Humberto M. Jorge

A multiple objective decision support model for the selection of remote load control strategies

Utilities frequently use remote load control as an effective means to achieve suitable network operational conditions. This procedure, usually designated load management (LM), is a part of the resources considered under the general designation of demand-side management (DSM). However, the use of LM in electric distribution network management is a problem that involves different conflicting aspects such as reducing peak demand, maximizing utility profits and minimizing discomfort caused to consumers. Hence, the problem is multiobjective in nature: economical, technical and quality of service aspects must all be explicitly accounted for in mathematical models. This paper presents a multiobjective decision support model which allows the consideration of the main concerns that have an important role in LM: minimize peak demand as perceived by the distribution network dispatch center, maximize utility profit corresponding to the energy services delivered by the controlled loads, maximize quality of service in the context of LM.

A Real-Time Energy Management Architecture for Multisource Electric Vehicles

This paper presents an energy management architecture for small urban electric vehicles based on hybrid energy sources and its real-time implementation. The energy management strategy uses an integrated rule-based metaheuristic approach to obtain solutions for sharing energy and power between two sources with different characteristics, namely, one with high specific energy and another with high specific power. A comprehensive real-time architecture for the energy management system is presented considering different management levels. The proposed approach determines an optimized real-time energy sharing between the sources without prior knowledge of the power demand profile. The multilevel energy management strategy has been validated using power-level reduced-scale hardware-in-the-loop simulations for a normalized urban driving cycle. The experimental results show the effectiveness of a real-time implementation based on particle swarm optimization supported by a set of rules restricting the search space. This strategy is effective in controlling the energy sources to work in their higher efficiency region and in satisfying the dynamic performance of the vehicle.

Simulation model and road tests comparative results of a small urban electric vehicle

Electric vehicles (EVs), due to its lack of local gas emissions, silent driving and much higher efficiency than internal combustion engine vehicles, are fundamental for the worlds sustainable mobility. In EVs projects, computer modeling and simulation can be used to reduce the expense and length of their design cycle by testing configurations and energy management strategies before starting the prototype construction. Accordingly, this paper presents the modeling and simulation of a small urban EV based on the conversion of an internal combustion engine vehicle in a fully EV, called VEIL. The ISEC electric vehicle VEIL project is shortly presented, along with the VEIL dynamic model. A real test circuit at ISEC campus is modeled and the results of simulation are compared with experimental results in order to validate the simulation model.

A Simulated Annealing Approach for Optimal Power Source Management in a Small EV

This paper presents a novel energy management strategy for a hybridized power source small urban electric vehicle (EV). First, an analysis of load requirements for typical urban driving cycles is presented. Thereafter, the energy and power management issues are addressed for a multisource EV to define an improved management architecture. A dynamically restricted search space strategy coupled with a simulated annealing technique is exploited to accomplish the global optimization of the energy management system (EMS). The control of the dc/dc converter operations based on this EMS is also presented. The multiple sources have been simulated using an overall model implemented in Matlab/Simulink. A reduced-scale prototype has been built to analyze the performance of the energy management strategy. The results obtained show that energy management has been enhanced leading to an increase of the vehicle performance with reduced size embarked sources.

Electric vehicles chargers characterization: Load demand and harmonic distortion

This paper presents electric vehicles chargers power quality characterization. The difference between domestic charge for small urban electric vehicle and industrial charge for forklift is addressed. Full charges of lead-acid and NiMH batteries are monitored by recent power analyser, allowing the study of power demands and the voltage and current waveform distortion of the considered charger. The data of the voltage and current waveforms is then used to study and analyse the best solution to mitigate the principal power quality problems. For this, a set of methods and techniques to attenuate the harmonics amplitude by network structural alteration is presented and discussed. The load levelling control concept has been introduced as an alternative option to structural alteration. This comprehensive and effective EV charging control system will respond to the undesirable increases in peak energy demand and power quality degradation, and avoid that the electrical energy network might not be prepared to respond to these requests.

Network reconfiguration to improve reliability and efficiency in distribution systems

This paper presents a new method to improve reliability and also minimize active power losses in radial distribution systems (RDS) through a process of network reconfiguration. The methodology adopted to enhance reliability, uses the Monte Carlo (MC) simulation and an historical data of the network such as the severity of the potential contingencies in each branch. Due to the greater number of possible configurations and the need of an efficient search, is also used an improved genetic algorithm (IGA), with adaptive crossover and mutation probabilities and with other new features. The method analyses the RDS in a perspective of optimization considering no investment, and a perspective of optimization where is given the possibility to place a limited number of tie-switches, defined by a decision agent, in certain branches. The effectiveness of the proposed method is demonstrated through the analysis of a 69 bus RDS.

A PC-based simulation package for supporting end-user demand side energy management strategies

A simulation package designated at SIMCA-E/sup 2/ (simulation for management, control and analysis of electricity end-use) is described. The package covers a wide area of applications-namely, the testing and performance evaluation of demand controllers and demand control algorithms, computer-aided analysis of data from energy audits, generation of alternative scenarios according to different consumption patterns and/or different contractual terms with the utility, and computer-aided teaching of demand-side management techniques and strategies. The package is written in Pascal and runs on IBM PC-AT, XT, or compatible computers. It may be used with different graphic cards. >

An automated energy management system in a smart grid context

The ongoing transformation of electric grids into smart grids provides the technological basis to implement demand-sensitive pricing strategies aimed at using the electric power infrastructure more efficiently. These strategies, also designated by demand response [1], already proved to be effective in altering patterns of electricity usage [2-6], and create benefits not only for end users (by lowering their electricity bill without degrading comfort levels), but also for the utilities (by managing the peak, flattening the aggregate demand curve, and meeting supply with demand) and the environment (by avoiding, or delaying, building new generation units and other network infrastructures). In fact, demand-sensitive pricing of electricity is expected to become the standard pricing mechanism in smart grids [3, 7, 8] and is considered essential to accelerate the deployment of variable renewable generation while maintaining electric system security and reliability at least cost [9].

An Energy Management System Aggregator Based on an Integrated Evolutionary and Differential Evolution Approach

The increasing penetration of renewable generation in the electric power system has been leading to a higher complexity of grid management due to its inherent intermittency, also with impact on the volatility of electricity prices. Setting the adequate operating reserve levels is one of the main concerns of the System Operator (SO), since the integration of a large share of intermittent generation requires an increased amount of reserve that is needed to balance generation and load. At the same time, the energy consumption in households has been steadily growing, representing a significant untapped savings potential due to consumption waste and load flexibility (i.e., the possibility of time deferring the use of some loads).

Analysis of operation modes for a neighborhood electric vehicle with power sources hybridization

Electric vehicles will have a fundamental place in sustainable mobility due to its very high efficiency and local/global emissions levels. To accomplish this role, both high power and high energy sources are desirable. In spite of recent advances in battery technology, this was not yet achieved and power sources hybridization can be a way to accomplish it. This is particularly true in the case of urban electric vehicles with very frequent speed variations and where regenerative braking can play an important role. In this paper the operation modes for a neighborhood electric vehicle with power sources hybridization are analyzed to define the convenient control strategy for its DC-DC converters. A system simulation is performed and an energetic analysis of different energy sources mix is done considering different acceleration requests. This is used to obtain a maximum energetic efficiency equation that can be used to optimize the control of the DC-DC converters.