Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment

Luthander, Rasmus; Widén, Joakim; Munkhammar, Joakim; Lingfors, David

doi:10.1016/j.energy.2016.06.039

Cited by 168 publications

(67 citation statements)

References 34 publications

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“…Many researchers have considered the application of energy storage to make demand patterns smooth [30], considering the impact of storage and solar photovoltaic (PV) systems. The limitation of survey study has been observed -that the storage was used to increase individual-consumption, instead of focusing on rebound peak shaving.…”

Section: Background Literaturementioning

confidence: 99%

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

et al. 2020

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In most demand response (DR) based residential load management systems, shifting a considerable amount of load in low price intervals reduces end user cost, however, it may create rebound peaks and user dissatisfaction. To overcome these problems, this work presents a novel approach to optimizing load demand and storage management in response to dynamic pricing using machine learning and optimization algorithms. Unlike traditional load scheduling mechanisms, the proposed algorithm is based on finding suggested low tariff area using artificial neural network (ANN). Where the historical load demand individualized power consumption profiles of all users and real time pricing (RTP) signal are used as input parameters for a forecasting module for training and validating the network. In a response, the ANN module provides a suggested low tariff area to all users such that the electricity tariff below the low tariff area is market based. While the users are charged high prices on the basis of a proposed load based pricing policy (LBPP) if they violate low tariff area, which is based on RTP and inclining block rate (IBR). However, we first developed the mathematical models of load, pricing and energy storage systems (ESS), which are an integral part of the optimization problem. Then, based on suggested low tariff area, the problem is formulated as a linear programming (LP) optimization problem and is solved by using both deterministic and heuristic algorithms. The proposed mechanism is validated via extensive simulations and results show the effectiveness in terms of minimizing the electricity bill as well as intercepting the creation of minimal-price peaks. Therefore, the proposed energy management scheme is beneficial to both end user and utility company.

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Section: Background Literaturementioning

confidence: 99%

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

et al. 2020

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“…[1][2][3] Similar to costs for PV panels, prices for lithium-ion batteries continue to decrease, [4][5][6] thus driving the remarkable growth of PV-BESS installations in countries like Australia and Germany and various other regions worldwide. [1][2][3] Similar to costs for PV panels, prices for lithium-ion batteries continue to decrease, [4][5][6] thus driving the remarkable growth of PV-BESS installations in countries like Australia and Germany and various other regions worldwide.…”

Section: Introductionmentioning

confidence: 99%

“…For homeowners who fit their house with photovoltaic (PV) panels, the installation of a battery energy storage system (BESS) can increase self-consumption and thus lower electricity costs. [1][2][3] Similar to costs for PV panels, prices for lithium-ion batteries continue to decrease, [4][5][6] thus driving the remarkable growth of PV-BESS installations in countries like Australia and Germany and various other regions worldwide.…”

Section: Introductionmentioning

confidence: 99%

Assessment of residential battery storage systems and operation strategies considering battery aging

et al. 2019

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With the increasing popularity of combining residential photovoltaic systems with battery storages, research, industry, and customers look for ways to determine if such an investment is economically profitable. Simulation programs may serve to predict the profitability and lifetime of the system. In this paper, we use techno-economic analysis with a specific account of battery degradation to determine profitability and lifetime of a residential photovoltaic (PV) battery system under different energy management and tariff regimes. This work presents two case studies: the first being a techno-economic comparison for a residential PV-battery system in New South Wales, Australia and Germany, and the second analyzing the profitability and degradation impact of three different operation strategies for a battery storage in Australia. The results reveal that site-specific conditions (i.e., geographical and energy-economic constraints) may have a significant impact on the ideal system configuration and ultimately the anticipated battery lifetime. Furthermore, statistical analysis of different storage operation strategies applied to various prosumer load and generation profiles reveals the effects of storage dispatch strategies on battery aging.

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“…They basically evaluate how well the generation and consumption profiles of the building match with respect to time. Hence, they have been found to be quite useful when studying the integration of rooftop PVs [15], batteries [16], thermal storage [17] and heat pumps [18]. The SCR is among the most commonly used load matching indicators [19].…”

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confidence: 99%

Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus

et al. 2020

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Three load matching indicators (self-consumption rate, self-sufficiency rate, loss of load probability) and the CO 2 emissions were evaluated for 55 Cypriot households with 3 kWp rooftop photovoltaic (PV) generators. The calculations were performed using 30-minute generation and consumption data from a large scale smart meter project in Cyprus. To investigate the effects of recent advances in local legislation, an analysis for higher PV capacities (5 kWp and 10 kWp) was also performed. The PV generation profiles for 5 kWp and 10 kWp PVs were obtained by scaling the 3 kWp PV generation profiles. The results showed that the self-consumption of the analyzed households varied seasonally, as it was related to their heating and cooling demand. More interestingly, the ratio between the households' annual electricity generation and demand, formally defined here as generation-to-demand ratio (GTDR), was found to be related to the value ranges of the studied load matching indicators. Hence, on average, households with 3 kWp PV generators annually self-consumed 48.17% and exported 2,415.10 kWh of their PV generation. On the other hand, households with larger PV generators were characterized by a higher GTDR, but lower load matching capabilities. For the cases of 5 kWp and 10 kWp PV generators, the average self-consumption fell to 34.05% and 19.31%, while the exported PV generation was equal to 5,122.47 kWh, and 12,534.90 kWh, respectively. Along with lower load matching capabilities, households that generated more than they consumed were also found to have a lower potential for CO 2 emissions reduction per installed kWp within the boundaries of the building. In this context, the GTDR could be used by stakeholders to characterize buildings, infer possible value ranges of more complex indicators and make evidence based decisions on policy and legislation.Energies 2020, 13, 1934 2 of 18 improvements in the building sector and the additional cooling load from the extreme temperature conditions [2]. Clearly, rapid and coordinated action is required to keep the building sector on track with sustainable development goals. In order to meet the 2-degree and 1.5-degree targets, in particular, the global emissions from the building sector should drop by 32 GtCO 2 , and 50 GtCO 2 , respectively [3]. This will likely be achieved through a combination of actions, such as the electrification of buildings [4], the integration of renewable energy sources (RES) [5] and deep retrofit measures [6]. Amidst this complexity, policy makers need to be guided by simple, but robust, indicators, in order to make evidence-based decisions.Very commonly, the energy performance of buildings is benchmarked by the intensity of primary energy use, the building material characteristics, and the minimum requirements of the renewable energy share or by the CO 2 emissions. These indicators are often found in the context of nearly zero energy buildings (NZEBs) which, in the EU, are regulated by the Energy Performance in Buildings Directive (EPBD) [7]. In essence,...

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Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment

Cited by 168 publications

References 34 publications

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

Assessment of residential battery storage systems and operation strategies considering battery aging

Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus

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