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• This paper presents a novel application of AHA to tune the controllers parameters. • The super twisting sliding mode controller is applied to microgrids. • The results of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. It is essential to improve optimal control for an autonomous microgrid operation. This paper presents a novel application of an artificial hummingbird algorithm for achieving optimal control of an autonomous microgrid. It also suggests a comparative analysis for the optimal super-twisting sliding mode control with both optimal fuzzy logic control and optimal proportional-integral control. The proposed super-twisting sliding mode controller parameters are adequately designed by using the AHA optimization method; meanwhile, the FLC and PI controllers are designed by using the African vultures and the Gorilla troops optimization algorithms, respectively. A MATLAB/Simulink environment is used to verify the effectiveness of the suggested microgrid control methods. The overall system detailed approaches for modeling, designing, and controlling are introduced. The investigation of the ST-SMC approach is also tested in a real-time setting. OPAL-RT 4510 rapid control prototyping and OPAL-RT 8660 data acquisition are used to implement the suggested controller. The verification results prove the superiority of the ST-SMC based on the AHA optimization technique compared with other optimal control techniques. [ABSTRACT FROM AUTHOR]

Since its beginning, the computational performance of numerical optimization techniques has depended on utilizing efficient mathematical formulations to deal with large-size problems successfully. This fact is manifested in the unit commitment literature. Several approaches have been proposed to handle the complexity of accurately modeling real power systems. However, most of these methodologies focus on strengthening the technical features' representations by reducing the number of constraints and variables of the associated optimization problem or approximating its relaxed feasible region to the integer one to improve resolution processes. Hence, the state-of-art of these effective procedures is periodically studied under operational research and commercial solvers developments. Nevertheless, the formulation comparisons frequently obviate analyzing the impact of the balance equations on the computational burden of the unit commitment problem. This constraint links every single technical restriction along the time span and sometimes provides an ample optimization space, sometimes a narrow one, directly affecting resolution proceedings. It can impose an electricity generation equal to demand, allow production excesses, include non-served energy, or establish profit-based relationships. This paper presents a computational analysis of the most popular balance equations, detailing solver performances and determining these methodologies' tightness, compactness, and arduousness. Therefore, 1010 case studies were run utilizing different input profiles and optimality-convergence criteria. • The computational burden of the unit commitment balance equations is studied. • The tightness and compactness of widely utilized demand constraints are determined. • The "arduousness" concept is introduced to improve predictions on MIP resolutions. • The implications of using stable or intermittent thermal-load profiles are analyzed. • A formulation comparison benchmark with big-size optimization problems is proposed. [ABSTRACT FROM AUTHOR]

• An explicit reliability assessment model considering flexible deployment of CB, RCS, and MS is formulated as a mixed integer linear programming (MILP). • A linear reliability-oriented switch planning model is proposed to optimize the placement CB, RCS, and MS simultaneously. • The necessity of considering the function of different switches and power flow constraints for elaborate reliability assessment is verified. • Higher computation efficiency compared with traditional simulation-based reliability assessment method. • A better comprehensive switch planning scheme can be obtained compared with conventional switch planning guideline. Different types of switches play different roles in the fault management procedure in particular context of smart grid, which have different impact on system reliability. The motivation behind this paper is to develop an explicit reliability assessment method adapting to flexible deployment of circuit breakers (CBs), remote-controlled switches (RCSs), and manual switches (MSs), which can be further integrated into switch planning models analytically. The peculiar of this paper is to use the propagation constraints of fictitious power flows to model three stages of fault management procedures. Based on the fictitious power flows, the placement and action for CBs, RCSs, and MSs are modeled in detail as linear constraints, considering power flow constraints in fault management procedure. Therefore, this MILP model can be easily integrated into an exact mathematical optimization model. Finally, the proposed method is tested in 37, 85, 137, 417 node distribution network systems to verify the efficiency of the algorithm. Besides, the necessity of considering flexible location of three types of switches and its application on switch planning are verified. [ABSTRACT FROM AUTHOR]

• To propose sooty-tern optimization (STO) for designing PSS parameters of multi-machine power system. • The performance of STO is evaluated using eigenvalue analysis, simulation results and performance indices. • The results are matched with other powerful optimization techniques. • Robustness is verified on hidden operating cases also under critical disturbances. This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO. [ABSTRACT FROM AUTHOR]

Reliability is a prerequisite for community integrated energy systems to provide consumers with uninterrupted high-quality energy supply. However, it is still challenging to consider reliability in the planning of community integrated energy systems due to the complexity of energy conversion relationships. Conventional reliability-constrained planning methods usually form a bi-level optimization problem in which the planning scheme optimization and reliability index calculation are separated into two subproblems. In this paper, a novel reliability-constrained planning method for community integrated energy systems is proposed. First, the fault incidence matrices are employed to construct the analytic reliability evaluation model of community integrated energy system. Then, the reliability evaluation model is linearized and explicitly integrated into the planning model, forming a mixed-integer linear programming model. This model can be efficiently solved by mathematical optimization algorithms while effectively balancing the operation cost and reliability at the planning stage. Case studies are conducted based on a practical community integrated energy system in North China to verify the effectiveness of the proposed method. • The reliability calculation is transformed into inequality constraints for planning. • The reliability-constrained planning is a mixed-integer linear programming model. • Challenge posed by complex energy conversion relationships in planning is addressed. • Optimal configuration is efficiently obtained considering economy and reliability. [ABSTRACT FROM AUTHOR]

Solving the unit commitment (UC) problem in a computationally efficient manner has become increasingly crucial, especially in the context of high renewable energy penetration. This paper tackles this challenge by employing the offline training of a model-free deep reinforcement learning (DRL) framework, thereby enhancing the optimization efficiency of the UC problem. The complex modeling of random variables is avoided by reformulating the UC problem as a Markov decision process, where the DRL-based method extracts knowledge regarding wind output forecasting errors from historical data. Finally, a discrete proximal policy optimization (PPO-D) algorithm is developed to generate UC solutions under the discrete action spaces necessitated by unit start-up/shut-down variables. Simulation results on the 5-unit system demonstrate that the proposed DRL-based UC model can yield an optimal solution with higher computational efficiency compared to the conventional mathematical optimization methods, while hedging against the wind power uncertainty. In addition, the case study on the IEEE 118-bus system involving 31 testing days further validates the generalization ability of the proposed DRL-based UC model. • The UC problem is formulated as a state-disturbed Markov decision process. • A discrete proximal policy optimization algorithm is developed for the UC problem. • Offline network learning is conducted based on real-world historical wind data. • The computation time is greatly reduced under the DRL-based model-free optimization. [ABSTRACT FROM AUTHOR]

This paper proposes an architectural solution to enhance the resilience of distributed decision-making and control in microgrids and smart grids with respect to communication delays. The paper develops a resource allocation framework to optimally place the multiple-input multiple-output (MIMO) communication technology on critical channels to reduce the communication bottle neck and expedite the convergence speed of distributed online optimization algorithms, which require convergence in the cyber network before the solution can be implemented on the physical grid. The paper shows that optimal placement of the MIMO technology can decrease communication transmission delays, which in turn improves the reliability of power system operational protocols, such as distributed frequency regulation. Illustrative case studies using data from real-world power systems show that only upgrading the critical communication channels is sufficient to achieve system-wide performance improvement. • Proposing an architectural solution to enhance the resilience of distributed optimization in smart grids with respect to communication delays. • Determining an upper bound on the communication delays under different MIMO communication architectures. • Developing a resource allocation algorithm to optimally place the MIMO technology and to increase the resilience of communication networks. [ABSTRACT FROM AUTHOR]

• Under the unique environment of high humidity and high salinity of nuclear power plant, the environment should be improved to ensure staff comfort and equipment safe operation through HVAC system, this paper investigates the HVAC system with ice storage in a nuclear plant. • Considering that the consistency and realizability of ice storage are more complex in two-timescale, the existing two-timescale induced model is updated with ice storage. • The effect of ice storage is shown by analyzing the output of HVAC that consists of chiller and ice storage. The multi-objective programming is used to attain the balance between the electricity cost and user comfort. Nuclear power is the world's largest source of clean energy. Nowadays the capacity shortage of electricity market for consuming nuclear power has appeared. Meanwhile, under the unique environment of high humidity and high salinity of nuclear power plant, the environment should be improved to ensure staff comfort and equipment safe operation through heating, ventilation, and air conditioning (HVAC) system. The joint operation of nuclear power and ice storage should be encouraged. Using nuclear power to provide electricity, the ice storage uses cheap off-peak electricity to make ice and uses the ice for supplemental cooling during peak demand time. The ice storage also provides a way to shift a large amount of electricity from off-peak time to peak time. With the objective of minimizing the electricity cost, while keeping the indoor temperatures of nuclear power plant near the desired temperatures, a two-timescale model of HVAC system with ice storage is proposed in this paper. Simulation results show that the proposed model can guarantee the consistency in different timescales, and meet the user desired temperature for comfort with the electricity cost reduced. [ABSTRACT FROM AUTHOR]

• A robust optimization for small-signal stability constrained optimal power flow (SSSCOPF-RO) model of a power system considering the dynamic characteristics and uncertain power output of DFIGs is established. • The SSSCOPF-RO model is transformed into an SDP model using the semi-definite relaxation (SDR) and McCormick envelop relaxation (MER) techniques to improve the convergence and computational efficiency. • The bilayer robust optimization model is transformed into a single-layer optimization model based on dual programming theory to be directly solved by commercial solvers. • Results show the high efficiency, accuracy and robustness of the proposed method. When substantial wind power generation is integrated into the power system, the low rotational inertia and uncertain power output of wind turbines pose a great challenge to the small-signal stability (SSS) of power system operation. In this paper, considering the dynamic model of a doubly fed induction generator (DFIG) and the uncertainty of wind power, we establish a bilayer robust optimization of the SSS constrained optimal power flow (SSSCOPF-RO) model for a power system with DFIGs. It uses Lyapunov's second theorem to describe the SSS constraint. By the semi-definite relaxation and McCormick envelope relaxation techniques, we transform the inner-layer optimization of the SSSCOPF-RO model into a semi-definite program (SDP) model to improve the convergence reliability and computational efficiency of solving the model. Based on the dual optimization theory, the inner-layer SDP model is converted into its dual SDP model, and the bilayer robust optimization model is transformed into a single-layer optimization model, which can be solved using the solver MOSEK in the software CVX. Finally, case studies were conducted on IEEE 9-, 39-, and 118-bus systems to validate the proposed method. The obtained optimal solution of the transformed single-layer optimization model could ensure the SSS of the system under the uncertain fluctuation of DFIGs' active power output and effectively reduce the network loss cost, and the results indicate the effectiveness and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

The massive integration of renewable-based distributed energy resources (DERs) inherently increases the energy system's complexity, especially when it comes to defining its operational schedule. Deep reinforcement learning (DRL) algorithms arise as a promising solution due to their data-driven and model-free features. However, current DRL algorithms fail to enforce rigorous operational constraints (e.g., power balance, ramping up or down constraints) limiting their implementation in real systems. To overcome this, in this paper, a DRL algorithm (namely MIP-DQN) is proposed, capable of strictly enforcing all operational constraints in the action space, ensuring the feasibility of the defined schedule in real-time operation. This is done by leveraging recent optimization advances for deep neural networks (DNNs) that allow their representation as a MIP formulation, enabling further consideration of any action space constraints. Comprehensive numerical simulations show that the proposed algorithm outperforms existing state-of-the-art DRL algorithms, obtaining a lower error when compared with the optimal global solution (upper boundary) obtained after solving a mathematical programming formulation with perfect forecast information; while strictly enforcing all operational constraints (even in unseen test days). • A deep reinforcement learning (DRL) algorithm is proposed for optimal energy systems scheduling. • The proposed DRL algorithm can strictly enforce all operational constraints in the action space. • A deployment procedure is introduced that exploit optimization theory for deep learning models. • The proposed DRL algorithm is compared with other models, such as DDPG, PPO, TD3. [ABSTRACT FROM AUTHOR]

• the distributionally robust optimization method is adopted for renewable uncertainty. • an alternating direction method of multipliers algorithm is used for privacy protection. • peer-to-peer transactive energy trading improves the profit of microgrids. • the network transmission cost will encourage the electricity transactions nearby. Distributed renewable energy requires market-based measures to remain competitive as subsidies are phased out. However, the intermittence and volatility of renewable energy power generation lead to great challenges in decision-making. To address the uncertainty issues induced by inaccurate RE forecast, this paper proposed a peer-to-peer transactive energy trading strategy for multiple microgrids based on distributionally robust optimization. First, an uncertainty fuzzy set based on Wasserstein distance is created for the renewable energy prediction errors in each microgrid. Second, a day-ahead microgrids peer-to-peer transactive energy trading model is proposed based on the distributionally robust optimization theory to address the power fluctuation problems of renewable energy. Third, using the dual theory, the proposed nonlinear model is addressed by transforming it into a linear and convex programming problem. Considering the independence of microgrids, a distributed strategy based on the alternating direction method of multipliers is then developed to preserve their privacy. Finally, the case study proves that the method proposed can increase the income of microgrids containing renewable energy through peer-to-peer transactions, protect the privacy of microgrids, and then promote the development of renewable energy. The distributionally robust optimization approach also guarantees the economy and reliability of the transaction results for real-time deployment. [ABSTRACT FROM AUTHOR]

• Sensors should be installed in strategic places for a reliable grid supervision. • Data criticalities refer to impending unobservability and incapacity of the state estimation functionality to handle bad data. • Metaheuristics can successfully tackle meter placement problems. In this paper, strategic observation of power grids refers to the optimized placement of measurements necessary to obtain the most likely operating condition of the grid, achieved in real-time monitoring via the state estimation (SE) functionality. The measuring system designed for SE considers the types, placement at various grid buses, and the total available measurements, which is crucial for reliable monitoring. The objective to be reached is to find the best places to install additional measuring units, in a limited number divided into pre-specified lots, to reduce data criticalities—whose importance is related to impending unobservability and incapacity of SE residual analysis to handle bad data. The process of finding an optimized solution for the meter placement problem is typically combinatorial and of considerable size, which makes metaheuristics the realistic option to solve the issue at hand. Metaheuristics are approximate optimization techniques with a pervasive success story in tackling various complex, real-world combinatorial optimization problems. This paper proposes the combined implementation of greedy randomized adaptive search procedure (GRASP), variable neighborhood descent (VND), and variable neighborhood search (VNS) optimization techniques. Numerical results performed on benchmark power grids (IEEE 30- and 118-bus) illustrate the application of the proposed approach and evince that high-quality solutions are achieved (validated by the exhaustive search results) in a computing time adequate for power grid operation planning studies. [ABSTRACT FROM AUTHOR]

• Four discrete/continuous-time online optimal power flow algorithms are designed. • The proposed algorithms are suitable for active distribution network optimization. • The proposed algorithms are fully evaluated and compared via detailed case studies. • The proposed discrete-time prediction-correction algorithm is the most recommended. This paper discusses and analyzes the latest online algorithm applications for time-varying optimal power flow problems in active distribution networks with numerous power-electronics-interfaced distributed energy resources, which bring significant time-varying characteristics to the optimal operation of active distribution networks. Conventional offline optimization algorithms are time-consuming and unable to track time-varying optimal solutions in highly variable environments. In contrast, online algorithms are characterized by fast calculation speeds and the ability to continuously track optimal solutions, thus having great potential in solving optimization problems that vary rapidly over time. In this paper, we design online time-varying optimal power flow algorithms based on the latest online algorithms, including three discrete-time algorithms (i.e., correction-only, prediction-correction, and correction-extra correction algorithms) and one continuous-time algorithm (i.e., prediction-correction interior-point method). According to our survey of the current applications of online algorithms, this study represents the first time that discrete-time prediction-correction and correction-extra correction algorithms have been applied in solving time-varying optimal power flow problems in active distribution networks. Detailed case studies, including correctness, validity, and sensitivity analysis, are carried out on a modified IEEE 33-node system. The numerical results reveal the advantages, disadvantages, and applicability of the proposed algorithms. Moreover, the characteristics of the discrete-time and continuous-time algorithms are compared intuitively. Finally, based on the numerical results, a guide for how to select an appropriate online time-varying optimal power flow algorithm for active distribution network optimization is provided, wherein the discrete-time prediction-correction optimal power flow algorithm is the most frequently recommended choice. [ABSTRACT FROM AUTHOR]

• Four-parameter PID consists of proportional, integral, derivative terms, and filter time constant; • AVR controller is optimized under constraints of robustness; • Proposed optimization method indirectly minimizes integral absolute error of load disturbance response; • The paper provides design guideline and it recommends set of design parameter values; • The procedure for tuning parameters of reference filter to modify step reference response is novel. A procedure for the optimization of the proportional-integral-derivative (PID) controller under constraints to robustness of model uncertainties and sensitivity to measurement noise for a synchronous generator (SG) automatic voltage regulator (AVR) is presented in this paper. Performance indices related to the load disturbance response such as integral error (IE) and integral absolute error (IAE) are used as key criteria for the design method. They are minimized for a given maximum modulus of sensitivity function M s. By maximizing loop gain regarding given sensitivities M s and M n we optimized load disturbance response. Contrary to majority AVR PID controller procedures, along with proportional, integral and derivative gain, a noise filter time constant is included in a proposed optimization algorithm to satisfy given sensitivity to the measurement noise M n. A simple and effective solution of the optimization problem is obtained by binding a ratio between the integral and the derivative time constant T i / T d. The optimal T i / T d ratio is determined subsequently. Besides this, a step response to voltage reference change is modified with a reference filter to avoid large overshoots. This is achieved by limiting the maximum modulus of the resulting close-loop transfer function sensitivity M sp. The fact that zeros and poles of the second order reference filter have the same damping factor simplifies the procedure for resolving two out of six parameters. Also, two additional conditions result in the fact that three design parameters { M s , M n , M sp } are sufficient to determine the required six parameters of the two-degree-of-freedom (2DOF) PID controller. Such controller outperforms recently presented AVR controllers especially regarding the load disturbance rejection. Experiment tests with no-load SG and SG connected on the grid are carried out to prove the claims stated in this work concerning robustness performance and sensitivity to measurement noise. Also, we showed practical use of the proposed controller parameter tuning procedure by experimental runs performed on a lab generator. [ABSTRACT FROM AUTHOR]

To address the challenges that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these strategies require accurate network models to work effectively. However, distribution network data are known to contain errors, and attention has been given to techniques that allow to derive improved network information. This paper presents a novel method to derive line impedance values from smart meter measurement time series, with realistic assumptions in terms of meter accuracy, resolution and penetration. The method is based on unbalanced state estimation and is cast as a non-convex quadratically constrained optimization problem. Both line lengths and impedance matrix models can be estimated based on an exact nonlinear formulation of the steady-state three-phase network physics. The method is evaluated on both the IEEE European Low Voltage Feeder (906 buses) and a real Belgian LV feeder, showing promising results. • Impedance values and operating conditions (state estimation) are jointly derived. • Accurate unbalanced impedance models are derived from smart meter measurements. • Impedance estimation is cast as a quadratic constrained optimization problem. • Constraints are added to impose structure of multi-conductor impedance matrices. • Steady-state network physics is exploited: impedance estimation is model-aware. [ABSTRACT FROM AUTHOR]

Peer-to-peer energy trading allows smart grid-connected parties to trade renewable energy among each other. It is widely considered as a scheme to mitigate the supply–demand imbalances during peak-hour. However, the main challenge of such a scheme is the lack of secure, efficient, and transparent systems that foster economic incentives for users. This paper proposes a novel system that combines cooperative game theory and blockchain technology to stimulate users to maximize their profit and trade energy securely. In our model, users can store renewable energy credits as assets in the blockchain and trade them with others. We also develop a modified version of Proof of Energy Generation (PoEG) by including distribution line loss as a consensus mechanism among blockchain energy trading members. Then, we propose a coalition formation algorithm based on PoEG protocol and optimization technique to determine the winning coalition as block miners. The system is validated through comprehensive theoretical analysis and simulation experiments utilizing a standard IEEE 14-bus system and a real-life dataset (Ausgrid). Also, the developed model is implemented on the Avalanche blockchain platform. The test results show that the proposed scheme improves prosumers' financial benefits by 6.5% in terms of energy savings compared to the baseline model. The experiments demonstrate the effectiveness, capabilities, and robustness of the proposed system. • Proposing a blockchain and cooperative game theory-based P2P energy trading system. • Developing a coalition formation algorithm based on PoEG to determine block miners. • Validating the model using comprehensive theoretical analysis and experiments. • Utilizing a standard IEEE 14-bus system and the Ausgrid Dataset for the experiments. • Implementing the model using the Avalanche blockchain platform. [ABSTRACT FROM AUTHOR]

The construction of large-scale offshore wind farms faces the difficulty of solving the optimization problem of collector systems. Due to its NP-hard feature, this problem cannot be solved straightforwardly. This paper proposes an efficient and precise solution framework suitable for large-scale collector system optimization and raises the main influencing factors of time complexity. First, according to a heuristic idea, an arc selection algorithm is established to tighten the feasible region. Second, an optimization framework based on the greedy algorithm and mixed integer quadratic programming (MIQP) is established with excellent efficiency and accuracy. The overall problem is decomposed into the master problem of topology optimization and the sub-problem of cable selection. A bi-level model that adopts both greedy algorithm and global optimization is proposed, iterating through the master problem and the sub-problem by transferring parameters and updating constraints. Third, a large number of examples are analyzed, and the advantages of the proposed algorithms and model are comprehensively verified. In the end, time complexity analysis is carried out, and the empirical formula of solution time is obtained by fitting. Based on the proposed method, large-scale problems can be solved much faster than traditional methods and reach a balance of both speed and quality. • A heuristic arc selection technique is proposed to reduce the candidate arcs. • The greedy algorithm and global optimization are combined. • Cases of 120 turbines can finish in 100 s. • Large-Scale problems are solved with a balance of both speed and quality. • Time complexity is analyzed and an empirical formula is proposed. [ABSTRACT FROM AUTHOR]

• A strategy is proposed to increase the PFC's reliability in co-phase TPSS. • Switching frequency conversion and modulation changes is combined in this paper. • The reliability assessment process is established to calculate failure rate of PFC. • This strategy can reduce the failure rate of IGBT module in PFC by 39.01%. Co-phase traction power supply system (TPSS) is proposed to solve the problems of neutral section and power quality in electrical railways. As the key component of co-phase TPSS, the lifetime of the power flow controller (PFC) largely relies on the power module (PM)'s reliability. The PMs' lifetime is affected by junction temperature, including average temperature and temperature fluctuation. In this paper, a lifetime extension strategy is presented for junction temperature management, which combines switching frequency conversion with modulation changes. Firstly, in the low-load area, switching frequency conversion is utilized to minimize temperature fluctuation; in the high-load area, an optimized single-phase space vector pulse width modulation (SVPWM) is introduced to reduce the average temperature. Secondly, the particle swarm optimization with constriction factor is applied to solve the load partition point, as well as optimize switching frequency for PFC. Finally, the validity of the proposed lifetime extension strategy is verified by case studies, the result is shown that the failure rate of IGBT module can be reduced by 39.01% and the mean time to failure (MTTF) of the PFC is prolonged by 16.28%. The optimization algorithm can effectively improve the reliability of PFC. [ABSTRACT FROM AUTHOR]

• Consider the spatial–temporal correlation of wind power during load restoration. • Construct a uncertainty set limited by the spatial and temporal correlation budget. • Linearize the robust model with quadratic terms by binary expansion. • Solve the robust model by the extended column-and-constraint generation algorithm. Wind power can be incorporated with load restoration of power systems, and the robust method is usually utilized to maintain feasibility considering the uncertainty of wind power. However, traditional robust load restoration always ignores the inherent time and spatial couplings of wind generation variability, which may increase the conservativeness of the solution. Therefore, this paper proposes a robust load restoration optimization considering the spatial and temporal correlation of wind power to reduce conservativeness. To eliminate unlikely-to-appear cases in time and space, the spatial–temporal correlation budget is introduced to construct a budget-constrained uncertainty set. Based on the constructed uncertainty set, the load restoration method is established as a two-stage robust model, which aims to maximize the restored load while guaranteeing the voltage and frequency security constraints. To make the proposed model computationally tractable, the binary expansion method is utilized to linearize the two-stage robust model with quadratic terms. In addition, due to inner-level binary variables in the subproblem, the extended column-and-constraint generation (EC&CG) algorithm is employed to iteratively solve the proposed model. Finally, the proposed robust method is tested on the modified IEEE 39-bus test system, and simulation results show that the proposed method can effectively reduce conservativeness while maintaining the feasibility of a robust solution. [ABSTRACT FROM AUTHOR]

This paper explores the possibilities of providing fast frequency support as an emergency support service to the disturbed AC system through the MTDC grid. A two-layer hierarchical control structure of the MTDC grid is proposed to assure the minimum cost of the frequency control actions, the minimum voltage deviations, or the minimal impact on the frequencies of not-affected AC systems while ensuring the stable operation of MTDC grid. An optimization algorithm is executed at the secondary control level to find the optimal reference values for the voltage-droop characteristics of the voltage-regulating converters, and consequently their DC voltages and active power references. Then, at the primary control level, the reference values are tuned with the optimization results. Implemented control structure confirms that MTDC can provide set values at its terminals without endangering its stability. The secondary control layer is implemented in MATLAB, while the performance of the controller is successfully evaluated through simulation in RSCAD. • Provides fast frequency support to the disturbed AC system through the MTDC grid. • Ensures optimal and stable operation of the MTDC grid including all constraints. • Enables optimal power-sharing for different criteria functions. • Droop coefficients are determined offline and do not depend on the disturbance • Enables rapid response and does not dependent on telecommunication delays. [ABSTRACT FROM AUTHOR]

• Optimal sizing of stand-alone solar PV systems with power quality constraints. • Performing transient simulations of detailed system model under varying conditions. • Non-optimal system parameters may cause inadmissible system voltages and harmonics. • Initial capital cost is marginally higher compared to conventional approach. • Proposed approach can help in designing more reliable and cost-effective PV systems. Designing reliable and cost-effective solar PV systems with compliance to national and international power quality standards is critical for their economical, efficient, and safe operation. The conventional approaches currently being used to optimally size the solar PV systems generally ignore power quality criteria during the initial design phase. This paper fills this gap by presenting a novel Genetic Algorithm (GA) based strategy to design a stand-alone solar PV system featuring optimal system size with conformance to power quality standards. MATLAB/Simulink environment was used to develop a detailed model of solar PV system, including a robust control mechanism for maximum power point tracking, battery bi-directional control, and inverter output control. An optimisation algorithm based on the single-objective GA was then designed to find the optimal PV array size, battery capacity, and the values of harmonic filter components so that the total investment cost is minimised and load demand as well as power quality criteria over the studied period are satisfied. The simulation results, corroborated with insightful discussions demonstrate that ignoring power quality criteria by selecting improper non-optimal system parameters can lead to inadmissible voltages and THD levels in the system. In contrast, the proposed design strategy can successfully devise a solar PV system that is optimally sized and complies with power quality standards. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

• Presenting a method based on traveling pattern to estimate the number of EVs. • Presenting an innovative and real-time approach for optimal scheduling of EVs. • Presenting a method based on Lagrange interpolation to minimize energy consumption. • Presenting a method based on LOC to compensate for operator's profit reduction. • Modeling the growth rate of EVs during the planning horizon using the UTM. In this paper, a practical approach is presented to long-term expansion planning of electric vehicle (EV) parking lots which aims to maximize the parking lot operator's profit. The optimal number, location, capacity, and time for construction or expansion of EV parking lots have also been determined. Moreover, a method based on drivers' travel patterns is proposed to estimate the number of EVs referring to each area, in which the unscented transformation method (UTM) is used to calculate the annual growth rate of EVs. An innovative and real-time method for optimal charge and discharge scheduling of EVs is also provided, which can calculate the parking lots' demand at different times. The most important advantages of this method are its independence from iterative-based optimization algorithms and from modeling some input parameters such as the initial state of charge (SOC) and the arrival time of EVs into the parking lot as uncertain parameters. Also, a Lagrange interpolation-based method is proposed to minimize the energy consumption required for EVs to reach a parking lot. In addition, in order to prevent the reduction of parking lot operator's profit in case of non-fulfillment of the obligations of the EV owners, an approach based on paying the lost opportunity cost (LOC) is presented. Real-world data has been used for numerical studies. The results show that based on travel patterns and estimating the number of EVs available in each municipal district and using the proposed real-time scheduling method, parking lot's demand can be calculated as the main prerequisite for parking lots expansion planning. Also, the proposed method for parking selection compared to two other methods will save energy by 26.6% and 8.33%, respectively. Moreover, the proposed approach increases the total parking lot operator's profit by 4.23%. [ABSTRACT FROM AUTHOR]

• To consider the real-time (RT) regulation of ESSs and feedback to day-ahead (DA) stage, a DA two-stage stochastic optimal dispatch model is established. • The Hodrick-Prescott filter is introduced to the power system optimization for the first time. • The limited flexible adjustment characteristics of LCC-HVDC power considering the actions of reactive power control equipment (RPCE) are modeled. This paper proposes a day-ahead (DA) two-stage stochastic optimal dispatch strategy of line commutate converter-based HVDC (LCC-HVDC) interconnected power system, considering two flexibility regulation measures of sending system. First, the energy storage systems (ESSs) are used to suppress the real-time (RT) power fluctuations of sending system net loads decomposed by the Hodrick-Prescott filter under the two-stage stochastic programming structure, which can feedback to DA stage. Second, the flexible adjustment characteristics of LCC-HVDC power are modeled, and the actions of reactive power control equipment (RPCE) are limited for the operation safety. The static operation model of converter stations is linearized, and the linearization errors are analyzed by the probability statistics of multiple scenarios. The effectiveness of the proposed strategy is verified on the wind power LCC-HVDC transmission system. [ABSTRACT FROM AUTHOR]

• A day-ahead interval optimization method for the CCHP system considering the uncertainty of wind power and PV is proposed. • The wind power and PV output are expressed in interval mathematics in the proposed model. • The model analyses the scheduling results of load demands and wind power, PV under four seasons. • The economy and environmental friendliness of the model are verified via interval optimization and robust optimization. To address the uncertainty of renewable energy and different load demands under different seasons in combined cooling, heating, and power (CCHP) system, a day-ahead interval scheduling optimization method is proposed for the CCHP system taking into account the uncertainty of wind power and photovoltaic (PV). Based on the interval theory, the uncertainty of wind power and PV are expressed by intervals. The total operating cost of carbon oxide emissions treatment cost and energy purchase cost is minimized as an objective function. A day-ahead interval optimization model of the CCHP system including wind power, PV, and energy storage is established considering different load demands in four seasons. The established nonlinear integer programming model is converted into the mixed-integer linear programming (MILP) model. The model is solved by interval linear programming. The CCHP system of a park is studied and compared with robust optimization. The simulation results verify the effectiveness of the method in this paper to achieve effective cooperative operation of the economy and environmental protection. [ABSTRACT FROM AUTHOR]

A Phasor measurement unit (PMU) is a device that gives a fast and accurate measurement of both current and voltage with time synchronization. This PMU data facilitates effective monitoring and control of power systems in a smart grid (SG). PMU collects data from various buses linked with it and led to an increase in the data generation of the particular PMU and it encourages cramming in data transmission. The delay in data reception at the control centre affects the performance of the SG protection. In a wide-area monitoring system (WAMS), the data traffic model is introduced to calculate the data traffic index. The net traffic can be reduced by installing the PMUs at suitable buses such that the number of PMUs must be reduced which, minimizes the system cost. This paper presents the optimal PMU placement technique with complete system observability by considering the WAMS data traffic index and installation cost index. The teaching learning-based optimization (TLBO) technique is used to obtain optimal PMU positions. In addition, the zero injection bus (ZIB) case is considered for further reduction in the PMU count which in turn reduces installation cost. The selection of an appropriate PMU location set is identified based on the installation cost and WAMS data traffic for normal and ZIB cases. The proposed optimum PMU placement (OPP) algorithm is tested on various test systems such as IEEE 14-bus, 24-bus, 30-bus, 57-bus, and 118-bus systems for normal and ZIB cases. The MATLAB-based simulation results are compared with the various optimization techniques existing in the state of art. The results show that the effectiveness of the proposed methodology for determining the best PMU locations. • Optimum PMU placement problem is solved by Teaching learning-based optimization. • The best PMU set is identified by WAMS Data traffic and installation cost index value. • Traffic analysis made for all control centres for considered bus systems. • Among all optimization techniques, TLBO is found to best for higher-order buses with the inclusion of Zero injection bus cases. [ABSTRACT FROM AUTHOR]

• Optimize the electricity prices based on the DR characteristics of multi-type users. • Construct a flexible power retail portfolio optimization model. • Propose a flexible electricity retail strategy with integrated DR. Since the electricity market is opening up in China, it is necessary for power retailers to participate in the market and find a way to gain benefits. In this context, this paper constructs a two-stage flexible power sales optimization model with multi-type demand response strategies. First, the demand response strategies of different types of users are analyzed. Second, two-stage optimization is conducted: In the first stage, the fuzzy K-means clustering method is used to divide a day into peaks, flats, and valleys, and then, a multi-objective time-of-use pricing model is constructed with the aims of minimizing the peak–valley load ratio and maximizing the response revenue rate of users. A flexible power retailing portfolio optimization model is built considering multiple types of users in the second stage and the model is solved by the chaotic particle swarm optimization algorithm. Finally, a case study with four scenarios is conducted, where the demand response is taken as a changeable factor. The results show that (1) the price-based demand response can shave peaks and fill valleys, whilst also reducing the electricity cost, and optimization for industrial users has the optimal effect, followed by that of commercial users and agricultural users; (2) different types of demand responses have different emphases on peak shaving and valley filling, and a combination can achieve the best effect; (3) with the demand response, an electricity retailer can effectively reduce the deviation assessment cost, and increase the revenue by flexibly allocating electricity sales, without resulting in unsatisfied users. The conclusions verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• Two metaheuristic algorithms for improvement of the MPPT technique are presented. • The first MPPT method is based on WOA and DE, called the WOADE-MPPT technique. • The second MPPT method, IWOADE-MPPT, is an improvement of the WOADE. • IWOADE-MPPT technique outperforms all traditional MPPT techniques. • Five various shading patterns are studied to analyze the proposed MPPT technique. The photovoltaic output power varies according to the solar radiation and ambient temperature because the operation of the photovoltaic arrays depends on them. Therefore, achieving the maximum photovoltaic power under different shading patterns is a key factor in the performance improvement of photovoltaic systems. An efficient Maximum Power Point Tracking technique is needed to distinguish the global maximum power point from the local ones, as the traditional techniques are prone to fail. This paper proposes two hybrid meta-heuristic algorithms to improve the maximum power point tracking technique of partially shaded photovoltaic systems. The first proposed maximum power point tracking technique is based on the Whale Optimization Algorithm and Differential Evolution algorithms. The second proposed maximum power point tracking technique is an improved version of the first proposed technique. Both proposed techniques are highly proficient in enhancing the photovoltaic system's efficiency in shaded and unshaded conditions. The maximum power point tracking technique is studied for evaluating the performance based on two traditional algorithms, Whale Optimization Algorithm and Differential Evolution, and two hybrid proposed algorithms. The simulation results show the second proposed maximum power point tracking technique finds the global power points faster and offers better performance than the first proposed technique, which itself outperforms two traditional maximum power point tracking techniques concerning a faster rate of convergence and higher efficiency. [ABSTRACT FROM AUTHOR]

• A novel optimization model is proposed to enable differentiated local control strategies for distributed generators with communication failures. • The relationship between cyber failure scenarios and cyber link states is modeled in detail. • Interval mathematics and intelligent algorithm are adopted to solve the optimization problem. • The influences of cyber system topologies on the control strategies are analyzed. The dependence of distribution network operation on cyber systems becomes heavier as the amount of grid connected distributed generation (DG) continually increases in the active distribution system (ADS). In order to reduce the operational risks under cyber failure scenarios, this paper proposes an optimization model to enable differentiated local control strategies for DGs with communication failures. First, the correspondence model between cyber failure scenarios and cyber link states is established based on the failure mode effect analysis (FMEA) method. Then a bi-level multi-objective optimization model of differentiated local control strategies for DGs is proposed, which considers the uncertainties of cyber failure scenarios and the uncertainties in power generations and load demands. It aims at minimizing the operational risks including voltage deviation and overcurrent (load curtailment) situations. Interval mathematics and intelligent optimization algorithm are adopted for solving the optimization problem. Finally, a test system is established to validate the effectiveness of the proposed local control strategies. Also, the influences of cyber system topologies on the control strategies are analyzed. This study is instrumental in devising effective operational strategies for DGs in ADS. [ABSTRACT FROM AUTHOR]

• Apply relaxed ADMM method to address DERs coordination in a decentralized way. • Introduce energy storage devices with charging/discharging efficiency taken into account. • Allow possibly non-smooth cost functions of DERs. Distributed optimization algorithms contribute to an insight for the optimal coordination of distributed energy resources (DERs). To tackle the DER coordination problem, this paper studies a composite constrained optimization problem over multi-agent networks, where all agents independently maintain convex objective functions while satisfying the local and coupled constraints. Within the Peaceman-Rachford splitting (PRS) framework, a distributed algorithm based on the relaxed ADMM method is proposed for the considered problem, where agents in the network commonly share a fixed step-size instead of the diminishing one. Finally, the simulations on the optimal coordination of DERs including distributed generators (DGs) and energy storages (ESs) are conducted to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A new multi-objective framework is presented for electricity retailers. • The selling price is calculated based on profits of electricity retailer and customers. • Normalized normal constraint methodology is used to generate the Pareto frontier. The electricity retail market provides a marketplace for domestic consumers and retailers to buy and sell electricity in a competitive environment. Permitting final consumers to choose electricity retailers promotes suppliers' pluralism and clients' freedom, which leads to improvements in the quality of electricity retailing service. The main aim of a retailer and consumers is profit maximization, which highly depends on retail-selling price. Retailer's profit may be explicitly reduced by decreasing retail selling-price or implicitly reduced by increasing retail selling-price as increasing these prices may decrease clients' consumption. Accordingly, the retail selling-price, retailer energy procurement strategy, and clients' consumption should be simultaneously analyzed from the viewpoints of retailer and clients. In this paper, a multi-objective optimization problem is introduced to obtain the retailer's energy procurement strategy in terms of the profits of energy buyers and sellers. Moreover, the normalized normal constraint method as a proficient multi-objective optimization technique is utilized to solve the proposed multi-objective optimization problem determining the retailers' energy procurement strategy and retail selling-prices. Since the wholesale energy price is a highly volatile uncertainty source affecting the retailer's profit, the point estimate method is utilized to characterize the uncertainty of the wholesale energy price. Simulation results illustrate the effectiveness of the proposed non-deterministic multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

• Modeling methods reduce the nonlinearity of the Previous studies. • Modeling methods make it easier to be implemented by computer. • The optimization model is applicable to multiple scenarios by unified matrix form. • The model facilitates the optimization of Regional Integrated Energy System. Regionally Integrated Energy System (RIES) combines electricity, heat, natural gas, and other energy sources to improve the overall utilization efficiency of the energy system as well as to satisfy the energy demand. However, the energy flows relationship in RIES is highly interconnected because of the energy network's topology complexity and other energy factors, creating challenges in optimizing the system operation. This paper proposes a unified modeling method by incorporating an energy hub (EH) to integrate various components and energy flow relationships in RIES. The RIES energy network equations are built in the matrix forms to describe the energy conversion and coupling via energy flow selection as state variable and branches/nodes definition based on network graph theory. Based on the energy network equations, the optimized operation model is proposed in unified form as the optimal operation of RIES that reduce the complexity of optimal operation compared with the traditional nonlinear model. Finally, a test system is presented to demonstrate the optimal operation model, compared and analyzed the optimized results between unified modeling and traditional nonlinear modeling to prove the effectiveness of the modeling method. The results show both optimality of the cost and computing time have been improved by the unified modeling method. [ABSTRACT FROM AUTHOR]

• CC-MPC and ML to trade off day-ahead participations and penalties during operation. • Feed-forward disturbance compensation layer for CC strategy and Enc-Dec for AI agent. • A case study is conducted to assess the proposed model. • Presentation of satisfactory results in the operation of Virtual Power Plants. The management of uncertainty is one of the most important issues affecting the optimal operation of Distributed Energy Resources (DERs). Virtual Power Plants (VPPs) aggregate different Energy Nodes (ENs) to enable them to participate in energy markets in an aggregated way. This participation requires very accurate forecast services to trade off between the revenue at bidding time, by making more aggresive bids, and the reduction of penalties at operation time due to deviations from the commitment. In this paper, a stochastic optimization layer is built over a Model Predictive Control (MPC) kernel to define a Stochastic Model Predictive Control (SMPC) scheme by combining Chance-Constrained (CC) and Machine Learning (ML) to handle the uncertainty related to generation and load profiles at optimization time.This technique can be applied to participate simultaneously in different energy markets depending on the configuration or capacity of the VPP, and according to the business model of the system operator. More accurate optimizations allow more profitable operations and more flexibility to redistribute the energy allocation to offer different energy services. The results are satisfactory as this scheme works better than the deterministic approach in terms of penalty reduction for most of the cases. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposing a new optimal transient stability-oriented protection coordination method; • Smartly selecting characteristics for double-inverse relays equipped with HS relay; • Increasing the speed of stability-oriented protection scheme; • 33.7% improvement in the sum of operating times of DOCRs compared to available methods; • Validating the optimum results and their CTI and CCT constraints in the DIgSILENT. Many studies have been devoted to developing the optimized protection schemes of smart grids. However, there is a research gap about studying the transient stability constraints in smart grids' optimized protection schemes. Also, there is a major challenge in the transient stability-oriented protection system's speed due to increased coordination constraints and transient stability for synchronous distributed generations (DGs). This paper tries to fill such a research gap by developing a new transient stability-oriented method for optimized settings of directional overcurrent relays (DOCRs), smartly selecting relay curves in double-inverse relays equipped with high-set (HS) relays. This study's main advantage is achieving a fast and optimal protection system, while all transient stability constraints have been concerned. The proposed method has been studied in the IEEE 33-bus test system, while the GA and PSO algorithms solve the optimization problem. The comparative analysis of the test results with those of other available methods illustrates that 33.7% improvement in the sum of DOCRs' operating time appears, while there is no stability constraint violation. The validation of speed and both coordination and stability constraints violations using the DIgSILENT simulations is another contribution of this research work. [ABSTRACT FROM AUTHOR]

• Six bioinspired optimization techniques used for the Dynamic TTC calculation. • Contingencies, operational and small-signal stability limits are considered. • Tools for power flow calculation and modal analysis are easily integrated. • Quality of solutions, computational effort and variability are discussed. • The proposed approach is suitable for planning studies. This paper proposes a straightforward approach to determine the Total Transfer Capability (TTC) between two power system areas taking into account security constraints. The main goal is to adjust the generator's control variables (active generated power and terminal voltage) to maximize the TTC considering contingency scenarios and small-signal stability constraints (minimum damping ratio requirement). It is a challenging nonconvex and nonlinear optimization problem that is generally solved in the literature by methods based on approximations in which the computational complexity involved outweighs the accuracy. Here, conventional power flow and modal analysis tools are easily integrated into an optimization approach solved by six bioinspired optimization techniques. Simulations are conducted for the well-known New-England test system, and transient stability studies validate the obtained results. [ABSTRACT FROM AUTHOR]

• New algorithm particle swarm optimization with oscillatory inertia factor with exponential decay. • Simultaneous adjustment of the AVR and PSS parameters through the PSO-OED optimization technique. • Better adjustment of the AVR and PSS providing a better dynamic performance and transient system response. • New objective function capable of analyzing the performance of several generators simultaneously. This paper presents a new algorithm for simultaneous tuning of the optimal parameters of Automatic Voltage Regulator (AVR) and the Power System Stabilizer (PSS) using a new technique of Particle Swarm Optimization with Oscillating Exponential Decay (PSO-OED). The proposed algorithm was applied both the Single-Machine Infinite-Bus (SMIB) Power System and the 9-Bus Multi-Machine Power System. The results of the simulation show that the proposed method is efficient and guarantees an improvement in the dynamic stability and convergence rate of the simulations. [ABSTRACT FROM AUTHOR]

• A multi-objective robust optimization model for active distribution systems has been proposed. • The range of the uncertainty set have been analyzed. • Effectiveness, robustness and scalabilities have been fully compared. The conventional single objective optimization cannot meet the requirements for the optimal operation of active distribution networks (ADNs) with multiple uncertainties brought by the distributed generation (DG), diverse demand responses and flexible topologies etc. In addition, the contradictions among multiple objectives, the tradeoff between the robustness, conservative, and deterioration of performance, as well as the computation cost have challenged the solution of multi-objective optimization, in particular for the ADNs with multiple uncertainties along with complicated operation scenarios. Considering the uncertainties of distributed photovoltaic (PV), a multi-objective robust optimization strategy for ADNs is proposed in this paper to maximize the utilization of renewables and to minimize the network losses at the same time. By constructing the uncertainty set of predicted PV's maximum outputs, the extreme scenarios of the uncertainty set are chosen to establish constraints, and reduce the dimension of calculation. Then, the multi-objective optimization algorithm can obtain the Pareto fronts under various ranges of uncertainty sets while satisfying the physical and operating constraints of the ADNs, the influence of various ranges of uncertainty set on the Pareto front is taken into consideration. The effectiveness and robustness of the proposed model are verified on a modified IEEE 33 bus system, and the scalability is tested on a 141 bus distribution network. [ABSTRACT FROM AUTHOR]

• Distributed transactive energy management framework is proposed for operation of MMGs. • Transactive signal in the context of ADMM facilitates independent operation of MGs. • Robust optimization technique makes a trade-off between economics and operational risk. • Developed transactive framework has improved the convergence of the ADMM algorithm. Presence of microgrids (MGs) and renewable energy sources (RESs) in distribution power systems could result in dramatic changes in operation and planning of these systems. In this regard, operational procedures employed by utilities should take into account the intermittent nature of RESs, while coping with the independent operation of MGs. In this perspective, this paper develops a distributed power management framework based on alternating direction method of multipliers (ADMM) for distribution networks with multi-MG (MMG) structures. The proposed approach develops a transactive signal in the context of ADMM to coordinate operation of MGs in a distributed manner. In this context, the configuration of MMG distribution systems is devised in a way that facilitates modern distributed control of the system by eliminating the role of a central coordinator in traditional distribution systems. Moreover, robust optimization technique is deployed in the MG operational scheduling to handle the uncertainty associated with RESs. Finally, through a numerical case study, the efficiency of the proposed framework to schedule local resources for MMG distribution systems is analyzed. [ABSTRACT FROM AUTHOR]

• Showing the effect of transient short-circuit currents of wind farms on mis-coordination between overcurrent relays. • Proposing a new coordination method based on dynamic model of overcurrent relay. • Presenting the highly complex and nonlinear proposed method in the binary linear programming form. • Presenting an initial process for reducing the simulation time of proposed method. The presence of wind farms in network makes short-circuit currents (SCC) seen by overcurrent relays (OCR) have not fixed magnitudes and contain transient states. The transient SCC changes real operating times of OCRs compared with calculated operating times, in addition to, mis-coordination between primary and backup OCRs. In this paper, a new method based on the dynamic model of OCR is proposed for considering the effects of transient SCCs of wind farms on the coordination of OCRs, and determining proper settings for them. Since the proposed optimization problem is highly complex and nonlinear, and also uses large amount of data, the intelligent optimization algorithms have not sufficient ability for utilizing in the proposed method. Therefore, the proposed coordination method is formulated as a binary linear programming (BLP)-based approach. It is also shown that the speed of proposed BLP-based method can be increased by removing some unusable variables. It is found from simulation results and hardware tests that the proposed method has better performance in comparison with conventional coordination methods. [ABSTRACT FROM AUTHOR]

• A hybrid optimization algorithm for energy management systems of microgrids. • Comparison of the hybrid approach with existing and novel optimization algorithms. • Effective management strategy of the charging station considering all stakeholders. • Optimal location of the charging station based on energy savings and system loss. This paper presents an investigation into finding an optimal location for a planned Electrical Vehicle Charging Station (EVCS) with coordinated charging within the Microgrid installation at Wroclaw University of Science and Technology. The study uses a hybrid optimization algorithm built to combine the speed of MATPOWER and the search capabilities of a meta -heuristic optimization algorithm based on an extended ant colony which serves as the Energy Management System (EMS) of the Microgrid. The location obtained is based on an analysis of energy savings calculated on representative days of the year obtained through clustering and placing the EVCS on different nodes of the Microgrid consisting of distributed energy sources. [ABSTRACT FROM AUTHOR]