Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

Abstract

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem.The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intel-ligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence charac-teristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered.The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Program-ming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Two-Stage Stochastic Model Using Benders’ Decomposition for Large-Scale Energy Resource Management in Smart Grids

“…The work developed in [8] adopts Benders' decomposition approach to solve a multi-objective model in day-ahead context. The authors were able to reduce the complexity of the original MINLP scheduling problem compared to a previous formulation proposed in [7]. However, it was found later in [9], that the slave problem formulated as an hourly distribution power flow in [8] leads to sub-optimal solutions, due to temporal dependencies in distributed energy resources.…”

“…MINLP techniques require significant computer resources. The computation time needed for solving these types of problems is not compatible with the time limitations of short-term energy scheduling [7].…”

“…It is assumed that the upstream grid is connected to the distribution network at b=1. The maximum admissible line flow is expressed by (7) and (8).…”

“…Furthermore, the work in [8] seems limited in the sense that it does not consider demand response (DR), renewable generation such as wind or PV, and energy storage systems (ESS), which are increasingly important in SGs. Although the proposed works have contributed to reducing the original problem complexity, uncertainty factors have not been considered in the mentioned works [7]- [9] and many others presented in the literature [10]- [15].…”

An Optimal Operation Model and Ordered Charging/Discharging Strategy for Battery Swapping Stations

“…Equation (21) Equations (9), (10), (12), (13), (17), (18) and Constraint (9) Batteries are arranged by the optimization model but discharging is not considered in these scenarios.…”