G.J. Osório

Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

Short-term wind power forecasting using a hybrid evolutionary intelligent approach

This paper presents a hybrid evolutionary intelligent approach, based on a combination of evolutionary particle swarm optimization (EPSO) with an adaptive-network-based fuzzy inference system (ANFIS), for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses challenges due to its intermittency and volatility. Hence, good forecasting tools are important for tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated, reporting the numerical results from a real-world case study.

A review of short-term hydro scheduling tools

This paper presents a review of short-term hydro scheduling tools reported by the scientific community, presenting different methodologies and models published over the last 20 years about operations, scheduling and optimization of hydro systems, in competitive electricity markets, considering the short-term horizon, i.e., between one day and one week, with the goal of maximizing profits and reducing losses, considering also different hydro configurations around the world. Hence, a comprehensive study is carried out in order to gather the maximum information possible to clarify and show the most recent advances in this field of research, contributing also for the appearance of future contributions.

Application of an intelligent system based on EPSO and ANFIS to price forecasting

This paper proposes evolutionary particle swarm optimization (EPSO) combined with an adaptive-network-based fuzzy inference system (ANFIS) for short-term electricity prices forecasting. In a deregulated framework, producers and consumers require short-term price forecasting to derive their bidding strategies to the electricity market. Accurate forecasting tools are required for producers to maximize their profits and for consumers to maximize their utilities. The accuracy of the price forecasting attained with the proposed intelligent system is thoroughly evaluated, reporting the numerical results from a real-world case study.

Hybrid evolutionary-adaptive approach to predict electricity prices and wind power in the short-term

Nowadays, with the new paradigm shift in the energy sector and the advent of the smart grid, or even with the mandatory imposition for a gradual reduction of greenhouse gas emissions, the renewable producers, namely the wind power producers are faced with the competitiveness and deregulated structure that characterizes the liberalized electricity market. In a liberalized electricity market, the most important signal for all market players corresponds to the electricity prices. In this sense, accurate approaches for short-term electricity prices prediction are needed, and also for short-term wind power prediction due to the increasing share of wind generation. Hence, this paper presents a new hybrid evolutionary-adaptive approach for wind power and electricity market prices prediction, in the short-term, based on mutual information, wavelet transform, evolutionary particle swarm optimization and adaptive neuro-fuzzy inference system, tested on real case studies, proving its superiority in a comprehensive comparison with other approaches previously published in the scientific literature.

Optimal behavior of electric vehicle parking lots as demand response aggregation agents

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, Plug-in Electric Vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based Demand Response Programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

A Probabilistic Approach to Solve Economic Dispatch Problem in Systems with Intermittent Power Sources

As a crucial factor in global energy consumption, environmental problems related to the greenhouse gas emissions and high oil prices have motivated the growth and incorporation of alternative sources of energy into power systems. However, one of the most important facets of such problems is their intermittent nature, which means that the operation and management of a power system is a difficult task. In this paper, a probabilistic approach to solving the economic dispatch (ED) problem under conditions of uncertainty is presented. The proposed methodology allows for obtaining the probability distribution function (PDF) of all generation units, the energy not supplied (ENS) and the total generation cost. A case study based on an insular power system is analyzed, with reference to the important relationship that exists between the PDF of net load demand and the PDF of ENS, power production and total generation cost.

Evaluating the security of electrical energy distribution networks in the presence of electric vehicles

Nowadays, Electric Vehicles (EVs) are the new technology to reduce the usage of fossil fuels and to prevent the environmental issues. But, increasing the number of EVs and mismanagement of their energy in distribution networks would cause higher operational costs and lower network security. This paper evaluates the voltage security of distribution networks in the presence of EVs. Accordingly, the maximization of voltage security margin (VSM) and the minimization of operational cost are considered as the main objective functions in the optimization problem of active and reactive power management. The constraints of the proposed optimization problem include power flow equations, system operating limits and EVs constraints. It is supposed that the EVs are equipped with bidirectional chargers to control active and reactive power, simultaneously. The proposed model is implemented on the 33-bus distribution network to evaluate the performance of the proposed optimization scheme for distribution network management in the presence of EVs.

Including forecasting error of renewable generation on the optimal load dispatch

In this paper a probabilistic model to solve the economic dispatch (ED) problem considering the uncertainty introduced by power sources, such as wind and solar, is presented. Assuming the forecasting error to be modeled by a beta probability distribution function (PDF), the proposed methodology presented in this paper allows the incorporation of this PDF in the optimization model, obtaining the PDF of power production of thermal and renewable generators, energy not supplied, excess of electricity, and generation cost. The results obtained from the proposed methodology are compared with those obtained from Monte Carlo Simulation (MCS) approach, observing a good agreement.

Probabilistic approach to describe greenhouse gas emissions and their impact in the operation of insular power systems

Environmental problems related to the conventional generators have motivated governmental policies all over the world in order to incorporate alternative power sources to reduce greenhouse gas (GHG) emissions and fossil-fuel consumption. On the one hand, wind power generation can increase GHG emissions of the others conventional generators connected to the system. On the other hand, wind energy is characterized by its variability that imposes challenges in the operation of the power system. In order to integrate GHG emissions estimation and the uncertainty related to the wind power generation, a probabilistic point of view is presented in this paper to estimate the probability distribution function (PDF) of the GHG emissions of a typical insular power system. The PDF of power production of each conventional generator is calculated, and then the expected value of the total GHG emissions is estimated.