Modeling wind power investments, policies and social benefits for deregulated electricity market – A review

Chinmoy, Lakshmi; Iniyan, S.; Goić, Ranko

doi:10.1016/j.apenergy.2019.03.088

Cited by 65 publications

(25 citation statements)

References 183 publications

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“…Predictive models can be used to estimate electricity consumption, either for the entire building or for some specific uses, including eating, cooling, washing and dryer machines [115]. They can also be used to predict micro generation for sources such as solar or wind [116]- [118]. In essence, this problem can be regarded as time-series forecasting, for which machine learning-based approaches can be considered [93], [111], [116], [119]:…”

Section: ) Building Energy Consumptionmentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and windbased systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed. INDEX TERMS Energy efficiency, home energy management systems, household appliance models, load management, optimal scheduling, smart homes, security.

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Section: ) Building Energy Consumptionmentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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“…Meanwhile, other recent studies have considered a social value of the power market. In Reference [16], the economic assessment could account for both the cost and the social value of the technology. Social value became integral to understanding the representation of risk in the large wind power industry.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…The reduction of the market value is not only driven by the variability of RE, but can also be interpreted as the economic costs of variability. Inserting equation 2into (14), the optimal condition equations (15) and (16) can be rephrased as follows: The market value decreases with RE penetration. The optimal deployment of RE is given by the point where the market value of RE equals their marginal generation costs.…”

Section: Evaluation Of Social Welfare Using Rlcoementioning

confidence: 99%

Real Levelized Cost of Energy with Indirect Costs and Market Value of Variable Renewables: A Study of the Korean Power Market

2019

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A levelized cost of energy (LCOE) is a methodology for comparing power generation costs in the transition to renewable energy (RE). However, the major limitation of evaluating RE based on the LCOE is that it does not consider indirect costs, such as the environmental and curtailment effect. This paper proposes the real LCOE (rLCOE) approach that accounts for indirect and direct generation costs. The mathematical approach to estimating indirect costs is derived from economic theory. The indirect effects, which quantify all benefits generated due to RE, is related to the variability of the share RE in the energy generation mix. The rLCOE enhances the accuracy of the economic comparison of power generation costs and the derivation of the optimal quantities of RE because external effects are incorporated into the LCOE principles. This approach has taken into account electricity demand, fuel prices, and environmental costs for each energy source to adequately compare generation costs. Simulations have been performed to demonstrate the application of the rLCOE approach in the Korean power market. Here, the unit variation of costs with the RE share were analyzed. The results show that indirect cost savings of an additional unit of RE begin to fall in scenario 3 in contrast to the result of LCOE approach indicating higher generation costs with RE share, especially, the proportion of RE in the generation mix is higher than 20%. Thus, the optimal power generation can be evaluated using the rLCOE approach.

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“…The amount of power reserves however, which is defined as the difference between the available and the produced power under curtailed operation, does depend on the available power in the wind for both delta and balance control. This is particularly critical for compensation schemes under mandatory down-regulation, as well as (existing and expected) flexible balancing market structures, where the reserve power is traded at different timescales depending on the regional balancing market schemes (Chinmoy et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Model-free estimation of available power using deep learning

et al. 2021

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Abstract. In order to assess the level of power reserves during down-regulation, the available power of a wind turbine needs to be estimated. The current practice in available power estimation is heavily dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a single-input model-free approach dynamic estimation of the available power using recurrent neural networks. Accordingly, it combines wind turbine control considerations and modern forecasting methodologies for a model-free, single-input estimation of available power. It enables a robust real-time implementation of dynamic delta control, as well as higher-accuracy provision of the reserves to the system operators. The model-free approach requires only 1 Hz wind speed measurements as input and estimates 1 Hz available power as output. The neural network is trained, tested and validated using the DTU 10 MW reference wind turbine HAWC2 model under realistic atmospheric conditions. The unsteady patterns in the turbulent flow are represented via long short-term memory (LSTM) neurons which are trained during a period of normal operation. The adaptability of the network to changing inflow conditions is ensured via transfer learning, where the last LSTM layer is updated using new measurements. It is seen that the sensitivity of the networks to changing wind speed is much higher than that of turbulence, and the updates are to be implemented solely based on the altering inflow velocity. The validation of the trained LSTM networks on time series with 7, 9 and 11 m s−1 mean wind speeds demonstrates high accuracy (less than 1 % bias) and capability of transfer-learning online. Including highly turbulent inflow cases, the networks have shown to comply with the most recent grid codes, which require the quality of the available power estimations to be evaluated with high accuracy (less than 3.3 % standard deviation of the error around zero bias) at 1 min intervals.

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Modeling wind power investments, policies and social benefits for deregulated electricity market – A review

Cited by 65 publications

References 183 publications

A Survey on Home Energy Management

A Survey on Home Energy Management

Real Levelized Cost of Energy with Indirect Costs and Market Value of Variable Renewables: A Study of the Korean Power Market

Model-free estimation of available power using deep learning

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