A Graphical Performance-Based Energy Storage Capacity Sizing Method for High Solar Penetration Residential Feeders

Zhu, Xiangqi; Yan, Jiahong; Liu, Ning

doi:10.1109/tsg.2016.2577030

Cited by 35 publications

(13 citation statements)

References 15 publications

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“…They showed that after the installation of ESS, energy exchanged with the grid decreased significantly. Zhu et al [142] presented a method for sizing ESS within distribution grid with high PV penetration and tested the method on three types of ESS: behind-the-meter, utility owned and merchant owned. They showed that it is more economical for the DSO to procure services from the latter two types of ESS.…”

Section: Behind-the-meter Ess Investmentsmentioning

confidence: 99%

Operating and Investment Models for Energy Storage Systems

2020

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In the context of climate changes and the rapid growth of energy consumption, intermittent renewable energy sources (RES) are being predominantly installed in power systems. It has been largely elucidated that challenges that RES present to the system can be mitigated with energy storage systems (ESS). However, besides providing flexibility to intermittent RES, ESS have other sources of revenue, such as price arbitrage in the markets, balancing services, and reducing the cost of electricity procurement to end consumers. In order to operate the ESS in the most profitable way, it is often necessary to make optimal siting and sizing decisions, and to determine optimal ways for the ESS to participate in a variety of energy and ancillary service markets. As a result, many publications on ESS models with various goals and operating environments are available. This paper aims at presenting the results of these papers in a structured way. A standard ESS model is first outlined, and that is followed by a literature review on operational and investment ESS models at the transmission and distribution levels. Both the price taking and price making models are elaborated on and presented in detail. Based on the examined body of work, the paper is concluded with recommendations for future research paths in the analysis of ESS.

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Section: Behind-the-meter Ess Investmentsmentioning

confidence: 99%

Operating and Investment Models for Energy Storage Systems

2020

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“…The total annual energy cost (Cost TOTAL/a ), in (7), could be calculated by adding the total energy consumed (Cost POWER/a ), in (4), to the cost of the ESS (Cost ESS/a ), in (5), and the cost of the solar PV panel (Cost SOLAR/a ), in (6) if considered. Solar Photovoltaic Systems: A Case Study Cost POWER/a = ∫C POW (t) * P (t) dt (4) Cost ESS/a = 105 CAD/kWh (5) Cost SOLAR/a = 160 CAD/kW (6) Cost TOTAL/a = C POWER/a + C ESS/a + C SOLAR/a (7) For a comparison, we use the energy cost from 2016 with given load profile, without ESS nor solar PV panel, by taking the power directly from the grid which corresponds to 165.75 CAD for direct market access or 1039.3 CAD for TOU pricing. For the following analysis the energy market-/solar-data from 2016 and 2017 are used and divided by two to get the annual average.…”

Section: Cost Calculationsmentioning

confidence: 99%

“…An optimization framework is proposed [5], for finding Solar Photovoltaic Systems: A Case Study optimal operation rule-based strategies for operating an energy storage device connected to a self-use solar generation system for minimizing the payments to the grid using real data for solar generation and building load, and it is found that they are able to achieve near-optimal performance without requiring forecasts. A graphical, performance-based energy storage capacity sizing method [6] is proposed for residential loads with high solar penetration levels, using the calculated rated power and storage capacity of an energy storage device. The historical solar radiation data, residential household load data, and residential load models are used for creating the net load, considering the solar generation.…”

Section: Introductionmentioning

confidence: 99%

An Energy Market Model for Homes with Battery Energy Storage and Solar Photovoltaic Systems: A Case Study

Abdel-Fattah¹,

Punt²

2019

IJEPE

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The energy market is tending to be more flexible and therefore the price range of energy might vary a lot over the day. As a result, it might be feasible to consider energy storage systems for private households. This paper presents an average Ontario household case study that will be evaluated on an economical basis. The integration of energy storage systems has many advantage and could play an important role in smart-grids technology. Energy storage systems could save the energy cost depending on the dynamic price levels and also form solar PV panels if available. The presented work in this paper started from a simple basic analysis, presented a simple algorithm, and investigated the economic aspect possibilities. Adding energy storage system to household case is considered at the beginning. Then, the work has been extended to consider the integration of the solar photovoltaic panels/system which is presenting an advanced system which is very interesting to investigate. Also, the savings from self-generated energy, for the considered case study is presented. Finally, the current situation is evaluated and the suggested solutions and possibilities are presented. The role the technology role in the system cost and saving is confirmed and, up to now, the subventions for solar PV systems is still expected for economic feasibility.

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“…They can also mitigate the negative impact of the randomness of renewable generation and load on distribution system reliability. Residential optimal EMSs with renewable power generation units are proposed by Rastegar et al [11], Mediwaththe et al [12], Li et al [13], Kwon et al [14] and Zhu et al [15]. Researchers treated multiple houses with controllable loads or distributed load groups in a smart grid to reduce the fluctuation of power flow caused by renewable energy [16,17].…”

Section: Introductionmentioning

confidence: 99%

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

Wang¹,

Liang²,

Dinavahi³

2019

IET Renewable Power Generation

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Renewable power generation combined with energy storage (ES) is expected to bring enormous economical and environmental benefits to the future smart grid. However, the ES management in smart grid is facing significant technical challenges due to the volatile nature of renewable energy sources and the buffering effect of ES units. The challenges are further complicated by the increasing size and complexity of the system, as well as the consideration of random usage patterns of electrical appliances by customers. To address these challenges, this study proposes a parallel decomposition method for large-scale stochastic programming in a distribution system with renewable energy sources and ES units. By leveraging nested decomposition, the problem can be converted into independent sub-problems with a series of time periods. In addition, the reformulated problem is fully parallel for speed up in execution. The performance of the proposed method is evaluated based on the IEEE 4-bus and 33-bus test distribution systems with real photovoltaic generation and electrical appliance usage data. The case study demonstrates that the proposed scheme can substantially reduce the system operation cost, with low computational complexity.

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A Graphical Performance-Based Energy Storage Capacity Sizing Method for High Solar Penetration Residential Feeders

Cited by 35 publications

References 15 publications

Operating and Investment Models for Energy Storage Systems

Operating and Investment Models for Energy Storage Systems

An Energy Market Model for Homes with Battery Energy Storage and Solar Photovoltaic Systems: A Case Study

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

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