• Energy Research
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• 9. Industry and infrastructure close
• IEEE Transactions on Smart Grid close

Smart grids have attracted increasing interest in the field of industrial research. These systems allow electricity consumers to communicate with electrical systems using bidirectional communications. However, these bidirectional channels may introduce privacy threats to consumers. Considerable research has been conducted with the aim of resolving these threats. Unfortunately, a practical smart grid design with adequate efficiency and security functionalities has not yet been produced. In this paper, we propose lightweight privacy preserving metering protocols by designing a distributed authentication method to further increase the speed of the message authentication process. We confirmed the efficiency and security of our method by performing a detailed analysis.

In this paper, a novel algorithm for calculating distribution load flow (DLF) for smart grid is presented. The algorithm can provide a DLF solution in a specific area of interest in the distribution system without the necessity of including all of the distribution system laterals in the DLF problem. The system laterals excluded from the DLF solution are replaced by equivalent loads using a proposed lumping technique. The proposed DLF zooming method thus reduces the size of the DLF problem and hence minimizes the effect of system size on the DLF solution time. The construction of the DLF problem is based on a novel automated recursive process, which enables fast accommodation of changes in the network topology of smart grid. The simulation results validate the accuracy of the proposed DLF zooming algorithm and provide a favorable evaluation of its performance for smart grid operation.

This aim of this special section is to present applications of electrical energy storage. The issue summarizes the most recent research and development and it identifies issues that must be addressed for the successful application of storage technologies. We were extremely impressed by the number of papers submitted in response to the call for papers and the spectrum of interests that the international community has for energy storage. The papers solicited discuss a broad range of environmental, economic, technical, market, and policy considerations associated with the application of energy storage on power systems. Out of 83 submissions, we were able to accept 22 exceptional contributions. The final paper selection was made based on the quality of the papers and an attempt to balance a broad topical representation. The final papers were divided into five topical areas as follows: 1) Hybrid Storage Management System and its Applications in Microgrids; 2) Storage for Active Distribution Networks; 3) Energy Storage for Provision of Ancillary Services; 4) Energy Storage for Integration of Renewable Resources; and 5) Operation of Energy Storage for Reliability.

This paper presents a novel hierarchical control approach and new mathematical optimization models of greenhouses, which can be readily incorporated into energy hub management systems (EHMSs) in the context of smart grids to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control systems consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is proposed, so that it can be implemented as a supervisory control in existing greenhouse control systems. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Therefore, the proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied through Monte Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

In this paper, we introduce the incremental welfare consensus algorithm for solving the energy management problem in a smart grid environment populated with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central energy-management unit, central price coordinator, or leader. The optimum energy solution is found through local peer-to-peer communications among smart devices. Each distributed generation unit is connected to a local price regulator, as is each consumer unit. In response to the price of energy proposed by the local price regulators, the power regulator on each generation/consumer unit determines the level of generation/consumption power needed to optimize the benefit of the device. The consensus-based coordination among price regulators drives the behavior of the overall system toward the global optimum, despite the greedy behavior of each unit. The primary advantages of the proposed approach are: 1) convergence to the global optimum without requiring a central controller/coordinator or leader, despite the greedy behavior at the individual level and limited communications; and 2) scalability in terms of per-node computation and communications burden.

This paper proposes the integration of multiple antenna technologies with compressed sensing algorithms to facilitate wireless multiple-access from smart meters to a central base station in smart grid home area networks. This integration is made possible by new precoding weight designs which utilize the multiple antennas for either achieving multiplexing or providing power-gain. For the multiplexing scheme, we show that the use of multiple antennas can significantly reduce the delay, particularly at high signal-to-noise ratios. For the power-gain scheme, we demonstrate that the use of multiple antennas enhances the performance of the compressed sensing algorithm by reducing the impact of unwanted noise, leading to a lower delay. Finally, we compare our distributed scheme with a carrier sense multiple-access scheme, and show that our scheme achieves a lower average delay for even a small number of transmitting nodes.

With the fast expansion and increasing complexity of power system to meet the ever growing load demand, more information needs to be transmitted for real-time monitoring, control and management purpose. The timely and accurate transmission of a huge quantity of data poses great challenge to the communication network. This paper proposes a novel real-time compressive sensing based strategy for the load frequency control of a multi-area interconnected power system. According to the proposed strategy, the measured data in each control area is compressed before being transmitted through the communication network, and then recovered accurately by the discrete central controller. The proposed strategy can significantly reduce the size of transmitted data and improve the reliability of the communication network by introducing model predictive control method. Simulation results demonstrate the effectiveness and applicability of the proposed compressive sensing based control strategy.

So-called smart technologies are at the forefront of modern energy research. Many existing works focus on the effects of smart technologies, with scales ranging from a single household to an entire city. One area that is less studied is the adoption of these technologies. In this paper, we extend our prior work to develop a more robust framework for exploring the diffusion of basic smart grid technologies using a social-network-based model to study demand response adoption. This network is based around considering an end-user as a node, and any relationship where mutual trust and communication exists as an edge. In addition, we have incorporated mathematical representations of user personality traits in the decision-making progress to better simulate real-world actions. We observe greater usage of demand response when conventional electricity is high, when conscientiousness is high, and when the network is densely connected; all of these are reasonable results given logical behavior of the individual agents. Our model includes many tunable parameters in the update stages, of which the effects of only a few are included in this paper. This quantity of potential parameters, as well as the broad nature of the model and algorithm, makes our model a candidate for future improvement and development based on including different parameters.

Advances in smart grid technology have yet to coalesce into a comprehensive solution integrating the landscape of future power systems. The microgrid concept may offer a solution for combining advanced components and enabling technologies within an infrastructure that must expand to meet emerging demands. As autonomous power system entities, microgrids require robust real-time power management and control to simultaneously operate jointly with the utility, provide reliable service, and help achieve customer-driven objectives utilizing local power system assets. In this paper, a decentralized control architecture for microgrids is presented, along with a simulation environment appropriate for on-going investigations into real-time, agent-based decision-making. Challenges faced by operating self-organizing multi-agent system (MAS) are presented, as well as results for a representative power management scenario for a multi-asset microgrid capable of operating in grid-interconnected or islanded mode. The system and formulations presented demonstrate the viability and capability of decentralized agent-based control for microgrids and illustrate their potential towards achieving smart grid goals.