在智能电网中,拥有可再生能源发电装备的用户可以与他人进行能源交易,以获取利润。自产能源不足的用户可通过从其他有剩余能源的用户购买所需的能源来满足需求。然而如果参与交易无法为用户带来额外收益,用户就不愿意参与此类交易。为...在智能电网中,拥有可再生能源发电装备的用户可以与他人进行能源交易,以获取利润。自产能源不足的用户可通过从其他有剩余能源的用户购买所需的能源来满足需求。然而如果参与交易无法为用户带来额外收益,用户就不愿意参与此类交易。为了提高能源交易参与者的收益,文中提出了一种新的点对点(peer to peer, P2P)能源交易方法,将能源交易描述为能源产消者和拍卖商之间的非合作博弈。买方根据不同的电价调整购买的能源数量,拍卖者控制博弈,卖方不参与博弈,但最终实现效益最大化,然后证明了存在唯一的博弈均衡,以确定市场能源交易价格和数量。利用区块链技术实现了所提出的能源交易方法,以显示实时P2P能源交易的可行性。仿真结果表明,与现有的两种方法相比,所提出的方法参与者累积效益提高了32%以上,验证了其有效性。展开更多
The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are ...The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.展开更多
文摘在智能电网中,拥有可再生能源发电装备的用户可以与他人进行能源交易,以获取利润。自产能源不足的用户可通过从其他有剩余能源的用户购买所需的能源来满足需求。然而如果参与交易无法为用户带来额外收益,用户就不愿意参与此类交易。为了提高能源交易参与者的收益,文中提出了一种新的点对点(peer to peer, P2P)能源交易方法,将能源交易描述为能源产消者和拍卖商之间的非合作博弈。买方根据不同的电价调整购买的能源数量,拍卖者控制博弈,卖方不参与博弈,但最终实现效益最大化,然后证明了存在唯一的博弈均衡,以确定市场能源交易价格和数量。利用区块链技术实现了所提出的能源交易方法,以显示实时P2P能源交易的可行性。仿真结果表明,与现有的两种方法相比,所提出的方法参与者累积效益提高了32%以上,验证了其有效性。
文摘The Stackelberg prediction game(SPG)is a bilevel optimization frame-work for modeling strategic interactions between a learner and a follower.Existing meth-ods for solving this problem with general loss functions are computationally expensive and scarce.We propose a novel hyper-gradient type method with a warm-start strategy to address this challenge.Particularly,we first use a Taylor expansion-based approach to obtain a good initial point.Then we apply a hyper-gradient descent method with an ex-plicit approximate hyper-gradient.We establish the convergence results of our algorithm theoretically.Furthermore,when the follower employs the least squares loss function,our method is shown to reach an e-stationary point by solving quadratic subproblems.Numerical experiments show our algorithms are empirically orders of magnitude faster than the state-of-the-art.
