Energy-Efficient Decentralized Routing with Localized Cooperation Suitable for Fast Fading

Wireless networks transmit information from a source to a destination via multiple hops in order to save energy and, thus, increase the lifetime of battery-operated nodes. The energy savings can be especially significant in cooperative transmission schemes, where several nodes cooperate during one hop to forward the information to the next node along a route to the destination. Finding the best multi-hop transmission policy in such a network which determines nodes that are involved in each hop, is a very important problem, but also a very difficult one especially when the physical wireless channel behavior is to be accounted for and exploited. We model the above optimization problem for randomly fading channels as a decentralized control problem – the channel observations available at each node define the information structure, while the control policy is defined by the power and phase of the signal transmitted by each node.In particular, we consider the problem of computing an energy-optimal cooperative transmission scheme in a wireless network for two different channel fading models: (i) slow fading channels, where the channel gains of the links remain the same for a large number of transmissions, and (ii) fast fading channels,where the channel gains of the links change quickly from one transmission to another. For slow fading, we consider a factored class of policies (corresponding to local cooperation between nodes), and show that the computation of an optimal policy in this class is equivalent to a shortest path computation on an induced graph, whose edge costs can be computed in a decentralized manner using only locally available channel state information(CSI). For fast fading, both CSI acquisition and data transmission consume energy. Hence, we need to jointly optimize over both these; we cast this optimization problem as a large stochastic optimization problem. We then jointly optimize over a set of CSI functions of the local channel states, and a corresponding factored class of control policies corresponding to local cooperation between nodes with a local outage constraint. The resulting optimal scheme in this class can again be computed efficiently in a decentralized manner. We demonstrate significant energy savings for both slow and fast fading channels through numerical simulations of randomly distributed networks.

Decentralized dynamic compensators for large multivariable systems

In this paper, we consider the synthesis of decentralized dynamic compensators for large systems. The eliminant approach is used to obtain sufficient conditions for the existence of proper, stable, decentralized observer-controllers for stabilizing a large system. An illustrative example is given.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Decentralized peak power control based on short-term forecast using modified Kalman filter

This paper proposes a method of short term load forecasting with limited data, applicable even at 11 kV substation levels where total power demand is relatively low and somewhat random and weather data are usually not available as in most developing countries. Kalman filtering technique has been modified and used to forecast daily and hourly load. Planning generation and interstate energy exchange schedule at load dispatch centre and decentralized Demand Side Management at substation level are intended to be carried out with the help of this short term load forecasting technique especially to achieve peak power control without enforcing load-shedding.

Decentralized linear quadratic power system stabilizers for multi-machine power systems

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead-lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron-Phillip's model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions.

Electrochemical capacitors: Technical challenges and prognosis for future markets

Electrochemical capacitors are potential devices that could help bringing about major advances in future energy storage. They are lightweight and their manufacture and disposal has no detrimental effects on the environment. A comprehensive description of fundamental science of electrochemical capacitors is presented. Similarities and differences between electrochemical capacitors and secondary batteries for electrical energy storage are highlighted and various types of electrochemical capacitors are discussed with special reference to lead-carbon hybrid ultracapacitors. Some envisaged applications of electrochemical capacitors are described along with the technical challenges and prognosis for future markets. (C) 2012 Published by Elsevier Ltd.

Dynamic mechanism design for markets with strategic resources

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

Game theoretic, decentralized, local information based algorithm for community detection in social graphs

Motivated by the observation that communities in real world social networks form due to actions of rational individuals in networks, we propose a novel game theory inspired algorithm to determine communities in networks. The algorithm is decentralized and only uses local information at each node. We show the efficacy of the proposed algorithm through extensive experimentation on several real world social network data sets.

Decentralized demand-supply matching using community microgrids and consumer demand response: A scenario analysis

Developing countries constantly face the challenge of reliably matching electricity supply to increasing consumer demand. The traditional policy decisions of increasing supply and reducing demand centrally, by building new power plants and/or load shedding, have been insufficient. Locally installed microgrids along with consumer demand response can be suitable decentralized options to augment the centralized grid based systems and plug the demand-supply gap. The objectives of this paper are to: (1) develop a framework to identify the appropriate decentralized energy options for demand supply matching within a community, and, (2) determine which of these options can suitably plug the existing demand-supply gap at varying levels of grid unavailability. A scenario analysis framework is developed to identify and assess the impact of different decentralized energy options at a community level and demonstrated for a typical urban residential community Vijayanagar, Bangalore in India. A combination of LPG based CHP microgrid and proactive demand response by the community is the appropriate option that enables the Vijayanagar community to meet its energy needs 24/7 in a reliable, cost-effective manner. The paper concludes with an enumeration of the barriers and feasible strategies for the implementation of community microgrids in India based on stakeholder inputs. (C) 2014 Elsevier Ltd. All rights reserved.

Quick, Decentralized, Energy-Efficient One-Shot Max Function Computation Using Timer-Based Selection

In several wireless sensor networks, it is of interest to determine the maximum of the sensor readings and identify the sensor responsible for it. We propose a novel, decentralized, scalable, energy-efficient, timer-based, one-shot max function computation (TMC) algorithm. In it, the sensor nodes do not transmit their readings in a centrally pre-defined sequence. Instead, the nodes are grouped into clusters, and computation occurs over two contention stages. First, the nodes in each cluster contend with each other using the timer scheme to transmit their reading to their cluster-heads. Thereafter, the cluster-heads use the timer scheme to transmit the highest sensor reading in their cluster to the fusion node. One new challenge is that the use of the timer scheme leads to collisions, which can make the algorithm fail. We optimize the algorithm to minimize the average time required to determine the maximum subject to a constraint on the probability that it fails to find the maximum. TMC significantly lowers average function computation time, average number of transmissions, and average energy consumption compared to approaches proposed in the literature.

Error exponent analysis of energy-based Bayesian decentralized spectrum sensing under fading

This paper considers decentralized spectrum sensing, i.e., detection of occupancy of the primary users' spectrum by a set of Cognitive Radio (CR) nodes, under a Bayesian set-up. The nodes use energy detection to make their individual decisions, which are combined at a Fusion Center (FC) using the K-out-of-N fusion rule. The channel from the primary transmitter to the CR nodes is assumed to undergo fading, while that from the nodes to the FC is assumed to be error-free. In this scenario, a novel concept termed as the Error Exponent with a Confidence Level (EECL) is introduced to evaluate and compare the performance of different detection schemes. Expressions for the EECL under general fading conditions are derived. As a special case, it is shown that the conventional error exponent both at individual sensors, and at the FC is zero. Further, closed-form lower bounds on the EECL are derived under Rayleigh fading and lognormal shadowing. As an example application, it answers the question of whether to use pilot-signal based narrowband sensing, where the signal undergoes Rayleigh fading, or to sense over the entire bandwidth of a wideband signal, where the signal undergoes lognormal shadowing. Theoretical results are validated using Monte Carlo simulations. (C) 2015 Elsevier B.V. All rights reserved.