The shift towards an energy Grid dominated by prosumers (consumers and producers of energy) will inevitably have repercussions on the distribution infrastructure. Today it is a hierarchical one designed to deliver energy from large scale facilities to end-users. Tomorrow it will be a capillary infrastructure at the Medium and Low Voltage levels that will support local energy trading among prosumers. In [74], we analyzed the Dutch Power Grid and made an initial analysis of the economic impact topological properties have on decentralized energy trading. In this paper, we go one step further and investigate how dierent networks topologies and growth models facilitate the emergence of a decentralized market. In particular, we show how the connectivity plays an important role in improving the properties of reliability and pathcost reduction. From the economic point of view, we estimate how the topological evolutions facilitate local electricity distribution, taking into account the main cost ingredient required for increasing network connectivity, i.e., the price of cabling.
In the framework the planning techniques might be revised especially for the Distribution Grid which is the segment that is likely to face the greatest changes due to the presence of Advanced Metering Infrastructure (i.e., bidirectional intelligent digital meters at customer location) and Distribution Automation (i.e., feeders can be monitored, controlled in automated way through two-way communication). In addition, the is no longer a layer where only energy is consumed, but Distributed Energy Generation facilities (small-scale photovoltaic systems and small-wind turbines) will be attached to this segment of the Grid; altogether these elements are likely to reshape the way planning for is realized @cite_54 .
@cite_54
2540877670 2050495423 2138272709 2024605755
In the delivery of electricity from the generating station to the consumers, Power Distribution System (PDS) plays a major role. Electric power utilities worldwide are adopting the computer aided monitoring, control and management of PDS to provide better services to the consumers. Recent advances in Information Technology and data communication system are being utilized to automate the electric PDS. The next-generation electricity grid, which is the smart grid, is addressing the major shortcomings of the existing grid. In essence, the smart grid envisages providing utility companies with full visibility and control over their assets and services. This paper presents a review of automation of the Power systems with special emphasis on distribution automation, leading to a smarter grid. The distribution automation and distribution management system functions are discussed in detail and the emergence of a smart grid. The wide area monitoring and control implementation in the transmission sector is also touched upon. The paper also provides some insight into the expansion of laboratory facilities for smart grid implementation and research. In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider. The widespread deployment of Automatic Metering Infrastructures in Smart Grid scenarios rises great concerns about privacy preservation of user-related data, from which detailed information about customer's habits and behaviors can be deduced. Therefore, the users' individual measurements should be aggregated before being provided to External Entities such as utilities, grid managers and third parties. This paper proposes a security architecture for distributed aggregation of additive data, in particular energy consumption metering data, relying on Gateways placed at the customers' premises, which collect the data generated by local Meters and provide communication and cryptographic capabilities. The Gateways communicate with one another and with the External Entities by means of a public data network. We propose a secure communication protocol aimed at preventing Gateways and External Entities from inferring information about individual data, in which privacy-preserving aggregation is performed by means of a cryptographic homomorphic scheme. The routing of information flows can be centralized or it can be performed in a distributed fashion using a protocol inspired by Chord. We compare the performance of both approaches to the optimal solution minimizing the data aggregation delay. The penetration of renewable distributed generation (DG) sources has been increased in active distribution networks due to their unique advantages. However, non-dispatchable DGs such as wind turbines raise the risk of distribution networks. Such a problem could be eliminated using the proper application of energy storage units. In this paper, optimal planning of batteries in the distribution grid is presented. The optimal planning determines the location, capacity and power rating of batteries while minimizing the cost objective function subject to technical constraints. The optimal long-term planning is based on the short-term optimal power flow considering the uncertainties. The point estimate method (PEM) is employed for probabilistic optimal power flow. The batteries are scheduled optimally for several purposes to maximize the benefits. A hybrid Tabu search particle swarm optimization (TS PSO) algorithm is used to solve the problem. The numerical studies on a 21-node distribution system show the advantages of the proposed methodology. The proposed approach can also be applied to the realistic sized networks when some sensitive nodes are considered as candidate locations for installing the storage units.