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Approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties
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Stochastic Search and Rescue Method for Day Ahead Economic Emission Load Dispatch Under Wind Uncertainty
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Convex Q-Learning in Continuous Time with Application to Dispatch of Distributed Energy Resources
Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with a finite-horizon optimal control objective. The main contributions are (i) The new Q-ODE: a model-free characterization of the Hamilton-Jacobi-Bellman equation. (ii) A formulation of Convex Q-learning that avoids approximations appearing in prior work. The Bellman error used in the algorithm is defined by filtered measurements, which is necessary in the presence of measurement noise. (iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an exploration condition on the training input. (iv) The theory is illustrated in application to resource allocation for distributed energy resources, for which the theory is ideally suited.
Model Predictive Dispatch of Energy Storage for Voltage Regulation in Active Distribution Systems.
Abstract not provided.
Real-time Optimal Dispatch of Behind-the-Meter DERs for Secondary Frequency Regulation
Active distribution networks (ADNs) can provide grid services such as peak load shaving, loss reduction, VoltNar control, congestion management, and frequency regulation. The fast response of inverter-based distributed energy resources (DERs) and battery storage systems enables them to provide ramping support and frequency control. This paper proposes a real-time optimization approach for behind-the-meter DERs to participate in the secondary frequency regulation. The proposed approach determines optimal set-points of output power of DERs to respond to requests from system operators (SOs) in realtime to maintain the frequency of the system at the nominal value. To satisfy the real-time requirement for the secondary frequency control, the nonlinear AC power flow model is linearized considering power losses in the system to achieve both high computational speed and acceptable accuracy. The optimization problem is rerun in a closed loop manner until the mismatch between the requested power by the system operators and actual delivered power at the substation reaches an acceptable tolerance. Furthermore, the proposed approach is validated using modified versions of the IEEE 33-bus and IEEE 69-bus distribution systems. Although the contribution of a single distribution system in the frequency regulation may not be significant, stacked and coordinated contributions from several distribution systems can provide frequency regulation and other grid services at scale.
A Task-Based Day-Ahead Load Forecasting Model for Stochastic Economic Dispatch
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Linearizing Bilinear Products of Shadow Prices and Dispatch Variables in Bilevel Problems for Optimal Power System Planning and Operations
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Data-Driven Mean-Corrected Recursive Estimation-Based Optimal DER Dispatch for Distribution System Voltage Control
Recent advances in smart inverters offer opportunities to mitigate adverse grid impacts caused by high penetrations of distributed photovoltaics (PV) in distribution grids, such as voltage violations. Here, this paper proposes a novel measurement-driven optimal power flow (OPF)-based distributed energy resource management system (DERMS) voltage regulation via recursive sensitivity estimation informed coordinated control of distributed PV inverters. The proposed approach leverages available grid and controllable DER measurements, eliminating reliance on system model information while being adaptive and robust to volatile operating conditions. A mean-corrected recursive ridge regression (MCRRR) algorithm is proposed for sensitivity estimation, continuously refining the sensitivity model through a closed-form solution. It effectively manages varying grid operating conditions, such as changes in power injections and topology reconfiguration, to facilitate a time-varying update of the Load Sensitivity Factors (LSF). The proposed approach is formulated as a linear programming (LP) problem and is thus scalable to larger-scale distribution systems. Its effectiveness and efficiency are demonstrated on a realistic distribution feeder with high PV penetrations in Southern California, USA.
Model Predictive Dispatch of Energy Storage for Voltage Regulation in Active Distribution Systems.
Abstract not provided.
Optimal Dispatch of Energy Storage Systems for Harmonic Mitigation and Power Factor Correction.
Abstract not provided.
Hydrogen Based Energy Storage System for Integration with Dispatchable Power Generator (Phase I Feasibility Study)
This project examined the feasibility of integrating hydrogen generation, storage, and use as a means to decarbonize campus activities while retaining the ability to utilize the existing natural gas fired combined heat and power system installed at the campus of the University of California @ Irvine. Analysis of specific potential sites for the integrated system identified a location adjacent to the existing central plant which resulted in minimization of interconnections. A strategy based on use of commercial electrolyzers and gas storage was identified. Primary technology advancements are required for the gas turbine to accommodate higher levels of hydrogen and the integrated controls. The project indicated challenges for adopting the proposed strategy with the present rates and constraints. The availability of a relatively low-cost biogas resource by the campus already decarbonizes the gas turbine to some extent. In the absence of this resource, procurement of electricity directly from large scale renewable operations could facilitate lower electricity costs. Additional solar resources on campus could also help in this regard. The gas turbine cannot be operated below 50% capacity due to air permit constraints. Using the otherwise curtailed gas turbine operation to generate hydrogen via electrolysis by consuming natural gas is not highly efficient and therefore leads to relatively high costs of electricity returned. Several scenarios demonstrate potential for effective decarbonization, yet most involve lower and lower capacity factor for the legacy gas turbine which is not a good use of the asset. A small gas turbine output with higher efficiency operation would help. As would ability to export electricity to the grid. Certainly current rate structures and operational scenarios are less attractive than other possible future structures which should be pushed for in the future.