The stake of distributed generation resources like fuel cell in daily market is proved to be a major uncertain problem. The volatile character of market price together with the unbalanced nature of power can take hold of economic advancement of distributed generation resources which in turn can culminate in diversion retribution while the market is being struck. This study introduces a market participation model in share conditions to improve the profit for Fuel Cell/wind turbine/storage/photovoltaic and demand response. To solve the mentioned problem, an accurate prediction model is presented in this paper. This model is based on complete ensemble empirical mode decomposition, and multiple artificial neural network which is coupled with Broyden water cycle algorithm. By this algorithm, the prediction accuracy of proposed forecast engine is enhanced and could get the better results. A sure-footed stochastic optimization approach was deployed in order to take prices of markets and distributed generation resources into account. In the generation of distributed generation resources, forecasting error database in everyday, modified, and depressed market was drawn on to induce probabilistic scenario. Improbable variables were discarded by a neuro-fuzzy model. Eventually, to illustrate the joint model strategy suggested in the study, a testing system contains fuel cell/wind turbine/storage unit/photovoltaic and demand response was utilized and the attained results were calculated in two different periods.

事实证明，燃料电池等分布式发电资源在日常市场中的股权是一个主要的不确定性问题。市场价格的波动性以及电力的不平衡性质可以控制分布式发电资源的经济发展，而反过来又可以在罢工市场时导致分配收益。这项研究引入了共享条件下的市场参与模型，以提高燃料电池/风力涡轮机/存储/光伏发电的利润以及需求响应。为了解决上述问题，本文提出了一种准确的预测模型。该模型基于完全集成的经验模式分解和结合Broyden水循环算法的多重人工神经网络。通过这种算法，提出的预测引擎的预测精度得到了提高，可以获得更好的结果。为了确定市场价格和分布式发电资源，部署了可靠的随机优化方法。在分布式发电资源的产生中，利用每天，经过修改和低迷的市场中的预测误差数据库来诱发概率情景。难以置信的变量被神经模糊模型丢弃。最终，为了说明研究中提出的联合模型策略，测试系统包含燃料电池/风力涡轮机/存储单元/光伏发电，并利用了需求响应，并在两个不同的时期内计算了得出的结果。为了确定市场价格和分布式发电资源，部署了可靠的随机优化方法。在分布式发电资源的产生中，利用每天，经过修改和低迷的市场中的预测误差数据库来诱发概率情景。难以置信的变量被神经模糊模型丢弃。最终，为了说明研究中提出的联合模型策略，测试系统包含燃料电池/风力涡轮机/存储单元/光伏发电，并利用了需求响应，并在两个不同的时期内计算了得出的结果。为了确定市场价格和分布式发电资源，部署了可靠的随机优化方法。在分布式发电资源的产生中，利用每天，经过修改和低迷的市场中的预测误差数据库来诱发概率情景。难以置信的变量被神经模糊模型丢弃。最终，为了说明研究中提出的联合模型策略，测试系统包含燃料电池/风力涡轮机/存储单元/光伏发电，并利用了需求响应，并在两个不同的时期内计算了得出的结果。难以置信的变量被神经模糊模型丢弃。最终，为了说明研究中提出的联合模型策略，测试系统包含燃料电池/风力涡轮机/存储单元/光伏发电，并利用了需求响应，并在两个不同的时期内计算了得出的结果。难以置信的变量被神经模糊模型丢弃。最终，为了说明研究中提出的联合模型策略，测试系统包含燃料电池/风力涡轮机/存储单元/光伏发电，并利用了需求响应，并在两个不同的时期内计算了得出的结果。