Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. Both functions turn out to be very similar according to the performance of these indicators, unlike the ranking index, in which the DRF has better performance. The DRF reduces to 25% the minimum demand to avoid non-optimal situations, such as non-supplying the demand and has potential benefits, such as the elimination of finite combinations and easy implementation.

Introducción: Las redes eléctricas tradicionales están migrando a nuevas configuraciones de redes inteligentes, que traen retos operacionales y de planeación. Con miras a avanzar en estos retos se propone resolver un problema de optimización usando programación en elementos de redes distribuidas inteligentes. Objetivo: El problema de optimización consiste en administrar el despacho energético de una red inteligente para optimizar los recursos disponibles, considerando la incertidumbre de energías renovables, viajes planeados de vehículos eléctricos, el pronóstico de carga y los precios del mercado. Metodología: Se propuso utilizar un ensamble entre dos métodos heurísticos. El algoritmo VNS (Variable Neighborhood Search) y el DEEPSO (Differential Evolutionary Particle Swarm). Resultados: El algoritmo VNS-DEEPSO fue evaluado en una competencia de “Smart Grids” con otros algoritmos con un valor de 18.21, siendo 7 % mejor que el segundo algoritmo clasificado en la competencia. Conclusiones: El algoritmo VNS-DEEPSO fue ganador entre 9 algoritmos metaheurísticos que solucionaron el problema, este problema tenía un mayor incremento de dificultad debida a la incertidumbre generada por factores ambientales, pronóstico de carga, viajes en vehículos eléctricos y el mercado de precios. Acorde a los resultados, el algoritmo VNS-DEEPSO demostró ser el más eficiente en minimizar los costos operacionales y maximizar los ingresos de la red inteligente.

Colombia has a hydroelectric power generation capacity of over 60% and thermal generation just above 30%. Smart Grids have as a common objective the reliability of the energy service and the economic growth using Information and Communication Technologies for the generation with minimum contributions of energy supply sources derived from fossil fuels. Therefore, some strategies are proposed for the implementation of components such as renewable energies, controlled loads, energy storage systems, electric vehicles which are managed by an aggregator, besides considering the uncertainty in the Colombian environment that includes climatic conditions, the prediction of user load and market prices. Smart Grids requires flexible management of loads in collaboration with users, it depends on their habits and control of charges (smart households). Energy storage is a tactic to control user loads and generation with non-conventional renewable energy. As energy storage methods are presented adapted to regional conditions.

Smart grids gain acceptance for promoting the efficient use of energy resources, based on market prices. These include energy storage systems and electric vehicles; in terms of operation they are complex for controlling the loading / unloading of energy or the buy / sell of it. These networks also encourage demand response programs, that is, according to the price, the users decide how much energy they consume. In addition to promoting the use of renewable energy. This research presents two contributions: 1) The implementation of market power indicators to a mathematical model of smart microgrid and 2) The implementation of a new multiobjective hybrid algorithm called “variable neighborhood search: the differential evolutionary particle swarm”. The results are close to the Pareto front with a uniform distribution. Then, the smart microgrid is evaluated with two restrictions: the Herfindahl-Hirschman index and the three biggest bidders’ index, the first contribution allows no bidder to exercise market power during the 24 hours, which guarantees a competitive electricity market. The second contribution consists in converting this single objective algorithm to a multiobjective version. The performance of the new multiobjective algorithm is verified with the test problems showing good performance.

The uncertainty of solar generation and the bull market are unavoidable in energy dispatch. The purpose of this research is to validate an uncertainty cost function of residential photovoltaic energy in a real microgrid by varying the number of auctions in intraday markets. Therefore, the following procedure is proposed. First, the variability of photovoltaic generation is quantified through Monte Carlo simulations. Second, a statistical function calculates the variability costs of photovoltaic generation. Third, the uncertainty costs are estimated by varying intraday auction markets. Other complementary services are added to the network, such as battery exchange stations for electric vehicles, demand response loads, market power restrictions, and energy storage systems, which are estimated as total costs in an index ranking. The total costs are optimized in a benchmark microgrid and take complimentary services as a black box. Only the uncertainty costs of residential solar generators are discriminated. The main findings were that (1) the uncertainty costs have an error of less than 0.0168% compared to the Monte Carlo simulations and that (2) the uncertainty costs of solar generation are reduced with a decreasing trend to a more significant number of auction markets in intraday markets.