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Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods.

The objective of this paper is to propose a simple transmission congestion pricing scheme based on tracing principle by considering generator fixed cost, cost for incurring loss and transmission congestion cost. Restructuring has brought about considerable changes by the virtue of which electricity is now a commodity and has converted into deregulated type. Such a competitive market has paved way for innumerable participants. This concept of restructuring has led to overloading of transmission lines. In this paper, power flow tracing has been employed by using suitable optimization algorithm, where the real power generation has been maximized. Congestion in the transmission line has been produced in a new fashion by maximizing the real power demand. The power flow under normal operating condition and congestion is determined and hence the difference in power flow is estimated. Based on the estimated power flow difference, the transmission line congestion cost is computed. Pool model and bilateral model has been considered in simulation study to introduce the concept of deregulation. The proposed method is tested and validated on Modified IEEE 30 bus test system and Indian utility 69 bus test system.

In this paper, an optimal power dispatch formulation is proposed in the operating horizon using real time measurements to enhance the performance of online energy scheduling in microgrid considering renewable energy, grid power trade and demand response. A stochastic weight tradeoff particle swarm optimization (SWT-PSO) based backward–forward sweep (BFS) optimal power flow (OPF) method is used by the microgrid central controller (MGCC) to obtain the online optimal schedules of distributed energy resources (DERs). The effectiveness of the suggested approach is investigated on the residential feeder of the CIGRE LV benchmark microgrid in terms of reduction in fuel & emission costs and voltage deviation. In addition, settling time in overall implementation is drastically reduced. Also, the paper analytically justifies the use of (1) renewable energy sources, grid trade and prioritized load curtailment, (2) SWT-PSO over priority listing method and (3) online OPF over online economic dispatch (ED).

In this paper a novel approach regarding the optimal penetration of Distributed Generation (DG) in Distribution Networks (DNs) towards loss minimization is proposed. More specific, a Local Particle Swarm Optimization (PSO) variant algorithm is developed in order to define the optimal active and reactive power generation and/or consumption requirements for the optimal number and location of nodes that yield loss minimization. Thus, the proposed approach provides the optimal number, siting and sizing of DGs altogether. In addition, based on the optimal power requirements of the resulted nodes, a combination of potential DG types to be installed is recommended. The proposed objective function in this paper is also innovative since it embeds the constraint of reverse power flow to the slack bus by the formation of a new penalty term. The proposed methodology is applied to 30 and 33 bus systems. The results indicate the optimal number, locations, and capacity of DG units, which were calculated simultaneously. Finally, the impact of the predefined amount of permissible reverse power flow to the optimal solution is also examined through two scenarios: the first considers zero reverse power flow and the second unlimited reverse power flow.

This paper develops a novel hybrid algorithm for solving transmission expansion planning (TEP) problems in electric power networks. Raising the awareness about immense contaminants produced by fossil fuels as well as depleting these resources have pushed energy companies toward considering more renewable energy resources (RERs). The RESs are beneficial for the society and the power system utility, however, taking into account the uncertainties, which are inherent in RERs, increase the complexity of the optimization problems. In this work, a Monte-Carlo simulation (MCS) is used to address the intermittent nature of wind energy. To handle the resulted model, by modifying and combining three well-known evolutionary algorithms such as shuffled frog leaping algorithm (SFLA), particle swarm optimization (PSO), and teaching learning-based optimization (TLBO), a potent hybrid MSFLA-MPSO-MTLBO, namely combinatorial heuristic-based profound-search algorithm (CHPSA), is proposed. A self-adaptive probabilistic mutation operator (SAPMO) is employed to enhance the effectiveness and computational efficiency of the CHPSA. Ten commonly-used benchmark problems are introduced to corroborate the performance of the CHPSA, while the IEEE RTS 24-bus test system is used to validate the model. Results show that the proposed CHPSA is capable of obtaining better solutions than other algorithms, either implemented in this paper or borrowed from the literature.

This paper presents modified particle swarm optimization to solve economic dispatch problems with non-smooth/non-convex cost functions. Particle swarm optimization performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes Gaussian random variables in velocity term which improves search efficiency and guarantees a high probability of obtaining the global optimum without considerably worsening the speed of convergence and the simplicity of the structure of particle swarm optimization. The efficacy of the proposed method has been demonstrated on four test problems and 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 reported in the literature. It is found that the proposed modified particle swarm optimization based approach is able to provide better solution.

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

This paper presents a novel Primal–Dual Interior Point Method (PDIPM) based sensitivity approach for efficient assessment of the impact of uncertainties in Multi-objective Optimization (MO). This shall aid in robust decision making. The MO problem considered, in this paper, is the Environmental–Economic dispatch (EED) problem. The two objectives, i.e. the emission and economic cost, are continuous convex functions. The uncertainties in the system parameters such as loads (or injections) and limits on line flows and voltage magnitudes, are assumed to be of fuzzy type, more specifically in an interval. Results for the IEEE 30 bus system have been obtained using the proposed approach and compared with those obtained by Monte Carlo Simulations (MCS) and Particle Swarm Optimization (PSO) based on Harmony Search (HSPSO). The results obtained provide interesting insights on how uncertainties in input data can affect decision making in MO.

Optimal power flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In this paper, particle swarm optimization (PSO) with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power system equipped with flexible ac transmission systems (FACTS). The two FACTS devices, namely, thyristor controlled series capacitor and thyristor controlled phase shifter are considered for this study. This study is implemented on modified IEEE 30-bus and IEEE 57-bus test power systems with four different objectives. The results presented in this paper demonstrate the potential of ALC-PSO algorithm and show its effectiveness for solving the OPF problem with FACTS devices over the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature.

In this paper a new, an Improved Particle Swarm Optimization (IPSO) is proposed for optimizing the power system performance. Recently, the Particle Swarm Optimization (PSO) technique has been applied to solve power engineering optimization problems giving better results than classical methods. Due to slow convergence and local minima, particle swarm optimization fails to give global results. To overcome these drawbacks, in this paper presents the application of improved particle swarm optimization for optimal sizing and allocation of a Static Compensator (STATCOM) and minimize the voltage deviations at all the buses in a power system. This algorithm finds an optimal settings for present infrastructure as well as optimal locations, sizes and control settings for Static Compensator (STATCOM) units. A 30 bus system is used as an example to illustrate the technique. Results show that the Improved Particle Swarm Optimization (IPSO) is able to find the best solution with statistical significance and a high degree of convergence. The simulation results are presented to show a significant improvement of the power system reliability and feasibility and potential of this new approach.

This paper presents a new approach to the solution of optimal power generation for economic dispatch (ED) using improved particle swarm optimization (IPSO) technique. In this paper an improved PSO technique is suggested that deals with equality and inequality constraints in ED problems. A constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process and simultaneously the dynamic process inherent in the conventional PSO algorithm is preserved. The application and statistical performance of various intelligent algorithms such as differential evolution (DE), particle swarm optimization (PSO) and improved particle swarm optimization (IPSO) are considered on economic dispatch problems with non-smooth cost functions considering valve point effects and multiple fuel options. To determine the efficiency and effectiveness of various intelligent algorithms, three experiments are conducted considering only multiple fuel options, considering both valve-point and multiple fuel options and also taking into account the valve point loadings, ramp rate limits and prohibited operating zones. The simulation results reveal that the proposed IPSO has provided the better solution with a very high probability to demonstrate its robustness over other intelligent techniques such as DE, PSO and improved genetic algorithm with multiplier updating (IGA_MU), ant colony optimization (ACO), artificial bee colony algorithm (ABC), hybrid swarm intelligent based harmony search algorithm (HHS) and fuzzy adaptive chaotic ant swarm optimization (FCASO). The proposed IPSO ensures convergence within least execution time and provides quality solutions as compared to earlier reported best results.

Optimal Power Flow (OPF) is used for developing corrective strategies and to perform least cost dispatches. In order to guide the decision making of power system operators a more robust and faster OPF algorithm is needed. OPF can be solved for minimum generation cost, that satisfies the power balance equations and system constraints. But, cost based OPF solutions usually result in unattractive system losses and voltage profiles. In the present paper the OPF problem is formulated as a multi-objective optimization problem, where optimal control settings for simultaneous minimization of fuel cost and loss, loss and voltage stability index, fuel cost and voltage stability index and finally fuel cost, loss and voltage stability index are obtained. The present paper combines a new Decoupled Quadratic Load Flow (DQLF) solution with Enhanced Genetic Algorithm (EGA) to solve the OPF problem. A Strength Pareto Evolutionary Algorithm (SPEA) based approach with strongly dominated set of solutions is used to form the pareto-optimal set. A hierarchical clustering technique is employed to limit the set of trade-off solutions. Finally a fuzzy based approach is used to obtain the optimal solution from the tradeoff curve. The proposed multi-objective evolutionary algorithm with EGA–DQLF model for OPF solution determines diverse pareto optimal front in just 50 generations. IEEE 30 bus system is used to demonstrate the behavior of the proposed approach. The obtained final optimal solution is compared with that obtained using Particle Swarm Optimization (PSO) and Fuzzy satisfaction maximization approach. The results using EGA–DQLF with SPEA approach show their superiority over PSO–Fuzzy approach.

A comparative study has been made on the solutions obtained using combined economic emission dispatch (CEED) problem considering line flow constraints using different intelligent techniques for the regulated power system to ensure a practical, economical and secure generation schedule. The objective of the paper is to minimize the total production cost of the power generation. Economic load dispatch (ELD) and economic emission dispatch (EED) have been applied to obtain optimal fuel cost of generating units. Combined economic emission dispatch (CEED) is obtained by considering both the economic and emission objectives. This bi-objective CEED problem is converted into single objective function using price penalty factor approach. In this paper, intelligent techniques such as genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE) are applied to obtain CEED solutions for the IEEE 30-bus system and 15-unit system. This proposed algorithm introduces an efficient CEED approach that obtains the minimum operating cost satisfying unit, emission and network constraints. The proposed algorithm has been tested on two sample systems viz the IEEE 30-bus system and a 15-unit system. The results obtained by the various artificial intelligent techniques are compared with respect to the solution time, total production cost and convergence criteria. The solutions obtained are quite encouraging and useful in the economic emission environment. The algorithm and simulation are carried out using Matlab software.

The objective of this paper is to evolve simple and effective methods for the economic load dispatch (ELD) problem with security constraints in thermal units, which are capable of obtaining economic scheduling for utility system. In the proposed improved particle swarm optimization (IPSO) method, a new velocity strategy equation is formulated suitable for a large scale system and the features of constriction factor approach (CFA) are also incorporated into the proposed approach. The CFA generates higher quality solutions than the conventional PSO approach. The proposed approach takes security constraints such as line flow constraints and bus voltage limits into account. In this paper, two different systems IEEE-14 bus and 66-bus Indian utility system have been considered for investigations and the results clearly show that the proposed IPSO method is very competent in solving ELD problem in comparison with other existing methods.

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.

This paper develops an optimal simultaneous hourly reconfiguration and day-ahead scheduling framework in smart distribution systems considering the operation of the protection devices. The objective function of the model is defined as the minimization of system’s costs in terms of the costs associated with the purchased power from the wholesale market as well as the distributed generation (DG) owners, cost of switching actions, power losses cost and the cost for implementation of demand response (DR) programs. Moreover, a novel switching index along with the maximum number of switching actions based on the switch ages and critical branches in the network is presented. Due to the nonlinearity and non-convexity nature of the problem, the proposed optimization problem is then solved using a metaheuristic approach based on particle swarm optimization (PSO). As the result of the optimization process, the optimal set-points of DGs and responsive loads together with the optimal radial configuration of the distribution system for each hour of the scheduling time horizon are determined. To investigate the effect of DR programs and hourly reconfiguration on the load profile of the system, different price-based DR actions combined with interruptible load programs are also considered. Moreover, to demonstrate the satisfactory performance of the proposed model, the IEEE 33-bus distribution test system is thoroughly interrogated.

The variability and intermittency of renewable energy and power load bring great pressure to the dispatch of Micro-Grid, especially intra-day dispatch. In order to reduce the intra-day dispatch pressure, this paper proposes a data-driven two-stage day-ahead dispatch model for islanded Micro-Grid. The first stage dispatch model considers multiple demand responses, and applies phase space reconstruction and machine learning to predict renewable energy output and power load. Multi-objective particle swarm optimization algorithm is used to solve the first stage dispatch model. For the obtained Pareto Front, weight multiple objectives by entropy weight method to get the optimal solution. Since the deviation between the predicted value and the actual value can lead to renewable energy curtailment or load loss, the role of the second stage is to predict the renewable energy curtailment and load loss after the first stage dispatch. Firstly, extreme gradient boosting is used to predict when renewable energy curtailment and load loss occur. Secondly, extreme learning machine is used to predict the amount of renewable energy curtailment and load loss at the corresponding time points. Finally, linear programming and mixed integer linear programming are used to solve the second stage dispatch model. By comparative cases analysis, simulation results show that the enhancement of demand response to system efficiency is at the cost of increasing dispatch cost and reducing reliability. In contrast, the proposed two-stage dispatch method using regulation reserve capacity not only reduces dispatch cost, but also improves system efficiency and reliability.

Unbalanced distribution of single-phase customers among the three phases is an important factor in causing unbalance in low-voltage (LV) distribution systems. This unbalance phenomenon has some important consequences as higher losses in the distribution system, increasing voltage unbalance, and higher neutral wire current. The key challenge is that these networks only have the monthly reading of customers’ meters which is called data-scarce distribution systems due to the lack of enough information. In this paper, a method is proposed in order to have a balanced distribution of single-phase customers between the phases based on particle swarm optimization (PSO) algorithm to reduce the average-load losses (which is calculated losses in the average load of all of the customers and is an attainable data in real-world data-scarce distribution systems). First, a comparative study is performed between two load balancing methods. In the first method, the transformer neutral wire current is reduced. This method is used generally in electricity distribution companies. In the second method, the balancing is performed in order to reduce the losses in the average-load. Then, a probabilistic approach is proposed in order to manage the uncertainty of single-phase customer's consumption, which is an important issue in low-voltage distribution systems balancing. Numerical studies are performed on a practical system and the results show that the proposed method has higher performance comparing to the first method used by distribution companies. Also, it is observed that by rephasing of some single-phase customers, losses are reduced significantly.

This paper proposes a peer-to-peer energy trading system based on multi-objective game-theoretic optimisation for a clustered microgrid to find suitable sizes of distributed generations, including energy storage systems. The chosen architecture includes three different microgrids, and each microgrid can be a combination of solar panels, wind turbines and batteries, to meet the requirements of their residential loads. In the clustered microgrid, all microgrids are connected with one another, and also with the main grid, and they have the ability to perform both peer-to-peer and peer-to-grid energy trading based on load requirements and to reach the optimum value of the outlined objective function. To formulate a multi-objective function, two different criteria’ the annual profit and the loss of power supply probability are considered, and the Nash equilibrium-based game theory technique is modelled for clustered microgrids using a particle swarm optimisation algorithm to obtain suitable sizes of the players and payoff values. In the end, for each microgrid, suitable sizes of solar panels, wind turbines and batteries are proposed to meet the residential load requirements in the grid-connected mode. The outcomes of the multi-objective function are analysed and compared for two energy trading schemes, including peer-to-grid and peer-to-peer. The trends of objective functions and sizes of the players are analysed to verify the design of the proposed architecture.

The variable and intermittent nature of large-scale wind power integration makes day-ahead unit commitment (UC) decision-making difficult. This paper establishes a novel and effective UC model with wind power integration by optimizing the utilization of the forecast error and reserve decision. First, considering the temporal feature of the UC model, a time sequence segment-fitting method (TSFM) for the wind power forecast error is presented, in which the non-parametric fitting method is used to address the ‘fat-tail’ effect of error distribution. Second, according to the probability intervals of the forecast error and characteristic of the reserve, a new reserve decision method is proposed to define three classes of reserve strategies and optimize the capacity for each type of reserve. Third, a UC model with time-varying confidence levels is established by introducing conditional value at risk (CVaR) and chance-constrained programming (CCP), and is linked with the TSFM. This novel model can balance the costs of fuel, various reserves, load shedding risk, and wind curtailment risk, which can improve the economy of the power grid operation. Finally, an improved hybrid particle swarm optimization algorithm with a heuristic searching strategy is applied to solve this multivariate mixed integer non-linear programming problem. The simulation results verify the effectiveness and practicality of the model proposed.

The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24 h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation.

Accurate medium- and long-term power consumption forecast is the premise and foundation of secure scheduling and layout planning of power system, which are conducive to the coordinated development of various power generation channels so that renewable energy generation can be improved. In view of this problem, this paper analyzes power consumption intensity in different regions in China and proposes a modified grey model with novel initial condition to enhance its forecast accuracy. Influence of initial condition in grey model is analyzed synthetically by several commonly used models, and the Inverse Square Root Unit (ISRU) function with adjustable parameters is defined as the novel initial condition to better reveal the characteristics of data growth. Particle Swarm Optimization (PSO) algorithm is adopted to calculate the introduced coefficients. Using data available from National Bureau of Statistics of China from 2007 to 2014, consumption data of 31 provinces (municipalities) from 2015 to 2018 is forecasted to attest the efficiency and adaptability of ISRU-GM(1,1). Forecast results are compared with other improved prediction models as well. Potential power consumption ability in China in the next few years from provincial perspective is studied, which guides the layout planning of power system.

This paper aims at adopting the Particle Swarm Optimization (PSO) technique to find the near-optimal solutions for the capacitor allocation problem in distribution systems for the modified IEEE 16-bus distribution system connected to wind energy generation based on a cost function. The proper allocation and the optimized number of capacitors have led to adequate power losses reduction and voltage profile enhancement. Because of the wind power generation variations due to the nature of wind speed intermittency and the lack of reactive power compensation, the problem under study have been presented involving a nonlinear fitness function. In order to solve it, the corresponding mathematical tools have to be used. The formulated fitness cost function has consisted of four terms: cost of real power loss, capacitor installation cost, voltage constraint penalty, and capacitor constraint penalty. PSO technique has been used to obtain the near-optimum solution to the proposed problem. Simulation results demonstrate the efficiency of the proposed fitness cost function when applied to the system under study. Furthermore, the application of PSO to the modified IEEE 16-bus system has shown better results in terms of power losses cost and voltage profile enhancement compared to Genetic Algorithm (GA). In order to verify the successful adaptation of PSO toward attaining adequate near-optimal capacitor allocations in distribution systems, this metaheuristic technique has been employed to the large-scale IEEE 30-bus system. The proposed PSO technique has provided adequate results while modifying the objective function and constraints to include the power factor and transmission line capacities for normal and contingency (N-1) operating conditions.

To meet ever increasing load demand in a sustainable way, reinforcement of photovoltaic (PV) array, wind turbine (WT) and capacitor bank in distribution network is proposed in this paper. A comprehensive planning model is presented to determine location and required installation capacity of multiple PV array, WT and capacitor units in an electric power distribution network under heavy load growth situation. Intermittent power generation of renewable energy sources (RESs) are quantified with suitable probability distribution functions and incorporated in the planning model. The planning approach considers several welfare areas in the distribution systems, viz., increment of profit margin, reduction of carbon-di-oxide emission, minimization of distribution power losses, enhancement of voltage stability level and improvement of the network security considering power flow, voltage limit, line capacity, RES penetration, capacitor penetration and utility economy constraint. Non-dominated sorting based multi-objective particle swarm optimization algorithm along with fuzzy decision making criteria is used to find the best allocation alternative for mix RES and capacitor planning problem. The effectiveness of the proposed model has been tested on a typical 28-bus Indian rural distribution network. The results show that more efficient techno-eco-environmental optimization can be obtained from combined RES and capacitor planning model.

Management of reactive power resources is essential for secure and stable operation of power systems in the standpoint of voltage stability. In power systems, the purpose of optimal reactive power dispatch (ORPD) problem is to identify optimal values of control variables to minimize the objective function considering the constraints. The most popular objective functions in ORPD problem are the total transmission line loss and total voltage deviation (TVD). This paper proposes a hybrid approach based on imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) to find the solution of optimal reactive power dispatch (ORPD) of power systems. The proposed hybrid method is implemented on standard IEEE 57-bus and IEEE 118-bus test systems. The obtained results show that the proposed hybrid approach is more effective and has higher capability in finding better solutions in comparison to ICA and PSO methods.

This paper presents a new and comprehensive Objective Function (OF) for capacitor placement in distribution networks. In this study, distribution network management’s viewpoint toward identifying comprehensive OF to maximize the benefit of a distribution company is considered. In addition to considering active power loss cost and capacitor cost, two other important terms, i.e. cost of buying reactive power and voltage drop penalty for maximizing the benefit of distribution companies are considered in the OF. All actual conditions including time varying nature of load, annual load growth, time varying price of active and reactive power, and switchable and fixed capacitor are taken into account based on the reality. The profit derived from the proposed OF is compared with two other common OFs, and it is shown that the benefit achieved from the proposed OF is more than the two other OFs. The proposed OF is validated and tested on radial distribution systems with differing topologies and varying sizes and complexities.

This paper proposes a practical formulation for the non-convex economic dispatch problem to consider multi-fuel options, ramp rate limits, valve loading effect, prohibited operating zones and spinning reserve. A new optimization algorithm based on the θ-bat algorithm (θ-BA) is suggested to solve the problem. The θ-BA converts the Cartesian search space into the polar coordinates such that more search ability would be achieved. According to the complex, nonlinear, and constrained nature of the problem, a new self-adaptive modification method is proposed. The proposed modified θ-BA (θ-MBA) is constructed based on the roulette wheel mechanism to effectively increase the convergence of the algorithm. The high ability and satisfying performance of the proposed optimization method is examined on IEEE 15-unit, 40-unit and 100-unit test systems.

Hydro–wind–thermal scheduling is one of the most important optimization problems in power system. An aim of the short term hydrothermal scheduling of power systems is to determine the optimal hydro, wind and thermal generations in order to meet the load demands over a scheduled horizon of time while satisfying the various constraints on the hydraulic, wind and thermal power system network. In this paper we present optimal hourly schedule of power generation in a hydro–wind–thermal power system applying PSO technique. The simulation results inform that the proposed PSO approach appears to be the powerful to minimize fuel cost and it has better solution quality and good convergence characteristics than other techniques.

An important objective of the Optimal Power Flow (OPF) problem is to minimize the generation cost and keep the power outputs of generators, bus voltages, bus shunt reactors/capacitors and transformer tap settings in their secure limits. Solving this OPF problem using classical methods suffer from the disadvantages of highly limited capability to solve the practical large scale power system problems. To overcome the inherent limitations of conventional optimization techniques, Swarm Intelligence (SI) methods have been developed. However, the environmental concern, dictate the minimization of emissions of the thermal plants. Individually, if one objective is optimized, other objective is compromised. Hence, Multi-Objective Optimal Power Flow (MO-OPF) problem has been formulated in this paper. Swarm Intelligence methods, such as Particle Swarm Optimization (PSO) and Glowworm Swarm Optimization (GSO) have been used to solve the OPF problem with generation cost and emission minimizations as objective functions. The effectiveness of the proposed algorithms are tested on IEEE 30 bus and practical Indian 75 bus systems for cost minimization as objective function, and IEEE 30 bus test system for minimization of cost and emission as objectives. The results obtained from both the networks, the PSO and GSO are compared with each other based on different parameters.

As distributed energy (DE) and storage devices being integrated into microgrids (MGs), demand side management (DSM) is getting more and more complicated. The real-time pricing (RTP) mechanism based on demand response (DR) is an ideal method for DSM, which can achieve supply–demand balance and maximize social welfare in the future. This paper proposes a hierarchical market framework to address RTP between the power supplier and multi-microgrids (MMGs). Firstly, an expectation bilevel model is proposed to adjust the energy scheduling of MMGs, including uncertain loads, multi-energy-supply and storage devices,etc. In the proposed bilevel model, the upper level aims to maximize the profit of the power supplier, while the lower level is formulated to maximize the expectation of total welfares for MMGs. Then, the lower level is transformed into a deterministic optimization problem by mathematical techniques. To solve the model, a hybrid algorithm-called distributed PSO-BBA, is put forward by combining the particle swarm optimization (PSO) and the branch and bound algorithm (BBA). In this algorithm, the PSO and BBA are employed to address the subproblems of upper and lower levels, respectively. Finally, simulations on several situations show that the proposed distributed RTP method is applicable and effective under uncertainties, and can reduce the computational complexity as well. The results show that the hierarchical energy dispatch framework is not only more reasonable but also can increase the profits of power suppliers and the welfare of MGs effectively.

Gradual depletion of fossil fuel resources, poor energy efficiency, and environmental pollution problems have led to the use of renewable energy sources (RESs) such as wind turbines (WTs) and solar photovoltaic (PV) cells in distribution networks all around the world. The uncertain nature of these sources, along with network power demands, necessitates probabilistic evaluation to extract results with high applicability and efficiency. Distribution network flexible AC transmission system (D-FACTS) devices such as distribution static compensator (D-STATCOM) can be efficiently used for making the modern distribution networks with high penetration of RESs more flexible. This paper presents a probabilistic technique for optimal allocation of the D-STATCOM, considering the correlation between uncertain variables. The proposed solution method helps to mitigate expected active power losses, improve expected voltage deviation index (VDI), and decrease D-STATCOM expected installation cost for radial/mesh distribution networks. The k-means based data clustering method (DCM) and Latin hypercube sampling (LHS) method are used for probabilistic evaluation of distribution networks. In addition, the particle swarm optimization (PSO) algorithm is employed as the optimization tool. The proposed algorithm is applied to the IEEE 69 node test network, and the results are compared with the Monte Carlo simulation (MCS) method. Also, the efficacy of the proposed study method has been investigated for a real meshed distribution network.

This paper presents binary particle swarm optimization (BPSO) technique for the optimal allocation of phasor measurement units (PMUs) for the entire observability of connected power network. Phasor measurement units are considered as one of the most important measuring devices in the prospect of connected power network. PMUs function may be incorporated to the wide-area connected power networks for monitoring and controlling purposes. The optimal PMU placement (OPP) problem provides reference to the assurance of the minimal number of PMUs and their analogous locations for observability of the entire connected power networks. Binary particle swarm optimization (BPSO) algorithm is developed for the solution of OPP problem. The efficacy and robustness of the proposed method has been tested on the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, IEEE 118-bus and Northern Regional Power Grid (NRPG) 246-bus test system. The results obtained by proposed approach are compared with other standard methods and it is observed that this BPSO based placement of phasor measurement units is found to be the best among all other techniques discussed.

This paper presents a newly developed hybrid optimization algorithm for solving the problem of Economic Dispatch (ED) for a multi-generator system. The hybrid algorithm named ACO–ABC–HS combines the framework of Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Harmonic Search (HS) algorithms to find the optimized solution for the system. The ACO algorithm is used to find the initial solution set, the ABC algorithm is employed to test and improve each of the probable solutions provided by the ACO module, while the HS module is used to discard the inferior solutions from the solution set and replace them with better ones. The performance of this hybrid algorithm is compared with those of conventional ED solving techniques like Gradient Search as well as other evolutionary algorithms namely ABC, ACO, HS and Particle Swarm Optimization (PSO). Valve point loading, environmental emissions, line losses and ramping rate constraints have been included in the ED analysis to provide more practical results. The algorithm’s performance is also tested for Multi-Area Economic Dispatch (MAED) with tie-line constraints. The results obtained clearly point out the superiority of the hybrid algorithm in finding out the optimum results, while satisfying the constraints of minimizing the generation costs, reducing the emissions as well as tie-line costs and transmission losses.

Multi-area dynamic economic dispatch determines the optimal scheduling of online generator outputs and interchange power between areas with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators, tie line constraints, and transmission losses. This paper presents quasi-oppositional group search optimization for solving multi-area dynamic economic dispatch problem with multiple fuels and valve-point loading. Group search optimization (GSO) inspired by the animal searching behavior is a biologically realistic algorithm. Quasi-oppositional group search optimization (QOGSO) has been used here to improve the effectiveness and quality of the solution. The proposed QOGSO employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. The QOGSO is tested on two multi-area test systems having valve point loading and mult-fuel option. Results of the proposed QOGSO approach are compared with those obtained from group search optimization (GSO), biogeography-based optimization (BBO), gravitational search algorithm (GSA), differential evolution (DE) and particle swarm optimization (PSO). It is found that the proposed QOGSO based approach is able to provide better solution.

Planning of reactive power sources is a serious issue for secure and economic operation of power systems. In this paper, a bi-level strategy is proposed to optimize the Reactive Power Planning (RPP) problem. In the first level, the weakest buses are selected to be the optimal placements to install the additional VAR sources and its corresponding suitable sizes are determined using a proposed Refined Heuristic Process (RHP). In the second level, two modified versions of Differential Evolution Algorithm (DEA) are proposed for optimizing the RPP control variables which able to minimize both the allocation costs of additional VAR sources throughout the system, and the system operational costs of real power losses. To validate the effectiveness of proposed strategy, several applications are carried out on three power systems networks namely IEEE 14-bus, IEEE 30-bus test systems and the West Delta region system as a part of the Egyptian Unified network. The proposed strategy is evaluated compared with other optimization methods as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and the commonly used Differential Evolution (DE) version (DE/rand/1). The robustness of the proposed versions of DEA is proven compared to other optimization techniques. Added to that, the control parameters of the proposed DEA are optimally identified. Numerical results show that the proposed version of DEA achieves highest reduction in the operation and investment costs compared to other optimizing algorithms in the literature which denotes that the proposed version of DEA can be efficiently applied to the RPP problem.

Comparing with the traditional distribution network, a significant feature of the active distribution network (ADN) is that the performance of distributed generation (DG) units, energy storage units and micro-grid (MG) in the network is controllable for the distribution network operator. Considering the characteristics of the distributed power supply and micro-grid, and giving full play to the advantages of distributed generation technology in the economic, environmental and energy aspects, this paper highlights an environmental protection and energy saving optimal schedule model for ADN. The scheduling model focuses on the minimum network loss, minimum voltage deviation and minimum difference between peak and valley load. In addition, the two stage algorithm is presented to solve the proposed multi-objective scheduling model of ADN. First, a set of Pareto solutions are obtained by using the proposed particle swarm optimization combined with bacterial foraging algorithm (PSO-BFO) to solve multi-objective optimization problems, then the optimal schedule strategy of ADN is gained through evaluating the Pareto solutions with entropy weight decision-making method. To avoid the search falling into local optimal solution, the two-value crossover operator is introduced to exchange the information among subpopulations and update the position of related particles. Meanwhile, the adaptive adjusting inertia constant strategy is used to improve the algorithm convergence speed. Finally, the case study results demonstrate the rationality of the proposed optimal schedule model and the validity of its solution algorithm for ADN.

Many electrical power systems are changing from a vertically integrated entity to a deregulated, open-market environment. This paper proposes an approach to optimally allocate multi-type flexible AC transmission system (FACTS) devices in restructured power systems with wind generation. The objective of the approach is to maximize the present value of long-term profit. Many factors like load variation, wind generation variation, generator capacity limit, line flow limit, voltage regulation, dispatchable load limits, generation rescheduling cost, load shedding cost, and multilateral power contracts are considered in problem formulation. The proposed method accurately evaluates the annual costs and benefits obtainable by FACTS devices in formulating the large-scale optimization problem under both normal condition and possible contingencies. The overall problem is solved using both Particle Swarm Optimization (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The efficacy of the proposed approach is demonstrated for modified IEEE 14-bus test system and IEEE 118-bus test system.

In this paper, using particle swarm optimization (PSO) based method is developed to determine the optimal allocation of distributed generators (DGS) on a multi phased unbalanced distribution network. PSO algorithm has been programmed in MATLAB using open source software called OpenDSS in a co-simulation environment to solve the unbalanced three-phase optimal power flow (TOPF) and to find the optimal location and sizing of different types of distributed generators. Using the IEEE 123 node distribution feeder as a test bed, results from the proposed method is compared to those from the repeated load flow (RLF) method. For a realistic study, mixes of all type of DGs are considered. Results indicate that integrating optimally sized DGs at the optimal locations not only reduces the total power loss in the distributed system but improves the voltage profile as well.

This paper proposes differential search (DS) algorithm to solve distribution system reliability optimization problem. Optimum number and location of remote control switch (RCS) have been found, in order to enhance reliability of a radial distribution system. A multi-objective formulation has been considered with a view to enhance reliability at a compromised cost. DS algorithm utilizes the Brownian-like random-walk movement used by an organism to migrate. Simulation results obtained by DS algorithm have been compared with that of particle swarm optimization (PSO), differential evolutionary algorithm (DE), genetic algorithm (GA), ant colony optimization (ACO) and gravitational search algorithm (GSA). Results show that DS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency.

A stochastic model for day-ahead Micro-Grid (MG) management is proposed in this paper. The presented model uses probabilistic reconfiguration and Unit Commitment (UC) simultaneously to achieve the optimal set points of the MG’s units besides the MG optimal topology for day-ahead power market. The proposed operation method is employed to maximize MG’s benefit considering load demand and wind power generation uncertainty. MG’s day-ahead benefit is considered as the Objective Function (OF) and Particle Swarm Optimization (PSO) algorithm is used to solve the problem. For modeling uncertainties, some scenarios are generated according to Monte Carlo Simulation (MCS), and MG optimal operation is analyzed under these scenarios. The case study is a typical 10-bus MG, including Wind Turbine (WT), battery, Micro-Turbines (MTs), vital and non-vital loads. This MG is connected to the upstream network in one bus. Finally, the optimal set points of dispatchable units and best topology of MG are determined by scenario aggregation, and these amounts are proposed for the day-ahead operation. In fact, the proposed model is able to minimize the undesirable impact of uncertainties on MG’s benefit by creating different scenarios.

Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.

The economic emission dispatch (EED) problem of thermal generating units is a highly complex combinatorial multi-constraint, non-convex optimization problem with conflicting objectives. This paper presents a Modulated Particle Swarm Optimization (MPSO) method to solve the EED problem of thermal units. The conventional PSO is modified by modulating velocity of particles for better exploration and exploitation of the search space. The modulation of particles’ velocity is controlled by introducing a truncated sinusoidal constriction function in the control equation of PSO. The conflicting objectives of the EED problem are combined in fuzzy framework by suggesting adjusted fuzzy membership functions which is then optimized using proposed PSO. The effectiveness of the proposed PSO is tested on three standard test generating systems considering several operational constraints like valve point effect, and prohibited operating zones (POZs). The application results and their comparison with other existing methods show that the proposed MPSO is promising for EED problem of thermal generating units.

This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.

In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.

In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multi-modal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brainstorm optimisation algorithm (BSOA) is a novel promising heuristic optimisation algorithm inspired by brainstorming process in human beings. In this paper, BSOA is employed to find optimal location and setting of FACTS devices. Static var compensators (SVC’s) and thyristor controlled series compensators (TCSC’s) are used as FACTS devices. FACTS allocation problem is formulated as a multi-objective problem whose objectives are voltage profile enhancement, overload minimisation and loss minimisation. The results of applying BSOA to FACTS allocation problem in IEEE 57 bus system demonstrate its high efficacy in solving this problem both with TCSC and SVC units. BSOA leads to better voltage profile and lower losses than particle swarm optimisation (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), hybrid of genetic algorithm and pattern search (GA–PS), backtracking search algorithm (BSA), gravitational search algorithm (GSA) and asexual reproduction optimisation (ARO). The findings of this research can be used by power system decision makers in order to establish a better voltage profile and lower voltage deviations during contingencies.

This paper proposes a new method of optimization, the Big Bang–Big Crunch (BB–BC) method, for the optimization of preventive and corrective control actions to enhance the dynamic security of a power system against transient instabilities. The control actions, generation rescheduling and load shedding, are considered as constrained optimization problems with static and dynamic security constraints. These optimization problems are solved through the BB–BC method to minimize some operational costs related to the control actions. To reduce the size of the search spaces and the computational burden, decision trees and correlation coefficients are used as feature selection tools, which determine the most effective generators and loads for shaping the system’s transient stability. The proposed method is applied to a test system and compared with genetic algorithms, particle swarm optimization, differential evolution and active set method. The BB–BC method is promising since it gives comparable results with the other population based optimization methods.

The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.

Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.

This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.