Remote Renewable Energy Monitoring & Control Mr. Shane Phelan, Dr. Stephen Daniels

School of Electronic Engineering , Energy Design Lab DCU and Cinergy Ltd

Introduction Renewable energy sources are inherently intermittent and when they power important equipment such as telecom’s towers, monitoring their real-time and long term performance becomes critical. These installations are often located in difficult to access and environmentally harsh locations so it is also important to model how they will perform in these regions. This process will facilitate more realistic cost projections with respect to operational costs and component lifetime. Traditional sources of energy for these types of sites are AC generators which are required to run 24/7. This mode of operation results in high running costs in terms of fuel and maintenance. The alternative is to operate a hybrid system which only runs for half the time thus reducing these costs.

Objectives • Establish efficient methods of acquiring, transmitting and

storing logged operational information

• Simulate end to end system to predict optimal operational settings and costs

• Compare physical systems with constructed models using the same [wind] data to verify the integrity of the simulated data

Research Techniques A Labview based data acquisition & sensor system was designed, built and installed into a multitude of hybrid generator systems, off grid sites and grid tied sites.

5.8kW Wind Turbine

10kW DC Generator

Battery Bank 40kWh

DC Bus

DC Load (0 – 100A)

5kw Inverter

Industrial

Computer

& Router

Data Acquisition

Unit

Results (Physical System) These results show the number of times the generator must run to make up for the intermittent wind resource. Over a period of 5 days, the generator run time was significant despite the load only being 1.5kW. The wind turbine hardly contributed during this period.

Future Research • Increase the level of model sophistication in terms of multi

stage charging, temperature compensation, battery degradation, wind power dumped and other parameters

• Refine model to include more accurate operational costs

• Expand model to cater for domestic and broader industrial installations

Acknowledgments

Conclusions • The most important parameter is the engine run hours per

day. Any significant increase/decrease indicates a problem in the system where the load is constant

• Hybrid Systems can offer significant savings

• Even a simple model can act as a viable alternative to the expense of real world testing

Results (Model) A simple Simulink based hybrid energy system was constructed to replicate the electrical performance of the core components of the system. These are the same components as seen in figure 2. The initial version of the model does not include the amount of energy dumped by the wind turbine or the 2nd stage of the DC generator charging profile.

Cinergy are an Irish based telecoms power solutions provider. They specialise in providing off-grid and grid-tied products which reduce fuel consumption and C02 emissions respectively. As this is a relatively new method of powering telecoms sites, the need for comprehensive component monitoring is paramount. They have been working directly with this project for the last 18 months.

Figure 1: Cinergy Off Grid Test Site

Figure 2: High level view of data acquisition system

Figure 3: System Voltage (Green), Generator Power (Red) and Wind Power (Purple)

Figure 4: High level Simulink model overview

This model demonstrates the core components of the system. Each sub element contains its own algorithm controlling how varying levels of bus voltage affect its input to the system. Simulink already contains sophisticated algorithms for controlling the performance and behavior of various types of battery banks. Parameters such as internal resistance, chemical composition and individual cell voltages are catered for which is extremely useful. The performance profile of the Fortis Montana wind turbine was integrated into the system to give an approximate power output for a given wind speed.

The main variable is system voltage which indicates the level of charge of the battery bank. This parameter controls when the generator cuts in and when the wind controller backs off. This graph is similar to the measured (real world) results in that the generator can be seen to operate during the periods of low wind input. These operation parameters can give the user a more accurate indication of the typical generator run time for a given environment.

Figure 5: Simulink Results

Voltage

Generator Current

Wind Turbine Current

Discussion The real world data roughly corresponds to that of the model data. This can be seen in terms of the number of times that the generator must cut in to make up for the intermittency of the wind power. The generator cycles are a little shorter due to the single stage charging implementation in the model. The variable input to the model is very noisy simulated wind speed data. This is causing a higher degree of variance than the real world data. The wind turbine controller deems the battery bank to be charged at 56.4V and backs off after this point. This problem is more evident in the simulated data. This means that most of the wind power is dumped while the generator is running which could be as much as 8 hours for a typical run. During the periods when the wind is providing enough power to supply the load the battery bank is undergoing micro-cycling .In the same way as full charge-discharge cycles, the battery bank can handle only a finite amount of micro-cycles before the quality of the batteries becomes affected. The long term effects of this activity are relatively unknown by the battery bank manufacturers.