ON THE ENERGY MANAGEMENT IN DATA CENTER’S MICROGRID Teemu Koski, Heikki Koivo, Vesa Hasu

Aalto University, Department of Automation and Systems Technology {teemu.koski, heikki.koivo, vesa.hasu}@tkk.fi

ABSTRACT This paper focuses on the energy management of microgrid connected to a data center. In our simulation studies, we use microgrid including a diesel generator, a micro turbine and the supply from the main grid. The energy supply management in microgrid is studied with a few allocation procedures. The first procedure is based on Mesh Adaptive Direct Search (MADS) optimization. The second procedure is an expert system. Based on the simulations, the paper suggests features for energy management algorithms of data centers’ microgrids.

Index terms: microgrid; energy management; simulation.

1. INTRODUCTION Microgrids have raised interest especially in countries,

in which a reliable electrical network is not available, or in the applications requiring a power reserve in the case of blackouts. One application for microgrids is the power supply of data centers. In the internet era, data centers and their energy demands have grown into new highs. Microgrids related to data centers can reduce the dependency on the local electric network operator’s quality of service and pricing. During the building a data center using megawatts of power, a microgrid might be cheaper than strengthening the local electrical network.

In this paper, we study the energy management procedures of microgrid through simulation studies. The idea is to study the effect of an energy management system of the total energy costs. The energy management can be done in several ways, such as using agents [1]. In this work, we use a mesh adaptive search (MADS) [2] and an expert system for optimizing costs. Additionally, we apply also a genetic algorithm (e.g. [3]). Its performance was not better than MADS and the computation took more than five times the MADS computation time. Hence, we omit its reporting in our paper. The energy management methods are discussed in Section III.

The applied energy sources are diesel generators and micro turbines. Both energy sources are introduced individually with its characteristics. Originally the idea was to include also e.g. solar and fuel cells to the simulations, but the chosen simulation procedure was poorly suited to converting DC to three-phase AC. The work’s essence, energy management, required simulation periods of hours and an efficient DC-AC-conversion would have required 1500 Hz simulation.

In order to MADS function to work, cost functions were introduced for each energy source. The costs were formulated so that costs were functions of produced energy. They were based on the manufacturers’ data or the research literature.

In the paper a complete microgrid model is built and simulated in a few working conditions: normal condition, during an islanding process and how fuel and electricity prices affect optimization and total costs. Microgrid was modeled using models for the energy source, an electricity

consumption of the data center and the optimization controller. Lastly the MADS controller was compared with an expert system controller. Finally, we discuss how the optimization controller should be improved if the controller was implemented in the data center. In addition, we discuss on future work and some recommendations how to improve the energy management.

In this paper, Section II and III introduce the models and the energy management procedures, respectively. Sections IV and V show the simulations and the conclusions, respectively.

2. SIMULATION MODELS The following simulations consider the real and

reactive powers of the data center’s microgrid. Simulations are made in Simulink-environment of MATLAB with SimPower Systems software [5], which also supplied the applied induction generator models. 2.1. Data center

In the data center modeling, we follow the paper by Fan et al. [4], which describes the power usage of a center with cdfs based on data center tasks. The center tasks are web search, webmail and large-scale data processing. The data center model includes 1 MW constant usage, which represents center cooling, power losses, and other local consumption. Additionally, we include a dynamic power usage of servers as in [4]. All three server groups are equal and their maximum power usage is 0.75 MW.

In the simulations, we assume that the data center has an Uninterruptible Power Supply (UPS) system in batteries. As a simplification, we treat UPS as one large battery, which is supplies undisturbed energy to servers. 2.2. Diesel generator

Diesel generators (DG) represent a well-known technology, which is very often used for reserve power. Their advantages are relatively fast start-ups (around 30 s), their reasonable price and the wide variety of scales. The efficiency of diesel motors can be around 50%, even though the traditional generators reach about 40% [6]. The downsides of DG are the dependency on fossil fuels and their large emissions of small particles.

The DG model and its parameters values were chosen as in [7]. First, the amount of injected fuel Φ is decided from the control signal by the transfer function

10.125 1( ) ( )ss l s+Φ = , (1)

where s is the Laplace-variable and l is the control signal. Second, the motor is modeled as a simple delay

0.5( ) 1.15 ( )sq s e s−= Φ , (2)

where q is the mechanical torque of the motor. Third, the flywheel with a constant friction is modeled by a function

0.30.03( ) ( )sn s q s+= , (3)

2011 IEEE GCC Conference and Exhibition (GCC), February 19-22, 2011, Dubai, United Arab Emirates

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where n is the wheel angular speed. The angular speed from (3) was fed into the generator modeled in SimPower. The maximum real power of the diesel engine is 2 MW

For the control signal l(s), we utilize a cascade controller structure (see e.g. [8]) with a PI-controller, in which the inner loop controller is twice as fast the outer loop controller. The discrete transfer function of the applied PI-controller is GPI(z) = KP + KI/(z – 1), (4) and KP = 0.3, KI = 0.002 for inner PI-controller, KP = 0.3, KI = 0.001 for outer controller.

For the diesel engine fuel consumption, we modeled the DG fuel consumption Fdg in liters per hour to the power relative to the generator’s peak power Pdg (scaled from 0 to 1) by the second order polynomial Fdg = 78.9 + 246⋅Pdg + 96⋅Pdg

2. (5) The model is based on the values Caterpillar 2000 kVA standby generator specifications [9]. In addition to the fuel costs, the simulations include operation and maintenance costs. For the diesel engine, the cost is 0.0852 €/kWh [10]. 2.3. Micro turbine

Micro turbines (MT) are small gas turbines with a high rotation speed. For the data center microgrid, an interesting aspect of MT is the ability to modify them to use other fuels than natural gas, such as ethanol or bio-gas [11]. The electrical efficiency of MTs is around 30% [11]. For the MTs, we use the model presented in Fig. 1 [12].

Figure 1. Micro turbine model diagram. Adapted from [12].

The MT fuel system model consists of two first degree transfer functions in series with saturation between them [12]. The time constants for the first and second transfer functions are 10 s and 0.1 s, respectively. The saturation restricts the fuel injection change between –0.1 and 1.2.

MT is controlled using either PI-controllers for rotation speed and power (left in Fig. 1) or a Watt governor (bottom of Fig. 1). The governor guarantees the turbine keeping in a safe operating zone. The parameters for the angular velocity PI-controller are KP = 1000, KI =12.5. The parameters of power PI-controller, are KP = 0.1, KI =1.0. The PI-controller structure is as in (4).

The MT efficiency decreases rapidly if it is not used with a nominal power [13]. Hence we use an efficiency normalization ηnorm(Pmt), where Pmt is the MT power relative to the turbine’s peak power, to scale the nominal efficiency ηnom. We use the efficiency normalization

( ) 20.54 0.89 0.43norm mt mt mtP P Pη = + ⋅ − ⋅ , (6)

which is based on the data in [13]. This normalization constant is multiplied with ηnom = 0.3 to get the proper efficiency in the simulations. In the simulation, the MT operation and maintenance costs are 0.0040 €/kWh [10].

3. MANAGEMENT METHODS

3.1. Mesh Adaptive Direct Search (MADS) MADS is an extension of the generalized pattern

search algorithm for constrained nonlinear optimization [2], which minimizes the cost function f: n → { }∪ ∞ under constraints x ∈ nΩ ⊂ . MADS advantages include that it does not require the derivative of the cost function. In the following, we present only the outline of MADS. For full mathematical details and set of parameters, see [2]. The general MADS algorithm for minimization is:

• Initialization: Let the initial x0 belong to the subspace defined by the constraints; and set parameters [2].

• Perform a poll operation until a new candidate xk is found so that f(xk) < f(xk–1) or the termination condition is fulfilled. In the poll operation, a minimization candidate is “polled” around the previous candidate.

• If the poll is successful, the poll domain is enlarged; if the poll is unsuccessful, the domain is reduced.

• An optional search using any additional algorithm. • Go back to poll stage.

The algorithm is terminated if any of the termination conditions is fulfilled; usually the poll area retraction under the minimum threshold or the number of iterations over the maximum threshold. The original MADS [2] does not rule how to do the different stages in the algorithm. The implementation is done with the Matlab’s Global Optimization Toolbox [14]. 3.2. The expert system

In order to gain more knowledge on the energy management, we compare MADS algorithm with an expert system. The system takes into account that the efficiencies of the generators are at its largest close to the maximum output.

In practice, the expert system computes the unit prices for each generator type and the electricity from the main grid. Then the system chooses the cheapest energy supply type and determines whether or not it can supply for the whole energy demand. If the whole demand cannot be satisfied, the surplus demand is allocated to the rest of the energy supply methods by the same criterion until the whole demand is satisfied.

4. SIMULATION RESULTS In the simulations, the prices are relative to the ones in

Finland. If not indicated otherwise, the applied prices are: diesel 1 €/l, natural gas 0.0410 €/kWh and electricity from the main grid 0.14 €/kWh. The electricity and natural gas prices are estimated based on the Nordic power market prices from Nordpool. The maintenance costs of DG and MT, 0.0852 €/kW and 0.0040 €/kW, are also included.

The energy management algorithm is run in every five minutes. If the energy demand increases between these instants, the additional energy is taken from the main grid. If generators produce more energy than required, the microgrid feeds the surplus to the main grid for free.

The simulations include two 200 kW MTs, a 2 MW DG and a connection to the main grid. The microgrid feeds its energy to UPS, from which the data center servers take their power. We use 75 MJ UPS. Simulations are done for 50 Hz AC. The data center power demand is around 750 kW for servers and 1 MW for the cooling and other fixed consumption.

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More simulation results can be found in [15]. 4.1. Reference simulation: constant energy pricing

The scope of this simulation is to examine the energy management during a mid-term period of 12 h. The reference powers from the MADS-optimization energy management are drawn in Fig. 2. It shows that, with the current prices, the algorithm buys most of the energy demand from the main grid. In a few occasions, the management starts MTs, which is a result of optimization getting stuck in a local optimum and the optimum being at the boundary of the optimization domain.

The real and reactive powers from MTs and DG are drawn in Fig. 3. It shows that MTs cause reactive power disturbances in the microgrid. Also, the management system starts DG occasionally, which causes reactive power to the main grid.

The power received from the main grid in Fig. 4 reflects the changes in the server load and the use of microgrid power generation. The server load variation of ca 100 kW is clearly visible in Fig. 4. The reflective power in Fig. 4 is proportional to the one in Fig. 3. The energy cost variation of the data center is shown in Fig. 5. The variation is very closely related to the amount of the energy bought from the main grid. 4.2. Main grid blackout

This simulation is for 3 hours, which includes a blackout between 0.28 and 1.7 hour. That is, during that period energy cannot be bought from the main grid. During the blackout, the management algorithm is run twice as fast, i.e. the period is reduced to 150 s. The microgrid is disconnected from the main grid between 0.4 h and 1.1 h, and connected without power supply between 1.1 h and 1.7 h. This is done to test the management while the DG can follow the load.

Fig. 6 shows the reference powers for the available electricity providing techniques. During the blackout, the buying from the main grid is disabled. Fig. 6 shows that the management algorithm asks DG to provide the most of the power compensation. In the microgrid, there is a quick disturbance between 0.4 h and 1.1 h, see the real and reactive powers in Fig. 7. The disturbances relate to a slow reaction of the optimization algorithm to the load changes. These disturbances end when the microgrid is connected back to the main grid in 1.1 h. Interestingly at that point, the oscillation caused by the DG in reactive powers of MTs increase and the oscillation in real powers decrease. 4.3. Comparison between management strategies: changing energy prices

In here, we compare the MADS-based energy management algorithm to the expert system described in Subsection III.B. In the simulation, the power prices of DG, MTs and electricity from the main grid vary according to the Fig. 8. Even though some prices may be unrealistic, they give an indication of the management success and behavior while prices are varying.

The reference powers and the total energy costs of the two management techniques are shown in Figs. 9 and 10, respectively. Fig. 9 shows that the references differ greatly from each other. The expert system results in very constant power management, which does not use the DG at all. The MADS optimization results in more dynamic management, which applies also the DG.

The costs of the management techniques in Fig. 10

indicate that the MADS optimization does not work well in the cost optimization. One of the reasons for the suboptimality of the MADS allocation is that it does not take into account the relative efficiencies of the microgrid power generators, i.e. the advantages of using generators

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Figure 2. Reference powers decided by the MADS optimization for microturbines (MT1, MT2), diesel generator (DG) and

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Figure 3. Real and reactive powers of MT1,2 and DG. Negative values are energy transfer from the generators to the microgrid.

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Figure 4. Real and reactive powers taken from the main grid. Positive values: energy transfer from the main grid to microgrid.

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Figure 5. Energy costs of the data center.

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Figure 6. Reference powers decided by the MADS optimization for MT1, 2, DG and power taken from the main grid (Network).

The blackout occurs at the time of 0.28 h and 1.67 h.

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close to their maximum power. The black-box optimization methods like MADS have

their advantages. For example, more power generation techniques are easy to incorporate into its cost function, whereas the expansion of the expert system can become tedious with increasing number of featured services.

5. CONCLUSIONS This paper discussed the use of microgrid energy

supply for data centers. The models of components were taken from the literature. The energy management was

studied through the cost and the powers in the microgrid. The paper presented two versions of energy

management techniques: a black-box optimization based technique and an expert system relying on the system knowledge. Simulations indicated that the expert system was more appropriate for the energy management of the data center microgrid. However, the expert system did have knowledge, e.g., on the generator efficiencies that the black-box –type MADS method did not utilize. The simulations indicated that the energy management algorithm should follow the load of the data center closer in order to reduce the disturbances if the microgrid is not connected in the main grid.

In practice, the management technique should also prefer a generator, which is able to follow rapid developments in the load in the case of blackout. In our framework, this would mean using more DG power. Another issue for practical energy management algorithms is the change from the power generation to the electricity buying from the main grid. The presented management techniques can cause disturbances in the microgrid due to rapid changes in the reference powers.

Comparison of results points out some problems with MADS algorithm like slowness to find a new optimum point when prices change. Also MADS controller output contains unnecessary variation for energy sources.

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Multiagent System for Microgrid Control," IEEE Trans. Power Systems, vol. 20, pp. 1447-1455, 2005.

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[3] R.L. Haupt, S.E. Haupt, Practical Genetic Algorithms. John Wiley & Sons, 1998, 272 p.

[4] X. Fan, W. Weber and L. A. Barroso, "Power provisioning for a warehouse-sized computer". Proc. 34th Annual International Symposium on Computer Architecture, pp. 13-23, 2007.

[5] The MathWorks, “SimPowerSystems 5.2”, accessible at: http:// www.mathworks.com/products/simpower/, accessed May 12th, 2010.

[6] L. Guzzella and A. Amstutz, "Control of diesel engines". IEEE Control Systems Magazine, vol. 18, pp. 53-71, 1998.

[7] Bo Kuang, Youyi Wang and Yoke Lin Tan, "An H∞ controller design for diesel engine systems," Proc. Int. Conf. Power System Technology, PowerCon 2000. Vol. 1, pp. 61-66, 2000.

[8] K. Dutton, S. Thompson, B. Barraclough, The art of control engineering. Prentice Hall, 1997, 811 p.

[9] Caterpillar, Standby 1600 ekW 2000 kVA 50 hz 1500 rpm 400 volts, accessible at: http://www.cat.com/cda/components/securedFile/disp laySecuredFileServletJSP?x=7&fileId=725527, accessed May 12th, 2010.

[10] F. A. Mohamed. “Microgrid modelling and online management,” Doctoral Thesis, Helsinki University of Technology, Finland, 2008.

[11] S. Haugwitz, “Modelling of Microturbine Systems”, Proc. European Control Conference, Cambridge, UK, 2003.

[12] M. Y. El-Sharkh, N. S. Sisworahardjo, M. Uzunoglu, O. Onar and M. S. Alam, "Dynamic behavior of PEM fuel cell and microturbine power plants," J. Power Sources, vol. 164, pp. 315-321, 1/10. 2007.

[13] L. Goldstein, B. Hedman, D. Knowles, S. Freedman I., R. Woods and T. Schweizer, "Gas-fired distributed energy resource technology characterizations," National Renewable Energy Laboratory, Tech. Rep. NREL/TP-620-34783, 2003.

[14] The MathWorks, “Global Optimization Toolbox 3.0”, accessible at: http://www.mathworks.com/products/global-optimization/, May 12th, 2010.

[15] T.P. Koski, “Tietokonekeskuksen mikroverkko-järjestel-män energianhallinta”, Master’s Thesis (in Finnish), Aalto University, Finland, 2010.

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Figure 7. Powers of micro turbines (MT1,2) and diesel generator (DG). Negative powers mean energy transfer from

generators to microgrid.

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Figure 8. Changing energy prices during the simulation: diesel, natural gas and electricity unit prices.

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Figure 9. Reference powers determined by the expert system

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Figure 10. Energy costs of the data center for expert system and MADS. The zero prices refer to unsuccessful optimization.

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