Economic assessment of multi-operator virtual power plants in electricity market : a game theory-based approach

In recent years, the penetration of distributed energy resources (DERs) has increased significantly due to their tremendous effect on network flexibility, economic indicators, and power loss. On the contrary, a diverse assortment of DERs can lead to some challenges in controlling these resources in the power grid. To acquire the maximum benefit of DERs and overcome their challenges the concept of virtual power plants (VPPs) has been suggested. Due to the ability of VPPs to participate in electricity markets and the competition of VPPs to gain more profit we are facing deregulated multi-operator markets, and it is necessary to define VPPs as price maker units. The optimal economic assessment of VPPs in a multi-operator market depends on two folds: modeling inner cooperation between its components and managing external competition with other VPPs. To this end, in this paper, a new framework for optimal economic assessment of a multi-operator VPP system is proposed by considering a combination of non-cooperative and cooperative game theory-based approaches. In the proposed methodology, VPPs compete with other rivals to determine the amount of power exchange and offer prices based on supply function equilibrium. Due to incomplete information of VPPs about other opponents and market construction, a combination of particle swarm optimization and genetic algorithm is proposed to find the Nash equilibrium point. Also, the Shapely value concept is used for fair distribution of shared profit among VPPs components. The effectiveness of the proposed method has been verified in two case studies for a multi-operator VPP with a diverse assortment of DERs. The results show that VPP profit and electricity market prices directly relate to the diversity of resources in VPP. In this regard, the mark-up coefficient of the VPP with a greater number of DERs is about 16% and 32% larger than the two other VPPs which leads to more profit for this VPP and resources in its coalition

A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively

A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively