Model-based algorithms have been introduced as a practical solution for transformer protection. Compared with conventional protection functions, these methods provide more secure and dependable performance under challenging network conditions, although their high computational burden remains a limitation. This paper presents a fast model-based algorithm that not only requires lower computational burden compared with existing model-based algorithms, but also delivers comparable effectiveness. The proposed approach uses multiple linear models (MLMs) to approximate the nonlinear magnetizing characteristic of the transformer core. By replacing a single nonlinear model with several linear models, the computational burden is reduced while maintaining modeling accuracy. Simulation results have been employed to show the effectiveness of the proposed algorithm.

In a centralized protection and control substation (CPC), all of the measurements of the substation, includes voltages and currents, are available to its centralized high-performance processor. This paper proposes a model-based algorithm for protection of the power transformer in a CPC-based substation. The main difficulty in the use of conventional model-based algorithms lies in modeling of the nonlinear behavior of the transformer core. To tackle this problem, in this paper, multiple linear models are employed to simulate the nonlinearity of the magnetic core. At each time instance, the dynamic behavior of the transformer will be followed by one of these linear models and, the proposed algorithm switches between these linear models based on the concept of interactive multiple model (IMM) algorithm. This way, not only the accuracy of the proposed algorithm increases but also the computational burden is significantly reduced compared to conventional model-based algorithms. Therefore, the proposed algorithm has better potential to be implemented in real-world microprocessors. Several experimental tests include turn-to-turn (TTF) and turn-to-ground faults (TGFs) have been employed to reveal the effectiveness of the proposed IMM-based protection algorithm.

While Renewable Energies (REs) are not cost-effective in Iran yet, making changes in the structure of the energy system under a long-term energy plan could offer a suitable solution for a more sustainable system. In this paper, energy planning conducted for the case study of Iran. The main goal of this study is to provide a practical solution that can reduce emissions and costs, in addition to meeting the demands of different sectors including electricity, household, transportation, and industry. In this regard, five scenarios designed and analyzed with a focus on the share of REs, the efficiency of power plants, and the capacity of combined cycle power plants. In this modeling, the share of Res including wind and solar in electricity production increased from 0.2 % to 3.3 %. The average efficiency of Iran’s power plants increased from 37 % to 40.5 %, and the share of combined cycle power plants of the total thermal power plants increased from 35 % to 50 % from 2016 to 2050. The results of the cost analysis showed that applying the integrated scenario of REs and combined cycles would be effective in the reduction of total annual cost, carbon dioxide (CO2) emissions, and fossil fuel consumption. The average saved cost by integrated scenario is about $8.7 billion over 30 years compared to the business as usual (BAU). Also, the reduction in fossil fuel consumption and CO2 emission is 294.74 TWh and 65 million tons of CO2, respectively.

The growing penetration of renewable energy sources, with intermittent and uncertain nature, brings new challenges to the secure and efficient operation of power systems. Expanding transmission networks and utilizing energy storage (ES) have been introduced as effective solutions to address these challenges. This paper presents a minimax regret robust co-planning model with mixed integer recourse for transmission and ES systems, designed from the perspective of a central planner. The model considers a polyhedral uncertainty set for future peak load growth, while uncertainties in wind farm expansion are addressed through internal scenario analysis. This approach will guarantee the robustness of investment decisions and provide the central planner with a clear picture of the maximum regret among all possible scenarios. Furthermore, the proposed minimax regret framework facilitates strategic planning for ES installation after the resolution of long-term uncertainties. In this paper, we reformulate the model into a standard min-max-min problem, in which the maximization level is only over uncertainties. Subsequently, a five-level solution strategy based on a modified nested column and constraint generation decomposition technique is represented to deal with the intractability and complexity of the problem caused by binary variables of transmission lines and ES blocks. The model is finally evaluated through comprehensive simulation studies to verify its tractability, practicality, and effectiveness.

The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR.

The growing penetration of renewable energy sources, with intermittent and uncertain nature, brings new challenges to the secure and efficient operation of power systems. Expanding transmission networks and utilizing energy storage (ES) have been introduced as effective solutions to address these challenges. This paper presents a minimax regret co-planning model for transmission and ES systems from the perspective of a central planner under a polyhedral uncertainty set of future peak load growth and uncertainties of wind farm expansion addressed through internal scenario analysis. This minimax regret robust approach will guarantee the robustness of the investment decisions and provide the central planner with a clear picture of the maximum regret among all possible scenarios. However, solving the proposed model is challenging due to the infinite number of scenarios and constraints associated with all realizations of uncertain parameters of polyhedral uncertainty sets. Therefore, a five-level solution strategy based on the nested column and constraint generation (C&CG) decomposition technique is represented to deal with the intractability and complexity of the problem caused by binary variables of transmission lines and ES blocks. The model is finally implemented on a modified IEEE 24-bus test system to verify its tractability, practicality, and effectiveness.

Remote controlled switches (RCSs) have the ability to isolate the faulted area from other parts of the distribution system. On the other hand, the dispatchable distributed generators (DDGs) and tie lines can supply the interrupted loads after fault occurrence trough microgrids and reduce the outage time. In this regard, this article proposes a planning model for simultaneous placement of RCSs, DDGs, and tie lines to improve distribution system reliability. The presence of renewable distributed generations (RDGs) and energy storage systems, which have an increasing penetration in today's distribution networks are also considered. Moreover, two different practical load shedding methods are considered to balance the total generation and consumption in microgrids. The predetermined expansion plan of RDGs is also considered. To simplify the implementation of the proposed optimization model and ensure the global optimal solution, the planning model is formulated as a mixed integer linear programming model which can be solved by various commercial solvers. Finally, the effectiveness of the proposed model is illustrated by implementation on bus 4 of Roy Billinton test system through various case studies and sensitivity analyses. The results demonstrate the significant impact of the proposed planning model on improving the distribution system reliability.

Remote-controlled switches (RCSs) have the ability to quickly isolate the faulted area from other parts of distribution systems. On the other hand, the distributed generation resources (DGs) and tie lines can supply the interrupted loads after fault occurrence as a microgrid. In this regard, this paper proposes a planning model for simultaneous placement of RCSs, dispatchable DGs (DDGs), and tie lines in distribution systems with complex topologies to improve their reliability. Presence of renewable DGs (RDGs) including photovoltaic cells (PVs) and wind turbines (WTs), as well as the uncertainties of loads, RDGs, and outage duration of the faulted areas are also considered in the proposed planning model. Conditional value at risk (CVaR) is used to manage the system risk. The proposed planning model is formulated as a mixed integer linear programming model (MILP), which can be solved using various commercial solvers and give the global optimal solution. Finally, the proposed planning model is implemented on the IEEE 33 bus system as various cases to illustrate its effectiveness on improving the reliability of distribution systems and managing the system risk.

Recent weather-related disasters experienced worldwide with considerable damages to the interconnected power infrastructure have highlighted the importance and urgency of enhancing the resiliency of the distribution grid. A Resilient distribution grid can withstand and recover from such rare events. Resiliency against extreme events is conceptualized in three distinct stages: prior, during, and after the event. Rapid recovery is a feature of after the event stage. In this paper, restoration strategies to restore maximum loads as quickly as possible are investigated. The proposed approach attempts to restore the critical loads by using tie-switches to reconfigure the network. In the case of isolated areas without the possibility of using upstream utility grid, sectionalizing the grid into several microgrids (MGs) is proposed to improve the system resiliency. The number of isolated MGs is an issue that is required to be correctly determined. So, a new approach is proposed to compromise between amount and reliability of supplied load to find the optimum number of MGs. The proposed method is simulated on the unbalanced IEEE-123 and 37-bus distribution grid with random locations for DERs.

Due to the increasing global warming, it is anticipated that the number and severity of natural disasters will increase in the coming years. In this regard, this paper proposes a planning model to improve the resilience of distribution systems against natural disasters. A mathematical model is developed to determine the optimal locations of remote-controlled switches (RCSs), distributed generation units (DGs), and tie lines in distribution systems with complex topologies or lateral branches. Simultaneous occurrence of multiple faults is considered to better simulate the extreme events. Moreover, the concept of multi-microgrids is used to supply the maximum possible interrupted load after the fault occurrence. To manage the system risk and different failure scenarios, the conditional value at risk (CVaR) is added to the planning model. The optimization model has been formulated as a mixed integer linear programming (MILP) problem, which can be easily solved using various commercial solvers and give the global optimal solution. Finally, to illustrate the effectiveness of the proposed planning model on improving the distribution system resilience, it is implemented on the IEEE 33-bus system using different case studies and sensitivity analyses.

Transactive energy (TE) provides joint market and control functionality to manage distributed energy resources (DERs) in distribution networks. This work develops a real-time TE management framework that allows residential customers to actively join in the real-time transactive market with fulfilling households' preferences including comfort, economical energy consumption, and privacy-preserving. In this regard, first, a user-friendly algorithm is developed to calculate the real-time willingness to pay (bid) for electric vehicles (EVs) and heating, ventilation, and air conditioning (HVAC) units considering customers' preferences and concerns. Then, to preserve the privacy of households, the centralized market-clearing problem to maximize social welfare is decomposed into several subproblems using the alternating direction method of multipliers (ADMM) approach. Also, closed-form solutions to all subproblems are derived to simplify implementation and mitigate the computational complexity instead of solving optimization subproblems directly. This model is then implemented in a case study with several numbers of smart homes. The numerical results illustrate that the proposed distributed transactive model not only satisfies households' comfort preferences but also decreases the average charging cost of EV batteries by 40% compared to the uncontrolled charging model. Further, the results show that our proposed model significantly mitigates the computational burden of the transactive market clearing problem compared to the centralized approach and the distributed approach without closed-form solutions.

In this paper, a new energy management model is proposed to determine the optimal scheduling of an office building which includes electric vehicle (EV) charging piles, batteries, and rooftop photovoltaic systems. To optimally manage the electricity procurement of the building and mitigate the rate of transformer aging, the building energy management system (BEMS) employs the flexibility of batteries and EV charging. In the proposed model, to incentivize EV owners to offer their flexibility, the BEMS organizes a transactive market among plugged-in EVs. To this end, EV owners submit their response curves and the target state-of-charge to the BEMS. Then, the transactive market is cleared to determine the market-clearing price for each EV, the optimal EV charging decisions, and accordingly, the scheduling of office building. Also, to model the correlated uncertainties of solar power generation and demand, the distributionally robust chance-constrained method is employed. Moreover, the “Big-M” technique and the piecewise linear approximation method are utilized to linearize the optimization problem. Finally, the case of a building with 100 charging piles is studied. The numerical results illustrate a decrease in the total operating cost of BEMS and the rate of transformer aging compared to uncontrolled charging and direct control approaches.

After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel objective function is proposed to model the impact of feeders' failure probability on the survivability of MGs. Then, a two-stage master/slave optimization is presented to optimize the number and configuration of MGs. In this optimization framework, a heuristic algorithm will determine the open/close status of feeders, a graph search method will find the formed MGs, and finally, an optimal power flow study will be run to maximize the amount of supplied loads. This paper shows that the proposed methodology will result in an optimal solution, which establishes an appropriate balance between the amount of supplied loads and their risk of interruption. IEEE 33-bus test system is employed to investigate the effectiveness of the proposed methodology.

In this article, a high-accuracy hybrid approach for short-term wind power forecasting is proposed using historical data of wind farm and Numerical Weather Prediction (NWP) data. The power forecasting is carried out in three stages: wind direction forecasting, wind speed forecasting, and wind power forecasting. In all three phases, the same hybrid method is used, and the only difference is in the input data set. The main steps of the proposed method are constituted of outlier detection, decomposition of time series using wavelet transform, effective feature selection and prediction of each time series decomposed using Multilayer Perceptron (MLP) neural network. The combination of automatic clustering and T2 statistic is employed for outlier detection. Effective feature selection is also carried out with the assistance of the Non-dominated Sorting Genetic Algorithm II (NSGA- ӀӀ) and the Radial Basis Function (RBF) Neural network. The evaluation of the proposed method using the data of Sotavento wind farm located in Spain demonstrates the very high accuracy of the proposed approach.

This paper proposes a new energy management model for residential buildings to handle the uncertainties of demand and on-site PV generation. For this purpose, the building energy management system (BEMS) organizes a transactive energy (TE) market among plug-in electric vehicles (PEVs) to determine their charge/discharge scheduling. According to the proposed TE framework, the PEV owners get reimbursed by the BEMS for the flexibility they offer. In this regard, the PEV owners submit their response curves for reimbursement upon arrival. Then, the BEMS solves an optimization problem to maximize its own profit and determine the real-time TE market-clearing price. Afterward, based on the clearing price, the real-time scheduling of PEV batteries and the reimbursements to the PEV owners for their responses are determined. Additionally, the original mixed-integer non-linear optimization problem is reformulated as a mixed-integer linear programming one using a set of linearization techniques. Finally, the proposed model is applied to a residential building with 50 PEV charging piles, and the simulation results show that the proposed model decreases the actual charging payment of PEV owners by 17.6% and 52.3%, and the total cost of BEMS by 5.1% and 10.8% compared to demand response concept-based and uncontrolled charging models, respectively.

Due to the increasing global warming caused by greenhouse gas emissions, it is anticipated that the number and severity of natural disasters such as hurricanes, wildfires, and floods will increase in the coming years. In this regard, this paper presents a planning framework for distribution systems to improve their resilience against natural disasters. A mathematical model is developed to simultaneously determine the optimal locations of tie lines and dispatchable distributed generation (DDG) units. Two different types of faults, i.e., open circuit and short circuit faults, are considered in the planning model to better simulate the natural disasters. Presence of renewable DGs including photovoltaic cells (PVs) and wind turbine (WTs), as well as energy storage systems (ESSs) are also considered. The conditional value at risk (CVaR) which is a well-known risk index is used to manage the system risk. The problem is formulated as a mixed integer linear programming (MILP) model, which can be solved using various commercial solvers and achieve the global optimal solution. Finally, to illustrate the effectiveness of the proposed model on improving the resilience of distribution systems and manage the system risk, it is implemented on IEEE 33 bus test system using various case studies and sensitivity analyses.

Due to the accelerated climate change, it is anticipated that the number and severity of natural disasters such as hurricanes, blizzards, and floods will be increased in the coming years. In this regard, this paper presents a distribution system planning model to improve the system resilience against hurricane. A scenario-based mathematical model is proposed to capture the random nature of weather events. Moreover, a stochastic optimization model is developed to simultaneously harden the distribution lines and place different types of distributed generation (DG) units such as microturbines (MTs), wind turbines (WTs), and photovoltaic cells (PVs). The conditional value at risk (CVaR) is used as a risk index to manage the system risk against different failure scenarios. The problem is formulated as a mixed integer linear programming (MILP) model that can be solved by various commercial solvers. Finally, to illustrate the effectiveness of the proposed model, it is implemented on the IEEE 33 bus system, and various case studies are defined. The results show the effectiveness of our mathematical model in improving the distribution system resiliency and managing the system risk.

Designing transactive energy (TE) markets in distribution systems has been a hot topic due to the increased presence of residential prosumers. In the literature, several residential market platforms have been proposed; however, they usually have two main drawbacks: 1) ignoring the effect of single-phase distributed energy resources on the voltage unbalance (VU) in active distribution networks to develop a transactive coordination model that will effectively mitigate the VU and 2) inability in providing a user-friendly strategy for home occupants to adjust the willingness to pay/accept of responsive assets according to their comfort and economic purposes. This article amends the shortcomings by proposing a network-constrained TE model to coordinate residential prosumers with the main goal of satisfying households’ preferences as well as mitigating distribution system problems. The case study is then carried out demonstrating that the proposed TE-concept-based coordination of residential prosumers is aligned with customers’ preferences (comfort and economic purposes) and concerns (plug-in electric vehicle battery life) and could effectively decrease the VU.

This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system (MRES) including distributed energy resources (DERs). The main objective of the proposed scheduling approach is the minimization of the total costs of an MRES consisting of both electricity and gas energy carriers. The proposed model considers both electrical and natural gas distribution networks, DER technologies including renewable energy resources, energy storage systems (ESSs), and combined heat and power. The uncertainties pertinent to the demand and generated power of renewable resources are modeled using the chance-constrained approach. The proposed model is applied on the IEEE 33-bus distribution system and 14-node gas network, and the results demonstrate the efficacy of the proposed approach in the matters of diminishing the total operation costs and enhancing the reliability of the system.

Uncoordinated adoption of plug-in electric vehicles (PEVs) imposes further load on the distribution network, and therefore may result in disruptive impacts on the grid. Transactive coordination of PEVs has been introduced as an effective approach to mitigate these negative consequences. This paper furthers efforts enabling PEVs to participate in a real-time retail electricity market under a transactive energy (TE) paradigm. In this regard, PEV owners will estimate their willingness to pay/accept using a user-friendly strategy and submit the estimated values to the retail market operator. Then using a network-constrained market clearing mechanism, the clearing prices, i.e., dual variables of active power balance constraints, will be calculated. Finally, these calculated clearing prices will be sent to whole PEVs and their actions have been determined. The proposed model is applied to the modified IEEE 33-bus test system combined with several low voltage feeders, and the results illustrate the effectiveness of the proposed model from viewpoints of PEV owners and utility.