Optimal Voltage Profile Research Articles

Multi-objective optimization of solar resource allocation in radial distribution systems using a refined slime mold algorithm

The integration of distributed generation resources in power systems offers various advantages, such as peak load management and reduced transmission line congestion. However, it also introduces challenges related to voltage stability. This paper presents a novel multi-objective model for optimizing the allocation of solar resources in radial distribution systems. The model aims to achieve an optimal voltage profile, minimize losses, and maximize penetration levels. To address the conflicting nature of these objectives, a refined multi-objective slime mold algorithm (MOSMA) is proposed. This algorithm demonstrates exceptional capabilities in finding Pareto fronts, avoiding local optima, and effectively solving multi-objective problems compared to other optimization methods. Additionally, the corrected social hierarchy method is integrated to enhance performance. The proposed method is evaluated using a standard system under various operational conditions, showing superior results in terms of maintaining an acceptable voltage profile and significantly reducing losses. The study reveals that while losses decrease for penetration levels ranging from low to medium, they start to increase for levels exceeding 100 %. Notably, the proposed method achieves approximately 12 % system efficiency improvement, as measured by the voltage profile, at a penetration level of 300 %. These findings highlight the effectiveness of the proposed method, even at high penetration levels, surpassing other optimization approaches based on the inverse generation distance parameter.

Enhancing voltage profile in islanded microgrids through hierarchical control strategies

This paper presents a novel approach aimed at enhancing the voltage profile of an islanded microgrid (MG) through the utilization of Phasor Measurement Units (PMUs) and a hierarchical control system for Distributed Generators (DGs). Recognizing the criticality of maintaining an optimal voltage profile for essential loads within an MG, we assume that PMUs are strategically placed across the MG based on existing placement methodologies to ensure the observability of load buses. The synchrophasor measurements captured by these PMUs are leveraged within an offline-designed tertiary control layer of the hierarchical framework, which in turn sets the voltage reference for the secondary control layer of DGs across the MG. This approach effectively enhances the voltage profile of vital load buses within the MG. The proposed hierarchical strategy encompasses primary, secondary, and tertiary levels of control, responsible for stabilizing voltage and frequency, computing power references, and managing power flow, respectively. PMU measurements play a crucial role in the secondary control level by continuously monitoring deviations. By ensuring the effective placement of PMUs, the proposed framework enables efficient control, leading to notable improvements in the voltage profile and power management. To evaluate the effectiveness of our approach, five test networks were used: 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus test networks. Simulation results demonstrate an impressive over 80 % enhancement in the MG's voltage profile achieved by our proposed method. Additionally, our heuristic strategy efficiently assigns the closest load bus to each DG while aggregating equidistant bus deviations. Collectively, our research underscores the significant impact of PMU measurements and a well-structured hierarchical voltage control system in substantially improving the voltage profile of MGs.

A New Framework for Active Loss Reduction and Voltage Profile Enhancement in a Distributed Generation-Dominated Radial Distribution Network

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks.

Voltage profile optimization using crow search algorithm modified with Lampnien’s criterion to account inequality constraints

Voltage profile optimization using crow search algorithm modified with Lampnien’s criterion to account inequality constraints

Profile Optimisation in Real Distribution Networks (Case Study Long MV Feeder)

Abstract In the dynamic field of power systems, integrating distributed generation (DG) sources like solar photovoltaic (PV) plants is crucial for enhancing reliability and fostering sustainability. However, this integration poses voltage profile management challenges in electrical grids. This study investigates voltage profile optimization in Bosnia and Herzegovina’s Gračanica network, focusing on a medium voltage feeder with 68 buses. Using DIgSILENT PowerFactory software, six scenarios with different configurations of solar plants are analyzed for their impact on voltage profiles and power losses. Results show that while DG integration offers benefits, incorrect sizing or placement can increase power losses. Optimal DG benefits are linked to specific sizes and locations. This research emphasizes the need for balancing PV generation with load demands and provides insights for optimal PV plant size and output to minimize negative impacts. These findings aid energy planners and policymakers in implementing distributed solar PV in medium voltage networks.

Voltage Profile Optimization of Active Distribution Networks Considering Dispatchable Capacity of 5G Base Station Backup Batteries

Voltage Profile Optimization of Active Distribution Networks Considering Dispatchable Capacity of 5G Base Station Backup Batteries

Molecular Size-Selective Electrodialytic Desalters for Electrospray Ionization-Mass Spectrometry.

A membrane-based electrodialytic desalter has been developed for the selective removal of buffer/salt constituents from a fluid stream while retaining larger charged molecules such as proteins prior to mass spectrometric (MS) detection. The salts are deleterious to MS causing signal suppression, formation of adducts, and eventual contamination of the inlet. The new device uses dialysis membranes (DMs) paired with ion exchange membranes (IEMs) flanking the carrier flow channel in a planar configuration. The DMs contact the carrier channel preventing adsorptive losses of large, charged molecules to the IEMs. Ions are removed under an applied electric field using four pairs of electrodes along the flow channel. Removal of both anions and cations is more energy intensive than conversion of a suitable MS friendly salt into its respective acid or base, for example, ammonium acetate into acetic acid. The energetics and optimal voltage profiles for both scenarios have been thoroughly investigated. The DMs resulted in nonlinear increases in energy required for desalting over standard IEM devices due to electroosmotic flow of water into the interstitial space between the membranes. For a device channel with nominal volume of 15.2 μL, a maximum concentration of 200 mM ammonium acetate flowing at 0.25 mL/min was converted into acetic acid. Recovery of bovine serum albumin measured at 280 nm was 67%-96% at tested salt concentrations, and dispersion volumes were less than 200 μL2 and may be suitable for coupling to liquid chromatography.

Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system

ABSTRACT This article presents the design of an improved smart grid model with an improved optimal VAr strategy using a bio-inspired approach. The integration of an artificial neural network technique with bio-inspired algorithms is the novelty of this research work. The objective of the work is attained by integrating the artificial neural network with three different evolving techniques like the Big Bang-Big Crunch algorithm, the gravitational search algorithm, and bacterial foraging algorithm. The three different layers of artificial neural network reconcile with the power grid for enhancing the voltage profile generated by bio-inspired techniques. The effectiveness and reliability of the algorithms are examined on the standard IEEE 30 bus system. The sample test bus system comprises six Generators, four Transformer tap changers, and 2 Static Shunt VAr Compensator. Artificial intelligence technology is applied for enhancing the voltage profile by managing reactive power across the power system network. The results produced by artificial intelligence technique are more competent and outstanding than any other conservative optimization techniques. It is found that the real power loss across the network has been reduced from 5.744 MW to 4.234 MW, while the bus voltage deviation has been minimized from 1.475 p.u. to 0.0918 p.u. and the voltage profile has been improved from 0.968 to 1.0985 p.u. after integration of the artificial neural network. The optimal values of objective functions are attained by evaluating the training and testing performance of artificial neural network criteria such as coefficient of determination, mean absolute error, root mean square error, and mean absolute percentage error. The obtained results show the remarkable improvement in voltage profile after integrating artificial intelligence technology with a power grid system.

Optimal distributed generation location and sizing for loss minimization and voltage profile optimization using ant colony algorithm

A system of power generation whereby the generating equipment is located close to the point of usage, thereby reducing losses and operation cost is called distributed generation (DG). However, it is imperative that DGs are sited such that the quality of power delivered is optimized and the total real power loss within the system minimized. This paper proposes an approach for optimum sizing and siting of DGs sizing in a power distribution system using Ant Colony Optimization (ACO) algorithm. To validate the algorithm the IEEE 30 bus standard test system was employed. A 92% decrease in real power loss within the system relative to the value before the connection of DGs was observed, while the minimum bus voltage increased from 0.656 per unit to 0.965 per unit. The results obtained from ACO are further verified by creating an ETAP model of the IEEE 30 bus system and simulating the impact of DG on the system. A significant reduction in total real power losses within the system and improvement in voltage profile was observed when the DGs are placed at the ACO derived sites relative to at other locations. Therefore, Ant Colony Algorithm can be used in deriving the optimum sites and sizes of DGs in a power distribution system.

Distributed Optimal Voltage Control With Asynchronous and Delayed Communication

The increased penetration of volatile renewable energy into distribution networks necessities more efficient distributed voltage control. In this paper, we design distributed feedback control algorithms where each bus can inject both active and reactive power into the grid to regulate the voltages. The control law on each bus is only based on local voltage measurements and communication to its physical neighbors. Moreover, the buses can perform their updates asynchronously without receiving information from their neighbors for periods of time. The algorithm enforces hard upper and lower limits on the active and reactive powers at every iteration. We prove that the algorithm converges to the optimal feasible voltage profile, assuming linear power flows. This provable convergence is maintained under bounded communication delays and asynchronous communications. We further numerically test the performance of the algorithm using the full nonlinear AC power flow model. Our simulations show the effectiveness of our algorithm on realistic networks with both static and fluctuating loads, even in the presence of communication delays.

Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles

Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method.

Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood

Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program; hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network.

Realization of Online Voltage Profile Optimization

Realization of Online Voltage Profile Optimization

Estimation of the efficiency of FACTS devices for voltage-stability enhancement with PV area criteria

As a result of the global decarbonisation policy, the increased penetration of RES (Renewable Energy Sources) into the electrical power system is introducing some serious technical challenges, one of the most serious being to maintain voltage profiles within acceptable/allowable limits. The unpredictable generation of a considerable share of the renewable energy can result in voltage deviations that system-network operators are not able to control effectively. At the present stage of the technology, the power electronic converters that are applied for most of the RES are used as a kind of adapter that ensures optimal RES operation and transmits the active power to the grid. But each of these converters, together with an energy source (or sink), inherently also exhibits the characteristics of a FACTS device. As a result, there should be no hindrance to applying the theories for enhanced EPS (Electric Power System) stability limits, developed for FACTS devices, also for RES converters. Many of the problems that are caused in an EPS by the large share of RES could be solved by the RES itself, also at the system level, if properly controlled and IT connected to each other and to the system operator. In this context, the present paper reviews the impact of FACTS devices on the voltage stability with methods based on the calculation of PV curves. An analytical, in-depth approach can clearly demonstrate the theoretical feasibility of FACTS devices for voltage-stability enhancement. The paper presents such an approach for CSC, SSSC, SVC and STATCOM, based on an analytical calculation of the area between the PV curves and the voltage margins in a PV diagram. The basic contribution of the paper is a theoretical explanation of the suitability of various FACTS devices for maintaining an optimal voltage profile, taking into account any variation of a device parameter.

Zoning Evaluation for Voltage Optimization in Distribution Networks with Distributed Energy Resources

This paper deals with the problem of the voltage profile optimization in a distribution system including distributed energy resources. Adopting a centralized approach, the voltage optimization is a non-linear programming problem with large number of variables requiring a continuous remote monitoring and data transmission from/to loads and distributed energy resources. In this study, a recently-proposed Jacobian-based linear method is used to model the steady-state operation of the distribution network and to divide the network into voltage control zones so as to reformulate the centralized optimization as a quadratic programming of reduced dimension. New clustering methods for the voltage control zone definition are proposed to consider the dependence of the nodal voltages on both active and reactive powers. Zoning methodologies are firstly tested on a 24-nodes low voltage network and, then, applied to the voltage optimization problem with the aim of analyzing the impact of the R/X ratios on the zone evaluation and on the voltage optimization solution.

Multiarea Voltage Controller for Active Distribution Networks

The aim of this paper was to develop a multi-area decentralized controller for improving the voltage profile of large active distribution networks. Voltages at control buses of each area are kept as close as possible to their reference values by managing the reactive power of distributed generators within the same areas. Moreover, in order to avoid exchanging a considerable amount of data on more or less large portions of the network, the proposed methodology adopted an equivalent reduced network for each area. This equivalent network model is seen at control buses and nodes where distributed generation units are connected. With this simplification, each area controller will have to evaluate simultaneously, the unknown parameters of the reduced network and the optimal control laws for the voltage profile optimization of its control area. To comply with this exigency, a multi-objective optimization problem was formulated. The solution of this problem formulation was found by adopting an algorithm operating in the continuous time domain. Test results are provided on a 49-bus distribution network, demonstrating the effectiveness of the developed methodology.

Comparative study of MPC based coordinated voltage control in LV distribution systems with photovoltaics and battery storage

This paper compares traditional local voltage control strategy with coordinated, optimization-based ones in low voltage (LV) distribution systems with photovoltaics and battery energy storage systems. Optimization-based strategies are formulated within a model predictive control (MPC) framework. Three strategies based on MPC are proposed and implemented, namely, centralized, decentralized and distributed MPC. The formulated strategies for voltage control are compared in a case study using a modified CIGRÉ European 3-area low-voltage network. Results indicate that decentralized MPC gives a better voltage profile in the network when compared to local voltage control strategy, since the latter inherently fails to maintain voltages of buses in the network not connected to photovoltaics or battery storage system within limits. Centralized MPC strategy is able to provide the optimal voltage profile across the network but utilizes 13% higher reactive power from the control devices to achieve this when compared to decentralized MPC. The latter performs well as long as the reactive power reserves within an area is sufficient but faces drawbacks similar to that of local voltage control strategy when the reactive reserves are completely exhausted. Distributed MPC utilizes 1.3% higher amount of reactive power reserves compared to centralized MPC in order to provide a network voltage profile similar to that of the latter while also yielding architectural advantages of decentralized MPC.

Alternative Improving the Quality of Sub-Voltage Transmission System using Static Var Compensator

This study discusses the voltage profile improvement method in the transmission sub-systems using one of the devices from Flexible Alternating Current Transmission Systems (FACTS), by using a Static Var Compensator (SVC) with the object focused on electric power system sub-systems in South Bandung area and Ujung Berung. The aims of the study were to determine the voltage profile sub-systems in South Bandung and Ujung Berung before and after installation of SVC and also to determine the position and capacity of SVC in maintaining optimal voltage profile transmission sub-system. The research method used the power flow simulation from Newton-Raphson method with ETAP 7.0. The flow of reactive power had been set so that the power loss on the network (losses) system obtained a minimum. The results of the simulation and power flow analysis shows that the average voltage profile and quality of installation SVC voltage sub-systems after the optimal position and capacity to be better compared to prior to installation. By minimizing the power loss on the network, the bus voltage profile can be maintained in the values that are allowed, so the quality and continuity of operation in the power system can be maintained.

Incentive Design for Voltage Optimization Programs for Industrial Loads

This paper presents a novel framework for planning and investment studies pertaining to the implementation of system-wide conservation voltage reduction (CVR). In the CVR paradigm, optimal voltage profiles at the load buses are determined so as to yield load reductions and hence energy conservation. The system modifications required to operate at such voltages is known to be capital intensive, which is not desirable by investors. Hence, the proposed model determines the system savings and the appropriate price incentives to offer industries such that a minimum acceptable rate of return is accrued. In this model, the industrial facilities are represented by a combination of constant impedance, constant current, and constant power loads. A detailed case study for Ontario, Canada, is carried out considering that industrial loads are investing in voltage optimization to reduce their energy costs. The optimal incentives that need to be offered by the system planner, over a long-term horizon, and across various zones of Ontario, are determined using the presented mathematical model. Furthermore, a comprehensive risk analysis comprising sensitivity studies and Monte Carlo simulations is carried out considering the variations in the most uncertain model parameters.

Testing New Reactive Power Control of DERs by Real-Time Simulation

Abstract In the smart grid paradigm, the reactive power control of distributed energy resources (DERs) plays a key role improving the voltage profile in the distribution systems. This topic has been addressed by previous papers in which the Optimal Set-Point Design (OSPD) of DER reactive control, based on a decentralized approach, has been developed. The OSPD determines the set point of a reactive power closed-loop regulation scheme according to an optimization strategy. After briefly recalling the OSPD procedure, the article presents validation studies aiming at testing the effectiveness of the OSPD. The validation is based on a hardware-in-the-loop real-time simulation facility. In particular, an experimental setup has been arranged and presented, in which the system is simulated using the real-time digital simulator (RTDS), while the OSPD has been implemented on a PC in the LabView environment. The OSPD has been developed by considering two different optimization objectives, namely the feeder voltage profile optimization and the distribution losses minimization. The achieved results are then presented and also compared with the ones obtained a classical regulation scheme.