Simulated Annealing in Optimization of Energy Production in a Water Supply Network

Abstract

In water supply systems, the potential exists for micro-hydropower that uses the pressure excess in the networks to produce electricity. However, because urban drinking water networks are complex systems in which flows and pressure vary constantly, identification of the ideal locations for turbines is not straightforward, and assessment implies the need for simulation. In this paper, an optimization algorithm is proposed to provide a selection of optimal locations for the installation of a given number of turbines in a distribution network. A simulated annealing process was developed to optimize the location of the turbines by taking into account the hourly variation of flows throughout an average year and the consequent impact of this variation on the turbine efficiency. The optimization is achieved by considering the characteristic and efficiency curves of a turbine model for different impeller diameters as well as simulations of the annual energy production in a coupled hydraulic model. The developed algorithm was applied to the water supply system of the city Lausanne (Switzerland). This work focuses on the definition of the neighborhood of the simulated annealing process and the analysis of convergence towards the optimal solution for different restrictions and numbers of installed turbines.Water Resour Manage

Energy Recovery Using Micro-Hydropower Technology in Water Supply Systems: The Case Study of the City of Fribourg

“…In the recently developed search algorithm [5,18], in each iteration, a full year is simulated with an hourly time step considering the installation of a given number of turbines in several locations or combined in one location. The produced energy is obtained by coupling the EPANET hydraulic model [20] that calculates the hydraulic state of the network for each time step with the characteristics of the turbines.…”

“…Besides the locations where PRVs are installed, there are also areas within the sub-grids that have more pressure than necessary, even if not excessive, because of their connection to other higher areas [5].…”

“…Distribution networks are complex systems, usually composed of multiple loops and asymmetric consumption in both time and space. The optimal location of turbines within such systems is a subject of research [5,18,19]. Moreover, before enduring deep analyses and simulations of turbine operation and water demand scenarios, a measure of the excess energy available within the city network, indicating the potential for that network to be used for hydropower, would be useful for WSS managers.…”

Energy Saving in a Water Supply Network by Coupling a Pump and a Pump As Turbine (PAT) in a Turbopump

“…In 2014 , Carravetta et al [38] compared the effectiveness of the different installation schemes of a PAT plant. Other preliminary studies have included an analysis of the optimal location of a PAT within a WDN [36,39,40] to maximize the power production.…”

Evidential reasoning-based airline network design for long-haul transportation in express delivery

“…Some intelligent searching algorithms, such as the genetic algorithm in [7] and simulated annealing in [8], also have been used to design networks. However, the conventional optimal solutions mentioned above have are very complex to use for designing networks in which cargo must be transferred to hubs, and there is no way to accurately estimate the conditions of the ports, such as the volume of rainfall, the volume of railway transportation, the average throughput, and the hardware infrastructure.…”