Abstract
Electric power systems (EPSs) are systems composed by several active agents as well as various physical and economical aspects. Electricity markets are systems for effecting the purchase and sale of electricity using supply and demand to set energy prices. This work looks at using software agents to help manage the complexity of power systems. Specifically, this paper presents a linear programming model that accounts for: (i) optimal allocation of thermal generation in EPSs, and (ii) the effect of introducing new generation technology in EPSs. To illustrate the model, the paper presents a case study involving a specific EPS and a simulated day-ahead market. The first part of the case study considers three different types of thermal power plants: coal (base), natural gas (intermediate) and oil fueled (peak). The results indicate a maximal thermal capacity of 21962 MW. The second part considers the addition of renewable generation (8000 MW) to the EPS, composed by three types of sources (wind, photovoltaic and mini-hydro). The results determine a reduction of 2400 MW on the thermal base capacity, and an over-cost between 10% and 17% (in relation to the initial system).
H. Algarvio and J. Santana—This work was supported by “Fundação para a Ciência e a Tecnologia” with references UID/CEC/50021/2013 and PD/BD/105863/2014.
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Algarvio, H., Lopes, F., Santana, J. (2017). A Linear Programming Model to Simulate the Adaptation of Multi-agent Power Systems to New Sources of Energy. In: Bajo, J., et al. Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems. PAAMS 2017. Communications in Computer and Information Science, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-60285-1_30
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