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Socially-and-environmentally-aware power management in residential neighborhoods exposed to heat waves considering uncertainties
Dindar, Amin
Dindar, Amin
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2020
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Abstract
Natural disasters threaten efficient and normal operation of power grids all around the world. These natural events may negatively affect the capacity of power delivery to the consumers or may cause power outage in all or parts of a region. To make electric power grids more resilient against natural hazards, power grid operators have to adopt optimal strategies in order to mitigate the effects of natural disasters and make post-disaster power grid recovery faster, easier, and with less costs. Another important factor in developing strategies to cope with a natural disaster is the type of the natural disaster. For instance, a heat wave event only causes a reduction in the ampacity of power grid assets such as lines and transformers; on the other hand, a flood event might destroy parts of power grid and cause disconnection of some regions from the power grid depending on the intensity of the event. In the course of extreme ambient temperatures that pushes various power grid components towards their operational limits, it is desired for the electric utility company to reduce the power consumption to alleviate the stress on assets. This can be achieved by implementing demand response targeting air conditioning units which typically account for the largest portion of residential demand during a heat wave. However, this is a delicate matter since excess indoor temperatures can affect the health of residents, especially children and the elderly. This places a limitation on how frequently and to what extent A/C demand response should be used. In addition to reducing demand, efficient asset utilization necessitates that impacts of temperature on capacity and lifetime of assets be considered. Therefore, the energy dispatch problem poses multiple objectives that need to be optimized simultaneously. To make matters more complicated, the problem is subject to uncertainties associated with parameters and input data, e.g. building occupancy levels, electric demand, and temperature-related correction factors for capacity of generation resources, to name a few. To address these uncertainties, the energy dispatch model needs to be able to guarantee feasibility even under worst-case conditions. In this study, a robust optimization solution is introduced that tries to solve the above multi-objective optimization problem subject to uncertainties in model parameters and input data. It is shown through a case study that considering the uncertainties makes the dispatch more conservative. However, this is necessary since failing to include them can lead to mismatches between demand and generation, which could jeopardize the security of the grid. The above-mentioned problem can be viewed from a customer's point of view as well. When equipped with a home energy management system (HEMS), residential customers can become important actors for enabling demand response. This can be done by changing the setpoint of the air-conditioning units or by shifting appliance loads from peak hours to o-peak hours. From the HEMS' standpoint, this can be modeled as an optimization problem where the goal is to reduce power consumption while maximizing nancial DR incentives received. However, another equally important goal would be to make sure that the comfort level of the residents is not compromised. This is in particular crucial during periods of extreme temperatures where maintaining an acceptable indoor temperature has a direct impact on the residents' health, especially for children and the elderly. What makes this multi-objective optimization problem more challenging is the uncertain nature of some model parameters, e.g. electricity rates, building occupancy levels, etc. In this part of this study, a novel solution for energy management of a smart home using demand response by considering the above factors is presented. The problem is formulated as a robust multiobjective one and is solved for a given time horizon. Simulation results are provided to illustrate the impact of uncertainties on the nal solution.
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