In today modern life smart electrical devices are used to make the human lives more comfortable. Actually, this is the combination of electronics and communications that provides the opportunity for real time communication while the measured electricity by smart meters is sent to the energy provider. In this way smart meters in residential areas play an important role for two way interaction between several users and energy provider. Solving an optimization problem with regard to consideration of satisfaction of both sides of users and energy providers tends to achieve the optimum price that is sent to the users to optimize their consumption in peak demand periods that is the main goal of demand response management programs. As nowadays the renewable energy plays an important role in providing the request of the users specially in residential areas consideration of the concept of uncertainty is an important issue that is considered in this thesis. Therefore, solving the optimization problem in presence of load uncertainty is important topic that is investigated. Another interesting issue is consideration of users' number variation in presence of load uncertainty in dynamic pricing demand response programs which gives the advantage of having good estimation of optimum consumption level of users according to the optimum announced price. In this thesis these issues are considered for solving an Income Based and Utility Base optimization problems that are further explained in upcoming chapters. In chapter III ,which provides the first contribution of the thesis a novel algorithm called Income Based Optimization (IBO) is defined and compared with previously proposed Utility Based Optimization problem (UBO). The price, users' consumption versus provided energy capacity by energy provider in 24 hours period are simulated and analyzed. The effect of variation in other parameters dependent to the cost imposed to the energy provider and the parameters that affect the users level of satisfaction is also evaluated. In Chapter IV, existence of load uncertainty is considered in proposed UBO algorithm when it is assumed that number of users in each time slot is varying based on different distributions such as Uniform or Poison. The results for the average gap between energy provider's generating capacity and consumption of the users are compared with when number of users kept constant in presence of load uncertainty in 24 hours period. Moreover, the effect of different distributions on the gap between generating capacity and the users consumption is evaluated assuming the number of users are increasing and following the distributions. The results for the announced price in 24 hours period is also evaluated and further is extended to the average announced price with respect to increase in number of users when it is assumed that user entry and departure type is varying based on different distributions and the load uncertainty also is existed. In chapter V, the proposed IBO algorithm in chapter three is further extended to the Uncertain IBO and is called UIBO. Therefore, it is assumed that bounded uncertainty is added to the users consumption. This algorithm is further extended in a way that variation in number of users is considered based on different distributions. The results are evaluated for the average gap between generating capacity and users consumption in 24 hours period and is further extended with respect to consideration of the increasing pattern for the number of users in presence of load uncertainty and different types of distributions for the users number variation. With respect to consideration of UIBO algorithm the price in 24 hours period is evaluated and the results are further extended to evaluate the average price with respect to increasing pattern for number of users that are varying based on different distributions when the bounded uncertainty is added to the users consumption. Moreover, the achieved gain of the proposed algorithm based on the ratio of the variation of the announced price to the varying number of users is evaluated. Finally chapter VI provides the conclusion and suggestion for future work.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:732611 |
| Date | January 2017 |
| Creators | Ahmadzadeh-Ghahnaviehei, Sahar |
| Publisher | University of Essex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://repository.essex.ac.uk/20790/ |
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