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This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimization techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate virtual power players (VPP). VPPs are represented as a coalition of...
Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps...
Power systems operation in a liberalized environment requires that market players have access to adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper...
Power systems are planned and operated according to the optimization of the available resources. Traditionally these tasks were mostly undertaken in a centralized way which is no longer adequate in a competitive environment. Demand response can play a very relevant role in this context but adequate tools to negotiate this kind of resources are required. This paper presents an approach to deal with...
Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems...
In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in GAMS (general algebraic modeling systems)...
This paper provides a different approach for electricity price forecast from risk management point of view. Making use of neural networks, the methodology presented here has as main concern finding the maximum and the minimum system marginal price (SMP) for a specific programming period, with a certain confidence level. To train the neural network, probabilistic information from past years is used...
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