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

Abstract

The invention relates to the technical field of alternating-current variable-frequency drilling machine energy storage, in particular to a variable-frequency drilling machine hybrid energy storage capacity configuration method based on a self-adaptive grey wolf algorithm. The method comprises the following steps: establishing a full-life-cycle cost model of the alternating-current variable-frequency drilling machine hybrid energy storage system as a target function; the capacity and the charge state of an energy storage unit, power balance, a load power shortage rate and the power of a diesel generator are used as constraint conditions; and solving a target function by adopting a self-adaptive grey wolf algorithm improved by a self-adaptive adjustment strategy and a jump-out local optimal strategy and taking the minimum full-life-cycle cost of the alternating-current variable-frequency drilling machine hybrid energy storage system as an optimization target to obtain the optimal capacity configuration of the alternating-current variable-frequency drilling machine hybrid energy storage system. According to the method, the optimal energy storage configuration can be obtained, the convergence speed is high in the configuration solving process, and local optimum can be avoided.

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] When the AC variable frequency drilling rig is drilling, the drill tool drives the winch motor to reverse, generating a large amount of feedback energy which is usually dissipated by the brake unit. At the same time, there are necessary intervals during the drilling process, during which the rig is in a light load state and the diesel generator set continues to work, resulting in a waste of diesel.

[0003] By introducing a hybrid energy storage system consisting of supercapacitors and batteries to recover the above energy, if the capacity of the hybrid energy storage system is configured too large, it will lead to unnecessary waste, thereby increasing the overall operating cost. On the contrary, 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] The commonly used methods in the capacity configuration of hybrid energy storage systems include particle swarm optimization and traditional grey wolf algorithm. In particular, the traditional grey wolf algorithm is prone to fall into local optimality and has a slow convergence speed during operation. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a hybrid energy storage capacity configuration method for a variable frequency drilling rig based on an adaptive grey wolf algorithm, which 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 a local optimum.

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

[0007] Establish a full life cycle cost model for AC variable frequency drilling rig hybrid energy storage system as the objective function; use 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 was adopted. The minimum life cycle cost of AC variable frequency drilling rig hybrid energy storage system was taken as the optimization goal. The objective function was solved and the optimal capacity configuration of AC variable frequency drilling rig hybrid energy storage system was 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 mxD c +(1+f ob +f mb +f db )f db nP b +F gen

[0013] In the formula, 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; 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 operation coefficient, maintenance coefficient, handling 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 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 energy storage unit capacity and state of charge constraints are:

[0018]

[0019] In the formula, 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 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 charge state 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 state of charge of the battery pack;

[0020] The power balance constraint is:

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

[0022] Where P gen P is the working power of the diesel generator set; bat is the battery pack power; P sc is the power of the supercapacitor group; 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 P is the working power of the diesel generator set; genmax It is the maximum operating power of the diesel generator set.

[0030] Further, 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 individual gray wolves 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 fitness value of the gray wolf 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] In the formula, 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] In the formula, 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], It is the optimal location for the current gray wolf population.

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

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

[0050] Figure 2 is a flow chart of the adaptive grey wolf algorithm of the present invention;

[0051] Figure 3 This is the structure 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 of the AC variable frequency drilling rig hybrid energy storage system as the objective function;

[0056] Among them, 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] In the formula, 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; 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 operation coefficient, maintenance coefficient, handling 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 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: Using 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 charge state constraints are:

[0066]

[0067] In the formula, 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 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 charge state 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 state of charge of the battery pack;

[0068] The power balance constraint is:

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

[0070] Where P gen P is the working power of the diesel generator set; bat is the battery pack power; P sc is the power of the supercapacitor group; 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 P is the working power of the diesel generator set; genmax It is the maximum operating power of the diesel generator set.

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

[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 individual gray wolves 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 fitness value of the gray wolf 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] In the formula, 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 large 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] In the formula, 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], It is the optimal individual position of the current gray wolf population.

[0097] The strategy of escaping from 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 from the local optimum and increasing population diversity.

[0098] The energy that can be stored in a series-parallel supercapacitor group composed of m single supercapacitors is:

[0099]

[0100] In the formula, E C The energy stored in the supercapacitor bank; U USC is the capacitance value of the single supercapacitor; E Cmax The upper limit of energy storage of the supercapacitor bank; E Cmin The lower limit of energy storage of supercapacitor bank; C USC is the capacitance value of the 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 bank 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] In the formula, E B E is the rated storage capacity of the battery pack; 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 the hybrid energy storage system is built. 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] There will be energy loss at each level of transmission in the energy storage system, so the efficiency of each level must be considered; considering that the drill string will be affected by the friction of the rock wall and the buoyancy of the mud during the drilling operation, a correction factor is used for 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 lifted. 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 Frequency conversion 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 acceleration <![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, experiments were 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 Super capacitor / unit 8059 7842 Battery / pcs 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 the full life cycle cost, and proposes an adaptive gray wolf algorithm to optimize the capacity of the hybrid energy storage system. It can be seen from the experimental simulation results that compared with the traditional gray wolf algorithm, the full life cycle cost is reduced by 1.08%, and it converges after about 78 iterations, which is better than the traditional gray wolf algorithm. The improved gray wolf algorithm proposed in this embodiment not only improves the convergence speed, but also reduces the full life cycle cost of the energy storage system, and reasonably configures the supercapacitors and batteries of the energy storage system.

[0119] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for configuring hybrid energy storage capacity of a variable frequency drilling rig based on an adaptive grey wolf algorithm, characterized in that: include: Establish a full life cycle cost model for AC variable frequency drilling rig hybrid energy storage system as the objective function; use 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 was adopted. The minimum life cycle cost of AC variable frequency drilling rig hybrid energy storage system was taken as the optimization goal. The objective function was solved and the optimal capacity configuration of AC variable frequency drilling rig hybrid energy storage system was obtained.

2. The variable frequency drilling rig hybrid energy storage capacity configuration method based on the adaptive grey wolf algorithm according to claim 1 is characterized in that: 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 In the formula, 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; 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 battery's operating factor, maintenance factor, disposal factor, depreciation factor and unit price; m and n are the number of supercapacitors and storage batteries respectively. Diesel generator 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.

3. The variable frequency drilling rig hybrid energy storage capacity configuration method based on the adaptive grey wolf algorithm according to claim 1 is characterized in that: The energy storage unit capacity and state of charge constraints are: In the formula, 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 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 charge state 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 state of charge of the battery pack; The power balance constraint is: P gen +P bat +P sc =P load Where P gen P is the working power of the diesel generator set; bat is the battery pack power; P sc is the power of the supercapacitor group; P load is the load power; The load power failure rate constraint is: f(LPSP)≤f(LPSPmax) In the formula, 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 P is the working power of the diesel generator set; genmax It is the maximum operating power of the diesel generator set.

4. The variable frequency drilling rig hybrid energy storage capacity configuration method 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 individual gray wolves 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).

5. The variable frequency drilling rig hybrid energy storage capacity configuration method based on the adaptive grey wolf algorithm according to claim 4 is characterized in that: The adaptive adjustment strategy is: Compare the current individual fitness value of the gray wolf 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.

6. The variable frequency drilling rig hybrid energy storage capacity configuration method based on the adaptive grey wolf algorithm according to claim 5 is characterized in that: The formula for the adaptive adjustment strategy is: In the formula, 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.

7. The variable frequency drilling rig hybrid energy storage capacity configuration method based on the adaptive grey wolf algorithm according to claim 1 is characterized in that: The formula for jumping out of the local optimal strategy is: In the formula, 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], It is the optimal location for the current gray wolf population.

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