Multi-criteria optimization of supply schedules in intermittent water supply systems

[EN] One of the problems for water supply systems with intermittent supply is the peak flow produced at some hours of the day, which is usually much larger than that in a system with continuous supply. The main consequence is the reduction of pressure and flow at the ends or highest points of the system network. This, in turn, generates inequity in water supply and complaints from users. To reduce the peak flow, some sectors of the system must be assigned a different supply schedule. As a result, the supply curve is modified, and the peak flow is reduced. This reorganization seeks some optimal allocation schedule and must be based on various quantitative and qualitative technical criteria. This paper hybridizes integer linear programming and multi-criteria analysis to contribute with a solution proposal to the technical management of intermittent water supply systems, which provides short-term results and requires little investment for implementation. This solution does not seek perpetuating intermittent water supply. On the contrary, this methodology can be a useful tool in gradual transition processes from intermittent to continuous supply. (C) 2016 Elsevier B.V. All rights reserved.Ilaya-Ayza, AE.; Benítez López, J.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Multi-criteria optimization of supply schedules in intermittent water supply systems. Journal of Computational and Applied Mathematics. 309:695-703. doi:10.1016/j.cam.2016.05.009S69570330

Implementation of DMAs in Intermittent Water Supply Networks Based on Equity Criteria

[EN] Intermittent supply is a common way of delivering water in many developing countries. Limitations on water and economic resources, in addition to poor management and population growth, limit the possibilities of delivering water 24 h a day. Intermittent water supply networks are usually designed and managed in an empirical manner, or using tools and criteria devised for continuous supply systems, and this approach can produce supply inequity. In this paper, an approach based on the hydraulic capacity concept, which uses soft computing tools of graph theory and cluster analysis, is developed to define sectors, also called district metered areas (DMAs), to produce an equitable water supply. Moreover, this approach helps determine the supply time for each sector, which depends on each sector¿s hydraulic characteristics. This process also includes the opinions of water company experts, the individuals who are best acquainted with the intricacies of the network.Ilaya-Ayza, AE.; Martins-Alves, C.; Campbell-Gonzalez, E.; Izquierdo Sebastián, J. (2017). Implementation of DMAs in Intermittent Water Supply Networks Based on Equity Criteria. Water. 9(11):1-20. doi:10.3390/w9110851S12091

Optimizing Household Water Decisions for Managing Intermittent Water Supply in Mexico City

Nearly 1 billion people around the world experience intermittent water supply (IWS), including about 70% of residents in the Mexico City area. Households with IWS often rely on multiple sources of water to meet their needs, including municipal piped water, trucked water, and rainwater. When calculating water costs and reliability of supply, models of these systems must account for household decision-making regarding the volume of water to use from each different source each day. Modeling these household decisions (or “control policies”) is challenging, especially when households use rainwater as a water source, due to the complexity of the input variables involved (e.g. intermittent water schedule, season, day of week), but is critical to understanding the role that household-level interventions, such as household storage and rainwater harvesting, may play in water access. Universal approximators provide a solution to this challenge by allowing for flexible shaping of these control polices. This study uses Radial Basis Function Networks to determine optimal household water management decisions, maximizing reliability of water supply while minimizing costs for an arbitrary household in Mexico City. The model design is informed using data collected during interviews with households in the city. The model produces Paretooptimal solution sets that demonstrate which household-level investments are most effective for improving the reliability of water access. Results show that household storage tanks are a critical component of water access, especially in households with very low access to the municipal piped water supply. A tank volume of around 1500-2500 liters can provide most of the savings, depending on the availability of municipal water, although a larger tank is better able to collect rainwater. IWS households with sufficient storage are able to meet their water needs with piped water nearly as reliably as those with continuous water supply, as long as a minimal threshold of water is delivered. When household storage is limited, households are more vulnerable to disruptions in the piped network, and costs increase if supply is not delivered consistently. Rainwater harvesting systems are shown to be economically viable at the DocuSign Envelope ID: 3F0D957B-51D2-42AE-9AE7-C1921FE89C34 iv household level regardless of the frequency of municipal piped water service. The techniques presented in this study are a crucial step in modeling water resources in cities with IWS

Distributed MPC for coordinated energy efficiency utilization in microgrid systems

To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal distributed model predictive control strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems, and some controllable loads, can maintain its own systemwide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.Accepted manuscrip

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation

DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to autonomously perform real-time load scheduling of dc platform supply vessel (PSV) with an objective to minimize specific fuel oil consumption (SFOC) for better fuel efficiency. Focus has been on the modeling of various components and control routines, which are envisaged to be an integral part of dc PSVs. Integration with photovoltaic-based energy storage system (ESS) has been considered as an option to cater for the short time load transients. In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling of generators and ESS using dc optimal power flow algorithm. This framework considers real dynamics of dc PSV during various marine operations with possible contingency scenarios, such as outage of generation systems, abrupt load changes, and unavailability of ESS. The proposed modeling and control routines with real-time transient simulation scheme have been validated utilizing the real-time marine simulation platform. The results indicate that the coordinated treatment of renewable based ESS with DGs operating with optimized speed yields better fuel savings. This has been observed in improved SFOC operating trajectory for critical marine missions. Furthermore, SFOC minimization at multiple suboptimal points with its treatment in the real-time marine system is also highlighted

Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section

Recent advances on data-driven services for smart energy systems optimization and pro-active management

Optimization and proactive management of energy systems are crucial for achieving sustainability, efficiency and resilience in future smart energy networks. Data-driven approaches offer promising solutions for tackling the complex and dynamic challenges of energy systems, such as uncertainty, variability, and heterogeneity. Meanwhile, recent advances in decreasing hardware costs and improving data accessibility have allowed for the collection of high-quality data, leading to the development of more accurate and robust data-driven models of different energy systems. In this study, a comprehensive overview of current and future trends in data-driven optimization for smart energy systems is presented. After introducing the motivation and the background of this research field, the potential applications and benefits of optimization in various domains is discussed, such as electric vehicles charge, district heating networks and energy districts. Subsequently this review focuses on different methods and techniques for data-driven optimization and proactive management, ranging from scientific models to machine learning algorithms. Finally, the novel European project, DigiBUILD, is introduced, where different case studies are tested in several pilots, including electric vehicle charging management for increasing renewable energy source consumption, district heating network operative costs optimization and building energy and comfort management