CN110705776A - Energy optimization scheduling method - Google Patents
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Abstract
The invention provides an energy optimization scheduling method, which comprises the following steps: constructing an optimized dispatching model of the energy system from the power generation cost, the environmental cost, the standby cost and the demand side response; establishing a corresponding objective function by an optimized scheduling model, wherein the constraint conditions of the objective function comprise system energy balance constraint, comprehensive energy output constraint, energy storage battery constraint, refrigerator constraint, demand side load constraint and transferable load system balance capacity constraint; and performing optimization solution on the objective function of the energy system according to the dragonfly algorithm, and outputting an optimal solution meeting an iteration termination condition to obtain an optimal scheme for energy system scheduling. The invention fully considers transferable loads in the electric load, the heat load and the cold load on the demand side in the comprehensive energy system, takes the objective function as an optimization target, and utilizes the dragonfly algorithm to carry out multi-objective optimization solution on the objective function to obtain the optimal scheme of energy system scheduling, and also solves the problems of multi-objective and non-linear optimization in the energy system optimization scheduling.
Description
Technical Field
The invention relates to the technical field, in particular to an energy optimization scheduling method.
Background
An energy system (i.e., a comprehensive energy system) integrates multiple energy sources such as coal, petroleum, natural gas, electric energy, heat energy and the like in an area by using an intelligent control strategy, and realizes coordinated planning, optimized operation, cooperative management, interactive response and complementary mutual assistance among multiple energy subsystems. On one hand, the diversified energy utilization requirements in the system are met, and on the other hand, the energy utilization efficiency is effectively improved, and the energy sustainable development is promoted. With the gradual depletion of fossil fuels and the increasingly serious problems of environmental pollution, climate change and the like brought by the development and utilization of traditional energy sources, renewable energy sources are developed and utilized on a large scale, the utilization efficiency of the energy sources is improved, the pollution emission is reduced, and the guarantee of energy supply and energy safety is the core content and inevitable choice of the energy revolution of China at present.
However, in the energy optimization process of the existing energy system, the economy is taken as the optimization purpose, and the demand side response is ignored, so that the optimization of the model in the energy scheduling process is unreasonable.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide an energy optimization scheduling method, which is used to solve the problem in the prior art that the energy system ignores the demand-side response during the energy optimization process, which results in unreasonable optimization of the model during the energy scheduling process.
To achieve the above and other related objects, in a first aspect of the present application, the present invention provides an energy optimization scheduling method, including:
constructing an optimized dispatching model of the energy system from the power generation cost, the environmental cost, the standby cost and the demand side response; the optimization scheduling model of the energy system establishes a corresponding objective function, and the constraint conditions of the objective function comprise system energy balance constraint, comprehensive energy output constraint, energy storage battery constraint, refrigerator constraint, demand side load constraint and transferable load system balance capacity constraint;
and performing optimization solution on the objective function of the energy system according to the dragonfly algorithm, and outputting an optimal solution meeting an iteration termination condition to obtain an optimal scheme for energy system scheduling.
As described above, the energy optimization scheduling method of the present invention has the following beneficial effects:
the optimization scheduling method of the energy system is based on the power generation cost, the environmental cost, the standby cost and the demand side response structure to construct an optimization scheduling model of the energy system, fully considers transferable loads in the electric load, the heat load and the cold load of the demand side in the comprehensive energy system, takes the objective function as an optimization target, and utilizes the dragonfly algorithm to carry out multi-objective optimization solution on the objective function to obtain the optimal scheme of energy system scheduling, and also solves the problems of multi-objective and non-linear optimization in the optimization scheduling of the comprehensive energy system.
Drawings
Fig. 1 is a block diagram illustrating an integrated energy system according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an energy optimization scheduling method according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating step S2 in the energy optimization scheduling method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.
In the following description, reference is made to the accompanying drawings that describe several embodiments of the application. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present disclosure. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "above," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first preset threshold may be referred to as a second preset threshold, and similarly, the second preset threshold may be referred to as a first preset threshold, without departing from the scope of the various described embodiments. The first preset threshold and the preset threshold are both described as one threshold, but they are not the same preset threshold unless the context clearly indicates otherwise. Similar situations also include a first volume and a second volume.
Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C "are only exceptions to this definition should be done when combinations of elements, functions, steps or operations are inherently mutually exclusive in some manner.
Referring to fig. 1, a structural block diagram of an integrated energy system according to an embodiment of the present invention is shown, in which a combined cooling, heating and power system is one of the most commonly used technologies in the integrated energy system. Under the energy internet view angle, a plurality of local power transmission and distribution micro-grids can be formed through connection, electric energy transmission and distribution in a local area can be achieved, and meanwhile energy intercommunication can be carried out with a centralized power grid, so that support and supplement are provided for a central energy supply system.
Referring to fig. 2, a flowchart of an energy optimization scheduling method according to an embodiment of the present invention includes:
step S1, constructing an optimized dispatching model of the energy system from the power generation cost, the environmental cost, the standby cost and the demand side response; the optimization scheduling model of the energy system establishes a corresponding objective function, and the constraint conditions of the objective function comprise system energy balance constraint, comprehensive energy output constraint, energy storage battery constraint, refrigerator constraint, demand side load constraint and transferable load system balance capacity constraint;
and step S2, carrying out optimization solution on the objective function of the energy system according to the dragonfly algorithm, and outputting the optimal solution meeting the iteration termination condition to obtain the optimal scheme of energy system scheduling.
In the embodiment, an optimal scheduling model of the energy system is constructed based on the power generation cost, the environmental cost, the standby cost and the demand side response structure, transferable loads in the electric load, the heat load and the cold load of the demand side in the comprehensive energy system are fully considered, the objective function is taken as an optimization target, the dragonfly algorithm is used for carrying out multi-objective optimal solution on the objective function, the optimal scheme of energy system scheduling is obtained, and the problem of multi-objective and nonlinear optimization in the optimal scheduling of the comprehensive energy system is solved.
In one embodiment, the optimized scheduling model of the integrated energy system is established under the condition that the system meets the normal operation and load consumption of each integrated energy, and the total operation cost of the integrated energy system is minimized by reasonably planning and arranging the output plans of each unit and timely adjusting the load. The model is a complex, nonlinear multi-objective optimization problem, the economic operation of which mainly considers the economic cost, the environmental cost and the standby cost of comprehensive energy, and the consideration of the demand side response is mainly reflected in the demand side load constraint. For energy sources such as photovoltaic energy, wind power energy and the like which utilize natural resources to generate electricity, the electricity generation cost is very low and can be ignored, so that only depreciation cost and operation cost of a unit are considered in economic operation. Micro gas power generation and fuel cell power generation need to consider fuel cost, unit operation and maintenance cost and depreciation cost. Because the comprehensive energy system is connected with an external large power grid in a grid mode, when the internal power supply is insufficient, the power purchase from the external grid needs to be considered; when the internal consumption is excessive, power needs to be transmitted to a power grid; therefore, the electricity purchasing and selling cost paid to the power grid is also considered in the total cost of the operation of the comprehensive energy system. In addition, the pollutant emission control cost (environmental cost) generated by the fuel unit and the compensation cost generated by the renewable energy power generator unit along with the fluctuation of weather change need to be considered. The objective function is:
minC＝CF+CH+CB(1)
c in the formulas (1) and (2)FFor the cost of electricity generation, CHCost for environmental pollution emission control, CBA reserve capacity fee; n is a radical oftM is the number of the units for the calculated total time period number; c. CfIs the fuel price; fi(Pi) Representing the fuel consumption of the unit; o isi(Pi) Representing the operation and maintenance cost of the unit; cdep(Pi) Representing the depreciation cost of the unit; cbuyAnd CsellRespectively representing the electricity purchase price and the internet price in the time period t; pgrid(t) represents the power value exchanged with the grid during the time period t; wherein, Fi(Pi)＝Cst(Pi)+Cop(Pi)，Cst(Pi) Fuel for the unit in generating electricity, Cop(Pi) Fuel used by the unit during starting; o isi(Pi)＝ko(Pi)PiΔt，ko(Pi) For operating maintenance parameters, PiIs the output power; cdep(Pi)＝CIfcr/Pcrτ，CIFor installation cost of the generator, fcrFor capital recovery factor, PcrThe rated power of the generator is shown, and tau is the maximum utilization hours; k is a pollutant type number; alpha is alphaikPollutant emission coefficients for different unit types; alpha is alphagrid，kPollutant emission coefficients for system power generation; pi(t) is the ith stagePower of the unit at time t, betakThe cost for treating pollutants;
load shortage caused by overlarge wind power generation dispatching value;the power is the nest electric quantity caused by the over-small wind power generation dispatching value;
load shortage caused by overlarge photovoltaic power generation dispatching value;
the grid power is the grid power caused by the over-small photovoltaic power generation scheduling value;wind power over-modulation compensation coefficient;
wind power under-modulation compensation coefficients;
an overshoot compensation factor for photovoltaic power generation,
and the compensation coefficient is the undermodulation compensation coefficient of the photovoltaic power generation.
In one embodiment, the demand side load constraint is:
in the formula (3), the reaction mixture is,
is the amount of load transferred over a time period Δ t;
the unit transfer quantity of the ith type load in the time period delta t;
is the number of units that can transfer the load over a time period Δ t;
and
respectively the maximum input quantity and the output quantity of the load in the ith time period delta t;
is the load amount of the i-th load before the transfer.
In one embodiment, the shiftable load system balancing capacity constraint is:
in formula (4):
the load quantity of the i-th load after the transfer;
is the maximum load amount in the time period delta t; pF(Δ t) is a fixed load amount; pR(Δ t) is the random load amount.
On the basis of the above embodiment, the constraint conditions of the objective function include system energy balance constraint, comprehensive energy output constraint, energy storage battery constraint, and refrigerator constraint, which specifically include:
system energy balance constraints
In the formula (5), Pi(t) is the ith unitPower at time period t; ps(t) photovoltaic output; pw(t) wind energy output; pbatt(t) the output of the energy storage battery; pgrid(t) power exchanged with the grid; pD(t) schedulable force; wMT，kIs the calorific value of the internal combustion turbine; wRT，lThe heat value of the waste heat of the fuel cell is generated; wDIs the grid thermal load;
is the power of the electric refrigerator in the time period t;
is the power of the absorption refrigerator in the time period t; pD(t) is the cooling load required for the period t.
Comprehensive energy output constraint
In the formula (6), Pmin、PmaxThe minimum/maximum output of the unit;
the maximum climbing speed and the maximum descending speed of the unit are obtained; u. ofi(t) the on-off state of the unit; n is a radical ofi，maxThe maximum starting times of the unit in the scheduling time period are set; t ison，i、Toff，iThe on-off time of the unit is set; t isoff，i，min、Ton，i，minThe minimum shutdown/startup time of the unit.
Energy storage battery restraint
In the formula (7), Pcharge，max、Pdischarge，maxCharging/discharging power for the battery, respectively; ebatt，min、Ebatt，maxRespectively representing the minimum value and the maximum value of the energy stored in the battery; ebatt(0) For scheduling battery energy value at initial time；Ebatt(Nt) The battery energy value at the scheduling end moment; u. ofcharge，i(t)、udischarge，i(t) the charge/discharge state of battery i at time t, respectively; eta1i，max、η2i，maxRespectively the maximum number of discharges/charges within the battery rescheduling period.
Refrigerator restraint
In the formula (8), the reaction mixture is,
the maximum power of the electric refrigerator and the absorption refrigerant in the period t are respectively.
In one embodiment, for example, the dragonfly algorithm in this embodiment is a meta-heuristic novel intelligent optimization algorithm proposed by Seyedali mirjali in 2015 by american scholars, has a strong optimization solving capability, and is more and more concerned by scholars in recent years. However, when the dragonfly algorithm is applied to long-term optimization scheduling, it is found that each dragonfly population is continuously close to the optimal position of the population in the optimization process, the diversity of the population is gradually reduced, and the dragonfly population is easy to fall into local optimal in the later period. Therefore, how to avoid premature convergence to improve the quality of the optimized result under the acceptable calculation time-consuming condition is an urgent problem to be solved.
In one embodiment, the dragonfly algorithm is optimized by chaotic traversal search to improve the quality of the initialized population.
Specifically, chaos traversal search is adopted to improve initial population quality
The chaos phenomenon generally exists in a nonlinear optimization system, has delicate internal structure, can not repeatedly experience all states in a specific area, has good ergodicity, randomness and regularity, and adopts Logistic mapping to carry out chaos search:
zn+1＝u(1-zn)zn(9)
in formula (9): z is a radical ofnIs the value of the variable z at the nth iteration, zn∈[0,1](ii) a u is a key parameter for controlling the state of the system, and u belongs to [0,4 ]]. Research shows that when u is 4, the system is in a complete chaotic state, and a chaotic sequence has no repeated phenomenon, so that the technology of the invention takes u as 4.
After the chaotic sequence is generated, each chaotic variable needs to be subjected to carrier processing and mapped into an original optimization variable Y feasible space, and the formula is as follows:
Yn＝Y-+zn(Y--Y-) (10)
in formula (10): y isnOptimizing variable Y and chaos variable z for originalnTaking a value correspondingly; y is-、Y-The upper limit and the lower limit of the original optimization variable Y are respectively taken.
In the embodiment, the chaos idea is utilized to initialize the population, so that the diversity and the distribution balance of the initial population are effectively improved, and the convergence speed and the search precision of the algorithm are enhanced.
In one embodiment, the dragonfly algorithm is optimized to improve population diversity by using a neighborhood variation search.
Specifically, neighborhood variation search is utilized to improve population diversity
In the evolution process, each particle is continuously close to the optimal position of the population and gradually gathered to a smaller area range, the diversity of the population is reduced, the searching capability is reduced, and if the global optimal position of the population is the local optimal solution, the premature convergence phenomenon is easy to occur. In order to improve the searching efficiency of the algorithm, the optimal population individuals are randomly varied in a neighborhood range which is reduced generation by generation, local refined search is carried out, if the fitness of new individuals obtained by variation is improved, the global optimal individuals of the population before variation are directly replaced, otherwise, the individuals in the population are randomly replaced with a certain probability. And (3) if the variable Y is varied to obtain Y', the calculation formula is as follows:
Y′＝Y+Rk(2r4-1) (11)
Rk＝(R--R-)(k--k)k-+R-(12)
in formulae (11) and (12): rkSearching the radius for the neighborhood at the kth iteration; r-、R-Are respectively asThe upper and lower limits of the neighborhood search radius; r is4Is [0, 1 ]]Random numbers are evenly distributed in intervals.
In the embodiment, the optimal individual of the varied population is obtained by utilizing the neighborhood variation search, so that the phenomenon of premature convergence of the population is avoided, and the diversity of the population is improved. The neighborhood variation search algorithm and the chaotic traversal search algorithm in the two embodiments can be used for the dragonfly algorithm independently, and can also be used in combination with the dragonfly algorithm, and the two algorithms are preferably used in combination in the embodiment.
In an embodiment, step S2 in the energy-optimization scheduling method includes:
importing data into the optimized scheduling model;
initializing dragonfly algorithm parameters;
initializing a population by utilizing chaotic traversal search;
calculating an initial fitness function value of dragonfly individuals in the population;
optimizing the target function according to the five behaviors of the dragonfly algorithm, continuously performing iterative computation, and updating the position of the dragonfly individual;
and searching the global optimal position of the population by using neighborhood variation search, and outputting a corresponding maximum fitness function value and an individual dragonfly when iteration reaches a preset maximum iteration number to obtain an optimal scheme for energy system scheduling.
In this embodiment, see fig. 3 in detail, which is a detailed flowchart implemented in step S2, including:
step 1: the operation parameters of all comprehensive energy sources, the electricity purchase and sale prices of all stages, the fuel price, the maximum utilization hours, the pollution emission coefficient, the compensation coefficient and other various cost coefficients are input. And simultaneously, predicting the electric load, the heat load, the cold load and the wind and light output conditions of the system at each stage according to the load parameters and the wind and light unit parameters at each stage.
Step 2: and initializing dragonfly algorithm parameters. And setting parameters such as population scale m, iteration times N, position parameter X, position change step length delta X and the like.
Step 3: the population is chaotically initialized using equations (9) and (10).
Step 4: and (5) initializing a population. N dragonfly individuals are randomly generated, and the fitness value of each individual is calculated according to the formula (1) and recorded.
Step 5: find the adjacent dragonfly. The Euclidean distance is used for judging whether an adjacent dragonfly exists between the dragonfly.
Step 6: the individual location is updated. The position and the position step length are updated, and the mathematical expression of the dragonfly individual position updating behavior is as follows:
① degree of separationWherein S isiDenotes the dispersive behavior of the ith individual, XjRepresenting the position of the jth adjacent individual dragonfly, X being the position of the current individual, N representing the number of individuals adjacent to the ith individual dragonfly, ② degree of alignment
Wherein A isiIndicates the degree of alignment, V, of the ith individualjExpressing the speed of j adjacent dragonfly individuals, j being the total number of adjacent individuals, N being the total number of individuals, ③ cohesion degree
Wherein, CiDenotes the degree of cohesion of the i-th individual, XjRepresenting the position of the jth adjacent dragonfly individual, N being the total number of adjacent individuals, X being the position of the current individual, ④ food attraction Fi＝X+-X, wherein FiDenotes the food attraction of the ith individual, X+Indicating the location of the food source, X being the current individual's location, ⑤ deterrent action EiX- -X, wherein EiIndicating the avoidance behavior of the ith individual, X-indicating the position of the natural enemy, and X being the position of the current individual. Judging according to Step 5; if there is an adjacent dragonfly, the position update step of the next generation dragonfly is calculated as Δ Xt+1＝(sSi+aAi+cCi+fFi+eEi)+wΔXiThe next generation dragonfly position is calculated as Xt+1＝Xt+ΔXi+1In the formula: t represents the current iteration number; i represents the ith dragonfly individual; xtRepresenting the individual position of the current t generation population; Δ Xt+1Representing the next generation population position updating step length; xt+1Representing the individual position of the next generation population; s represents a separation degree weight; siRepresenting the degree of separation of the ith individual; a represents an alignment weight; a. theiIndicating the alignment of the ith individual; c represents a cohesion weight; ciExpressing the cohesion degree of the ith individual; f represents a food factor; fiIndicating the attraction of the food location to the ith individual; e represents a natural enemy factor; eiRepresenting the repulsive force of the natural enemy position to the ith individual; w represents the value of the adaptive inertial weight coefficient according to the formulaUpdating the adaptive inertia weight w, where wmax、wminThe maximum value and the minimum value of w are respectively, and J is the current fitness function value of the dragonfly individual; j. the design is a squareavgAnd JminRespectively is the average fitness function value and the minimum fitness function value of all current dragonfly individuals.
If there is no adjacent dragonfly, the position of the next generation dragonfly is updated to Xt+1＝Xt+Levy(d)×XtIn the formula: d denotes the location vector dimension.
Step 7: and (5) performing domain variation search on the global optimal position of the population according to the formula (11) and the formula (12).
Step 8: judging whether the flow meets the convergence condition (termination condition) of the algorithm, and if the algorithm reaches the preset maximum iteration times, jumping out of the program; if not, return to Step3 to continue the iteration.
Step 9: and calculating and outputting the result. And comparing the fitness value of the optimal individual in each iteration, selecting the optimal individual corresponding to the optimal fitness value in all iterations, outputting the dragonfly individual corresponding to the maximum fitness value and the optimal value of the dragonfly, obtaining the convergence result of the optimization calculation as an optimal scheme, and calculating and outputting the target function value corresponding to the optimal individual, the output condition of the comprehensive energy in each stage, the load transfer condition and the like.
In the embodiment of the application, the energy optimization scheduling method has the following advantages:
(1) according to the method, the three aspects of the power generation cost, the environmental cost and the standby cost are comprehensively considered, the optimal scheduling model of the comprehensive energy system compatible with the demand side response is comprehensively established, and the electric load, the heat load and the cold load in the comprehensive energy system are fully considered.
(2) The dragonfly algorithm is introduced to solve the problem of multi-target and non-linear optimization in the optimization scheduling of the comprehensive energy system, and meanwhile, the dragonfly algorithm is improved aiming at the defects of the dragonfly algorithm.
(3) The method introduces chaotic search to enhance the quality of the dragonfly population; meanwhile, the dragonfly population is subjected to field variation search, so that the diversity of the population is enhanced, the problem that an algorithm is easy to fall into local optimization is solved, and the defects of premature convergence, poor optimizing capability and the like in the process of group optimization are overcome.
In conclusion, an optimal scheduling model of the energy system is constructed based on the power generation cost, the environmental cost, the standby cost and the demand side response structure, transferable loads in the electric load, the heat load and the cold load of the demand side in the comprehensive energy system are fully considered, the objective function is used as an optimization target, the dragonfly algorithm is used for carrying out multi-objective optimal solution on the objective function, the optimal scheme of energy system scheduling is obtained, and the problem of multi-objective and non-linear optimization in the optimal scheduling of the comprehensive energy system is solved. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. An energy optimization scheduling method, comprising:
constructing an optimized dispatching model of the energy system from the power generation cost, the environmental cost, the standby cost and the demand side response; the optimization scheduling model of the energy system establishes a corresponding objective function, and the constraint conditions of the objective function comprise system energy balance constraint, comprehensive energy output constraint, energy storage battery constraint, refrigerator constraint, demand side load constraint and transferable load system balance capacity constraint;
and performing optimization solution on the objective function of the energy system according to the dragonfly algorithm, and outputting an optimal solution meeting an iteration termination condition to obtain an optimal scheme for energy system scheduling.
2. The energy optimization scheduling method according to claim 1, wherein the dragonfly algorithm is optimized to improve the initialized population quality by using a chaotic traversal search.
3. The energy optimization scheduling method of claim 1, wherein the dragonfly algorithm is optimized to improve population diversity by using neighborhood variation search.
4. The energy optimization scheduling method according to claim 1, wherein the objective function is:
min C＝CF+CH+CB(1)
c in the formulas (1) and (2)FFor the cost of electricity generation, CHCost for environmental pollution emission control, CBA reserve capacity fee; n is a radical oftM is the number of the units for the calculated total time period number; c. CfIs the fuel price; fi(Pi) Representing the fuel consumption of the unit; o isi(Pi) Representing the operation and maintenance cost of the unit; cdep(Pi) Indicating depreciation of unitsThen, the process is carried out; cbuyAnd CsellRespectively representing the electricity purchase price and the internet price in the time period t; pgrid(t) represents the power value exchanged with the grid during the time period t; wherein, Fi(Pi)＝Cst(Pi)+Cop(Pi)，Cst(Pi) Fuel for the unit in generating electricity, Cop(Pi) Fuel used by the unit during starting; o isi(Pi)＝ko(Pi)PiΔt，ko(Pi) For operating maintenance parameters, PiIs the output power; cdep(Pi)＝CIfcr/Pcrτ，CIFor installation cost of the generator, fcrFor capital recovery factor, PcrThe rated power of the generator is shown, and tau is the maximum utilization hours; k is a pollutant type number; alpha is alphaikPollutant emission coefficients for different unit types; alpha is alphagrid，kPollutant emission coefficients for system power generation; pi(t) is the power of the ith unit in time period t, betakThe cost for treating pollutants;load shortage caused by overlarge wind power generation dispatching value;the power is the nest electric quantity caused by the over-small wind power generation dispatching value;load shortage caused by overlarge photovoltaic power generation dispatching value;
the grid power is the grid power caused by the over-small photovoltaic power generation scheduling value;
wind power over-modulation compensation coefficient;
wind power under-modulation compensation coefficients;
an overshoot compensation factor for photovoltaic power generation,
and the compensation coefficient is the undermodulation compensation coefficient of the photovoltaic power generation.
5. The energy optimization scheduling method according to claim 1, wherein the demand side load constraint is:
in the formula (3), the reaction mixture is,
is the amount of load transferred over a time period Δ t;
the unit transfer quantity of the ith type load in the time period delta t;
is the number of units that can transfer the load over a time period Δ t;
and
respectively the maximum input quantity and the output quantity of the load in the ith time period delta t;for loads of type i before transferAmount of the compound (A).
6. The energy optimization scheduling method of claim 1 wherein the shiftable load system balancing capacity constraint is:
in formula (4):
the load quantity of the i-th load after the transfer;is the maximum load amount in the time period delta t; pF(Δ t) is a fixed load amount; pR(Δ t) is the random load amount.
7. The energy optimization scheduling method according to claim 1, wherein the step of performing optimization solution on the objective function of the energy system according to a dragonfly algorithm, outputting an optimal solution meeting an iteration termination condition, and obtaining an optimal scheme for energy system scheduling comprises:
importing data into the optimized scheduling model;
initializing dragonfly algorithm parameters;
initializing a population by utilizing chaotic traversal search;
calculating an initial fitness function value of dragonfly individuals in the population;
optimizing the target function according to the five behaviors of the dragonfly algorithm, continuously performing iterative computation, and updating the position of the dragonfly individual;
and searching the global optimal position of the population by using neighborhood variation search, and outputting a corresponding maximum fitness function value and an individual dragonfly when iteration reaches a preset maximum iteration number to obtain an optimal scheme for energy system scheduling.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112241508A (en) * | 2020-10-26 | 2021-01-19 | 深圳供电局有限公司 | Computing method, device and storage medium for comprehensive energy optimization scheduling result |
| CN112598167A (en) * | 2020-12-17 | 2021-04-02 | 上海电力大学 | Power station boiler NO based on dragonfly algorithm and fast learning netxEmission amount prediction method |
| CN113052498A (en) * | 2021-04-23 | 2021-06-29 | 国核电力规划设计研究院有限公司 | Electric-to-gas two-stage conversion device scheduling method based on comprehensive energy system |
| CN113392513A (en) * | 2021-05-28 | 2021-09-14 | 国网河北省电力有限公司衡水供电分公司 | Multi-objective optimization method, device and terminal for combined cooling, heating and power system |
| CN113537841A (en) * | 2021-09-09 | 2021-10-22 | 河南科技学院 | Method for optimizing dispatching of yard crane by considering customer satisfaction |
| CN113742994A (en) * | 2021-07-28 | 2021-12-03 | 广东电网有限责任公司 | Multi-objective optimization method for power distribution network with distributed power supplies |
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| CN114565244A (en) * | 2022-02-16 | 2022-05-31 | 石河子大学 | Optimized scheduling method and device of comprehensive energy system |
| CN114565238A (en) * | 2022-02-15 | 2022-05-31 | 石河子大学 | Comprehensive energy low-carbon scheduling method and device |
| CN114580278A (en) * | 2022-02-24 | 2022-06-03 | 中冶赛迪工程技术股份有限公司 | Novel urban peak clipping and valley filling double-layer optimized operation method considering park electric automobile participation |
| CN114723174A (en) * | 2022-05-06 | 2022-07-08 | 国网江苏省电力有限公司南通供电分公司 | Energy delivery parameter adjusting method and system based on state evaluation |
| CN115063003A (en) * | 2022-06-30 | 2022-09-16 | 华北电力大学（保定） | Time-interval scheduling method of comprehensive energy system based on multivariate load time sequence analysis |
| CN117332991A (en) * | 2023-11-24 | 2024-01-02 | 国网天津市电力公司电力科学研究院 | System energy efficiency optimization method and device based on comprehensive energy demand |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685285A (en) * | 2019-01-11 | 2019-04-26 | 中冶赛迪工程技术股份有限公司 | A kind of micro-grid load electricity consumption Optimized Operation new method based on multiple target dragonfly algorithm |
-
2019
- 2019-09-27 CN CN201910921396.6A patent/CN110705776A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685285A (en) * | 2019-01-11 | 2019-04-26 | 中冶赛迪工程技术股份有限公司 | A kind of micro-grid load electricity consumption Optimized Operation new method based on multiple target dragonfly algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 冯仲恺等: "改进量子粒子群算法在水电站群优化调度中的应用", 《水科学进展》 * |
| 徐斌等: "基于需求响应的区域微电网优化调度", 《电力科学与技术学报》 * |
| 曾鸣等: "兼容需求侧可调控资源的分布式能源系统经济优化运行及其求解算法", 《电网技术》 * |
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