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Papers in international journals

In this section one finds papers submitted to international journals. They are subject to Copyright Laws and limitations and can only be downloaded and shared by authorized persons or for purposes related with the EPSO project.

File Stochastic star
This paper reports the results of the adoption of a probabilistically defined communication structure in a special algorithm coined as EPSO – Evolutionary Particle Swarm Optimization, which is classified as an evolutionary algorithm using a particle movement rule as the recombination operator. Alternatively, EPSO may be seen as an algorithm of the family of PSO (Particle Swarm Optimization) but with a self-adaptive mechanism applied to make the weights of the movement rule evolve improving the performance of the algorithm. The paper presents results showing that a probabilistically controlled communication (to the particles of a swarm) of the location of the best-so-far point leads to better convergence and that the optimal value of the probability of communication depends on the topology of the surface being searched. Also, full communication (similar to classical PSO) has in all cases been shown to be worse than probabilistically constrained communication. This is demonstrated by comparing results in different test functions and also in the application of EPSO to an industrially relevant application – the reactive power planning in large scale power systems. ..
File Energy markets
This paper presents an overview of a simulation platform for studying the behavior of energy retail markets where multiple energies enter in competition. This platform is based on autonomous agent techniques. The simulations include agents representing Residential, Commercial and Industrial Consumer Groups, Electricity, Gas, Heat Retail Suppliers and Energy Deliverers, Regulators, Market Operators, Economy and Information Environment. Each pursues its own interests and from their interaction a complex collective behavior emerges. Agents formulate their strategies namely by inner complex simulation process that try to guess other agent moves and define optimum decisions in energy purchasing, price fixing, market share wining, investing and capturing new consumers, among other. The process works on a FIPA complying platform being able to run in a parallel cluster machines. The paper shows the results of experiments illustrating that consumer awareness and rapid response are important to have a real market while lack of timely response allows retailers to take advantage of them. ..
File Maintenance optimization
This paper propose an approach to multi-objective maintenance policy definition for electrical networks. Maximum asset performance is one of the major goals for electric power system managers. To reach this goal, minimal life cycle cost and maintenance optimization becomes crucial, while meeting demands from customers and regulators. This necessitates the determination of the optimal balance between preventive and corrective maintenance in order to obtain the lowest total cost. The approach of this paper is to study the problem of balance between preventive and corrective maintenance as a multiobjective optimization problem, where we have the customer interruptions on one hand and on the other hand the maintenance budget of the network operator. The problem is solved with meta-heuristics developed for the specific problem, as well as with an Evolutionary Particle Swarm Optimization algorithm. The maintenance optimization is applied in a case study to an urban distribution system in Stockholm, Sweden. Despite a general decreased level of maintenance (lower total maintenance cost) a better network performance can be given to the customers. This is achieved by focusing the preventive maintenance on components with a high potential for improvements. Beside this, the paper constitutes a display of the value in introducing more maintenance alternatives for every component and to choose the right level of maintenance for the components with respect to network performance.
File EPSO vs. Monte Carlo
This paper reports the application of a population based method (EPSO – Evolutionary Particle Swarm Optimization) to calculate power system reliability. Population based methods appear as competitors to the traditional Monte Carlo simulation because they can be much more computationally efficient in estimating a number of reliability indices. The work reported in this paper demonstrates that EPSO variants, suited to the problem of exploring a zone in the state space instead of searching for a single optimizing state, are efficient in calculating a number of system reliability indices such as power not supplied. The results obtained with EPSO are compared with Monte Carlo and with work of other researchers in population based methods.
File Reactive Power Planning
Evolutionary Particle Swarm Optimization (EPSO) is a robust optimization algorithm belonging to evolutionary methods. EPSO borrows the movement rules from Particle Swarm Optimization (PSO) and uses it as a recombination operator that evolves under selection. This paper presents a reactive power planning approach taking advantage of EPSO robustness, in a model that considers simultaneously multiple contingencies and multiple load levels. Results for selected problems are summarized including a trade-off analysis of results.
File Wind-Hydro coordination
This paper reports the application of neural networks denoted “autoencoders” in order to reduce the dimension of the search space in complex optimization problems. This allows a more efficient search by meta-heuristic algorithms, with a reduction in computing time and an improvement in the quality of results. The technique, coined as miranda, is illustrated with an application of an EPSO (Evolutionary Particle Swarm Optimization) algorithm to problems of medium term wind-hydro coordination, where the operation of cascading river dams with pumping-storage capability must be combined with decisions on the available wind power generation, depending on tariffs and market prices. One shows that an EPSO running of a reduced space generated by an autoencoder with solutions evaluated in a reconstructed space runs many times faster to obtain the same results as an EPSO running in the original problem space.
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