Search for: All records

This paper investigates integration of distributed energy resources (DERs) in microgrids (MGs) through two-stage power conversion structures consisting of DC-DC boost converter and DC-AC voltage source converter (VSC) subsystems. In contrast to existing investigations that treated DC-link voltage as an ideal constant voltage, this paper considers the non-ideal dynamic coupling between both subsystems for completeness and higher accuracy, which introduces additional DC-side dynamics to the VSC. The analysis shows parameters of the boost converter's power model that impact stability through the DC-link. Carefully selecting these parameters can mitigate this effect on stability and improve dynamic performance across the DC-link. Hence, an optimization framework is developed to facilitate in selecting adequate boost converter parameters in designing a stable voltage source converter-based microgrid (VSC-MG). The developed optimization framework, based on particle swarm optimization, considers dynamic coupling between both subsystems and is also effective in avoiding inadequate boost converter parameters capable of propagating instability through the DC-link to the VSC. Simulations are performed with MATLAB/Simulink to validate theoretical analyses. 

The electric power distribution network (PDN) and the transportation network (TN) are generally operated/coordinated by different entities. However, they are coupled through electric vehicle charging stations (EVCSs). This paper proposes to coordinate the operation of the two systems via a fully decentralized framework where the PDN and TN operators solve their own operation problems independently, with only limited information exchange. Nevertheless, the operation problems of both systems are generally mixed-integer programs (MIP), for which mature algorithms like the alternating direction method of multipliers (ADMM) may not guarantee convergence. This paper applies a novel distributed optimization algorithm called the SD-GS-AL method, which is a combination of the simplicial decomposition, gauss-seidel, and augmented Lagrangian, which can guarantee convergence and optimality for MIPs. However, the original SD-GS-AL may be computationally inefficient for solving a complex engineering problem like the PDN-TN coordinated optimization investigated in this paper. To improve the computational efficiency, an enhanced SD-GS-AL method is proposed by redesigning the inner loop of the algorithm, which can automatically and intelligently determine the iteration number of the inner loop. Simulations on the test cases show the efficiency and efficacy of the proposed framework and algorithm. 

The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. 

This paper explores the micro Energy-Water-Hydrogen (m-EWH) nexus, an engineering system designed to reduce carbon emissions in the power sector. The m-EWH nexus leverages renewable energy sources (RES) to produce hydrogen via electrolysis, which is then combined with carbon captured from fossil fuel power plants to mitigate emissions. To address the uncertainty challenges posed by RES, this paper proposes a real-time decision-making framework for the m-EWH nexus, which requires the rapid solution of large-scale mixed-integer convex programming (MICP) problems. To this end, we develop a machine learning-accelerated solution method for real-time optimization (MARO), comprising three key modules: (1) an active constraint and integer variable prediction module that rapidly solves MICP problems using historical optimization data; (2) an optimal strategy selection module based on feasibility ranking to ensure solution feasibility; and (3) a feature space extension and refinement module to improve solution accuracy by generating new features and refining existing ones. The effectiveness of the MARO method is validated through two case studies of the m-EWH nexus, demonstrating its capability to swiftly and accurately solve MICP problems for this complex system. 

Emergency response resources (ERRs) such as mobile energy resources (MERs) and repair crews (RCs) play a pivotal role in the efficient restoration of power distribution systems after disasters. This paper presents a computationally tractable approach to utilize ERRs and post-disaster available distributed energy resources (PDA-DERs) in the restoration of disaster-impacted distribution systems. The post-disaster restoration model is proposed to co-optimize the dispatch of pre-allocated ERRs and PDADERs to minimize the impact of high-impact low-frequency (HILF) events on customers, i.e., energy not served for the entire restoration window. Compared with existing restoration strategies using ERRs, the proposed approach is more tractable since, in the restoration model, a super-node approximation (SNA) of distribution networks and the convex hulls relaxation (CHR) of non-linear constraints are introduced to achieve the best trade-off between computational burden and accuracy. Tests of the proposed approach on IEEE test feeders demonstrated that a combination of SNA and CHR remarkably reduces the solution time of the post-disaster restoration model. 

This paper investigates a novel engineering problem, i.e., security-constrained multi-period operation of micro energy-water nexuses. This problem is computationally challenging because of its high nonlinearity, nonconvexity, and large dimension. We propose a two-stage iterative algorithm employing a hybrid physics and data-driven contingency filtering (CF) method and convexification to solve it. The convexified master problem is solved in the first stage by considering the base case operation and binding contingencies set (BCS). The second stage updates BCS using physics-based data-driven methods, which include dynamic and filtered data sets. This method is faster than existing CF methods because it relies on offline optimization problems and contains a limited number of online optimization problems. We validate effectiveness of the proposed method using two different case studies: the IEEE 13-bus power system with the EPANET 8-node water system and the IEEE 33-bus power system with the Otsfeld 13-node water system.