发表机构
Management \& Technology University of Pennsylvania Philadelphia, PA, USA; Systems Engineering Institute Technology of Sepuluh Nopember (ITS) Surabaya, Indonesia; Systems Engineering King Fahd University of Petroleum（; ; ）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究成熟与新兴经济体中社会公平的可再生能源预算分配问题，将其建模为马尔可夫决策过程，用单一求解器接口比较政策，结果显示滚动时域值迭代政策占优，还揭示简单启发式方法在不同经济体效果不同。
AI 中文摘要
公平的可再生能源规划是一个序贯决策问题，但公共规划者可用的决策变量在成熟和新兴经济体之间存在显著差异。在前者中政府主要建设发电项目，而在后者中则通过激励措施和配额引导私人投资。我们将社会公平的可再生能源预算分配制定为马尔可夫决策过程（MDP），并使用单一的与问题无关的求解器接口，比较在两种情况下相同的政策：八个美国城市（成熟经济体）和印度尼西亚西爪哇省（新兴经济体）。结果表明，在两种情况下，滚动时域值迭代政策占主导地位。在美国，它实现了66%的可再生能源渗透率，同时与随机基线相比，将服务不足的低收入人口减少了96%。在西爪哇省，它缩小了低接入差距，同时吸引了最多的私人资本。更有趣的是，一种简单的追逐市场启发式方法，在美国略有次优，在印度尼西亚可能会产生灾难性后果，因为一旦规划者通过私人开发商采取行动，追逐有吸引力的市场和服务不足目标就会产生分歧，导致每个低接入地区服务不足。
英文摘要
Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies. In the former the government largely builds generation, while in the latter it steers private investment through incentives and quotas. We formulate socially-equitable renewable-energy budget allocation as a Markov Decision Process (MDP) and, using a single problem-agnostic solver interface, compare the same policies across the two settings: eight U.S. cities (a mature economy) and West Java, Indonesia (an emerging economy). The results show that across both settings, a receding-horizon value-iteration policy dominates. In the U.S., it reaches 66% renewable penetration while cutting the underserved low-income population by 96% versus a random baseline. In West Java it closes the low-access gap while crowding in the most private capital. More interestingly, a naive market-chasing heuristic, which is mildly sub-optimal in the U.S., could yield catastrophic outcomes in Indonesia, by underserving every low-access region, because chasing attractive markets and serving the underserved goals diverge once the planner acts through private developers.
