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Summary The unprecedented penetration of distributed generation in distribution energy networks provides utilities with a unique opportunity to manage portions of networks as microgrids (MGs). The implementation of an MG may offer many benefits, such as capital investments deferral, reduction of greenhouse gas emissions, improvement in reliability, and reduction in network losses. Future energy networks will contain various forms of energy which are acquired by mixing several sources and energy storages in the concept called multicarrier microgrid (MCMG). In order to draw the most effective performance from MCMG systems, appropriate design and operation are essential. This paper represents a compound co‐optimization strategy to find the best type and size of components and the associated optimum dispatch in a grid‐tied community MCMG implementing reliability criteria. Here, the required level of reliability is handled within the optimization process to fulfill multiple demands. The mixed‐integer nonlinear programming (MINLP) technique of general algebraic modeling system (GAMS) and the genetic algorithm of MATLAB software are utilized to solve the co‐optimization problem. Additionally, a contemporary time‐based demand response program is modeled to reshape the load curve, as well as prevent the undue use of energy in peak hours. Eventually, the proposed strategy is applied to a test case to select the best components while respecting reliability restrictions. Numerical simulations prove the effectiveness of the proposed expansion planning.

Abstract:A microgrid (MG) is a small-scale version of the power system which makes possible the integration of renewable resources as well as achieving maximum demand side management (DSM) utilization. The future power system will be faced with severe uncertainties owing to penetration of renewable resources. Consequently, the uncertainty assessment of system performance is essential. The conventional energy scheduling in an MG may not be suitable for active distribution networks. Hence, this study focuses on the probabilistic analysis of optimal power dispatch considering economic aspects in a multi-carrier networked microgrid. The aim is to study the impact of uncertain behavior of loads, renewable resources, and electricity market on the optimal management of a multi-carrier networked microgrid. Furthermore, a novel time-based demand side management is proposed in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. The optimization model is formulated as a mixed integer nonlinear program (MINLP) and is solved using MATLAB and GAMS software. Results show that the energy sharing capability between MCMGs and MCMGs and the main grids as well as utilization of demand side management can decrease operating costs for smart distribution grids.

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Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are addressed. The DRPs unlock the flexibility potentials of domestic demands to compensate for the power shortage during critical hours. The heat-power dual dependency characteristic of combined heat and power systems as a substantial technology in microgrids is considered in the model. The simulation results confirm that the suggested NMCMG not only integrates the flexibility potentials into the microgrids but also enhances the resilience of the energy systems.