CN117134409A - Micro-grid system considering electro-hydro-thermal complementation and multi-objective optimal configuration method thereof - Google Patents
Micro-grid system considering electro-hydro-thermal complementation and multi-objective optimal configuration method thereof Download PDFInfo
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Abstract
The invention discloses a micro-grid system taking electric-hydrogen-heat complementation into consideration and a multi-objective optimization configuration method thereof, wherein the method aims at optimizing economy, power supply stability and highest renewable energy consumption rate, adopts an electric-hydrogen-heat multi-energy complementation control strategy, establishes an electric-hydrogen-heat multi-energy complementation capacity optimization configuration model facing high-proportion renewable energy sources, and solves objective problems by using a particle swarm optimization (SAPSO) based on simulated annealing. The method can realize the combined supply of the heat and electricity of the community micro-grid, effectively reduce the 'wind abandon and light abandon' of the community micro-grid, and improve the multi-energy coupling utilization efficiency, economy, reliability and environmental protection of the community micro-grid. The method is applied to the park microgrid under the high-proportion distributed power application scene, and has positive effects of improving the economy and the power supply reliability of the system, promoting the consumption of renewable energy sources and improving the energy utilization efficiency of the terminal.
Description
Technical Field
The invention belongs to comprehensive energy technology, and particularly relates to a micro-grid capacity multi-objective optimization configuration method considering electric-hydrogen-thermal multi-energy complementation.
Background
With the rapid growth of distributed renewable energy installations and the diversity of consumer side loads, terminal side grids face a number of problems and challenges. In this context, new energy micro-grid has been developed, which is based on distributed power generation technology, and is mainly based on small power stations close to distributed resources or users, and is a small modularized and distributed energy supply network formed by combining end user power quality management and energy cascade utilization technology. The micro-grid is an important component of the intelligent power grid, can realize the efficient utilization of internal renewable energy, can also provide clean energy supply for the power grid and users, and is an integrated energy system of electric, thermal and gas coupling. The energy storage is an essential important component in the micro-grid, the conventional electrochemical energy storage such as a storage battery is mainly used at present, but the electrochemical energy storage has the defects of short energy storage time, small capacity scale grade and the like, is mainly used for power grid frequency modulation and peak shaving, and smoothing new energy output fluctuation, realizes short-period response and adjustment at an hour level, cannot be suitable for high-capacity renewable energy consumption and storage, cannot convert different energy sources, and has limited effect on improving the comprehensive energy efficiency of the micro-grid. Aiming at the problems, the academic world applies the hydrogen energy technology with the advantages of large energy storage capacity, long storage time, cleanness, no pollution and the like to the end user power grid, and plays an advantage in each link of a novel power system of source, network and load. Firstly, in the aspect of new energy consumption, the capability of the power grid for absorbing new energy can be effectively improved by utilizing renewable energy to electrolyze hydrogen production/hydrogen storage. Secondly, in the aspect of power grid auxiliary regulation, the quick response capability of the hydrogen energy power station is utilized, the second-level and millisecond-level response of the input power can be realized, and meanwhile, 10% -150% of wide power input can be adapted, so that peak regulation and frequency modulation services are provided for the power grid, the safety, reliability and flexibility of the power system are improved, and the method is an important means for constructing a zero-carbon power grid and a novel power system. Finally, in the aspect of comprehensive energy utilization, hydrogen energy is used as flexible and efficient secondary energy, an electrolytic tank and a fuel cell can be utilized at an energy consumption end, and through electric hydrogen conversion, the interconnection complementation and collaborative optimization of various energy networks such as electric power, heat supply, fuel and the like are realized, the development of distributed energy is promoted, the energy utilization efficiency of a terminal is improved, and the comprehensive utilization way of electric energy is expanded. In addition, the development of the hydrogen energy industry is quickened, and the hydrogen energy industry is also a strategic choice for coping with global climate change, realizing carbon peak and carbon neutralization targets, guaranteeing national energy safety and realizing high-quality development of economy and society. Under the pushing of technology, cost, policy and the like, hydrogen energy is possible to be used as a tie for connecting renewable energy sources and an electric power energy storage medium, and plays an increasingly important role in a novel electric power system mainly comprising new energy sources.
In view of the above advantages of hydrogen energy, the method can be applied to micro-grids from the aspect of energy storage, can store waste wind and waste light electrolytic hydrogen production, and is suitable for long-term peak shaving technology. Moreover, in the scenario of large capacity long period regulation, hydrogen storage is more competitive in terms of technical economy. However, the current hydrogen energy utilization cost is generally higher, and how to reduce the cost of the hydrogen energy in different application scenes of the micro-grid through an optimal configuration technology, so that the improvement of the comprehensive energy efficiency of the system is a key for promoting the development of the hydrogen energy technology in the comprehensive energy system.
Disclosure of Invention
In order to solve the technical problems provided by the background technology, the invention aims to provide an electric-hydrogen-thermal multi-energy complementary micro-grid capacity multi-objective optimal configuration method. The electric-hydrogen-heat multifunctional complementary micro-grid structure and the electric-hydrogen-heat multifunctional complementary micro-grid capacity multi-objective optimal configuration model which are provided by the research and take the hydrogen energy storage and wind-light unit as cores can effectively solve the wind and light discarding phenomena of the community micro-grid in a high-proportion wind-light resource application scene, and improve the community energy source treatment. Finally, by improving the SAPSO algorithm, the method can obtain an optimal solution under economical conditions.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a micro-grid system that accounts for electro-hydro-thermal complementarity, the system comprising: distributed power, energy storage, electro-hydro-thermal and user load, control system;
the distributed power supply comprises a wind turbine generator and a photovoltaic turbine generator;
the energy storage device comprises a storage battery, a hydrogen storage tank and a heat storage tank;
the electro-hydro-thermal device comprises an electrolytic bath water electrolysis hydrogen production device, a fuel cell and a hydrogen boiler;
the control system is used for determining the power and the photovoltaic power which are provided for the user load and the fan to generate electricity for producing hydrogen, and when the power of the power generation system is larger than the power of the electric load, the energy storage system absorbs the residual power and distributes the residual power to the electrolytic tank or the storage battery;
when the power of the power generation system cannot meet the power required by the electric load, the energy storage system releases energy, the storage battery pack or the fuel cell discharges, the system shortage power is provided, and the system power balance is met.
The multi-objective optimization configuration method for the micro-grid system considering electro-hydro-thermal complementation comprises the following steps:
(1) Determining an objective function of a multi-objective optimal configuration method for a microgrid system that takes into account electro-hydro-thermal complementarity
The capacity of a wind turbine generator, a photovoltaic unit, an energy storage device and an electric-hydrogen-heat device in a micro-grid is taken as a decision variable, an objective function is formed by the minimum annual average total cost, the minimum load electricity shortage rate and the maximum renewable energy source absorption rate of a system, and the specific objective function is represented by the following formula:
wherein C is init Representing the planned construction costs taking into account the electro-hydro-thermal coupling system, including the inherent installation costs of the equipment to be configured, C oper Representing annual benefits from consideration of operating and maintenance costs, renewable energy subsidy costs and electric-hydrogen-thermal coupling systems of the equipment to be configured, C rep Representing depreciated revenues at the end of a device lifecycle considering replacement costs of the devices to be configured; n is n 1 Representing the number of sampling points which do not meet the power load demand in the planning construction period, k represents the number of renewable energy types in the system, and P i (t) represents the output power at time t of the ith type of renewable energy source, P load (t) represents electric load power, P bd (t) represents the discharge power of the battery, P fc (t) represents the fuel cell output electric power, P drump (t) represents the lack of power of the system at time t; n is n 2 Representing the number of sampling points with abandoned wind and abandoned light in planning construction period, P bc (t) represents the battery charging power, P ele (t) represents the input power of the electrolytic cell, P loss (t) represents the renewable energy source lost power of the system at the time t;
(2) Constraint conditions of a multi-objective optimization configuration method considering an electro-hydro-thermal complementary micro-grid system are set
The constraint conditions of the proposed optimal configuration method include: decision variable capacity constraint, energy storage state constraint, unit output constraint, power balance constraint and hydrogen supply and demand balance constraint.
Further, on the basis of meeting the electric load demand preferentially, the energy storage device needs to continuously adjust the energy storage state according to the relation between the wind-solar power generation output power and the electric heating load demand;
defining the net output power at system t time as P net The expression is as follows:
when the wind-solar power generation output power at the moment t is greater than the electric load demand, P net (t)>0, the electrolytic tank works at the moment, the redundant electric quantity is converted into hydrogen and stored, and part of heat requirement is supplied through a hydrogen boiler; when the wind-solar power generation output power at the moment t is smaller than the electric load demand, P net (t)<0, when the fuel cell works, the stored hydrogen is converted into electric energy to supply the part with insufficient electric load, and the heat energy generated in the process supplies part of heat requirement; when the sum of the wind-solar power generation and the output electric power of the fuel cell at the moment t can not meet the electric load requirement, P fc (t)＜|P net (t) | at this time, there is load-lacking electric power P drump (t)。
Further, the improved SAPSO algorithm is used for solving, optimizing is carried out for the capacity of each device to be configured, convergence of the PSO algorithm is ensured by selecting proper parameters, and boundary limitation on the speed can be canceled, and the method specifically comprises the following steps:
(1) Determining an objective function of an improved SAPSO algorithm
According to the SAPSO algorithm, the load power failure rate f of the objective function is ensured at the same time 2 Less than 8 percent, new energy consumption rate f 3 More than 95%, the objective function of the formula (1) is rewritten into a penalty function form, and the expression is as follows:
minf＝f 1 +l 1 (f 2 -0.08)x 1 +l 2 (0.95-f 3 )x 2
wherein x is i ∈{0,1}(i＝1,2)，x i =0 indicates that the target is not out of limit, x i =1 indicates that the item is out of limit; l (L) i (i=1, 2) is a penalty factor for the corresponding term;
(2) Constraint of profile domains
The contour line domain is used for restraining the position parameters obtained by initializing the particles, namely, the horizontal position parameters (x, y) of the particles are required to be positioned in the contour domain corresponding to the control points, so that the constraint is satisfied:
readmap(x,y)∈[M-ξ,M+ξ]
wherein readmap represents a function of reading a map; the value of ζ is the standard deviation of the measurement noise, M is the center of the contour;
(3) Selection of adaptive parameters
Adjusting the three parameters according to the obtained fitness value of each particle
Wherein omega i Is the dynamic inertial weight of the ith particle, c i1 And c i2 The control is the dynamic learning factor of the ith particle, N is the number of particles, and k (i) represents the ordinal number corresponding to the ith particle after the fitness value of the particles in the current search state is arranged. Omega max And omega min Represents the maximum value of ω and the maximum value of n max Represents the maximum number of iterations and n represents the current number of iterations.
The beneficial effects brought by adopting the technical scheme are that:
1. the characteristics of local natural resources and load demand are fully considered by combining the characteristics of hydrogen energy storage, long-period large-scale storage across seasons and multi-energy combined storage and supply, so that the economy, the power supply reliability and the renewable energy consumption rate of the micro-grid are improved.
2. The improved SAPSO solving algorithm adds some constraint conditions in particle swarm optimization searching, optimizes static parameters into dynamic variables, improves the speed of global searching, and obtains accurate matching tracks to obtain an optimal solution.
3. The formulated control strategy of the electric-hydrogen-thermal multi-energy complementary system is beneficial to improving the energy utilization efficiency of the micro-grid system terminal, and provides a certain reference for the optimal configuration of a 100% renewable energy system and the application of hydrogen energy storage.
Drawings
FIG. 1 is a schematic diagram of an electro-hydro-thermal multi-energy complementary independent micro-grid structure.
Fig. 2 is a flow chart of an electro-hydro-thermal multi-energy complementary microgrid solution based on a modified SAPSO algorithm.
Detailed Description
The technical scheme of the present invention will be described in detail below with reference to the accompanying drawings.
The present invention first provides a micro-grid system that considers electro-hydro-thermal complementarity, the system comprising: distributed power, energy storage, electro-hydro-thermal and user load, control system;
the distributed power supply comprises a wind turbine generator and a photovoltaic turbine generator;
the energy storage device comprises a storage battery, a hydrogen storage tank and a heat storage tank;
the electro-hydro-thermal device comprises an electrolytic bath water electrolysis hydrogen production device, a fuel cell and a hydrogen boiler;
the control system is used for determining the power and the photovoltaic power which are provided for the user load and the fan to generate electricity for producing hydrogen, and when the power of the power generation system is larger than the power of the electric load, the energy storage system absorbs the residual power and distributes the residual power to the electrolytic tank or the storage battery;
when the power of the power generation system cannot meet the power required by the electric load, the energy storage system releases energy, the storage battery pack or the fuel cell discharges, the system shortage power is provided, and the system power balance is met.
Further, the micro-grid system is as follows:
mainly comprises a distributed power supply (distributed generation, DG), an energy storage device, an electro-hydro-thermal device and a load. Wherein DG mainly comprises a Wind Turbine (WT), a photovoltaic (photovoltaic cell, PV), an energy storage device mainly comprises a Storage Battery (SB), a hydrogen storage tank (hydrogen storage tank, HST), a heat storage tank (thermal storage tank, TST), and an electro-hydro-thermal device mainly comprises an electrolytic water production device (ELE), a Fuel Cell (FC), and a Hydrogen Boiler (HB). Determining, by the control system, a fan, photovoltaic power to provide to the user load and hydrogen production: the fan and the photovoltaic power generation preferably meet the requirement of a user on power consumption, when the power of the power generation system is larger than the power of the power consumption load, the energy storage system absorbs the residual power, the residual power is distributed to the electrolytic tank or the storage battery, and the electric energy is stored in a hydrogen energy or chemical energy mode, wherein the hydrogen energy can be converted into the electric energy through the fuel cell when necessary, the heat load of the user can be supplied in the process, and meanwhile, when the heat load is high, the hydrogen energy can be converted into heat energy through the hydrogen boiler; when the power of the power generation system cannot meet the power required by the electric load, the energy storage system releases energy, the storage battery pack or the fuel cell discharges, the system shortage power is provided, and the system power balance is met.
The invention also provides a multi-objective optimization configuration method of the micro-grid system considering electro-hydro-thermal complementation, which comprises the following steps:
(1) Determining objective function of multi-objective optimization configuration method of micro-grid system considering electro-hydro-thermal complementation
Taking the economy, the power supply reliability and the new energy utilization rate of the system into comprehensive consideration, taking the capacities of WT, PV, an energy storage device and an electro-hydro-thermal device in a micro-grid as decision variables, wherein an objective function consists of the minimum annual average total cost, the minimum load electricity shortage rate and the maximum renewable energy consumption rate of the system, and the specific objective function comprises the following formula:
wherein C is init 、C oper 、C rep Representing the planning construction costs, the operation maintenance costs and the replacement costs, respectively, taking into account the electro-hydro-thermal coupling system. Planning construction costs mainly considering inherent installation costs of equipment to be configured, C oper Mainly considers the operation and maintenance cost of equipment to be configured, the renewable energy subsidy cost and the annual income brought by an electric-hydrogen-thermal coupling system, C rep Mainly considering replacement cost of equipment to be configured and depreciated income at the end of the life cycle of the equipment; n is n 1 Representing the number of sampling points which do not meet the power load demand in the planning construction period, k represents the number of renewable energy types in the system, and P i (t) represents the output power at time t of the ith type of renewable energy source, P load (t) represents electric load power, P bd (t) represents the discharge power of the battery, P fc (t) represents the fuel cell output electric power, P drump (t) represents the lack of power of the system at time t; n is n 2 Representing the number of sampling points with abandoned wind and abandoned light in planning construction period, P bc (t) represents the battery charging power, P ele (t)Represents the input power of the electrolytic cell, P loss And (t) represents the renewable energy source loss power of the system at the time t.
Wherein CRF (r, N age ) Representing the fund recovery coefficient of the equipment to be configured, r is the actual discount rate, N age For the operational life of the devices to be configured, Γ is the set of devices to be configured, N k ，π k The number and unit price of the kth equipment to be configured are respectively; c (C) k,rep ，C k,rem Respectively the kth device N to be configured age Replacement costs and depreciated revenue after year; r is (r) 1 Delta is the inflation rate for long-term discount rate.
C oper ＝C om -C sub -C ex
Wherein C is om Maintenance cost for the equipment to be configured; c (C) sub Annual benefit is subsidized for the wind-solar power generation country; c (C) ex The method is beneficial to the operation year of the electric-hydrogen-heat multi-energy complementary system, and is specifically shown as follows:
wherein C is k,om Maintenance cost for the k-th class of equipment to be configured; pi l,sub ，E l,sub The standard and the annual total power generation amount are respectively complemented for the output unit of the first renewable energy source; p (P) el,t ，Q hl,t The user electric load and the heat load power are respectively provided for the system at the moment t; pi el,t For the electricity price of the user at the moment t, the time-sharing electricity price is adopted for calculation, pi hl,t For the user's heat unit price at time t, 0.255 yuan/kWh is taken here.
(2) Constraint conditions of a multi-objective optimization configuration method considering an electro-hydro-thermal complementary micro-grid system are set
The constraint conditions of the proposed optimal configuration model mainly comprise: decision variable capacity constraint, energy storage state constraint, unit output constraint, power balance constraint and hydrogen supply and demand balance constraint.
1) Decision variable constraints
0≤N k ≤N k,max
Wherein k is Γ; n (N) k,max The upper limit value of the number of the k-th type equipment to be configured can be set according to factors such as actual investment budget, installation occupied area and the like, and the upper limit value of the number is defined according to load conditions, so that solving efficiency is improved.
2) Energy storage state constraints
The ratio of the remaining capacity of a battery after a period of use or prolonged rest to the capacity of its fully charged state is often expressed as a percentage. The value range is 0-1, and the battery is completely discharged when soc=0 and completely full when soc=1. It is english, SOC (state of charge). The energy storage states of the hydrogen storage tank and the heat storage tank are defined as above, and the upper limit value and the lower limit value of the energy storage level are required to be set in consideration of the influence of the energy storage level value on the energy storage life.
Wherein: SOC (t), LOT (t) and LOH (t) are respectively the energy storage states of the storage battery, the heat storage tank and the hydrogen storage tank at the moment t, ESS, TSS, HSS are respectively the total capacities of the storage battery, the heat storage tank and the hydrogen storage tank, and SOC max 、SOC min 、LOT max 、LOT min 、LOH max 、LOH min Upper and lower limit values of energy storage levels of the storage battery, the heat storage tank and the hydrogen storage tank respectively, W bt,t 、W tst,t 、VL t The storage battery electric quantity, the heat energy stored in the heat storage tank and the hydrogen storage quantity of the hydrogen storage tank at the moment are respectively.
3) Unit output constraint
Wherein:for the output of the k-th set at time t, < >>And->The upper and lower limits of the allowable output for the k-th set.
4) Power balance constraint
The operating power of each module should comply with the balance condition, and the expression is as follows:
P pv (t)+P wt (t)-P bt_ch (t)+P bt_dis (t)-P ele (t)+P fc (t)＝P el (t)
Q fc (t)+Q hb (t)+Q tst,dis (t)＝Q hl (t)
wherein: p (P) pv (t)、P wt (t)、P el (t)、Q hl (t) represents the output power of PV and WT at time t, the electrical load, the thermal load power, P provided by the system bt_ch (t)、P bt_dis (t)、P ele (t)、P fc (t) represents the charge/discharge power of the storage battery, the input power of the electrolytic cell, the output electric power of the fuel cell, Q fc (t)、Q hb (t)、Q tst,dis And (t) represents the output heat power of the fuel cell and the hydrogen boiler at the moment t and the heat release power of the heat storage tank respectively.
5) Hydrogen supply and demand balance constraint
V fc (t)+V hb (t)+V cs (t)＝V dcs (t)+V h (t)
Wherein: v (V) fc (t)、V hb (t)、V cs (t) represents the hydrogen consumption rate of the fuel cell and the hydrogen boiler at time t and the hydrogen charging rate of the hydrogen storage tank, V dcs (t)、V h And (t) represents the hydrogen supply rate of the hydrogen storage tank and the hydrogen production rate of the electrolytic cell at the moment t.
In order to improve the system efficiency, an electric-hydrogen-thermal multifunctional complementary system control strategy is formulated, and the specific expression is as follows:
because wind-solar power generation is obviously affected by factors such as environment, the intermittence, the fluctuation and the uncertainty, the output power of the non-grid-connected wind-solar power generation fluctuates up and down on the electric load. On the basis of preferentially meeting the electric load demand, in order to improve the system efficiency and meet part of the heat load demand, the energy storage device needs to continuously adjust the energy storage state according to the relation between wind-solar power generation output power and the electric heating load demand. The net output power at time t of the system is defined herein as P net (t) the expression is as follows:
when the wind-solar power generation output power at the moment t is greater than the electric load demand, P net (t)>0, at this time, the electrolyzer is operated, the surplus electricity is converted into hydrogen and stored, and a part of heat demand is supplied through a hydrogen boiler, and the expression is as follows:
V h (t)＝min{P net (t)·η ele /H hv ,V h，max (t)}
Q hb (t)＝min{V h (t),V hb，max (t)}·H hv ·η hb
wherein: v (V) h,max (t) is the maximum hydrogen production rate allowed by the electrolyzer, V hb,max (t) is the maximum hydrogen consumption rate, eta, allowed by the hydrogen boiler ele For the efficiency of the alkaline cell H hv Is the heat value of hydrogen, eta hb The heating efficiency of the hydrogen boiler is improved.
When the wind-solar power generation output power at the moment t is smaller than the electric load demand, P net (t)<0, at which time the fuel cell is operated, the stored hydrogen is converted to electrical energy to supply the portion of insufficient electrical load, and the thermal energy generated by this process supplies a portion of the thermal demand as follows:
P fc (t)＝min{|P net (t)|,P fc,max (t)}
Q fc (t)＝P fc (t)·η fc_h /η fc_e
wind-solar power generation at time tAnd the sum of the output electric power of the fuel cells still cannot meet the electric load demand, P fc (t)＜|P net (t) | at this time, the load lacks electric power P drump (t) the following:
P drump (t)＝|P net (t)|-P fc (t)
finally, an improved SAPSO algorithm is used for solving an independent micro-grid multi-objective optimization configuration model with complementary electric-hydrogen-thermal multi-energy.
In consideration of the uncertainty of loads in the electric-hydrogen-thermal multi-energy complementary system, fluctuation of wind-solar power generation, large-scale energy conversion and a large amount of multi-source data generated by energy storage equipment, and complex objective functions, a large amount of constraint conditions exist, so that the particle swarm algorithm is adopted for solving. The particle swarm algorithm has the advantages of strong robustness, high convergence speed, high optimization efficiency, easiness in implementation and the like, but has the defects of low search precision, easiness in sinking into local optimum and the like. Therefore, a particle swarm optimization (SAPSO) algorithm based on simulated annealing is adopted to surround a preset configuration optimization target, the capacity of each device to be configured is optimized, the convergence of the PSO algorithm can be ensured by selecting proper parameters, and the boundary limitation on the speed can be canceled.
According to the SAPSO algorithm, the load power failure rate f of the objective function is ensured at the same time 2 Less than 8 percent, new energy consumption rate f 3 Greater than 95%, the objective function herein is rewritten as a penalty function, expressed as follows:
minf＝f 1 +l 1 (f 2 -0.08)x 1 +l 2 (0.95-f 3 )x 2
wherein x is i ∈{0,1}(i＝1,2)，x i =0 indicates that the target is not out of limit, x i =1 indicates that the item is out of limit; l (L) i (i=1, 2) is the penalty factor of the corresponding term.
The computational complexity and efficiency of SAPSO is affected by the size of the uncertainty domain, the number of particles, and the number of iterations. A larger uncertainty domain can result in an excessively large search range, thereby reducing the speed of global searches and significantly extending search times. The smaller uncertainty region may result in a missing optimal solution, failing to obtain an accurate matching trajectory. If the number of particles and the number of converging iterations are too large, the calculation time of the whole algorithm is directly affected. The convergence iteration times are less, so that the optimization algorithm cannot obtain an optimal solution and is terminated in advance, and the accuracy of the algorithm is seriously affected. If the amount of calculation is to be reduced to improve the real-time performance of the system, it is necessary to reduce the number of particles, select an appropriate uncertainty region, or increase the convergence speed of the particle swarm. To solve these problems, constraints are added to the particle swarm optimization search and static parameters are optimized as dynamic variables to improve the SAPSO algorithm.
1) Constraint of profile domains
The particle initialization process has a great influence on the performance of the PSO. As the number of particles increases, the coverage of the solution space expands accordingly, thereby obtaining an optimal solution with a greater probability. However, this also increases the computational effort of the overall algorithm, violating the original intent of optimizing the search. In order to improve the matching probability and searching efficiency of the algorithm, the contour line domain is used for restraining the position parameters obtained by initializing the particles, namely the horizontal position parameters (x, y) of the particles are required to be positioned in the contour domain corresponding to the control points, so that the constraint is satisfied
readmap(x,y)∈[M-ξ,M+ξ]
Wherein: readmap represents a function of reading a map; the value of ζ is the standard deviation of the measurement noise. Under this constraint, the particles can be distributed around the real trajectory with a greater probability during the initialization process. The method realizes the goal of obtaining the optimal solution with less particles, and greatly improves the searching efficiency and the performance of the algorithm.
2) Selection of adaptive parameters
The inertia weight omega is taken as an important parameter of PSO, and has great influence on the convergence of the algorithm. The method can balance the capability of local searching and global searching, and fully reflect the influence of the historical speed on the current speed of the particles. When the inertia weight is large, the particles can explore new fields, and the global searching capability is strong. When the inertial weight is small, the particles tend to search locally, i.e., an evolving search. Learning factor c 1 And c 2 Controlling particle inThe ability to find optimal and self-optimal in the population. The appropriate selection of inertial weights and learning factors may improve the efficiency and accuracy of the optimized search. Therefore, it is necessary to adjust these three parameters according to the obtained fitness value of each particle
Wherein: omega i Is the dynamic inertial weight of the i-th particle. c i1 And c i2 Control is the dynamic learning factor of the ith particle. N is the number of particles. k (i) represents the ordinal number corresponding to the i-th particle after the fitness value of the particle in the current search state is arranged. Omega max And omega min Represents the maximum value and the maximum value of ω. n is n max Represents the maximum number of iterations and n represents the current number of iterations.
The optimization calculation flow chart of the specific improved SAPSO algorithm is shown in fig. 2, and the steps are as follows:
1) Initializing parameters, annealing initial temperature T, temperature cooling coefficient C and crossover probability P c Probability of variation P m Learning factor c 1 And c 2 . Reading basic parameters, load conditions, regional natural resources and other parameters of equipment to be configured of the system;
2) In order to improve the matching probability and the searching efficiency of the algorithm, the contour line domain is used for restraining the position parameters obtained by initializing the particles, namely, the horizontal position parameters (x, y) of the particles are required to be positioned in the contour domain corresponding to the control points, so that the constraint is satisfied:
readmap(x,y)∈[M-ξ,M+ξ]
wherein: readmap represents a function of reading a map; the value of ζ is the standard deviation of the measurement noise. Under this constraint, the particles can be distributed around the real trajectory with a greater probability during the initialization process. The method realizes the goal of obtaining the optimal solution with less particles, and greatly improves the searching efficiency and the performance of the algorithm.
3) Adjusting the three parameters according to the obtained fitness value of each particle:
wherein: omega i Is the dynamic inertial weight of the i-th particle. c i1 And c i2 Control is the dynamic learning factor of the ith particle. N is the number of particles. k (i) represents the ordinal number corresponding to the i-th particle after the fitness value of the particle in the current search state is arranged. Omega max And omega mi n represents the maximum value of ω and the maximum value. n is n max Represents the maximum number of iterations and n represents the current number of iterations.
4) Operating the particles in the population according to the following three formulas;
wherein d is the search space dimension, here taken as 3;the current and global optimal position of the particle i in the d-th dimension in the k-th iteration; />And->A random value of 0 to 1; psi is a compression factor.
5) Cross probability P for the population generated in 3) c Performing crossover operation: randomly selecting individuals x from a sub-population j 、x k Performing crossover operation according to 3) 4) to generate new individual x' j 、x' k Calculate fitness function f (x j )、f(x k )、f(x' j ) And f (x' k )。
If min {1, exp (- (f (x)' j )-f(x j ) X 'is given by } > T } > random } >, x' j As a new individual;
if min {1, exp (- (f (x)' k )-f(x k ) X 'is given by } > T } > random } >, x' k As a new individual;
wherein random is a random value of 0 to 1;
6) With variation probability P for new populations after crossing m Performing mutation operation: randomly selecting individuals x from a sub-population j Performing Gaussian mutation operation to generate new individual x' j Calculate fitness function f (x j ) And f (x' j )，
If min {1, exp (- (f (x)' j )-f(x j ) X 'is given by } > T } > random } >, x' j As a new individual;
7) If the current optimal individual meets the convergence condition, terminating the operation and outputting an optimal result;
8) If the maximum iteration number is not reached, modifying the population annealing temperature, enabling T to be CT, and turning to the step 3, and adding 1 to the iteration number. The specific solving process is shown in figure 2.
The embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by the embodiments, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the present invention.
Claims (4)
1. A micro-grid system that accounts for electro-hydro-thermal complementarity, the system comprising: distributed power, energy storage, electro-hydro-thermal and user load, control system;
the distributed power supply comprises a wind turbine generator and a photovoltaic turbine generator;
the energy storage device comprises a storage battery, a hydrogen storage tank and a heat storage tank;
the electro-hydro-thermal device comprises an electrolytic bath water electrolysis hydrogen production device, a fuel cell and a hydrogen boiler;
the control system is used for determining the power and the photovoltaic power which are provided for the user load and the fan to generate electricity for producing hydrogen, and when the power of the power generation system is larger than the power of the electric load, the energy storage system absorbs the residual power and distributes the residual power to the electrolytic tank or the storage battery;
when the power of the power generation system cannot meet the power required by the electric load, the energy storage system releases energy, the storage battery pack or the fuel cell discharges, the system shortage power is provided, and the system power balance is met.
2. A multi-objective optimization configuration method for a micro-grid system taking electro-hydro-thermal complementation into account as defined in claim 1, comprising the steps of:
(1) Determining an objective function of a multi-objective optimal configuration method for a microgrid system that takes into account electro-hydro-thermal complementarity
The capacity of a wind turbine generator, a photovoltaic unit, an energy storage device and an electric-hydrogen-heat device in a micro-grid is taken as a decision variable, an objective function is formed by the minimum annual average total cost, the minimum load electricity shortage rate and the maximum renewable energy source absorption rate of a system, and the specific objective function is represented by the following formula:
wherein C is init Representing the planned construction costs taking into account the electro-hydro-thermal coupling system, including the inherent installation costs of the equipment to be configured, C oper Representing annual benefits from consideration of operating and maintenance costs, renewable energy subsidy costs and electric-hydrogen-thermal coupling systems of the equipment to be configured, C rep Representing depreciated revenues at the end of a device lifecycle considering replacement costs of the devices to be configured; n is n 1 Representing the number of sampling points which do not meet the power load demand in the planning construction period, k represents the number of renewable energy types in the system, and P i (t) represents the output power at time t of the ith type of renewable energy source, P load (t) represents electric load power, P bd (t) represents the discharge power of the battery, P fc (t) represents combustionThe material battery outputs electric power, P drump (t) represents the lack of power of the system at time t; n is n 2 Representing the number of sampling points with abandoned wind and abandoned light in planning construction period, P bc (t) represents the battery charging power, P ele (t) represents the input power of the electrolytic cell, P loss (t) represents the renewable energy source lost power of the system at the time t;
(2) Constraint conditions of a multi-objective optimization configuration method considering an electro-hydro-thermal complementary micro-grid system are set
The constraint conditions of the proposed optimal configuration method include: decision variable capacity constraint, energy storage state constraint, unit output constraint, power balance constraint and hydrogen supply and demand balance constraint.
3. The multi-objective optimal configuration method for a micro-grid system taking into account electro-hydro-thermal complementation according to claim 2, wherein: on the basis of meeting the electric load demand preferentially, the energy storage device is required to continuously adjust the energy storage state according to the relation between wind-solar power generation output power and the electric heating load demand;
defining the net output power at system t time as P net The expression is as follows:
when the wind-solar power generation output power at the moment t is greater than the electric load demand, P net (t)>0, the electrolytic tank works at the moment, the redundant electric quantity is converted into hydrogen and stored, and part of heat requirement is supplied through a hydrogen boiler; when the wind-solar power generation output power at the moment t is smaller than the electric load demand, P net (t)<0, when the fuel cell works, the stored hydrogen is converted into electric energy to supply the part with insufficient electric load, and the heat energy generated in the process supplies part of heat requirement; when the sum of the wind-solar power generation and the output electric power of the fuel cell at the moment t can not meet the electric load requirement, P fc (t)＜|P net (t) | at this time, there is load-lacking electric power P drump (t)。
4. A multi-objective optimization configuration method for a micro-grid system taking into account electro-hydro-thermal complementation according to claim 2 or 3, wherein: the improved SAPSO algorithm is used for solving, optimizing is carried out for the capacity of each device to be configured, the convergence of the PSO algorithm is ensured by selecting proper parameters, and the boundary limit on the speed can be canceled, and the method comprises the following steps:
(1) Determining an objective function of an improved SAPSO algorithm
According to the SAPSO algorithm, the load power failure rate f of the objective function is ensured at the same time 2 Less than 8 percent, new energy consumption rate f 3 More than 95%, the objective function of the formula (1) is rewritten into a penalty function form, and the expression is as follows:
minf＝f 1 +l 1 (f 2 -0.08)x 1 +l 2 (0.95-f 3 )x 2
wherein x is i ∈{0,1}(i＝1,2)，x i =0 indicates that the target is not out of limit, x i =1 indicates that the item is out of limit; l (L) i (i=1, 2) is a penalty factor for the corresponding term;
(2) Constraint of profile domains
The contour line domain is used for restraining the position parameters obtained by initializing the particles, namely, the horizontal position parameters (x, y) of the particles are required to be positioned in the contour domain corresponding to the control points, so that the constraint is satisfied:
readmap(x,y)∈[M-ξ,M+ξ]
wherein readmap represents a function of reading a map; the value of ζ is the standard deviation of the measurement noise, M is the center of the contour;
(3) Selection of adaptive parameters
Adjusting the three parameters according to the obtained fitness value of each particle
Wherein omega i Is the dynamic inertial weight of the ith particle, c i1 And c i2 The control is the dynamic learning factor of the ith particle, N is the particleK (i) represents the ordinal number corresponding to the i-th particle after the fitness value of the particle in the current search state is arranged. Omega max And omega min Represents the maximum value of ω and the maximum value of n max Represents the maximum number of iterations and n represents the current number of iterations.
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