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As traditional large hydropower has been extensively exploited, micro-hydro systems have caught research increasing interest. New engineering challenges arise in developing micro-hydro systems in areas with significant elevation but prohibitive horizontal distances between primary reservoirs. This study addresses these challenges by proposing a cascade-pumped micro-hydro storage (CPMHS) system that leverages intermediate reservoirs to bridge long horizontal distances, enabling efficient energy transfer and storage. The methodology utilizes naturally occurring lakes with substantial head heights but limited feasibility for direct pumped storage due to horizontal separations. Integrating smaller, strategically placed intermediate reservoirs maximizes energy capture along the cascading path, making pumped storage viable in geographically constrained locations. The proposed system will enhance energy generation potential and provide additional benefits for water management. Using geographical data and a detailed case study focused on Mountain Lake and surrounding lakes, this paper demonstrates the energy efficiency and viability of cascade-based micro-hydro storage. A practical methodology for implementing CPMHS systems is proposed and validated by case studies. An optimization framework is developed for efficient energy capture in regions with challenging topography.

Classical monofacial solar photovoltaic systems have gained prevalence and are widely reported in the literature because they have a lower initial cost compared with bifacial systems. However, limited investigation of both systems has been done on a utility scale with different performance indicators. This paper introduces a multifaceted comparative analysis including various aspects like energy generation, reliability, environmental effect, economic viability, and footprint area. Real measured data, including ambient temperature, solar irradiance, and a utility-scale load, were used for studying both systems in the City of Detroit. The optimal system sizing and energy management strategy are attained using the Whale optimization algorithm. Minimizing the loss of power supply probability and sizing the number of photovoltaic panels (NPV) are carried out for both cases. Results revealed that the bifacial solar system generates more power with a lower NPV, a smaller installation area, and hence a lower levelized cost of energy for the entire project lifetime compared to the monofacial system. Accordingly, the bifacial system outlined in this paper is recommended and can be implemented in various locations to establish a sustainable solar energy system that is economically feasible with clean energy production for the entire project's lifespan.

Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing profits. This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market. The proposed model leverages various time-series features, including load forecasts, solar and wind generation forecasts, and temporal attributes. The model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. Based on the price spread prediction results, several trading strategies are proposed and the most effective strategy for maximizing cumulative profit under realistic market conditions is identified through backtesting. The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period. The proposed method underscores the importance of an accurate electricity price forecasting model and introduces a new method of evaluating the price forecast model from a virtual bidder's perspective, providing valuable insights for future research., Comment: Submitted to 2025 IEEE PES General Meeting

The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences. This paper presents a novel simulation framework for assessing and enhancing the resilience of power distribution networks under such conditions. Resilience is estimated through Monte Carlo simulations, which simulate extreme weather scenarios and evaluate the impact on infrastructure fragility. Due to the proprietary nature of power networks topology, a distribution network is synthesized using publicly available data. To generate the weather scenarios, an extreme weather generation method is developed. To enhance resilience, renewable resources such as solar panels and energy storage systems (batteries in this study) are incorporated. A customized Genetic Algorithm is used to determine the optimal locations and capacities for solar and battery installations, maximizing resilience while balancing cost constraints. Experiment results demonstrate that on a large-scale synthetic distribution network with more than 300,000 nodes and 300,000 edges, distributed energy resources (DERs) can significantly improve resilience metrics, providing utilities with valuable insights for community-level power system resilience enhancement., Comment: Submitted to 2025 IEEE PES General Meeting