Real-time pricing for smart grid with multi-energy microgrids and uncertain loads: a bilevel programming method

Quadratic function based price adjustment strategy on monitoring process of power consumption load in smart grid

“…Regarding the problem ( 10)- (13) as the primal problems, the Lagrangian is defined as [8] ( ) where t  is a Lagrange multiplier for a fixed t T .…”

“…Lately, RTP models have been prosperously developed and widely applied in model establishment and algorithm improvement. Under a hierarchical market framework between the power supplier and multi-microgrids, Yuan et al came up with a real-time pricing model, and they solved the model with a hybrid algorithm combining the particle swarm optimization (PSO) and the branch and bound algorithm (BBA) [13] . Chiu et al put forward an energy sale and redemption pricing framework that exploits a time-dependent pricing strategy [14] .…”

A cosh-based smoothing Newton algorithm for the real-time pricing problem in smart grid

“…The literature [8] gave a multitype-user welfare equilibrium via RTP scheme to reduce cost and improve 1 INTRODUCTION efficiency. In the literature [9], a hierarchical market framework was proposed to solve the RTP problem of a two-layer programming model. A hybrid algorithm of distributed PSO and the branch and bound algorithm (BBA) were used.…”

Forecasting electricity consumption in Shanghai using a nonlinear interval grey model

“…And China's carbon peak and carbon neutrality are also implemented under this circumstance. On the other hand, the level of electricity consumption will not only affect the cost of electricity production, but also affect social welfare, which is an important research object of demand side management (Yuan et al 2020). Therefore, accurately electricity consumption forecasting is beneficial to achieving a balance between supply and demand, and has a guiding role in the supply management of power resources, which to a certain extent alleviates the tightness of energy supply (Tao et al 2020).…”