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

Wide area monitoring (WAM), wide area protection (WAP), and wide area control (WAC) systems will enhance the future of smart grid operation in terms of reliability and security. In part I of this paper, a proposed architecture for a hybrid ac/dc smart grid hardware test-bed system was presented. Design details of the various components and their connectivity in the overall system architecture were identified. In part II of the paper, the focus is on the design of monitoring, control, and protection systems and their integrated real-time operation. Various control scenarios for system startup and continuous operation are examined. We have developed a control system based on wide area measurements. The advanced measurement system based on synchrophasors was also implemented using DAQs real-time synchronous data. The developed system features a wide variety of capabilities such as online system parameters calculation and online voltage stability monitoring. These are implemented as an experimental case to enhance wide area monitoring systems. Moreover, the protection system was designed inside of the real-time software environment to monitor the real-time wide area data, and make a comprehensive and reliable coordination for the whole system. Ideas related to the interaction of a dc microgrid involving sustainable energy sources with the main ac grid have been also implemented and presented. The implemented system is explicit and achievable in any research laboratory and for real-time real-world smart grid applications.

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.

Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This paper presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The paper further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.

Demand-side management (DSM) has emerged as an important smart grid feature that allows utility companies to maintain desirable grid loads. However, the success of DSM is contingent on active customer participation. Indeed, most existing DSM studies are based on game-theoretic models that assume customers will act rationally and will voluntarily participate in DSM. In contrast, in this paper, the impact of customers' subjective behavior on each other's DSM decisions is explicitly accounted for. In particular, a noncooperative game is formulated between grid customers in which each customer can decide on whether to participate in DSM or not. In this game, customers seek to minimize a cost function that reflects their total payment for electricity. Unlike classical game-theoretic DSM studies which assume that customers are rational in their decision-making, a novel approach is proposed, based on the framework of prospect theory (PT), to explicitly incorporate the impact of customer behavior on DSM decisions. To solve the proposed game under both conventional game theory and PT, a new algorithm based on fictitious player is proposed using which the game will reach an epsilon-mixed Nash equilibrium. Simulation results assess the impact of customer behavior on demand-side management. In particular, the overall participation level and grid load can depend significantly on the rationality level of the players and their risk aversion tendency. 9 pages, 7 figures, journal, accepted

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.

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.