Subir Majumder

Subir Majumder

Large, fast-controllable loads such as Bitcoin mining facilities are increasingly viewed as potential sources of flexibility in modern power systems, yet the conditions under which this flexibility is realized remain incompletely understood. Using the Texas power market as an empirical setting, we examine how Bitcoin-mining load responds to two distinct electricity-sector cost channels: contemporaneous wholesale electricity prices and incentives created by coincident-peak-based transmission charges. We find that mining load responds to both cost channels in a manner consistent with miners operating around a breakeven point. At the aggregate level, we observe that mining load decreases as electricity-sector costs rise, but the strength of this response depends on hashprice, a measure of expected mining revenue from the crypto-financial sector. When hashprice is higher, aggregate load responsiveness is weaker. This mechanism is especially evident in the wholesale-price response. Mining load remains largely online at low prices and begins to decline only when electricity costs become large relative to expected mining revenue, with higher hashprice shifting the implied curtailment threshold toward higher wholesale prices. These findings indicate that Bitcoin-mining demand response to electricity-sector costs is economically state-dependent and shaped by revenue conditions in the crypto-financial sector. Treating such loads as stable demand-response resources may therefore overstate available grid flexibility, with implications for power-system planning, market design, and reliability assessment.

Subir Majumder, Minlan Yu, Le Xie

Artificial intelligence (AI) is driving rapid growth in electricity demand, yet the grid-facing power dynamics of AI data centers remain poorly understood. Here we show that, in shared-GPU systems, the composition of batch and inference workloads decouples aggregate power variability from short-horizon ramping. As the inference share rises, variability becomes U-shaped, whereas ramping becomes hump-shaped, particularly under higher loading. The magnitude and turning points of these patterns also depend on system loading. Using a trace-calibrated framework linking workload arrivals, queueing, scheduling, and GPU power, we show that the underlying mechanism is asymmetric. At intermediate workload mixes, queued batch jobs fill capacity left idle by fluctuating inference demand, reducing aggregate power variability. However, short-horizon ramping remains elevated because inference-side fluctuations propagate more directly into realized power. AI data centers should therefore be understood as dynamic systems whose workload composition shapes their grid impact.

Salah U. Kadir, Subir Majumder, Ajay D. Chhokra et al.

Power grid operation subject to an extreme event requires decision-making by human operators under stressful condition with high cognitive load. Decision support under adverse dynamic events, specially if forecasted, can be supplemented by intelligent proactive control. Power system operation during wildfires require resiliency-driven proactive control for load shedding, line switching and resource allocation considering the dynamics of the wildfire and failure propagation. However, possible number of line- and load-switching in a large system during an event make traditional prediction-driven and stochastic approaches computationally intractable, leading operators to often use greedy algorithms. We model and solve the proactive control problem as a Markov decision process and introduce an integrated testbed for spatio-temporal wildfire propagation and proactive power-system operation. We transform the enormous wildfire-propagation observation space and utilize it as part of a heuristic for proactive de-energization of transmission assets. We integrate this heuristic with a reinforcement-learning based proactive policy for controlling the generating assets. Our approach allows this controller to provide setpoints for a part of the generation fleet, while a myopic operator can determine the setpoints for the remaining set, which results in a symbiotic action. We evaluate our approach utilizing the IEEE 24-node system mapped on a hypothetical terrain. Our results show that the proposed approach can help the operator to reduce load loss during an extreme event, reduce power flow through lines that are to be de-energized, and reduce the likelihood of infeasible power-flow solutions, which would indicate violation of short-term thermal limits of transmission lines.