With the upgrade of the voltage level and power level of energy storage systems,the number of cells in a series-connected battery pack increases,making the imbalance between battery packs more complex.Therefore,a transformer-based direct equalization topology for multi-path series-connected battery packs is proposed.The topology,which uses a transformer as the energy transfer carrier,is able to switch between Buck-Boost mode and flyback mode.By controlling a low-conduction bi-directional switching array,the topology structure can achieve equalization between any battery cells.This topology is of multiple equalization modes,flexible equalization paths and fast equalization speed.In order to distribute energy effectively and achieve equalization efficiently,a fuzzy control strategy with State of Charge(SOC)as its equalization index is designed.Finally,a series of simulation experiments are carried out to verify the effectiveness of the proposed topology and its control strategy under different working conditions.The results show that the proposed balancing topology and control strategy have good effects in overcoming the inconsistency between single cells in a battery pack.This study is of great significance for improving the capacity difference between the battery cells,the capacity utilization rate of the energy storage system and the service life of the battery pack.
随着大量分布式新能源接入配电网,其电能损耗和节点电压偏差增大等问题愈发严重,仅依赖安装无功补偿设备、改变传统发电机机端电压等手段已无法满足高比例新能源配电网的高调节性能需求.为此,研究深度挖掘不同种类新能源对于配电网的无功调控潜力,详细分析了目标函数以及约束条件的设置,规划了基于数据驱动的无功调控算法流程,实现了合理调节配电网的节点电压与降低配电线路电能损耗的目标.首先,构建了高比例新能源参与配电网无功优化的数学模型,并应用多种智能算法求解.随后,采用多种算法得到的无功调控策略作为深度学习长短期记忆(LSTM)神经网络的训练数据,通过训练的网络在此类模型下可以预测得到优质的、高效率的无功调控策略.最后,利用IEEE 14与IEEE 33节点算例验证了所提算法的有效性和优化性能.
根据电网企业在电力市场中扮演的角色,在供给侧企业间两方博弈的基础上,将电网企业引入竞价,构建异质性企业与电网代理购电模式下供大于求的三方演化博弈模型.由于购、售双方信息不对称,为保证自身收益最大化而风险最小化,双方通过补偿机制来抵御风险.研究市场出清价格(MCP)和按报价结算(PAB)竞价机制下的电力市场竞价博弈行为,研究结果表明:在MCP机制下,三方博弈系统存在2个不受补偿影响的竞价演化稳定策略(ESS),改变补偿系数只会影响系统收敛至不同ESS的路径;PAB机制下,调整补偿系数会出现不同的ESS,且在一定条件下PAB机制下的购电成本低于MCP机制.
随着新型电力系统建设的推进,用户侧小型储能装置愈发受到重视.然而无序的充放电管理模式既给电网带来安全运行隐患,又造成资源浪费.基于此,提出一种通过储能聚合商云服务平台实现用户侧小储能装置联合调度的运行模式.建立综合考虑储能运营商、储能装置投资者和电网等各参与主体的最优运行模型,然后基于纳什均衡理论建立各参与主体在约束条件下的合作博弈模型.通过算例仿真分析,对模型求解得出最优共享储能定价方案.结果表明:多主体合作最优定价方案达到了削峰填谷、节约资源的目的,同时实现了电网、储能聚合商、储能装置投资者的多方共赢.提出的定价机制被验证具有一定的可行性和科学性.
针对集中供热系统滞后性等因素导致的短期热负荷预测精度较低问题,提出一种基于互补集合经验模态分解(CEEMD)数据处理技术与灰狼优化算法-长短期记忆网络(GWO-LSTM)的精细非侵入性负荷监测方法.采用CEEMD将采集得到的原始热负荷数据分解为若干个平稳的固有模态函数(IMF)并对每个IMF分别进行建模预测,叠加每个IMF的预测值作为最终的预测输出.为了提高预测精度,利用GWO算法对LSTM的隐藏层神经元数目、训练次数和初始学习率进行参数寻优,建立CEEMD-GWO-LSTM短期热负荷预测模型.以实际热负荷数据进行仿真试验并与单一LSTM模型、CEEMD-LSTM模型进行对比,试验结果表明,CEEMD-GWO-LSTM模型的均方根误差、平均绝对误差和平均绝对百分比误差分别为0.591 5 MW,0.460 2 MW和8.083 8%,显著低于其他对比模型.
