Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS

Tools for optimal operation and planning or urban distribution systems

This paper reports on the results of the implementation of a set of software tools for the comprehensive analysis and optimisation of distribution systems, developed jointly by Azienda Energetica Metropolitana (AEM) Torino S.p.A. and Dipartimento di Ingegneria Elettrica Industriale - Politecnico di Torino. The software tools cover several aspects of distribution system analysis, fault current calculation, reliability assessment, total operating cost evaluation, optimal reconfiguration, optimal planning and service restoration

Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

Improving reliability on distribution systems by network reconfiguration and optimal device placement.

Masters Degree. University of KwaZulu-Natal, Durban.A distribution system without reliable networks impacts production; hinders economy and affects day to day activities of its customers who demand uninterrupted supply of high quality. All power utilities try to minimize costs but simultaneously strive to provide reliable supply and achieve customer satisfaction. This research has focused on predicting and thereafter improving the South African distribution network reliability. Predictive reliability modelling ensures that utilities are better informed to make decisions which will improve supply to customers. An algorithm based on Binary Particle Swarm Optimization (BPSO) was implemented to optimize distribution network configuration as well as supplemental device placement on the system. The effects on reliability, network performance and system efficiency were considered. The methodology was applied to three distribution networks in KwaZulu-Natal, each with diverse topology, environmental exposure and causes of failure. The radial operation of distribution networks as well as the practical equipment limitations was considered when determining the optimal configuration. The failure rates and repair duration calculated unique to each network was used to model the performance of each component type. Historical performance data of the networks was used as a comparison to the key performance indicators obtained from DigSILENT PowerFactory simulations to ensure accuracy and evaluate any improvement on the system. The results of a case study display improvements in System Average Interruption Duration Index (SAIDI) of up to 20% and improvements in System Average Interruption Frequency Index (SAIFI) of up to 24% after reconfiguration. The reconfiguration also reduced the system losses in some cases. Network reconfiguration provides improved reliable supply without the need for capital investment and expenditure by the utility. The BPSO algorithm is further used to optimally place and locate switches and reclosers on the networks to achieve maximum improvement in reliability for minimal cost. The results show that the discounted future benefit of adding additional protection devices to a network is approximately R27 million over a twenty-five-year period. The maximum SAIDI improvement from adding reclosers to a network was 21%, proving that additional device placement is a cost-effective means to improve system reliability

Optimizing Rehabilitation and Maintenance of Hospitals

Hospitals are one of the core elements of a health care system that provide medical service to the patients. Hospital facility management is a complex issue as it involves the management of several complex systems that have a direct impact on the delivery of health care issues. This research focuses on two vital aspects of hospital facility management, (1) level of service provided by the hospital and (2) technical aspects of mission critical hospital subsystems. This study proposes two models in order to maintain and improve the level of service delivered to the patients. The first model operates at the macro-level and undertakes the Network-level Hospital Rehabilitation Trade off model (NEHIR). The model optimizes the scheduling of rehabilitation works through the use of genetic algorithm optimization engine. The model features through five modules, (1) Database module that stores the hospitals data, (2) Backward Markov chain module that estimates the transition probability matrix, (3) Deterioration prediction module that predict the future condition of the asset, (4) Rehabilitation Cost optimization and (5) Multi-objective rehabilitation schedule optimization that conducts a tradeoff between the modified rehabilitation cost and the number of unserved patients. The second model operates at the micro-level and undertakes the Hospital-level Reliability Centered Maintenance model (HOREM). The model optimizes the maintenance tasks for critical subsystems and optimize the allocation of maintenance budget among the hospital subsystems. HOREM model is consisted of five modules as follows, (1) Reliability Centered Maintenance module that was used to define the components, functions, functional failure, failure modes, failure consequence and maintenance type for subsystems components, (2) fuzzy logic system module for determining the probability of failure of different replacement/restoration intervals, (3) Monte-Carlo simulation module determining the probability of failure of different inspection intervals, (4) Multi-objective maintenance optimization module that tradeoff between the downtime and maintenance costs and (5) Systems Integration optimization module that optimize the top management maintenance budget on hospitals subsystems. Two case studies were considered for verification and validation. The first case study is comprised of four hospitals was used for NEHIR model validation. The results of NEHIR model showed 8% decrease in number of unserved patients and 20% saving in rehabilitation costs. The second case study was one hospital that was used for validating HOREM model. The results of HOREM model showed 17% reduction in maintenance costs compared to traditional methods for the same downtime

Application of System Reliability Indices in Electric Power System

System reliability indices deal with the adequacy of overall system supply and indicates the system behaviour and response. The index express interruption statistics in terms of the customers which can be an individual, firm, or organization that purchases electric services at one location under one rate classification, contract or schedule. The system reliability indices include: System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), Average Service Unavailability Index (ASUI), Expected Energy Not Supplied (EENS) and Average Energy Not Supplied (AENS). These indices are used as performance evaluation for power system reliability assessment. This assessment helps in system planning for long and short terms. This study therefore focuses reviewed past study on application of SAIFI, SAIDI and CAIDI for system reliability assessment on electric power system. These indices imply how often an average customer experiences sustained interruption over a predefined time of year and they are most used reliability indices for power system. Keywords: Reliability Indices, SAIDI, SAIFI, CAIDI, ASAI, ASUI, AENS, Reliability Assessment. DOI: 10.7176/JETP/11-6-03 Publication date: November 30th 202

A method for the evaluation and optimisation of power losses and reliability of supply in a distribution network

This paper presents two methods for evaluating and optimizing the configuration of a distribution network. A new loss-optimization method is described which partitions, optimizes and then recombines the network topology to identify the lowest loss configurations available. A reliability evaluation method is presented which evaluates, on a load-by-load basis, the most effective restoration path and the associated time. In contrast to previously-reported methods, the operation of different types of switch is integrated into this approach, reducing dependency on pre-determined restoration times for each load each fault location. This provides a more accurate estimate of the outage durations through identification of the specific restoration method for each load under each fault condition. The optimization method applied is shown to be effective in identifying optimally-reliable network topologies. Significant benefits are shown to be available

Immune System Based Control and Intelligent Agent Design for Power System Applications

The National Academy of Engineering has selected the US Electric Power Grid as the supreme engineering achievement of the 20th century. Yet, this same grid is struggling to keep up with the increasing demand for electricity, its quality and cost. A growing recognition of the need to modernize the grid to meet future challenges has found articulation in the vision of a Smart Grid in using new control strategies that are intelligent, distributed, and adaptive. The objective of this work is to develop smart control systems inspired from the biological Human Immune System to better manage the power grid at the both generation and distribution levels. The work is divided into three main sections. In the first section, we addressed the problem of Automatic Generation Control design. The Clonal Selection theory is successfully applied as an optimization technique to obtain decentralized control gains that minimize a performance index based on Area Control Errors. Then the Immune Network theory is used to design adaptive controllers in order to diminish the excess maneuvering of the units and help the control areas comply with the North American Electric Reliability Corporation\u27s standards set to insure good quality of service and equitable mutual assistance by the interconnected energy balancing areas. The second section of this work addresses the design and deployment of Multi Agent Systems on both terrestrial and shipboard power systems self-healing using a novel approach based on the Immune Multi-Agent System (IMAS). The Immune System is viewed as a highly organized and distributed Multi-Cell System that strives to heal the body by working together and communicating to get rid of the pathogens. In this work both simulation and hardware design and deployment of the MAS are addressed. The third section of this work consists in developing a small scale smart circuit by modifying and upgrading the existing Analog Power Simulator to demonstrate the effectiveness of the developed technologies. We showed how to develop smart Agents hardware along with a wireless communication platform and the electronic switches. After putting together the different designed pieces, the resulting Multi Agent System is integrated into the Power Simulator Hardware. The multi Agent System developed is tested for fault isolation, reconfiguration, and restoration problems by simulating a permanent three phase fault on one of the feeder lines. The experimental results show that the Multi Agent System hardware developed performed effectively and in a timely manner which confirms that this technology is very promising and a very good candidate for Smart Grid control applications