This study explores the crucial interplay between aggregators and building
occupants in activating flexibility through Demand Response (DR) programs, with
a keen focus on achieving robust decarbonization and fortifying the resilience
of the energy system amidst the uncertainties presented by Renewable Energy
Sources (RES). Firstly, it introduces a methodology of optimizing aggregated
flexibility provision strategies in environments with limited data, utilizing
Discrete Fourier Transformation (DFT) and clustering techniques to identify
building occupant's activity patterns. Secondly, the study assesses the
disaggregated flexibility provision of Heating Ventilation and Air Conditioning
(HVAC) systems during DR events, employing machine learning and optimization
techniques for precise, device-level analysis. The first approach offers a
non-intrusive pathway for aggregators to provide flexibility services in
environments of a single smart meter for the whole building's consumption,
while the second approach carefully considers building occupants' thermal
comfort profiles, while maximizing flexibility in case of existence of
dedicated smart meters to the HVAC systems. Through the application of
data-driven techniques and encompassing case studies from both industrial and
residential buildings, this paper not only unveils pivotal opportunities for
aggregators in the balancing and emerging flexibility markets but also
successfully develops end-to-end practical tools for aggregators. Furthermore,
the efficacy of this tool is validated through detailed case studies,
substantiating its operational capability and contributing to the evolution of
a resilient and efficient energy system.

通过数据驱动技术和工业及住宅建筑的案例研究，本文不仅揭示了在平衡和新兴灵活性市场中机构的关键机遇，还成功开发了机构的端到端实用工具，验证了其操作能力，并为弹性和高效能源系统的演进作出了贡献。

借助实用数据驱动工具赋能汇聚者：利用聚合和分解的灵活性进行需求响应

Empowering Aggregators with Practical Data-Driven Tools: Harnessing  Aggregated and Disaggregated Flexibility for Demand Response

Economic and policy factors are driving the continuous increase in the
adoption and usage of electrical vehicles (EVs). However, despite being a
cleaner alternative to combustion engine vehicles, EVs have negative impacts on
the lifespan of microgrid equipment and energy balance due to increased power
demand and the timing of their usage. In our view grid management should
leverage on EVs scheduling flexibility to support local network balancing
through active participation in demand response programs. In this paper, we
propose a model-free solution, leveraging Deep Q-Learning to schedule the
charging and discharging activities of EVs within a microgrid to align with a
target energy profile provided by the distribution system operator. We adapted
the Bellman Equation to assess the value of a state based on specific rewards
for EV scheduling actions and used a neural network to estimate Q-values for
available actions and the epsilon-greedy algorithm to balance exploitation and
exploration to meet the target energy profile. The results are promising
showing that the proposed solution can effectively schedule the EVs charging
and discharging actions to align with the target profile with a Person
coefficient of 0.99, handling effective EVs scheduling situations that involve
dynamicity given by the e-mobility features, relying only on data with no
knowledge of EVs and microgrid dynamics.

使用深度强化学习进行电动车辆在微电网中的充电和放电活动调度，以对齐配电系统运营商提供的目标能源配置文件。

基于深度 Q 学习的智能电动车需求响应在智能电网中的智能调度

A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in  Smart Grids

The surge in electricity use, coupled with the dependency on intermittent
renewable energy sources, poses significant hurdles to effectively managing
power grids, particularly during times of peak demand. Demand Response programs
and energy conservation measures are essential to operate energy grids while
ensuring a responsible use of our resources This research combines distributed
optimization using ADMM with Deep Learning models to plan indoor temperature
setpoints effectively. A two-layer hierarchical structure is used, with a
central building coordinator at the upper layer and local controllers at the
thermal zone layer. The coordinator must limit the building's maximum power by
translating the building's total power to local power targets for each zone.
Local controllers can modify the temperature setpoints to meet the local power
targets. The resulting control algorithm, called Distributed Planning Networks,
is designed to be both adaptable and scalable to many types of buildings,
tackling two of the main challenges in the development of such systems. The
proposed approach is tested on an 18-zone building modeled in EnergyPlus. The
algorithm successfully manages Demand Response peak events.

通过使用 ADMM 和深度学习模型进行分布式优化，本研究通过采用两层分层结构的方法，将建筑物的总功率限制为对应的各个区域的局部功率目标，从而有效地规划室内温度设定点，并成功管理需求响应的高峰事件。

分布式基于 ADMM 的深度学习方法用于多区域建筑热控制

A Distributed ADMM-based Deep Learning Approach for Thermal Control in  Multi-Zone Buildings

The rapid growth of decentralized energy resources and especially Electric
Vehicles (EV), that are expected to increase sharply over the next decade, will
put further stress on existing power distribution networks, increasing the need
for higher system reliability and flexibility. In an attempt to avoid
unnecessary network investments and to increase the controllability over
distribution networks, network operators develop demand response (DR) programs
that incentivize end users to shift their consumption in return for financial
or other benefits. Artificial intelligence (AI) methods are in the research
forefront for residential load scheduling applications, mainly due to their
high accuracy, high computational speed and lower dependence on the physical
characteristics of the models under development. The aim of this work is to
identify households' EV cost-reducing charging policy under a Time-of-Use
tariff scheme, with the use of Deep Reinforcement Learning, and more
specifically Deep Q-Networks (DQN). A novel end users flexibility potential
reward is inferred from historical data analysis, where households with solar
power generation have been used to train and test the designed algorithm. The
suggested DQN EV charging policy can lead to more than 20% of savings in end
users electricity bills.

使用深度强化学习（特别是深度 Q 网络）确定户用电动汽车的成本减少充电策略，该策略可为终端用户节约超过 20% 的电费开支。