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

Abstract

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.

Smart Microgrids Operation Considering a Variable Neighborhood Search: The Differential Evolutionary Particle Swarm Optimization Algorithm

“…The international Genetic and Evolutionary Computation Conference (GECCO 2019)/IEEE congress on evolutionary computation (IEEE-CEC 2019) and IEEE World Congress on Computational Intelligence (IEEE-WCCI 2018) competitions call to present complex metaheuristic optimization solutions to complex real-life problems, specifically for smart microgrid operation [5,6]. The test bed competition was developed by the group GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University [5,7]. The organizers of theses competitions provide a free framework (an encrypted test bed) in order to test heuristic algorithms for the smart microgrids scheduling, the framework can be downloaded freely from Supplementary Materials [5,7].…”

“…The test bed competition was developed by the group GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University [5,7]. The organizers of theses competitions provide a free framework (an encrypted test bed) in order to test heuristic algorithms for the smart microgrids scheduling, the framework can be downloaded freely from Supplementary Materials [5,7].…”

“…Although there are several explicit mathematical formulations, due to the complexity of smart microgrids models, they cannot be solved efficiently [6]. In cases where analytical model-based formulations become intractable, it has been demonstrated that heuristic and metaheuristic algorithms are a reliable option [7]. Additionally, some heuristic-based algorithms with acceptable results considering environmental uncertainties include Monte Carlo simulations, which allow analyzing more complex models, such as including planning of trips with EVs, penetration of renewables, variability of market prices and demand forecast [5].…”

Heuristic optimization of electrical energy systems: Refined metrics to compare the solutions

“…Specific competitions on heuristic optimization exist also in the power and energy systems area. For example, the 2017 IEEE competition on modern heuristic optimizers for smart grid operation [5] constructed two testbeds run for a given number of scenarios. Each scenario required the execution of a given number NMC of Monte Carlo objective function assessments, resulting in NMC best solutions.…”

Optimal Routing an Ungrounded Electrical Distribution System Based on Heuristic Method with Micro Grids Integration

“…For the reasons exposed above, the raised problem is not trivial and it must be solved applying heuristic models. The solution of the mathematical model of the EDS planning is proposed as a routing problem which is approached through a complex network analysis and graph theory [34]. Hence, it is necessary to perform a heuristic model that can reach a near optimal solution or sub-optimal solution.…”