CN116910965A - Ship energy management strategy real-time optimization method - Google Patents
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Abstract
The invention discloses a ship energy management strategy real-time optimization method, which comprises the following steps: step 1: acquiring the total power consumption requirement of a load side of a current ship power system and machine parameters of each device, and constructing a current ship power supply constraint condition, an output power change constraint condition and a residual power supply capacity constraint condition; step 2: according to the energy management strategy, actually optimizing target requirements, and constructing an objective function of a real-time energy management strategy optimizing model; step 3: constructing a real-time energy management strategy optimization model according to the constraint conditions constructed in the step 1, the objective function constructed in the step 2 and the pelican algorithm; step 4: and generating control instructions of the adjusting devices corresponding to the power supply devices based on the real-time data by using a real-time energy management strategy optimization model, and optimizing the energy of the ship. The invention combines various constraint conditions and the improved pelican algorithm, and improves the accuracy and applicability of energy management.
Description
Technical Field
The invention relates to the technical field of ship management, in particular to a ship energy management strategy real-time optimization method.
Background
With the recent worldwide increasing development of low-carbon calls, the "green transformation" of various industries has become an international consensus. Because the ship has the advantages of large cargo carrying capacity, ocean navigation and the like, the shipping industry becomes an important ring in international trade cooperation, but the ship consumes a large amount of fossil fuel while guaranteeing the global material flow. According to the statistics of UK Law classification society, the annual emission of carbon dioxide in the current shipping industry accounts for 2.33% of the total world, and the annual emission of sulfur oxide and nitrogen oxide accounts for 20% and 30% respectively.
With the development and maturity of electric propulsion technology, electric energy storage technology and new energy power generation technology, and the gradually increasing emission limit requirements, the shipbuilding industry is also faced with the problem of replacing the main power source of the ship. Compared with a ship main engine using traditional energy, such as an internal combustion engine, a steam turbine and the like, after the electric motor is used as a main power device of the full-electric drive ship, electric energy becomes a tie for connecting all ship-mounted electric device equipment. Therefore, the electric energy flowing condition in the ship power grid directly influences the sailing power of the ship.
In order to reduce the fuel cost and pollution emission of the ship in the navigation process, a proper energy management strategy is required to be formulated for the ship on the premise of not interfering the normal energy supply of the ship so as to coordinate the working states of all distributed power supplies in the power grid. Likewise, in comparison with conventional vessels using mechanical power, the new electric propulsion vessels integrate the power system and the electric power system into a comprehensive electric power system, and the electric power links the whole vessel equipment together, but there is a problem in that the process of formulating the vessel energy management strategy needs to consider not only the power supply but also the energy distribution problem.
Specifically, energy management of the traditional ship only needs to be carried out for a power system and an electric power system respectively, and the power system and the electric power system are in a relatively independent relationship, and when the emerging electric propulsion ship carries out energy management, not only electric energy requirements of an electric propulsion load and other electric loads need to be met, but also the output specific gravity of different power generation equipment needs to be coordinated at a power generation end. Compared with the energy management problem of the traditional ship, the complexity and difficulty of the energy management problem of the novel electric propulsion ship are higher, so that the method for formulating the traditional energy management strategy is not suitable for formulating the energy management strategy of the novel electric propulsion ship. Moreover, the multi-energy electric propulsion ship is different from a ship using a single energy supply mode, different types of power generation equipment have different operation characteristics, and particularly, energy storage equipment can be used as power generation equipment and electric equipment, so that the difficulty in formulating an energy management strategy is further improved. Therefore, the conventional method lacks practical application value in such problems.
Disclosure of Invention
The invention provides a real-time optimization method for a ship energy management strategy, which aims to solve the problems that the prior art is not applicable to novel high-electrification electric propulsion ships and novel multi-energy ships.
The invention provides a ship energy management strategy real-time optimization method, which comprises the following steps:
step 1: acquiring the total power consumption requirement of a load side of a current ship power system and machine parameters of each device, and constructing a current ship power supply constraint condition, an output power change constraint condition and a residual power supply capacity constraint condition;
step 2: according to the energy management strategy, actually optimizing target requirements, and constructing an objective function of a real-time energy management strategy optimizing model;
step 3: constructing a real-time energy management strategy optimization model according to the constraint conditions constructed in the step 1, the objective function constructed in the step 2 and the pelican algorithm;
step 4: and generating control instructions of the adjusting devices corresponding to the power supply devices based on the real-time data by using a real-time energy management strategy optimization model, and optimizing the energy of the ship.
Further, the current ship power supply constraint condition is: the sum of the power requirements of each electric equipment is always equal to the sum of the output electric quantity of each electric energy supply equipment;
the output power constraint conditions are: the rated output power variation range provided by the electric energy supply equipment manufacturer;
the output power variation constraint conditions are: the electric energy supply equipment is limited by a self-generating mechanism, and the output power of the electric energy supply equipment is in an adjustable range between two adjacent sampling moments;
the remaining power supply capacity constraint conditions are: only the maximum power supply quantity achievable by the current power supply device itself is used in a manner calculated in terms of energy conversion efficiency.
Further, the specific expression of the ship electric energy supply constraint condition is as follows:
Q G ＝Q U
wherein,,
Q G ＝P DE (t)+P PGE (t)+P SBP (t)+P SC (t)+P RE (t)
Q U ＝P EPS (t)+P L (t)
after the replacement of the components,
P DE (t)+P PGE (t)+P SBP (t)+P SC (t)+P RE (t)＝P EPS (t)+P L (t)
wherein P is DE (t) is the output power of the diesel generator at the moment t; p (P) PGE (t) is the real-time output power of the photovoltaic power generation equipment at the moment t; p (P) EPS (t) and P L (t) power requirements of the electric propulsion system and other electric loads at time t, respectively; p (P) SBP (t) and P SC (t) when the values are positive, respectively, the two are in a power generation state, namely, the electric energy is output outwards; p (P) RE (t) about the power supply redundancy capability at time tThe beam conditions calculate the resulting values.
Further, the specific calculation formula of the power supply redundancy capacity constraint condition is as follows:
in the method, in the process of the invention,the upper limit of rated output power of the diesel generating set is set.
Further, the specific setting mode of the output power constraint condition is as follows:
when the electric energy supply device is a distributed power supply capable of generating only, the specific expression of the output power constraint condition is as follows:
P i max (t)≤P i (t)≤P i max (t)
wherein P is i max (t)、P i min (t) and P i (t) the upper power limit, the lower power limit and the output power of each distributed power supply at the time t respectively;
when the electric energy supply device is a distributed power source which can be used as a power source or a load, the specific expression of the output power constraint condition is as follows:
in the method, in the process of the invention,and E is i (t) the upper capacity limit, the lower capacity limit and the current capacity of each distributed power supply at the time t respectively.
Further, in the step 4, the specific process of generating the control instruction of the adjusting device corresponding to each power supply device based on the real-time data by using the real-time energy management policy optimization model is as follows:
step 41: generating a plurality of energy management strategies according to the real-time data, wherein each energy management strategy is used as a candidate solution, and a set of the plurality of candidate solutions is used as an initial candidate solution population;
step 42: judging that each candidate solution belongs to a feasible solution or an infeasible solution by using a plurality of constraint conditions;
step 43: evaluating each feasible solution by using an objective function, and selecting the feasible solution with the best evaluation as a prey of the pelican algorithm;
step 44: updating the candidate solution based on the obtained prey through a pelican algorithm;
step 45: judging that each candidate solution belongs to a feasible solution or an infeasible solution by using a plurality of constraint conditions;
step 46: evaluating each feasible solution by using an objective function, and selecting the feasible solution with the best evaluation as a prey of the pelican algorithm;
step 47: steps 44-46 are repeated until the iteration is completed, and the best possible solution for objective function evaluation is taken as the output of the real-time energy management strategy optimization model.
Further, in the steps 42 and 45, after updating the candidate solution, the method further includes: the infeasible solution is corrected to a feasible solution using the boundary values of the constraint.
Further, in the pelicans algorithm, the update operation in the first stage is determined using a hybrid discrimination condition.
Further, in the algorithm, the water surface flight calculation is performed based on the current calculation time and the maximum calculation time.
The invention has the beneficial effects that:
the invention combines various constraint conditions and the improved pelican algorithm to optimize the energy management strategy, thereby improving the accuracy and applicability of energy management.
According to the invention, after each candidate solution is generated/changed, the candidate solution is judged, the infeasible solution in the population is corrected into the feasible solution by using the boundary value of the constraint condition, the improved algorithm can at least ensure that an energy management strategy which can be practically applied to each device of a ship power system and the regulating device thereof can be provided, the possibility that the standard algorithm cannot finally search the feasible solution is avoided, and the optimization capability of the energy management strategy of the improved algorithm is improved, namely the robustness and the performance of the algorithm are improved.
The invention improves the judging mode of the effectiveness of the hunting behavior of the first stage of the standard algorithm, namely the pelican, into a mixed distinguishing condition, and increases the position diversity of the pelican before the second stage. Due to the improvement provided by the invention, the algorithm is applied to a constrained optimization problem with time limitation, and algebra for iteration can be severely limited. Therefore, the improved algorithm improves the phenomenon that the diversity of the pelican population is reduced under the condition that the hunting behavior in the first stage fails to find enough better solution positions, thereby providing candidate solution populations with larger difference for the hunting behavior in the second stage, and improving the local mining capability of the algorithm on the feasible solutions obtained at present so as to improve the searching efficiency of the algorithm.
According to the invention, the calculation method of the flying behavior of the pelican water surface is changed into a mode based on the current calculation time and the maximum calculation time, and the algorithm is improved to avoid the defect that the maximum iteration algebra needs to be set manually by experience, so that the algorithm is greatly improved in the aspects of optimal solution precision and convergence speed, and the algorithm is more accurate and reasonable in the aspect of energy management problem application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The embodiment of the invention provides a real-time optimization method for a ship energy management strategy, taking a direct current networking type full-electric drive ship as an example, wherein an electric power system comprises a distributed power supply, an electric propulsion system and other electric loads, and all electric equipment is connected to a direct current bus. The distributed power supply is electric energy supply equipment, comprises a diesel generator, photovoltaic power generation equipment, a storage battery pack and a super capacitor, and takes each power supply and electric energy conversion equipment thereof as a whole; the electric equipment in the electric propulsion system is a propulsion motor, and can convert electric energy in the power grid into mechanical energy so as to enable the ship to obtain power; other electrical loads refer to all remaining consumers that do not include the first two classifications.
Real-time parameter information of electric equipment in the electric power system is obtained through necessary electric power system monitoring equipment of the ship, a preset sampling time interval is 1 second, and the real-time output power P of the diesel generator is contained DE Real-time output power P of photovoltaic power generation equipment PGE Real-time output power P of battery pack SBP Real-time output power P of super capacitor SC The power requirements of the electric propulsion system required to specify the speed of the ship, the power requirements of other electrical loads. Wherein, since the storage battery and the super capacitor can be used as both a power supply and an electric load, P is the same as that of the power supply SBP 、P SC When the value is positive, the two are in a power generation state, namely, the electric energy is output outwards, and when the value is negative, the two are in a power utilization state and the electric energy is absorbed inwards.
Collecting the total power consumption requirement of the load side of the current ship power system, namely the sum Q of the power requirements of all electric equipment in the current ship power system U The total quantity Q of the supplied electric energy required by the ship at the current moment G ：
Q G ＝Q U
Here, Q U No energy storage device is involved, whether it is in a charged state or a discharged state, it is considered a distributed power source rather than a load.
Regardless of how the output power of each power supply device is regulated, the sum of the output power of each power supply device is always equal to the Q G The calculation mode of the ship electric energy supply constraint condition is as follows:
Q G ＝P DE (t)+P PGE (t)+P SBP (t)+P SC (t)+P RE (t)
Q U ＝P EPS (t)+P L (t)
P DE (t)+P PGE (t)+P SBP (t)+P SC (t)+P RE (t)＝P EPS (t)+P L (t)
wherein P is DE (t) diesel generator output power, denoted as time t; p (P) EPS (t) and P L (t) power requirements of the electric propulsion system and other electrical loads, respectively; p (P) RE And (t) represents a value calculated by the power supply redundancy capability constraint.
The redundant constraint condition of the power supply capacity means that in order to cope with the interference caused by unpredictable environmental changes encountered in the actual sailing process of the ship, the main power device of the ship, namely, a part of the power supply capacity of the diesel generator set is reserved so as to ensure that the sudden change of the load end in the ship power system is dealt with, and the specific numerical value can take the value of 15% of the rated output power upper limit of the diesel generator set, namely, the command
The output power constraint condition refers to the rated output power variation range provided by an electric energy supply equipment manufacturer, and the calculation mode of the output power constraint condition is divided into two types, and for a distributed power supply such as a diesel generator and the like, which can only generate electricity, the formula is as follows:
P i max (t)≤P i (t)≤P i max (t)
wherein P is i max (t)、P i min (t) and P i (t) the upper power limit, the lower power limit and the output power of each distributed power supply at the time t respectively;
for a distributed power supply which can be used as a power supply or a load for a storage battery pack and the like, the formula is as follows:
in the method, in the process of the invention,and E is i (t) the upper capacity limit, the lower capacity limit and the current capacity of each distributed power supply at the time t respectively.
The output power variation constraint condition refers to that the electric energy supply equipment is limited by a self-generating mechanism, and the output power of the electric energy supply equipment is adjustable within the range between two adjacent sampling moments. The output power of the diesel generator and the storage battery cannot be changed drastically in a short time, but the super capacitor can be used. The output power variation constraint is calculated in the following manner:
ΔP DE ＝|P DE (t+1)-P DE (t)|≤P DE，limit
ΔP SBP ＝|P SBP (t+1)-P SBP (t)|≤P SBP，limit
wherein P is DE (t) and P DE (t+1) is the output power of the diesel generator at the t and t+1 times respectively; p (P) SBP (t) and P SBP (t+1) is the output power of the storage battery at the t and t+1 times respectively; ΔP DE And DeltaP SBP The climbing power of the diesel generator and the battery pack are respectively; p (P) DE，limit And P SBP，limit The maximum climbing power of the diesel generator and the storage battery respectively.
The remaining power supply capacity constraint condition refers to that the power generation capacity of the energy storage device is limited not only by the rated output power change range, but also by the current capacity of the energy storage device, and the calculation mode of the remaining power supply capacity constraint condition is as follows:
in the formula, the main chain,and E is i (t) the upper capacity limit, the lower capacity limit and the current capacity of each distributed power supply at the time t respectively.
Taking economy as a real-time energy management strategy optimization model target, the calculation mode of an objective function is as follows:
minCost＝C DE +C SBP +C SC
C DE ＝price·V fuel +α(L FH )·P DE
C SBP ＝C Deg，SBP ＝β SBP ·(B CD +B E )
C SC ＝C Deg，SC ＝β SC ·C CD
the Cost is the total Cost of each electric energy supply device in the ship electric power system when in use; c (C) DE The use cost of the diesel generator is; c (C) SBP The use cost of the storage battery pack is; c (C) SC The use cost of the super capacitor is realized; price is diesel oil price; v (V) fuel Fuel consumption for a diesel generator; alpha (·) is the operation and maintenance cost coefficient of the diesel generator and is the load rate L FH Is a function of (2); c (C) Deg，SBP The decay loss cost of the storage battery pack; beta SBP The degradation loss coefficient of the storage battery pack; b (B) CD The charge and discharge alternating cycle coefficients of the storage battery pack; b (B) E The excess use amplitude of the storage battery pack is given; c (C) Deg，SC The decay loss cost of the super capacitor; beta SC Is the decay loss coefficient of the super capacitor; c (C) CD The alternating cycle coefficient of the charge and discharge of the super capacitor.
For three kinds of electric energy supply equipment capable of being actively regulated, namely a diesel generator, a storage battery and a super capacitor, the intersection of calculation results of all calculation modes of corresponding constraint conditions is used as the regulating range of the output power of the electric energy supply equipment.
The improved pelican algorithm is used for driving the real-time energy management strategy optimization model to carry out iterative operation, the optimized ship energy management strategy is obtained, and the obtained energy management strategy is evaluated by using an objective function, and the method comprises the following specific steps:
ST1: generating a plurality of groups of energy management strategies, namely candidate solutions, wherein the candidate solution set is used as an initial candidate solution population;
ST2: judging whether each candidate solution belongs to a feasible solution or an infeasible solution by using constraint conditions, wherein the feasible solution is not against any constraint condition, and the infeasible solution is against at least any constraint condition;
ST3: correcting the infeasible solution in the population into a feasible solution by using the boundary value of the constraint condition;
ST4: evaluating each candidate solution using an objective function, selecting the best of which is evaluated as a prey;
ST5: in the algorithm of pelican, the hunting behavior is divided into two stages, and in the first stage, a candidate solution, namely pelican, approaches a hunting region:
in the method, in the process of the invention,for the j-th dimension position of the i-th pelican updated in the first stage, rand is [0,1 ]]Random numbers in the range, I is a random integer of 1 or 2, p j For the j-th dimension of the prey, F p Objective function value for hunting;
ST6: the mixed discrimination conditions are then used to determine the update action of the first stage of the present time, which is considered a valid update only if the conditions are met:
wherein X is i For the final position of the i-th pelicant in the present iteration,for the new position of the i-th pelican after the first stage update, F i Is an objective function based on the new position of the i-th pelican after the first stage update;
ST7: in the second phase, the candidate solution, namely the pelargonic object, flies on the water surface to continue searching for hunting objects:
in the middle of，For the second phase updated dimension position of the second pel, rand is [0,1]Random numbers in the range, R is a random integer with a value of 0 or 2, TIME is the current simulation TIME, and TIME is the maximum simulation TIME;
ST8: the updating action of the second stage is judged, and only after the objective function value is improved, the updating action is considered to be one effective updating:
wherein X is i For the final position of the i-th pelicant in the present iteration,for the new position of the i-th pelican after the second stage update, F i Is an objective function based on the new position of the i-th pelican after the second stage update;
ST9: judging whether each candidate solution belongs to a feasible solution or an infeasible solution by using constraint conditions, wherein the feasible solution is not against any constraint condition, and the infeasible solution is against at least any constraint condition;
ST10: correcting the infeasible solution in the population into a feasible solution by using the boundary value of the constraint condition;
ST11: evaluating each candidate solution using an objective function, selecting the best of which is evaluated as a prey;
ST12: is the upper computation time limit reached? If yes, executing ST13, otherwise executing ST5;
ST13: and ending the calculation process, and outputting the candidate solution represented by the prey as a final result.
Although embodiments of the present invention have been described, various modifications and changes may be made by those skilled in the art without departing from the spirit and scope of the invention, which is intended to be within the scope of the appended claims.
Claims (9)
1. The real-time optimization method for the ship energy management strategy is characterized by comprising the following steps of:
step 1: acquiring the total power consumption requirement of a load side of a current ship power system and machine parameters of each device, and constructing a current ship power supply constraint condition, an output power change constraint condition and a residual power supply capacity constraint condition;
step 2: according to the energy management strategy, actually optimizing target requirements, and constructing an objective function of a real-time energy management strategy optimizing model;
step 3: constructing a real-time energy management strategy optimization model according to the constraint conditions constructed in the step 1, the objective function constructed in the step 2 and the pelican algorithm;
step 4: and generating control instructions of the adjusting devices corresponding to the power supply devices based on the real-time data by using a real-time energy management strategy optimization model, and optimizing the energy of the ship.
2. The method for optimizing a ship energy management strategy in real time according to claim 1, wherein the current ship power supply constraint condition is: the sum of the power requirements of each electric equipment is always equal to the sum of the output electric quantity of each electric energy supply equipment;
the output power constraint conditions are: the rated output power variation range provided by the electric energy supply equipment manufacturer;
the output power variation constraint conditions are: the electric energy supply equipment is limited by a self-generating mechanism, and the output power of the electric energy supply equipment is in an adjustable range between two adjacent sampling moments;
the remaining power supply capacity constraint conditions are: only the maximum power supply quantity achievable by the current power supply device itself is used in a manner calculated in terms of energy conversion efficiency.
3. The ship energy management strategy real-time optimization method according to claim 1 or 2, wherein the specific expression of the ship electric energy supply constraint condition is:
Q G ＝Q U
wherein,,
Q G ＝P DE (t)+P PGE (t)+P SBP (t)+P SC (t)+P RE (t)
Q U ＝P EPS (t)+P L (t)
after the replacement of the components,
P DE (t)+P PGE (t)+P SBP (t)+P SC (t)+P RE (t)＝P EPS (t)+P L (t)
wherein P is DE (t) is the output power of the diesel generator at the moment t; p (P) PGE (t) is the real-time output power of the photovoltaic power generation equipment at the moment t; p (P) EPS (t) and P L (t) power requirements of the electric propulsion system and other electric loads at time t, respectively; p (P) SBP (t) and P SC (t) when the values are positive, respectively, the two are in a power generation state, namely, the electric energy is output outwards; p (P) RE And (t) is a value obtained by calculating the power supply redundancy capacity constraint condition at the moment t.
4. The ship energy management strategy real-time optimization method according to claim 3, wherein the specific calculation formula of the power supply redundancy capacity constraint condition is as follows:
in the method, in the process of the invention,the upper limit of rated output power of the diesel generating set is set.
5. The ship energy management strategy real-time optimization method according to claim 1 or 2, wherein the specific setting mode of the output power constraint condition is as follows:
when the electric energy supply device is a distributed power supply capable of generating only, the specific expression of the output power constraint condition is as follows:
P i max (t)≤P i (t)≤P i max (t)
wherein P is i max (t)、P i min (t) and P i (t) the upper power limit, the lower power limit and the output power of each distributed power supply at the time t respectively;
when the electric energy supply device is a distributed power source which can be used as a power source or a load, the specific expression of the output power constraint condition is as follows:
wherein E is i max (t)、E i min (t) and E i (t) the upper capacity limit, the lower capacity limit and the current capacity of each distributed power supply at the time t respectively.
6. The method for optimizing the energy management strategy of the ship in real time according to claim 1, wherein in the step 4, the specific process of generating the control command of the adjusting device corresponding to each power supply device based on the real-time data by using the real-time energy management strategy optimizing model is as follows:
step 41: generating a plurality of energy management strategies according to the real-time data, wherein each energy management strategy is used as a candidate solution, and a set of the plurality of candidate solutions is used as an initial candidate solution population;
step 42: judging that each candidate solution belongs to a feasible solution or an infeasible solution by using a plurality of constraint conditions;
step 43: evaluating each feasible solution by using an objective function, and selecting the feasible solution with the best evaluation as a prey of the pelican algorithm;
step 44: updating the candidate solution based on the obtained prey through a pelican algorithm;
step 45: judging that each candidate solution belongs to a feasible solution or an infeasible solution by using a plurality of constraint conditions;
step 46: evaluating each feasible solution by using an objective function, and selecting the feasible solution with the best evaluation as a prey of the pelican algorithm;
step 47: steps 44-46 are repeated until the iteration is completed, and the best possible solution for objective function evaluation is taken as the output of the real-time energy management strategy optimization model.
7. The method for optimizing energy management strategies of a ship in real time according to claim 6, wherein in the steps 42 and 45, after updating the candidate solution, the method further comprises: the infeasible solution is corrected to a feasible solution using the boundary values of the constraint.
8. The method for optimizing energy management strategies of a ship in real time according to claim 1, wherein the pelican algorithm determines the update operation in the first stage using a hybrid discrimination condition.
9. The method according to claim 1 or 8, wherein the pelican algorithm performs the calculation of the water surface flight based on the current calculation time and the maximum calculation time.
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