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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 31, NO. 2, MARCH 2016
Residential Demand Response: Dynamic Energy
Management and Time-Varying Electricity Pricing
Matteo Muratori and Giorgio Rizzoni, Fellow, IEEE
Abstract—Demand response programs are currently being proposed as a solution to deal with issues related to peak demand and
to improve the operation of the electric power system. In the demand response paradigm, electric utilities provide incentives and
benefits to private consumers as a compensation for their flexibility
in the timing of their electricity consumption. In this paper, a dynamic energy management framework, based on highly resolved
energy consumption models, is used to simulate automated residential demand response. The models estimate the residential demand using a novel bottom-up approach that quantifies consumer
energy use behavior, thus providing an accurate estimation of the
actual amount of controllable resources. The optimal schedule of
all of the controllable appliances, including plug-in electric vehicles, is found by minimizing consumer electricity-related expenditures. Recently, time-varying electricity rate plans have been proposed by electric utilities as an incentive to their customers with the
objective of re-shaping the aggregate demand. Large-scale simulations are performed to analyze and quantitatively assess the impact of demand response programs using different electricity price
structures. Results show that simple time-varying electricity price
structures, coupled with large-scale adoption of automated energy
management systems, might create pronounced rebound peaks in
the aggregate residential demand. To cope with the rebound peaks
created by the synchronization of the individual residential demands, innovative electricity price structures—called Multi-TOU
and Multi-CPP—are proposed.
Index Terms—Demand response, electricity pricing, rebound
peaks, residential energy management, TOU and CPP.
I. INTRODUCTION
N
OWADAYS, to match peak demand, follow seasonal and
daily fluctuations, and ensure reliable operation of the
electric power system, utilities are forced to maintain a substantial amount of underutilized power capacity. This capacity,
often outdated and environmentally harmful, drives the cost of
electricity as indicated by the exponential increase in wholesale
electricity price during peak operation [1].
Instead of adapting electricity generation to match changes
in demand, the demand itself could be made more flexible to
Manuscript received August 07, 2014; revised November 04, 2014, January
07, 2015; accepted March 17, 2015. Date of publication April 03, 2015; date
of current version February 17, 2016. This work was performed at The Ohio
State University—Center for Automotive Research supported by the National
Science Foundation under Grant 1029337. Paper no. TPWRS-01072-2014.
M. Muratori is with the Joint Glogal Change Research Institute, Pacific
Northwest National Laboratory, College Park, MD 20740 USA (e-mail:
matteo.muratori@pnnl.gov).
G. Rizzoni is with the Department of Mechanical Engineering, The Ohio State
University, Columbus, OH 43212 USA (e-mail: rizzoni.1@osu.edu).
Digital Object Identifier 10.1109/TPWRS.2015.2414880
reduce requirements on the electric power generation infrastructure and allow for an easier integration of nondispatchable
resources. Demand response is a promising techno-economical
solution to make electricity demand more flexible, allowing
private customers to modify their demand profiles to fit the
needs of energy supply. In the demand response paradigm,
electric utilities provide some sort of incentive to their residential customers as a compensation for their flexibility in
the timing of their energy consumption. Utilities also provide
a signal to their customers (typically electricity price) that is
intended to guide the power consumption so as to obtain an
aggregate demand that better matches the needs of the power
generation. Demand response has proved effective at shifting
consumption away from peak hours, thus increasing system
efficiency and stability, reducing the need for investment in
peaking generation, and bringing several environmental and
financial benefits [2]. A power system equipped with demand
response capabilities can lead to a reduction in systems costs,
CO emissions, and price volatility by shifting power consumption to periods characterized by low prices and high renewable
power production [3].
The goal of demand response programs is to influence consumers to change their demand, in response to the needs of the
supplier [4]. To achieve this objective the proper signal must be
sent to the final customers. In order to develop proper electricity
price structures advanced modeling, simulation, and optimization tools are needed to properly analyze a complex system that
includes interactions between humans, energy infrastructures,
and local conditions.
In this paper, a state-of-the-art dynamic energy management
framework is used to evaluate the potential of residential demand response. The dynamic energy management framework is
based on highly-resolved personal energy consumption models
developed using a novel bottom-up approach that quantifies
consumer energy use behavior in the United States [5]. The
highly-resolved personal energy consumption models capture
the entire energy footprint of American households, including
energy consumption for personal mobility. The dynamic energy management framework simultaneously optimizes the
scheduling of controllable appliances and in-home charging
of Plug-in electric vehicles (PEVs). The automated dynamic
energy management framework introduced in this paper is
decentralized, in the sense that each single household receives
a signal from the electric utility, and independently optimizes
its own demand. Even though this leads to a local optimum, the
signal sent from the electric utility can be developed in such a
way as to achieve one or more system-level objectives, such as
reduce electricity generation and grid operation costs for electric utilities, manage demand peaks (better interaction between
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MURATORI AND RIZZONI: RESIDENTIAL DEMAND RESPONSE: DYNAMIC ENERGY MANAGEMENT AND TIME-VARYING ELECTRICITY PRICING
demand and generation), reduce overall pollutant and carbon
dioxide emissions from electricity generation, improve overall
grid efficiency, and minimize primary energy consumption.
The study proposed in this paper improves existing residential energy management systems in five important ways.
First, the optimization framework is based on highly-resolved
models that capture consumer behavior. The incorporation of
stochastic consumer behaviors provides more accurate estimation of the actual amount of available controllable resources,
allowing for a better understanding of the potential of residential demand response programs. Second, the model captures the
entire residential electricity consumption and the recharging of
plug-in electric vehicles, allowing for estimating the impact of
the gradual electrification of the fleet of passenger vehicles on
demand response. Third, a realistic scenario of residential demand response deployment is simulated, where no coordination
among consumers or direct control from the electric utilities are
assumed, in line with the distributed nature of smart grids. Also,
simple, transparent, and deterministic, i.e., known a priori and
predictable by the customers, time-varying pricing schemes
coupled to automated management systems are considered in
this study, a required condition for consumer acceptance [6].
Fourth, the model is intended to capture system-level impact
of demand response, rather than local effects on a single consumer. Large-scale simulations are performed to explore and
evaluate the impact of different electricity price structures on
the aggregate residential electricity demand. Fifth, simulation
results confirms prior evidence that demand synchronization
among consumers might be created by the introduction of
automated energy management systems when time-varying
electricity pricing is used (phenomenon known as “rebound
peaks”). To cope with these issues, innovative electricity price
structures based on group pricing are proposed in this paper,
called multi-time-of-use (Multi-TOU) and multi-critical peak
pricing (Multi-CPP).
II. DEMAND RESPONSE AND TIME-VARYING
ELECTRICITY PRICING
Demand response models are based on the assumption that
demand is elastic, and that consumers respond to higher electricity price by changing their demand (in particular, the timing
of their electricity consumption) to reduce electricity-related
expenditures. Studies have shown that under certain conditions
and in some markets this is the case. Espey and Espey [7]
summarize several studies of residential electricity demand
elasticities, confirming the elasticity of residential demand for
electricity in the United States. Caves and Christensen [8] use
data from five experimental implementations of residential
demand response in the United States, and concluded that
customers responded to higher prices during the peak period by
reducing peak period usage and/or shifting it to less expensive
off-peak periods. Torriti shows that Italian customers, when
proposed with time-varying electricity price, also respond with
a significant load shift. In this example, demand response leads
to higher average electricity consumption and lower payments
by consumers [9].
In 2011, the demand response market in the United States
generated approximately $6 billion in direct revenues for
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local businesses, industry, and households as well as enabling
avoided investment costs. In the PJM interconnection, demand
response allowed for cutting 7% of their seasonal peak [2].
Currently, demand response programs focus mainly on industrial and large commercial consumers, with fixed compensations attributed to small numbers of large end-users, using direct load control and interruptible loads. Walawalkar et al. [10]
predict that, in order for demand response to scale up and enable
greater level of renewable integration, technologies that are suitable for participating every day to demand response are needed.
The vast potential of building energy management strategies is
still untapped, not to mention future possibilities as home automation and smart appliances become standardized along with
deployment of smart grid infrastructure [10]. Thus, in the future,
large numbers of end-users, including small commercial customers and residential households could be involved in demand
response programs, involving deliberate shifts in electricity demand in correspondence with peak loads (and thus high electricity price) [11].
Controllable appliances and intelligent demand-side energy
management platforms make residential demand response an attractive option for residential customers, since these technologies allow for an automatic response, without requiring direct
monitoring of the electricity price by a user and limiting the inconvenience intrinsic in changing the timing of the electricity
consumption. Other than overall reduction of electricity-related
expenditures, residential customer would also benefit from a
series of advantages deriving from a better operation of the
system, including reduced power sags and interruptions, better
service continuity and reliability, and improved power quality
(reduced voltage and frequency variations and transients phenomena).
Customer acceptance is a conditio sine qua non for the effective deployment of demand response programs. Empirical
results show that consumers are open to dynamic pricing, but
prefer simple programs to complex and highly dynamic ones
[6]. Also, smart home technologies allowing for response automation (of the kind assumed in this study) are seen as a prerequisite for an active participation of residential customers in demand response programs [6]. Studies performed at EPRI1 identified automated costumer-side equipment able to intelligently
monitor and manage residential energy use as a critical enabling
technology needed to move toward realizing a smart grid [12].
Time-varying electricity price is the signal adopted by electric
utilities to influence and guide residential energy consumption.
The most common pricing structures include time of use pricing
(TOU), critical peak pricing (CPP), peak time rebates (PTRs),
or real time pricing (RTP) [13]–[16].
TOU rates are defined as different electricity prices for different periods of the day or of the year. TOU programs can
be characterized by two or more price tiers. CPP is essentially
a TOU program, with a significantly higher price tier during
peak periods. The objective of CPP is to enhance a TOU price
structure with the ability of promptly responding to emergency
1The Electric Power Research Institute (EPRI) is an independent, nonprofit
company performing research, development and demonstration in the electricity
sector.
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events. Two variants of this type of price structure exist: one
where the time and duration of the peak periods are predetermined and another where the time and duration of the peak periods may vary based on the needs of the electric power infrastructure. In this case customers are notified a few hours prior
to a critical peak event, that typically lasts for no more than a
few hours. Outside these peak periods TOU prices are typically
in effect.
In the PTR paradigm, customers receive electricity bill rebates for reducing their electricity consumption during peak periods (established a priori by the electric utility) relative to a
previously established baseline, which is determined for each
individual customer. The baseline is usually identified using
historical household electricity consumption. Wolak notes that
PTR provides an incentive for house-holders to elevate their
electricity use during the period in which baselines are established, and found evidence for such behavior [17]. A study conducted at MIT finds that demand response programs that pay
customers for reducing consumption from a baseline generally
provide excessive compensation and give customers incentives
for strategic behavior [18].
RTP is a highly dynamic electricity price structure that adjusts electricity prices on an ongoing basis throughout the day,
following the wholesale electricity generation cost, on an hourly
or subhourly basis (generally 10–15-min time intervals). RTP is
intended to convey actual generation cost to the final consumer
allowing for optimal use of generation resources.
Navigant Research estimates less than 3% of U.S. residential
customers have access to time-varying pricing today and well
below 1% have actually adopted it. In a best-case scenario, as
predicted by Navigant, time-varying pricing will be available to
60% of residential customers in the U.S. by 2020, with about
20% participating to demand response [19].
Muratori et al. [1] review in depth the role of residential
demand response in modern electricity markets and show that
simple technical solutions (like time-varying pricing associated
with decentralized automated energy management) may lead
to undesired demand dynamics. LeMay et al. [20] suggest that
there is a threat of rebound peaks in which consumers delay their
demands to avoid a peak, but cause a new peak when trying to
satisfy delayed demand. Similar trends are observed in an experiment performed by Pacific Gas and Electric to monitor the substation-level load impacts of end-use load control [21]. Lenhoff
et al. [22] reports that if many consumers react to time-varying
electricity pricing in an un-coordinated manner, the coincidence
factor of load increases significantly and the electric system
may face strongly increased load fluctuations. Mishra et al. [23]
suggest that current pricing plans incentivize all consumers to
shift their energy consumption during low-price periods. Thus,
at large scales, simultaneous energy request during off-peak periods will trigger rebound peaks if prices do not change to reflect
the resulting increases in off-peak demand.
Load “pickup” effects and formation of rebound peaks are
observed in several studies and pilot projects. For example, results from the EV Project2 show how the introduction of a TOU
rate plan does not necessarily prevent the charging demand from
peaking [24]. If anything, the TOU rate plan increases and shifts
the peak demand, as shown in Fig. 1, where weekdays-charging
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 31, NO. 2, MARCH 2016
Fig. 1. Weekday average time-of-day charging demand of EVSE. From The
EV Project reports. (a) Average charging demand with flat electricity price.
(b) Average charging demand with TOU electricity price.
demand profiles normalized per electric vehicle supply equipment (EVSE) for two locations are reported. Fig. 1(a) shows
how, in Nashville, where the project participants do not have
access to any TOU rate, the charging demand starts to increase
gradually after 4 p.m. and peaks around 8 p.m. when residents
are mostly at home. On the other hand, Fig. 1(b) shows charging
demand profiles in San Francisco where a special TOU rate plan
is available. In this case, the introduction of the TOU rate plan
has the effect of synchronizing the demand exactly at the moment when electricity price changes (at midnight). Therefore,
the demand increase is steeper and presents a higher peak value
(note the different scale on the -axis). This example shows that
the TOU electricity plan can be successful at shifting the electricity demand, but could also incur large demand rebound peaks
when the electricity price changes.
The tools proposed in this paper allow studying and comparing the impact of different electricity price structures on automated distributed residential demand response.
III. DYNAMIC ENERGY MANAGEMENT FRAMEWORK
In this paper, a state-of-the-art dynamic energy management
(DEM) tool is used to simulate automated decentralized residential demand response [25]. The proposed energy management
framework relies on highly-resolved models of all the components, providing a more accurate estimation of the actual amount
of available controllable resources.
A detailed residential energy eco-system (REES) model, developed at The Ohio State University [5], is used to simulate
residential electricity demand. The REES model captures the
entire energy footprint of American households, to include all
appliances, lighting, HVAC system, and in-home charging of
plug-in electric vehicles, viewing residential and transportation
2The EV Project is the largest deployment of electric vehicle charge infrastructure in history. [online] available: http://www.theevproject.com/
MURATORI AND RIZZONI: RESIDENTIAL DEMAND RESPONSE: DYNAMIC ENERGY MANAGEMENT AND TIME-VARYING ELECTRICITY PRICING
Fig. 2. Sample output of the REES model for a winter week in the Midwest
region of the United States.
energy needs as an integrated continuum. The model is based on
a novel bottom-up approach that simulates and quantifies consumer energy use behaviors. The REES model is based on the
integration of highly-resolved (10-min resolution) bottom-up
models for residential [26] and personal transportation [27] energy consumption in the United States. Residential power profiles are proven to have the same statistical features of metered
data and show typical diurnal and seasonal patterns (for more
details on the residential power demand model refer to Muratori et al. [26]).
As an example, Fig. 2 reports the per-household average
weekly electric power profile of 100 residential households.
This large-scale simulation is aimed at representing the total
residential electric load of a heterogeneous group of households
and related PEVs (the 100 households differ in terms of size,
insulation, and number and demographic characteristics of
household members). Two scenarios are depicted in the figure:
a case where no PEVs are deployed (reference scenario representing the current status quo), and a second case assuming a
10% PEV market share, equally subdivided between PHEVs
and EVs.3
Fig. 2 clearly shows the typical trait of aggregate residential
electricity demand, characterized by significant daily fluctuations. Significant seasonal variations are also present, which are
not depicted in the figure.
Starting from the REES model, an optimal control problem
for the scheduling of all of the controllable appliances (i.e.,
laundry machine, dryer, and dishwasher) and in-home charging
of plug-in electric vehicles is formulated. The management
problem is numerically solved using dynamic programming
(DP), and considers the behavior of household members and
their energy consumption, as predicted by the REES model.
Residential energy management has been studied extensively
in the literature. Kirschen [29] discusses demand-side aspects of
electricity markets and concludes that enhancing the ability of
the demand for electricity to respond to price signals could benefit not only the consumers who choose to participate actively
in electricity markets, but would also help these markets operate
more efficiently.
The expected time-varying retail price motivates the studies
of various algorithms to schedule smart appliances to minimize
cost for the users.
Mohesenian-Rad and Leon-Garcia [30] propose a residential
energy consumption scheduling framework which attempts to
achieve a desired tradeoff between minimizing the electricity
1111
payment and minimizing the waiting time for the operation of
each appliance in household in presence of a real-time pricing
tariff by doing price prediction based on prior knowledge.
Sou et al. [31] use mixed-integer programming to minimize
the electricity cost scheduling smart appliances. Kim and Poor
[32] use stochastic dynamic programming to solve an appliance
scheduling problem assuming statistical knowledge of future
electricity price.
The capability of scheduling thermostatically controlled
loads is at the core of several appliance commitment algorithms
prosed in the literature. Du and Lu [33] propose an algorithm to
control a water heater based on price and consumption forecasts
considering user comfort settings to meet an optimization objective such as minimum payment or maximum comfort. Avci
et al. [34] propose a two-stage cost- and energy-efficient HVAC
load control strategy under a dynamic pricing. Livengood and
Larson [35] describe a prototype model to illustrate the use
of approximate stochastic dynamic programming to optimally
control a few residential devices accounting for uncertain
weather forecast.
Nevertheless, these approaches do not focus specifically on
solving a decentralized (not coordinated) residential energy
management problem, and do not capture properly stochastic
user behavior. Moreover, the study proposed in this paper is
intended to capture system-level impact of demand response,
rather than local effects on a single consumer.
In this paper, a DEM framework is proposed to simultaneously find the optimal schedule for all the controllable
appliances and in-home charging of PEVs. The framework
proposed is flexible enough that different cost functions, e.g.,
minimization of carbon footprint, and different price structures,
e.g., TOU, CPP, or others, can be easily simulated to reproduce
different policy decisions and evaluate their impact on the
electricity demand. In this paper, the optimization framework
is aimed at minimizing cost for households owners.
For each controllable appliance, the enabling time
(time
at which a user enables the th controllable appliance), the completion time
(time required to complete the run-cycle of
the th controllable appliance), the deadline
(time at which
the user requires the th controllable appliance run-cycle to be
completed), and the maximum waiting time
(time that can
be waited before the run-cycle of the th controllable appliance must start to match the desired deadline) are predicted by
the REES model, and could differ for different appliances executions or charging events. Deadlines are set by the user and
may be the result of a compromise between cost and convenience. In this study it has been assumed that each PEV must
finish charging before the next driving event, when possible,
while dish-washing and laundry appliances are given an 8-hour
window to complete their execution.4
3A PEV is defined by the U.S. Department of Energy as a vehicle that draws
electricity from a battery with a capacity of at least 4 kWh and is capable of
being charged from an external source [28]. The definition of PEV includes
plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs). In this
paper, a battery capacity of 16 kWh and 24 kWh is assumed for PHEVs and
EVs, respectively.
4Laundry is modeled as two separate parts: washing and drying [26]. The
latter has been constrained in this framework not to start until the former has
been completed.
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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 31, NO. 2, MARCH 2016
The state vector of the optimal control problem, namely ,
is defined to capture the timing dynamics of the controllable
appliances that are used to solve the control problem.
is a
vector including information on which activity is currently performed by each of the household's members, as predicted by
the REES model. The controllable appliances timing dynamics,
, are different for non-interruptible and interruptible appliances. In the former case, the timing dynamics are represented
by a counter, as described by
if
if
if
if
if
(1)
is defined as:
The notation
such that
, meaning that at least one household
member
is performing activity during
time-step . When the value of the timing state of a controllable appliance is
the appliance has not been enabled
by any household member and it sits waiting. When
is
negative, and finite
, its value represents
the maximum waiting time before the th appliance must start
running to satisfy the deadline constraint (time at which the
execution of the appliance must be completed). Finally, when
the timing state of a controllable appliance has a positive value
, this value represents the portion of the appliance
execution that has been already completed, terminating with
, the completion time required to complete the run-cycle of
the th non-interruptible appliance. When
the job is
completed and the state is re-initialized:
. For all the
interruptible appliances, the timing dynamics are represented
by two counters, an up counter and a down counter, as reported
by
if
if
if
if
if
if
if
if
(2)
The first part of (2),
, is an up counter that increases whenever the appliance is on, e.g., battery of a plug-in electric vehicle is being charged, until reaching the completion time
required to complete the run-cycle (fully charging PEV battery)
of the th interruptible controllable appliance. When
the job is completed and the states are re-initialized:
. The second part of (2),
, is a down counter that,
starting from the maximum waiting time
, increases until
reaching the value of 0, threshold at which the appliance, e.g.,
PEV charging station, must start running in order to complete
its run-cycle before the deadline. The state vector is initialized
so that
for all the non-interruptible controllable
appliances and
for all the interruptible appliances.
An exogenous signal, sent by the electric utility, is needed as
input to the DEM tool. In this case, the price of electricity
is used to define the cost function in the following form:
(3)
is the number of controllable appliances,
is the
where
control variable of the th appliance (
when the appliance
is running), and
is the wattage of the th appliance, expressed
in [W]. Since the problem has been implemented on a 10-min
time basis, the term
is used to convert the electric energy
consumption to [kWh].
The cost function described by (3) represents a scenario in
which electric utilities send a time-varying electricity price
signal to residential customers as a way to steer the demand.
Each consumer receives a price signal and tries to optimally
manage his/her electricity consumption to minimize cost. The
optimal control policy
is defined as the optimal
control
with the initial state being
, and it is found
by solving the cost-to-go function,
, as shown in (4),
at the bottom of the page, where is the cost function reported
in (3) evaluated at time .
The DP method solves
by using backwards induction. The cost-to-go function at time
is
exactly
. The algorithm proceeds from
to
considering every possible state
per each
time-step , finding the optimal control policy
for
every state-time pair.
The proposed automated optimization framework finds the
optimal schedule for all the controllable appliances so as to minimize the cost function—based on the dynamic programming
algorithm. When finding the optimal schedule for the controllable appliances, more than one solution may result in the same
(4)
MURATORI AND RIZZONI: RESIDENTIAL DEMAND RESPONSE: DYNAMIC ENERGY MANAGEMENT AND TIME-VARYING ELECTRICITY PRICING
Fig. 3. Aggregate residential electric power demand for a winter week in the
Midwest region of the United States assuming TOU electricity price and automated demand response.
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Fig. 4. Distribution of the average aggregate electric power demand assuming
TOU electricity price.
minimum cost. If this is the case, the dynamic energy management framework implemented in this study schedules the controllable appliances to run as early as possible during lowest
price periods, so as to minimize customer inconvenience.
IV. IMPACT OF DIFFERENT ELECTRICITY PRICE STRUCTURES
ON AUTOMATED RESIDENTIAL DEMAND RESPONSE
Recently, time-of-use rate plans have been proposed to residential costumers with the objective of re-shaping the aggregate
demand. Nevertheless, several studies and smart grid demonstration projects show that, when each household independently
optimizes its demand leveraging off-peak electricity prices in
order to reduce its own cost, the resulting aggregate demand
may be affected by an even higher rebound peak shifted toward
the off-peak period (see Fig. 1).
The modeling proposed in this study allows for exploring the
implications of automated residential demand response, considering the impact of time-varying electricity pricing on residential demand response. The goal of the simulations presented in
this section is to assess the impact of widespread adoption of decentralized automated residential demand response programs,
assuming that all residential customers adopt the DEM framework proposed in Section III (each single customer optimizes
his own energy consumption so as to minimize cost and customer inconvenience). The impact of electricity price structures
on the aggregate residential demand is analyzed and quantitatively assessed via large-scale simulations.
First, a two-tier TOU electricity pricing system is considered,
where price of electricity is high between 7 a.m. and 10 p.m.
(High-Tier Price), and low between 10 p.m. and 7 a.m. (LowTier Price). Fig. 3 shows the same simulation depicted in Fig. 2,
after each household optimizes its own demand to minimize the
electricity-related expenditure assuming TOU electricity price.
Even though the demand has been deferred towards low-cost
periods, thus filling the load valleys (this phenomenon would
be accentuated if more loads where deferrable or distributed energy storage were present), Fig. 3 also shows that, whenever
the electricity price drops, a peak appears in the aggregate demand. Such peaks, called rebound peaks, are higher and steeper
than the peaks originally present in the residential demand that
the TOU electricity pricing structure was intended to eliminate.
This occurs because all the deferrable activities wait for the
price to drop before starting, leading to a contemporaneous request of power when the electricity price changes.
Fig. 5. Aggregate residential electric power demand for a winter week in the
Midwest region of the United States assuming CPP electricity price and automated demand response.
Fig. 4 shows the impact of the considered TOU electricity
price structure on the statistical distribution of the electricity
demand when an automated energy management system is introduced. Whether or not plug-in electric vehicles are present,
dynamic energy management changes the statistical distribution
of the residential electricity demand, with the effect of shrinking
the demand towards lower power regions, while introducing
peaks of higher demand that were not present before.
Overall, the introduction of TOU electricity pricing eliminates the smoothing effect due to the natural stochastic features
of residential demand, forcing demand synchronization among
all the REESs. Even though the demand is being effectively deferred toward night periods, results show that pronounced rebound peaks are created in the aggregate demand.
The introduction of CPP shows effects similar to those obtained using TOU electricity rates. Still, adopting CPP electricity pricing, electric utilities have the opportunity of calling a
critical peak event, with the objective of drastically reducing the
demand and relieving the electric power infrastructure in case of
emergency. This is shown in Fig. 5, where the same simulation
depicted in Fig. 2 is reported, but a CPP electricity price structure is assumed and each household optimizes its own demand
to minimize the electricity-related expenditure. The CPP structure is identical to the TOU pricing previously considered, but
a critical event (Critical Peak Price) is called during the highest
demand peak, namely between 9:00 p.m. and 11:30 p.m. of Saturday night for the representative week shown in Fig. 5.
The introduction of a critical event is proven to be effective at
removing one of the peaks in the demand, which is essential for
guaranteeing the reliable operation of the electric grid in case of
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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 31, NO. 2, MARCH 2016
TABLE I
STATISTICAL FEATURES OF THE PER-HOUSEHOLD AGGREGATED RESIDENTIAL ELECTRICITY DEMAND ASSUMING FLAT, TOU, AND CPP PRICING
emergency. Overall, CPP can be considered as a case of TOU
price structure, leading to the creation of similar rebound peaks,
but with the ability of effectively dealing with a few emergency
events during the year.
Table I summarizes the statistical features of the per-household aggregate residential electricity demand (averaged over
the total number of households) in the six scenarios considered
so far: residential eco-systems with only conventional vehicles
(REES CV) versus residential eco-systems with 10% market
penetration of plug-in electric vehicles (REES 10% PEV) assuming flat electricity price, two-tier TOU price, and critical
peak price (CPP). In the table,
is a severity term, introduced in this study, defined as the percentage of time during
which the demand is higher that times the average demand.
For example,
represents the percentage of time during
which the aggregate residential demand was higher than 1.5
times its average. This severity term can be used to rapidly evaluate the “peakiness” of a power profile.
The results summarized in Table I suggest that, when an
automated decentralized (uncoordinated) dynamic energy
management system is largely adopted by residential customers, the introduction of TOU electricity price decreases
the overall demand variability and pushes the demand toward
the average load.
is significantly reduced compared
to the reference case of flat electricity price. This partially
achieves the provider's goal of having a residential demand
as flat as possible, filling demand valleys and reducing peaks.
Nevertheless, higher demand peaks are introduced, and
increases compared to the reference case. The adoption of a
CPP electricity price produces results similar to those achieved
with time-of-use rates, but CPP can be used as a means for
effectively dealing with a few emergency events.
These effects are accentuated by the presence of plug-in electric vehicles, that represent a substantial portion of deferrable
load.
The main goal in the deployment of demand response programs is to smooth consumption and reduce overall “peakiness”
of the aggregate demand. However, as the results show, introduction of either TOU or CPP rates in a system where each individual consumer automatically optimizes demand responding to
the same signal may actually exacerbate the very problem that
the demand response program was designed to address. Still,
CPP rates are proven to be extremely effective in dealing with
a limited number of emergencies.
Different techno-economic solutions can be used to cope with
the rebound peaks created by simple time-varying electricity
price structures that lead to the synchronization of the individual residential demands. For example, electricity price struc-
Fig. 6. Aggregate residential electric power demand for a winter week in the
Midwest region of the United States assuming Multi-TOU electricity price and
automated demand response.
Fig. 7. Distribution of the average aggregate electric power demand assuming
Multi-TOU electricity price.
tures based on group pricing can be used. In such a structure
the customers are divided in different groups, and each group is
proposed with a different pricing scheme, e.g., the same timevarying pricing staggered in time, so as to avoid the synchronization of the aggregate demand. Alternatively, the starting
time of the controllable appliances could be randomized within
low-price periods, or some sort of time-based or location-based
randomization can be added to the price signal.
The development of effective demand response programs
results from a compromise between costumers' acceptance,
i.e., keeping the electricity price simple and predictable, and
achieving the system-level objectives of the electric utilities.
In this paper the use of deterministic electricity price structures
based on group pricing, Multi-TOU and Multi-CPP, is explored.
Due to their intrinsically simple structure and predictability
these pricing schemes are more likely to be adopted by American residential customers during the early roll-out of demand
response programs, while making demand response appropriate for increasingly complex supply–demand balancing. A
Multi-TOU structure is essentially a TOU structure in which the
MURATORI AND RIZZONI: RESIDENTIAL DEMAND RESPONSE: DYNAMIC ENERGY MANAGEMENT AND TIME-VARYING ELECTRICITY PRICING
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TABLE II
STATISTICAL FEATURES OF THE PER-HOUSEHOLD AGGREGATED RESIDENTIAL ELECTRICITY DEMAND ASSUMING FLAT AND MULTI-TOU ELECTRICITY PRICING
residential customers are divided into groups. Each group sees
different TOU electricity prices, leading to a multi time-of-use
price structure [5]. In particular, a two-tier TOU electricity
pricing system is assumed, where the price of electricity is high
during day hours (High-Tier Price), and low during night hours
(Low-Tier Price). The moment at which price changes between
low and high tier is deferred by one hour going from one group
of customers to the following. For example, for the first group
of customers the daily (Low-Tier) price goes from 6 a.m. to 9
p.m., for the second group it goes from 7 a.m. to 10 p.m. and so
forth. In this way each customer sees a TOU rate, but not all of
the customers are synchronized. To avoid disparities between
customers, each can rotate among the four groups during the
year, so that each residential customer is faced with the same
overall electricity price during a calendar year.
Results of the introduction of this Multi-TOU rates are
shown in Fig. 6, in which the simulations described in Fig. 2 are
repeated, after each household optimizes its own demand in
order to minimize the electricity-related expenditure assuming
Multi-TOU electricity price.
The adoption of Multi-TOU pricing does not lead to the creation of any rebound peak when automated energy management
is introduced. Moreover, the natural “peakiness” of the demand
shown in Fig. 2 appears to be significantly reduced, leading
to a smoother aggregate demand. This achieves the objective
for which residential demand response programs are designed,
which is to alleviate the requirements on the electric system to
follow the fluctuations in the demand.
Fig. 7 shows the impact of Multi-TOU electricity price on the
statistical distribution of the electricity demand when an automated energy management system is introduced.
Fig. 7 confirms that the introduction of a Multi-TOU electricity price is effective in reducing the variability in the demand, with the effect of shrinking the demand towards lower
power regions. In this case no rebound peak is introduced.
Table II summarizes the statistical features of the per-household aggregate residential electricity demand (averaged over
the total number of households) when Multi-TOU electricity rates are introduced. From the table it appears that
when an automated decentralized (un-coordinated) dynamic
energy management system is introduced, the adoption of
multi-time-of-use electricity pricing (Multi-TOU) decreases
the overall demand variability and pushes the demand toward
the average load.
is significantly reduced compared to
the reference case of flat electricity price. Also, peaks in the
demand are significantly reduced, and
is maintained at
0. The maximum demand during the simulated week is reduced
by 12% when no PEV are considered, and by 17% when a PEV
market share of 10% is considered.
The simulated results suggest that Multi-TOU electricity
pricing could be an effective policy to achieve the objective of
electric utilities to have residential demand as flat as possible,
filling demand valleys and reducing peaks. A multi-CPP price
structure has the same effects, also providing the ability of
dealing with emergency events, as shown previously.
V. CONCLUSION
In this paper, an automated dynamic energy management
framework is proposed to find the optimal schedule of residential controllable appliances, including in-home charging
of plug-in electric vehicles. The management framework is
decentralized, in that each individual household independently
optimizes its own demand, without any coordination among
different consumers. The optimal control problem is solved
using dynamic programming, finding the global solution that
minimizes a cost function. The algorithm is general and different cost function could be selected to achieve different
objectives. For the purpose of simulating demand response
programs the cost function is chosen to minimize consumer
electricity-related expenditure, thus providing some sort of
compensation to the residential costumers for their flexibility
in timing the energy consumption.
This work is among the first that systematically considers stochastic correlations among different end-use activities in the design of the energy management framework [5]. The incorporation of stochastic consumer behaviors provides more accurate estimation of the actual amount of available controllable
resources, and hence enables better estimation of the impact of
automated demand response. Moreover, the proposed study of
integrated load modeling for both PEVs and other smart appliances represent a significant advance over most existing works
in aggregate load modeling that have mainly focused on firstorder thermostatically controlled loads.
Large-scale simulations are performed to explore and evaluate the impact of different electricity price structures on the
aggregate residential electricity demand. In particular, results
show that when an automated decentralized (uncoordinated) dynamic energy management system is largely adopted by residential customers, the smoothing effect due to the natural stochastic
features of residential demand is eliminated, forcing demand
synchronization among all the consumers. Even though the demand is being effectively deferred toward night periods, results
show that pronounced rebound peaks are created in the aggregate demand, that are higher and steeper than the original demand peaks that the time-varying electricity pricing structures
were intended to eliminate.
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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 31, NO. 2, MARCH 2016
To cope with the rebound peaks created by simple timevarying electricity price structures, that lead to the synchronization of the individual residential demands, the use of
Multi-TOU and Multi-CPP electricity pricing is proposed. In
the Multi-TOU paradigm residential customers are divided into
groups, and each group sees different TOU electricity prices.
In this way each customer sees a time-varying electricity price,
but not all the customers are synchronized.
The adoption of multi-time-varying electricity pricing does
not lead to the creation of any rebound peak when automated
energy management is introduced. Moreover, the natural “peakiness” of the residential demand appears to be significantly reduced. This achieves the objective for which residential demand
response programs are designed, which is to alleviate the requirements on electric power generation infrastructure to follow
the fluctuations in the demand. That is, instead of adapting electricity generation to match changes in the demand, the demand
itself is made more flexible to reduce requirements on electric
power generation. This also allows for an easier integration of
nondispatchable renewable resources. Simulation results show
that the modeling proposed in this paper can serve as a tool to
study energy policy solutions, including evaluating and comparing the effects of different electricity price structures, and
developing effective residential demand response programs.
ACKNOWLEDGMENT
The authors would like to thank Prof. W. Zhang, Dr. E. Serra,
and C. Y. Chang for their contribution to the early development
of this study and Prof. R. Sioshansi for his thorough review.
REFERENCES
[1] M. Muratori, B.-A. Schuelke-Leech, and G. Rizzoni, “Role of residential demand response in modern electricity markets,” Renewable Sustain. Energy Rev., vol. 33, no. 0, pp. 546–553, 2014.
[2] Smart Energy Demand Coalition, “The demand response snapshot—The reality for demand response providers working in Europe
today,” 2011.
[3] J. Morales, A. Conejo, H. Madsen, P. Pinson, and M. Zugno, Integrating Renewables in Electricity Markets: Operational Problems, ser.
Int. Series in Oper. Res. Manag. Sci.. Berlin, Germany: Springer,
2014, vol. 205.
[4] U.S. Department of Energy, “Benefits of demand response in electricity markets and recommendations for achieving them. A report to
the United States Congress pursuant to Section 1252 of the Energy
Policy Act of 2005,” Feb. 2006.
[5] M. Muratori, “Dynamic management of integrated residential energy
systems,” Ph.D. dissertation, Dept. Mech. Eng., The Ohio State Univ.,
Columbus, OH, USA, 2014.
[6] E. Dütschke and A.-G. Paetz, “Dynamic electricity pricing: Which programs do consumers prefer?,” Energy Policy, vol. 59, pp. 226–234,
2013.
[7] J. A. Espey and M. Espey, “Turning on the lights: A meta-analysis of
residential electricity demand elasticities,” J. Agricultural Appl. Econ.,
vol. 36, no. 1, pp. 65–82, 2004.
[8] D. W. Caves, L. R. Christensen, and J. A. Herriges, “Consistency of
residential customer response in time-of-use electricity pricing experiments,” J. Econometrics, vol. 26, no. 1–2, pp. 179–203, 1984.
[9] J. Torriti, “Price-based demand side management: Assessing the impacts of time-of-use tariffs on residential electricity demand and peak
shifting in northern Italy,” Energy, vol. 44, no. 1, pp. 576–583, 2012.
[10] R. Walawalkar, S. Fernands, N. Thakur, and K. R. Chevva, “Evolution
and current status of demand response (DR) in electricity markets: Insights from PJM and NYISO,” Energy, vol. 35, no. 4, pp. 1553–1560,
2010.
[11] J. Torriti, M. G. Hassan, and M. Leach, “Demand response experience
in Europe: Policies, programmes and implementation,” Energy, vol.
35, no. 4, pp. 1575–1583, 2010.
[12] C. W. Gellings and R. J. Lordan, “The power delivery system of the
future,” Electricity J., vol. 17, no. 1, pp. 70–80, 2004.
[13] J. R. Roncero, “Integration is key to smart grid management,” in Proc.
SmartGrids for Distribution, IET-CIRED. CIRED Seminar, 2008, pp.
1–4.
[14] G. R. Newsham and B. G. Bowker, “The effect of utility time-varying
pricing and load control strategies on residential summer peak electricity use: A review,” Energy Policy, vol. 38, no. 7, pp. 3289–3296,
2010.
[15] S. Borenstein, M. Jaske, and A. Ros, “Dynamic pricing, advanced metering, and demand response in electricity markets,” J. Amer. Chem.
Soc., vol. 128, no. 12, pp. 4136–4145, Mar. 2002.
[16] H. Chao, “Price-responsive demand management for a smart grid
world,” Electricity J., vol. 23, no. 1, pp. 7–20, 2010.
[17] F. A. Wolak, “Residential customer response to real-time pricing: The
Anaheim critical peak pricing experiment,” UC Berkeley Center for
the Study of Energy Markets, 2007 [Online]. Available: http://escholarship.org/uc/item/3td3n1x1
[18] J. G. Kassakian and R. Schmalensee, “The future of the electric grid:
An interdisciplinary MIT study,” 2011.
[19] Navigant—Electric Light and Power, “Dynamic pricing adoption will
be very low without action from utilities,” Oct. 9, 2013 [Online]. Available: http://www.elp.com/articles/2013/10/dynamic-pricing-adoptionwill-be-very-low-without-action-from-utilities.html
[20] M. LeMay, R. Nelli, G. Gross, and C. A. Gunter, “An integrated architecture for demand response communications and control,” in Proc.
41st Annu. Hawaii Int. Conf. Syst. Sci., 2008, pp. 174–174, IEEE.
[21] G. Heffner and D. Kaufman, “Distribution substation load impacts of
residential air conditioner load control,” IEEE Trans. Power App. Syst.,
vol. PAS-104, no. 7, pp. 1602–1608, Jul. 1985.
[22] S. Lehnhoff, O. Krause, and C. Rehtanz, “Dezentrales autonomes
energiemanagement,” At-Automatisierungstechnik Methoden und
Anwendungen der Steuerungs-, Regelungs-und Informationstechnik,
vol. 59, no. 3, pp. 167–179, 2011.
[23] A. Mishra, D. Irwin, P. Shenoy, and T. Zhu, “Scaling distributed energy
storage for grid peak reduction,” in Proc. 4th Int. Conf. Future Energy
Syst., 2013, pp. 3–14.
[24] S. Schey, D. Scoffield, and J. Smart, “A first look at the impact of electric vehicle charging on the electric grid in the EV project,” in Proc.
26th Electric Vehicle Symp., Los Angeles, CA, USA, 2012.
[25] M. Muratori, C.-Y. Chang, G. Rizzoni, and W. Zhang, “Dynamic energy management of a residential energy eco-system,” in Proc. ASME
Dynamic Syst. & Control Conf., Oct. 21–23, 2013.
[26] M. Muratori, M. C. Roberts, R. Sioshansi, V. Marano, and G. Rizzoni,
“A highly resolved modeling technique to simulate residential power
demand,” Appl. Energy, vol. 107, no. 0, pp. 465–473, 2013.
[27] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, “Highly-resolved
modeling of personal transportation energy consumption in the United
States,” Energy, vol. 58, no. 0, pp. 168–177, 2013.
[28] U.S. Department Of Energy, “Alternative Fuels Data Center,” 2013
[Online]. Available: http://www.afdc.energy.gov/laws/law/NC/9355
[29] D. Kirschen, “Demand-side view of electricity markets,” IEEE Trans.
Power Syst., vol. 18, no. 2, pp. 520–527, May 2003.
[30] A.-H. Mohsenian-Rad and A. Leon-Garcia, “Optimal residential load
control with price prediction in real-time electricity pricing environments,” IEEE Trans. Smart Grid, vol. 1, no. 2, pp. 120–133, Sep. 2010.
[31] K. C. Sou, J. Weimer, H. Sandberg, and K. H. Johansson, “Scheduling
smart home appliances using mixed integer linear programming,” in
Proc. IEEE Conf. Decision and Control, Orlando, FL, USA, Dec. 2011,
pp. 5144–5149.
[32] T. T. Kim and H. V. Poor, “Scheduling power consumption with price
uncertainty,” IEEE Trans. Smart Grid, vol. 2, no. 3, pp. 519–527, Sep.
2011.
[33] P. Du and N. Lu, “Appliance commitment for household load scheduling,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 411–419, Jun. 2011.
[34] M. Avci, M. Erkoc, and S. S. Asfour, “Residential HVAC load control
strategy in real-time electricity pricing environment,” in Proc. IEEE
EnergyTech Conf., Cleveland, OH, USA, May 29–31, 2012.
[35] D. Livengood and R. Larson, “The energy box: Locally automated optimal control of residential electricity usage,” Service Science, vol. 1,
no. 1, pp. 1–16, 2009.
MURATORI AND RIZZONI: RESIDENTIAL DEMAND RESPONSE: DYNAMIC ENERGY MANAGEMENT AND TIME-VARYING ELECTRICITY PRICING
Matteo Muratori received the B.S. and M.S. degrees
(summa cum laude) in energy engineering from Politecnico di Milano, Milan, Italy, and the M.S. and
Ph.D. degrees in mechanical engineering from The
Ohio State University, Columbus, OH, USA.
He is currently a Researcher with the Pacific
Northwest National Laboratory (PNNL) Joint Global
Change Research Institute, College Park, MD, USA,
where he works on evaluating and assessing the role
of new technologies in responding to global energy
issues, including climate change and energy security.
Prior to joining PNNL, he was with the The Ohio State University—Center
for Automotive Research, where his research focused on sustainable mobility,
residential energy systems modeling, energy management techniques, smart
grids and demand response programs, and energy policy.
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Giorgio Rizzoni (F’04) received the B.S., M.S. and
Ph.D. degree in electrical and computer engineering
from the University of Michigan, Ann Arbor, MI,
USA, in 1980, 1982, and 1986, respectively.
He is currently the Ford Motor Company Chair
in ElectroMechanical Systems, a Professor of Mechanical and Electrical Engineering, and an Adjunct
Professor of Industrial Design at The Ohio State
University, Columbus, OH, USA. Since 1999, he
has been the Director of The Ohio State University
Center for Automotive Research (CAR), an interdisciplinary university research center. CAR conducts research on advanced
automotive and transportation technologies and systems engineering, focusing
on sustainable mobility, advanced propulsion systems, human safety, and the
environment.