results:

[0116] algorithm Traditional Grey Wolf Algorithm Adaptive Grey Wolf Algorithm Supercapacitor / unit 8059 7842 Battery / unit 467 422 Minimum fee / yuan 159750 158032 Iterations / times 128 78

[0117] like Figure 4 As shown: Using the traditional grey wolf algorithm, the full life cycle cost of the hybrid energy storage system is 159,750 yuan, and it converges after about 128 iterations; using the adaptive algorithm, the full life cycle cost of the hybrid energy storage system is 158,032 yuan, and it converges after about 78 iterations.

[0118] This embodiment establishes an energy storage configuration model with the goal of minimizing lifecycle costs and proposes an adaptive gray wolf algorithm to optimize the capacity of a hybrid energy storage system. Experimental simulation results show that compared to the traditional gray wolf algorithm, the lifecycle cost is reduced by 1.08%, and convergence is achieved after approximately 78 iterations, both outperforming the traditional gray wolf algorithm. The improved gray wolf algorithm proposed in this embodiment not only improves convergence speed but also reduces the lifecycle cost of the energy storage system, effectively configuring the supercapacitors and batteries in the energy storage system.

[0119] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A hybrid energy storage capacity configuration method for variable frequency drilling rigs based on an adaptive grey wolf algorithm, characterized in that: include: A full life cycle cost model for an AC variable frequency drilling rig hybrid energy storage system was established as the objective function, with energy storage unit capacity and state of charge, power balance, load power shortage rate, and diesel generator power as constraints. An adaptive grey wolf algorithm improved by adaptive adjustment strategy and jumping out of local optimal strategy is adopted. With the minimization of the life cycle cost of the AC variable frequency drilling rig hybrid energy storage system as the optimization goal, the objective function is solved and the optimal capacity configuration of the AC variable frequency drilling rig hybrid energy storage system is obtained. The expression of the objective function is: LCC =C I +C O +C M +C D +F gen =(1+f oc +f mc +f dc )f dc mP c +(1+f ob +f mb +f db )f db nP b +F gen Where C I 、C O 、C M 、C D They are equipment purchase cost, operation cost, maintenance cost and disposal cost; F gen is the operating cost of the diesel generator set; f oc 、f mc 、f dc 、f dc 、P c They are the overcapacity operation coefficient, maintenance coefficient, treatment coefficient, depreciation coefficient and unit price; f ob 、f mb 、f db 、f db 、P b are the battery's operating coefficient, maintenance coefficient, handling coefficient, depreciation coefficient, and unit price; m and n are the excess capacity and number of batteries, respectively; Diesel generator set operating cost F gen The expression is: F gen =a i +b i P i (t)+c i (P i (t)) 2 Where, P i (t) is the power of the i-th diesel generator; i = 1, 2, 3, ..., n, n is the number of diesel generators; a i 、b i 、c i is the coefficient of the fuel cost function of the i-th diesel generator; The capacity and state of charge constraints of the energy storage unit are: Where, E c is the capacity of the supercapacitor bank; E cmin is the minimum capacity of the overcapacity group; E cmax is the maximum capacity of the overcapacity group; E b is the battery pack capacity; E bmin is the minimum capacity of the battery pack; E bmax is the maximum capacity of the battery pack; S c S is the state of charge of the supercapacitor bank; cmin S is the lower limit of the charge state of the overcapacity group; cmax S is the upper limit of the state of charge of the overcapacity group; b S is the state of charge of the battery pack; bmin S is the lower limit of the battery pack charge state; bmax The upper limit of the battery pack's state of charge; The power balance constraint is: P gen +P bat +P sc =P load Where, P gen is the working power of the diesel generator set; P bat is the battery pack power; P sc is the supercapacitor group power; P load is the load power; The load power failure rate constraint is: f(LPSP)≤f(LPSPmax) Where, f(LPSP) is the load power failure rate, f(LPSPmax) is the maximum load power failure rate, E ps (t) is the load power shortage; E(t) is the load demand; The power constraint of the diesel generator is: 0≤P gen ≤P genmax Where, P gen is the working power of the diesel generator set; P genmax It is the maximum operating power of the diesel generator set.

2. The method for configuring hybrid energy storage capacity of a variable frequency drilling rig based on the adaptive grey wolf algorithm according to claim 1 is characterized in that: The steps of the adaptive grey wolf algorithm are: (1) Initialize the gray wolf population; initialize a, A, and C; initialize the values ​​of α, β, and δ; (2) Calculate the fitness value of each gray wolf individual and the average fitness value of the gray wolf group f avg ; (3) Compare the fitness values ​​of the gray wolf individuals and select the optimal solutions α, β and δ under the current number of iterations; (4) Update the current position of the gray wolf, update a, A and C; (5) Improve the position update equation through adaptive adjustment strategy, and use the fitness values ​​of the three gray wolves, α, β, and δ, to mutate the position of the current gray wolf individual; (6) Generate new individuals outside the current optimal solution area by jumping out of the local optimal strategy, compare the fitness values ​​and retain the optimal solution; (7) Determine whether the maximum number of iterations has been reached. If so, the algorithm stops; otherwise, return to step (2).

3. The method for configuring hybrid energy storage capacity of a variable frequency drilling rig based on an adaptive gray wolf algorithm according to claim 2, characterized in that: The adaptive adjustment strategy is: Compare the current individual gray wolf fitness value with the average fitness value of the gray wolf group; If the current individual gray wolf fitness value does not exceed the average fitness value of the gray wolf group, continue to use the traditional strategy to update the current individual gray wolf position; If the current individual gray wolf fitness value is greater than the average fitness value of the gray wolf group, the fitness values ​​of the three gray wolves α, β and δ are used to mutate the current individual gray wolf position.

4. The hybrid energy storage capacity configuration method for variable frequency drilling rigs based on the adaptive grey wolf algorithm according to claim 3 is characterized in that: The formula for the adaptive adjustment strategy is: Where, fα,f β , fδ are the fitness values ​​of α, β and δ respectively, The three gray wolf individuals α, β and δ are obtained by random vector changes.

5. The method for configuring hybrid energy storage capacity of a variable frequency drilling rig based on an adaptive grey wolf algorithm according to claim 1, characterized in that: The formula for jumping out of the local optimal strategy is: Where, To jump out of the local optimal individual position; is the individual position after the position update; r is a random number between [-2, -1] and [1, 2], This is the optimal location for the current gray wolf population.

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