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• Importance and challenges of relay coordination in Microgrid. • Overview of different adaptive schemes opted for relay coordination. • Different optimization techniques which have been used for the calculation of relay settings. • Evolvement of algorithms from conventional to Hybrid methods which combines two different metaheuristic methods to solve a complex, non- linear optimization problem in an efficient way for optimized relay coordination. • Future directions of research for further modifications possible in Algorithms are stated at the end. Concept of microgrids (MGs) have gained significant attention in order to enhance the resilience and efficiency of modern Power Systems. MG offers bi-directional power flow so an effective protection and control becomes a vital requirement to ensure the reliable operation of MGs. Relay coordination being an integral part of the protection system, plays a critical role in prevention of power disturbances and equipment damage due to the presence of bi-directional power flow. This review paper focuses on the optimization of relay coordination in MGs, and highlights the importance of adaptive and intelligent approaches involved for the efficacy of MG operation. This paper overviews different schemes used to adapt while determining the DOCRs settings with different contingencies considered. The paper depicts the key challenges associated with relay coordination in MGs; varying fault currents, and bidirectional power flows. To overcome these challenges, recent researches have explored advanced techniques that includes intelligent algorithms to operate on collected real-time data. Additionally, the impact of communication technologies for relay coordination in real-time has been discussed for Grid connected and Islanded mode of operation. This review covers a wide range of optimization methods such as; Traditional, Meta-Heuristic, and Hybrid algorithms, which allows adaptive and reliable protection schemes during different modes of MG operations. [ABSTRACT FROM AUTHOR]

The hosting capacity region defines a joint feasible region in the operational space involving various installations. By facilitating their coordinated operation, this region succeeds in tapping into the grid's power delivery potential. Meanwhile, when approximating this region using an expansive polytope, facet selection should be carefully considered. Random facet selection will cause the Matthew effect, indicating that a facet which was more frequently selected owns a higher likelihood of being selected again. Such effect eventually harms the region expansion efficiency. Beyond static facet selection measures, this paper proposes adaptive measures for further improved assessment performances. The adaptive scheme is engineered to cyclically use original measures, thereby merging and leveraging their potentials on efficient facet selection. Relevant 3-dimensional region assessment experiments are conducted on a 10.5 kV Dutch grid case, which is modeled on Pandapower toolkit. The results demonstrate that, compared to static alternative measures, adaptive measures contribute to larger region space consistently, with potential region space gain being up to 62.2%. This validates the superior efficacy of adaptive mechanism in facet selection for region assessment. • HCR defines a feasible region in the combined operational space of energy units. • HCR is beneficial for coordination operation to unleash grid power potential. • Analyze the Matthew effect in facet selection problem during HCR computation. • Integrate metaheuristics and RL to proposed adaptive facet selection algorithms. • Validate the methods by comparative numerical tests with state-of-art ones. [ABSTRACT FROM AUTHOR]

Faults are an inherent part of power distribution systems, and fault indicators play a crucial role in self-healing smart distribution systems by assisting in fault detection and location. However, the effectiveness of fault indicators depends on their number and location in the distribution system. This paper addresses the optimal decisions on the allocation of fault indicators in distribution networks to minimize the expected fault location time. To build upon the existing knowledge, a comprehensive review of the literature on the allocation of fault indicators is provided. Additionally, a novel approach is given by formulating the fault location time as a quadratic function of the network's section sizes. From a relationship established between the fault indicator allocation problem and connected edge-partitions, a mathematical model and a metaheuristic are developed. To evaluate the proposed methodologies, a benchmark comprising 19 networks is employed, including the largest network solved for this problem to date. The results demonstrate the superiority of the new methodologies over previous approaches, as evidenced by the quality of the solutions and their optimality gaps. • A quadratic programming model is proposed for the fault indicator allocation problem. • A scalable BRKGA metaheuristic was proposed to tackle large instances. • Results are given for 19 networks, among which the largest being solved to date. • Significant improvements were attained w.r.t. solution quality and optimality gaps. • Source codes and data are made public to encourage further investigations. [ABSTRACT FROM AUTHOR]

· Consideration of reactive power tariffs in optimizing the sizing and siting of distributed generation systems (DGs) for improvement of efficiency. · Applying water flow optimization (WFO) method on the problem of optimal sizing and siting of DGs for the first time. · Taking into account power factor limitations for DGs capable of injecting both active and reactive power. · Examination of multi-objective functions, including DG allocation to minimize economic losses and annual active/reactive power loss. Distributed generation (DG) systems are gaining popularity because they generate electricity closer to the point of consumption. This reduces transmission and distribution losses and enhances system reliability. For the efficient and cost-effective use of these systems, optimal sizing and siting of DGs is crucial. This paper introduces a Water Flow Optimization (WFO)-based method for the optimal placement and sizing of DG systems capable of injecting both active and reactive power simultaneously. This study assesses both the technical and economic facets of the system, taking into account power losses and the comprehensive annual economic costs, including capital investment, deployment, operation, and maintenance. A significant aspect of this research is the DG units' capability to enhance voltage and minimize losses, factoring in the reactive power tariff. The findings show that the WFO method surpasses other optimization techniques, such as particle swarm optimization, whale optimization algorithm, genetic algorithm and gray wolf optimization. Furthermore, the study emphasizes the cost advantages of injecting reactive power. The minimum annual economic losses (AEL) recorded in this study are $19,207 and $13,852 for IEEE-33 and IEEE-69 bus systems, respectively. In comparison, for DGs only injecting active power, the AEL increases to $24,749 and $41,381 for the corresponding systems. [ABSTRACT FROM AUTHOR]

• A time based two types of dynamic pricing strategies are employed for microgrid-based CS. • The microgrid-based CS's cumulative profit is maximized, peak to valley ratio of CS and electricity purchase cost for the EV owner are reduced. • The uncertainties associated with the sources and EV SoC variation are effectively handled. • The EVs are used as a source of flexibility through V2G/G2V operation to offer ancillary services to the utility grid. • Risk management is included to cope with the uncertainties and risks in the electricity market. The FHO algorithm is applied to solve the proposed complex problem on the IEEE 33 bus modified system for two operational cases. This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature. [ABSTRACT FROM AUTHOR]

• The slow convergence rate and lack of positive semi-definiteness issues of UKF are surmounted. • Whale Optimization Algorithm (WOA) and Tunicate Swarm Algorithm (TSA) are used to develop HW-TO-UKF. • The proposed HW-TO-UKF is used to optimize scaling parameters that are involved in computing the weights of the sigma points of UKF. • WSCC-3 machine and NPCC_48 machine systems are used for simulations. • The efficacy of proposed HW-TO-UKF is compared with various heuristic-based existing methods. The Energy Management Systems (EMS) utilizes Dynamic State Estimation to monitor the operating condition of the power grid. For a synchronous generator, the speed and angle of a rotor are the most important dynamic states. These states are responsible for producing uncertainty in the power system models. Extended Kalman Filter (EKF) and Unscented Kalman Filters (UKF) are utilized to estimate the dynamic states of the power system when it perceives significant disturbances or noise. The conventional Unscented Kalman Filter (UKF) has limitations in terms of slow convergence speed and its lack of positive semi-definiteness. This prohibits its ability to accurately track the transients of the system. The scaling parameters of UKF (alpha and beta) are optimally tuned to overcome these issues. In this paper, a novel Hybrid Whale-Tunicate-based UKF (HW-TO-UKF) is proposed for tuning the scaling parameters of UKF for reducing the mean difference between the real state and the measured state. During the validation of the proposed method, better robustness and statistical efficiency are achieved by defining the objective function as the minimization of the error difference between the actual and measured states. The simulation is carried out on the WSCC-3 and NPCC-48 machine systems to demonstrate the performance of the proposed HW-TO-UKF over the existing models. [ABSTRACT FROM AUTHOR]