Pricing of reactive power support provided by distributed generators in transmission systems

• Autor(a) principal: Rueda-Medina, A. C. [UNESP]
• Data de Publicação: 2011
• Outros Autores: Padilha-Feltrin, A. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/PTC.2011.6019300; http://hdl.handle.net/11449/72740
• topic: distributed generation; multi-objective optimization; Reactive power support; transmission systems; Active power; Active power generation; Ancillary service; Demand response programs; Distributed generators; Micro grid; Monte Carlo Simulation; Multi objective; Multi-objective optimal power flow; Opportunity costs; Optimization methodology; Optimization process; Reactive power capacity; Sales opportunities; Smart grid; Test systems; Two way communications; Voltage stability margins; Communication systems; Computer simulation; Costs; Distributed power generation; Fuzzy logic; Markov processes; Monte Carlo methods; Multiobjective optimization; Reactive power; Smart power grids; Sustainable development; Time series; Transmissions; Turbines; Voltage stabilizing circuits; Electric power transmission

Distributed Generation, microgrid technologies, two-way communication systems, and demand response programs are issues that are being studied in recent years within the concept of smart grids. At some level of enough penetration, the Distributed Generators (DGs) can provide benefits for sub-transmission and transmission systems through the so-called ancillary services. This work is focused on the ancillary service of reactive power support provided by DGs, specifically Wind Turbine Generators (WTGs), with high level of impact on transmission systems. The main objective of this work is to propose an optimization methodology to price this service by determining the costs in which a DG incurs when it loses sales opportunity of active power, i.e, by determining the Loss of Opportunity Costs (LOC). LOC occur when more reactive power is required than available, and the active power generation has to be reduced in order to increase the reactive power capacity. In the optimization process, three objectives are considered: active power generation costs of DGs, voltage stability margin of the system, and losses in the lines of the network. Uncertainties of WTGs are reduced solving multi-objective optimal power flows in multiple probabilistic scenarios constructed by Monte Carlo simulations, and modeling the time series associated with the active power generation of each WTG via Fuzzy Logic and Markov Chains. The proposed methodology was tested using the IEEE 14 bus test system with two WTGs installed. © 2011 IEEE.