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The many well-established advantages of distributed generation (DG) make their usage in active distribution networks prevalent. However, uncontrolled operation of DG units can negatively interfere with the performance of other equipment, such as tap-changers, in addition to resulting in sub-optimal usage of their potential. Thus, adequate scheduling/control of DG units is critical for operators of the distribution system to avoid those adverse effects. A linearised model of a multi-objective method for coordinating the operation of photovoltaics, battery storage systems, and tap-changers is proposed. Three objective functions are defined for simultaneously enhancing voltage profile, minimising power losses, and reducing peak load power. The formulated multi-objective problem is solved by means of the epsilon-constraint technique. A novel decision-making methodology is offered to find the Pareto optimality and select the preferred solution. To assess to proposed model's performance, it is tested using 33-bus IEEE test system. Consequently, tap-changers suffer lessened stress, the batteries state-of-charge is kept within adequate limits, and the DG units operation is at higher efficiency. The obtained results verify the effectiveness of this approach.

This paper is intended to give an effective coordination method between high penetration of plug in electric vehicles (PEVs) and optimal operation management (OOM) of distribution network problem. An effective fuzzy logic controller (FLC) is proposed which focuses on coordinated smart charge/discharge control of high penetration of PEVs with OOM in an iterative optimization process. In each iteration, the proposed FLC receives the voltage of the connection node of the PEVs, the remaining time to the peak load and the available energy of the PEVs while appropriately specifies the amount of absorbed or injected active power of the PEVs. This coordination leads to the minimization of the electric network losses, voltage flattening and reducing the produced pollutant. A modified version of Sine-Cosine Algorithm (MSCA) is used to minimize the objective functions while the problem constraints are satisfied. An appropriate 33-bus distribution network with 1000 PEVs in 100 groups is considered in order to show the authenticity and efficiency of this work. The results show that the proposed FLC-based coordination procedure could effectively adjust the exchange power of the PEVs with the OOM decision variables in an acceptable time duration.

This study presents a new solution to cope with the intermittent nature of renewable generations (RGs) and facilitate the integration of RGs in the smart active distribution network. Electric spring (ES) as one of the most influential solutions in demand-side management is proposed as a flexible resource for flexible operation of grid-connected microgrid against other sources of uncertainty such as forecasted load demand and energy price as well as the RGs output. This innovative approach has been considered in the context of a stochastic problem by presenting the static model of ES for the first time. The modelling of uncertainties is done by the roulette wheel mechanism as a scenario generation process and backward method for reducing the number of scenarios. In the proposed optimisation problem, the objective function is minimising the operating cost and voltage deviation as well as maximising system flexibility, subject to the AC power flow, ES and RGs constraints and technical system limitations. Finally, the proposed solution is tested on 33-bus IEEE test system by the general algebraic modelling system software. The case studies demonstrate the efficiency of the proposed ES model in different simulation and experimental cases in providing flexi-renewable microgrid.

Recently, demand-response (DR) programmes are one of the appropriate tools for energy systems to encourage flexible customers to participate in the operation of energy systems. One of the complex tasks in multi-energy environments is optimal energy flow (OEF) problem of these systems. In this regard, this study investigates the OEF of an integrated electrical, heat, and gas system considering flexible heat and electrical demands. The conventional DR programme has been combined with the demand-side energy supplying management activity by introducing switching concept among input energy carriers. The way of the supplying energy of flexible customer can be changed by switching among input energy carriers. Here, the integrated system operator minimises the system operation costs subject to supply flexible consumers’ energy. To solve the complex OEF problem, this study presents a new optimisation algorithm named modified biogeography-based optimisation (BBO) algorithm. In this study, the proposed modification for the original BBO increases the robustness and the capability of the proposed optimisation method. The numerical results show that the flexible DR programme creates smoother energy demand curves in heat and electrical networks and reduce the operating costs of the integrated system.

The integration of various energy supplying systems increases the energy efficiency and energy system reliability. Smart microgrids are an ideal area to use multi-carrier energy systems. In this context, this study presents a novel modelling framework for optimal day-ahead scheduling of a networked multi-carrier energy microgrid (NMCEMG) system. The NMCEMG system in this study is composed of heat, gas, and power supply networks. The presented framework has enhanced the multi-carrier microgrid modelling against the previous works that modelled the multi-carrier microgrid as an energy hub due to difficulties in the energy flow analysis of heat and gas networks. The proposed framework optimises the day-ahead operating cost of the NMCEMG system considering nodal and energy flow constraints of each network. The proposed microgrid includes various energy interdependent equipment such as combined heat and power units, gas-fired boilers, power to gas units, electrical and heat storages and electric heat pumps. This study presents a new optimisation algorithm named self-adaptive modified whale optimisation algorithm based on wavelet theory to solve the day-ahead optimal scheduling of the NMCEMG problem. The numerical results corroborate the proposed modelling framework as superior over conventional hub-based multi-carrier microgrid models in terms of energy system security.

Considering the independency of areas as a challenge in multi-area economic dispatch (MAED) problems, this study presents a novel parallel process MAED to preserve independency in MAED problems and, at the same time, supply the demanded power in each area. Although distributed power networks are physically connected to each other to improve the financial and technical aspects of the operation, they have to operate with few information about other areas. In addition, utilising renewable energies, especially wind turbines, are unavoidable in the future to supply the demanded power. However, the uncertainty that arises from wind behaviour makes the MAED problems more complicated, meaning that the exact amount of output power in each time instant is not known. Hence, the authors analyse the wind and load uncertainty effect on the MAED cost function using the unscented transformation (UT) method. Moreover, the applicability of the UT method in terms of accuracy and speed is evaluated with Monte–Carlo simulation method. Finally, four different cases, both small and large, are studied to evaluate the effectiveness of the proposed parallel process and UT method in MAED problems.

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

This paper presents a comprehensive model of dynamic optimal operation management of the smart nano-grids (NGs) including the micro wind turbines (WTs) and micro photovoltaics (PVs) as renewable energy sources (RESs) while micro turbines (MTs) and fuel cell (FC) are considered as non-RESs. Furthermore, two types of lead-acid and lithium-ion batteries are considered besides the three types of controllable, curtailment-able and must run loads to increase the flexibility of the proposed formulation. The different objective functions of NG operation problem to be minimised include operating cost, environmental damage cost of pollution gases and exchanged power cost with the main grid. Also the power losses of batteries are modelled using the quadratic functions based on types and output powers of considered batteries, while these losses impose additional cost to operation cost functions of batteries. A modified teaching-learning-based optimisation (MTLBO) algorithm is used to cope with the multiobjective problem considering the constraints.

Here, an optimisation framework is proposed for integrated resource expansion planning (IREP) including conventional generation units, wind generation units, and transmission lines while taking into account the role of demand response program (DRP) aggregators. This problem is a bi-level optimisation problem. In the upper-level problem, the objective function is to maximise the profit for each resource, that is, the generation company (GENCO), wind generation company (WINDCO), and transmission company (TRANSCO) as well as the DRP aggregator. Also, the lower-level problem considers a market model with the participation of private GENCOs, WINDCOs, TRANSCOs, and DRP aggregators. The lower-level problem minimises energy cost subject to AC power flow (PF) equations, power network limitations, pollution constraint, GENCO, WINDCO, TRANSCO constraints, and technical limitations of DRPs. Here, the lower-level problem is non-linear and non-convex. Accordingly, to facilitate the solution of the proposed bi-level optimisation problem, a linear model is proposed. Then, the proposed bilevel optimisation problem is converted into an integrated single-level one using the Karush-Kuhn-Tucker (KKT) conditions. Eventually, a mixed-integer linear programming (MILP) model is proposed. The proposed method is applied to the IEEE 6-bus and the IEEE 30-bus test systems, and finally, the capabilities of the proposed scheme are evaluated.

This study recommends a stochastic optimization model for the security constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. unit/line outages. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC OPF). The AC OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and the OTS action. While the stochastic SCUC model with the OTS action based on AC OPF is a mixed-integer non-linear programming model, this study transforms it into a mixed-integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of AC OPF, which allows the reactive power and voltage to be considered directly in power flow model. The proposed stochastic SCUC problem is evaluated on the 6 bus, IEEE 118-bus and 662-bus test systems in pre- and post-OTS action. Obtained results demonstrate the effectiveness of the proposed model.

The wind and hydro technologies express a significant part of the electricity generation section. This study presents an optimal coordinated bidding strategy of wind, cascaded hydro generation, and pumped-storage (PS) units. One of the chief purposes of this study is maximisation the profit of the wind and hydro plants by participating in the day-ahead energy and ancillary service markets. The regulation and spinning reserve markets are regarded as ancillary services. Thanks to the inherent variability and uncertainty of wind power, it does not participate in the ancillary service market. Hydro company is constructed of several cascaded hydro units which design alongside a river basin as well as a PS unit. In this study, the risk is modelled by using conditional value at risk. To reach the optimum solution, a new improved clonal selection algorithm is applied which shows the effectiveness of the proposed method for optimising a generation companies (GENCOs) profit.

The purpose of this study is to suggest an optimal multi-objective fuzzy fractional-order P I λ D μ controller (MOFFOPID) for the speed control of EV systems with time-delay. It is presumed that while the EV is in movement, the armature winding resistance of the direct current (DC) motor varies with time due to temperature changes and this makes it impossible to develop an accurate dynamic model for an EV system. Consequently, in order to have a fast and accurate tracking of the set-point, besides a smooth control signal, a new multi-objective stochastic optimisation is used for the online adjustment of the parameters of MOFFOPID controller. Moreover, a comparison is made between the results of the current study and those of some of the most recent studies on the same topic, which have used online multi-objective PI and online multi-objective fuzzy PI, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are implemented on a DC motor to verify the effectiveness of the proposed MOFFOPID controller in controlling the speed of the DC motor which has non-linear features. The results of the simulation confirm the desirable performance of suggested controller.

The intensive non-linear system and plants of modern industry highly motivating researcher to extend and evolve non-linear control systems. In this study, in order to control a class of non-linear uncertain power systems in the presence of large and fast disturbances, a new simple indirect adaptive proportional-integral is proposed. For handling dynamic uncertainties, the proposed controller utilises the advantages of least squares support vectors regression (LS-SVR) to approximate unknown non-linear actions and noisy data. The LS-SVR is used to approximate the non-linear uncertainties which must be bounded, whereas no requirement needs for bounds to be known. The globally asymptotic stability of the closed-loop system is mathematically proved by using Lyapunov synthesis. To show the merits of the proposed approach, a non-linear electric vehicle (EV) system is considered as a case study. The goal is to force the speed of EV to track a desired reference in the present of structured and unstructured uncertainties. The experimental data, new European driving cycle, is used in order to examine the performance of proposed controller. The simulation studies on a second-order EV with the presence of fast against slow and large against small disturbances demonstrate the effectiveness of the proposed control scheme.

This study jointly addresses two major challenges in power system operations: (i) sustained growth of intermittent offshore wind farms (OWFs) connected to AC grid via multi-terminal voltage source converter (VSC)-based high-voltage DC (HVDC) grid that brings new challenges to the power system operation, and (ii) dealing with non-linearity of the AC power flow equations with the multi-terminal VSC-based HVDC grid model. To overcome these challenges, firstly, to deal with the uncertainties caused by the high penetration of the intermittent OWFs, this study introduces a robust optimisation approach. The proposed framework is computationally efficient and does not require the probability density function of the wind speed. The proposed decision-making framework finds the optimal decision variables in a way that they remain robust against the set of uncertainties. Secondly, the mathematical representation of the full AC optimal power flow (OPF) problem, with the added modelling of multi-terminal VSC-based HVDC grid in a day-ahead scheduling problem, is a mixed-integer non-linear programming (MINLP) optimisation problem, which is computationally burdensome for large-scale systems. Accordingly, this paper proposes a computationally efficient method for adjustment of solutions set points, which is also compatible with existing customary solvers with minimal modification efforts. This paper is a postprint of a paper submitted to and accepted for publication in [IET Renewable Power Generation] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library

Parameter estimation and dynamic modelling of power systems and their components are basis of design, planning and stability or security assessment in power systems. This study considers the estimation of power system model parameters by a global identification framework based on the maximum-likelihood principle. The proposed framework is formulated as a non-linear optimisation problem, which is solved by a hybrid method based on the bat-inspired algorithm and differential evolution method. The combination of these algorithms makes the hybrid method faster and it obtains closer to the global minimum than a pure global method. Since noise and model uncertainties are inherent parts of system identification, the effect of these factors on the performance of the proposed identification framework are studied. Results based on synthetic data in frequency domain show that the estimated parameters are close to the correct values even in the presence of significant measurement noise and considerable uncertainties.

This study aims to investigate the role of the reconfiguration strategy to enhance the reliability of the distribution systems. In this regard, the idea of failure rate reduction approach is employed to assess three significant reliability indices including the System Average Interruption Frequency Index, System Average Interruption Duration Index and Average Energy Not Supplied. In addition, as a result of attractiveness and importance of the power loss objective function in the system, this target is also considered in the problem. The problem is then formulated in a stochastic framework based on 2m + 1 point estimate method to capture the uncertainty of load, failure rate and repair rate forecast error. In order to make the analysis more practical, the idea of yearly multi-level load modelling is employed. The feasibility and effectiveness of the proposed method are examined on the IEEE 32-bus test system.

This study suggests a new stochastic framework based on point estimate method to capture the uncertainty associated with the forecast error of the active and reactive loads and failure rate and repair rate values. The objective functions to be investigated are system interruption frequency index, system average interruption duration index and average energy not supplied. Meanwhile, since the total MW cost is an attractive issue to the power utilities, the active power losses is also considered as an objective function. In order to explore the problem search space globally, a novel self-adaptive modification method based on firefly algorithm is proposed. The feasibility and effectiveness of the proposed method are examined on the IEEE 34-bus test system. According to the simulation results, the proposed framework based on capacitor placement problem can improve the reliability of the system effectively.

This study proposes a new method to employ the distribution feeder reconfiguration as a reinforcement strategy to enhance the reliability of the distribution systems. Also, several wind power sources are considered to assess their effects on the reliability indices. In order to make the final results more dependable, a stochastic framework based on the probabilistic power flow is utilised to consider the uncertainty of forecast/measurement error of the active and reactive loads, failure rate, repair rate and output generation of wind units, concurrently. The objective functions to be investigated are (i) system average interruption frequency index, (ii) average energy not supplied, (iii) active power losses and (iv) total cost. Moreover, a new modified optimisation method based on harmony search algorithm is proposed to improve the total ability of the algorithm to explore the problem search space globally. The effectiveness and efficiency of the proposed method are examined through two radial distribution test systems.

This paper presents a unit commitment formulation for micro-grid that includes a significant number of grid parallel Proton Exchange Membrane-Fuel Cell Power Plants (PEM-FCPPs) with ramping rate and minimum up/down time constraints. The aim of this problem is to determine the optimum size of energy storage like battery storages and use the efficient hydrogen and thermal energy storages and to schedule the committed units' output power while satisfying practical constraints and electrical/thermal load demand over one day with 15 min time step. In order to best use of multiple PEM-FCPPs, hydrogen storage management is carried out. Also, since the electrical and heat load demand are not synchronised, it could be useful to store the extra heat of PEM-FCPPs in the peak electrical load in order to satisfy delayed heat demands. Due to uncertainty nature of electrical/thermal load, photovoltaic and wind turbine output power and market price, a two-stage scenario-based stochastic programming model, where the first stage prescribes the here-and-now variables and the second stage determines the optima value of wait-and-see variables under cost minimization is implemented. For solving the problem, a new enhanced cuckoo optimisation algorithm is presented and successfully applied to two typical micro-grids. Quantitative results show its usefulness.

This study proposes a multi-objective optimal static and dynamic scheduling of thermoelectric power systems considering the conflicting environmental and economical objectives. Meantime, some restrictions such as valve-point effects, prohibited operating zones, multi-fuel options, line flow limits as well as spinning reserve should be taken into account in order to ensure secure real-time power system operation. A novel multi-objective θ-improved cuckoo optimisation algorithm is projected to solve the optimisation problems by defining a set of nondominated points as the solutions. The suggested method moves forward the particles to the problem search space in the polar coordinates as a substitute of the Cartesian one. In addition, in order to achieve better performance and higher-convergence speed, several improvement strategies are utilised. This algorithm is equipped with a novel powerful mutation strategy in order to increase the population diversity and to amend the convergence criteria. Furthermore, a fuzzy-based clustering is used to control the size of the repository and a niching method is utilised to choose the best solution during the optimisation process and to ensure diversity among non-dominated solutions. Performance of the proposed algorithm is tested on 6-, 10-, 14-, 40- and 100-unit test systems and compared with those of other well-known methods.

This study deals with the problem of controlling class of uncertain non-linear systems in the presence of external disturbances. To achieve this goal, a robust fuzzy sliding mode (RFSM) controller is introduced. First known dynamics of the system are eliminated through feedback linearisation and then fuzzy sliding mode controller is designed using Takagi-Sugeno (TS) method, based on the Lyapunov method which is capable of handling uncertainties. There is no sign of the undesired chattering phenomenon in the proposed method. The globally asymptotic stability of the closed-loop system is mathematically proved. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by optimal H ∞ adaptive proportional integral derivative controller and an optimal Type-2 fuzzy proportional integral derivative, which are the latest researches in the problem in hand. Simulation results show the effectiveness of the RFSM controller.

Nowadays, the rising concern about the costs of fuel and operation of generating units in the energy control centre deserve progress of solution methodologies for the practical economic dispatch (ED) problems. ED aims to schedule the committed units' output power while satisfying practical constraints and load demand. The generator ramp rate limits, non-convex and discontinuous nature of prohibited operating zones, non-smooth characteristic of valve-point effects, multi-fuel type of generation units, transmission line losses and the large number of units in practical power plants make this problem very hard to solve. In this study, a new solution method of integrating the classical gradient-based optimisation technique and a new enhanced simplified swarm optimisation algorithm is comprehensively presented and successfully applied to determine the feasible, robust, fast and globally or near-globally optimal solution within a rapid timeframe for the ED problems. The simulations are carried out on four-test systems, including 10-, 15-, 40- and 80-units using the proposed optimisation technique in the Fortran Power Station 4.0 software. The current proposal outperforms other methods showcased in the recent state-of-the art literature in the area.

In power systems, dynamic economic dispatch (DED) is one of the most significant non-linear problems showing non-convex characteristic because of the valve-point effects. In this study, an enhanced adaptive particle swarm optimisation (EAPSO) algorithm is proposed to solve the DED problem where the valve-point effects, ramp-rate limits and transmission power losses are taken into account. In the proposed optimisation algorithm, a mutation technique is devised to prevent premature phenomena and lead the swarm search space much more effectively; also a novel non-linear approach is designed to adjust the inertia weight factor dynamically according to the optimisation process performance. Social and cognitive factors are self-adaptively tuned, so the swarm can search the space smartly for global optimum solution. The efficiency of the proposed method is validated on three popular test systems in the area including 5, 10 and 30 thermal units. The results are compared with most of the other works in the area. The superiority of the method is shown over earlier methods.

In this study, a novel modified adaptive θ-particle swarm optimisation (MA θ-PSO) algorithm is presented to investigate the multiobjective economic/emission dispatch (MEED). θ-PSO algorithm is based on the phase angle vector and can generate a high-quality solution within the shorter calculation time in comparison with the original PSO and other evolutionary methods. However, θ-PSO algorithm is easy to fall into stagnation when the position of a particle is not improved for several generations. In order to avoid this shortcoming, the authors have proposed a modified θ-PSO algorithm by using a new mutation. Moreover, the inertia weight factor as a significant adjusting parameter of θ-PSO algorithm is tuned by using fuzzy IF/THEN rules such that the cognitive and the social parameters are self-adaptively adjusted. The proposed MA θ-PSO algorithm maintains a finite-sized repository of non-dominated solutions. As the cost and emission functions have conflicting behaviours, a fuzzy clustering technique is used to control the size of the repository. The proposed algorithm is tested on two standard IEEE test systems. The obtained results demonstrate the satisfying capability of the proposed method to generate well-distributed Pareto optimal non-dominated solutions of the MEED problem.

In this study, a stochastic multiobjective framework is proposed for distribution feeder reconfiguration (DFR). The proposed multiobjective framework can concurrently optimise competing objective functions including total power losses, voltage deviation and total cost. Moreover, system uncertainties including wind power generation and active and reactive load uncertainty are explicitly considered in the stochastic DFR problem. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism in conjunction with Weibull/Gaussian probability distribution function of wind/load forecast variations for random scenario generation wherein the stochastic multiobjective DFR problem is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective formulation based on the adaptive modified particle swarm optimisation (AMPSO) is implemented for each deterministic scenario of the first stage. Utilisation of the stochastic framework would capture more uncertainty spectrum of the investigated multiobjective DFR problem rather than that of the deterministic framework. Consequently, the results of the stochastic framework are more realistic and dependable. Moreover, the new adaptive optimisation algorithm (AMPSO) has much more ability than the other well-known algorithms in the area of optimisation applications.

This study presents multiperiod multiobjective generation expansion planning (MMGEP) model of power electric system including renewable energy sources (RES). The model optimises simultaneously multiple objectives (i.e. minimisation of total costs, emissions, energy consumption and portfolio investment risk as well as maximisation of system reliability). The mixed-integer linear programming (MILP) is used for the proposed optimisation and an efficient linearisation technique is proposed to convert the non-linear reliability metrics into a set of linear expressions. The proposed solution for multiobjective mathematical programming (MMP) framework includes a hybrid augmented-weighted epsilon constraint and lexicographic optimisation approach to obtain the Pareto optimal or efficient solutions for the MMGEP problem. Finally, fuzzy decision making is implemented to select the most preferred solution among Pareto solutions based on the goals of decision makers (DMs). A synthetic test system including seven types of candidate units is considered here for GEP in a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail.

In this study, the operating benefits of considering thermal recovery and hydrogen production in the economic model of a grid-parallel proton exchange membrane fuel cell power plant (PEM-FCPP) are investigated. Also the study considers the simultaneous effect of distribution feeder reconfiguration (DFR) on the operating management of PEM-FCPPs in a stochastic environment. In this regard, a new method based on a probabilistic approach called point estimate method (PEM) is proposed to consider the uncertainty associated with the load demand prediction error as well as the variation of (i) price of natural gas for PEM-FCPPs, (ii) tariff for buying electricity from PEM-FCPPs and the grid, (iii) tariff for selling electrical energy, (iv) operation and maintenance cost, (v) hydrogen selling price and (vi) fuel cost for supplying residual loads (thermal energy). The objective functions to be optimised are the total active power losses, the total emission produced and the total cost related to the grid and PEM-FCPPs. As a result of conflicting behaviours of the investigated objective functions, an interactive fuzzy approach is utilised to let the decision maker to apply his/her preferences to the system. The feasibility and efficiency of the proposed method is shown through the use of Tai-power distribution test system.

The study presents an improved particle swarm optimisation (IPSO) method for the multi-objective optimal power flow (OPF) problem. The proposed multi-objective OPF considers the cost, loss, voltage stability and emission impacts as the objective functions. A fuzzy decision-based mechanism is used to select the best compromise solution of Pareto set obtained by the proposed algorithm. Furthermore, to improve the quality of the solution, particularly to avoid being trapped in local optima, this study presents an IPSO that profits from chaos queues and self-adaptive concepts to adjust the particle swarm optimisation (PSO) parameters. Also, a new mutation is applied to increase the search ability of the proposed algorithm. The 30-bus IEEE test system is presented to illustrate the application of the proposed problem. The obtained results are compared with those in the literatures and the superiority of the proposed approach over other methods is demonstrated.

Fuel cells are environmentally clean, have low emission of oxides of nitrogen and sulfur and, at the same time, they can operate with a very low level of noise. In addition, they can provide energy in a controlled way with higher efficiency compared to conventional power plants. This study presents an efficient multi-objective new fuzzy self adaptive particle swarm optimisation evolutionary algorithm to solve the multi-objective optimal operation management considering fuel cell power plants in the distribution network. The objective functions of the problem are to decrease the total electrical energy losses, the total electrical energy cost, the total pollutant emission and deviation of bus voltages. The proposed algorithm is tested on a real distribution test feeder and the results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions.

This study proposes a Modified Honey Bee Mating Optimisation (MHBMO) to solve the dynamic optimal power flow (DOPF) problem of power system considering the valve-point effects. DOPF is a complicated non-linear problem that occupies an important role in the economic operation of power system. It has non-smooth and non-convex characteristics when generation unit valve-point effects are taken into account. Non-linear characteristics of the power generators and practical constraints, such as ramp rate constraint, transmission constraints and non-linear cost functions, are all considered for the realistic operation and they cause more complication of the proposed problem. Recently, evolutionary algorithms are devoted to solve compliment problems like the OPF problem. HBMO is one of the evolutionary algorithms considered as a typical swarm-based approach to optimisation, in which the search algorithm is inspired by the process of real honey-bee mating. Besides the privileges of HBMO, it has some drawbacks such as probability of trapped in local optima and converge to global optima in long time. Therefore this study proposes an algorithm profit from a mutation to overcome the above drawbacks. In order to validate the proposed algorithm, it has been tested on the 14, 30 and 118-bus test systems. The proposed algorithm provides better results in comparison with original HBMO and other methods in the literature as demonstrated by simulation results.