Bi-objective optimization of a grid-connected decentralized energy system

Abstract

Summary Motivated by the increasing transition from fossil fuel–based centralized systems to renewable energy–based decentralized systems, we consider a bi‐objective investment planning problem of a grid‐connected decentralized hybrid renewable energy system. In this system, solar and wind are the main electricity generation resources. A national grid is assumed to be a carbon‐intense alternative to the renewables and is used as a backup source to ensure reliability. We consider both total cost and carbon emissions caused by electricity purchased from the grid. We first discuss a novel simulation‐optimization algorithm and then adapt multi‐objective metaheuristic algorithms. We integrate a simulation module to these algorithms to handle the stochastic nature of this bi‐objective problem. We perform extensive comparative analysis for the solution approaches and report their performances in terms of solution time and quality based on well‐known measures from the literature.

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

“…LP is mostly applied to power system optimisation problems to determine optimal DG sizes, as it gives exact solutions. 5,8 Hamam et al, 55 Dicorato et al, 56 Keane and O'Malley, 57,58 Altintas et al, 41 and Alturki et al 40 41 The performance of the algorithm was evaluated by evaluating solutions' spread using spacing, maximum spread values, and Central Processing Unit (CPU) time metrics. Also, sensitivity analysis on the effects of investment costs in relation to solar and wind DGs was conducted.…”

“…Multi‐objective function of a DG allocation problem involves combination or addition of many single objectives in Section with an inevitable conflict in which a single solution is incapable of solving all the diverse objectives. These multi‐objective functions are required to be minimised or maximised simultaneously into a single‐objective formulation . The multi‐objective formulations are grouped into (i)Multi‐objective weighted sum formulation: weighted sum of each objective is used to transform multi‐objective formulation into a single objective function problem with the use pre‐specified weights.…”

“…LP is mostly applied to power system optimisation problems to determine optimal DG sizes, as it gives exact solutions . Hamam et al, Dicorato et al, Keane and O'Malley, Altintas et al, and Alturki et al use LP to achieve high penetration and maximum energy harvesting of DGs, respectively, while maintaining voltage magnitude limits. Hamam et al describes a partitioning algorithm based on LP method to solve excessive memory requirement and computation time of long‐term generation plant mix problems .…”

“…Maximisation of DG capacity and profit are the objective functions of the works respectively. Altintas et al presented a bi‐objective LP optimisation algorithm to solve distribution expansion planning problem that incorporates renewable generation resources (wind and solar) with the objective to minimise total cost of investment and carbon emissions . The performance of the algorithm was evaluated by evaluating solutions' spread using spacing, maximum spread values, and Central Processing Unit (CPU) time metrics.…”

“…These multi-objective functions are required to be minimised or maximised simultaneously into a single-objective formulation. 33,[38][39][40][41] The multi-objective formulations are grouped into 5,7 (i) Multi-objective weighted sum formulation: weighted sum of each objective is used to transform multi-objective formulation into a single objective function problem with the use pre-specified weights. Weighted sum approach is simple to formulate but difficult to apply in non-convex optimisation problems.…”