arXiv subjects

Arash Khojaste

Publications and source records attributed to Arash Khojaste.

5 recordsLinked to original sources

arXiv2026

The Value of Perfect Endpoint Forecasts for Offshore-Wind Thermal Firming

Forecast value depends not only on accuracy but also on the information structure available to the operating model. We study a diagnostic current-endpoint-only forecast for offshore-wind thermal firming: at hour t, the controller observes the current net-demand state z_t and one perfect future target z_{t+h}, but not the intermediate path or earlier endpoint messages. Unlike rolling path forecasts, these endpoint information sets are not nested in h. We embed the signal in a cyclostationary MDP using quantile Fourier regression states estimated from ISO New England load and offshore wind data, and solve annual state-action-frequency LPs for h = 1, . . . , 6. A one-hour endpoint forecast reduces annual firming cost by 8.07%, while a six-hour endpoint reduces it by 2.28%. The decreasing profile shows that a single farther endpoint is less actionable for a one-step ramping decision, without implying that longer rolling forecasts are less valuable.

math.OC↗

arXiv2026

Risk-Averse Markov Decision Processes: Applications to Electricity Grid and Reservoir Management

This paper develops risk-averse models to support system operators in planning and operating the electricity grid under uncertainty from renewable power generation. We incorporate financial risk hedging using conditional value at risk (CVaR) within a Markov Decision Process (MDP) framework and propose efficient, exact solution methods for these models. In addition, we introduce a power reliability-oriented risk measure and present new, computationally efficient models for risk-averse grid planning and operations.

math.OC↗

arXiv2025

Electricity Price-Aware Scheduling of Data Center Cooling

Data centers are becoming a major consumer of electricity on the grid, with cooling accounting for about 40\% of that energy. As electricity prices vary throughout the day and year, there is a need for cooling strategies that adapt to these fluctuations to reduce data center cooling costs. In this paper, we present a model for electricity price-aware cooling scheduling using a Markov Decision Process(MDP) framework to reliably estimate the cooling system operational costs and facilitate investment-phase decision-making. We utilize Quantile Fourier Regression (QFR) fits to classify electricity prices into different regimes while capturing both daily and seasonal patterns. We simulate 14 years of operation using historical electricity price and outdoor temperature data, and compare our model against heuristic baselines. The results demonstrate that our approach consistently achieves lower cooling costs. This model is useful for grid operators interested in demand response programs and data center investors looking to make investment decisions.

math.OC↗

arXiv2025

Baseline hydropower generation offer curves

We outline a mathematical model for pricing hydropower generation. The model involves a Markov decision process that reflects the seasonal variation in historical time series of water inflows. The procedure is computationally efficient and easy to interpret.

math.OC↗

arXiv2024

Quantile Fourier regressions for decision making under uncertainty

Weconsider Markov decision processes arising from a Markov model of an underlying natural phenomenon. Such phenomena are usually periodic (e.g. annual) in time, and so the Markov processes modelling them must be time-inhomogeneous, with cyclostationary rather than stationary behaviour. We describe a technique for constructing such processes that allows for periodic variations both in the values taken by the process and in the serial dependence structure. We include two illustrative numerical examples: a hydropower scheduling problem and a model of offshore wind power integration.

math.OC↗