Time-varying Acceleration Coefficients Research Articles

Optimal Congestion Management by Load Curtailment in Electricity Market

A novel bi-level framework is developed for congestion management in electric power market by load curtailment and generator rescheduling. Interruptible load auction schemes are performed to detect the load buses which require corrective measures to relieve congestion. The proposed congestion management problem is solved in two different levels. The objective functions formulated as non-linear equation is solved using swarm intelligence techniques. In first level, the load curtailment problem is solved, while in second level the generator rescheduling problem is solved. Generator rescheduling through swarm based optimization techniques minimizes the curtailment of contracted power in a deregulated market. A comparative study has been carried out on the swarm intelligence techniques such as Cauchy Gaussian infused PSO (CGI-PSO) and Particle Swarm Optimization with Time Varying Acceleration Coefficient (PSO-TVAC) to solve the proposed problem. The formulated methodology is simulated on standard IEEE 30 bus system and verified through Indian utility-69 bus system.

Fuel constrained dynamic economic dispatch with demand side management

Owing to slowly reduction of fossil fuel, the cost-effective use of available fuel for electric power generation has turn out to be a very important concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work recommends social group entropy optimization (SGEO) technique to solve fuel constrained dynamic economic dispatch (FCDED) with demand side management (DSM) integrating renewable energy sources and pumped hydro storage plant. Here the dynamic economic dispatch (DED) problem is solved with and without fuel constrained. The effectiveness of the recommended technique is revealed on two test systems. Simulation results of two test systems have been compared with those acquired from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Test results show that fuel consumption can be sufficiently controlled for fulfilling constraints imposed by suppliers. It has been observed from the comparison that the suggested SGEO has the capability to confer with superior-quality solution.

Heat and power generation augmentation planning of isolated microgrid

Abstract- Due to raising heat and power demand the microgrid has to be expanded to meet increased power and heat demand. A short-range heat and power generation augmentation planning (HPGAP) of an isolated microgrid incorporating plug-in electric vehicles (PEVs) is described and evaluated here. The objective of HPGAP is to find out the most cost-effective and reliable expansion plan for meeting the forecasted power demand and heat demand over a short-range horizon while fulfilling a large number of technical, reliability and social constraints. An archetypal test system with existing diesel generators, small hydro power plant (SHPP), solar PV plant, wind turbine generator (WTG), biomass-fuel-fired combined heat and power (BCHP) unit, battery energy storage system (BESS), plug-in electric vehicles (PEVs) and candidate solar PV plant, WTG, SHPP, BCHP units and PEVs is considered here. The problem is solved by using self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE).

Intrusion Detection System Using FKNN and Improved PSO

Intrusion detection system (IDS) techniques are used in cybersecurity to protect and safeguard sensitive assets. The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism. The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor (FKNN) algorithm. Using this method, two parameters, i.e., the neighborhood size (k) and fuzzy strength parameter (m) were characterized by implementing the particle swarm optimization (PSO). In addition to being used for FKNN parametric optimization, PSO is also used for selecting the conditional feature subsets for detection. To proficiently regulate the indigenous and comprehensive search skill of the PSO approach, two control parameters containing the time-varying inertia weight (TVIW) and time-varying acceleration coefficients (TVAC) were applied to the system. In addition, continuous and binary PSO algorithms were both executed on a multi-core platform. The proposed IDS model was compared with other state-of-the-art classifiers. The results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy, precision, recall, and f-score. The results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two datasets. Moreover, the proposed method was able to obtain a large number of true positives and negatives, with minimal number of false positives and negatives.

Оптимизация расположения и мощности возобновляемых распределенных источников энергии с использованием модифицированного метода роя частиц

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

Optimal solutions for fixed head short-term hydrothermal system scheduling problem

In this paper, optimal short-term hydrothermal operation (STHTO) problem is determined by a proposed high-performance particle swarm optimization (HPPSO). Control variables of the problem are regarded as an optimal solution including reservoir volumes of hydropower plants (HdPs) and power generation of thermal power plants (ThPs) with respect to scheduled time periods. This problem focuses on reduction of electric power generation cost (EPGC) of ThPs and exact satisfactory of all constraints of HdPs, ThPs and power system. The proposed method is compared to earlier methods and other implemented methods such as particle swarm optimization (PSO), constriction factor (CF) and inertia weight factor (IWF)-based PSO (FCIW-PSO), two time-varying acceleration coefficient (TTVACs)-based PSO (TVAC-PSO), salp swarm algorithm (SSA), and Harris hawk algorithm (HHA). By comparing EPGC from 100 trial runs, speed of search and simulation time, the suggested HPPSO method sees it is more robust than other ones. Thus, HPPSO is recommended for applying to the considered and other problems in power systems.

Optimal Parameters of OUPFC and GUPFC Under Renewable Energy Power Variation Using Cuckoo Search Algorithm Variants

In this paper, improved variants of cuckoo search algorithm are proposed for optimizing the controlling variables of flexible ac transmission system devices towards voltage stability enhancement and active power loss minimization by considering renewable energy sources intermittency in the network. Primarily, the optimal location for flexible ac transmission system devices is determined using line stability index and later the control parameters of generalized unified power flow controller and optimal unified power flow controller are optimized at different intermittency levels of renewable energy sources using three cuckoo search algorithm variants. The case studies are performed on standard IEEE 14-, 30-, 57-bus test systems. The superiority of proposed cuckoo search algorithm variants (linearly increased switching parameter, exponentially increased switching parameter and increased switching parameter in a power of three) in solving the multi-objective, non-linear complex optimization problem over time varying acceleration coefficient—particle swarm optimization variants is presented by illustrating various case studies.

Particle swarm optimisation with opposition learning-based strategy: an efficient optimisation algorithm for day-ahead scheduling and reconfiguration in active distribution systems

In operation of active electric distribution networks, optimal configuration and schedule of distributed generation and reactive power resources are determined. This represents a formidable multi-modal constrained optimisation problem with discrete decision variables. Metaheuristics are the most common approaches for solving this problem. However, due to its multi-modal nature, metaheuristics commonly converge prematurely into local optima and cannot find near-global solutions. In this research, a new particle swarm optimisation (PSO) variant is put forward for finding optimal configuration and schedule of distributed generation and reactive power resources in distribution systems including both dispatchable and renewable distributed energy resources. In the proposed PSO variant, opposition-based learning concept is incorporated into PSO which reduces premature convergence probability through enhancement of swarm leaders. The results of the proposed opposition-based PSO in IEEE 69 bus system indicate its outperformance over conventional PSO, time-varying acceleration coefficient PSO, fractal optimisation algorithm and evolutionary programming.

Multi-group grey wolf optimizer (MG-GWO) for estimating photovoltaic solar cell model

The use of photovoltaic (PV) solar systems as a direct convertor of electricity is increasing on daily basis at industrial and domestic scales. Such systems are still under worldwide investigation due to their low efficiency to have better performance. In this regard, the parameters effecting the PV solar system performance should be defined and investigated using mathematical models, to determine the optimum values of these parameters that result in best possible performance. In this paper, a new and novel method based on grey wolf optimizer (GWO) algorithm is developed for the estimation of the photovoltaic solar cell model. The new proposed method is called multi-group grey wolf optimizer (MG-GWO) where several clans/packs of wolves are searching for the prey. The GWO and MG-GWO are metaheuristic techniques that mimic the leadership hierarchy and hunting behavior of clan or clans of grey wolfs. The clan consists of four levels of leadership ranked from the highest to the lowest as Alpha, Beta, Delta and Omega. On the other hand, the hunting behavior consists of three steps, searching, encircling, and attacking the prey. The algorithm mimics these levels and behavior to find the solution. In the present study, these metaheuristic techniques are used to extract the parameters of a single-diode photovoltaic (PV) solar cell model. The optimization results showed that MG-GWO is better in terms of robustness, and speed of convergence compared to conventional GWO. For more comprehensive comparison, these two methods are compared to the conventional PSO and some recent versions of PSO like time-varying accelerated coefficient PSO (PSOTAC), asymmetric time-varying acceleration coefficient PSO (PSOM), and its improved version (PSOI). The results show that MG-GWO has a superior performance compared to the other algorithms. They also show that MG reduces the values of the RMSE and MAE of GWO up to 77% depending on the number of the packs and population in each pack.

Day-ahead profit-based reconfigurable microgrid scheduling considering uncertain renewable generation and load demand in the presence of energy storage

Due to the penetration of renewable energy sources (RESs) with probabilistic natures into microgrids (MGs), optimal scheduling and reconfiguration (RG) processes are associated with uncertainty. This paper presents the stochastic profit-based optimal day-ahead scheduling of a reconfigurable microgrid (RMG) as a new generation of the conventional microgrid. The proposed algorithm finds the optimal RMG's topology from the profit maximization point of view, the optimal hourly MG's unit set-points like micro-turbines (MTs) and energy storage, and power exchange with the main grid, simultaneously. The generated power of wind turbine (WT) and PV panel, as well as load demand are considered as uncertain parameters. To solve the profit maximization problem of RMG, time-varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is employed. Also, to ensure simulation accuracy in the presence of high-level uncertainties, the autocorrelation model is used based on actual data for the uncertainty of renewable power output. The feasibility and applicability of the proposed framework are demonstrated on a 69-bus radial RMG with various distributed generators in different cases. The results show the effectiveness of the proposed model.

Hydropower Production Optimization from Inflow: Case Study of Songloulou Hydroplant

The model of nonlinear power generation function is developed to generate optimal operational policies for Songloulou inflow in Cameroon and test these policies in real time conditions. Our model is used to adjust operational regimes for the Songloulou reservoir under varying flows (turbined and deversed) using a dynamic program. A more interesting approach, proposed in this article, consists of combining both the principle of decomposition by resources (or quantities) and the technique of dynamic programming. Dynamic programming is an appropriating optimization algorithm that is used for complex non-linear inflow operational policies and strategies. In this case study, our optimization model is used and confirmed maximizing large scale of hydropower in a period of time step by the integration of several. The high non linearity of our study object is the first stage of difficulty which brought us to combined least squared and Time Varying Acceleration Coefficients Particle Swarm (TVACPSO) to obtain appropriate production function which generated optimal operational policies for the Songloulou hydropower in sub-Saharan region and after we tested it in the company policies operational at real time conditions. The model could be successfully applied to other hydropower dams in the region.

A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch

In power generating plants, the expenses on combustible fuel is extremely costly and the concept of ELD (Economic Load Dispatch) make possible to save the considerable portion of profits. Practically generators have economic dispatch problems in terms of non-convexity. These kinds of problem cannot be resolved by conventional optimization techniques because the complication escalates due to manifold constrained that require to be fulfilled in all operating conditions. Recently a Particle Swarm Optimization (PSO) algorithm stimulated by collective conduct of swarm can be applied effectively to translate the ELD problems. The classical PSO bears the difficulty of early convergence mainly when the space of search is asymmetrical. To overcome the trouble “Crazy PSO with TVAC (Time Varying Acceleration Coefficients)” is launched which improve the search ability of the PSO by rebooting the vector of velocity whenever diffusion or saturation locate inside and to employ a scheme of parameter automation to maintain correct equilibrium between global hunt and local hunt and also circumvent the congestion. This arrangement is developed crazy PSO with TVAC and also demonstrated on two different model experimental structures of three generation units and six generation units. The result acquired from proposed method is evaluate with classical PSO and Real coded genetic algorithm (RGA) and it is found to be superior. This method is mathematically simple, gives fast convergence and robustness to resolve the rigid optimization inconvenience.

An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units

This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at https://github.com/ebrahimakbary/MNHPSO-JTVAC.

A Framework for $$H_{2} /H_\infty$$ Synthesis in Damping Power Network Oscillations with STATCOM

The escalating in load demand prioritized the incorporation of additional renewable energy generation plants to the existing grid. In parallel, the necessity of reactive power balance and damping characteristics has advised the incorporation of flexible AC transmission system controllers to the existing power network. Hereby, the modern power system has been driven towards the much more composite system which in turn necessitates a healthy controller technique. The objective of this paper is to contribute recommendations to incorporate robust controller technique in the field of the electrical power system. Detailed design considerations for the $$H\infty$$ controller design of a modern power network have been specified, and the same has been demonstrated with mathematical modelling of power network with static synchronous compensator (STATCOM) connected in the middle of the transmission line and in a multi-machine power system. To comment on the suitability of the controller design, a deep stability analysis has been presented and compared with the traditional power system stabilizer, power system stabilizer optimized using particle swarm optimization with time-varying acceleration coefficients algorithm and whale optimization algorithm under adverse system operating conditions. The proposed controller framework has been presented by considering the case studies of single machine infinite bus system, two-machine system model and the benchmark two-area four-generator multi-machine systems connected with STATCOM. The controller performance analysis has been verified by considering eigenvalues analysis, singular value analysis and dynamic response of system states during perturbations for the first two case studies, and system analysis under faulty condition has been investigated for the multi-machine system.

A parameter-free particle swarm optimization algorithm using performance classifiers

This paper presents an investigation into the short-term versus long-term performance of various particle swarm optimization (PSO) control parameter configurations. While evidence suggests that the best PSO parameter values to employ are time-dependent, this paper provides an in-depth examination of a small set of parameter values to provide a more concrete quantification of the performance degradation observed with specific control parameter configurations over time. Given that the short-term performance is not necessarily indicative of long-term performance, this paper proposes that machine learning techniques be used to build predictive models based on two easily-observable landscape characteristics. Finally, using the predictive models as a basis, this paper also proposes a parameter-free PSO algorithm, which performs on par with other top-performing PSO variants, namely the three best performing static PSO configurations, particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC), and particle swarm optimization with improved random constants (PSO-iRC).

Optimization of a True Moving Bed unit and determination of its feasible operating region using a novel Sliding Particle Swarm Optimization

The optimization of Simulated Moving Bed units has become an important topic to be developed in this field. The present work proposes a novel methodology to optimize a True Moving Bed unit and draw the feasible operating region through a novel improved Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients and Mutable Searching Region, here called Sliding Particle Swarm Optimization, concomitantly with an adapted method to define the feasible operating region. Two main contributions can be highlighted, first the process optimization with its feasible operational regions, leading to better results when compared with the numerical optimization based on the equilibrium theory, second a new particle swarm method is presented which can deal more efficiently with multi local minima problems. Finally, the concept of feasible operating region concept is presented as a support tool for the process operation. Three different operational scenarios were simulated here in order to verify the consistence and efficiency of the methodology. The main advantage of the methodology here proposed is the possibility to tracking all the possible operating regime of the unit while meeting a given process requirement.

Particle swarm optimization of 2D magnetotelluric data

We implement the particle swarm optimization (PSO) algorithm for the two-dimensional (2D) magnetotelluric (MT) inverse problem. We first validate PSO on two synthetic models of different complexity and then apply it to an MT benchmark for real-field data, the COPROD2 data set (Canada). We pay particular attention to the selection of the PSO input parameters to properly address the complexity of the 2D MT inverse problem. We enhance the stability and convergence of the solution of the geophysical problem by applying the hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC). Moreover, we parallelize the code to reduce the computation time because PSO is a computationally demanding global search algorithm. The inverse problem was solved for the synthetic data both by giving a priori information at the beginning and by using a random initialization. The a priori information was given to a small number of particles as the initial position within the search space of solutions, so that the swarming behavior was only slightly influenced. We have demonstrated that there is no need for the a priori initialization to obtain robust 2D models because the results are largely comparable with the results from randomly initialized PSO. The optimization of the COPROD2 data set provides a resistivity model of the earth in line with results from previous interpretations. Our results suggest that the 2D MT inverse problem can be successfully addressed by means of computational swarm intelligence.

Efficient Allocation Strategy of Energy Storage Systems in Power Grids Considering Contingencies

This paper addresses the allocation of Energy Storage Systems (ESSs) in power grids by finding the optimal number of ESSs and their locations and sizes with the goal of improving reliability in contingency states. We propose a contingency-sensitivity-based heuristic to decide the optimal number of ESSs and the most effective locations for ESSs support, while circumventing the combinatorial nature of the siting problem. A contingency sensitivity index (CSI) is proposed which represents the impacts of contingencies on the network buses. The CSI ranks the buses, such that those with higher impacts have the privilege for installing ESSs. For the ESSs being fixed, the sizing is formulated as a multi-period AC optimal power flow (OPF) problem and solved by Self-Organizing Hierarchical Particle Swarm Optimization with Time Varying Acceleration Coefficients (HPSO TVAC). The optimal ESSs sizes are selected by minimizing a total cost, which includes investment cost of storage devices, bus voltage deviation cost and average network losses cost. Uncertainties of the renewable generation are accounted by considering different realizations of the generation profiles, then, ESSs sizes are selected by taking the worst case approach. The proposed methodology has been demonstrated on the modified IEEE 30-bus system and Tunisian Grid. The obtained results show the effectiveness of the proposed methodology and the related reliability merits.

Path Planning for Unmanned Underwater Vehicle Based on Improved Particle Swarm Optimization Method

Path planning of Unmanned Underwater Vehicle (UUV) is of considerable significance for the underwater navigation, the objective of the path planning is to find an optimal collision-free and the shortest trajectory from the start to the destination. In this paper, a new improved particle swarm optimization (IPSO) was proposed to process the global path planning in a static underwater environment for UUV. Firstly, the path planning principle for UUV was established, in which three cost functions, path length, exclusion potential field between the UUV and obstacle, and attraction potential field between UUV and destination, were considered and developed as an optimization objective. Then, on the basis of analysis traditional particle swarm optimization (PSO), the time-varying acceleration coefficients and slowly varying function were employed to improve performance of PSO, time-varying acceleration coefficients was utilized to balance the local optimum and global optimum, and slowly varying function was introduced into the updating formula of PSO to expand search space and maintain particle diversity. Finally, numerical simulations verify that, the proposed approach can fulfill path planning problems for UUN successfully.

Research on Environmental/Economic Dispatch for Microgrid Incorporating Reserve Cost

Based on the minimum principle of reserve cost, operating cost and environmental cost, an environmental/economic dispatch model is constructed in this paper. The improved particle swarm optimization algorithm with time-varying acceleration coefficient is used to solving the model. Through the example simulation, the results show that the built model can make the optimization of the microgrid more reasonable. It can effectively reduce the reserve cost, the operating cost and the environmental cost. This is a good exploration of the environmental/ economic dispatch for microgrids.