Performance Analysis and Comparison on Energy Storage Devices for Smart Building Energy Management

Xu, Zhanbo; Guan, Xiaohong; Jia, Qing‐Shan; Wu, Jiang; Wang, Dai; Chen, Siyun

doi:10.1109/tsg.2012.2218836

Cited by 200 publications

(94 citation statements)

References 7 publications

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“…In several state-of-the-art smart grid studies, where the control of devices on the network has been comprehensively tested, as in this work, it has also been important to select the correct algorithm based on the demand characteristics. Xu et al concluded that it is important to study the application in detail before selecting a control technique [44]. This result confirms, for the data presented, that selecting the correct storage control technique for peak reduction, using DNO owned storage devices to achieve the best possible demand reduction, will depend on the accuracy of the forecast and the variation in actual demand reduction that can be achieved.…”

Section: Mpc Vs Set-point Control: Varying Aggregation Sizesupporting

confidence: 72%

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The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid's infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, where the objective is to achieve the greatest peak reduction in demand, for a given storage device specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-point controller and bench marked against a control algorithm with a perfect forecast. A specific case study, using storage on the LV network, is presented, and the results of each algorithm are compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

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Section: Mpc Vs Set-point Control: Varying Aggregation Sizesupporting

confidence: 72%

“…this is similar to assumptions with solar radiation and demand profiles explored in [44]. The algorithm is shown below (Algorithm 1).…”

Section: Selecting Nodesmentioning

confidence: 97%

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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“…In this paper, we define the combination of one charging and one discharging as a cycle and roughly believe the life of EV battery just depends on the number of charging/discharging cycles. Therefore, the battery degradation cost is indicated by charging/discharging cycles [3]. (30), the objective of aggregator can be interpreted as minimizing state transitions between charging and discharging, which is obviously harmful to the EV battery.…”

Section: Ev Aggregator Scheduling Problemmentioning

confidence: 99%

“…Renewable energy such as photovoltaic generation and wind power may be utilized in a microgrid as distributed energy sources for avoiding the long distance transmission and reducing carbon emission [2]. Storage devices in a micorgrid not only help make better use of renewable energies but also regulate the load peaks [3].…”

Section: Introductionmentioning

confidence: 99%

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Wang

Guan

et al. 2016

IEEE Trans. Smart Grid

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153

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Abstract-Electric vehicles (EVs) can be considered as flexible mobile battery storages in microgrids. For multiple microgrids in an area, coordinated scheduling on charging and discharging are required to avoid power exchange spikes between the multimicrogrid system and the main grid. In this paper, a two-stage integrated energy exchange scheduling strategy for multimicrogrid system is presented, which considers EVs as storage devices. Then several dual variables, which are representative of the marginal cost of proper constraints, are utilized to form an updated price, thereby being a modification on the original electricity price. With this updated price signal, a price-based decentralized scheduling strategy is presented for the Microgrid Central Controller (MGCC). Simulation results show that the two-stage scheduling strategy reduces the electricity cost and avoids frequent transitions between battery charging/discharging states. With the proposed decentralized scheduling strategy, each microgrid only needs to solve its local problem and limits the total power exchange within the safe range.Index Terms--Microgrid, electric vehicle, energy exchange, dual variable, updated price signal, decentralized scheduling strategy. I. NOMENCLATURE Index:i Index for microgrid,, ( ) i s B t Lower bound of the aggregate batteries' remaining energy in microgrid i during t in scenario s (kWh).Upper bound of the aggregate batteries' remaining energy in microgrid i during t in scenario s (kWh). ( ) C tOriginal electricity price during t (CNY/kWh).

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“…Moreover, frameworks [7][8][9] provide either real-time or day-ahead energy consumption schedules on the basis of behavioural profiling mechanisms aimed at deducing dwellers habits and at identifying possible energy saving actions. Frameworks integrating local RESes and energy storage banks have also been designed [10,11]. A comparative survey on demand side management systems involving smart buildings and electricity grid can be found in [12].…”

Section: Related Workmentioning

confidence: 99%

Energy Optimization and Management of Demand Response Interactions in a Smart Campus

et al. 2016

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Abstract:The proposed framework enables innovative power management in smart campuses, integrating local renewable energy sources, battery banks and controllable loads and supporting Demand Response interactions with the electricity grid operators. The paper describes each system component: the Energy Management System responsible for power usage scheduling, the telecommunication infrastructure in charge of data exchanging and the integrated data repository devoted to information storage. We also discuss the relevant use cases and validate the framework in a few deployed demonstrators.

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Performance Analysis and Comparison on Energy Storage Devices for Smart Building Energy Management

Cited by 200 publications

References 7 publications

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Energy Optimization and Management of Demand Response Interactions in a Smart Campus

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