CN114741960A - Comprehensive energy resource economic environment scheduling optimization method and system - Google Patents
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Abstract
The invention discloses a comprehensive energy economic environment scheduling optimization method and a system, wherein the method comprises the following steps: acquiring basic data of a generator set and a load of an integrated energy system, wherein the integrated energy system comprises a thermal power generator set, a renewable energy generator set and an energy storage device; according to the basic data and the economic target and the environmental target to be considered, a dynamic economic environment scheduling model of the comprehensive energy system containing thermal power, wind power, photovoltaic and energy storage is constructed; the optimizing ability of the mayfly optimization algorithm is improved by introducing crossover, variation and chain strategies when the optimal result is calculated; the improved mayflies algorithm, the renewable energy output control strategy and the comprehensive energy dynamic economic environment scheduling model are combined to construct a comprehensive energy capacity distribution system, and then the optimal scheme of economic environment scheduling of each power generation system is obtained. The method realizes the optimization of economic indexes and environmental indexes of the power system on the premise of ensuring the utilization rate of renewable energy sources and the stable output.
Description
Technical Field
The invention belongs to the field of power system scheduling, and particularly relates to a comprehensive energy, economic and environmental scheduling optimization method and system.
Background
The global energy crisis and the change of climate change cause more and more countries to reduce the proportion of fossil fuel in energy systems, and pay attention to the development and utilization of various renewable energy sources such as wind energy, solar energy and tidal energy to solve the problems of energy shortage, greenhouse effect and the like. Although each country strives to save energy and reduce emissions to achieve these goals, the problems faced remain severe. The uncertainty and uneven spatial-temporal distribution of the intermittent renewable energy power generation enable the renewable energy power generation power to be increased and the scheduling cost of the power system to be increased correspondingly. Therefore, continuous research is needed to improve the installed capacity of renewable energy sources such as wind power, photovoltaic energy and the like, improve the utilization rate of wind-solar power generation, and further research and develop clean energy technology to ensure sufficient and reliable power supply.
The comprehensive energy system of wind power, photovoltaic, thermal power and an energy storage device is taken as a main component of an energy internet, the utilization rate of renewable energy is improved through multi-energy complementation on the basis of electric power, and the comprehensive energy system is a main direction of energy and electric power development in China. However, thermal power generation is still the main factor in China at the current stage, and more thermal power generating units are needed to be added into power grid peak shaving as rotary standby while the installed capacity of renewable energy is increased. With the development of energy storage technology, the energy storage power station can effectively restrain wind and light intermittent problems and adjust the output of the unit, and can well act on power grid peak regulation. Despite these advantages, at present, electrochemical energy storage devices cannot be largely put into grid dispatching due to high cost, so how to reasonably optimize an economic environment dispatching system including renewable energy devices by using energy storage facilities with certain capacity limitation becomes a key problem to be solved currently.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the system takes wind power generation, photovoltaic power generation, thermal power generation and energy storage devices into consideration, and realizes the optimization of economic indexes and environmental indexes of a power system on the premise of ensuring the utilization rate of renewable energy sources and the stable operation of a power grid.
The first aspect of the embodiment of the invention for solving the technical problem proposes an optimization method for scheduling the economic environment of the integrated energy, which optimizes the output of each part of the integrated energy system based on an improved mayflies optimization algorithm and a renewable energy output control strategy, and the method comprises the following specific steps:
acquiring generator set operation data, pollutant emission data and load basic data of a comprehensive energy system, wherein the comprehensive energy system comprises a thermal power generator set, a renewable energy generator set and an energy storage device;
constructing a dynamic economic environment scheduling model of the comprehensive energy system containing thermal power, wind power, photovoltaic and energy storage according to the basic data of the generator set and the load;
as a further improvement of the invention, on the basis of the intelligent mayflies algorithm, aiming at promoting the optimization effect of the algorithm, an improved mayflies optimization algorithm is constructed as an optimization tool for the model, wherein the positions of the improved mayflies optimization algorithm are updated by variable weight, chaotic initialization, variation and chain motion;
and combining an improved intelligent algorithm, a renewable energy output control strategy and a comprehensive energy dynamic economic environment scheduling model to construct a comprehensive energy capacity distribution system so as to obtain an optimal scheme for economic environment scheduling of each power generation system.
In a second aspect of the embodiment for solving the technical problem, the invention provides a comprehensive energy resource economic environment scheduling system, which includes:
the acquisition module is used for acquiring basic data of a generator set and a load of the comprehensive energy system;
the model establishing module is used for establishing a comprehensive energy system economic environment scheduling model considering various constraint conditions based on the generator set operation data in the scheduling period, the pollutant emission data and the basic data of the load;
the computing module is used for computing a comprehensive energy dynamic economic environment scheduling model combining the improved mayflies optimization algorithm and the renewable energy control strategy so as to determine an output power scheduling scheme of each power generation device of the comprehensive energy system;
and the output module is used for outputting the output power distribution result.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the comprehensive energy dynamic economic environment scheduling model provided by the embodiment of the invention, the total cost of the system is taken as the economic target of the comprehensive energy system, the operation cost of the thermal power generating unit, the operation cost of the wind power generation, the operation cost of the photovoltaic device and the operation cost of the energy storage device are comprehensively considered, and the power balance constraint, the operation state constraint and the change rate constraint of each device of the system are considered, so that the economic optimization of the comprehensive energy system is realized; pollutant emission of a thermal power generating unit is taken as an environmental target of the comprehensive energy system, and a wind power device and a photovoltaic device are taken as environment-friendly power generation devices, so that the environment of the comprehensive energy system is optimal; according to the output control strategy of the renewable energy, the energy supply of the comprehensive energy system is optimized, and meanwhile, the aim of simply improving the generating capacity of the renewable energy is not taken as the target, so that the stable output of the renewable energy power generation in the whole dispatching period is realized.
(2) The comprehensive energy and economic environment scheduling optimization method is not limited to the economic environment scheduling optimization method, and can also be used for expanding optimization methods of other equipment.
Drawings
In order to more clearly show the technical solutions of the embodiments of the present invention, the drawings required in the embodiments are briefly described below, and it is obvious that the described drawings are only a part of the embodiments of the present invention, and not all of them. For persons skilled in the art, other figures can be obtained according to the figures without creative labor.
FIG. 1 is a schematic structural diagram of an integrated energy-economic environment scheduling optimization system according to the present invention;
FIG. 2 is a flowchart illustrating the implementation of an improved mayfly algorithm according to an embodiment of the invention;
FIG. 3 is a block diagram illustrating an implementation of a renewable energy control strategy according to an embodiment of the present invention;
FIG. 4 is a pareto solution set obtained by the calculation module according to an embodiment of the present invention;
fig. 5 is a comparison of a renewable energy grid-connected power scheduling scheme provided by an embodiment of the invention;
FIG. 6 is a final scheduling result obtained by the integrated energy-economic environment scheduling optimization system according to the present invention;
FIG. 7 is a flow chart of the integrated energy-saving environment scheduling optimization system according to 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. Meanwhile, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of embodiments of the invention.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following detailed description will be made with reference to the accompanying drawings by way of specific embodiments.
The global energy crisis and the change of climate change cause more and more countries to reduce the proportion of fossil fuel in energy systems, and pay attention to the development and utilization of various renewable energy sources such as wind energy, solar energy and tidal energy to solve the problems of energy shortage, greenhouse effect and the like. Although all countries strive to save energy and reduce emission to achieve the goals, the problems still remain serious, and although wind turbines and photovoltaic devices are connected to the power system in a large scale, due to the characteristics of uncertainty and uneven space-time distribution of intermittent renewable energy power generation, the wind-solar power generation power is increased, and meanwhile, the scheduling cost of the power system is correspondingly increased. In order to ensure the stable operation of a power system, more thermal power generating units are added into a power grid peak shaving as rotary standby. With the development of the energy storage technology, the energy storage power station can effectively inhibit wind and light intermittency and adjust the output of the unit, and can well act on the peak regulation of the power grid. Despite these advantages, electrochemical energy storage devices cannot be used in large quantities in grid regulation due to the high costs. As more and more renewable energy devices are added into the system, the scheduling problem of the power grid becomes more and more complex, and therefore how to reasonably optimize the hybrid dynamic economic discharge containing the renewable energy devices by using energy storage facilities with certain capacity limitation becomes a key problem to be solved by power system operators of various countries.
Therefore, the comprehensive energy economic environment scheduling optimization system provided by the invention controls the output of the renewable energy and improves the optimization capability of the solving algorithm, so that the stability of the output of the renewable energy is ensured while the economic and environmental problems are solved, and the optimal economic environment scheduling optimization scheme is finally obtained.
Fig. 1 is a schematic structural diagram of the integrated energy-saving economic environment scheduling optimization system of the present invention. The structure of the system comprises:
s101, an acquisition module is used for acquiring generating set operation data, pollutant emission data and basic data of load of an integrated energy system, wherein the integrated energy system comprises a thermal power generating set, a renewable energy generating set and an energy storage device;
in this embodiment, the basic data may include, but is not limited to, at least one of the following: the method comprises the following steps of using electric load power, generating power and generating power change rate of a generating set, generating cost of the generating set, predicted generating power of wind power and photovoltaic power, transmission loss coefficient of a system, pollutant discharge amount of a thermal power generating set, an energy transfer formula and charging and discharging constraint conditions of an energy storage device and the like.
And S102, a model establishing module is used for establishing a comprehensive energy system economic environment scheduling model considering various constraint conditions based on the generator set operation data and pollutant emission data in the scheduling period and basic data of load.
In this embodiment, the economic environment scheduling model of the integrated energy system may include, but is not limited to, at least one of the following: the system comprises a thermal power generating unit operation cost model, a renewable energy device operation cost model, an energy storage device operation cost model and a thermal power generating unit pollutant discharge model.
S103, a calculating module is used for calculating a dynamic economic environment scheduling model of the integrated energy system in combination with the improved mayflies optimization algorithm and the renewable energy control strategy so as to determine an output power scheduling scheme of each power generation device of the integrated energy system.
In this embodiment, an improved mayfly optimization algorithm is constructed as an optimization tool for the model, wherein the positions of the improved mayfly optimization algorithm are updated by variable weight, chaotic initialization, variation and chain motion; the constructed objective functions and constraint conditions are then input into the improved mayflies algorithm, while considering the effects of the renewable energy output control strategy on renewable energy processing, and finally a set of reference power scheduling schemes is obtained.
And S104, an output module for outputting the optimal scheme of the power distribution of each unit.
In this embodiment, each group of power scheduling schemes output by the calculation module is stored and then input to a pareto compromise selection scheme preset in the system, so as to finally obtain an optimal power scheduling scheme of the integrated energy system, and output the scheme to the user interface.
Fig. 2 is a flowchart illustrating an implementation of calculating an output power result of each unit in the method for scheduling and optimizing an integrated energy-saving environment according to the embodiment of the present invention. The calculation flow is as follows:
s301: acquiring basic data based on a thermal power generating unit, a wind power generating unit, a photovoltaic device, an energy storage device and a load, determining a weight coefficient omega and a proportionality coefficient Q, and starting to run a mayfly optimization algorithm in a calculation module;
s302: initializing the positions and speeds of the mayflies and male mayflies populations and establishing a fitness formula for the mayflies populations according to said objective function, evaluating fitness values of the initialized populations, and selecting locally optimal and globally optimal solutions;
s303: the positions and speeds of the males are updated by a male population position updating formula based on the relationships of male mayflies and the global optimal positions mayflies, the positions and speeds of the female mayflies are updated by a female population position updating formula based on the positional relationships of the male and female mayflies;
s304: evaluating the fitness value of the individual in the population after the position is updated based on the established fitness function, and selecting new global optimal and local optimal values;
s305: generating a certain number of offspring populations based on a crossover strategy and a mutation strategy, and sequencing the offspring and the parent according to the fitness value to select a new generation of mayflies and mayflies;
s306: and judging whether the current iteration reaches the iteration times, if so, outputting a power distribution scheme of the thermal power units in the comprehensive energy system, and otherwise, returning to the S303.
In this embodiment, parameters such as the number of iterations L, the weight coefficient ω, and the scale coefficient Q of the algorithm operation are set, and then the algorithm operation is started.
In this embodiment, the positions and fitness of mayfly populations are initialized. The initialization position matrix for male mayflies is as follows:
wherein, XlIs the male population after the l-th iteration,
is the position of the ith individual in j dimension after the ith iteration, N is the size of the population, and D is the dimension of the problem to be solved, i.e. the number of the units.
The initialized position matrix for female dayflies is as follows:
wherein, YlAre female populations after the l-th iteration, with female and male mayflies of the same size, both N.
The fitness matrix corresponding to the male mayfly population can be obtained by substituting the positions of the male mayfly population in the fitness function with the objective function as the fitness function of the population as follows:
wherein the content of the first and second substances,
is a fitness matrix of male mayflies,
is the fitness corresponding to the nth male mayflies in the first iteration. Substituting the positions of female mayfly populations into the fitness function, the fitness matrix corresponding to the female population can be obtained as follows:
wherein, the first and the second end of the pipe are connected with each other,
is a fitness matrix of female mayflies,
is the fitness corresponding to the nth mayfly in the ith iteration.
In this embodiment, in order to obtain the optimal distribution scheme, two search modes, i.e., the location update formulas for the two mayflies of different species, are set. The positions and speeds of male mayflies are updated based on the relationships of male mayflies and the positions and speeds of the female mayflies based on the positional relationships of the male and female mayflies. The male dayflies update their positions by jumping near the water surface, i.e. the unit speeds incorporated in a dayfly in the current position, the male dayflies have a population, do not have a greater moving speed, can only search near the optimal position, this mode of operation being more conducive to the search for the optimal power configuration of the units of the integrated energy system. Unlike male dayflies, female dayflies do not accumulate in one place, but are attracted by males moving to the male position, large-scale movement of female dayflies facilitates access to more individual genset power distribution schemes.
In this embodiment, the specific position updating formula of the male mayflies is as follows:
wherein the content of the first and second substances,
is the speed of the ith male mayflies at the time of the first search,
is the position, k, of the ith mayfly at the time of the first search1，k2Is a positive attraction coefficient, rp is the distance of the local optimum position from the ith male mayfly at the time of the current search, rg is the distance of the global optimum position from the ith male mayfly, pbestAnd gbestRespectively a local optimum position and a global optimum position.
The location update formula for the female population is as follows:
wherein the content of the first and second substances,
is to search the updated position of the ith female dayflies,
is the speed of searching for the updated ith female dayflies, fl is the random wandering coefficient of female dayflies, e is a random number that varies with the number of iterations between-1 and 1, k is3Is the coefficient of attraction of fixed female mayflies, r is the distance between a male mayflies and a female mayflies.
In the embodiment, in order to enhance the searching capability of the algorithm and improve the speed of the algorithm for searching the optimal solution, a chaotic mapping initialization strategy, a crossing strategy, a mutation strategy and a chain motion are introduced to generate a new individual so as to enhance the overall searching capability and the searching speed.
The purpose of optimizing the initialization is to obtain a better position matrix in the initial stage and keep the randomness of the initial population as much as possible so as to quickly obtain the optimal output power of each generator set of the comprehensive energy system. The chaotic map initialization operation of this embodiment is as follows:
xiand yiAre the initialized locations of male and female mayflies that are improved by introducing logistic chaotic maps, ub and lb represent the upper and lower limits of the mayflies location search; z is a radical of formulaiIs with xiAnd (3) mapping chaotic sequences with the same dimension, wherein mu is an adjustable parameter and is a vector randomly generated between 0 and 1, and when the value is 4, all values of z can be chaotic.
In the offspring in the mayflies algorithm, the best male and female individuals are mated in turn to produce offspring, which makes it easy for the algorithm to fall into local optima during iteration, and cross-mutation operations are added to the mayflies in order to improve the global search ability. Search results with poor fitness values refer to search results with good fitness values, which is beneficial to improving the local searching capability of mayflies to obtain an optimal scheme. The crossover formula is as follows:
wherein Q is a random number with the same dimension of x and y from-1 to 1, and x is1、y1The method is characterized in that the method is an ith individual with different searching modes after being sequentially sorted according to fitness, and offset 1 and offset 2 are two new individuals. Meanwhile, random disturbance variation occurs in the search result of the new individual, and in order to reduce invalid variation as much as possible and enable the offspring to be varied in the search range, the method for solving the variation value psi is improved. The variation value is increased in the early stage, and variation is carried out in the global range as many times as possible; and correspondingly reducing the variation value in the later period, and improving the variation efficiency. Adding a degree of random search patterns to mayflies helps to prevent searches from falling into local optima and failing to find a globally optimal solution. Random perturbation variation is as follows:
mutnew＝new+ψ (9)
wherein mutnew is a variant progeny; new is the new individual generated after crossover; psi is a random variation value.
The mayflies are subjected to a chain movement to create new individuals, i.e. a search of multiple results is averaged to determine if a better search result is produced, the chain movement being determined according to the following equation:
in any of the above embodiments, the objective function of the integrated energy system economic environment scheduling model to be solved is used as a basis, including the total operation cost objective and the pollutant emission objective of the integrated energy system. The renewable energy source may be wind energy or light energy, which is not limited herein, and in this embodiment, wind power and photovoltaic are used as an example of new energy power generation for analysis.
The total operation cost target comprises the power generation cost of a thermal power generating unit, a wind power generating unit and a photovoltaic device and the energy storage cost of an energy storage power station.
The thermal power generating unit operation function expression considering the valve point effect is as follows:
wherein f is1cThe total power generation cost of the thermal power generating unit, N is the number of the thermal power generating units, Pi，tIs the generated power of the ith unit in the t hour, ai、bi、ciA fuel cost coefficient of the thermal power generator, T is a time period required for calculating the total cost, giAnd hiFor valve loading factor, P, of a single generatori minIs the lower active power limit of the ith generator.
Meanwhile, considering the higher manufacturing cost and depreciation cost of the fan, the photovoltaic panel and the energy storage power station, the total cost of the fan, the photovoltaic and the energy storage device is as follows:
wherein, cw，cpv，cbatThe cost coefficients of the wind power, photovoltaic and energy storage devices, respectively, J and K are the number of the wind power devices and the photovoltaic devices, respectively,
the power of the ith wind turbine generator scheduling output at the moment t is shown,
represents the power, P, of the scheduling output of the ith photovoltaic power station at the moment tt batAnd the output or input power of the energy storage power station at the moment t is represented.
Therefore, the total operating cost target of the integrated energy system is as follows:
FC＝f1c+f2c (13)
② target of pollutant discharge amount
When the system generates electricity, the thermal power generating unit can generate a large amount of carbon dioxide and other polluted gases, and meanwhile, the environment can be greatly influenced. Wind power and photovoltaic power generation are used as environment-friendly renewable energy power generation modes, and pollutant emission can be reduced by increasing corresponding power generation proportion, so that the pollutant emission of the wind power and the photovoltaic power generation does not need to be considered.
The pollutant emission amount formula of the thermal power generating unit can be expressed as follows:
wherein o isi，pi，qi，θi，
Representing the pollutant emission coefficient of the ith thermal generator.
For an economic environment scheduling model comprising a plurality of solving targets, the targets are combined into a whole through a weight coefficient w, and when the magnitude of each target has a large difference, a proportionality coefficient Q is introduced into a fitness function, so that the optimization algorithm can obtain a better compromise solution. The objective function is formulated as follows:
min F＝wFC+Q(1-w)FE (15)
wherein F is an objective function combining operating cost and pollutant emissionCIs the running cost of the comprehensive energy system, FEIs the pollutant discharge amount of the comprehensive energy system, w is the weight coefficient for changing the operation cost and the pollutant discharge ratio in the objective function, Q is a proportionality coefficient, and the value of Q depends on the order of magnitude difference between different targets.
The use of the objective function promotes the search algorithm to obtain the optimal operation cost and pollutant emission under the weight coefficient, and for the whole integrated energy system, the operation cost and pollutant emission corresponding to a group of weight coefficients are compared to obtain the most appropriate weight coefficient and compromise solution. The optimal compromise solution is obtained by operating a pareto satisfaction formula after various data are normalized, and finally the solution with the highest satisfaction is selected as the optimal compromise solution.
The membership function for normalization is as follows:
wherein phi isk，iIs the satisfaction of the ith solution in the kth objective in the pareto solution set,
and
is the upper and lower bounds of the kth target.
And calculating the satisfaction degree of each group of target solutions by calculating the satisfaction degree of each target and further operating a satisfaction degree function, wherein the formula is as follows:
wherein phi isiIs the satisfaction of the final solution, n is the number of targets, in this embodiment n is 2, i.e. the total cost and emission of the integrated energy system; i is the number of solutions in the pareto solution set.
In some embodiments, on the basis of any one of the above embodiments, the method for scheduling and optimizing an integrated energy-saving environment further includes: and establishing constraint conditions according to the economic environment scheduling model. Optionally, the constraint condition includes at least one of: system power balance constraints, generator set output power constraints, and energy storage device capacity constraints.
System power balance constraint
Wherein the content of the first and second substances,
is the load demand at time t and,
is the transmission loss at time t. When the output power of the energy storage power station scheduling is larger than zero, the sum of the output powers of the thermal power generating unit, the wind power generating unit, the photovoltaic power station and the energy storage power station is equal to the total load sum and the transmission loss of the system; and when the output power of the energy storage power station is less than zero, the output power does not consider the energy storage power station.
② constraint of output power of generator set
The output power constraint of the thermal power generating unit is as follows:
Pi min≤Pi≤Pi max (19)
the generator set can be damaged by the rapid increase or decrease of the output power, the output power is controlled within a certain range by setting a limit value of power change, and the slope is restricted as follows:
wherein, Pi minAnd Pi maxIs the minimum output and maximum output limit of the ith unit; pi upAnd Pi downRespectively the ascending limit and the descending limit of the power change of the ith unit;
capacity constraint of energy storage power station
The energy storage device power constraints are as follows:
wherein
Is the maximum power of the energy storage power station;
is the maximum charging power;
is the charging or discharging power of the energy storage power station at the moment t, when Pt batAnd the energy storage power station discharges when the voltage is greater than zero, and the power station charges when the voltage is less than zero. In order to ensure the continuity of the scheduling, the charging and discharging amount in one scheduling period is set to be equal.
The energy constraints of the energy storage device are as follows:
wherein E ist、Et+1The electric quantity of the energy storage power station at the moment t and the electric quantity of the energy storage power station at the next moment are respectively; the charging and discharging efficiencies of the energy storage power station are set to be equal and are all etabat；EmaxThe capacity of the battery is represented, and the battery capacity is within the constraint range at each moment.
Fig. 3 is a block diagram of an implementation of a renewable energy control strategy according to an embodiment of the present invention. The renewable energy output control strategy can regard the wind turbine generator, the photovoltaic device and the energy storage device as a whole and aims at improving the power stability of the renewable energy accessed to the power grid. As shown in fig. 4, after the wind power and the photovoltaic power are accessed to the system, firstly, the power is subjected to stability reduction according to set data, then the fluctuation of the generated power is analyzed, the data after control reduction is transmitted to an algorithm calculation layer for optimization, then the wind power and the photovoltaic power output by the algorithm are transmitted to a charge and discharge control part for energy storage, and finally the wind power, the photovoltaic power and the energy storage power processed by a plurality of control instructions are output.
In order to further illustrate the appropriate effect of the comprehensive energy-saving environment scheduling optimization method provided by the invention, the following shows and analyzes with reference to specific examples.
In this example, the scheduling interval of the system power is 1 hour, the scheduling length is 24 hours, and 10 thermal power generating units, 1 wind power generating unit, 1 photovoltaic device and 1 energy storage device are adopted for scheduling optimization. The formulas and models of this embodiment have been described in other embodiments, and are not described herein again.
Step 1, acquiring generator set operation data, pollutant emission data and basic data of load of a comprehensive energy system, wherein the comprehensive energy system comprises a thermal power generator set, a renewable energy generator set and an energy storage device.
And 2, constructing a comprehensive energy system economic environment scheduling model considering various constraint conditions based on the generator set operation data in the scheduling period, the pollutant emission data and the basic data of the load. The method comprises a thermal power generating unit operation cost model, a renewable energy device operation cost model, an energy storage device operation cost model and a thermal power generating unit pollutant discharge model.
The function expression of the thermal power unit operation cost model is as follows:
the functional expression of the total cost model of the fan, the photovoltaic and the energy storage device is as follows:
the functional expression of the pollutant emission model of the thermal power generating unit is as follows:
table 1 the power generation parameters of the generator set are as follows:
min F＝wFc+Q(1-w)FE
step 4, operating the improved dayflies optimization algorithm in the calculation module to obtain a group of target solutions
(4.1) setting the iteration number in the algorithm search to be 1000, the search dimension to be the number of thermal power generating units, wind power generating units, photovoltaic devices and energy storage devices, the scale of the algorithm search to be 100, and presetting the search weight w of the mayfly algorithm. Using a chaotic mapping strategy to initialize the positions and speeds of the mayfly and male mayfly populations, establishing a fitness formula for the mayfly populations according to said objective function, evaluating fitness values of the initialized populations, and selecting locally optimal and globally optimal solutions;
(4.2) updating the positions and speeds of male dayflies based on the relationships of male dayflies and global optimal positions mayflies by a male population position updating formula, the positions and speeds of mayflies based on the positional relationships of male and female dayflies by a female population position updating formula;
(4.3) evaluating the fitness value of the individual in the population after the position is updated based on the established fitness function, and selecting new global optimal value and local optimal value;
(4.4) generating a number of offspring populations based on the crossover strategy, the mutation strategy and the chain motion, sorting the offspring and parents according to fitness values to select new generations of female and male mayfly populations;
and (4.5) judging whether the current iteration reaches the iteration times of 1000, if so, outputting the power distribution scheme of the thermal power unit in the integrated energy system corresponding to the weight coefficient w, and if not, returning to the step (4.2).
And step 5, determining a weight coefficient omega, linearly changing the value of the weight coefficient omega from 0 to 1, operating the mayfly search algorithm for multiple times to obtain a group of pareto frontier solutions, and using a satisfaction formula to obtain the optimal weight coefficient w with the highest satisfaction value, and the operating cost and pollutant emission corresponding to the w.
In this embodiment, the comprehensive energy system model is searched for optimal operating costs and pollutant emissions using the Particle Swarm Optimization (PSO) algorithm, the moth fire suppression (MFO) algorithm, the mayfly optimization (MA) algorithm, and the improved mayfly optimization (IMA) algorithm, respectively, to obtain a comparison of the search results of the four algorithms.
TABLE 2 comparison of the run results of different algorithms
TABLE 3 output power of each power generation device of the integrated energy system
The results of the embodiment show that the comprehensive energy, economic and environmental scheduling optimization method can obtain lower operation cost and pollutant discharge amount, and the line loss of the system for obtaining the optimal search result is also minimum.
Fig. 4 is a pareto solution set obtained by the calculation module according to an embodiment of the present invention. Fig. 4 shows a pareto solution set comparison between an objective function with a proportional coefficient Q and an objective function without the proportional coefficient Q, where the horizontal axis represents system pollutant emission and the vertical axis represents system operation cost, and it can be seen that the proposed proportional coefficient can effectively promote the integrated energy system to obtain a better unit power scheduling scheme.
Fig. 5 is a comparison of a renewable energy grid-connected power scheduling scheme provided by an embodiment of the invention. By using a renewable energy output control strategy and not using the renewable energy output control strategy for the comprehensive energy system, 24-hour renewable energy dispatching power curve comparison is obtained, and it can be seen that the comprehensive energy system can obtain stable wind-solar energy storage output with stable output and small fluctuation through the renewable energy output control strategy, and the capability of the system for coping with uncertainty is increased.
Fig. 6 is a final scheduling result obtained by the integrated energy-saving environment scheduling optimization system provided by an embodiment of the present invention. Fig. 6 shows a stacked histogram of the schedule optimized power of each unit obtained by modifying the mayday algorithm, in which examples G1-G10 represent 10 units, and legend renewable energy represents the sum of wind power, photovoltaic power and stored energy charge-discharge power. As can be seen from the figure, the output of each unit of the comprehensive energy system is relatively uniform, and the wind and light storage output is stable.
It will be appreciated by those skilled in the art that the present invention has been described with reference to flow diagrams, model block diagrams, and simulation diagrams of the operation of the system, which are provided in accordance with embodiments of the present invention, it being understood that the contents of the flow diagrams, model block diagrams, and simulation diagrams of the operation of the system can be implemented by computer program instructions which are provided to a general purpose computer, special purpose computer, embedded processor, or the like, to produce a scheduling system such that the instructions, which are executed by a processor of the computer or other programmable data processing apparatus, produce a generalized system for implementing the functions specified in the flow diagram flow or flows and/or block or blocks of the model block diagrams.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; 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 be modified or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. An economic environment scheduling optimization method of an integrated energy system is characterized by comprising the following steps:
acquiring generator set operation data, pollutant emission data and load basic data of a comprehensive energy system, wherein the comprehensive energy system comprises a thermal power generator set, a renewable energy generator set and an energy storage device;
constructing a dynamic economic environment scheduling model of the comprehensive energy system containing thermal power, wind power, photovoltaic and energy storage based on basic data of the generator set and the load;
the improved mayflies optimization algorithm (IMA) is combined with a renewable energy output control strategy and a comprehensive energy dynamic economic environment scheduling model to construct a comprehensive energy capacity distribution system, so that an optimal scheme for economic environment scheduling of each power generation system is obtained.
2. The method for optimizing the economic environment scheduling of the integrated energy system according to claim 1, wherein: the comprehensive energy system comprises a thermal power generating unit, a wind power generating unit, a photovoltaic device and an energy storage device.
3. The method for optimizing the economic environment scheduling of the integrated energy system according to claim 1, wherein: the optimal scheme is solved through an improved mayfly optimization algorithm, and the specific flow is as follows:
s1: acquiring basic data based on a thermal power generating unit, a wind power generating unit, a photovoltaic device, an energy storage device and a load, determining a weight coefficient omega and a proportionality coefficient Q, and starting to operate an IMA algorithm;
s2: initializing the positions and speeds of the female and male mayflies populations and evaluating fitness values according to said fitness formula, selecting locally optimal and globally optimal solutions;
s3: the positions and speeds of male dayflies and female dayflies are updated by means of a position updating formula, based on the relationship between male and global optimal positions dayflies;
s4: evaluating the fitness value of the individual after the position is updated based on the fitness function, and updating the global optimal value and the local optimal value;
s5: generating a certain number of offspring populations based on a cross-mutation strategy, sorting the offspring and parents according to fitness values into new generation female and male mayfly populations;
s6: and judging whether the current iteration reaches the iteration times, if so, outputting a power distribution scheme of the thermoelectric generator set in the comprehensive energy system, and otherwise, returning to S3.
4. The integrated energy system economic environment scheduling optimization method of claim 1, wherein the position update formulas of the mayflies optimization algorithms comprise position update formulas of female mayflies and of male mayflies, as follows:
the location update formula for the female population is as follows:
wherein the content of the first and second substances,
and
are the positions of male dayflies and male dayflies at the current iteration,
and
are the positions of the male dayflies and male dayflies the next iteration after the position update,
and
is the speed of the individual, k, before and after the iteration1、k2、k3Is the attraction coefficient of the population, rp is the distance of the local optimum to the male mayflies at the current iteration, rg is the distance of the global optimum to the male mayflies, r is the distance between the male and female individuals, p is the distance between the male and female individualsbestAnd gbestRespectively a population local optimal position and a global optimal position.
5. The method for optimizing the economic environment scheduling of the integrated energy system according to claim 1, wherein: the fitness function of the mayfly optimization algorithm is an objective function of dynamic economic environment scheduling, which is as follows:
min F＝wFC+Q(1-w)FE
wherein F is an objective function combining operating cost and pollutant emissionCIs the running cost of the comprehensive energy system, FEIs the pollutant emission of the comprehensive energy system, w is a weight coefficient for changing the operation cost and the pollutant emission ratio in the objective function, and Q is a proportionality coefficient.
6. The method for optimizing the economic environment scheduling of the integrated energy system according to claim 1, wherein: the scaling coefficient Q in the objective function depends on the order of magnitude difference between different targets, and when the order of magnitude difference of each target is large, the weight is introduced into the fitness function, so that the optimization algorithm can obtain a better compromise solution.
7. The method for optimizing the economic environment scheduling of the integrated energy system according to claim 1, wherein: the optimal compromise solution is obtained by operating a pareto satisfaction formula after various data are normalized, and finally the solution with the highest satisfaction is selected as the optimal compromise solution.
The membership function used for normalization is as follows:
wherein phi isk，iIs the satisfaction of the ith solution in the kth objective in the pareto solution set,
and
is the upper and lower bound limit of the kth target.
The satisfaction function is as follows:
wherein phiiIs the satisfaction of the final solution, and n is the number of targets, i.e., the total cost and emissions of the integrated energy system; i is the number of solutions in the pareto solution set.
8. The method for optimizing the economic environment scheduling of the integrated energy system according to claim 1, wherein: the renewable energy output control strategy can enable the wind turbine generator, the photovoltaic device and the energy storage device to be integrated, and aims to improve the power stability of the renewable energy accessed to a power grid.
9. The method for optimizing economic dispatch of an integrated energy system according to claim 1, further comprising:
considering the valve point effect phenomenon of the thermal power generating unit;
constructing a pareto satisfaction formula selected by a plurality of target values;
considering corresponding constraint conditions according to the dynamic economic environment scheduling model;
the constraint conditions comprise power balance constraint of a dynamic economic environment scheduling model, power generation constraint and power climbing constraint of a thermal power generating unit, output constraint of wind power and photovoltaic power generation, and charge and discharge power and energy constraint of an energy storage power station.
10. An integrated energy, economic and environmental dispatch optimization system, comprising:
the acquisition module is used for acquiring basic data of the generator set and the load of the comprehensive energy system;
the model establishing module is used for establishing a comprehensive energy system economic environment scheduling model considering various constraint conditions based on the generator set operation data in the scheduling period, the pollutant emission data and the basic data of the load;
the computing module is used for determining an output power scheduling scheme of each power generation device in the dynamic economic environment scheduling model of the comprehensive energy system;
and the output module is used for outputting the power distribution result of each unit.
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| CN117154736A (en) * | 2023-09-01 | 2023-12-01 | 华能罗源发电有限责任公司 | Method and system for optimizing deep peak shaving of thermal power unit by participation of hybrid energy storage system |
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| CN117154736A (en) * | 2023-09-01 | 2023-12-01 | 华能罗源发电有限责任公司 | Method and system for optimizing deep peak shaving of thermal power unit by participation of hybrid energy storage system |
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