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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]

• Frequency adaptive: multioscillatory control and repetitive control are studied. • Methods are applied for a grid-tie converter. • Memory and computational burden analysis of both methods are presented. • A linear prewarping for frequency adaptive multioscillatory control is proposed. • Practical implementation hints are provided. The paper describes two main approaches of periodic control under variable frequency conditions: frequency adaptive multioscillatory control (FAMOSC) and fractional delay repetitive control (FDRC). The methods are applied in the control system of a grid-tie converter to increase the quality of phase currents in case of distorted grid voltages. The paper compares structures, performance and implementation of FAMOSC, FDRC and the conventional approach of repetitive and multioscillatory control. Performances of the control systems are illustrated with experiments on a laboratory test bench. The article describes tuning process of FDRC and FAMOSC with the usage of Particle Swarm Optimization algorithm. A detailed analyse and research of the techniques in terms of computational complexity is presented. Practical hints for implementation and resource optimization of both techniques on DSP are included. For FAMOSC a fast implementation of prewarping is proposed. [ABSTRACT FROM AUTHOR]

Highlights • A novel peer to peer decision-making mechanism for capacity expansion is introduced. • A two-stage stochastic math-heuristic formulation for capacity expansion is proposed. • A multi-agent system with agent configuration for coordination is developed. • An Evolutionary Vertical Sequencing protocol produces smart permutations of grids. • The advantage of coordinated decision making is validated over traditional approaches. Abstract Power system planning for modern distribution networks is undergoing a change because of distributed power generation and grid ancillary services. The vast electricity retail utility industry with many distribution network operators plans generation and transmission expansion planning for determining optimal investment decisions. This paper addresses this planning problem in a decentralized and distributed context. This paper introduces a coordinated decision making approach for optimal investments in generation and transmission expansion planning problems for distribution networks. The distribution networks are further classified into microgrids. Considering an agent as the functional information exchanging entity just like an energy meter with the objective to coordinate the expansion decisions of participants; a novel math-heuristic optimization model Coordinated Microgrid is presented. To simulate the coordination of information a multi-agent-system based coordinated decision making method is adopted and the value of coordination is investigated. The evolutionary vertical sequencing protocol, a heuristic method, is developed and implemented to simulate the coordination process among agents on the top level. The proposed protocol produces smart permutations of microgrids for coordination. On the bottom level, a two-stage chance-constrained stochastic MILP formulation for investment decisions with operational uncertainties is modeled. For market clearing a nodal-pricing scheme is adopted that maintains the Nash equilibrium among and across the microgrids for energy transactions. The proposed model is tested with consumption, network configuration data from three islands in west-coast of Norway. The models are solved to optimality and results lead to the observations that the value of coordination lies in profit increment of individual microgrid. The novel protocol proposed demonstrates an advantage of retrieving smart permutations from combinations of microgrids. In summary, CoMG is a novel expansion planning model for optimal investments in modern power distribution networks. [ABSTRACT FROM AUTHOR]

Highlights • The LFC problem is formulated as a constrained optimization problem. • The optimal PI controllers are tuned by the proposed CPEO algorithm. • Four different interconnected power systems are used to evaluate the proposed method. • The effectiveness of the CPEO is demonstrated compared with CPSO. • The proposed PI-type controller is verified compared with other controllers. Abstract This paper proposes a robust proportional-integral (PI) controller with its parameters designed by constrained population extremal optimization for load frequency control problem of multi-area interconnected. During the process of optimization, the robust performance index is used as fitness function, where linear matrix inequalities technique is employed to describe the H ∞ constraint, and the taking error performance requirement such as integral time absolute error is incorporated as another constraint. Three different two-area interconnected power systems are used as test systems to demonstrate the effectiveness of the proposed controller by comparing with other PI control methods and one optimized model predictive control. In addition, in order to investigate the performance of proposed controller for the LFC problem of large scale system, a three-area interconnected power system is used as another test system. The comprehensive experimental results fully demonstrate that the proposed control scheme in this paper performs better than other control strategies on the most considered scenarios under the conditions of load disturbance and parameters uncertainties in terms of system response and control performance indices. [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]

Reliability is an overriding factor in power system operations. Corrective security-constrained economic dispatch (SCED) satisfying the “ N  − 1” criterion is difficult because of a large number of contingencies and the strict time limits for real-time operations. The existence of conflicting contingencies further complicates the problem. To overcome these difficulties, this paper develops a new iterative contingency filtering approach to manage “ N  − 1” transmission and generator contingencies via decomposition and coordination. Instead of always removing conflicting contingencies as in existing papers, we offer system operators an important option to keep them for increased reliability, enabled by identifying multiple conflicting contingencies simultaneously. To satisfy the strict time requirements in real-time operations, the computational performance of our approach is significantly enhanced by novel warm-start of subproblem models and by parallel computing. Numerical results demonstrate that our new approach is computationally efficient and scalable, and increases the system reliability. In particular, the Polish 2383-bus system with all transmission contingencies is solved within two minutes. [ABSTRACT FROM AUTHOR]

• A systematic planning method to allocate dynamic VAR sources is proposed. • The concept of voltage control area is proposed to partition a bulk power system. • A zone-based voltage supporting index is defined in the site selection process. • A zone-based distributed optimization framework is put forward. In this paper, a practical planning method to allocate dynamic VAR sources (DVSs) using the concept of voltage control areas (VCAs) is proposed to enhance short-term voltage stability of power systems. Firstly, based on the defined transient distance index, a spectral clustering method is employed to partition a system into VCAs. Then, candidate DVS sites in each zone are determined by the proposed voltage supporting index. In each VCA, the capacity of candidate sites is preliminarily estimated by use of the dichotomy method to decide the most effective control bus and the initial capacity guess, based on which further system-wide optimization is implemented using particle swarm optimization (PSO) to obtain the final allocation scheme. Case study on the New England 39-bus and 68-bus test systems along with the comparisons with the existing methods confirm the effectiveness and advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

• A hybrid prediction method is designed to improve the prediction accuracy of data. • An energy management strategy based on demand-side response is proposed. • A neighbor prediction method is designed to solve data interruption problem. • An improved particle swarm optimization algorithm is proposed to solve the optimization model. Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study. [ABSTRACT FROM AUTHOR]

Economic dispatch problem (EDP) is an elementary optimization problem of power systems whose purpose is to minimize the total generation cost while meeting total demands and complying with individual generator output constraints. This paper proposes a distributed optimization algorithm with noisy gradient based on stochastic gradient-push approach to solve the EDP on time-varying directed communication networks potentially with time delays. It shows that the proposed algorithm can be ensured to solve the EDP when the time-varying directed communication networks are uniformly jointly strongly connected. The algorithm with asynchronous step-sizes can deal with the finite time-varying delays on communication links as well. The generation cost functions are considered as strictly convex and strongly convex functions respectively with convergence rate of O l n k / k and O ln k / k . Simulation results are provided to validate the availability of the algorithm. [ABSTRACT FROM AUTHOR]

• A kind of device is utilized to mitigate three-phase imbalance in distribution system. • A joint optimization model from the perspective of the whole system is proposed. • The current imbalance and voltage imbalance are simultaneously considered. • Some linearization techniques are applied to simplify the model. Three-phase imbalance is a common phenomenon in three-phase four-wire distribution network systems (DNSs), which may cause power quality deterioration, increase power losses, and can even damage appliances as well. The situation is becoming even worse because of the increasing integration of the distributed generations (DGs) and storage-capable loads into DNSs. In this paper, a joint optimization model based on the Y-connected and Δ-connected static reactive power compensation devices are proposed to mitigate the three-phase imbalance and minimize the active power losses in DNSs. The model is formulated to optimize the operation of all the devices in order to mitigate the imbalance of the whole DNS rather than a single feeder or a particular area. Meanwhile, the current imbalance of the transformer and the voltage imbalance of all three-phase nodes are simultaneously considered in the model according to the practical requirements. Several linearization techniques are applied to simplify the computation and accelerate the solving speed. These techniques convert the non-linear and non-convex model into a mixed-integer linear programming model, which can be effectively solved by off-the-shelf solvers. The effectiveness and scalability of the proposed method are verified by the test results obtained from the modified IEEE 34 and IEEE 123 test systems. [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]

In a deregulated power system, incentive regulations for network owners are designed to direct the network investment. An innovative method that assesses the incentive regulation in distribution networks is proposed in this paper. The method quantifies the interplay between incentive regulation, network investment, and network performances. It allows regulators and the distribution system operators (DSOs) evaluating the economic effects of investments within the incentive regulation framework. Considered network investments include the investment in network infrastructure and performance improving. The assessment is based on a network investment optimization model considering multi-period optimal power flow and regulatory constraints. The main contributions of this paper include the modeling of the incentive regulations and the quantification of the impacts of incentive regulation on network infrastructure investment and performance improving investment. [ABSTRACT FROM AUTHOR]

The paper proposes a decentralized control scheme for scheduling the flexible charging demand of plug-in electric vehicles in residential distribution networks. This control scheme is designed for execution by a multi-agent system at two consecutive stages of static and dynamic scheduling. The distinctive attributes of the developed control scheme are (i) to realistically prioritize both the customers’ and the utility’s objectives, (ii) to incorporate the uncertainty in the forecasted demand, (iii) to account for customers’ flexibility in their charging demand, and (iv) to specify a fair pricing scenario to all customers while protecting their privacy. The paper includes extensive numerical studies using a set of recorded real-world driving data, representing heterogeneous vehicular demand. In order to assess the efficacy of the proposed scheduling scheme, comparative assessments are also presented against an optimization-based charging scheduling scheme. [ABSTRACT FROM AUTHOR]

The presence of power electronic-based wind turbines and photovoltaic systems in distribution networks has provided distribution companies an opportunity to implement voltage control through using the reactive power of these systems. In this paper, a decentralized method based on fuzzy logic is proposed to control the reactive power of distributed generations (DGs) regarding the technical constraints. The fuzzy system is optimized by gradient descent algorithm (GDA) and then implemented on various DG technologies including a photovoltaic (PV) system, permanent magnet synchronous generator (PMSG) wind turbine and also a doubly fed induction generator (DFIG) wind turbine. The system under study is tied to a real distribution network. Having simulated the system, the paper shows that the fuzzy system can appropriately determine the desired reactive power that should be produced by each DG based on the voltage variation of the bus at which the DG is connected. Furthermore, a centralized voltage control is also applied to the same network to verify the performance of the method proposed. The verification indicates that the method is capable of finding the near-optimal solution. A scenario in which an unwanted conflict appears in the DGs’ function is defined in detail and then a strategy is presented to resolve the situation. In addition to this, the coordination between the stator of the wind turbines and grid side converter (GSC) is examined. To investigate the robustness of the proposed method in different distribution networks, simulation results are also presented for IEEE 33-bus distribution test system. The numerical results show that the fuzzy system can effectively control the voltage of the DG connection bus. [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]

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]

Highlights • A scalable optimization method is proposed to maximize a DG hosting capacity. • The method enumerates all possible optimal configurations while simultaneously considering optimal DG placement. • A compressed data structure is utilized to represent the solution space compactly. • The method obtains 1.49 × 1018 optimal configurations in 17.1 h for a network containing 235 switches. Abstract At the stage of planning distributed generation (DG) for a distribution network, the network configuration is a key factor in increasing the DG hosting capacity. The determination of a configuration that maximizes the hosting capacity is a highly complex, nonlinear combinatorial optimization problem. No existing method can yield the global optimal solution for practical-scale networks. Therefore, this paper proposes a scalable optimization method. Specifically, the proposed method enumerates all optimal configurations while simultaneously considering optimal DG placement. The proposed method first optimizes the DG placement for possible partial networks using a second-order cone programming technique. Next, it enumerates possible combinations of the partial networks while avoiding a combinatorial explosion using a highly compressed data structure. Finally, it finds the optimal configurations by exploring solutions over the data structure. In experiments involving a large-scale network containing 235 switches, our enumeration method obtained 1.49 × 10 18 global optimal configurations in 17.1 h. Another powerful feature of our method is that it enables distribution system operators to select the preferred optimal configuration interactively. [ABSTRACT FROM AUTHOR]

Highlights • Converters transmit electricity in the presence of intermittent production from WPPs. • A model for determining optimal characteristics of power converters is proposed. • With optimal characteristics, converters provide real time minimal losses in the system. • Interior Point Method is used for optimization problem solution. • The proposed methodology has been tested on IEEE 14-bus and MTDC 5-bus test systems. Abstract The construction of offshore wind power plants usually requires transmission of the large amount of energy over the large distances with cable power lines. The most effective solution for the connection of large number of remote offshore wind power plants to the rest of the system is with MTDC transmission network. In this paper, the methodology for optimal control of power converters for minimizing power losses in the system is presented. The main task of the proposed methodology is to determine, based on the forecasted production of wind power plants, the performance characteristics of the power converters that would enable their optimal decentralized work in the presence of intermittent production from wind power plants. The determination of the power converter characteristics is conducted during day-ahead power system operation planning, based on which the power converters, during a real time control, maintains optimal operation of the system locally, without communication with each another. The proposed methodology is tested on the example of MTDC network with 5 buses connected to the IEEE 14-bus test system. [ABSTRACT FROM AUTHOR]

Highlights • Multi PSS multi-objective optimal design is developed. • Combined stability and nonlinearity indices objective function. • The nonlinearity index is derived from the 2th order participation factor. • The new design strongly preserves under stress systems performances. • Employed a newly developed HSS optimization algorithm. Abstract Design of power system stabilizer (PSS) withstanding critical situations is a challenging task. In this paper, an effective method for the design of a coordinated multiple PSS in power systems are offered and weaknesses of the existing methods are disclosed. The setup includes the nonlinear model of the system, a specific multi-objective function and a newly developed meta-heuristic Hyper-Spherical Search (HSS) optimization algorithm. The objective function includes both eigenvalue stability index (SI) and nonlinearity index (NI). The employed new NI is the second order modal interaction of the lightly damped electromechanical modes. The performance of the proposed Stability-Nonlinearity index (SN) based PSS design by HSS (SNPSS-HSS) is compared with SNPSS-GA, SI based PSS (SPSS) and the conventional design method (CPSS) under various operating conditions and disturbances. The investigations are conducted over both a single lead PSS and PSS2B. The performance is detailed using time domain simulations, SI and NI analysis. A four-machine two-area system and the IEEE 39-bus system are utilized for test purpose. The results of extensive simulations indicate the superiority of SNPSS-HSS design with respect to the others. The obtained relative stability is higher and the nonlinearity index is lower than the product of the other designs. Besides, as it is expected, using a high degree of freedom PSS2B improves the performance of the aforementioned algorithms. [ABSTRACT FROM AUTHOR]

Highlights • A coordinated high-order sliding mode control method. • Power optimization and grid synchronization of a doubly-fed induction generator. • A novel sliding stage reaching law. Abstract This paper aims to propose a coordinated high-order sliding mode control (HOSMC) method for power optimization and grid synchronization of a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The super-twisting algorithm is employed to maximize the captured wind energy with optimal rotor speed tracking, and regulate the voltage to fulfill the grid requirement. Subsequently, a grid synchronization scheme is proposed, where the wind turbine stator terminal voltage is controlled to synchronize with the grid voltage without employing the current control loop. Meanwhile, a novel reaching law is employed to accelerate the reaching speed. Simulations are carried out to verify the effectiveness of the novel approach, where the first order sliding mode control and the proportional-integral control are considered for comparison. A stepwise wind speed scenario, a variable wind speed condition, and the scenario under parameter perturbations are included in the comparative study. The simulation results fully reveal the robustness, chattering free property and high tracking precision of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • A method is proposed to calculate the optimal oversize of the DG inverters. • The reactive power capability and active power curtailment are considered. • Different control schemes of the inverter are considered in the proposed method. • The effect of OLTC operation on the total costs and inverter oversize is studied. • IEEE 69-bus and IEEE 33-bus distribution systems are used for simulation. Abstract The penetration of renewable distributed generations (DG) is recently being increased in distribution systems. Due to the intermittent nature of these generation units, several technical problems are expected based on the environmental conditions and load profiles, such as voltage rise and increasing losses. A traditional way to alleviate these technical problems is to oversize their interfacing inverters for releasing their capacities to inject/absorb further reactive power. In the literature, the DG inverter is normally oversized by a certain percentage (e.g., 10%) for this purpose. In turn, an optimization-based method is proposed in this paper to calculate the optimal oversize of the interfaced inverter employed in various utility-owned DG types to regulate voltages and to reduce losses with minimum total costs. The proposed method considers the active power curtailment (APC) feature in the DG inverter and the transformer taps. Different control schemes of the interfaced inverter are considered and incorporated in the proposed optimization model. The simulation results demonstrate the effectiveness of the proposed method compared with existing methods. [ABSTRACT FROM AUTHOR]

Multi-Contingency Transient Stability Constrained Optimal Power Flow (MC-TSCOPF) models optimize the economic dispatch of power systems while ensuring their stability after a series of reference incidents. This paper proposes a MC-TSCOPF model that represents the power balance at each node of the system and at each sample time. The proposed model includes non-linear loads, synchronous generators, a windfarm, and a Flywheel Energy Storage system (FESS). The model is written on GAMS and solved using a standard Interior Point algorithm. This study focuses on the Fuerteventura-Lanzarote insular grid in Spain, where stability problems and load shedding cause high additional costs due to the low inertia of the system. A FESS has been recently installed in the system to improve its stability, taking advantage of its high-power capacity and rapid response. The proposed TSCOPF model has been applied to optimize the operation of the FESS to support stability in the event of a contingency. The results of the study show that 1) a proper model of non-linear loads is essential in TSCOPF studies; 2) the proposed MC-TSCOPF provides a tool for minimizing the generation costs while ensuring transient and frequency stability; and 3) it is possible to further reduce the generation costs by using the proposed model to calculate an optimal dynamic response of the FESS. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) technology is a promising solution to manage the uncertainties of renewable energy in demand side. Because of various uncertainties, VPPs’ dispatch models are always solved by stochastic optimization, robust optimization and interval optimization. However, these approaches always require high computational complexity, be over conservative or cannot describe VPPs’ profitability precisely. Thus, this paper combined interval and deterministic optimization together and adopted the combined approach to solve a VPP’s dispatch problem. The combined optimization not only maximized VPPs’ deterministic profits under forecasted scenarios to estimate the VPP’s most likely profits, but also maximized VPPs’ profit intervals to manage uncertainties. The proposed model was in a regulated electricity market environment, and the VPP’s traded energy was cleared by time-of-use prices. A case study from real world was adopted to prove the validity of this model. Comparison with other optimizations like stochastic and robust optimization was also studied. The combined optimization can manage the VPP’s uncertainties within limited computational time. [ABSTRACT FROM AUTHOR]

In the smart grid, retail price determination by electricity retailer is necessary in the presence of hydrogen storage systems and plug-in electric vehicles under pool market price uncertainty. Therefore, in this paper, real-time pricing is proposed in comparison with time-of-use pricing and fixed pricing. Furthermore, an interval optimization approach is proposed for pool market price uncertainty modeling. In this approach, uncertainty-based profit function of retailer is reformulated as a deterministic bi-objective problem with average and deviation profits as the conflict objective functions which average profit should be maximized while deviation profit should be minimized. Furthermore, epsilon constraint method is used to solve the proposed bi-objective model in order to obtain Pareto solutions. Finally, fuzzy satisfying approach is used to select the trade-off solution from Pareto solutions. The obtained results show that average profit of retailer increases in the real-time pricing in comparison with time-of-use pricing and fixed pricing. Also, deviation profit of retailer decreases in the proposed interval optimization approach in comparison with deterministic approach. The proposed mixed-integer linear programming model is solved using CPLEX solver under GAMS optimization software. To validate the better performance of proposed model, three types of retail price determination under deterministic and interval optimization approaches are utilized and the results are compared with each other. [ABSTRACT FROM AUTHOR]

The security constrained optimal power flow (SCOPF) is a fundamental tool to analyze the security and economy of a power system. To ensure the safe and economic operation of a system considering demand uncertainties and to acquire economic and reliable solutions, in this paper, a parallel method for solving the interval DC SCOPF with demand uncertainties is presented. By using the interval optimization method, the uncertain nodal load can be expressed as interval variables and integrated into the DC SCOPF model, which is then formed as a large scale nonlinear interval optimization formulation. According to the theory of interval matching and selection of the extreme value intervals, the interval DC SCOPF problem can be transformed into two deterministic nonlinear programming problems and solved by alternating direction method of multipliers (ADMM) to obtain the range information of interval formulation variables. Using ADMM, the above two deterministic problems, which are large in scale because of the large number of preconceived contingencies, all can be split into independent sub-problems corresponding to pre-contingency status and each individual post-contingency cases. These small-scale sub-problems can be solved in parallel to improve the computing speed. Compared with the Monte Carlo (MC) method, the simulation results of the IEEE 30-, 57- and 118-bus systems validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, technological progress in power electronics and in battery storage systems, along with the introduction of smart meters, have made possible the application of improvements in the control of power peaks for residential customers. The demand charge is an important component in the residential customer’s electricity invoice in Spain, which has increased by nearly 100% in the last few years, prompting the consumers to reduce their peak usage. This paper proposes a model which allows residential consumers to save a significant part of their invoice by lowering contracted capacity through the use of a battery energy storage system. Recent academic literature has already studied in-depth the benefits of load-shifting energy throughout the day in the context of time of use (ToU) tariffs, but, regarding power optimization, there is a long way to go; not much research has been conducted in this field. In this article, a model to calculate optimum battery size which reduces contracted power is proposed. The model starts with a given load curve from a typical consumer in Spain in a context of constant energy prices. This system could be financed by the end user directly or by an independent company which participates in the savings. [ABSTRACT FROM AUTHOR]

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy), installed capacity (power) and actual instantaneous power delivery is proposed. Therefore, in this model, the uncertainties of wind power and ramp-up/down constrains of traditional power plants are correctly considered in the investment cost. The numerical results show the great potential of proposed model with lowest risk on generation cost. Also, they are show that MABC approach is successful in portfolio optimization. [ABSTRACT FROM AUTHOR]

Over-current relays provide primary as well as backup protection to electrical distribution systems. These relays should be coordinated and set at the optimum values, to minimize the total operating time and hence ensure that least damage is caused when a fault occurs. While they provide backup protection, it is also imperative to ensure that their settings should not cause their inadvertent operation and subsequent sympathy trips. This paper describes a Chaotic Firefly algorithm (CFA) for optimal time coordination of these relays. The algorithm has been implemented in MATLAB and tested on several systems, out of which two have been illustrated in this paper. The results obtained by the Chaotic Firefly algorithm are compared with those obtained by the conventional Firefly algorithm (FA). The novel feature of this paper is the application of the Chaotic Firefly algorithm to the problem of over-current relay coordination. [ABSTRACT FROM AUTHOR]

This paper presents a mixed-integer second-order conic programming (MISOCP) model to solve the reconfiguration problem of electrical distribution systems, considering the simultaneous minimization of total active power losses and improvement of customer-oriented reliability indices. The reliability indices considered in this paper are the system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI), and the energy not supplied (ENS). Under radiality, the proposed model satisfies the operational constraints of the reconfiguration problem, i.e., the voltage magnitude limits of the nodes and the current capacities of the conductors are not violated. The use of an MISOCP model guarantees convergence to optimality via convex optimization software tools. A multi-objective optimization approach is used to generate a full Pareto front surface that shows the conflict between the active power loss minimization and the improvement of the reliability indices in the reconfiguration problem. Finally, in order to test and verify the proposed methodology, a 43-node test system and a real 136-node system were employed. [ABSTRACT FROM AUTHOR]

Energy Storage Systems (ESSs) will have an important role in the optimal operation of Active Distribution Networks (ADNs). Within this context, this paper focuses on the problem of ESSs optimal siting and sizing. Following similar approaches already proposed by the Authors, this paper proposes a multi-objective procedure that accounts for various ancillary services that can be provided by ESSs to ADNs. The proposed procedure takes into account the voltage support and network losses minimization along with minimization of the cost of energy from external grid and congestion management. For the case of large-scale problems, accounting for networks with large number of nodes and scenarios, the selection of the solution methodology is a non-trivial problem. In this respect, the paper proposes and discusses the use of the Alternative Direction Method of Multipliers in order to define an efficient algorithm capable to treat large-scale networks and, also, address the issue of the optimality of the solution. A real large-scale network with real profiles of load and distributed photovoltaic generation is used as the case study to analyze the effectiveness of the proposed methodology. [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]

This paper deals with the subject of finding the site and size of distributed generation in distribution systems. This problem corresponds to a mixed-integer nonlinear problem which is difficult and hard to solve with classical optimization techniques. Many approaches and with different objective functions have been applied to solve it. In this paper, the problem to solve comprises multiple distributed generation sources and the objective of minimizing power losses and generation costs, both for the distributed and conventional generators. Since it is highly combinatorial, a search space reduction is needed. So, an approximate model is used to reduce the search space of the possible buses where the distributed generation has to be located. Then, for each combination of the reduced space search, a nonlinear equations system is solved by a numerical method to get the size of the distributed generation, checking the voltage limits and the lines’ capacities. To test the method, a comparison between the proposed algorithm and a force brute algorithm is performed on a 69 and 118 bus test systems. The obtained results indicate that this method find the optimal or near optimal solution in a reasonable computation time. [ABSTRACT FROM AUTHOR]

The planning and design of an electric power system, including high-voltage direct-current transmission, is a complex optimization problem. The optimization must integrate and model the engineering requirements and limitations of the generation, while simultaneously balancing the system electric load at all times. The problem is made more difficult with the introduction of variable generators, such as wind and solar photovoltaics. In the present paper, we introduce two comprehensive linear programming techniques to solve these problems. Linear programming is intentionally chosen to keep the problems tractable in terms of time and computational resources. The first is an optimization that minimizes the deviation from the electric load requirements. The procedure includes variable generators, conventional generators, transmission, and storage, along with their most salient engineering requirements. In addition, the optimization includes some basic electric power system requirements. The second optimization is one that minimizes the overall system costs per annum while taking into consideration all the aspects of the first optimization. We discuss the benefits and disadvantages of the proposed approaches. We show that the cost optimization, although computationally more expensive, is superior in terms of optimizing a real-world electric power system. The present paper shows that linear programming techniques can represent an electrical power system from a high-level without undue complication brought on by moving to mixed integer or nonlinear programming. In addition, the optimizations can be implemented in the future in planning tools. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to determine the optimal location of phasor measurement units (PMUs) in any network to make it observable. This proposed methodology is based on network connectivity information and unreachability index (URI), where URI is the difficulty to observe any node in the network and it is computed using the inverse of connectivity. In order to choose the optimal bus, it is basically considered to observe a low connectivity bus from an adjacent bus selected by weighting factors that are based on logical analysis of the observability theory combined with the URI; this process stops until the network is observable. The purpose is minimize the number of PMUs in a network with the optimal location and the aim to get a low number of critical measurements (CM) with a high total redundancy (TR), in order to obtain an optimal distribution of PMUs on the network. The proposal is considered as an easy solver for PMU’s placing on the network due to important reduction in complexity and computational cost, besides comparable results are as good as those papers using recent optimization methods such as metaheuristics and stochastics, without taking into account that the proposal can handle huge networks. The algorithm is applied to the IEEE 14, 30, 57, 118 and 300-bus systems, and also to medium and large power systems of 1006, 3305, 15,000, 20,000 and 30,000 buses with success. [ABSTRACT FROM AUTHOR]

This paper aims at the investigation of an active power filter (APF) comprised of a transformerless multilevel inverter (MLI) for power conditioning in three-phase three-wire distribution network. The inverter topologies used here are three, five, seven and nine-level. The system configuration mainly involves cascaded MLI structure of APF, generation of compensation filter currents based on instantaneous active and reactive current component ( i d – i q ) method and dc-link voltage regulation using a PI controller. Not many papers focus on the regulation of dc-link capacitor voltage. Here we have proposed the implementation of bacterial foraging optimization (BFO) to extract the gains of PI controller. The proposed work provides improved dc-link voltage regulation, quick prevail over current harmonics and reduction of overall source current THD. Adequate MATLAB/Simulink simulation results are presented for the different cascaded MLIs discussed above. Additionally, the performance has been validated in real-time using Opal-RT Lab simulator considering three different conditions of supply i.e., balanced sinusoidal, balanced non-sinusoidal and unbalanced sinusoidal. [ABSTRACT FROM AUTHOR]

Traditional optimization and control techniques are no longer suitable to account for the interactions introduced by new technologies like combined heat and power units (CHP) and renewable sources. In this paper a general optimization framework and a hierarchical control architecture are presented for systems with multiple energy carriers, i.e. electricity, heat, gas, etc. The proposed framework is based on the energy hub approach and is an extension of previous research done in this field. Two-level architectures can be adjusted to fulfill the needs of future power systems, where there will be a higher participation of decentralized generation. Nevertheless, the papers generally focus exclusively on electricity flows, even when they include cogeneration units like fuel cells. For this reason the contribution of this paper is to propose a two-level control strategy that can be applied in systems with multiple energy carriers and to provide an illustrative example in which the results of using the strategy can be observed. The complete framework presented in this paper consists of an optimization algorithm and a real-time control algorithm. The optimization algorithm indicates when to turn on and turn off a generation unit and how much power it should deliver at a certain time period. The optimization is done for a forecasted period of 24 h and the real-time control strategy runs continuously to compensate for the mismatches between the scheduled load and the real load by means of control actions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Moreover, comparisons are performed with other optimization algorithms. The simulation results show that our hybrid method is better in terms of the speed and accuracy. The main contribution of this paper is the development of a emission unit commitment model integrating with wind energy and combining the SQP and PSO methods to achieve faster and better performance optimization [ABSTRACT FROM AUTHOR]

This paper presents an improved differential evolution to solve economic dispatch problem of thermal generating units with non-smooth/non-convex cost functions due to valve-point loading taking into account transmission losses and nonlinear generator constraints such as prohibited operating zones. Differential evolution exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective function toward the optimization process and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. This paper proposes Gaussian random variable instead of scaling factor which improves search efficiency. The proposed method has been applied to four different non-convex economic dispatch problems with valve-point effects, prohibited operating zones with transmission losses, multiple fuels with valve point effects and the large-scale Korean power system with valve-point effects and prohibited operating zones. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed improved differential evolution based approach is able to provide better solution. [ABSTRACT FROM AUTHOR]

This paper sheds new lights on data uncertainty in fuzzy reliability computations to achieve the best possible accuracy using minimum data. To the best of our knowledge, there are major drawbacks to implement Conventional Fuzzy Arithmetic (CFA): catastrophic overestimation, non-applicability to real-world systems and complex computations based on impractical optimizations. A novel approach namely Fuzzy Transformation Method (FTM) has recently been put forward that eliminates the core problems of CFA. The present paper aims to validate the applicability of FTM to the reliability issues. Few researchers have addressed the issues of both symmetric and asymmetric uncertainty distributions in Fuzzy Failure Rates and how a fuzzy result can profoundly come under the influence of them. Hence, a comprehensive investigation is launched to evaluate the uncertainties and some beneficial observations are provided as well as sensitivity analysis and component importance measure in fuzzy frameworks. An interesting correlation between the component importance and input-output uncertainties is observed. Additionally, pictorial representations, results and remarks are numerically discussed. We opted for All-Digital Protection Systems (ADPSs) due to novelty of them and their impacts on substation reliability. Until now, there has not been provided a fuzzy perspective concerning reliability of ADPSs. [ABSTRACT FROM AUTHOR]

It is often necessary to investigate the output power against load demand in a system having distributed generation (DG) resources connected to the existing conventional power system. In this paper, the load frequency control (LFC) problem is presented using different optimization algorithms for two types of power system configurations: (i) hybrid configuration of thermal power system (TPS) integrated with DG, comprising wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyzer (AE) and battery energy storage system (BESS); (ii) two area interconnected power system with DG connected in area-1. The inclusion of wind energy system in DG, having high variability in its output power, results into a challenging task for the realization of an effective controller design. This difficulty is further enhanced with random variation of load demand. The control scheme proposed in this paper is based on linear matrix inequalities (LMI) with its parameters tuned by particle swarm optimization (PSO), as a new contribution to earlier studies. The robustness of this controller is thoroughly demonstrated in the above hybrid power systems with different conditions of load disturbances, wind power and parameter variations. [ABSTRACT FROM AUTHOR]

This paper presents a novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network. The basic disadvantage of the original teaching learning based optimization (TLBO) algorithm is that it gives a near optimal solution rather than an optimal one in a limited iteration cycles. In this paper, opposition based learning (OBL) and quasi OBL concepts are introduced in original TLBO algorithm for improving the convergence speed and simulation results of TLBO. In order to show the effectiveness and superiority, the proposed algorithms are tested on 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results of the proposed methods are compared with those obtained by other artificial intelligence techniques like GA/PSO, GA, PSO and loss sensitivity factor simulated annealing (LSFSA). The results show that the QOTLBO surpasses the other techniques in terms of solution quality. [ABSTRACT FROM AUTHOR]

This paper presents the Parallel Vector Evaluated Improved Honey Bee Mating Optimization (VEIHBMO) as a novel multi objective technique to obtain a set of optimal Power System Stabilizers (PSSs) parameters. It includes a feedback signal of a remote machine and local and remote input signal ratios for each machine in a multi-machine power system under various operating conditions. This goal is formulated as a multi objective optimization process with two competing and non-commensurable fitness functions. The objectives considered in this paper are the time domain based integral square time of square error (ISTSE) and eigenvalues based comprising the damping factor. The effectiveness of the proposed method is demonstrated on 4-machine two-area and 16-machine five-area power system under different loading conditions. The system performance is assessed through the time multiplied absolute value of the error (ITAE), eigenvalues and figure of demerit (FD) analysis performance indices. The simulation results show that the performance of the proposed stabilizer is comparable to that which could be obtained by the conventional design, but without the need for the estimation and computation of the external system parameters. [ABSTRACT FROM AUTHOR]