IEEE 30-bus System Research Articles

PMSG-based wind farm high-voltage ride-through improvement for single-pole tripping of circuit breakers

For transient single phase-to-ground (SPG) faults in transmission lines, single-pole circuit breaker (SPCB) operation can improve system reliability. This issue is more critical in transmission lines emanating from wind farms (WFs). In the large-scale WFs, the grid codes necessitate that wind generation remains in operation during the transmission line faults. For this purpose, the grid codes focus on the development of fault-ride-through (FRT) requirements. In this paper, it is shown that after the SPCB operation, the terminal voltage of the permanent magnet synchronous generator (PMSG)-based wind turbine (WT) rapidly increases and may cross the high-voltage ride-through (HVRT) boundary. Therefore, the WT terminal overvoltage after the SPCB operation may result in undesirable tripping of the WT. The obtained results in this paper show that in most cases both the coupled and decoupled sequence controllers of the WT converter equipped with the FRT requirements are not able to improve the HVRT performance after the SPCB operation. To enhance the HVRT performance of PMSG-based wind turbines after the SPCB operation, a dc-link chopper controller is proposed. EMTP-RV simulation results on the IEEE 118-bus and 5-bus systems show the success and effectiveness of the proposed method.

Defense strategy selection based on incomplete information game for the false data injection attack

With the rapid development and wide application of sensing devices and communication networks, the traditional power system has transformed into a cyber physical power system (CPPS) gradually. The constant interaction of information flow and power flow exposes the grid to potential cyber-attack risks. In this paper, the defense strategy selection approaches are proposed based on incomplete information game against false data injection attack (FDIA) in different scenarios. Firstly, the attack-defense tree model containing economic indicators and a measure of attack is established, and the expected revenues of both attacker and defender are calculated in detail. Secondly, the static Bayesian attack-defense game model in which the defender grasps incomplete information is constructed, considering two cases of low-tech attacker and high-tech attacker. Then, the zero-sum attack-defense game model in which the attacker masters incomplete information is structured, considering the changing attack space. Finally, the simulations are carried out on the IEEE 30-bus system, and the optimal defense strategies in the case of two incomplete information are obtained by solving the equilibrium point. The simulation results demonstrate that the proposed method is effective in cyber security defense system, and can provide guiding ideology and theoretical basis for operators to deploy defense measures.

Stochastic optimal PV placement and command based power control for voltage and power factor correction using MOPSO algorithm

This paper addresses a multi-objective particle swarm optimization (MOPSO) algorithm to locate the best position at which a single distributed generator (DG) with optimum voltage amplitude and phase can be installed. The objective is to maximize the power factor and voltage in IEEE 9-Bus system and voltage in IEEE 15-Bus system using MATLAB/SIMULINK software. Systematic simulations revealed that a single DG at Bus-5 with voltage amplitude/phase of 230 kV/-13∘ caused highest objective function value of 1.9919 with an improvement in power factor and voltage of 1.39% and 1.15% respectively. In IEEE 15-Bus system, DG at Bus-3 caused the highest objective function (0.8828) with DG voltage amplitude/phase of 10.735 kV/23.12∘ and 11.89% voltage improvement. Finally, a robust control program in dq0 domain is developed to achieve calculated powers from photovoltaic generator at DG bus whose effect is validated to be the same at all buses as given by the MOPSO algorithm.

An Improved Voltage Sag Monitor Placement Method by using Simple Probability Reach Area Matrix

Aim: A novel optimization model is introduced in this paper to achieve monitor placements of voltage sag. Background: Considering the uncertainty of system parameters, calculation errors and measurement errors, the method of MRA is not suitable. Objective: The contradiction between the number of monitors and observation areas can be coordinated to achieve voltage sag observation with fewer monitor quantities. Method: A new generalized approach defined as a simple probability reach area matrix (SPMRA) is proposed. Result: SPMRA method is simulated by the IEEE-39 bus system, which reveals that monitor placement quantities can be reduced effectively under probabilistic observability. Conclusion: SPMRA method is a general method for monitor placements of voltage sag. This method not only performs the functions of conventional MRA methods, but also addresses the challenges of errors and conflicts between observation requirements and installation costs.

Power efficiency improvement in reactive power dispatch under load uncertainty

Nowadays, there is a significant rise in electricity demand, posing challenges for power grid operators due to inaccurate forecasting, leading to excessive power losses and voltage instability. This paper addresses these issues by focusing on solving optimal reactive power dispatch (ORPD) while considering load demand uncertainty. The main objective of solving ORPD is to reduce power losses by adjusting generator voltage ratings, transformer tap ratio, and shunt capacitors' reactive power. Monte Carlo simulation (MCS) is employed to generate load scenarios using the normal probability density function, while a reduction-based technique is implemented to decrease the number of those scenarios. The improved gray wolf optimization (I-GWO) algorithm is introduced for the first time to address the stochastic ORPD problem. Experimentation is conducted on an IEEE-30 bus system when results are contrasted with conventional gray wolf optimization (GWO) and five other algorithms as stated in the literature. The I-GWO algorithm's performance is assessed with and without considering load demand uncertainty. Through Friedman's statistical tests, a significant decrease of 20.96% in active power losses and 63.06% in the summation of expected power losses is observed. The I-GWO algorithm's results on the ORPD problem demonstrate its effectiveness and robustness.

A probabilistic distributed digital twins approach for short-term stability and islanding of smart grid

The simultaneous evaluation of voltage, frequency, and transient stability enhances the reliability of the power systems, due to the transient effect of stability aspects on each other. However, different quantitative and qualitative parameters in the aspect and nonlinear structural analysis of the system make simultaneous and accurate predictions difficult. For this reason, the simultaneous probabilistic prediction of the stability state of three aspects with high certainty can be considered the most accurate method. This study presents a simultaneous prediction method for the stability aspect states by means of the decision theory and the game theory matrix on the basis of distributed digital twins (DDTs). The decision matrix weighs the aspect states in each bus using the distributed structure measurement and prioritizes the weak buses. In addition, the stability of different bus aspects is predicted using the game matrix. Furthermore, the changes in the system stability are predicted with high probability and certainty in the digital twins (DTs) graph, and if required, islanding is performed on the basis of the weak buses' priorities. The effects of the three aspects on each other, and the use of real-time data of the DDTs, have contributed to the high accuracy and speed of the proposed method. Simulations were performed on IEEE 14-bus, 39-bus, and 118-bus systems by means of MATLAB and DIgSILENT. The results show the advantages of the proposed method over other methods.

Multi-stage planning method for independent energy storage based on dynamic updating of key transmission sections

A multi-stage planning method for independent energy storage (IES) based on dynamically updating key transmission sections (KTS) is proposed to address issues such as uneven power flow distribution and transmission congestion resulting from the high penetration of renewable energy sources and load growth. First, an IES planning model considering KTS constraints is established by searching and updating the KTS and associated constraints. This model takes into account IES investment and operation costs, maintenance costs, revenue from spot price differences, and penalties for system congestion. Then, a multi-stage planning method for energy storage is proposed based on the dynamic updating of KTS and the annual planning results. To verify the effectiveness and feasibility of the proposed method, a case study simulation is conducted using the improved IEEE 39-bus New England standard system. The results demonstrate the ability of the method to achieve multi-stage rolling optimization of IES, providing a reference for future efforts toward multi-stage planning of IES.

Multi-objective optimal reconfiguration of distribution networks using a novel meta-heuristic algorithm

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution.

Multi-source coordinated low-carbon optimal dispatching for interconnected power systems considering carbon capture

The overall electricity consumption of electrolytic aluminum and ferroalloy loads is significant, and some of these loads have dispatch potential that can be used to locally absorb wind power while reducing dependence on conventional thermal power. To characterize the uncertainty of wind power, a fuzzy set of wind power forecasting error probability distribution based on the Wasserstein distance was first established, and the approximate radius of the fuzzy set was corrected under extreme scenarios. By introducing joint chance constraints, the inequalities of uncertain variables were established at the lowest confidence level to improve the reliability of the model. Next, a two-stage distributed robust optimal scheduling model for source-load coordination was developed. In the first stage, wind power forecasting information was fully utilized to schedule the electrolytic aluminum load and optimize unit commitment. In the second stage, the uncertainty of wind power output was considered to schedule the ferroalloy load and optimize unit output. The model was approximately transformed into a mixed-integer linear programming problem and solved using a sequential algorithm. The IEEE 24-bus system was used for case validation. The validation results show that the model can effectively improve wind power absorption capacity, reduce overall operating costs, and achieve a balance between low carbon emissions and robustness.

Importance-driven denial of service attack strategy design against remote state estimation in multi-agent intelligent power systems

This paper introduces a novel importance-driven denial of service (IDoS) attack strategy aimed at impairing the quality of remote estimators for target agents within multi-agent intelligent power systems. The strategy features two key aspects. Firstly, the IDoS attack strategy concentrates on target agents, enabling attackers to determine the voltage sensitivity of each agent based on limited information. By utilizing these sensitivities, the proposed strategy selectively targets agents with high sensitivity to amplify disruption on the target agent. Secondly, unlike most existing denial of service attack strategies that adhere to predefined attack sequences, IDoS attacks can selectively target important packets on highly sensitive agents, causing further disruption to the target agent. Simulation results on the IEEE 39-Bus system demonstrate that, compared to existing denial of service attack strategies, the proposed IDoS attack strategy significantly diminishes the estimation quality of the target agent, confirming its effectiveness from an attacker's perspective.

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Improving islanded distribution system stability with adaptive decision-making framework

Abstract In an integrated distribution system incorporating distributed generation (DG), various technical challenges must be addressed when the grid becomes disconnected and transforms into an islanded system. The main focus in such circumstances revolves around ensuring the stability of the islanded network. This study presents an advanced decision-making framework for supporting islanded networks by integrating metaheuristic Black Widow Optimization (BWO) and the rate of change of the voltage stability index (RoCVSI). The Rate of Change of the Voltage Stability Index (RoCVSI) detects instability in islanded networks by continuously monitoring rapid changes in the voltage stability margin. Upon identifying potential instability, an advanced decision-making strategy utilizing the Black Widow Optimization (BWO) algorithm is deployed. BWO generates multiple load-shedding scenarios and evaluates their impact on system stability, iteratively refining the solutions through processes similar to selection and cannibalism in black widow spiders. The optimal load-shedding strategy is then implemented to selectively shed specific loads, thereby reducing demand and enhancing island stability. The proposed scheme’s effectiveness for islanded network stability is assessed by extensively analyzing the IEEE 33-bus system. The efficiency of the proposed approach is confirmed through a comparative analysis, with results indicating the better efficiency of the proposed method in the islanded network.

Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Decentralized Multi-Area Economic Dispatch in Power Systems Using the Consensus Algorithm

A multi-area power system requires coordination to enhance reliability and reduce operating costs. Economic dispatch in such systems is crucial because of the uncertainties associated with variable loads and the increasing penetration of renewable energy sources. This paper presents a hierarchical consensus algorithm designed to determine the economic dispatch in a multi-area power system, accounting for the uncertainties in load and renewable generation. The proposed algorithm, which utilizes distributed agents, operates across three levels. Level 1 coordinates all areas, while levels 2 and 3 form a leader–follower consensus algorithm for overall economic dispatch. Breadth-first search is employed to identify the leader agent within each area. To address the uncertainties in loads and renewable generation, Monte Carlo simulations are performed. The efficacy of the proposed method is validated using the IEEE 39-bus and 118-bus systems, as well as a realistic 1968-bus power system in Taiwan. The traditional equal lambda method is employed to verify that the proposed approach is suitable for multi-area power systems using distributed computation.

Distributionally robust CVaR optimization for resilient distribution system planning with consideration for long-term and short-term uncertainties

Climate change is exerting excessive strain on the environment, leading to an ever-increasing frequency of extreme weather events that negatively impact distribution systems. To mitigate these impacts, this paper presents a distributionally robust conditional value-at-risk (DR-CVaR) optimization approach for resilience-oriented distribution system planning. Firstly, a resilience-oriented distribution system planning model is developed, which incorporates flexible operational resources, such as mobile energy storage prepositioning and demand response program, in the emergency response phase. Then, the uncertainties associated with extreme weather events are addressed using a DR-CVaR optimization approach, where the short-term uncertainty in the occurrence of outages caused by specific extreme weather events is addressed through a distributionally robust optimization approach, while the long-term uncertainty regarding the risk of potential extreme weather events is addressed by a conditional value-at-risk optimization approach. The DR-CVaR model is eventually transformed into an equivalent three-level model and solved using a customized nested column-and-constraint generation algorithm. Finally, case studies are conducted based on the IEEE 33-bus distribution system under several extreme weather events. The numerical results show that the proposed model can reduce the load shedding cost as well as investment cost, which confirms the effectiveness and superiority of the proposed model.

Northern goshawk optimization for optimal reactive power compensation in photovoltaic low-voltage radial distribution networks

In recent years, photovoltaic systems (PVs) have been increasingly integrated into radial distribution networks (RDNs). However, network operators experience significant issues because they are sporadic. To reduce the load on the main grid and improve the network performance, capacitor banks (CBs) are frequently employed for power factor correction and VAr compensation. However, a large number of CBs must be switched at once in accordance with variations in load and PV generation to maintain feeder voltage regulation and improve its performance. This study's optimization strategy for CB control considers a variety of goals. Using seasonal load profiles and PV generation patterns, a novel and efficient northern goshawk optimization (NGO) was created to determine the appropriate control of CBs. By resolving the typical CBs allocation problem in EDNs and comparing the findings with those in the literature, the effectiveness of NGO was initially cross-verified. In the second stage, an NGO is used to maximize annual net savings while maintaining the best possible control over CBs. Considering the PVs and CBs of the network, a modified IEEE 33-bus test system was used to evaluate the effectiveness of the suggested methodology.

Research on Parameter Correction of Distribution Network Model Based on CIM Model and Measurement Data

The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China’s distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction.

Impact assessment of electric vehicle charging on distribution networks

Electric Vehicles are emerging as preferred means of transport due to rising concerns regarding the increased harmful emission with fossil fuels. The integration of the EV in the distribution network has severe effects which create unbalance in the network and will lead to a reduction in the reliability of the network. This work deals with the impact analysis of the electric vehicle charging stations on the distribution system and assesses the hosting capability of the distribution system by formulating several scenarios. In this paper, the IEEE-33 & IEEE-69 bus systems have been modified to mimic the practical distribution network. The proposed formulation is done on MATLAB, and results show that up-to 40% EV penetration is tolerable by the existing distribution networks. and as the penetration increases, the permissible limit gets violated. This research work provides a comprehensive analysis of the impact caused due to residential and public based charging of electric vehicles and can be applied to different networks to assess the hosting capacity of the distribution network.