Leonel M. Carvalho

Probabilistic Analysis for Maximizing the Grid Integration of Wind Power Generation

This paper presents a sequential Monte Carlo simulation algorithm that can simultaneously assess composite system adequacy and detect wind power curtailment events. A simple procedure at the end of the state evaluation stage is proposed to categorize wind power curtailment events according to their cause. Furthermore, the dual variables of the DC optimal power flow procedure are used to identify which transmission circuits are restricting the use of the total wind power available. In the first set of experiments, the composite system adequacy is assessed, incorporating different generation technologies. This is conducted to clarify the usual comparisons made between wind and thermal technologies which, in fact, depend on the performance measure selected. A second set of experiments considering several wind penetration scenarios is also performed to determine the operational rules or system components responsible for the largest amount of wind energy curtailed. The experiments are carried out on configurations of the IEEE-RTS 79 power system.

Coping with wind power uncertainty in Unit Commitment: A robust approach using the new hybrid metaheuristic DEEPSO

The uncertainty associated with the increasingly wind power penetration in power systems must be considered when performing the traditional day-ahead scheduling of conventional thermal units. This uncertainty can be represented through a set of representative wind power scenarios that take into account the time-dependency between forecasting errors. To create robust Unit Commitment (UC) schedules, it is widely seen that all possible wind power scenarios must be used. However, using all realizations of wind power might be a poor approach and important savings in computational effort can be achieved if only the most representative subset is used. In this paper, the new hybrid metaheuristic DEEPSO and clustering techniques are used in the traditional stochastic formulation of the UC problem to investigate the robustness of the UC schedules with increasing number of wind power scenarios. For this purpose, expected values for operational costs, wind spill, and load curtailment for the UC solutions are compared for a didactic 10 generator test system. The obtained results shown that it is possible to reduce the computation burden of the stochastic UC by using a small set of representative wind power scenarios previously selected from a high number of scenarios covering the entire probability distribution function of the forecasting uncertainty.

Statistical tuning of DEEPSO soft constraints in the Security Constrained Optimal Power Flow problem

The optimal solution provided by metaheuristics can be viewed as a random variable, whose behavior depends on the value of the algorithms strategic parameters and on the type of penalty function used to enforce the problems soft constraints. This paper reports the use of parametric and non-parametric statistics to compare three different penalty functions implemented to solve the Security Constrained Optimal Power Flow (SCOPF) problem using the new enhanced metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO). To obtain the best performance for the three types of penalty functions, the strategic parameters of DEEPSO are optimized by using an iterative algorithm based on the two-way analysis of variance (ANOVA). The results show that the modeling of soft constraints significantly influences the best achievable performance of the optimization algorithm.

The STABALID project: Risk analysis of stationary Li-ion batteries for power system applications

This work presents a risk analysis performed to stationary Li-ion batteries within the framework of the STABALID project. The risk analysis had as main objective analysing the variety of hazards and dangerous situations that might be experienced by the battery during its life cycle and providing useful information on how to prevent or manage those undesired events. The first task of the risk analysis was the identification of all the hazards (or risks) that may arise during the battery life cycle. Afterwards, the hazards identified were mapped in the different stages of the battery life cycle and two analyses were performed for each stage: an internal problem analysis and an external peril analysis. For both, the dangerous phenomena and the undesirable events resulting from each hazard was evaluated in terms of probability of occurrence and severity. Then, a risk assessment was carried out according to a predefined risk matrix and a preliminary set of risk mitigation measures were proposed to reduce their probability of occurrence and/or their severity level. The results obtained show that it is possible to reduce the probability of occurrence/severity of all the risks associated to the battery life cycle to acceptable or tolerable levels.

Probabilistic analysis of stationary batteries performance to deal with renewable variability

Stationary batteries are currently seen as an interesting solution to deal with the variability of the renewable energy sources. In the same way as other types of storage, e.g. pumped-hydro units, this new type of storage equipment can improve the use of Renewable Energy Sources (RES). Additionally, the stationary batteries location in the grid is not as physically constrained as other storage systems and can be optimally selected to maximize its overall benefits. This paper proposes a new methodology to represent the unique stochastic behavior of stationary batteries while integrated into an electrical power system. This methodology includes not only the technical restrictions of this type of storage system but also how its operation strategy affects its lifetime. The methodology was tested on a small test system, which is based on the IEEE-RTS 79, using sequential Monte Carlo simulation as its core to accurately reproduce the chronology of events of stationary batteries. The results of the simulation are focused on the potential impacts of these storage devices not only in terms of renewable energy used but also in the adequacy of supply.

Modeling wind power uncertainty in the long-term operational reserve adequacy assessment: A comparative analysis between the Naïve and the ARIMA forecasting models

The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements but also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Naïve forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naïve model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naïve and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.

Solving security constrained optimal power flow problems: a hybrid evolutionary approach

A hybrid population-based metaheuristic, Hybrid Canonical Differential Evolutionary Particle Swarm Optimization (hC-DEEPSO), is applied to solve Security Constrained Optimal Power Flow (SCOPF) problems. Despite the inherent difficulties of tackling these real-world problems, they must be solved several times a day taking into account operation and security conditions. A combination of the C-DEEPSO metaheuristic coupled with a multipoint search operator is proposed to better exploit the search space in the vicinity of the best solution found so far by the current population in the first stages of the search process. A simple diversity mechanism is also applied to avoid premature convergence and to escape from local optima. A experimental design is devised to fine-tune the parameters of the proposed algorithm for each instance of the SCOPF problem. The effectiveness of the proposed hC-DEEPSO is tested on the IEEE 57-bus, IEEE 118-bus and IEEE 300-bus standard systems. The numerical results obtained by hC-DEEPSO are compared with other evolutionary methods reported in the literature to prove the potential and capability of the proposed hC-DEEPSO for solving the SCOPF at acceptable economical and technical levels.

Enhancing stochastic unit commitment to include nodal wind power uncertainty

The Unit Commitment (UC) problem consists on the day-ahead scheduling of thermal generation units. The scheduling process is based on a forecast for the demand, which adds uncertainty to the decision of starting or shutting down units. With the increasing penetration of renewable energy sources, namely wind power, the level of uncertainty is such that deterministic UC approaches that rely uniquely on point forecasts are no longer appropriate. The UC approach reported in this paper considers a stochastic formulation and includes constraints for the technical limits of thermal generation units, like ramp-rates and minimum and maximum power output, and also for the power flow equations by integrating the DC model in the optimization process. The objective is to assess the ability of the stochastic UC approach to decrease the expected value of load shedding and wind power loss when compared to the deterministic UC approach. A case study based on IEEE-RTS 79 system, which has 24 buses and 32 thermal generation units, for two different penetrations of wind power and a 24-hour horizon is carried out. The computational performance of the methodology proposed is also discussed to show that considerable performance gains without compromising the robustness of the stochastic UC approach can be achieved.

Using VaR and CVaR techniques to calculate the long-term operational reserve

Generally, the more Renewable Energy Sources (RES) in generation mix the more complex is the problem of reliability assessment of generating systems, mainly because of the variability and uncertainty of generating capacity. These short-term concerns have been seen as a way of controlling the amount of spinning reserve, providing operators with information on operation system risks. For the medium and long-term assessment, such short-term concerns should be accounted for the system performance [1,2], assuring that investment options will result in more robust and flexible generating configurations that are consequently more secure. In order to deal with the spinning reserve needs, this work proposes the use of a risk based technique, Value-at-Risk and Conditional Value-at-Risk, to assist the planners of the Electric Power Systems (EPS) as regards the design of the flexibility of generating systems. This methodology was applied in the IEEE-RTS-96 HW producing adequate results.

A successful parallel implementation of NSGA-II on GPU for the energy dispatch problem on hydroelectric power plants

Nowadays, hydraulic sources are responsible for most of the Brazils energy production. Hydroelectric power plants (HPP) operators in Brazil usually distribute equally the total power required among the generator units available in the plant. However, studies show that this configuration does not guarantee that each generator unit operate close to its optimal operation point. The energy dispatch optimization problem consists in determining which generation units need to be on or off and what is their respective power-set, so that both the overall HPP costs is minimized and the power required by the plant is met. This paper presents a GPU-based parallel implementation of NSGA-II, to solve the energy dispatch problem of a HPP complaying with the real time restrictions posed by the operation of a real HPP from the reception of the power demand to the energy dispatch. Our implementation obtains better solutions than the sequential implementation currently available.