Alternating Direction Method of Multipliers for decentralized electric vehicle charging control

Abstract

Abstract-The integration of Electric Vehicles (EVs) into the power grid is a challenging task. From the control perspective, one of the main challenges is the definition of a comprehensive control structure that is scalable to large EV numbers. This paper makes two key contributions: (i) It defines the EV ADMM framework for decentralized EV charging control. (ii) It evaluates EV ADMM using actual data and various EV fleet control problems. EV ADMM is a decentralized optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM). It separates the centralized optimal fleet charging problem into individual optimization problems for the EVs plus one aggregator problem that optimizes fleet goals. Since the individual problems are coupled, they are solved consistently by passing incentive signals between them. The framework can be parameterized to trade-off the importance of fleet goals versus individual EV goals, such that aspects like battery lifetime can be considered. We show how EV ADMM can be applied to control an EV fleet to achieve goals such as demand valley filling and minimal-cost charging. Due to its flexibility and scalability, EV ADMM offers a practicable solution for optimal EV fleet control.

On the effects of signal design in electric vehicle charging using vehicle-originating-signals

“…For instance, [2] proposes a decentralized algorithm that controls EV charging to fill demand valleys via incentive signals, [3] presents a framework for incentives-based distributed EV charging control for different objectives, [4] evaluates the supply of frequency reserves via centralized and direct EV control, [5] presents a decentralized optimization with direct control to minimize charging costs, [6] presents a local direct control technique for DN voltage stability, [7] aims at minimizing power losses with a centralized, direct control approach, and [8] presents a decentralized direct control method that includes other flexible loads. Most of these use formal optimization methods.…”

Complexity Analysis of Optimal Recharge Scheduling for Electric Vehicles

“…If such graph includes any node v ∈ V whose edges have all infinite weight, then v is removed along with the farthest departure (lines [13][14][15][16]. This is the case in which a vehicle cannot be charged in time for any departure in the list.…”

“…A game theoretical framework is adopted also by Zou et al [15], who design a distributed charging coordination method for EVs relying on an auction mechanism based on progressive second price. A distributed approach aimed at increasing scalability is applied by Rivera et al [16]: they propose a decentralized optimization algorithm based on the Alternating Direction Method of Multipliers and separate the centralized optimal fleet charging problem into individual optimization problems for the single EVs, which are coupled and solved consistently by exchanging incentive signals between them, plus one aggregated problem that optimizes fleet goals. The framework can be parameterized to trade-off the importance of fleet goals versus the objectives of the individual EVs.…”

Demand response for aggregated residential consumers with energy storage sharing

“…Aggregators are new entities in the electricity market that act as mediators between users and the utility operator, and possess the technology to perform DR signals and communicate with both users and utilities [15]. In [16], an algorithm is built on the alternating directions method of multipliers (ADMM), focusing on decentralized algorithms for Electric Vehicles charging. In addition, a coordination framework based on ADMM is proposed in [17] to negotiate among the households and a coordinator, with the main goal being to minimize the imbalance among communities, while including objectives and constraints for each community and taking into account each user's quality of life/activities.…”