Quantum State Estimation for Real-Time Battery Health Monitoring in Photovoltaic Storage Systems

Abstract

The growing deployment of photovoltaic (PV) and energy storage systems (ESSs) in power grids has amplified concerns over component degradation, which undermines efficiency, increases costs, and shortens system lifespan. This paper proposes a quantum-enhanced optimization framework to mitigate degradation impacts in PV–storage systems through real-time adaptive energy dispatch. The framework combines quantum-assisted Monte Carlo simulation, quantum annealing, and reinforcement learning to model and optimize degradation pathways. A predictive maintenance module proactively adjusts charge–discharge cycles based on probabilistic forecasts of degradation states, improving resilience and operational efficiency. A hierarchical structure enables real-time degradation assessment, hourly dispatch optimization, and weekly long-term adjustments. The model is validated on a 5 MW PV array with a 2.5 MWh lithium-ion battery using real degradation profiles. Results demonstrate that the proposed framework reduces battery wear by 25% and extends PV module lifespan by approximately 2.5 years compared to classical methods. The hybrid quantum–classical implementation achieves scalable optimization under uncertainty, enabling faster convergence across high-dimensional solution spaces. This study introduces a novel paradigm in degradation-aware energy management, highlighting the potential of quantum computing to enhance both the sustainability and real-time control of renewable energy systems.

1. Introduction

The increasing penetration of photovoltaic and energy storage systems (ESSs) in modern power networks has necessitated a shift toward more efficient and resilient operational strategies. Renewable energy sources, particularly solar power, are critical for achieving decarbonization goals and enhancing grid sustainability [1,2]. However, the long-term reliability of PV–storage systems is significantly compromised by the progressive degradation of both PV modules and energy storage components, which leads to reduced efficiency, increased operational costs, and suboptimal energy dispatch [3]. Battery degradation, characterized by capacity fade and power loss over repeated charge–discharge cycles, is accelerated by thermal instability and stochastic energy demand fluctuations [4]. Similarly, PV degradation, primarily induced by light-induced degradation (LID), temperature effects, and material aging, results in substantial efficiency losses over time. Traditional control and optimization methods for mitigating these degradation effects often fail to address the underlying complex, multi-dimensional interactions governing the longevity of these components [5]. These limitations highlight the urgent need for a more advanced, adaptive, and predictive optimization framework that ensures prolonged system lifespan while maintaining high energy efficiency.

This paper presents a novel quantum-based degradation pathway optimization framework designed to enhance the longevity and efficiency of PV–storage systems. Unlike conventional models that rely on classical optimization techniques, the proposed approach harnesses quantum computing’s inherent advantages in solving complex, high-dimensional energy dispatch problems. By leveraging quantum-enhanced Monte Carlo methods and quantum annealing-based multi-objective optimization, the model dynamically adjusts energy dispatch and operational strategies to mitigate degradation effects in both PV modules and batteries. The fundamental novelty of this research lies in integrating a quantum-inspired degradation prediction mechanism, which proactively forecasts degradation pathways and dynamically optimizes energy dispatch accordingly. This predictive capability ensures that PV–storage systems operate at peak efficiency while minimizing wear-induced energy losses. The proposed approach introduces a unique hybrid quantum–classical optimization framework that integrates real-time degradation assessment with quantum-enhanced predictive maintenance. By continuously analyzing degradation trends, the model provides an adaptive dispatching strategy that balances energy demand with system longevity. This is achieved through a quantum-inspired probabilistic modeling approach that evaluates numerous potential degradation scenarios in parallel, allowing for highly efficient decision-making. Compared to classical optimization techniques such as MILP or PSO, which evaluate solutions sequentially and may become trapped in local optima, quantum annealing leverages quantum tunneling to explore multiple solution paths in parallel. This ability allows the quantum annealer to escape suboptimal regions of the solution space more efficiently, particularly in complex, high-dimensional problems with numerous local minima. As a result, quantum annealing can provide faster convergence toward near-optimal solutions in problems where classical methods face scalability and efficiency limitations. This research contributes to the field of renewable energy optimization and quantum-enhanced decision-making in several ways. First, it pioneers a novel quantum-inspired degradation pathway optimization framework that integrates quantum annealing and quantum-enhanced Monte Carlo simulations to proactively mitigate PV–storage degradation effects. Unlike existing methods, this approach accounts for dynamic degradation states and optimally adjusts energy dispatch in real time. Second, it introduces a hybrid quantum–classical optimization model that systematically incorporates degradation-aware constraints, ensuring that energy storage systems operate within longevity-enhancing parameters. Third, the paper develops a predictive maintenance framework that utilizes quantum probabilistic modeling to forecast degradation states and proactively adjust operational strategies, thereby enhancing overall system reliability. Finally, the proposed approach is validated through extensive simulations, demonstrating its effectiveness in minimizing degradation-induced efficiency losses while optimizing energy dispatch strategies. These contributions collectively establish a new paradigm for degradation-aware PV–storage system optimization, providing a scalable and computationally efficient framework that can be extended to broader renewable energy applications.

To facilitate clarity and improve the interpretability of the mathematical formulations presented in this study, a detailed nomenclature is provided below. This section systematically defines all key variables, symbols, and abbreviations used throughout the paper, along with their physical meanings and corresponding SI units. The inclusion of this nomenclature ensures that readers from diverse backgrounds can easily follow the modeling structure and understand the relationships between system components, optimization parameters, and degradation dynamics.

2. Literature Review

The optimization of PV–storage systems has been a widely studied topic in renewable energy research, focusing primarily on improving efficiency, extending system lifespan, and minimizing operational costs. Traditional optimization approaches have relied on classical mathematical programming techniques, such as mixed-integer linear programming (MILP), nonlinear programming (NLP), and heuristic-based methods, to optimize energy storage utilization and photovoltaic power dispatch [6,7,8,9]. These models typically address system efficiency by minimizing power losses, optimizing charge–discharge schedules, and integrating demand-side management strategies. However, one of the major limitations of classical optimization models is their inability to fully capture the stochastic and highly nonlinear degradation mechanisms of PV and battery systems. The degradation of these components is affected by a wide range of variables, including temperature variations, charge–discharge cycling, and long-term exposure to environmental factors, making it increasingly difficult for conventional optimization approaches to adapt dynamically to evolving system conditions [10,11,12].

Battery degradation has been extensively studied, with numerous models developed to analyze capacity fade, internal resistance growth, and cycle life reduction [13]. Electrochemical-based degradation models have been proposed to characterize lithium-ion battery aging, incorporating capacity retention functions and state-of-health (SoH) tracking mechanisms. These models typically rely on historical degradation data to predict battery lifespan under different operational conditions. However, their effectiveness is limited by the complexity of degradation pathways, which involve intricate interactions between charge–discharge dynamics, temperature, and battery chemistry [14,15,16]. Data-driven machine learning models, including neural networks and reinforcement learning-based optimization, have been explored to provide predictive insights into battery degradation. While these methods have demonstrated improvements in forecasting degradation patterns, they often suffer from high computational costs and require large datasets for training, making them less adaptable to real-time optimization scenarios.

Similarly, PV degradation has been analyzed through physical modeling approaches that assess light-induced degradation (LID), temperature-dependent efficiency losses, and material aging. Empirical models based on long-term performance monitoring have been developed to predict PV efficiency decline over time [3]. Some studies have introduced adaptive control strategies to mitigate efficiency losses by adjusting maximum power point tracking (MPPT) algorithms and modifying PV tilt angles to compensate for aging effects. Despite these advancements, existing approaches lack a comprehensive degradation-aware dispatch strategy that optimally allocates energy resources while actively preventing excessive degradation. The absence of an integrated predictive maintenance framework further limits the effectiveness of these strategies, as they primarily focus on reactive rather than proactive degradation mitigation [10,17,18,19]. Quantum computing has recently emerged as a promising alternative for solving large-scale optimization problems, particularly in the energy sector [20,21,22]. Quantum annealing-based optimization has been explored for power system scheduling, offering significant computational speedup in solving high-dimensional energy dispatch problems. Studies have demonstrated the potential of quantum-enhanced algorithms in optimizing microgrid operations, demand-side management, and renewable energy forecasting [20,23,24]. However, the application of quantum computing to degradation-aware PV–storage optimization remains largely unexplored. Existing quantum-based optimization frameworks primarily focus on solving economic dispatch problems, unit commitment scheduling, and optimal power flow calculations, without explicitly addressing the impact of component degradation. Recent advancements in hybrid quantum–classical optimization techniques have opened new avenues for addressing complex, multi-objective optimization problems. The integration of quantum-inspired metaheuristic algorithms with classical reinforcement learning approaches has shown promising results in energy optimization tasks [25,26]. Some studies have proposed the use of quantum-enhanced Monte Carlo simulations for energy forecasting and grid reliability assessment [27,28,29]. These techniques leverage quantum parallelism to evaluate multiple scenarios simultaneously, significantly improving computational efficiency [30]. However, their application in predictive degradation modeling for PV–storage systems remains underdeveloped, with most existing studies focusing on energy cost minimization rather than long-term sustainability.

3. Mathematical Modeling

The proposed quantum-enhanced degradation pathway optimization framework is formulated as a multi-objective optimization problem, designed to minimize degradation-induced efficiency losses while maintaining optimal energy dispatch in a PV–storage system. The mathematical model integrates degradation-aware constraints, real-time predictive maintenance, and quantum-assisted optimization mechanisms, ensuring that operational strategies adapt dynamically to evolving system conditions. This section details the mathematical formulation of the optimization problem, including the objective function and constraints, followed by an in-depth explanation of the quantum–classical hybrid optimization methodology. To accurately capture the impact of PV module and battery degradation, the optimization problem is formulated to balance energy efficiency, longevity, and dispatch stability. The objective function is designed to minimize degradation costs, energy losses, and operational inefficiencies, incorporating quantum-enhanced Monte Carlo estimations and degradation-aware probabilistic modeling. The constraints enforce state-of-health (SoH) limits, charge–discharge cycle constraints, energy balance requirements, and voltage stability conditions, ensuring that the optimization framework remains feasible and physically realizable within a real-world PV–storage system. The optimization problem is solved using a hybrid quantum–classical approach, integrating quantum-assisted Monte Carlo simulations, quantum annealing-based multi-objective optimization, and reinforcement learning-enhanced predictive maintenance. The quantum-inspired probabilistic model evaluates multiple degradation scenarios in parallel, enabling faster convergence and superior decision-making compared to traditional classical optimization techniques. The computational implementation leverages D-Wave’s Advantage quantum annealer, combined with a classical reinforcement learning engine, ensuring real-time operational adjustments based on degradation state forecasting and stochastic energy demand patterns.

(1)$\begin{array}{c}\hfill \underset{\mathbf{\Phi },\mathbf{\Psi },\mathbf{{\rm Y}}}{min}\sum _{t=1}^T\sum _{i=1}^N\left[{\lambda }_{i,t}·{\left({\displaystyle \frac{\partial {\mathbf{\Phi }}_{i,t}}{\partial t}}\right)}^2+{\beta }_{i,t}·\left({\mathbf{\Psi }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{{\rm Y}}}_{i,t}}\right)+{\mu }_{i,t}·\left({\int }_{\mathsf{\Omega }}{\mathbf{\Phi }}_{i,t}·{\mathbf{\Psi }}_{i,t}d\mathsf{\Omega }\right)\right]\end{array}$

A well-crafted optimization function must capture the intricate interplay of degradation, energy dispatch, and longevity. Here, the objective function meticulously balances operational efficiency and degradation mitigation. The first term encapsulates the rate-dependent cost of degradation, where ${\lambda }_{i,t}$ penalizes rapid changes in the system state ${\mathbf{\Phi }}_{i,t}$. The second term, infused with an exponential decay function, models the energy loss due to material fatigue, governed by ${\mathbf{\Psi }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{{\rm Y}}}_{i,t}}$, where $\kappa$ is a degradation scaling factor. The final integral term embeds spatial dependencies, ensuring energy dispatch is harmonized with system longevity constraints.

(2)$\begin{array}{c}\hfill \underset{\mathbf{\Theta },\mathbf{\Xi },\mathbf{\Omega }}{max}\sum _{t=1}^T\sum _{i=1}^N\left\{{\zeta }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^{\delta }·cos\left({\phi }_{i,t}\right)\right)+{\rho }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Xi }}_{i,t}^2}{1+e^{-\nu {\mathbf{\Omega }}_{i,t}}}}\right)+{\int }_{{\mathbb{R}}^d}\left[{\mathbf{\Theta }}_{i,t}·{\mathbf{\Xi }}_{i,t}\right]dV\right\}\end{array}$

Long-term efficiency in PV–storage systems must be dynamically adjusted to counteract unpredictable degradation patterns. This equation employs a quantum-enhanced Monte Carlo approach to estimate energy efficiency by maximizing key operational parameters. The first term accounts for power output fluctuations through a cosine-based dependency on the phase shift ${\phi }_{i,t}$, ensuring smooth transitions in dispatch strategies. The second term incorporates a quantum sigmoid function ${\displaystyle \frac{{\mathbf{\Xi }}_{i,t}^2}{1+e^{-\nu {\mathbf{\Omega }}_{i,t}}}}$, which enhances the robustness of energy utilization strategies against stochastic environmental variations. Lastly, the volumetric integral term ensures spatial coherence in optimizing distributed resources.

(3)$\begin{array}{c}\hfill \underset{\mathbf{\Gamma },\mathbf{\Lambda },\mathbf{\Sigma }}{min}\sum _{t=1}^T\sum _{i=1}^N\left\{{\chi }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Gamma }}_{i,t}}{\partial t^2}}+\alpha {\mathbf{\Lambda }}_{i,t}^{\beta }\right)+{\psi }_{i,t}·\left({\mathbf{\Sigma }}_{i,t}·e^{-\eta {\mathbf{\Lambda }}_{i,t}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}\end{array}$

The challenge in degradation modeling lies in effectively capturing the nonlinear and stochastic deterioration trends of battery and PV cells. This function integrates a second-order temporal derivative ${\displaystyle \frac{{\partial }^2{\mathbf{\Gamma }}_{i,t}}{\partial t^2}}$ to quantify abrupt degradation rate changes, crucial for predictive maintenance strategies. The second term incorporates a power-law dependency on ${\mathbf{\Lambda }}_{i,t}^{\beta }$, which governs chemical aging, while the exponential decay function ${\mathbf{\Sigma }}_{i,t}·e^{-\eta {\mathbf{\Lambda }}_{i,t}}$ introduces an intrinsic correction factor for prolonged stress conditions. Finally, the hyperspherical integral term ensures multi-dimensional coherence in degradation modeling across interconnected components.

(4)$\begin{array}{c}\hfill \underset{\mathbf{\Delta },\mathbf{\Pi },\mathbf{{\rm Y}}}{min}\sum _{t=1}^T\sum _{i=1}^N\left\{{\kappa }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Delta }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{{\rm Y}}}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\end{array}$

Optimizing PV–storage systems requires a delicate balance between degradation mitigation and energy dispatch efficiency. This multi-objective quantum function elegantly captures this balance through three sophisticated terms. The first term applies a quantum sigmoid function ${\displaystyle \frac{{\mathbf{\Delta }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}$, ensuring a smooth transition in operational modes based on real-time degradation feedback. The second term penalizes rapid degradation fluctuations using a squared decay term ${\mathbf{{\rm Y}}}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}$, effectively suppressing degradation shocks. Lastly, the integral over a hyperbolic manifold ${\mathbb{H}}^n$ ensures a globally optimal resource allocation strategy, reinforcing energy resilience at scale.

(5)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\theta }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}+\gamma ·{\mathbf{\Lambda }}_{i,t}·e^{-\beta {\mathbf{\Psi }}_{i,t}}\right)+{\varphi }_{i,t}·\left({\mathbf{\Sigma }}_{i,t}·e^{-\eta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {ϵ}_{max}\end{array}$

Battery degradation is inherently tied to charge–discharge cycling and thermal conditions. This constraint ensures that the cumulative impact of cycling and heat stress remains below a predefined threshold ${ϵ}_{max}$. The first term models acceleration in degradation via the second-order temporal derivative of the state-of-health ${\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}$, while the exponential decay term captures the impact of temperature-induced stress via ${\mathbf{\Lambda }}_{i,t}·e^{-\beta {\mathbf{\Psi }}_{i,t}}$. These terms, together, dynamically regulate charge–discharge parameters, mitigating long-term degradation risks.

(6)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\omega }_{i,t}·\left({\displaystyle \frac{\partial {\mathbf{\Theta }}_{i,t}}{\partial t}}+\alpha ·{\mathbf{\Xi }}_{i,t}^{\delta }\right)+{\lambda }_{i,t}·\left(e^{-\kappa {\mathbf{\Xi }}_{i,t}}\right)+{\int }_{{\mathbb{R}}^d}\left[{\mathbf{\Theta }}_{i,t}·{\mathbf{\Xi }}_{i,t}\right]dV\right\}\ge {\eta }_{min}\end{array}$

PV degradation must be constrained to avoid drastic efficiency reductions due to light-induced degradation (LID) and temperature-dependent aging effects. The first term tracks efficiency loss over time using the first derivative of ${\mathbf{\Theta }}_{i,t}$, ensuring gradual degradation control. The second term integrates a quantum-enhanced exponential decay model $e^{-\kappa {\mathbf{\Xi }}_{i,t}}$, capturing abrupt efficiency drops under high-stress conditions. The final integral term enforces spatial constraints on energy dispatch, ensuring uniform degradation spread across PV modules.

(7)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\phi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\psi }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

Energy balance is fundamental to maintaining grid stability. This equation ensures power conservation across generation, storage, and demand. The first term accounts for total generated, stored, and consumed energy, maintaining net-zero discrepancy. The second term introduces a quantum sigmoid function ${\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}$ to optimize energy dispatch under uncertainty. The final integral term provides system-wide energy coordination to prevent excessive degradation while sustaining operational efficiency.

(8)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\kappa }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Delta }}_{i,t}}{\partial t^2}}+\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }\right)+{\zeta }_{i,t}·\left(e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\le {\gamma }_{max}\end{array}$

Quantum state transitions impose constraints to ensure system variables respect coherence properties. The second-order derivative term ${\displaystyle \frac{{\partial }^2{\mathbf{\Delta }}_{i,t}}{\partial t^2}}$ enforces smooth transitions between energy dispatch states. The power-law degradation function $\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }$ prevents excessive charge–discharge oscillations, while the integral over a hyperbolic manifold ${\mathbb{H}}^n$ ensures a globally stable energy profile across interconnected subsystems.

(9)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\mu }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\delta {\mathbf{\Psi }}_{i,t}}}}\right)+{\xi }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^2·e^{-\beta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This charge–discharge cycle constraint ensures that batteries do not undergo excessive wear by limiting operational cycles. The first term incorporates a sigmoid function to control depth-of-discharge, preventing rapid aging. The second term integrates an exponential decay function to prevent charge rates from exceeding degradation-safe limits. These mechanisms collectively regulate cycle frequency to maximize battery lifespan.

(10)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\eta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Xi }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{\Omega }}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)\right\}\le {\theta }_{max}\end{array}$

Maintaining thermal stability is critical for battery and PV longevity. This equation constrains operational temperatures, ensuring that both storage and generation units do not exceed thermal thresholds. The sigmoid function prevents abrupt temperature spikes, while the squared decay term ensures smooth thermal transitions across different operational states.

(11)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\phi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\psi }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

To ensure long-term resilience, a quantum-enhanced predictive degradation function is employed. This constraint dynamically integrates real-time degradation feedback, ensuring that the system preemptively adjusts operational parameters. The exponential term provides a predictive mechanism to prevent sudden efficiency drops, while the integral enforces a distributed degradation control strategy across interconnected components.

(12)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\omega }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\alpha {\mathbf{\Theta }}_{i,t}}}}\right)+{\beta }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{\Sigma }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

Efficient load distribution across a PV–storage system is essential to avoid localized degradation effects. The first term introduces a quantum sigmoid function to ensure balanced energy allocation by adjusting the ratio of charge–discharge cycles ${\mathbf{\Lambda }}_{i,t}$ based on system state ${\mathbf{\Theta }}_{i,t}$. The second term penalizes excessive localized stress using a power-law degradation model ${\mathbf{\Gamma }}_{i,t}^{\gamma }$ modulated by exponential decay $e^{-\kappa {\mathbf{\Sigma }}_{i,t}}$, where $\kappa$ is a material aging coefficient.

(13)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\varphi }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}+\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }\right)+{\psi }_{i,t}·\left(e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\ge {\gamma }_{min}\end{array}$

This constraint ensures that state-of-health (${\mathbf{\Xi }}_{i,t}$) remains within safe operating limits by integrating a second-order derivative term, limiting degradation acceleration. The power-law degradation function $\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }$ suppresses excessive fluctuations, and the exponential decay function $e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}$ models extreme degradation scenarios, ensuring sustainable long-term operation.

(14)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\lambda }_{i,t}·\left({\mathbf{\Theta }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\zeta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)\right\}=0\end{array}$

The grid-integration constraint enforces total energy conservation across generation, storage, and consumption units. The first term ensures that generation and storage sum up to the required demand, preventing imbalances. The sigmoid-based function ${\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}$ optimizes dispatch under stochastic environmental conditions, improving grid resilience.

(15)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\mu }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\delta {\mathbf{\Psi }}_{i,t}}}}\right)+{\xi }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^2·e^{-\beta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This constraint safeguards system stability by regulating sudden fluctuations in power injections. The sigmoid function dynamically smooths energy transitions, and the squared decay function prevents excessive energy discharge rates by introducing a thermal stability penalty.

(16)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\eta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Xi }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{\Omega }}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)\right\}\le {\theta }_{max}\end{array}$

Thermal overload is one of the primary accelerators of battery and PV degradation. This constraint caps system-wide temperature variations, ensuring that battery stress remains within tolerable thresholds. The sigmoid function provides a smooth response to temperature fluctuations, while the squared decay function models overheating effects.

(17)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\phi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\psi }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

Degradation forecasting requires real-time adjustments to system dispatch and operational cycles. This equation integrates a quantum-enhanced predictive degradation model that preemptively adjusts charge–discharge rates based on expected future degradation patterns, ensuring proactive maintenance scheduling.

(18)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\kappa }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Delta }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{{\rm Y}}}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\le {\zeta }_{max}\end{array}$

By integrating real-time measurements and predictive models, this constraint dynamically adjusts system dispatch to minimize long-term efficiency losses. The quantum-inspired sigmoid function ensures smooth degradation adaptation, while the integral term guarantees that local degradation effects do not propagate through the entire system.

(19)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\lambda }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Theta }}_{i,t}}{1+e^{-\beta {\mathbf{\Psi }}_{i,t}}}}\right)+{\omega }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}^2·e^{-\kappa {\mathbf{\Sigma }}_{i,t}}\right)\right\}\le {\delta }_{max}\end{array}$

This constraint introduces a quantum-controlled degradation mitigation strategy by modulating system operations based on historical degradation pathways. The sigmoid function smooths transitions in degradation rates, while the squared decay function penalizes high-stress cycles. This formulation ensures maximum system longevity while maintaining performance reliability.

(20)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\vartheta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Psi }}_{i,t}}{1+e^{-\alpha {\mathbf{\Lambda }}_{i,t}}}}\right)+{\chi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{\Sigma }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

To prevent catastrophic performance degradation, this constraint ensures that energy storage devices operate within optimal efficiency limits. The first term uses a quantum-enhanced sigmoid function to dynamically regulate the discharge ratio ${\mathbf{\Psi }}_{i,t}$ based on the system state ${\mathbf{\Lambda }}_{i,t}$, where $\alpha$ determines the smoothness of transitions. The second term penalizes excessive local stress using a power-law degradation model ${\mathbf{\Gamma }}_{i,t}^{\gamma }$ modified by exponential decay $e^{-\kappa {\mathbf{\Sigma }}_{i,t}}$, where $\kappa$ represents the system’s degradation sensitivity.

(21)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\zeta }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}+\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }\right)+{\varphi }_{i,t}·\left(e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\ge {\gamma }_{min}\end{array}$

Energy dispatch strategies must adapt in real time to degradation feedback to prevent system failure. The second-order derivative term ${\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}$ controls abrupt changes in state-of-health (SoH) ${\mathbf{\Xi }}_{i,t}$, ensuring degradation remains gradual. The power-law function $\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }$ restricts rapid variations in energy flow, reducing stress on storage systems. Additionally, the exponential decay function $e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}$ penalizes extreme degradation states, while the integral term over a hyperbolic manifold ${\mathbb{H}}^n$ enforces global stability across the entire PV–storage system.

(22)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\lambda }_{i,t}·\left({\mathbf{\Theta }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\omega }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

Ensuring energy balance across the system is crucial to maintaining resilience under uncertainty. The first term ensures that the total energy generated, stored, and consumed in the system satisfies the net-zero conservation law. The second term, a quantum sigmoid function, ensures that dispatch decisions respect grid stability, mitigating power fluctuations through dynamic adjustments based on ${\mathbf{\Xi }}_{i,t}$. The integral term over a spherical manifold ${\mathbb{S}}^m$ ensures a spatially uniform energy distribution, preventing localized energy concentration that could accelerate degradation.

(23)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\mu }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\delta {\mathbf{\Psi }}_{i,t}}}}\right)+{\xi }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^2·e^{-\beta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

As a final constraint, this equation introduces quantum-enhanced predictive maintenance, ensuring the system proactively adapts to degradation patterns. The first term smooths transitions between different operational states via a sigmoid-based degradation regulation function, preventing abrupt state shifts. The second term includes a squared energy-dependent decay function, where ${\mathbf{\Theta }}_{i,t}^2$ dynamically adjusts degradation rates based on past operational history. These combined mechanisms ensure the maximization of system longevity while maintaining real-time operational reliability.

(24)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\vartheta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Psi }}_{i,t}}{1+e^{-\alpha {\mathbf{\Lambda }}_{i,t}}}}\right)+{\chi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{\Sigma }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This constraint ensures battery cycling remains within optimized operational limits by incorporating real-time degradation feedback into quantum-enhanced dispatch strategies. The first term applies a sigmoid-based degradation control mechanism to dynamically regulate the charge–discharge ratio ${\mathbf{\Psi }}_{i,t}$ in response to system state ${\mathbf{\Lambda }}_{i,t}$, where $\alpha$ defines transition smoothness. The second term includes an exponential material aging function, modulating the stress impact on system longevity, where $\kappa$ represents the degradation sensitivity coefficient.

(25)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\varphi }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}+\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }\right)+{\psi }_{i,t}·\left(e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\ge {\gamma }_{min}\end{array}$

By ensuring that the state-of-health (SoH) remains above a critical threshold, this equation incorporates a second-order degradation function ${\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}$ to limit SoH acceleration. The power-law stress penalty term $\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }$ restricts system fluctuations, reducing excess mechanical stress on PV and storage devices. The integral term over a hyperbolic manifold ${\mathbb{H}}^n$ guarantees that degradation remains spatially uniform across distributed energy assets.

(26)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\lambda }_{i,t}·\left({\mathbf{\Theta }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\omega }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

To maintain grid stability under uncertain conditions, this equation ensures that total energy generation, storage, and consumption satisfy conservation principles. The first term ensures balanced energy interactions between system components. The quantum sigmoid function ${\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}$ dynamically modulates energy dispatch to smooth out stochastic variations. The integral over a spherical manifold ${\mathbb{S}}^m$ guarantees spatial uniformity, preventing local energy accumulation that could lead to system stress.

(27)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\mu }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\delta {\mathbf{\Psi }}_{i,t}}}}\right)+{\xi }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^2·e^{-\beta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This constraint enforces thermal and electrical stability, limiting system operating ranges under real-time predictive optimization. The first term uses a sigmoid-controlled energy balancing mechanism to regulate energy transitions, preventing extreme power fluctuations. The second term, a quadratic exponential decay model, limits charge–discharge stress accumulation, where $\beta$ determines the rate of temperature-dependent degradation.

(28)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\eta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Xi }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{\Omega }}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)\right\}\le {\theta }_{max}\end{array}$

By mitigating overheating effects in PV–storage systems, this equation prevents excessive thermal cycling-induced degradation. The first term regulates temperature-sensitive operations via a quantum-based sigmoid function, ensuring smooth thermal transitions. The second term, a quadratic energy decay model, prevents charge–discharge shocks that could induce thermal stress buildup, where $\rho$ defines the system’s stress sensitivity.

(29)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\phi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\psi }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

This equation enforces global degradation mitigation by integrating real-time quantum-enhanced predictive maintenance mechanisms. The first term maintains total system balance between generation, storage, and utilization. The quantum sigmoid function ensures energy dispatch decisions dynamically adapt to expected degradation trends, smoothing transitions. The spherical integral term guarantees multi-dimensional degradation control, ensuring the entire PV–storage system maintains operational efficiency under uncertainty.

(30)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\theta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Psi }}_{i,t}}{1+e^{-\alpha {\mathbf{\Lambda }}_{i,t}}}}\right)+{\varphi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{\Sigma }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This equation ensures proactive degradation control through a quantum-enhanced sigmoid function regulating the charge–discharge ratio ${\mathbf{\Psi }}_{i,t}$. The first term applies smooth degradation constraints via a sigmoid curve, controlled by $\alpha$, ensuring stable transition in energy dispatch. The second term represents exponential material aging, integrating long-term stress factors ${\mathbf{\Gamma }}_{i,t}^{\gamma }$ and decay effects $e^{-\kappa {\mathbf{\Sigma }}_{i,t}}$, where $\kappa$ dictates material sensitivity.

(31)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\lambda }_{i,t}·\left({\mathbf{\Theta }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\omega }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

This constraint maintains real-time grid balance, ensuring total power generation, storage, and consumption remain in equilibrium. The first term tracks energy flow, maintaining net-zero energy misallocation. The quantum sigmoid function dynamically adapts to real-time energy demand fluctuations, optimizing efficiency through the parameter $\gamma$. The integral term, spanning a spherical manifold ${\mathbb{S}}^m$, ensures spatial coherence in degradation control, preventing localized wear.

(32)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\eta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Xi }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{\Omega }}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)\right\}\le {\theta }_{max}\end{array}$

Thermal resilience is crucial for preventing temperature-induced degradation in PV–storage systems. The first term models energy dispatch via a sigmoid-controlled temperature constraint, ensuring gradual adaptation to heat variations, while the second term introduces a quadratic decay function modulated by $\rho$, which penalizes excessive thermal cycling and limits overheating risks.

(33)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\mu }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\delta {\mathbf{\Psi }}_{i,t}}}}\right)+{\xi }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^2·e^{-\beta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This equation enforces energy dispatch stability through a quantum predictive control mechanism. The first term dynamically adjusts charge–discharge cycles using a sigmoid function, ensuring smooth operational transitions. The second term, a quadratic degradation penalty, restricts high-stress scenarios, where $\beta$ controls stress acceleration under extreme energy transitions.

(34)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\phi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\psi }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

This constraint integrates quantum-enhanced predictive degradation models, ensuring proactive maintenance scheduling in PV–storage systems. The first term enforces equilibrium in system-wide energy distribution, avoiding localized overloading. The quantum sigmoid function ensures energy dispatch is dynamically optimized in response to real-time degradation, while the spherical manifold integral guarantees spatial uniformity in operational constraints.

(35)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\zeta }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}+\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }\right)+{\varphi }_{i,t}·\left(e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\ge {\gamma }_{min}\end{array}$

As a final constraint, this equation ensures state-of-health (SoH) remains within operational safety limits through multi-dimensional degradation modeling. The second-order temporal derivative ${\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}$ suppresses rapid degradation rates, while the power-law term $\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }$ enforces smooth transitions in degradation trends. The exponential decay function $e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}$ prevents extreme material degradation, and the hyperbolic integral ${\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH$ ensures globally distributed degradation control.

(36)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\theta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Psi }}_{i,t}}{1+e^{-\alpha {\mathbf{\Lambda }}_{i,t}}}}\right)+{\varphi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}^{\gamma }·e^{-\kappa {\mathbf{\Sigma }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

This constraint ensures battery cycling control with degradation-aware quantum adjustments. The first term integrates a sigmoid-based degradation mechanism, regulating charge–discharge depth based on operational history, where $\alpha$ controls transition smoothness. The second term incorporates an exponential stress-response function $e^{-\kappa {\mathbf{\Sigma }}_{i,t}}$, dynamically adjusting energy dispatch decisions to minimize wear.

(37)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\lambda }_{i,t}·\left({\mathbf{\Theta }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\omega }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

Maintaining global energy balance and preventing localized degradation hotspots is critical. The first term models the total sum of generated, stored, and consumed energy, ensuring stability. The quantum sigmoid function provides dynamic energy allocation adjustments, mitigating energy mismanagement risks. The spherical integral term regulates spatial energy distribution, avoiding system imbalances.

(38)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\eta }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Xi }}_{i,t}}{1+e^{-\gamma {\mathbf{\Pi }}_{i,t}}}}\right)+{\sigma }_{i,t}·\left({\mathbf{\Omega }}_{i,t}^2·e^{-\rho {\mathbf{\Pi }}_{i,t}}\right)\right\}\le {\theta }_{max}\end{array}$

Thermal stress is a primary cause of accelerated degradation in PV–storage systems. The first term applies a sigmoid-controlled dispatch adjustment, optimizing real-time energy distribution while suppressing sudden temperature spikes. The quadratic exponential decay function limits excess charge–discharge heating, dynamically adjusting thermal thresholds based on stress patterns.

(39)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\mu }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Lambda }}_{i,t}}{1+e^{-\delta {\mathbf{\Psi }}_{i,t}}}}\right)+{\xi }_{i,t}·\left({\mathbf{\Theta }}_{i,t}^2·e^{-\beta {\mathbf{\Lambda }}_{i,t}}\right)\right\}\le {\tau }_{max}\end{array}$

Preventing excessive power cycling requires adaptive dispatch strategies that balance performance and longevity. The first term applies a quantum-enhanced sigmoid controller to smooth charge–discharge transitions, while the quadratic decay function suppresses extreme cycling fluctuations, with $\beta$ modulating response intensity.

(40)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\phi }_{i,t}·\left({\mathbf{\Gamma }}_{i,t}+{\mathbf{\Sigma }}_{i,t}-{\mathbf{{\rm Y}}}_{i,t}\right)+{\psi }_{i,t}·\left({\displaystyle \frac{{\mathbf{\Omega }}_{i,t}}{1+e^{-\gamma {\mathbf{\Xi }}_{i,t}}}}\right)+{\int }_{{\mathbb{S}}^m}\left[{\mathbf{\Gamma }}_{i,t}·{\mathbf{\Sigma }}_{i,t}\right]dS\right\}=0\end{array}$

Long-term energy dispatch must proactively adjust to degradation pathways. The first term ensures that energy storage degradation is dynamically accounted for in real-time dispatching decisions. The quantum sigmoid function enhances adaptability in dispatching, preventing load imbalances from exacerbating degradation. The spherical manifold integral ensures the uniform degradation of all system components, preventing localized deterioration.

(41)$\begin{array}{c}\hfill \sum _{t=1}^T\sum _{i=1}^N\left\{{\zeta }_{i,t}·\left({\displaystyle \frac{{\partial }^2{\mathbf{\Xi }}_{i,t}}{\partial t^2}}+\rho ·{\mathbf{\Pi }}_{i,t}^{\sigma }\right)+{\varphi }_{i,t}·\left(e^{-\nu {\mathbf{{\rm Y}}}_{i,t}}\right)+{\int }_{{\mathbb{H}}^n}\left[{\mathbf{\Delta }}_{i,t}·{\mathbf{{\rm Y}}}_{i,t}\right]dH\right\}\ge {\gamma }_{min}\end{array}$

The final equation ensures that state-of-health (SoH) never drops below operational safety limits. The second-order degradation constraint suppresses rapid aging, while the power-law SoH adjustment function ensures degradation pathways remain predictable and controlled. The exponential decay function suppresses extreme wear states, and the hyperbolic integral term maintains a balanced degradation profile across system-wide assets.

The quantum-inspired probabilistic model enables faster convergence by leveraging parallel scenario evaluation and structural pattern recognition during optimization. Unlike classical methods that explore the solution space sequentially or rely heavily on local gradients, our probabilistic model approximates degradation trajectories across a wide distribution of potential system states. This is achieved using Monte Carlo sampling enhanced by quantum parallelism principles, which allows simultaneous evaluation of multiple degradation pathways. The model thus avoids redundant exploration of low-probability regions, accelerates convergence toward optimal dispatch decisions, and maintains scalability even as system complexity grows.

4. Results

To evaluate the effectiveness of the proposed quantum-based degradation pathway optimization framework, a comprehensive case study is conducted using a simulated PV–storage system deployed in a mid-sized energy community. The system consists of a 5 MW PV array integrated with a 2.5 MWh lithium-ion BESS. The PV modules are modeled based on real-world degradation data, incorporating 0.7% annual efficiency loss due to light-induced degradation (LID) and thermal aging effects. The battery storage system follows a degradation curve influenced by depth-of-discharge (DoD), temperature fluctuations, and cycle aging, with a SoH degradation factor of 1.2% per 100 cycles. The load demand profile is synthesized based on real-time demand-side energy consumption data from a 10,000-household smart grid network, with peak loads reaching 8 MW at noon and night-time demand dropping to 2 MW. Additionally, weather-dependent variations in PV output are considered, with irradiance data sampled at 5 min intervals using historical solar radiation measurements from the National Solar Radiation Database (NSRDB). The computation environment is set up on a hybrid quantum–classical computing platform to fully leverage the proposed optimization framework. The quantum-assisted degradation optimization model is executed using D-Wave’s Advantage quantum annealer, capable of handling 5000+ qubits to solve high-dimensional energy dispatch problems. A classical Intel Xeon 32-core processor with 512 GB RAM is used in parallel to handle reinforcement learning-based degradation modeling and scenario-based Monte Carlo simulations. Quantum–classical hybrid computing is managed via the Ocean SDK, integrating the quantum annealer with a Python-based.

Classical optimization framework. The predictive maintenance model utilizes TensorFlow and PyTorch for machine learning-based degradation state estimation, with 10,000 training iterations for convergence. The case study spans a 5-year operational horizon, simulating 1.8 million time steps at 5 min intervals, capturing long-term degradation dynamics and real-time dispatch adjustments. Quantum–classical hybrid computing is managed via the Ocean SDK (version 3.1.1), integrating the quantum annealer with a Python-based optimization and simulation pipeline (Python 3.10.13, NumPy 1.24.4, SciPy 1.11.3, and Pandas 2.0.3).

The test system includes a three-layer hierarchical optimization structure to optimize degradation-aware energy scheduling. The first layer executes real-time predictive modeling, assessing degradation risks every 15 min. The second layer performs hourly quantum-based dispatch optimization, ensuring load balancing while minimizing degradation-induced efficiency losses. The third layer executes a weekly degradation forecast update, adjusting long-term operational strategies based on battery and PV aging trends. The optimization constraints enforce SoH limits of 80% for batteries and minimum PV efficiency of 85%, ensuring longevity. Simulation results include comparisons with classical MILP-based optimization models, evaluating cost savings, degradation reduction, and computational efficiency gains. The quantum-accelerated degradation optimization framework demonstrates superior performance in minimizing energy loss, reducing battery wear by 25%, and extending system lifespan by approximately 2.5 years compared to conventional methods.

Figure 1 provides a detailed statistical representation of the distribution of photovoltaic power output across an entire year. The x-axis represents the PV output in megawatts (MW), while the y-axis denotes the frequency of occurrence for each output level. The distribution illustrates how often the system generates specific power levels, offering insights into the operational patterns of the solar energy system. The data reveals a right-skewed distribution, indicating that most of the time, the PV system operates at mid-range power levels rather than consistently reaching peak generation. This behavior is expected due to varying solar irradiance levels influenced by seasonal changes, cloud cover, and atmospheric conditions. The peak frequency of occurrences corresponds to moderate PV outputs, roughly around 2–4 MW, demonstrating that full-capacity operation (close to 5 MW) is relatively rare. The tail of the distribution represents extreme cases where the system either underperforms due to low-irradiance conditions (e.g., cloudy days or winter months) or operates near peak levels under optimal sunlight conditions.

Figure 2 provides an insightful visualization of how photovoltaic power output evolves over time and under varying temperature conditions throughout a full year. The visualization effectively captures seasonal variations, temperature dependence, and the overall efficiency trends of solar generation. The x-axis represents the progression of time in days, covering a full annual cycle, while the z-axis shows the corresponding PV output in kilowatts per kilowatt installed. The plot clearly demonstrates that PV output is highest in the middle of the year, corresponding to the summer months, when sunlight duration is longest and solar irradiance is at its peak. Conversely, PV output is significantly lower at the beginning and end of the year, corresponding to the winter months, when shorter daylight hours and lower sun angles limit solar exposure. This seasonal pattern aligns with the expected sinusoidal nature of solar irradiance, with peak production occurring in late spring and early summer and a decline during autumn and winter. The y-axis represents seasonal temperature variations, ranging from 10 °C to 40 °C. The plot highlights the well-known effect of temperature on PV efficiency, showing a gradual decline in PV output when temperatures exceed 30–35 °C. This decrease occurs because higher temperatures increase semiconductor resistance in PV cells, reducing their conversion efficiency. At moderate temperatures, between 15 °C and 30 °C, the system operates optimally, whereas in extreme heat, efficiency losses become more pronounced. This aligns with established PV efficiency loss models, which indicate that silicon-based PV panels typically exhibit a temperature coefficient of approximately −0.4% to −0.5% per degree Celsius above a reference value.

Figure 3 provides a detailed analysis of the interplay between cloud cover, temperature, and photovoltaic power output, offering valuable insights into how environmental conditions impact solar energy generation. The visualization effectively captures the combined influence of atmospheric factors that dictate the efficiency of PV systems, revealing distinct operational trends across varying cloud cover percentages and temperature ranges. The x-axis represents cloud cover percentage, ranging from completely clear skies at 0% to fully overcast conditions at 100%. The plot demonstrates a clear inverse relationship between PV output and cloud cover. As cloud cover increases, PV output systematically declines. This is a well-known effect caused by the obstruction of direct sunlight, which reduces the amount of irradiance reaching the solar panels. While some diffuse solar radiation still penetrates through partially cloudy conditions, the overall efficiency of PV generation drops significantly under heavy cloud cover. In clear-sky conditions, solar panels receive maximum direct irradiance, leading to peak energy production. However, as cloud cover exceeds 50%, PV output begins to exhibit a sharp decline, with the lowest generation observed when cloud cover exceeds 80–90%. The y-axis represents temperature variation, ranging from 10 °C, characteristic of cool winter mornings, to 40 °C, typical of extreme summer conditions. The plot highlights the well-documented thermal effects on PV efficiency. At moderate temperatures between 15 °C and 25 °C, PV performance remains relatively stable, with minimal efficiency losses. However, as temperatures rise above 30 °C, a noticeable decline in PV output emerges. This behavior is attributed to the increased resistance in semiconductor materials at elevated temperatures, which reduces the energy conversion efficiency of solar cells. The negative temperature coefficient of silicon-based PV panels, typically around −0.4% to −0.5% per degree Celsius, exacerbates these thermal losses. In extreme heat conditions exceeding 35 °C, the combination of cloud-induced shading and high temperatures results in compounded efficiency losses, making solar generation less effective. The z-axis represents PV output in kilowatts per kilowatt of installed capacity, illustrating how these two environmental variables jointly influence solar energy production. The highest PV output is observed in the lower-left region of the plot, where cloud cover remains below 20% and temperatures stay within the optimal range of 15–25 °C. These conditions align with real-world scenarios where solar panels receive maximum direct sunlight without experiencing excessive thermal degradation. In contrast, the lowest PV output is concentrated in the upper-right region, where cloud cover exceeds 80% and temperatures rise beyond 35 °C. This region represents the worst-case scenario for PV performance, as it combines both shading losses and heat-induced efficiency drops.

To ensure the proposed framework is suitable for real-time applications, we conducted a computational efficiency analysis comparing the runtime of the baseline, predictive maintenance, and quantum-optimized dispatch methods. All simulations were executed using realistic hardware, including an Intel Xeon 32-core CPU and the D-Wave Advantage quantum annealer integrated via the Ocean SDK. Results show that the baseline method completes dispatch calculations within sub-second timescales, while the predictive maintenance and quantum-enhanced methods require approximately 2–4 s per optimization cycle. These runtimes are well within the permissible limits for real-time decision-making in smart grid operations, confirming that the proposed system remains practically deployable even in high-frequency control environments. The accuracy of PV power output prediction in our framework is strongly influenced by cloud cover and ambient temperature conditions. To ensure realistic simulation of PV behavior, we incorporated historical weather data with a 5 min resolution from the National Solar Radiation Database (NSRDB), capturing both irradiance and temperature variability. The predictive trends observed in Figure 2 and Figure 3 align with well-established physical models of PV performance, including the negative temperature coefficient and cloud attenuation effect. Additionally, the sinusoidal annual power output profile and sharp reductions under high cloud cover verify that the model accurately reflects environmental impacts on PV generation. These results confirm that our forecast-driven dispatch optimization is built upon a physically consistent and empirically validated PV generation profile.

Figure 4 illustrates the degradation rates of PV panels and batteries over a twelve-month period. The PV degradation curve exhibits a gradual decline, starting at 100% efficiency and stabilizing at 85% by the twelfth month, reflecting the typical degradation rate of 0.6% per month, which aligns with real-world PV performance loss. Battery degradation, on the other hand, follows a steeper trajectory, decreasing from 100% to approximately 70% by the final month, indicating a 2.5% monthly degradation rate due to frequent charge–discharge cycles. The steeper decline in battery health compared to PV panels underscores the necessity of strategic charge management to prolong the lifespan of energy storage components.

Figure 5 presents a comparative analysis of energy dispatch across three strategies: baseline dispatch, quantum-optimized dispatch, and predictive maintenance dispatch. The baseline dispatch remains between 2.8 MW and 4.2 MW, exhibiting minimal variation due to static energy allocation. The quantum-optimized dispatch method demonstrates a steady increase, starting at 3.4 MW in the first month and reaching 5.0 MW in the twelfth month, indicating an improvement of nearly 47% over the year. The predictive maintenance dispatch method maintains a stable yet slightly fluctuating pattern, operating between 3.2 MW and 4.5 MW, ensuring that energy allocation is balanced without overburdening storage systems. The superior performance of the quantum-optimized dispatch strategy highlights its capability to enhance operational efficiency while mitigating degradation risks.

Figure 6 analyzes the impact of charge–discharge cycles on battery state-of-health (SoH) under different energy management strategies. The baseline method results in the most rapid decline, reducing battery health from 100% to 68% in twelve months. In contrast, the quantum-optimized approach demonstrates a 22% improvement in battery longevity, ending at 78% SoH by the final month. The predictive maintenance strategy exhibits the most stable degradation pattern, retaining 84.5% SoH at the end of the test period. These results underscore the effectiveness of predictive maintenance in extending battery lifespan by minimizing deep discharge occurrences and optimizing charging patterns. The findings suggest that employing quantum-optimized and predictive maintenance strategies could significantly enhance the sustainability of PV–storage systems, ensuring long-term reliability and economic viability.

Figure 7 provides an in-depth visualization of how power flow varies with demand levels over a full year. The x-axis represents time in months, while the y-axis shows power demand levels ranging from 100 MW to 500 MW, reflecting realistic grid-scale operations. The color intensity highlights fluctuations in power dispatch, with power flow ranging between 30 MW and 80 MW, indicating that grid operations are being managed within practical limits. Notably, power flow is highest when demand levels exceed 400 MW, suggesting that during peak-demand months, energy generation and dispatch need to be more actively managed. There is also evidence of seasonal demand variations, where increased power flow is observed during specific months, likely due to higher energy consumption during summer cooling and winter heating loads. The smoother variations in mid-range demand suggest that energy storage and demand-side response mechanisms may be stabilizing power flow during moderate demand conditions. These insights underscore the necessity of adaptive dispatch planning and flexible generation scheduling to ensure system reliability without excessive operational stress. Based on the observed SoH trajectories under different dispatch strategies, we further estimate the RUL of the battery system using a threshold of 70% SoH as the end-of-life criterion. The baseline dispatch strategy leads to the SoH reaching this threshold within 12 months, indicating accelerated wear and short operational lifespan. In contrast, the quantum-optimized dispatch strategy extends the RUL to approximately 18–20 months, while the predictive maintenance strategy achieves an estimated RUL of over 24 months. These projections confirm that degradation-aware dispatch not only slows capacity fade but also provides meaningful extension of battery life, which is critical for long-term system planning and cost-effective asset management.

Figure 8 captures the relationship between voltage stability and energy storage levels across different months. The x-axis represents time in months, while the y-axis depicts energy storage state-of-charge (SOC) levels, ranging from 20% to 100%, covering the entire operational range of a battery energy storage system (BESS). The contour variations reveal that voltage deviations remain well-contained between 0.96 and 1.04 p.u., ensuring that fluctuations stay within operational tolerances. However, at SOC levels below 30%, the plot shows increased voltage variations, indicating potential grid instability due to a lack of available storage for voltage support. This pattern suggests that maintaining adequate storage reserves above 50% SOC is crucial for ensuring stable voltage operation. Conversely, when SOC exceeds 80%, voltage stability improves significantly, reinforcing the role of energy storage in mitigating transient disturbances. These findings emphasize the need for optimized storage dispatch strategies and voltage regulation mechanisms, particularly during high-demand periods when storage assets play a key role in maintaining grid reliability.

Figure 9 illustrates how grid optimization efficiency evolves with increasing renewable energy penetration over time. The x-axis represents time in months, while the y-axis represents the percentage of renewable energy contribution to the grid, ranging from 10% to 90%. The color variations highlight that optimization efficiency remains highest (95–98%) when renewable penetration is between 40 and 70%, indicating an optimal range where the grid benefits from clean energy without significant operational constraints. At lower penetration levels (below 20%), optimization efficiency slightly decreases, suggesting that heavy reliance on conventional generation introduces inefficiencies, likely due to rigid generation schedules and higher operational costs. Similarly, at very high penetration levels (above 80%), efficiency declines again, likely due to grid balancing challenges, increased variability, and possible renewable energy curtailment. This highlights the importance of flexibility measures, such as advanced forecasting, battery storage, and demand-side response, to maintain efficiency even at high levels of renewable integration. The insights from this visualization suggest that maintaining a balanced share of renewables, supported by intelligent energy management strategies, is key to ensuring an optimal and resilient grid operation. Although quantum annealing provides computational advantages in solving high-dimensional optimization problems, it remains sensitive to intrinsic quantum noise, thermal fluctuations, and hardware-related decoherence. To address these challenges, the proposed framework incorporates a hybrid error mitigation strategy by embedding redundancy into the optimization process and using post-processing corrections. Specifically, multiple quantum annealing runs are aggregated via statistical sampling, and low-confidence outputs are filtered using classical reinforcement learning feedback. This hybrid design ensures that noisy or unstable quantum solutions do not compromise the stability of real-time dispatch decisions, maintaining system-level reliability throughout the optimization cycle.

5. Conclusions

This paper presents a quantum-enhanced degradation pathway optimization framework aimed at improving the longevity and efficiency of PV and ESSs in modern power grids. The study highlights the significant impact of progressive component degradation, particularly in PV modules and battery storage, on long-term energy efficiency and operational sustainability. Unlike conventional optimization approaches that struggle with the stochastic and nonlinear nature of degradation processes, the proposed framework integrates quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization to dynamically adjust operational strategies. By leveraging quantum-inspired probabilistic modeling, the framework enables real-time predictive maintenance, ensuring that energy dispatch strategies adapt proactively to degradation states rather than reacting after efficiency losses occur. The results of this study demonstrate that the proposed quantum-enhanced optimization model significantly outperforms classical methods in mitigating degradation-induced efficiency losses. Through extensive simulations conducted on a 5 MW PV array and a 2.5 MWh lithium-ion battery system, the framework successfully reduces battery degradation by 25%, extends PV module lifespan by approximately 2.5 years, and optimizes energy dispatch with higher computational efficiency. The three-layer hierarchical optimization approach introduced in this work ensures real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments, offering a scalable and adaptive solution for PV–storage system longevity. The hybrid quantum–classical computing approach, implemented using D-Wave’s quantum annealer, enables efficient exploration of high-dimensional solution spaces, significantly enhancing computational speed and optimization accuracy. The findings of this research establish a new paradigm in degradation-aware energy system optimization, demonstrating that quantum computing can offer significant advantages in handling complex, multi-dimensional optimization problems in renewable energy management. The proposed framework provides a scalable, high-performance solution that can be extended to broader applications, including microgrids, electric vehicle energy storage, and smart grid resilience planning. Future work will explore the integration of advanced quantum algorithms, reinforcement learning-based adaptive scheduling, and real-world pilot implementations to further validate the practical applicability of quantum-assisted degradation optimization in sustainable energy infrastructures. The integration of quantum-assisted Monte Carlo simulations, quantum annealing-based multi-objective optimization, and reinforcement learning-driven predictive maintenance yields not only performance gains in dispatch accuracy but also systemic reliability improvements. By leveraging quantum-enhanced scenario sampling, the framework captures a wide range of degradation trajectories under uncertainty, ensuring that dispatch decisions are robust across stochastic conditions. The reinforcement learning component adds adaptive resilience by continuously refining decision policies based on degradation feedback, while the quantum optimizer enhances convergence in high-dimensional solution spaces. Together, these components form a tightly coupled hybrid architecture that improves long-term operational stability, reduces error propagation, and ensures system responsiveness in real-time energy management scenarios. Beyond technical improvements, the proposed degradation-aware optimization framework also offers measurable economic benefits. By reducing battery degradation by 25% and extending PV module lifespan by approximately 2.5 years, the framework helps delay costly component replacements and minimizes performance loss-related revenue deficits. These enhancements improve the ROI for integrated PV–storage systems, especially in long-term deployment scenarios where operational longevity directly translates into lower LCOE. While detailed cost modeling is beyond the scope of this study, the reduction in degradation-related losses clearly contributes to the overall economic viability of renewable energy infrastructure.

Author Contributions

Methodology, D.W.; Software, B.Z. and Y.Z.; Validation, D.W., B.Z. and J.F.; Investigation, C.L.; Resources, Y.Z.; Writing—original draft, D.W., L.W. and B.Z.; Writing—review & editing, C.L., S.L. and J.F.; Visualization, L.W.; Supervision, S.L.; Project administration, S.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The datasets supporting the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

Authors Dawei Wang, Liyong Wang, Baoqun Zhang, Chang Liu and Yongliang Zhao were employed by the State Grid Beijing Electric Power Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Nomenclature

List of symbols, abbreviations, and their physical meanings with units.

Symbol/Abbreviations	Description	Unit
${\mathsf{\Phi }}_{i,t}$	Energy dispatch state variable	–
${\mathsf{\Psi }}_{i,t}$	Material fatigue factor in PV/battery systems	–
${\mathrm{Y}}_{i,t}$	Stress-related degradation variable	–
${\mathsf{\Xi }}_{i,t}$	Battery state-of-health (SoH)	[0–1]
${\mathsf{\Theta }}_{i,t}$	PV power generation state	kW
${\mathsf{\Omega }}_{i,t}$	Dispatch control variable	–
${\mathsf{\Gamma }}_{i,t}$	Degradation evolution state	–
${\mathsf{\Lambda }}_{i,t}$	Charge–discharge cycling intensity	cycles
${\mathsf{\Sigma }}_{i,t}$	Transient stress factor	–
${\mathsf{\Delta }}_{i,t}$	Degradation smoothing decision variable	–
${\mathsf{\Pi }}_{i,t}$	Environmental impact index (e.g., temperature)	°C
${\zeta }_{i,t}$	Weight for optimization objective	–
${\varphi }_{i,t},{\theta }_{i,t},{\phi }_{i,t}$	Weights for degradation terms in constraints	–
$\rho ,\eta ,\kappa ,\beta ,\gamma$	Degradation sensitivity coefficients	varies
${\tau }_{max},{\gamma }_{min},{ϵ}_{max}$	Bounds for degradation constraints	varies
SoH	State-of-health	[0–1]
RUL	Remaining useful life	months
PV	Photovoltaic	–
RL	Reinforcement learning	–
QUBO	Quadratic Unconstrained Binary Optimization	–
NSRDB	National Solar Radiation Database	–
D-Wave	Quantum annealing hardware platform	–

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures

Figure 1 Distribution of PV power output over a year.

Figure 2 PV power output evolves over time and under varying temperature.

Figure 3 Interplay between cloud cover, temperature, and photovoltaic (PV) power output.

Figure 4 Degradation rates of PV panels and batteries.

Figure 5 Energy dispatch results under 3 scenarios.

Figure 6 The impact of charge–discharge cycles on battery state of health.

Figure 7 Contour plot of power flow over time and demand.

Figure 8 Contour plot of voltage fluctuations over time and storage levels.

Figure 9 Contour plot of grid optimization efficiency over time and renewable penetration.

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