The Experts below are selected from a list of 26493 Experts worldwide ranked by ideXlab platform

Hans Auer - One of the best experts on this subject based on the ideXlab platform.

  • economic viability of renewable energy communities under the framework of the renewable energy directive transposed to austrian law
    Energies, 2020
    Co-Authors: Bernadette Fina, Hans Auer
    Abstract:

    This study is concerned with the national transposition of the European Renewable Energy Directive into Austrian law. The objective is to estimate the economic viability for residential Customers when participating in a renewable energy community (REC), focused on PV electricity sharing. The developed simulation model considers the omission of certain electricity levies as well as the obligatory proximity constraint being linked to grid levels, thus introducing a stepwise reduction of per-unit grid charges as an incentive to keep the inner-community electricity transfer as local as possible. Results show that cost savings in residential RECs cover a broad range from 9 EUR/yr to 172 EUR/yr. The lowest savings are gained by Customers without in-house PV systems, while owners of a private PV system make the most profits due to the possibility of selling as well as buying electricity within the borders of the REC. Generally, cost savings increase when the source is closer to the sink, as well as when more renewable electricity is available for inner-community electricity transfer. The presence of a Commercial Customer impacts savings for households insignificantly, but increases local self-consumption approximately by 10%. Despite the margin for residential participants to break even being narrow, energy community operators will have to raise a certain participation fee. Such participation fee would need to be as low as 2.5 EUR/month for Customers without in-house PV systems in a purely residential REC, while other Customers could still achieve a break-even when paying 5 EUR/month to 6.7 EUR/month in addition. Those results should alert policy makers to find additional support mechanisms to enhance Customers’ motivations to participate if RECs are meant as a concept that should be adopted on a large scale.

Haiping Du - One of the best experts on this subject based on the ideXlab platform.

  • Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures
    Renewable Energy, 2018
    Co-Authors: Orlando Talent, Haiping Du
    Abstract:

    Abstract This paper builds upon previous research to develop a new mixed integer linear program (MILP) for optimal PV-battery sizing and energy scheduling. Unlike previous formulations, the MILP optimises under both time-of-use (TOU) and demand tariff structures. Optimisation is based on the highest system net present value (NPV). One residential and one Commercial Customer are used as case studies to contrast optimisation under TOU and demand tariff structures. Optimal PV-battery sizing is not found to be affected by the tariff structures analysed. Optimal solutions under both tariffs prefer larger PV systems coupled with small battery systems. Energy consumption from the grid under TOU tariff optimisation reflects a scaled profile of the consumer’s energy demand curve. Peak consumption from the grid is heavily reduced under demand tariff optimisation to decrease the associated demand charge. In the residential case study, peak grid consumption over one year is reduced from 5.98 kWh to 2.25 kWh under demand tariff optimisation. In the Commercial case study, peak grid consumption over one year is reduced from 450.3 kWh to 348.6 kWh. The reduction of peak grid consumption is achieved by using the stored energy in the battery.

Isidoro Seguraheras - One of the best experts on this subject based on the ideXlab platform.

  • methodology for validating technical tools to assess Customer demand response application to a Commercial Customer
    Energy Conversion and Management, 2011
    Co-Authors: Manuel Alcazarortega, Guillermo Escrivaescriva, Isidoro Seguraheras
    Abstract:

    The authors present a methodology, which is demonstrated with some applications to the Commercial sector, in order to validate a Demand Response (DR) evaluation method previously developed and applied to a wide range of industrial and Commercial segments, whose flexibility was evaluated by modeling. DR is playing a more and more important role in the framework of electricity systems management for the effective integration of other distributed energy resources. Consequently, Customers must identify what they are using the energy for in order to use their flexible loads for management purposes. Modeling tools are used to predict the impact of flexibility on the behavior of Customers, but this result needs to be validated since both Customers and grid operators have to be confident in these flexibility predictions. An easy-to-use two-steps method to achieve this goal is presented in this paper.

Bernadette Fina - One of the best experts on this subject based on the ideXlab platform.

  • economic viability of renewable energy communities under the framework of the renewable energy directive transposed to austrian law
    Energies, 2020
    Co-Authors: Bernadette Fina, Hans Auer
    Abstract:

    This study is concerned with the national transposition of the European Renewable Energy Directive into Austrian law. The objective is to estimate the economic viability for residential Customers when participating in a renewable energy community (REC), focused on PV electricity sharing. The developed simulation model considers the omission of certain electricity levies as well as the obligatory proximity constraint being linked to grid levels, thus introducing a stepwise reduction of per-unit grid charges as an incentive to keep the inner-community electricity transfer as local as possible. Results show that cost savings in residential RECs cover a broad range from 9 EUR/yr to 172 EUR/yr. The lowest savings are gained by Customers without in-house PV systems, while owners of a private PV system make the most profits due to the possibility of selling as well as buying electricity within the borders of the REC. Generally, cost savings increase when the source is closer to the sink, as well as when more renewable electricity is available for inner-community electricity transfer. The presence of a Commercial Customer impacts savings for households insignificantly, but increases local self-consumption approximately by 10%. Despite the margin for residential participants to break even being narrow, energy community operators will have to raise a certain participation fee. Such participation fee would need to be as low as 2.5 EUR/month for Customers without in-house PV systems in a purely residential REC, while other Customers could still achieve a break-even when paying 5 EUR/month to 6.7 EUR/month in addition. Those results should alert policy makers to find additional support mechanisms to enhance Customers’ motivations to participate if RECs are meant as a concept that should be adopted on a large scale.

Cao Jinping - One of the best experts on this subject based on the ideXlab platform.

  • cloud computing based analysis on residential electricity consumption behavior
    Power system technology, 2013
    Co-Authors: Cao Jinping
    Abstract:

    To research residential electricity consumption behavior in intelligent residential area,based on cloud computing platform and parallel k-means clustering algorithm the time series features such as electricity consumption rate during peak hour,load rate,valley load coefficient,namely the ratio of electricity consumption during valley hour to total electricity consumption,and so on are established and the weights of various features are calculated by entropy weight method.Experimental data is from 600 users living in a certain built smart community.Experimental results show that the residential users in the smart community are divided into five categories,i.e.,vacant dwellings,office staff,office staff living with elders,aged families and Commercial Customer,and the clustering accuracy reaches 91.2%,and thus it is proved that the proposed model for residential electricity consumption behavior analysis is correct and effective.