多微网多时间尺度交易机制设计和交易策略优化

Design of Multi-time Scale Trading Mechanism and Trading Strategy Optimization for Multiple Microgrids

为了在多微网交易中充分发挥分散调度的优势、保护各子微网的隐私,以及进行高效快速的计算,文中提出了一种并网型多微网系统多时间尺度交易机制和基于深度学习的交易策略优化算法。首先,建立了多微网内部交易价格模型,该模型可根据多微网内部供需形势的变化,动态调整内部交易价格,使多微网内部交易相对于微网直接与配电网交易更具经济性,从而激励各子微网参与内部交易。其次,建立了"报量不报价"的日前、日内交易机制。在日前交易中通过交易电量和内部交易价格的迭代,形成日前交易计划和交易价格,并进行日前电力交易出清;在日内交易中,各子微网仅申报一次不平衡功率的购售电需求,申报结束后直接出清。此外,基于多微网系统与配电网之间日前预期和实际交互功率的偏差,提出了联络线功率偏差的补偿方案,以降低多微网系统功率波动对配电网运行的影响。然后,基于生成的日内交易样本数据,引入深度神经网络算法训练学习各子微网的交易策略,以便子微网在日内交易阶段快速、准确地得到自身最优的购售电计划。最后,通过算例验证了所提出模型和算法的有效性。

In order to give full play to the advantages of decentralized dispatching of multiple microgrids(MMGs)trading,protect the privacy of each sub-microgrid,and perform efficient and fast calculations,this paper proposes a multi-time scale trading mechanism of the grid-connected MMGs system and an optimization algorithm of trading strategy based on deep learning.Firstly,an internal electricity pricing model of MMGs is established,which can dynamically adjust the internal trading price according to the changes of supply and demand situations in MMGs,and make the internal trade among the MMGs more economical than the direct trade between the microgrid and the distribution network,thus encourages each sub-microgrid to participate in internal trade.Secondly,a day-ahead and intra-day trading mechanism of"quoted volume without quotation"is established.In the day-ahead trading,the trading plan and trading price are formed through iterations of the trading power and the internal trading prices,and the day-ahead power trade is cleared;in the intra-day trading,each sub-microgrid only declares the trading quantity of the unbalanced power once,and it is cleared directly after the declaration.In addition,based on the deviation of the expected and actual power exchange between the MMGs system and the distribution network,a compensation scheme is proposed to reduce the influence of the power fluctuations on the operation of distribution network.Then,based on the generated intra-day trading sample data,the deep neural network algorithm is introduced to train and learn the trading strategies of each sub-microgrid,so that the submicrogrids can quickly and accurately obtain its own optimal power trading plan during the intra-day trading stage.Finally,an example is given to verify the effectiveness of the proposed model and algorithm.