Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints

Abstract

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.

Uncertainty Costs Optimization of Residential Solar Generators Considering Intraday Markets

“…In addition, the MG has restrictions that prevent the appearance of monopolies, pivotal agents, and a minimum supply of demand. [9,13]. Furthermore, the mathematical formulation of the uncertainty caused by deviations in solar energy dispatch is stated [8,15].…”

“…In addition, the smart MG considers restrictions such as the Herfindahl-Hirschman concentration index and the index of the three most prominent bidders to avoid monopolies and pivotal agents [9]. There is also the demand welfare, which ensures a minimum consumption of the demand [13]. The MG model operates in a black box.…”

“…Additionally, MG can transfer energy with intraday markets [11]. It also has penalties for costs of uncertainty in the Gen of solar energy, market power restrictions, and constraints on the minimum supply of energy for demand [9,13,15]. MG is optimized using the VNS-DEEPSO algorithm, which improves MG in the first stage and MIs in the second stage, as shown in Figure 3 [17,18].…”

“…The user is given reliability, and study on the reserves that allow absorbing market volatilities is imperative. The literature suggests planning with multiple agents to reduce greenhouse gas costs, separately evaluating operating expenses and revenues obtained in markets, and assessing the demand curve [10,12,13]. Another approach takes price information from a real EM to conduct energy dispatch planning [14].…”

Rethinking the Distribution Power Network Planning and Operation for a Sustainable Smart Grid and Smooth Interaction with Electrified Transportation

“…The introduction of renewable energy generation and the forthcoming EV revolution disrupted distribution power network planning and operation. The new paradigm of the smart grid is a real challenge that is being taken seriously by many experts and researchers around the world, which are leveraging the full potential of artificial intelligence tools and classical optimization to solve challenging real-world problems [1,2]. Artificial intelligence methods will certainly play a very important role in this field.…”