Large Scale Energy Management Problem
- Manisha Badgurjar
- Anand Kumar Chaturvedi
Genetic algorithms, Real coding, Continuous search spaces.
Electric energy is an essential resource in today's life. So we are try to solving Challenge ROADEF/EURO 2010: A large-scale energy management problem with varied constraints. No matter if we make our first cup of coffee or tea in the morning or run a multi-million euro business, all the time we rely on a secure and inexpensive supply of electricity. Our goal is to full the respective demand of energy over a time horizon of several years, with respect to the total operating cost of all machinery. Determining optimal maintenance schedules and production plans is not easy because of the number of alternatives to assess. As the exact electricity demand of each forthcoming day is unknown and depends on a large variety of factors, this leads to the need of multiple uncertainty scenarios. Additionally, the increasing proportion of renewable energies in today's energy mix makes things more complicated for an utility company, because it has to feed the energy of thirdparty solar or wind power plants into its electricity networks and regulate its own power plants accordingly. The examined problem comprises three fields of optimization: maintenance scheduling, production planning and determining refueling amounts. It is a tactical model, neither considering short-term operational restrictions (like intraday load following) nor containing strategic decisions (like adding new power plants). However, the proposed model allows a generic formulation of other concerns like electricity network stability, safety considerations, availability of staff and tools, as well as legal restrictions. All of these limitations can be expressed as mathematical constraints. Here we are using genetic algorithms to solve its problem. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the real coding. In this paper we review the features of real coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behavior of this type of genetic algorithms are revised and compared.
Manisha Badgurjar, Anand Kumar Chaturvedi. "Large Scale Energy Management Problem".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.1, Issue 2, pp.137 - 143, URL :https://rjwave.org/ijedr/papers/IJEDR1302028.pdf
Volume 1 Issue 2
Pages. 137 - 143
