arXiv：cs.AI· Riya Kinnarkar, Mansur M. Arief, Yan Pratama Akhra, Dino Arla·· 5 小时前
成熟与新兴经济体中社会公平的可再生能源预算分配 MDP 策略对比
Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies
AI 导读
研究将社会公平的可再生能源预算分配建模为 MDP，用同一求解器接口在美国 8 座城市（成熟经济体）与印尼西爪哇（新兴经济体）对比相同策略。滚动时域值迭代策略在两地均占优：美国实现 66% 可再生能源渗透率，并将服务不足的低收入人群较随机基线减少 96%；西爪哇则引入约 7 倍于朴素追市场启发式的私人资本。该启发式在美国仅轻微次优，在印尼却可能让每个低准入地区都被忽视，造成灾难性后果。
正文
Abstract: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 about seven times the private capital of a naive market-chasing heuristic. More interestingly, this 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 diverge once the planner acts through private developers.
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Optimization and Control (math.OC) |
| Cite as: | arXiv:2607.10201 [eess.SY] |
| (or arXiv:2607.10201v2 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2607.10201 arXiv-issued DOI via DataCite |
Submission history
From: Mansur Arief [view email]
[v1]
Sat, 11 Jul 2026 08:26:17 UTC (82 KB)
[v2]
Sun, 11 Oct 2026 21:28:24 UTC (31 KB)
来源：arXiv：cs.AI · arxiv.org