CN114330983A - Multi-power user power supply energy scheduling method under power supply deficiency environment of regional power grid - Google Patents
Multi-power user power supply energy scheduling method under power supply deficiency environment of regional power grid Download PDFInfo
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Abstract
The invention relates to a multi-power user power supply energy scheduling method under the condition of power supply loss of a regional power grid, which comprises the following steps: s1: acquiring park electricity utilization data of a multi-power user; s2: building a multi-power user electric energy optimization scheduling model in a power supply deficiency environment; s3: and solving the multi-power supply user electric energy optimization multi-objective optimization model under the power supply deficiency environment to obtain a scheduling scheme. Compared with the prior art, the method can effectively promote the effective utilization of the limited electric quantity when the power supply of the power grid is lost, and improve the balance of the micro-power grid in an island state and the electricity economy of users.
Description
Technical Field
The invention relates to the field of power dispatching, in particular to a method for dispatching power supply energy of multiple power users in a power supply deficiency environment of a regional power grid.
Background
At present, energy optimization scheduling is mainly energy scheduling when the electric quantity is sufficient under the grid-connected condition, and in the prior art, a load working time optimization scheduling strategy which ensures that loads can be continuously supplied in all time periods and considers factors such as time-of-use electricity price and peak clipping and valley filling under the condition of sufficient energy exists. And a form of optimizing strategy is established by using the renewable energy utilization rate, the network loss and the user satisfaction degree as targets through scheduling and controlling the distributed power supply, the energy storage equipment and the flexible load.
However, in the prior art, the problems of increasing the benefit and improving the energy utilization rate and the like only by considering the scheduling load and the energy storage equipment are not considered, and the problem that the maximum demand can be reduced by reasonably configuring the energy storage and scheduling load because the demand electric charge exists in users with larger electric power in an industrial park and the like is not considered. At present, the electric charge of a power grid for many users is charged by two electricity prices, so that the electric charge is charged according to the actual electricity use condition, and the required electric charge is also charged according to the maximum required quantity of the users. Wherein, the maximum calculated value of the average power of the user power consumption in each time period (generally 15min) in one month is used as the maximum demand in the month. The existing research mainly considers the problems of optimizing user income, energy utilization rate and the like when the energy is sufficient, and the situation of insufficient electric quantity caused by adopting an island-type micro-grid is not analyzed. In areas affected by natural disasters, equipment damage, power failure maintenance and repair and other factors, and in remote areas, islands and other environments, the situation that power supply of a large power grid is lost can exist, and at the moment, power can be supplied to users through an island-type micro power grid. However, due to the constraint of the capacity of the power generation equipment of the user and the characteristic that the output of the renewable energy is easily influenced by external factors, the problems of insufficient power supply quantity, power failure of the user and the like can occur, so that the energy scheduling of the island-type micro-grid becomes more difficult and complicated.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for scheduling power supply energy of multiple power users in the power supply deficiency environment of a regional power grid.
The purpose of the invention can be realized by the following technical scheme:
a multi-power user power supply energy scheduling method under the condition of power supply loss of a regional power grid comprises the following steps:
s1: acquiring park electricity utilization data of a multi-power user;
s2: building a multi-power user electric energy optimization scheduling model in a power supply deficiency environment;
s3: and solving the multi-power supply user electric energy optimization multi-objective optimization model under the power supply deficiency environment to obtain a scheduling scheme.
Preferably, the multi-power user electric energy optimization scheduling model comprises a power failure loss model, an energy storage configuration cost model and a user dissatisfaction degree model.
Preferably, the power outage loss model is as follows:
wherein L is a model of total power failure loss of load in a multi-power user area, Lk,iIs a gardenLoss of power outage for class i adjustable loads in zone k,
cost per unit of electricity outage, Qcut,k,iThe power failure amount of the ith type adjustable load in the park k in the originally planned working time is shown, m is the number of the parks, and n is the number of the types of the adjustable loads.
Preferably, in the power outage loss model,
wherein the content of the first and second substances,
respectively the starting time and the ending time of the originally planned working time,
respectively start and end times, P, of the actual operating timeadj,k,i(t) is the working power of the ith type adjustable load in the park k in the time period t,
the model of the unit electric quantity power failure cost and the power failure time is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the planned working time length and the actual working time length of the ith type adjustable load in the park k;
indicating the length of the reduced operating time of the i-th class of adjustable loads in park k, Ak,iIs a stable value of the power failure cost, tau, of the i-th type adjustable load unit electric quantity in the park kk,iIs the i-th type adjustable load loss time constant in the park k;
preferably, the energy storage configuration cost model is as follows:
Pmax,k＝max(|Pstorage,k(t)|)t∈Γ
wherein, CcostConfiguring cost, P, for energy storage per time periodmax,kAnd Smax,kRespectively the maximum charge-discharge power and the maximum electric storage capacity P in the k operation period of the parkstorage,k(t) is the charge and discharge power of the park k during the operating cycle, Sk(t) is the stored energy in k operating periods of the park, ε1、ε2For ESS conversion of cost parameter, etamaxAnd the upper limit coefficient of the charging and discharging capacity of the energy storage equipment is obtained.
Preferably, the user dissatisfaction model is:
wherein the content of the first and second substances,
in order to be at a level of user dissatisfaction,
for economic dissatisfaction with electricity, Ecom,k,iFor electricity consumption comfort satisfaction degree mu of the i-th type adjustable load in the park kk,iRepresenting the impact factor of the adjustable load of the ith class in the park k,
indicating the loss of power outage, L, due to total power outage of the i-th class of adjustable loads in park kk,iThe power failure loss of the i-th type adjustable load in the park k is solved.
Preferably, the calculation formula of the power consumption comfort degree in the user dissatisfaction degree model is as follows:
preferably, in the step S3, the electrical energy optimization scheduling model for multiple power users is directly solved by using an NSGA-II algorithm.
Preferably, the step S3 specifically includes:
and solving the multi-power user electric energy optimization scheduling model by adopting NSGA-II to obtain a Pareto solution set, respectively constructing a fuzzy membership function for each target by adopting a fuzzy membership method, converting the fuzzy membership function into the satisfaction degree of the optimization result, and finding out the optimal solution in the Pareto solution set by comparing the satisfaction degrees.
Preferably, the fuzzy membership function is:
wherein D isrRepresents the r-th objective function satisfaction value; f. ofrIs the r-th objective function; fmaxAnd FminRespectively the upper and lower limits of the r-th objective function in the Pareto solution set,
calculating a normalized satisfaction value of each solution, wherein the solution with the maximum normalized satisfaction value is the optimal solution:
wherein D ishNormalized satisfaction value for the h solution; r is the number of objective functions; h is the number of solutions in the Pareto solution set, Dh,rIs the satisfaction value of the r-th objective function in the h-th solution.
Compared with the prior art, the invention has the following advantages:
the invention considers the influence of factors such as power equipment damage, power failure maintenance and repair and the like caused by natural disasters and accidents and the existence of special power supply environments such as remote mountainous areas, islands and the like, and can generate the condition of power supply loss of a local large power grid. At the moment, due to the characteristics that the capacity of the power generation equipment of the island-type microgrid and the output of renewable energy are easily influenced by external factors, the power supply quantity is insufficient, a reasonable energy scheduling strategy can promote the effective utilization of limited electric quantity in the power supply loss environment of the power grid, and the operating balance of the island-type microgrid and the electricity utilization economy of users are improved. Therefore, aiming at the condition that the electric quantity is limited under the power supply deficiency environment of the power grid, the invention takes multi-electric-force users considering electric energy sharing as research objects, reasonably distributes the electric quantity to different types of loads in different parks, adjusts the working time of the loads, establishes a multi-electric-force-user limited electric quantity optimized scheduling model by taking the minimum of power failure loss, energy storage cost and user dissatisfaction as the target, solves the multi-target optimized scheduling model by adopting the non-dominated sorting genetic algorithm based on the elite strategy to obtain a Pareto solution set, then constructs a fuzzy membership function for each target by adopting a fuzzy membership method, converts the fuzzy membership function into the satisfaction of an optimization result, finds out the optimal solution in the Pareto solution set by comparing the satisfaction, thereby well balancing the optimization effect among the targets and effectively scheduling the power supply energy of the multi-electric-force users under the power supply deficiency environment of the regional power grid, the power utilization efficiency and the power distribution effect are improved, so that the optimization effect among all targets can be well balanced, the effective utilization of limited electric quantity when the power supply of a power grid is lost can be effectively promoted, and the balance of the micro-grid in an island state and the power utilization economy of users are improved.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic representation of the energy flow information flow of the campus of the present invention;
FIG. 3 is a flow chart of model solution of the present invention;
FIG. 4 is a PV force and load operating curve for an embodiment of the present invention;
FIG. 5 is a Pareto solution graph under different algorithms of the present invention;
FIG. 6 is an illustration of the optimal solution for outage loss for different iterations of the present invention;
FIG. 7 is an optimal solution of the energy storage configuration cost for different iterations of the present invention;
FIG. 8 is a graph of optimal solution values for user dissatisfaction at different iterations of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
A method for scheduling power supply energy of multiple power users in a power supply deficiency environment of a regional power grid is disclosed, as shown in FIG. 1, and comprises the following steps:
s1: acquiring park electricity utilization data of a multi-power user;
the electric energy optimization scheduling model for the multiple power users comprises a power failure loss model, an energy storage configuration cost model and a user dissatisfaction degree model. Fig. 2 shows a schematic diagram of energy flow and information flow for power sharing and management of multiple power users in a power-off environment. In a multi-power user area in the power supply deficiency environment, the situation that the power consumption of certain parks is excessive and the power consumption of other parks is deficient exists at a certain moment, and at the moment, the electric energy can be shared among the parks to realize the effective utilization of energy. A multi-power consumer energy management system (MEMS) is a control system that integrates distributed power supplies and loads such as gas turbines, gas boilers, ESS, photovoltaic cells (PV) and the like in a multi-power consumer into a whole. The MEMS comprises a data acquisition system, a management scheduling center and a control center. The MEMS collects and processes information such as PV, ESS, gas turbine, load running power and running time through a data collection system, and transmits the data to a management dispatching center. The management scheduling center makes an electric energy scheduling plan of multiple power users in a power supply deficiency environment according to system requirements and intelligent decisions, reasonably schedules load power supply according to load importance and unit power failure loss, and simultaneously sends the scheduling plan to the control center to realize control over switching of a distributed power supply, charging and discharging of an ESS and removal or transfer of an adjustable load, so that the purposes of improving the electric energy utilization rate, the power utilization economy and the user satisfaction degree are achieved. In addition, the MEMS can make a gas boiler working plan according to the heat load data in the park and the operation condition of the gas turbine so as to meet all heat load requirements.
S2: and constructing a multi-power user electric energy optimization scheduling model in the power supply deficiency environment.
(1) Power failure loss model
The power failure loss of the invention is the power failure cost of unit electric quantity
And the amount of power failure Qcut,k,iMultiplying to obtain the power failure loss L of the i-th type adjustable load in the park kk,iCan be expressed as:
the unit cell blackout cost is not generally considered to be a constant value that is constant, which is a comprehensive variable. The variable is influenced by the power failure time and the type of the user, and the variable trend is that the variable increases and then gradually approaches a stable value along with the extension of the power failure time. The mathematical model of the unit electricity power failure cost and the power failure time is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the planned working time length and the actual working time length of the ith type adjustable load in the park k;
indicating the length of time the class i adjustable load was reduced in campus k. A. thek,iThe power failure cost of the unit electric quantity of the i-th type adjustable load in the park k is a stable value, and the power failure cost of the unit electric quantity is close to the value after a period of power failure. Tau isk,iThe time constant of the i-th type adjustable load loss in the park k reflects the speed of the unit electric quantity power failure cost along with the change of the load power failure duration time, and determines the duration time of the power failure
Is close to the steady value ak,i. Parameter A in the modelk,iAnd τk,iAll depending on the type of adjustable load。
The originally planned working time is
To
Class i adjustable load power failure Q in park kcut,k,iThe model is as follows:
the model L of the total power failure loss of the load in the multi-power user area is as follows:
wherein m is the number of parks, n is the number of adjustable load types, Padj,k,iAnd (t) the working power of the ith type adjustable load in the park k in the time period t.
(2) Energy storage configuration cost model
In the process of optimizing and scheduling the limited power quantity and the load time, the ESS adjusts the power supply quantity and the power supply time through charging and discharging. The emphasis of ESS capacity configuration is to let the ESS perform its functions optimally within a limited capacity range. Since the cost of configuring the ESS converted to each period is related to the maximum power storage amount and the maximum charge/discharge power in the operation cycle, the ESS needs to be reasonably utilized to schedule the power, and the maximum power storage amount and the maximum charge/discharge power of the ESS during the scheduling process are reduced. The energy storage configuration cost formula converted into each time interval can be obtained through the relationship as follows:
Pmax,k＝max(|Pstorage,k(t)|)t∈Γ
in the formula, Pmax,kAnd Smax,kRespectively the maximum charge-discharge power and the maximum power storage capacity in the k operation period of the park; epsilon1、ε2Converting cost parameters for the ESS; etamaxIs the upper limit coefficient of the charging and discharging capacity of the energy storage device, SkAnd (t) is the electricity storage capacity in the k operation period of the park.
(3) User dissatisfaction model
Because the influence degrees of power failure on different users are different, the power utilization economical efficiency serving as a scheduling target cannot meet diversified power utilization requirements of the users, and the power utilization satisfaction degree of the users needs to be considered. From the perspective of the user, the user satisfaction degree is composed of the electricity utilization comfort satisfaction degree and the electricity utilization economy satisfaction degree. The power utilization comfort satisfaction degree is defined as the percentage of the actual working time of the power utilization load in the planned power utilization time period; the electricity utilization economic satisfaction is defined as the percentage of the loss of power failure which is not generated by the electricity utilization load and the total loss of power failure generated by the total power failure. In the embodiment, only the influence of the adjustable load on the user satisfaction is considered.
According to the definition, the electricity utilization comfort satisfaction degree E of the ith type adjustable load in the park k can be obtainedcom,k,iThe expression is as follows:
the total electricity consumption comfort satisfaction degree E of the multi-power userscomComprises the following steps:
total economic satisfaction degree E of multiple electric power userseconThe expression is as follows:
then the user satisfaction EuserComprises the following steps:
Euser＝Ecom+Eecon
degree of user dissatisfaction
Comprises the following steps:
in the formula, muk,iRepresenting the impact factor of the adjustable load of the ith class in the park k,
representing the power failure loss generated by the power failure of all the ith type adjustable loads in the park k;
indicating the dissatisfaction degree of the electricity comfort;
indicating the dissatisfaction of the electricity economy.
According to the objective function model, it is easy to know that in order to achieve the economic objective of multiple power users, if the power failure loss minimization is considered to distribute the electric quantity proportion of each load and adjust the running time, the phenomena that more electric quantities are distributed to the load with larger unit power failure loss, the actual power consumption behavior of the user deviates from the expected power consumption behavior and the like due to excessive electric energy storage are caused, so that the problems of increased energy storage configuration cost, reduced user satisfaction and the like are caused. However, the proportion of each load electric quantity and the scheduling operation time are distributed only by considering the degree of satisfaction of users, the maximum charge-discharge power and the maximum electric storage quantity are increased, and the unit power failure loss is not distinguished, so that the energy storage configuration cost and the power failure loss are increased. Therefore, from the overall view, three targets of power failure loss, energy storage configuration cost and user dissatisfaction degree need to be coordinated to determine the optimal limited electric quantity scheduling scheme.
S3: and solving the multi-power supply user electric energy optimization multi-objective optimization model under the power supply deficiency environment to obtain a scheduling scheme.
The method takes the minimum power failure loss, energy storage configuration cost and user dissatisfaction in the park in the power supply loss environment of the power grid as optimization targets, and the model can know that the 3 targets have a mutual constraint relation, and the 3 targets have different dimensions. Therefore, the NSGA-II is adopted to directly solve the multi-objective optimization model. It is known that NSGA-II has good convergence and distribution, can obtain a more ideal Pareto solution set, and can well balance the optimization effect among various targets. The optimization scheduling model solving flow is shown in fig. 3. The method comprises the steps of solving an optimized scheduling model by adopting NSGA-II to obtain a Pareto solution set, then constructing a fuzzy membership function for each target by adopting a fuzzy membership method, converting the fuzzy membership function into satisfaction of an optimization result, and finding out an optimal solution in the Pareto solution set by comparing the satisfaction.
The fuzzy membership function is:
in the formula, DrRepresents the r-th objective function satisfaction value; f. ofrIs the r-th objective function; fmaxAnd FminRespectively the upper and lower limits of the r-th objective function in the Pareto solution set.
Then, a normalized satisfaction value of each solution is calculated, wherein the solution with the largest normalized satisfaction value is the optimal solution.
In the formula, DhNormalized satisfaction value for the h solution; r is the number of objective functions; h is the number of solutions in the Pareto solution set, Dh,rIs the satisfaction value of the r-th objective function in the h-th solution.
In this embodiment, the PV and the gas turbine power generation amount in the typical day of the multicenter area in 5 months and the various load power consumption conditions are obtained specifically for the agricultural park, the industrial park, the commercial park, and the residential park as shown in table 1. The total PV output of all parks and the working time and power of the fixed load, various adjustable loads are shown in figure 4.
TABLE 1 Multi-park Power Generation and load Power consumption
Compared with the NSGA-II, the constraint conditions and the function parameters of the three target models are drawn to draw a target function scatter diagram which represents all solutions which can be obtained by the target function under the constraint conditions, and the fitness of all solutions is calculated according to the fitness function. The color of the square represents the fitness, the darker the color indicates the better the solution, and the darker the color represents the optimal region. Meanwhile, in the embodiment, the optimization scheduling model is solved through an MATLAB platform by using multi-objective optimization algorithms such as NSGA-II, NSGA, MOPSO and the like, the population scale is set to be 100, and the iteration number is set to be 100. The Pareto solution set under 3 algorithms is obtained through simulation, and is shown in fig. 5.
As can be seen from FIG. 5, the Pareto solution set obtained by NSGA-II converges to the region with the deepest fitness color, which proves that the Pareto solution set obtained by NSGA-II converges to the global optimum, and the Pareto solution sets obtained by the other two algorithms are located outside the optimum region, which represents that the local optimum phenomenon is trapped.
The method can find the solution with the maximum standard satisfaction value in the Pareto solution set according to a fuzzy membership method, and takes the solution as the optimal solution. Therefore, in addition to comparing the Pareto solution sets of different algorithms, the present embodiment also compares the optimal solution values of 3 targets in different iteration times under the three algorithms, and the results are shown in fig. 6 to 8. As can be seen from FIGS. 6-8, the optimal solution values obtained by NSGA-II under different iteration times are smaller than those obtained by NSGA and MOPSO, i.e., the optimal solution obtained by NSGA-II is better. In addition, when the iteration number is less than 50, the optimal solution value obtained by NSGA-II under different iteration numbers is gradually reduced, when the iteration number reaches 50, the optimal solution value reaches a stable level, and when the iteration number continues to increase, the optimal solution value is always in a stable state, and the total running time of the program is about 225 s. However, in NSGA and MOPSO, the optimal solution value reaches a plateau when the number of iterations reaches 70 and 60, respectively, and the total run time of the program is about 480s and 274s, respectively. It is easy to know that the convergence rates of NSGA and MOPSO are significantly lower than those of NSGA-II. The method can quickly and effectively obtain the scheme for scheduling the power supply energy of the multi-power user.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (10)
1. A multi-power user power supply energy scheduling method under the condition of power supply loss of a regional power grid is characterized by comprising the following steps:
s1: acquiring park electricity utilization data of a multi-power user;
s2: building a multi-power user electric energy optimization scheduling model in a power supply deficiency environment;
s3: and solving the multi-power supply user electric energy optimization multi-objective optimization model under the power supply deficiency environment to obtain a scheduling scheme.
2. The method for scheduling the power supply energy of the multiple power users in the power supply deficiency environment of the regional power grid according to claim 1, wherein the optimal scheduling model of the power of the multiple power users comprises a power failure loss model, an energy storage configuration cost model and a user dissatisfaction degree model.
3. The method for scheduling the power supply energy of the multiple power users in the power supply deficiency environment of the regional power grid according to claim 2, wherein the power failure loss model is as follows:
wherein L is a model of total power failure loss of load in a multi-power user area, Lk,iFor the power failure loss of the i-th type adjustable load in the park k,
cost per unit of electricity outage, Qcut,k,iThe power failure amount of the ith type adjustable load in the park k in the originally planned working time is shown, m is the number of the parks, and n is the number of the types of the adjustable loads.
4. The method according to claim 3, wherein in the power outage model,
wherein the content of the first and second substances,
respectively the starting time and the ending time of the originally planned working time,
respectively start and end times, P, of the actual operating timeadj,k,i(t) is the working power of the ith type adjustable load in the park k in the time period t,
the model of the unit electric quantity power failure cost and the power failure time is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the planned working time length and the actual working time length of the ith type adjustable load in the park k;
indicating the length of the reduced operating time of the i-th class of adjustable loads in park k, Ak,iIs a stable value of the power failure cost, tau, of the i-th type adjustable load unit electric quantity in the park kk,iIs the i-th type adjustable load loss time constant in the park k;
5. the method for scheduling the power supply energy of the multiple power users in the power supply deficiency environment of the regional power grid according to claim 2, wherein the energy storage configuration cost model is as follows:
Pmax,k＝max(|Pstorage,k(t)|)t∈Γ
wherein, CcostConfiguring cost, P, for energy storage per time periodmax,kAnd Smax,kRespectively the maximum charge-discharge power and the maximum electric storage capacity P in the k operation period of the parkstorage,k(t) is the charge and discharge power of the park k during the operating cycle, Sk(t) is the stored energy in k operating periods of the park, ε1、ε2For ESS conversion of cost parameter, etamaxAnd the upper limit coefficient of the charging and discharging capacity of the energy storage equipment is obtained.
6. The method for dispatching the power supply energy of the multiple power users in the power supply deficiency environment of the regional power grid according to claim 4, wherein the user dissatisfaction model is as follows:
wherein the content of the first and second substances,
in order to be at a level of user dissatisfaction,
for economic dissatisfaction with electricity, Ecom,k,iFor electricity consumption comfort satisfaction degree mu of the i-th type adjustable load in the park kk,iRepresenting the impact factor of the adjustable load of the ith class in the park k,
indicating the loss of power outage, L, due to total power outage of the i-th class of adjustable loads in park kk,iThe power failure loss of the i-th type adjustable load in the park k is solved.
7. The method for dispatching the power supply energy of the multiple power users in the power supply deficiency environment of the regional power grid according to claim 6, wherein a calculation formula of the power consumption comfort satisfaction degree in the user dissatisfaction degree model is as follows:
8. the method for scheduling power supply energy of multiple power consumers in the power supply deficiency environment of the regional power grid according to claim 1, wherein in the step S3, an NSGA-II algorithm is adopted to directly solve the optimal scheduling model of the power supply energy of the multiple power consumers.
9. The method according to claim 8, wherein the step S3 specifically includes:
and solving the multi-power user electric energy optimization scheduling model by adopting NSGA-II to obtain a Pareto solution set, respectively constructing a fuzzy membership function for each target by adopting a fuzzy membership method, converting the fuzzy membership function into the satisfaction degree of the optimization result, and finding out the optimal solution in the Pareto solution set by comparing the satisfaction degrees.
10. The method for scheduling the power supply energy of the multiple power users in the power supply deficiency environment of the regional power grid according to claim 9, wherein the fuzzy membership function is as follows:
wherein D isrRepresents the r-th objective function satisfaction value; f. ofrIs the r-th objective function; fmaxAnd FminRespectively the upper and lower limits of the r-th objective function in the Pareto solution set,
calculating a normalized satisfaction value of each solution, wherein the solution with the maximum normalized satisfaction value is the optimal solution:
wherein D ishNormalized satisfaction value for the h solution; r is the number of objective functions; h is the number of solutions in the Pareto solution set, Dh,rIs the satisfaction value of the r-th objective function in the h-th solution.
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| CN105955931A (en) * | 2016-05-10 | 2016-09-21 | 东南大学 | High-density distributed photovoltaic absorption-oriented regional energy network optimizing and scheduling method |
| CN106655248A (en) * | 2016-10-21 | 2017-05-10 | 中国电建集团中南勘测设计研究院有限公司 | Power capacity allocation method of grid-connected microgrid |
| CN110417015A (en) * | 2019-06-18 | 2019-11-05 | 湖北追日电气股份有限公司 | Micro-capacitance sensor Multiobjective Optimal Operation method and system based on Model Predictive Control |
| CN111340299A (en) * | 2020-02-29 | 2020-06-26 | 上海电力大学 | Multi-objective optimization scheduling method for micro-grid |
-
2021
- 2021-11-12 CN CN202111339766.9A patent/CN114330983A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105955931A (en) * | 2016-05-10 | 2016-09-21 | 东南大学 | High-density distributed photovoltaic absorption-oriented regional energy network optimizing and scheduling method |
| CN106655248A (en) * | 2016-10-21 | 2017-05-10 | 中国电建集团中南勘测设计研究院有限公司 | Power capacity allocation method of grid-connected microgrid |
| CN110417015A (en) * | 2019-06-18 | 2019-11-05 | 湖北追日电气股份有限公司 | Micro-capacitance sensor Multiobjective Optimal Operation method and system based on Model Predictive Control |
| CN111340299A (en) * | 2020-02-29 | 2020-06-26 | 上海电力大学 | Multi-objective optimization scheduling method for micro-grid |
Non-Patent Citations (1)
| Title |
|---|
| 江友华等: "电网供电缺失环境下多园区有限电量优化调度策略", 《电力建设》, vol. 42, no. 1, 31 January 2021 (2021-01-31), pages 105 - 116 * |
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