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Reducing the operating energy costs of commercial buildings is an important problem faced by the facility managers. Additionally, energy cost from heating-ventilation-air-conditioners (HVAC) is a significant fraction (often greater than 50%) of the overall operational cost of these buildings. In this paper, we propose a pre-cooling framework that uses a “gray box” thermal model of a building to compute...
This paper assesses the potential cost-saving incentives for content distribution networks to shift traffic load among geographically distributed data centers in response to hourly variation in electricity prices. Such incentives are likely aligned with benefits to utilities and grid operators, which might take the form of peak-shaving or ancillary services. However, private cost savings are not strictly...
In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based...
Micro-grid integrates load, renewable energy power generation and energy storage devices as a whole system, providing an effective way for renewable energy accessing power grid. In the micro-grid system, the cost of energy storage devices is high, which has bad influences on the economic operation. For this reason, this paper proposes the model which takes advantage of micro-grid having an internal...
Identifying a small group of power and communication lines whose removal initiates a cascading failure and results in a blackout is critical for the operation of the smart grid. Once identified by an attacker, such a group becomes a threat to the availability and throughput of the entire system. In this paper, we introduce a vulnerability analysis based on the dynamics of the power grid, and the associated...
Electricity grids are facing challenges due to peak consumption and renewable electricity generation. In this context, demand response offers a solution to many of the challenges, by enabling the integration of consumer side flexibility in grid management. Commercial buildings are good candidates for providing flexible demand due to their volume and the stability of their loads. However, existing...
The smart charging techniques of electrical vehicles (EVs) are developed to minimize EVs' adverse effects on the power system. However, the existing techniques have some drawbacks. First, they use direct methods, which take the decision authority away from EVs' owner and reduce the EVs' acceptance and security. Second, they do not consider real-time effects of EVs' charging and discharging demand...
The development of smart grid, electric vehicles (EV), and integration of renewable energy sources (RES) changes the existing power grid. Volt-VAR optimization (VVO) is a well-known problem to reduce the losses along the distribution lines. Current VVO does not endorse the role of elastic and inelastic loads, EVs, and RESs to decrease the reactive losses and hence the cost of generation. We propose...
There is a growing interest by power system operators to encourage load resources to offer frequency regulation. There are several studies that evaluate the system-wide benefits of such load resource participation. However, the current literature often overlooks the potential adverse impact on power distribution feeders. This paper seems to address this open problem. We focus on a scenario where load...
Electric vehicles (EV) is a promising solution for reducing the environment adverse effect of road transport. In this study, a multi-objective, multi-stage collaborative planning model is proposed for the integrated EV charging stations and power distribution network. The proposed model aims to minimize the investment & operation costs of the distribution system and maximize the annually captured...
One of the critical requirements in power grid operation and planning is the ability to accurately forecast expected load. This allows for a heightened enhancement in grid operations, energy management, and planning. Load forecasting is historically based on aggregated spatial and temporal consumption data; with the deployment of Advanced Metering Infrastructure (AMI) systems, it can be achieved not...
Utilities have deployed tens of millions of smart meters, which record and transmit home energy usage at fine-grained intervals. These deployments are motivating researchers to develop new energy analytics that mine smart meter data to learn insights into home energy usage and behavior. Unfortunately, a significant barrier to evaluating energy analytics is the overhead of instrumenting homes to collect...
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