Minimum Voltage Deviation Research Articles

Location and size selection using hybrid Jaya‐Luus‐Jaakola algorithm for decentralized generations in distribution system considering demand‐side management

AbstractIn the distribution system, decentralized generation is incorporated to maximize the system reliability and economic stability, and to avoid redundancy in system. The efficient implementation of distributed‐generations (DGs) in a distribution‐system (DS) can be done by employing demand‐side management (DSM). In this research proposal, location and size selection of DGs in a DS considering DSM is accomplished using a hybrid Jaya‐Luus‐Jaakola (JLJ) algorithm. The hybridization of Jaya algorithm (JA) with Luus‐Jaakola algorithm (LJA) is done with a view to improving the rate of convergence of the algorithm. Three design objectives of minimization of active power loss and voltage deviation along with maximization of voltage stability index are considered in this research proposal. These design objectives under consideration are integrated together to form a problem of a single objective by assignments of weights. The formulated problem is minimized using proposed hybrid algorithm subjected to system constraints such as balance of power, limits on bus voltage and line capacity, and generation level of DGs. A static penalty is added to the formulated problem in case of any constraint violations. A photo‐voltaic‐based DG is used in the 33‐bus standard radial‐distribution‐system (RDS). A DSM scenario is created by shifting some loads of peak hours to off‐peak hours in view of minimizing the total cost of generation. The potency and superiority of the proposed algorithm is validated with the help of comparison of the results obtained by exploiting proposed method with results acquired from available state‐of‐art algorithms in literature. The comparison proves the comparative superior performance of the proposed algorithm.

Optimal integration and coordination of distributed generation and shunt compensators using improved African vultures optimizer

This article aims to improve the performance of radial distribution systems (RDSs) by accurately solving the optimal energy management of the active powers of distributed generation units based on renewable energy and the reactive powers of shunt compensator-based flexible AC transmission system technology. An adaptive African vultures optimizer (AAVO) is proposed and applied to optimize individually and simultaneously various objective functions, including the minimization of total power loss, minimization of total voltage deviation and maximization of the loading margin. The efficacy of the proposed AAVO method is validated on three RDSs, the 33-bus, the 85-bus and the large RDS with 141-bus, under normal conditions and considering uncertainties of loads. Compared to many other techniques, the results clearly demonstrate the robustness and competitiveness of the proposed new variant in solving the power management optimization of a large-scale RDS.

Network partitioning and hierarchical voltage regulation for distribution networks using holomorphic embedding method‐based sensitivity

The fluctuation of photovoltaics (PVs) output may cause the voltage out-of-limit, while the PV inverters and hybrid distribution transformers (HDTs) bring flexible controllability to distribution networks (DNs). However, there are differences in response speeds between power electronic equipment and traditional discrete regulating equipment. Therefore, a network partitioning and hierarchical voltage regulation method using holomorphic embedding method (HEM) -based sensitivity is proposed. Firstly, the HEM-based voltage sensitivity is derived, and the network partitioning method is proposed using the HEM-based sensitivity matrix. Then, the hierarchical voltage regulation framework is constructed. The objects of the upper layer are the minimum network loss and voltage deviation, which centrally optimizes the operation status of discrete regulation equipment. The lower layer exploits the fast control ability of PV inverters and HDTs, so as to autonomously regulate the voltage of each partition. Finally, a practical 10 kV DN in China is used for case studies. The results demonstrate that the HEM-based voltage sensitivity calculation method has advantages in computational efficiency and accuracy compared with the conventional method. Moreover, the proposed voltage regulation method can suppress the voltage fluctuation.

Model Predictive Control Based Coordinated Voltage Control for Offshore Radial DC-connected Wind Farms

In this study, a coordinated voltage control strategy based on model predictive control (MPC) is proposed for offshore radial DC-connected wind farms. Two control modes are designed in this strategy. In the economic operation mode, the wind farm controller generates optimal active power references as well as bus voltage references of medium-voltage collector for DC-connected wind turbine (DCWT) systems and high-voltage DC/DC converters, where the goal is to minimize power losses inside the wind farm and ensure that voltages are within a feasible range, all while tracking the power references. In the voltage control mode, the main control objective for the wind farm controller is to minimize voltage deviations from the rated voltage. With the MPC, the control objective and operation constraints can be explicitly represented in the optimization problem while considering the dynamic response of the DCWT system. In addition, a sensitivity coefficient calculation method for radial DC-connected wind farms is developed to improve computational efficiency. Finally, DC-connected wind farms with 20 wind turbines are used to demonstrate the performance of the proposed strategy.

Intelligent Control for Voltage Regulation in the Distribution Network Equipped with PV Farm

A combined PSO-ANN control is proposed in this work to achieve the best voltage regulation in a distribution network, based on quick response and minimum average voltage deviation. The Jordanian Sabha Distribution Network (JSDN) with PV Farms is used as a real case study to examine a voltage variation issue. Two STATCOMs are used to solve the voltage fluctuation problem on the network’s three buses. The required reactive powers of STATCOMs for voltage regulation during load variation are calculated in offline mode using a particle swarm optimization (PSO) algorithm. Despite its high performance in solving voltage issue in the JSDN network, the PSO controller is unable to react promptly to dynamic changes in the network. An artificial neural network (ANN) is therefore suggested as an online mode controller for quick and efficient voltage regulation. The offline dataset is used to train the ANN for online voltage regulation utilizing the MATLAB-Tool Box. At an average voltage deviation (AVD) of 1.168%; (whereas an acceptable one is 6%), the results revealed the proposed ANN controller’s competence for voltage regulation in the distribution network. Moreover, to find the best position based on an efficient voltage regulation, many sites for STATCOMs are taken into consideration.

Bi-level planning model of distributed PV-energy storage system connected to distribution network under the coordinated operation of electricity-carbon market

The disordered connection of Distributed PV-Energy Storage Systems (DPVES) in the Distribution Network (DN) will have negative impacts, such as voltage deviation and increased standby costs, which will affect the demand of urban consumers for reliable and sustainable power consumption. Therefore, reasonable planning of the location and capacity of the DPVES in the DN is one of the key measures to reduce these negative impacts, while promoting the consumption of renewable energy and protecting the income of the DPVES. In this paper, under the background of coordinated operation of China's electricity market and carbon market, considering the uncertainty of distributed photovoltaic (DPV) output power and the demand response (DR) behavior of users, a bi-level planning model of DPVES in DN is established. The upper-level model aims at the maximum revenue of the DPVES in the DN and the highest local consumption rate of DPV, and the decision variables are the connection location and capacity of the DPVES. The lower-level model aims at the minimum average voltage deviation of the DN nodes, and the decision variables are the implementation of load-side DR and the output power of the DPVES. Then, the solution method and process of the model are given. Finally, four planning scenarios are set up in the IEEE-33 node distribution system, and the planning results of different scenarios are analyzed and discussed. The research results show that the bi-level planning model proposed in this paper can reduce the average voltage deviation of DN nodes while effectively increasing the average return of the DPVES and improving the local consumption rate of DPV.

Optimal joint operation of coupled transportation and power distribution urban networks

The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).

Optimal Reactive Power Dispatch by Success History Based Adaptive Differential Evolution Salp Swarm Algorithm

In this study, a novel hybrid algorithm success history-based adaptive differential evolution salp swarm algorithm (SHADE-SSA) is proposed to solve two different cases of IEEE 30 bus reactive power dispatch problems integrated with thermal generators, wind farms and solar photovoltaic plants. Real power loss minimization and voltage deviation minimization are considered as main objectives in the present work. The performance and robustness of the proposed hybrid SHADE-SSA algorithm are compared with the results of five different metaheuristic algorithms for the same test system and consider the same control variables and constraints. The results of the simulation of the proposed algorithm conform to the effective choice for the solution of optimal reactive power dispatch problems of power systems.

Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program

Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme.

A hybrid IMFO-fuzzy algorithm for performance enhancement of microgrid under uncertainty via DGs integration and reconfiguration

Microgrids (MGs) are distributed generation and distribution systems that include distributed generation (DG) units, energy storage systems (ESSs), distributed reactive sources (DRSs), and resilient loads that can operate in either connected or isolated modes. When dealing with uncontrolled DGs such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES), MGs planners have a difficult time making decisions. The work proposed in this paper addresses three interconnected works: (i) the implementation of a rigorous hybrid optimization approach for reconfiguration and DGs placement; (ii) the performance investigation under uncertain behavior of RES-based DGs and demand; and (iii) performance enhancement realization through the replacement of hybrid DGs for RES-based DGs. An Improved Moth Flame Optimization (IMFO), which is a multi-objective optimization method, has been linked with fuzzy logic in order to handle multiple objectives in an efficient manner. These objectives include the minimization of voltage deviation, the reduction of generation cost, and the reduction of loss. The quality of the power, the amount of money saved by consumers, and the benefits to the Distribution System Operator (DSO) might all be improved with the help of a hybrid algorithm. This research is also extended to address the uncertainties of RES-based DGs by replacing hybrid DGs in the most optimal locations. IEEE 33 bus RDS is used to test radial distribution system (RDS) microgrids. For validation purposes, uses 24-hour load patterns to mimic WES and PVES’ 24-hour load dispatching behavior. The research findings clearly demonstrate the advantages of microgrids over traditional architectures. The hybrid DG requires an average generating cost of 185.33 $/kW in order to produce 100 kW of power throughout the day with significantly reduced emissions.

Supplementary Feedforward Voltage Control in a Reconfigurable Distribution Network using Robust Optimization

Network reconfiguration (NR) has attracted much attention due to its ability to convert conventional distribution networks (DNs) into self-healing grids. This paper proposes a new strategy for real-time voltage regulation (VR) in a reconfigurable DN, whereby optimal feedforward control of distributed generators (DGs) is achieved in coordination with the operation of line switches (SWs). This enables preemptive compensation of upcoming deviations in DN voltages resulting from NR-aided load restoration. A robust optimization problem is formulated using a dynamic analytical model of NR to design the feedforward voltage controllers (FVCs) that minimize voltage deviations with respect to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norm. Errors in the estimates of DG modeling parameters and load demands are reflected in the design of optimal FVCs via polytopic uncertainty modeling. Small-signal analysis and case studies are conducted, verifying the effectiveness and robustness of the optimal FVCs in improving real-time VR when NR is activated for load restoration. The performance of the proposed FVCs is confirmed under various conditions of a self-healing DN,characterized by network islanding and size, parameter errors, SW operations, and communication time delays.

Two-layer control structure for enhancing frequency stability of the MTDC system

This paper explores the possibilities of providing fast frequency support as an emergency support service to the disturbed AC system through the MTDC grid. A two-layer hierarchical control structure of the MTDC grid is proposed to assure the minimum cost of the frequency control actions, the minimum voltage deviations, or the minimal impact on the frequencies of not-affected AC systems while ensuring the stable operation of MTDC grid. An optimization algorithm is executed at the secondary control level to find the optimal reference values for the voltage-droop characteristics of the voltage-regulating converters, and consequently their DC voltages and active power references. Then, at the primary control level, the reference values are tuned with the optimization results. Implemented control structure confirms that MTDC can provide set values at its terminals without endangering its stability. The secondary control layer is implemented in MATLAB, while the performance of the controller is successfully evaluated through simulation in RSCAD.

A robust controlling and management of load with minimum frequency and voltage deviation in network employing genetic algorithm

In this work optimal autonomous controlling of frequency and voltage deviation of the power system network has been presented. The frequency deviations and the voltage fluctuations are assessed with conventional and genetic algorithm method. Initial model of power system network has been developed which is based on Tie line power flow. There were several unknown parameters observed in the objective functions of conventional tie line power method. These unknown parameters were trained with genetic algorithm. The genetic algorithm consist of three major steps: reproduction, crossover, and mutations. The suitable value of frequency and voltage deviations are obtained for various loading conditions. But due loading conditions, high value of transient or peak overshoot and settling time were attained for frequency and voltage variations. It is observed that optimal minimum value of peak overshoot and settling time for frequency and voltage deviations are attained with genetic algorithm in comparison to conventional methods.

Solution of the Multi-Objective Optimal Power Flow Problem Using Oppositional-Based Algorithm

An efficient optimal power flow (OPF) algorithm allows the finest setting of the plant by solving multi-objective optimization problem to minimise the overall operating cost. This paper proposes the quasi oppositional backtrack search algorithm (QOBSA) for optimal setting of OPF control variables. The QOBSA is stochastic algorithm which gives committed and robust results compared to the traditional methods. This technique has been implemented to test the control parameters for the IEEE 30-bus with single and multi-objective functions like the minimization of fuel cost, minimization of total voltage deviation (TVD), voltage stability enhancement, emission reduction, and multi-fuel cost minimization. The result provides better voltage profile at every bus based on L-index which in turn greatly reduces the burden on load buses. The QOBSA code has been developed in the MATLAB platform and tested with the help of IEEE 30-bus and the outcomes have been compared with ongoing literature.

Parameter-Free Improved Best-Worst Optimizers and Their Application for Simultaneous Distributed Generation and Shunt Capacitors Allocation in Distribution Networks

This paper proposes parameter-free two best-worst optimizers (BWOs) that combine the searching capabilities of Jaya and Rao-1 algorithms to intensify their exploration and exploitation capabilities. For the proposed first optimizer, BWO-1, parallel taxonomy has been adopted for the Jaya and Rao-1 combination to obtain dual sets of updated solutions for a given current solution set. For the proposed second optimizer, BWO-2, along with the parallel taxonomy, a subloop of random scaling factors has been introduced in the solution updating mechanism of both standard Jaya and Rao-1 algorithms to generate multiple sets of updated solutions in a single iteration. The best solutions from obtained multiple solution-sets will survive only and lead to the next iteration. Hence, the proposed solution updating mechanisms for BWO-1 and BWO-2 increase the probability of getting a quality solution under set conditions. The ability and scalability of BWO-1 and BWO-2 were assessed, solving the optimization problem of power loss reduction and voltage deviation minimization in the IEEE 33-bus and 69-bus test systems. The results show that the BWO-2 exhibited a lead of 2.97%, 1.83%, and 0.72% over the Rao-1, Jaya, and BWO-1 techniques, respectively. Besides, the BWO-2 achieves up to 38.76% more reduction in power losses against the existing standard, improved, and hybrid optimization techniques.

A dynamic optimization method for power distribution network operation with high ratio photovoltaics

With the high ratio distributed photovoltaics (PVs) penetration to the distribution network (DN), the bearing capacity, integration capacity, consuming capacity and controlling capacity of DN for PVs are all facing great challenges. Enhancing the flexible regulation and operation optimization capabilities of DN have become key factors to satisfy these new challenges. For the operation optimization problem of high ratio PVs penetration, a novel quasi-equal curtailment ratio (QCR) constraint of PVs is proposed in the dynamic optimization method for DN operation. Firstly, the uncertainty of high ratio PVs and their active power deviations are discussed. And a dynamic optimization framework of PVs based on optimal power flow (OPF) has been proposed. Secondly, the optimization model for high ratio PVs is formulated with multi-objective of maximum PVs output, minimum voltage deviation and minimum line loss, under the equality and inequality constraints of power flow, PVs power generation limit, QCR of PVs and so on. Then the multi-objective optimization problem (MOP) is transformed into a single-objective optimization problem (SOP) by weighting coefficients, and solved by sequential quadratic programming (SQP) with trust-region (TR) searching algorithm. Finally, the proposed method is tested, verified and compared with the primal dual interior point (PDIP) and traditional SQP algorithms in IEEE 33-bus, PG&E 69-bus, 292-bus and 1180-bus test cases. The experimental results show the rapidity and robustness of the proposed method.

Optimal operation of QAB-based unified power quality conditioner integrated with PV arrays under VA capacity constraints

This study presents an optimal operation scheme for a quadruple-active-bridge (QAB) based unified power quality conditioner (UPQC) integrated with photovoltaic (PV) under VA capacity constraints. Due to the removal of the low frequency isolation transformer and the energy router functionality of the QAB converter, higher power density and more flexibility can be achieved for the UPQC-QAB. Thus, it is convenient for the PV arrays to be integrated at the DC-links to supply the load through the UPQC-QAB. In the constrained multi-objective optimization problem, the maximum power generation of PV arrays, the minimum load voltage deviation, the maximum power factor of the power grid and the minimum total apparent power of the UPQC-QAB are determined. Based on hierarchical optimization idea, the multi-objective optimization problem is simplified as several single-objective optimization ones. Finally, simulation results verify the correctness and effectiveness of the proposed optimal operation scheme.

Bi-level planning and scheduling of electric vehicle charging stations for peak shaving and congestion management in low voltage distribution networks

Most existing algorithms for electric vehicle charging station (EVCS) planning and scheduling of electric vehicles (EVs) do not consider the uncertain behavior of EVs and distribution networks, including the EV arrival and departure times, user driving needs, and realistic residential load and generation profiles. This paper proposes a bi-level planning and operation framework that determines the optimal locations of EVCS and scheduling of EVs to reduce peak load and network congestion in low voltage distribution networks by considering uncertain EV and distribution network behavior. Planning of EVCS is performed to minimize voltage deviations and distribution network losses. The priority-based scheduling algorithm uses the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technology to charge the EVs during high priority periods to meet the user requirements and discharge during low or medium priority periods to reduce the peak load and thermal overloading in distribution feeders. Based on an Australian scenario, the proposed method has been tested on the IEEE low voltage test feeder. Uncoordinated scheduling of EVs is also implemented and compared with the proposed strategy. The results show that installing EVCS at the optimal locations can reduce the network losses and voltage deviations by 39.38 per cent and 15.32 per cent, respectively. It is also shown that the proposed scheduling of EVs can reduce the peak load of distribution substations and lower the network congestion without affecting the EV charging requirements. With a 55 percent penetration of EVs, the peak load and thermal overload in distribution lines are reduced by 20.53 per cent and 5.88 per cent, respectively.

Real Power Loss Reduction by Accipitridae Optimization Algorithm

In this paper, Accipitridae Optimization Algorithm (AOA) is used to solve the power loss lessening problem. Authorizing a satisfactory magnitude of reactive power is effervescent for the steadfast operation of power transmission structures, as the power meagreness may tip to staid power catastrophe and leading power upheavals. Loss reduction problem is intended as one of the prominent conditions for compassionate and financial action of edifice. It is exceptional by appropriate union of the network apparatus castoff to deal with up the power flow with the dispassionate of decreasing the losses and evolution the power outline of the assemblage. Plentiful previous studies unravelled the power problem as the optimization objective tasks premeditated noticeably. Real power loss reduction, Voltage stability expansion and voltage deviation minimization are the key objectives of the work. Hunting methods of Accipitridae are imitated to design the Accipitridae Optimization Algorithm (AOA). Accipitridae does hunting in a single-mode on the prey. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae is formulated. As soon as the prey spot is precise then Accipitridae is geared up for landing and assault, the Accipitridae move down perpendicularly with a preface assault to determine the prey response. AOA initiates the upgrading measures by engendering an arbitrary solution. In the course of the curve of reiteration, the explore stratagem of the AOA is the rational location of the most excellent optimal solution. The authenticity of the Accipitridae Optimization Algorithm (AOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Voltage stability escalated with minimization of power deviance. Then the proportion of actual power loss lessening is amplified.

Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique

This paper proposes a simultaneously allocating electric vehicle charging station (EVCS) and photovoltaic (PV) energy resources in smart grid considering uncertainties using a hybrid technique. The proposed hybrid technique is the combination of capuchin search algorithm (CapSA) and wild horse optimizer (WHO), and hence it is named as ECapSA technique. The dynamic behaviour of capuchin monkeys is enhanced by the wild horse's social life behaviour. That's why, it is called as enhanced CapSA (ECapSA) technique. In the proposed work, the following four main objectives are considered and formulated by the ECapSA technique: (a) minimization of energy loss, (b) minimization of 24-h voltage deviation, (c) maximization of weightage to offer electric vehicle charging service for maximal users, and (d) minimization of land cost. The locations of electric vehicle charging station and distributed generation (DG) affect the power loss along voltage at the distribution network. Besides, the locations on the road network determine the land cost together with weightage. The proposed formulation solution is carried out in two layers: upper and lower layers. The electric vehicle charging station allocation is modelled in upper layer and distributed generation of the optimization in lower layer. Here, 24 h slots are considered for load variation and photovoltaic generation variation at the charging station (CS). The energy loss reduction is an objective of problem formulations instead of power loss reduction. The ECapSA technique is implemented in the MATLAB platform, its performance is analyzed with existing techniques.