Time-varying Voltage-dependent Load Research Articles

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

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.

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 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.

The role of demand response in optimal sizing and siting of distribution energy resources in distribution network with time-varying load: An analytical approach

In the most studies related to allocation of distributed generation the load model assumed in constant. Such assumptions may lead to inconsistent and misleading results in the reduction of energy losses, improve system stability, etc. However, this paper is considered several different types of time-varying to allocation of units. In this study, Optimal Sizing and Siting of Distribution Generation units (Consisting of dispatchable, nondispatchable and photovoltaic) Problem are provided at the same time with considering the role of demand response and time-varying voltage-dependent load models (Consisting of commercial, industrial and residential). This method first identifies a set of buses to be placed of DG units. The optimal sizes of DG units at the identified buses are then evaluated by minimizing Multiobjective Index (Consisting of active, reactive power losses). The proposed method has been tested and validated on 33-buses test distribution system. The results show that demand response and different load models have a significant impact on the planning of Distribution Generation units in radial distribution network.

Impact of time varying load models on PV DG planning

A constant load and generation model is generally considered in planning studies that pertain to Distributed Generation (DG) integrated distribution systems. However, such considerations may result in misleading and inconsistent values for the voltage profile, loss reduction, payback period, deferral values, and other related calculations due to the variation of both renewable energy-based generation and load demand. This paper investigates the impact of time varying voltage dependent load models on the DG planning studies. The study is based on the comparative assessment of different impact indices, penetration level, active and reactive power intake, active and reactive power loss, and Mega Volt Ampere support offered by the installation of photovoltaic based DG for different time varying load models. The outcomes of the current research work reveal that the time varying load modeling approach has significant impact on the distribution systems.

Probablistic generation model for optimal allocation of PV DG in distribution system with time-varying load models

Distributed generation (DG) planning studies are generally conducted while assuming constant generation and load models. However, in this paper, optimal allocation of photovoltaic (PV) based DG is carried out while considering probabilistic generation and time varying voltage dependent load models. First, a novel probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power produced via PV units. Subsequently, the obtained power values are used to determine the optimum PV allocation in the distribution system through particle swarm optimization to minimize the multi-objective function. The proposed algorithm has been validated on 33-bus and 69-bus distribution test systems. The results demonstrate that the proposed model is suitable for solar irradiance modeling and can be employed in power system planning studies.

Determining PV Penetration for Distribution Systems With Time-Varying Load Models

A constant or voltage-dependent load model is usually assumed in most distributed generation (DG) planning studies. However, this paper proposes several different types of time-varying voltage-dependent load models to determine the penetration level of photovoltaic (PV) units in a distribution network. Here, a new analytical expression is first proposed to size a PV unit, which can supply active and reactive powers. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of three indices, namely active power loss, reactive power loss and voltage deviation. The expression is then adapted to allocate PV units while considering the time-varying load models and probabilistic PV generation. The effectiveness of the proposed approach was validated on 69- and 33-bus test distribution systems. The results showed that PV allocation with different types of time-varying load models can produce dissimilar penetration levels.

A profit-centric strategy for distributed generation planning considering time varying voltage dependent load demand

This paper proposes a new algorithm to determine the best connection of multiple distributed generations (DGs) on distribution networks according to type of customers. The considered DG technology is combined heat and power (CHP) in various sizes which can be extended to other technologies. Different types of demand, such as industrial, residential and commercial are defined by the load model that is voltage dependent and the load patterns from real life hourly demand data. This method which is based on multi-year multi-period power flow is useful for Distribution Network Operators (DNOs) when they are faced with voltage dependent load models in their networks. However it can be used in constant load models with multi-period or single demand. The proposed optimization is from the DNO’s perspective and aims to maximize their present value of profit against the supplying costs of the network. It is shown that by definition of an appreciated economic based objective function, the customer type can significantly affect the optimal DG options in distribution network planning. Finally, this method is tested using the 69-bus radial system. Furthermore, a comparative study for different customer demand types is shown for various cases and results are discussed in this paper.