Majid Babaei Nik

This paper introduces a comprehensive three-stage strategic framework designed to enable home microgrids (H-MGs) to collaborate effectively within multiple interconnected electrical and thermal grids. By forming coalitions, H-MGs can enhance their competitiveness in the energy market. The framework is supported by a bi-level optimization model that addresses the optimal management of both electrical and thermal energy within home microgrids, while also integrating electricity and heat distribution networks. A key aspect of this model is the incorporation of a specialized demand-side management strategy, which focuses on solving the optimization problem to maximize the overall system’s profit, despite the presence of variable uncertainties. The bi-level optimization model operates on two levels. The upper-level model focuses on maximizing the profit of the network operator, taking into account Combined Heat and Power (CHP) resources. The lower-level model, on the other hand, is designed to minimize the cost of electricity supply for H-MGs. To solve this complex optimization problem, the paper proposes a Multi-Stage Stochastic Programming approach based on the Golf Optimization Algorithm (MSSP-GOA). The effectiveness of the proposed method is demonstrated through a detailed simulation study. The results show that the method significantly reduces the market clearing price (MCP) for approximately 26% of the time intervals. Additionally, it leads to a 42% increase in the consumption of responsive loads within H-MGs and a threefold increase in local generation. The MSSP-GOA algorithm not only enhances market participation but also significantly boosts profits for all participants involved, underscoring its potential as a robust solution for optimizing energy management in collaborative microgrid environments.

Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system.

With the increasing penetration of renewable energy resources, such as wind and photovoltaic (PV) production, in future microgrids, challenges arise due to the potential interruption of these resources caused by changing weather conditions. In this paper, we propose a mixed-integer quadratic programming (MIQP) based bi-level model for the optimal operation of microgrids under worst-case (WC) scenarios of renewable energy resource outages. The upper-level problem formulates the minimization of energy loss and load shedding in a demand-side management (DSM) program, as well as optimal charging and discharging of electric vehicles (EVs) and energy storage systems (ESSs). The lower-level problem models the maximization of renewable energy curtailment to account for the worst-case realization of renewable resource outages. A decomposition and re-formulation method is adopted to solve the proposed bi-level optimization model, which includes binary variables in both levels. The proposed model and algorithm are implemented in the Julia programming language and solved with the Gurobi commercial solver. The model is analyzed using a 33-node microgrid under different cases to evaluate its performance, showcasing optimal microgrid operation results under worst-case renewable resource interruptions.