Sarthak Gupta's research while affiliated with Los Alamos National Laboratory and other places

Publications (15)

Preprint
Analytical and practical evidence indicates the advantage of quantum computing solutions over classical alternatives. Quantum-based heuristics relying on the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) have been shown numerically to generate high-quality solutions to hard combinatorial problems, y...
Preprint
Volt/VAR control rules facilitate the autonomous operation of distributed energy resources (DER) to regulate voltage in power distribution grids. According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage. Howeve...
Article
A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF...
Preprint
Given their intermittency, distributed energy resources (DERs) have been commissioned with regulating voltages at fast timescales. Although the IEEE 1547 standard specifies the shape of Volt/VAR control rules, it is not clear how to optimally customize them per DER. Optimal rule design (ORD) is a challenging problem as Volt/VAR rules introduce nonl...
Preprint
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options. Such rules enable DERs to respond autonomously in response to time-varying grid loading conditions. The rules comprise affine droop control augm...
Preprint
A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty. Most SOPF...
Article
Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inv...
Preprint
Coordinating inverters at scale under uncertainty is the desideratum for integrating renewables in distribution grids. Unless load demands and solar generation are telemetered frequently, controlling inverters given approximate grid conditions or proxies thereof becomes a key specification. Although deep neural networks (DNNs) can learn optimal inv...
Preprint
Aiming for the median solution between cyber-intensive optimal power flow (OPF) solutions and subpar local control, this work advocates deciding inverter injection setpoints using deep neural networks (DNNs). Instead of fitting OPF solutions in a black-box manner, inverter DNNs are naturally integrated with the feeder model and trained to minimize...
Preprint
Full-text available
Deep learning for distribution grid optimization can be advocated as a promising solution for near-optimal yet timely inverter dispatch. The principle is to train a deep neural network (DNN) to predict the solutions of an optimal power flow (OPF), thus shifting the computational effort from real-time to offline. Nonetheless, before training this DN...
Article
With increasingly favorable economics and bundling of different grid services, energy storage systems (ESS) are expected to play a key role in integrating renewable generation. This work considers the coordination of ESS owned by customers located at different buses of a distribution grid. Customers participate in frequency regulation and experienc...
Conference Paper
Energy storage systems are becoming a key component in smart grids with increasing renewable penetration. Storage technologies feature diverse capacity, charging, and response specifications. Investment and degradation costs may require charging batteries at multiple timescales, potentially matching the control periods at which grids are dispatched...

Citations

... The maximum number of iterations was set to 1, 000, and we used the aer simulator statevector quantum simulation backend. For the dual update in (14), constraint violations were measured over the observables H m using the minimum eigenstate returned by VQE. The stopping criteria λ t − λ t−1 2 ≤ 1 · 10 −5 was utilized to ascertain the convergence of the dual updates (14). ...
... The novel solver for (12) was implemented in Python using the Qiskit library [13]. The VQE class in Qiskit was used to solve the minimization for the primal update (15). In addition to providing the ansatz described in Section 3, the VQE class was configured with the 'SLSQP' optimizer. ...
... Controlling inverters using control rules has been advocated as an effective means to reduce the computational overhead. In such a scheme, inverter setpoints are decided as a (non)linear function of solar, load, and/or voltage data; see e.g., [7], [8] and references therein. Although such approaches reduce the computational burden, they still have high communication needs if driven by non-local data. ...
... We next expound upon how our work differs from prior works utilizing machine learning for smart inverter control. DNNs have been extensively employed before for optimal DER control under OPF formulations, with the objective of minimizing energy losses and energy costs; see e.g., [17]- [20]. Support vector machines and Gaussian processes have also been suggested for reactive power control using smart inverters [21], [22]. ...
... There are also some models which are not based on RL framework. Some studies reach the equilibrium among agents through their own iteration rules [139,150] or other supervised machine learning algorithm, such as support vector machines SVM [154] . ...
... In Ref. [138], the twotimescale Lyapunov optimisation method is used for predictive service placement; the algorithm here incorporates the prediction of user mobility in the near future. The real-time operation of heterogeneous energy storage units is investigated in Ref. [139]; two battery units collaborate to support a microgrid in different timescales: the discharging power of slower battery exchange and the inflow power from the main grid serve the basic demand; the difference between real-time renewable generation and demand is filled by the fast battery as well as the energy brought from the real-time market. The slower problem involves the expected cost of the fast process, which is solved via a projected subgradient scheme. ...