CN110932312A - Reliability evaluation method for wind-solar storage micro-grid system - Google Patents
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Abstract
The disclosure relates to a reliability evaluation method for a wind-solar storage micro-grid system, which comprises the following steps: establishing mathematical models of a wind power generation system, a photovoltaic power generation system and an energy storage system of the wind-solar energy storage micro-grid system; establishing a reliability evaluation index; estimating WTG capacity and PV capacity, ESS power and PV capacity based on reliability estimation indexes by adopting an acquisition enumeration method; and integrating the reliability evaluation result to obtain a final reliability evaluation result. According to the method, the capacity configuration scheme of the wind-solar storage micro-grid system is obtained by establishing and implementing the reliability evaluation scheme of the wind-solar storage micro-grid system, and the operation stability of the wind-solar storage micro-grid system is improved.
Description
Technical Field
The disclosure relates to the field of electricity, in particular to a reliability evaluation method for a wind-solar energy storage micro-grid system.
Background
The method has the advantages that the breadth of our country is broad, the wind energy and solar energy resources are abundant, if the resources can be fully utilized, the power generation pressure of the domestic power system can be relieved well, and new vitality can be injected for the development of regional economy. However, wind power generation and photovoltaic power generation are easily affected by the environment, and the generated power has strong randomness and volatility. If the direct access is in the electric wire netting, can produce the impact to the safe and stable operation of electric wire netting. In order to smooth the fluctuation of the output of the wind-light system, an energy storage system with a certain capacity is usually matched to establish a wind-light storage micro-grid system.
The capacity and power configuration of a wind power generation system, a photovoltaic power generation system and an energy storage system in the wind-solar energy storage micro-grid system can affect the stability of the wind-solar energy storage micro-grid system, and at present, no complete reliability evaluation system is used for carrying out reliability evaluation on the wind-solar energy storage micro-grid system to obtain a configuration scheme of the wind-solar energy storage micro-grid system.
Accordingly, there is a need for one or more methods to address the above-mentioned problems.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a method, an apparatus, an electronic device, and a computer-readable storage medium for reliability evaluation of a wind-solar-storage micro-grid system, thereby overcoming, at least to some extent, one or more of the problems due to the limitations and disadvantages of the related art.
According to one aspect of the disclosure, a method for evaluating reliability of a wind-solar storage micro-grid system is provided, which includes:
the method comprises the steps of establishing a model, namely establishing a mathematical model of a wind power generation system WTG of the wind-solar energy storage micro-grid system, a mathematical model of a photovoltaic power generation system PV and a mathematical model of an energy storage system ESS;
an evaluation index determining step, namely establishing a reliability evaluation index, and determining the reliability index as follows according to mathematical models of a wind power generation system, a photovoltaic power generation system and an energy storage system in the wind-solar energy storage micro grid system: the system comprises a load loss probability LOLP, a user average power failure duration index CAIDI, a system average power failure frequency index SAIFI, a system average power failure duration index SAIDI and an average power supply availability index ASAI;
an acquisition enumeration evaluation step, wherein according to the reliability index, a WTG capacity and PV capacity, an ESS capacity and PV capacity and an ESS power and PV capacity are evaluated based on the reliability evaluation index by adopting an acquisition enumeration method, the load loss probability LOLP, a user average power failure duration index CAIDI, a system average power failure frequency index SAIFI, a system average power failure duration index SAIDI and an average power supply availability index ASAI are observed along with the minimum values of the change of the WTG capacity and PV capacity, the ESS capacity and PV capacity and the ESS power and PV capacity, and the evaluation results of the change of the WTG capacity and PV capacity, the ESS capacity and PV capacity and the ESS power and PV capacity on the system reliability are determined respectively;
and an evaluation index integration step, integrating the evaluation results of WTG capacity and PV capacity, ESS power and PV capacity change on system reliability to obtain the final reliability evaluation result.
In an exemplary embodiment of the present disclosure, the model building step further includes:
establishing a mathematical model of a WTG (wind turbine generator system), wherein the relation between the output power of a wind turbine generator and the wind speed meets the following preset conditions:
wherein, A, B, C parameters are:
in the formula: vciFor cutting into wind speed, VcoFor cutting out wind speed, VrAt rated wind speed, PrIs rated power;
building a PV mathematical model of a photovoltaic power generation system, wherein the photovoltaic instantaneous power is as follows:
PV(t)＝Beta(α,β)PVmax
wherein α, β are shape parameters of Beta distribution, PVmaxIs the maximum output power of the photovoltaic power generation system;
establishing an Energy Storage System (ESS) mathematical model, wherein the energy storage system comprises a lead-acid storage battery and a super capacitor, and the charge and discharge state equation of the lead-acid storage battery is as follows:
wherein SOC (t +1) and SOC (t) are respectively the residual capacity of the battery at t +1 and t, and pch (t) and Pdch (t) are the charging and discharging power of the battery at t, ηch、ηdchRespectively charge and discharge efficiency, and delta t is interval time; the capacity limit range set by the energy storage capacity is as follows:
SOCmin≤SOC(t+1)≤SOCmax。
in an exemplary embodiment of the present disclosure, the collecting enumeration evaluating step further includes:
when reliability evaluation is carried out on the WTG capacity and the PV capacity, the PV capacity is increased from 0MW to 2MW when the WTG capacity is set to be 1.3MW, 1.4MW and 1.5MW respectively, and reliability evaluation index values are calculated and evaluated;
during reliability evaluation of ESS capacity and PV capacity, setting the WTG capacity to be 1.5MW, increasing the PV capacity from 0.4MW to 1.6MW, increasing the ESS capacity from 0.2MW & h to 1.4MW & h, calculating a reliability evaluation index value and evaluating;
and when reliability of the ESS power and the PV capacity is evaluated, setting the WTG capacity to be 1.5MW, increasing the PV capacity from 0.4MW to 1.6MW, increasing the ESS power from 0MW to 0.6MW, calculating a reliability evaluation index value and evaluating.
In an exemplary embodiment of the present disclosure, the evaluation index synthesizing step further includes:
and respectively obtaining the influence of the WTG capacity, the PV capacity, the ESS power and the capacity on the system reliability according to the estimation results of the WTG capacity and the PV capacity, the ESS capacity and the PV capacity and the ESS power and the PV capacity, and obtaining an analysis result of the variation trend according to the influence.
The reliability evaluation method of the wind-solar energy storage micro-grid system in the exemplary embodiment of the disclosure establishes mathematical models of a wind power generation system, a photovoltaic power generation system and an energy storage system of the wind-solar energy storage micro-grid system; establishing a reliability evaluation index; estimating WTG capacity and PV capacity, ESS power and PV capacity based on reliability estimation indexes by adopting an acquisition enumeration method; and integrating the reliability evaluation result to obtain a final reliability evaluation result. According to the method, the capacity configuration scheme of the wind-solar storage micro-grid system is obtained by establishing and implementing the reliability evaluation scheme of the wind-solar storage micro-grid system, and the operation stability of the wind-solar storage micro-grid system is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 shows a flowchart of a wind-solar-storage micro-grid system reliability evaluation method according to an exemplary embodiment of the present disclosure;
FIG. 2 illustrates an improved IEEE RBTS BUS6 main feeder F4 system of a wind-solar-storage microgrid system reliability assessment method according to an exemplary embodiment of the present disclosure;
FIG. 3 shows a wind turbine power output characteristic curve of a wind power generation set reliability evaluation method of a wind-solar energy storage micro-grid system according to an exemplary embodiment of the disclosure;
fig. 4 shows a photovoltaic 48h output power variation curve with a photovoltaic capacity of 1.6MW of the wind-solar energy storage micro-grid system reliability evaluation method according to an exemplary embodiment of the disclosure;
5A-5E illustrate reliability assessment index maps for different wind turbine and photovoltaic capacity configurations of a wind-solar storage microgrid system reliability assessment method according to an exemplary embodiment of the present disclosure;
6A-6C illustrate reliability assessment index maps for different photovoltaic and energy storage capacity configurations of a wind-solar energy storage microgrid system reliability assessment method according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other methods, components, materials, devices, steps, and so forth. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in the form of software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.
In the embodiment of the example, firstly, a reliability evaluation method for a wind-solar storage micro-grid system is provided; referring to fig. 1, the wind-solar-storage micro-grid system reliability evaluation method may include the following steps:
a model establishing step S110, establishing a mathematical model of a wind power generation system WTG of the wind-solar energy storage micro-grid system, a mathematical model of a photovoltaic power generation system PV and a mathematical model of an energy storage system ESS;
an evaluation index determining step S120 of establishing a reliability evaluation index, and determining the reliability index as follows according to mathematical models of a wind power generation system, a photovoltaic power generation system and an energy storage system in the wind-solar energy storage micro grid system: the system comprises a load loss probability LOLP, a user average power failure duration index CAIDI, a system average power failure frequency index SAIFI, a system average power failure duration index SAIDI and an average power supply availability index ASAI;
a collection enumeration evaluation step S130, according to the reliability index, adopting a collection enumeration method to respectively evaluate the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity based on the reliability evaluation index, observing the load loss probability lopp, the user average power outage duration index CAIDI, the system average power outage frequency index SAIFI, the system average power outage duration index SAIDI, and the minimum value of the average power supply availability index ASAI when changing with the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity, and respectively determining evaluation results of the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity on the system reliability;
an evaluation index integration step S140 integrates the evaluation results of the WTG capacity and PV capacity, ESS capacity and PV capacity, and ESS power and PV capacity variation on the system reliability to obtain a final reliability evaluation result.
The reliability evaluation method of the wind-solar energy storage micro-grid system in the exemplary embodiment of the disclosure establishes mathematical models of a wind power generation system, a photovoltaic power generation system and an energy storage system of the wind-solar energy storage micro-grid system; establishing a reliability evaluation index; estimating WTG capacity and PV capacity, ESS power and PV capacity based on reliability estimation indexes by adopting an acquisition enumeration method; and integrating the reliability evaluation result to obtain a final reliability evaluation result. According to the method, the capacity configuration scheme of the wind-solar storage micro-grid system is obtained by establishing and implementing the reliability evaluation scheme of the wind-solar storage micro-grid system, and the operation stability of the wind-solar storage micro-grid system is improved.
Next, the reliability evaluation method of the wind-solar-storage micro-grid system in the present exemplary embodiment will be further described.
In the model building step S110, a mathematical model of the wind power generation system WTG of the wind-solar-storage micro-grid system, a mathematical model of the photovoltaic power generation system PV, and a mathematical model of the energy storage system ESS may be built.
In an embodiment of the present example, the model building step further comprises:
establishing a mathematical model of a WTG (wind turbine generator system), wherein the relation between the output power of a wind turbine generator and the wind speed meets the following preset conditions:
wherein, A, B, C parameters are:
in the formula: vci is cut-in wind speed, Vco is cut-out wind speed, Vr is rated wind speed, and Pr is rated power;
building a PV mathematical model of a photovoltaic power generation system, wherein the photovoltaic instantaneous power is as follows:
PV(t)＝Beta(α,β)PVmax
α and β are shape parameters of Beta distribution, and PVmax is the maximum output power of the photovoltaic power generation system;
establishing an Energy Storage System (ESS) mathematical model, wherein the energy storage system comprises a lead-acid storage battery and a super capacitor, and the charge and discharge state equation of the lead-acid storage battery is as follows:
wherein SOC (t +1) and SOC (t) are respectively the residual capacity of the battery at t +1 and t, pch (t), Pdch (t) are the charging and discharging power of the battery at t, η ch and η dch are respectively the charging and discharging efficiency, delta t is the interval time, and the capacity limit range set by the energy storage capacity is as follows:
SOCmin≤SOC(t+1)≤SOCmax。
in the embodiment of the present example, as shown in fig. 2, in order to implement a wind/light/storage microgrid system, the blades of the wind turbine in the wind turbine model rotate to drive the generator to convert wind energy into electric energy, and the power generated by the wind turbine is determined by the installed capacity and the wind speed. Fig. 3 shows a wind turbine power output characteristic curve. When the actual wind speed is less than the cut-in wind speed Vci or greater than the cut-out wind speed Vco, the output of the wind turbine generator is 0; when the wind speed is between the cut-in wind speed and the rated wind speed Vr, the output of the wind turbine generator is increased; when the wind speed exceeds the rated wind speed, the output of the wind turbine generator is maintained at the rated power Pr output. The relationship between the output power of the wind turbine generator and the wind speed is as follows:
wherein A, B, C is a parameter, and specifically comprises:
in the formula: vci is cut-in wind speed, Vco is cut-out wind speed, Vr is rated wind speed, and Pr is rated power.
The photovoltaic power generation system is influenced by the ambient temperature and the illumination intensity, but the illumination intensity within a certain time approximately follows Beta distribution, as shown in fig. 4, which is a 48h output power change curve of the photovoltaic system, specifically:
wherein α, β are shape parameters of Beta distribution.
The photovoltaic output instantaneous power should satisfy:
PV(t)＝Beta(α,β)PVmax
in the formula: PVmax is the maximum output power of the photovoltaic power generation system.
Wherein, energy storage system includes: an energy storage system with a lead-acid storage battery and a Super Capacitor (SC) as energy storage units is established, wherein the lead-acid storage battery has the advantages of large capacity, low price, deep charging and discharging and the like, and is the most widely applied energy storage equipment of the existing micro-grid. The charging and discharging states of the lead-acid storage battery meet the following conditions:
in the formula, SOC (t +1) and SOC (t) are respectively the residual capacity of the battery at the time t +1 and the time t, pch (t) and Pdch (t) are the charging and discharging power of the battery at the time t, η ch and η dch are respectively the charging and discharging efficiency, delta t is an hour time interval, and the energy storage capacity at a certain time is within a set capacity limit range, specifically:
SOCmin≤SOC(t+1)≤SOCmax。
in the evaluation index determining step S120, a reliability evaluation index may be established, and according to the mathematical models of the wind power generation system, the photovoltaic power generation system, and the energy storage system in the wind-solar energy storage microgrid system, the reliability index is determined as follows: the system comprises a load loss probability LOLP, a user average power failure duration index CAIDI, a system average power failure frequency index SAIFI, a system average power failure duration index SAIDI and an average power supply availability index ASAI.
In the embodiment of the example, the microgrid reliability evaluation method is provided, 5 reliability evaluation indexes are introduced, a reliability index system is established, and the reliability level of the microgrid is reflected. The specific reliability indexes of the micro-grid comprise: load Loss Probability (LOSS of Load Probability, LOLP), user Average power outage Duration Index (CAIDI), System Average power outage Frequency Index (SAIFI), System Average power outage Duration Index (SAIDI), and Average power supply availability Index (ASAI).
In the collection enumeration evaluating step S130, the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity may be evaluated based on the reliability evaluation index by using a collection enumeration method according to the reliability index, and the minimum values of the loss probability low, the user average power outage duration index CAIDI, the system average power outage frequency index SAIFI, the system average power outage duration index SAIDI, and the average power supply availability index ASAI varying with the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity may be observed to determine the evaluation results of the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity variation on the system reliability.
In this exemplary embodiment, the collecting enumeration evaluating step further includes:
when reliability evaluation is carried out on the WTG capacity and the PV capacity, the PV capacity is increased from 0MW to 2MW when the WTG capacity is set to be 1.3MW, 1.4MW and 1.5MW respectively, and reliability evaluation index values are calculated and evaluated;
during reliability evaluation of ESS capacity and PV capacity, setting the WTG capacity to be 1.5MW, increasing the PV capacity from 0.4MW to 1.6MW, increasing the ESS capacity from 0.2MW & h to 1.4MW & h, calculating a reliability evaluation index value and evaluating;
and when reliability of the ESS power and the PV capacity is evaluated, setting the WTG capacity to be 1.5MW, increasing the PV capacity from 0.4MW to 1.6MW, increasing the ESS power from 0MW to 0.6MW, calculating a reliability evaluation index value and evaluating.
In the example of the present example, the experimental parameters: an improved IEEE RBTS BUS6 main feeder F4 part is used as an example system, and experimental parameters are as follows: rated power of WTG: 1.5 MW; rated power of PV: 2 MW; ESS energy storage capacity: 2MW · h, maximum charge-discharge power: 0.4MW (20% ESS).
Experiment one: influence of micro-grid on system reliability
The reliability index of the micro-grid can be obtained from the reliability index of the load point, and the calculation result is as follows: does not contain the microgrid system SAIFI: 1231. SAIDI: 2511. ASAI: 0.8432, respectively; the method comprises the following steps: SAIFI: 894. SAIDI: 2423. ASAI: 0.9241. the calculation result shows that: the distributed power supply has great influence on the reliability of the load point of the microgrid, and compared with the reliability indexes of the system before and after the microgrid is added, the reliability index of the system under the condition of the microgrid is obviously superior to the reliability index under the condition of no microgrid, so that the power supply reliability of the system can be effectively improved by proper access of the microgrid.
Experiment two: effect of PV and WTG capacity allocation on microgrid reliability
The invention researches the influence of the capacity configuration of PV and WTG on the reliability of a microgrid. When the WTG capacity is 1.3MW, 1.4MW and 1.5MW respectively, the PV capacity is increased from 0MW to 2MW, and the variation of each reliability index is shown in FIGS. 5A-5E.
The probability indexes (such as LOLP, SAIDI, ASAI) can be studied to see that: first, increasing PV capacity can increase the reliability level of the microgrid, but the effect of the increase is limited by the WTG capacity. Taking LOLP as an example:
1) when the WTG capacity is 1.3MW, the PV capacity is increased to 2MW from 0MW, the LOLP is reduced to 0.229 from 0.435, and the LOLP is reduced by 47.35%.
2) After the WTG capacity is increased, the contribution of the PV capacity to the reliability index of the microgrid is reduced, for example, when the PV capacity is 0.6MW and the WTG capacity is increased from 1.3MW to 1.4MW, the LOLP is reduced from 0.242 to 0.16 and reduced by 33.88%; when the WTG capacity is increased from 1.4MW to 1.5MW, the LOLP is reduced from 0.16 to 0.0292, and the LOLP is reduced by 81.75%. It can be seen that there is a reliability knee value P WTG (1.4MW) for the WTG capacity. When the WTG capacity is larger than P WTG, the reliability of the microgrid is mainly determined by the WTG; when the WTG capacity is less than P WTG, reliability is greatly affected by PV capacity.
Second, similar to the reliability knee of WTG capacity, PV capacity also has a reliability knee P PV (0.6 MW). When the PV capacity is lower than P × PV, the reliability index has a higher sensitivity to the PV capacity. Taking the LOLP indicator for a WTG capacity of 1.3MW as an example, when the PV capacity is increased from 0MW to 0.6MW, the LOLP is reduced from 0.435 to 0.242, and is reduced by 44.38%. Whereas when the PV capacity is higher than P × PV, the sensitivity decreases significantly, e.g. when the PV capacity increases from 0.6MW to 2MW, the lop decreases from 0.242 to 0.229 and 5.37% of the lop decreases. It can be seen that at a certain WTG capacity, there is an optimal configuration value for PV capacity.
The frequency and duration indexes (such as CAIDI and SAIFI) are studied, and the following can be seen: first, increasing WTG capacity can improve the frequency and duration metrics of the microgrid system as a whole. When the WTG capacity is fixed and the PV capacity is increased, the CAIDI and SAIFI change in a non-monotonicity trend, and the main reason is that the frequency and duration indexes are not only related to the real-time matching degree of a power supply and a load, but also influenced by energy balance and are closely related to energy storage configuration, so that the invention further researches the capacity configuration scheme of the PV and the ESS and the influence of the capacity configuration scheme on the reliability.
Experiment three: impact of PV and ESS capacity configuration on microgrid reliability
The comprehensive capacity configuration scheme of the PV and the ESS in the optical storage micro-grid system has a remarkable influence on the reliability of the micro-grid. Under the condition that the WTG capacity is 1.5MW, the PV capacity is increased from 0.4MW to 1.6MW, the ESS capacity is increased from 0.2MW & h to 1.4MW & h, and the change condition of each reliability index is calculated. Taking SAIDI as an example, SAIDI under different PV and ESS capacity configurations is obtained as shown in FIG. 6A.
1) ESS capacity is 0.2MW · h, when PV capacity is increased from 0.4MW to 1.6MW, SAIDI is reduced from 971hr/customer yr to 812hr/customer yr, which is reduced by 16.37%; when the ESS capacity is 0.4MW & h, the PV capacity is increased from 0.4MW to 1.6MW, SAIDI is reduced from 843hr/customer & yr to 638hr/customer & yr, and the reduction amplitude is up to 24.31%. The above data indicate that the contribution of PV capacity to reliability of the microgrid is closely related to ESS capacity, and insufficient ESS capacity may limit the effect of PV capacity on reliability improvement.
2) When the PV capacity is 0.4MW and the ESS capacity is 0.2MW & h, the ESS capacity is increased to 1.4MW & h, and SAIDI is reduced from 971hr/customer & yr to 279hr/customer & yr by 71.27%. The above data demonstrate that the reliability benefits generated by increasing the ESS capacity configuration alone are significantly better than measures to increase PV capacity when ESS capacity is insufficient, further demonstrating the necessity to configure a full capacity ESS in an optical storage microgrid system.
Experiment four: the effects of PV capacity and ESS power configuration on microgrid reliability.
The charging and discharging power is an important technical indicator of the ESS. The invention considers that the charging power and the discharging power are equal, and researches the influence of the PV capacity and the power configuration of the ESS on the reliability of the microgrid. Similarly, this section still assumes that the WTG capacity is 1.5MW, the PV capacity is planned to range from 0.4MW to 1.6MW, the ESS power is increased from 0MW to 0.6MW, and the variation of each reliability index is calculated. Taking SAIDI and ASAI as examples, SAIDI and ASAI under different PV and ESS capacity configurations are obtained as shown in FIGS. 6B-6C.
Analyzing the data in FIGS. 6B-6C, it can be seen that when the PV capacity is 1.2MW and the ESS power is increased from 0MW to 0.6MW, the SAIDI is decreased from 949hr/customer yr to 547hr/customer yr, which is a 42.36% decrease; the ASAI is increased from 0.892 to 0.938 by 5.16%. When the PV capacity is 1.4MW, the ESS power is also increased from 0MW to 0.6MW, SAIDI is reduced from 905hr/customer yr to 536hr/customer yr, and is reduced by 40.77%; the ASAI is increased from 0.897 to 0.939, and the ASAI is improved by 4.68 percent. The above data indicate that ESS power has a boost effect on the PV reliability contribution, and that the boost effect increases accordingly as the PV capacity increases.
In the evaluation index integration step S140, the evaluation results of the WTG capacity and PV capacity, ESS capacity and PV capacity, and ESS power and PV capacity variation on the system reliability may be integrated to obtain the final reliability evaluation result.
In an embodiment of the present example, the evaluation index integrating step further includes:
and respectively obtaining the influence of the WTG capacity, the PV capacity, the ESS power and the capacity on the system reliability according to the estimation results of the WTG capacity and the PV capacity, the ESS capacity and the PV capacity and the ESS power and the PV capacity, and obtaining an analysis result of the variation trend according to the influence.
In the embodiment of the present example, the analysis result of the variation trend is derived from the influence:
1) compared with the reliability indexes of the system before and after the micro-grid is added, the reliability index of the system under the condition with the micro-grid is obviously superior to the reliability index under the condition without the micro-grid, and the fact that the power supply reliability of the system can be effectively improved through the proper access of the micro-grid is shown.
2) Increasing the WTG capacity can improve the frequency and duration metrics of the microgrid system as a whole. The reliability level of the micro-grid system can be improved by increasing the PV capacity, the probability index can be continuously improved, but the improvement effect is limited by the WTG capacity; the frequency and duration indexes are influenced by the real-time matching degree of the power supply and the load, the energy balance and the energy storage configuration, and the change trend of the frequency and duration indexes is non-monotonous.
3) The contribution of PV capacity to reliability of the microgrid is closely related to ESS capacity, and insufficient ESS capacity limits the reliability improvement effect of PV capacity. Significant reliability gains can be generated by increasing the ESS capacity configuration when the ESS capacity is insufficient.
4) ESS power has a boost effect on the PV reliability contribution, and the boost effect increases accordingly as PV capacity increases.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (4)
1. A reliability evaluation method for a wind-solar storage micro-grid system is characterized by comprising the following steps:
the method comprises the steps of establishing a model, namely establishing a mathematical model of a wind power generation system WTG of the wind-solar energy storage micro-grid system, a mathematical model of a photovoltaic power generation system PV and a mathematical model of an energy storage system ESS;
an evaluation index determining step, namely establishing a reliability evaluation index, and determining the reliability index as follows according to mathematical models of a wind power generation system, a photovoltaic power generation system and an energy storage system in the wind-solar energy storage micro grid system: the system comprises a load loss probability LOLP, a user average power failure duration index CAIDI, a system average power failure frequency index SAIFI, a system average power failure duration index SAIDI and an average power supply availability index ASAI;
an acquisition enumeration evaluation step, wherein according to the reliability index, a WTG capacity and PV capacity, an ESS capacity and PV capacity and an ESS power and PV capacity are evaluated based on the reliability evaluation index by adopting an acquisition enumeration method, the load loss probability LOLP, a user average power failure duration index CAIDI, a system average power failure frequency index SAIFI, a system average power failure duration index SAIDI and an average power supply availability index ASAI are observed along with the minimum values of the change of the WTG capacity and PV capacity, the ESS capacity and PV capacity and the ESS power and PV capacity, and the evaluation results of the change of the WTG capacity and PV capacity, the ESS capacity and PV capacity and the ESS power and PV capacity on the system reliability are determined respectively;
and an evaluation index integration step, wherein a final reliability evaluation result is obtained based on the evaluation results of the WTG capacity and PV capacity, the ESS capacity and PV capacity, and the ESS power and PV capacity change on the system reliability.
2. The method of claim 1, wherein the modeling step further comprises:
establishing a mathematical model of a WTG (wind turbine generator system), wherein the relation between the output power of a wind turbine generator and the wind speed meets the following preset conditions:
wherein, A, B, C parameters are:
in the formula: vciFor cutting into wind speed, VcoFor cutting out wind speed, VrAt rated wind speed, PrIs rated power;
building a PV mathematical model of a photovoltaic power generation system, wherein the photovoltaic instantaneous power is as follows:
PV(t)＝Beta(α,β)PVmax
wherein α, β are shape parameters of Beta distribution, PVmaxIs the maximum output power of the photovoltaic power generation system;
establishing an Energy Storage System (ESS) mathematical model, wherein the energy storage system comprises a lead-acid storage battery and a super capacitor, and the charge and discharge state equation of the lead-acid storage battery is as follows:
wherein, SOC (t +1) and SOC (t) are respectively the residual capacity of the battery at t +1 and t, Pch (t) and Pdch (t) are the charging and discharging power of the battery at t time ηch、ηdchRespectively charge and discharge efficiency, and delta t is interval time; the capacity limit range set by the energy storage capacity is as follows:
SOCmin≤SOC(t+1)≤SOCmax。
3. the method of claim 1, wherein the collecting enumeration evaluating step further comprises:
when reliability evaluation is carried out on the WTG capacity and the PV capacity, the PV capacity is increased from 0MW to 2MW when the WTG capacity is set to be 1.3MW, 1.4MW and 1.5MW respectively, and reliability evaluation index values are calculated and evaluated;
during reliability evaluation of ESS capacity and PV capacity, setting the WTG capacity to be 1.5MW, increasing the PV capacity from 0.4MW to 1.6MW, increasing the ESS capacity from 0.2MW & h to 1.4MW & h, calculating a reliability evaluation index value and evaluating;
and when reliability of the ESS power and the PV capacity is evaluated, setting the WTG capacity to be 1.5MW, increasing the PV capacity from 0.4MW to 1.6MW, increasing the ESS power from 0MW to 0.6MW, calculating a reliability evaluation index value and evaluating.
4. The method of claim 1, wherein the evaluation index integration step further comprises:
and respectively obtaining the influence of the WTG capacity, the PV capacity, the ESS power and the capacity on the system reliability according to the estimation results of the WTG capacity and the PV capacity, the ESS capacity and the PV capacity and the ESS power and the PV capacity, and obtaining an analysis result of the variation trend according to the influence.
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