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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]

• 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]

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]

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]

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]

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]

• 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]

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]

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]

This paper presents a novel Flexible AC Transmission Systems (FACTS) placement method based on mixed integer quadratically constrained programming (MIQCP). The method is capable of allocating both Static VAR Compensator (SVC) and Thyristor-Controlled Series Capacitor (TCSC) either separately or simultaneously, to determine concurrently their numbers, locations as well as sizes. The optimization objective is to maximize loadability and minimize compensation. The proposed MIQCP formulation partially incorporates key quadratic constraints, which were absent in mixed integer linear programming (MILP) placement methods reported in the past. The authors demonstrate significant improvements in accuracy over past MILP methods, with case studies presented on the simplified Southeast Australian network, and on a large scale real-world power system, Powerlink Queensland's HV transmission network, with 886 buses and 1087 branches. In addition, results and observations for simultaneous allocation of multiple TCSCs and SVCs in these networks are demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Transmission dispatch is a nonlinear optimisation problem due to the nonlinearity of the power flow equations. In the open literature, linearisation of power flow that yields only the active power is used for transmission dispatch problems. The solutions obtained are unacceptable when verified in nonlinear power flow equations, especially for smart grid applications because of the instability issues that result from such formulations. This paper overcomes this limitation by proposing a model that accounts for both active and reactive power in transmission dispatch problem formulations. Furthermore, this paper develops new formulations for transmission dispatch that covers the overall spectrum of operation of a power system network based on the load duration curve rather than the single period considered in the open literature. Transmission dispatch is considered in the context of minimising active power losses in this paper. The advantage of the proposed approach is that it gives acceptable results when verified with nonlinear power flow compared to the classical approach used in transmission dispatch problems. Also, the paper demonstrates that the global set of switchable lines that can minimise the active power losses of a network is obtainable from the multi-period formulations based on the consideration of varying load levels. The results indicate that only this set of switchable lines can reduce losses and ensure the stability of a network, hence useful for smart grid applications. [ABSTRACT FROM AUTHOR]

The short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a series of hydraulic and electric system constraints. This paper presents a hybrid algorithm for solving SHS problem by combining real coded genetic algorithm and artificial fish swarm algorithm (RCGA-AFSA), which takes advantage of their complementary ability of global and local search for optimal solution. Real coded genetic algorithm (RCGA) is applied as global search, which can explore more promising solution spaces and give a good direction to the global optimal region. Artificial fish swarm algorithm (AFSA) is used as local search to obtain the final optimal solution for improving the exploitation capability of algorithm. The water transport delay between connected reservoirs is taken into account in this paper. Moreover, new coarse and fine adjustment methods without any penalty factors and extra parameters are proposed to deal with all equality and inequality constraints. To verify the feasibility and effectiveness of RCGA-AFSA, the proposed method is tested on two hydrothermal systems. Compared with other methods reported in the literature, the simulation results obtained by hybrid RCGA-AFSA are superior in fuel cost and computation time. [ABSTRACT FROM AUTHOR]

This paper presents the binary group search optimization algorithm (BGSO) for solving the optimal distribution network reconfiguration problem. The objective of the optimization is loss reduction in distribution system. The proposed GSO algorithm is introduced with fundamental modifications, such as introducing binary form of the GSO in order to be fit for reconfiguration and all binary form problems. All formulation of conventional GSO has been modified for accessing a novel powerful binary searching algorithm. Additionally, the forward-backward sweep, load flow is used in this paper, due to its accuracy. The Comparative studies are conducted on standard networks to verify the validity of the proposed algorithm. The simulations on different test cases confirm validity of the BGSO in solving optimal distribution network reconfiguration problem. [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]

• 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]

Abstract: In this paper, a method which employs a Modified Teaching–Learning Based Optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Generation (DG) units in distribution systems. For the sake of clarity, and without loss of generality, the objective function considered is to minimize total electrical power losses, although the problem can be easily configured as multi-objective (other objective functions can be considered at the same time), where the optimal location of DG systems, along with their sizes, are simultaneously obtained. The optimal DG site and size problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used by researchers to solve this problem because of their independence from type of the objective function and constraints. Recently, a new evolutionary method called Teaching–Learning Based Optimization (TLBO) algorithm has been presented, which is modified and used in this paper to find the best sites to connect DG systems in a distribution network, choosing among a large number of potential combinations. A comparison between the proposed algorithm and a brute force method is performed. Besides this, it has also been carried out a comparison using several results available in other articles published by others authors. Numerical results for two test distribution systems have been presented in order to show the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Transformers in electrical distribution systems for buildings and industries are set up to ensure the continuity of supply. Modern distribution transformers are reaching increasingly higher levels of energy efficiency but contribute to between 16% and 40% of the energy losses associated with electrical distribution systems. This paper examines and proposes a method to reduce losses in transformation systems and thus to reduce the associated emission of greenhouse gases (GHGs). This paper proposes a method of system optimization for transformers in parallel, called Parallel Losses Optimization (PLO), and is applicable to existing or future facilities and is adaptable to any type of transformer system. The method has been validated in 12 real facilities of different power and efficiency levels. Reductions in the losses of the studied transformers were up to 41% with respect to the initial losses. This demonstrates the beneficial operation of the PLO method proposed in this paper in a wide range of existing transformers or for future installations. The research has obtained patent pending status P201101267 in Spain. [Copyright &y& Elsevier]

Abstract: This paper addresses the security assessment of power systems when the simultaneous loss of K components is considered. The problem is formulated as a bilevel program. The upper-level optimization identifies a set of simultaneous out-of-service components in the power system, whereas the lower-level optimization models the reaction of the system operator against the outages selected in the upper level. The system operator reacts by determining the optimal power system operation under contingency. Due to the inherent nonconvexity and nonlinearity of the resulting bilevel problem, efficient solution procedures are yet to be explored. A genetic algorithm is proposed in this paper to attain high-quality near-optimal solutions with moderate computational effort. The modeling flexibility provided by this evolution-inspired methodology makes it suitable for this kind of bilevel programming problems. Numerical results demonstrate the effectiveness of the proposed approach in the identification of critical power system components. [Copyright &y& Elsevier]

Abstract: This paper presents an innovative Tribe-Modified Differential Evolution (Tribe-MDE) for solving multi-objective Environmental/Economic Dispatch (EED) problems. By using this method the multi-objective problem will be changed into a mini-max problem on this first stage and then will be solved using the Tirbe-MDE algorithm. The operator, a person who is competent with respect to the problem, can initiate all the necessary actions to determine an optimal solution. Because of the fact that all of Pareto obtained solutions are of the same level of preference an operator will be able to affect his/her opinions with respect to different conflicting objectives. Using this methodology the operator can achieve the optimal solution. The DE algorithm is an advanced stochastic method that can prepare the necessary preliminaries for an operator to solve the EED problem. DE method has several advantages including its few control variables, local searching capability, fast results, easy using process and simple structure and using the control variables logically has a significant effect on the results. The subject of this paper is studying the modified DE algorithm which is founded on self-adaptive control. In this method a diversity-preserving method is used to solve the premature convergence problem and for proving the effectiveness of this method in solving EED problems the presented algorithms is applied on different systems. [Copyright &y& Elsevier]

Abstract: This paper presents a new framework for the valuation of small signal stability as an ancillary service through Power System Stabilizers (PSSs). PSSs can improve small signal stability and simultaneously reduce the generation costs in a competitive market. This saving could be treated as a source to compensate the service provided by PSSs. This paper proposes an allocation approach to compensate the PSSs, proportional to their effectiveness in overall system small signal behavior. NSGA-II approach is used to solve the resulting multi objective optimization problem. A test system is used to represent the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents results of research into the use of factorial experimental design for constructing sensitivity as well as functionally oriented models for power system planning, operation, and control. The rationality of applying factorial experimental design for this goal is associated with the possibility to “build” secants but not tangents (how it is done in the use of existing techniques) to improve the adequateness of constructed models. Considering that the experiments with mathematical models of systems (but not with real systems) create difficulties in all stages of statistical evaluating the results of experiments (the impossibility to estimate reproducibility dispersions without the results of so-called parallel experiments), questions of their overcoming are discussed. The sensitivity models obtained with the use of experimental design techniques are applicable to planning, operation, and control technologies related to applying knowledge based approaches (in particular, a fuzzy logic based approach) as well as to utilizing optimization approaches. Taking this into account, the results of the paper are illustrated by their applications to solving the following power engineering problems: (a) fuzzy logic based coordinated voltage and reactive power control in regulated and deregulated environments and (b) power system operation, based on the application of procedures of sequential multicriteria optimization to realize dispatch on several objectives. The paper also describes procedures for constructing functionally oriented models (models with given or predefined structures, which can serve, in particular, as flexible equivalents for diverse functionalities in power engineering) on the basis of applying experimental design techniques. Their application is illustrated by the construction of functionally oriented equivalents to evaluate power system reaction in solving problems of monocriteria or multicriteria optimizing network configuration in distribution systems. [Copyright &y& Elsevier]