arXiv subjects

Minjae Jeon

Publications and source records attributed to Minjae Jeon.

5 recordsLinked to original sources

arXiv2026

Threshold Pricing for Distributed Scheduling of Flexible Demands in Energy Communities

This paper develops a price-based distributed scheduling in an energy community whose members own behind-the-meter renewable generation with deferrable EV charging and price-elastic thermostatic loads. A coordinator transacts with the distribution utility under a Net Energy Metering tariff and broadcasts a community price to which each household responds in its own interest, giving a bilevel stochastic dynamic program that is intractable in general. Our main result characterizes that the joint optimal centralized policy is a two-threshold policy on the community's aggregate renewable generation. Building on this structure, we adopt the Threshold Pricing Rule, which is uniform, individually rational, revenue adequate, and asymptotically optimal in terms of community welfare under a light-traffic condition. Simulations using synthetic and real world data confirm asymptotic optimality and individual surplus gains.

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arXiv2026

Price-Based Distributed Scheduling of Flexible Demands in Energy Communities

We study price-based distributed scheduling of flexible demand in an energy community, where a coordinator broadcasts electricity prices and individual households schedule their consumption. Household demand includes deferrable and non-deferrable loads, such as electric vehicle charging with completion deadlines and thermostatically controlled loads. The coordinator transacts with a distribution utility on behalf of community members under the regulated Net Energy Metering tariff. We formulate distributed demand scheduling as a bilevel stochastic dynamic program. The upper level optimizes the coordinator's pricing policy to minimize the community's energy costs subject to operating, revenue adequacy, and individual rationality constraints. The lower level involves stochastic dynamic programs that maximize households' consumption benefits subject to the availability of renewable generation. The computational cost of such a distributed stochastic dynamic program is prohibitive in general. By uncovering the structure of optimal centralized scheduling, we derive Threshold Pricing Rule (TPR) -- a simple community pricing policy with linear computational costs for the upper- and lower-level optimizations. Being independent of parameters of the underlying stochastic dynamic program, TPR is robust against modeling uncertainties and is shown to guarantee revenue adequacy for the community and individual rationality for community members. As the community size grows, TPR is shown to be asymptotically optimal.

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arXiv2025

Joint Scheduling of Deferrable and Nondeferrable Demand with Colocated Stochastic Supply

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.

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arXiv2023

On the Optimality of Procrastination Policy for EV charging under Net Energy Metering

We consider the problem of behind-the-meter EV charging by a prosumer, co-optimized with rooftop solar, electric battery, and flexible consumptions such as water heaters and HVAC. Under the time-of-use net energy metering tariff with the stochastic solar production and random EV charging demand, a finite-horizon surplus-maximization problem is formulated. We show that a procrastination threshold policy that delays EV charging to the last possible moment is optimal when EV charging is co-optimized with flexible demand, and the policy thresholds can be computed easily offline. When battery storage is part of the co-optimization, it is shown that the net consumption of the prosumer is a two-threshold piecewise linear function of the behind-the-meter renewable generation under the optimal policy, and the procrastination threshold policy remains optimal, although the thresholds cannot be computed easily. We propose a simple myopic solution and demonstrate in simulations that the performance gap between the myopic policy and an oracle upper bound appears to be 0.5-7.5%.

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arXiv2022

Co-optimizing Consumption and EV Charging under Net Energy Metering

We consider the co-optimization of flexible household consumption, electric vehicle charging, and behind-the-meter distributed energy resources under the net energy metering tariff. Using a stochastic dynamic programming formulation, we show that the solution to the dynamic programming co-optimization is a procrastination threshold policy that delays and minimizes electricity purchasing for EV charging in each time interval. The policy thresholds can be computed off-line, simplifying the continuous action space dynamic optimization to decoupled closed-form charging and consumption decisions. Empirical studies using renewable, consumption, and EV data demonstrate the benefits of co-optimization.

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