Agent-Based Simulation of Electric Energy Consumers: The NetLogo Tool

Agent-Based Simulation of Electric Energy Consumers: The NetLogo Tool

Fernanda Mota (Furg, Brazil), Iverton Santos (Furg, Brazil), Graçaliz Dimuro (Furg, Brazil), Vagner Rosa (Furg, Brazil) and Silvia Botelho (Furg, Brazil)
DOI: 10.4018/978-1-4666-5954-4.ch014
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The electric energy consumption is one of the main indicators of both the economic development and the quality of life of a society. However, the electric energy consumption data of individual home use is hard to obtain due to several reasons, such as privacy issues. In this sense, the social simulation based on multiagent systems comes as a promising option to deal with this difficulty through the production of synthetic electric energy consumption data. In a multiagent system the intelligent global behavior can be achieved from the behavior of the individual agents and their interactions. This chapter proposes a tool for simulation of electric energy consumers, based on multiagent systems concepts using the NetLogo tool. The tool simulates the residential consumption during working days and presented as a result the synthetic data average monthly consumption of residences, which varies according to income. So, the analysis of the produced simulation results show that economic consumers of the income 1 in the summer season had the lowest consumption among all other consumers and consumers noneconomic income 6 in the winter season had the highest.
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The simulated patterns of consumption for Consumer Units individual is of paramount importance to provide data relating to the distribution of electricity to studies and research, since the actual data is difficult to obtain for privacy of service users electricity distribution and because the data available are scarce and outdated. Furthermore, the marked expansion of energy consumption has some negative aspects such as the possibility of exhaustion of resources used for energy production and environmental impact produced by this activity, although it may reflect warming economic and improving the quality of life of the population. Because of this, high investments in research of new energy sources and construction of new plants are being conducted by researchers around the world, (Castro, 2004). It should be noted that the factors linked to the growth of electricity demand in the residential sector are extremely complex and varied, because it involves variables that range from the type of user, his or her social class, type of equipment used, until the time of use and consumption habits, constraints often difficult to define. The knowledge, understanding and verification of their relationship are of utmost importance for the planning of activities, both aiming to supply this market, and for those who seek to optimize the use of electricity (Hansen, 2000).

An important aspect to be studied concerns the influence of climate on electricity consumption. It is known that in southern Brazil, and more specifically in the city of Porto Alegre, are recorded very adverse conditions of temperature, humidity and temperatures associated with equally high in summer and low in winter. Furthermore, we believe that all these investigations listed above can be of great value for both the planning area of the energy sector as well as for the planning of urban development (Hansen, 2000).

Unfortunately household energy consumption sources are a mystery for most of the people. This is due to the lack or scarce data of peoples’ habits on the use of home appliances. Modeling the relationship of people with electric goods is very difficult due to the absolute absence of in-house data. One of the possible approaches is use of the average data available from Brazilian’s Family Organization Program (a government initiative to generate data of population behavior in several aspects – also known as POF) (POF, 2010) to build a behavioral model of consumer’s energy usage. The work developed in this chapter is focused on providing standards for synthetic data that approximate the actual patterns of Consumer Units by the technique of simulation-based multi-agent systems with NetLogo framework support. Finally, this chapter also describes a technique to simulate variations according to seasons.

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