Smart grid

About: Smart grid is a research topic. Over the lifetime, 37536 publications have been published within this topic receiving 627844 citations. The topic is also known as: intelligent grid.

Achieving Differential Privacy of Data Disclosure in the Smart Grid

Abstract: The smart grid introduces new privacy implications to individuals and their family due to the fine-grained usage data collection. For example, smart metering data could reveal highly accurate real-time home appliance energy load, which may be used to infer the human activities inside the houses. One effective way to hide actual appliance loads from the outsiders is Battery-based Load Hiding (BLH), in which a battery is installed for each household and smartly controlled to store and supply power to the appliances. Even though such technique has been demonstrated useful and can prevent certain types of attacks, none of existing BLH works can provide probably privacy-preserving mechanisms. In this paper, we investigate the privacy of smart meters via differential privacy. We first analyze the current existing BLH methods and show that they cannot guarantee differential privacy in the BLH problem. We then propose a novel randomized BLH algorithm which successfully assures differential privacy, and further propose the Multitasking-BLH-Exp3 algorithm which adaptively updates the BLH algorithm based on the context and the constraints. Results from extensive simulations show the efficiency and effectiveness of the proposed method over existing BLH methods. Index Terms—Smart Grid, Smart Meter, Privacy, Differential Privacy, Data Disclosure

Incentive Mechanism for Demand Side Management in Smart Grid Using Auction

Abstract: Smart pricing methods using auction mechanism allow more information exchange between users and providers, and they can meet users' energy demand at a low cost of grid operation, which contributes to the economic and environmental benefit in smart grid. However, when asked to report their energy demand, users may have an incentive to cheat in order to consume more while paying less, causing extra costs for grid operation. So it is important to ensure truthfulness among users for demand side management. In this paper, we propose an efficient pricing method that can prevent users' cheating. In the proposed model, the smart meter can record user's consumption information and communicate with the energy provider's terminal. Users' preferences and consumption patterns are modeled in form of a utility function. Based on this, we propose an enhanced AGV (Arrow-d'Aspremont-Gerard-Varet) mechanism to ensure truthfulness. In this incentive method, user's payment is related to its consumption credit. One will be punished to pay extra if there is a cheat record in its consumption history. We prove that the enhanced AGV mechanism can achieve the basic qualifications: incentive compatibility, individual rationality and budget balance. Simulation results confirm that the enhanced AGV mechanism can ensure truth-telling, and benefit both users and energy providers.

Power System Reliability Impact of Energy Storage Integration With Intelligent Operation Strategy

Abstract: Electric power industry is experiencing a movement from the existing conventional electric grid to a more reliable, efficient and secure smart grid. In order to achieve these goals, components such as energy storage will be included, and potentially in large scale. Many feasible applications of energy storage in power systems have been investigated. The major benefits of energy storage include electric energy time-shift, frequency regulation and transmission congestion relief. In this paper, we focus on the reliability improvement of the bulk power system brought by the utilization of energy storage in the local distribution systems integrated with renewable energy generation. An intelligent operation strategy for energy storage which improves reliability considering the renewable energy integration is presented. The smart grid communication and control network is utilized to implement the proposed energy storage operation. A bulk power system reliability evaluation framework is proposed to study the reliability impact brought by the energy storage integration and operation. A detailed case study and sensitivity analysis is performed to demonstrate the effectiveness of the presented operation strategy and evaluation framework, and to provide valuable insights on the power system reliability impact derived from the energy storage integration.

A Supervisory Power Management System for a Hybrid Microgrid With HESS

Abstract: This paper proposes a supervisory power management system (PMS) for a grid interactive microgrid with a hybrid energy storage system. The key feature of the proposed PMS is reduced number of sensors required to implement the PMS. The PMS considers renewable power variation, grid availability, electricity pricing, and changes in local loads. It can detect the operating mode of system without measuring load currents and powers. A single-phase voltage source converter (VSC) transfers real power between dc grid and utility grid besides offering ancillary services such as harmonic mitigation, reactive power support, and unity power factor at the point of common coupling (PCC). In the proposed system, a better dc-link voltage regulation is achieved and the usage of supercapacitors reduces the current stress on the battery. The PMS also addresses extreme operating conditions such as load shedding, off-maximum power point tracking operation of photovoltaic, elimination of critical oscillation of hybrid energy storage systems power, islanded operation, and resynchronization with grid. The performance of the proposed PMS is verified by digital simulation and experimental studies.

Home Appliance Load Modeling From Aggregated Smart Meter Data

Abstract: With recent developments in the infrastructure of smart meters and smart grid, more electric power data is available and allows real-time easy data access. Modeling individual home appliance loads is important for tasks such as non-intrusive load disaggregation, load forecasting, and demand response support. Previous methods usually require sub-metering individual appliances in a home separately to determine the appliance models, which may not be practical, since we may only be able to observe aggregated real power signals for the entire-home through smart meters deployed in the field. In this paper, we propose a model, named Explicit-Duration Hidden Markov Model with differential observations (EDHMM-diff), for detecting and estimating individual home appliance loads from aggregated power signals collected by ordinary smart meters. Experiments on synthetic data and real data demonstrate that the EDHMM-diff model and the specialized forward-backward algorithm can effectively model major home appliance loads.