Voltage Security Research Articles

DMPC-Based Coordinated Voltage Control for Integrated Hybrid Energy System

High penetration and fluctuation of renewable energy resources are threatening the voltage security of the integrated hybrid energy system (IHES). This article focuses on providing a fully distributed method to ensure the voltage security and information privacy of the IHES. First, a multiagent system based control scheme is presented, in which the upper level agent is responsible for the voltage security of the whole system, and the local dynamic performance of each distributed energy resource (DER) unit is formulated in the lower level unit agent. Second, a distributed model predictive control (DMPC) based coordinated voltage control method is proposed, by which only partial information exchange is needed in the interaction. Furthermore, the diverse requirements on voltage quality of each microgrid in the IHES are considered in this article and it provides a novel paradigm in this field compared with the traditional methods. To study the stability of the resultant DMPC, the Nash equilibrium is achieved and theoretically proved to ensure the convergence of the proposed method. Finally, the validity of the proposed method is verified by virtue of the simulation and experimental test results.

Modified Spider Monkey Optimization-Based Optimal Placement of Distributed Generators in Radial Distribution System for Voltage Security Improvement

Due to increase in load demand the use of distributed generation has been increased in the distribution system. Voltage profile as well as voltage security state of the distribution system can be improved by incorporating Distributed Generator (DG) of particular sizes at specific locations. In this article, modified spider monkey optimization (MSMO) technique has been proposed for finding out the optimal size and location of DGs to improve stability of the network. This method is a modification of recently developed swarm intelligent technique called spider monkey optimization (SMO) technique. Here minimization of voltage deviation is considered as objective function. The performance of MSMO technique has been tested on IEEE 33 bus, IEEE 69 bus, and Indian 85 bus practical radial distribution system. The results obtained by MSMO-based technique are compared with the results of other existing methods, which show that MSMO algorithm outperforms other standard optimization techniques in terms of voltage profile improvement of the system which proves the efficiency of the proposed method. MSMO-based DG insertion also enhances voltage security state of the system.

Technical barriers for harnessing the green hydrogen: A power system perspective

Extracting green hydrogen from renewable energy sources is a new concept in the energy industry. As an energy carrier, hydrogen is well capable of facilitating a strong coupling between various energy sectors, as well as integration of renewable energy sources. This paper investigates the system-wide technical factors that might limit the amount of producible hydrogen in a given power system. A non-linear programming formulation is proposed to quantify the impact of voltage security constraints, the location and size of power to hydrogen facilities, and finally the wind penetration levels on the harvest-able green hydrogen. The applicability of the proposed framework is demonstrated on the IEEE 39 bus system.

Risk-averse multi-objective optimal combined heat and power planning considering voltage security constraints

In this paper, a comprehensive model is proposed for long-term planning of various combined heat and power units in an integrated heat and electricity network. The proposed model takes into account the uncertain electric loads, and market price applying the information gap decision theory. Furthermore, the security of the power grid from the voltage stability viewpoint utilizing the L-index approach is considered. The model is based on the risk-averse multi-objective combined heat and power planning methodology, which maximizes the profit of combined heat and power owners and minimizes the system operator costs over the planning horizon in the presence of the environmental emissions cost. The best compromise solution is achieved via a fuzzy logic-based min-max method. The risk-averse strategy of Information gap decision theory is applied to the obtained solution, which demonstrates the impact of data uncertainty. The proposed mixed-integer non-linear programming model is solved using the general algebraic modeling system package and tested on the IEEE 14-bus standard system. The results indicate that the risk-averse strategy improves the robustness of the network against the uncertainty. Also solving the model using the multi-objective framework gives comprehensive results, and shows that the voltage stability constraints affect the planning decisions.

Test Systems for Voltage Stability Studies

This paper describes the two test systems for voltage stability studies set up by the IEEE PES Task Force on “Test Systems for Voltage Stability Analysis and Security Assessment” under the auspices of the Power System Stability Subcommittee of the Power System Dynamic Performance Committee. These systems are based on previous test systems, making them more representative of voltage stability constraints. A set of representative results are provided for both systems, with emphasis on dynamic simulation. They illustrate various aspects such as long-term dynamics, voltage security assessment, real-time detection, and corrective control of instabilities. The value for educators, researchers and practitioners are emphasized.

The multi-objective optimal allocation method of static var compensator based on transient voltage security control

Based on the detailed consideration of the dynamic characteristics of Static Var Compensator (SVC), the mathematical method for installing location and optimal capacity of SVC considering transient voltage security is proposed. Firstly, the trajectory sensitivity index is used to determine the key fault set and SVC candidate location. Then, transient voltage security constraint of the bus voltage is restored to 0.75pu within 1s and the bus voltage is restored to 0.9pu within 3s after remove fault, Finally, considering the transient voltage recovery characteristics and dynamic characteristics of SVC, the minimum of bus voltage deviation after fault removed and minimum of installtion capacity of SVC as objective function for hybrid optimal allocation model. The bus voltage and capacity of SVC are introduced as variables into the Crisscross Optimization Algorithm (CSO) for solving. The effectiveness of the proposed SVC installation location and capacity optimization method is verified by the time domain simulation of the IEEE39-bus system.

The impact of feeder reconfiguration on automated distribution network with respect to resilience concept

Owners of power systems incur high costs and investments to transfer the electrical power to customers in the distribution network. Hence, the reliability of the distribution network is particularly important in the lives of customers. Natural disasters such as hurricane and earthquake or sudden faults may threaten the reliability of the distribution network. Therefore, the focus of this study is to provide a new formulation for the distribution system resilience and voltage security. To improve the mentioned objectives in the automated distribution system, the concept of distribution feeder reconfiguration (DFR) is used in this study. Due attention of distribution network resilience and voltage security issues, energy not supplied and voltage stability index along with power loss are considered as objective functions. According to the increase of distributed generators in the distribution network, the effect of these units is considered in the DFR problem. An improved particle swarm optimization is presented to solve the complex and non-convex DFR problem. The proposed algorithm is tested into two distribution systems including IEEE 33 and 70-node.

Fuzzy Neural Network Based Optimal and Fair Real Power Management for Voltage Security in Distribution Networks with High PV Penetration

The high penetration of distributed generation (DG) sources in distribution networks (DN) can induce overvoltage issues. In this paper, an artificial intelligence based fair and optimal method for voltage regulation in DN with high photovoltaic (PV) penetration is proposed. Based on the forecasting of solar radiance and load profiles, the method optimally dispatches the generation of PVs to prevent overvoltage with the objective of minimizing the energy curtailment of PVs for a given long period. In addition, the RPCM can adaptively adjust the curtailment of PVs based on fuzzy neural network algorithm so that the PV systems in the DN could reach and keep similar accumulated curtailments during the period. Steady state simulation studies under various scenarios have been carried out on a 69-bus distribution feeder and an actual distribution network to demonstrate the effectiveness of the proposed method.

RETRACTED ARTICLE: Multi objective dragonfly algorithm for congestion management in deregulated power systems

Congestion in transmission corridors are the major bother for deregulated power system’s operation. Generator rescheduling along with demand alteration is a traditional remedy for transmission line congestion. According to market clearing process, the system operator (SO) has to pay a certain amount of cost to the market participants for rescheduling the generation and demand. This kind of redispatch related congestion management (CM) procedure is mainly carried out to reduce the congestion cost, but they are failing to provide an attention in power systems security. The risky generator’s power shifts may diminish the voltage and transient stability of the power system. So power system security should be included in the congestion management procedure. In this proposed multi objective congestion management procedure, rescheduling of active power is carried out to improve/retain the power systems security along with a congestion cost reduction. Voltage security margin (λ) and corrected transient energy margin (CTEM) provides a measure for power system security level. Multi Objective Dragonfly Algorithm (MODA) is employed to trace the non dominated solutions for three conflicting objectives. Fuzzy decision making principle is applied to select the best Pareto solution depends on the objective’s significances. The goodness of the MODA optimization approaches is experimented in congestion alleviation of New England 39 bus systems and solutions are compared with some reputed methods.

Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm

This paper presents a temporally coupled distributed online (TDO) algorithm to aggregate and coordinate numerous networked distributed energy resources (DERs) as a virtual power plant (VPP). A centralized stochastic optimization problem is formulated to minimize the long-term social utility loss while satisfying the voltage security, operational requirements of DERs, and VPP service requests. After that, we propose the TDO algorithm to reformulate the primary problem as an adaptation of online convex optimization (OCO). In particular, the temporally coupled constraints are well separated to each timeslot. In real-time operation, the VPP operator updates the incentives according to the measurement feedback. The smart energy gateways (SEGs) equipped at each node maximize their income and utility based on the received incentive signals through adjusting the setpoints of the governed photovoltaics (PV) inverters and electric vehicles (EVs). Unlike conventional distributed optimization algorithms where complicated iterative procedures between agents are unavoidable, the proposed TDO algorithm is computation- and communication-efficient since the SEG can directly employ the closed-form optimal setpoints without iterative communications once receiving the incentives. Furthermore, we design an incentive scheme to coordinate the SEGs based on the privacy protected nonintrusive measurements instead of direct control. Optimality and convergency of TDO are analyzed mathematically. Finally, the proposed method is corroborated numerically on a modified 33-node test feeder. A larger system is tested to validate the computational time performance.

Optimal participation of demand response aggregators in reconfigurable distribution system considering photovoltaic and storage units

Feeder reconfiguration is an important operational process in power distribution grids, which is implemented to enhance the system’s performance by managing the switches. Given variations of the electricity price and load pattern in smart distribution networks, the operational problems of the distribution system are largely time-dependent and very complex. To deal with these temporal dependencies, it is important to extend the problem across different time intervals. Toward this end, a multi-objective optimization model is presented in this study for dynamic feeder reconfiguration problem in the distribution system over multiple time intervals considering distributed generators, energy storage systems, and solar photovoltaic units. The demand response program including interruptible/curtailable service is proposed to enable energy consumers to rethink their energy consumption patterns based on incentive and punitive policies. The common objectives considered in the feeder reconfiguration problem are power loss and voltage deviation which are important objectives for traditional distribution systems, but less attention has been paid to distribution network voltage security. This study considers operational cost, energy loss and voltage stability index as objective functions that can meet operational and voltage security expectations. Dynamic feeder reconfiguration problem is a complex integer nonlinear program problem and hence it is difficult to solve which requires the use of appropriate optimization algorithms to converge to global optima or find close to global optima. In this paper, a modified honey bee matting optimization algorithm based on the new mating mechanism is presented to solve the multi-objective dynamic feeder reconfiguration problem. The proposed approach uses an eliminating zone concept to finds a set of non-dominated solutions during the search process. Furthermore, a fuzzy decision-maker is adopted to select the best compromise solution among the non-dominated solutions. The suggested approach is tested on the IEEE 33-node and 136-node test systems and its superiorities are shown through comparison with other evolutionary algorithms.

Sustainable energy planning for cost minimization of autonomous hybrid microgrid using combined multi-objective optimization algorithm

With the development of scattered energy resources in the rural areas of Sarawak (Malaysia), various operational problems due to the unplanned installation of autonomous microgrids become gradually remarkable. To address this concern, the paper proposes an optimal strategy to evaluate the performance of different hybrid microgrid configurations for the Long San Village in Sarawak. A mathematical model is presented for sizing the component of the system to meet the maximum load demand under changing weather conditions and at the lowest possible cost. The developed approach simulates different microgrid models using deterministic and stochastic optimization methods to find the exact dynamic energy price of the selected optimal configuration in the context of system uncertainties. Furthermore, the operational feasibility of the system in terms of reliability and voltage security is studied in addition to economic feasibility with a comparative analysis of the environmental impact. The results show that the optimal configuration with the lowest cost of energy and net present cost can be achieved if the installed solar PV is less than 61 kW with 85 kW h of energy storage and 11 kW of hydro generation, where such system has 55,725 (kg/year) Carbon Dioxide and 330 (kg/year) Nitrogen Oxides. The findings also indicate that the dynamic energy pricing increases to 0.71 $/kWh when the power generation from renewable resources drops to zero. Further, the dynamic analysis shows that in order to reduce the voltage drop during disturbances, it is crucial to carefully install the sources in the buses connected to high energy demand.

Hybrid voltage stability and security assessment using synchrophasors with consideration of generator Q‐limits

Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operate power systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse, and power blackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential to take proactive control actions and minimise the impact on the system. This study presents a novel online algorithm for (a) hybrid perturbation analysis based voltage stability monitoring (HPVSM), (b) including Q-limit in voltage stability index, and (c) real-time security analysis using voltage stability index. HPVSM-based voltage stability index is computed using the data obtained from linear state estimator and PMU measurements. Typically measurement-based schemes ignore the impact of generator Q-limits and security analysis is not feasible. The proposed HPVSM-based index considers the impact of generator Q-limits violations by anticipating the critical generators using real-time PMU measurements. Contingencies are ranked using the proposed voltage stability index for security analysis. Results simulated for the 9-bus WECC, IEEE 14, 57, and 118 bus systems highlight the superiority of the proposed method in real-time voltage stability and security analysis.

Generation Equivalence Index (GEI) to assign low-order models to synchronous generators in voltage security assessment based on dynamic simulation

This paper proposes a novel method, suitable for online applications, which is able to identify those synchronous generators (SGs) that can be presented by low order models in dynamic voltage stability assessment when an outage occurs. The proposed identification algorithm is based on a novel Generation Equivalence Index (GEI) and deciding according to its value about model reduction. The proposed GEI determines a critical electrical distance so that those SGs with farther electrical distance from the disturbance location can be represented by a simple two-order model and other ones should be modeled precisely to accurately assess the dynamic voltage stability status. Obviously, in any interconnected large scale power system, determination of such a criterion reduces the computational burden of time domain simulations performed for voltage stability assessment and eliminates the need for applying dynamic data of all SGs. The dynamic simulation results performed in Nordic32, IEEE 39-bus, and IEEE 118-bus test systems using DIgSILENT PowerFactory software verify the efficiency of the proposed approach.

Wide‐area agent‐based scheme for Volt/Var control with a cooperative framework based on game theory

A novel method for dynamic voltage control, followed by a power loss reduction in a wide area scheme, is presented in this study. The proposed scheme is based on a combination of multi-agent system and cooperative game theory concepts and determines appropriate coordination of reactive power resources for maintaining voltage security as well as lowering post-disturbance power loss of power system. In addition, the algorithm takes the advantages of previously proposed mechanisms by the authors, i.e. a predictive mechanism in taking remedial actions according to the trends of voltage magnitudes and impending remedial actions and a preventive approach providing an effective number of changes of taps in on-load tap changers. The acquired results of simulations, performed in the Nordic 32 test system, show the effectiveness and robustness of the proposed scheme.

Index-based transmission for distributed generation in voltage stability and loss control incorporating optimization technique

<span lang="EN-US">The integration of Distributed Generation (DG) in a distribution network may significantly affect distribution performance. With the penetration of DG, voltage security is no longer an issue in the transmission network. This paper presents a study of Distributed Generation on the IEEE 26-Bus Reliability Test System (RTS) with the use of Fast Voltage Stability Index (FVSI) for determining its location and incorporated with Grasshopper Optimization Algorithm (GOA) to optimize the sizing of the DG. The study emphasizes the power loss of the system in which a comparison between Evolutionary Programming (EP) and Grasshopper Optimization Algorithm is done to determine which optimization technique gives an optimal result for the DG solution. The results show that the proposed algorithm is able to provide a slightly better result compared to EP.</span>

Optimizing hybrid power systems with compressed air energy storage

Concern for the environment necessitates the reduction in use of fossil fuels. A solution is to use more renewable power generation facilities. However, the intermittency of renewable energy makes operational scheduling challenging. An optimization model is developed here to determine the performance of a hydro-thermal-wind-solar hybrid power system with the possibility of integrating a compressed air energy storage system. The hybrid power system is implemented in the IEEE-30 bus system. Real-time operational constraints such as varying renewable power availability and disruptions are considered. The model was solved using the meta-heuristic approaches of differential evolution and modified bacteria foraging algorithm. Results indicate that the modified bacteria foraging algorithm arrived at better solutions, making it possible to achieve lower power loss, higher annual savings and reduced variability in voltage security. The best performance is obtained using a hybrid power system which incorporates the compressed air energy storage. Results indicate that higher renewable energy penetration with proper scheduling strategy can result in improvements in system performance.

Fast dynamic voltage security margin estimation: concept and development

This paper develops a machine learning-based method for fast estimation of the dynamic voltage security margin (DVSM). The DVSM can incorporate the dynamic system response following a disturbance and it generally provides a better measure of security than the more commonly used static voltage security margin (VSM). Using the concept of transient P-V curves, the paper first establishes and visualizes the circumstances when the DVSM is to prefer to the static VSM. To overcome the computational difficulties in estimating the DVSM, the paper proposes a method based on training two separate neural networks on a data set composed of combinations of different operating conditions and contingency scenarios generated using time-domain simulations. The trained neural networks are used to improve the search algorithm and significantly increase the computational efficiency in estimating the DVSM. The machine learning-based approach is thus applied to support the estimation of the DVSM, while the actual margin is validated using time-domain simulations. The proposed method was tested on the Nordic32 test system and the number of time-domain simulations was possible to reduce with approximately 70 %, allowing system operators to perform the estimations in near real-time.

Estimation of Dynamic Compensation for Renewable-Based Hybrid DG in Radial Distribution System Using Least Error Iterative Method

In this paper, wind diesel-based hybrid electrical system is proposed as distributed generator (DG) for radial distribution system (RDS). Since renewable-based DGs and especially wind operated electric generating system requires reactive power for its excitation along with the reactive power demand for maintaining the voltage profile at all buses in RDS, a fast and adequate dynamic compensation is required along with DG. The main contribution of this paper is to optimize the amount of reactive power supplied by dynamic compensator using new proposed approach of least error voltage constraint iterative approach. The paper is starting with the selection procedure of most sensitive bus satisfying three voltage stability indices fast voltage security index, line quality factor and voltage stability index followed by the selection of DG optimal size using optimization approach with multiconstraints all together, namely total real and reactive power loss, voltage limits voltage stability indices and voltage stability indices. The result shows how the dynamic compensator can maintain voltage level by releasing additional reactive power with the adjustment of firing angle for different levels for multidynamic load changes.

Safety Evaluation of Microgrid Using Chaotic Time Series and RBF Neural Network

In order to evaluate safety of the Microgrid(MG) after distributed energy resources and different types of loads accessed in or disconnected, chaotic time series and RBF neural network are essential and beneficial tools. In this paper, Voltage Security Assessment Index(VSAI) is established to assess whether the MG is able to achieve equilibrium state for voltage. The application of Solar Photo Voltaic(SPV) or Wind Turbine(WT) generation alone is tested in the process of the phase space reconstruction and compared to the consorted hybrid example. Chaos algorithm can be used to define and optimal the RBF center and the connection weights of output layer, which can further improve the convergence speed of RBF neural network. Comparing the result of output between actual and prediction in three different conditions, if the difference of their values is above or below a certain threshold, the safety of MG will be judged unsafe. Test is conducted on an autonomous MG bus feeder to verify the usefulness of the posed system. The research conclusion shows that chaotic time series and RBF neural network is effective and feasible for the safety evaluation of MG.