This paper investigates the feasible reserve of diesel generators in day-ahead unit commitment (DAUC) in order to handle the uncertainties of renewable energy sources. Unlike other studies that deal with the ramping of generators, this paper extends the ramp rate consideration further, using dynamic limits for the scheduling of available reserves (feasible reserve) to deal with hidden infeasible reserve issues found in the literature. The unit commitment (UC) problem is solved as a two-stage day-ahead robust scenario-based unit commitment using a metaheuristic new variant of particle swarm optimization (PSO) called partitioned step PSO (PSPSO) that can deal with the dynamic system. The PSPSO was pre-optimized and was able to find the solution for the base-case UC problem in a short time. The evaluation of the optimized UC schedules for different degrees of reserve consideration was analyzed. The results reveal that there is a significant advantage in using the feasible reserve formulat... [more]

Installing renewable energy systems for zero-energy buildings has become increasingly common; building integrated photovoltaic (BIPV) systems, which integrate PV modules into the building envelope, are being widely selected as renewable systems. In particular, owing to the rapid growth of information and communication technology, the requirement for appropriate operation and control of energy systems has become an important issue. To meet these requirements, a computational model is essential; however, some unmeasurable parameters can result in inaccurate results. This work proposes a calibration method for unknown parameters of a well-known BIPV model based on in situ test data measured over eight days; this parameter calibration was conducted via an optimization algorithm. The unknown parameters were set such that the results obtained from the BIPV simulation model are similar to the in situ measurement data. Results of the calibrated model indicated a root mean square error (RMSE) o... [more]

The building sector is an energy-consuming sector, and the development of zero-energy buildings (ZEBs) is necessary to address this. A ZEB’s active components include a system that utilizes renewable energy. There is a heat-pump system using geothermal energy. The system is available regardless of weather conditions and time, and it has attracted attention as a high-performance energy system due to its stability and efficiency. However, initial investment costs are higher than other renewable energy sources. To solve this problem, design optimization for the capacity of geothermal heat-pump systems should be performed. In this study, a capacity optimization design of a geothermal heat-pump system was carried out according to building load pattern, and emphasis was placed on cost aspects. Building load patterns were modeled into hospitals, schools, and apartments, and, as a result of optimization, the total cost over 20 years in all building load patterns was reduced.

This paper presents a compensation method for unbalanced voltage through active and reactive power control by utilizing a smart inverter that improves the voltage unbalance index and detects an unbalanced state of voltage magnitude and phase, and thus enhances power quality by minimizing the voltage imbalance. First of all, this paper presents an analysis of a mathematical approach, which demonstrates that the conventional voltage unbalanced factor (VUF) using the symmetrical component cannot correctly detect the imbalanced state from index equations; and by only minimizing the VUF value, it cannot establish a balanced condition for an unbalanced state of the voltage profile. This paper further discusses that intermittent photovoltaic (PV) output power and diversified load demand lead to an unexpected voltage imbalance. Therefore, considering the complexity of unbalanced voltage conditions, a specific load and an PV profile were extracted from big data and applied to the distribution s... [more]

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. E... [more]

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical... [more]

Recently, there was a great focus on integrating renewable energy sources (RESs) into electrical power systems (hybrid systems) due to their many environmental and economic advantages. The output of most of these RESs is DC; some power electronic devices, including inverters, must be used to integrate these RESs into the electrical grid. Any maloperation, faults, or improper control in these power electronic devices will enormously affect these hybrid systems’ performance. This paper aims to mitigate the misfire and fire-through faults that occur at the switching of the inverter that connects three renewable sources: PV, wind, and the fuel cell to the grid. This mitigation of such inverter faults (misfire and fire-through) is performed through optimal tuning of the PI controller driving a dynamic voltage restorer (DVR) connected at the system’s AC side. The optimization technique used is particle swarm optimization (PSO). While mitigating these two inverter faults using the PI-PSO cont... [more]

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 31... [more]

A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems.

Grid operators are now considering using distributed energy resources (DERs) to provide distribution voltage regulation rather than installing costly voltage regulation hardware. DER devices include multiple adjustable reactive power control functions, so grid operators have the difficult decision of selecting the best operating mode and settings for the DER. In this work, we develop a novel state estimation-based particle swarm optimization (PSO) for distribution voltage regulation using DER-reactive power setpoints and establish a methodology to validate and compare it against alternative DER control technologies (volt−VAR (VV), extremum seeking control (ESC)) in increasingly higher fidelity environments. Distribution system real-time simulations with virtualized and power hardware-in-the-loop (PHIL)-interfaced DER equipment were run to evaluate the implementations and select the best voltage regulation technique. Each method improved the distribution system voltage profile; VV did n... [more]

Reasonable configuration of equipment capacity can effectively improve the economics of wind-photovoltaic-battery hybrid generation system (WPB-HGS). Based on the current needs of investors to pay more attention to the economic benefits of WPB-HGS, this paper proposes a capacity configuration method for WPB-HGS considering return on investment (ROI). A bi-level planning model for integrated planning and operation of WPB-HGS was established. The lower-level model optimizes the system’s operating status with the goal of maximizing the daily power sales of the system. The upper-level model plans the equipment capacity of the WPB-HGS with the goal of maximizing the annual net income and return on investment. The model is solved using adaptive weighted particle swarm optimization. According to actual engineering examples, the specific equipment capacity is configured, and the configuration results are analyzed to verify the effectiveness of the method.

This paper proposes an approach to develop building dynamic thermal models that are of paramount importance for controller application. In this context, controller requires a low-order, computationally efficient, and accurate models to achieve higher performance. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems can be developed using thermal network models using thermal resistances and capacitances. In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference one. Additionally, extensive simulations are carried to evaluate the proposed simplified model with solar radiation and identified model pa... [more]

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to... [more]

With the development of smart devices and information technology, it is possible for users to optimize their usage of electrical equipment through the home energy management system (HEMS). To solve the problems of daily optimal scheduling and emergency demand response (DR) in an uncertain environment, this paper provides an opportunity constraint programming model for the random variables contained in the constraint conditions. Considering the probability distribution of the random variables, a home energy management method for DR based on chance-constrained programming is proposed. Different confidence levels are set to reflect the influence mechanism of random variables on constraint conditions. An improved particle swarm optimization algorithm is used to solve the problem. Finally, the demand response characteristics in daily and emergency situations are analyzed by simulation examples, and the effectiveness of the method is verified.

The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.

This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is due to the battery ESS efficiency being a multiplication of inverter and battery efficiency and the dependency of an inverter and any associated battery efficiencies on load and charging and discharging. We propose a combined mixed-integer linear programming and particle swarm optimization (MILP-PSO) algorithm as a novel means of addressing these considerations. In the algorithm, MILP is used to find some initial points of PSO, so that it can find better solution. Moreover, some additional algorithms are added into PSO to modify and, hence, improve its ability of dealing with constraint conditions and the local minimum problem. The simulation results show that the proposed algorithm performs better than MILP and PSO alone... [more]

The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization... [more]

This study discusses the evaluation of oscillatory stability based on the synchronizing K s and damping K d torque coefficients for a single-machine system connected to an infinite bus (SMIB). Particle swarm optimization (PSO) technique is used to determine K s and K d values and subsequently identify the oscillatory stability conditions in the SMIB. The ability of PSO is compared with those of evolutionary programming (EP) techniques and artificial immune system (AIS). The least square (LS) method is selected as the benchmark for K s and K d values determined by PSO, EP, and AIS. Simulation results show that PSO successfully estimated K s and K d values closest to LS compared with EP and AIS. PSO also uses lower computational time compared with those of the two other techniques. The proposed technique is suitable for solving oscillatory stability problems based on the determination of eigenvalues and minimum damping ratio... [more]

In this paper, the optimal site and size selection of wind turbines (WTs) is presented considering the maximum allowable capacity constraint with the objective of loss reduction and voltage profile improvement of distribution grids based on particle swarm optimization (PSO as a multi-objective problem using weighted coefficients method. The optimal site, size, and power factor of the WTs are determined using PSO. The proposed method is implemented on 84- and 32-bus standard grids. In this study, PSO algorithm is applied to determine the size, site, and power factor of WTs considering their maximum size constraint (with constraint, variant size) and also not considering their maximum size constraint (without constraint, constant size). The simulation results showed that the PSO is effective to find the site, size, and power factor of WTs optimally in the single and multi-objective problem. The results of this method showed that the power loss is reduced more and voltage profile improved... [more]

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mi... [more]

Deterministic forecasting models have been used through the years to provide accurate predictive outputs in order to efficiently integrate wind power into power systems. However, such models do not provide information on the uncertainty of the prediction. Probabilistic models have been developed in order to present a wider image of a predictive outcome. This paper proposes the lower upper bound estimation (LUBE) method to directly construct the lower and upper bound of prediction intervals (PIs) via training an artificial neural network (ANN) with two outputs. To evaluate the PIs, the minimization of a coverage width criterion (CWC) cost function is proposed. A particle swarm optimization (PSO) algorithm along with a mutation operator is further implemented, in order to optimize the weights and biases of the neurons of the ANN. Furthermore, wavelet transform (WT) is adopted to decompose the input wind power data, in order to simplify the pre-processing of the data and improve the accur... [more]

Chlorine demand as a disinfectant for water utility impacts on unintended energy consumption from electrolysis manufacture; thus, diminishing the chlorine consumption also reduces the environmental impact and energy consumption. Problems of disinfectant distribution and uniformity in Water Distribution Networks (WDN) are associated with the exponential urban growth and the physical and biochemical difficulties within the network. This study optimizes Chlorine Booster Stations (CBS) location on a network with two main objectives; (1) to deliver minimal Free Residual Chlorine (FRC) throughout all demand nodes according to country regulations, and (2) to reduce day chlorine mass concentration supplied in the system by applying an hour time pattern in CBS, consequently associated economic, energy and environmental impacts complying with regulatory standards. The application is demonstrated on a real-world WDN modeled from Guanajuato, Mexico. The resulting optimal location and disinfectant... [more]

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible “N-1” and “N-2” forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutua... [more]

The small modular dual fluid reactor is a novel variant of the Generation IV molten salt reactor and liquid metal fast reactor. In the primary circuit, molten salt or liquid eutectic metal (U-Pu-Cr) is employed as fuel, and liquid lead works as the coolant in the secondary circuit. To design the control system of such an advanced reactor, the uncertainties of the employed computer model and the physicochemical properties of the materials must be considered. In this paper, a one-dimensional model of a core is established based on the equivalent parameters achieved via the coupled three-dimensional model, taking into account delayed neutron precursor drifting, and a power control system is developed. The performance of the designed controllers is assessed, taking into account the model and property uncertainties. The achieved results show that the designed control system is able to maintain the stability of the system and regulate the power as expected. Among the considered uncertain par... [more]