Smart grid and impact analysis of the application hourly rate for residential consumers using the Monte Carlo method

Figueiro, I. C.; Abaide, Alzenira da Rosa; Bernardon, Daniel Pinheiro; Neto, Nelson Knak

doi:10.1109/powereng.2013.6635654

Cited by 5 publications

(3 citation statements)

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“…In distribution networks when investigating new smart grid technologies, reliability, cost estimation, loading capability, peak loads occurrences etc. Monte Carlo method is widely used [1], [10], [11], [13]. In each case authors make a large number of simulations to analyze all possible operation scenarios.…”

Section: A Monte Carlo Methodsmentioning

confidence: 99%

Distribution network development based on stochastic modelling approach

Kolenc

Papič

Blažič

2014

2014 49th International Universities Power Engineering Conference (UPEC)

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The paper analyses the influence of distributed generation (DG) on network planning methods in low voltage (LV) networks. Due to environmental considerations, legal constraints, price, and introduction of new elements into the network, classical network planning is not any more sufficient. To form the basis of planning decisions approach which provides information on the probabilities of attributes such as voltage limits or power losses, should be provided. The paper presents statistical Monte Carlo approach which can be used in LV distribution network planning. The method enables evaluation of the future voltage conditions and therefore the comparison of different possible network development strategies, taking into account the stochastic natures of future DG location and loads consumption. The results are demonstrated on an actual LV Slovenian distribution network model.

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Section: A Monte Carlo Methodsmentioning

confidence: 99%

Distribution network development based on stochastic modelling approach

Kolenc

Papič

Blažič

2014

2014 49th International Universities Power Engineering Conference (UPEC)

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“…Historical measurements of temperature, solar radiation incidence, and wind speed recorded in the year of 2014 for the southern region of Brazil were used to determine the microgeneration technology to be installed. According to a previous study, 49 heating corresponds to nearly 30% of the monthly energy consumption of residential and rural consumers and contribute around to 40% to the formation of the peak, which normally occurs in weekdays at around 6 PM to 9 PM. Figure 4 shows (a) the monthly average solar irradiation and (b) the normalized daily average generation considering a PV module of 0.25 kW, in which P STC , G STC , and T STC are, respectively, 0.25 kW, 1 kW/m 2 , and 25°C.…”

Section: Case Study Characterizationmentioning

confidence: 99%

“…The LPs with DR considered reflect the influence of a ToU tariff on the use of heating equipment in Brazil. According to a previous study, 49 heating corresponds to nearly 30% of the monthly energy consumption of residential and rural consumers and contribute around to 40% to the formation of the peak, which normally occurs in weekdays at around 6 PM to 9 PM. What is more, residential and rural consumers represent around 50% of the Brazilian LV energy market, which are the groups in which the ToU tariff is applied to.…”

Section: Case Study Characterizationmentioning

confidence: 99%

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Neto

Abaide

Miranda

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper proposes a new probabilistic model for active low‐voltage prosumers suitable for distribution expansion planning studies. The load uncertainty of these consumers is considered through a range of load profiles by segmenting the energy consumption according to the different energy uses. Then, consumption adjustments are simulated using a nonhomogenous Poisson process based on the energy usage preferences and the financial gains according to the tariff scheme. A case study based on the modified IEEE 33‐Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of the load profiles in scenarios with high penetration of renewable distributed generation (DG). The experiments carried out reveal that considerable monetary savings in the expansion of the distribution grid can be achieved for this case study (up to 37%) as compared with the alternative with no active demand (AD) by exploiting the flexibility associated with the active behavior of prosumers as a response to price signals and/or by permitting adequate levels for the integration of DG into the distribution grid.

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