• Energy Research
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• IEEE Transactions on Smart Grid close

Integrated generation systems are increasingly considered suitable to supply remote areas, less developed countries, and small isolated communities with power. The energy management investigated in this paper concerns a smart grid encompassing a photovoltaic park. We propose a novel cloud-distributed solution to determine the best energy dispatch, i.e., where energy is going to be used and whether to change the operating points for some consumption devices. Neural networks have been used to predict both energy production and consumption, making it possible to strategically set the activation time of loading devices and to minimize energy flow changes. Moreover, cloud computing resources make it possible to have fast and distributed computation on the big amount of data gauging power production and consumption.

This paper proposes a novel measurement procedure for load flow analysis in medium voltage (MV) smart grids. The new approach is based on power measurements at low voltage (LV) side of MV/LV distribution substations. Such measurements are carried out by means of power quality analyzers. If compared with the load flow methods based on measurements at the MV side, the proposed approach has a lower cost and it offers an equally reliable measurement system. The proposed algorithm takes the basic concept of the current summation backward/forward method; some modifications are introduced to use LV load powers measurements. To this aim, the MV/LV power transformer is properly modeled and taken into account in the algorithm. The proposed approach is validated by means of both simulations and experimental measurements; the test system is the MV distribution grid of Ustica Island, Italy. First, the proposed algorithm is compared with the MATLAB/Simulink load flow tool, which is based on the traditional Newton-Raphson method. Second, a comparison is carried out between the proposed method and the measurement data collected in the actual MV network of Ustica. Such comparison takes into account the uncertainties of both proposed method, and experimental active and reactive MV power measurements.

Making electric power grids smart is invariably linked to the implementation of an advanced communication infrastructure that is able to transport sensing, control and automation information timely, reliably and efficiently. Reusing the power line themselves to realize (part of) this infrastructure is an obvious choice that has a long and successful track record with power utilities. In this paper, we propose to make use of the fact that the nodes of a power line communications (PLCs) network are stationary to deliver messages fast and energy efficiently through routes consisting of a series of communication links. In particular, we exploit location information of PLC nodes to route a message along a favorable path. Such geographic or geo-routing is particularly apt for PLC networks with time-varying link qualities, where optimal routes will change with time. We present routing algorithms and protocols for unicast, broadcast, and multicast transmission that use network topology knowledge to determine the message path taking energy and delay constraints into account. We focus on the distribution domain of the power grid and propose decentralized solutions that use of information about the communication neighborhood of a node, which is acquired through proper signaling in the grid.

In this paper, we propose a profit-maximization-based pricing optimization model for the demand response (DR) management with customer behavior learning in the context of smart grids. By recognizing the different consumption patterns between shiftable and curtailable appliances, two different and distinguished behavior models are proposed. For shiftable appliances whose energy consumption can be shifted from high price periods to low price periods but total energy consumption is fixed, a probabilistic behavior model and its learning algorithm are proposed to model an individual customer’s shifting probabilities dependent on different hourly prices. For curtailable appliances whose energy consumption cannot be shifted but total energy consumption can be adjusted, a regression model is proposed to model an individual customer’s usage patterns dependent on prices and temperatures. After proposing the learning algorithms to identify these proposed behavior models, this paper further develops a genetic algorithm-based distributed pricing optimization algorithm for DR management with the aim to maximize the retailer’s profit. Numerical results indicate the applicability and effectiveness of the proposed models and their benefits to the retailer by improving its profit.

Energy storage is traditionally well established in the form of large scale pumped-hydro systems, but nowadays is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the forecasted wider integration of intermittent renewable resources in future electrical distribution systems (Smart Grids). This paper is intended to offer a useful tool for analyzing potential advantages of distributed energy storages in Smart Grids with reference to both different possible conceivable regulatory schemes and services to be provided. The Smart Grid Operator is assumed to have the ownership and operation of the energy storage systems, and a new cost-based optimization strategy for their optimal placement, sizing and control is proposed. The need to quantify benefits of both the Smart Grid where the energy storage devices are included and the external interconnected grid is explored. Numerical applications to a Medium Voltage test Smart Grid show the advantages of using storage systems related to different options in terms of incentives and services to be provided.

This paper describes the development of a multifunction control strategy for the stable operation of distributed generation (DG) units during the integration with the power grid. The proposed control model is based on direct Lyapunov control (DLC) theory and provides a stable region for the proper operation of DG units during the integration with the power grid. The compensation of instantaneous variations in the reference current components in ac-side and dc-voltage variations in the dc-side of the interfacing system are adequately considered in this control plan, which is the main contribution and novelty of this paper in comparison with previous control strategies. Utilization of the DLC technique in DG technology can confirm the continuous injection of maximum active power in fundamental frequency from the DG source to the power grid, compensating all the reactive power and harmonic current components of grid-connected loads through the integration of DG link into the grid. Application of this concept in smart grids system can guarantee to reduce the stress on the utility grid during the peak of energy demand. Simulation and experimental test results are presented to demonstrate the proficiency and performance of the proposed DLC technique in DG technology.

This paper proposes and verifies the performance of an innovative and low cost coupling system for power line communication (PLC) on medium voltage (MV) smart grids. The coupling system makes use of the capacitive divider of the voltage detecting systems (VDSs) to inject and receive the PLC signal. VDS are usually already installed in the MV switchboards of the major electrical manufacturer all over the world according to IEC 61243-5. VDS are used to detect the presence of the mains voltage to guarantee personnel safety. An interface circuit has been developed to be connected between the PLC transceiver and the VDS socket. In this way, the PLC signal can be coupled to the MV network without installing a dedicated MV coupler, thus avoiding the related costs of the coupler, the installation, and the temporary service interruption. The innovative device is able to couple digitally modulated narrowband PLC signals with modulation rate up to 19.2 kbit/s. In this paper, first a description of the proposed solution is reported. Second, its communication performance has been tested in laboratory. Finally, different tests have been carried out in two MV smart grid real installations under normal operation, i.e., in the presence of the mains voltage. ? 2010-2012 IEEE.