Efficient Utilization of Energy Employing Meta-Heuristic Techniques with the Incorporation of Green Energy Resources in Smart Cities

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A smart city is an efficient, reliable, and sustainable urban center that facilitates its inhabitants with a high quality of life standards via optimal management of its resources. Energy management of smart homes (SHs) is one of the most challenging and demanding issues which needs significant effort and attention. Demand side management in smart grids authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. In demand side management, scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller. However, user discomfort is caused by the scheduling of home appliances based on the demand response or limiting its time of use. Further, rebound peaks that are regenerated in the off-peak hours are also a major challenge in demand side management. An increase in the world’s population results in high energy demand; thus, causing a huge consumption of fossil fuels. This ultimately results in severe environmental problems for mankind and nature. Renewable energy sources (RESs) emerge as an alternative to fossil fuels. The RESs are eco-friendly and sustainable, which are incorporated in SHs via two modes: grid-connected or stand-alone. The reliability of RESs is usually met with the use of hybrid RESs along with the integration of energy storage systems (ESS). The efficient usage of these components in the hybrid RESs requires an optimum unit sizing that achieves the objectives of cost minimization and reliability in stand-alone mode. These are some of the main concerns of a decision-maker. This thesis focuses on employing meta-heuristic techniques for efficient utilization of energy and RESs in SH. At first, an evolutionary accretive comfort algorithm is developed based on four postulations which allow the time-varying priorities to be quantified in time and device based features. Based on the input data, considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the evolutionary accretive comfort algorithm generates an optimal energy consumption pattern which gives maximum satisfaction at a predetermined user budget. A cost per unit comfort index, which relates the consumer’s expenditure to the achievable comfort is also demonstrated. To test the applicability of theproposed evolutionary accretive comfort algorithm, three budget scenarios of 1.5 $/day, 2.0 $/day, and 2.5 $/day are taken. Secondly, a priority-induced demand side management strategy based on the load shifting technique considering various energy cycles of an appliance is presented. The day-ahead load shifting technique is mathematically formulated and mapped with multiple knapsack problem to mitigate the rebound peaks. The proposed autonomous home energy management controller embeds three meta-heuristic optimization techniques: genetic algorithm, enhanced differential evolution, and binary particle swarm optimizationalong with the optimal stopping rule, which is used for solving the load shifting problem. Next, the RESs and ESS are integrated into a residential sector considering grid-connected mode. The proposed optimized home energy management system minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of the electricity market. Here the appliances are classified into shiftable and non-shiftable categories, and a hybrid genetic particle optimization scheme outperforms to other algorithms in terms of cost and a peak-to-average ratio. Besides, meta-heuristic schemes that do not depend on algorithmic-specific parameters are considered for integrating the RESs and ESS in a stand-alone system. Preliminary, the Jaya algorithm is used for finding the optimal unit sizing of RESs, including photovoltaic panels, wind turbines, and fuel cells to reduce the consumer’s total annual cost. The methodology is applied to real solar irradiation and wind speed data taken from Hawksbay, Pakistan. Next, an improved Jaya and the learning phase as depicted in teaching learning-based optimizationis proposed for optimal unit sizing of photovoltaics, wind turbines, and battery systems using real data obtained from another site, located in Rafsanjan, Iran. The system’s reliability is considered using the maximum allowable loss of power supply probability concept. Finally, a diesel generator is integrated into the RESs to assess its environmental and economic aspects. Thus, the thesis objectives achieved are to have a green, reliable, economical, and sustainable power supply in the SH.

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