Optimal participation of a virtual power plant in electricity market considering renewable energy: A deep learning-based approach

Gougheri, Saleh Sadeghi; Jahangir, Hamidreza; Golkar, Mahsa A.; Ahmadian, Ali; Golkar, Masoud Aliakbar

doi:10.1016/j.segan.2021.100448

Cited by 41 publications

(34 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MI between the two variables is defined as following [29]:

\begin{equation} I(x,y) = \mathop \sum \limits_{i = 1}^n \mathop \sum \limits_{j = 1}^m P({x_i,y_j}){\log _2}\left( \frac{P(x_i,y_j)}{P(x_i)P(y_j)} \right)\end{equation}

…”

Section: Mrmi Feature Selection Methodsmentioning

confidence: 99%

“…The objectives of this paper contain revenue calculation from the electricity market, curtailment penalty caused by wind power, and degradation cost of the battery. Gougheri et al [29] presented an optimal bidding strategy for virtual power plants (VPP) to participate in energy and spinning reserve markets. A deep learning approach based on bi-directional long short-term memory (BLSTM) networks modelled the uncertainties parameters of this system, including load demand, electricity prices, and wind speed.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Gougheri et al. [29] presented an optimal bidding strategy for virtual power plants (VPP) to participate in energy and spinning reserve markets. A deep learning approach based on bi‐directional long short‐term memory (BLSTM) networks modelled the uncertainties parameters of this system, including load demand, electricity prices, and wind speed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system

Keynia

Memarzadeh

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

Nowadays, with the development of the global economy, traditional non‐renewable energy resources can not only meet the increasing energy demand but also bring severe ecological and environmental problems. Wind power, as one of the most economical types of renewable energies source, has become one of the essential resources for clean energy production. Because of the randomness, intermittency, and fluctuation of natural wind, the output power of wind turbines is greatly affected by wind uncertainty. Therefore, wind power generation forecasting and scheduling may be challenging. In this paper, for the successful and accurate presence of wind power producers in the electricity energy market, a method based on forecasting wind power production, the electricity price, and Financial Loss/Gain (FLG) in coordination with energy storage is proposed. To predict the electricity price, wind power production, and FLG for the next 24 h, the hybrid method based on deep learning time series prediction based on the LSTMs method and input selection based on the MRMI method has been used. For this purpose, first, based on historical data, the wind power producer forecasts the electricity price and wind power production for the next 24 h. According to the same predicted values, initial offers are set for participation in the day‐ahead electricity market. After that, wind power producers modify their wind power production based on the FLG prediction method. Since the FLG signal has highly volatile behaviour, therefore it is not efficient to forecast and apply it directly to the proposed wind power production offers. Therefore, classifying FLG by the FCM method and predicting the FLG class labels is much more helpful in improving the proposed bid of wind power producers to the electricity market. Finally, the wind power producer can improve its profit from participating in the electricity market by interacting with the energy storage unit. The presented numerical results demonstrate the efficiency of the proposed method. For example, if the initial offers are modified based on the FLG method, it has caused the expected profit to improve by 4.44–27.69% in the desired months of the year 2018. Also, in three months, the total profit of the wind power producer and energy storage will be higher than if the profit of the wind power producer participates in the day‐ahead market with 100% accuracy.

show abstract

“…The MI between the two variables is defined as following [29]:

\begin{equation} I(x,y) = \mathop \sum \limits_{i = 1}^n \mathop \sum \limits_{j = 1}^m P({x_i,y_j}){\log _2}\left( \frac{P(x_i,y_j)}{P(x_i)P(y_j)} \right)\end{equation}

…”

Section: Mrmi Feature Selection Methodsmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system

Keynia

Memarzadeh

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…The boiler and CHP are supplied by natural gas. As mentioned before, the electricity demand is supplied by upstream grid and the CHP unit and the heat demand is supplied by the boiler and CHP unit [27]. Equation (21) denotes the energy hub's objective (cost) function.…”

Section: Energy Hub Model Formulationmentioning

confidence: 99%

“…where L elec d,h (kW) and L heat d,h (kW) are the hourly electricity and heat demands, respectively. Furthermore, Equations ( 24) and (25) ensure that the power produced by the CHP and heat by the boiler at any time are within their corresponding generation capacities as follows [27]:…”

Section: Energy Hub Model Formulationmentioning

confidence: 99%

Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub

2021

Self Cite

View full text Add to dashboard Cite

In modern systems, there is a tendency to model issues more accurately with low computational cost and considering multiscale decision-making which increases the complexity of the optimization. Therefore, it is necessary to develop tools to cope with these new challenges. Supply chain management of enterprise-wide operations usually involves three decision levels: strategic, tactical, and operational. These decision levels depend on each other involving different time scales. Accordingly, their integration usually leads to multiscale models that are computationally intractable. In this work, the aim is to develop novel clustering methods with multiple attributes to tackle the integrated problem. As a result, a clustering structure is proposed in the form of a mixed integer non-linear program (MINLP) later converted into a mixed integer linear program (MILP) for clustering shape-based time series data with multiple attributes through a multi-objective optimization approach (since different attributes have different scales or units) and minimize the computational complexity of multiscale decision problems. The results show that normal clustering is closer to the optimal case (full-scale model) compared with sequence clustering. Additionally, it provides improved solution quality due to flexibility in terms of sequence restrictions. The developed clustering algorithms can work with any two-dimensional datasets and simultaneous demand patterns. The most suitable applications of the clustering algorithms are long-term planning and integrated scheduling and planning problems. To show the performance of the proposed method, it is investigated on an energy hub as a case study, the results show a significant reduction in computational cost with accuracies ranging from 95.8% to 98.3%.

show abstract

Optimal operation of a sustainable water desalination unit integrated a green energy system

Ahmadian

Almansoori

Elkamel

2023

Computer Aided Chemical Engineering

View full text Add to dashboard Cite

Optimal participation of a virtual power plant in electricity market considering renewable energy: A deep learning-based approach

Cited by 41 publications

References 33 publications

A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system

A new financial loss/gain wind power forecasting method based on deep machine learning algorithm by using energy storage system

Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub

Optimal operation of a sustainable water desalination unit integrated a green energy system

Contact Info

Product

Resources

About