面向能源转型中价值创造的战略投资决策：一种强化学习方法

中文摘要 AI 辅助

气候变化的全球挑战推动了以2015年《巴黎协定》等国际协议为指导的大幅减排举措。行动过慢可能导致未来损失和声誉损害，而行动过快则可能因许多可再生能源项目的边际盈利能力或技术不成熟带来的潜在损失，损害股东价值。为应对这一复杂转型，能源公司必须采用序列决策（SDM）策略，以在不确定性下通过决策灵活性最大化价值创造。为此，我们开发了一个定制模拟环境，用于模拟直至2050年的动态能源格局。在此基础上，我们设计了一个多准则序列决策（SDM）框架，该框架探索与三个部门（石油与天然气、可再生能源、二氧化碳减排）资金分配的不同投资组合相关的各类决策策略，旨在在转型期间最大化价值，同时考虑产量、能源价格和成本的不确定性。该框架有三个目标：最大化利润、最小化二氧化碳社会成本、增强可再生能源领域的竞争优势。本研究评估强化学习（RL）在定义的序列决策（SDM）框架内识别最优投资策略的应用。智能体的序列决策通过影响石油与天然气产量、可再生能源产出、二氧化碳排放量和收入等关键变量来塑造虚拟动态环境。通过反复交互，强化学习算法探索状态空间并在不确定性下学习最优策略。我们将该强化学习策略与一组手动定义的基准策略进行对比，发现其在适应性和长期价值创造方面始终优于基准策略。

英文摘要

The global challenge of climate change has driven significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement of 2015. Acting too slowly could result in future losses and reputational damage, while moving too quickly could jeopardize shareholder value due to the marginal profitability or potential losses due to technology immaturity of many renewable projects. To navigate this complex transition, energy companies must adopt Sequential Decision Making (SDM) strategies to maximize value creation from decision flexibility under uncertainties. To support this, we developed a custom simulation environment to model the dynamic energy landscape up to 2050. Building on this, we designed a multi-criteria SDM framework that explores various decision strategies related to different portfolios for allocating funds across three sectors: oil & gas, renewables, and CO2 reduction. It aims to maximize value during the transition while accounting for uncertainties in productions, energy prices, and costs. This framework has three objectives: maximizing profit, minimizing CO2 social costs, and enhancing competitive advantage in the renewable energy sector. This research evaluates the use of Reinforcement Learning (RL) to identify optimal investment policies within the defined SDM framework. The agent's sequential decisions shape a virtual dynamic environment by influencing key variables such as oil and gas production, renewable energy output, CO2 emissions, and revenues. Through repeated interaction, the RL algorithm explores the state space and learns an optimal policy under uncertainty. We benchmark the RL strategy against a set of manually defined baseline policies and find it consistently outperforms them in adaptability and long-term value creation.

发表机构

• University of Stavanger（斯塔万格大学）
• NORCE Norwegian Research Centre（挪威研究中心）

机构由 AI 辅助整理，请以论文原文为准。