arXiv subjects

Eiko Furutani

Publications and source records attributed to Eiko Furutani.

9 recordsLinked to original sources

arXiv2026

Optimal design of solar-battery hybrid resources considering multi-market participation under weather and price uncertainty

The rapid growth of variable renewable energy has increased the need for flexible and efficiently coordinated energy resources. In this context, hybrid resources that combine renewable generation and battery storage within a single market-participating entity have attracted growing attention. Such hybrid resources can have multiple revenue streams, while allocating limited power and energy capacity across multiple electricity markets including energy and ancillary services. This multi-market coordination increases operational complexity and complicates profitability assessment, making optimal system sizing a challenging design problem. In addition, uncertainty in renewable generation and market prices makes it difficult for conventional optimization approaches to determine system designs that remain effective under stochastic operating conditions. To address these challenges, this paper proposes a deep reinforcement learning-based co-optimization framework for hybrid solar-battery resources. The framework embeds system design variables directly into the policy learning process, enabling joint optimization of hybrid system sizing and coordinated multi-market bidding strategies within a unified stochastic formulation. Case studies using historical renewable generation and market data demonstrate the effectiveness of the proposed framework in identifying economically rational hybrid system design considering multi-market operation.

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arXiv2026

A Reinforcement Learning-based Transmission Expansion Framework Considering Strategic Bidding in Electricity Markets

Transmission expansion planning in electricity markets is tightly coupled with the strategic bidding behaviors of generation companies. This paper proposes a Reinforcement Learning (RL)-based co-optimization framework that simultaneously learns transmission investment decisions and generator bidding strategies within a unified training process. Based on a multiagent RL framework for market simulation, the proposed method newly introduces a design policy layer that jointly optimizes continuous/discrete transmission expansion decisions together with strategic bidding policies. Through iterative interaction between market clearing and investment design, the framework effectively captures their mutual influence and achieves consistent co-optimization of expansion and bidding decisions. Case studies on the IEEE 30-bus system are provided for proof-of-concept validation of the proposed co-optimization framework.

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arXiv2026

Sizing of Battery Considering Renewable Energy Bidding Strategy with Reinforcement Learning

This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.

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arXiv2026

Probabilistic Reachability Analysis of Multi-scale Voltage Dynamics Using Reinforcement Learning

Voltage stability in modern power systems involves coupled dynamics across multiple time scales. Conventional methods based on time-scale separation or static stability margins may overlook instabilities caused by the coupling of slow and fast transients. Uncertainty in operating conditions further complicates stability assessment, and high computational cost of Monte Carlo simulations limit its applicability to multi-scale dynamics. This paper presents a deep reinforcement learning-based framework for probabilistic reachability analysis of multi-scale voltage dynamics. By formulating each instability mechanism as a distinct absorbing state and introducing a multi-critic architecture for mechanism-specific learning, the proposed method enables consistent learning of risk probabilities associated with multiple instability types within a unified framework. The approach is demonstrated on a four-bus system with load tap changers and over-excitation limiters, illustrating effectiveness of the proposed learning-based reachability analysis in identifying and quantifying the mechanisms leading to voltage collapse.

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arXiv2024

Combined Plant and Control Co-design via Solutions of Hamilton-Jacobi-Bellman Equation Based on Physics-informed Learning

This paper addresses integrated design of engineering systems, where physical structure of the plant and controller design are optimized simultaneously. To cope with uncertainties due to noises acting on the dynamics and modeling errors, an Uncertain Control Co-design (UCCD) problem formulation is proposed. Existing UCCD methods usually rely on uncertainty propagation analyses using Monte Calro methods for open-loop solutions of optimal control, which suffer from stringent trade-offs among accuracy, time horizon, and computational time. The proposed method utilizes closed-loop solutions characterized by the Hamilton-Jacobi-Bellman equation, a Partial Differential Equation (PDE) defined on the state space. A solution algorithm for the proposed UCCD formulation is developed based on PDE solutions of Physics-informed Neural Networks (PINNs). Numerical examples of regulator design problems are provided, and it is shown that simultaneous update of PINN weights and the design parameters effectively works for solving UCCD problems.

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arXiv2024

Model Predictive Online Trajectory Planning for Adaptive Battery Discharging in Fuel Cell Vehicle

This paper presents an online trajectory planning approach for optimal coordination of Fuel Cell (FC) and battery in plug-in Hybrid Electric Vehicle (HEV). One of the main challenges in energy management of plug-in HEV is generating State-of-Charge (SOC) reference curves by optimally depleting battery under high uncertainties in driving scenarios. Recent studies have begun to explore the potential of utilizing partial trip information for optimal SOC trajectory planning, but dynamic responses of the FC system are not taken into account. On the other hand, research focusing on dynamic operation of FC systems often focuses on air flow management, and battery has been treated only partially. Our aim is to fill this gap by designing an online trajectory planner for dynamic coordination of FC and battery systems that works with a high-level SOC planner in a hierarchical manner. We propose an iterative LQR based online trajectory planning method where the amount of electricity dischargeable at each driving segment can be explicitly and adaptively specified by the high-level planner. Numerical results are provided as a proof of concept example to show the effectiveness of the proposed approach.

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arXiv2024

Iterative Linear Quadratic Regulator With Variational Equation-Based Discretization

This paper discusses discretization methods for implementing nonlinear model predictive controllers using Iterative Linear Quadratic Regulator (ILQR). Finite-difference approximations are mostly used to derive a discrete-time state equation from the original continuous-time model. However, the timestep of the discretization is sometimes restricted to be small to suppress the approximation error. In this paper, we propose to use the variational equation for deriving linearizations of the discretized system required in ILQR algorithms, which allows accurate computation regardless of the timestep. Numerical simulations of the swing-up control of an inverted pendulum demonstrate the effectiveness of this method. By the relaxing stringent requirement for the size of the timestep, the use of the variational equation can improve control performance by increasing the number of ILQR iterations possible at each timestep in the realtime computation.

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arXiv2024

Simultaneous Modeling of In Vivo and In Vitro Effects of Nondepolarizing Neuromuscular Blocking Drugs

Nondepolarizing neuromuscular blocking drugs (NDNBs) are clinically used to produce muscle relaxation during general anesthesia. This paper explores a suitable model structure to simultaneously describe in vivo and in vitro effects of three clinically used NDNBs, cisatracurium, vecuronium, and rocuronium. In particular, it is discussed how to reconcile an apparent discrepancy that rocuronium is less potent at inducing muscle relaxation in vivo than predicted from in vitro experiments. We develop a framework for estimating model parameters from published in vivo and in vitro data, and thereby compare the descriptive abilities of several candidate models. It is found that modeling of dynamic effect of activation of acetylcholine receptors (AChRs) is essential for describing in vivo experimental results, and a cyclic gating scheme of AChRs is suggested to be appropriate. Furthermore, it is shown that the above discrepancy in experimental results can be resolved when we consider the fact that the in vivo concentration of ACh is quite low to activate only a part of AChRs, whereas more than 95% of AChRs are activated during in vitro experiments, and that the site-selectivity is smaller for rocuronium than those for cisatracurium and vecuronium.

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arXiv2024

Screening Curve Method for Economic Analysis of Household Solar Energy Self-Consumption

The profitability of solar energy self-consumption in households, the so-called photovoltaic (PV) self-consumption, is expected to boost the deployment of PV and battery storage systems. This paper develops a novel method for economic analysis of PV self-consumption using battery storage based on an extension of the Screening Curve Method (SCM). The SCM enables quick and intuitive estimation of the least-cost generation mix for a target load curve and has been used for generation planning for bulk power systems. In this paper, we generalize the framework of existing SCM to take into account the intermittent nature of renewable energy sources and apply it to the problem of optimal sizing of PV and battery storage systems for a household. Numerical studies are provided to verify the estimation accuracy of the proposed SCM and to illustrate its effectiveness in a sensitivity analysis, owing to its ability to show intuitive plots of cost curves for researchers or policy-makers to understand the reasons behind the optimization results.

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