Techno-Economic Assessment of Grid-Level Battery Energy Storage Supporting Distributed Photovoltaic Power

Lopez‐Lorente, Javier; Liu, Xueqin; Best, Robert; Makrides, George; Morrow, D. John

doi:10.1109/access.2021.3119436

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…Technologies such as rooftop photovoltaic (PV) systems, small scale wind generation, battery energy storage systems (BESS), among others, cause operational constraints such as overvoltage, power network imbalances and reverse power flow just to mention but a few [15]. Similarly, with the decarbonization of the transport and heating sectors, numbers of low carbon technology loads such as EVs and heat pumps have been rising at the end of the electrical network.…”

Section: Challenges Of LV Network With High Der Integrationmentioning

confidence: 99%

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

et al. 2023

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Increasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utility sides. Non-intrusive load monitoring (NILM) methods provide an opportunity to add value to conventional electric measurements and to increase the observability of LV networks for the implementation of active management network techniques and intelligent control of DER. This work proposes a novel implementation of NILM methods for the identification of DER electrical signatures from aggregated measurements taken at the LV side of a distribution transformer. The implementation evaluates three machine learning algorithms such as k Nearest Neighbours (kNN), random forest and a multilayer perceptron under 100 scenarios of DER integration. A year of minutely reported values of electric current, voltage, active power, and reactive power are used to train and test the proposed model. The F 1 scores achieved of 73% and 93% for Electrical Vehicles (EV) and rooftop photovoltaic (PV) respectively and processing times below 314 µs on an Intel Core i7-8700 machine. These results confirm the relevance of the NILM method based on low frequency electric measurements from the real-time identification of DER.INDEX TERMS Distributed energy resources (DER), non-intrusive load monitoring (NILM), low voltage networks distribution networks (LVN), random forest, supervised machine learning.The associate editor coordinating the review of this manuscript and approving it for publication was Wentao Fan .

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Section: Challenges Of LV Network With High Der Integrationmentioning

confidence: 99%

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

et al. 2023

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“…Within the discourse of communal energy solutions, the terminologies front of meter (FTM) and behind the meter (BTM) delineate the spatial relationship of energy st age systems vis-à-vis the electricity network [23]. FTM battery storage systems are sea lessly connected to the grid and typically characterised by larger scales, whereas BT counterparts are established on-site, encompassing residences or commercial premi [5,14].…”

Section: Literature Review: Knowledge Gapsmentioning

confidence: 99%

Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study

Mohseni,

Rutovitz,

Smith

et al. 2023

Sustainability

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Amidst the evolving paradigms of the contemporary energy landscape, marked by the imperative of sustainability and efficiency, the integration of energy storage has emerged as a transformative strategy that seeks to recalibrate the dynamics of electricity distribution and consumption. However, there remains a pressing need to determine the most economically viable approach for deploying energy storage solutions in residential low-voltage (LV) feeders, especially in rural areas. In this context, this paper presents the results of an economic evaluation of energy storage solutions for a residential LV feeder in a rural town in Australia. Specifically, the study compares the financial viability of a front-of-the-meter (FTM) battery installed on the feeder with that of a fleet of behind-the-meter (BTM) batteries. The FTM battery, with a size of 100 kW/200 kWh, is assumed to be operated by the retailer but owned by the community, with any profits assigned to the community. In this scenario, we studied a battery operating under standard network tariffs and three different trial tariffs that distribution network service providers currently offer in Australia. On the other hand, the fleet of BTM batteries (3 kW, 3.3 kWh) are individually owned by households with solar installations, and their cumulative capacity matches that of the FTM battery. The comparison is based on key economic parameters, including network charges, retail margins, frequency control ancillary service (FCAS) revenues, wholesale energy costs, technology costs associated with community batteries, and net profit or loss for the community, as well as considerations of utility grid arbitrage and solar photovoltaic (PV) self-consumption. The study also assumes different grant levels to assess the impact of subsidies on the economic feasibility for both battery configurations. The findings indicate that, while both require some form of subsidy for profitability, the BTM batteries outperform the FTM battery in terms of economic viability and so would require lower grant support. The FTM battery case finds a need for grants ranging from 75% to 95% to break even, while the BTM fleet requires approximately 50% in grants to achieve a similar outcome. In conclusion, this study highlights the importance of grant support in making energy storage solutions economically feasible. In particular, it highlights how the less mature segment of FTM batteries will need higher support initially if it is to compete with BTM. The outcomes of this study inform decision-making processes for implementing energy storage solutions in similar communities, fostering sustainable and cost-effective energy systems.

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“…However, these studies did not consider the reactive power control in sizing the BESS power conversion system (PCS), which is an important feature that needs to be considered to maximize the BESS utilization. The BESS allocation in PV-rich networks has been addressed in [26], where different sizes were simulated in order to evaluate the effectiveness of BESS in enhancing the network performance as well as driving profits to investors. The study emphasized the importance of reactive power control in maximizing profits which can support the economic viability of grid-scale BESS.…”

Section: A Literature Review and Contributionsmentioning

confidence: 99%

Stacking Battery Energy Storage Revenues in Future Distribution Networks

et al. 2022

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Distribution system operators are attracted to battery energy storage systems (BESS) as a smart option to support the distribution network. However, due to its high capital cost, BESS profitability is dependent on the participation in multiple services to stack revenues and rationalize their existence. Yet, revenue stacking is location-dependent based on the available services and regulations. In this paper, specific revenue stacking frameworks are proposed for BESS installed in modern distribution networks that consider the conflicts and synergies that may occur from the involvement in multiple services in practice. A simple yet effective sizing formulation is introduced to find the BESS system size based on the primary service which is to solve the distribution network violations. BESS scheduling is simulated in accordance with the proposed frameworks to maximize the stackable profits for a case study of Northern Ireland. The BESS profitability is investigated through cost-benefit analyses of different technologies for the sole and stacked services. The results show that revenue stacking can boost the annual revenues by 129% with a payback period of 8 years on average. The presented insights are useful for network operators and energy investors in understanding and assessing the profitability of different BESS technologies for various applications.

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Techno-Economic Assessment of Grid-Level Battery Energy Storage Supporting Distributed Photovoltaic Power

Cited by 15 publications

References 38 publications

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study

Stacking Battery Energy Storage Revenues in Future Distribution Networks

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