Optimization of fuel consumption and NO_(x) emission for mild HEV via hierarchical model predictive control

In this paper,we consider the fuel economy optimization problem for a mild hybrid electric vehicle(HEV)using hierarchical model predictive control.In the proposed algorithm,two problems are addressed:eco-driving and torque distribution.In the eco-driving problem,vehicle speed was controlled.Considering the reduction in fuel consumption and NOx emissions,the torque required to follow the target speed was calculated.Subsequently,in the torque distribution problem,the distribution between the engine and motor torques were calculated.In this phase,engine characteristics were considered.These problems differ in terms of time scales;therefore,a hierarchical model predictive control is proposed.Lastly,the numerical simulation results demonstrated the efficacy of this research.

Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid

This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity market in an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity price while considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supply balance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method.