2. LITERATURE REVIEW
2.7 TECHNIQUES AND SOFTWARE TOOLS USED TO MODEL MICROGRIDS
MICROGRIDS 2.7.1 TECHNIQUES
A number of techniques for modelling a microgrid system are available. The subsections below provides a brief description of some of these suggested by [26][15] [27]
i. Linear Programming
Linear programming is a technique that describes the system constraints as functions and plots them in a plane as boundary conditions that contain possible solutions. A cost function for the overall system is then developed and the optimal solution(s) are calculated. Benefits to the method of Linear Programming include its ease of use and traceability. It is however considered expensive computationally particularly as the numbers of parameters increase.
ii. Stochastic Modelling & Numerical Methods
These methods use design constrains for performance of components and/or subsystems. First a robust stochastic model of the system under survey is developed and a set of design constraints on performance and are translated mathematically. This method, comparatively, is more difficult to understand and thereby harder to trace. It does have the benefit from being easier to code and implement from high level coding platforms such as MATLAB.
iii. Neural Networks Algorithms
This method employs a machine learning algorithm to determine the optimal parameters of a solution given a set of design constraints. The learning makes use of “training data” to produce meaningful values for the parameters of the defined system. Neural network algorithms require large amounts of training data for larger scale systems which can be a
38
barrier under extreme cases of uncertainty. It is also a method that proves to be expensive computationally.
iv. Genetic Algorithm, Particle Swarm Optimisation
The Genetic Algorithm is an optimisation method that mimics the biological process of gene selection. It is an iterative method that parameterises the Microgrid into different categories, populates the sizing of the system randomly then through numerous processes of “selection”
and “mutation” arrive at the optimal size through reference each stage to the optimisation constraints.
This algorithm can also be adapted to use a Particle Swarm Optimisation method, which uses a similar structure with the central difference being in the iterative procedure. This method, while covered widely in literature for complex problems, is regarded as increasing sharply in complexity with the rise in the number of parameters. Some bio mimicry methods are also difficult to represent in code.
2.7.2 SOFTWARE
The modelling of Microgrid systems including Distributed Energy Resources, Energy Storage Systems, Control schemes and Energy management can be conducted on many commercially available platforms some of which have the capacity to perform optimisation and sizing of components. Table 2-6 to follow briefly discusses some of the available software define tools for Microgrid system modelling [27]. In particular, the HOMER, SIMULINK, HYBRID2, Ret Screen, iHoga software tools , see [28][29][30][31][32][33] , are described in the sections to follow
i. HOMER
Hybrid Optimization of Multiple Electric Renewable (HOMER) software package models components from a number of distributed energy sources, energy storage devices and configurations and provides a techno-economic projections. Some of the features of the HOMER system include its ease of use, expansive component libraries, wide use in literature and application in industry. It does however have inexact models of energy sources and other components, the design scheme is also rigid with marginal customisation options.
39 ii. SIMULINK
Simulink is a graphical programming operating in the MATLAB simulation environment and is used for modelling, simulating and analysing multi-domain dynamic systems. The benefits of Simulink include its ease of use, modular design and notably the high level of customisation enabling inexact existing component models to be more suitably defined.
iii. HYBRID 2
HYBRID2 is the second iteration of a simulation platform based on time series/probabilistic models of hybrid energy generation systems designed for long term performance and economic analysis. HYBRID2’s platform, while rigid in its component definition capabilities offers input-error checking capabilities. It also has the capacity to simulate the operability of a system using up to 3 distribution buses as well as incorporating the capacity to consider shading over solar panels in its calculations, a feature well suited for urban, industrial contexts.
iv. RETscreen
RETScreen is a software tool designed to assess the feasibility of energy efficient renewable energy technologies using climate and system component data. RETscreen’s platform does not compute component sizing, nor does it consider the impact of temperature on solar PV systems. It is also very limited in its ability to input time series data.
v. iHoga
Improved Hybrid Optimization by Genetic Algorithm (iHOGA) optimization software tool developed for systems a number of renewable energy technologies using Genetic algorithm it also has the capability to help asses and has purchase and selling energy options to the electrical grid. The iHoga system can only compute up to 10kW daily.
vi. Other commercially available packages
There are a number of commercially available software packages such as the Hybrid Power System Simulation Model HYBRID 2, the General Algebraic Modelling system (GAMS) and the Simulation of Photovoltaic Energy Systems (SimPhoSys) among others, some of which focussing in on specific technologies.
40
In a 2014 paper titled “Review of software tools for hybrid renewable energy systems” Sinha et al [28] divides the software tools available for Microgrid design into four key categories.
They are as follows:
Pre-Feasibility tools: This set of tools generally comprises of capabilities to conduct rough sizing of the system under survey and subsequent financial estimation
Sizing tools: This set of tools primarily provides capabilities to determine the optimal set of system components, based on input characteristics and a database of define components, including a description of the energy flows between components
Simulation tools: Simulation tools are used to model behavioural dynamics of a system under investigation. This is achieved by characterising all the system components and using the software tool to observe, analyse and some cases optimise aspects of the system based the performance of the model in the software domain.
Open Architecture tools: Software in this category are described as a research tool, a layer lower than Simulation tools. Open Architecture platforms allow users to modify model algorithms for individual components and their interactions within the system under investigation; this could involve different model representations for various types of battery storage, for example.Since this study uses synthetic data ( see meaning shortly), its scope with respect to the theory, planning, and design of a microgrid system based on renewable energy technology is limited to both rough system-sizing and rough financial cost estimation and will not involve in-depth investigation into the behavioural dynamics and performance of either the proposed or the preferred candidate systems or the final proposed system.
For this reason, in lieu of the descriptions given of the various software packages above, HOMER is chosen as most suitable out the alternatives given in the table. To operate the HOMER software package in this environment the following will be needed:
Load Profile ModelsA model described in the next chapter will be used to forecast the expected loads for the energy consumption in the villages in question. The resolution of this model will need to in kW/h projected over a period of time (potentially several years) when fed into the HOMER system
41
Necessary Time Series Input data:Distributed energy resource data such as hydro potential sources (rivers, streams, etc)and biomass sources will be statistically estimated in the absence of available physical survey data
Relevant Component Data:Component data such as generator size, solar panel rating among other technical specifications will need to be specified for the candidate solutions to be produced by the HOMER algorithm
Meteorological data:Since reliable solar radiation data and wind speed data for remote areas are difficult and expensive to obtain, this study will limit itself to making use of publicly available data sourced from the NASA archives.
42