Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

Abstract

This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory

Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization

“…Muttaqi et al 18 presents a review of the operational and control issues during the design and operation of distribution systems and MGs and the corresponding solutions have also been suggested. A robust chance‐constrained optimization framework has been proposed by Esmaeel Nezhad et al 19 for the uncertain scheduling of multicarrier energy hubs. An alternating direction method of multipliers‐based decentralized method is developed by Javadi et al 20 for the scheduling of energy hubs in MGs.…”

General Modeling of Interconnected Hubs in Series and Parallel Structures

“…In this study, the alternate direction technique of multiplier technique is used to represent distributed power flow. In [36], a robust chance-bound model is developed to handle production and consumption uncertainty in multicarrier energy hubs. The broad formulation of the problem is established in the framework of mixed integer linear programming.…”