• Energy Research

AbstractThe integration of energy efficiency programs and renewable resources in energy communities (EC) incorporating smart microgrids might be the future development of sustainable cities. Cooperation among EC with high penetration of renewable energy resources will support energy procurement. Mutual energy efficiency programs in EC associated with deploying renewable resources can guarantee sustainable development significantly. This paper proposes a new sustainability index (SI) influenced by the loss of load reduction while increasing the penetration of renewable energies. A multi‐objective optimization method is used to determine the optimal size and location of renewable resources incorporating energy efficiency programs. The objective functions, that is, the total cost and SI, are based upon social, economic, environmental, and technical (SEET) considerations. Since the outputs of renewable energies are uncertain, it may increase the loss of load probability. Therefore, this paper introduces a model that can handle the uncertainties of renewable energies. A new method so‐called adaptive multi‐objective crow search algorithm (AMCSA) is developed for the trade‐off between cost and sustainability in the optimization problem. The proposed methodology cannot only minimize the total cost in EC but also will maximize the SI. Simulation studies and results analysis indicates the effectiveness of the proposed methodology for the development of sustainability and renewable energies in the smart grid.

AbstractThe integration of energy efficiency programs and renewable resources in energy communities (EC) incorporating smart microgrids might be the future development of sustainable cities. Cooperation among EC with high penetration of renewable energy resources will support energy procurement. Mutual energy efficiency programs in EC associated with deploying renewable resources can guarantee sustainable development significantly. This paper proposes a new sustainability index (SI) influenced by the loss of load reduction while increasing the penetration of renewable energies. A multi‐objective optimization method is used to determine the optimal size and location of renewable resources incorporating energy efficiency programs. The objective functions, that is, the total cost and SI, are based upon social, economic, environmental, and technical (SEET) considerations. Since the outputs of renewable energies are uncertain, it may increase the loss of load probability. Therefore, this paper introduces a model that can handle the uncertainties of renewable energies. A new method so‐called adaptive multi‐objective crow search algorithm (AMCSA) is developed for the trade‐off between cost and sustainability in the optimization problem. The proposed methodology cannot only minimize the total cost in EC but also will maximize the SI. Simulation studies and results analysis indicates the effectiveness of the proposed methodology for the development of sustainability and renewable energies in the smart grid.

The flexibility of smart grids has become an important issue due to the increasing penetration of uncertain energy resources, such as renewable as well as virtual power plants in the smart grids. Flexibility sources, such as demand response (DR) programs and plug-in electric vehicles (PEVs), can help the smart grid to be more productive. Although the renewable power plants are considered as flexible tools, they are somehow uncertain by themselves. In this article, the uncertainty of power generation of renewable resources has been resolved by incorporating the DR programs and PEVs. A stochastic decision making model for the coordinated operation of renewable resources and some virtual power generation is presented to solve a risk-constrained optimal bidding strategy for a smart grid. The participation of DR and PEV aggregators in the day-ahead market is considered. The uncertainty in day-ahead prices associated with renewable power generation is discussed throughout this article. As a well-known measure, the conditional value at risk is employed in the model to cope with all aforementioned uncertainties. Numerical studies and result analysis show that the expected profit of these resources is increased and the related risk is reduced significantly.

The flexibility of smart grids has become an important issue due to the increasing penetration of uncertain energy resources, such as renewable as well as virtual power plants in the smart grids. Flexibility sources, such as demand response (DR) programs and plug-in electric vehicles (PEVs), can help the smart grid to be more productive. Although the renewable power plants are considered as flexible tools, they are somehow uncertain by themselves. In this article, the uncertainty of power generation of renewable resources has been resolved by incorporating the DR programs and PEVs. A stochastic decision making model for the coordinated operation of renewable resources and some virtual power generation is presented to solve a risk-constrained optimal bidding strategy for a smart grid. The participation of DR and PEV aggregators in the day-ahead market is considered. The uncertainty in day-ahead prices associated with renewable power generation is discussed throughout this article. As a well-known measure, the conditional value at risk is employed in the model to cope with all aforementioned uncertainties. Numerical studies and result analysis show that the expected profit of these resources is increased and the related risk is reduced significantly.

Abstract Energy efficiency program (EEP) is one of the demand-side management programs that can solve the demand-side and network-side problems. In this paper, a new concept of energy efficiency (EE) on the microgrid considering economic and technical impacts is introduced. The impacts of EE are considered on sitting of green virtual resources such as EEP as well as wind and solar power plants. The EE consists of two terms, economic and technical terms that are introduced in this paper. New indexes such as the efficient reliability index (ERI) are introduced to evaluate the EE. The presented model is implemented on the IEEE standard 33-bus test system. The model of the problem is the mixed integer non-linear programming (MINLP) and the problem is solved by the genetic algorithm in the MATLAB software. The results show the effectiveness of the proposed method and the presented model.

Abstract Energy efficiency program (EEP) is one of the demand-side management programs that can solve the demand-side and network-side problems. In this paper, a new concept of energy efficiency (EE) on the microgrid considering economic and technical impacts is introduced. The impacts of EE are considered on sitting of green virtual resources such as EEP as well as wind and solar power plants. The EE consists of two terms, economic and technical terms that are introduced in this paper. New indexes such as the efficient reliability index (ERI) are introduced to evaluate the EE. The presented model is implemented on the IEEE standard 33-bus test system. The model of the problem is the mixed integer non-linear programming (MINLP) and the problem is solved by the genetic algorithm in the MATLAB software. The results show the effectiveness of the proposed method and the presented model.

Abstract Maintenance of distribution systems has become more important due to the need to increase energy availability, quality and safety, and also reduce operation cost. Accordingly, the maintenance strategy in distribution systems is one of the most important decision-making activities. One of the most important evaluations for deciding the adequate performance of power distribution system is the identification of the critical components. On the other hand, the budget for maintenance of components is limited, so the maintenance should only be performed on critical components. Identifying the critical components is used to achieve the objective of minimising the cost for maintenance actions. This paper presents a new weighted importance (WI) reliability index model and proposes an appropriate method in order to prioritise the elements of distribution system for reliability-centered maintenance (RCM) at two different levels. At the first level, the feeders of a sample distribution substation are prioritised for the RCM actions. The second level is more detailed than the first level. At the second level, the components of a sample feeder are prioritised for the RCM actions. The reliability of system can be increased to as much as required level while minimal maintenance to be done by the maintenance prioritisation of the system components and performing the maintenance actions on the critical components. Appropriate maintenance decision-making for the critical components of distribution system can lead to the improvement in the reliability of distribution system.

Abstract Maintenance of distribution systems has become more important due to the need to increase energy availability, quality and safety, and also reduce operation cost. Accordingly, the maintenance strategy in distribution systems is one of the most important decision-making activities. One of the most important evaluations for deciding the adequate performance of power distribution system is the identification of the critical components. On the other hand, the budget for maintenance of components is limited, so the maintenance should only be performed on critical components. Identifying the critical components is used to achieve the objective of minimising the cost for maintenance actions. This paper presents a new weighted importance (WI) reliability index model and proposes an appropriate method in order to prioritise the elements of distribution system for reliability-centered maintenance (RCM) at two different levels. At the first level, the feeders of a sample distribution substation are prioritised for the RCM actions. The second level is more detailed than the first level. At the second level, the components of a sample feeder are prioritised for the RCM actions. The reliability of system can be increased to as much as required level while minimal maintenance to be done by the maintenance prioritisation of the system components and performing the maintenance actions on the critical components. Appropriate maintenance decision-making for the critical components of distribution system can lead to the improvement in the reliability of distribution system.