Smart microgrid for sustainable rural development
February 13, 2019
by Dr Lai Chean Hung
Energy is one of the core components in economic development. Like in many developing countries in the world, socio-economic development in rural communities has been one of the major focus areas in ensuring sustainable growth in Malaysia.
In Sarawak, nearly ten percent of the populations (approximately forty thousand households) are still living off the utility grid, underdeveloped and lacking of basic infrastructure such as affordable electricity supply, clean water supply, access roads, communication infrastructure and so forth. Generally, these rural villages are sparsely distributed across the inland of Borneo Island where utility grid extension to these remote villages is often not viable due to financial and technical challenges.
Conventional petrol or diesel generators have been the primary source of energy to support their basic electricity needs such as lighting, food refrigeration and other essential electrical appliances. However, the difficulties in transporting the diesel fuels to the remote areas increase the cost of energy which further jeopardise their quality of life.
These rural communities are generally low-income smallholder farmers that rely heavily on exporting agricultural produce to earn a living. The vicious cycle of ‘expensive energy-low productivity-low income’ often pushes the communities into poverty trap, which greatly impacts the quality of life and widening the social-economy divide in Malaysia.
Fortunately, many rural development initiatives and efforts have been put in place over the past decades, such as expanding the main utility grid and deploying self-sufficient renewable-based energy systems (also known as microgrid) to remote rural communities in the pursuit of equitable development.
However, most of these rural electrification projects tend to focus on the supply-side issues and they are designed to provide sufficient energy based on the projected average consumption on basic necessities such as lighting and food refrigeration, with rudimentary consideration of the environment that they operate in. The human aspect and environmental variation are often neglected in the system design and implementation processes.
For instance, an evaluation of rural electrification projects reveals a number of sustainability issues, including inability to meet the increasing electricity demands, continued reliance on conventional diesel or petrol generator, heavy reliance on external support, and cost ineffective solutions (Subhes C. Bhattacharyya 2012). Also, a study on quality-of-life change through deployment of solar hybrid systems shows that most one-off rural electrification projects are not sustainable mainly due to human aspects such as lack of awareness, improper usage and poor local technical support (Hideaki Ohgaki 2017).
Studies have shown that microgrid operation can be optimised and more cost-effective by considering the dynamics of renewable energy generation (supply) and consumption (end-use electricity demand). With the emerging technology in sensors, internet-of-things (IOT) and affordable computing hardware, real-time supply-demand data can be measured and processed through smart algorithm such as Artificial Intelligence (AI) and machine learning, in order to optimally manage and control the switching between different generation devices at different supply-demand conditions.
These data can give an invaluable insight into the behaviour of the users, system components and environment which can be used to accurately model and predict the future state of the system parameters, so that remedial actions can be taken by the control system to prevent detrimental operation that may deteriorate the system components. For example, minimising the use of petrol or diesel generators, implementing demand-side management to minimise the supply-demand mismatch, or to ensure battery is fully charged for rainy days forecasted ahead.
To achieve ‘smart’ microgrid system, there are three core areas that need to be addressed. Firstly, the dynamics of supply, storage and demand must be fully understood. This can be achieved by developing a predictive supply-storage-demand model by using AI or deep learning algorithm and the observation of historical data collected from sensors.
Secondly, a cost-effective demand-side management strategy that aim to control the distributed loads as well as to influence the end-user behaviour. And finally, an intelligent energy management and control strategy to perform switching between different generation and storage devices, in an optimal way.
Nevertheless, technologies alone do not guarantee long-term sustainability. The human aspects must be carefully taken into consideration when planning and implementing rural electrification schemes. For example, increasing the local people’s awareness on sustainable energy and developing local technical support should be included to ensure long-term sustainability of such systems.
Dr Lai Chean Hung is a lecturer from the Faculty of Engineering, Computing and Science at Swinburne University of Technology, Sarawak Campus. He can be reached via email at email@example.com.