Multiple Timescale Dispatch and Scheduling for Stochastic Reliability in Smart Grids with Wind Generation Integration

Integrating volatile renewable energy resources into the bulk power grid is challenging, due to the reliability requirement that at each instant the load and generation in the system remain balanced. In this study, we tackle this challenge for smart grid with integrated wind generation, by leveraging multi-timescale dispatch and scheduling. Specifically, we consider smart grids with two classes of energy users - traditional energy users and opportunistic energy users (e.g., smart meters or smart appliances), and investigate pricing and dispatch at two timescales, via day-ahead scheduling and realtime scheduling. In day-ahead scheduling, with the statistical information on wind generation and energy demands, we characterize the optimal procurement of the energy supply and the day-ahead retail price for the traditional energy users; in realtime scheduling, with the realization of wind generation and the load of traditional energy users, we optimize real-time prices to manage the opportunistic energy users so as to achieve systemwide reliability. More specifically, when the opportunistic users are non-persistent, i.e., a subset of them leave the power market when the real-time price is not acceptable, we obtain closedform solutions to the two-level scheduling problem. For the persistent case, we treat the scheduling problem as a multitimescale Markov decision process. We show that it can be recast, explicitly, as a classic Markov decision process with continuous state and action spaces, the solution to which can be found via standard techniques. We conclude that the proposed multi-scale dispatch and scheduling with real-time pricing can effectively address the volatility and uncertainty of wind generation and energy demand, and has the potential to improve the penetration of renewable energy into smart grids.Comment: Submitted to IEEE Infocom 2011. Contains 10 pages and 4 figures. Replaces the previous arXiv submission (dated Aug-23-2010) with the same titl

An IoT Multi-Interface Gateway for Building a Smart Space

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Privacy-preserving Energy Scheduling for Smart Grid with Renewables

We consider joint demand response and power procurement to optimize the average social welfare of a smart power grid system with renewable sources. The renewable sources such as wind and solar energy are intermittent and fluctuate rapidly. As a consequence, the demand response algorithm needs to be executed in real time to ensure the stability of a smart grid system with renewable sources. We develop a demand response algorithm that converges to the optimal solution with superlinear rates of convergence. In the simulation studies, the proposed algorithm converges roughly thirty time faster than the traditional subgradient algorithm. In addition, it is fully distributed and can be realized either synchronously or in asynchronous manner, which eases practical deployment

Adaptive Electricity Scheduling in Microgrids

Microgrid (MG) is a promising component for future smart grid (SG) deployment. The balance of supply and demand of electric energy is one of the most important requirements of MG management. In this paper, we present a novel framework for smart energy management based on the concept of quality-of-service in electricity (QoSE). Specifically, the resident electricity demand is classified into basic usage and quality usage. The basic usage is always guaranteed by the MG, while the quality usage is controlled based on the MG state. The microgrid control center (MGCC) aims to minimize the MG operation cost and maintain the outage probability of quality usage, i.e., QoSE, below a target value, by scheduling electricity among renewable energy resources, energy storage systems, and macrogrid. The problem is formulated as a constrained stochastic programming problem. The Lyapunov optimization technique is then applied to derive an adaptive electricity scheduling algorithm by introducing the QoSE virtual queues and energy storage virtual queues. The proposed algorithm is an online algorithm since it does not require any statistics and future knowledge of the electricity supply, demand and price processes. We derive several "hard" performance bounds for the proposed algorithm, and evaluate its performance with trace-driven simulations. The simulation results demonstrate the efficacy of the proposed electricity scheduling algorithm.Comment: 12 pages, extended technical repor

Adaptive stochastic energy flow balancing in smart grid

A smart grid can be considered as an unstructured network of distributed interacting nodes represented by renewable energy sources, storage and loads. The nodes emerge or disappear in a stochastic manner due to the intermittent nature of natural sources such as wind speed and solar irradiation. Prediction and stochastic modelling of electrical energy flow is a critical characteristic in such a network to achieve load balancing and/or peak shaving in order to minimise the fluctuation between off peak and peak demand by power consumers. Before contributing energy to the network, a node acquires information about other nodes in the grid and the state of the grid in order to adjust its power injection to or consumption from the grid. The unpredictable behaviour of nodes in a smart grid is modelled and administered through a scheduling strategy control and learning algorithm using the historical data collected from the system. The stochastic model predicts future power consumption/injection to determine the power required for storage components. In the proposed stochastic model and the deployed learning and adaptation processes, two indicators, based on moving averages of different subsets of the time series are implemented to satisfy two objectives. The first objective is to predict the most efficient state of electrical energy flow between a distribution network and nodes. Whereas the second objective is to minimise the peak demand and off peak consumption of acquiring electrical energy from the main grid by using ant colony search algorithm (ACSA). The performance of the indicators is validated against limited autoregressive integrated moving average (LARIMA) and second order Markov Chain model. It is shown that proposed method outperforms both LARIMA and Markov Chain model

MultiGreen: Cost-Minimizing Multi-source Datacenter Power Supply with Online Control

Session 4: Data Center Energy ManagementFulltext of the conference paper in: http://conferences.sigcomm.org/eenergy/2013/papers/p13.pdfFaced by soaring power cost, large footprint of carbon emis- sion and unpredictable power outage, more and more mod- ern Cloud Service Providers (CSPs) begin to mitigate these challenges by equipping their Datacenter Power Supply Sys- tem (DPSS) with multiple sources: (1) smart grid with time- varying electricity prices, (2) uninterrupted power supply (UPS) of finite capacity, and (3) intermittent green or re- newable energy. It remains a significant challenge how to operate among multiple power supply sources in a comple- mentary manner, to deliver reliable energy to datacenter users over time, while minimizing a CSP’s operational cost over the long run. This paper proposes an efficient, online control algorithm for DPSS, called MultiGreen. MultiGreen is based on an innovative two-timescale Lyapunov optimiza- tion technique. Without requiring a priori knowledge of system statistics, MultiGreen allows CSPs to make online decisions on purchasing grid energy at two time scales (in the long-term market and in the real-time market), leveraging renewable energy, and opportunistically charging and dis- charging UPS, in order to fully leverage the available green energy and low electricity prices at times for minimum op- erational cost. Our detailed analysis and trace-driven sim- ulations based on one-month real-world data have demon- strated the optimality (in terms of the tradeoff between min- imization of DPSS operational cost and satisfaction of data- center availability) and stability (performance guarantee in cases of fluctuating energy demand and supply) of Multi- Green