CN113887973A - Multi-target planning method and device for comprehensive energy system and terminal equipment - Google Patents
Multi-target planning method and device for comprehensive energy system and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of power grids, and provides a multi-target planning method, a multi-target planning device and terminal equipment of a comprehensive energy system, wherein the method comprises the following steps: acquiring equipment parameters corresponding to each piece of equipment contained in the comprehensive energy system, and establishing a physical model of the comprehensive energy system according to the equipment parameters; establishing a target function and constraint conditions of multi-target planning of the comprehensive energy system according to the physical model; and solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system. The comprehensive energy system multi-target planning scheme provided by the invention can accurately and efficiently determine the optimal planning scheme according to the actual equipment parameters of the equipment in the system, thereby improving the energy utilization efficiency and reducing the operation cost and carbon emission of the comprehensive energy system.
Description
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a multi-target planning method and device for a comprehensive energy system and terminal equipment.
Background
Nowadays, the traditional energy system has the problems of excess productivity, low benefit and unfriendly environment, is difficult to plan and coordinate in time, has the relatively serious phenomenon of abandoning light and wind, and has strong dependence on fossil energy. In contrast, comprehensive energy has a wider and wider development prospect. The comprehensive energy system can realize the cooperation and complementation of the heterogeneous energy subsystems in a certain area through an information interaction technology, meet the increasingly complex user requirements, increase the utilization rate of energy and promote the sustainable development of energy.
The operation of the comprehensive energy system comprises a plurality of processes of energy production, energy transmission, energy consumption and the like, and the proper planning of the comprehensive energy system is beneficial to the safety, environmental protection, economy and high efficiency of the system operation. In the planning of the comprehensive energy system, the traditional annealing algorithm and hill climbing algorithm are low in calculation speed, small in calculation scale and low in accuracy.
Disclosure of Invention
In view of this, the embodiment of the invention provides a multi-target planning method and device for an integrated energy system, and a terminal device, which can improve the efficiency and accuracy of the multi-target planning for the integrated energy system.
The first aspect of the embodiment of the invention provides a multi-target planning method for an integrated energy system, which comprises the following steps:
acquiring equipment parameters corresponding to each piece of equipment contained in the comprehensive energy system, and establishing a physical model of the comprehensive energy system according to the equipment parameters;
establishing a target function of multi-target planning of the comprehensive energy system according to the physical model;
establishing a constraint condition of multi-target planning of the comprehensive energy system according to the physical model;
and solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system.
A second aspect of the embodiments of the present invention provides a multi-objective planning apparatus for an integrated energy system, including:
the physical model establishing module is used for acquiring equipment parameters corresponding to each piece of equipment contained in the comprehensive energy system and establishing a physical model of the comprehensive energy system according to the equipment parameters;
the objective function establishing module is used for establishing an objective function of the multi-objective planning of the comprehensive energy system according to the physical model;
the constraint condition establishing module is used for establishing a constraint condition of multi-target planning of the comprehensive energy system according to the physical model;
and the optimal planning scheme generation module is used for solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as described above.
A fifth aspect of embodiments of the present invention provides a computer program product, which, when run on a terminal device, causes the electronic device to perform the steps of the method according to any one of the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the multi-target planning scheme of the comprehensive energy system comprises the steps of obtaining equipment parameters corresponding to all equipment contained in the comprehensive energy system, and establishing a physical model of the comprehensive energy system according to the equipment parameters; establishing a target function and constraint conditions of multi-target planning of the comprehensive energy system according to the physical model; and solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system. The multi-target planning scheme of the comprehensive energy system provided by the embodiment of the invention can accurately and efficiently determine the optimal planning scheme according to the actual equipment parameters of the equipment in the system, thereby improving the energy utilization efficiency and reducing the operation cost and the carbon emission of the comprehensive energy system.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic diagram of an application scenario of a multi-objective planning method for an integrated energy system according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an implementation of the multi-objective optimization method for an integrated energy system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an implementation process of a multi-target differential grayish wolf algorithm provided by the embodiment of the present invention;
FIG. 4 is a schematic diagram of system parameters corresponding to an integrated energy system according to an embodiment of the present invention;
FIG. 5 is a user daily load line graph provided by an embodiment of the present invention;
FIG. 6 is a time of use power price line graph provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of a supply and demand balance output provided by an embodiment of the present invention;
FIG. 8 is a schematic diagram of an integrated energy system multi-objective planning apparatus according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic view of an application scenario of the multi-objective planning method for an integrated energy system according to an embodiment of the present invention. Referring to fig. 1, an integrated energy system provided by an embodiment of the present invention includes a fan power generation device, a photovoltaic power generation device, an electric boiler device, a gas boiler device, and a CHP (combined heat and power) unit. The system supplies the electric load and the heat load of a user by using natural gas, solar energy, wind energy transmitted by a natural gas network and electric energy of an external power grid.
In order to improve the energy utilization efficiency of the comprehensive energy system and reduce the cost and carbon emission, an optimal planning method of the system, namely the capacity of each device and the output power in the operation process, needs to be solved.
Fig. 2 is a schematic diagram illustrating an implementation process of the multi-objective planning method for an integrated energy system according to an embodiment of the present invention. Referring to fig. 2, the multi-objective planning method for an integrated energy system according to an embodiment of the present invention may include S101 to S104.
S101: and acquiring equipment parameters corresponding to each piece of equipment contained in the comprehensive energy system, and establishing a physical model of the comprehensive energy system according to the equipment parameters.
In some embodiments, the physical model includes one or more of the following: the system comprises a fan power model, a photovoltaic power model, an electric boiler heat supply model, a gas boiler heat supply model and a cogeneration unit model.
In some embodiments, the expression of the fan power model is:
in the formula (1), PWTIs the power of the fan, v is the wind speed, vciFor cutting into wind speed, vcoTo cut out wind speed, vrRated wind speed, P, of the fanrThe rated power of the fan.
According to the formula (1), the main factor influencing the power and the output of the fan is the wind speed, and when the wind speed is higher than the cut-in wind speed or is lower than the cut-in wind speed, the fan cannot work normally.
In some embodiments, the photovoltaic power model is expressed as:
in the formula (2), PpvIs the photovoltaic power; f. ofpvThe energy conversion efficiency for photovoltaic power output can be 0.9; pr,pvThe actual radiation intensity of the position where the photovoltaic panel is located; a is the rated illumination intensity of the photovoltaic panel; a. thesIs the temperature power coefficient;
is a constant coefficient; t ispvIs the actual temperature of the photovoltaic module; t isrIs the rated temperature of the photovoltaic module.
As can be seen from equation (2), the generated power of the photovoltaic panel is related to not only the energy conversion efficiency of the photovoltaic panel, but also the intensity of the illumination radiation and the external temperature.
The electric boiler is an electric heat coupling device, and generates heat energy by using electric energy to supply heat to users.
In some embodiments, the expression of the electric boiler heating model is:
QEHB(t)＝ηEHB(1-μloss)PEHB(t) (3)
in the formula (3), QEHB(t) is the heat supply quantity of the electric boiler in the time period t, etaEHBIs the electric heat conversion efficiency of the electric boiler, mulossIs the heat loss rate of the electric boiler, PEHB(t) is the electrical power used by the electric boiler for a period of time t.
A gas boiler is a plant that occurs by means of alternating coupling and conversion between air and thermal energy flows, the thermal load being supplied by natural gas. In the working process of the comprehensive energy system, the gas-fired boiler has better environmental protection and is also beneficial to improving the coupling relation between gas and heat energy flow. The external environment also affects the performance of the gas boiler during its operation.
In some embodiments, the expression for the gas boiler heating model is:
in the formula (4), the reaction mixture is,
for the natural gas power interval consumed by the gas boiler during the period t, [ Q ]GB(t)]±Interval of natural gas flow consumed by gas boiler during t period, LGLCVIs natural gas low heat value (the value is 9.7 kW.h/m 3), delta t is scheduling time,
is the thermal power interval of the external output of the gas boiler within the period of t [. eta. ]GB]±In the interval of gas-heat conversion efficiency, [ H ]GB(t)]±Is the final output heat interval of the gas boiler.
The cogeneration unit generally comprises a micro gas turbine and a waste heat boiler, and natural gas is fully combusted in a high-temperature heating combustion chamber to release a large amount of heat in the combustion process. The heat is used to drive a micro-combustion engine to generate electricity, and the residual heat can be supplied to a heat load by means of a waste heat boiler. During the operation of the cogeneration unit,
in some embodiments, the co-generator model is expressed as:
in the formula (5), the reaction mixture is,
is the natural gas power interval consumed by the micro-combustion engine in the t period, [ Q ]MT(t)]±Is the natural gas flow interval value L consumed by the micro-combustion engine in the t periodGLCVThe natural gas has a low heat value (the value is 9.7 kW.h/m 3), and delta t is scheduling time;
is the electric power interval output by the micro-combustion engine within the time period t, [ eta ]MT]±The power generation efficiency interval of the micro-combustion engine in the time period t is shown;
is the power interval of the high-temperature flue gas waste heat within the time period t, [ eta ]q]±The efficiency is lost for waste heat transmission;
presetting a thermal power interval output by the boiler in the t period,
is a heating coefficient of the waste heat boiler,
for flue gas recovery, [ H ]HRSG(t)]±The heat interval value is the heat interval value output by the waste heat boiler.
S102: and establishing a multi-target planning objective function of the comprehensive energy system according to the physical model.
In some embodiments, the independent variables of the target model are the capacity and output power of each device, and the dependent variables of the target model are the operating cost and carbon emission of the integrated energy system.
In some embodiments, fixed parameters such as gas prices, equipment unit purchase costs, operation and maintenance costs, and electricity prices for each period of time may be included in the objective function.
In one particular application scenario, the objective model may include a cost objective function and a carbon emissions objective function.
The cost objective function is expressed as:
in the formula (6), F1For cost purposes, CopFor the system running cost, CinvFor annual investment costs, Cop-NGCost for purchase of natural gas, Cop-MFor equipment operating maintenance costs, Cop-GAnd (5) purchasing electricity cost for the power grid.
For the purchase price of electricity in the time period t,
for the purchased electric power in the time period t,
is the electricity selling price in the time period t,
is the selling power in the time period T, and T is the time period number.
For the price of natural gas, PCCHP(t) is the natural gas price participating in demand response over time period t, Δ t is the dispatch time, δCCHPFor operating costs of heating installations, LHVNGBeing of low heating value of natural gas, aNG_stTo conserve power costs of natural gas plants, PNG_st(t) conserving power from the natural gas plant for a time period t. CiAnd P is the output power of the ith distributed generator. I is the set of system equipment element types (including fans, photovoltaics, electric boilers, gas boilers, and cogeneration units), I is the equipment category,
and MiRespectively the investment cost per unit volume and the total configuration volume of the equipment i, r is the discount rate, and 8 percent is taken as the conversion rate, y is taken as the conversion rateiThe life of the device.
The expression of the carbon emission objective function is:
in the formula (7), F2For carbon emission purposes, QcarbAlpha is the carbon emission coefficient in the power generation process, Pgrid(t) is the grid input power in time period t, beta is the carbon emission coefficient in the combustion of natural gas, Pin,NG(t) Natural gas input Power, HV, over time period tgasThe natural gas content is.
S103: and establishing a constraint condition of multi-objective planning of the comprehensive energy system according to the physical model.
In some embodiments, the constraints include: grid balance constraints, thermal system balance power constraints, equipment output constraints, and climb rate constraints.
In a specific application scenario, the expression of the grid balance constraint is as follows:
Ppv+Pwt-Pch_e+Pdis_e+Pgrid＝Lele+Php+Pac+Peb (8)
in the formula (8), PpvFor photovoltaic real power, PwtIs the actual power of the fan, Pch_eFor the power of the battery in the charging operating state, Pdis_eIs the power of the battery in the discharge operating state, PgridThe input power of the power grid; l iseleFor consumer electrical load power, PhpFor the load power of the heat pump, PacFor electrical refrigeration load power, PebThe load power of the electric boiler.
The thermal system equilibrium power constraint is expressed as:
in the formula (9), the reaction mixture is,
for heat pump heating power, PgbPower for heating of heating equipment, PebPower for supplying heat to electric boilers, Pch_hAnd Pdis_hFor the heat-releasing power of the heat-storage system, LhotIs the heat load.
The expression for the device contribution constraint is:
in the formula (10), Pi,tFor output values of non-dispatchable equipment (e.g. fans and photovoltaic), Pi,t,minMinimum value of output, P, for non-dispatchable devicesi,t,maxThe maximum output value of the non-dispatchable equipment; pf,tFor schedulable device values of output, Pf,t,minFor minimum value of output of schedulable device, Pf,t,maxTo be at leastAnd scheduling the maximum output value of the equipment.
The expression for the ramp rate constraint is:
in the formula (11), Pi,tFor the power of the device in the current time period, Pi,t-1Is the power of the device during the last period of time,
is the maximum power allowed by the device to rise per unit time period,
the maximum power reduced for the device operation in the unit time period.
S104: and solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system.
In a specific embodiment, a multi-objective differential grayish wolf algorithm is applied to solve the objective function under the constraint condition, and a planning scheme corresponding to the comprehensive energy system is generated.
The grey wolf algorithm is a multi-target solving algorithm and is mainly characterized in that the solution of a multi-target problem is used as a grey wolf individual, and the action path and the operation process of the grey wolf population during hunting and hunting are simulated according to a grade mechanism. The gray wolf algorithm corrects the searching direction of the optimal solution, and continuously updates the position of the gray wolf by using an information cross mechanism when the gray wolf algorithm selects the head wolf, so that the convergence speed is increased, the optimal solution is obtained in a short time, and the optimal equipment construction capacity and the operation strategy of the comprehensive energy system can be accurately and efficiently determined.
In this embodiment, each individual gray wolf in the gray wolf population represents a planning plan, and the game corresponding to the gray wolf population is the optimal planning plan.
The expression for the wolfsbane population surrounding the prey is:
X(k+1)＝Xp(k)-A|CXp(k)-X(k)| (12)
in the formula (12), X (k +1) is the position vector of the wolf body after k +1 iterations, X (k) is the position vector of the wolf body after k iterations, Xp(k) Is the location vector of the prey at k iterations, and a and C are coefficient vectors.
Specifically, a ═ 2a (k) r1-a(k)，C＝2r2. Wherein a is a convergence factor, and a is a convergence factor,
kmaxis the maximum number of iterations, r1And r2Are all greater than or equal to zero and less than or equal to 1. As can be seen from the expression of the convergence factor, the convergence factor decreases linearly from 2 to 0 as the number of iterations increases.
In some embodiments, S104 may include S201 to S205.
S201: acquiring system parameters corresponding to the comprehensive energy system, and initializing a planning scheme set according to the system parameters; each planning scenario includes the capacity and power output of the respective device.
Specifically, the system parameters include, but are not limited to, wind speed, light, energy price, and equipment base parameters.
In some embodiments, initializing a set of planning schemes according to system parameters includes: and initializing a planning scheme set according to system parameters based on the chaotic mapping method.
In a specific application scenario, data is introduced into a dynamic and globally generated initial value within a search area range based on a Logistic fuzzy mapping method. The fuzzy mapping method can increase the complexity and diversity of population data initialization, namely, the schemes contained in the planning scheme set have larger diversity, and the optimization calculation process is facilitated.
The expression of the Logistic mapping is as follows:
Mn+1＝Mn×μ×(1-Mn) (13)
in the formula (13), M is from (0, 1), and mu is from [0, 4 ]; when mu is 4, the equation is in a completely chaotic state, and the M sequence is a full sequence on (0, 1).
S202: and calculating the satisfaction corresponding to each planning scheme in the initial planning scheme, and determining the target planning scheme in the planning scheme set according to the satisfaction.
In some embodiments, calculating the satisfaction of each of the initial planning scenarios includes:
and calculating the satisfaction degree of the first planning scheme according to each objective function value corresponding to the first planning scheme, wherein the first planning scheme is any one of the initial planning scheme set.
In a specific application scenario, the wolf individual corresponding to the target planning scheme is a wolf, and the wolf may include an optimal individual, a suboptimal individual, and a third-best individual in a wolf population.
Optionally, a fuzzy membership function is introduced to select the wolf. And performing satisfaction calculation on each individual gray wolf in the solution set, namely the gray wolf population, and evaluating the compatibility between the gray wolf population and each target.
Specifically, the satisfaction degree of each gray wolf individual relative to the target function is calculated, descending order arrangement is carried out, and in the sequence after the ordering, the first three gray wolfs are determined as head wolfs which are sequentially the optimal individual, the suboptimal individual and the third-best individual. According to the position vector of the wolf, the position vector of each wolf individual in the next iteration can be calculated, and continuous optimization is realized.
The satisfaction calculation formula is as follows:
in formula (14), μnIs the satisfaction of the nth Grey wolf individual, Y is the number of objective functions, munySatisfaction of the yth objective function for the nth graywolf individual; f. ofnyThe y objective function value of the nth individual wolf,
and
respectively the maximum and minimum of the y-th objective function. The higher the satisfaction degree of a wolf individual is, the closer the value is to 1, the stronger the compatibility of the solution corresponding to the wolf individual in the multi-target problem is, and the solution approaches to the optimal solution.
S203: and updating the position of each planning scheme in the planning scheme set according to the target planning scheme, and generating an updated planning scheme set.
In a specific application scenario, the positions of the planning schemes in the planning scheme set are updated according to the target planning scheme, that is, the positions of the individual wolfs in the wolf population are updated according to the positions of the wolfs.
The location update formula is:
in formula (15), Xn(k +1) is the position vector of the nth Grey wolf individual after k +1 iterations, Xn(k) Is the position vector of the nth grey wolf individual after k iterations; xα(k)、Xβ(k)、Xγ(k) Respectively obtaining the position vectors of the optimal individual, the suboptimal individual and the third optimal individual in the wolf population after k iterations; a. the1、A2、A3、C1、C2、C3Are all coefficients.
And after the position is updated, punishing the boundary crossing of the wolf individual.
The traditional wolf algorithm leads the iterative optimization process of the wolf population by means of the wolf with search capability, however, this method is prone to the following problems: the population initially comprises a plurality of search targets and has large difference, so that the wolf has high possibility of selecting poor solutions, thereby influencing the subsequent iteration process; the parameters simulating the natural obstacles can be embodied only when the position of the wolf is updated, and the real undertaker of the optimization task does not consider the natural obstacles; the wolf head easily appears local optimal solution in some initial populations with high randomization, and influences the final optimizing result.
Aiming at the defects of the traditional wolf algorithm, the embodiment improves by applying the crossing and variation strategy in the difference algorithm in the population updating process. By introducing the crossover and mutation strategies, natural obstacles can be well simulated when the position of the wolfsbane population is updated, and the problem of local optimal solution is avoided.
In some embodiments, after updating the position of each planning solution in the planning solution set according to the target planning solution, S203 may further include: and carrying out cross variation processing on each updated planning scheme, and updating the position of each planning scheme in the planning scheme set again.
Specifically, three wolf individuals are randomly selected in the k-th iteration, and the position vector of the variant individual is calculated according to the three individuals.
The calculation formula of the position vector of the variant individual is as follows:
in the formula (16), Vn(k) Is the position vector, X, obtained by the variation of the nth Grey wolf individual after k iterations1(k)、X2(k)、X3(k) Are the position vectors of random wolf individuals, FkIs a scaling factor. FupAnd FdownUpper and lower limits, respectively, for the scaling factor. The scaling factor may control the influence of the difference vector, i.e. control the step size. If the value of the scaling factor is larger, the occurrence of a local optimal solution is avoided.
Specifically, the diversity of the wolfsbane population is improved by means of a cross method.
The calculation formula of the individual position vector generated by the intersection process is as follows:
in the formula (17), Pn(k) Is the position vector V obtained by the n-th individual cross of the wolf after k iterationsn(k) Is composed ofThe position vector R obtained by the variation of the nth Grey wolf individual after k iterationscrtThe cross probability value is equal to or greater than zero and equal to or less than one. RupAnd RdownUpper and lower limits of the crossover probability, k, respectivelymaxIs the maximum number of iterations.
In the process of crossing and mutation, the crossing probability of the scaling factors is subjected to precision adjustment and optimization processing, so that the local and global data search and analysis capabilities can be improved.
In a specific application scenario, an updated planning scheme set is generated, i.e., the generation of the descendant wolf population.
Optionally, population update is performed using fast non-dominated sorting and elite retention strategies. After the cross mutation processing is carried out on the wolf population, the current wolf individuals are sorted according to the rapid non-dominant ranking, the wolf individuals with the grade of one are regarded as non-dominant solutions, and the non-dominant solutions are put into an external solution set. After differential processing, the new populations after mutation and crossover are subjected to non-dominated sorting. And repeating the process until all the solutions stored in the final external solution set are non-dominant solutions. Specifically, the solution set is a Pareto solution set.
Optionally, after the cross mutation processing is performed on the gray wolf population, the gray wolf populations of the parents and the offspring are mixed, and the current gray wolf individuals are sorted according to the sequence of the crowding distance from large to small. The remaining positions in the wolf population are filled with wolf individuals in crowded areas, creating a new population.
S204: s202 and S203 are repeatedly performed until a preset number of iterations is reached.
In some embodiments, S204 comprises: randomly assigning a value to the convergence factor at each iteration; and repeatedly executing S202 and S203 based on the randomly assigned convergence factor until the preset iteration number is reached.
Specifically, in the formula (12), when | a | ≧ 1, the gray wolf population surrounding area will be enlarged to find the chasing target in a larger range, at this time, the algorithm is in the global exploration phase; when the A < 1 >, the surrounding range of the wolf population is reduced to accurately position the chasing target, and the algorithm is in a local accurate searching stage. The value of A is determined by a convergence factor a (k), and the value of the convergence factor determines the balance of the global exploration phase and the local search phase of the algorithm. When a complex multi-target problem is faced, the linearly decreasing convergence factor is difficult to adapt to a multi-peak search process, and the problem of local optimal solution is easily caused.
To avoid the occurrence of locally optimal solutions, the convergence factor may be assigned randomly. After setting the convergence factor to a random number obeying a certain distribution, the algorithm can be made to obtain larger and fewer values at the beginning of the iteration and smaller and more values at the end of the iteration, thus better balancing the global exploration phase with the local search phase.
In a specific embodiment, the adjustment formula of the convergence factor is:
a(k)＝ainitial-(ainitial-afinal)rand+σrandn (18)
in the formula (18), a (k) is a convergence factor at the k-th iteration; a isinitialAnd afinalThe initial value and the final value of the convergence factor are respectively; σ is a variance, which is used for representing the deviation degree between the random variable and the mathematical expectation value; rand is [0, 1 ]]Random numbers are uniformly distributed in the interval, and randn is a random number which follows normal distribution.
By setting the value taking method of the convergence factor, on one hand, the optimal solution can be avoided, and on the other hand, the convergence speed of the algorithm can be improved.
S204: and taking the target planning scheme when the preset iteration times are reached as the optimal planning scheme corresponding to the comprehensive energy system.
The optimal planning scheme comprises the design capacity of each device and the output power of each device in the system operation process.
The multi-objective planning method provided by the embodiment of the invention can optimize the expansibility of the non-inferior solution set, so that the solution set is more uniformly distributed, the distance between the calculated non-inferior solution set and the actual non-inferior solution set is shorter, and the end point of the non-inferior solution set is closer to the end point of a single target extreme point.
Fig. 3 shows a schematic implementation flow diagram of a multi-target differential grayish wolf algorithm provided by the embodiment of the invention. Referring to fig. 3, in a specific application scenario, the optimization process of the planning scheme is as follows.
Firstly, relevant data such as wind speed, illumination, energy price, basic parameters of equipment and the like are input into an algorithm. And (3) carrying out chaos initialization on the grey wolf population according to the data, and calculating an objective function of each grey wolf individual. According to the method of fast non-dominant ranking, the wolf is selected according to the fuzzy satisfaction degree of the solution. And judging whether the iteration times are smaller than a preset threshold value, if so, updating the gray wolf position, and punishing the updated wolf group. And adaptively processing the variation and crossing processes according to the variation coefficient and the crossing coefficient to generate a progeny population. Parent and child populations are mixed and new populations are selected based on crowding distance. Based on a fast non-dominated sorting method, the wolf is selected in the new population according to the fuzzy satisfaction degree of the solution. And iteratively executing the processes of updating the grey wolf position, generating a new population and selecting the head wolf until the iteration times are greater than or equal to a preset threshold value. And in the iteration process, the convergence factor is utilized to improve the convergence speed, so that the condition of local optimal solution is avoided. Based on the process, 8760 time intervals are calculated, and the optimal operation strategy of the multi-objective operation optimization is obtained. Optionally, the duration of each period is 1 hour.
In a specific application scenario, the integrated energy system comprises a photovoltaic system, a fan, a power generation boiler, a gas boiler and a CHP unit.
Fig. 4 shows system parameters corresponding to the integrated energy system in this scenario. Wherein, fig. 4(a) is the annual illumination condition corresponding to the integrated energy system, and fig. 4(b) is the annual wind speed condition corresponding to the integrated energy system. The illumination data and the wind speed data are basic data for determining photovoltaic output and wind output, and need to be input into the algorithm provided by the embodiment.
Tables 1 to 4 respectively show the device parameters of the fan, the photovoltaic device parameters, the electric boiler device parameters, and the gas boiler system parameters in this scenario. The device parameters in tables 1 to 4 were entered into the algorithm provided in this example.
TABLE 1
TABLE 2
TABLE 3
TABLE 4
Fig. 5 shows a daily load line graph of the user in the present scenario.
Fig. 6 shows a time-of-use power rate line graph in the present scenario.
Referring to fig. 6, the fixed electricity price in the scene is 0.5 yuan/kw · h, and the remaining electricity in the integrated energy system can be on line at the fixed electricity price. Meanwhile, the power shortage in the system and the power storage amount stored in the off-peak period purchase power by adopting a time-of-use power price. Specifically, the electricity price at peak (8:00-12:00, 17:00-21:00) is 1.0697 yuan/kW.h, the electricity price at valley (24:00-8:00) is 0.4139 yuan/kW.h, and the electricity price at ordinary times (12:00-17:00 and 21:00-24:00) is 0.4139 yuan/kW.h.
The daily load data and the time-of-use electricity price data of the user are input into the algorithm provided by the embodiment.
And calculating an optimal planning scheme of the comprehensive energy system by using the multi-target differential grayish wolf algorithm shown in fig. 3.
Fig. 7 shows a schematic diagram of the supply and demand balance output provided by the embodiment of the invention. Fig. 7(a) is a schematic diagram of the electric load supply and demand balance output, and fig. 7(b) is a schematic diagram of the thermal load supply and demand balance output. Referring to fig. 7, the supply and consumption of the system should be balanced at each time in the optimal planning scheme.
Table 5 shows comparative data of plant capacity results for the conventional single-target planning scheme and the present example, and table 6 shows comparative data of economic efficiency and carbon emissions for the conventional single-target planning scheme and the present example.
TABLE 5 comparison of the equipment capacity results of the conventional single-target planning scheme and this example
| Unit: kW (power of kilowatt) | Fan blower | Photovoltaic system | Electric boiler | Gas boiler | CHP |
| This example | 3074 | 2800 | 544 | 1370 | 1022 |
| Traditional single-target planning scheme | 3664 | 3000 | 600 | 1447 | 1178 |
TABLE 6 comparison of economics and carbon emissions of the conventional single-target planning scheme and the present example
As can be seen from table 6, the multi-target differential grayish wolf algorithm provided by the embodiment of the present invention can reduce the cost by 9.52% and reduce the carbon emission by 36%.
The multi-target differential gray wolf algorithm provided by the embodiment can fully perform analog simulation tests on the comprehensive energy system, and combines the methods of variation, intersection and multi-target processing in the fusion differential algorithm. By utilizing the randomness of the convergence factor, the convergence speed and multi-target balance problem in the solving process can be considered, and the situation that the solution falls into the local optimal solution is avoided.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 8 is a schematic structural diagram of the multiple target planning apparatus for an integrated energy system according to an embodiment of the present invention, and referring to fig. 8, the multiple target planning apparatus 80 for an integrated energy system may include: a physical model building module 810, an objective function building module 820, a constraint building module 830 and an optimal planning scheme generating module 840.
The physical model establishing module 810 is configured to obtain device parameters corresponding to each device included in the integrated energy system, and establish a physical model of the integrated energy system according to the device parameters;
an objective function establishing module 820, configured to establish an objective function of the multi-objective planning of the integrated energy system according to the physical model;
the constraint condition establishing module 830 is configured to establish constraint conditions of multi-objective planning of the integrated energy system according to the physical model;
and the optimal planning scheme generation module 840 is configured to solve the objective function under the constraint condition to generate an optimal planning scheme corresponding to the integrated energy system.
The comprehensive energy system multi-target planning scheme provided by the invention can accurately and efficiently determine the optimal planning scheme according to the actual equipment parameters of the equipment in the system, thereby improving the energy utilization efficiency and reducing the operation cost and carbon emission of the comprehensive energy system.
In some embodiments, the physical model may include one or more of the following: the system comprises a fan power model, a photovoltaic power model, an electric boiler heat supply model, a gas boiler heat supply model and a cogeneration unit model.
In some embodiments, the independent variables of the target model are the capacity and output power of each device, and the dependent variables of the target model are the operation cost and carbon emission of the integrated energy system;
in some embodiments, the constraint may include: grid balance constraints, thermal system balance power constraints, equipment output constraints, and climb rate constraints.
In some embodiments, the optimal planning solution generation module may include: the system comprises a planning scheme set initialization unit, a target planning scheme determination unit, a planning scheme set updating unit, an iteration unit and an optimal planning scheme determination unit.
The planning scheme set initialization unit is used for executing the first step, namely acquiring system parameters corresponding to the comprehensive energy system, and initializing a planning scheme set according to the system parameters; each planning scenario includes the capacity and power output of the respective device.
And the target planning scheme determining unit is used for executing a second step, namely calculating the satisfaction corresponding to each planning scheme in the planning scheme set, and determining the target planning scheme in the planning scheme set according to the satisfaction.
And the planning scheme set updating unit is used for executing a third step, namely updating the positions of all the planning schemes in the planning scheme set according to the target planning scheme and generating an updated planning scheme set.
And the iteration unit is used for repeatedly executing the second step and the third step until the preset iteration times are reached.
And the optimal planning scheme determining unit is used for taking the target planning scheme when the preset iteration times are reached as the optimal planning scheme corresponding to the comprehensive energy system.
In some embodiments, the planning scheme set initialization unit has means for: and initializing the planning scheme set according to the system parameters based on a chaotic mapping method.
In some embodiments, the target planning scenario determination unit is specifically configured to: calculating the satisfaction degree of the first planning scheme according to each objective function value corresponding to the first planning scheme; the first planning scheme is any one of the set of initial planning schemes.
In some embodiments, the planning scheme update unit is further configured to: and carrying out cross variation processing on each updated planning scheme, and updating the position of each planning scheme in the planning scheme set again.
In some embodiments, the iteration unit is specifically configured to: randomly assigning a value to the convergence factor at each iteration; and repeatedly executing the second step and the third step based on the randomly assigned convergence factor until the preset iteration times are reached.
Fig. 9 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 9, the terminal device 90 of this embodiment includes: a processor 900, a memory 910, and a computer program 920, such as an integrated energy system multi-objective planning program, stored in the memory 910 and executable on the processor 900. The processor 90, when executing the computer program 920, implements the steps of the aforementioned embodiments of the multiple objective planning method for an integrated energy system, such as the steps S101 to S104 shown in fig. 2. Alternatively, the processor 900 executes the computer program 920 to implement the functions of the modules/units in the device embodiments, such as the functions of the modules 810 to 840 shown in fig. 8.
Illustratively, the computer program 920 may be partitioned into one or more modules/units that are stored in the memory 910 and executed by the processor 900 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 920 in the terminal device 90. For example, the computer program 920 may be divided into a physical model building module, an objective function building module, a constraint building module, and an optimal plan generating module (module in a virtual device).
The terminal device 90 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 900, a memory 910. Those skilled in the art will appreciate that fig. 9 is merely an example of a terminal device 90 and does not constitute a limitation of the terminal device 90 and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 900 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 910 may be an internal storage unit of the terminal device 90, such as a hard disk or a memory of the terminal device 90. The memory 910 may also be an external storage device of the terminal device 90, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 90. Further, the memory 910 may also include both an internal storage unit and an external storage device of the terminal device 90. The memory 910 is used for storing the computer programs and other programs and data required by the terminal device. The memory 910 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A multi-objective planning method for an integrated energy system is characterized by comprising the following steps:
acquiring equipment parameters corresponding to each piece of equipment contained in the comprehensive energy system, and establishing a physical model of the comprehensive energy system according to the equipment parameters;
establishing a target function of multi-target planning of the comprehensive energy system according to the physical model;
establishing a constraint condition of multi-target planning of the comprehensive energy system according to the physical model;
and solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system.
2. The multi-objective planning method for integrated energy system according to claim 1,
the physical model comprises one or more of the following: the system comprises a fan power model, a photovoltaic power model, an electric boiler heat supply model, a gas boiler heat supply model and a cogeneration unit model;
the independent variables of the target model are the capacity and the output power of each device, and the dependent variables of the target model are the running cost and the carbon emission of the comprehensive energy system;
the constraint conditions include: grid balance constraints, thermal system balance power constraints, equipment output constraints, and climb rate constraints.
3. The method for multi-objective planning of an integrated energy system according to claim 1, wherein the solving the objective function under the constraint condition to generate the optimal planning solution corresponding to the integrated energy system comprises:
acquiring system parameters corresponding to the comprehensive energy system, and initializing a planning scheme set according to the system parameters; each planning scheme comprises the capacity and the output power of each device;
step two, calculating the satisfaction corresponding to each planning scheme in the planning scheme set, and determining a target planning scheme in the planning scheme set according to the satisfaction;
updating the position of each planning scheme in the planning scheme set according to the target planning scheme, and generating an updated planning scheme set;
repeatedly executing the second step and the third step until the preset iteration times are reached;
and taking the target planning scheme when the preset iteration times are reached as the optimal planning scheme corresponding to the comprehensive energy system.
4. The method of claim 3, wherein the initializing a set of planning schemes based on the system parameters comprises:
and initializing the planning scheme set according to the system parameters based on a chaotic mapping method.
5. The method of claim 3, wherein the calculating the satisfaction level of each of the planning scenarios in the initial set of planning scenarios comprises:
calculating the satisfaction degree of the first planning scheme according to each objective function value corresponding to the first planning scheme; the first planning scheme is any one of the set of initial planning schemes.
6. The method of claim 3, wherein after updating the location of each plan in the set of plans based on the target plan, the method further comprises:
and carrying out cross variation processing on each updated planning scheme, and updating the position of each planning scheme in the planning scheme set again.
7. The multi-objective planning method for integrated energy systems according to claim 3, wherein the repeatedly performing step two and step three until a predetermined number of iterations is reached comprises:
randomly assigning a value to the convergence factor at each iteration;
and repeatedly executing the second step and the third step based on the randomly assigned convergence factor until the preset iteration times are reached.
8. A multi-objective planning device for an integrated energy system is characterized by comprising:
the physical model establishing module is used for acquiring equipment parameters corresponding to each piece of equipment contained in the comprehensive energy system and establishing a physical model of the comprehensive energy system according to the equipment parameters;
the objective function establishing module is used for establishing an objective function of the multi-objective planning of the comprehensive energy system according to the physical model;
the constraint condition establishing module is used for establishing a constraint condition of multi-target planning of the comprehensive energy system according to the physical model;
and the optimal planning scheme generation module is used for solving the objective function under the constraint condition to generate an optimal planning scheme corresponding to the comprehensive energy system.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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