Probabilistic ESS sizing and scheduling for improved integration of PHEVs and PV systems in residential distribution systems

ElNozahy, Mohamed S.; Abdel-Galil, T.K.; Salama, M.M.A.

doi:10.1016/j.epsr.2015.03.029

Cited by 46 publications

(26 citation statements)

References 22 publications

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“…Uncertainties like price or tariff developments or weather effects are addressed either by a sensitivity analysis (Komiyama and Fujii, 2014), by integrating probabilistic approaches (ElNozahy et al, 2015) or by approaches to minimize forecast uncertainties (Ghofrani et al, 2014;Moshövel et al, 2015). Not in all cases are the uncertainties of relevance to the results (Cai et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Solar energy storage in German households: profitability, load changes and flexibility

2016

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Highlights• Domestic photovoltaics (PV) and storage systems are techno-economically analyzed.• PV & storage are profitable in the medium term due to high self-consumption rates.• Controlled electric vehicle charging improves load flexibility and self-generation.• External procurement of electricity drastically changes and decreases to 48-58 %.• Dynamic tariffs e. g. with load limits or demand charges incentivize load shifting. AbstractThe developments of battery storage technology together with photovoltaic (PV) roof-top systems might lead to far-reaching changes in the electricity demand structures and flexibility of households. The implications are supposed to affect the generation mix of utilities, distribution grid utilization, and electricity price. Using a techno-economic optimization model of a household system, we endogenously dimension PV system and stationary battery storage (SBS). The results of the reference scenario show positive net present values (NPV) for PV systems of approx. 500-1,800 EUR/kW p and NPV for SBS of approx. 150-500 EUR/kWh. Main influences are the demand of the households, selfconsumption rates, investment costs, and electricity prices. We integrate electric vehicles (EV) with different charging strategies and find increasing NPV of the PV system and self-consumption of approx. 70 %. With further declining system prices for solar energy storage and increasing electricity prices, PV systems and SBS can be profitable in Germany from 2018 on even without a guaranteed feed-in tariff or subsidies. Grid utilization substantially changes by households with EV and PV-SBS. We discuss effects of different incentives and electricity tariff options (e. g. load limits or additional demand charges). Concluding, solar energy storage systems will bring substantial changes to electricity sales. Keywordsdemand flexibility; optimization model; tariff design; electric vehicles; controlled charging; battery storage profitability Abbreviations CHP -combined heat and power; EEG -(Erneuerbare Energien Gesetz) Renewable Energy Act; EVelectric vehicle; EV2H -electric vehicle to home; NPV -net present value; PV -photovoltaics; RMSD -root-mean-square deviation; SBS -stationary batteries system; SoC -state of charge; ToU -time of use; UoSC -use of system charge

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Section: Literature Reviewmentioning

confidence: 99%

Solar energy storage in German households: profitability, load changes and flexibility

2016

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“…In a parallel way, other researchers have investigated the technical impacts of PV systems on RDSs . The assessment of technical impact of PV systems and EVs has recently emerged in literature . Nonetheless, up to the present, potentially negative technical impacts have been minimised by strict interconnection requirements (eg, PV systems and EVs).…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, probabilistic techniques are the key to a more accurate assessment of the combined impact of uncertainties regarding RDS output variables. Of these techniques, the ones that are most frequently used are the following: probabilistic load flow methods based on Monte Carlo simulation (MCS), analytical techniques, and approximation methods …”

Section: Introductionmentioning

confidence: 99%

Voltage behaviour in radial distribution systems under the uncertainties of photovoltaic systems and electric vehicle charging loads

Ruíz-Rodríguez

Hernández

Jurado

2017

Int Trans Electr Energ Syst

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Summary This paper presents a probabilistic method that accurately verifies the fulfilment of voltage constraints in radial distribution systems (RDSs) with photovoltaic (PV) systems and electric vehicle (EV) charging loads. This problem has not been comprehensively discussed. The proposed probabilistic method (PPM) involves the calculation of input variable cumulants, the linearization of load‐flow equations, and the application of the cumulant method and the Cornish‐Fisher expansion. Obviously, it is first necessary to model the PV systems and EV charging loads as random variables. In addition, the correlation of the input variables is suitably handled. As part of this research, a Monte Carlo simulation was performed, which confirmed the accuracy of the results obtained with the PPM. As an added value, when compared to the Monte Carlo simulation, there was a significant reduction in computational cost. The results obtained by the PPM demonstrate that the connection of PV systems in the IEEE 33‐node RDS with EVs clearly contributes to keeping voltages within regulatory limits, once the distributions of the RDS output variables are inspected. Nonetheless, the greater dispersion in the distributions in combined PV and EV scenarios means that voltage constraint fulfilment cannot be absolutely guaranteed. This probabilistic approach provides a more accurate assessment than one based on a simple deterministic vision. Thus, the PPM is extremely useful because it provides a better understanding of the combined technical impact under different correlated and uncorrelated scenarios.

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“…According to the requirement of a power system, the power balance in the hybrid wind/diesel/ESS system should be followed as Equation (4).…”

Section: Electrical Energy Forecastingmentioning

confidence: 99%

“…Studies [3,4] show that an optimal planning of locations and sizes of ESSs in power systems can reduce a power system's costs and enhance a its reliability and power quality. A novel method has been presented in [5] for designing an energy storage system dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. Wang et al in [6] proposed a determination methodology for optimizing the capacity of an ESS that enables a wind power generator to meet the requirements of grid integration.…”

Section: Introductionmentioning

confidence: 99%

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

et al. 2017

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Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

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Probabilistic ESS sizing and scheduling for improved integration of PHEVs and PV systems in residential distribution systems

Cited by 46 publications

References 22 publications

Solar energy storage in German households: profitability, load changes and flexibility

Solar energy storage in German households: profitability, load changes and flexibility

Voltage behaviour in radial distribution systems under the uncertainties of photovoltaic systems and electric vehicle charging loads

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

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