Public profile
Hou Shengren
Building decision systems for electricity markets and energy assets
I work at the boundary of power-market research, market practice, and AI systems for energy operations. This site is my public layer for essays, research, publications, media links, and collaboration.
Public focus
What I work on
A compact map of the public topics behind my research, practice, and product judgment.
Electricity Markets
Power-market reform, market design, short-term market behavior, price formation, and cross-border mechanisms.
Energy Assets
Decision workflows for storage, flexibility, and market-facing energy assets under operational and regulatory constraints.
Decision Agents
AI systems that connect rules, forecasts, strategy, execution, risk control, and review into accountable decision loops.
Research Translation
Turning research methods into useful workflows, public analysis, and decision systems for real energy contexts.
Current work
The decision layer for market-facing energy assets
Power markets are becoming more operational, more volatile, and more data-driven. Energy assets now need better ways to combine market rules, forecasts, strategy, execution, and risk review.
My work focuses on that decision layer: how market-facing energy assets can reason about prices, constraints, uncertainty, and accountability with support from research methods and AI systems.
Decision systems
From market facts to accountable action
The public framing behind my current work: decision agents should help energy teams connect evidence, constraints, decisions, and review instead of only generating analysis.
Rules
Market design, tariffs, bidding constraints, asset limits, and operational boundaries.
Forecasts
Prices, uncertainty, renewable output, demand, flexibility, and scenario distributions.
Strategy
Dispatch logic, participation choices, risk appetite, and portfolio-level priorities.
Execution
Human-reviewed actions, operating receipts, exceptions, and traceable workflow states.
Risk control
Policy boundaries, anomaly checks, override paths, and failure-mode awareness.
Review
Post-event learning, model feedback, decision records, and accountable improvement.
Experience
Public background
The through-line is research training, market practice, AI decision-system building, and public writing.
Research foundations
The analytical base behind the work
Research remains the method base for product judgment, market interpretation, and decision-system design.
Electricity markets
Market design, cross-border mechanisms, short-term markets, and price behavior.
Energy AI
Forecasting, probabilistic modeling, optimization, reinforcement learning, and safe decision systems.
Storage and flexibility
Battery dispatch, flexibility strategy, and market participation under operational constraints.
Research translation
Turning strong methods into public writing, useful workflows, and decision systems that can be examined.
Selected publications
Representative academic work
Selected public research related to energy systems, storage, and decision methods.
- RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks Energy and AI, 2025.
- Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks arXiv, 2024.
- Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm International Journal of Electrical Power & Energy Systems, 2023.
Selected writing
Essays and public notes
Long-form writing on electricity markets, AI systems, entrepreneurship, and research translation.
2026-06-09
认识一个问题
认清一个问题的过程，就是在解决这个问题。
Read Essay2026-03-20
Ten Years of Study and Research
Notes on imitation learning, scientific curiosity, mentorship, and growth, adapted from a previous interview.
Reflections from a decade of study, from undergraduate education to doctoral research, on decision-making, curiosity, scientific practice, and mentorship.
Read EssayContact
Open to public conversations around energy technology, power markets, and AI decision systems.
If your work touches electricity markets, storage, energy AI, speaking, or research translation, email is the cleanest starting point.
公开方向
我关注的工作
这里是支撑我研究、实践和产品判断的几个长期问题。
电力市场
电力市场改革、市场设计、短期市场行为、价格形成和跨区机制。
能源资产
面向储能、灵活性和市场化能源资产的决策工作流，关注运行约束和规则约束。
决策 Agent
把规则、预测、策略、执行、风控和复盘连接成可追踪的 AI 决策闭环。
研究转化
把研究方法转化为真实能源场景中的工作流、公开分析和决策系统。
当前工作
面向市场化能源资产的决策层
电力市场正在变得更运营化、更波动、更数据驱动。能源资产需要更好地把市场规则、预测、策略、执行和风险复盘结合起来。
我的工作聚焦在这个决策层：面向市场的能源资产如何在价格、约束、不确定性和责任边界之间做出更好的判断，并通过研究方法和 AI 系统获得支持。
决策系统
从市场事实到可追踪行动
这是我当前工作的公开表达：决策 Agent 不应只是生成分析，而应帮助能源团队连接证据、约束、决策和复盘。
规则
市场机制、价格规则、报价约束、资产边界和运行限制。
预测
价格、不确定性、新能源出力、负荷、灵活性和场景分布。
策略
调度逻辑、参与选择、风险偏好和组合层面的优先级。
执行
人工确认的行动、运行回执、异常处理和可追踪状态。
风控
策略边界、异常检查、覆盖路径和失败模式意识。
复盘
事后学习、模型反馈、决策记录和可问责的持续改进。
经历
公开背景
这条主线可以概括为：研究训练、市场实践、AI 决策系统建设和公开写作。
研究基础
支撑当前工作的分析底座
研究仍然是我进行产品判断、市场理解和决策系统设计的方法基础。
电力市场
市场设计、跨区机制、短期市场和价格行为。
能源 AI
预测、概率建模、优化、强化学习，以及安全可控的决策系统。
储能与灵活性
电池调度、灵活性策略，以及运行约束下的市场参与逻辑。
研究转化
把强方法转化为公开写作、可用工作流和可被审视的决策系统。
代表论文
公开学术工作
与能源系统、储能和决策方法相关的部分公开研究。
- RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks Energy and AI, 2025.
- Safe Imitation Learning-based Optimal Energy Storage Systems Dispatch in Distribution Networks arXiv, 2024.
- Optimal energy system scheduling using a constraint-aware reinforcement learning algorithm International Journal of Electrical Power & Energy Systems, 2023.
公开写作
随笔与公开笔记
围绕电力市场、AI 系统、创业和研究转化的长文写作。
2026-06-09
认识一个问题
认清一个问题的过程，就是在解决这个问题。
阅读随笔2026-03-20
十年研学路：关于模仿学习、科研与成长的几点感悟
整理自“研途风采”访谈，关于模仿学习、科研、导师与成长的几点感悟。
从本科到博士十年求学路上的一些感悟，关于决策、好奇心、科研训练与导师的重要性。
阅读随笔联系
欢迎围绕能源科技、电力市场和 AI 决策系统进行公开交流。
如果你的工作涉及电力市场、储能、能源 AI、演讲或研究转化，邮件是最清晰的起点。