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Register a new userBattery Life-Cycle Optimization and Runtime Control for Commercial Buildings Demand Side Management: A New York City Case Study
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Added by
Yubo Wang
Publication date
2018
fields
Electronic Engineering
and research's language is
English
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In metropolitan areas populated with commercial buildings, electric power supply is stringent especially during business hours. Demand side management using battery is a promising solution to mitigate peak demands, however long payback time creates barriers for large scale adoption. In this paper, we have developed a design phase battery life-cycle cost assessment tool and a runtime controller for the building owners, taking into account the degradation of battery. In the design phase, perfect knowledge on building load profile is assumed to estimate ideal payback time. In runtime, stochastic programming and load predictions are applied to address the uncertainties in loads for producing optimal battery operation. For validation, we have performed numerical experiments using the real-life tariff model serves New York City, Zn/MnO2 battery, and state-of-the-art building simulation tool. Experimental results shows a small gap between design phase assessment and runtime control. To further examine the proposed methods, we have applied the same tariff model and performed numerical experiments on nine weather zones and three types of commercial buildings. On contrary to the common practice of shallow discharging battery for preventing phenomenal degradation, experimental results show promising payback time achieved by optimally deep discharge a battery.
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Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge, charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents battery cycle life prognosis and its integration with parallel asset management to reduce lifecycle cost of the Battery Energy Storage System (BESS). A nonlinear capacity fade model is incorporated in the parallel asset management model to update battery capacity. Parametric studies have been conducted to explore the influence of different model inputs (e.g. usage rate, unit battery capacity, operating condition and periodical demand) for a five-year time horizon. Experiment results verify the reasonableness of this new framework and suggest that the increase in battery lifetime leads to decrease in lifecycle cost.
Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data has been collected that provides an indication of a sensors health. This telemetry data was analyzed over an 18 month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.
Commercial buildings account for approximately 36% of US electricity consumption, of which nearly two-thirds is met by fossil fuels [1] resulting in an adverse impact on the environment. Reducing this impact requires improving energy efficiency and lowering energy consumption. Most existing studies focus on designing methods to regulate and reduce HVAC and lighting energy consumption. However, few studies have focused on the control of occupant plugload energy consumption. In this study, we conducted multiple experiments to analyze changes in occupant plugload energy consumption due to monetary incentives and/or feedback. The experiments were performed in government office and university buildings at NASA Research Park located in Moffett Field, CA. Analysis of the data reveal significant plugload energy reduction can be achieved via feedback and/or incentive mechanisms. Autoregressive models are used to predict expected plugload savings in the presence of exogenous variables. The results of this study suggest that occupant-in-the-loop control architectures have the potential to reduce energy consumption and hence lower the carbon footprint of commercial buildings.
We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators states or the ISOs state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregators energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities.
The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.
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