OPTIMIZING WIND-DIESEL HYBRID ENERGY SYSTEMS WITH A DEMAND SIDE MANAGEMENT TECHNIQUE
Aims
While the benefits of added wind power to diesel generator based remote area communities is well known, optimizing wind-diesel systems is problematic. Each remote energy system includes unique energy loading patterns together with a unique and variable wind resource resulting in system design on a case by case basis. When wind turbines are oversized, an abundance of renewable energy must be dumped in order to maintain the integrity of the hybrid system. This paper explains the potential fuel savings that could be realized by introducing demand side management techniques to wind-diesel hybrid energy systems and a software tool to model them. Scott Base, New Zealand’s Antarctic research station is used as a case study.
Scott Base, an isolated research facility located in the harsh environment of Antarctica is completely reliant on fuel oil (AN8) to meet all of its heat and electricity loads. Antarctic New Zealand, the government body that manages all aspects of Scott Base’s existence, has expressed the desire to operate on Antarctica with as small an environmental footprint as possible. This desire includes minimizing green house gas emissions and using renewable energy where possible. The year round wind resource along with the success of wind turbines at Australia’s Mawson Station has placed wind power at the top of the list of renewable technologies under consideration at Scott Base.
Methodology
The creation of each proposed wind-diesel hybrid energy system configuration is based on two identified wind turbines suitable for Antarctic installation. Utilising the two identified turbines, five possible wind-diesel hybrid energy system configurations are modelled utilizing the HOMER software package.
- Existing diesel generator sets with 1 Northwind 100 turbine
- Existing diesel generator sets with 2 Northwind 100 turbines
- Existing diesel generator sets with 3 Northwind 100 turbines
- Existing diesel generator sets with 1 Enercon E33 turbine
- Existing diesel generator sets with 2 Enercon E33 turbines
For each proposed system configuration there are multiple simulation structures possible. The simulation structures represent how the architecture of the energy system is built. Different protocols on how power supply meets demand can result in different levels of fuel savings. The primary analysis is based on a standard simulation structure for each model. A demand side management structure is also modelled which utilizes a Matlab algorithm in coordination with HOMER modelling software to represent load shifting. Therefore, the results of ten different theoretical wind-diesel hybrid energy systems for Scott Base are reported. A specific load has been identified as appropriate for load shifting: the laundry facilities. For each hour of a year the laundry facility load is evaluated against the wind turbine(s) electrical production. To compare the laundry facility load with the wind turbine(s) electrical production at each time-step, a Matlab algorithm is created and used as an add-on to the HOMER model.
Results
Two sets of results are outlined in the following sections. The first set, Standard Structure, details the potential fuel savings for each of five wind-diesel hybrid system models with varying levels of installed wind power capacity (from 100kW in Model #2 to 660kW in Model #6). The second set, Demand Side Management Structure, details the additional fuel savings that could be achieved by incorporating a novel DSM technique to the Scott Base laundry facilities load.
Standard Structure
Simulated over the course of one full year, the following results represent the predicted performance of the five proposed wind-diesel hybrid energy system models. Using the standard simulation structure, any wind generated electricity is supplied to the Scott Base electrical load with the generator sets supplying any unmet demand. A 30% minimum diesel load is enforced to ensure the generator set’s life-spans are not adversely effected by low loading. After supplying the electrical load, excess wind energy is applied to the base thermal load with the remaining thermal demand being supplied by recovered heat from the generator sets and the base boilers. Model #1 is the benchmark that all other simulations are measure against, being the simulation of the existing Scott Base energy system.
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Model #1
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Benchmark
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Standard System Structure
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Total:
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392592
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L
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Litres of Fuel Consumed
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Predicted Fuel Savings Per Year (L)
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Pay Back Period (Yrs.)
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Total
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Generator
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Boiler
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Model #2
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One Northwind 100kW Wind Turbine
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331151
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274731
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56420
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61441
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13.5
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Model #3
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Two Northwind 100kW Wind Turbines
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295556
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255028
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40528
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97036
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17.1
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Model #4
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Three Northwind 100kW Wind Turbines
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270064
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237216
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32848
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122528
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20.3
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Model #5
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One Enercon 330kW Wind Turbine
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219907
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191750
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28157
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172685
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7.9
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Model #6
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Two Enercon 330kW Wind Turbines
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154766
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133924
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20842
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237826
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11.4
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Demand Side Management Structure
The demand side management simulation structure is evaluated to determine potential fuel savings if a novel laundry facilities load shifting technique were implemented at Scott Base. This novel technique is a theoretical automation of the laundry facilities at the base. By coordinating available wind power with the laundry load, surplus wind power, which may have otherwise been unused, may be applied to a time flexible service such as the base laundry.
Table 2: Demand Side Management Structure Simulation Results
<!--[if !supportMisalignedColumns]--> <!--[endif]-->
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All values in litres of AN8 fuel
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Allowable Laundry Facility Load Delay
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3 hours
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6 hours
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12 hours
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24 hours
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Model #2
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One Northwind 100kW Wind Turbine
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54
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85
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139
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166
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Model #3
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Two Northwind 100kW Wind Turbines
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286
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424
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793
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1204
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Model #4
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Three Northwind 100kW Wind Turbines
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292
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520
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727
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1177
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Model #5
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One Enercon 330kW Wind Turbine
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266
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499
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800
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1227
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Model #6
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Two Enercon 330kW Wind Turbines
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295
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514
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831
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1110
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Conclusions
Predicted fuel use for all models follows an expected pattern with predicted generator and boiler fuel use decreasing as wind power capacity increases. Significant additional fuel savings are possible using the demand side management technique with a minimum installed wind capacity. A suggested limit on the amount of additional fuel savings possible is discovered for the specific Scott Base load modelled.
People: Jake Frye