Renewable Energy Sources

  1. Overview
  2. Solar Energy
    1. Detailed Solar Model
    2. Simplified Solar Model
  3. Concentrated Solar Power (CSP)
  4. Wind Energy
  5. Geothermal Energy
  6. Hydro Energy
  7. Biomass
  8. Tidal Energy
  9. Chronology and Resolution Consideration

Overview

Renewable energy sources (RES) are modeled using combinations of classes as described in the following table.

Table 1: Renewable Energy Sources
Technology Description Classes
Solar Solar energy is the most abundant energy source and an increasingly important contributor to global energy production. Solar power plants are a large collection of interconnected solar panels. Solar production is a function of solar intensity which varies across the day and seasonally and intermittently with weather conditions. Generator, Storage (CSP), Variable
Wind Wind power is one of the fastest growing renewable energy sectors. Wind power production is a function of wind speed which is highly variable, depending on weather conditions, location and to some extent time of day. To increase reliability, wind farms are being built offshore as well as onshore, and offshore wind energy could in years to come become dominant way of harnessing wind energy. Generator, Variable
Geothermal Geothermal energy refers to power extracted from heat stored in the Earth. It is mostly used to generate electricity, and in some countries also for heating purposes in form of geothermal heating. Unlike solar and wind energy geothermal energy doesn't suffer from intermittency issue. Generator
Hydro Hydropower is renewable energy source that has been used for many hundreds of years in form of waterwheels and mills. Hydropower is one of the most important renewable energy sources globally. Hydropower plants have long lifespan, and relatively low maintenance costs since almost everything is automated. Hydropower also doesn't suffer from intermittency issue like wind and solar power do and ensures constant supply of energy. Hydropower is also very efficient energy source. Generator, Storage, Waterway, Variable
Biomass Biomass refers to biological material deriving from living, or recently living organisms such as wood, waste or biofuels. Biomass has many different forms, but most of these forms are different plant matter grown to generate electricity or produce heat. Generator
Tidal Tidal energy can be harnessed by turbines underwater powered by the movement of the tides. This emerging technology may prove an important energy source especially in regions with large tide differentials. Generator, Variable

Solar Energy

Modeling any size of solar from small scale rooftop installations to full solar power plants is simple using the Generator class. Resources can be static or dynamic in size and set up for optimal expansion with LT Plan.

At the most fundamental level, solar production is determined by:

  • The number of panels;
  • solar irradiance;
  • solar panel efficiency; and
  • ambient temperature.

There are two approaches you can take here. You can model solar irradiance and conversion efficiency in detail, or simply model the solar generation directly.

Detailed Solar Model

Modeling solar irradiance and conversion efficiency is possible using the Variable class to define a Profile for the solar irradiance and a look-up table to convert irradiance to electrical output. There are many online resources for solar irradiance data and conversion efficiency including Renewables Ninja. Using a source such as this you can create input like that in Table 2. Here the solar irradiance is read from an external Data File. That files contains values for each hour of the simulation horizon. The values of Lookup x and Lookup y convert solar irradiance to electrical output. The solar Generator object would then refer to the Variable as in Table 3.

In this example there is 100 MW of solar generation modeled as 100 one megawatt units. Note the use of Fixed Load which makes the solar generation 'must take' and the setting of Fixed Load Global = "no" which means the Fixed Load is interpreted as the load per Unit. To model dispatchable solar i.e. solar that can be turned off if needed, swap out the Fixed Load property for Rating.

Table 2: Solar Irradiance Conversion Table
Variable Property
 Value
 Data File
 Units
 Band
Solar Profile
 0
 Solar Irradiance.csv
 -
 1
Solar Lookup x
 0
-
 1
Solar Lookup x 0.1
- 2
Solar Lookup x 0.2
- 3
Solar Lookup x 0.3
-
 4
Solar Lookup x 0.4
-
 5
Solar Lookup x 0.5
-
 6
Solar Lookup x 0.6
-
 7
Solar Lookup x 0.7
-
 8
Solar Lookup x 0.8
-
 9
Solar Lookup x 0.9
-
 10
Solar Lookup x 1.0
- 11
Solar Lookup y
 0
-
 1
Solar Lookup y 0.1664
- 2
Solar Lookup y 0.2989
- 3
Solar Lookup y 0.4145
-
 4
Solar Lookup y 0.5131
-
 5
Solar Lookup y 0.5949
-
 6
Solar Lookup y 0.6596
-
 7
Solar Lookup y 0.7074
-
 8
Solar Lookup y 0.7383
-
 9
Solar Lookup y 0.7522
-
 10
Solar Lookup y 0.7492
- 11

Table 3: Detailed Solar Generator
Generator Property Value Units Action Expression
Solar Units 100 -

Solar Max Capacity 1 MW

Solar Fixed Load 0 MW = Solar
Solar Fixed Load Global No -


Simplified Solar Model

Table 4 illustrates a simpler approach which ignores the conversion from irradiance to electrical output. As in the detailed case, the Units property scales up the Max Capacity to give the total Installed Capacity while the Rating property, read from a Data File, defines the normalized (values between 0 and 1) solar electrical output in each interval of the horizon. For Monte Carlo simulation or stochastic optimization you can read the solar generation from a Variable object which in turn reads its expected values from the Data File instead, as in Table 3.

Table 4: Simplified Solar Generator
Property Value Data File Units
Units 100
-
Max Capacity 1
MW
Rating 0 Solar Generation MW

When running LT Plan with solar as an expansion option define the additional properties as shown in Table 5 in particular the Max Units Built which is the key property for toggling a Generator in/out of the expansion planning optimization. Note the definition of Firm Capacity which determines the generator's contribution to Capacity Reserves.

Table 5: Solar Generator in LT Plan
Property Value Data File Units
Units 0
-
Max Capacity 1
MW
Rating 0 Solar Generation MW
Firm Capacity 0.15
MW
Max Units Built 100
-
Build Cost 750
$/kW
Economic Life 25
years
WACC 10
%

Note: If the system is small scale e.g. a single household/industrial unit or micro grid you can change the underlying unit of electrical power from the default megawatt (MW) to kilowatt (kW) in the Units of Data Settings.

Concentrated Solar Power (CSP)

Concentrated Solar Power (CSP) systems (also known as concentrated solar thermal (CST) systems) are systems that use mirrors or lenses to concentrate solar thermal energy onto a small area. Electrical power is produced when the concentrated light is converted to heat which drives a steam turbine connected to an electrical power generator. In addition, the heat can be stored, generally in a molten salt medium, and used to drive the steam turbine engine when the sun is not shining.

This system can modeled using a Generator connected to a Storage. Power coming in from solar radiation is modeled as Natural Inflow. Heat loss is modeled as a Spill. Losses on start-up of the Generator are modeled with the Head Storage Flow at Start property.

Wind Energy

Wind generators can be modeled in the same fashion as solar e.g. by a generator with Max Capacity of 1 and a Rating (or Rating Factor) read from a Data File directly or via a Variable which represents a normalized wind profile. The capacity installed at the wind site is then input using the Units property as in Table 6.

Table 6: Wind Generator
Property Value Data File Units Action Expression
Units 1 - - - -
Max Capacity 1 - MW - -
Rating 0 - MW = Wind Variable


Wind is generally more variable than solar and it is important to model that intermittency. The Variable class is ideal for this purpose and the example in Table 6 shows how the wind generation can be linked to a Variable. In this case, the Variable is providing normalized wind production data over time. A further step would be to base this on raw wind speed and to use a Lookup Table to convert wind speed to generation as in Table 2 for the solar example.

Geothermal Energy

Geothermal generation is generally more controllable and less intermittent and thus is modeled more like conventional thermal generation as in Table 7. If there is any variability in the output then the Rating property can be applied.

Table 7: Geothermal Generator
Property Value Data File Units
Units 1 - -
Max Capacity 200 - MW
VO&M Charge 5 - $/MWh

Hydro Energy

Hydro generation models range from simple energy-constrained resources to complex multi-stage stochastic optimization of cascading river systems. These topics are covered in a series of articles beginning with Hydro Classes.

Biomass

Biomass is modeled similarly to geothermal, except that its production is often more constrained e.g. by availability of biomass fuel at different times of year, or by schedules for waste disposal. Time of year availability can be controlled with the Rating property and fixed schedules can be modeled with the Fixed Load or Min Load properties.

Tidal Energy

Tidal energy has similar characteristics to solar in that it is predictable with some degree of weather-related variability.

Chronology and Resolution Considerations

As described above, RES can be intermittent in nature. This intermittency implies that conventional generation sources fill the 'gaps' in RES generation e.g. when the sun is not shining or the wind blowing. To correctly capture the physical and financial effects of intermittency your simulation should be as high resolution as practicable.

The resolution of ST Schedule is controlled by Horizon Periods per Day. It is typical to model systems with high levels of RES at resolutions as fine as 5-minutes, but the step size (window over which the optimization occurs) needs to be long enough to optimize the unit commitment of thermal plant and to optimize hydro and pumped storage. For example, you might require an optimization step of one week to fully optimize pumped storage and batteries. However, at 5-minute resolution a week has 168 × 12 = 2016 periods. This can lead to very large optimization problems and long run times.

There are (at least) two approaches to solving this problem:

  1. Use look-ahead: For example, the step size is one day at 5-minute resolution, but each step has six days of look-ahead and those days are at a lower resolution e.g. hourly resolution. These settings are made through the Horizon class.
  2. Use interleaved simulations: One Model can be interleaved with another meaning that the interleaved model performs its step optimization after the corresponding step of the master model. Here the master model can be lower resolution and fix the unit commitment of large thermal and other resources for the high-resolution interleaved model. Further, the interleaved model can pass back the starting position of hydro and pumped storage resources for the next step of the master model. Interleaving of models is described in the topic Balancing Markets but the key point here is that the interleaved models can use different Horizon objects and therefore run at different resolutions making this approach very useful for RES modelling.

LT Plan presents additional challenges due to the very long simulation step. Ideally one would run LT Plan in either 'Fitted' or 'Sampled' mode as controlled by the Chronology setting. However, it can be difficult to obtain enough resolution and have a solvable optimization problem. A solution is to break use multiple steps in LT Plan with overlap.

The fastest Chronology mode is 'Partial' which uses load duration curves to simplify the chronology, but the weakness of this approach is that the load curve slicing can place blocks of time with disparate RES generation in the same time block. This can cause the optimization to overvalue the energy value of RES and underplay the intermittency. A solution is to use the Global Slicing Block input to ensure that the slicing preserves the intermittency to the maximum extent possible.