Voltage Dependent Load Models Research Articles

Techno-economic and environmental assessments of optimal planning of waste-to-energy based CHP-DG considering load growth on a power distribution network

Load growth puts pressure on existing electric infrastructure and impacts on the system's performance parameters which may necessitate network expansion. Conventionally, electric network expansion is done by building new substations or reinforcing the existing ones with new transformers and upgrading the network feeders. However, optimal allocation of combined heat and power distributed generators (CHP-DGs) on distribution networks (DNs) can be adopted for network expansion planning problem. Optimal DG allocation is an optimisation problem which requires an efficient optimisation approach. In this paper, an improved particle swarm optimisation (IPSO) based on weighted randomised acceleration coefficient and adaptive inertia weight is proposed for optimal DG allocation problem. The considered CHP-DGs include internal combustion engine (ICE) and fuel cells (FCs) powered by biogas obtained from anaerobic digestion of food wastes. The proposed IPSO is tested on IEEE 69 bus radial distribution network under single and multi-objectives considering constant and mixed seasonal voltage-dependent load models. Some of the key findings show that integrating ICE-based CHP-DGs operating at optimal power factor in winter day mixed voltage dependent load in base year achieves 97.63 % active power loss reduction in comparison to 77.14 % loss reduction for unity power factor operating FC-based CHP-DG. Economic and environmental evaluation indicate that FC-based CHP-DG records a net present value of over 29.29 million $, levelised cost of energy of 0.0493 $/kWh and zero pollutant emission in comparison with 28.40 million $, 0.0501 $/kWh and 0.2817 million kg pollutant emission for ICE-based CHP-DG over the planning horizon. In comparison with the standard PSO, the proposed IPSO performs better in terms of solution quality, convergence speed and statistical results.

Investigating effect of various time varying load models in grid connected microgrid system integrated with renewables and cogeneration units

ABSTRACT The optimal allocation of sources considering voltage-dependent load models is essential as the power consumption of the loads is sensitive to voltage magnitude. In this paper, optimal allocation of microgrid system with cogeneration units, renewable energy sources (RES), shunt capacitors and heat boiler are determined for constant, residential, industrial, commercial and mixed load models. The cost, emission, energy loss, voltage deviation and voltage stability values for each load model is determined using particle swarm optimisation (PSO) integrated fuzzy max–min approach while the uncertainties in RES are taken into account. Results demonstrate that the cost, energy loss and voltage deviation values are maximum for constant power (CP) load model compared with other load models. Furthermore, the system with constant impedance (CZ) load achieves maximum loading capability than with other types of constant loads. The proposed method is validated on 33-bus grid-connected microgrid system with all the load models.

Optimal dynamic multi-microgrid structuring for improving distribution system resiliency considering time-varying voltage-dependent load models

This paper presents an innovative approach for enhancing distribution network resiliency against severe weather-related faults. Given the importance of loads in the resiliency concept, an exact and realistic load model is developed along with the consumption pattern and voltage-dependency of each load type. By defining two technical and economic objective functions, a unique method is used to combine and optimize the objective functions through Imperialist Competitive Algorithm (ICA). Two main strategies, namely the dynamic arrangement of microgrid regions via hourly network reconfiguration as well as grid voltage control through Automatic Voltage Regulation (AVR) of distributed generation facilities, are utilized for increased load restoration. The effect of each of these strategies on grid resiliency is evaluated along with the overall approach efficiency based on simulations on a sample 33-bus network comprising renewable and non-renewable resources and commercial, residential and industrial loads. Using this approach, the strategy to encounter each particular type of failure is obtained.

Probabilistic Optimal Active and Reactive Power Dispatch including Load and Wind Uncertainties considering Correlation

The increased integration of renewable energies (REs) raised the uncertainties of power systems and has changed the approach to dealing with power system challenges. Hence, the uncertain nature of all the power system variables needs to be considered while dealing with the optimal planning and operation of modern power systems. This paper presents a probabilistic optimal active and reactive power dispatch (POARPD) based on the point estimate method (PEM), considering the uncertainties associated with load variation and wind power generation. In the POARPD, the deterministic optimal active and reactive power dispatch (OARPD) is performed in two stages, which gives a deterministic two-stage OARPD (TSOARPD). The objectives of TSOARPD are the operating cost (OC) minimization in stage 1 and voltage stability (VS) maximization in stage 2, whereas the VS is improved by maximizing the system’s reactive power reserve (RPR). In this paper, instead of using multiobjective optimal power flow, this TSOARPD is used to give more importance to VS when the system is substantially loaded. The POARPD problem is solved using PEM for modified IEEE-9 bus and standard IEEE-30 bus test systems by considering the correlation between the loads. The results are compared with Monte Carlo simulation (MCS). While solving POARPD, the voltage-dependent load model is used to account for the real-time voltage dependency of power system loads. This paper discusses the detailed procedure of solving POARPD by considering correlation and the increased nonlinearities by giving more importance to VS when the system is heavily loaded.

IMPACT AND EVALUATION OF OPTIMIZED PV GENERATION IN THE DISTRIBUTION SYSTEM WITH VARYING LOAD DEMANDS

Most distributed renewable energy generation (DREG) planning studies are performed using a constant load model and a dispatchable generation unit. However, the renewable generation unit and load demand vary in real life, and the generation size at the peak demand varies accordingly with loading levels. Such considerations may lead to the erroneous conclusion: the power loss reduction and bus voltage improvement may not be optimal. Consequently, the generation unit must be adequately integrated to offer optimal capacity in the distribution system while considering non-constant load demand as a part of DREG planning. Therefore, the impact of integrating photovoltaic (PV) considering historical solar weather data and varying load demand for five different voltage-dependent load models is proposed in this study. Particle swarm optimization (PSO) is employed to find the optimal location and size of PV with the objective to minimize power losses in the distribution system using IEEE 33-bus and IEEE 69-bus test systems. The findings are evaluated based on the comparative analysis of power losses reduction, PV penetration level, power loss index, and voltage deviation index. Findings revealed that the proposed model is effective in determining the optimal location and size of PV with a significant reduction of power losses that varies between 13.84% to 32.71% in 33-bus, and between 18.56% to 43.80% in 69-bus. In addition, the improvement in minimum bus voltage and other performance indices are also significant.

Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models

Over the last few decades, distributed generation (DG) has become the most viable option in distribution systems (DSs) to mitigate the power losses caused by the substantial increase in electricity demand and to improve the voltage profile by enhancing power system reliability. In this study, two metaheuristic algorithms, artificial gorilla troops optimization (GTO) and Tasmanian devil optimization (TDO), are presented to examine the utilization of DGs, as well as the optimal placement and sizing in DSs, with a special emphasis on maximizing the voltage stability index and minimizing the total operating cost index and active power loss, along with the minimizing of voltage deviation. The robustness of the algorithms is examined on the IEEE 33-bus and IEEE 69-bus radial distribution networks (RDNs) for PV- and wind-based DGs. The obtained results are compared with the existing literature to validate the effectiveness of the algorithms. The reduction in active power loss is 93.15% and 96.87% of the initial value for the 33-bus and 69-bus RDNs, respectively, while the other parameters, i.e., operating cost index, voltage deviation, and voltage stability index, are also improved. This validates the efficiency of the algorithms. The proposed study is also carried out by considering different voltage-dependent load models, including industrial, residential, and commercial types.

Multi-objective optimal allocation of multiple capacitors and distributed generators considering different load models using Lichtenberg and thermal exchange optimization techniques

Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics.

Three-Phase Second-Order Analytic Probabilistic Load Flow With Voltage-Dependent Load

This paper proposes a fully analytic second-order probabilistic load flow (PLF) method to realize an accurate and fast three-phase load flow analysis considering unbalanced uncertainties from voltage-sensitive loads and photovoltaic (PV) generation in distribution systems. The load flow equations are modelled by an accurate quadratic expression based on the bus injection model (BIM). To work at the distribution level, the voltage dependence of the load is considered based on the constant impedance, constant current, and constant power (ZIP) model, with the ZIP parameters acting as more realistic random variables. The PV generation is also considered as a constant power model. The uncertainties are modelled in time series as conditional probabilities, reducing the complexity of their probability distribution functions (PDFs). The PLF is modelled in a fully analytic second-order stochastic formulation, which can accurately and easily handle the PDFs of voltage and current by computing the first two moments. The computation is accelerated by an analytical calculation of the quadratic coefficients over the ZIP parameters. Case studies on a practical distribution network show the significance of considering the voltage-dependent load model and the high accuracy of the proposed method.

Optimal siting for different level of DG penetration and Its Impact on Radial Distribution system under different voltage-dependent load Model

Optimal siting for different level of DG penetration and Its Impact on Radial Distribution system under different voltage-dependent load Model

Integrated T&D Voltage Stability Assessment Considering Impact of DERs and Distribution Network Topology

Electric distribution systems are going through a major upgrade with increasing penetration of distributed energy resources (DERs) and impacting transmission system operational analysis. Traditionally, the transmission system’s voltage stability margin is computed by performing a continuation power flow (CPF) analysis. In transmission-CPF, loads in distribution systems are aggregated at the transmission-distribution (T&D) interface buses, and the distribution system’s electrical characteristics, like feeder length, increasing penetration of DERs, and associated control, are not considered. The traditional CPF thus considers the distribution network as a purely passive network without active network management and distributed energy resources. In this paper, two integrated T&D CPF methods are implemented using a co-simulation framework that uses individual transmission and distribution solvers. The first method is iteratively-coupled T&D CPF (IC-TDCPF), where the iterative coupling concept forces convergence of boundary variables at T&D PCC (Point of common coupling) at every CPF step. The second method, Loosely Coupled T&D CPF (LC-TDCPF), relaxes the boundary convergence constraints at PCC to achieve faster calculation speed. Moreover, a reduced distribution network equivalent is also developed to enable faster computation of VSM by reducing the number of integrated T&D buses. The developed T&D CPF co-simulation framework uses an open-source platform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MATPOWER</i> for solving transmission system and open-source software <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OPENDSS</i> for solving distribution system. Three large integrated T&D systems with 4196, 10587, and 12295 buses are developed to demonstrate the impact of DERs, voltage-dependent load models, and distribution network topology on integrated voltage stability margin using the proposed T&D CPF and co-simulation.

Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

Single objective constant PQ load models were extensively considered for site and size of distributed generation (DG) and shunt capacitor (SC) allocation. Which may lead to single non-dominated solution of unpredictable and misleading results about their site and size, loss reduction and payback period. Therefore, primary objective of this study is to investigate the effects of seven nonlinear voltage-dependent load models for the siting and sizing of DG and SC considering various conflicting Multiobjective functions. These objective functions are minimization of active power loss, voltage deviation, cost of energy loss per year, the total cost of installed DG and SC, and emission. Three study cases of simultaneous optimization of two and three objective functions are intended to find optimal integration of DG and SC in the standard 33 and 118-bus radial distribution network considering seven nonlinear voltage-dependent load models. A new Multiobjective evolutionary algorithm (MOEA) called Bidirectional Coevolutionary (BiCo) is applied to the proposed study cases to demonstrate the impact of load model on DG and SC allocation. Further, to show the superiority and performance of proposed algorithm, six state-of-the-art MOEAs are implemented and statistically compared with proposed algorithm using a representative hypervolume indicator (HVI). The maximum savings in annual cost of annual energy loss reaches 58.99% in case1 of PQ load model, 59.4% in case 2 and 64.96% in case 3 of industrial load model considering only DG allocation, whereas, 93.673% in constant PQ load model of case1, 78.908% in constant current load model of case2 and 93.403% of case3 of PQ load model considering simultaneous DG and SC. Simulation results show that the proposed algorithm is adept and suitable to find a better trade-off between various conflicting objective functions compared to other recently designed MOEAs.

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

The ever-increasing penetration of intermittent renewable resources in low-voltage power grids necessitates efficient operational strategies for voltage regulation as well as power scheduling of the available resources. In this paper, a risk-aware Volt/VAR support framework followed by a real-time reinforcement learning controller is presented for three-phase distribution systems. In the risk-aware stochastic scheduling stage, the legacy voltage regulating assets along with inverter-based photovoltaics (PVs) and energy storage system (ESS) are optimized considering day-ahead and intra-day markets. Moreover, demand response (DR) and voltage reduction plans are included in the proposed scheduling framework. By incorporating voltage-dependent load modeling in this study, the implementation of the voltage reduction plan reduces energy consumption in feeders by running the network at lower permissible voltage limits. The shiftable loads under the DR program are employed for peak shaving and to reduce operational costs. The result shows that DR implementation also reduces dependencies on the operation of traditional devices. The stochasticity of abrupt changes in PV generations is represented as the Gaussian Mixture Model (GMM), indicating a non-unimodal probability distribution in day-ahead PV forecasting errors. The scenario sets for uncertain variables are then reduced using a fuzzy clustering technique. Decisions made in the scheduling, associated with PV inverters and ESS operation, are revised with a real-time controller, i.e., Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The DDPG is adopted in the control stage of the framework considering the detailed modeling of unbalanced three-phase distribution grids to minimize the voltage deviation and power ramping of ESS. The performance of the proposed multi-stage scheme is verified using a three-phase active distribution grid under different scenarios.

Distribution locational marginal pricing for congestion management of an active distribution system with renewable-based microgrids under a privacy-preserving market clearing approach and load models

Microgrid formation has changed the one-direction and passive structure of distribution systems. Increasing the energy demand makes it more sophisticated to keep the operational constraints for the distribution system operator, especially during on-peak hours, which may lead to line congestion. In this study, a distributed locational marginal pricing strategy for congestion management of a radial distribution system with several renewable-based MGs is proposed. The proposed model considers the independent operation for each MG, confidentiality aspects for sensitive data, and the computational burden of problem-solving based on a distributed problem-solving approach. The results show the achievement of the optimal global solution for the feasible problem with more than 99.99% accuracy. Due to the prominent role of load in line congestion, voltage-dependent load models at the distribution system and price-based load models at the MGs side have been considered. The results show that the ignorance of accurate load models can lead to the estimation error for energy trading revenue of MGs up to 29.83% and up to 1.47% for the distribution system compared to non-consideration of load models in a congestion state. Distribution locational marginal prices have also changed by up to 4.18% compared to the case of no-load model considerations.

The Optimal Integration of Multiple DGs under Different Load Models using Artificial Bee Colony-Hill Climbing Algorithm

With the continual advances in technologies, there is a significantly increased demand for electricity leads to a drastic increase in usage and penetration of DGs into the distribution network. The incorrect addition of DGs into the distribution system resulting in declining stability, power losses, voltage profile, reliability, and power quality of the system. Therefore, the optimal planning of DGs (OPDG) has always been under constant investigation and development for the maximum beneficiary. The proposed framework presents a novel meta-heuristic hybrid optimization technique called Artificial Bee Colony-Hill Climbing (ABC-HC) to find the best siting and sizing of multiple DGs using a weighted multi-objective function which includes real and reactive power loss index and voltage deviation index. The results clearly indicate the significant reduction in losses and voltage deviation achieved by integration of single and multiple DGs. The research work is also extended to assess the voltage harmonics of the system before and after the placement of DGs. The effectiveness of the proposed ABC-HC algorithm has been demonstrated on the standard IEEE-33 bus RDS under different voltage dependent load models using MATLAB 2017a.&#x0D; HIGHLIGHTS&#x0D; &#x0D; As demand grows, technologies advance, and power system infrastructure changes, there are more opportunities to integrate distributed generators into the distribution system&#x0D; The maximum benefit from the DG integration is achieved by selecting the optimal size and optimal site in the distribution system&#x0D; A new hybrid optimization technique has been developed for optimal planning of DGs under different voltage dependent loads. The work was also extended to carry out harmonic analysis&#x0D; &#x0D; GRAPHICAL ABSTRACT

Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem

In the present markets, transmission congestion is contemplated to play a crucial role in the initiation of cascading outages, which compels the system to collapse. It is to be alleviated as soon as possible to maximise profits; or else the cost effective supply will be restricted from reaching the end consumers. Furthermore, global warming, the most serious issue being overlooked by conventional congestion management approaches, causes severe environmental consequences like increase in temperature and declining water supplies. The incorporation of renewable energy (RE) or associated technologies is indeed an excellent countermeasure to global warming. Hence, in this work, optimal capacities of solar based Distributed Generation (DG) are inserted to acknowledge the transmission network's N-1 contingency scenario. A multi-objective framework utilising the Grey Wolf Optimizer (GWO) is presented to obtain the exact size of solar DG by minimizing the costs, voltage deviations and power losses of the network simultaneously. Also, a new heuristic ranking based on the MW flow and net power injections is proposed to determine the most critical buses for the optimal placement of the solar DG. To assess the viability of the proposed approach, it is evaluated using a variety of case studies, including constant and voltage dependent load models in both planning stage and real time operation stage. The capacities of unity and 0.9 lagging power factor (pf) DGs have been obtained for standard IEEE system to check the effectiveness of the approach. The final optimal solutions obtained by GWO are compared with the existing algorithms available in the literature to show the superiority and the potentiality in solving the multi-objective framework. The simulation results emphasise the importance of the proposed approach in terms of costs, voltages and losses as it delivers better performance when compared to existing models in the literature.

Holomorphic Embedding Power Flow Algorithm for Isolated AC Microgrids With Hierarchical Control

Power flow (PF) calculation plays a fundamental role in the steady-state analysis of isolated microgrids (MGs). In this paper, a novel analytical PF algorithm based on holomorphic embedding (HE) is proposed for isolated AC MGs considering hierarchical control (HC). With a deliberate design in embedding technique, the presented algorithm not only inherits the deterministic property of the canonical embedding that enables to derive the upper-branch (operable) solution explicitly, but also is compatible with the HC characteristic of isolated MGs. Furthermore, the embedded PF model features a general structure with a constant recursive matrix, which can accommodate various bus types and network re-configurations (including both radial and meshed networks), as well as non-nonlinearities of voltage-dependent load models. Case studies are carried out on 3 test systems: a 12-bus MG and modified IEEE 33-bus and 123-bus distribution systems. Numerical results reveal the applicability and efficacy of the proposed algorithm.

A Bi-level optimizer for reliability and security assessment of a radial distribution system supported by wind turbine generators and superconducting magnetic energy storages

This paper is aimed at improving the reliability and security of radial distribution system supported by wind turbine generators (WTGs) and superconducting magnetic energy storages (SMESs). For reliability indices assessment, the load-oriented indices including energy not supplied (ENS) and average energy not supplied (AENS) as well as the customers-oriented indices including system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), customer average interruption duration index (CAIDI), and average service unavailability index (ASUI) are evaluated. Network security index (NSI) is also addressed, which refers to the risk level for current flow in the lines prior reaching to extremis. A multi-objective function is composed and formulated in order to simultaneously minimize ENS, SAIFI, SAIDI, ASUI, and NSI as indices which characterize the performance of distribution system using a hybrid approach based on equilibrium optimizer (EO) along with the loss sensitivity factors. A bi-level optimizer based on the hybrid approach addressing all constraints is applied for simultaneous minimization of reliability and security indices of distribution system. The outer level looks for an optimal sizing and sitting of WTGs and SMESs as well as optimal weighting coefficients whose evaluation is no longer assumed arbitrarily or left to the preferences of the decision maker. The inner level optimizes charging and discharging powers as well as initial state of charge of SMESs. Modeling of feeder's failure rate applying several mathematical functions is considered. The effectiveness of proposed approach is validated on the IEEE 33-bus test system considering commercial, industrial, residential, and mixed time-varying voltage-dependent load models. The performance of the distribution system is also checked as regards the energy loss in feeders, the bus voltage deviation from rated value as well as the voltage stability of the system. For validating the effectiveness of the EO algorithm, the obtained results using the EO algorithm are compared with those predicted by particle swarm optimization (PSO) and genetic algorithm (GA). Moreover, the obtained results display that the bi-level optimization can successfully achieve satisfactory improvement of the reliability and security indices of the distribution system.

Optimal allocation of capacitor banks in distribution systems using particle swarm optimization algorithm with time-varying acceleration coefficients in the presence of voltage-dependent loads

ABSTRACT In this paper, a novel solution algorithm is presented for the capacitor allocation problem in radial distribution systems according to the type of load model with the primary objective of power loss minimisation and network savings maximisation. The voltage-dependent load model considers three types of demands, namely industrial, residential, and commercial. Loss sensitivity factors and particle swarm optimisation with time-varying acceleration coefficients (TVAC-PSO) are employed to optimise the location and size of the capacitors, respectively. The loss sensitivity factors identify the sequence of buses to be compensated by the placement of capacitors. The feasibility of the presented method is evaluated on standard 34 and 85-bus radial distribution systems. The results of the comparative study suggest that our proposed algorithm outperforms the existing heuristic techniques in solution quality and convergence speed, which renders it viable and applicable for implementation.

Reallocation of Distributed Generation for Optimal Placement in Real-Time Delivery System Using Evolutionary Algorithms

Integration of renewable energy sources at the distribution side can boost the benefits of a smart grid. Due to a growing awareness of the environmental effect of electric power production and current technology utilized in many generations that are more environmentally friendly than traditional plants, distributed generation (DG) is gaining traction. The growing demand for power in emerging nations necessitates the use of DG to generate additional energy. Because of advancements such as greater competition, real-time pricing, and spot pricing, precise energy loss assessment is critical for obtaining more technical and economic advantages.The modeling of load is very important while finding location and size of DG. The improper modeling of load may give rise to poor benefits. This paper examines the impact of load models on the estimated energy loss in DG planning using Genetic Algorithm (GA) and Ant Lion Optimization (ALO). With the provided information, the suggested technique investigates the impact of load models on the estimation of energy loss based on the year's seasonal pattern. Comparative tests of the traditional static load model and the Voltage Dependent Load Model (VDLM) for energy loss at various load levels are conducted using one of the substations delivering energy from a state utility and a 13-bus system.

Optimal Placement and Sizing of Wind Turbine Generators and Superconducting Magnetic Energy Storages in a Distribution System

High penetration of intermittent wind-turbine generation (WTG) into electric distribution system along with large variations of load demand introduce many problems to the system such as high power losses, voltage sag, and low voltage stability. To mitigate such problems, the distribution system is supported by superconducting magnetic energy storages (SMESs). This paper is aimed at determining the optimal placement and sizing of WTGs and SMESs in a distribution system using a proposed multi-objective-function based optimization method. The method is a hybrid one being based on an efficient algorithm called Equilibrium Optimizer (EO) along with loss sensitivity factor (LSF). The weighted-sum multi-objective function (IMO) is formulated for simultaneous minimization of energy-loss and voltage-deviation as well as enhancement of voltage-stability as indices characterizing the distribution system performance. The weight factors are no longer assumed or left open to the preferences of the decision maker. They are computed while optimizing the indices of the IMO in order to determine the optimal placement and sizing of WTGs and SMESs. The proposed method for optimal placement and sizing of WTGs and SMESs is tested and validated on the standard IEEE 33-bus distribution system with time-varying voltage-dependent load models including residential, industrial, commercial, and mixed loads as well as variable wind-speed. The results obtained using EO algorithm are compared with those obtained by particle swarm optimization (PSO) and genetic algorithm (GA) to validate the effectiveness of EO. The numerical results and simulations imply that the combination of WTGs and SMESs can successfully achieve minimization of energy-loss and voltage-deviation as well as enhancement of voltage-stability, and thereby significantly improve the performance of distribution system.