Enabling Sustainable Smart Homes: An Intelligent Agent ... · We propose a Mobility Integrated Energy Management artifact (MIEM) implemented through an intelligent agent that represents

Thirty Fifth International Conference on Information Systems, Auckland 2014 1

Enabling Sustainable Smart Homes: An Intelligent Agent Approach

Completed Research Paper

Konstantina Valogianni

Rotterdam School of Management Erasmus University Rotterdam, The

Netherlands [email protected]

Wolfgang Ketter Rotterdam School of Management

Erasmus University Rotterdam, The Netherlands

[email protected]

John Collins Computer Science Department

University of Minnesota [email protected]

Dmitry Zhdanov School of Business

University of Connecticut [email protected]

Abstract

Smart homes can offer significant benefits to residents. Specifically, their combination with electric vehicles (EVs) can support environmental sustainability, as they can use part of their battery to cover household consumption needs. We present a Mobility Integrated Energy Management IS artifact that supports a smart home owner’s decisions with regard to using household appliances and charging electric vehicles. The artifact offers personalized energy consumption recommendations based on individual characteristics using information available. We observe that by adopting it, owners reshape their energy consumption curve and can save on their electricity bill. At the same time they create benefits for the electricity grid by reducing peak demand and increasing sustainability. We conclude by offering energy policy recommendations with regard to EV and smart home appliances adoption rates.

Keywords: IT artifact, Sustainability, Information Systems Design, Smart Homes, Machine learning, Intelligent agents

Introduction

Sustainability is a major concern of modern societies that aspire to minimize their negative impacts on the environment using technological advancements (Watson, et al., 2012a). A core component of sustainability, being defined as “development that meets the needs of the present without compromising the ability of future generations to meet their needs” (Brundtland, 1987), is a sustainable energy supply. The energy grid of the future is known as smart grid (Fadlullah, et al., 2011) and according to Gharavi & Ghafurian (2011) “can be defined as an electric system that uses information, two-way, cyber-secure communication technologies, and computational intelligence in an integrated fashion across the entire spectrum of the energy system from the generation to the end points of consumption of the electricity”. Smart grid’s main characteristic is the large scale integration of decentralized renewable sources. These renewables, apart from clean energy sources, are also volatile and highly dependent on the weather conditions. Therefore, they may easily destabilize the grid, threatening its reliability. Consequently, there is a need for reducing this volatility, while at the same time benefiting from the sustainable renewable sources.

Building a Better World Through IS

2 Thirty Fifth International Conference on Information Systems, Auckland 2014

The main challenge in balancing the smart grid is to mitigate the peak demand, which is additional to the base load and match volatile demand with supply. Base load power is provided around the clock and typically comes from large nuclear or coal-fired plants. Peak demand is the extra consumption that results for example on hot summer afternoons and is typically generated by power plants that can be switched on for shorter periods, such as gas turbines (Kempton & Tomic, 2005). This demand is difficult to be matched with the supply, because of its stochastic and volatile nature and more importantly determines the total capacity that must be available in the grid. One solution to cover extra peak demand is to install additional generation and transmission infrastructure. This solution will be extremely costly and unsustainable. Therefore, finding ways to reduce these peaks and avoid increasing supply is of high importance. Scaling up supply would require building costly new power plants and infrastructure that decreases efficiency and consequently reduces environmental sustainability. Modern societies would be more sustainable if they could totally offset this peak demand or adapt energy use to availability of sustainable sources. Evidence that the usage of IS or investments in smart grid solutions are financially more attractive than the investment in generation capacity or grid capacity, has been provided by Schmidt & Busse (2013).

A key role in the smart grid management process is played by the energy customer, who is transformed from a passive energy consumer to an active participant in the smart grid. Energy customers are now able to adapt consumption to availability and also produce amounts of energy using for example photovoltaic panels or wind turbines. These new energy customers are consequently involved both consuming and producing energy, and are commonly called prosumers (Lampropoulos et al., 2010; Rathnayaka et al., 2011). Prosumers mostly reside in smart homes, where with the help of information and communication technology (ICT) infrastructure they can control their appliances’ energy use. Smart homes appeal to the end customers because of the ease and comfort they offer (Aldrich, 2003) and their sustainability. For example by adjusting heating based on households’ occupancy the residents can save significant amounts of energy (Urieli, 2013). According to Paetz et al. (2012) smart appliances and home automation are perceived by the household residents as the inevitable future. Furthermore, many of the smart home residents, in their effort to be sustainable, own an electric vehicle (EV). The combination of EVs with smart household appliances that can shift their function over time, to benefit from low off peak prices, appears to be promising for alleviating the smart grid from excessive peak demand (Brandt et al. 2013). Electric vehicles are especially useful sustainability tools because of their energy storage capability. They can be used to offset some of the volatility coming from the renewable sources. If they are charged properly they can store energy in high supply periods and feed it back to the grid during high demand periods. Furthermore, they use energy more efficiently than fuel-powered vehicles. High levels of sustainability are reached if they are charged with energy from renewable energy sources, such as wind and solar.

The uncoordinated use of EVs, though, is likely to lead to high demand peaks during the charging time, since EV charging without coordination, mostly occurs during peak hours when EV owners return home from work. Therefore, during these hours occur demand peaks that lead to high prices. Specifically, considering customers range anxiety (Franke, et al., 2011), this charging may reinforce this behavior. Range anxiety represents the fear that the individual might run out of battery while driving. To cope with this, people usually plug in their EVs at night, while during the day they charge their EVs whenever and wherever it’s possible, just to feel that their battery is always charged. If such charging happens during peak hours it can create additional demand increase on the grid. Therefore, without controlled charging the grid might get outages or extreme peak loads.

Consequently, the combination of EVs and smart homes has the potential to change the logic behind individual energy consumption, since part of the EV battery capacity can be fed back to the grid during energy shortage periods, reducing the peaks. This is known as vehicle-to-grid (V2G) capability of EVs (Kempton & Tomic, 2005) and is anticipated to offer significant value to the smart grid. However, in order for owners to make optimal decisions for their energy consumption and EV charging, they need to be aware of the information available such as energy prices in the future, renewable source availability, weather conditions, driving schedule etc.

All this information may exceed the limits of human cognitive ability (Simon, 1979). Therefore, we propose an intelligent software agent (Wooldridge & Jennings, 1995) that can process the large amount of information available, related to energy consumption and prices, and facilitate a customer’s decisions.



These decisions are related to household consumption EV charging, ensuring lower peak consumption and protecting grid’s stability. The software agent has the role of an intelligent decision support system (Hevner et al., 2004) that is able to explore and understand the complex environment of the smart grid (Bichler et al., 2010) and suggest an optimized consumption and charging schedule for energy customers. Besides mitigating the information overload concerns, intelligent agents support customers’ convenience through personalization of decision making. Examples of agents used in our real-life are the software agents used in various Global Positioning Systems (GPS) to provide routing recommendations or agents used in smart phones to provide personalized recommendations regarding battery usage, brightness of the display, etc.

We propose a Mobility Integrated Energy Management artifact (MIEM) implemented through an intelligent agent that represents a household (Smart Home) combined with an EV. At the core of this agent is an energy information system (Energy IS) that processes price and consumption information obtained by the environment and offers personalized household consumption and EV charging suggestions. Its main objective is to minimize the total energy consumption (household and EV) cost. The agent must satisfy household consumers’ electricity needs and at the same time benefit from energy price variations, yielding savings to the household. Therefore, following Seidel’s et al. (2013) categorization, satisfying the home owner’s comfort and increasing the savings on electricity bills are the two main affordances of our Information System. The main contributions of our approach are that: a) MIEM reduces peak demand increasing sustainability of the grid, b) as a result of the peak demand reduction and demand reallocation, we observe that the average energy prices are reduced. This brings savings to the individual customers in the form reduction of their electricity bill, c) examining the effect of MIEM penetration in the energy market, we provide specific policy recommendations related adoption rates depending on population’s heterogeneity, to prevent congestion and herding behavior.

Related Work

Our approach builds on the academic literature of Green IS, creating an artifact that supports environmental sustainability. We follow the Design Science stream (Hevner et al., 2004; Gregor et al., 2013; Walls et al. 2004) to create this artifact and describe its sustainability affordances (Seidel et al. 2013). The artifact itself, adds to the existing literature of smart home and EV energy management, by proposing a decentralized approach, which by satisfying the individual household comfort, yields benefits for the whole smart grid.

Green IS

Information systems (IS) can play a significant role in improving environmental sustainability. The research domains that focus on improving environmental sustainability through IS, vary from purely theoretical and philosophical to practical and action driven (Melville, 2010). Melville (2010) outlines possible research questions that lead to supporting sustainability through the use of IS. Our approach could be categorized under RQ6b: “What design approaches are effective for developing information systems that influence human actions about the natural environment?” The proposed IS artifact influences energy customers to modify their natural behavior, which is to perform all the household activities without any scheduling and charge the EV whenever the person is close to a charging point. Adopting MIEM, the household customer schedules consumption and EV charging in an effective way so that it reduces peak demand. Consequently, it saves natural resources that would be used to produce this extra demand.

An IS stream adjacent to environmental sustainability is Green IS (Watson, et al., 2010; Dao, et al., 2011). Watson et al. (2012a) stress the importance of the stream, analyzing four green projects under the Green IS lens. They conclude that there is a challenge for societies to design IS that will lower consumption and support sustainability. Loeser et al. (2011) suggest a typology for Green IS strategies in firms, derived from case study analyses. They conclude with some propositions for Green IS strategies followed by business leaders. In the heart of Green IS lies the sustainability principle: with the use of information, the demand can be “re-shaped”. According to this, societies are able to reduce high CO2 levels coming from energy generated by fossil fuels, using available information. MIEM supports this principle firstly by reducing excess levels of energy and secondly is becoming even more effective by imposing the constraint



that the energy used by customers must come exclusively from renewable sources. The second option offers higher CO2 reduction, since no fossil fuels are involved. According to Peffers, et al. (2008) the IS that support the new logic of environmental sustainability adopt simulations, optimization techniques and design science principles. Finally, Green IS artifacts differ from others that support different dominant logics (such as Subsistence, Agricultural, Industrial, Service), in the sense that environmental sustainability implies apart from creating customers or providing services, to be sustainable with regard to resources used. This creates the main challenge for the new societies (Watson, et al., 2012b).

We show how our artifact contributes to Green IS by extending the “integrated sustainability framework” (Table 1) proposed by Dao et al. (2011). They propose the framework to describe the value of sustainability within firms. We extend and modify this framework to be applicable to a smart home which has different objectives than a firm. However, we see direct analogies in the processes within a firm and a smart home that both aim to be sustainable. The interal strategies refer to practicies within the smart home and the external refer to practices aiming at benefiting the smart grid. The rows referring to today depict features of the current situation whereas the rows referring to tomorrow outline the effects of long term adoption of MIEM to sustainability.

Table 1. Integrated sustainability framework as proposed by Dao et al. (2011) adapted for MIEM

Internal (Smart Home) External (Smart Grid)

Today (Current Situation)

-Strategy -Reduce redundant energy consumption by minimizing costs (based on Watson Watson’s et al. (2010) proposition Energy+Information <Energy) -Pay-off -Reduced electricity bills

-Strategy -By minimizing the costs using MIEM, the peak demand is reallocated to other time periods, based on individual preferences -Pay-off -Average energy prices reduced, the smart grid is alleviated from critical strains

Tomorrow (Future Situation)

-Strategy -While MIEM’s adoption increases, the energy customers feel more positive towards sustainable solutions as they see the benefits they gain from it -Pay-off -Lower range anxiety since MIEM makes sure the EV battery is charged

-Strategy -Use only energy tariffs with energy stemming from renewable sources

-Pay-off -Establish a sustainability culture, reduce the CO2 emissions and support environmental awareness

Smart Homes and Electric Vehicles

The combination of smart home energy management with EVs or other storage facilities has been studied under the prism of control engineering and computer science. However, the sustainability dimension remains a new perspective that needs to be addressed. In Table 2 we gathered literature focused on smart homes and EVs and describe the characteristics of this work. We see that most of the previous works schedule the smart home consumption and EV charging from the point of view of an external party (charging coordinator) or assume only EV charging without combining it with a smart home. In the current work we examine this topic from the individual consumer’s perspective. From this point of view we see that if each individual minimizes cost, we have energy peak demand reduction, not being originated by external signals. Other manuscripts such as Garcia et al. (2013) and Pipattanasomporn et al. (2012) adopt control engineering methodologies focusing more on the physical infrastructure than the economic incentives and sustainable peak reduction that we are interested in. The articles that deal with individual consumers, such as Vytelingum et al. (2010) and Mishra et al. (2012), focus on storage batteries without accounting for their use in EVs.



Brandt et al. (2013) describe an energy IS for a household assuming a synergy between the EV and a photovoltaic panel. They see promising results by making the EV charging follow the photovoltaic panel’s energy production. However, they assume that the EV is the second car of the household, so that the energy customers do not rely solely on electric mobility. Brandt et al. (2012), Kahlen et al. (2014) and Wagner, et al. (2013) propose new business models that outline the EV integration towards the new era of sustainable societies. Furthermore, Gerding et al. (2013) propose a two sided market approach to allocate charging timeslots among the EV customers and avoid charging congestion. There, EV customers bid to obtain charging timeslots. Gerding et al. (2011) present an online auction mechanism where the owners of EVs state their timeslots available for charging and also bid for power. Finally, Stein et al. (2011) describe an online mechanism with pre-commitment for EV charging coordination.

Table 2. Smart Home and EV literature overview

Methodology Objective Manuscript

Control engineering approach

-Smart home appliance management with cloud computing -Demand response event analysis for household appliance scheduling

Garcia et al. (2013) Pipattanasomporn et al. (2012)

Storage within a smart home without assuming EV driving

-Reduce the electricity costs for the individuals with charging coordination -Benefit form the price difference with use of storage

Vytelingum et al. (2010) Mishra et al. (2012)

EV charging without combining with smart homes

Schedule EV charging in optimal way for the individual or the market

Gerding et al. (2013), Gerding et al. (2011), Stein et al. (2011), Valogianni et al. (2014), Vandael, et al. (2013a), Kahlen et al. (2014), Chen et al. (2012), Vandael, et al. (2013b), Vytelingum et al. (2010), Vaya & Andersson (2012)

Forecasting methods for smart home appliances scheduling

-Schedule smart home appliances using forecasting

Truong et al. (2013)

EV usage coupled with renewables

-Create an energy IS for a household assuming a synergy between the EV and a photovoltaic panel

Brandt et al. (2013)

New IS business models -Create new business models outline the EV integration towards the new era of sustainable societies

Brandt et al (2012), Kahlen et al. (2014), Wagner, et al. (2013)

Market mechanism -EV charging coordination through a market mechanism

Gerding et al. (2013), Gerding et al. (2011), Stein et al. (2011)

EV fleet aggregator -EV charging coordination mechanisms which is fully performed by an aggregator (EV fleet aggregator)

Vandael, et al. (2013a), Kahlen et al (2014), Chen et al. (2012), Vandael, et al. (2013b), Vytelingum et al. (2010), Vaya & Andersson (2012)

Mobility Integrated Energy Management Model

Energy Customer’s Decision problem

Presenting the Mobility Integrated Energy Management (MIEM) artifact we aim to ease the complex decision making problem of an energy customer that lives in a smart home and owns an EV. Considering that we are experiencing a transition phase moving towards a fully smart energy grid, there is a plethora of information available to the customer. Similarly to other complex economic environments (Bichler, et al., 2010), there is evidence that by making optimal use of this information abundance, the energy



customer can arrive to better consumption decisions. Namely, the energy customer needs to decide in real time how much energy to consume, given changing energy prices and energy availability. Breaking this decision problem down to smaller ones, the energy customer is expected to decide when each individual household activity is to be performed. Additionally, there is the need of satisfying the daily routine activities (commuting to work, etc.). Therefore, assisting energy customers with intelligent agents prevents them from sub-optimal decisions but most importantly mitigates feelings such as range anxiety or anxiety of having a high electricity bill at the end of the month.

Model Assumptions

MIEM, except for assisting energy customers with their real time power consumption decisions, aims to enable sustainability as a key decision-making dimension. It is based on the following assumptions related to the boundaries of the artifact as well as its communication with the environment:

Each household is represented by an intelligent agent using MIEM. With this assumption we disentangle the real time consumption decision from the household customers’ presence. The intelligent agent can provide consumption recommendations even when the customer is not present at home and the customer can accept this recommendation or intervene based on personal preferences. Any change in the personal preferences is observed by the intelligent agent and used as input for the learning algorithm.

Each household owns one EV which is fully electric. With this assumption we make sure that the households are fully electric, and consequently independent from costly and unsustainable petrol mobility solutions.

Each household can have either flexible or non-flexible household appliances. Considering the rapid technology advancements, we can safely assume that the household appliances will be in future totally flexible reacting to price signals (Gottwalt et. al, 2011). However, we live in a transition period where both solutions, flexible and non-flexible exist. Therefore, we simulate scenarios where we take into account both cases.

The household customers are exposed to variable pricing schemes, constructed to account for taxation and extra charges included within the retail price. This assumption lies in the core of the definition of sustainability. A sustainable society needs to make their members aware of the amount of energy they consume, as well as when they consume: consuming a considerable amount of energy during a low demand period will not have the same effect on the energy grid and the environment, as consuming the same amount during a high demand period, where each energy unit is valuable and perhaps scarce. Therefore, we construct variable pricing schemes that will communicate the actual shortage or abundance of energy to the household customers. With this assumption we aim to steer the customers towards more sustainable decisions (e.g. consuming where power demand is low).

All cars drive within the same distribution system. This assumption ensures that all the energy customers belong to the same region, pay the same taxes and consequently face the same energy prices.

We assume V2G which is actually implemented as Vehicle2Home (V2H). The actual procedure is that the energy customer when having surplus of energy can sell it to the grid and then buy it back, so that the customer pays taxes and additional fees in both processes. However, we assume that previously stored energy is used within the same household. For this process we assume 3% distribution line losses as suggested by Chukwu et al. (2014).

Mobility Integrated Energy Management IS Artifact

Our artifact offers a new solution to the existing problem of managing household consumption and EV charging in an effective way. Therefore, from the Design Science point of view, it is categorized under the solution group “Improvement: New Solutions for Known Problems” (Gregor et. al, 2013). This new solution strives to yield the maximum possible benefits to the individual household customers using the information available. With respect to relevance it is strongly related to environmental sustainability as well as smart grid and energy information systems (Energy IS) (Watson et al., 2012b) where the use of information incurs reduction to the amount of energy consumed by the smart home. With regard to rigor, our IS artifact is connected to the general Green IS research stream but also to the general power systems



engineering area. Specifically, according to Elliot (2011) this approach is categorized under IT that aims at changing human behavior in order to increase sustainability. An illustration of the proposed design is given in Figure 1. There we observe that the intelligent agent (MIEM) interacts with the household appliances and the EV by receiving inputs that will help forming the personalized recommendations.

Figure 1. Mobility Integrated Energy Management Artifact Overview (Smart Home & Vehicle)

In the evaluation section we will evaluate the IS artifact with respect to validity, utility, quality and efficacy (Hevner, et al., 2004). To this end, we will demonstrate through large scale simulations the performance of the artifact in realistic conditions. Furthermore, using some metrics related to sustainability (peak-to-average ratio, demand volatility, absolute peak reduction) we will evaluate the artifact compared to the current situation where no intelligent agents are employed as well as compared to other proposed methods applicable in the energy domain. Following Kane & Alavi’s (2007) work for building effective simulations we specify our criteria for an effective simulation. For our simulation environment these are the high accuracy in learning the household consumption behavior, the ability to minimize individual costs, the prevention of herding behavior in energy consumption. All the aforementioned factors, if satisfied, will ensure effective simulation environment for our evaluation.

The presented IS artifact engages in two different processes: a) scheduling the household consumption and b) scheduling the EV charging. For the first process it gets as inputs the energy prices per hour (Pt), learns the household consumption pattern from experiencing the household residents behavior and

depending on their flexibility gives as output the household demand, h,tx . For the second process it

receives as inputs the energy prices, the charging availability and the driving needs (charging demand)

and gives as output the optimal EV charging schedule c,tx . These two outputs summed, yield the total

household demand: overall,t h,t c,tx =x x . In the following sections all aforementioned inputs and outputs will

be analyzed. Following Walls’ et al. (1992) framework for design science, we summarize the most important meta-requirements for MIEM in Table 3. Following these meta-requirements the meta-design features of MIEM are outlined in Table 4 and the design hypotheses are presented in Table 5. Confirming these hypotheses will create both savings to the individual smart home residents but also to the overall electricity grid by reducing peak demand and demand volatility and preventing herding effects.

Table 3. Meta-requirements for MIEM

MR1. MIEM should schedule household consumption and EV charging based on the cost minimization objective

MR2. MIEM should account for individual driving preferences and profiles MR3. MIEM should reduce range anxiety by ensuring enough battery capacity for EV owners

MR4. MIEM should learn and update the household demand pattern using new data entries



Table 4. Meta-design features of MIEM

MD1. Household demand pattern learn and update using RL based on past behavior

MD2. Charging availability get and update based on recorded inputs MD3. Charging demand calculate based on driving profile, car and battery characteristics

MD4. Optimal household and EV charging demand vector calculate and update based on minimal costs

Table 5. Design hypotheses for MIEM

DH1. MIEM will reduce peak demand

DH2. MIEM will reduce demand volatility DH3. MIEM will prevent herding effects in electricity consumption

DH4. MIEM will bring savings to individual consumers

Benchmarks

Important part of the Evaluation cycle of a designed artifact, is the quality of the produced results (Hevner, et al., 2004). MIEM consists of the reinforcement learning component and the optimization component. Since the latter one yields always the optimal solution given the prevailing conditions, we evaluate our artifact based on the RL part which shows a forecasting error. Therefore, we assess the reinforcement learning component compared to state of the art methods used in similar settings. Below we compare the Autoregressive Moving Average Method (ARMA), the Exponential Smoothing (ES) and a 6th degree polynomial regression, (all trained on the same data). For the comparison we use the Mean Percentage Error (MPE) that shows the bias in the predicted curves and the Root Means Square Error (in Wh). The results of these comparisons are described in the Numerical Results section.

The first benchmark uses an Autoregressive Moving Average Method (ARMA) which is based on time series and forecasts the next value of the series based on previous ones. This model uncovers hidden periodicity patterns in the times series and in our case is purely trained on household consumption data. From our data set an ARMA(1,1)×(1,1)21 was derived. This means we have 22 lags for the (autoregressive) AR part and 21 for the (moving average) MA part (this proved to be the best estimator coming from the data, another option would be ARMA(1,1)×(1,1)24 with similar accuracy). It is stationary; therefore we did not include any integrative components. The second benchmark is an exponential smoother that uses the following recursive equation to forecast the next time step’s household consumption:

, 1 t 1(1 )st h ts x where , 1h tx is the previous household demand observation and is the

smoothing factor. Finally, the third benchmark derives a 6th degree polynomial regression model from the training set, and based on this forecasts the coming time step’s household demand.

Household Demand Learning

The Mobility Integrated Energy Management artifact plans the household demand based on prices and resident’s price elasticity (or flexibility). Therefore, it learns the household demand pattern using reinforcement learning (RL) (Sutton & Barto, 1998). RL is based on a reward mechanism that provides an algorithm with positive and negative rewards for optimal or non-optimal decisions, respectively. In this particular problem the customer agent has to decide on the customer’s individual consumption value, based on training on previous consumption entries. Because of this reward mechanism, RL can be more flexible and adaptive than other static learning methods. Furthermore, it can be adapted based on new entries (new household consumption values), being independent from the initial training (online learning (Mitchell, 1997)). Finally, it can capture the evolution of the household demand accounting both for old and new household consumption values. For the previous reasons, we selected RL as the learning mechanism of MIEM. The customer agents’ decision problem is described by the following Markov Decision Process (MDP) (Puterman, 1994):



1,1 t,j

0 i

1,1 t,j

{S ...S }

Action Space {A , ...,A }

Rewards {r ...r }

State Space

S T L

A

R

(1)

where max[1, ]t t and

maxt is the size of the horizon over which we want to learn the consumption and

[1,M]j with M the maximum consumption level. T is the set with the time intervals, t (T={t}) and L is

the discrete set with the consumption levels. The Action Space, A includes all the transitions from ,t jS to

,m nS max, [1, t ]t m and , [1,M]j n , under the temporal constraint that m > t.

More specifically, MIEM uses Q-learning, as described by (2) and (3) (Mitchell, 1997). The customer agent learns the optimal policy through rewards R={rt,j} that are offered to it for each state St,j. In this specific problem, the learned policy corresponds to the individual household consumption over a weekly horizon. Each reward is denoted as rt,j and expresses the consumer benefit for each state. After experimenting with various reward schemes we concluded to the following yielding the highest predictive accuracy:

2

, , , , tP̂t j t j t j t jr L L L (2)

The energy prices ( tP̂ ) at each hour are predicted by the intelligent agent using a moving window of the 7

past days, averaged over each hour respectively. This simple moving window forecasting gives a good indication of the price level in the coming hour. In future, we plan to enrich this price forecasting with including influences from exogenous factors such as availability of renewable sources and weather conditions (Feuerriegel, et al. 2014). Regarding the reward function, we plan to experiment with more reward structures and compare the results. So far the current scheme seems to be the most accurate for thissetting. The optimal policy is the one that yields the maximum consumer benefit evaluation for each

action iA . In other words, the agent selects the policy with the highest rewards, which correspond to the

highest benefit obtained from each state (consumption level). Therefore, the valuation of each state t,jQ is

summarized as (Watkins & Dayan, 1992):

t,j t,jQ = r + *( (S,A)) (3)

The function *( ) represents the discounted cumulative reward achieved by the policy starting from

state S. The function ( ) determines the next state that the agent should proceed, i.e. 1 (S,A)tS .

And the optimal evaluation gives the learned household consumption:

t, jS t,j = argmax {Q }hx * (4)

Here maxh,1 h,[x ....x ]thx * is a vector over the temporal dimension, [0,1] is the discount factor and

practically expresses the weight of the previous state rewards.

If the household is flexible ( 0 ) with respect to shifting household appliances demand to cheaper time periods, the total household demand will be modified by the agent as follows:

1, , 1 , 1

1

* *t th t h t h t

t

P Px x x

P

(5)

where is the price elasticity of demand and (5) results from the definition of elasticity of demand

as

, , 1

, 1

1

1

*

*

h t h t

h t

t t

t

x x

x

P P

P

. We simulate scenarios where the customers are flexible or non-flexible with respect

to price changes. For the non-flexible scenarios we have 0 and , , *h t h tx x .



Electric Vehicle Charging Scheduling

The intelligent agent schedules EV charging in order to minimize the household electricity cost, c,tc(x ) .

Since we assume both Grid-to-Vehicle and Vehicle-to-Grid flows we adopt the following convention: if

c,tx 0 , the EV customer charges from the grid. Otherwise the customer feeds energy back to the grid.

Therefore the charging scheduling problem is defined as follows:

max

, c,t

1

argmin { c(x ) }c t

t

x t

t

CA

cx * (6)

subject to the constraints (10), (11), (12):

max, , max, max t [1,...,t ]t c t tX x X (7)

The upper bound max,tX represents the maximum energy that the customer agent can charge from the

network per timeslot t. This represents the main network constraint and is dependent on the characteristics of the residential connection. In our numerical results we perform sensitivity analysis of this parameter to investigate its effect on the result.

c,t t t-1 t maxx = SoC -SoC + E{D } t [1,...,t ] (8)

0 minSoC = SoC (9)

, max t [1,...,t ]c t tx CA M (10)

where M is a sufficiently large number, tSoC is the state of charge on timeslot t, and minSoC is the

minimum allowed state of charge that does not destroy the battery’s lifetime. The variable tCA is the

charging availability of the household customer (if the customer is driving he/she cannot charge) and

tE{D } is the expected driving demand (in kms). Both of these variables are calculated from the driving

data described later in the Data Description section. All the aforementioned constraints ensure that the agents do not violate the customer’s comfort and have the EV always charged to cover the driving needs of the coming day. Constraint (10) ensures that charging only occurs when the customer is not driving and is

available for charging. Defining cost as c,t t ,ˆc(x ) P c tx , (6) becomes:

max

, t c,

1

ˆargmin { P }c t

t

x t t

t

x CA

cx * (11)

subject to (7)-(10). The prices tP̂ at each hour are predicted by the intelligent agent using a moving

window of the 7 past days, averaged over each hour respectively. This way we take into account the most recent values that are usually the most correlated values. We chose Dynamic Programming (Bellman, 1956, Betsekas, 1995) to solve the problem by breaking it down into smaller sub-problems assuming backwards induction. In summary, with (11) and (5) the agent constructs the total household demand

as overall,t h,t c,tx =x x . Main objective is to schedule overall,tx such that the individual household customer

gets the maximum savings on the electricity bill without violating the daily routine in terms of household activities and driving.

Experimental Evaluation

Simulation Environment

To evaluate the proposed artifact under realistic conditions we create a simulation where each household is represented by an intelligent agent using MIEM. The simulation environment consists of variable energy prices, reflecting the energy availability (see more information in Data Description section). Each household has an individual household consumption profile, which the agent tries to learn via RL. Furthermore, each household customer has a particular driving profile, based on which the agent schedules the charging to provide an optimal recommendation. Our simulation is based on Power TAC (Ketter et al., 2013).



In Table 6 we present the simulation parameters’ values used in our numerical simulation experiments. At the end of this section we perform sensitivity analysis with respect to other values commonly used in literature. By Level of Charging, Xmax, we denote the maximum charging speed (in KWs) that the EV can charge from the grid. This is now set to the single phase charging speed, which is 3.3 KW (230 VAC, 16 A) and is used in real world for the lowest level charging. It allows charging from a household socket and does not even require a three phase installation. We chose this particular charging speed, since we investigate the household consumption and charging behavior and we assume that there are no other (fast) charging options available, as it is the current state in most European countries. For the number of customers, N, we assume 1000 households in our simulation. This number is sufficiently large to represent all household and driving pattern, but also sufficiently small to comply with the same distribution network assumption. A population over 1000 household is quite likely to belong on different

distribution networks, paying different energy prices. For the price elasticity of demand we use the value 0.39 as derived by Greenwood et al. (2013) for the household energy customers. For the planning horizon we assume tmax=168h (1 week).

Table 6. Simulation Parameters

Parameter Notation Value

Level of Charging Xmax 3.3KW

Number of Customers N 1.000 households Price Elasticity of Demand 0.39

Planning Horizon tmax 168h (1 week)

Data Description

The customers simulated within our simulation experiments have a) a particular daily driving pattern, b) a particular household consumption pattern and c) respond to energy market via variable prices. Regarding the energy consumption data, we use the household consumption data from the Netherlands obtained in collaboration with a European Utility Company. This data set includes detailed consumption per 15 minutes for 24 different households. We aggregate this consumption data over hourly intervals to maintain the granularity of our analysis to be 1 hour. The measurements are gathered in 2010. Based on these 24 households we create large customer populations drawing randomly one of the 24 household consumption profiles. The driving patterns are derived from the Dutch Central Bureau of Statistics

(Central Bureau of Statistiek, CBS). The driving demand is denoted as tD and is displayed in Figure 2 for a

24 hour horizon. We assume customers that own purely electric cars like Nissan Leaf or Tesla S. Since Tesla S cars are not as affordable as Nissal Leaf cars, we assume that in our population 40% own a Tesla S and 60% own a Nissan Leaf. Regarding customer’s charging and discharging availability we assume that the customers can charge the EV’s battery when they are not only at home but also at work (”standard” charging with direct billing to the customer), which is nowadays implemented by companies in order to encourage their employees to drive ”green.” We assume that the EV batteries can pull power from the grid but also feed it back to the grid when they have excess capacity (Grid-to-Vehicle and Vehicle-to-Grid). By excess capacity we mean the extra capacity coming from charging during time periods that the battery is already charged enough to cover the driving needs. Furthermore we examine our approach under real time pricing, and as an example we create a variable pricing scheme based on the European Power Exchange (EPEX) intraday price-curve over weekly horizons1. Since we use as case study the Netherlands we consider that the Dutch retail energy prices account on 44% for the wholesale energy price2 (EPEX prices) while the rest 56% represents the distribution network fees, the energy taxes and VAT. With this assumption we create the retail price scheme that accounts both for wholesale prices, distribution

network fees and taxes:0.44

EPEXpriceP (€/KWh) assuming EPEXprice is the price given by EPEX. The

average price curve over a horizon of 3 weeks together with the variability bars is shown in Figure 3. Furthermore, in Figure 4 we display the histograms of price and household demand.

1 http://www.epexspot.com/en/market-data/intraday/intraday-table/ [Date Accessed: 02/05/2014] 2 http://www.nuon.nl/energie/energieprijzen-vergelijken/opbouw-energieprijs.jsp [Date Accessed: 02/05/2014]



Figure 2. Distribution of Driving Demand of the population over a 24h horizon

Figure 3. Example variable pricing scheme based on EPEX prices.

Figure 4. Price and household demand distributions

Numerical Results

We use a 2x2 design for our simulation (Table 7). One dimension deals with the household flexibility in terms of the appliances used. Such appliances may be potentially used at specific times (inflexible households) or usage may be “shifted” to time periods of greater energy availability (flexible households). Another dimension deals with whether the EV has the ability to serve as a local power source for the household (V2H capability) or not (no V2H capability). We should note that these simulation design choices cover a range of conceivable current and future scenarios. Therefore, we assume a current scenario (Scenario 1) a future scenario (Scenario 4) and two “transition phase” scenarios (Scenarios 2, 3).

Table 7. Simulation Scenarios

Scenario 1 Inflexible Household Appliances No V2H

Scenario 2 Inflexible Household Appliances V2H

Scenario 3 Flexible Household Appliances No V2H

Scenario 4 Flexible Household Appliances V2H



Demand Curve Reshaping

Using MIEM, households reshape their demand curve both as a result of their price elasticity but also as a result of the dynamic EV charging scheduling. In Figure 5 there is an illustration of the overall household demand after running Scenarios 1-4, compared to the unconstrained behavioral charging (Unconstrained Charging - baseline scenario) where practically the human customer is responsible for charging and discharging the car and there is no shifting of household activities. This data in our case is collected in collaboration with a European charging infrastructure company. The data points refer to charging demand of EV owners throughout the day without any restriction or control, therefore they serve as our baseline scenario. Additionally to the unconstrained behavioral charging, we compare the MIEM output to the household demand curve without the presence of EVs or any kind of household appliance shifting. This comparison is selected to show how the EV batteries can offset peak demand and reveal any potential that storage might have. It is expected that most of the simulated scenarios will yield higher peak demand compared to the plain household demand without any EVs in the market, because of the presence of EVs adding extra load to the grid. However, in some cases we observe that the presence of EVs creates peak demand reduction compared to the plain household demand curve (without any EVs in the market). This is a rather important incentive to adopt EVs and promote sustainability. The cause of this peak demand is that the EV storage can act as a buffer, achieving better demand allocation and reducing volatility.

Figure 5. Power demand reshaping across Scenarios 1-4 of using MIEM artifact.

Comparing Scenarios 1, 3 with Scenarios 2, 4 we observe that the presence of V2H feature creates higher peak demand reduction (right graph, Figure 5) compared to the situation where only shiftable appliances or EVs exist in the household. This finding combined with the fact that V2H creates lower peaks than the household itself without an EV (Household Demand curve), could be used to promote EV adoption. Apart from the steady state power demand, MIEM reduces also the volatility of results. Below, in Figure 6, we illustrate an indicative result which corresponds to comparison between Scenario 1 and 3 and Unconstrained Charging. We observe that the unconstrained charging is highly volatile mostly because it stems from the customers intrinsic behavior to charge the EV. On the other hand, MIEM output in most of the cases reduces the volatility spectrum, yielding more robust results and preventing “spikes” in the household demand. This result is rather important for the grid’s stability and reliability since higher volatility is associated with higher black out probability.

To compare the performance of the various scenarios we adopt the peak-to-average ratio (PAR) reduction

(

2

1

1

peak peak

Nrms

i

i

x xPAR

xx

N

) metric and the peak demand reduction. PAR is also known as “crest factor”

and indicates how extreme the peaks in a waveform are. In other words, the demand can be covered by base load which is more sustainable and does not need extra resources for peak coverage. Tables 8 and 9 display the comparisons of the 4 scenarios to the baseline unconstrained charging and to the household demand without any EVs or shiftable appliances present (the minus sign indicates negative reduction, i.e. increase). Table 9 specifically, presents a contrast between the 4 scenarios with the plain household demand without any EVs adopted by the household. Through this comparison, we want to see how much



value, the adoption of an EV will bring to a household that currently owns a conventional car. If this value proves to be significant, then there might be an incentive for EV adoption.

Figure 6. Demand volatility reduction as result of adopting MIEM artifact (Scenario 1 and Scenario 3).

Table 8. Scenarios performance compared to the Unconstrained Charging (baseline)

PAR reduction Absolute Peak reduction Scenario 1 7.52% 9.53% Scenario 2 8.31% 10.44% Scenario 3 7.10% 8.78% Scenario 4 7.74% 9.57%

Table 9. Scenarios performance compared to the household demand without EVs

PAR reduction Absolute Peak reduction

Scenario 1 14.53% -9.04% Scenario 2 9.75% 7.23% Scenario 3 14.07% -9.94% Scenario 4 9.19% 6.33%

We see that the maximum peak demand reduction is created by Scenarios 2 and 4 that have the V2H feature incorporated. It is interesting to mention that MIEM adoption incurs peak demand reduction of around 7% (Scenarios 2 and 4) compared to the case where no artifacts are used and no EVs exist on the market. This practically means that, against the expectations of EVs to increase peak demand, if EV storage is properly integrated in the energy market, there will be higher sustainability levels (lower peaks). This creates an incentive for electric mobility stakeholders to promote EV adoption and increase grid’s sustainability. Furthermore, it creates benefits for individual household customers who have now a flatter demand curve compared to the situation where no artifact was in place, paying lower energy prices and supporting environmental sustainability.

Energy Price Reduction

Following the peak demand reduction, we observe reduction on the energy prices. This is an immediate result of demand shifting towards cheaper time periods. In Figure 7 we display this average price reduction as a function of MIEM adoption. We see that the IS adoption brings savings to the household customers up to 11% for Scenarios 1 and 3 and 14% for Scenarios 2 and 4. These price reductions reach their maximum at around 80% and 100% adoption rates. However, with such adoption rates there is the risk of having herding behavior (Gottwalt et. al, 2011), which is a phenomenon typically showing up in demand shifting solutions. This herding is caused in many demand response settings because of too much shifting towards off-peak time periods, creating new peaks just moved in earlier timeslots. With Figure 7 we investigate any herding behavior that might be caused by excessive demand shifting. More specifically for Scenarios 2 and 4 we observe that after adoption of around 80% the price reduction declines mostly caused by excessive household demand shifting (herding). Therefore, the energy policy makers should not



encourage MIEM adoption with V2H capabilities at a rate higher than 80%. Similarly, for Scenarios 1 and 3 an adoption around 90% would be a safe upper bound to prevent herding behavior and shifting the demand peaks to earlier time periods.

Figure 7. Average Energy Price Reduction as a result of MIEM artifact adoption.

Sensitivity Analysis

The sensitivity analysis demonstrates how the results of Scenario 1 would be adjusted if the charging level would be different. For this analysis we investigate the effect on the result using the charging levels currently available in the energy market. Apart from (3.3KW, 230VAC, 16 A) single-phase charging which is used for the main numerical results, we use medium single-phase charging (7 KW, 230VAC, 32 A), low level three phase charging (10 KW, 400VAC, 16A), medium level three phase charging (24 KW, 400VAC, 32A) and high level three phase charging (43 KW, 400VAC, 63A) which is the extreme case and is not available in the US and might have different configuration for Japan or Germany. This parameter is crucial for the energy policy makers, since EVs adoption is radically increasing and heavily incentivized by governmental parties. The charging speed (levels) is one of the main features to make EVs more appealing to general public, since the car batteries can be charged faster. For example for charging the battery installed in a Nissan Leaf (24KWH) with the single phase charging of 3.3KW the owner needs to plug the car for 6-8 hours. However, using the other charging speeds (7KW, 10KW, 24KW, 43KW) the customer needs to plug the car for 3-4, 2-3,1-2, or 0.5 hours, respectively. Therefore, it becomes clear that the higher the charging speed the more appealing the EV concept becomes for the individuals. On the contrary, from the sustainability point of view, the charging speed needs to be carefully investigated before allowing massive high speed charging for the general public. As the results in Tables 10 and 11 demonstrate, increasing the charging speed reduces the peak reduction and the highest charging speed case creates significant increase on the peaks. This means that extra generation infrastructure needs to be built to cater power for these peaks, increasing carbon footprint and mitigating the sustainability levels.

Table 10. Sensitivity Analysis compared to the Unconstrained Charging (baseline)

PAR reduction Absolute Peak reduction

Level of Charging (Xmax =7KW) 2.24% 5.12%

(Scenario 1) (Xmax =10KW) 1.67% 4.62% (Xmax =24KW) 0.84% 4.29% (Xmax =43KW) -9.12% -7.50%

This last situation of 43KW charging speed is currently not an option available to the general public in many countries like USA, so it is an extreme situation which acts as an upper bound of the fast charging capability. However, if fast charging capabilities become available, the policy makers should impose constrains (or counterincentives) on the number of concurrent charging (depending on the fast charging speed) to prevent peak demand increase.

Looking at the sensitivity analysis of Scenario 1 in comparison with the case with no EVs in the market (plain household demand curve without any EVs in the market), we observe absolute peak demand increase. This increase was expected, since energy previously produced by gas in the conventional car, is now produced by electricity, increasing the electricity demand. However, the fact that PAR is actually



reduced, indicates that peaks are not extreme compared to the average demand, leading to lower demand volatility.

Table 11. Sensitivity Analysis compared to the household demand without EVs

PAR reduction Absolute Peak reduction Level of Charging (Xmax =7KW) 27.44% -16.81% (Scenario 1) (Xmax =10KW) 29.10% -23.10% (Xmax =24KW) 32.57% -52.98% (Xmax =43KW) 28.76% -140.33%

Benchmark Results

In this section, we present the results of the performance comparison of our artifact with the benchmarks presented in the Benchmark section. We see (Table 12) that in terms of MPE, RL is significantly better than the standard prediction methods resulting from its ability to adapt. However in terms of RMSE, RL has lower error compared to all the methods but higher compared to the ES. We are more interested in the MPE since it gives the percentage error and measures the algorithms flexibility. For the ARMA model we get 55.8% prediction error. The reason is that the ARMA prediction weakens over time, having at the end significant deviation from the actual value. This result is in accordance with the result of Reddy & Veloso (2012), where the authors use a similar ARIMA model and have 60% error. One would expect that energy consumption would be a standard pattern, easily predicted by typical prediction methods. However, we observe much volatility in the data that cannot be captured by standard prediction methods. Therefore, we chose to apply RL on this setting, which is adaptive and can follow changes in the demand capturing variation of individual preferences.

Table 12. Predictive Accuracy

RL ARMA ES 6th Polynomial

MPE (%) 6.8 55.8 18.5 39.6

RMSE (Wh) 112.1 552.8 80.5 172.8

Conclusion & Policy Recommendations

Home appliances and vehicle batteries can be used to match demand to energy supply stemming from renewable energy sources. Balancing supply and demand incurs peak demand reduction and mitigates the volatility of renewable sources. Therefore, we designed MIEM artifact, which supports energy customers in the data intensive environment of smart grid by offering personalized consumption recommendations. It contributes to Green IS theory as shown in Table 1, since it is essentially an artifact that supports sustainability, making optimal use of the data available. More specifically, it learns individual household and driving behavior and schedules the shifting of flexible household appliances and the usage of EV batteries, to minimize household energy cost without sacrificing comfort. As a result of better allocation of household consumption, we observe peak demand reduction of around 10%. Furthermore, MIEM reduces demand volatility which is an important metric for sustainability, since the capacity of the grid must be able to cover the worst-case demand scenario. A highly volatile demand curve requires that the capacity of the network can always cover the worst case scenario of demand, requiring inefficient and unsustainable fossil-fuel resources. On the contrary a lower volatility demand curve can be more predictable and does not necessitate installing new capacity infrastructure to cover extreme demand spikes.

On the household customer level, MIEM using the intelligent agent approach eliminates range anxiety associated with the EV battery charging but also any other anxiety related to household appliance shifting. That is because the household customer has no responsibility to schedule his/her household consumption based on the price signals and is sure that the EV battery will have sufficient charge. MIEM helps encourage adoption of EVs and smart appliances that are more energy efficient and reduce the household’s carbon footprint. Therefore, it can be used by policy makers as a tool to support green technology adoption (EVs and energy efficient appliances). In Table 13 below we present an overview of the policy recommendations as derived by our simulation results, showing the practical contribution of MIEM to energy business. The maximum MIEM adoption rate without herding behavior is the upper bound of penetration that would be advised in a population. However, given that this upper bound is



expected to stress the electricity grid, it is recommended to encourage slightly lower penetration rates and protect the grid from critical strains.

Table 13. Policy Recommendations

Population Type (Scenarios)

Maximum MIEM adoption rate without

herding behavior

Recommended MIEM adoption rate without

herding and grid stress Inflexible Household Appliances & No V2H (current scenario)

100% 90%

Inflexible Household Appliances & V2H (transition phase scenario)

80% 70%

Flexible Household Appliances & No V2H (transition phase scenario)

100% 90%

Flexible Household Appliances & V2H (future scenario)

76% 66%

One limitation of the presented IS artifact is that it does not account for the charging station subsidies that exist in many countries aiming to promote electric mobility. The presence of subsidies is expected to create potentially perverse incentives and will change the artifact adoption landscape. In our future work we plan to examine the effect of various types of electric mobility subsidization on sustainability levels. Another limitation is that we currently focus on a European country (Netherlands) with particular assumptions relating to this. One of our future extensions is to apply our IS artifact on countries outside Europe and compare the outcomes. Furthermore, we currently predict energy prices using a moving average window without accounting for exogenous parameters (weather conditions, expected renewable feed-in, renewable sources availability in the area) that will influence price variation. Therefore, in our future work, we will include exogenous factors in our predictions to increase forecasting accuracy. Finally, we plan to examine the influence of the presented artifact in a smart energy neighborhood setting where the customers can decide for collective peak reduction, accounting for network effects.

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Enabling Sustainable Smart Homes: An Intelligent Agent ... · We propose a Mobility Integrated Energy Management artifact (MIEM) implemented through an intelligent agent that represents