Carmen L. T. Borges

Active Distribution Network Integrated Planning Incorporating Distributed Generation and Load Response Uncertainties

Summary form only given: This paper presents a model for active distribution systems expansion planning based on Genetic Algorithms, where Distributed Generation (DG) integration is considered together with conventional alternatives for expansion, such as, rewiring, network reconfiguration, installation of new protection devices, etc. The novel approach of planning DG integration together with network expansion is a requirement for the modern active distribution network. However, the uncertainties related to DG power generation and load response growth must be taken into account in order to plan a safe system at a minimum cost. Thus, two different methodologies for uncertainties incorporation through the use of multiple scenarios analysis are proposed and compared. The multiple objectives optimization algorithm applied in the model takes into account the costs of reliability, losses, power imported from transmission, and network investments.

Impact of distributed generation allocation and sizing on reliability, losses and voltage profile

This work presents a methodology for evaluating the impact of DG units installation on electric losses, reliability and voltage profile of distribution networks. The losses and voltage profile evaluation is based on a power flow method with the representation of generators as PV buses. The reliability indices evaluation is based on analytic methods modified to handle multiple generations. The methodology may be used to evaluate the influence of the local of installation and the capacity of DG on these system performance characteristics for different generation expansion planning alternatives. The results obtained with the proposed methodology for systems extracted from the literature demonstrates its applicability.

State-of-the-Art Techniques and Challenges Ahead for Distributed Generation Planning and Optimization

It is difficult to estimate how much distributed generation (DG) capacity will be connected to distribution systems in the coming years; however, it is certain that increasing penetration levels require robust tools that help assess the capabilities and requirements of the networks in order to produce the best planning and control strategies. The work of this Task Force is focused on the numerous strategies and methods that have been developed in recent years to address DG integration and planning. This paper contains a critical review of the work in this field. Although there have been numerous publications in this area, widespread implementation of the methods has not taken place. The barriers to implementation of the advanced techniques are outlined, highlighting why network operators have been slow to pick up on the research to date. Furthermore, key challenges ahead which remain to be tackled are also described, many of which have come into clear focus with the current drive towards smarter distribution networks.

Probabilistic Wind Farms Generation Model for Reliability Studies Applied to Brazilian Sites

This paper presents a computer model for the probabilistic representation of wind farms generation for reliability studies, which can provide an annual estimation of energy production and calculate several performance indexes. The model combines the stochastic characteristics of wind speed with the operational information of the turbines, such as the failure and repair rates, representing the wind farm by a Markov process. The simulations are made with real time series of wind speed of several Brazilian regions and actual turbine models. The influence of some wind farm and installation site characteristics on the results are evaluated, such as the wind speed statistical clustering technique, the number and type of the turbines, and the failure and repair rates. The results obtained reproduce successfully the behavior of the components considered in the model

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.

Small Hydro Power Plants Energy Availability Modeling for Generation Reliability Evaluation

This paper presents a model for evaluating small hydro power plants (SHPP) generation availability that can be applied to generation systems reliability and to generation planning studies. The model considers the uncertainties of rivers inflows and generation units operation. The river inflow is modeled as a stationary stochastic process by a multiple states Markov chain and the generator unit by a two states Markov model. The large number of different inflow values is reduced by the application of the statistical clustering techniques K-means in two different approaches: inflow clustering and power clustering. The steady state probabilities of each power generation value of the SHPP are calculated by the solution of the stochastic system. The expected value of the annual power generation of the SHPP, the duration curve, and several reliability indices are then calculated in a more accurate way than conventional approaches, since the model considers both the river inflow variation and the generation unit operation. Results obtained with actual Brazilian rivers inflows used for SHPP generation are reported in the paper and demonstrate the accuracy and applicability of the presented method for reliability evaluation.

A Flexible Mixed-Integer Linear Programming Approach to the AC Optimal Power Flow in Distribution Systems

In this paper, we propose a flexible mixed-integer linear programming formulation of the AC OPF problem for distribution systems, using convexification and linearization techniques. The proposed formulation allows the representation of discrete decisions via integer decision variables, captures the nonlinear behavior of the electrical network via approximations of controllable accuracy, and can be solved to global optimality with commercial optimization solvers. The formulation is based on conventional variables that describe network behavior, which ensures its flexibility and the possibility of application to various distribution system problems, as we indicate with case studies.

A Comparison of Load Models for Composite Reliability Evaluation by Nonsequential Monte Carlo Simulation

This paper presents a comparison of Markov load models for composite reliability evaluation by nonsequential Monte Carlo simulation. The proposed models represent the whole system load curve. The first model (M1) is an aggregated Markov model that represents all different states present in the load curve, without using any clustering technique. The second model (M2) consists of a hybrid Markov model, where all different levels of the load curve are also represented but it tries to preserve some chronology of the load curve. The third model (M3) consists of a non-aggregated Markov model. The frequency and duration (F&D) indices are calculated by the conditional probability method for all models. The indices calculated using these models are compared with the indices obtained when the usual clustered aggregated Markov model (M0) is used. The indices obtained by sequential Monte Carlo simulation with a chronological system load curve are used as comparison reference in order to validate the presented models.

Modeling reliability aspects of distributed generation connected to distribution systems

This paper presents a discussion about modeling reliability aspects of distributed generation connected to distribution systems. Three different models developed by the authors are presented. The first is based on analytical method and is applicable to DG units of non-intermittent energy sources. The second considers the uncertainty of generation associated with wind generation and proposes a model based on multiple states Markov process. The third combines some aspects of the two previous models and aggregates the load variation curve in a sequential Monte Carlo simulation method. Practical operational premises are explored in all models

Impact of the Combined Integration of Wind Generation and Small Hydropower Plants on the System Reliability

The purpose of this paper is to assess the impact of the integration of wind generation, together with small hydropower plants (SHPs), in the reliability of the power system. In order to preserve the characteristics of the time series of the variable energy sources (wind and river inflows) and the variable load, the analyses are based on the sequential Monte Carlo simulation. By calculating the reliability indices, we intend to evaluate how the uncertainty of wind energy production impacts on system planning, especially with the reduction of capacity of reservoirs associated with SHPs. We also intend to evaluate the existence of complementarity between wind and hydro-generation, or even between different wind generation and/or the load. Finally, we intend to analyze how the correlation between these energy sources can benefit the supply of the future foreseen demand.