Signal processing for distributed nodes in smart networks

With increasing environmental concern for energy conservation and mitigating climate change, next generation smart networks are bound to provide improved performance in terms of security, reliability, and energy efficiency. For instance, future smart networks will work in highly complex and dynamic environments and will have distributed nodes that need to interact with each other and may also interact with an energy provider in order to improve their performance. In this context, advanced signal processing tools such as game theory and distributed transmit beamforming can yield tremendous performance gains in terms of energy efficiency for demand management and signal trans-mission in smart networks. The central theme of this dissertation is the modeling of energy usage behavior of self-seeking distributed nodes in smart networks. The thesis mainly looks into two key areas of smart networks: 1) smart grid networks and 2) wireless sensor networks, and contains: an analytical framework of the economics of electric vehicle charging in smart grids in an energy constrained environment; a study of a consumer-centric energy management scheme for encouraging the consumers in a smart grid to voluntarily take part in energy management; an outage management scheme for efficiently curtailing energy from the consumers in smart grids in the event of a power outage; a comprehensive study of power control of sensors in a wireless sensor network using game theory and distributed transmit beamforming; and finally, an energy aware distributed transmit beamfoming technique for long distance signal transmission in a wireless sensor network. This thesis addresses the challenges of modeling the energy usage behavior of distributed nodes through studying the propriety of energy users in smart networks, 1) by capturing the interactions between the energy users and energy provider in smart grids using non-cooperative Stackelberg and generalized Nash games, and showing that the socially optimal energy management for users can be achieved at the solution of the games, and 2) by studying the power control of sensors in wireless sensor networks, using a non-cooperative Nash game and distributed transmit beamforming that demonstrates significant transmit energy savings for the sensors. To foster energy efficient transmission, the thesis also studies a distributed transmit beamforming technique that does not require any channel state information for long distance signal transmission in sensor networks. The contributions of this dissertation are enhanced by proposing suitable system models and appropriate signal processing techniques. These models and techniques can capture the different cost-benefit tradeoffs that exist in these networks. All the proposed schemes in this dissertation are shown to have significant performance improvement when compared with existing solutions. The work in this thesis demonstrates that modeling power usage behavior of distributed nodes in smart networks is both possible and beneficial for increasing the energy efficiency of these networks

Management of Renewable Energy for a Shared Facility Controller in Smart Grid

This paper proposes an energy management scheme to maximize the use of solar energy in the smart grid. In this context, a shared facility controller (SFC) with a number of solar photovoltaic panels in a smart community is considered that has the capability to schedule the generated energy for consumption and trade to other entities. In particular, a mechanism is designed for the SFC to decide on the energy surplus, if there is any, that it can use to charge its battery and sell to the households and the grid based on the offered prices. In this regard, a hierarchical energy management scheme is proposed with a view to reduce the total operational cost to the SFC. The concept of a virtual cost is introduced that aids the SFC to estimate its future operational cost based on some available current information. The energy management is conducted for three different cases, and the optimal cost to the SFC is determined for each case by the theory of maxima and minima. A real-time algorithm is proposed to reach the optimal cost for all cases, and some numerical examples are provided to demonstrate the beneficial properties of the proposed schem

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.Comment: 7 pages, 4 figures, 3 tables, conference pape

Energy Management for a User Interactive Smart Community: A Stackelberg Game Approach

This paper studies a three party energy management problem in a user interactive smart community that consists of a large number of residential units (RUs) with distributed energy resources (DERs), a shared facility controller (SFC) and the main grid. A Stackelberg game is formulated to benefit both the SFC and RUs, in terms of incurred cost and achieved utility respectively, from their energy trading with each other and the grid. The properties of the game are studied and it is shown that there exists a unique Stackelberg equilibrium (SE). A novel algorithm is proposed that can be implemented in a distributed fashion by both RUs and the SFC to reach the SE. The convergence of the algorithm is also proven, and shown to always reach the SE. Numerical examples are used to assess the properties and effectiveness of the proposed scheme.Comment: 6 pages, 4 figure

Policy Design for Controlling Set-Point Temperature of ACs in Shared Spaces of Buildings

Air conditioning systems are responsible for the major percentage of energy consumption in buildings. Shared spaces constitute considerable office space area, in which most office employees perform their meetings and daily tasks, and therefore the ACs in these areas have significant impact on the energy usage of the entire office building. The cost of this energy consumption, however, is not paid by the shared space users, and the AC's temperature set-point is not determined based on the users' preferences. This latter factor is compounded by the fact that different people may have different choices of temperature set-points and sensitivities to change of temperature. Therefore, it is a challenging task to design an office policy to decide on a particular set-point based on such a diverse preference set. As a result, users are not aware of the energy consumption in shared spaces, which may potentially increase the energy wastage and related cost of office buildings. In this context, this paper proposes an energy policy for an office shared space by exploiting an established temperature control mechanism. In particular, we choose meeting rooms in an office building as the test case and design a policy according to which each user of the room can give a preference on the temperature set-point and is paid for felt discomfort if the set-point is not fixed according to the given preference. On the other hand, users who enjoy the thermal comfort compensate the other users of the room. Thus, the policy enables the users to be cognizant and responsible for the payment on the energy consumption of the office space they are sharing, and at the same time ensures that the users are satisfied either via thermal comfort or through incentives. The policy is also shown to be beneficial for building management. Through experiment based case studies, we show the effectiveness of the proposed policy.Comment: Journal paper accepted in Energy & Buildings (Elsevier

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Economics of Electric Vehicle Charging: A Game Theoretic Approach

In this paper, the problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game. In this game, on the one hand, the smart grid that acts as a leader, needs to decide on its price so as to optimize its revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs, which act as followers, need to decide on their charging strategies so as to optimize a tradeoff between the benefit from battery charging and the associated cost. Using variational inequalities, it is shown that the proposed game possesses a socially optimal Stackelberg equilibrium in which the grid optimizes its price while the PEVGs choose their equilibrium strategies. A distributed algorithm that enables the PEVGs and the smart grid to reach this equilibrium is proposed and assessed by extensive simulations. Further, the model is extended to a time-varying case that can incorporate and handle slowly varying environments