Smart technology, deployed in rural village energy systems, has emerged as a prominent concept to relieve global energy poverty. It promotes the integration of renewable distributed energy resources by allowing improved data capture, energy management, and control automation. The development of new technologies and control processes, in order to bring clean energy to people living in rural off-grid areas, is needed as an alternative to paraffin and wood-fuel.
Solar cogeneration systems have the ability to attain considerably high levels of energy efficiency. These systems are designed to produce heat and electricity from the same device by recovering energy that would otherwise have been wasted making them suitable for district energy systems in off-grid areas. Solar cogeneration systems are inspiring a large number of developers to come up with packaged cogeneration systems, their emphasis being on functional controller design and scheduling optimization.
Unfortunately, control optimization for systems operating as stand-alone power packs in eco-villages often lack access to wideband wireless and cellular linkage infrastructure. Online control and optimization, therefore, becomes a challenge. Gerro Prinsloo and Robert Dobson from Stellenbosch University, South Africa, in collaboration with Andrea Mammoli from University of New Mexico, USA, focused on an integrated on-board control and optimization technique in a bid to realize load demand balancing with specific communication and cost restraints. Their work is published in Energy.
A storage optimization solution was proposed in the study and evaluated within the confines of an energy storage scenario for a rural solar microgrid. They used predicted generation and energy consumption profiles. Using this information along with other economical, physical and environmental restraints, the researchers were able to come up with a day-ahead control plan using an integrated integer linear programming technique.
The authors overlaid the discrete rural village electrical demand per hour onto the main supply contribution. Doing so, they highlighted the mismatch between peak load demand and the solar supply. It was possible to schedule the supply from a consumer point of view, but it wasn’t possible to shift produced loads to a period when excess energy was generated. Load shifting carefully took into consideration daily schedules of the villagers as not to be disruptive. Optimizing the generation schedule allowed the self-consumption and self-generation microgrid controller to reduce the effect on the consumer budget.
It was clear that a significant amount of solar energy was lost during the peak sunlight hours. This was mainly due to a limited microgrid storage capacity. The researchers, therefore, developed a storage optimization decision support system in a bid to curtail the added cost effect on the user. The system could now compare and track operating costs and storage extension capital cost. This would alert the user of billing cost versus operating cost savings that could be obtained by increasing the battery and hot water storage capacity.
Experimental results obtained by the authors indicate that incremental storage optimization can provide energy management efficiency and reduced customer bills. The developed hybrid decision support system ensured that less energy was dumped due to automated microgrid storage capacity specifications. The storage cost optimization gave approximately 66% reduction in daily microgrid liquefied petroleum gas (LPG) costs while the storage optimization posted an approximate annual financial gain of $130, as a result of reduced waste of energy.
Gerro Prinsloo2, Andrea Mammoli1, and Robert Dobson1. Discrete cogeneration optimization with storage capacity decision support for dynamic hybrid solar combined heat and power systems in isolated rural villages. Energy, volume 116 (2016), pages 1051-1064Show Affiliations
- Centre for Renewable and Sustainable Energy Studies, Thermodynamic Research Group, Stellenbosch University, Stellenbosch, South Africa
- Centre for Emerging Energy Technologies, University of New Mexico, Albuquerque, USA
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