No abstract

Plug-in hybrid electric vehicles (PHEV) are expected to become widespread in the near future. However, high penetration of PHEVs can overload the distribution system. In smart grid, the charging of PHEVs can be controlled to reduce the peak load, known as demand-side management (DSM). In this paper, we focus on the DSM for PHEV charging at low-voltage transformers (LVTs). The objective is to flatten the load curve of LVTs, while satisfying each consumer's requirement for their PHEV to be charged to the required level by the specified time. We first formulate this problem as a convex optimization problem and then propose a decentralized water-filling-based algorithm to solve it. A moving horizon approach is utilized to handle the random arrival of PHEVs and the inaccuracy of the forecast nonPHEV load. We focus on decentralized solutions so that computational load can be shared by individual PHEV chargers and the algorithm is scalable. Numerical simulations are given to demonstrate the effectiveness of our algorithm.Index Terms-Decentralized convex optimization, demand-side management (DSM), plug-in hybrid electric vehicle (PHEV), smart grid, water filling.

In this paper, we present a fully distributed bisection algorithm for the economic dispatch problem (EDP) in a smart grid scenario, with the goal to minimize the aggregated cost of a network of generators, which cooperatively furnish a given amount of power within their individual capacity constraints. Our distributed algorithm adopts the method of bisection, and is based on a consensus-like iterative method, with no need for a central decision maker or a leader node. Under strong connectivity conditions and allowance for local communications, we show that the iterative solution converges to the globally optimal solution. Furthermore, two stopping criteria are presented for the practical implementation of the proposed algorithm, for which sign consensus is defined. Finally, numerical simulations based on the IEEE 14-bus and 118-bus systems are given to illustrate the performance of the algorithm.

No abstract

Plug-in hybrid electric vehicles (PHEV) tend to become more widespread in the next decades. However, large penetration of PHEVs will overload the distribution system. In smart grid, the charging of PHEVs can be controlled to reduce the peak load, which is known as demand side management (DSM). In this paper, we focus on the impact of PHEV charging on low-voltage transformers. The objective is to flatten the load curve of low-voltage transformers, while each consumer's requirement for their PHEV to be charged to the required level by their specified time is satisfied. We first formulate it as a convex optimization problem and then propose a distributed water-filling-based algorithm to solve it. Proofs of optimality and numerical simulations are given to demonstrate the effectiveness of our algorithm.