Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Large-scale deployment of intermittent renewable energy (namely wind energy and solar PV) may entail new challenges in power systems and more volatility in power prices in liberalized electricity markets. Energy storage can diminish this imbalance, relieving the grid congestion, and promoting distributed generation. The economic implications of grid-scale electrical energy storage technologies are however obscure for the experts, power grid operators, regulators, and power producers. A meticulous techno-economic or cost-benefit analysis of electricity storage systems requires consistent, updated cost data and a holistic cost analysis framework. To this end, this study critically examines the existing literature in the analysis of life cycle costs of utility-scale electricity storage systems, providing an updated database for the cost elements (capital costs, operational and maintenance costs, and replacement costs). Moreover, life cycle costs and levelized cost of electricity delivered by electrical energy storage is analyzed, employing Monte Carlo method to consider uncertainties. The examined energy storage technologies include pumped hydropower storage, compressed air energy storage (CAES), flywheel, electrochemical batteries (e.g. lead–acid, NaS, Li-ion, and Ni–Cd), flow batteries (e.g. vanadium-redox), superconducting magnetic energy storage, supercapacitors, and hydrogen energy storage (power to gas technologies). The results illustrate the economy of different storage systems for three main applications: bulk energy storage, T&D support services, and frequency regulation.

Electrical energy storage systems: A comparative life cycle cost analysis

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Partial shading is a serious obstacle to effective utilization of photovoltaic (PV) systems since it can result in significant output power degradation for the system. A PV system is organized as a series connection of PV modules, each module comprising of a number of series-parallel connected cells. This paper presents modified PV cell structures with integrated switches, imbalanced cell connection topologies for PV modules, and a dynamic programming algorithm to produce near-optimal reconfigurations of each PV module with the goal of maximizing the system output power level under any partial shading patterns. Through simulations, we have demonstrated up to a factor of 2.3X improvement in the output power level of a PV system comprised of 3 PV modules with 60 PV cells per module.

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Near-optimal, dynamic module reconfiguration in a photovoltaic system to combat partial shading effects

This paper is the first to present a comprehensive analysis of the profitability of the hybrid electrical energy storage (HEES) systems while further providing a HEES design and control optimization framework to maximize the total return on investment (ROI). The solution consists of two steps: (i) Derivation of an optimal HEES management policy to maximize the daily energy cost saving and (ii) Optimal design of the HEES system to maximize the amortized annual profit under budget and system volume constraints. We consider a HEES system comprised of lead-acid and Li-ion batteries for a case study. The optimal HEES system achieves an annual ROI of up to 60% higher than a lead-acid battery-only system (Li-ion battery-only) system.

/pdf/maximizing-return-on-investment-of-a-grid-connected-hybrid-521j2g8w33.pdf

Maximizing return on investment of a grid-connected hybrid electrical energy storage system

Integrating residential-level photovoltaic (PV) power generation and energy storage systems into the smart grid will provide a better way of utilizing renewable power. This has become a particularly interesting problem with the availability of dynamic energy pricing models in which electricity consumers can use their PV-based generation and controllable storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential-level storage controller should possess the ability of forecasting future PV-based power generation and load power consumption profiles for better performance. In this paper we present novel PV power generation and load power consumption prediction algorithms, which are specifically designed for a residential storage controller. Furthermore, to perform effective storage control based on these predictions, we separate the proposed storage control algorithm into two tiers, one which is performed at decision epochs of a billing period (e.g., a month) to globally "plan" the future discharging/charging schemes of the storage system, and another one performed locally and more frequently as system operates to compensate prediction errors. The first tier of algorithm is formulated and solved as a convex optimization problem at each decision epoch of the billing period, while the second tier has O(1) complexity.

/pdf/a-hierarchical-control-algorithm-for-managing-electrical-1kx2vxrofy.pdf

A Hierarchical Control Algorithm for Managing Electrical Energy Storage Systems in Homes Equipped with PV Power Generation

Energy harvesting is a promising technique to overcome the limitation imposed by the finite energy capacity of batteries in conventional battery-powered embedded systems. In particular, the question of how one can achieve full energy autonomy (i.e., perpetual, battery-free operation) of a real-time embedded system with an energy harvesting capability (RTES-EH) by applying a global control strategy is investigated. The energy harvesting module is comprised of a Photovoltaic (PV) panel for harvesting energy and a supercapacitor for storing any excess energy. The global controller performs optimal operating point tracking for the PV panel, state-of-charge management for the supercapacitor, and energy-harvesting-aware real-time task scheduling with dynamic voltage and frequency scaling (DVFS) in the embedded load device. The controller, which accounts for dynamic V-I characteristics of the PV panel, terminal voltage variation and self-leakage of the supercapacitor, and power losses in voltage converters, employs a cascaded feedback control structure with an inner control loop determining the V-I operating point of the PV panel and an outer supervisory control loop performing real-time task scheduling and setting the voltage and frequency level in the embedded load device (to keep the state-of-charge of the supercapacitor in a desirable range). Experimental results show that the proposed global controller lowers the task drop rate in a RTES-EH by up to 60% compared with baseline controller within the same service time.

A framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting

Dynamic power management (DPM) in battery-powered mobile systems attempts to achieve higher energy efficiency by selectively setting idle components to a sleep state. However, re-activating these components at a later time consumes a large amount of energy, which means that it will create a significant power draw from the battery supply in the system. This is known as the energy overhead of the “wakeup” operation. We start from the observation that, due to the rate capacity effect in Li-ion batteries which are commonly used to power mobile systems, the actual energy overhead is in fact larger than previously thought. Next we present a model-free reinforcement learning (RL) approach for an adaptive DPM framework in systems with bursty workloads, using a hybrid power supply comprised of Li-ion batteries and supercapacitors. Simulation results show that our technique enhances power efficiency by up to 9% compared to a battery-only power supply. Our RL-based DPM approach also achieves a much lower energy-delay product compared to a previously reported expert-based learning approach.

/pdf/reinforcement-learning-based-dynamic-power-management-with-a-29xxtqvvvi.pdf

Siyu Yue

Papers

Near-optimal, dynamic module reconfiguration in a photovoltaic system to combat partial shading effects

Maximizing return on investment of a grid-connected hybrid electrical energy storage system

A Hierarchical Control Algorithm for Managing Electrical Energy Storage Systems in Homes Equipped with PV Power Generation

A framework of concurrent task scheduling and dynamic voltage and frequency scaling in real-time embedded systems with energy harvesting

Reinforcement learning based dynamic power management with a hybrid power supply