基于储能优化的微电网经济调度模型研究

发布时间：2018-05-05 02:25

本文选题：微电网经济调度 + 蓄电池组　；参考：《山东大学》2017年硕士论文

【摘要】：利用风力、太阳能等可再生能源进行发电的分布式电源越来越多;分布式电源与附近的负荷构成了一个微型的发电、供电系统,称之为微电网;微电网相关技术解决了可再生能源接入大电网的问题。作为新的发电方式及可再生能源接入的途径,微电网的经济调度问题一直是深受关注的研究课题。本文在分析分布式电源、蓄电池组运行特性的基础上,实现了某实际微电网的经济调度模型。该模型的目标函数考虑了蓄电池组的使用寿命以及公共电网的峰谷电价影响。微电网中蓄电池组的使用寿命与其充放电过程有关,本文基于分布式电源出力及微电网负荷的预测结果,改进了蓄电池组优化的充、放电策略,即尽可能地利用分布式电源输出的剩余功率对蓄电池组进行充电,以减少公共电网对蓄电池组充电的电费成本,同时为了延长蓄电池组的使用寿命又要控制蓄电池组在短时间频繁充、放电次数。主要工作说明如下:1)改进了蓄电池组在微电网经济调度中的费用损失模型。一般采用充电或放电功率绝对值与蓄电池组总吞吐量之比计算蓄电池组单次充电或放电的损失费用。这个方法有不足之处,本文采用放电功率的2倍值与总吞吐量之比来计算,可以克服蓄电池组荷电状态较低时,充电功率过大导致充电费用较高而不得不减小充电功率的问题。同时,在对微电网的分布式电源与负荷预测的基础上,通过出力与负荷之间的功率平衡,对蓄电池组的充、放电策略进行优化,其基本思路是尽可能地采用可再生能源发出的电能对其进行充电,而在电价峰时段且分布式电源出力不足时,则尽可能地利用蓄电池组的储能对负荷进行供电。而且通过控制蓄电池组小功率充电或放电的次数,以提高蓄电池组的使用寿命。2)根据风力发电机输出功率损耗率相关数据与光伏电站逆变环节输出损耗率,分别建立了风电机组与光伏电站在输出功率时的费用损耗模型。这两个模型应用于分布式电源出力大于负荷时的微电网经济调度问题。3)实现了微电网经济调度的目标函数;在分布式电源出力与微电网负荷的比较基础上,将目标函数分成两段,即当分布式电源的出力大于微电网负荷时,以风力及光伏输出功率损耗最小为调度目标;当出力小于负荷时而需要蓄电池组或公共电网提供额外功率时,则以微电网内的用电成本最低为调度目标。4)在满足微电网相关约束条件,计及蓄电池组使用寿命,考虑蓄电池组优化充、放电策略,计及公共电网峰谷电价的基础上,采用粒子群算法对微电网的经济调度模型进行求解,给出了微电网内各单元的经济调度结果并进行简要分析。本文实现的经济调度模型,基于微电网中各个分布式电源的实际情况,兼顾了蓄电池组的寿命损耗与公共电网峰谷电价的影响,能够更加全面、有效地实现微电网用电成本最低的目标。通过采用粒子群算法对该经济调度模型的求解,以及对经济调度结果的分析,说明本文实现的经济调度模型具有一定的参考意义,能够为微电网的规划、调控提供较强的指导作用。
[Abstract]:A growing number of distributed power sources, such as wind, solar and other renewable sources of energy, are becoming more and more distributed; distributed power and nearby loads constitute a micro power generation, power supply systems, called microgrids; microgrid related technologies have solved the problems of renewable energy access to large power grids as new generation methods and renewable energy access. In this paper, based on the analysis of the distributed power and the running characteristics of the battery group, the economic dispatch model of a real microgrid is realized. The objective function of this model takes into account the service life of the battery group and the influence of the peak valley electricity price of the public grid. The service life of the battery group in the power grid is related to the charging and discharging process. Based on the result of the distributed power supply and the prediction of the micro grid load, this paper improves the charging and discharging strategy of the battery group optimization, that is to use the residual power of the distributed power output to recharge the battery pack as much as possible in order to reduce the public grid to the battery group. At the same time, to prolong the service life of the battery, to prolong the service life of the battery group, and to control the number of batteries in a short time and the number of discharge. The main work is as follows: 1) the cost loss model of the battery group in the economic dispatch of the microgrid is improved. The ratio of the absolute value of the charging or discharging power to the total throughput of the battery group is generally adopted. The loss cost of single charge or discharge of battery group is calculated. This method is inadequacies. In this paper, the 2 times of discharge power is calculated with the ratio of total throughput. It can overcome the problem that charging power is too high and has to reduce charging power when the charge state of battery group is low. At the same time, the micro grid can be used. On the basis of distributed power and load forecasting, through the power balance between the output and the load, the charging and discharging strategy of the battery group is optimized. The basic idea is to use the electricity generated by renewable energy to charge it as much as possible, while in the peak period of electricity price and the shortage of distributed power supply, it is used as much as possible. The storage energy of the battery group supplies power to the load. And by controlling the number of small power charging or discharging of the battery group to increase the service life of the battery group.2), the output power of the wind turbine and the photovoltaic power station is established according to the output loss rate related data of the output power of the wind turbine and the output loss rate of the inverter link of the photovoltaic power station. The two models are applied to the microgrid economic scheduling problem.3 when the distributed power supply exceeds the load. The objective function of the microgrid economic dispatch is realized. On the basis of the comparison of the distributed power supply and the microgrid load, the target function is divided into two segments, that is, when the output of the distributed power is larger than the microelectricity. When the load is on the net, the minimum power loss of the wind and photovoltaic output is the scheduling goal. When the power is less than the load, the battery group or the public grid is required to provide the extra power, the minimum power cost in the micro grid is the.4 of the scheduling target. The service life of the battery group is considered, and the battery group is considered. On the basis of the chemical charging, discharge strategy and the peak valley electricity price of the public grid, the particle swarm optimization algorithm is used to solve the economic dispatch model of the microgrid, and the economic dispatch results of each unit in the microgrid are given and a brief analysis is made. The economic dispatch model realized in this paper is based on the actual situation of the distributed power supply in the micro grid and takes into account the consideration of the actual situation of the various distributed power sources in the microgrid. The life loss of the battery group and the peak valley electricity price of the public grid can be more comprehensive and effective to achieve the lowest cost of the power consumption of the microgrid. By using the particle swarm optimization algorithm to solve the economic dispatch model and the analysis of the results of the economic dispatch, the economic dispatch model realized in this paper has a certain reference meaning. It can provide a strong guidance for the planning and regulation of the microgrid.

【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73

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