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Economic, technological, and environmental causes are moving energy toward smart distribution networks. The change from fossil fuels to renewable energy sources is extremely environmentally friendly in both the building and transport sectors. Microgrids could be efficiently used in remote areas utilizing renewable energy resources for supplying different energy demands namely power, heat and cold. This paper presents a mixed-integer linear programming (MILP) model for optimizing planning and sizing decisions in microgrids connected to main grid. Planning decisions the amount of generation of each distributed generation (DG) technology and the amount of power transmission to other nodes. Also, sizing of different DGs including photovoltaic and combined heat and power facilities are performed by the proposed model. According to the results, the model is able to optimize sizing and planning decisions in grid-connected microgrids. [ABSTRACT FROM AUTHOR]

In this paper, a robust bi-level decision-making framework is presented for distributed generation (DG) owning retailers to supply the electricity to price-sensitive customers. Uncertainties about client demand and wholesale prices are the main difficulties faced by the electricity retailer. Clients can adjust their consumption according to the retailer's selling price. A higher selling price increases retailers' profit but decreases client consumption. Hence, the retailer faces a tradeoff between the price and sales. In the proposed model, the optimal selling price and the retailer's energy-supply strategy are modeled in the lower sub-problem. According to the proposed selling price, the optimal energy consumption of price-sensitive clients is determined in the upper sub-problem. To evaluate the financial risk arising from uncertain prices, the Information Gap Decision Theory (IGDT) approach is addressed in the lower sub-problem. Additionally, the risk-based optimization problem is formulated for risk-averse and risk-taker retailers via the robustness and opportunity functions, respectively. The robustness of the optimal solution against price variations is evaluated such that the associated profit will be more than the electricity retailer's acceptable threshold. The efficiency and performance of the decision-making framework are analyzed via a case study, and the numerical results are discussed. [ABSTRACT FROM AUTHOR]