Fernando Lezama

2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results

This paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems.

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

Complex Optimization and Simulation in Power Systems

1GECAD, Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (IPP), R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal 2National Institute of Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico 3BiSITE, University of Salamanca, Calle Espejo, 2, 37007 Salamanca, Spain 4Électricité de France R&D, Paris, France

Differential evolution strategies for large-scale energy resource management in smart grids

Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, Evolutionary Algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter setting on four state-of-the-art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the setting of their parameters, they can find better solutions than other EAs, and near-optimal solutions in acceptable times compared with an MINLP approach.

Evolutionary framework for multi-dimensional signaling method applied to energy dispatch problems in smart grids

In the smart grid (SG) era, the energy resource management (ERM) in power systems is facing an increase in complexity, mainly due to the high penetration of distributed resources, such as renewable energy and electric vehicles (EVs). Therefore, advanced control techniques and sophisticated planning tools are required to take advantage of the benefits that SG technologies can provide. In this paper, we introduce a new approach called multi-dimensional signaling evolutionary algorithm (MDS-EA) to solve the large-scale ERM problem in SGs. The proposed method uses the general framework from evolutionary algorithms (EAs), combined with a previously proposed rule-based mechanism called multi-dimensional signaling (MDS). In this way, the proposed MDS-EA evolves a population of solutions by modifying variables of interest identified during the evaluation process. Results show that the proposed method can reduce the complexity of metaheuristics implementation while achieving competitive solutions compared with EAs and deterministic approaches in acceptable times.

Electrical Load Pattern Shape Clustering Using Ant Colony Optimization

Electrical Load Pattern Shape (LPS) clustering of customers is an important part of the tariff formulation process. Nevertheless, the patterns describing the energy consumption of a customer have some characteristics (e.g., a high number of features corresponding to time series reflecting the measurements of a typical day) that make their analysis different from other pattern recognition applications. In this paper, we propose a clustering algorithm based on ant colony optimization (ACO) to solve the LPS clustering problem. We use four well-known clustering metrics (i.e., CDI, SI, DEV and CONN), showing that the selection of a clustering quality metric plays an important role in the LPS clustering problem. Also, we compare our LPS-ACO algorithm with traditional algorithms, such as k-means and single-linkage, and a state-of-the-art Electrical Pattern Ant Colony Clustering (EPACC) algorithm designed for this task. Our results show that LPS-ACO performs remarkably well using any of the metrics presented here.

Load pattern clustering using differential evolution with Pareto Tournament

Load Patterns (LPs) clustering has a broad range of applications, such as tariff formulation, power system planning, load forecasting, and so on. In this paper, we develop a multi-objective version of Differential Evolution (DE) using a Pareto Tournament (PT) selection to solve the LP clustering problem. Our automatic DE LP clustering (ADE-LPC) algorithm provides an entire Pareto front, and by incorporating a locus-based adjacency encoding, it inherent determines the number of clusters associated with the solutions during the optimization process. We analyze the results using two metrics for clustering, one based on compactness (the overall deviation (DEV)) and the other based on connectedness (connectivity (CONN)). These two metrics have complementary behavior and, in some cases, the correct clustering solution can be found in a trade-off between the two objectives. In addition, we compare our ADE-LPC algorithm against traditional clustering algorithms, such as k-means and single-linkage, and against two evolutionary algorithms, one based on ACO, and the well-known Non-dominated Sorted Genetic Algorithm II (NSGA-II). The results show that our ADE-LPC algorithm finds the best Pareto set, measure by the hypervolume, compared to the other algorithms.

Closed frequent similar pattern mining: Reducing the number of frequent similar patterns without information loss

The concept of closed frequent similar pattern mining is introduced.Several lemmas to prune the search space are introduced and proved.A novel closed frequent similar pattern mining algorithm (CFSP-Miner), is proposed.CFSP-Miner is more efficient than the frequent pattern mining algorithms.CFSP-Miner has excellent scalability properties. Frequent pattern mining is considered a key task to discover useful information. Despite the quality of solutions given by frequent pattern mining algorithms, most of them face the challenge of how to reduce the number of frequent patterns without information loss. Frequent itemset mining addresses this problem by discovering a reduced set of frequent itemsets, named closed frequent itemsets, from which the entire frequent pattern set can be recovered. However, for frequent similar pattern mining, where the number of patterns is even larger than for Frequent itemset mining, this problem has not been addressed yet. In this paper, we introduce the concept of closed frequent similar pattern mining to discover a reduced set of frequent similar patterns without information loss. Additionally, a novel closed frequent similar pattern mining algorithm, named CFSP-Miner, is proposed. The algorithm discovers frequent patterns by traversing a tree that contains all the closed frequent similar patterns. To do this efficiently, several lemmas to prune the search space are introduced and proven. The results show that CFSP-Miner is more efficient than the state-of-the-art frequent similar pattern mining algorithms, except in cases where the number of frequent similar patterns and closed frequent similar patterns are almost equal. However, CFSP-Miner is able to find the closed similar patterns, yielding a reduced size of the discovered frequent similar pattern set without information loss. Also, CFSP-Miner shows good scalability while maintaining an acceptable runtime performance.

CML-WSN: A Configurable Multi-layer Wireless Sensor Network Simulator

Wireless Sensor Networks (WSNs) have large applications in environments where access to human cannot be constant or where reliable and timely information is required to support decisions. WSNs must show high reliability, robustness, availability of information, monitoring capabilities, self-organization, among other aspects. Also, engineering requirements, such as low-cost implementation, operation, and maintenance are necessary. In this context, a simulator is a powerful tool for analyzing and improving network technologies used as a first step to investigate protocol design and performance test on large-scale systems without the need of real implementation. In this paper, we present a Configurable Multi-Layer WSN (CML-WSN) simulator. The CML-WSN simulator incorporates a configurable energy model to support any sensor specification as a one of its main features. The CML-WSN simulator is useful because it allows exploring prototypes with much less cost and time compared to the requirements needed in real networks implementations.

Optimal Scheduling of On/Off Cycles: A Decentralized IoT-Microgrid Approach

The current energy scenario requires actions towards the reduction of energy consumptions and the use of renewable resources. To this end, the energy grid is evolving towards a distributed architecture called Smart Grid (SG). Moreover, new communication paradigms, such as the Internet of Things (IoT), are being applied to the SG providing advanced communication capabilities for management and control. In this context, a microgrid is a self-sustained network that can operate connected to the SG (or in isolation). In such networks, the long-term scheduling of on/off cycles of devices is a problem that has been commonly addressed by centralized approaches. In this paper, we propose a novel IoT-microgrid architecture to model the long-term optimization scheduling problem as a distributed constraint optimization problem (DCOP). We compare different multi-agent DCOP algorithms using different window sizes showing that the proposed architecture can find optimal and near-optimal solutions for a specific case study.