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Project Abstract :

Due to Large Industrial and overall development of the country, demand for electricity has seen to be exponentially increased during last decade. Current practice for managing the energy deficit is rolling blackouts, also referred to as load shedding (i.e., an intentionally engineered electrical power shutdown where electricity delivery is stopped for non-overlapping periods of time over different parts of the country). Especially Rural areas where load shedding may extend up to 18 Hours. The Amount of load being shedded is determined by the rate of fall of supply frequency. When the load on a system is greater than the generation, a sudden fall of system frequency occurs, and to manage this, load shedding is carried out. This can be viewed as a crude and ineffective, form of demand response.

In this project, we propose a novel optimization algorithm for handling loads using Demand Side Management (DSM). DSM consists of planning, implementing, and monitoring activities of electrical utilities which encourage consumers to modify their level and pattern of electricity usage. Fortunately, DSM focuses only on cost reduction by flattening the load curve, and energy management is not considered. Introducing Fuzzy controlled energy management technique along with existing DSM strategies results in increased sustainability. Simulations are carried out for a university infrastructure, utilizing the existing photo-voltaic array with battery storage. These existing assets are treated as local DC grid, and plays a vital role in managing the overall cost. In addition to this, DSM also encourage consumers to lower energy consumption which leads to a significant drop in emission of harmful gases into the atmosphere thereby helping curtail the global warming process. The bulk proportion of CO2 emissions is from fossil fuel based power plants. Reducing environmental degradation and saving our future generation from this huge complication, reduction of CO2 footprint has also been accounted in our objective function.

Thus, along with the primary function (i.e. DSM), truncation of carbon emissions is considered as secondary function and together is treated as a multi objective problem and is solved using evolutionary computation. Simulations are carried out on campus infrastructure system considering both economic efficiency and load curve, based on economic theory. The crux of this project is to implement DSM with optimal scheduling of available renewable resources with the reduction of carbon outrush solved using genetic algorithm.