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 envir...
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ISBN:
(纸本)9781424452293
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 these issues, by using a multi-agent simulator able to model demand side players and simulate their strategic behavior. The paper includes an illustrative case study that considers an incident situation. The distribution company is able to reduce load curtailment due to load flexibility contracts previously established with demand side players.
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...
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ISBN:
(纸本)9781424452293
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 more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.
This paper presents a new architecture for MASCEM, a multi-agent electricity market simulator. The main focus is the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are re...
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This paper presents a new architecture for MASCEM, a multi-agent electricity market simulator. The main focus is the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. VPPs can reinforce the importance of distributed generation technologies, mainly based on renewable energy sources, making them valuable in electricity markets. The new features are implemented in Prolog which is integrated in the JAVA program by using the LPA Win-Prolog Intelligence Server (IS) that provides a DLL interface between Win-Prolog and other applications.
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 ...
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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 agents, with specific characteristics and goals. The paper details some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study based on real data.
Electricity market players operating in a liberalized environment require adequate decisionsupport tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services repr...
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Electricity market players operating in a liberalized environment require adequate decisionsupport 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 the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.
Electroluminescence measurements of color coded multi-quantum-well structures were used to improve the charge carrier distribution over three quantum wells with emission in the blue spectral region. Laser performance ...
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ISBN:
(纸本)9781424417827
Electroluminescence measurements of color coded multi-quantum-well structures were used to improve the charge carrier distribution over three quantum wells with emission in the blue spectral region. Laser performance was improved by optimized quantum barrier design.
Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach fo...
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Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level plusmn Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.
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