A. C. G. Melo

Composite reliability evaluation by sequential Monte Carlo simulation on parallel and distributed processing environments

This paper describes two parallel methodologies for composite reliability evaluation using sequential Monte Carlo simulation. The methodologies are based on coarse grain asynchronous implementations. In the first methodology, a complete simulation year is analyzed on a single processor and the many simulated years necessary for convergence are analyzed in parallel. In the second methodology, the adequacy analysis of the system operating states within the simulated years is performed in parallel and the convergence is checked on one processor at the end of each simulated year. The methodologies are implemented on a 10 nodes IBM RS/6000 SP scalable distributed memory parallel computer and on a network of 8 IBM RS/6000 43P workstations. The results obtained in tests with actual power system models showed high speedup and efficiency on both parallel platforms.

Concurrent composite reliability evaluation using the state sampling approach

Abstract This paper presents a parallel methodology for composite reliability evaluation using Monte Carlo simulation by the state sampling approach. The methodology is based on coarse-grain parallelism, in which the adequacy analyses of the sampled system states are performed by different processors and the parallel process convergence is controlled by an asynchronous algorithm. The methodology was implemented on parallel and distributed processing environments: a ten-node IBM RS/6000 SP distributed memory parallel computer and a network of eight IBM RS/6000 43P workstations. The results obtained in tests with actual power system models show high speed-up and efficiency on both platforms and very good scalability.

The Carbon Market Incremental Payoff in Renewable Electricity Generation Projects in Brazil: A Real Options Approach

The objective of this work is to present a methodology capable of estimating the incremental payoff of the carbon market for grid-connected renewable power generation projects in Brazil. The proposed methodology consists of using the NEWAVE model to determine the optimal operating policy for the Brazilian Interconnected System over the medium-term, and to establish hydrological dispatch scenarios for the power plants connected to this system. Based on these results, the criteria defined in the methodology ACM0002 were used to estimate the baseline of the projects developed in response to the Clean Development Mechanism of the Kyoto Protocol. Having estimated the baseline for the project, the incremental payoff obtained from the sale of Certified Emission Reductions is estimated using the real options theory. Subsequently, the value of the option is added to the conventional cash flow of the project, determining the impact of the carbon market on its internal rate of return.

Assessing investment analysis of wind power generation projects in Brazil

This paper presents a set of methodologies developed in order to support investment decision on electric power generation projects in Brazil. The main goal of such methodologies is to properly consider the unsystematic risk that affects the cash flow of those projects. Additionally, a typical wind power generation project will be evaluated in accordance with the rules of the Brazilian power market. Dominant hiring strategies will be determined for such project, i.e., the strategies that, for a given risk level, offer the biggest expected return.

Incorporating multi-area reliability concepts to the evaluation of the Brazilian power system capacity to supply the peak load

This paper discusses the adoption of multi-area reliability concepts in the evaluation of Brazilian power system capacity to supply the peak load. In the assessment of the interconnected hydrothermal power systems reliability it is adopted a computational model that takes into account uncertainties on the streamflows into the hydro plants reservoirs, random forced outages of generating units and interconnections among subsystems, system load curve and programmed outage of generating units. The model allows both single area and multi-area analysis. The calculated reliability indices can help the identification of possible power system deficiencies to supply the forecasted maximum peak load as well as the computation of power marginal costs in libelarized electricity markets. In addition, it is discussed an approach to estimate the maximum peak load that a hydrothermal generation system can supply considering a multi-area reliability criterion based on adequacy analysis. The obtained results encourage the continuity of studies seeking the establishment of such criterion for hydrothermal interconnected systems.

Application of stochastic programming and probabilistic analyses as key parameters for real decision making regarding implementing or not energy rationing - a case study for the Brazilian hydrothermal interconnected system

During 2014 and 2015, the Brazilian hydrothermal interconnected system faced critical hydrological conditions, such as extremely low multivariate inflow values on February 2014 and January 2015. Therefore, an issue that arose in early 2014 was whether the Brazilian government would have to implement or not an energy rationing. In this sense, this paper summarizes a proposed approach to technically support this decision, based on dual stochastic dynamic programming, multivariate inflows scenarios generation and probabilistic analyses, and that utilized the chain of optimization models developed by CEPEL and real configurations of the Brazilian large scale interconnected hydrothermal system. These studies, inserted in a very comprehensive and detailed technical analyses carried out by the Brazilian Monitoring Committee of the Electrical Sector, led to the decision of not implementing an energy rationing in 2014, and to continue to closely monitoring the electric power system performance.

DETERMINATION OF THE CARBON MARKET INCREMENTAL PAYOFF CONSIDERING A STOCHASTIC JUMP-DIFFUSION PROCESS

The objective of this paper is to verify the robustness of the Least Square Monte Carlo and Grant, Vora & Weeks methods when used to determine the incremental payoff of the carbon market for renewable electricity generation projects, considering that the behavior of the price of Certified Emission Reductions, otherwise known as Carbon Credits, may be modeled using a jump-diffusion process. In addition, this paper analyses particular characteristics, such as absence of monotonicity, found in trigger curves obtained through use of the Grant, Vora & Weeks method to valuate these types of project.