An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

[EN] Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints

Development of a smart grid for the proposed 33 KV ring main Distribution System in NIT Rourkela

The non-reliability of fossil fuels has forced the world to use energy efficiently. These days, it is being stressed to use the electrical power smartly so that energy does not go waste. And hence comes the concept of a Smart Grid. So it becomes necessary for reputed places of academics to develop the prototype of the same in their campus. National Institute of Technology (NIT) Rourkela intends to set up a 33KV Ring Main Distribution System including 33/0.433 KV substations in its campus. The present 11KV line will be discarded and replaced by the 33KV system. The main driving force behind this step by the management is to accommodate the stupendously increased power requirement of the institute. The above mentioned plan also includes, set up of Data Acquisition System (DAS) that intends to monitor the electrical equipment in the substations. This is being done not only to increase the accountability and reliability of the distribution system but also to encourage academic research in the distribution automation domain. All in all, an excellent step towards make the Grid, Smart. In this project work the focus is laid on getting load flow solution of the 33KV ring main system. Here the authors use a specialized algorithm for distribution network with high R/X value to obtain the load flow solution. Then using artificial neural networks computation, algorithms are implemented to do the load forecasting and dynamic tariff setting. At the end a Web Portal, the NITR e-Power Monitoring System is developed that will be an excellent interface to the public in general and will help the students of the institute to know their grid well. In short a conscious effort is put to make the grid more interactive

Investigation of energy storage system and demand side response for distribution networks

PhD ThesisThe UK government has a target of achieving an 80% reduction in CO2 emissions with respect to the values from 1990 by 2050. Therefore, renewables based distributed generations (DGs) coupled with substantial electrification of the transport and heat sectors though low carbon technologies (LCTs), will be essential to achieve this target. The anticipated proliferation of these technologies will necessitate major opportunities and challenges to the operation and planning of future distribution networks. Smartgrid technologies and techniques, such as energy storage systems (ESSs), demand side response (DSR) and real time thermal ratings (RTTRs), provide flexible, economic and expandable solutions to these challenges without resorting to network reinforcement. This research investigates the use of ESS and DSR in future distribution networks to facilitate LCTs with a focus on the management and resolution of thermal constraints and steady state voltage limit violation problems. Firstly, two control schemes based on sensitivity factors and cost sensitivity factors are proposed. Next, the impacts of a range of sources of uncertainties, arising from existing and future elements of the electrical energy system, are studied. The impacts of electric vehicle charging are investigated with Monte Carlo simulation (MCS). Furthermore, to deal with uncertainties efficiently, a scheduling scheme based on robust optimization (RO) is developed. Two approaches have been introduced to estimate the trade-off between the cost and the probability of constraint violations. Finally, the performance of this scheme is evaluated. The results of this research show the importance of dealing with uncertainties appropriately. Simulation results demonstrate the capability and effectiveness of the proposed RO based scheduling scheme to facilitate DG and LCTs, in the presence of a range of source of uncertainties. The findings from this research provide valuable solution and guidance to facilitate DG and LCTs using ESS, DSR and RTTR in future distribution networks

GENETIC ALGORITHM FOR THE REDUCTION OF REACTIVE POWER LOSSES IN RADIAL DISTRIBUTION SYTEM

Power losses in distribution system have become the most concerned issue in power losses analysis in any power system. In the effort of reducing power losses within distribution system, reactive power compensation has become increasingly important as it affects the operational, economical and quality of service for electric power systems. Hence, the objective of the project is to perform a study and analysis on the power losses in radial distribution network by applying genetic algorithm approach for reduction of the reactive power losses. In this project, IEEE 34-bus Standard Test System is used together with the MATLAB and ERACS as powerful tools for the analysis and simulation work. Necessary literature reviews and research are conducted extensively in order to achieve the objectives of the project. The total loss saving for both single and multiple capacitor placements is 22.52% and 22.07% respectively. Single capacitor insertion is more cost effective as compare to multiple capacitor insertion because it have higher kW/kVAR ratio which is 2.696 and 2.163 respectively. Heuristic Search Strategies has total loss saving of 24.18% and 23.82% respectively for single and multiple capacitor insertions while GA has 22.52% and 22.07%). However, Genetic Algorithm is identified to be more cost effective because it has higher IcW/kVAR ratio which is 2.696 and 2.163 for single and multiple capacitor insertion respectively for while 1.9885 and 2.158 for Heuristic Search Strategies. The objective and goal towards the end of the project is to achieve the reduction of reactive power loss using genetic algorithm. The final results of the project successfully provide solutions to the reduction of reactive power losses, which eventually further contribute to the entire electrical power system in achieving superior performance in the context of operational, economical and quality of service

Statistical method for identification of sources of electromechanical oscillations in power systems

The use of real-time continuous dynamics monitoring often indicates dynamic behaviour that was not anticipated by model-based studies. In such cases it can be difficult to locate the sources of problems using conventional tools. This thesis details the possibility of diagnosing the causes of problems related to oscillatory stability using measurement-based data such as active power and mode decay time constant, derived from system models. The aim of this work was to identify dynamics problems independently of an analytical dynamic model, which should prove useful in diagnosing and correcting dynamics problems. New statistical techniques were applied to both dynamic models and real systems which yielded information about the causes of the long decay time constants observed in these systems. Wavelet transforms in conjunction with General Linear Models (GLMs) were used to improve the statistical prediction of decay time constants derived from the system. Logic regression was introduced as a method of establishing important interactions of loadflow variables that contribute to poor damping. The methodology was used in a number of case studies including the 0.62Hz Icelandic model mode and a 0.48Hz mode from the real Australian system. The results presented herein confirm the feasibility of this approach to the oscillation source location problem, as combinations of loadflow variables can be identified and used to control mode damping. These ranked combinations could be used by a system operator to provide more comprehensive control of oscillations in comparison to current techniques