Reduced environmental effect, lower operating costs, and a stable and
sustainable energy supply for current and future generations are the main
reasons why power optimization is important. Power optimization makes ensuring
that energy is used more effectively, cutting down on waste and optimizing the
utilization of resources.In today's world, power optimization and artificial
intelligence (AI) integration are essential to changing the way energy is
produced, used, and distributed. Real-time monitoring and analysis of power
usage trends is made possible by AI-driven algorithms and predictive analytics,
which enable dynamic modifications to effectively satisfy demand. Efficiency
and sustainability are increased when power consumption is optimized in
different sectors thanks to the use of intelligent systems. This survey paper
comprises an extensive review of the several AI techniques used for power
optimization as well as a methodical analysis of the literature for the study
of various intelligent system application domains across different disciplines
of power consumption.This literature review identifies the performance and
outcomes of 17 different research methods by assessing them, and it aims to
distill valuable insights into their strengths and limitations. Furthermore,
this article outlines future directions in the integration of AI for power
consumption optimization.

能源优化、人工智能、实时监测、智能系统和能源供应是该研究论文的主要关键词和研究领域，并通过对 17 个不同研究方法的评估揭示了它们的优势和局限性，为未来的人工智能与能源消耗优化的整合指明了方向。

基于人工智能的优化能耗方法综述

AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive  Survey

The transition towards carbon-neutral electricity is one of the biggest game
changers in addressing climate change since it addresses the dual challenges of
removing carbon emissions from the two largest sectors of emitters: electricity
and transportation. The transition to a carbon-neutral electric grid poses
significant challenges to conventional paradigms of modern grid planning and
operation. Much of the challenge arises from the scale of the decision making
and the uncertainty associated with the energy supply and demand. Artificial
Intelligence (AI) could potentially have a transformative impact on
accelerating the speed and scale of carbon-neutral transition, as many decision
making processes in the power grid can be cast as classic, though challenging,
machine learning tasks. We point out that to amplify AI's impact on
carbon-neutral transition of the electric energy systems, the AI algorithms
originally developed for other applications should be tailored in three layers
of technology, markets, and policy.

电力电网向碳中和转变是解决气候变化最大的游戏规则之一，人工智能在技术、市场和政策等三个层面上进行定制化可以提高电能系统向碳中和的转变速度和规模。

人工智能时代的能源系统数字化：朝着碳中和的三层方法论

Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

The use of electric vehicles (EV) in the last mile is appealing from both
sustainability and operational cost perspectives. In addition to the inherent
cost efficiency of EVs, selling energy back to the grid during peak grid
demand, is a potential source of additional revenue to a fleet operator. To
achieve this, EVs have to be at specific locations (discharge points) during
specific points in time (peak period), even while meeting their core purpose of
delivering goods to customers. In this work, we consider the problem of EV
routing with constraints on loading capacity; time window; vehicle-to-grid
energy supply (CEVRPTW-D); which not only satisfy multiple system objectives,
but also scale efficiently to large problem sizes involving hundreds of
customers and discharge stations. We present QuikRouteFinder that uses
reinforcement learning (RL) for EV routing to overcome these challenges. Using
Solomon datasets, results from RL are compared against exact formulations based
on mixed-integer linear program (MILP) and genetic algorithm (GA)
metaheuristics. On an average, the results show that RL is 24 times faster than
MILP and GA, while being close in quality (within 20%) to the optimal.

本研究基于强化学习算法，从车辆装载容量、时间窗口、能量供应等多个因素出发，在大规模客户和配电站环境下，构建了满足多个系统目标的电动车路由模型，并通过实验证明，该方法不但在速度上快于混合整数线性规划和遗传算法元启发式算法，而且结果质量接近最优解。