Accurate price predictions are essential for market participants in order to
optimize their operational schedules and bidding strategies, especially in the
current context where electricity prices become more volatile and less
predictable using classical approaches. Locational Marginal Pricing (LMP)
pricing mechanism is used in many modern power markets, where the traditional
approach utilizes optimal power flow (OPF) solvers. However, for large
electricity grids this process becomes prohibitively time-consuming and
computationally intensive. Machine learning solutions could provide an
efficient tool for LMP prediction, especially in energy markets with
intermittent sources like renewable energy. The study evaluates the performance
of popular machine learning and deep learning models in predicting LMP on
multiple electricity grids. The accuracy and robustness of these models in
predicting LMP is assessed considering multiple scenarios. The results show
that machine learning models can predict LMP 4-5 orders of magnitude faster
than traditional OPF solvers with 5-6\% error rate, highlighting the potential
of machine learning models in LMP prediction for large-scale power models with
the help of hardware solutions like multi-core CPUs and GPUs in modern HPC
clusters.

该研究评估了在多个电力网格上使用流行的机器学习和深度学习模型预测 LMP 的性能，表明机器学习模型相比传统的最优功率流求解器在 LMP 预测方面具有更快的速度和更低的误差率，这为大规模电力模型中的 LMP 预测提供了潜力。

基于人工智能的近实时边际定价方法：可行性和鲁棒性研究

AI Driven Near Real-time Locational Marginal Pricing Method: A  Feasibility and Robustness Study

This paper introduces a deep reinforcement learning (RL) framework for
optimizing the operations of power plants pairing renewable energy with
storage. The objective is to maximize revenue from energy markets while
minimizing storage degradation costs and renewable curtailment. The framework
handles complexities such as time coupling by storage devices, uncertainty in
renewable generation and energy prices, and non-linear storage models. The
study treats the problem as a hierarchical Markov Decision Process (MDP) and
uses component-level simulators for storage. It utilizes RL to incorporate
complex storage models, overcoming restrictions of optimization-based methods
that require convex and differentiable component models. A significant aspect
of this approach is ensuring policy actions respect system constraints,
achieved via a novel method of projecting potentially infeasible actions onto a
safe state-action set. The paper demonstrates the efficacy of this approach
through extensive experiments using data from US and Indian electricity
markets, comparing the learned RL policies with a baseline control policy and a
retrospective optimal control policy. It validates the adaptability of the
learning framework with various storage models and shows the effectiveness of
RL in a complex energy optimization setting, in the context of multi-market
bidding, probabilistic forecasts, and accurate storage component models.

该研究介绍了一种深度强化学习框架，用于优化可再生能源和储能发电厂的操作，以最大化能源市场收益，同时最小化储能损耗成本和可再生资源废弃。

多市场可再生能源优化及强化学习

Multi-market Energy Optimization with Renewables via Reinforcement  Learning

Energy markets can provide incentives for undesired behavior of market
participants. Multi-agent Reinforcement learning (MARL) is a promising new
approach to determine the expected behavior of energy market participants.
However, reinforcement learning requires many interactions with the system to
converge, and the power system environment often consists of extensive
computations, e.g., optimal power flow (OPF) calculation for market clearing.
To tackle this complexity, we provide a model of the energy market to a basic
MARL algorithm, in form of a learned OPF approximation and explicit market
rules. The learned OPF surrogate model makes an explicit solving of the OPF
completely unnecessary. Our experiments demonstrate that the model additionally
reduces training time by about one order of magnitude, but at the cost of a
slightly worse approximation of the Nash equilibrium. Potential applications of
our method are market design, more realistic modeling of market participants,
and analysis of manipulative behavior.

通过提供一个学习了的 OPF 近似和显式的市场规则的能源市场模型，以 MARL 算法的形式来确定预期的能源市场参与者的行为，从而减少与系统的交互次数，并减少培训时间，同时稍微糟糕地近似了纳什均衡。