Emission-aware Energy Storage Scheduling for a Greener Grid

Reducing our reliance on carbon-intensive energy sources is vital for reducing the carbon footprint of the electric grid. Although the grid is seeing increasing deployments of clean, renewable sources of energy, a significant portion of the grid demand is still met using traditional carbon-intensive energy sources. In this paper, we study the problem of using energy storage deployed in the grid to reduce the grid's carbon emissions. While energy storage has previously been used for grid optimizations such as peak shaving and smoothing intermittent sources, our insight is to use distributed storage to enable utilities to reduce their reliance on their less efficient and most carbon-intensive power plants and thereby reduce their overall emission footprint. We formulate the problem of emission-aware scheduling of distributed energy storage as an optimization problem, and use a robust optimization approach that is well-suited for handling the uncertainty in load predictions, especially in the presence of intermittent renewables such as solar and wind. We evaluate our approach using a state of the art neural network load forecasting technique and real load traces from a distribution grid with 1,341 homes. Our results show a reduction of >0.5 million kg in annual carbon emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the ACM International Conference on Future Energy Systems (e-Energy 20) June 2020, Australi

A Subgradient Method for Free Material Design

A small improvement in the structure of the material could save the manufactory a lot of money. The free material design can be formulated as an optimization problem. However, due to its large scale, second-order methods cannot solve the free material design problem in reasonable size. We formulate the free material optimization (FMO) problem into a saddle-point form in which the inverse of the stiffness matrix A(E) in the constraint is eliminated. The size of A(E) is generally large, denoted as N by N. This is the first formulation of FMO without A(E). We apply the primal-dual subgradient method [17] to solve the restricted saddle-point formula. This is the first gradient-type method for FMO. Each iteration of our algorithm takes a total of $O(N^2)$ foating-point operations and an auxiliary vector storage of size O(N), compared with formulations having the inverse of A(E) which requires $O(N^3)$ arithmetic operations and an auxiliary vector storage of size $O(N^2)$. To solve the problem, we developed a closed-form solution to a semidefinite least squares problem and an efficient parameter update scheme for the gradient method, which are included in the appendix. We also approximate a solution to the bounded Lagrangian dual problem. The problem is decomposed into small problems each only having an unknown of k by k (k = 3 or 6) matrix, and can be solved in parallel. The iteration bound of our algorithm is optimal for general subgradient scheme. Finally we present promising numerical results.Comment: SIAM Journal on Optimization (accepted

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

A placement problem for multiple Battery Energy Storage System (BESS) units is formulated towards power system transient voltage stability enhancement in this paper. The problem is solved by the Cross-Entropy (CE) optimization method. A simulation-based approach is adopted to incorporate higher-order dynamics and nonlinearities of generators and loads. The objective is to maximize the voltage stability index, which is setup based on certain grid-codes. Formulations of the optimization problem are then discussed. Finally, the proposed approach is implemented in MATLAB/DIgSILENT and tested on the New England 39-Bus system. Results indicate that installing BESS units at the optimized location can alleviate transient voltage instability issue compared with the original system with no BESS. The CE placement algorithm is also compared with the classic PSO (Particle Swarm Optimization) method, and its superiority is demonstrated in terms of a faster convergence rate with matched solution qualities.Comment: This paper has been accepted by IEEE Transactions on Sustainable Energy and now still in online-publication phase, IEEE Transactions on Sustainable Energy. 201

Integrated optimal design and sensitivity analysis of a stand alone wind turbine system with storage for rural electrification

In this paper, the authors investigate a robust Integrated Optimal Design (IOD) devoted to a passive wind turbine system with electrochemical storage bank: this stand alone system is dedicated to rural electrification. The aim of the IOD is to find the optimal combination and sizing among a set of system components that fulfils system requirements with the lowest system Total Cost of Ownership (TCO). The passive wind system associated with the storage bank interacts with wind speed and load cycles. A set of passive wind turbines spread on a convenient power range (2 – 16 kW) are obtained through an IOD process at the device level detailed in previous papers. The system cost model is based on data sheets for the wind turbines and related to battery cycles for the storage bank. From the range of wind turbines, a “system level” optimization problem is stated and solved using an exhaustive search. The optimization results are finally exposed and discussed through a sensitivity analysis in order to extract the most robust solution versus environmental data variations among a set of good solutions

Sequential joint signal detection and signal-to-noise ratio estimation

The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance. This optimization problem is reduced to a more tractable formulation by transforming the observed signal and noise sequences to a single sequence of Bernoulli random variables; joint detection and estimation is then performed on the Bernoulli sequence. This transformation renders the problem easily solvable, and results in a computationally simpler sufficient statistic compared to the one based on the (untransformed) observation sequences. Experimental results demonstrate the advantages of the proposed method, making it feasible for applications having strict constraints on data storage and computation.Comment: 5 pages, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 201