The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each user's preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.

https://www.ece.ubc.ca/~vincentw/J/SMSWjTSG12.pdf

Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches

In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.

/pdf/optimal-real-time-pricing-algorithm-based-on-utility-48z3qlh5gj.pdf

Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

In this paper, we propose a novel optimization-based real-time residential load management algorithm that takes into account load uncertainty in order to minimize the energy payment for each user. Unlike most existing demand side management algorithms that assume perfect knowledge of users' energy needs, our design only requires knowing some statistical estimates of the future load demand. Moreover, we consider real-time pricing combined with inclining block rate tariffs. In our problem formulation, we take into account different types of constraints on the operation of different appliances such as must-run appliances, controllable appliances that are interruptible, and controllable appliances that are not interruptible. Our design is multi-stage. As the demand information of the appliances is gradually revealed over time, the operation schedule of controllable appliances is updated accordingly. Simulation results confirm that the proposed energy consumption scheduling algorithm can benefit both users, by reducing their energy expenses, and utility companies, by improving the peak-to-average ratio of the aggregate load demand.

/pdf/tackling-the-load-uncertainty-challenges-for-energy-24fv6nts7z.pdf

Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid

In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.

Load Scheduling and Power Trading in Systems With High Penetration of Renewable Energy Resources

In a residential area where many households have installed rooftop photovoltaic (PV) units, there is a reverse power flow from the households to the substation when the power generation from PV units is larger than the aggregate load of the households. This reverse power flow causes the voltage rise problem. In this paper, we study the use of demand side management to mitigate the voltage rise problem. We propose an autonomous energy consumption scheduling algorithm, which schedules the operation of deferrable loads to jointly shave the peak load and reduce the reverse power flow. The proposed algorithm shifts the operation of deferrable loads from peak consumption hours to hours with high-power generation from the PV units. We use stochastic programming to formulate an energy consumption scheduling problem, which takes into account the uncertainty related to the amount of power generation from PV units. The formulated cost function comprises a monetary cost for energy consumption, the revenue from energy export, and an external cost for the voltage rise. Numerical results show that our proposed algorithm can mitigate the voltage rise problem in areas with high penetration of PV units and reduce the peak-to-average ratio of the aggregate load.

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Pedram Samadi

Papers

Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid

Load Scheduling and Power Trading in Systems With High Penetration of Renewable Energy Resources

Residential Demand Side Management Under High Penetration of Rooftop Photovoltaic Units