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Model Predictive Controller Design for a Load Management Research Facility in a Distributed Power System
Model Predictive Controller Design for a Load Management Research Facility in a Distributed Power System Yi Zong*, Anders Thavlov*, Daniel Kullmann*, Oliver Gehrke*, Henrik Bindner* *Wind Energy Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Frederiksborgvej 399,VEA-118, dk-4000 Roskilde, Denmark (Tel: 0045-46775045; e-mail: [email protected], [email protected] [email protected], [email protected],[email protected]).
Abstract: This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system with a high penetration of renewable energy. A description of the facility based on a distributed power system (SYSLAB) is followed by a discussion of the software platform on which building controllers can be executed. Finally, this paper shows how to design a thermal model predictive controller for the power consumption prediction in this distributed power system. The control of the PowerFlexHouse allows studies to be performed on the reaction of this intelligent house to a hybrid power grid. With this demand side control study, we hope we can not only dramatically improve grid reliability, but also raise energy efficiencies and reduce power cost for users. Keywords: Load-management, Model predictive control, Distributed power system. 1. INTRODUCTION
Anton Andersson, Oliver Gehrke, Francesco Sottini, Henrik Bindner, Per Nørgård 2007).
There is an increasing level of wind power in the power system. i.e. the integration of 50% wind energy into electricity system by 2025 in Denmark places strong demands on flexibility elsewhere in the system and the task of controlling the system is becoming increasingly demanding. Fluctuations in wind power put large demands for flexibility on the rest of the system to achieve a balance between generation and consumption. Figure 1 shows power demand and wind power generation in a power system over a period of two days. It is clearly seen that the load has a daily pattern with some stochastic variations and that the wind power varies stochastically as well, but with no correlation with the load. It is also clear that the variations in wind power can be very large even in rather short timescales, i.e. from minutes to hours. Both the lack of correlation and the stochastic nature of the wind power put very strong requirements for flexibility on the rest of the system. Traditionally, there has been a separation between the production and consumption of electricity: Consumption has been regarded a passive part of the system with respect to control, and therefore any generation mismatch caused by variations in wind power production has had to be compensated by other generating units. In recent years, it has been realised that there is a large potential for additional flexibility in the control of power systems in enabling the active participation of the consumption side in the balancing of power supply and demand. Most of this potential is to be found in the ability to defer consumption in which the exact time of power use is not critical for the application (Yi Zong,
Fig. 1. Load and wind power variations Load management can be used in certain large industrial applications such as the refrigeration of cold stores (Yi Zong, Tom Cronin, Oliver Gehrke, Henrik Bindner, Jens Carsten Hansen, Mikel Iribas Latour, Oihane Usunariz Arcauz 2009) or the lighting in greenhouses. Residential buildings offer possibilities as well; examples include many heating and cooling applications – space heaters, heat pumps, refrigerators, water heaters, etc. – where thermal energy can be stored in the heat capacitances of buildings, water or food. The household as main element of the local energy system’s demand side consists of several appliances. To minimize comfort losses for the user, only appliances with a storage
capacity for thermal energy, such as refrigerators, freezers, and electric water boilers, are considered for fulfilling normal-operation, non-emergency control tasks. Load management can offer various advantages for the participating parties as outlined in the following (S. Koch, M. Zima, and G. Andersson 2008): • Increase in grid security and enhance control possibilities, peak load reduction and ancillary service opportunities for electricity service providers. • Grid integration of intermittent renewable energy sources and reduced final consumer prices are examples for possible positive impacts. • Device-dependent load shedding in the case of a contingency can be an attractive option to complement or replace the less graceful automatic load shedding schemes which cause blackouts in entire regions.
•
Gaia wind turbine (11 kW)
•
Bonus wind turbine (55 kW)
•
Diesel generator set (48 kW/60kVA)
•
Solar panels (7 kW)
•
Vanadium battery (15 kW/120kWh)
•
Capacitor bank (46 kVAr)
•
Back-to-back converter (30 kW/45kVA)
•
Dump load (75 kW)
•
Office building-PowerFlexHouse (20 kW)
•
Plug-in hybrid car (9 kWh)
Current developments in information and communication technologies make it possible to exploit this potential. It is, however, uncertain how it can be realised. There is a great need to investigate how flexible consumption should be implemented, seen from the perspective of power system control as well as from that of a consumer. Any such system will have to be integrated with the rest of the power grid’s control system – probably by means of a market for system services and an aggregation mechanism. In order to gain acceptance, the user’s needs would need to be met without much noticeable compromise on perceived comfort. Ideally, the user would stay in control while providing flexibility to the grid. Model predictive control (MPC) is the only advanced control technology that has made a substantial impact on industrial control problems: its success is largely due to its almost unique ability to handle, simply and effectively, hard constraints on control and states. (D.Q.Mayne, 2001).It has been widely applied in industry and it was selected to meet the problems that mentioned above. This paper introduces PowerFlexHouse, a new experimental facility for exploring the technical potential of actively controlled buildings in power grids with a high penetration of renewable energy. A description of the facility is followed by a discussion of the requirements for the software platform on which building controllers can be executed. Finally, the design of a thermal model predictive controller for the power consumption prediction is presented in detail. 2. SYSTEM DESCRIPTION AND REQUIREMENTS 2.1 Distributed Power System -SYSLAB Description In recent years, Risø DTU has established SYSLAB, a new laboratory for intelligent, active and distributed power systems (http://www.risoe.dtu.dk/research/sustainable_energy/wind_e nergy/projects/syslab.aspx). SYSLAB is built around a small power grid with renewable (wind, solar) and conventional (diesel) power generation, battery storage, and various types of consumers. Currently components connected to the SYSLAB grid are listed as following (See Fig. 2):
Fig. 2. Components in SYSLAB All these components in the grid are remotely controllable and locally supervised by a “node” computer collocated with each unit. A high-speed data network connects all these nodes; together they form a distributed platform for testing new concepts for system controllers. The whole system can be run centrally from any point on the network, or serve as a platform for fully decentralised control, for example through software agents. One of the loads on the SYSLAB grid is a small, freestanding office building, PowerFlexHouse, which has been designed as a research facility for active load management. The building contains seven offices, a meeting room and a kitchen. The electrical load of the building consists of heating, lighting, air-conditioning, a hot-water supply and various household appliances, such as a refrigerator and a coffee machine. The combined peak load of the building is close to 20kW and dominated by ohmic loads, with the exception of fluorescent-tube lighting. Unlike most other buildings at Risø, its energy supply is purely electrical. All individual loads in the building are remote-controllable from a central building controller. The same controller is able
to receive input from a multitude of indoor and outdoor sensors (see Fig. 3): Each room is equipped with a motion detector, temperature and light sensors, window and door contacts. A meteorology mast on the outside of the building supplies local environmental measurements of ambient temperature, wind speed, wind direction, and solar irradiation. These environmental measurements can be used for obtaining precise estimates of the heat flux in and out of the building.
3. SOFTWARE PLATFORM DESCRIPTION In accordance with the above requirements for the whole software design, we divided the whole system into different domains. The architecture of the software can be looked upon as a network in which each node is an independent and autonomous module (See Fig. 4). Each of them has different functionalities and can be developed individually. They can all provide an interface to communicate with the other entities through standard network technologies. A client/server architecture was used to carry out the communication and collaboration among the different modules.
Fig 3. Sensors and actuators in PowerFlexHouse 2.2 System Requirements The main purpose of the PowerFlexHouse facility is to serve as a test bench for comparing various strategies for active and passive load management. The building controller is able to communicate with the SYSLAB grid through its own node computer. This interface can be used to provide the controller with power system information, either as raw data or processed into e.g. a price signal. Information may also flow in the other direction, for example providing the power system controller with the expected near-future behaviour of the building loads. Comfort settings can be adjusted individually for each room in the building by means of small touch-screen user interfaces. This approach allows the adaptation of the user interface depending on the chosen control strategy, and a comparison of user acceptance between different types of user interface. To realize the operation of the PowerFlexHouse as a flexible load for SYSLAB, there are general requirements for the PowerFlexHouse software design. Firstly, it should interact successfully as a SYSLAB component and negotiate electrical demand with the SYSLAB environment. Secondly, the events and conditions in and around the PowerFlexHouse should be collected and preserved in a database in order to provide information for the controller to make decisions and display the history records in some user interfaces. Thirdly, the controller should perform some automatic control algorithm to make the PowerFlexHouse react to the external power grid (SYSLAB). Finally, user visualization via t touch screen and user-interaction via PDA should be supplied in the house.
Fig. 4. Architecture of the PowerFlexHouse software platform The Hardware module (HW) is dedicated to the management of all the EnOcean devices (sensors, switches and actuators) that are in or around the house. There are motion sensors, temperature sensors, window/door sensors, light switches, radiators and light actuators for collecting information or control. First of all, this module should achieve wireless communication between the sensors and a transceiver connected to the RS232 port of an embedded PC. At the same time, this embedded PC is connected to the PowerFlexHouse node unit and thereby interfaces with the rest of SYSLAB’s network. So it is possible for us to take the SYSLAB status, such as available power, wind speed and diesel load into account for controlling the house. Next, when new measurement data are received from sensors, the HW module raises events and sends them to the other modules. Meanwhile, the HW module also receives commands from the Controller module. In addition, it should manage a state model for all the hardware devices. The Controller module processes the information provided by the HW, User Interface (UI), SYSLAB and Forecast modules so that it can make control decisions on how to manage the house. Control commands from the controller should be sent out to control the different loads in the PowerFlexHouse. With the purpose of optimising the behaviour of the house via the developed control algorithms, the evolution of the controller needs to collect information from the SYSLAB
environment, Database (DB), UI module, events from the HW module and energy model from the Forecast module. 4. MODEL-BASED PREDICTIVE CONTROLLER DESIGN 4.1 Thermal Model Predictive Control Concept for PowerFlexHouse’s Demand Side Management (DSM) Heating Strategies From the mathematic point of view, storage based appliances are similar from algorithmic point of view but apply different parameters. Therefore for the first step, we studied the thermal model predictive control for the PowerFlexhouse’s demand side management heating strategies. There are totally 10 heaters in the PowerFlexHouse. Each of them has the power of 1kW. So, maximum permitted electrical power consumption of heater units: Pheat-max=10×1kW=10kW. The aim of a DSM strategy is the reduction of the peak demand. Currently, taking into account the fact that the peak demand is mainly caused by the operation of heater units during winter for PowerFlexHouse, the DSM heating strategy intends to reduce the consumption of these units during the peak periods. However, this reduction should not be against the thermal comfort. This means that the indoor temperature and the humidity should not be increased more than a specified limit, so that the users still feel comfortable in it. MPC refers to a class of control algorithms that compute a sequence of manipulated variable adjustments by utilizing a process model to optimize forecasts of process behaviour based on a linear or quadratic open-loop performance objective, subject to equality or inequality constraints over a future time horizon. (Ronald Soeterboek 1992). The block diagram of a model predictive controller with one estimator is shown in Fig. 5. Here, we decided to use MPC to implement the first controller for the load management of PowerFlexHouse, because, as an optimization-based control technique, the periodic re-optimization characteristic of MPC, can not only provide stability during external disturbances, but also can overcome some effects of poor model.
u
Dynamic Optimizer
Tref
^
Model
To reduce the problem’s complexity, the model of heat dynamics of PowerFlexHouse is formulated as one large room exchanging heat with an ambient environment. That is to say, we regard 8 rooms in PowerFlexHouse building as a large room. The control object is the one representing indoor temperature Tin. The plant model is given as a stochastic discrete-time linear state-space model, which was directly obtained from the reference (Anders Thavlov 2008). The states space equations were expressed in (1) and (2): T(t + 1) = ФT(t) + ГU(t)
(1)
⎡ Ti (t ) ⎤ ⎢ ⎥ Output: y(t) =C T(t) = [1 0 0] Tim (t ) ⎢ ⎥ ⎢⎣Tom (t ) ⎥⎦
(2)
where
⎡ 9.9288 × 10−1 ⎢ Φ = ⎢ 2.7410 × 10−1 ⎢1.5641 × 10−4 ⎣ ⎡1.2844 × 10−3 ⎢ Γ = ⎢1.8592 × 10−4 ⎢ 3.3551 × 10−3 ⎣
1.8661 × 10−4 7.2489 × 10−1 1.5459 × 10−8
2.9990 × 10−2 2.6053 × 102 1.6128 × 10−6
5.6429 × 10−3 ⎤ ⎥ 8.1923 × 10−4 ⎥ 9.9649 × 10−1 ⎥ ⎦ −2 ⎤ 1.0226 × 10 ⎥ 1.4838 × 10−3 ⎥ 8.0402 × 10−7 ⎥ ⎦
T=[Tin,Tim, Tom] is the state vector and U =[Ta,Фs, Фh]is the input vector to the system. Here, Tin (t ) is the indoor air temperature; Tim (t ) and Tom (t ) , which are the temperature of heat accumulating layer in the building envelope and the temperature in the heat accumulating layer in the inner walls and floor, can not be measured. State estimator-Kalman filter can be used to estimate these two states; Ta is the ambient (outdoor) temperature; Фs is the solar radiation; and Фh is the energy input from the electrical heaters. We change (1) into (3): T(t + 1) = ФT(t) + Г1Z(t) + Г2U(t)
(3)
where [Г1 Г2] = Г; Z(t) = [Ta(t), Ф s(t)]’ is the environment input, at time t, which cannot be controlled and Ut is the heat input that the MPC heat controller determines.
MPC Controller
Cost Funtion + Constraints
4.2 Simple Lineal Thermal Model for PowerFlexHouse
T
Plant
y
State Estimator
Set-point Reference
4.3 Control Algorithm The control strategy for the electrical space heaters should be found such that the total cost of the energy used in heating is minimized over a horizon (Hp). At the same time, it should keep the indoor air temperature around the given reference temperature Tref = 21.5°C. The objective function can be formulated as: H
Fig. 5. Block diagram of a model predictive controller with one state estimator
J = m in [
p
−1
∑
k =0
Subject to:
H p −1
C (k ) × u (k ) +
∑
k =0
(
k Tm in − T r e f
2
)]
(4)
ui (t ) Є int [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], which means the heat input that the MPC heat controller determines by mixed integer optimization approach. Here, C(t) is the price signal which varies with time. The price signal is provided at noon 12:00 each day for the following day in hourly intervals. i. e. the current time is noon, then t=12:00, (24−t)+24=12+24=36 hours price signal known. Thus, there is an actual maximal prediction horizon of 36 hours. In addition, the minimum indoor temperature Tin_min=19ºC; the maximum indoor temperature Tin_max=22 ºC. Firstly, the controller output is initialised by the vector of dimension 1 ×Hp, u0 = [u0, …, uHp-1], containing the input variables of the plant which are optimised. However, the initialization of the algorithm assumes that the heating units are switched off, u0 = [0, …, 0]. This way, the search direction is always positive.
m = ⎡Tl in ,..., Tl in Use the model to predict T in k +1
⎢⎣
k + Hp Tl in − Tref ≤ ε , optimal solution is achieved and only
the first element of the controller output sequence (u0) is used to control the process. At the next sample (hence, at k+1), the whole procedure is repeated. Otherwise, the first element of the controller output sequence with the maximum or minimum power consumption of the heating units is used to control the process (u0= Pheat –max or u0=0). To overcome the model’s error, here we use the process’s real-time output and model (precious) predictive output to structure one model output feedback correction (See Fig. 6.) Uh(k+Hp/k)
+
Tref
optimizer _ +
Uh(k/k)
plant
Tin(k/k)
Tin(k+Hp/k)
model
+
Tin(k/k) _
correction
+
Fig. 6. Block diagram of PowerFlexHouse’s MPC The MPC control law is described as following: Step 1: Initialization step. u0 = [u0, …, uHp-1], where Hp is the
l in prediction horizon, u0 =oldu = 0 and T
k + Hp
− Tref is a huge
number. Step 2: At current time k, measure Tin(k/k) and compare it
l in with the precious predictive value T
( k / k −1)
l e = Tin(k/k) - T
( k / k −1) in
;
;
⎤ and ⎥⎦
m + e. correct the predictive error by T in l in Step 3: If T
k+Hp
−Tref ≤ ε and
k +i k +i ∀Tl in , Tin _ min ≤ Tl in ≤ Tin _ max , where 0 ≤ i ≤ H p and
ε is a small number. Calculate the optimal control sequence that minimizes the cost function. {u(k)= [u0, …, uHp-1]} = arg H
J = m in [
p
−1
H
C (k ) × u (k ) +
∑
k =0
p
−1
∑
k =0
(
k Tm in − T r e f
2
)]
Goto step 4.
l in else if T
k+Hp
The difference between the predicted indoor temperature at the end of the predictive period and the desired Tref one is evaluated at each control cycle. If this difference is small enough to be acceptable:
k + Hp
−Tref > ε
l in , Tl in ≥ T if ∀T in _ max , where 0 ≤ i ≤ H p k +i
k +i
u0=0
l in , Tl in ≤ T else if ∀T in _ min ,where 0 ≤ i ≤ H p k +i
k +i
u0= Pheat –max end if end if Step 4: Apply u0 to heating units. Next sample (hence, at k+1), k=k+1, and repeat from step 2 to step 4 until the whole predictive period. 5. FUTURE WORK In the near future, we hope that some projects based on this research facility can be done, for example, after we get satisfying results from the single-agent MPC, as described in section 4, a multi-agent MPC should be taken into account to find an acceptable near-optimal solution for the whole distributed power system. Meanwhile, the next controller will be integrated with the frequency control for power grid. The frequency of grid shows clearly the balance between the power supply and demand. For example, it is known that the grid frequency has the nominal value of 50 Hz in Europe. When demand exceeds supply, the frequency falls, when supply is over demand, the frequency rises. Depending on a measured difference from the nominal value of the frequency, the appliances can decide to work or to delay the activation for a short-time. Millions of such devices acting together would act like a huge, fast-reacting back-up system and keep the grid stable. It is expected that rapid and widespread implementation of this system will ensure these to be done with least impact on power grid and most benefit to ratepayers.
6. CONCLUSIONS Active management of the consumption side, i.e. the load in a power grid is widely seen as one of the keys to achieving additional operational flexibility to ensure the stability of the grid as penetration levels rise. However, efficient use of load management demands a tight integration with the power grid’s control system. It shows that various control strategies and theories can be investigated on load management and also on how this intelligent house is used to stabilize fluctuations in the power grid with a high penetration of renewable energy, in comparison with the actual power system presented within the SYSLAB.
REFERENCES Anders Thavlov (2008). Dynamic Optimization of Power Consumption. Chapter 7. Kongens Lynby 2008. D.Q. Mayne (2001). Constrained optimal control. European Control Conference, Plenary Lecture. Ronald Soeterboek (1992). Predictive control-a Unified Approach, chapter 1. Prentice Hall(UK) Limited,1992, Great Britain. S. Koch, M. Zima, and G. Andersson (2008). Local Load Management: Coordination of a diverse set of thermostat-controlled household appliances (available online). In Smart Energy Strategies 08, Zurich, Switzerland, 2008. Yi Zong, Anton Andersson, Oliver Gehrke, Francesco Sottini, Henrik Bindner, Per Nørgård (2007). Implementation of a load management research facility in a distributed power system. In: Proceeding of Nordic Wind Power Conference, Risø National Laboratory, Demark. pp.1-2. ISBN: 978-87-550-3640-6 Yi Zong, Tom Cronin, Oliver Gehrke, Henrik Bindner, Jens Carsten Hansen, Mikel Iribas Latour, Oihane Usunariz Arcauz (2009). Application Genetic Algorithms for Load Management in Refrigerated Warehouses with Wind Power Penetration. In: Proceeding of 2009 IEEE Bucharest Power Tech, Bucharest, Romania, pp. 1-6. IEEE Catalog Number: CFP09815-CDR, ISBN: 978-14244-2235-7
Abstract: This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system with a high penetration of renewable energy. A description of the facility based on a distributed power system (SYSLAB) is followed by a discussion of the software platform on which building controllers can be executed. Finally, this paper shows how to design a thermal model predictive controller for the power consumption prediction in this distributed power system. The control of the PowerFlexHouse allows studies to be performed on the reaction of this intelligent house to a hybrid power grid. With this demand side control study, we hope we can not only dramatically improve grid reliability, but also raise energy efficiencies and reduce power cost for users. Keywords: Load-management, Model predictive control, Distributed power system. 1. INTRODUCTION
Anton Andersson, Oliver Gehrke, Francesco Sottini, Henrik Bindner, Per Nørgård 2007).
There is an increasing level of wind power in the power system. i.e. the integration of 50% wind energy into electricity system by 2025 in Denmark places strong demands on flexibility elsewhere in the system and the task of controlling the system is becoming increasingly demanding. Fluctuations in wind power put large demands for flexibility on the rest of the system to achieve a balance between generation and consumption. Figure 1 shows power demand and wind power generation in a power system over a period of two days. It is clearly seen that the load has a daily pattern with some stochastic variations and that the wind power varies stochastically as well, but with no correlation with the load. It is also clear that the variations in wind power can be very large even in rather short timescales, i.e. from minutes to hours. Both the lack of correlation and the stochastic nature of the wind power put very strong requirements for flexibility on the rest of the system. Traditionally, there has been a separation between the production and consumption of electricity: Consumption has been regarded a passive part of the system with respect to control, and therefore any generation mismatch caused by variations in wind power production has had to be compensated by other generating units. In recent years, it has been realised that there is a large potential for additional flexibility in the control of power systems in enabling the active participation of the consumption side in the balancing of power supply and demand. Most of this potential is to be found in the ability to defer consumption in which the exact time of power use is not critical for the application (Yi Zong,
Fig. 1. Load and wind power variations Load management can be used in certain large industrial applications such as the refrigeration of cold stores (Yi Zong, Tom Cronin, Oliver Gehrke, Henrik Bindner, Jens Carsten Hansen, Mikel Iribas Latour, Oihane Usunariz Arcauz 2009) or the lighting in greenhouses. Residential buildings offer possibilities as well; examples include many heating and cooling applications – space heaters, heat pumps, refrigerators, water heaters, etc. – where thermal energy can be stored in the heat capacitances of buildings, water or food. The household as main element of the local energy system’s demand side consists of several appliances. To minimize comfort losses for the user, only appliances with a storage
capacity for thermal energy, such as refrigerators, freezers, and electric water boilers, are considered for fulfilling normal-operation, non-emergency control tasks. Load management can offer various advantages for the participating parties as outlined in the following (S. Koch, M. Zima, and G. Andersson 2008): • Increase in grid security and enhance control possibilities, peak load reduction and ancillary service opportunities for electricity service providers. • Grid integration of intermittent renewable energy sources and reduced final consumer prices are examples for possible positive impacts. • Device-dependent load shedding in the case of a contingency can be an attractive option to complement or replace the less graceful automatic load shedding schemes which cause blackouts in entire regions.
•
Gaia wind turbine (11 kW)
•
Bonus wind turbine (55 kW)
•
Diesel generator set (48 kW/60kVA)
•
Solar panels (7 kW)
•
Vanadium battery (15 kW/120kWh)
•
Capacitor bank (46 kVAr)
•
Back-to-back converter (30 kW/45kVA)
•
Dump load (75 kW)
•
Office building-PowerFlexHouse (20 kW)
•
Plug-in hybrid car (9 kWh)
Current developments in information and communication technologies make it possible to exploit this potential. It is, however, uncertain how it can be realised. There is a great need to investigate how flexible consumption should be implemented, seen from the perspective of power system control as well as from that of a consumer. Any such system will have to be integrated with the rest of the power grid’s control system – probably by means of a market for system services and an aggregation mechanism. In order to gain acceptance, the user’s needs would need to be met without much noticeable compromise on perceived comfort. Ideally, the user would stay in control while providing flexibility to the grid. Model predictive control (MPC) is the only advanced control technology that has made a substantial impact on industrial control problems: its success is largely due to its almost unique ability to handle, simply and effectively, hard constraints on control and states. (D.Q.Mayne, 2001).It has been widely applied in industry and it was selected to meet the problems that mentioned above. This paper introduces PowerFlexHouse, a new experimental facility for exploring the technical potential of actively controlled buildings in power grids with a high penetration of renewable energy. A description of the facility is followed by a discussion of the requirements for the software platform on which building controllers can be executed. Finally, the design of a thermal model predictive controller for the power consumption prediction is presented in detail. 2. SYSTEM DESCRIPTION AND REQUIREMENTS 2.1 Distributed Power System -SYSLAB Description In recent years, Risø DTU has established SYSLAB, a new laboratory for intelligent, active and distributed power systems (http://www.risoe.dtu.dk/research/sustainable_energy/wind_e nergy/projects/syslab.aspx). SYSLAB is built around a small power grid with renewable (wind, solar) and conventional (diesel) power generation, battery storage, and various types of consumers. Currently components connected to the SYSLAB grid are listed as following (See Fig. 2):
Fig. 2. Components in SYSLAB All these components in the grid are remotely controllable and locally supervised by a “node” computer collocated with each unit. A high-speed data network connects all these nodes; together they form a distributed platform for testing new concepts for system controllers. The whole system can be run centrally from any point on the network, or serve as a platform for fully decentralised control, for example through software agents. One of the loads on the SYSLAB grid is a small, freestanding office building, PowerFlexHouse, which has been designed as a research facility for active load management. The building contains seven offices, a meeting room and a kitchen. The electrical load of the building consists of heating, lighting, air-conditioning, a hot-water supply and various household appliances, such as a refrigerator and a coffee machine. The combined peak load of the building is close to 20kW and dominated by ohmic loads, with the exception of fluorescent-tube lighting. Unlike most other buildings at Risø, its energy supply is purely electrical. All individual loads in the building are remote-controllable from a central building controller. The same controller is able
to receive input from a multitude of indoor and outdoor sensors (see Fig. 3): Each room is equipped with a motion detector, temperature and light sensors, window and door contacts. A meteorology mast on the outside of the building supplies local environmental measurements of ambient temperature, wind speed, wind direction, and solar irradiation. These environmental measurements can be used for obtaining precise estimates of the heat flux in and out of the building.
3. SOFTWARE PLATFORM DESCRIPTION In accordance with the above requirements for the whole software design, we divided the whole system into different domains. The architecture of the software can be looked upon as a network in which each node is an independent and autonomous module (See Fig. 4). Each of them has different functionalities and can be developed individually. They can all provide an interface to communicate with the other entities through standard network technologies. A client/server architecture was used to carry out the communication and collaboration among the different modules.
Fig 3. Sensors and actuators in PowerFlexHouse 2.2 System Requirements The main purpose of the PowerFlexHouse facility is to serve as a test bench for comparing various strategies for active and passive load management. The building controller is able to communicate with the SYSLAB grid through its own node computer. This interface can be used to provide the controller with power system information, either as raw data or processed into e.g. a price signal. Information may also flow in the other direction, for example providing the power system controller with the expected near-future behaviour of the building loads. Comfort settings can be adjusted individually for each room in the building by means of small touch-screen user interfaces. This approach allows the adaptation of the user interface depending on the chosen control strategy, and a comparison of user acceptance between different types of user interface. To realize the operation of the PowerFlexHouse as a flexible load for SYSLAB, there are general requirements for the PowerFlexHouse software design. Firstly, it should interact successfully as a SYSLAB component and negotiate electrical demand with the SYSLAB environment. Secondly, the events and conditions in and around the PowerFlexHouse should be collected and preserved in a database in order to provide information for the controller to make decisions and display the history records in some user interfaces. Thirdly, the controller should perform some automatic control algorithm to make the PowerFlexHouse react to the external power grid (SYSLAB). Finally, user visualization via t touch screen and user-interaction via PDA should be supplied in the house.
Fig. 4. Architecture of the PowerFlexHouse software platform The Hardware module (HW) is dedicated to the management of all the EnOcean devices (sensors, switches and actuators) that are in or around the house. There are motion sensors, temperature sensors, window/door sensors, light switches, radiators and light actuators for collecting information or control. First of all, this module should achieve wireless communication between the sensors and a transceiver connected to the RS232 port of an embedded PC. At the same time, this embedded PC is connected to the PowerFlexHouse node unit and thereby interfaces with the rest of SYSLAB’s network. So it is possible for us to take the SYSLAB status, such as available power, wind speed and diesel load into account for controlling the house. Next, when new measurement data are received from sensors, the HW module raises events and sends them to the other modules. Meanwhile, the HW module also receives commands from the Controller module. In addition, it should manage a state model for all the hardware devices. The Controller module processes the information provided by the HW, User Interface (UI), SYSLAB and Forecast modules so that it can make control decisions on how to manage the house. Control commands from the controller should be sent out to control the different loads in the PowerFlexHouse. With the purpose of optimising the behaviour of the house via the developed control algorithms, the evolution of the controller needs to collect information from the SYSLAB
environment, Database (DB), UI module, events from the HW module and energy model from the Forecast module. 4. MODEL-BASED PREDICTIVE CONTROLLER DESIGN 4.1 Thermal Model Predictive Control Concept for PowerFlexHouse’s Demand Side Management (DSM) Heating Strategies From the mathematic point of view, storage based appliances are similar from algorithmic point of view but apply different parameters. Therefore for the first step, we studied the thermal model predictive control for the PowerFlexhouse’s demand side management heating strategies. There are totally 10 heaters in the PowerFlexHouse. Each of them has the power of 1kW. So, maximum permitted electrical power consumption of heater units: Pheat-max=10×1kW=10kW. The aim of a DSM strategy is the reduction of the peak demand. Currently, taking into account the fact that the peak demand is mainly caused by the operation of heater units during winter for PowerFlexHouse, the DSM heating strategy intends to reduce the consumption of these units during the peak periods. However, this reduction should not be against the thermal comfort. This means that the indoor temperature and the humidity should not be increased more than a specified limit, so that the users still feel comfortable in it. MPC refers to a class of control algorithms that compute a sequence of manipulated variable adjustments by utilizing a process model to optimize forecasts of process behaviour based on a linear or quadratic open-loop performance objective, subject to equality or inequality constraints over a future time horizon. (Ronald Soeterboek 1992). The block diagram of a model predictive controller with one estimator is shown in Fig. 5. Here, we decided to use MPC to implement the first controller for the load management of PowerFlexHouse, because, as an optimization-based control technique, the periodic re-optimization characteristic of MPC, can not only provide stability during external disturbances, but also can overcome some effects of poor model.
u
Dynamic Optimizer
Tref
^
Model
To reduce the problem’s complexity, the model of heat dynamics of PowerFlexHouse is formulated as one large room exchanging heat with an ambient environment. That is to say, we regard 8 rooms in PowerFlexHouse building as a large room. The control object is the one representing indoor temperature Tin. The plant model is given as a stochastic discrete-time linear state-space model, which was directly obtained from the reference (Anders Thavlov 2008). The states space equations were expressed in (1) and (2): T(t + 1) = ФT(t) + ГU(t)
(1)
⎡ Ti (t ) ⎤ ⎢ ⎥ Output: y(t) =C T(t) = [1 0 0] Tim (t ) ⎢ ⎥ ⎢⎣Tom (t ) ⎥⎦
(2)
where
⎡ 9.9288 × 10−1 ⎢ Φ = ⎢ 2.7410 × 10−1 ⎢1.5641 × 10−4 ⎣ ⎡1.2844 × 10−3 ⎢ Γ = ⎢1.8592 × 10−4 ⎢ 3.3551 × 10−3 ⎣
1.8661 × 10−4 7.2489 × 10−1 1.5459 × 10−8
2.9990 × 10−2 2.6053 × 102 1.6128 × 10−6
5.6429 × 10−3 ⎤ ⎥ 8.1923 × 10−4 ⎥ 9.9649 × 10−1 ⎥ ⎦ −2 ⎤ 1.0226 × 10 ⎥ 1.4838 × 10−3 ⎥ 8.0402 × 10−7 ⎥ ⎦
T=[Tin,Tim, Tom] is the state vector and U =[Ta,Фs, Фh]is the input vector to the system. Here, Tin (t ) is the indoor air temperature; Tim (t ) and Tom (t ) , which are the temperature of heat accumulating layer in the building envelope and the temperature in the heat accumulating layer in the inner walls and floor, can not be measured. State estimator-Kalman filter can be used to estimate these two states; Ta is the ambient (outdoor) temperature; Фs is the solar radiation; and Фh is the energy input from the electrical heaters. We change (1) into (3): T(t + 1) = ФT(t) + Г1Z(t) + Г2U(t)
(3)
where [Г1 Г2] = Г; Z(t) = [Ta(t), Ф s(t)]’ is the environment input, at time t, which cannot be controlled and Ut is the heat input that the MPC heat controller determines.
MPC Controller
Cost Funtion + Constraints
4.2 Simple Lineal Thermal Model for PowerFlexHouse
T
Plant
y
State Estimator
Set-point Reference
4.3 Control Algorithm The control strategy for the electrical space heaters should be found such that the total cost of the energy used in heating is minimized over a horizon (Hp). At the same time, it should keep the indoor air temperature around the given reference temperature Tref = 21.5°C. The objective function can be formulated as: H
Fig. 5. Block diagram of a model predictive controller with one state estimator
J = m in [
p
−1
∑
k =0
Subject to:
H p −1
C (k ) × u (k ) +
∑
k =0
(
k Tm in − T r e f
2
)]
(4)
ui (t ) Є int [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], which means the heat input that the MPC heat controller determines by mixed integer optimization approach. Here, C(t) is the price signal which varies with time. The price signal is provided at noon 12:00 each day for the following day in hourly intervals. i. e. the current time is noon, then t=12:00, (24−t)+24=12+24=36 hours price signal known. Thus, there is an actual maximal prediction horizon of 36 hours. In addition, the minimum indoor temperature Tin_min=19ºC; the maximum indoor temperature Tin_max=22 ºC. Firstly, the controller output is initialised by the vector of dimension 1 ×Hp, u0 = [u0, …, uHp-1], containing the input variables of the plant which are optimised. However, the initialization of the algorithm assumes that the heating units are switched off, u0 = [0, …, 0]. This way, the search direction is always positive.
m = ⎡Tl in ,..., Tl in Use the model to predict T in k +1
⎢⎣
k + Hp Tl in − Tref ≤ ε , optimal solution is achieved and only
the first element of the controller output sequence (u0) is used to control the process. At the next sample (hence, at k+1), the whole procedure is repeated. Otherwise, the first element of the controller output sequence with the maximum or minimum power consumption of the heating units is used to control the process (u0= Pheat –max or u0=0). To overcome the model’s error, here we use the process’s real-time output and model (precious) predictive output to structure one model output feedback correction (See Fig. 6.) Uh(k+Hp/k)
+
Tref
optimizer _ +
Uh(k/k)
plant
Tin(k/k)
Tin(k+Hp/k)
model
+
Tin(k/k) _
correction
+
Fig. 6. Block diagram of PowerFlexHouse’s MPC The MPC control law is described as following: Step 1: Initialization step. u0 = [u0, …, uHp-1], where Hp is the
l in prediction horizon, u0 =oldu = 0 and T
k + Hp
− Tref is a huge
number. Step 2: At current time k, measure Tin(k/k) and compare it
l in with the precious predictive value T
( k / k −1)
l e = Tin(k/k) - T
( k / k −1) in
;
;
⎤ and ⎥⎦
m + e. correct the predictive error by T in l in Step 3: If T
k+Hp
−Tref ≤ ε and
k +i k +i ∀Tl in , Tin _ min ≤ Tl in ≤ Tin _ max , where 0 ≤ i ≤ H p and
ε is a small number. Calculate the optimal control sequence that minimizes the cost function. {u(k)= [u0, …, uHp-1]} = arg H
J = m in [
p
−1
H
C (k ) × u (k ) +
∑
k =0
p
−1
∑
k =0
(
k Tm in − T r e f
2
)]
Goto step 4.
l in else if T
k+Hp
The difference between the predicted indoor temperature at the end of the predictive period and the desired Tref one is evaluated at each control cycle. If this difference is small enough to be acceptable:
k + Hp
−Tref > ε
l in , Tl in ≥ T if ∀T in _ max , where 0 ≤ i ≤ H p k +i
k +i
u0=0
l in , Tl in ≤ T else if ∀T in _ min ,where 0 ≤ i ≤ H p k +i
k +i
u0= Pheat –max end if end if Step 4: Apply u0 to heating units. Next sample (hence, at k+1), k=k+1, and repeat from step 2 to step 4 until the whole predictive period. 5. FUTURE WORK In the near future, we hope that some projects based on this research facility can be done, for example, after we get satisfying results from the single-agent MPC, as described in section 4, a multi-agent MPC should be taken into account to find an acceptable near-optimal solution for the whole distributed power system. Meanwhile, the next controller will be integrated with the frequency control for power grid. The frequency of grid shows clearly the balance between the power supply and demand. For example, it is known that the grid frequency has the nominal value of 50 Hz in Europe. When demand exceeds supply, the frequency falls, when supply is over demand, the frequency rises. Depending on a measured difference from the nominal value of the frequency, the appliances can decide to work or to delay the activation for a short-time. Millions of such devices acting together would act like a huge, fast-reacting back-up system and keep the grid stable. It is expected that rapid and widespread implementation of this system will ensure these to be done with least impact on power grid and most benefit to ratepayers.
6. CONCLUSIONS Active management of the consumption side, i.e. the load in a power grid is widely seen as one of the keys to achieving additional operational flexibility to ensure the stability of the grid as penetration levels rise. However, efficient use of load management demands a tight integration with the power grid’s control system. It shows that various control strategies and theories can be investigated on load management and also on how this intelligent house is used to stabilize fluctuations in the power grid with a high penetration of renewable energy, in comparison with the actual power system presented within the SYSLAB.
REFERENCES Anders Thavlov (2008). Dynamic Optimization of Power Consumption. Chapter 7. Kongens Lynby 2008. D.Q. Mayne (2001). Constrained optimal control. European Control Conference, Plenary Lecture. Ronald Soeterboek (1992). Predictive control-a Unified Approach, chapter 1. Prentice Hall(UK) Limited,1992, Great Britain. S. Koch, M. Zima, and G. Andersson (2008). Local Load Management: Coordination of a diverse set of thermostat-controlled household appliances (available online). In Smart Energy Strategies 08, Zurich, Switzerland, 2008. Yi Zong, Anton Andersson, Oliver Gehrke, Francesco Sottini, Henrik Bindner, Per Nørgård (2007). Implementation of a load management research facility in a distributed power system. In: Proceeding of Nordic Wind Power Conference, Risø National Laboratory, Demark. pp.1-2. ISBN: 978-87-550-3640-6 Yi Zong, Tom Cronin, Oliver Gehrke, Henrik Bindner, Jens Carsten Hansen, Mikel Iribas Latour, Oihane Usunariz Arcauz (2009). Application Genetic Algorithms for Load Management in Refrigerated Warehouses with Wind Power Penetration. In: Proceeding of 2009 IEEE Bucharest Power Tech, Bucharest, Romania, pp. 1-6. IEEE Catalog Number: CFP09815-CDR, ISBN: 978-14244-2235-7