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The universal acceptance of electric vehicles depends on the widespread presence of charging stations. These stations have to be designed intelligently so as not to overwhelm the fragile power grid with the additional load. In this paper we extend our previous work in [1] and examine how the charging station performance, namely the blocking probability, is affected both by the energy storage technology...
The optimal power flow (OPF) problem is generally nonconvex. Recently a second-order cone relaxation for OPF has been proposed using the branch flow model. In this paper, we provide sufficient conditions under which the relaxation is exact, and demonstrate that these conditions hold for a wide class of practical power distribution systems.
Electric vehicles (EVs) offer an attractive long-term solution to reduce the dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs with different EV battery charging rate constraints, that is distributed across a smart power grid network requires a coordinated charging schedule to minimize the power generation and EV charging costs. In this paper, we study a joint optimal...
Wireless Sensor Networks (WSNs) have been widely recognized as a promising technology that can enhance various aspects of today's electric power systems, making them a vital component of the smart grid. Efficient aggregation of data collected by sensors is crucial for a successful WSN-based smart grid application. Existing works on the Minimum Latency Aggregation Scheduling (MLAS) problem in WSNs...
Monitoring the energy consumptions of the massive electrical appliances in buildings has attracted great attentions for smart, green and sustainable living. Traditional approaches generally require large-scale smart sensor/meter networks, and thus suffer from the high deployment, maintenance and data collection costs. In this paper, we propose methodologies and algorithms to optimize the smart meter...
This paper considers the placement of m sensors at n > m possible locations. Given noisy observations, knowledge of the state correlation matrix, and a mean square error criterion, the problem can be formulated as an integer programming problem. The solution for large m and n is infeasible, requiring us to look at approximate algorithms. Using properties of matrices, we come up with lower and upper...
This paper presents a comparative study of three real-time algorithms for power system model identification, parameter estimation and state prediction using real-time Phasor Measurement (PMU) data available from various selected nodes in a power system. Current modeling and state estimation algorithms in power control centers only use limited amount of data, leading to local observability. Our approach,...
We proposed the concept of i-Energy [1] as a novel smart demand-side energy management scheme to realize efficient and versatile control of e-power flows among decentralized energy generation/storage devices and appliances in homes, offices, and neighboring communities. To embody the concept, we developed a novel energy management method named Energy on Demand (EoD) for a single power source [3]....
In this paper, we address the case where two microgrids are isolated from the main power grid but can exchange energy with each other in a peer-to-peer (P2P) manner. The goal is to minimize the total cost resulting from energy generation and transportation, while each microgrid satisfies its local power demand. We first propose a centralized solution. In this approach, a central controller must have...
We propose a framework for demand response in smart grids that integrate renewable distributed generators (DGs). In this framework, some users have DGs and can generate part of their electricity. They can also sell extra generation to the utility company. The goal is to optimize the load schedule of users to minimize the utility company's cost and user payments. We employ parallel autonomous optimization,...
We consider the problem of optimal demand response with energy storage management for a power consuming entity. The entity's objective is to find an optimal control policy for deciding how much load to consume, how much power to purchase from/sell to the power grid, and how to use the finite capacity energy storage device and renewable energy, so as to minimize his average cost, being the disutility...
With the proposed penetration of electric vehicles and advanced metering technology, the demand side is foreseen to play a major role in flexible energy consumption scheduling. On the other hand, over the past several years, there has been a growing interest for the utility companies to integrate more renewable energy resources. Such renewable resources, e.g., wind or solar, due to their intermittent...
An economical way to manage demand-side energy storage systems in the smart grid is proposed by using an H∞ design. The proposed design can adjust the stored energy state economically according to the price signal, while tolerating a certain degree of system uncertainty and having physical constraints on the stored energy level satisfied. Roughly speaking, batteries in the proposed design are charged...
With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This...
This paper presents a stochastic modeling framework to employ adaptive control strategies in order to provide short term ancillary services to the power grid by using a population of heterogenous thermostatically controlled loads. The problem is cast anew as a classical Markov Decision Process (MDP) to leverage existing tools in the field of reinforcement learning. Initial considerations and possible...
Day-ahead DSM techniques in the smart grid allow the supply-side to know in advance an estimation of the amount of energy to be provided to the demand-side during the upcoming day. However, a pure day-ahead optimization process cannot accommodate potential real-time deviations from the expected energy consumption by the demand-side users, neither the randomness of their renewable sources. This paper...
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