Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply

2025/07/13 by Minjae Jeon, Lang Tong, Jeon, Minjae +3
Economics, Econometrics and Finance · #Economic theories and models #FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2507.09794

openalex publication_date 2025/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We investigate the problem of serving deferrable and nondeferrable electric demands with colocated stochastic supply and grid-imported electricity. Deferrable demands arrive randomly and can be delayed within their service deadlines. Nondeferrable demands are always present and must be served immediately, but the quantity served depends on the cost of electricity. Colocated supply is stochastic with zero marginal cost. It can be used to meet demand or exported to the grid to maximize profit. The stochasticity of demands and local supply makes optimal scheduling a Markov decision process with continuous (uncountable) state and action spaces. Under deterministic, time-varying, and piecewise-linear retail pricing of electricity, we show that the optimal demand scheduling follows the \em Principle of Procrastination, which reduces the infinite-dimensional policy space to a finite-dimensional Euclidean space defined by three procrastination parameters for each deferrable demand. For settings in which the underlying probability distributions are unknown, we propose a \em Procrastination Threshold Reinforcement Learning algorithm. Numerical experiments based on real-world test data confirm that the proposed threshold learning algorithm closely approximates the optimal policy and outperforms standard benchmarks.

Citations

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• DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
• Online Reinforcement Learning of Optimal Threshold Policies for Markov\n Decision Processes
• Soft Actor-Critic Algorithms and Applications
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• On-line Building Energy Optimization using Deep Reinforcement Learning
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Deadline Scheduling as Restless Bandits
• Dynamic Scheduling for Charging Electric Vehicles: A Priority Rule
• Continuous control with deep reinforcement learning
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• A Model Predictive Control Approach for Low-Complexity Electric Vehicle Charging Scheduling: Optimality and Scalability
• Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints
• Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant\n Consumers
• The Construction of Utility Functions from Expenditure Data
• Reinforcement Learning: An Introduction

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