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Author (up) González-Castillo, M.; Navarrete, P.; Tapia, T.; Lorca, A.; Olivares, D.; Negrete-Pincetic, M.
Title Cleaning scheduling in photovoltaic solar farms with deterministic and stochastic optimization Type
Year 2023 Publication Sustainable Energy Grids & Networks Abbreviated Journal Sustain. Energy, Grids Netw.
Volume 36 Issue Pages 101147
Keywords Cleaning scheduling; Operations and maintenance; Optimization; Photovoltaic solar farms; Soiling
Abstract Soiling in solar panels causes a decrease in their ability to capturing solar irradiance, thus reducing the module's power output. To reduce losses due to soiling, the panels are cleaned. This cleaning represents a relevant share of the operation and maintenance cost for solar farms, for which there are different types of technologies available with different costs and duration. In this context, this paper proposes a method that allows scheduling the dates on which cleaning generates greater utility in terms of income from energy sales and costs associated with cleaning. For this, two optimization models that deliver a schedule of dates where the best income-cost balance is obtained, are proposed and compared: a deterministic Mixed Integer Linear Problem and a stochastic Markov Decision Process. Numerical results show that both models outperform the baseline case by similar to 4.6%. A simulator was built and both models were compared to the baseline case for 10,000 rainfall and irradiance scenarios. The stochastic model outperformed both models for all scenarios, thus proving that modeling rainfalls increases profitability in the face of uncertainty.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2352-4677 ISBN Medium
Area Expedition Conference
Notes WOS:001095657200001 Approved
Call Number UAI @ alexi.delcanto @ Serial 1904
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Author (up) Rodriguez, R.; Negrete-Pincetic, M.; Lorca, A.; Olivares, D.; Figueroa, N.
Title The value of aggregators in local electricity markets: A game theory based comparative analysis Type
Year 2021 Publication Sustainable Energy Grids & Networks Abbreviated Journal SEGAN
Volume 27 Issue Pages 100498
Keywords Information; Flexibility; Potential game; Adaptive expectations method
Abstract Demand aggregators are expected to have a key role in future electricity systems. More specifically, aggregators can facilitate the harnessing of consumers' flexibility. This paper focuses on understanding the value of the aggregator in terms of aggregation of both flexibility and information. We consider the aggregation of flexibility as the ability to exercise a direct control over loads, while the aggregation of information refers to knowledge of the flexibility characteristics of the consumers. Several game theory formulations are used to model the interaction between the energy provider, consumers and the aggregator, each with a different information structure. We develop a potential game to obtain the Nash equilibrium of the non-cooperative game with complete information and we analyze the system dynamics of consumers using the adaptive expectations method in an incomplete information scenario. Several key insights about the value of aggregators are found. In particular, the value of the aggregator is mainly related to the aggregation of information rather than flexibility, and flexibility is valuable only when it can be coordinated. In this sense, prices are not enough to guarantee an effective coordination.
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Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2352-4677 ISBN Medium
Area Expedition Conference
Notes WOS:000692827300020 Approved
Call Number UAI @ alexi.delcanto @ Serial 1399
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