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Cost-effective water distribution network (WDN) design with acceptable pressure performance is crucial for the management of drinking water in cities. This paper presents a Microsoft Excel tool to model, simulate, and optimize WDNs with looped pipelines under steady-state incompressible flow simulations. Typically, the hardy-cross method is applied using spreadsheet calculations to estimate discharges. This method requires mass-conservative initial estimates and requires successive iterations to converge. In this paper, however, we develop an alternative method that uses the built-in solver capabilities of Excel, does not require initial mass-conservative estimation, and is free of flow corrections. The main objective of this paper is to develop an open-source accessible tool for simulating hydraulic networks also adapted for teaching and learning purposes. The governing equations and the mathematical basis for the hydraulic modeling of the system are mathematically described, considering the topology of the network, mass and energy conservation, cost of tank material, foundation, and cost of pumping energy to fill the tank. The use of this tool is encouraged at the undergraduate and graduate engineering levels, as it offers the opportunity to address complex concepts in a comprehensive way using a spreadsheet that does not require coding expertise. Hence, users can debug all cells and understand all equations used in the hydraulic model, as well as modify them. To demonstrate the model capabilities, three practical examples are presented, with the first one solved step by step, and the results are compared with the EPANET and with the results reported in the literature. Using the optimization method presented in this paper, it was possible to achieve a cost reduction of 151,790 USD (9.8% of the total cost) in a network that supplies a 44,416 population.

This paper deals with the joint reduction of dynamic states (internal states of generator, solar, and loads, etc) and algebraic variables (states of the network e.g., voltage and phase angles) of a nonlinear differential-algebraic equation (NDAE) model of power networks. Traditionally, in the current literature of power systemmodel order reduction (MOR), the algebraic constraints are usually neglected and the power network is commonly modeled via a set of ordinary differential equations (ODEs) instead of NDAEs. Thus, reduction is usually carried out for the dynamic states only and the algebraic variables are kept intact. This leaves a significant part of the system's size and complexity unreduced. This paper addresses this aforementioned limitation, by jointly reducing both dynamic and algebraic variables. As compared to the literature the proposedMOR techniques herein are endowed with the following features: (i) no system linearization is required, (ii) requires no transformation to an equivalent or approximate ODE representation, (iii) guarantee that the reduced order model to be NDAE and thus preserves the differential-algebraic structure of original power system model, and (iv) can seamlessly reduce both dynamic and algebraic variables while maintaining high accuracy. Case studies performed on a 2000-bus power system reveal that the proposedMOR techniques are able to reduce system order while maintaining accuracy

The rapid increase in the integration of intermittent and stochastic renewable energy resources (RER) introduces challenging issues related to power system stability. Interestingly, identifying grid nodes that can best support stochastic loads from RER, has gained recent interest. Methods based on Lyapunov stability are commonly exploited to assess the stability of power networks. These strategies approach quantifying system stability while considering: (i) simplified reduced order power system models that do not model power flow constraints, or (ii) datadriven methods that are prone to measurement noise and hence can inaccurately depict stochastic loads as system instability. In this paper, while considering a nonlinear differential algebraic equation (NL-DAE) model, we introduce a new method for assessing the impact of uncertain renewable power injections on the stability of power system nodes/buses. The identification of stable nodes informs the operator/utility on how renewables injections affect the stability of the grid. The proposed method is based on optimizing metrics equivalent to the Lyapunov spectrum of exponents; its underlying properties result in a computationally efficient and scalable stable node identification algorithm for renewable energy resources allocation. The proposed method is validated on the IEEE 9-bus and 200-bus networks

Floods in urban areas are becoming more intense due to unplanned urbanization and more frequent due to climate change. One of the most effective strategies to alleviate the effects of flooding is the use of flood control reservoirs such as detention ponds, which attenuate flood waves by storing water and slowing the release after the storm. Detention ponds can also improve water quality by allowing the settlement of pollutants inside the reservoir. The operation of most detention ponds occurs passively, where the outflows are governed by fixed hydraulic structures such as fully open orifices and weirs. The operation of detention ponds can be enhanced with active controls: orifices can be retrofitted with controlled valves, and spillways can have controllable gates such that their control schedule can be defined in real-time with a model predictive control (MPC) approach. In this paper, we develop a distributed quasi-2D hydrologic-hydrodynamic coupled with a reservoir flood routing model and an optimization approach (MPC) to identify the opening or closing of valves and movable gates working as spillways. We adapt the optimization problem to switch from a flood-related cost function to a heuristic function that aims to increase the detention time when no inflow hydrographs are predicted within a prediction horizon. The numerical case studies show the potential results of applying the methods herein developed in a real-world watershed in Sao Paulo, Brazil. We test the performance of MPC compared to static (i.e., fixed hydraulic device opening) alternatives with valves either fully or partially opened. The results indicate that the control algorithm presented in this paper can achieve greater flood and proxy water quality performance compared to passive scenarios.

Observability quantification is a key problem in dynamic network sciences. While it has been thoroughly studied for linear systems, observability quantification for nonlinear networks is less intuitive and more cumbersome. One common approach to quantify observability for nonlinear systems is via the Empirical Gramian (Empr-Gram) -- a generalized form of the Gramian of linear systems. In this technical note, we produce three new results. First, we establish that a variational form of nonlinear systems (computed via perturbing initial conditions) yields a so-called Variational Gramian (Var-Gram) that is equivalent to the classic Empr-Gram; the former being easier to compute than the latter. Via Lyapunov exponents derived from Lyapunov's direct method, the technical note's second result derives connections between existing observability measures and Var-Gram. The third result demonstrates the applicability of these new notions for sensor selection/placement in nonlinear systems. Numerical case studies demonstrate these three developments and their merits.

The main objective of the connected and automated vehicle (CAV) platoon control problem is to regulate CAVs' position while ensuring stability and accounting for vehicle dynamics. Although this problem has been studied in the literature, existing research has some limitations. This paper presents two new theoretical results that address these limitations: (i) the synthesis of unrealistic high-gain control parameters due to the lack of a systematic way to incorporate the lower and upper bounds on the control parameters, and (ii) the performance sensitivity to the communication delay due to inaccurate Taylor series approximation. To be more precise, taking advantage of the wellknown Pade approximation, this paper proposes a constrained CAV platoon controller synthesis that (i) systematically incorporates the lower and upper bounds on the control parameters, and (ii) significantly improves the performance sensitivity to the communication delay. The effectiveness of the presented results is verified through conducting extensive numerical simulations. The proposed controller effectively attenuates the stop-and-go disturbance -- a single cycle of deceleration followed by acceleration -- amplification throughout the mixed platoon (consisting of CAVs and human-driven vehicles). Modern transportation systems will benefit from the proposed CAV controls in terms of effective disturbance attenuation as it will potentially reduce collisions.

The operation of water distribution networks is a complex procedure aimed at efficiently delivering consumers with adequate water quantity while ensuring its safe quality. An added challenge is the dependency of the water quality dynamics on the system's hydraulics, which influences the performance of the water quality controller. Prior research has addressed either solving the optimum operational hydraulic setting problem or regulating the water quality dynamics as separate problems. Additionally, there have been efforts to couple these two problems and solve one compact problem resulting in trade-offs between the contradictory objectives. In contrast, this paper takes a novel approach by examining the water quality dependency on the hydraulics from a control-theoretic standpoint. More specifically, we explore the influence of accountability for water quality controllability improvement when addressing the pump scheduling problem. We examine its effects on the cumulative cost of the interconnected systems as well as the subsequent performance of the water quality controller. To achieve this, we develop a framework that incorporates different controllability metrics within the operational hydraulic optimization problem; its aim is attaining an adequate level of water quality control across the system. We assess the aforementioned aspects' performance on various scaled networks with a wide range of numerical scenarios.