Pengcheng You

Optimal Cooperative Charging Strategy for a Smart Charging Station of Electric Vehicles

This paper proposes a novel cooperative charging strategy for a smart charging station in the dynamic electricity pricing environment, which helps electric vehicles (EVs) to economically accomplish the charging task by the given deadlines. This strategy allows EVs to share their battery-stored energy with each other under the coordination of an aggregator, so that more flexibility is given to the aggregator for better scheduling. Mathematically, the scheduling problem is formulated as a constrained mixed-integer linear program (MILP) to capture the discrete nature of the battery states, i.e., charging, idle and discharging. Then, an efficient algorithm is proposed to solve the MILP by means of dual decomposition and Benders decomposition. At last, the algorithm can be implemented in a distributed fashion, which makes it scalable and thus suitable for large-scale scheduling problems. Numerical results validate our theoretical analysis.

Optimal Charging Schedule for a Battery Switching Station Serving Electric Buses

We propose a model of a battery switching station (BSS) for electric buses (EBs) that captures the predictability of bus operation. We schedule battery charging in the BSS so that every EB arrives to find a battery ready for switching. We develop an efficient algorithm to compute an optimal schedule. It uses dual decomposition to decouple the charging decisions at different charging boxes so that independent subproblems can be solved in parallel at individual charging boxes, making the algorithm inherently scalable as the size of the BSS grows. We propose a direct projection method that solves these subproblems rapidly. Numerical results illustrate that the proposed approach is far more efficient and scalable than generic algorithms and existing solvers.

Optimal privacy-preserving load scheduling in smart grid

With the wide deployment of smart meters in the power grid, it is becoming much easier to gather the detailed power consumption data of residential users, which enables the possibility of smarter and greener power grid. However, the fine-grained load profile of the individual user also introduces the severe concern of privacy leakage as the private information such as personal living habits may be inferred by the malicious third parties for unauthorized use and benefits. Different from most existing privacy-preserving energy management works which are solely based on the control of rechargeable batteries, we further introduce the proactive scheduling of widely used thermostatically controlled devices, including air conditioner, water heater, and laundry drier for effective load hiding. To minimize the weighed sum of financial cost, the deviation from the pre-defined load profile, and the user dissatisfaction, we formulate a novel load scheduling problem which is subject to both the device/battery physical dynamics and the practical user requirements. In order to solve the overall problem effectively under the uncertain price, we decompose the primal problem into a series of subproblems through dual composition, and design a stochastic gradient based two-level iterative distributed algorithm. Extensive simulations under various parameters are employed to demonstrate the effectiveness of our design.

Battery Swapping Assignment for Electric Vehicles: A Bipartite Matching Approach

This paper formulates an optimal station assignment problem for electric vehicle (EV) battery swapping that takes into account both temporal and spatial couplings. The goal is to reduce the total EV cost and station congestion due to temporary shortage in supply of available batteries. We show that the problem is reducible to the minimum weight perfect bipartite matching problem. This leads to an efficient solution based on the Hungarian algorithm. Numerical results suggest that the proposed solution provides a significant improvement over a greedy heuristic that assigns nearest stations to EVs.

Scheduling of EV battery swapping, I: centralized solution

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information.

Optimal charging schedule for a battery switching station serving electric buses

Summary form only given. We propose a model of a battery switching station (BSS) for electric buses (EBs) that captures the predictability of bus operation. We schedule battery charging in the BSS so that every EB arrives to find a battery ready for switching. We develop an efficient algorithm to compute an optimal schedule. It uses dual decomposition to decouple the charging decisions at different charging boxes so that independent subproblems can be solved in parallel at individual charging boxes, making the algorithm inherently scalable as the size of the BSS grows. We propose a direct projection method that solves these subproblems rapidly. Numerical results illustrate that the proposed approach is far more efficient and scalable than generic algorithms and existing solvers.

Efficient battery charging schedule of battery-swapping station for electric buses

This paper investigates the battery charging schedule problem of a battery-swapping station for electric buses (EB). An EB assignment policy is proposed such that there is a one-to-one correlation between EBs and batteries. By this means, the battery charging schedule problem aiming to minimize the total cost of the battery-swapping station is formulated as a constrained convex program with both spatially and temporally coupled constraints. Based on dual decomposition and our proposed EB assignment policy, the battery charging schedule problem can be decomposed into a series of local subproblems, which can be independently tackled. Furthermore, a fast search method in combination with binary search is put forward to deal with subproblems. Therefore, the battery charging schedule problem can be solved efficiently in a distributed manner. Numerical results confirm the validity of our proposed approach.

Stabilization of Power Networks via Market Dynamics

This work investigates the increasing interactions between power network dynamics and market dynamics. A dynamical spot pricing mechanism for rational market behavior of generators and loads is designed to model market dynamics, which provably drives a power network to an equilibrium operating point, achieving secondary frequency control and economic dispatch.

Efficient Online Station Assignment for EV Battery Swapping

This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that make battery swapping requests to a central operator, with the aim of minimizing cost to EVs and congestion at service stations. Inspired by a polynomial-time solvable offline solution via a bipartite matching approach, we develop an efficient online station assignment algorithm that provably achieves a tight (optimal) competitive ratio under mild conditions.

Scheduling of EV Battery Swapping, II: Distributed Solutions

In Part I of this paper, we formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. We propose there a centralized solution based on second-order cone programming relaxation of optimal power flow and generalized Benders decomposition that is applicable when global information is available. In this paper, we propose two distributed solutions based on the alternating direction method of multipliers and dual decomposition, respectively, that are suitable for systems where the distribution grid, stations, and EVs are managed by separate entities. Our algorithms allow these entities to make individual decisions, but coordinate through privacy-preserving information exchanges to solve a convex relaxation of the global problem. We present simulation results to show that both algorithms converge quickly to a solution that is close to optimum after discretization.