Active human intelligence for smart grid (AHISG) : feedback control of remote power systems.
| dc.contributor.author | Fulhu, Miraz Mohamed | |
| dc.date.accessioned | 2014-08-31T20:57:53Z | |
| dc.date.available | 2014-08-31T20:57:53Z | |
| dc.date.issued | 2014 | en |
| dc.description.abstract | Fuel supply issues are a major concern in remote island communities and this is an engineering field that needs to be analyzed in detail for transition to sustainable energy systems. Power generation in remote communities such as the islands of the Maldives relies on power generation systems primarily dependent on diesel generators. As a consequence, power generation is easily disrupted by factors such as the delay in transportation of diesel or rises in fuel price, which limits shipment quantity. People living in remote communities experience power outages often, but find them just as disruptive as people who are connected to national power grids. The use of renewable energy sources could help to improve this situation, however, such systems require huge initial investments. Remote power systems often operate with the help of financial support from profit-making private agencies and government funding. Therefore, investing in such hybrid systems is uncommon. Current electrical power generation systems operating in remote communities adopt an open loop control system, where the power supplier generates power according to customer demand. In the event of generation constraints, the supplier has no choice but to limit the power supplied and this often results in power cuts. Most smart grids that are being established in developed grids adopt a closed loop feedback control system. The smart grids integrated with demand side management tools enable the power supplier to keep customers informed about their daily energy consumption. Electric utility companies use different demand response techniques to achieve peak energy demand reduction by eliciting behavior change. Their feedback information is commonly based on factors such as cost of energy, environmental concerns (carbon dioxide intensity) and the risk of black-outs due to peak loads. However, there is no information available on the significant link between the constraints in resources and the feedback to the customers. In resource-constrained power grids such as those in remote areas, there is a critical relationship between customer demand and the availability of power generation resources. This thesis develops a feedback control strategy that can be adopted by the electrical power suppliers to manage a resource-constrained remote electric power grid such that the most essential load requirements of the customers are always met. The control design introduces a new concept of demand response called participatory demand response (PDR). PDR technique involves cooperative behavior of the entire community to achieve quality of life objectives. It proposes the idea that if customers understand the level of constraint faced by the supplier, they will voluntarily participate in managing their loads, rather than just responding to a rise in the cost of energy. Implementation of the PDR design in a mini-grid consists of four main steps. First, the end-use loads have to be characterized using energy audits, and then they have to be classified further into three different levels of essentiality. Second, the utility records have to be obtained and the hourly variation factors for the appliances have to be calculated. Third, the reference demand curves have to be generated. Finally, the operator control system has to be designed and applied to train the utility operators. A PDR case study was conducted in the Maldives, on the island of Fenfushi. The results show that a significant reduction in energy use was achieved by implementing the PDR design on the island. The overall results from five different constraint scenarios practiced on the island showed that during medium constrained situations, load reductions varied between 4.5kW (5.8%) and 7.7kW (11.3%). A reduction of as much as 10.7kW (15%) was achieved from the community during a severely constrained situation. | en |
| dc.identifier.uri | http://hdl.handle.net/10092/9582 | |
| dc.identifier.uri | http://dx.doi.org/10.26021/1265 | |
| dc.language.iso | en | |
| dc.publisher | University of Canterbury. Mechanical Engineering | en |
| dc.relation.isreferencedby | NZCU | en |
| dc.rights | Copyright Miraz Mohamed Fulhu | en |
| dc.rights.uri | https://canterbury.libguides.com/rights/theses | en |
| dc.subject | Participatory Demand Response | en |
| dc.subject | Demand Response | en |
| dc.subject | Demand Side Management | en |
| dc.subject | Remote Power Systems | en |
| dc.subject | Energy Management | en |
| dc.subject | Transition Engineering | en |
| dc.subject | Remote Area Power Supply | en |
| dc.subject | Mini Grids | en |
| dc.subject | Energy Audit | en |
| dc.title | Active human intelligence for smart grid (AHISG) : feedback control of remote power systems. | en |
| dc.type | Theses / Dissertations | |
| thesis.degree.discipline | Mechanical Engineering | |
| thesis.degree.grantor | University of Canterbury | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
| uc.bibnumber | 2040402 | |
| uc.college | Faculty of Engineering | en |