Day-Ahead Scheduling of Multi-Energy Microgrids Based on a Stochastic Multi-Objective Optimization Model

Abstract

Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the transaction of electricity and natural gas (NG). Electricity and gas are traded by MGs with DISCO at prices that are dynamic and fixed, respectively. Through scenario generation and probability density functions, the uncertainties of wind speed, solar irradiation, electrical, and heat demands have been considered. By using mixed-integer linear programming (MILP) for scenario reduction, the high number of generated scenarios has been significantly reduced. The ɛ-constraint approach was used and solved as mixed-integer nonlinear programming (MINLP) to obtain a solution that meets the needs of both of these nonlinear objective functions.

Energy storage device sizing and energy management in building‐applied photovoltaic systems considering battery degradation

“…This process is iterated until the performance of all available batteries in the relevant building is investigated. Fuzzy decision-making (FDM) has become popular due to its ability to capture human beings' inherent vagueness and subjectivity when it comes to the decision-making of various complexity levels [41,42]. If it has reached the maximum available battery capacity, then FDM is used to find the optimal type of battery, and this type is shown along with its KPIs.…”

Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids

“…Previous studies on energy management have identified the following shortcomings: • A lack of a comprehensive online energy-management system for minimizing cost function and maximizing the use of DERs. Most of the presented approaches focus on off-line control and scheduling over day-ahead [29][30][31][32][33] • An absence of an intelligent approach for providing a structure that guarantees stability and reliability [34][35][36]. Generally, most studies in smart grids mainly focus on the generation and demand side without investigating the structure of the control units.…”

“…The ES agent sends suitable set points to the converter after ending a tender. At the end of each tender, the ES agent calculates its revenue from each tender and updates its offer price for the next bid using Equations ( 30) and (31), respectively.…”

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

“…Multi-objective optimization not implemented 57 2023 CES, EES, CAES, EHP, AC, heat pump Operation cost, emission Blue whale optimization algorithm Integration of WT, PV, and MT not considered. Different charging modes of EV not analyzed 58 2023 PV, WT, battery MG and EV cost Enhanced variant multi-objective particle swarm optimization algorithm Analysis of environmental pollution is ignored 59 2023 CHP, gas boiler, WT, PV, HS, BS Operating cost of multi-microgrid, profit of the distribution company Mixed-integer linear programming, ε-constraint approach, mixed-integer nonlinear programming Analysis of environmental pollution is ignored 60 2023 WT, PV, battery, MT, diesel generator, FC and grid Operation cost, emission Improved shuffled frog leaping algorithm Different charging modes of EV not analyzed 61 2023 thermal generators, battery and grid operation cost, emission efficient black widow optimization algorithm, Integration of renewable energy sources is ignored 62 2023 PV, diesel generator, grid and battery Energy consumption, life cycle of battery, practicality of the renewable energy usage Extended optimal ε-variable technique Analysis of operating cost and emission is ignored 63 2024 Battery, supercapacitor Battery capacity loss, state of charge NSGA-III, Integration of renewable energy sources is ignored 64 ...…”