Demand response programs (DRPs) have paved a meaningful role in the power supplydemand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT-based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively.
This paper presents a new energy management system (EMS) for an islanded microgrid (MG) to increase power system security cost-effectively. The small size of MGs, variations in renewable energy sources (RESs) output power, and low inertia of power electronic interfaced distributed energy resources (DERs) convert voltage fluctuations into a vital issue for the MG EMS. This paper proposes a two-stage stochastic multiobjective framework to simultaneously attain economic, environmental, and technical aims. In the first stage, many scenarios related to RESs output power variations, load demand changes, and DERs contingency outages are generated using Monte Carlo simulation. Afterwards, generated scenarios are reduced and applied to the second stage. A novel objective function based on the expected voltage fluctuations (EVFs) caused by active and reactive power variations is proposed, which is minimized alongside the active and reactive costs and MG emission. The proposed model is applied to a test MG, and active and reactive power and primary and secondary reserve scheduling are correctly accomplished for five different cases. Precise analysis confirms the notability and effectiveness of MG voltage fluctuations management in energy and reserve scheduling. Numerical results show more than 60% reduction in voltage fluctuations.
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