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Investigation of Cost and Energy Optimization of Drinking Water Distribution Systems Carla Cherchi, Mohammad Badruzzaman, Matthew Gordon, Simon Bunn, and Joseph G. Jacangelo Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03666 • Publication Date (Web): 13 Oct 2015 Downloaded from http://pubs.acs.org on October 15, 2015

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Environmental Science & Technology

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Investigation of Cost and Energy Optimization of Drinking Water Distribution

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Systems

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Carla Cherchi1*, Mohammad Badruzzaman1, Matthew Gordon2, Simon Bunn2, Joseph G.

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Jacangelo1,3

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1

MWH Americas, Inc., 300 N. Lake Avenue, Pasadena, CA, USA

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2

Derceto, Ltd., 63 Albert Street, Auckland 1141, New Zealand

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3

The Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe Street,

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Baltimore, MD 21205, USA

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* Corresponding Author: MWH Americas, Inc., 300 N. Lake Avenue, Pasadena, CA, USA,

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Email: [email protected], Phone: +1 (626)-568-6009, Fax: +1 (626)-568-6101

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Abstract

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Holistic management of water and energy resources through Energy and Water Quality

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Management Systems (EWQMS) have traditionally aimed at energy cost reduction with limited

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to no emphasis on energy efficiency or greenhouse gas minimization. This study expanded the

16

existing EWQMS framework and determined the impact of different management strategies for

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energy cost and energy consumption (e.g., carbon footprint) reduction on system performance at

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two drinking water utilities in California, US. The results show that optimizing for cost led to 4%

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(Utility B, summer) to 48% (Utility A, winter) cost reduction. The energy optimization strategy

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was successfully able to find the lowest energy use operation and achieved 3% (Utility B,

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summer) to 10% (Utility A, winter) reduction in energy usage. The findings of this study

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revealed that there may be a trade-off between cost optimization (dollars) and energy use

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(kilowatt-hour), particularly in summer when optimizing the system for the reduction of kWh to

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a minimum incurred 64% and 184% increase in cost compared to the cost optimization scenario.

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Water age simulations through hydraulic modeling did not reveal any adverse effects on the 1

ACS Paragon Plus Environment


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distribution system water quality and in tanks from pump schedule optimization targeting either

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cost or energy minimization.

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Keywords. Energy and Water Quality Management System, Drinking Water Utilities, Cost

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Optimization, Energy Optimization, Energy Efficiency, Greenhouse Gas Emissions.

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TOC

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1

Introduction

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Over the past several decades, water organizations have been challenged by increasing energy

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costs and demand and new stringent environmental regulatory requirements.1 An approximate

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3% share of United States (U.S.) annual electricity consumption is attributable to water utilities

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operations and future forecasts estimate this percentage to increase due to greater water demand

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and use of energy intensive treatment solutions.2,

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requirements for conveyance and distribution system operations vary in relation to the service

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area topography, raw water sources and adopted management strategies.6 In states where water is

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moved across large distances and elevations, this energy usage can become substantially higher.

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For example, in California, the water-related energy consumption accounts for 7.7% of the

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state’s energy usage.7 More than 90% of GHGs emitted into the atmosphere annually by drinking

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water utilities in the U.S. is due to electricity usage, which contributes significantly to an

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increasing carbon footprint estimated to be 45 million tons of CO2-eq per year.8, 9

3, 4, 5

In drinking water utilities, the energy

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Regional and national regulations have been promulgated or are anticipated in the U.S. and

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other parts of the world to achieve GHG emissions reduction. Cap-and-trade market mechanisms 2


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are well-recognized and are viable tools to manage these environmental challenges. In the U.K.,

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the Carbon Reduction Commitment (CRC), a cap-and-trade market addressing climate change

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and issued by the Climate Change Act, requires water companies with half-hourly metered

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electricity consumption equal to or greater than 6,000 MWh per year to reduce CO2 emissions by

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at least 26% by 2020, against a 1990 baseline.10 In the U.S., the California Air Resources Board

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has designed a cap-and-trade program that is enforceable and meets the requirements of the

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Assembly Bill No.32 (AB 32), the California Global Warming Solutions Act of 2006, which is

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expected to return to 1990 emission levels by 2020 and cover 85% of the state’s GHG

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emissions.11 Although U.S. water utilities are currently not qualified to participate in cap and

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trade programs, any future change in regulation will require both water and energy suppliers to

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consider the GHG implications of their systems.12

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Energy management programs can assist water utilities in reducing their carbon footprint and

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meeting current and future cap-and trade requirements, in addition to achieving operating energy

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cost savings. To facilitate the design of these energy management programs, holistic life cycle

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approaches have been recently applied as tools to identify the GHG impact of water distribution

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systems and identify opportunities for emission reduction.13 The majority of the GHG footprint

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of a water distribution system is related to pumping energy. Electricity and energy use reduction

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can be achieved at the infrastructure level (e.g., pumps, motors, pipelines, etc.), through energy

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efficient system designs, rehabilitation or equipment replacement, or by implementing energy

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optimization strategies, particularly when substantial inefficiencies due to leakage in distribution

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pipes occurs.13, 14, 15, 16, 17 The need for addressing the reduction of energy use and related GHG

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emissions in the water sector through system optimization and the importance of evaluating the

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trade-offs between different optimization objectives and various control strategies (cost vs. kWh

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minimization) has been emphasized in previous studies.2, 12, 18, 19, 20, 21 Despite this need for multi-

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objective analysis and optimization, these trade-offs in water conveyance and distribution

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systems operations remain poorly investigated by the water industry and specific tools for

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integrated system planning and operations are not widely available. The reluctance of water

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utilities to implement energy optimization programs and investigate these trade-offs is mostly

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due to concerns over constraints tied to water quality, the potential impact on production levels

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and maintenance, in addition to the cultural challenge that such implementation implies at all

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levels of the organization. 3



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Energy and Water Quality Management Systems (EWQMS) have been increasingly

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implemented at drinking water utilities to provide the foundation for a system control

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management tool to cohesively address water quality, supply and energy management within

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operational constraints.22 An EWQMS is a collection of individual application software

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programs that interface with existing monitoring and pre-existing control systems (Supervisory

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Control and Data Acquisition) in real-time to achieve operating cost reduction through real-time

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scheduling of pumping during lower cost tariff periods and best efficiency practices within the

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boundaries of water quality requirements and operating rules.23 Although traditional EWQMS

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design and operation focuses on achieving energy cost reductions, very little emphasis has been

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given to better understand the impact in EWQMS performance when the reduction of kilowatt-

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hours is prioritized. Energy optimization, in fact, can conflict with cost optimization, particularly

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for utilities, such as those in California, where high differential tariffs result from Time-of-Use

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(TOU) structures. Furthermore, in current EWQMS systems, the accounting of GHGs emissions

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associated to pumping system operations is lacking. Therefore, it is of paramount importance

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that the EWQMS enables water utilities to consider operational options to reduce kWh and GHG

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emissions and identify the trade-offs between energy management for cost reduction, energy and

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carbon footprint reduction, and conveyance and distribution system water quality.

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This study was undertaken to investigate the potential of control strategy optimization for the

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reduction of energy (e.g., GHG emissions) from water distribution system operations and

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examine the trade-offs between various control objectives (cost vs. energy minimization). This is

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achieved by developing and testing an integrated EWQMS modified with a kWh optimization

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and GHG accounting component that expands the existing and historical EWQMS framework to

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address the nexus between energy cost management, energy (kWh) use reduction, and system

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water quality. The functional specifications of the modified EWQMS were appropriately

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developed and evaluated at two drinking water utilities in California. Off-line simulations were

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performed on selected areas of these utilities’ distribution networks to reproduce the optimal

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pumping schedule of different optimization scenarios. Energy costs and kWh minimization were

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the targeted optimization goals simulated and compared with baseline of historical operations for

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conditions of high and low water demands. Water age simulations through hydraulic modeling

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were used to determine the potential impact of these optimization scenarios on tanks and

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distribution system water age. 4


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2

Materials and Methods

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2.1

Site Selection

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Two water utilities in California, namely Utility A and Utility B, were selected for testing and

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assessment of the EWQMS modified with a kWh optimization and GHG accounting component.

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Both water utilities currently have an operational EWQMS system utilizing Aquadapt (Derceto,

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Ltd). Details on the conveyance and distribution networks controlled by EWQMS at these

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utilities are presented in Table S1 (Supporting Information).

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For this study, the analysis was limited to a portion of the distribution network, which did not

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include the conveyance system and was hydraulically isolated from the remaining areas under

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EWQMS control. The conceptual schematics of these selected sites at Utility A and Utility B are

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reported in Figure 1. Each site includes three pumping stations, subjected to different tariff

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structures by the energy providers, and three water storage tanks. Each pumping station (PS) at

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Utility A includes three electric pumps with a maximum of two pumps that can run

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simultaneously. At Utility B, the PS-B1 pump station has two gas pumps (not operated for this

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study) and one electric pump, whereas PS-B2 and PS-B3 pumping stations only have electric

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pumps. Graphical representations of the daily and seasonal tariff profiles (summer and winter) at

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these pumping stations are reported in Supporting Information (Figure S1 and Figure S2). At

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Utility A, all pumping stations were subject to seasonal TOU tariffs by Pacific Gas & Electric,

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and the PS-A2 pumping station includes three peak period demand charges. At Utility B, PS-B1

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pump station was subjected to seasonal TOU tariffs and two peak period demand charges by

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Southern California Edison, whereas PS-B2 and PS-B3 pump stations were under flat tariffs

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throughout the year.

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Figure 1: Conceptual schematic of the Utility A (top) and Utility B (bottom) distribution system areas selected for testing.

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2.2

Selection of optimization scenarios

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Two optimization strategies were implemented in separate evaluations to investigate the

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potential cost and benefits associated with different goals (e.g., objective functions) and by

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imposing distinct constraints. The analysis compared historic manual (baseline) operation with

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the optimization objectives of cost and kWh reduction during the high (summer) and low

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(winter) demand seasons. Table 1 summarizes the fundamental principles of the optimization

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goals analyzed in this study, also detailed in the following sections.

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Minimization of Energy Costs. The pump energy cost depends on the electricity tariff rate

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applied (on peak, mid-peak and off-peak) and by the related kilowatt (kW) demand charge

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costs.24 The cost optimization scenario targets the lowest cost for pumping by scheduling the

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pumps at a lower tariff period and by reducing the demand charges, through the minimization of

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the following function fc (1):

min ∙ ∙ 1


6

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where N is the total number of pumps in the distribution system network; Tr = electricity tariff

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rate ($/kWh); Ep = electricity consumption of a given pump (kWh); Dc is the demand charge

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($/kW) and D is the peak demand (kW) in the study period. The amount of energy required for

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pump operations (Ep) depends on the flow through the pump, the head supplied by the pump, and

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the wire-to-water efficiency, which is the ratio of the energy imparted to the water and the

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energy delivered to the pumping system by the electrical conductors (ηp·ηm·ηd).16,

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each pump, the electricity consumption (Ep) is calculated based on the following equation (2)

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and the total energy consumption (ET) is defined by equation (3): !"

!#

25

Thus, for

∙∙ ∙ 2 ∙ ∙

159 %!

" % 3 %!

160 161

where Q is the flow rate; H is the total pumping head; ηm, ηp and ηd are the efficiency of the

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motor, pump and drive, respectively; γ is the specific weight of water, t = duration of pumping at

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a given operating point i; i is the summation index for the operating points; T is the number of

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operating points within the analysis period; n is the summation index for the number of pumps in

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the distribution network.16 As a result of the optimization, pump operations under TOU tariff

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structures are primarily scheduled during off-peak tariff periods. After achieving minimization of

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the fc function, as secondary objective, the optimizer seeks to minimize the pump energy

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consumption (EC) through operational pump efficiency strategies described in the following

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section.

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Minimization of Energy Consumption/GHG Emissions. The optimization targeting kWh

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reduction limits the amount of energy used to move water, by selecting the lowest hydraulic

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resistance path and operating near the best efficient point on the pump curve. Thus, the

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minimization of the total energy consumption, previously calculated in Equation (2), is achieved

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through the minimization of the following function fe : 7



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min ' " 4

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where N is the number of pumps in the distribution system network and ET is the total energy

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consumption. The strategies adopted to reduce the system energy consumption include: i)

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decreasing of the total system head; ii) selecting and operating pumps close to their best

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efficiency point; iii) selecting efficient pump and pump combinations to meet a given demand;

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iv) running pumps in series in order to move water with a lower lift; v) properly managing the

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water storage tanks (e.g., pumping into emptier tanks).

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The majority of the GHG emissions in water conveyance and distribution systems are from

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pumping energy and their estimation depends on the kWh consumed as well as on the fuel source

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mix for their generation. Since the marginal GHG emissions factors from the energy providers

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are not currently available, the kWh optimization scenario did not account for the energy source

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providing electricity (e.g., coal, hydropower, nuclear, wind, solar, etc.), which would result in

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different GHG emissions levels than those obtained using average GHG emission factors.

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Therefore, for this study, the kWh optimization corresponds to the GHG optimization since

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constant GHG emission factors were used. The average emission rate used in the GHG

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calculations at Utility A were those provided by the Pacific Gas & Electric for that utility (0.187

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metric tons-CO2 per MWh of electricity); at Utility B the fixed emission rate were those assigned

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per kWh of electricity consumed in California by eGrid for the Western Electricity Coordinating

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Council sub-region (0.296 metric tons-CO2 per MWh) due to the unavailability of the GHG

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emission factor specific to the SCE service area. 26

195 196

Table 1. Description of the optimization scenarios Optimization Scenario Optimization Scenario

Description

Baseline

Primary principle: System under no EWQMS operation (manual control by operators)

Cost Optimization

Primary principle: Pump operated when the cost is minimum (selection order: off-peak, mid-peak, on-peak)

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Optimization Scenario

Description Secondary principle: Pump operated at the lowest specific energy (kWh/ML pumped)

Water Demand

1

197 198

kWh1 Optimization

Primary principle: Pump operated at the lowest specific energy (kWh/ML pumped) Secondary principle: Assumes flat tariff operations

High Demand (Summer)

Utility A: 14 ML/day Utility B: 2.9 ML/day

Low Demand (Winter)

Utility A: 4 ML/day Utility B: 0.9 ML/day

kWh optimization is the GHG optimization when flat GHG emission factors are used and does not assume variability of GHG emissions factors with different energy sources.

199 200 201

2.3

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The EWQMS optimizer consists of an Excel spreadsheet that runs a Visual Basic for

203

Applications (VBA) program. The VBA program solves the optimization problem (cost and

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kWh minimization) using linear and nonlinear programming combined with advanced heuristics

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and includes a GHG accounting component for estimation of the carbon footprint of pumping

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operations. A simplified representation of the optimizer’s logic, related input and output, is

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presented in Figure 2. The optimizer requires input such as pump curves and efficiency curves;

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historical operational data such as pump flows, outflows or pump outflows, tank levels/volumes

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for each half hourly period and pump total dynamic head values. In addition a number of

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constraints are applied and influence the convergence and results of the optimization solution.23

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These constraints ensure compliance with water quality regulations, consumptive use criteria,

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energy and mass conservation principles in the distribution network and at junction nodes, and

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optimal pump operations (e.g., minimum run times; maximum starts per hour; minimum flow

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rates, minimum and maximum plant production rates). Energy rate structures and demand

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charges, energy efficient measures and asset management practices are other implicit and explicit

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constraining boundaries typically applied. The model uses these data to forecast water demand

Development of the EWQMS Optimizer Functional Specifications

9



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for each individual pressure zone for the following 48 hours of operation. The demand forecaster

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uses either average historical demand for the day type and season for each zone at the starting

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point of the day and re-calculates water demand every 30 minutes using a combination of the

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starting point and the last 6 hours of measured values, in order to enable detection of fast

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changing trends and sudden weather changes. At the start of the day, the optimizer develops a

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pump station’s schedule in half hourly periods for the upcoming 24 hours based on specific

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optimization goals (cost savings vs. energy savings), after replacing the historical data with the

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new water demand prediction profile. The software also improves water quality by seeking to

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increase turnover of storage and minimize production flow disturbances.

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The pump schedule generated is then exported into EPANET format and allows the user to run

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more realistic “extended-period” simulations in order to select a hydraulically feasible schedule

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with the lowest objective function value.24 The system iterates and continuously adapts to refine

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its schedule in response to changing conditions including variability in water demand, equipment

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availability and treatment plant capacity until the hydraulic and water quality results are

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acceptable.

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The contribution of leakage loads into pump energy use and operation was not considered as part

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of the optimization results for this study. The minimization of water losses in water distribution

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systems and the implementation of pressure management strategies have the potential to improve

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the energy efficiency of pumping, therefore result in additional GHG emission reduction.27

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Figure 2: Input and output from the EWQMS optimizer.

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2.4

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Water Quality Simulations

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Calibrated hydraulic models in EPANET format were provided by Utility A and Utility B for

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the offline simulations. The cost and kWh optimization goals were modeled in the InfoWater

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model using pump switching derived from the simulations of system operations minimizing cost

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and kWh. Water age, a useful proxy for water quality, was modeled using the Age of Water

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quality option in InfoWater. Modeling of water quality based on water age is a recommended

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practice for water distribution system management to overcome the monitoring constraints of

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chlorine residuals and DBPs based modeling.28, 29 Water quality modeling results were used to

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determine whether planned on-line optimization scenarios could materially affect the quality of

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water being delivered to customers.

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3

Results

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The analysis compared historic operations with the optimization objectives of cost reduction

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and kWh minimization for high (summer) and low (winter) water demand scenarios within the

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distribution sub-systems at Utility A and Utility B. In addition, water quality predictions were

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made to foresee the potential impacts of these optimization strategies on the water quality in

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tanks and that water provided to customers.

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3.1 Impact of Cost and kWh Optimization Strategies on Distribution System Performance at Utility A

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Figure 3 shows the distribution of pumping to different tariff windows at Utility A, obtained

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under cost and kWh optimization scenarios. In conditions of high water demand and within a

259

given infrastructure, the system characterized by a fixed storage capacity has less flexibility for

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pumping higher volumes of water than in low water demand conditions of winter, due to a lower

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storage to demand ratio. In addition, the tariff system in summer has a higher differential than

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that of winter.

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In high water demand conditions, the cost optimization scenario shifted 94% (at PS-A1) and

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100% (at PS-A2 and PS-A3) of the load to off-peak periods, with no pumping occurring during

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peak times. Due to the avoidance of pumping during the peak and mid-peak periods at PS-A1

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and the peak period at PS-A2, the cost optimization simulation operated against a higher average

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head than any other scenario. At PS-A1 and PS-A3, when the system was optimized for kWh, 12


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27% and 18% of the pumping occurred during peak hours. As there was less demand in winter

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all optimization scenarios were able to operate exclusively with the PS-A1 pump station and,

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with the exclusion of the baseline period, no pumping occurred at PS-A2. The PS-A3 pumping

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station, mostly avoided the mid-peak periods (

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