No abstract

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.

Context: Currently, renewable energy sources are playing an important role in counteracting the environmental impact of traditional energy sources. For this reason, system operators must have analytical tools that allow them to incorporate these new forms of energy. In electrical power systems, when incorporating renewable resources such as photovoltaic solar generation, wind power generation or electric vehicles, uncertainty is introduced due to the stochasticity of primary resources.Method: Uncertainty costs are proposed that incorporate the injected power variability of the main sources of renewable energy (solar and wind) and the consumed power (electric vehicles). Variability is considered by the probability distributions of the primary sources of renewable energy (solar irradiation and wind speed).Results: The main result of this research is the application of analytical costs of uncertainty. In this way it is possible to modify the cost function of a traditional economic dispatch. Additionally, it is proposed to solve the problem with a heuristic optimization method of economic dispatch of active-reactive power. Finally, a comparison is made with the operating cost of the system without the incorporation of renewable energies.Conclusions: The proposed model in this article is a potential decision-making tool that power system operators may consider in the operation of the system. The tool is capable of considering the uncertainties of the primary sources of renewable energy. The probability distribution of the primary source forecast is assumed to be known. An opportunity in order to extend the model is to study its applicability to dynamic time horizons, contemplating the constraints of the unit commitment problem

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.