Sizing of a stand-alone microgrid considering electric power, cooling/heating and hydrogen

Abstract

Microgrids are small-scale power systems that include local generation and storage units to serve their loads. The effectiveness of such systems depends on both sizing and operations, that need to be efficient to minimize costs while ensuring reliable power delivery. In this paper, we build a stand-alone microgrid while considering not only electric power, but also cooling, heating, and hydrogen consumption. A unit commitment algorithm, formulated as a mixed integer linear programming problem, is used to determine the best operation strategy of the system. A genetic algorithm is used to search for the best size of each component. Results show the feasibility of the proposed sizing method.

Robust optimal dispatch of integrated energy system considering with coupled wind and hydrogen system

“…3) The fuel cell can produce electricity by consuming hydrogen, which is the critical equipment of the system [19]. are the consumed hydrogen and the generated electricity.…”

“…The basic requirement of a classic robust optimization model is the toughness that the system is able to combat interruptions under the most extreme scenarios [19]. The model can be described as (19).…”

Optimal Design of Isolated Mini-Grids with Deterministic Methods: Matching Predictive Operating Strategies with Low Computational Requirements

“…On the other hand, Mixed-Integer Linear Programming (MILP) is a numerical technique for mathematical optimization that guarantees convergence towards the global optimum; however, the computational burden grows significantly with the size of the problem [31,32]. Instead, when MILP tackles the optimal system operation, the computational burden is generally lower [18,33,34].…”

“…In sizing methodologies, MILP is often used with a deterministic approach, where an assumption for the yearly profiles of load and renewable generation is done beforehand the simulation of the operation; stochastic approaches have also been proposed, but they require simplifications to handle a large time horizon [35,36]. Computational requirements are a challenge of MILP: as reported by Li and colleagues [33], in order to optimally design of a stand-alone energy system with provision of electricity and heat/cool, only 12 representative days instead of a full yearly profile had to be used. In another example [34], a MILP formulation was proposed in order to optimize an off-grid system considering the simulation of an entire year at hourly resolutions; however, the convergence criterion was either a maximum of 3 h of computational time or a 5% of tolerance on the objective function.…”