Pump Scheduling Optimization in Four US Cities: Case Studies

2008 ◽  
Author(s):  
Simon Bunn
Keyword(s):  
Case Studies ◽  
Scheduling Optimization ◽  
Pump Scheduling ◽  
Us Cities

Improving energy efficiency in water supply systems with pump scheduling optimization

2019 ◽  
Vol 213 ◽  
pp. 342-356 ◽  
Author(s):  
Tiago Luna ◽  
João Ribau ◽  
David Figueiredo ◽  
Rita Alves
Keyword(s):  
Energy Efficiency ◽  
Water Supply ◽  
Water Supply Systems ◽  
Scheduling Optimization ◽  
Pump Scheduling ◽  
Supply Systems

scholarly journals Optimal Scheduling of a Microgrid Including Pump Scheduling and Network Constraints

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Ashok Krishnan ◽  
L. P. M. I. Sampath ◽  
Y. S. Foo Eddy ◽  
H. B. Gooi
Keyword(s):  
Case Studies ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Renewable Energy Sources ◽  
Management Strategies ◽  
Electricity Cost ◽  
Network Constraints ◽  
Battery Energy Storage Systems ◽  
Pump Scheduling ◽  
Battery Energy

This paper proposes an efficient energy management system (EMS) for industrial microgrids (MGs). Many industries deploy large pumps for their processes. Oftentimes, such pumps are operated during hours of peak electricity prices. A lot of industries use a mix of captive generation and imported utility electricity to meet their energy requirements. The MG considered in this paper includes diesel generators, battery energy storage systems, renewable energy sources, flexible loads, and interruptible loads. Pump loads found in shipyard dry docks are modelled as exemplar flexible industrial loads. The proposed EMS has a two-stage architecture. An optimal MG scheduling problem including pump scheduling and curtailment of interruptible loads (ILs) is formulated and solved in the first stage. An optimal power flow problem is solved in the second stage to verify the feasibility of the MG schedule with the network constraints. An iterative procedure is used to coordinate the two EMS stages. Multiple case studies are used to demonstrate the utility of the proposed EMS. The case studies highlight the efficacy of load management strategies such as pump scheduling and curtailment of ILs in reducing the total electricity cost of the MG.

A Novel Depth-First Searching Approach for Detailed Scheduling of Operations in a Multi-Product Pipeline With Multiple Pump Stations

2018 ◽  
Author(s):  
Qi Liao ◽  
Bohong Wang ◽  
Zhengbing Li ◽  
Haoran Zhang ◽  
Yongtu Liang ◽  
...  
Keyword(s):  
Large Scale ◽  
Computing Time ◽  
Hydraulic Calculation ◽  
Current Status ◽  
Time Interval ◽  
Scheduling Optimization ◽  
Pipeline Scheduling ◽  
Pump Scheduling ◽  
Pump Stations ◽  
Product Pipeline

Considering market’s diversified demand and transport economy, large volumes of various refined products commonly move down the pipeline in batches, which are pumped at pump stations and delivered to respective delivery stations. The integrate detailed scheduling optimization is a sophisticated problem due to the characteristics of multi-product pipelines, such as market-oriented, fluctuated demand, various processing technique and complicated hydraulic calculation during batch migration. The integrate detailed scheduling optimization has been widely studied during the last decade, however, most of them studied pipeline scheduling and pump scheduling separately. Besides, the proposed methods are mathematical models, whose computational efficiency greatly decreases in large-scale pipeline scheduling, let alone in the problems coupling with pump scheduling. Aiming at this problem, this paper presents a novel depth-first searching approach based on flowrate ratio to deal with the detailed scheduling of operations in a multi-product pipeline with multiple pump stations. As for each single time interval, the proposed method decides an ideal flowrate ratio according to current status, then solves out the optimal flowrate that mostly conforms to the ideal ratio and satisfies all operational constraints, and finally updates information for next time interval. However, during the computational procedure, backtracking method would be adopted to modify the previous flowrate ratios and recalculate new flowrate when the actual delivered products are insufficient. Finally, a case tested on a Chinese real-world pipeline with 6 delivery stations is given to demonstrate the veracity and practicability of the proposed method. From the results, computing time of the case is within 1 minute, and the solved detailed scheduling plans can fulfill demand with stable pump operations. Besides, the proposed approach is scarcely influenced by the scale of pipeline structure and time horizon, so it is also applicable to the long-term scheduling of a pipeline with many delivery stations.

Assessing City Responses to Federal Subventions

1986 ◽  
Vol 4 (3) ◽  
pp. 289-298 ◽  
Author(s):  
J W Fossett
Keyword(s):  
Risk Aversion ◽  
Case Studies ◽  
Urban Areas ◽  
General Purpose ◽  
Utility Functions ◽  
Federal Grants ◽  
Federal Funds ◽  
Local Officials ◽  
The Impact ◽  
Us Cities

This paper reports on a series of case studies in which the consequences are examined of the major buildup in federal grants to urban areas over the 1970s in eleven major US cities. Conventional economic models of the impact of this buildup are argued to rely overmuch on assumptions about the manner in which local officials perceive and structure choices, neglecting important program features and ignoring variations across place and time in the structure of utility functions and in the types of behavior seen as likely to achieve a desired outcome. The case studies suggest that local uses of federal funds are structured by the uncertainty in both level and form of grant receipts and by a propensity for risk-aversion behavior on the part of local general-purpose officials.

Multiobjective Pump Scheduling Optimization Using Harmony Search Algorithm (HSA) and Polyphonic HSA

2012 ◽  
Vol 27 (5) ◽  
pp. 1249-1261 ◽  
Author(s):  
Ioannis P. Kougias ◽  
Nicolaos P. Theodossiou
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Scheduling Optimization ◽  
Pump Scheduling

Active learning of optimal controls for pump scheduling optimization

2021 ◽  
Author(s):  
Antonio Candelieri ◽  
Riccardo Perego ◽  
Ilaria Giordani ◽  
Francesco Archetti
Keyword(s):  
Active Learning ◽  
Real World ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Search Space ◽  
Scheduling Optimization ◽  
The Real ◽  
Decision Variables ◽  
Pump Scheduling

<p>Two approaches are possible in Pump Scheduling Optimization (PSO): <em>explicit</em> and <em>implicit control</em>. The first assumes that decision variables are pump statuses/speeds to be set up at prefixed time. Thus, the problem is to efficiently search among all the possible schedules (i.e., configurations of the decision variables) to optimize the objective function – typically minimization of the energy-related costs – while satisfying hydraulic feasibility. Since both the energy cost and the hydraulic feasibility are black-box, the problem is usually addressed through simulation-optimization, where every schedule is simulated on a “virtual twin” of the real-world water distribution network. A plethora of methods have been proposed such as meta-heuristics, evolutionary and nature-inspired algorithms. However, addressing PSO via explicit control can imply many decision variables for real-world water distribution networks, increasing with the number of pumps and time intervals for actuating the control, requiring a huge number of simulations to obtain a good schedule.</p><p>On the contrary, implicit control aims at controlling pump status/speeds depending on some control rules related, for instance, to pressure into the network: pump is activated if pressure (at specific locations) is lower than a minimum threshold, or it is deactivated if pressure exceeds a maximum threshold, otherwise, status/speed of the pump is not modified. These thresholds are the decision variables and their values – usually set heuristically – significantly affect the performance of the operations. Compared to explicit control, implicit control approaches allow to significantly reduce the number of decision variables, at the cost of making more complex the search space, due to the introduction of further constraints and conditions among decision variables. Another important advantage offered by implicit control is that the decision is not restricted to prefixed schedules, but it can be taken any time new data from SCADA arrive making them more suitable for on-line control.</p><p>The main contributions of this paper are to show that:</p><ul><li>thresholds-based rules for implicit control can be learned through an active learning approaches, analogously to the one used to implement Automated Machine Learning;</li> <li>the active learning framework is well-suited for the implicit control setting: the lower dimensionality of the search space, compared to explicit control, substantially improves computational efficiency;</li> <li>hydraulic simulation model can be replaced by a Deep Neural Network (DNN): the working assumption, experimentally investigated, is that SCADA data can be used to train and accurate DNN predicting the relevant outputs (i.e., energy and hydraulic feasibility) avoiding costs for the design, development, validation and execution of a “virtual twin” of the real-world water distribution network.</li> </ul><p>The overall system has been tested on a real-world water distribution network.</p>

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