Design, architecture and implementation of a residential energy box management tool in a SmartGrid

Energy xxx (2014) 1e15

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Design, architecture and implementation of a residential energy box management tool in a SmartGrid Christos S. Ioakimidis a, b, *, Luís J. Oliveira b, Konstantinos N. Genikomsakis c, Panagiotis I. Dallas d INþ, Center for Innovation, Technology and Policy Research-Instituto Superior T
ecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal b MITjPortugal Program, Sustainable Energy Systems, Campus IST-TagusPark, Av. Professor Cavaco Silva, 2744-016 Porto Salvo, Portugal c Deusto Institute of Technology, DeustoTech Energy, University of Deusto, Avda.de las Universidades 24, 48007 Bilbao, Spain d Wireless Network Systems Division, INTRACOM Telecom S.A., 19.7 km Markopoulo Ave., 19002 Peania, Athens, Greece a

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 December 2013 Received in revised form 2 July 2014 Accepted 3 July 2014 Available online xxx

This paper presents the EB (energy box) concept in the context of the V2G (vehicle-to-grid) technology to address the energy management needs of a modern residence, considering that the available infrastructure includes micro-renewable energy sources in the form of solar and wind power, the electricity loads consist of “smart” and conventional household appliances, while the battery of an EV (electric vehicle) plays the role of local storage. The problem is formulated as a multi-objective DSP (dynamic stochastic programming) model in order to maximize comfort and lifestyle preferences and minimize cost. Combining the DSP model that controls the EB operation with a neural network based approach for simulating the thermal model of a building, a set of scenarios are examined to exemplify the applicability of the proposed energy management tool. The EB is capable of working under real-time tariff and placing bids in electricity markets both as a stand-alone option and integrated in a SmartGrid paradigm, where a number of EBs are managed by an aggregator. The results obtained for the Portuguese tertiary electricity market indicate that this approach has the potential to compete as an ancillary service and sustain business with benefits for both the microgrid and residence occupants. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Electric vehicle Microgrid Vehicle-to-grid Electricity management Ancillary service Energy market

1. Introduction The shift towards small scale distributed generation of electricity is indicative of the major changes that are taking place on energy generation, distribution and storage technologies [1]. Over the years, considerable research efforts have focused on exploring technically feasible and practical solutions for integrating the decentralized generation units into the main grid [2e6]. Prominent among them is the microgrid concept, with multiple benefits to customers, electricity utilities and society in general, including economic advantages, reduced environmental impact, as well as enhanced reliability and quality of energy supply [7e10].

* Corresponding author. INþ, Center for Innovation, Technology and Policy
cnico, Universidade de Lisboa, Av. Rovisco Pais 1, Research-Instituto Superior Te 1049-001 Lisboa, Portugal. Tel.: þ351 21 040 70 25; fax: þ351 21 423 35 98. E-mail addresses: [email protected], [email protected] (C.S. Ioakimidis), [email protected] (L.J. Oliveira), kostas.genikomsakis@ deusto.es (K.N. Genikomsakis), [email protected] (P.I. Dallas).

The idea of a consumer, and potentially micro-producer, having an energy management device, the so-called EB (energy box), installed at his residence or small business and working under a real-time tariff was proposed in Ref. [11]. In the context of the present work, this concept is extended to the case of deploying multiple EBs integrated in a microgrid (Fig. 1), having local renewable energy sources, electricity loads and EVs (electric vehicles), while being managed by an aggregator, which exchanges information with the multiple EBs to guarantee the grid's frequency stability. Under these conditions, this platform can be considered a SmartGrid with the capability of placing bids to electricity markets. The structural characteristics of the available markets are closely related to the purpose that the production of this commodity serves, having obviously different control regimes, prices, power dispatched and contract terms. The distinctive characteristics of the proposed EB implementation as a residential energy management tool include the consideration of the EV battery as a local storage option coupled with an improved discretization of the battery charge profile, and

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Fig. 1. Energy box paradigm (Scale 1) as a part of the SmartGrid MSP (MultiScale Programming) architecture.

its capability to participate in electricity markets. To make the optimal set of decisions for the energy management of the residence, such as charge or discharge the EV battery, control the heating/cooling system to cover the thermal needs, and even buy and/or sell electricity from/to the grid, the EB exchanges information with the aggregator and uses forecasted values of several parameters within a time window of 24 h, considering that it operates both in the peak power market and as an ancillary service. The use of the V2G (vehicle-to-grid) technology as an ancillary service is introduced and discussed by several researchers [12e14], and in this direction, the EB approach acts as a merger between V2G capacity and DR (demand response) to present a competitive solution. To this end, this work examines a number of scenarios for a typical residence in Portugal that is controlled by an EB working under a real-time tariff, and considers the case of a number of EBs integrated in a microgrid in order to supply power and compete as an ancillary service in the Portuguese tertiary electricity market. The rest of the paper is structured as follows. Section 2 provides the general outline and theoretical framework of the EB operation. Section 3 introduces the main mathematical model of the EB and the auxiliary tool that simulates the thermal model of the building. Section 4 analyzes the inputs and algorithms for the implementation of the EB, while Section 5 describes its operation as an ancillary service. The subsequent section presents the results from a number of scenarios and cases using the EB for the energy management at residential level and as an ancillary service in the Portuguese tertiary electricity market respectively. Section 7 discusses the limitations of the proposed approach and the last section underlines the main conclusions. 2. Problem context 2.1. Infrastructure at the residence In the frame of this work, it is considered that the physical infrastructure under the control of the proposed EB management tool includes the following components:

(i) A micro-wind turbine installed on a tower annex to the residence in order to avoid the potential noise and structural problems of roof mounting [15]. In this paper, two cases are examined for the power output of the turbine, having a rotor diameter of 3 m and 5 m respectively. It is further assumed that the micro-wind turbine is equipped with a small battery bank to smooth the voltage variation and prevent damaging the inverter. (ii) A PV (photovoltaic) panel placed on the roof of the residence. Similarly, two cases are examined for the PV panel, with corresponding rated power output of 0.5 kW and 1 kW under a SSI (standard solar irradiation) of 1000 W/m2. (iii) A plug-in EV, e.g. a PHEV (plug-in hybrid electric vehicle) or a BEV (battery electric vehicle), similar to the Mitsubishi i-Miev [16], equipped with a 20 kWh battery pack providing a range of 160 km under an approximate power consumption of 125 Wh/km [17]. Given that the battery charging time depends on the available power and voltage, a slow charging option at 200 V a.c. (alternating current) is also assumed, requiring approximately 8 h (to be conservative). (iv) A smart meter, that is an advanced metering device capable of recording, among other parameters, the consumption on an hourly or more frequent basis and transmitting the daily measurements via a communication network to a central collection point [18], which in this case is the EB. (v) A dish washing machine as a typical example of a smart appliance that can be controlled remotely by using the EB. In this case, the instructions needed are given by the occupant of the residence and the EB schedules the operation of the device in the most beneficial instant (within a predefined time frame). It is considered that the corresponding load is 2 kW and, for simplicity, the time for a complete wash is 1 h. (vi) An AC (air conditioning) system, which is also remotely controlled by the EB. The latter one determines the temperature to set for each decision point and uses the residential thermal model (refer to Subsection 3.3) to compute the power required for the AC. Without loss of generality, it is

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assumed that the maximum power is 3.8 kW, both for heating and cooling. 2.2. Information technology resources The IT (information technology) resources required to implement the EB are the following  A CPU (central processing unit) with sufficient computational power to make the energy management decisions in the desired time frame. The algorithm running on the EB is dynamic in nature and all the variables in the process have to be discretized. Increasing the number of discretization points refines the solution quality, yet results in increased computational requirements. In this context, the control process of the EB is expected to enhance the quality of the decisions made to the extent that a further incremental increase in the number of discretization points does not perceptively affect the solution.  A communication network capable of transmitting the information flux, such as the dynamic pricing, between the aggregator and the several EBs in the microgrid. Indicatively, the Internet could serve this purpose provided that network security issues are controlled. 2.3. Participation in power markets For the peak power market, it is assumed that the EB is using a real-time price tariff (dynamic pricing), managed by an aggregator, and thus reflecting the current conditions on the microgrid [11,19,20]. The introduction of such a tariff would surpassingly create a flatter demand curve by “shaving the peaks and filling the valleys” [11], allowing the current installed generation capacity to meet the forecasted demand growth for the following years [21-23]. As already pointed out, the present work also examines, among the available ancillary services, the case of a number of EBs sup
ria) in Portugal plying power to the “tertiary reserve” (reserva tercia [24,25]. This reserve is used to replace the secondary reserve allowing it to maintain the level established by the system. It is important to note that the secondary reserve is negotiated on the previous day according to forecasts of the demand and the probability of generators failure [25,26]. The tertiary reserve is negotiated posteriorly, having a higher cost and requiring a fast response time, characteristics that can be matched by the EB. In this paradigm, the grid operator negotiates directly with the aggregator (similar to what happens nowadays with the regular market agents), which collects the bids of all the EBs inside the microgrid and computes an overall bid. 3. Methodology 3.1. Assumptions The EB decisions for a specific moment in time depend on the next 24 h conditions and thus an extremely large number of states arises. The situation is further exacerbated by the fact that the fluctuation of certain variables, such as the wind speed and the electricity price, is not negligible, and therefore decisions are made inherently under uncertainty. This necessitates the formulation of the EB model as a stochastic optimization problem with the introduction of the simplifications below:  The problem has the optimal substructure property, i.e. an optimal solution can be constructed efficiently from the optimal solutions of its sub-problems [27]. In the context of our problem,

3

this means that the optimum decision for a specific moment in time is obtained recursively through the set of optimal decisions corresponding to the future states of the 24th hour and backwards. This is not a completely accurate property in the problem, but is a fairly acceptable assumption.  The domain of the several state variables is discretized in order to avoid the curse of dimensionality, “the phenomenon that the number of states grows exponentially with the number of dimensions of that state space” [28]. The problem under study is of highly complex computational nature and finding optimum solutions is rather impractical, even with current advances in microprocessor technology. To this end, DSP (dynamic stochastic programming) is employed as an approximation approach to gain computational efficiency, without considerably sacrificing optimality. The algorithm for solving the corresponding DSP model (presented in the following subsection) has a polynomial complexity O(nc), with c ¼ 7, using the big O notation [27]. 3.2. Dynamic stochastic programming model In general, a DSP model can be formulated in terms of five basic concepts, namely decisions, states, stages (decision times), transition rules, and rules for following an optimal policy (policies) [11,28]. The applicability of this technique for an EB implementation is proposed in Ref. [11], the present approach however considers the EV battery as a local storage option coupled with an improved discretization of the battery charge profile. For each moment in time i, decisions are represented by the vector di that contains two fields, namely dEVBattery and dTemp , i i which denote the options to charge or discharge the battery and the AC temperature setting respectively. Microgrid State vectors sHome , si and sWeather , are used to model the i i future conditions for the residence, the microgrid and the weather respectively, at time instance i. Analytically, the state sHome contains i future information for the indoor temperature, the EV presence, the energy and the level of the EV battery, the renewable electricity generation from the micro-wind turbine and the PV panel, as well as for the electricity loads that are either uncontrolled or controlled remotely by the EB. The state sMicrogrid includes forecasts of i microgrid electricity prices, while the state sWeather consists of i outdoor temperature, wind speed and solar radiation forecasts. Table 1 outlines the corresponding information for all the aforementioned states. Home:EVBatteryEnergy The energy stored in the EV battery si is Home:EVBatteryLevel defined as a function of the battery level si , which is an integer between 1 and 9 resulting from the discretization of the battery charge profile. Table 2 shows the energy stored in the EV battery that corresponds to each level. It is noted that the number of battery levels accessed by the EB algorithm is restricted according to the specifications set by the EV owner, so that an energy reserve is always available to allow traveling a minimum distance in case of an emergency. Moreover, the simplifications given in Eqs. (1) and (2), retrieved from Refs. [29,30], are employed for the output (in kW) of the micro-renewable sources sHome:Wind and sHome:PV that depend on i i the forecasts of wind speed sWeather:Wind and solar radiation i sWeather:Rad respectively. i 3

sHome:Wind ¼ 0:0005*r*A*Cp *sWeather:Wind *Ng *Nb i i

(1)

where r is the air density that approximately equals 1.225 kg/m3, A is the rotor swept area exposed to the wind (m2), Cp is the

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Table 1 Description of the states and their scope in the EB algorithm. Scope

State

Perfectly known states at present time (basic states)

si sHome:EVBatteryLevel i sHome:WashingMachine i

Perfectly known states at all time

sHome:OtherLoads i

States as a function of other states

sHome:Wind i

Home:Temp

sHome:PV i sHome:EVBatteryEnergy i sWeather:Temp i sWeather:Wind i sWeather:Rad i Microgrid:Price si sHome:EVPresence i

Random states

coefficient of performance (assumed equal to 0.35), Ng is the generator efficiency (assumed equal to 0.80), and Nb is the gearbox efficiency (assumed equal to 0.95).

( sHome:EVBatteryLevel þ 1; Home:EVBatteryLevel siþ1

¼

i Home:EVBatteryLevel  si Home:EVBatteryLevel ; si

sHome:PV ¼ Panel rating* i

sWeather:Rad i SSI

1;

EVBattery

if di

if

EVBattery di

Obtained from

[16,28] ºC [1, …, 9] 0 e Not available 1 e Available [Hsb, HM] kWh Hsb e Min load (standby) HM e Max load possible [0, Ws] kW Ws e Max production output [0, Ps] kW Ps e Max production output [0, Es] kWh Es e Battery capacity Random Random Random Random 0 e Not available 1 e Available

Temperature sensor at home Electric vehicle battery Occupant Smart meter providing house consumption profile

Þ f1 ðsWeather:Wind i Þ f2 ðsWeather:Rad i Þ f3 ðsHome:EVBatteryLevel i Forecast Forecast Forecast Forecast Occupation patterns

Home:EVBatteryLevel

The next stage EV battery level siþ1 is given in Eq. (4) and depends only on the decision dEVBattery to charge or discharge i the battery.

¼ charge and battery not full

¼ discarge and battery not empty

(4)

otherwise

! (2)

where Panel rating is the rated power output of the PV panel under a standard solar irradiation (SSI) of 1000 W/m2. The main EB algorithm uses hourly decision points in time, which are referred to as stages in the context of DSP, while transition rules are employed in order to obtain the states corresponding to future stages, using as parameters the current states and the decisions made in the current stage. In the present model, the next stage temperature in the residence sHome:Temp is obtained iþ1 by Eq. (3) and depends on the maximum and minimum reachable temperatures by turning the AC to maximum power for heating or Home:maxTemp Home:minTemp cooling, i.e. si and si respectively. If the EB decision to set the AC temperature, dTemp , is within the aforei mentioned range, then the next stage indoor temperature is the one targeted by the AC, otherwise it is set equal to the maximum or minimum reachable temperature, depending on the case. The corresponding power needed by the AC to reach the next stage temperature is obtained via the residential thermal model and depends on the current indoor and outdoor temperatures and the characteristics of the residence (refer to Subsection 3.3).

( sHome:maxTemp Home:Temp siþ1 ¼

Admissible range

>sHome:maxTemp if dTemp i i i Temp Home:minTemp Temp Home:maxTemp if si di  di  si Home:minTemp Temp Home:minTemp si if di
The multiple objectives of the EB decisions, namely maximizing comfort and lifestyle preferences and minimizing cost, are combined in an AOF (aggregate objective function) with a linear weight relationship between the factors, as in Ref. [11]. The optimal policy rule is given in Eq. (5).

maximize di



E si

 h   i cost cost lcomfort ucomfort  l u i i

(5)

where lcomfort þ lcost ¼ 1, with lcomfort and lcost denoting the weight coefficients, while ucomfort being the comfort preference i function illustrated in Fig. 2 and ucost being the cost preference i function given in Eq. (6). In the context of this work, lcomfort and lcost are adjusted to the values 0.0033 and 0.9967 respectively, so that the variation of the terms in the AOF reaches the same dimensionality.

Table 2 Battery energy as a function of battery level. EV battery level

Energy stored in EV battery (in kWh)

1 2 3 4 5 6 7 8 9

0 4.33 8.67 13.00 15.05 16.64 17.94 19.04 20.00

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Because of the lack of field measurements from an actual residence, the required data to train the neural networks were generated for the parameter values shown in Table 3 by the model given in Eq. (7) that was first proposed in Ref. [31], and later employed in Ref. [11].

 h*en Tnþ1 ¼ ε*Tn þ ð1  εÞ* Tno ± ATC

(7)

where Tnþ1 represents the temperature for the next period, Tn and Tno denote the indoor and outdoor temperature respectively in current period n, ε is the factor of inertia given in Eq. (8), h is the thermal conversion efficiency for heating or coefficient of performance for cooling, en is the electric power input in period n, ATC is the overall thermal conductivity, while the plus (þ) and minus () signs are used for heating and cooling respectively.

ε ¼ expð  t=TCÞ

Fig. 2. Comfort preference function.

 * sHome:OtherLoads þ DBatteryEnergy i  *Dt  sHome:PV *Dt  sHome:Wind i i Microgrid:Price

ucost ¼ si i

Home:EVBatteryEnergy

where DBatteryEnergy ¼ siþ1 and Dt ¼ 1 h.

(6)

Home:EVBatteryEnergy

 si

3.3. Thermal model of the building To cover the thermal needs and comfort conditions in the residence according to the occupant's preferences by taking into account the thermal losses of the building, the main EB model is combined with an auxiliary tool based on neural networks that provide as output the amount of power required to supply the AC system to reach the desired temperature. Neural networks are employed in this work because of their modeling power in terms of function approximation capability, given that they are known to be well suited to dealing with uncertainty and noise in the data, as well as nonlinear applications without requiring any prior knowledge of the functional form of the relationship between dependent and independent variables. In this context, a set of three neural networks is developed, each one for the corresponding AC operation mode, namely heating, turned off and cooling. All the neural networks share the same structure; yet they are trained with their individual data sets, as they are used to model different operating conditions. Specifically, each multilayer feedforward neural network has the following characteristics: 1) Input layer with 3 nodes corresponding to the indoor and outdoor temperatures for the current decision point, and the desired temperature for the next decision point. 2) Hidden layer 1 with 5 nodes and a logarithm sigmoid transfer function. 3) Hidden layer 2 with 2 nodes and a logarithm sigmoid transfer function. 4) Output layer with 1 node and a linear transfer function.

(8)

where t is the duration of control periods and TC is the time constant of the system. Table 4 outlines the simulation results obtained by using Eq. (7) with the parameter values of Table 3. The first column shows the indoor temperature for a hypothetic present moment. The second column represents the corresponding outdoor temperature as a random number between 5 and 20  C in order to simulate the conditions where heating is needed. The third column consists of the next stage temperatures predicted by Eq. (7), while the last column includes the results of adding a random deviation of the order of ±0.2  C to the previous data. The purpose of the latter process is to approximate the simulation data with temperature measurements from an actual residence that are likely to contain errors associated with the thermometer. A similar procedure was followed to obtain the data for the cooling simulation. To train the neural networks with backpropagation as a typical example of a supervised learning algorithm, the following considerations are made:  The MSE (mean squared error) is used as performance function, resulting in values of 0.0036 for heating, 0.0006876 for cooling and 0.00038 for turned off AC.  The LevenbergeMarquardt backpropagation is used as training function.  To improve the generalization, the input data are randomly divided into three sets, for training, validation and testing respectively. Indicatively, Fig. 3 illustrates the behavior of both the neural network and the thermal model in Eq. (7) under the conditions of the green-colored outdoor temperature profile and the AC heating at full power. The analysis to obtain the 24 h profiles was based on recursively applying the neural network and Eq. (7). The results confirm the capability of the neural network to serve as a reliable thermal model under these conditions. Similar graphs, in support

Table 3 Parameter values for simulation purposes. Parameter

Value

ATC (kW/oC) TC (h)

0.1 25 (mixed insulation) 1-heating (efficiency) 3-cooling (COP) 1 0.96 max 3.8

h t (h)

ε en (kW)

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Table 4 Neural network training data for heating. Indoor temperature

Outdoor temperature

Next decision point temperature

Next decision point temperature ±0.2  C

16 16 … 25 25

16.7 6.2 … 15.9 8.7

17.9 16.6 … 25.7 24.9

17.9 16.5 … 25.9 24.9

of our proposed approach, may be obtained for the other two AC operating modes, namely cooling and turned off. Our extensive experiments show that the neural networks can sufficiently fit the residential thermal model with 9 measurements for each indoor temperature and thus hint to the fact that this also applies to the case of having available data from an actual residence. 4. Implementation This section presents the implementation of the main EB algorithm, and specifically a comprehensive analysis of the inputs required and an outline of the programming approach employed. The day under study for the purposes of this analysis is the 4th of April, 2010, while it is considered that the EB is installed at a residence in the south of Portugal, near the anemometer at the weather station of Portim~ ao. Retrieved hourly data for 04/04/2010 and 05/ 04/2010 provide input to the proposed EB implementation, given that it utilizes a 24 h time frame where future states influence the current decision. In this context, it is noted that computing the whole decision set for the present instance requires access to forecasts of the future states for the following day. 4.1. Electricity load profile Fig. 4 represents a general residential load profile that accounts for lighting and conventional household appliances present inside the house. As mentioned in subsection 2.1, this profile is typically

Fig. 4. Residential electricity load profile.

provided by the smart meter installed at the residence and is assumed to be deterministic and known in all states. 4.2. Simple forecasted states Hourly forecasted values for the future states of electricity prices, outdoor temperatures and solar radiation are assumed to be available to the EB via a remote internet service. Online weather services typically provide these forecasts for wide areas of interest at a significantly lower cost, compared to that required for a customized wind speed forecast at a given residence for energy production purposes, in which case the influence of locationspecific characteristics to the wind flow that reaches the microwind turbine must be taken into account. For more realistic results, it is also considered that these forecasts may deviate from the predicted mean by a maximum of ±5%, under the schema specified in Table 5. 4.2.1. Hourly electricity prices This study considers the current tariff for residential houses in Portugal [32] as a basis for the forecasted electricity prices, since the EB is working under a real-time tariff. For each hourly price of the 48 h time horizon, a random component within the range ±5% is introduced to the current tariff, and the result serves as the forecasted expected value of the real-time electricity price. Then, upper and lower bounds are computed and a random selection is made among all the three possibilities (forecasted mean, lower bound, upper bound), obeying the probability distribution presented in Table 5. The adjusted electricity prices having random deviations are illustrated in Fig. 5. An important aspect to address is that the principle of net metering is not employed in this work, in contrast to the approach presented in Ref. [11], even though a real-time tariff is used as well. Electricity is considered to have a 5% lower price when supplied to

Table 5 Probability of simple forecasted states.

Fig. 3. Simulation of 24 h with AC operating in heating mode.

Probability

Expected value m

Upper bound mþ5%

Lower bound m-5%

0.60

0.20

0.20

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Fig. 7. Hourly solar radiation for the 48 h time horizon (04/04/2010 and 05/04/2010).

Fig. 5. Hourly electricity prices for the 48 h time horizon (04/04/2010 and 05/04/2010).

the microgrid, compared to the buying price in every specific instance. Accordingly, this approach ensures two important issues: (i) A sustainability scenario is examined, where the entity responsible for the electricity distribution network, which in this case may be a service provided to the microgrid, buys the electricity for a price and sells it with a profit margin, allowing hence to possibly cover maintenance costs and unexpected problems of the distribution network. (ii) The EB algorithm interprets as more valuable to charge the EV with electricity generated from installed renewable sources than that provided by the microgrid, leading to a higher penetration of renewable energy at the residence under study, and thus reducing the concerns about power quality inside the microgrid.

4.2.2. Hourly outdoor temperatures Hourly forecasted temperatures were retrieved from the WRF (weather research forecast) model at 9 km resolution [33] for the days under study. The adjusted data containing deviations of the order of ±5%, according to the distribution shown in Table 5, are presented in Fig. 6. A simple examination of the figure reveals that the second day (from 25 until 48 h) is expected to be warmer than the first day, implying that lower energy consumption is expected for the AC to ensure the desirable comfort temperatures. 4.2.3. Hourly solar radiation Similarly, the hourly forecasted solar radiation was retrieved from the WRF model at 9 km resolution [33] for the days under

Fig. 6. Hourly outdoor temperatures for the 48 h time horizon (04/04/2010 and 05/04/ 2010).

study, while the same procedure is applied to generate the revised data that contain random deviations (Fig. 7). For a specific time of the year (given that the distance from the sun varies during the earth's annual orbit), the main factor that influences the daily profile of solar radiation reaching the PV panels is the atmospheric attenuation, however Fig. 7 indicates that it is similar for the two days. 4.3. Occupation patterns e EV presence The EV battery provides energy storage capacity, and its availability patterns are important for the EB algorithm to obtain the optimal decision set. Fig. 8 shows the assumed profile of the EV presence at the residence, based on the premise that it usually leaves the house at about 07:30 and returns at about 19:30. 4.4. Wind speed forecast Wind speed data for a residential location depend on a number of variables and location characteristics, such as surrounding buildings, mountains and trees, which influence the actual wind energy that can be potentially utilized by a micro-wind turbine. In this context, relying solely on regional wind data forecasts that refer to a wide area, in which the residence is located, may result, under certain circumstances, in unacceptable modeling errors. A framework for forecasting the wind speed at a residential location for micro-wind energy generation purposes is developed in Refs. [34], while preliminary wind data forecasts for the days under study obtained by applying this approach are detailed in Fig. 9. Specifically, a 3 h time window is considered for the forecasted wind speeds, which are approximated with a discrete probability function taking 8 values. Naturally, these values differ both in speed and probability depending on the forecast parameters. To exemplify this, the forecast for the two periods from 00:30 until 03:30 and from 06:30 until 09:30 on 04/04/2010 (the middle graph of the first row in Fig. 9) presents a 47% probability for wind speed at

Fig. 8. Probability of the EV presence at the residence during a day.

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Fig. 9. Wind speed forecasts at the residence location.

about 5.2 m/sec, 26% for 2.4 m/sec, 14% for 1.3 m/sec, and the remaining 13% is described by the other 5 points of the discretization. 4.5. Programming structure 4.5.1. Object oriented programming The set of EB algorithms are developed in MATLAB, while object oriented programming is employed as a means of keeping the code clear and simple. In this paradigm, the data structures are objects and their associated methods allow performing a number of essential tasks [35]. The complete list of the instances contained in

the class constructed for modeling the object EB, which is referred to as EBC (energy box class), is shown in Table 6. The object EBC represents a possible state with the available set of decisions in a specific moment in time. Among the developed methods for the object EBC, the following ones are highlighted:  Update forecasts: This method updates some instances with the information corresponding to the specific time (given in Table 6(i)). As an example, the instance wind speed is a vector with 16 values, 8 representing the wind speed itself and 8 representing their associated probabilities, as shown in Fig. 9.

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Table 6 Instances of the object EB. Instances (i). Time (v). Other loads (ix). EV presence

(ii). Indoor temperature (vi). Grid price

(iii). EV battery level (vii). Wind speed

(iv). EV battery energy (viii). Radiation

(x). Outdoor temperature

(xi). Decision for the AC

(xii). Decision for the battery

 Obtain power of the AC: This method uses the residential thermal model (refer to subsection 3.3) to obtain the necessary power for the AC if the target decision given by instance (xi) can be fulfilled according to Eq. (3).  Obtain battery power: This method is based on Eq. (4) to obtain the battery power supplied or drained from the residence.  Obtain wind and solar power: These methods return the forecasted power output from the micro-wind turbine and the solar panel, using the information stored in the instances and Eqs. (1) and (2) respectively.  Compute the expected value: This method calls all the previous methods in order to compute the expected value of the considered object EB by recurring to the AOF function given in Eq. (5).

Fig. 10. Obtaining the optimal decision set.

ds23 ¼

max

lcost ucost 23 þTR 4.5.2. Decision set For a given moment in time and a specific combination of the individual decisions for the AC and the EV battery (shown in Table 6(xi) and (xii)), the decision set ds consists of all possible combinations between the basic states (shown in Table 6(ii) and (iii)). It is derived that the decision set contains 120 objects, corresponding to 15 indoor temperature states and 8 accessible battery states. 4.5.3. Obtaining the best set of decisions for an instance within the forecasting time window As already pointed out, the problem under study is considered to have an optimal substructure, which is composed of the best set of decisions (with the highest expected value), as represented in Eq. (5), for any given moment within the 24 h forecasting time window. The first decision set to be computed corresponds to the 24th hour, referred to as ds24 , and it is obtained from Eq. (9) by summing the expected values of all the states for each decision set, and then choosing the one with the higher expected value.

ds24 ¼

max

EVBattery Temp ;d24 d24

 lcost ucost 24

i



E

Microgrid Home ;s24 ;s24 sWeather 24

h lcomfort ucomfort 24

(9) The optimal decision set ds24 is then applied to all possible combinations between the basic states sHome:Temp and 24 sHome:EVBatteryLevel , and the values are stored in the transition matrix 24 T24 . The whole process is illustrated in Fig. 10. The next decision set to be computed corresponds to the preceding moment in time, which in this case is the 23rd. The process continues in a similar fashion, with the difference being on the computation of the expected value for a given combination of decisions and basic states. Specifically, the computation involves the summation of the expected value of the basic state resulting from that combination of decisions and basic states multiplied by the probability of that to be the proceeding state. This is summarized in Eq. (10).



;dTemp dEVBattery 23 23

i

h E comfort comfort u23 Microgrid Home l ;s ;s sWeather 23 23 23

(10) where

TR ¼

6 X

  T24 temperaturel ; battery_levell *probabilityl :

(11)

l¼1

It follows from Eq. (11) that, for each combination of decisions and basic states, there are 6 possibilities of resulting states, each one with its individual probability. This is due to the fact that when stage to stage transition rules of a current hypothetical state are applied (see Eqs. (3) and (4)), the output is stochastic because of its dependence on the outdoor temperature forecast and the availability of the EV at the residence, which are both stochastic parameters. The whole process is repeated until the decision set for the first moment in time is computed. 5. The EB acting as an ancillary service This section discusses the capability of the EB to work as an ancillary service in order to place bids on the Portuguese tertiary market. The technique developed by the authors is clearly a first approach to verify the applicability of the EB to compete on this market, and as such it might be considered conservative and, in some aspects, not conforming to the current regulation of the Portuguese market. The methodology to obtain the bids at a specific moment utilizes the information stored in the matrix T24 (Fig. 10), which is generated during the computation of the best decision set for this instance in time. This matrix stores the expected value of the AOF, shown in Eq. (5), for all possible states and therefore an estimation of the monetary incentive needed to shift the EB decision to another state becomes readily available. In this context, the EB can provide bids for changing the state of the battery from charging to discharging and vice-versa, as well as from shifting to all possible temperatures reached by the AC. It is important to report some combinations of particular interest, between these two subsets of decisions, given the specific prices and power negotiated. For instance, the scenario of

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C.S. Ioakimidis et al. / Energy xxx (2014) 1e15

supplying maximum power to the microgrid would involve discharging the battery and setting the AC temperature to the minimum possible level. Under an ideal discretization with a sufficiently large number of points, this would literally mean to turn off the AC, while in our implementation we consider increments of 0.5  C for the temperature states and turning off the AC would fall in a non accessible state. Similarly, depending on the particular conditions of a given moment, some decisions may not be accessible and no bid to supply power to the microgrid can be placed by the EB. This is the case, for example, of discharging the EV battery when its level is below the threshold set by the EV owner and the EB has already set the decision for the AC to minimum power consumption. The bids in the tertiary market are placed as contracted power for a specified period of time, as long as it is a multiple of the time between stages (60 min), and the winning bid determines the agreement price for ensuring the power reserve and an extra for the active energy actually used (as a simplification this is considered here to be the real-time price during the contracted period). It is of methodological interest to note that a bid placed by the EB is calculated based on the hypothesis that the EB would be supplying the entire amount of energy corresponding to the contracted power over the agreed period. Since this is a rather unlikely situation, the EB could yield benefits by utilizing this amount of energy for other tasks until active energy is actually demanded. Under these conditions, this approach may be considered conservative, while a more sophisticated model that has as input the probability distribution of the actual active energy demanded would lead to more competitive bids in the tertiary market. It is also noted that individual bids from multiple EBs integrated in a microgrid can be sent to the aggregator (Fig. 1) that will afterwards submit its own bids in the national market, as a market agent (refer to subsection 6.4). 6. Applications and results 6.1. Preliminary considerations A set of hypothetical scenarios is introduced in order to identify the interrelations between the input variables and the EB decisions, as well as to examine the feasibility of the EB to compete as an ancillary service. In an intuitive approach to the energy management of a residence having the characteristics presented in subsection 2.1, the following indicative outline of interactions would be expected in an arbitrary moment in time:

6.2. Simulation scenarios for residential energy management This subsection presents a number of alternative scenarios in order to assess the performance of the EB. To this end, different sets of parameters are employed to simulate distinct cases in terms of EV availability and electricity generation. To account for different EV availability profiles, the following subjects are considered:  Subject 0 represents the case that there is no EV present at the residence.  Subject 1 leaves the residence at 07:00, it travels during the day a distance of about 50 km that equals a consumption of about 6 kWh, and it returns back at 18:00 (the vehicle under consideration has a power consumption of nearly 125 Wh/km [17]). The emergency energy reserve for the EV required by subject 1 corresponds to the level 3 of the battery (8.56 kWh) that allows covering a distance of about 70 km.  Subject 2 leaves the residence one hour later (at 08:00), traveling a shorter route to the workplace and returning at 18:00. The distance covered during the day is approximately 16.5 km that corresponds to a consumption of about 2 kWh, while the emergency energy reserve for the EV required by subject 2 corresponds to the level 2 of the battery (4.33 kWh) that allows covering a distance of about 35 km.  Subject 3 represents the case that the EV remains at the residence for the whole day, having the same emergency energy reserve for the EV with subject 2. For the power output of the micro-renewables installed at the residence, the following distinct cases are examined:  In the low power case, the installed micro-renewables consist of a wind turbine with a rotor diameter of 3.5 m and a solar panel with a rating of 0.5 kW, while their power outputs are given in Eqs. (1) and (2). Fig. 11 shows the actual wind speed profile that corresponds to the forecast presented in Fig. 9.  In the medium power case, it is assumed that a wind turbine with a rotor diameter of 5 m and a PV panel rated at 1 kW are installed at the residence. To highlight the effect of the microrenewables, a wind speed profile with higher values is also considered, as shown in Fig. 12.

 High electricity prices cause the EB to save energy by scheduling controllable loads wisely.  High micro-renewables generation induce the EB to use energy.  In cases of micro-renewables producing more power than the residence needs (e.g. a small difference between outdoor and indoor temperature requires considerably less energy to ensure the conditions of comfort), the EB is expected to save energy and make use of it at a later period, in which the demand exceeds micro-renewable generation. This is due to the fact that the EB considers the energy produced by the micro-renewable sources to be more efficiently used within the residence, given that the buying price from the microgrid is higher than the selling price at each moment in time. Other inputs, such as the probability of EV presence at the residence, have a significant impact on the decisions to buy or sell electricity, however, no direct relationship can be determined in advance as a general rule, because of the complexity of the decision making process and its dependence on the conditions that prevail at each moment in time.

Fig. 11. Actual wind speeds for the day under study, 4th April 2010.

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C.S. Ioakimidis et al. / Energy xxx (2014) 1e15

11

Table 7 Main characteristics of scenarios.

Fig. 12. Hypothetical wind speeds.

In this frame of this work, the following scenarios are simulated and analyzed considering that the EB is responsible for the energy management of the residence:  Scenario I: subject 1 with low power output from renewables,  Scenario II: subject 2 with low power output from renewables,  Scenario III: subject 2 with medium power output from renewables, and  Scenario IV: subject 3 with medium power output from renewables.

micromicromicromicro-

To establish a common basis for comparing the impact of the EB on the residential energy management, two baseline scenarios are also employed, namely A and B, representing low and medium power output from micro-renewables respectively. Both baseline scenarios consider that the EB is deactivated under the following assumptions:  The AC temperature is set to 20  C during the 24 h of the simulation period to attain a 100% comfort level in the residence.  The price of the electricity bought from the grid is 5% higher than the selling price in every time instant, to consider a sustainable scenario. The base prices forecasted for the 48 h time window are shown in Fig. 5.  No storage capacity from the EV is available (subject 0), implying that the electricity produced by the micro-renewable sources is consumed in the residence and the exceeding electricity (if any) is sold to the grid with the corresponding market price.  The washing machine starts its operation at the first possible moment (at 01:00). Table 7 summarizes the main characteristics of the whole set of scenarios considered in this work. 6.3. Simulation results of the EB for residential energy management Fig. 13 details the evolution of temperature in the residence and the EV battery level for the scenario I. The results indicate that there is a tendency of the EB to use the maximum amount of energy possible before 10:00 and save energy after 19:00. This means that

Scenarios

EB operation

EV availability profile

Power output from micro-renewables

Baseline scenario A Scenario I Scenario II Baseline scenario B Scenario III Scenario IV

deactivated activated activated deactivated activated activated

subject subject subject subject subject subject

low low low medium medium medium

0 1 2 0 2 3

the EB realizes that the best scenario is to store energy both in the form of heat (e.g. in the walls and furniture of the house) and electrical energy in the battery that is later utilized. In theory, all EB inputs should influence its proposed decisions, however in this case the micro-renewables production influences to a lesser extent the decisions made by the EB. This is primarily due to the low microrenewables production, and therefore having or not the forecasting has a reduced impact on the solutions. This hints to the effect that, in cases with very low micro-renewables production, the EB could potentially rely less on the forecasts and use the savings in computational resources to increase the discretization points in order to have a better control on setting the AC temperature. The cost value obtained by the EB for the scenario I is equal to 4.569 V/d. It is important to note though two key factors: (i) the EV left the residence with battery level 8 and returned with level 4, and (ii) an amount of energy is stored as heat remaining in the residence (there is an indoor temperature difference between states 1 and 24). To allow direct comparisons with the other scenarios, all the (thermal and electric) energy stored is evaluated at the lowest value verified in the 24 h period. In this case, the corrected cost of the day under study becomes 4.277 V. The EB solution for the scenario II yields the evolution of temperature in the residence and the EV battery level shown in Fig. 14, while Fig. 15 depicts the corresponding diagrams for the scenario III. The similarity on the results between these scenarios may be attributed to the fact that the micro-renewables power output is for the most of the time lower than the consumption in the residence, with the exception of two periods that both occur when the EV is not present, and therefore the impact of selling the electricity at a 5% lower price has a low impact to the optimal decisions. The corresponding cost obtained by the EB for the scenarios II and III amounts to 3.847 V/d and 2.227 V/d, while taking into account the total energy stored the corrected costs become 3.731 V/d and 2.111 V/d respectively. Similarly, the outcome of the EB solution in terms of temperature in the residence and EV battery level evolution over time for the scenario IV is illustrated in the diagrams of Fig. 16, while the expected daily cost amounts to 1.969 V/d. Table 8 compares the results of scenarios IeIV over those obtained from the baseline line scenarios A and B in terms of comfort level and renewable energy penetration in the residence, as well as cost of the day under study. A close examination of the results reveals the following:  A comfort level of 100% is attained in all scenarios.  The low power output of the micro-renewable sources installed at the residence results in a 100% penetration of renewable energy (scenarios I and II).  The medium power output of the micro-renewable sources installed at the residence results in cases with renewable energy penetration lower than 100% (scenarios III and IV), where the generation is larger than the consumption and the excess electricity is sold to the grid at the market price.

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C.S. Ioakimidis et al. / Energy xxx (2014) 1e15

Fig. 13. Indoor temperatures and EV battery levels for scenario I.

Fig. 14. Indoor temperatures and EV battery levels for scenario II.

Fig. 15. Indoor temperatures and EV battery levels for scenario III.

Fig. 16. Indoor temperatures and EV battery levels for scenario IV.

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C.S. Ioakimidis et al. / Energy xxx (2014) 1e15 Table 8 Comparison of scenario results. Scenarios

Comfort level

Renewable energy penetration in the residence

Cost of the day under study

Baseline scenario A Scenario I Scenario II Baseline scenario B

100% 100% 100% 100%

4.598V 4.277V 3.731V 2.972V

Scenario III

100%

Scenario IV

100%

100% 100% 100% 100% except for: 72% at 14:00 69% at 15:00 100% except for: 81% at 14:00 76% at 15:00 100% except for: 21% at 14:00

2.111V

1.969V

 Scenario I represents the “worst case” for the EB as a result of the conjunction of subject 1 with low power output from microrenewables, but even under these unfavorable conditions, the potential cost savings amount to about 0.321 V/d (or equivalently 7% cost decrease) compared to the baseline scenario A.  The cost savings in scenario II amount to 0.867 V/d in absolute terms, corresponding to a percentage cost reduction of approximately 19%, with respect to the baseline scenario A.  The estimated cost of the day under study in scenario III is reduced by 0.861 V, leading to approximately 29% cost savings, compared to baseline scenario B.  The anticipated cost savings in scenario IV are 1.003 V/d, or equivalently the potential percentage cost reduction is roughly equal to 34%, with respect to baseline scenario B. As expected, the higher power output of the renewable sources, combined with the longer availability of the EV as electricity storage, yields the most favorable results in all scenarios considered. The simulation experiments were performed on a PC (personal computer) with Intel Core Duo processor T2300 and 1 GB of RAM, which is a representative example of a home computer with low computational power. The execution time of a single simulation run was approximately 10 h, a fact that at present hinders the online execution of the EB algorithms on a low-cost computer, considering that the EB updates the information and forecasts for the current and future states after the time interval between two successive stages passes, i.e. 1 h for the present application, in order to make the optimal energy management decisions for the subsequent 24 h. However, with computer performance roughly doubling every 1.5 years, it is reasonable to anticipate that in the not-so-distant future even typical home PCs will have the capacity to execute the EB algorithms in less than 1 h so that the energy management decisions are updated online. 6.4. EB competing in the tertiary market To evaluate the capability of the EB to act in the tertiary market of Portugal, this section demonstrates two examples of bids for the scenario IV, namely at 02:00 and 15:00 on the 4th of April, 2010 [36]. More specifically, the following analysis examines the possibility of supplying power to the main grid and placing bids based on the methodology presented in detail in section 5. 6.4.1. EB bid for 02:00 For the day under study at 02:00, Fig. 16 shows that the indoor temperature is 21  C and the battery level equal to 3, while the optimal decision set calculated by the EB consists of charging the EV battery and setting the AC temperature to 20.5  C. The only

13

alternative option in this instance is to discharge the battery, since the AC is already in the minimum power consumption. The expected values of the AOF, given in Eq. (5), for the decisions to charge or discharge the EV battery are 1.9897 and 1.9845 respectively. The process of placing a bid requires the determination of the monetary incentive needed to cover the difference between these two expected values that correspond to the aforementioned situations. The bid obtained for this case is detailed in Table 9. In a microgird composed of several EBs, each one with its individual bid, the decision for which bids to accept is a function of the aggregator. The aggregator conducting its own business negotiates with the national grid an amount of power to ensure for the tertiary reserves. In the case of 1000 EBs with the same bid integrated in a microgrid, the aggregator could place its own bid in the national market for 7.80 MW at a price of 0.669 þ (aggregator's margin) in V/MW. Table 10 presents the top 18 competitors at 02:00 in the Portuguese tertiary market for the day under study, retrieved from Ref. [37]. A comparison of the results in Tables 9 and 10 reveals that the EB concept can be a very competitive option to supply power to the tertiary market, as in this case it would rank first by a wide margin. 6.4.2. EB bid for 15:00 For the day under study at 15:00, the indoor temperature is 22.5  C and the battery level equal to 6, as shown in Fig. 16. Accordingly, the optimal decision set calculated by the EB is to charge the EV battery and set the AC temperature to 22.5  C. This case exemplifies the fact that the EB can post multiple bids of varying power and cost by setting the AC temperature to 22 or 21.5  C and discharging the battery. The resulting combinations are analyzed in Table 11. The results shown in Table 11 indicate that reducing the temperature in the residence to 21.5  C would significantly increase the cost of the final bid providing only a slight increase in the available power. This is due to the fact that reducing the indoor temperature to this extent at 15:00 would prove more costly later, because of the considerable power need to maintain the comfort conditions when the electricity prices are at or near their peak. The best bid involves “supplying” less power to the grid by discharging only the EV battery, while keeping the AC temperature unchanged. This bid, however, is still substantially higher compared to the bid calculated for 02:00. Table 12 shows the top 18 competitors at 15:00 in the Portuguese tertiary market, retrieved from Ref. [37]. On the basis of the real data for the day under study, it is evident that the bids differ considerably, while in this case the EB bid is of reduced competitiveness and it would rank in the 12th place. 7. Discussion To demonstrate the feasibility of the proposed approach, conservative assumptions are consistently employed throughout our analysis. To highlight its potential, the following main points that limit the benefits and the significance of the results obtained are posteriorly identified:  The present analysis and experimentation with the proposed model considers a residence having interior insulation and

Table 9 EB possible bid for Portuguese tertiary market at 02:00 on 04/04/2010. Time

Power (kW)

Monetary incentive

Bid (V/MW)

02:00

7.80

0.0052

0.669

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C.S. Ioakimidis et al. / Energy xxx (2014) 1e15

Table 10 Real bids in Portuguese tertiary market at 02:00 on 04/04/2010. Up

Down

Agent

MW

Price (V/MW)


Area Bal.

MW

Price (V/MW)

ACAVADO ALIMA ATEJZEZ ALIMA ACAVADO ATEJZEZ ACAVADO ATEJZEZ ADOUNAC ACAVADO AGUADIA ATEJZEZ ACAVADO ATEJZEZ ATEJZEZ ARTG ARIBAT1 ARTG

80 160 50 135 80 50 80 50 8 80 70.4 50 10 50 33 560 100 466

21.9 22.8 22.9 24.8 25.4 26.4 26.9 27.9 28.0 29.4 29.9 30.4 31.9 32.9 49.9 50.0 59.5 60.0

ACAVADO ATEJZEZ ACAVADB ACAVADO ADOUINT ADOUNAC ALIMA AMONDEB ATEJZEZ

30 17 65 154 850 659 145 276 200

3.10 1.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Table 11 EB possible bids for the Portuguese tertiary market at 15:00 on 04/04/2010. Decisions forAC/EV battery

Total power c ompared to baseline (kW)

Expected value from AOF of the proceeding state

Monetary incentive

Bid (V/MW)

Baseline: 22.5  C/charge 22  C/discharge 21.5  C/discharge 22.5  C/discharge

n.a

3.1205

n.a

n.a

2.97 3.03 2.61

2.9325 2.8763 3.0061

0.189 0.245 0.114

63.55 80.88 43.83

therefore a low thermal constant. In this case, there is a limitation on the possibility of storing energy inside the residence in the form of heat. This is due to the fact that if the EB decides to store energy of this form to benefit from a lower electricity price for the present moment, in the posterior decision point the percentage of energy lost is often high enough to dissipate the potential gain from the price difference. Therefore, in this situation, the EB optimizes only the EV battery decisions, while the AC decision is simply the one with the highest value of the objective function for the moment under consideration,

Table 12 Real bids in Portuguese tertiary market at 15:00 on 04/04/2010. Up

Down

Agent

MW

Price (V/MW)


Area bal.

MW

Price (V/MW)

ACAVADO ALIMA ATEJZEZ ACAVADO ATEJZEZ ACAVADO ATEJZEZ ACAVADO AGUADIA AGUADIA AGUADIA ARTG ARIBAT1 ARTG ARIBAT2 ARIBAT1 ARIBAT2 ARIBAT1

67.4 146.1 50.0 80.0 50.0 80.0 43.0 78.0 80.0 80.0 70.0 560.0 100.0 442.0 50.0 100.0 50.0 100.0

21.90 22.80 22.90 25.40 26.40 26.90 27.90 29.40 29.90 33.00 35.80 50.00 59.50 60.00 60.48 60.50 60.58 60.70

ACAVADB ACAVADO ADOUINT ADOUNAC AGUADIB ALIMA AMONDEB AMONDEG ATEJZEZ

142.0 199.6 760.0 592.0 240.0 183.9 276.0 20.0 357.0

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

meaning that future states do not influence the current decision (the AC temperature was equal to 20  C for all the time instances of the day under study).  The remaining energy stored in the form of heat inside the residence and the electricity consumed by the EV to travel was evaluated at the lowest registered price in order to estimate the minimum level of the potential gain.  For simplicity reasons, it is assumed that the charge and discharge rates of the EV battery are the same. In a real-world situation additional benefits can occur, as the EV battery can discharge faster and thus provide its stored energy at a considerably higher rate compared to that of charging.  Under the dynamic pricing tariff, the price for selling electricity to the main grid was considered 5% lower than the buying (market) price. This assumption however is conservative only for today's scenarios, in which the electricity produced from micro-renewables is currently subsidized. Implementing the proposed approach as a residential energy management tool integrated in a SmartGrid, raises some non negligible concerns regarding the smooth operation of the local grid:  The possibility of several EBs in the microgrid becoming inadvertently synchronized might result in undesirable oscillations of the local network frequency and voltage. To prevent the occurrence of such phenomena, a method to slightly desynchronize the real-time pricing affecting the EBs needs to be developed; yet this is clearly beyond the scope of the present work.  Having an automated system managing the microgrid through a communication network can potentially lead to complications and problems regarding the security of the electrical grid. Control mechanisms that effectively address unauthorized access to the IT components of the system have to be considered and solutions to guarantee its security need to be developed in future works.

8. Conclusions This paper presents the implementation of the EB in the context of the V2G technology along with analytical examples of its application in residential energy management both as a standalone tool and integrated in a SmartGrid. The automated control of the electricity management decisions in this paradigm is proved to have several advantages for the residence occupant and the grid. As a general rule, the EV presence at the residence has a considerable effect on the revenue associated with the charging and discharging operations of the EV battery. Reducing the daily distance traveled, which affects the battery state of charge, and/or increasing the time that the EV serves as a local energy storage results in significant cost savings on the electricity bill. In this context, notable differences may be observed between weekend days and working days, as the availability of the electricity storage capacity during all the 24 h of a day maximizes the opportunities of revenue due to the dynamic pricing. Moreover, in the examples presented, the decisions regarding the AC temperature setting and the EV battery level seem to have a low influence on each other. This is attributed to the fact that, in the case considered, the electricity generation rarely surpasses the consumption and their interactions are only observed when combinations between them can change the regime from buy/sell electricity. Additionally, the experimentation with the DSP model reveals that there is a limit of how low micro-renewable electricity

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C.S. Ioakimidis et al. / Energy xxx (2014) 1e15

production can be until the effect of having forecasts on the weather conditions might be considered negligible in order to allocate the computational resources for increasing the number of discretization points. Above that threshold, having the forecasts available increases the quality of EB decisions. This phenomenon occurs because the EB perceives the electricity obtained from the micro-renewable sources as the most economical option and if in any given period the electricity generation at the residence exceeds the consumption, the EB is induced to store the energy produced by the micro-renewables in order to allocate it at later periods, in which buying more expensive energy would be required. The results obtained indicate that the proposed model can be a feasible option for the EB to compete as an ancillary service through the intermediation of the aggregator managing the microgrid, for some moments of the day. This analysis is not rigorous enough to be able to sustain this affirmation by presenting a particular and precise bid containing all the costs associated with the aggregator. However, it is clearly a first step to verify the applicability of the EB on this field and, from a quantitative point of view, it seems to have a margin to sustain business with benefits for both the microgrid and residence occupants. As a concluding remark, it is noted that the decisions made by the proposed model are not the optimum solutions from a strictly mathematical viewpoint, but they are the best feasible solutions among all those that the EB is capable of simulating. The primary reason for this is the inevitable use of a finite number of discretization points to describe the possible states in the model, as well as the stochastic nature of the problem at hand.

References [1] Bayod-Rújula AA. Future development of the electricity systems with distributed generation. Energy 2009;34(3):377e83.
J. [2] Pouresmaeil E, Montesinos-Miracle D, Gomis-Bellmunt O, Bergas-Jane A multi-objective control strategy for grid connection of DG (distributed generation) resources. Energy 2010;35(12):5022e30. [3] Sanseverino ER, Di Silvestre ML, Ippolito MG, De Paola A, Lo Re G. An execution, monitoring and replanning approach for optimal energy management in microgrids. Energy 2011;36(5):3429e36. [4] Mehleri ED, Sarimveis H, Markatos NC, Papageorgiou LG. A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level. Energy 2012;44(1):96e104. [5] Doagou-Mojarrad H, Gharehpetian GB, Rastegar H, Olamaei J. Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy 2013;54:129e38. [6] Kriett PO, Salani M. Optimal control of a residential microgrid. Energy 2012;42(1):321e30. [7] Blumsack S, Fernandez A. Ready or not, here comes the smart grid! Energy 2012;37(1):61e8. [8] Hawkes AD, Leach MA. Modelling high level system design and unit commitment for a microgrid. Appl Energy 2009;86(7e8):1253e65. [9] Chen YH, Lu SY, Chang YR, Lee TT, Hu MC. Economic analysis and optimal energy management models for microgrid systems: a case study in Taiwan. Appl Energy 2013;103:145e54. [10] Obara S, Kawai M, Kawae O, Morizane Y. Operational planning of an independent microgrid containing tidal power generators, SOFCs, and photovoltaics. Appl Energy 2013;102:1343e57.

15

[11] Livengood D, Larson R. The energy box: locally automated optimal control of residential electricity usage. Serv Sci 2009;1(1):1e16. [12] Tomi
c J, Kempton W. Using fleets of electric-drive vehicles for grid support. J Power Sources 2007;168(2):459e68. [13] Turton H, Moura F. Vehicle-to-grid systems for sustainable development: an integrated energy analysis. Technol Forecast Soc Change 2008;75(8): 1091e108. € ransson L, Karlsson S, Johnsson F, [14] Andersson SL, Elofsson AK, Galus MD, Go et al. Plug-in hybrid electric vehicles as regulating power providers: case studies of Sweden and Germany. Energy Policy 2010;38(6):2751e62. [15] Clarke S. Electricity generation using small wind turbines at your home or farm. Ontario: Ministry of Agriculture, Food and Rural Affairs; 2003. [16] Mitsubishi Motors press releases. Mitsubishi Motors builds new research EV, “i MiEV” for joint research with power companies. Available from: http:// www.mitsubishi-motors.com/en/corporate/pressrelease/corporate/ detail1533.html. [17] Green Car Congress. Mitsubishi motors begins production of i-MiEV; targeting 1,400 units in fiscal; 2009. Available from: http://www.greencarcongress. com/2009/06/imiev-20090605.html. [18] US Federal Energy Regulatory Commission. 2008 Assessment of demand response and advanced metering; 2008. Staff Report. [19] Kempton W, Tomi
c J. Vehicle-to-grid power implementation: from stabilizing the grid to supporting large-scale renewable energy. J Power Sources 2005;144(1):280e94. [20] Hammerstrom DJ. Pacific northwest gridwise testbed demonstration projects. Springfield, VA: Pacific Northwest National Laboratory; 2007. [21] Black JW, Larson RC. Strategies to overcome network congestion in infrastructure systems. J Ind Syst Eng 2007;1(2):97e115. [22] Kempton W, Tomi
c J, Letendre S, Brooks A, Lipman T. Vehicle-to-grid power: battery, hybrid, and fuel cell vehicles as resources for distributed electric power in California. Prepared for California Air Resources Board and the California Environmental Protection Agency, and Los Angeles Department of Water and Power, Electric Transportation Program; 2001. [23] Kempton W, Letendre SE. Electric vehicles as a new power source for electric utilities. Transp Res Part D: Transp Environ 1997;2(3):157e75.
ticas Nacionais (REN). Fluxos de Informaç~ [24] Redes Energe ao entre os Agentes de Mercado e a REN. REN; 2010. [25] Manual de Procedimentos do Gestor do Sistema; Dezembro 2008. Available from: http://www.mercado.ren.pt/DocReg/BibSubregula/MPGS.pdf. [26] Brooks AN. Vehicle-to-grid demonstration project: grid regulation ancillary service with a battery electric vehicle. Prepared for the California Air Resources Board and the California Environmental Protection Agency; December 2002. [27] Cormen TH, Leiserson CE, Rivest RL, Stein C. Introduction to algorithms. 2nd ed. The MIT Press and McGraw-Hill Book Company; 2001. [28] Kleywegt AJ, Shapiro A. Stochastic optimization. In: Salvendy G, editor. Handbook of industrial engineering. New York: John Wiley; 2001. p. 2625e50. [29] American Wind Energy Association. Available from: http://www.awea.org. [30] Solar estimate. Available from: http://www.solar-estimate.org/. [31] Constantopoulos P, Schweppe FC, Larson RC. Estia: a real-time consumer control scheme for space conditioning usage under spot electricity pricing. Comput Oper Res 1991;18(8):751e65.
ticos (ERSE). Available from: http:// [32] Entidade Reguladora dos Serviçoes Energe www.erse.pt. [33] WindGURU: Weather forecasts for windsurfing, kitesurfing and other wind related sports. Available from: http://www.windguru.cz/int/. [34] Ioakimidis CS, Oliveira LJ, Genikomsakis KN. Wind power forecasting in a residential location as part of the energy box management decision tool, IEEE Trans Ind Informat, [in press]. [35] Berntson C. Introduction to object oriented programming. 3M Health Information Systems; 2004. [36] Ioakimidis CS, Oliveira LJ. Use of the energy box acting as an ancillary service. In: Proc of the 2011 8th International Conference on the European Energy Market (EEM 11), Zagreb; 2011. p. 574e9.
ticas Nacionais (REN). Sistema de Informaç~ [37] Redes Energe ao de Mercados de
ctrica. Available from: http://www.mercado.ren.pt/Paginas/ Energia Ele default.aspx.

Please cite this article in press as: Ioakimidis CS, et al., Design, architecture and implementation of a residential energy box management tool in a SmartGrid, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.07.068