Energy and Buildings 128 (2016) 713–722

Contents lists available at ScienceDirect

Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild

Cooperative optimization of building energy systems in an economic model predictive control framework Andrea Staino, Himanshu Nagpal, Biswajit Basu ∗ Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, Ireland

a r t i c l e

i n f o

Article history: Received 30 November 2015 Received in revised form 17 June 2016 Accepted 5 July 2016 Available online 16 July 2016 Keywords: Cooperative optimization Control systems Building energy management systems Heat-pumps Economic model predictive control

a b s t r a c t A concept of ‘cooperative’ optimization of building energy systems is proposed in this paper. A cooperative optimization framework for a group of buildings connected to heat pumps in the context of economic model predictive control is formulated. Two optimization scenarios have been considered for analysis – a ‘selﬁsh’ optimization of an individual building and a cooperative optimization of a group of buildings. The impact of cooperative optimization on the energy usage patterns and cost of electricity for operating the heat pumps have been investigated. The proposed cooperative optimization approach is able to achieve up to 15% reduction in energy demand cost in comparison with selﬁsh optimization. The beneﬁts arising out of the cooperative optimization concept may be a major drive for the future smart cities and will play a signiﬁcant role in advancing the concept of smart energy systems. © 2016 Elsevier B.V. All rights reserved.

1. Introduction A signiﬁcant amount of research in the recent years has been devoted towards performance of building energy systems and their optimization and control. Most of the results reported in the literature are mainly focused on individual buildings, though recently groups of buildings and cities have been also considered [1,2]. In this context, further research about energy performance of a cluster of buildings is therefore needed. Traditional controllers which are most widely used in buildings tend to be compensated and use current outdoor temperature, i.e. they are feedforward in nature [3]. These controllers may not lead to optimal energy management in buildings. In recent years, there has been signiﬁcant development on control of heating, ventilation and air conditioning (HVAC) systems [4–7]. Model predictive control (MPC) in particular has gained remarkable momentum due to its suitability for control of slow dynamics systems. In the MPC framework, a numerical model of the process to be controlled is employed to predict the behaviour of the system over a certain future horizon. The predictions are used to formulate an optimization problem that aims at minimizing a prescribed objective function, typically quantifying the performance of the system. In this context, economic model predictive control (E-MPC) denotes a class of MPC strategies for which the objective function contains also economic

∗ Corresponding author. Tel.: +353 1896 2389. E-mail address: basub@tcd.ie (B. Basu). http://dx.doi.org/10.1016/j.enbuild.2016.07.009 0378-7788/© 2016 Elsevier B.V. All rights reserved.

criteria. The application of MPC in building energy control has been investigated for climate control [8–10] and appliance scheduling applications [11–14]. In [15] a randomized MPC approach based on weather and occupancy predictions is proposed to regulate comfort levels in buildings and to minimize the buildings energy consumption. Recently, a review on optimal control systems applied to energy management in smart buildings has been given by Shaikh et al. [16]. In spite of the requirement of a model and additional computation burden, the work by Privara et al. [17] is an evidence of one of the early implementation for building heating systems. The major advantage of predictive control algorithms is that the energy controllers can adjust the control input in advance of future requirements based on prediction. In this way, the effects of slow thermal dynamic response of building systems can be counteracted by the predictive control algorithm. Lee et al. [18] and Cho et al. [19] studied control methods for ﬂoor heating in Korea. They concluded from their studies that predictive control methods performed better than on/off controllers with regard to energy consumption. Ma et al. [20] and Vieira et al. [21] shows in their studies that predictive control performs better than the proportional control in context of energy and cost saving. Chen [22,23] and Pyeongchan [24] have applied model base predictive control techniques in ﬂoor heating applications. Different forms of objective functions have been explored by them leading to different formulations, such as based on minimizing the operating costs or accumulated heat supply ﬂux with indoor temperature target interval band. This paper proposes a formulation for cooperative optimization of a cluster of building energy system connected with heat pumps

714

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

in the framework of an E-MPC problem [9,25]. E-MPC is particularly suitable for the implementation of the demand response approach [9,26]. The optimization problem formulated in this paper leads to the concept of a cooperative optimization. The results from the cooperative optimization scenario are compared with those from a selﬁsh optimization of a single building using E-MPC. The impact of the proposed controller on energy usage pattern, load shifting trends and total cost of energy consumption is also investigated.

describing the underlying physics) with building energy performance data leads to a grey-box system identiﬁcation approach [29]. The use of grey-box models for modelling of heat dynamics of buildings has been investigated in the literature in [30]. For the purpose of illustration of the concept proposed in the present paper, the approach described in [25,30] has been adopted to model the thermal dynamics of a building subjected to multiple heat inputs, including ﬂoor heating and solar radiation. The modelling approach is brieﬂy outlined in Section 3.

2. Review on building climate controllers Before discussing E-MPC formulation some of the current building energy control strategies are discussed ﬁrst. 2.1. Current building energy control strategies Some of major and commonly used building HVAC control strategies are: (i) on-off temperature control, (ii) weather compensated control and (iii) PID control [7,17]. 2.1.1. On-Off room temperature control This is the simplest type of control strategy. The heating/cooling system in a room is turned on or off according to the deviation from a set point, i.e. based on an error threshold. This is implemented on a hysteresis curve. The main drawback of this controller is that it does not take into account the dynamics of the building. However, it is simple to implement. 2.1.2. Weather compensated control This is a form of feedforward control. The temperature of the heating element/machine in the room is set according to the outdoor temperature by means of a heating curve. This curve is a function of the difference between the indoor and the outdoor temperature. This controller also does not account for the dynamics of the building. In spite of this drawback, it is a robust controller and tuning is easy. 2.1.3. PID control This is one of the most popular industrial control strategies [27,28]. This is a feedback control strategy and can take into account some information about the system dynamics and heating/cooling temperature is determined according to the room temperature set-point error. PID controllers are widely used in industrial applications but they do not account for outdoor temperature explicitly in the design of the control law. Further, in the case of multiple input multiple output (MIMO) systems the tuning of multivariable PID controllers is still a challenging task in practice. Hence, the use of such controllers are limited in controlling HVAC systems for buildings. 2.2. Model identiﬁcation In comparison with the model free approach, model-based control offers the advantage of providing useful design indications through simulations at a design stage. The model-based approach is also well-suited for the application of optimal control strategies in which the control algorithm is re-cast as an optimization problem (e.g. MPC), with the possibility of modelling physical constraints. The performance of a model-based controller is, however, heavily dependent on the accuracy of the model, i.e. on how good the model is in predicting the behaviour of the real system. Thanks to the advances in sensing and metering technologies, data-driven characterization of the heat dynamics of buildings has gained particular attention in recent years due to the increased availability of detailed data. The integration of knowledge of the physical characteristics of the building (in terms of equations

3. Building energy system with heat pump and economic-MPC For the purpose of formulating the problem a simpliﬁed model for thermal dynamics in a building including heat pump is developed following [25]. The building considered is connected to a ground-source heat pump through ﬂoor heating pipes and it is subjected to solar radiation and outdoor ambient temperature. As the dynamics of a heat pump is much faster as compared to the thermal dynamics of a building in general, the amount of heat transferred by a heat pump can be represented by the following algebraic equation: Qc = Wc

(1)

where Qc = heat transferred from the condenser to the water, = coefﬁcient of performance of the heat pump, Wc = work done by the compressor. The simpliﬁed model of the building energy balance with a heat pump under the assumption discussed previously can be represented by the following three differential equations [25] Cp,r T˙r = (UA)fr (Tf − Tr ) − (UA)ra (Tr − Ta ) + (1 − p)s Cp,f T˙f = (UA)wf (Tw − Tf ) − (UA)fr (Tf − Tr ) + ps

(2)

˙ = Wc − (UA)wf (TW − Tf ) Cp,w Tw where the state variables Tr , Tf and Tw represent the room air temperature, the ﬂoor temperature and the water temperature in ﬂoor heating pipes, respectively. The external disturbances considered in the simpliﬁed model are the ambient air temperature Ta and the solar radiation power s . In (2), Cp,r , Cp,f and Cp,w denote the heat capacity of the room air, of the ﬂoor and of the water in ﬂoor heating pipes, respectively; (UA)ra , (UA)f,r and (UA)w,f represent the heat transfer coefﬁcients between room air and ambient, ﬂoor and room air, water and ﬂoor, respectively. The parameter p is the fraction of incident solar radiation on ﬂoor and is the compressor coefﬁcient of performance (COP). The system of differential equations (2) can be used to develop a state-space model of the building energy system with a heat pump with x˙ = [A]{x} + [B]u + [E]{d}

(3)

y = [C]{x}

In (3) the states are Tr

Tf

Tw

T

and the control input is u = Wc ;

T

the disturbances are {d} = Ta s ; and the output variable is the indoor temperature y = Tr . These lead to the matrices in the statespace model [25]. MPC is an advanced control method which stems from applications in process control industries in late 70s and early 80s [31]. MPC represents a class of control strategies where the model of the system of the process is explicitly considered and the control input is determined based on the minimization of an objective function with certain constraints. The optimization is performed iteratively over a ﬁnite horizon with a multi step-ahead control action prediction and is updated in time progressively. There have been several attempts to use MPC for HVAC control system [32–36] in

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

715

The state-space model of the system of buildings with heat pumps converted to a discrete-time state-space model using zero order holding sampling of the input signals is represented by {xsk+1 } = [As ]{xsk } + [Bs ]{usk } + [Es ]{dsk } {ysk } = [Cs ]{xsk } Fig. 1. Group of ‘n’ buildings connected to ‘m’ heat pumps.

the last decade with some real implementation study [17]. The main difference between MPC and E-MPC is that the objective function in E-MPC is based on an economic cost function. As in an MPC strategy, E-MPC strategy also consists of two basic steps: 1. The future outputs are predicted using the model. The future control signals (also termed as moves, i.e. a sequence of control inputs over time) are calculated by optimizing the objective function. 2. The ﬁrst component of the control sequence (signal move) is fed back (applied) to the system while the rest are discarded. At the next time step, the prediction horizon is moved forward one step (i.e. the starting and ending time steps of the cost function are shifted one time step forward according to the receding-horizon strategy), the process of output prediction and control action calculation is repeated and the ﬁrst component of the control sequence again is fed back (applied). This is repeated for future steps. On using zero-order-hold sampling of the input signals, the continuous version of the state space model in Eq. (3) of the building energy system with a heat pump can be represented by the following discrete form {xk+1 } = [Ad ]{xk } + [Bd ]uk + [Ed ]{dk } yd = [Cd ]{xk }

(4)

In classical MPC formulation, a quadratic criterion accounting for the state deviation and for the input usage is generally used to formulate the optimization problem [37]. In the E-MPC framework, an economic objective function is designed and a linear program is formulated to minimize the electricity expense while keeping the indoor temperature inside a prescribed comfort region. 4. Cooperative optimization for a group of buildings A model for a group of buildings sharing a number of heat pumps is developed here. Let us consider a system of n buildings and m heat pumps. Each heat pump may be connected to a number of buildings as shown in Fig. 1. The ith building is denoted by Ri and the jth heat pump is represented as Hj . The connectivity between the heat pumps and the buildings are represented by a connectivity matrix [L] of dimension n × m. Let us assume that the heat pump Hj is connected to nj number of buildings i.e. Rj1 , Rj2 , . . ., Rjn . In that case the column entries at j

j1 , j2 , . . ., jnj for the jth row in [L] will be 1 and the rest of the column entries for the same row will be 0. The control inﬂuence matrix for the system is given by ¯ [Bs ] = [B][L]

(5)

¯ is expressed as where [B]

¯ = [B1 ] [B]

[B2 ]

...

[Bn ]

(6)

and [Bj ], j = 1, 2, . . ., n denotes the control inﬂuence vector corresponding to the case of the building Rj connected with a heat pump.

(7)

In (7), {xs }, [As ], {us }, [Es ], {ds }, {ys } and [Cs ] represent the augmented state vector, system matrix, control input vector, disturbance inﬂuence matrix, disturbance vector, output vector and the output inﬂuence matrix, respectively. These are given by

{xs } = {x1 }T

{x2 }T

[As ] = Diag [A1 ]T

{us } = u1

⎡

u2

[E1 ]

⎢ [0 ] ⎢ 1 [Es ] = ⎢ ⎢. ⎣ .. [01 ]

{ds } = {d1 }T

{ys } = y1

⎡

y2

[C1 ]

[A2 ]T

...

um

T

{xn }T

...

[An ]T

...

⎤

[01 ]

[E2 ]

[01 ]. . .

[01 ] ⎥

.. .

..

.. .

...

...

... [02 ]

.

⎥ ⎥ ⎥ ⎦

(8d)

[En ]

... yn

{dn }

T

T

(8e)

T

[02 ]

(8f) ...

[02 ]

⎤

⎢ [0 ] [C ] [0 ] . . . [0 ] ⎥ 2 2 2 ⎥ ⎢ 2 ⎥ [Cs ] = ⎢ ⎢ ⎥ . . ⎣· ⎦ . · ... · [02 ]

...

...

(8b) (8c)

[01 ]. . .

T

T

T

[01 ]

{d2 }

(8a)

...

(8g)

[Cn ]

The matrices [01 ] and [02 ] are the zero matrices of dimension ns × q and r × ns respectively, where ns , q and r, are the number of states, number of disturbances and number of outputs for an individual building. The state vector, system matrix, disturbance inﬂuence matrix, disturbance vector, output vector and the output inﬂuence matrix for the building Rj , j = 1, . . ., n are denoted by {xj }, [Aj ], [Ej ], {dj }, {yj } and [Cj ], respectively. The control input vector from each heat pump is denoted by {uh }, h = 1, . . ., m. 5. Concept of cooperative optimization with E-MPC Under a cooperative framework, an E-MPC problem can be formulated for the group of buildings described in the previous section leading to a cooperative optimization concept. If the buildings collaborate in sharing the heat pumps in energy distribution, then using the discrete-time linear state-space formulation to predict the future outputs of the system, we can formulate an optimization problem to minimize the total electricity cost for operating the heat pump while keeping the indoor room temperatures in the buildings within prescribed intervals. It is assumed that there are multiple the control input to a building from the heat pumps to which the building is connected and the output consisting of room temperatures in the building. We minimize the total electricity cost of operating all the heat pumps connected to the buildings. The variable cost of electricity is considered in formulating the objective function. The constraints on the temperature set-points and building dynamics for each of the buildings have to be satisﬁed. Hence, this leads to a cooperative optimization problem in an E-MPC setting. The E-MPC problem formulated is then employed to investigate the impact of the concept

716

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

Table 1 Estimated building parameters. Parameter

Value

Value

Description

Cp,r Cp,f Cp,w (UA)ra

810 3315 836 28

kJ/◦ C kJ/◦ C kJ/◦ C kJ/◦ C h

(UA)f r

624

kJ/◦ C h

(UA)wf

28

kJ/◦ C h

Heat capacity of the room air Heat capacity of ﬂoor Heat capacity of water Heat transfer coefﬁcient between room air and ambient Heat transfer coefﬁcient between ﬂoor and the room Heat transfer coefﬁcient between water and ﬂoor

of cooperative optimization on the energy usage pattern and operating costs in relation to operation of heat pumps for the buildings.

min ˚ =

xs ,us ,ys

cu,k {G}usk +

k∈N

n

vik

i=1

s.t. {xsk+1 } = [As ]{xsk } + [Bs ]{usk } + [Es ]{dsk }, {ysk } = [Cs ]{xsk }, uimin ≤ uik ≤ uimax ,

k ∈ N k ∈ N, ∀i ∈ [0, m]

uimin ≤ uik ≤ uimax ,

k ∈ N, ∀i ∈ [0, m]

yik,min ≤ yik + vik ,

k ∈ N, ∀i ∈ [0, n]

yik,max ≥ yik − vik ,

k ∈ N, ∀i ∈ [0, n]

vik ≥ 0,

k ∈ N

(9)

k ∈ N, ∀i ∈ [0, n]

where N ∈ {0, 1, . . ., N} is the prediction horizon, {G} is a 1 × m vector and cu,k is the time-of-use electricity price. In (9), yk,min and yk,max indicate the time-varying upper and lower limits of the temperature comfort zone, respectively. A slack variable vik i = 1, . . ., n with the associated penalty cost is introduced to allow violations of the constraints on the output variable (the so-called soft-constraints). In this way, if the desired temperature demand cannot be achieved the optimization routine seeks a compromise between minimizing the economic cost and minimizing the constraint violations expressed by vik . The Economic MPC formulation also contains constraints on the control variable uik and on its rateof change uik = uik+1 − uik (hard constraints). It should be noted that the future electricity prices and also the future ambient temperature and solar radiation are employed in the optimal control problem (9). In the literature researchers have explored a large variety of techniques for the forecasting of electricity prices [38,39] including methods based on time-series [40,41], artiﬁcial neural networks and wavelets [42–44]. In [26] a leastsquares support vector machines approach is used for electricity tariff price forecasting in the context of an MPC-based method to reduce peak electricity demand in building climate control. For the purpose of simplicity in the implementation of the concept illustrated in this paper perfect forecasts for electricity prices and weather conditions have been assumed. 6. Comparison of selﬁsh and cooperative optimization The application of an Economic-MPC controller has been simulated using MATLAB to control the indoor temperature for two representative buildings, namely a small low-energy residential building with a ﬂoor heating system connected to a heat pump and a larger building equipped with similar heating system. The parameters for the small buildings are used as provided in [25] and are detailed in Table 1. For the large building, the value of the

parameters increases according to the size of the building in appropriate proportion. For the purpose of simplicity, non-ventilated single-storeyed, single-zone buildings have been considered. The buildings are subjected to the same outdoor weather conditions. The ambient temperature Ta and solar radiation s are extrapolated from ASHRAE IWEC (International Weather for Energy Calculations) weather data ﬁles for Dublin, Ireland [45]. These variables represent measured non-controllable disturbance inputs to the building energy system. The weather data time-histories could be replaced by weather forecasts in a real-time implementation of the proposed algorithm. Electricity tariffs from the Nordic power exchange market [46] corresponding to the actual electricity prices in Western Denmark have been employed. Both weather data and electricity prices data have been sampled using a sampling time of 30 min which is also the sampling time used to discretize the continuous-time system equations (3). At each time step, the optimization problem is solved using the ILOG CPLEX Optimizer via MATLAB. A two-week period has been simulated using a prediction horizon N = 96 (i.e. 48 h). The result of application of the selﬁsh optimization for two individual buildings is presented ﬁrst. One of them is three times larger in size than the other resulting in one small and one large building. The maximum electrical input power absorbed by heat pump compressor for the small building is umax = 2 kW with a maximum rate of change u = 1 kW/ [unit time]. Thermal comfort is deﬁned as in ASHRAE Standard 55-Thermal Environmental Conditions for Human Occupancy [47]. This standard deﬁnes the range of indoor thermal environmental conditions acceptable to a majority of occupants. In the paper, temperature ranges have been selected to test the performance of the proposed algorithm around the temperature comfort zone presented in the ASHRAE standard. Real-time constraints for small building are speciﬁed for the zone temperatures as following [47]:

⎧ ◦ ◦ ⎨ yk,min = 17 C, yk,max = 22 C, k ∈ [6am–8 : 30am] ⎩

or k ∈ [6pm–0.00am]

yk,min = 15 ◦ C, yk,max = 19 ◦ C,

(10) otherwise

For the large building, the maximum electrical input power absorbed by heat pump compressor is umax = 4 kW with a maximum rate of change u = 2 kW/unit time. Real time constraint for desired temperature zone for the large building are as following:

⎧ ◦ ◦ ⎨ yk,min = 18 C, yk,max = 23 C, k ∈ [6am–8 : 30am] ⎩

or k ∈ [6pm–0.00am]

yk,min = 16 ◦ C, yk,max = 20 ◦ C,

(11) otherwise

Subsequently, three buildings are connected to a shared centralized heat pump. In this case, two buildings are small and identical. Third building is three times larger in size than other two buildings. The maximum electrical input power absorbed by heat pump is umax = 8 kW. The performance obtained by cooperative control scheme proposed in (9) is then discussed and compared. Another scenario illustrating the collaboration of a residential building and an ofﬁce building in the proposed framework is simulated. The ofﬁce building is two times larger in size than the residential building. The maximum power absorbed by the heat pump is considered umax = 4 kW. The constraints for the zone temperature of residential building are as follows:

⎧ ◦ ◦ ⎨ yk,min = 18 C, yk,max = 23 C, k ∈ [6am–8 : 30am] ⎩

or k ∈ [6pm–0.00am]

yk,min = 16 ◦ C, yk,max = 20 ◦ C,

(12) otherwise

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

717

Fig. 2. Selﬁsh E-MPC optimization for a small building.

The desired temperature constraints for the ofﬁce building are

yk,min = 18 ◦ C, yk,max = 23 ◦ C,

k ∈ [9am–5pm]

yk,min = 16 ◦ C, yk,max = 20 ◦ C,

otherwise

(13)

6.1. Selﬁsh optimization First the application of the selﬁsh E-MPC optimization is investigated. 6.1.1. Small building – variable tariff The results in Fig. 2 illustrate the performance of the controller under time varying electricity prices, time-varying soft constraints and time varying weather conditions over 7 days of simulation (winter temperatures have been considered to justify usage of the heating system). It can be observed (upper ﬁgure) that the controller is successful in keeping the indoor temperature Tr within the prescribed comfort zone. The optimal schedule for the heat pump and the corresponding electricity spot price are shown in the middle ﬁgure. It is clear that not only the hard constraints on the control input are satisﬁed (as expected in conventional MPC) but also most of the time the compressor is operating when the electricity spot price is low. As a consequence, the optimal schedule for the heat pump exhibits an ‘impulsive’ proﬁle and input power peaks are occurring when the price is minimum. According to the E-MPC strategy then, the heat pump power consumption is shifted to times with cheaper energy cost while ensuring that the room temperature is within the desired limits. The outdoor temperature and the associated solar radiation are plotted in the lower ﬁgure. It should be noted that because of the solar gains room temperature is high initially. In this case, heat pump is not turned on. 6.1.2. Small building – ﬁxed tariff The selﬁsh optimization scheme has then been applied to the same building subjected to the same outdoor weather conditions and constraints as in the previous case but using constant electricity price. A ﬁxed tariff of 52.15 EUR/MWh (corresponding to the average electricity price over the 2 weeks time considered) has been assumed. The results are illustrated in Fig. 3. Clearly,

the controller is capable of maintaining acceptable indoor temperature without violating the constraint on the maximum input power required from the heat pump. Since there is no variation of the cost in the objective function, effectively the E-MPC in this case minimizes energy usage. As a consequence the power required to operate control is more ‘spread’ over time as shown in the lower ﬁgure. According to the results carried out in this study, for the small building considered the E-MPC controller with variable tarifﬁng can provide a reduction of the energy cost of about 39% compared to controller with ﬁxed electricity price.

6.1.3. Big building – variable tariff The performance of the selﬁsh economic MPC controller applied to a three times large (size) house is then examined. The controller is again operated under a variable tarifﬁng scheme. The room temperature is plotted in the upper ﬁgure in Fig. 4 while the corresponding optimal heat pump compressor schedule is plotted in the lower ﬁgure. Similar considerations as in the E-MPC control for the small building with variable tariff analyzed previously are applicable. It should be noted that due to the larger size of the building in this case the amount of energy required for heating is greater than in the previous case (i.e. the heating is switched on more often) and this obviously leads to a higher ﬁnal cost. Further, it can be seen that small violations of the soft constraints are occurring at some points in time and the room temperature is not strictly contained in the prescribed comfort zone. Appropriate selection of the penalty cost associated with the slack variable allows to compute optimal solutions for the E-MPC (i.e. the feasibility of the problem is maintained) at the expense of small violations of the output constraints for short periods of time. Finally, it can be observed that the room temperature proﬁle is skewed towards the lower bounds of the comfort region. This is because the objective function contains only economic criteria (i.e. the cost of operating the heat pump and the cost of violating soft output constraints). The set-point regulation performance of the controller is not incorporated in the synthesis of the control move. It should be mentioned that the tracking performance could be improved for instance by adding a quadratic term in the objective function to include the set-point tracking error and

718

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

Fig. 3. Selﬁsh E-MPC optimization for a small building with ﬁxed electricity cost.

the energy usage as an additional criteria to be minimized by the controller.

different constraint scenarios have been analyzed to ascertain the effectiveness of the proposed approach.

6.2. Cooperative optimization

6.2.1. Three buildings with shared heat pump The results in Fig. 5 illustrate the performance of the controller synthesized by solving the cooperative optimization problem deﬁned in (9) for the three buildings under consideration. The buildings are connected to a centralized heat pump heating system. Same variable tarifﬁng scheme, same weather conditions as in the previous cases have been employed. The indoor temperature Tr1 , Tr2 and Tr3 for the two small buildings and the large building over 7 days of simulation are shown in Fig. (5) respectively. It can be

The two buildings individually considered above are introduced as ‘small’ and ‘large’ buildings based on their size. For investigating the cooperative optimization, two small and one large building is considered and all of them have been connected to a shared heat pump according to the cooperative optimization model described in Section 4 in order to illustrate the application of the cooperative optimization concept proposed in this paper. In what follows, two

Fig. 4. Selﬁsh E-MPC optimization for a large building.

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

719

Fig. 5. Cooperative E-MPC optimization for the three buildings with shared heat pump of 8 kW capacity.

seen that the E-MPC controller in the proposed cooperative framework is able to keep the temperature for all the buildings within the comfort level. The desired control performance is achieved without violations of the hard constraints as shown in the lower ﬁgure. The minimization of the total energy cost in this case results in the heat pump compressor schedule shown in Fig. (5). The energy required for the two small and the large building are Wc1 , Wc2 and Wc3 respectively. It is found that for the larger building the heat pump compressor is operated daily and only when the cost of electricity is minimum and for shorter periods of time. For the small buildings, the compressor is allowed to be on also when the electricity price is not cheap. As a consequence, in comparison with the selfish E-MPC optimization the ﬁnal cost increases by 13% for both of the small buildings. However, for the large building the cooperative approach leads to an economic savings of about 16%. By considering the total cost of heating of the three buildings, according to the numerical analysis carried out in this study the cooperative optimization provides an economic savings of about 15% compared to the selﬁsh optimization scheme. It must be noted that since the overall cost reduction for cooperative optimization is substantial, there is a large enough margin to eliminate the slight additional cost which the small buildings incur. A discounted fare could be offered to the owners of the small buildings to incentivize their participation in the cooperative program. For instance, if a 13% reduction is offered for the small houses (so that they don’t incur in additional costs), it leads to a ﬁnal reduction of 15% in total cost compared to the selﬁsh scheme. This indicates that there is scope of providing discount to the participating houses so that the proﬁt is shared on an agreed basis. Finally, by scaling the approach to a large number of houses additional optimization variables such as electricity rates and discounted tariffs could be incorporated into the optimization problem, leading to a more ﬂexible (and more complex) cooperative framework. However, optimization of tariffs and of discounted

rates is beyond the scope of this paper and it is left as a potential for future study. It should be mentioned that the minimization of the total economic cost does not necessarily leads to the minimization of the energy consumption. This means that the optimal solution in the economic sense does not necessarily coincide with the best green solution. Fig. 6 shows the simulation results when the capacity of shared heat pump is considered to be 12 kW. In this case, energy cost savings is 3% more than in the case of 8 kW heat pump. So if more capacity is added to heat pump then obviously the purchasing cost of heat pump increases but it leads to more energy cost savings. 6.2.2. Cooperative optimization with residential and ofﬁce buildings Finally, the proposed cooperative optimization approach has been used to determine the optimal schedule for a heat pump shared between a residential house and an ofﬁce building. The model of the small building discussed above has been assumed to represent the residential house while the larger structure has been associated with ofﬁce use. In order to simulate a realistic scenario, it is assumed that for the residential house the temperature should be warmer in the morning between 6am and 8.30am and also in the evening between 6pm and midnight. Instead for the ofﬁce, a more comfortable climate should be ensured during the working hours (9am to 5pm). The indoor temperature constraints considered in this case are reported in (12) and (13) for the residential house and the ofﬁce building, respectively. The top ﬁgure in Fig. 7 shows the room temperature in the house over 7 days of simulation using the cooperative optimization algorithm. It can be seen that initially the output constraints are violated due to initial conditions outside of the comfort region. However, after some time the cooperative E-MPC controller is capable of bringing the indoor temperature Tr,res inside the desired band. Similarly, the air

720

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

Fig. 6. Cooperative E-MPC optimization for one ofﬁce building and one residential building with shared heat pump of 12 kW capacity.

Fig. 7. Cooperative E-MPC optimization for one ofﬁce building and one residential building with shared heat pump.

temperature Tr,off in the ofﬁce is regulated within the prescribed limits after some time of the simulations. By looking at the heat pump compressor schedule indicated by Wc,res and Wc,off in Fig. 7 for the house building and the ofﬁce building, respectively, it is found that the heat pump compressor for the ofﬁce is operated daily according to a pulse-like fashion and only when the cost of electricity is minimum. Instead, for the residential house the heat pump supplies heat every other day and at times the compressor is switched on even when the electricity price is high. Again, in comparison with selﬁsh E-MPC optimization, under the cooperative scheme the heat pump is operated in such a way that cost for the smaller structure (i.e. the residential building) increases almost

by 2% while the cost for larger structure i.e. ofﬁce building decreases by around 13%. By considering the total cost of energy for the two cases (selﬁsh and cooperative), a reduction of about 12% can be obtained by applying the cooperative optimization algorithm. In case of cooperative optimization, controller minimizes the overall electricity cost for all the buildings while keeping the room temperatures for all the buildings in the desired temperature range. It does not take into account the individual cost for every building to minimize. As a consequence, the small buildings might incur in an increase of the energy cost because the target (i.e. the objective function) is the global cost, not the individual ones. However, from a practical point of view this is not an issue because the overall

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

cost reduction for cooperative optimization is substantial, there is a large enough margin to eliminate the slight additional cost which the small buildings incur (e.g. discounted fare for small buildings). For the larger building, the lower energy cost is due to the possibility of better exploiting the additional capacity provided by the shared heat pump, (by accumulating the heat necessary to satisfy the constraints when the price of electricity is low). In case of an individual building, the key idea behind E-MPC (for selﬁsh optimization) is to store the amount of energy required to meet the thermal comfort demand primarily when the cost of electricity is minimum. The reduced order numerical model, which is based on energy and mass balance equations, is employed in real-time to simulate the thermal dynamics of the building and to predict the point in time when room temperature will fall outside of the desired range. Based on this information, an optimal schedule for the heat pump compressor can be determined to minimize energy costs. The same principle applies also to the case of a group of buildings. However, improved energy performance can be achieved in this case (by cooperative optimization) due to the ﬂexibility provided by sharing of the heat pump. In fact, the additional capacity offered by the shared heat pump can be conveniently exploited by dynamically allocating the energy stored based on the actual demand. This means that the energy accumulated by one building during low electricity prices can be made available to another building that needs to be heated (possibly when the price of electricity is high).

7. Conclusions A concept of cooperative optimal control to reduce energy demand costs in the context of building energy systems has been developed in this paper via MPC with an economic objective. Previous works in the literature have mainly focused on the minimization of the energy costs for individual buildings. This approach can be regarded as a selﬁsh optimization in which each building is considered as a single, independent unit whose energy cost is to be minimized. The Economic MPC strategy has been extended in this paper to minimize the total cost of operating the heating system for a cluster of buildings. Instead of minimizing the energy demand costs for each single building, the cooperative E-MPC aimed at optimizing the total (cumulative) electricity cost for all of the buildings. At each time step, the cooperative strategy is implemented by appropriately scheduling the activation of the heat pump compressors in order to provide to each building the amount of heat required to keep the temperature in the desired comfort zone. At the same time, the scheduling is designed to minimize the aggregated cost for the operation of the heat pumps. In the numerical case study, the optimal control of three buildings connected to a shared centralized heat pump has been considered under different time-varying constraint scenarios. The investigation carried out reveals that using the selﬁsh E-MPC algorithm the power consumption load is shifted to periods with low electricity prices on a daily basis. However, different energy usage patterns are observed when the cooperative optimization is performed. Using the cooperative strategy, the controller operates such that for larger buildings the heat pump compressor is switched on daily at maximum power for short time periods when the price of electricity is minimum; for small buildings the energy usage pattern is modiﬁed in such a way that the heat is supplied less frequently (e.g. every other day) but for longer time periods and also at peak hours. Results demonstrate that using the cooperative approach the individual costs for larger buildings is signiﬁcantly reduced compared to the selﬁsh optimization (savings around 15% are achieved), while only a marginal or practically

721

no increase is observed for smaller buildings. Indeed, simulation results have shown that the under a time-of-use electricity pricing scheme the cooperative optimization with E-MPC provides substantial total cost savings compared to the selﬁsh approach. The proposed formulation could represent a promising implementation of demand response approach in the context of building climate control. The framework developed in this paper for the cluster of buildings can be easily extended to energy optimization of smart cities. Further work is ongoing to incorporate weather forecasts and electricity prices forecasts in the synthesis of the control move in order to examine the impact of uncertainty on the performance of the predictive controller. Acknowledgement This work was conducted as part of the INDICATE project under the European Unions 7th Framework Programme for research under Grant Number 608775. References [1] P. Stadler, A. Ashouri, F. Marechal, Distributed model predictive control for energy systems in microgrids. arXiv:1508.00794. [2] F. Yik, J. Burnett, I. Prescott, Predicting air-conditioning energy consumption of a group of buildings using different heat rejection methods, Energy Build. 33 (2) (2001) 151–166. [3] H. Karlsson, C.-E. Hagentoft, Application of model based predictive control for water-based ﬂoor heating in low energy residential buildings, Build. Environ. 46 (3) (2011) 556–569, http://dx.doi.org/10.1016/j.buildenv.2010.08.014. [4] S. Soyguder, M. Karakose, H. Alli, Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system, Expert Syst. Appl. 36 (3) (2009) 4566–4573, http://dx.doi.org/10.1016/j.eswa.2008.05.031. [5] J. Bai, S. Wang, X. Zhang, Development of an adaptive smith predictor-based self-tuning PI controller for an HVAC system in a test room, Energy Build. 40 (12) (2008) 2244–2252, http://dx.doi.org/10.1016/j.enbuild.2008.07.002. [6] C. Ghiaus, I. Hazyuk, Calculation of optimal thermal load of intermittently heated buildings, Energy Build. 42 (8) (2010) 1248–1258, http://dx.doi.org/10. 1016/j.enbuild.2010.02.017. [7] C.P. Underwood, HVAC Control Systems: Modelling Analysis and Design, Routledge, 2002. [8] F. Oldewurtel, A. Parisio, C.N. Jones, D. Gyalistras, M. Gwerder, V. Stauch, B. Lehmann, M. Morari, Use of model predictive control and weather forecasts for energy efﬁcient building climate control, Energy Build. 45 (2012) 15–27, http://dx.doi.org/10.1016/j.enbuild.2011.09.022. [9] J. Ma, J. Qin, T. Salsbury, P. Xu, Demand reduction in building energy systems based on economic model predictive control, Chem. Eng. Sci. 67 (1) (2012) 92–100, http://dx.doi.org/10.1016/j.ces.2011.07.052. [10] Y. Ma, F. Borrelli, B. Hencey, B. Coffey, S. Bengea, P. Haves, Model predictive control for the operation of building cooling systems, IEEE Trans. Control Syst. Technol. 20 (3) (2012) 796–803, http://dx.doi.org/10.1109/TCST.2011. 2124461. [11] C. Chen, J. Wang, Y. Heo, S. Kishore, MPC-based appliance scheduling for residential building energy management controller, IEEE Trans. Smart Grid 4 (3) (2013) 1401–1410. [12] A.-H. Mohsenian-Rad, A. Leon-Garcia, Optimal residential load control with price prediction in real-time electricity pricing environments, IEEE Trans. Smart Grid 1 (2) (2010) 120–133, http://dx.doi.org/10.1109/TSG.2010. 2055903. [13] A.-H. Mohsenian-Rad, V.W. Wong, J. Jatskevich, R. Schober, A. Leon-Garcia, Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid, IEEE Trans. Smart Grid 1 (3) (2010) 320–331, http://dx.doi.org/10.1109/TSG.2010.2089069. [14] Y. Zong, D. Kullmann, A. Thavlov, O. Gehrke, H.W. Bindner, Application of model predictive control for active load management in a distributed power system with high wind penetration, IEEE Trans. Smart Grid 3 (2) (2012) 1055–1062, http://dx.doi.org/10.1109/TSG.2011.2177282. [15] X. Zhang, G. Schildbach, D. Sturzenegger, M. Morari, Scenario-based MPC for energy-efﬁcient building climate control under weather and occupancy uncertainty, in: 2013 European Control Conference (ECC), IEEE, 2013, pp. 1029–1034. [16] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renew. Sustain. Energy Rev. 34 (2014) 409–429, http://dx.doi.org/10.1016/j.rser.2014.03.027. ˇ [17] S. Privara, J. Sirok‘y, L. Ferkl, J. Cigler, Model predictive control of a building heating system: the ﬁrst experience, Energy Build. 43 (2) (2011) 564–572, http://dx.doi.org/10.1016/j.enbuild.2010.10.022. [18] J.-Y. Lee, I.-H. Yang, S.-Y. Song, H.-S. Kim, K.-W. Kim, A study of the predictive control of the ondol system in apartments, in: Proc. Building Simulation, vol. 99, 1999, pp. 215–222.

722

A. Staino et al. / Energy and Buildings 128 (2016) 713–722

[19] S. Cho, M. Zaheer-Uddin, Predictive control of intermittently operated radiant ﬂoor heating systems, Energy Convers. Manag. 44 (8) (2003) 1333–1342, http://dx.doi.org/10.1016/S0196-8904(02)00116-4. [20] Y. Ma, A. Kelman, A. Daly, F. Borrelli, Predictive control for energy efﬁcient buildings with thermal storage, IEEE Control Syst. Mag. 32 (1) (2012) 44–64. [21] J.A.B. Vieira, A.M. Mota, Adaptable PID Versus Smith Predictive Control Applied to an Electric Water Heater System, INTECH Open Access Publisher, 2012. [22] T. Chen, A Methodology for Thermal Analysis and Predictive Control of Building Envelope Heating Systems, Concordia University, 1998. [23] T. Chen, Real-time predictive supervisory operation of building thermal systems with thermal mass, Energy Build. 33 (2) (2001) 141–150, http://dx. doi.org/10.1016/S0378-7788(00)00078-5. [24] I. Pyeongchan, Modeling and Optimization in Slab-on-Grade Radiant Heating Floor Systems, Doctor Dissertation, University of Colorado. [25] R. Halvgaard, N.K. Poulsen, H. Madsen, J.B. Jørgensen, Economic model predictive control for building climate control in a smart grid, in: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, IEEE, 2012, pp. 1–6, http://dx. doi.org/10.1109/ISGT.2012.6175631. [26] F. Oldewurtel, A. Ulbig, A. Parisio, G. Andersson, M. Morari, Reducing peak electricity demand in building climate control using real-time pricing and model predictive control, in: 2010 49th IEEE Conference on Decision and Control (CDC), IEEE, 2010, pp. 1927–1932, http://dx.doi.org/10.1109/CDC. 2010.5717458. [27] K.H. Ang, G. Chong, Y. Li, PID control system analysis, design, and technology, IEEE Trans. Control Syst. Technol. 13 (4) (2005) 559–576, http://dx.doi.org/10. 1109/TCST.2005.847331. [28] Y. Li, K.H. Ang, et al., PID control system analysis and design, IEEE Control Syst. 26 (1) (2006) 32–41, http://dx.doi.org/10.1109/MCS.2006.1580152. [29] N.R. Kristensen, H. Madsen, S.B. Jørgensen, Parameter estimation in stochastic grey-box models, Automatica 40 (2) (2004) 225–237, http://dx.doi.org/10. 1016/j.automatica.2003.10.001. [30] P.D. Andersen, M.J. Jiménez, H. Madsen, C. Rode, Characterization of heat dynamics of an arctic low-energy house with ﬂoor heating Building Simulation, vol. 7, Springer, 2014, pp. 595–614, http://dx.doi.org/10.1007/ s12273-014-0185-4. [31] A. Zhang, M. Morari, Stability of model predictive control with soft constraints, in: Proceedings of the 33rd IEEE Conference on Decision and Control, 1994, vol. 2, IEEE, 1994, pp. 1018–1023, http://dx.doi.org/10.1109/ CDC.1994.411277. [32] R.Z. Freire, G.H. Oliveira, N. Mendes, Predictive controllers for thermal comfort optimization and energy savings, Energy Build. 40 (7) (2008) 1353–1365, http://dx.doi.org/10.1016/j.enbuild.2007.12.007. [33] P.D. Moros¸an, R. Bourdais, D. Dumur, J. Buisson, Building temperature regulation using a distributed model predictive control, Energy Build. 42 (9) (2010) 1445–1452, http://dx.doi.org/10.1016/j.enbuild.2010.03.014.

[34] T. Chen, Application of adaptive predictive control to a ﬂoor heating system with a large thermal lag, Energy Build. 34 (1) (2002) 45–51, http://dx.doi.org/ 10.1016/S0378-7788(01)00076-7. [35] M. Gwerder, B. Lehmann, J. Tödtli, V. Dorer, F. Renggli, Control of thermally-activated building systems, Appl. Energy 85 (7) (2008) 565–581, http://dx.doi.org/10.1016/j.apenergy.2007.08.001. [36] Z. Liao, A.L. Dexter, An inferential model-based predictive control scheme for optimizing the operation of boilers in building space-heating systems, IEEE Trans. Control Syst. Technol. 18 (5) (2010) 1092–1102, http://dx.doi.org/10. 1109/TCST.2009.2033667. [37] C.V. Rao, J.B. Rawlings, Linear programming and model predictive control, J. Process Control 10 (2) (2000) 283–289, http://dx.doi.org/10.1016/S09591524(99)00034-7. [38] R. Weron, Electricity price forecasting: a review of the state-of-the-art with a look into the future, Int. J. Forecast. 30 (4) (2014) 1030–1081, http://dx.doi. org/10.1016/j.ijforecast.2014.08.008. [39] A. Motamedi, H. Zareipour, W.D. Rosehart, Electricity price and demand forecasting in smart grids, IEEE Trans. Smart Grid 3 (2) (2012) 664–674, http://dx.doi.org/10.1109/TSG.2011.2171046. [40] J.C. Cuaresma, J. Hlouskova, S. Kossmeier, M. Obersteiner, Forecasting electricity spot-prices using linear univariate time-series models, Appl. Energy 77 (1) (2004) 87–106, http://dx.doi.org/10.1016/S03062619(03)00096-5. [41] J. Contreras, R. Espinola, F.J. Nogales, A.J. Conejo, Arima models to predict next-day electricity prices, IEEE Trans. Power Syst. 18 (3) (2003) 1014–1020, http://dx.doi.org/10.1109/TPWRS.2002.804943. [42] D. Singhal, K. Swarup, Electricity price forecasting using artiﬁcial neural networks, Int. J. Electr. Power Energy Syst. 33 (3) (2011) 550–555, http://dx. doi.org/10.1016/j.ijepes.2010.12.009. [43] N. Pindoriya, S. Singh, S. Singh, An adaptive wavelet neural network-based energy price forecasting in electricity markets, IEEE Trans. Power Syst. 23 (3) (2008) 1423–1432, http://dx.doi.org/10.1109/TPWRS.2008.922251. [44] J. Catal ao, H. Pousinho, V. Mendes, Neural networks and wavelet transform for short-term electricity prices forecasting, in: 15th International Conference on Intelligent System Applications to Power Systems, 2009. ISAP’09. IEEE, 2009, pp. 1–5, http://dx.doi.org/10.1109/ISAP.2009.5352834. [45] Energyplus Energy Simulation Software-International Weather for Energy Calculations (IWEC) Data for Ireland. http://apps1.eere.energy.gov/buildings/ energyplus/cfm/weather data3.cfm/region=6 europe wmo region 6/ country=IRL/cname=Ireland (accessed January 2015). [46] Danish Electricity Wholesale Market. http://www.energinet.dk/EN/El/ Engrosmarked/Udtraek-afmarkedsdata/Sider/default.aspx (accessed January 2015). [47] ASHRAE, Standard 55-2013, Thermal Environmental Conditions for Human Occupancy, American Society of Heating, Refrigerating and Air-Conditioning Engineering, Atlanta, GA.

Advertisement