消纳大规模风电的备用容量在线滚动决策与模型研究

发布时间：2018-05-16 02:23

本文选题：消纳风电 + 风电预测偏差　；参考：《华北电力大学》2015年硕士论文

【摘要】：近年来风电发展迅速,成为21世纪重要的绿色能源。随着风力发电在电网中的比重不断提高,由于风电具有随机性、间歇性和波动性的特点,发电功率较难准确预测,在时间和空间尺度上给接入风电的系统备用容量优化配置带来了新的挑战。日内风电预测的误差以及超短期负荷预测偏差、机组故障等因素的存在,导致日内功率预测与日前发电计划存在较大偏差,严重影响了传统的日前发电计划在日内的执行。同时目前建立在高可靠性常规电源基础上的备用配置方法还不能符合含大规模风电的电力系统的可靠性要求。因此,需要依据发电偏差滚动制定备用容量优化分配策略,以更好地协调系统的可靠性和经济性,实现发电计划由日前到日内的平滑过渡。首先,本文介绍了引起发电偏差的各种因素,其中,重点介绍了风电功率预测偏差较大的原因。在采用正态分布与拉普拉斯分布联合的模型来拟合风电预测偏差的基础上,提出了基于置信度的风电出力极值的求解方法,以确定在某一时段风电出力极值。随后,对含大规模风电场的电力系统进行随机生产模拟,以求取含风电场电力系统的最大备用容量。在对三种典型随机生产模拟计算方法探讨的基础上,确定本文所用方法。将风电机组视为固定出力为极值的常规电源优先安排,然后安排系统中容量最大机组,采用等效持续负荷曲线法进行随机生产模拟,求取在某一可靠性指标下的含风电系统的最大备用容量。最后,在上述研究的前提下,确定了消纳大规模风电的备用容量在线滚动决策,并建立了备用容量滚动优化模型。根据发电偏差与最大备用容量的关系,以备用容量购买成本最低为目标,建立优化模型。采用改进粒子群算法对模型进行求解,计算得到了滚动计划各时段的最优旋转备用容量。在减少弃风量的同时,提高了系统运行的经济性和可靠性,对各时段的备用容量调整具有指导意义。
[Abstract]:With the rapid development of wind power in recent years, it has become an important green energy in the twenty-first Century. With the increasing proportion of wind power in the power grid, due to the randomness, intermittence and volatility of wind power, the power generation power is difficult to accurately predict. The optimal allocation of standby capacity for wind power generation has brought a new choice on the time and space scale. The error of the intra day wind power forecast, the ultra short term load forecast deviation, the unit fault and other factors lead to the large deviation of the intra day power forecast and the pre day generation plan, which seriously affects the implementation of the traditional day forward generation plan in the day. At the same time, the standby configuration method based on the high reliability conventional power supply is also established. It can not meet the reliability requirements of the power system with large scale wind power. Therefore, it is necessary to formulate the optimal allocation strategy of reserve capacity according to the generation deviation, so as to better coordinate the reliability and economy of the system and realize the smooth transition from day to day. On the basis of fitting wind power forecast deviation with the combined model of normal distribution and Laplasse distribution, the method of solving the extreme value of wind power output based on confidence degree is proposed to determine the extreme value of wind power output at a certain period of time. On the basis of three typical stochastic production simulation calculation methods, the method used in this paper is determined on the basis of three typical stochastic production simulation calculation methods. The equivalent continuous load curve method is used for stochastic production simulation to obtain the maximum reserve capacity of the wind power system under a certain reliability index. Finally, on the premise of the above research, the on-line rolling decision of the reserve capacity of large scale wind power is determined, and a rolling optimization model of reserve capacity is established. With the relationship of capacity, the optimization model is set up with the minimum purchase cost of reserve capacity as the goal. The improved particle swarm optimization algorithm is used to solve the model, and the optimal rotation reserve capacity of each period of the rolling plan is calculated. At the same time, the economy and reliability of the system operation are improved and the reserve capacity of each period is adjusted. The whole is instructive.

【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM614

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