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 user's individual daily electricity charges.

/pdf/autonomous-demand-side-management-based-on-game-theoretic-2xyp3ii3r8.pdf

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

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.

/pdf/smart-grid-the-new-and-improved-power-grid-a-survey-4jypbbgv3j.pdf

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

Smart Grid - The New and Improved Power Grid:

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

/pdf/optimal-residential-load-control-with-price-prediction-in-2g95w0kebu.pdf

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

The paper reviews different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power. We consider both supply and demand side measures. In addition to presenting energy system flexibility measures, their importance to renewable electricity is discussed. The flexibility measures available range from traditional ones such as grid extension or pumped hydro storage to more advanced strategies such as demand side management and demand side linked approaches, e.g. the use of electric vehicles for storing excess electricity, but also providing grid support services. Advanced batteries may offer new solutions in the future, though the high costs associated with batteries may restrict their use to smaller scale applications. Different “P2Y”-type of strategies, where P stands for surplus renewable power and Y for the energy form or energy service to which this excess in converted to, e.g. thermal energy, hydrogen, gas or mobility are receiving much attention as potential flexibility solutions, making use of the energy system as a whole. To “functionalize” or to assess the value of the various energy system flexibility measures, these need often be put into an electricity/energy market or utility service context. Summarizing, the outlook for managing large amounts of RE power in terms of options available seems to be promising.

https://research.aalto.fi/files/6557604/lund_et_al_review.pdf

Review of energy system flexibility measures to enable high levels of variable renewable electricity

Many believe the electric power system is undergoing a profound change driven by a number of needs. There's the need for environmental compliance and energy conservation. We need better grid reliability while dealing with an aging infrastructure. And we need improved operational effi ciencies and customer service. The changes that are happening are particularly signifi cant for the electricity distribution grid, where "blind" and manual operations, along with the electromechanical components, will need to be transformed into a "smart grid." This transformation will be necessary to meet environmental targets, to accommodate a greater emphasis on demand response (DR), and to support plug-in hybrid electric vehicles (PHEVs) as well as distributed generation and storage capabilities. It is safe to say that these needs and changes present the power industry with the biggest challenge it has ever faced. On one hand, the transition to a smart grid has to be evolutionary to keep the lights on; on the other hand, the issues surrounding the smart grid are signifi cant enough to demand major changes in power systems operating philosophy.

Grid of the future

Techniques are described for incorporating distribution network analysis in the demand response scheduling process. In one example, a method includes receiving demand response (DR) request from a DR initiator, transmitting the DR request to an aggregator, for each customer, receiving customer location information and a customer DR value from the aggregator, aggregating DR at a distribution transformer in a distribution system, determining if there are any violations in the distribution system, determining an aggregated DR value for each electric node affected by the DR request, and transmitting each determined DR value to the DR initiator.

Systems and methods for demand response and distributed energy resource management

The Electric Industry Landscape is undergoing significant changes due to the increased use of intermittent renewable resources at both the bulk-power and distribution levels, the emergence of new technologies, and the increased participation of distributed and demand-side capabilities in power system operations. Two major complementary developments are gaining momentum to help address the challenges associated with the emerging industry landscape: a new distribution system operator (DSO) construct and the transactive energy (TE) systems framework.

The Changing Electrical Landscape: End-to-End Power System Operation Under the Transactive Energy Paradigm

A system and process/method is provided, which economically optimizes the dispatch of various electrical energy resources. The disclosed process/method is linked to and communicates with various sources of input data, including but not limited to, EMS/SCADA legacy Energy Management Systems (EMS), legacy Supervisory Control and Data Acquisition (SCADA) Systems, Demand Response (DR) and Distributed Energy Resources (DER) monitor, control, schedule, and lifecycle management systems (DR/DER Management System), and Energy Markets, electrical energy commodity trading systems (Trading Systems), and Operations System (OPS) in order to compute optimal day-ahead, day-of, and real-time schedules of various durational length for generation, demand response and storage resources while taking into account bilateral contracts and market-based trade opportunities.

Systems and Methods of Determining Optimal Scheduling and Dispatch of Power Resources

A system and process/method of modeling demand-response (DR) and distributed-energy resource (DER) assets is provided, facilitating the aggregation of said assets in virtual power plants (VPPs), and using VPPs to provide energy balancing services needed to minimize the impact of Variable Energy Resources (VER) on power system. The method accurately accounts for assets by qualifying assets based on various parameters and by forecasting the capabilities of the resulting VPPs for provision of balancing services. Asset performance factors that may affect the aggregated VPP's capabilities are monitored and recalculated when necessary. Near-term forecasted VPP capability is compared to near-term forecasted imbalances in the electric-power-supply system, and the VPP is dispatched to minimize the system imbalances. The dispatch signal is disaggregated to control commands to individual assets. This process provides a reliable and cost effective approach to support higher penetrations of renewable generation in the electric power system.

Ali Ipakchi

Papers

Grid of the future

Systems and methods for demand response and distributed energy resource management

The Changing Electrical Landscape: End-to-End Power System Operation Under the Transactive Energy Paradigm

Systems and Methods of Determining Optimal Scheduling and Dispatch of Power Resources

Use of Demand Response (DR) and Distributed Energy Resources (DER) to mitigate the impact of Variable Energy Resources (VER) in Power System Operation