Monica Alonso

Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms

Nowadays, modern power networks have to face a number of challenges such as growing electricity demand, aging utility infrastructure and not to forget the environmental impact of the greenhouse gases produced by conventional electric generation. In order to increase renewable energy penetration but without disregarding security and reliability matters during the process, distribution power networks need to evolve to a flexible power network, better known as smart grid, in which distributed intelligence, communication technologies and automated control systems work as the driving factors. Taking into consideration this new frame, intelligent optimization techniques emerge as the only suitable way to optimally design this smart grid. In this paper, a generalized optimization formulation is introduced to determine the optimal location of distributed generators to offer reactive power capability. In order to find a suitable solution to such Reactive Power Management problem, genetic algorithms are applied in those cases where different multiobjective functions are to be considered. A more detailed description of the genetic algorithm evolution process is shown in a microgrid example.

Voltage stability in distribution networks with DG

This paper presents a methodology for optimal placement of DG units in power networks to guarantee the voltage profile, maximize loadability conditions in normal and in contingencies situations. The methodology aims in finding the configuration, among a set of system components, which meets the desired system reliability requirements taking into account stability limits. Results shown in the paper indicate that the proposed formulations can be used to determine which the best buses are where the addition of small distributed generator units can greatly enhance the voltage stability of the whole network and power transfer capability under contingencies.

Computation of voltage sag initiation with Fourier based algorithm, Kalman filter and Wavelets

Dynamic Voltage Restorers (DVR) have been successfully applied for voltage dip mitigation in the last years. Especially in systems with nonlinear loads and wind turbine generation DVR units support the Power Quality enhancement. The reliability and quality of DVR operation depends mostly on fast and accurate voltage dip detection. Detection methodologies must be able to detect a voltage dip as fast as possible and be immune to other types of perturbations. In this paper we address the problem of voltage dip estimation using carefully selected advanced signal processing methods such as Fourier based algorithm, Kalman filtering and Wavelets. Additionally, the traditional and common technique of RMS value tracking has been mentioned. The algorithms have been tested under different conditions: voltage dip with phase jump, noise, frequency variations.

Application of advanced signal processing methods for accurate detection of voltage dips

Custom Power Devices like the dynamic voltage restorer have been applied for voltage dip mitigation in the last years. These electronic equipments need fast and reliable voltage dip detection algorithms. Such detection methodologies must be able to detect a voltage dip as fast as possible and be immune to other types of perturbations. In this paper we address the problem of voltage dip estimation by using different advanced signal processing methods such as Kalman filtering, Fourier based algorithms and Wavelet processing. The three algorithms have been tested under different conditions: voltage dip with phase jump, noise, frequency variations. The final implementation on a Texas Instrument DSP TMS320F2812DSP has been done.

Photovoltaic reactive power limits

Today, Photovoltaic (PV) inverters are working with very small values of reactive power. Then, the Power Factor (PF) is very close to the unit. So, the PV installations only inject active power into the grid. This paper aims to investigate the limits of reactive power capacity in PV generators. In this way, PV generators could be used as a controlled reactive power sources. In this paper, an introduction to the voltage control in photovoltaic generators is described and implemented in Simulink/Matlab and PSS/E.

Reactive Power Management of Power Networks with Wind Generation

As the energy sector shifts and changes to focus on renewable technologies, the optimization of wind power becomes a key practical issue. Reactive Power Management of Power Networks with Wind Generation brings into focus the development and application of advanced optimization techniques to the study, characterization, and assessment of voltage stability in power systems. Recent advances on reactive power management are reviewed with particular emphasis on the analysis and control of wind energy conversion systems and FACTS devices.

LVRT capability of wind farms

Nowadays wind turbines are generally required to offer ancillary services similar to those provided by conventional generators. One of the most important services wind turbines must offer is to stay connected to the grid in fault situations delivering the reactive current specified in the recent grid codes. In this article, FACTS solutions for fixed speed wind farms such as DVR are presented as well as classic control and crowbar solutions for variable speed wind turbines. Real results are also presented.

Reactive Power Management

Reactive Power Management is a critical issue when dealing with the planning and operation of power networks with high wind energy penetration. Reactive Power Management entails the requested operation and planning actions to be implemented in order to improve the voltage profile and the voltage stability in power networks [1]. An efficient reactive power planning could be obtained by choosing an optimum location of var sources during the planning stage, whereas efficient reactive power dispatch could be achieved by scheduling an optimum regulation of the voltage set point at the generators connection point and at the VAR settings during the reactive power dispatch [2] and [3].

Reactive Power Optimization

Every optimization problem employed to study electric power systems consists of an objective function and a group of constraints to be observed by this function that concurrently define the problem itself. The main constraints associated with the problem of reactive power planning are related to load flow equations. These problems are widely known as optimum load flow. Current operating conditions of reactive systems imply the necessity of redefining the reactive power planning problem, though bearing in mind its performance under normal conditions and faults or disturbances. The employment of these constraints by the planning of reactive power leads to a second optimization model known as optimum load flow with security constraints. Finally, more recent studies have raised the possibility of including voltage stability into the objective function, as well as into the problem constraints of reactive power planning, to maximize the voltage stability margin. Thus, a third optimization problem is derived, namely optimum load flow with security and voltage stability constraints. This optimization method aims to guarantee the existence of voltage stability margins in the system under faults and disturbances.

Voltage Stability in Power Networks with Wind Power Generation

Beyond any doubt, we may consider century 21st as the one devoted to renewable energy. According to the International Energy Agency (IEA) [1], Renewable Energy Sources shall provide about 35 % of the European Union’s (EU) electricity by 2020, and within this context, wind energy is set to contribute the most – nearly 35 % – of all the power coming from renewable sources. This evolution is based on sustainability scenarios, like the BLUE one [2] related to the reduction of greenhouse emissions. However, the appropriate integration of such renewable energy into power networks still presents major challenges to Power Systems Operators (PSO) and planners.