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This paper proposes a rule-based power management algorithm to enable the dispatch of a utility-scale PV power plant consisting of a hybrid energy storage system, in accordance with the Australian national electricity rules. This algorithm is purposely designed to regulate the instantaneous power of a PV plant with the same level of dispatchability as conventional power plants. Additionally, the proposed algorithm is robust under large forecasting errors (up to 60%) of solar irradiance and it can be easily implemented in practice. Specifically, this algorithm can be executed within seconds even when considering detailed market operation and model nonlinearities. A 30 MW PV plant, including a 7.5 MW/1.25 MWh vanadium redox battery and a 1.5 MW/0.25 MWh suppercapacitors bank, has been modeled using MATLAB/Simulink and PLECS software environment. The simulation results were based on various scenarios and confirmed the effectiveness of the proposed power management algorithm. It has been shown that even in the worst-case scenario of solar forecasting error of 60%, the proposed algorithm still managed to deliver over 94% of the available energy for a given solar irradiance profile of one month. [ABSTRACT FROM AUTHOR]

Electricity expenditure comprises a significant fraction of the total operating cost in data centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this paper, we consider utilizing existing energy storage capabilities in data centers to reduce electricity cost under wholesale electricity markets, where the electricity price exhibits both temporal and spatial variations. A stochastic program is formulated by integrating the center-level load balancing, the server-level configuration, and the battery management while at the same time guaranteeing the quality-of-service experience by end users. We use the Lyapunov optimization technique to design an online algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. We demonstrate the effectiveness of our proposed algorithm through extensive numerical evaluations based on real-world workload and electricity price data sets. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both the spatial and temporal variations in wholesale electricity prices and workload arrival processes. [ABSTRACT FROM AUTHOR]

In this paper, we show how to achieve close-to-optimal utility performance in energy-harvesting networks with only finite capacity energy storage devices. In these networks, nodes are capable of harvesting energy from the environment. The amount of energy that can be harvested is time-varying and evolves according to some probability law. We develop an online algorithm, called the Energy-limited Scheduling Algorithm (ESA), which jointly manages the energy and makes power allocation decisions for packet transmissions. ESA only has to keep track of the amount of energy left at the network nodes and does not require any knowledge of the harvestable energy process. We show that ESA achieves a utility that is within O(\epsilon) of the optimal, for any \epsilon>0, while ensuring that the network congestion and the required capacity of the energy storage devices are deterministically upper-bounded by bounds of size O(1/\epsilon). We then also develop the Modified-ESA (MESA) algorithm to achieve the same O(\epsilon) close-to-utility performance, with the average network congestion and the required capacity of the energy storage devices being only O([\log (1/\epsilon)]^2), which is close to the theoretical lower bound O(\log (1/\epsilon)). [ABSTRACT FROM AUTHOR]

This paper deals with a new control algorithm for a voltage and frequency controller (VFC) of an isolated wind energy conversion system (IWECS) using an isolated asynchronous generator to feed three-phase four-wire consumer loads. The reference source currents are estimated for the indirect current control of a voltage source converter (VSC) using the single-phase PQ theory to control the voltage and frequency of IWECS. A three-leg VSC with a battery energy storage system is used as a VFC for an IWECS. A nonisolated star-polygon transformer is used to provide a neutral terminal for four-wire loads and to compensate the neutral current resulting in reduction in rating of VSC. The proposed VFC is implemented using a DSP. Experimental results are presented to demonstrate the additional capabilities of VFC as a load leveler, a load balancer, a harmonic eliminator, and a neutral current compensator. [ABSTRACT FROM AUTHOR]

Recent advances in energy harvesting materials and ultra-low-power communications will soon enable the realization of networks composed of energy harvesting devices. These devices will operate using very low ambient energy, such as energy harvested from indoor lights. We focus on characterizing the light energy availability in indoor environments and on developing energy allocation algorithms for energy harvesting devices. First, we present results of our long-term indoor radiant energy measurements, which provide important inputs required for algorithm and system design (e.g., determining the required battery sizes). Then, we focus on algorithm development, which requires nontraditional approaches, since energy harvesting shifts the nature of energy-aware protocols from minimizing energy expenditure to optimizing it. Moreover, in many cases, different energy storage types (rechargeable battery and a capacitor) require different algorithms. We develop algorithms for calculating time fair energy allocation in systems with deterministic energy inputs, as well as in systems where energy inputs are stochastic. [ABSTRACT FROM PUBLISHER]