In order to decide which software would be best suited to this project, a selection criteria matrix was developed based on the requirements of the project. This page describes the selection process for choosing a suitable energy modelling software and describes the key selection criteria that had to be met.
Table 1: Detailed energy modeling software selection matrix
Many energy modelling software packages are available, however not all are appropriate as certain criteria need to be met. The software needs to be able to model large scale electricity grids and provide feasibility information such as CO2 emissions and costing calculations. Also, the software needs to be readily available for use. The selection matrix above was used as part of the selection process and shows the main criteria needed for selection. Further explanation of these criteria are given below:
Each software is assessed against the criteria described above and is given a score between 0 and 5. 0 meaning not possible or disagree and 5 being very possible or strongly agree. The results of the selection process are shown in table 1 above.
As shown in Table 1, EnergyPLAN scored the most points and therefore was chosen as the grid modelling software for the project. The principle reasons it was selected was it's capability of modelling the influence of dynamic factors such as varying demand and weather, calculating CO2 emissions associated with electricity generation and it is free of charge to access.
- Availability and Cost: the software has to be readily available for use and free of charge;
- Usability and Training Period: Due to the time constraints of the project, the amount of training time necessary to sufficiently operate the software needs to be kept to a minimum although 1 to 2 weeks is accepted, useful training exercises also need to be available.
- Geological Area/Scale: The project involves modelling the UK electricity grid so which ever software package was chosen, it has to be capable of modelling large scale nationwide systems;
- Time-frame: The project looks to assessing the modelled grid over a one year span assessing the varying demand and supply as affected by seasonal changes throughout the year, hence the software has to be capable of handling and generating data for a year long time-frame.
- Time-step: the number of data points in a time span must be frequent enough to sufficiently represent the dynamic nature of the electricity of the grid but not too frequent as to require large amounts of memory to save the data sets. Gridwatch stores data sets at 5 minute intervals. A time-step of between one every 5 minutes to 1 every hour is considered sufficient for simulation and assessment in this project.
- Sectors investigated: The electricity grid is made up of many sectors of supply, storage and demand subsystems, the software must allow for the inclusion of these sectors in the modelling process.
- Renewable generation: The principle aim of this project is to model an electricity grid with increased penetration of supply from renewable energy systems. Therefore, this is a required feature of the software. The project is looking to model systems such as wind, Photovoltaic and tidal so the software is required to allow for this.
- Conventional generation: As well as renewable energy systems, the project is looking at making considerable changes to conventional plants and nuclear power plant generating capacities. Therefore the software needs to allow for the modelling of thermal power plant electricity generation.
- Economic Analysis: As part of the project, economic assessment needs to carried out on the energy mix scenarios. The software needs to have a function that provides costing information associated with operation and maintenance of the different sectors in order to carry out the feasibility assessment.
- CO2 Emissions. Another significant aspect of feasibility assessment is the reduction of CO2 emissions, as is the motive behind this project. The software needs to have the capability of calculating CO2 emissions associated with electricity generation, not only in the burning of fuels in conventional power plants, but also to calculated the embedded CO2 emissions associated with all sources of electricity generation, such as CO2 emissions associated with wind farm construction and operation.
- Demand Matching: The software needs to provide a function where it dynamically adjusts and prioritises different generators in order to meet the variable demand throughout the year.
- Storage Inclusion: Large scale storage systems such as pumped hydro, are considered to be a critical part of a future low carbon electricity grid. The software has to model the dynamic energy trends and incorporate the storage energy when it is needed in time of deficit and charge the storage in times of surplus. The software has to be capable of modelling this dynamic system.
- Input Own Dynamic Data Trends: as recorded grid information will be sourced for creating scenario energy trends such as demand profile and power plant electricity outputs, the software has to allow the manual input if different data trends to capture this information.
Each software is assessed against the criteria described above and is given a score between 0 and 5. 0 meaning not possible or disagree and 5 being very possible or strongly agree. The results of the selection process are shown in table 1 above.
As shown in Table 1, EnergyPLAN scored the most points and therefore was chosen as the grid modelling software for the project. The principle reasons it was selected was it's capability of modelling the influence of dynamic factors such as varying demand and weather, calculating CO2 emissions associated with electricity generation and it is free of charge to access.
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