Smart Grid Energy Simulation, The Strengths And Opportunities

April 26, 2021
smart grid simulator||smart grid analytics|Smart grid simulation

The idea of simulation models has been attributed to how innovations have avoided pitfalls in the technology sector.Simulations are imitations of a particular process or situation. In technology, simulation has a more streamlined definition that centres on creating a computer model of a proposed design for study and analysis. This step in creating technology-based products saves costs and helps in evaluating performance capabilities and product reliability. Experts can also give projections and predictions that will help with the innovative and business side of technology production.

Understanding Grid Simulation and Varying Models

If it was just a physical grid simulation, an alternating current power supply that is capable of emulating dynamic grid conditions is used to test the reliability of equipment connected to the grid. But smart grid energy simulation not only relies on the physical aspects but also combines all areas of the grid, including the electrical power, the communication technologies between all electrical network components, IT and intelligence systems, and the control centre. So, we have the hardware and software components all working in tandem to deliver the best energy results with renewables in tow.

Grid simulation

General quantitative and qualitative simulations revolve around three ideas: event simulation, discrete event simulation, and Monte Carlo simulation. In smart grid energy simulation, the focus centres on the discrete event simulation model.

Discrete Energy Simulation

The discrete event simulation (DES) model is an approach used to model real-world systems that can be broken down into logical dynamic processes. The results may create new events that observers should take into account in a future time. The simulation makes a simple sense of information given to it and gives projections that can come in handy in future situations.DES is used because the grid interacts with the hardware and software of energy grids and humans, including grid operators and consumers. This creates a loop of events in relation to changing time. It is then very important for simulations to occur with all aspects taken into account.Simulations cannot give holistic results without the human factor. Despite DES being the simulation model that is generally relied on, another type that gives better results when used in conjunction with DES is an agent-based simulation (ABS).

Agent-based Simulation

This simulation takes it up a notch by simulating the simultaneous operations and interactions of multiple agents to recreate and predict the appearance of complex phenomena. These are computer models that are highly intuitive and attempt to capture individuals' behaviour within society.

Smart grid simulation

For smart grids to perform at the best level, analysts realise that putting both simulation types together to predict real-world user behaviour helps immensely when it has to do with calculating the impact of consumer behaviour and energy use. Accuracy is important since energy use and user behaviour tie closely to how consumers feel with changing conditions like weather.

In-loop Model

Another popular simulation model is the In-loop model that helps keep up with innovative technology speed and maintain the highest quality services within smart grid energy solutions. Model-based simulations help to meet costs, quality and time constraints.These in-loop model simulations can be physical or virtual prototypes or a combination of the two, adjusted to give results that are as close as possible to real-world behaviours.Some In-loop model applications used in smart grids include:

  • Hardware in the loop, according to ni.com, is a "technique where real signals from several components are connected to a test system that simulates reality, tricking the components into thinking it is in the assembled product". This way, the simulation results are as close to reality as possible, and they only have to do with the hardware.
  • Software in the loop "represents the integration of compiled production source code into a mathematical model simulation, providing engineers with a practical, virtual simulation environment for the development and testing of detailed control strategies for large and complex systems". The major difference here is that this simulation deals with software alone.
  • Model in the loop combines both of these designs for a full test of hardware and software designs to ensure the systems can give the best results.

Strengths and Opportunities in Smart Grid Energy Simulations

When reviewing strengths and opportunities in technological innovations, considerations of the future is always at the forefront, and the same goes for smart grid simulations. A major question and opportunity identified by analysts is the expected reliability of today's smart grids in transitioning to future needs. Can they handle more complexities as efficiently as the grids we have today?

How much effect will prosumers have on the future grid systems?

In conventional grid systems, consumers are mostly passive; however, with smart grids, there's a need for two-way information exchange, since consumers are producing energy now through DERs and can control flexible devices.[caption id="attachment_8310" align="aligncenter" width="650"]

microgrid

image credit: www.humless.com[/caption]Consumers and suppliers are expected to make the grid operate in a more transparent, interactive, and efficient way, giving way to prosumers. These prosumers will be a major dictator in the future of smart grid energy as well as simulation models.The opportunity to formally educate everyday people on these new technologies will be massive, especially with the consistent rise of a tech-dominated society. This should inspire a link between information and personal lives as far as their participation in the smart energy transition. Finally, smart grid energy simulations can supplement expert decision making and projections, allowing better-informed decisions within the energy sector. Renewables are known to be unreliable, but with simulations systems that have high prediction accuracies, grid managers can take proper measures to keep their smart grid systems efficient and reliable.

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