Sami Shamoon College of Engineering

In this study, we compare time-series and neural networks models capturing long memory for electricity price forecasting in the Russian day-ahead market. We identify the presence of long memory in hourly wholesale electricity price series across six regions of the Russian power grid. Using dedicated statistical tests, we confirm strong long-range dependence and estimate the corresponding long memory parameters. To investigate the potential enhancement in forecasting accuracy offered by long-memory models, we implement a set of fractionally integrated time series models, alongside Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) machine learning models. We evaluate forecasting performance using both in-sample and out-of-sample tests: the in-sample evaluation corresponds to one-hour-ahead predictions, while the out-of-sample evaluation simulates actual day-ahead market conditions. In most cases, the Seasonal Autoregressive Fractionally Integrated Moving Average model with calendar regressors (SARFIMAX) outperforms other time series models and neural networks. The best-performing model, SARFIMAX(1,0,0)(0,D,0)24, achieves an average forecasting improvement of approximately 0.5% compared to the DNN.

Several previous works examine the use of cryptographic mining machines, for instance Bitcoin mining machines, in demand-response mechanisms, as part of the portfolio of assets managed by the grid operator. This paper extends this formulation by addressing the effects of fast ramping transients, which may often occur in power grids rich with renewable energy sources. The resulting optimization problem is solved based on Pontryagin's minimum principle. The solution is used to examine the profitability and usage of these machines in a real-world settings, based on data from the California ISO and the "Noga" grid operator. A sensitivity analysis is conducted, considering the effects of several key parameters, such as the electricity price, and the machines' price, hashrate and monetary revenue. These are examined for several different machine types that are available in the market today. The main conclusion is that the profitability of the discussed mechanism is highly influenced by the cost of the mining machines, and the percentage of renewable sources within the energy mix, where some scenarios are more profitable then others.

This paper considers some algebraic surfaces that can deform to planar Zappatic surfaces with a unique singularity of type En. We prove that the Galois covers of these surfaces are all simply connected of general type, for n >= 4. We also give a formula for a local Zappatic singularity of a Zappatic surface of type En. As an application, we prove that such surfaces do not exist for n > 30. Furthermore, Koll & aacute;r improves the result to n > 9 in Appendix A.

The real-world performance and optimization potential of solar photovoltaic-battery energy storage system microgrids is implemented in public buildings across Mediterranean environments. Using data collected over an extended period, several aspects are evaluated, including system commissioning outcomes, operational behavior under varying time and seasonal conditions, and the effectiveness of scenario-based strategies. The latter aim at improving key performance indicators, such as self-consumption and self-sufficiency rates, which are analyzed across daily, weekly, and seasonal timescales. Moreover, scenario simulations explore the impact of varying photovoltaic and storage capacities as well as different levels of demand-side flexibility. Results show that instead of simply increasing the component capacity, it is more effective to increase load flexibility, which leads to the enhancement of system performance and the reduction of battery cycling. Moreover, factors such as occupancy schedules and climatic conditions significantly affect system behavior and optimization outcomes. The study demonstrates that integrating high-resolution monitoring data with scenario modeling offers valuable insights into the dynamic operation of photovoltaic-battery energy storage system microgrids. Across the four pilot sites, measured annual self-sufficiency rates reach up to 70-95 % under baseline operation, while scenariobased demand-side flexibility increases SSR by 10-25 percentage points compared to capacity scaling alone. Results consistently show that moderate load flexibility yields higher performance gains and lower battery cycling than equivalent increases in PV or storage capacity.

This study examined cast AZ31 magnesium alloy and its variant containing micro-alloying elements of Y and Ca (AZXW alloy), evaluating their potential as anode materials in magnesium–air batteries. The AZXW alloy was fabricated via two manufacturing techniques: casting and extrusion. The synergistic influence of Y and Ca, in conjunction with the production procedure, on the microstructure, electrochemical characteristics, and anodic discharge behavior of the examined alloys was investigated. The addition of Y and Ca results in the formation of secondary phases that affect grain size, particle size, and distribution, as well as the electrochemical performance and discharge properties of the Mg–air battery constructed for this study, over 24 h or until fully discharged. This work demonstrates the potential to enhance discharge performance and electrochemical behavior by adjusting the aqueous electrolyte solution in the battery through the incorporation of Citric Acid (C.A) at varying concentrations. The incorporation of citric acid into the aqueous electrolyte improves battery stability and specific energy as long as citric acid is present in the solution. Magnesium hydroxide (Mg(OH)2) begins to form on the anode surface as its concentration progressively decreases due to complexation with dissolved magnesium ions. This diminishes the effective anode area over time, ultimately resulting in the distinctive “knee-type” collapse characteristic of electrolytes containing citric acid.