Water Research 200 (2021) 117224
Contents lists available at ScienceDirect
Water Research
journal homepage: www.elsevier.com/locate/watres
Dynamic load shifting for the abatement of GHG emissions, power
demand, energy use, and costs in metropolitan hybrid wastewater
treatment systems
Samuel Reifsnyder a,b, Francesca Cecconi a,b, Diego Rosso a,b,∗
a
b
Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697-2175, USA
Water-Energy Nexus Center, University of California, Irvine, CA 92697-2175, USA
a r t i c l e
i n f o
Article history:
Received 8 January 2021
Revised 30 April 2021
Accepted 3 May 2021
Available online 9 May 2021
Keywords:
Water-energy nexus
Demand response
Integrated modeling
Decentralized wastewater treatment
Urban water management
Real-time control
a b s t r a c t
The installation of satellite water resource recovery facilities (WRRFs) has strengthened the ability to
provide cheap and reliable recycled water to meet the increasing water demand of expanding cities. As
a result, recent studies have attempted to address the problem of how to optimally integrate satellite
systems with other sectors of the urban sphere, such as the local economy, the power supply, and the
regional carbon footprint. However, such studies are merely based on the spatial domain, thus neglecting
potential time-dependent strategies that could further improve the sustainability of metropolitan water
systems. Therefore, in this study a new conceptual framework is proposed for the dynamic management
of hybrid systems comprised of both centralized and satellite WRRFs. Furthermore, a novel set of integrated real-time control (RTC) strategies are considered to analyze three different scenarios: 1) demand
response, 2) flow equalization of the centralized WRRF and 3) reduction of greenhouse gas emissions.
Data from a case study in California is used to develop an integrated dynamic model of a system of 8 facilities. Our results show that by dynamically shifting the dry-weather influent wastewater flows between
hydraulically connected WRRFs, a reduction in power demand (up to 25%), energy use (4%), operating
costs (8.5%) and indirect carbon emissions (4.5%) can be achieved. Therefore, this study suggests that a
certain degree of hydraulic interconnection coupled with dynamic load-shifting strategies, can broaden
the operational flexibility and overall sustainability of hybrid WRRF systems.
© 2021 Elsevier Ltd. All rights reserved.
1. Introduction
The continued increase in population density in urbanized areas, coupled with today’s climatic uncertainty, has necessitated
the pivot toward more resilient and sustainable urban planning
(Rogers et al., 2020; Wong and Brown, 2009). A critical challenge
for several metropolises pursuing this shift has been posed by the
need to adopt low energy solutions to meet the growing water
demand and treat the increasing volumes of generated wastewater (Qin et al., 2018). As a result, a multitude of research studies have highlighted various opportunities for the synergistic optimization of the urban water-energy system, including the implementation of cutting-edge technologies (Castrillo et al., 2019;
Tatinclaux et al., 2018), the adoption of energy-sensitive water policies (Kenway et al., 2019; Xu, 2020) and the optimization of existing energy-intensive water infrastructure, such as pumps and
∗
Corresponding author.
E-mail address: bidui@uci.edu (D. Rosso).
https://doi.org/10.1016/j.watres.2021.117224
0043-1354/© 2021 Elsevier Ltd. All rights reserved.
blowers (Capodaglio and Olsson, 2020; Vakilifard et al., 2019). Concurrently, the traditional idea of a linear urban water system, characterized by the “collection-usage-treatment-release” approach, is
currently being called into question by the unsustainable exploitation of accessible water resources. As such, the diversification of
the available water resources has been one of the key strategies for providing low-energy water supply alternatives in major metropolises such as Melbourne (Low et al., 2015), Singapore
(Lafforgue and Lenouvel, 2015), Tokyo (Edwin et al., 2014), and
Los Angeles (Porse et al., 2017). In addition to water conservation
strategies, the introduction of new alternative water sources such
as harvested stormwater and water reclamation have shown the
promise of improving local water security in a cost-effective and
energy-efficient way (Sedlak, 2014).
Among the possible water resource alternatives, water reclamation represents an attractive solution as it provides a reliable fitfor-purpose source of water near point of generation while still requiring lower energy and operating costs compared to imported
water or costal desalination (Capodaglio, 2020; Leverenz et al.,
S. Reifsnyder, F. Cecconi and D. Rosso
Water Research 200 (2021) 117224
2011). Moreover, the need to implement local water reclamation,
aimed at quenching the water demand of the growing peri–urban
areas, has shifted the global discourse concerning sustainable water supply toward decentralized water resource recovery facilities
(WRRFs) (Garrido-Baserba et al., 2018; Yerri and Piratla, 2019). Decentralized WRRFs that are hydraulically connected to the centralized sewer collection system are known as satellite WRRFs
(Larsen et al., 2013). Satellite WRRF systems provide the opportunity to collect, treat, and reclaim local wastewater, thus limiting
the need for dual-piping systems that stem from a single centralized facility and creating consequent long-term cost and energy
savings linked to construction and pumping (Hering et al., 2013).
Moreover, satellite wastewater treatment plants allow the production of a variety of treated effluent streams that can be tailored
to match the needs of local end-use applications such as industrial
reuse, landscape irrigation, groundwater recharge, streamflow augmentation, and service water used in households (Sharma et al.,
2010). Satellite wastewater treatment systems can be categorized
by three different types: i) interception type; ii) extraction type;
and iii) upstream type (Gikas and Tchobanoglous, 2009). In the interception type, wastewater is intercepted prior entering the sewer
laterals, diverted to a satellite system for treatment, and reused
locally. The extraction type, also known as wastewater “mining”
or “scalping”, differs from the previous type as the wastewater is typically extracted from larger sewer trunk lines or interceptors (Daigger, 2009; González-Viar et al., 2016). The upstream
type is usually adopted for suburban communities that experience
an increased demand for reclaimed water and are more peripheral with respect to the centralized collection system (Gikas and
Tchobanoglous, 2009). For such systems the wastewater is collected from the sewer main that pertains the remote community
prior to its introduction in the centralized collection system.
Although satellite wastewater facilities are considered selfsufficient, they typically lack the ability to process the generated
solids on-site. On the contrary, the solids are discharged into the
hydraulically connected sewer system from which they are transported downstream and processed in a centralized wastewater
treatment facility, thus taking advantage of the economies of scale
associated with biogas production. In addition, because the expansion of several of these centralized wastewater treatment facilities
is often limited due to the adjacent civic and industrial land development, satellite wastewater systems also provide the benefit of
hydraulically relieving downstream centralized wastewater infrastructure, thus deferring capital costs linked to expansion/upgrades
of centralized infrastructure (Marlow et al., 2013). Satellite treatment facilities, which are already recognized as a promising way
to meet the growing water demand of urban areas, do not serve
as a replacement for existing centralized water and wastewater
infrastructure. Instead, they are projected to be integrated gradually within existing urban water systems, thus establishing a hybrid urban water system (Larsen et al., 2013). In fact, it is precisely this integration that has been the subject of recent research
aimed at assessing the optimal management of hybrid centralized
and satellite wastewater treatment systems (Eggimann et al., 2015;
Opher and Friedler, 2016). Several of these studies perform spatial
modeling using site-specific conditions and geospatial information
for the quantification of sustainability metrics such as greenhouse
gas emissions (Kavvada et al., 2016), energy use (Bradshaw and
Luthy, 2017), and costs (Eggimann et al., 2016). These studies provide the important groundwork for the design and planning of integrated decentralized satellite wastewater systems. Nonetheless,
the focus of these recent studies has been posed merely on the
spatial domain, therefore neglecting the dynamics that characterize the day-to-day operation of wastewater treatment systems. In
fact, a time-invariant analysis of such systems precludes the ability to realistically examine real-time control (RTC) strategies that
are based on instrumentation, control and automation (ICA), which
are fundamentally dynamic (Yuan et al., 2019). It must be underscored that integrated RTC strategies have indeed been proposed
for wastewater systems as a whole (treatment plants and sewer
network), however their function has been limited to (i) the minimization of flooding and combined sewer overflows (CSO) during
wet weather events (Borsányi et al., 2008; van Daal et al., 2017),
and (ii) the minimization of the impact of wastewater on receiving
riverine ecosystems (Meng et al., 2017). Therefore, integrated RTC
strategies that target the optimization of energy, costs or carbon
footprint of hybrid centralized and satellite wastewater treatment
systems still remain unexplored.
Thus, the goals of this study are: i) to provide the first example of an integrated dynamic modeling analysis of hybrid wastewater treatment systems; ii) to develop a set of novel integrated RTC
strategies for hybrid wastewater treatment systems; iii) to quantify the benefits that can be achieved through the adoption of such
strategies in terms of energy, costs and indirect carbon emissions.
Three different scenarios are considered in this study: i) demand
response; ii) flow equalization of the centralized WRRF and iii) reduction of greenhouse gas (GHG) emissions. Each scenario is based
on an open-loop time of day control strategy, which is used to
lower the pumping rate at the influent lift stations of the different satellite WRRFs, with the intent to dynamically shift the influent loads to the downstream centralized treatment facility. A
case study of an existing regional hybrid wastewater system is considered herein, comprising a set of seven satellite extraction type
WRRFs and one centralized facility which are hydraulically connected. Data and specifications from each facility are utilized in
conjunction with specific assumptions for the development of a
dynamic process model. Data from each facility comprises various
operational variables (i.e., reactor dimensions, influent flows, constituent concentrations, aeration specifications, etc.). Local energy
tariff structures are considered in order to estimate the operating
costs of the aeration process and influent pumping of each treatment facility. In addition, dynamic profiles of the carbon intensity
(CI) of the electrical grid are used to estimate the carbon footprint
linked to the energy use of the overall system of plants.
2. Materials and methods
2.1. Background and description of the system of WRRFs
The system of hydraulically connected WRRFs presented in this
study is modeled after the interconnected system of wastewater
conveyance and treatment facilities known as the Joint Outfall System (JOS), which is managed and maintained by the Los Angeles
County Sanitation District (LACSD). The JOS provides wastewater
treatment services for 4.8 million residents in 73 cities. Designed
to take advantage of the regional topography, the JOS conveys the
collected wastewater to 7 satellite and 1 centralized WRRF mainly
by gravity (See Fig. 1).
The satellite facilities provide the dual purpose of hydraulically relieving the downstream centralized system and of supplying an alternative water resource to the local communities. Each
of the satellite WRRFs can be categorized as an extraction type
because the wastewater is directly mined out of the sewer system. The harvested wastewater is subsequently treated to title
22 standards via a treatment train comprising primary sedimentation, activated sludge treatment, coagulation, filtration, chlorination, and de-chlorination. The residual solids produced on-site
are discharged in the main trunk sewers of the JOS from which
they are transported downstream and treated at the centralized
wastewater treatment facility.
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S. Reifsnyder, F. Cecconi and D. Rosso
Water Research 200 (2021) 117224
Fig. 1. Maps and schematic of the case study of hydraulically connected WRRFs.
2.2. Modeling framework and boundaries
detailed features of each WRRF, specific assumptions are made to
limit the model complexity as well as to reduce excessive computational overhead required for the simulation. Hence, modeling results are always discussed in reference to simulated uncontrolled
conditions and not the utility’s actual operations. Such assumptions are presented in detail for each sub-model in the following
section. Equations and further description of each sub-model are
reported in the Appendix.
The scope of this study includes a system of hydraulically connected WRRFs with an emphasis on the dynamics of variables such
as effluent quality, power demand, energy use, costs, and GHG
emissions. In order to provide a realistic representation of the behavior of each facility, dynamic process sub-models are adopted
along with site-specific data used for model inputs and parameters.
The model boundary includes the typical units found in WRRFs:
influent pump station, primary clarification, bio-reactor tanks, aeration system, secondary clarification and control systems. The most
up-to-date modeling protocols for wastewater treatment are used
as the framework for these sub-models (Rieger et al., 2013). Additional systems are also modelled such as sewer hydraulics, indirect
GHG emissions and energy tariff structures. Each sub-model equation was coded in Matlab® and implemented in the Simulink®
platform using customizable diagram blocks (see Appendix: Fig. A1,
A4, A5–7, A9–10, A12). Although the integrated model incorporates
2.3. Model structure
2.3.1. Primary clarification
The primary clarification step is modelled by implementing an
ideal solid/liquid separation sub-model. Such a model is similar to
point separation models (i.e., non-spatial models with a set percent
removal) with the exception that the solids mass balance is performed around a defined control volume. In order to capture the
effects of the primary clarification on the delay and attenuation of
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S. Reifsnyder, F. Cecconi and D. Rosso
Water Research 200 (2021) 117224
the influent dynamics, the need for a primary settling model with
a specified volume was deemed necessary. For more information
see Appendix §3.
2.3.6. Sewer model
In the JOS network, the wastewater is conveyed primarily via
gravity by a system of separate sewer interceptors and trunk lines.
The biochemical transformations taking place in the sewer system, such as hydrolysis and heterotrophic decomposition of organic matter, have the potential to affect the wastewater composition (Hvitved-Jacobsen et al., 2013). However, to limit the number of state variables used, and therefore the computational time
required to numerically solve them, the sewer lines were assumed
non-reactive. Further information on the sewer model and relative
assumptions is reported in Appendix §6.
2.3.2. Bioreactors tanks
The activated sludge model 1 (ASM1) was selected among
the available model structures used to describe the bio-processes
found in activated sludge reactors (Henze et al., 1987; Van Loosdrecht et al., 2015). The selection of the ASM1 structure in lieu
of other models (i.e., ASM2, ASM2d, ASM3, etc.) is driven by the
absence of large anaerobic/anoxic zones in each of the facility’s
bio-reactors and by the nonessential need to model the biological phosphorus removal processes, as these are not part of the engineered process layout of the facilities considered (Rieger et al.,
2013). Stochiometric parameters are selected based on typical values found in the literature, whereas the kinetic parameters are estimated using temperature correction functions based on average
site-specific liquid temperatures (on average, 25 °C) (Rieger et al.,
2013). For the centralized WRRF, a modified ASM1 model for the
high purity oxygen (HPO) process was adopted based on the structure presented earlier in the literature (Tzeng et al., 2003).
2.3.7. Pump model
Wet-well pump stations are used at the front head of each
plant in order to provide the hydraulic head necessary for transporting the influent wastewater across the various treatment processes. In order to capture the diurnal dynamics of the power
demand required for the pumps to operate, the hydraulic pump
power equation is used (see Appendix, Eq. A31). A total dynamic
head of 20 m was assumed for each plant. The wire-to-water
(WTW) efficiency of the pumps was selected based on the size of
each facility (large η=0.7, medium η=0.6, small η=0.5).
2.3.3. Air supply model
The mechanical power demand from the blower groups
is estimated using the adiabatic compression power equation
(Tchobanoglous et al., 2014) (see Appendix, Eq. A29). Average wireto-air efficiency values were used based on the relative size of the
turbomachinery group of each facility (range: 0.55–0.75). The wireto-air efficiency includes the various efficiency losses during the
power transfer chain, which includes the motor efficiency, the mechanical efficiency (bearings, gears, etc.), and the blower efficiency.
In addition, the power demand of each blower group was estimated by including ambient temperature profiles obtained from local measurements for the period under study (sampling frequency
of 10 min). For each plant, a PI DO-control structure is used in order to maintain an oxygen set-point of 2.0 mg l−1 in the last aeration tank (with the exception of the centralized WRRF that uses a
set-point of 4.0 mg l−1 , see Section 4.3 in the Appendix for more
information).
2.3.8. Flow diversion control
The scheduled amount of diverted flow of each satellite WRRF
is determined by a two-level control structure composed by i) an
open-loop time of day integrated control system, which determines
when the shifting events of each satellite plant occur, and ii) an
equipment level PI-control feedback system used to vary the split
ratio of the influent over the diverted flow (Qinf / Qdiv ) in order
to maintain the desired influent flow set-point of the satellite WRRFs. For scenario 1 and 3, the average of the daily minimum flows
was selected as a set-point for the influent flows entering each
satellite facility during the scheduled load diversion (see Fig. 3).
Lower influent flow set-points were not considered, as they would
drift outside of the normal operating range of each satellite facility. A Boolean signal is used for the activation of the flow diversion strategy in the specified operating time-window (1= ‘ON’, 0=
‘OFF’). Differently, in scenario 2, three different influent flow tiers
are considered for the PI-control set-points (see sub-Section 2.5.2
for more information).
2.3.4. Oxygen transfer model
The oxygen transfer rate for each zone is estimated using the
equation presented in (ASCE, 2018), which incorporates correction factors that account for temperature, salinity, site elevation,
diffuser depth, diffuser fouling, and the effect of surface active
agents (see Appendix, Eq. A18). Consistently with previous studies
(Amerlinck et al., 2016; Reifsnyder et al., 2020), a spatiotemporal
variable α F factor was implemented based on previously obtained
off-gas measurements, which allows to estimate the oxygen transfer efficiency (OTE) under actual process conditions (see Appendix,
Section 4.4.1).
2.4. Data acquisition and pre-processing
A combination of site-testing, operational data, and equipment
specifications were used for developing the overall hybrid WRRFs
system model. Table 1 shows the equipment specification as well
as the average daily operational data that were used to develop a
dynamic model for each of the eight WRRFs.
2.4.1. Influent data
Diurnal influent flow and concentration profiles for two of the
eight facilities (Sat2 and Sat4) were obtained from facility records.
For the other six facilities, the circadian load and concentration
curves were constructed consistently from the available profiles
(see Appendix, Fig. A2). The characterization of the influent aggregate variables, such as chemical oxygen demand (COD) and total Kjeldahl nitrogen (TKN), was conducted by implementing daily
average grab sample measurements. In order to ensure that the
fractional proportion of each aggregate variable was in the typical
range for municipal wastewater, the influent fractionation toolbox
from SUMO® v.20 (Dynamita; Nyons, France) was utilized.
2.3.5. Secondary clarification
The sub-model for the secondary clarifiers is based on the
solids flux between spatially discretized layers. The solids mass
balance around each layer accounts for the bulk liquid flux and
the gravity separation flux (based on concentration driven settling
behavior) (Takacs et al., 1991; Torfs et al., 2017). The selection of
a layered flux clarification model in lieu of an ideal settling model
is driven by the desire to capture the variation in solids storage
as well as the sludge blanket profile of the clarifier in response
to the scheduled hydraulic diversions among the different facilities
considered herein. Typical solids settling parameters have been selected for the activated sludge processes of the satellite facilities,
whereas the settling parameters for the centralized facilities were
chosen based on typical HPO sludge settling characteristics with
lower sludge volume indices (SVIs) (Daigger, 1995).
2.4.2. Cost analysis and tariff structure
For each of the three scenarios described in section §2.5, the
costs are estimated from a costs analysis approach, while other
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S. Reifsnyder, F. Cecconi and D. Rosso
Water Research 200 (2021) 117224
Table 1
Equipment specification and average operational data of the individual WRRFs. (Satj = satellite (upstream) plant (j = 1–7); Cen = central (downstream) plant).
Water Resource Recovery Facilities
Sat1
Sat2
Sat3
Units
Influent Data
Influent Flow
Sanitary Peak
Flow
TSSa
BODb
CODc
Prim. Clarifiers
Number
Configuration
Dimensions (L x
W x D)
TSS Removal
Aeration Tanks
Number of Trains
Process
Configuration
Dimensions (L x
W x D)
Blowers
Number
Type
Capacity (per
unit)
Sec. Clarifiers
Number
Configuration
Dimensions (L x
W x D)
Other
parameters
Altitude
Sewer Distance
from Facility Cen
Sewer Trunk
Hydraulic Delay
a
b
c
d
e
f
g
h
− 1
Sat4
Sat5
Sat6
Sat7
Cen
]
1900
1.5h
3.9 × 104
1.28
5.3 × 104
1.8
2.2 × 105
1.48
1.4 × 105
1.77
1.3 × 105
1.48
7.9 × 104
1.77
1.1 × 106
1.24
[mg l − 1 ]
[mg l−1 ]
[mg l−1 ]
290h
220h
558h
267
229
567
356
353
738
340
295
688
340
295
688
315
296
634
303
274
640
496
426
758
[ –]
[ –]
[m]
N/A
N/A
N/A
2
Rectangular
91×6 × 3.5
3
Rectangular
8
Rectangular
91×6 × 3.5
5
Rectangular
91×6 × 3.5
4
Rectangular
91×6 × 3.5
4
Rectangular
91×6 × 3.5
52
Rectangular
82×6 × 3
[%]
N/A
61
30.5 × 6 × 3
66
65
62
60
67
75
[ –]
[ –]
2
CEAd
3
MLEe
3
MLEe
20
SFAf
12
SFAf
12
SFAf
8
SFAf
24
HPOg
[m3 d
[–]
79×9 × 4.5
[m]
9.1 × 7.5 × 4.5 91.5 × 9 × 4.5
68.5 × 9 × 4.5 68.5 × 9 × 4.5 68.5 × 9 × 4.5 68.5 × 9 × 4.5
76.2 × 17×5.1
3
Positive
Displacem.
600
3
Centrifugal
3
Centrifugal
5
Centrifugal
3
Centrifugal
5
Centrifugal
4
Centrifugal
N/A
N/A
1@
9.3 × 103
2@
1.9 × 104
2.2 × 104
2@
3.4 × 104
3@
7.4 × 104
7.4 × 104
3@
3.4 × 104
2 @ 1 × 105
2@
1.7 × 104
3@
3.4 × 104
N/A
2
Rectangular
6
Rectangular
6
Rectangular
30
Rectangular
18
Rectangular
18
Rectangular
13
Rectangular
45.5 × 6 × 3
208
Rectangular
60×6.5 × 4.3
7.8 × 3 × 2.7
45.5 × 5.5 × 3 45.5 × 6 × 3
45.5 × 6 × 3
45.5 × 6 × 3
45.5 × 6 × 3
[m asl]
[km]
457
62
64
36.7
229
65
75
39.6
72
39.6
19
21.6
7
21.6
11
N/A
[h]
8.6h
5.1h
9h
5.5h
5.5h
3h
3h
N/A
[ –]
[ –]
[m3 h−1 ]
[ –]
[ –]
[m]
total suspended solids
biological oxygen demand
chemical oxygen demand.
conventional extended aeration.
modified Ludzack-Ettinger.
step-feed aeration.
high purity oxygen.
value subject to assumption.
economic costs/benefits are excluded (González-Viar et al., 2016).
The main operational costs (OPEX) of our analysis derive from the
energy use and power demand charge of the blowers and pumps of
each facility, as these costs are directly impacted when shifting the
diurnal influent flows among plants. Since each scenario does not
require the installation of new processes/technologies, but only a
change in operations, there are no capital expenses (CAPEX) in our
cost analysis. As underscored by previous studies (Aymerich et al.,
2015), it is often the case for energy costs to vary throughout the
day according to a tier structure set by the energy provider. For
the system under study, a 2-tier energy tariff structure is adopted
by a local energy provider with a high energy cost between 16:00
and 21:00 (on-peak) and a lower energy cost during the remaining hours (off-peak). In order to estimate total cost deriving from
each facility’s energy consumption, the delivery and the generation
costs were considered for both the weekdays and weekends (see
Fig. A11b). In addition, a maximum power demand charge of 5.08
USD kW−1 is also included in the final energy costs projections
(see Eq. (1)).
where t1 and tfin are the initial and final simulation time, respectively.
2.4.3. Carbon intensity of the electrical grid
To account for the diurnal variations of indirect GHG emissions
associated with the daily energy consumption, dynamic carbon intensity (CI) profiles (also known as carbon output rates) were retrieved from historical data made available by the California Inde-
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S. Reifsnyder, F. Cecconi and D. Rosso
Water Research 200 (2021) 117224
Fig. 2. Scheduled load shifting strategies. Scenario 1 focuses on shifting the flow during on-peak energy tariff times (grid ramping hours – 16:00 to 21:00); Scenario 2
attempts to equalize the flow of the centralized plant by reducing the dip of the flow profile (low loading hours – 2:00 to 11:00); in Scenario 3 the flow is shifted during
the indirect GHG emission peak of the satellite facilities (19:00 to 0 0:0 0). Dashed black line denotes uncontrolled conditions (no load shifting), whereas red lines show the
influent flow during the different load shifting scenarios.
pendent System Operator (CAISO) (see Appendix, Fig. A11c). The
cumulative carbon footprint of each facility is then calculated by
computing the product of the CI and the energy consumption at
each time step, and then integrating it over the entire simulation
period (see Eq. (2)).
C O2,
eq [Mg]
=
t f in
t=t1
C I
· Power (t )
[kW ]
kgC O2,eq /kW h
t[h]
2.5.1. Scenario 1: Demand response
In the first scenario, the scheduled load shifting is implemented
as a demand response measure toward peak time-based rates that
are aimed at shifting the user’s energy consumption during the
day. In this study, the demand response strategy is based on the 2tier time-of-use (TOU) energy tariff structure previously introduced
(see Section 2.4.2 and Fig. A11b in the Appendix). More specifically,
during grid ramping hours (16:0 0–21:0 0) each satellite WRRF diverts the incoming load to the centralized plant while still maintaining their diurnal minimum influent flow according to the control strategy outlined in Section 2.3.8.
(2)
where t1 and tfin are the initial and final simulation time, respectively.
2.5.2. Scenario 2: Centralized flow equalization
The focus of the second scenario is on the equalization of the
centralized plant’s daily influent flows. In general, a flow is said
to be equalized if the flow is more or less constant with time
(Tchobanoglous et al., 2021). For this scenario, the diverted flows
from each satellite WRRF are scheduled with the intent of increasing the incoming loads of the centralized plant during low
loading hours (see Fig. 2, central column). The centralized plant
thus benefits from having a lower peak-to-peak amplitude of the
influent flow with a resulting improvement in process stability
and consequent reduction of the operational variability of mechanical equipment such as pumps and aeration apparatus. The
Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) algorithm, also implemented in previous studies (Rahnamay Naeini
et al., 2018; Reifsnyder et al., 2020), was used to minimize the variability of the centralized influent flows during low loading hours
(02:0 0–11:0 0). For this scenario, three progressively decreasing influent set-points are considered for each of the satellite plants with
the lowest being the average minimum daily flow (see Fig. 3). Once
2.5. Scenarios and evaluation metrics
Three different scenarios are considered in this study, and each
is based on shifting the influent load of seven satellite wastewater treatment plants to the centralized treatment facility. This is
achieved by lowering the pumping rate at the influent lift stations of each satellite facility according to an open-loop time of
day control strategy. By exploiting the hydraulic delay of sewer
trunk lines, this strategy effectively allows to defer the treatment
intensity between hydraulically connected facilities during specific
hours of the day. The assessment of each scenario is performed in
terms of indirect GHG emissions, energy use, and operating costs.
Additional metrics such as effluent quality and equipment limitation are also taken into consideration. The dynamic load shifting
strategy of each scenario is illustrated in Fig. 2 considering a single satellite WRRF and the receiving centralized WRRF. The three
scenarios are further discussed in the following sections.
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S. Reifsnyder, F. Cecconi and D. Rosso
Water Research 200 (2021) 117224
respect to uncontrolled conditions during the receiving hours of
the diverted flows (see Fig. 4b). Such increase in power demand
stems from the need to increase influent pumping rates and also
the need to maintain desired oxygen concentrations in the HPO
reactors by increasing the speed of the surface agitators. Fig. 4c
also shows an increase in the effluent total suspended solids (TSS)
concentrations during the receiving hours of the diverted flows,
yet still complies with the discharge limit (<30 mgTSS l−1 ). When
considering other effluent metrics, such as total nitrogen (TN) and
chemical oxygen demand (COD), no significant changes are observed (see Fig. 4d and e).
Fig. 5a provides a view of the simulated total power profile
of the entire system of WRRFs for 1 week under both the uncontrolled condition (dashed black line) and Scenario 1 (continuous line). The reduction in power demand during grid ramping
hours (dashed gray lines) is highlighted in yellow. Such reduction
is followed by an increase in total power demand as a result of
the shifting in treatment intensity to the centralized facility (highlighted in light red). However, the increase in power demand relative to uncontrolled conditions is lower compared to the achievable
power reduction during load shifting operations. This occurs as a
result of higher efficiency of the larger centralized influent pumps
as well as of the typically lower amplitude of the aeration power
profile that characterizes the HPO process in the centralized plant
(Wilcox and McWhirter, 1971).
The box plots shown in Fig. 5b provide a comparison between
the power distribution for the uncontrolled system and for the demand response strategy (Scenario 1) during the grid ramping period. It can be observed how the flow diversion of the satellite
facilities allows the shifting down of the operating power distribution of the overall system of plants with a reduction in mean
power demand during grid ramping hours of 3.7 MW. The distribution in Fig. 5c illustrates the probability of achieving a specific
reduction in power demand during the load shifting event with
an estimated maximum power reduction of 5.5 MW (or approximately 25% of the overall system).
Fig. 3. Example of the parameters used to identify the optimal scheduling of the
3-tier influent flow set-points during the load shifting events of Scenario 2 (Sat6).
The four time interval parameters i1 , i2 , i3 and i4 are varied during the optimization
phase, whereas the time of the day when the daily minimum typically occurs is
fixed (t4 ).
the daily minimum influent flow time has been set (t4 ), the time
intervals (i) of the three set-points are parameterized (i1 , i2 , i3
and i4 ) and are used as decision variables during the optimization
phase (see Fig. 3). During each simulation, the four parameters do
not change and are applied to each day of simulation.
Once the convergence to the optimized set of time interval parameters is reached, the daily scheduled times of the 3-tier influent flow set-points are obtained for each satellite facility (t1 , t2 and
t3 ,) which will remain fixed during the entire 2-week simulation
period of Scenario 2. Additional information on the load shifting
strategy for Scenario 2 can be found in Appendix §9.2. For more
information on the SC-SAHEL algorithm see section §10 in the Appendix or refer directly to Rahnamay Naeini et al., 2018.
2.5.3. Scenario 3: Greenhouse emissions offset
The third scenario targets the curtailment of the indirect daily
GHG peak emissions of the system of satellite WRRFs. For this scenario, the load-shifting operations are scheduled during the highest indirect GHG emission hours of each satellite facility with a
maximum load diversion time set to 5 h. Although the peak of
indirect GHG emissions varies according to the daily energy contribution from renewables, peak emission hours for the considered
system of facilities typically fall between 19:00 and 00:00.
3.2. Scenario 2: Centralized flow equalization
In Scenario 2, the equalization of the influent flow entering
the centralized plant is prioritized. An approximate 10,0 0 0 simulations were required in order to satisfy the convergence criteria
of the SC-SAHEL optimization method. Following the optimization
phase, the time interval parameters of the 3-tier flow diversions
(i1 , i2 , i3 , and i4 ) of the satellite plants are identified and saved (see
Table 2). These parameters also provide information on the time of
day when each influent set-point scheduled time (t1 , t2 and t3 ) is
triggered for each satellite facility. The optimized 3-tier scheduling time is used to trigger the flow shifting event of each satellite
facility for each day of the entire simulation. Profiles of the resulting influent flow profiles of each satellite plant for Scenario 2 are
reported in the appendix (Fig. A13).
In Fig. 6a and b, the 3-tier influent set-points of the plants
Sat2 and Sat4, respectively, are illustrated for the first 3 days of
simulation. The scheduling times of the 3-tier influent set-points
are different for each satellite plant and are mainly dependent on
their size and on the relative distance of the sewer line that links
the plant to the centralized facility. By correctly timing the influent set-point according to a 3-tier strategy, the compounded diverted flows of all the satellite plants (Fig. 6c) allow the significant reduction of the drop in influent flow of the centralized plant
during low loading hours, as seen in Fig. 6d. It is worth noting
that, following the optimization phase, two of the satellite WRRFs (Sat1 and Sat3) are found to not divert any of their influent
flow, and thus for the purpose of equalization do not require to
be connected to the centralized system. The convergence to such a
3. Results
3.1. Scenario 1: Demand response
In the first scenario, the action of shifting the influent load of
the satellite facilities is used as a demand response strategy which
allows the deferral of the power required for treatment after grid
ramping hours (16:00 to 21:00). It is, however, important to ensure that effluent quality goals of the centralized facility are met
throughout the receiving hours of the diverted flows from the upstream satellite plants. The centralized facility primarily targets the
abatement of carbonaceous constituents via an HPO process, which
is not uncommon for large plants in the United States. Discharge
effluent requirements do not require the centralized facility to remove nitrogenous compounds, and as such nitrification does not
take place in the aeration tanks.
Fig. 4 shows the simulated profiles for the main variables of
interest in the centralized WRRFs. An increase in peak flows to
the centralized facility is observed as a result of the scheduled
flow diversion. Nevertheless, such flow peaks still maintain values
equal or lower than the design capacity of the facility (see Fig. 4a).
Furthermore, an increase in power demand is also observed with
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Water Research 200 (2021) 117224
Fig. 4. Variables of interest under Scenario 1 for the centralized WRRF. (a) Influent flow rate, (b) power demand, (c) effluent suspended solids, (d) effluent total nitrogen and
(e) effluent chemical oxygen demand.
solution can be explained by the relatively long sewer delay time
required to transport the diverted flow from Sat1 and Sat3 to the
central facility (8.6 and 9 h respectively). In fact, for more distant
plants like Sat1 and Sat3, the diversion of any influent flow prior to
low loading conditions is likely to end up contributing to the diurnal sanitary peak flow entering the centralized plant (past 10:00–
11:00). As such, any hydraulic disturbance generated from Sat1 and
Sat3 will always overshoot the target time of low loading conditions of the centralized plants (2:0 0–11:0 0). From Fig. 6d it can be
observed that, although a perfect flow equalization is not achievable for each of the simulated days, a significant reduction in the
centralized influent flow variability is achieved, especially during
weekdays.
(continuous line) during the first week of simulation. The avoided
GHG emissions during the load shifting (dashed gray lines) are
highlighted in yellow. An approximate 17% reduction in GHG emission is achievable during the load shifting event.
3.4. Overall system performance
When considering different scenarios based on inter-facility
load shifting strategies, it is important to also track the volumes
of water displaced. In fact, as oftentimes satellite facilities emerge
from the need to reclaim the locally generated wastewater, scheduled load diversions can be in conflict with recycled water targets of the individual satellite facility. To address this consideration, Fig. 8a, b, and c provide a bubble-pie chart illustration of the
monthly cumulate volumes of water displaced for each scenario.
The color-coded slices of each pie chart exhibit the water volumes
shifted from each satellite plant. Scenario 1 results in the highest volume of water diverted to the centralized plant, with an approximate 2.4 × 106 m3 mo−1 of diverted water (or 12.7% of the
overall satellite influent, Fig. 8a). Scenario 3, although assuming a
different strategy, diverts an equally commensurate volume of water (2.4 × 106 m3 mo−1 or 12.3%, Fig. 8c). Conversely, the strategy
that prioritizes centralized flow equalization (Scenario 2) displays
3.3. Scenario 3: Greenhouse emissions offset
For Scenario 3, the scheduled flow diversion is deliberately selected to lower the total indirect GHG emissions by targeting the
daily peak emission hours of each facility. Similarly to Scenario 1,
the influent flow rates of each satellite WRRF are shifted during
a 5-hour timeslot (19:0 0–0 0:0 0). Fig. 7 shows the equivalent CO2
emission rates expressed in megagrams (or metric tons) per hour
for the uncontrolled system (dashed black line) and scenario 3
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Water Research 200 (2021) 117224
Fig. 5. Total power of the system of 8 WRRFs. (a) Total power demand profiles for uncontrolled conditions and for the demand response load shifting strategy (Scenario 1).
(b) Box plots showing the quartiles of the power demand during the electrical grid ramping hours (16:0 0–21:0 0) for the uncontrolled condition and Scenario 1. (c) Probability
distribution of the reduction in power demand during ramping hours.
the lowest amount of diverted water (1.3 × 106 m3 mo−1 or 6.8%,
Fig. 8b).
Figs. 8d shows the remaining water volumes available for reuse
at each satellite plant location. For Scenario 1 (demand response),
between 83% (Sat3) and 89% (Sat4) of the original influent flows
of each individual satellite plant are retained for possible reclamation. In Scenario 3, an analogous range 83% (Sat3) and 90% (Sat4)
are also observed. The attempt to equalize the centralized flow
(Scenario 2) instead leads to a solution that forsakes diversion of
the farthest satellite WRRFs (100% of the original volumes for Sat1
and Sat3). Furthermore, when compared to the other Scenarios,
the satellite plants that do participate in the load shifting strategy
of Scenario 2 still retain between 88% (Sat2) and 96%(Sat6) of the
original influent flows of the uncontrolled condition, thus available
for local reclamation.
The overall performance of the system of eight plants in terms
of total costs, energy use, and GHG emissions is presented in
Fig 9d. As a result of prioritizing load shifting operations during hours of high energy cost, Scenario 1 provides the most cost-
effective option with a monthly savings of 138,0 0 0 USD mo−1
with respect to uncontrolled conditions. Scenario 1 also yields
the highest saving in terms of energy use (490 MWh mo−1 ), although still commensurate to energy savings presented in Scenario
3 (480 MWh mo−1 ). Scenario 3 curbs GHG emissions to the lowest
value among the three scenarios (150 Mg CO2eq mo−1 ) by lowering
the power demand during the peak indirect emission hours. More
contained reductions for each performance metric are observed for
Scenario 2.
In addition, for the three scenarios an individualized view of
the performance of each of the 8 plants is shown by use of radar
plots (Fig. 9a, b, and c). For scenario 1 (Fig. 9a), the smaller facilities (i.e., Sat1, Sat2, and Sat3) display a higher percent reduction in
terms of each of the three metrics (26–28% costs, 17–18% energy,
and 17–18% GHG) compared to the larger plants (20–24% costs,
12–15% energy, and 12–15% GHG). Such a difference comes as a
result of the higher equipment inefficacies (pumps, blowers, etc.)
found in smaller facilities which consequently result in a higher
percent savings per mass of load diverted. Similar considerations
9
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Water Research 200 (2021) 117224
Fig. 6. Flow profiles. 3-day influent flow profiles of uncontrolled conditions and Scenario 2 for (a) Sat2 and (b) Sat4. (c) Time evolution of the flows diverted from the
satellite WRRFs at the receiving end of the centralized facility. (d) Uncontrolled conditions and Scenario 2 flow profiles for the centralized (Cen) plant.
reduction with the exception of Sat 1 and Sat 3 which do not display any change since they do not divert any flow (black continuous line in the center represents a 0% reduction, Fig. 9b).
4. Discussion
4.1. General implications and stakeholder breakdown
The results obtained from our analysis provide a new facet of
discourse to the ongoing dialog on sustainable urban water management. Moreover, this analysis deliberately aims to complement
the over decadal discussion on the optimal degree of centralization (ODC) of municipal wastewater infrastructures of growing
cities around the world, as discussed in detail in Eggimann et al.,
2015; Poustie et al., 2015 and Libralato et al., 2012. Thus, showing that a certain degree of interconnection between facilities can
widen their collective operational flexibility, which can be tailored
to serve a specific shared objective. In contrast to previous work,
Fig. 7. Total GHG emissions profiles for uncontrolled conditions and for the GHG
offset strategy (scenario 3). For such strategy the flow is shifted during the peak
indirect GHG emissions hours (19:00 to 0 0:0 0).
are also observed when viewing the individual plant performance
under scenario 3 (Fig. 9c). Compared to the aforementioned scenarios, the individual facilities in scenario 2 show a lower percent
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Water Research 200 (2021) 117224
Fig. 8. Water volumes for the 7 satellite WRRFs. The monthly diverted volumes of water are displayed for (a) Scenario 1, (b) Scenario 2 and (c) Scenario 3. Each coloured
slice shows the contribution of diverted volumes of water of each satellite plant. The percentage of water treated by each satellite plant is shown for each of the three
scenarios (d).
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Water Research 200 (2021) 117224
Fig. 9. Summary of the performance metrics. Radar charts illustrate the performance metrics in percent reduction (positive sign) of each plant for (a) Scenario 1; (b)
Scenario 2; (c) Scenario 3. (d) Bar plot of the showing the hybrid system’s total percent reduction of the performance metrics for each of the three scenarios considered. The
continuous black line in the center of the radar plots denotes a 0% improvement of the performance metrics.
the dynamic analysis of hybrid wastewater treatment systems presented here provides the ability to access a holistic set of operational strategies which would not otherwise be achievable via
a conventional time-invariant analysis. Through each of the three
scenarios, we illustrate the ability to use an inter-facility influent
RTC load shifting strategy that appeals to different stakeholders.
4.2. Scenario 1: Demand response services for electrical grid load
flexibility
In the first scenario, we employ a demand response strategy
as a reactive measure to the ramping up of the electrical grid
in the late afternoon hours. The pronounced electrical grid ramp12
07:16
01:20
23:20
04:00
07:16
07:16
06:40
04:10
00:40
02:20
00:50
0 h, 36min
3 h, 6min
3 h, 16min
6 h,0min
1 h, 50min
2 h, 40min
22:00
–
07:16
07:00
23:00
0 h, 16min
–
0 h, 0min
8 h, 0min
4.3. Scenario 2: Centralized load equalization
In the second scenario, the equalization of the centralized facility takes priority over other operational schemes. The load shifting of each satellite facility was optimized to obtain a satisfactory
downstream equalization while also preventing an increase in the
number of on/off switching events of equipment at the satellite
plants. The equalization of the centralized influent flow via the dynamic load shifting of each satellite plant provides a resourceful
tool for centralized facilities that do not have access to neighbouring land for the construction of equalization tanks. As such, flow
equalization can still be achieved without the need to undertake
expensive equalization projects. Although savings in terms of energy use, greenhouse gas emissions, and costs are lower compared
to the first and third scenario, in the second scenario the sanitation
district (the main stakeholder here) gains benefits in terms of process stability and improved asset management at the centralized
facility (Tchobanoglous et al., 2021).
0
1
2
1
2
h,
h,
h,
h,
h,
0min
50min
40min
30min
0min
0 h, 0min
21:10
–
–
–
03:30
01:20
2 h, 10min
3 h, 0min
2 h, 56min
22:20
–
–
–
t3
t2
t1
0 h, 0min
0 h, 0min
0 h, 0min
i3
i2
i4
3-tier scheduling time
3-tier time interval parameters
ing phenomenon originates from the progressive incorporation of
renewable systems into the power generation portfolio of a specific region. This condition, also known as “duck curve”, has become an increasingly observed trend for several cities worldwide
as they transition towards greener sources of energy. Therefore,
the evolution of the urban electrical grids toward a more sustainable state bears the challenge of dynamically matching the rapid
increase in net power demand in the late afternoon hours. To address this emerging challenge, in the first scenario we adopt a demand response strategy to lower the power required for wastewater treatment during grid ramping hours by taking advantage of
the interconnected nature of a hybrid system of wastewater treatment plants. In contrast to other demand response studies that
propose the suspension of the treatment operations of a single
facility (Schäfer et al., 2017), the integrated multi-plant approach
proposed here allows wastewater utilities to partake in demand
response programs without the need to shutdown treatment operations. For the case study considered, a maximum reduction of
25% (5.5 MW) in total power demand was estimated. Such a finding informs the potential for hybrid WRRF systems to introduce
flexibility into the power markets, and consequently promotes the
opportunity for the wastewater utilities to enter incentive-based
energy programs such as demand response or time-of-use (ToU)
energy tariff structures. Thus, the prime stakeholders involved in
this scenario are the electric grid operators and the sanitation district, both of which respectively gain benefits in terms of operational flexibility and economic incentives.
1
2
3
4
5
6
7
0 h, 0min
8 h, 6min
0 h, 0min
10 h, 6min
9 h, 16min
6 h, 26min
7 h, 56min
The third scenario centres its attention on reducing the indirect GHG emissions linked to the energy use required for treating
the wastewater. Here, the load of each satellite facility is shifted
downstream to the centralized plant during peak emission hours
(19:0 0–0 0:0 0). Hence, in contrast to isolated decentralized treatment facilities, hydraulically connected hybrid systems have the
ability to curtail their indirect carbon emissions by shifting their
electricity use to periods when the power supplied has a lower
carbon intensity. Such a capability allows the synergistic reduction
of the carbon footprint of the system as a whole, and therefore
expands the ability of local sanitation districts to enter climate
change solution initiatives. Furthermore, energy markets that include technologies with different emission factors provide the intriguing ability of hybrid WRRF systems to partake in emissionbased demand response programs. In an emission-based demand
management program the customer’s normal energy consumption pattern is changed in order to avoid emission-intensive hours
Sat
Sat
Sat
Sat
Sat
Sat
Sat
i1
4.4. Scenario 3: Emission-based demand response
Satellite WRRFs
Table 2
Optimized 3-tier scheduling parameters (i1 , i2 , i3 , and i4 ) for each satellite facility. The times of the day at which each tier is scheduled are also shown (t1 , t2 , t3 and t4 ).
06:26
Water Research 200 (2021) 117224
t4
S. Reifsnyder, F. Cecconi and D. Rosso
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Water Research 200 (2021) 117224
(Kopsakangas-Savolainen et al., 2017). Emission-based demand response programs would also provide ancillary benefits, such as the
reduction of emissions of air pollutants linked to power generation which were not considered in this study but deserve future
research. It is foreseeable that the enrolment in emission-based
demand response programs would involve a set of different stakeholders such as the electric grid operators, the air emissions regulatory agencies, and the sanitation districts.
work by also incorporating in-sewer process models. In addition,
for pressurized sewer systems, the pumping stations will also contribute to the overall power exerted by the hybrid system of WRRFs. Therefore, subsequent studies should also examine the aforementioned influent load shifting strategies for sewer systems that
are predominantly pressurized.
Finally, although this study is based on the analysis of a preexisting hybrid WRRF system, it also initiates a new discussion on
how to plan for future satellite systems. The system dynamics need
be granted a central role in the infrastructural planning. Therefore,
the selection of factors such as site location, plant capacity, and
process design should also be informed by the ability of the plant
to interact dynamically with other hydraulically connected facilities.
4.5. Compatibility with regional water reclamation targets
When considering the implementation of inter-facility influent
load shifting strategies, it is important to perform an analysis of
the volumes of water displaced. In fact, as satellite systems often
stem from the need to provide locally reclaimed water, the diversion of influent flows toward the centralized facility may conflict
with the reclamation targets of the individual satellite plant. In
our study we demonstrated the potential to achieve a reduction
in each of the performance metrics considered (GHG emissions,
energy, and costs), while avoiding an excessive withdrawal of water from the satellite plants (6.8–12.7% of the total influent flow
of the satellite plants). However, it is noteworthy to point out that
the constraints posed by water reclamation targets will also vary
seasonally with the winter months usually defined by a lower demand for reclaimed water when compared to the summer months.
Therefore, inter-facility RTC load shifting strategies are expected to
also vary according to the seasonal water reclamation constraints
of the region. This analysis, although not considered here, should
be informed by future studies.
5. Conclusions
The increased pressure for sustainable water is likely to result
in the further implementation of decentralized WRRFs. However,
this ubiquitously observed trend raises the question about the degree of interconnectedness between decentralized and centralized
wastewater infrastructure. This conundrum has been analysed by
previous studies, although mainly in terms of spatial optimization.
Herein, we have further extended such analysis to the temporal
domain and have illustrated a set of novel integrated strategies
for the dynamic management of hybrid systems comprised of both
satellite and centralized WRRFs. The key findings of this study are
outlined in the following points:
•
4.6. Dry-weather and wet-weather RTC
The case study presented herein is characterized by a sewer
network that is separate from the storm drainage system. Therefore, the proposed RTC strategy is not expected to be significantly influenced during wet weather events. However, for combined sewer systems (CSSs) or separate sewer systems with a high
degree of rainfall derived infiltration and inflow (RDII), it is expected that the dry-weather load shifting RTC strategies proposed
here would need to be temporarily suspended throughout the wetweather event or until normal sewer flowrates are re-established.
Moreover, during heavy precipitation events a possible alternative
would be to switch to a wet-weather RTC that instead focuses on
minimizing CSOs and reducing the impact on receiving water bodies. Therefore, future studies should address the feasibility of alternating between dry-weather and wet-weather integrated RTC
strategies when considering hybrid systems served by a CSS or by
systems with high levels of RDII.
•
•
•
•
4.7. Limitations and future studies
Several aspects surrounding the dynamic analysis of hybrid
wastewater treatment systems have not been investigated in this
paper, and thus require further examination.
Firstly, the analysis herein incorporates specific process models
that are characteristic of the case study considered. Hence, future
studies on dynamic load strategies of hybrid systems should investigate the improvements that can be achieved in terms of carbon
emissions, energy, and costs under different combinations of process configurations at each facility.
Secondly, this study considered a gravity driven non-reactive
sewer trunk model, which allows the significant simplification
of the overall model complexity. However, in reality, biochemical
transformations taking place in the sewer system will influence the
composition of the wastewater during its transit in the sewer lines.
Future studies can provide a complementary contribution to this
We introduce for the first time the analysis of dynamic load
shifting strategies for a hybrid system of 8 WRRFs using sitespecific data and wastewater treatment process models.
Our analysis reveals an untapped opportunity to lower greenhouse gas emissions, power demand, energy use, and costs
by dynamically shifting the influent loads among hydraulically
connected facilities.
Three different load scheduling strategies are identified, each
tailored for a specific goal. These are: demand response services, centralized flow equalization, and greenhouse gas emission offset.
The load shifting strategy used to improve the performance
metrics in each of the three scenarios (i.e., GHG emissions, energy, and costs), constitutes a relatively simple approach, which
is based on a scheduled influent flow set-point control operation. The intrinsic simplicity of the proposed approach offers
an appealing feature as it can be easily applied to real systems
without the need for complex operations or infrastructure upgrades.
Our study suggests that a certain degree of hydraulic interconnection between WRRFs can broaden their collective operational flexibility.
In general, this paper invites wastewater treatment facilities
that would normally operate in isolation to pursue integrated dynamic strategies that align with mutually shared goals which benefit the system as a whole. Therefore, analogous to smart energy grids, the dynamic interaction between connected wastewater treatment facilities presented here would provide an important
step toward the achievement of a corresponding smart water grid.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
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Water Research 200 (2021) 117224
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This project was funded by the This material is based upon
work supported by the United States Department of Energy under Award Number DE-IA0 0 0 0 018 (CERC-WET US Project 2.5) with
additional support from the Sanitation Districts of Los Angeles
County, and the Water-Energy Nexus (WEX) Center of the University of California, Irvine. Special thanks to the support and feedback provided by Nikos Melitas, which made the compilation of
this work possible.
Supplementary materials
Supplementary material associated with this article can be
found, in the online version, at doi:10.1016/j.watres.2021.117224.
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