Amir-Hamed Mohsenian-Rad

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each users individual daily electricity charges.

Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid

In this paper, we consider deployment of energy consumption scheduling (ECS) devices in smart meters for autonomous demand side management within a neighborhood, where several buildings share an energy source. The ECS devices are assumed to be built inside smart meters and to be connected to not only the power grid, but also to a local area network which is essential for handling two-way communications in a smart grid infrastructure. They interact automatically by running a distributed algorithm to find the optimal energy consumption schedule for each subscriber, with an aim at reducing the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Incentives are also provided for the subscribers to actually use the ECS devices via a novel pricing model, derived from a game-theoretic analysis. Simulation results confirm that our proposed distributed algorithm significantly reduces the PAR and the total cost in the system.

Distributed Internet-Based Load Altering Attacks Against Smart Power Grids

With the increase in use of information technology in advanced demand side management and given the growth in power consumption in the computation and communications sectors, a new class of cyberintrusion plans is emerging that aims to alter the load through the Internet and by means of automatic and distributed software intruding agents. These attacks work by compromising direct load control command signals, demand side management price signals, or cloud computation load distribution algorithms to affect the load at the most crucial locations in the grid in order to cause circuit overflow or other malfunctions and damage the power system equipment. To gain insights into these less-examined yet important intrusion strategies, in this paper, we identify a variety of practical loads that can be volnurable to Internet-based load altering attacks. In addition, we overview a collection of defense mechanisms that can help in blocking these attacks or minimizing the damage caused by them. Our simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that our proposed cost-efficent load protection strategy can significantly reduce the cost of load protection while it guarantees that no Internet-based load altering attack may overload the power distribution system.

Joint logical topology design, interface assignment, channel allocation, and routing for multi-channel wireless mesh networks

A multi-channel wireless mesh network (MC-WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each NIC operates on a distinct frequency channel. Two neighboring routers establish a logical link if each one has an NIC operating on a common channel. Given the physical topology of the routers and other constraints, four important issues should be addressed in MC-WMNs: logical topology formation, interface assignment, channel allocation, and routing. Logical topology determines the set of logical links. Interface assignment decides how the logical links should be assigned to the NICs in each wireless router. Channel allocation selects the operating channel for each logical link. Finally, routing determines through which logical links the packets should be forwarded. In this paper, we mathematically formulate the logical topology design, interface assignment, channel allocation, and routing as a joint linear optimization problem. Our proposed MC-WMN architecture is called TiMesh. Extensive ns-2 simulation experiments are conducted to evaluate the performance of TiMesh and compare it with two other MC-WMN architectures Hyacinth [1] and CLICA [2]. Simulation results show that TiMesh achieves higher aggregated network throughput and lower end-to-end delay than Hyacinth and CLICA for both TCP and UDP traffic. It also provides better fairness among different flows.

Coordination of Cloud Computing and Smart Power Grids

The emergence of cloud computing has established a trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers and cloud computing also provide opportunities to help the grid with respect to robustness and load balancing. To gain insights into these opportunities, we formulate the service request routing problem in cloud computing jointly with the power flow analysis in smart grid and explain how these problems can be related. Simulation results based on the standard setting in the IEEE 24-bus Reliability Test System show that a grid-aware service request routing design in cloud computing can significantly help in load balancing in the electric grid and making the grid more reliable and more robust with respect to link breakage and load demand variations.

Utility-optimal random access: Reduced complexity, fast convergence, and robust performance

In this paper, we propose two distributed contention-based medium access control (MAC) algorithms for solving a network utility maximization (NUM) problem in wireless ad hoc networks. Most of the previous NUM-based random access algorithms have one or more of the following performance bottlenecks: (1) extensive signaling among the nodes to achieve semi-distributed implementations, (2) synchronous updates of contention probabilities, (3) small update stepsizes to ensure convergence but with typically slow speed, and (4) supporting a limited range of utility functions under which the NUM is shown to be convex. Our proposed algorithms overcome the bottlenecks in all four aspects. First, only limited amount of message passing among nodes is required. Second, fully asynchronous updates of contention probabilities are allowed. Furthermore, our algorithms are robust to arbitrary large message passing delay and message loss. Third, we do not utilize any stepsize during updates, thus our algorithms can achieve faster convergence. Finally, our proposed algorithms have provable convergence, optimality, and robustness properties under a wider range of utility functions, even if the NUM problem is non-convex. Simulation results show the optimality and fast convergence of our algorithms, performance improvements compared with the subgradient-based MAC, and better efficiency-fairness tradeoff compared with the IEEE 802.11 distributed coordination function.

Connection Admission Control for Multiservice Integrated Cellular/WLAN System

The complementary characteristics of wireless cellular networks and wireless local area networks (WLANs) make them suitable to jointly offer seamless wireless access services to mobile users. In an integrated cellular/WLAN system, the quality-of-service (QoS) requirements for different services (e.g., voice, real-time video) require admission control to limit the number of connections in each access network. In this paper, we first develop an analytical model to facilitate the evaluation of different admission control policies in a multiservice integrated cellular/WLAN system. We then formulate two different revenue-maximization problems. Each problem takes different QoS requirements into account. By solving the equivalent cost-minimization problems, we evaluate the system performance when different combinations of cutoff priority and fractional guard channel admission control policies are being used. Results show that using a cutoff priority policy in both wireless access networks can achieve the optimal solution for the two optimization problems under a wide range of network conditions.

Lexicographically Optimal Routing for Wireless Sensor Networks With Multiple Sinks

In wireless sensor networks (WSNs), the field information (e.g., temperature, humidity, airflow) is acquired via several battery-equipped wireless sensors and is relayed toward a sink node. As the size of the WSNs increases, it becomes inefficient (in terms of power consumption) when gathering all information in a single sink. To tackle this problem, one can increase the number of sinks. The set of sensor nodes that are sending data to sink k is called commodity k. In this paper, we formulate the lexicographically optimal commodity lifetime (LOCL) routing problem. A stepwise centralized algorithm called the LOCL algorithm is proposed, which can obtain the optimal routing solution and lead to lexicographical fairness among commodity lifetimes. We then show that, under certain assumptions, the lexicographical optimality among commodity lifetimes can be achieved by providing lexicographical optimality among node lifetimes. This motivates us to propose our second algorithm, which is called the lexicographically optimal node lifetime (LONL) algorithm, which is suitable for practical implementation. Simulation results show that our proposed LOCL and LONL algorithms increase the normalized commodity and node lifetimes compared with the maximum lifetime with multiple sinks (MLMS) and lexicographical max-min fair (LMM) routing algorithms.

Random access for elastic and inelastic traffic in WLANs

In this paper, we consider the problem of random access in wireless local area networks (WLANs) with each station generating either elastic or inelastic traffic. Elastic traffic is usually non-real-time, while inelastic traffic is usually coming from real-time applications. We formulate a network utility maximization (NUM) problem, where the optimization variables are the persistent probabilities of the stations and the utilities are either concave or sigmoidal functions. Sigmoidal utility functions can better represent inelastic traffic sources compared to concave utility functions commonly used in the existing random access literature. However, they lead to non-convex NUM problems which are not easy to solve in general. By applying the dual decomposition method, we propose a subgradient algorithm to solve the formulated NUM problem. We also develop closed-form solutions for the dual subproblems involving sigmoidal functions that have to be solved in each iteration of the proposed algorithm. Furthermore, we obtain a sufficient condition on the link capacities which guarantees achieving the global optimal solution when our proposed algorithm is being used. If this condition is not satisfied, then we can still guarantee that the optimal value of the objective function is within some lower and upper bounds. We perform various simulations to validate our analytical models when the available link capacities meet or do not meet the sufficient optimality condition.

Distributed channel selection and randomized interrogation algorithms for large-scale and dense RFID systems

Radio frequency identification (RFID) is an emerging wireless communication technology which allows objects to be identified automatically. An RFID system consists of a set of readers and several objects, equipped with small and inexpensive computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. High probability of collision impairs the benefit of multiple reader deployment and results in misreading. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not achieve optimal system performance. In this paper, we systematically design two optimization-based distributed channel selection and randomized interrogation algorithms for dense RFID systems. For this purpose, we develop elaborate models for the reader-to-tag and reader-to-reader collision problems. The first algorithm is fully distributed and is guaranteed to find a local optimum of a max-min fair resource allocation problem for RFID systems. The second algorithm is semi-distributed and achieves the global optimal system performance. Max-min fair optimality balances the performance and the processing load among readers. Simulation results show that our algorithms have significantly better performance than the previous heuristic algorithms.