Arikiez, MK
(2016)
Algorithms for energy management in micro-grids.
PhD thesis, University of Liverpool.
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Abstract
Population explosion is one of the primary causes for concern in the power sector nowadays because residential buildings consume a high percentage of available electricity in the market. Also, the majority of current power plants use fossil fuel to generate electricity which makes the situation even worse due to the high price of fossil fuel. Consequently, electricity bills have soared dramatically in the last decade. If that was not enough, many countries have a shortage of electricity because they cannot increase their generation capacity to cover electricity demand. Many solutions have been introduced to improve the efficiency of the power grid and reduce electricity price for the users. For instance, Demand Side Management and Demand Response, domestic top-roof renewable micro-plants, and distributed renewable plants are introduced as a part of the solution to improve the situation. However, users are still paying a high percentage of their monthly income to electricity companies, that is because the surplus renewable power is not well utilized. The primary problem here is to find an efficient way to minimize the electricity cost and maximize the utilization of renewable power without using storage systems (batteries). Another issue is to solve the massive power allocation optimization problem in polynomial time. In this thesis, heuristic optimization algorithms are proposed to cope with the complexity of the problem as these kinds of problems are NP-hard. Furthermore, a set of different power allocation problems has been addressed in this thesis. The first one uses an online algorithm to solve power allocation problem that is modeled as a Knapsack problem. Additionally, the thesis has coped with the computational issue of a massive LP-based optimization problem of large buildings. Finally, an MILP-based heuristic algorithm has been used to solve power allocation problem in micro-grids (a set of houses shares renewable power for particulate rate). The empirical experiments and evaluations, in general, show promising results. The findings depict how an appropriate knapsack formulation can be used to address a significant dynamic energy allocation problem in a straightforward and flexible way and how good our heuristic algorithms can solve enormous power optimization problem in polynomial time. Finally, the results prove that our micro-grid model can reduce power bills by using the principle of renewable power sharing for a fair price.
| Item Type: | Thesis (PhD) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Aug 2016 10:17 |
| Last Modified: | 19 Jan 2023 07:35 |
| DOI: | 10.17638/03001776 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3001776 |
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