Alighalehbabakhani F.,Wayne State University |
Miller C.J.,Wayne State University |
Abkenar S.M.S.,Wayne State University |
Fracasso P.T.,University of Sao Paulo |
And 2 more authors.
Sustainable Computing: Informatics and Systems | Year: 2015
Pump station is the biggest energy consumer in a water distribution system (WDS). A large amount of money is expended to provide energy for pumps. The environmental footprint associated with these excess energy demands is a source of concern. By implementing an optimum pump schedule that needs a minimum amount of energy to provide enough pressure and flow for water system, operational cost will be reduced and water system will be more environmentally friendly.Researchers are trying to find practical tools and methods to optimize pump operation. In this research, Pollutant Emission Pump Station Optimization (PEPSO), Darwin Scheduler (DS) and another approach that uses Markov Decision Processes (MDP) have been used as three different tools for optimizing pump operation of WDS of Monroe, MI, USA. In all three methods pumping optimizations have been done based on reducing energy usage, at the end results of running these three tools have been compared. The comparison results show that pump operation that has been taken from MDP algorithm has the best result in terms of energy usage and the number of pump switches, while pump operation taken from DS can be more effective at volume stored in tanks. The simulations showed PEPSO to be considerably faster than the other two evaluated methods in arriving at the optimum solution. © 2014 Elsevier Inc.
Jin S.X.,Tull Inc. |
Loya-Smalley C.,Tull Inc. |
Tucker E.,Tull Inc. |
Qaqish A.,Tull Inc. |
And 3 more authors.
2013 International Green Computing Conference Proceedings, IGCC 2013 | Year: 2013
This paper presents a quantitative approach to estimating the carbon dioxide (CO2) emission reduction by optimizing water storage operations in water delivery systems. This approach uses hydraulic models of water delivery systems to perform pumping energy optimization analyses with equalization water storage and identifies real-time electrical generation types based on Locational Marginal Price (LMP) data available in open electrical markets. The real-time pollutant emission reduction has been evaluated based on hourly on-duty generation types and pollutant emission rates for different types of generation. An example is presented that applied the proposed approach to a large water delivery system in the Metro Detroit area, Michigan. The analysis results showed a daily CO2 emission reduction of 26.1 tonnes, which accounted for approximately 3% of the total CO2 emission produced by the electricity consumption for pumping water under the maximum day demand condition of 2012. © 2013 IEEE.