Electric vehicle (EV) charging stations represent a substantial load with
significant flexibility. The exploitation of that flexibility in demand
response (DR) algorithms becomes increasingly important to manage and balance
demand and supply in power grids. Model-free DR based on reinforcement learning
(RL) is an attractive approach to balance such EV charging load. We build on
previous research on RL, based on a Markov decision process (MDP) to
simultaneously coordinate multiple charging stations. However, we note that the
computationally expensive cost function adopted in the previous research leads
to large training times, which limits the feasibility and practicality of the
approach. We, therefore, propose an improved cost function that essentially
forces the learned control policy to always fulfill any charging demand that
does not offer any flexibility. We rigorously compare the newly proposed batch
RL fitted Q-iteration implementation with the original (costly) one, using
real-world data. Specifically, for the case of load flattening, we compare the
two approaches in terms of (i) the processing time to learn the RL-based
charging policy, as well as (ii) the overall performance of the policy
decisions in terms of meeting the target load for unseen test data. The
performance is analyzed for different training periods and varying training
sample sizes. In addition to both RL policies performance results, we provide
performance bounds in terms of both (i) an optimal all-knowing strategy, and
(ii) a simple heuristic spreading individual EV charging uniformly over time

本研究基于强化学习模型，使用马尔可夫决策过程来协调多个充电站，通过提出改进的成本函数使得学到的控制策略始终满足不具备任何灵活性的充电需求，同时使用真实世界数据对比了两种方法在负荷平整的情况下学习强化策略的性能和策略决策的总体性能。

使用强化学习进行多个电动汽车充电站需求响应协调的优化成本函数

Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning

In this paper, we propose novel approaches using state-of-the-art machine
learning techniques, aiming at predicting energy demand for electric vehicle
(EV) networks. These methods can learn and find the correlation of complex
hidden features to improve the prediction accuracy. First, we propose an energy
demand learning (EDL)-based prediction solution in which a charging station
provider (CSP) gathers information from all charging stations (CSs) and then
performs the EDL algorithm to predict the energy demand for the considered
area. However, this approach requires frequent data sharing between the CSs and
the CSP, thereby driving communication overhead and privacy issues for the EVs
and CSs. To address this problem, we propose a federated energy demand learning
(FEDL) approach which allows the CSs sharing their information without
revealing real datasets. Specifically, the CSs only need to send their trained
models to the CSP for processing. In this case, we can significantly reduce the
communication overhead and effectively protect data privacy for the EV users.
To further improve the effectiveness of the FEDL, we then introduce a novel
clustering-based EDL approach for EV networks by grouping the CSs into clusters
before applying the EDL algorithms. Through experimental results, we show that
our proposed approaches can improve the accuracy of energy demand prediction up
to 24.63% and decrease communication overhead by 83.4% compared with other
baseline machine learning algorithms.

本文介绍了使用机器学习技术预测电动汽车网络的能量需求的方法，包括一种使用聚类和联邦学习的方法，以减少通信开销和保护数据隐私。实验结果表明，与其他基线机器学习算法相比，我们的方法可以提高能量需求预测的准确性高达 24.63％，并降低 83.4％的通信开销。

电动汽车网络联邦学习能源需求预测

Energy Demand Prediction with Federated Learning for Electric Vehicle  Networks

Effective placement of charging stations plays a key role in Electric Vehicle
(EV) adoption. In the placement problem, given a set of candidate sites, an
optimal subset needs to be selected with respect to the concerns of both (a)
the charging station service provider, such as the demand at the candidate
sites and the budget for deployment, and (b) the EV user, such as charging
station reachability and short waiting times at the station. This work
addresses these concerns, making the following three novel contributions: (i) a
supervised multi-view learning framework using Canonical Correlation Analysis
(CCA) for demand prediction at candidate sites, using multiple datasets such as
points of interest information, traffic density, and the historical usage at
existing charging stations; (ii) a mixed-packing-and- covering optimization
framework that models competing concerns of the service provider and EV users;
(iii) an iterative heuristic to solve these problems by alternately invoking
knapsack and set cover algorithms. The performance of the demand prediction
model and the placement optimization heuristic are evaluated using real world
data.

本文提出了一个多视图学习框架，使用了 CCA 模型对候选站点进行需求预测，并使用混合填充和覆盖优化框架来解决服务提供商和电动汽车用户之间的竞争关系，通过交替调用背包和集合覆盖算法来解决这些问题，该模型制定的方案使用真实世界数据进行了评估。