CN110417002B - Optimization method of island micro-grid energy model - Google Patents

Optimization method of island micro-grid energy model Download PDF

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CN110417002B
CN110417002B CN201910614293.5A CN201910614293A CN110417002B CN 110417002 B CN110417002 B CN 110417002B CN 201910614293 A CN201910614293 A CN 201910614293A CN 110417002 B CN110417002 B CN 110417002B
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power
period
optimization
energy storage
constraint
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CN110417002A (en
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刘昱良
曹新慧
苗世洪
赵军
董昱廷
李忠政
田淼
张三春
白胜利
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Huazhong University of Science and Technology
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Ningxia Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Xinjiang Electric Power Co Ltd
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Huazhong University of Science and Technology
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Ningxia Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Xinjiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention discloses an optimization method of an island micro-grid energy model, belonging to the field of island micro-grid energy optimization and specifically comprising the following steps: establishing an island microgrid energy model; dividing the optimization cycle into a plurality of time intervals by taking the current time interval as a first time interval of the island micro-grid energy model optimization cycle, and acquiring the predicted values of wind-light predicted output and load power corresponding to each time interval; calculating the confidence coefficient of the positive backup constraint and the confidence coefficient of the negative backup constraint corresponding to each time period in the island micro-grid energy model according to the wind-solar predicted output and the 1-level load ratio of each time period; updating an island micro-grid energy model according to the confidence coefficients of the positive standby constraint and the negative standby constraint and the predicted values of the wind-light predicted output and the load power; according to the method, an optimization cycle is divided into a plurality of time periods, the length of each time period is not fixed, and the wind-solar predicted output and the confidence coefficients of the positive and negative standby constraints are updated by taking the time periods as a reference to update an island micro-grid energy model; the operation cost of the island microgrid is reduced.

Description

Optimization method of island micro-grid energy model
Technical Field
The invention belongs to the field of island micro-grid energy optimization, and particularly relates to an optimization method of an island micro-grid energy model.
Background
The over-development and utilization of fossil energy bring about serious problems of resource exhaustion and environmental pollution, and the global energy situation is more and more severe. Renewable energy power generation is vigorously developed in all countries of the world, and a micro-grid composed of distributed power sources, energy storage units, loads and the like is concerned more and more.
The micro-grid is flexible in operation mode, can normally operate in a grid-connected state to exchange power with an external power grid, and can also operate in an off-grid state as a completely independent island system. However, the microgrid under certain specific scenes, such as a small offshore island, a remote pasture and a frontier sentry, has no large power grid coverage area, and can only operate autonomously. For the island microgrid, because no large power grid is used for supporting, and a certain error exists in distributed power supply and charge prediction, how to fully utilize renewable energy sources and improve economic benefits while ensuring safe and stable operation of a system becomes a problem to be solved urgently.
In the prior art, energy optimization is mostly performed on an island microgrid from an optimization perspective, for example, an operation constraint is established with the aim of lowest operation cost, and then an energy optimization control strategy is solved and obtained; aiming at application scenes such as an island microgrid with an energy storage power station, a remote mountain area and the like, the daily operation energy control optimization strategy of a microgrid system with wind, light, diesel and storage is researched on the basis of considering factors such as the service life (use cost) of energy storage equipment, the working efficiency of a diesel engine and the like; considering the characteristics of an island microgrid, the energy management research still faces some non-negligible challenges: firstly, because power scenes such as remote areas, islands, frontier defense posts and the like have severe environments and large weather condition changes, and the output quantity of a photovoltaic power generation system is closely related to weather conditions and environmental factors, the prediction precision can influence the economy and safety of energy scheduling of the microgrid system, but most of the current energy optimization technologies do not consider prediction errors and prediction precision; secondly, the prior art can not realize the dynamic self-adaptive adjustment of confidence coefficient, so that the charge and discharge time period of the energy storage system can be reasonably arranged, and the operation cost of the islanding micro-grid system is reduced.
Therefore, in the prior art, the economic operation of the island microgrid cannot be realized, and an energy optimization control strategy of the island microgrid needs to be further researched so as to realize the dynamic self-adaptive charging and discharging regulation of the multiple energy storage units under the conditions of sudden changes in weather and the like, the energy supply and demand changes caused by distributed power generation fluctuation and sudden load changes, and effectively reduce the operation cost of the island microgrid.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an optimization method of an island microgrid energy model, and aims to solve the problem that the island microgrid operation cost is high due to distributed generation fluctuation caused by sudden changes of weather and energy supply and demand changes of the island microgrid caused by sudden changes of load.
In order to achieve the above object, the present invention provides an island microgrid energy model optimization method, which includes:
(1) establishing an island microgrid energy model by taking the minimum total running cost of an island microgrid system in an optimization period as a target function based on a source load uncertainty model, a power balance constraint, a positive and negative standby constraint, a diesel generator running constraint and an energy storage power station SoC constraint of a triangular fuzzy variable;
(2) dividing the optimization cycle into a plurality of time intervals by taking the current time interval as a first time interval of the island micro-grid energy model optimization cycle, and acquiring the predicted values of wind-light predicted output and load power corresponding to each time interval;
the current time interval is the time interval of the current moment; the length of each time interval is not fixed;
(3) calculating the confidence coefficient of the positive backup constraint and the confidence coefficient of the negative backup constraint corresponding to each time period in the island micro-grid energy model according to the wind-solar predicted output and the 1-level load ratio of each time period;
(4) updating an island microgrid energy model according to the confidence coefficient of the positive standby constraint, the confidence coefficient of the negative standby constraint, the wind and light predicted output and the predicted value of the load power corresponding to each time period;
the updated island microgrid energy model is used for outputting optimization variables corresponding to each time period in an optimization period;
(5) if the current time enters the next time period of the optimization cycle, taking the next time period as the current time period of the updated island micro grid energy model, and repeating the steps (2) to (4);
the wind-solar predicted contribution comprises: the predicted value of the wind power and the predicted value of the photovoltaic power;
the optimization variables include: the output power of the diesel engine; the generated power of the energy storage power station; charging power of the energy storage power station.
Preferably, the source-load uncertainty model of the triangular fuzzy variable is:
Figure BDA0002123385210000031
Figure BDA0002123385210000032
Figure BDA0002123385210000033
wherein,
Figure BDA0002123385210000034
and
Figure BDA0002123385210000035
respectively carrying out wind power prediction error ambiguity, photovoltaic prediction error ambiguity and load prediction error ambiguity;
Figure BDA0002123385210000036
and
Figure BDA0002123385210000037
respectively obtaining a predicted value of the wind power in a t period, a predicted value of the photovoltaic power in the t period and a predicted value of the load power in the t period; k is a radical ofW,kPVAnd kLRespectively obtaining a wind power output prediction error membership parameter, a photovoltaic output prediction error membership parameter and a load prediction error membership parameter;
Figure BDA0002123385210000038
and
Figure BDA0002123385210000039
respectively representing the predicted value of the wind power in the t period, the predicted value of the photovoltaic power in the t period and the predicted value of the load power in the t period;
preferably, the objective function in the island microgrid energy model is as follows:
minC＝CDG+CESS
wherein C represents the total running cost of the island micro-grid system in the optimization period; cDGThe operating cost of the diesel generator; cESSThe operating cost of the energy storage power station;
preferably, the power balance constraint is:
Figure BDA00021233852100000310
wherein, PG(t) represents the output power of the diesel engine during time period t; pEg(t) represents the generated power of the energy storage power station in a time period t;
Figure BDA00021233852100000311
and
Figure BDA00021233852100000312
respectively representing the fuzzy expectation of wind power in a t period, the fuzzy expectation of photovoltaic power in the t period and the fuzzy expectation of load power in the t period; pEc(t) represents the charging power of the energy storage power station during a period t; pRL(t) represents the power of the transferable load during time t;
preferably, the positive and negative standby constraints are:
Figure BDA0002123385210000041
wherein, PG maxAnd PG minRespectively representing the upper output limit and the lower output limit of the diesel generator; pG upAnd PG downThe power of climbing upward and the power of climbing downward are respectively the unit time of the diesel generator; alpha is alphaupAnd alphadownThe confidence of the positive standby constraint and the confidence of the negative standby constraint are respectively set; u. ofEg(t) represents the discharge state of the energy storage power station, 1 represents discharge, and 0 represents no discharge; u. ofEc(t) represents the energy storage power station charging state, 1 represents charging, and 0 represents non-charging; cr(. h) represents the magnitude of the confidence; rup(t)、Rdown(t) is an intermediate variable;
preferably, the diesel generator operation constraints are:
Figure BDA0002123385210000042
wherein, PG maxAnd PG minRespectively representing the upper output limit and the lower output limit of the diesel generator; t ist onAnd Tt offRespectively representing the duration running time and the duration shutdown time of the diesel generator before the time period t; pG upAnd PG downThe power of climbing upward and the power of climbing downward are respectively the unit time of the diesel generator; pG(t) represents the output power of the diesel engine during time period t; pG(t-1) represents the output power of the diesel engine during a period t;
preferably, the energy storage plant SoC constraints are:
Figure BDA0002123385210000043
wherein SOC (t) represents the state of charge of the energy storage power station in a time period t; SOC (t +1) represents the state of charge of the energy storage power station in a period of t + 1; SOCmin、SOCmaxRespectively representing the minimum charge state and the maximum charge state of the energy storage power station in a time period t; deltaEThe self-discharge rate of the energy storage power station is obtained; qEThe total capacity of the energy storage power station; etacAnd ηgRespectively the charging and discharging efficiency of the energy storage power station; SOC (0) and SOC (T) are respectively an initial charge state and a final charge state of an optimization period of the island micro-grid.
Preferably, the method for dividing an optimization cycle into a plurality of time intervals in step (2) is as follows:
dividing the first hour of the optimization cycle into 12 5-minute interval periods; after 1 hour, dividing a time period by 1 hour at time intervals; wherein, one optimization cycle is 24 hours;
preferably, the confidence of the positive backup constraint and the confidence of the negative backup constraint are:
Figure BDA0002123385210000051
Figure BDA0002123385210000052
wherein alpha isupAnd alphadownThe confidence of the positive standby constraint and the confidence of the negative standby constraint are respectively set;
Figure BDA0002123385210000053
and
Figure BDA0002123385210000054
respectively obtaining a predicted value of the wind power in a t period, a predicted value of the photovoltaic power in the t period and a predicted value of the load power in the t period; k is a radical ofα1、kα1'、kα2、kα2' is a proportionality coefficient; alpha is alpha0Is a constant term.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts a mixed duration rolling optimization method of a sliding data window, an optimization cycle is divided into a plurality of time intervals, the length of each time interval is not fixed, for example, one optimization cycle is 24 hours a day, and the first hour of the optimization cycle is divided according to the length of the time interval of 5 minutes; dividing the second hour into one hour according to the time interval length, and fully utilizing the short-term and ultra-short-term to obtain the wind-solar predicted output, namely: the method comprises the steps of (1) predicting a wind power value, a photovoltaic power value and a load power value; the island micro-grid energy model is updated, the updated island micro-grid energy model is adopted to output optimization variables corresponding to all time periods after the current time period, the optimization variables comprise output power of a diesel engine, generating power of an energy storage power station, charging power of the energy storage power station and the like, so that the island micro-grid can still stably run when the weather suddenly changes or the load fluctuates, the running cost of a diesel engine set and the energy storage power station, which is caused by large prediction errors, is prevented from being increased, and the running cost of the island micro-grid is reduced in general terms.
2. The invention provides an adaptive adjustment method for confidence coefficient of a fuzzy opportunity constraint model, which updates the confidence coefficient of positive standby constraint and the confidence coefficient of negative standby constraint in different time periods, and updates an island micro-grid energy model by taking wind and light predicted output as an updating variable.
Drawings
FIG. 1 is a schematic diagram of an optimization method of an island microgrid energy model provided by the invention;
FIG. 2 is a schematic diagram of an island microgrid structure provided by the embodiment;
FIG. 3 is a mixed duration scrolling optimization time segment division diagram provided by an embodiment;
FIG. 4 is wind power, photovoltaic and load forecast data at time 8:00 according to an embodiment;
FIG. 5 illustrates confidence values at different time intervals according to an embodiment;
FIG. 6 is a diesel storage operation plan with fixed confidence provided by an embodiment;
FIG. 7 is a schematic diagram of an adaptive confidence diesel fuel storage and transportation plan provided by an embodiment;
FIG. 8 is a SOC curve of an energy storage power station provided by the embodiment;
fig. 9 is a curtailment renewable energy power curve provided by an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the present invention provides an optimization method for an island microgrid energy model, including:
(1) establishing an island microgrid energy model by taking the minimum total running cost of an island microgrid system in an optimization period as a target function based on a source load uncertainty model, a power balance constraint, a positive and negative standby constraint, a diesel generator running constraint and an energy storage power station SoC constraint of a triangular fuzzy variable;
(2) dividing the optimization cycle into a plurality of time intervals by taking the current time interval as a first time interval of the island micro-grid energy model optimization cycle, and acquiring the predicted values of wind-light predicted output and load power corresponding to each time interval;
the current time interval is the time interval of the current moment; the length of each time interval is not fixed;
(3) calculating the confidence coefficient of the positive backup constraint and the confidence coefficient of the negative backup constraint corresponding to each time period in the island micro-grid energy model according to the wind-solar predicted output and the 1-level load ratio of each time period;
(4) updating an island microgrid energy model according to the confidence coefficient of the positive standby constraint, the confidence coefficient of the negative standby constraint, the wind and light predicted output and the predicted value of the load power corresponding to each time period;
the updated island microgrid energy model is used for outputting optimization variables corresponding to each time period in an optimization period;
(5) if the current time enters the next time period of the optimization cycle, taking the next time period as the current time period of the updated island micro grid energy model, and repeating the steps (2) to (4);
the wind-solar predicted contribution comprises: the predicted value of the wind power and the predicted value of the photovoltaic power;
the optimization variables include: the output power of the diesel engine; the generated power of the energy storage power station; charging power of the energy storage power station.
(1) The source load uncertainty model of the triangular fuzzy variable is as follows:
Figure BDA0002123385210000071
Figure BDA0002123385210000072
Figure BDA0002123385210000073
wherein,
Figure BDA0002123385210000074
and
Figure BDA0002123385210000075
respectively carrying out wind power prediction error ambiguity, photovoltaic prediction error ambiguity and load prediction error ambiguity;
Figure BDA0002123385210000076
and
Figure BDA0002123385210000077
respectively obtaining a predicted value of the wind power in a t period, a predicted value of the photovoltaic power in the t period and a predicted value of the load power in the t period; k is a radical ofW,kPVAnd kLRespectively obtaining a wind power output prediction error membership parameter, a photovoltaic output prediction error membership parameter and a load prediction error membership parameter;
Figure BDA0002123385210000078
and
Figure BDA0002123385210000079
respectively representing the predicted value of the wind power in the t period, the predicted value of the photovoltaic power in the t period and the predicted value of the load power in the t period;
(2) the objective function in the island microgrid energy model is as follows:
minC＝CDG+CESS
wherein C represents the total running cost of the island micro-grid system in the optimization period; cDGThe operating cost of the diesel generator; cESSThe operating cost of the energy storage power station;
the invention does not take into account the running cost of wind power and photovoltaic and the flexible load scheduling cost. Wherein:
Figure BDA0002123385210000081
wherein, PG(t) represents the output power of the diesel engine during time period t;
Figure BDA0002123385210000082
representing the starting cost of the diesel engine; u. ofG(t) is a state variable which represents the starting and stopping state of the diesel engine in the time period t, if the state is the starting state, u is the starting stateG(t) 1, whereas uG(t) ═ 0; f (t) is the fuel cost of the diesel engine in the time period t, and a, b and c are the fuel cost coefficients of the diesel engine; k is a radical ofessA comprehensive cost coefficient for the operation of the energy storage power station; pEg(t) represents the generated power of the energy storage power station in a time period t;
(3) the constraint conditions of the island microgrid energy model are as follows:
a. and power balance constraint:
Figure BDA0002123385210000083
wherein, PG(t) represents the output power of the diesel engine during time period t; pEg(t) represents the generated power of the energy storage power station in a time period t;
Figure BDA0002123385210000084
and
Figure BDA0002123385210000085
respectively representing the fuzzy expectation of wind power in a t period, the fuzzy expectation of photovoltaic power in the t period and the fuzzy expectation of load power in the t period; pEc(t) represents the charging power of the energy storage power station during a period t; pRL(t) represents the power of the transferable load during time t;
b. the positive and negative standby constraints are:
Figure BDA0002123385210000091
wherein, PG maxAnd PG minRespectively representing the upper output limit and the lower output limit of the diesel generator; pG upAnd PG downRespectively the power of diesel generator climbing upward in unit timeAnd a downward hill climbing power; alpha is alphaupAnd alphadownThe confidence of the positive standby constraint and the confidence of the negative standby constraint are respectively set; u. ofEg(t) represents the discharge state of the energy storage power station, 1 represents discharge, and 0 represents no discharge; u. ofEc(t) represents the energy storage power station charging state, 1 represents charging, and 0 represents non-charging; cr(. h) represents the magnitude of the confidence; rup(t)、Rdown(t) is an intermediate variable;
c. and (3) operation constraint of the diesel generator:
Figure BDA0002123385210000092
wherein, PG maxAnd PG minRespectively representing the upper output limit and the lower output limit of the diesel generator; t ist onAnd Tt offRespectively representing the duration running time and the duration shutdown time of the diesel generator before the time period t; pG upAnd PG downThe power of climbing upward and the power of climbing downward are respectively the unit time of the diesel generator; pG(t) represents the output power of the diesel engine during time period t; pG(t-1) represents the output power of the diesel engine during a period t;
d. the SoC constraint of the energy storage power station is as follows:
Figure BDA0002123385210000093
wherein SOC (t) represents the state of charge of the energy storage power station in a time period t; SOC (t +1) represents the state of charge of the energy storage power station in a period of t + 1; SOCmin、SOCmaxRespectively representing the minimum charge state and the maximum charge state of the energy storage power station in a time period t; deltaEThe self-discharge rate of the energy storage power station is obtained; qEThe total capacity of the energy storage power station; etacAnd ηgRespectively the charging and discharging efficiency of the energy storage power station; SOC (0) and SOC (T) are respectively an initial charge state and a final charge state of an optimization period of the island micro-grid.
Example 1
Table 1 shows parameters that can be directly obtained and specific values used in the present embodiment;
TABLE 1
Figure BDA0002123385210000101
Fig. 2 is a schematic diagram of an islanded microgrid structure provided in an embodiment, which mainly includes a distributed power supply, a diesel generator, an energy storage device, a load, and the like. The distributed power supply mainly comprises a wind driven generator for photovoltaic power generation, the energy storage device is an energy storage power station comprising a storage battery, and the load consists of a flexible load and a rigid load;
fig. 3 is a schematic diagram of a hybrid duration rolling optimization method based on a sliding data window according to the present embodiment, in which an optimization cycle is divided into different time periods, prediction accuracy of wind-solar power and load increases with a decrease in time period scale, short-term prediction can provide prediction data with a time interval of 1 hour within 24 hours, but due to a large time period scale, a prediction error is relatively large; ultra-short term prediction can provide data at time intervals of 5 minutes or 15 minutes over 4 hours, with small time scale but relatively high accuracy. In order to fully utilize predicted data and reduce the influence of prediction errors on an optimization result, the embodiment provides a mixed duration rolling optimization method based on a sliding data window for the optimization of the islanding microgrid energy source, the optimization cycle is 24 hours, and the first hour of the optimization cycle is divided into 12 time intervals of 5 minutes according to the prediction scale and progress of short-term and ultra-short-term prediction; the time interval was 1 hour from the second small hour as one period, and 23 times. And updating the wind-solar predicted output, namely the predicted value of the wind power, the predicted value of the photovoltaic power and the predicted value of the load power, of the island micro-grid energy model once in each time period, and sliding the optimization data window for 1 hour backwards to keep the optimization duration at 24 hours.
It is worth noting that: because the translatable load has the characteristic of being uninterrupted after starting, the translatable load does not participate in rolling optimization, and flexible load data is updated and optimized once only in the optimization initial time period every day; the interruptible load can be interrupted and delayed after being started, so that the mixed duration rolling optimization can be participated, but in order to avoid that the interruptible load on the current day can not enter the next optimization day, the optimization time window of the interruptible load can not slide backwards to the next day. The rolling optimization based on the sliding data window is the optimization of a limited time domain, the dependence on accurate prediction of power supply power during distribution can be greatly reduced, and the capability of coping with emergencies such as weather change and the like is improved.
The confidence coefficient of fuzzy opportunity constraint in the island microgrid energy model at the current time interval is updated according to the system states at different time intervals, and the coordination relation between robustness and economy in the whole optimization cycle of the microgrid system can be effectively guaranteed. When the prediction error percentage is the same, the larger the wind and light prediction output is, the larger the absolute value of the error is, the larger the influence on the load is, and therefore, the required confidence coefficient is higher. Higher confidence is also required when class 1 of loads are more heavily loaded. Accordingly, the confidence of the positive standby constraint and the confidence of the negative standby constraint are:
Figure BDA0002123385210000111
Figure BDA0002123385210000112
wherein alpha isupAnd alphadownThe confidence of the positive standby constraint and the confidence of the negative standby constraint are respectively set;
Figure BDA0002123385210000113
and
Figure BDA0002123385210000114
respectively obtaining a predicted value of the wind power in a t period, a predicted value of the photovoltaic power in the t period and a predicted value of the load power in the t period; k is a radical ofα1、kα1'、kα2、kα2' is a proportionality coefficient; alpha is alpha0Is a constant term.
In general, in this embodiment, a hybrid duration rolling optimization method of a sliding data window is adopted, an optimization cycle is divided into a plurality of time periods, the lengths of the time periods are not fixed, and short-term and ultra-short-term predicted wind and light output is obtained by fully utilizing the following steps: the method comprises the steps of (1) predicting a wind power value, a photovoltaic power value and a load power value; the island micro-grid energy model is updated, the updated island micro-grid energy model is adopted to output optimization variables corresponding to all time periods after the current time period, the optimization variables comprise output power of a diesel engine, generating power of an energy storage power station, charging power of the energy storage power station and the like, so that the island micro-grid can still stably run when the weather suddenly changes or the load fluctuates, the running cost of a diesel engine set and the energy storage power station, which is caused by large prediction errors, is prevented from being increased, and the running cost of the island micro-grid is reduced in general terms.
The invention provides an adaptive adjustment method for confidence coefficient of a fuzzy opportunity constraint model, which updates the confidence coefficient of positive standby constraint and the confidence coefficient of negative standby constraint in different time periods, and updates an island micro-grid energy model by taking wind and light predicted output as an updating variable.
The present embodiment sets the following scenarios to analyze the effectiveness of the present invention:
the current time is 8 points, the island micro-grid performs one-time mixing duration rolling optimization, the wind and light predicted output, namely the predicted value of wind power, the predicted value of photovoltaic power and the predicted value of load power are shown in fig. 4, and the optimization result of the system is shown in table 2:
TABLE 2
Confidence level Operating costs
Fixing 2575 yuan
Adaptive modulation 2389A Chinese medicinal composition
And respectively carrying out rolling optimization on the fixed confidence coefficient and the self-adaptive confidence coefficient on the islanding micro-grid system at the moment of 8:00, wherein the confidence coefficient value is shown in figure 5, and the daily operating cost is reduced by 186 yuan after the self-adaptive confidence coefficient optimization method is adopted. Fig. 6 and 7 are output optimization results of the diesel engine and the energy storage power station at a fixed confidence level and an adaptive confidence level, respectively, and it can be seen that in order to meet the requirement of the confidence level, the isolated island microgrid system needs to be added with a standby mode, and therefore the starting time of the diesel engine set is increased. Fig. 8 shows the SOC variation of the energy storage station with fixed confidence and adaptive confidence, and the adaptive confidence reduces the number of transitions of the charging and discharging conditions of the energy storage station, which is beneficial to prolonging the service life of the energy storage station. Fig. 9 is a wind curtailment and optical power curve of a fixed confidence level and an adaptive confidence level, the wind curtailment and optical power are 105.06kW when the confidence level is fixed, and the wind curtailment and optical power are reduced to 31.85kW when the adaptive confidence level is adopted, so that the utilization rate of renewable energy is increased.
The verification results of the scenes show that the method disclosed by the invention can effectively coordinate the robustness and economy of system operation, and can effectively and fully utilize the short-term and ultra-short-term source-load prediction data, wherein the prediction data are wind-light prediction output (a prediction value of wind power, a prediction value of photovoltaic power and a prediction value of load power), so that the influence caused by prediction errors is reduced.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. An optimization method for an island micro-grid energy model is characterized by comprising the following steps:
(1) establishing an island microgrid energy model by taking the minimum total running cost of an island microgrid system in an optimization period as a target function based on a source load uncertainty model, a power balance constraint, a positive and negative standby constraint, a diesel generator running constraint and an energy storage power station SoC constraint of a triangular fuzzy variable;
(2) dividing the optimization cycle into a plurality of time intervals by taking the current time interval as a first time interval of the island micro-grid energy model optimization cycle, and acquiring the predicted values of wind-light predicted output and load power corresponding to each time interval;
the current time interval is the time interval of the current moment; the length of each time interval is not fixed;
(3) calculating the confidence coefficient of the positive backup constraint and the confidence coefficient of the negative backup constraint corresponding to each time period in the island micro-grid energy model according to the wind-solar predicted output and the 1-level load ratio of each time period;
(4) updating an island microgrid energy model according to the confidence coefficient of the positive standby constraint, the confidence coefficient of the negative standby constraint, the wind and light predicted output and the predicted value of the load power corresponding to each time period;
the updated island microgrid energy model is used for outputting optimization variables corresponding to each time period in an optimization period;
(5) if the current time enters the next time period of the optimization cycle, taking the next time period as the current time period of the updated island micro grid energy model, and repeating the steps (2) to (4);
the wind-solar predicted contribution comprises: the predicted value of the wind power and the predicted value of the photovoltaic power;
the optimization variables include: the output power of the diesel engine; the generated power of the energy storage power station; charging power of the energy storage power station.
2. The optimization method according to claim 1, wherein the step (2) of dividing an optimization cycle into a plurality of time segments comprises:
dividing the first hour of the optimization cycle by a period length of 5 minutes; dividing the second hour into one hour according to the time interval length;
the optimization period is 24 hours.
3. The optimization method according to claim 1, wherein the confidence of the positive back-up constraint and the confidence of the negative back-up constraint are:
Figure FDA0002782850950000021
Figure FDA0002782850950000022
wherein alpha isupAnd alphadownThe confidence of the positive standby constraint and the confidence of the negative standby constraint are respectively set;
Figure FDA0002782850950000023
and
Figure FDA0002782850950000024
respectively obtaining a predicted value of the wind power in a t period, a predicted value of the photovoltaic power in the t period and a predicted value of the load power in the t period; k is a radical ofα1、kα1'、kα2、kα2' is a proportionality coefficient; alpha is alpha0Is a constant term.
4. The optimization method according to claim 1, wherein the source-to-load uncertainty model of the triangular fuzzy variable is:
Figure FDA0002782850950000025
Figure FDA0002782850950000026
Figure FDA0002782850950000027
wherein,
Figure FDA0002782850950000028
and
Figure FDA0002782850950000029
respectively carrying out wind power prediction error ambiguity, photovoltaic prediction error ambiguity and load prediction error ambiguity;
Figure FDA00027828509500000210
and
Figure FDA00027828509500000211
respectively obtaining a predicted value of the wind power in a t period, a predicted value of the photovoltaic power in the t period and a predicted value of the load power in the t period; k is a radical ofW,kPVAnd kLRespectively obtaining a wind power output prediction error membership parameter, a photovoltaic output prediction error membership parameter and a load prediction error membership parameter;
Figure FDA00027828509500000212
and
Figure FDA00027828509500000213
the fuzzy representation of the predicted value of the wind power in the t period, the fuzzy representation of the predicted value of the photovoltaic power in the t period and the fuzzy representation of the predicted value of the load power in the t period are respectively.
5. An optimization method according to any one of claims 1 to 4, wherein the objective function in the island microgrid energy model is as follows:
min C＝CDG+CESS
wherein C represents the total running cost of the island micro-grid system in the optimization period; cDGThe operating cost of the diesel generator; cESSWhich is the operating cost of the energy storage power station.
6. The optimization method of claim 4, wherein the power balance constraint is:
Figure FDA0002782850950000031
wherein, PG(t) represents the output power of the diesel engine during time period t; pEg(t) represents the generated power of the energy storage power station in a time period t;
Figure FDA0002782850950000032
and
Figure FDA0002782850950000033
respectively representing the fuzzy expectation of wind power in a t period, the fuzzy expectation of photovoltaic power in the t period and the fuzzy expectation of load power in the t period; pEc(t) represents the charging power of the energy storage power station during a period t; pRL(t) represents the power of the transferable load during time t.
7. The optimization method of claim 6, wherein the positive and negative backup constraints are:
Figure FDA0002782850950000034
wherein, PG maxAnd PG minRespectively representing the upper output limit and the lower output limit of the diesel generator; pG upAnd PG downThe power of climbing upward and the power of climbing downward are respectively the unit time of the diesel generator; alpha is alphaupAnd alphadownThe confidence of the positive standby constraint and the confidence of the negative standby constraint are respectively set; u. ofEg(t) represents the discharge state of the energy storage power station, 1 represents discharge, and 0 represents no discharge; u. ofEc(t) represents the energy storage power station charging state, 1 represents charging, and 0 represents non-charging; cr(. h) represents the magnitude of the confidence; rup(t)、Rdown(t) is an intermediate variable.
8. The optimization method according to claim 6 or 7, wherein the diesel generator operation constraints are:
Figure FDA0002782850950000041
wherein, PG maxAnd PG minRespectively representing the upper output limit and the lower output limit of the diesel generator; t ist onAnd Tt offRespectively representing the duration running time and the duration shutdown time of the diesel generator before the time period t; pG upAnd PG downThe power of climbing upward and the power of climbing downward are respectively the unit time of the diesel generator; pG(t) represents the output power of the diesel engine during time period t; pG(t-1) represents the output power of the diesel engine during the period t-1.
9. The optimization method according to claim 6 or 7, wherein the energy storage utility SoC constraints are:
Figure FDA0002782850950000042
wherein SOC (t) represents the state of charge of the energy storage power station in a time period t; SOC (t +1) represents the state of charge of the energy storage power station in a period of t + 1; SOCmin、SOCmaxRespectively representing the minimum charge state and the maximum charge state of the energy storage power station in a time period t; deltaEThe self-discharge rate of the energy storage power station is obtained; qEThe total capacity of the energy storage power station;ηcand ηgRespectively the charging and discharging efficiency of the energy storage power station; SOC (0) and SOC (T) are respectively an initial charge state and a final charge state of an optimization period of the island micro-grid.
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