Mohammad Shahidehpour

Sina Parhizi, Amin Khodaei, Mohammad Shahidehpour

An optimal scheduling model for a microgrid participating in the electricity distribution market in interaction with a Distribution Market Operator (DMO) is proposed in this paper. The DMO administers the established electricity market in the distribution level, sets electricity prices, determines the amount of the power exchange among market participants, and interacts with the Independent System Operator (ISO). Considering a predetermined main grid power transfer to the microgrid, the microgrid scheduling problem will aim at balancing the power supply and demand while taking financial objectives into account. Numerical simulations exhibit the application and the effectiveness of the proposed market-based microgrid scheduling model and further investigate merits over a price-based scheme.

Ren Wang, Yuxuan Li, Can Chen et al.

Adversarial robustness is a critical measure of a neural network's ability to withstand adversarial attacks at inference time. While robust training techniques have improved defenses against individual $\ell_p$-norm attacks (e.g., $\ell_2$ or $\ell_\infty$), models remain vulnerable to diversified $\ell_p$ perturbations. To address this challenge, we propose a novel Robust Mode Connectivity (RMC)-oriented adversarial defense framework comprising two population-based learning phases. In Phase I, RMC searches the parameter space between two pre-trained models to construct a continuous path containing models with high robustness against multiple $\ell_p$ attacks. To improve efficiency, we introduce a Self-Robust Mode Connectivity (SRMC) module that accelerates endpoint generation in RMC. Building on RMC, Phase II presents RMC-based optimization, where RMC modules are composed to further enhance diversified robustness. To increase Phase II efficiency, we propose Efficient Robust Mode Connectivity (ERMC), which leverages $\ell_1$- and $\ell_\infty$-adversarially trained models to achieve robustness across a broad range of $p$-norms. An ensemble strategy is employed to further boost ERMC's performance. Extensive experiments across diverse datasets and architectures demonstrate that our methods significantly improve robustness against $\ell_\infty$, $\ell_2$, $\ell_1$, and hybrid attacks. Code is available at https://github.com/wangren09/MCGR.

Dev Mistry, Feng Qiu, Bo Chen et al.

Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insight for high-stakes energy infrastructure. Here we introduce REVL-PV, a vision-language framework that embeds domain-specific diagnostic reasoning into multimodal learning across electroluminescence, thermal, and visible-light imagery. By requiring the model to link visual evidence to plausible defect mechanisms before classification, the framework produces structured diagnostic reports aligned with professional photovoltaic inspection practice. Evaluated on 1,927 real-world modules spanning eight defect categories, REVL-PV achieves 93\% classification accuracy while producing interpretable diagnostic rationales and maintaining strong robustness under realistic image corruptions. A blind concordance study with a certified solar inspection expert shows strong semantic alignment between model explanations and expert assessments across defect identification, root-cause attribution, and visual descriptions. These results demonstrate that reasoning-aware multimodal learning establishes a general paradigm for trustworthy AI-assisted inspection of photovoltaic energy infrastructure.

Hongxing Ye, Yinyin Ge, Mohammad Shahidehpour et al.

The increasing penetration of renewable energy in recent years has led to more uncertainties in power systems. These uncertainties have to be accommodated by flexible re- sources (i.e. upward and downward generation reserves). In this paper, a novel concept, Uncertainty Marginal Price (UMP), is proposed to price both the uncertainty and reserve. At the same time, the energy is priced at Locational Marginal Price (LMP). A novel market clearing mechanism is proposed to credit the gener- ation and reserve and to charge the load and uncertainty within the Robust Unit Commitment (RUC) in the Day-ahead market. We derive the UMPs and LMPs in the robust optimization framework. UMP helps allocate the cost of generation reserves to uncertainty sources. We prove that the proposed market clearing mechanism leads to partial market equilibrium. We find that transmission reserves must be kept explicitly in addition to generation reserves for uncertainty accommodation. We prove that transmission reserves for ramping delivery may lead to Financial Transmission Right (FTR) underfunding in existing markets. The FTR underfunding can be covered by congestion fund collected from uncertainty payment in the proposed market clearing mechanism. Simulations on a six-bus system and the IEEE 118-bus system are performed to illustrate the new concepts and the market clearing mechanism.

Hongxing Ye, Yinyin Ge, Mohammad Shahidehpour et al.

In Part II of this two-part paper, we analyze the marginal prices derived in Part I of this two-part paper within a robust optimization framework. The load and generation are priced at Locational Marginal Price (LMP) while the uncertainty and generation reserve are priced at Uncertainty Marginal Price(UMP). The Financial Transmission Right (FTR) underfunding is demonstrated when there is transmission reserve. A comparison between traditional reserve price and UMP is presented. We also discuss the incentives for market participants within the new market scheme.